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International Studies in Entrepreneurship
Nancy J. Hodges
Albert N. Link
KnowledgeIntensive
Entrepreneurship
An Analysis of the European Textile and
Apparel Industries
International Studies in Entrepreneurship
Series editors:
Zoltan J. Acs
George Mason University
Fairfax, VA, USA
David B. Audretsch
Indiana University
Bloomington, IN, USA
More information about this series at http://www.springer.com/series/6149
Nancy J. Hodges • Albert N. Link
Knowledge-Intensive
Entrepreneurship
An Analysis of the European Textile
and Apparel Industries
Nancy J. Hodges
Department of Consumer, Apparel,
and Retail Studies
University of North Carolina at Greensboro
Greensboro, NC, USA
Albert N. Link
Department of Economics
University of North Carolina at Greensboro
Greensboro, NC, USA
ISSN 1572-1922 ISSN 2197-5884 (electronic)
International Studies in Entrepreneurship
ISBN 978-3-319-68776-6 ISBN 978-3-319-68777-3 (eBook)
https://doi.org/10.1007/978-3-319-68777-3
Library of Congress Control Number: 2017954914
© Springer International Publishing AG 2018
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Contents
1Setting the Stage�������������������������������������������������������������������������������������� 1
2The European Textile and Apparel Industries: An Institutional
and Literature Review ���������������������������������������������������������������������������� 15
3Trends in the European Textile and Apparel Industries���������������������� 29
4The AEGIS Database������������������������������������������������������������������������������ 45
5Characteristics of KIE Textile and Apparel Firms and Founders������ 53
6Sources of Knowledge Used by KIE Textile and Apparel Firms �������� 81
7The Strategic Behavior of KIE Textile and Apparel Firms������������������ 105
8The Entrepreneurial Performance of KIE Textile
and Apparel Firms ���������������������������������������������������������������������������������� 135
9The Antecedents of Entrepreneurial Performance in KIE Textile
and Apparel Firms ���������������������������������������������������������������������������������� 139
10Prescriptions for Growth for US Textile and Apparel Firms�������������� 145
11Concluding Remarks ������������������������������������������������������������������������������ 163
References �������������������������������������������������������������������������������������������������������� 167
v
List of Figures
Fig. 1.1 Representation of direct and indirect paths from sources
of knowledge to entrepreneurial performance������������������������������������ 14
Fig. 3.1 Annual growth rate in industrial production
in the EU textile and apparel industries, 2002–2013�������������������������� 32
Fig. 3.2 Annual growth rate in employment in the EU textile
and apparel industries, 2002–2013����������������������������������������������������� 32
Fig. 3.3 Annual growth rate in hours worked in the EU textile
and apparel industries, 2002–2013����������������������������������������������������� 33
Fig. 3.4 Annual growth rate in labor productivity per person
employed in the EU textile and apparel industries, 2002–2013��������� 33
Fig. 6.1 Illustration of mean firm responses about the importance
of factors for the formation of the company, by industry������������������� 84
Fig. 6.2 Illustration of mean firm responses about the importance
of alternative sources of knowledge for exploring
new business opportunities, by industry��������������������������������������������� 86
Fig. 6.3 Representation of direct and indirect paths from sources
of knowledge to entrepreneurial performance���������������������������������� 103
Fig. 7.1 Representation of direct and indirect paths from sources
of knowledge to entrepreneurial performance���������������������������������� 105
Fig. 7.2 Illustration of mean firm contribution of factors
in creating and sustaining the competitive advantage
of the company, by industry�������������������������������������������������������������� 109
Fig. 7.3 Illustration of mean firm agreement to statements about
the sensing and seizing of opportunities within the firm,
by industry���������������������������������������������������������������������������������������� 117
Fig. 8.1 Illustration of descriptive data on measures
of entrepreneurial performance, by industry������������������������������������� 137
Fig. 9.1 Representation of the indirect paths from sources
of knowledge to entrepreneurial performance���������������������������������� 144
Fig. 10.1 Annual growth rate in US employment in the textile
mills industry, 2006–2015����������������������������������������������������������������� 148
vii
viii
List of Figures
Fig. 10.2 Annual growth rate in US employment in the textile
product mills industry, 2006–2015��������������������������������������������������� 149
Fig. 10.3 Annual growth rate in US employment in the apparel
industry, 2006–2015�������������������������������������������������������������������������� 149
Fig. 10.4 Textile fibers (HS codes 50–53, 55, and 63) import
and export, 2006–2015���������������������������������������������������������������������� 152
Fig. 10.5 Textile (NAICS 313) domestic shipment (Production),
1997–2012���������������������������������������������������������������������������������������� 152
Fig. 10.6 Textile yarn, fabrics, made-up articles, NES, and retail
products (HS codes 50–60, 63, and 65) import and export,
2006–2015���������������������������������������������������������������������������������������� 153
Fig. 10.7 Apparel (NAICS 315) domestic shipment (production),
1997–2012���������������������������������������������������������������������������������������� 153
Fig. 10.8 Articles of apparel and clothing accessories
(HS codes 61, 62, and 65) import and export, 2006–2015��������������� 154
List of Tables
Table 1.1 Characterization of the static versus dynamic roles
of an entrepreneur����������������������������������������������������������������������������� 5
Table 1.2 Definitions of knowledge-intensive entrepreneurship (KIE)������������ 8
Table 3.1 Taxonomy of the European textile industry������������������������������������� 30
Table 3.2 Taxonomy of the European apparel industry������������������������������������ 31
Table 3.3 Number of EU textile industry enterprises,
by country, 2005–2014��������������������������������������������������������������������� 35
Table 3.4 Number of EU textile industry employees,
by country, 2005–2014��������������������������������������������������������������������� 37
Table 3.5 Number of EU apparel industry enterprises,
by country, 2005–2014��������������������������������������������������������������������� 39
Table 3.6 Number of EU apparel industry employees,
by country, 2005–2014��������������������������������������������������������������������� 41
Table 4.1 AEGIS sampling population and survey sample,
by country����������������������������������������������������������������������������������������� 48
Table 4.2 Distribution of AEGIS firms, by country and by sector������������������� 49
Table 4.3 Distribution of AEGIS firms in the European textile
and apparel industries, by country���������������������������������������������������� 50
Table 4.4 Segmentation of EU industries, by sector���������������������������������������� 52
Table 5.1 Distribution of AEGIS firms in the European textile
and apparel industries, by country���������������������������������������������������� 55
Table 5.2 Characteristics of textile and apparel firms�������������������������������������� 56
Table 5.3 Correlation matrix among firm age, number
of full-time employees, number of part-­time employees,
and percent of workers that are part-time employees,
by industry���������������������������������������������������������������������������������������� 58
Table 5.4 Characteristics of textile and apparel firm founders������������������������� 59
Table 5.5 Human capital and financial capital characteristics
of textile and apparel firms��������������������������������������������������������������� 60
Table 5.6 Percent of textile founders by most recent
occupational experience, by country (n = 91)���������������������������������� 63
ix
x
List of Tables
Table 5.7 Percent of apparel founders by most recent occupational
experience, by country (n = 84)�������������������������������������������������������� 64
Table 5.8 Correlation matrix among founder age, education,
and experience, by industry�������������������������������������������������������������� 65
Table 5.9 Firm founder characteristics in the textile
and apparel industries, by gender����������������������������������������������������� 66
Table 5.10 Characteristics of nascent entrepreneurs
and established entrepreneurs in firms in the textile
and apparel industries����������������������������������������������������������������������� 68
Table 5.11 Annotated literature review related to financial capital�������������������� 70
Table 5.12 Annotated literature review related
to nascent entrepreneurs������������������������������������������������������������������� 78
Table 6.1 Mean firm responses about the importance
of factors for the formation of the company, by industry����������������� 84
Table 6.2 Mean firm responses about the importance
of alternative sources of knowledge for exploring
new business opportunities, by industry������������������������������������������� 86
Table 6.3 Mean textile firm responses about the importance
of factors for the formation of the company,
by country (n = 91)��������������������������������������������������������������������������� 88
Table 6.4 Mean apparel firm responses about the importance
of factors for the formation of the company,
by country (n = 84)��������������������������������������������������������������������������� 89
Table 6.5 Mean textile firm responses about the importance
of alternative sources of knowledge for exploring
new business opportunities, by country (n = 91)������������������������������ 90
Table 6.6 Mean apparel firm responses about the importance
of alternative sources of knowledge for exploring
new business opportunities, by country (n = 84)������������������������������ 91
Table 6.7 Correlation matrix between the importance
of factors for the formation of the company
from the perspective of textile firms (n = 91)����������������������������������� 94
Table 6.8 Correlation matrix between the importance of factors
for the formation of the company from the perspective
of apparel firms (n = 84)������������������������������������������������������������������� 95
Table 6.9 Correlation matrix between the importance of alternative
sources of knowledge for exploring new business
opportunities from the perspective of textile firms (n = 91)������������� 97
Table 6.10 Correlation matrix between the importance
of alternative sources of knowledge for exploring
new business opportunities from the perspective
of apparel firms (n = 84)������������������������������������������������������������������� 99
Table 7.1 Mean firm responses about the contribution
of factors in creating and sustaining the competitive
advantage of the company, by industry������������������������������������������ 108
List of Tables
xi
Table 7.2 Mean textile firm responses about the contribution
of factors in creating and sustaining the competitive
advantage of the company, by country (n = 91)����������������������������� 110
Table 7.3 Mean apparel firms’ responses about the contribution
of factors in creating and sustaining the competitive
advantage of the company, by country (n = 84)����������������������������� 111
Table 7.4 Correlation matrix between the textile firm responses
to the contribution of factors in creating and sustaining
the competitive advantage of the company (n = 91)����������������������� 113
Table 7.5 Correlation matrix between the apparel firms’ responses
to the contribution of factors in creating and sustaining
the competitive advantage of the company (n = 84)����������������������� 114
Table 7.6 Mean firm agreement to statements regarding
the sensing and seizing of opportunities within the firm,
by industry�������������������������������������������������������������������������������������� 116
Table 7.7 Mean agreement by textile firms to statements
about the sensing and seizing of opportunities within
the firm, by country (n = 91)���������������������������������������������������������� 118
Table 7.8 Mean agreement by apparel firms to statements about
the sensing and seizing of opportunities within the firm,
by country (n = 84)������������������������������������������������������������������������� 120
Table 7.9 Correlation matrix between textile firms’ agreement
to statements about the sensing and seizing
of opportunities within the firm (n = 91)���������������������������������������� 122
Table 7.10 Correlation matrix between apparel firms’
agreement to statements about the sensing and seizing
of opportunities within the firm (n = 84)���������������������������������������� 125
Table 7.11 Correlation matrix among textile firms’ source
of knowledge and strategic behavior indices (n = 91)�������������������� 131
Table 7.12 Correlation matrix among apparel firms’ sources
of knowledge and strategic behavior indices (n = 84)�������������������� 132
Table 8.1 Descriptive data on measures of entrepreneurial
performance, by industry���������������������������������������������������������������� 137
Table 9.1 Correlation matrix between sources of knowledge,
strategic behavior, and entrepreneurial performance
for textile firms (n = 91)����������������������������������������������������������������� 141
Table 9.2 Correlation matrix between sources of knowledge,
strategic behavior, and entrepreneurial performance
for apparel firms (n = 84)��������������������������������������������������������������� 142
Table 10.1 Taxonomy of the US textile mills industry������������������������������������� 146
Table 10.2 Taxonomy of the US textile product mills industry����������������������� 147
Table 10.3 Taxonomy of the US apparel industry�������������������������������������������� 148
Table 10.4 The largest occupations in the US textile
and apparel industries from May 2015������������������������������������������� 150
xii
List of Tables
Table 10.5 Establishments in textile, textile product,
and apparel manufacturing (in thousands), 2014��������������������������� 151
Table 11.1 Correlation matrix between strategic behavior
and entrepreneurial performance for textile firms (n = 91)������������ 165
Table 11.2 Correlation matrix between strategic behavior
and entrepreneurial performance for apparel firms (n = 91)���������� 165
About the Authors
Nancy J. Hodges is the Burlington Industries Professor and Head of the Department
of Consumer, Apparel, and Retail Studies (CARS) at the University of North
Carolina, Greensboro (UNCG). Her research focuses on issues of higher education
and employment relative to the US and North Carolina textile, apparel, and retail
industries. She has published over 50 peer reviewed articles in scholarly journals
including the Clothing and Textiles Research Journal, Family and Consumer
Sciences Research Journal, and the Journal of Retailing and Consumer Services.
She has presented her research at numerous juried national and international conferences, where she has received multiple awards for best research papers. She received
the Outstanding Paper Award for 2010 from the Emerald Literati Network for one
of her articles published in the Journal of Fashion Marketing and Management. She
has garnered more than $1 M in funds in support of her research, including a recently
completed 10-year project supported by the NC Agricultural Research Service on
the changing workforce of North Carolina’s textile sector. She is also the Project
Director and Co-PI of two recently completed USDA Higher Education Challenge
projects focused on investigating global industry issues and trends, including the
link between higher education and industry employment.
Professor Hodges earned her Ph.D. from the University of Minnesota in 1998
and has since been on the faculty at UNCG. She served as Director of Graduate
Studies for the CARS program from 2004 to 2014, has served as thesis or dissertation chair for more than 50 M.S. and Ph.D. students, and advised more than 20
doctoral dissertations to completion. She has served as the Vice President for
Planning for the International Textile and Apparel Association and is on the
Executive Board of the Costume Society of America’s southeastern region. Professor
Hodges has also served on the Editorial Board for the Clothing and Textiles Research
Journal and is presently on the Advisory Board of Fashion Practice: The Journal of
Design, Creative Process and the Fashion Industry. She is currently Associate
Editor for the Clothing and Textiles Research Journal.
Professor Hodges has received several college and university awards, including
college awards for Outstanding Teaching in 2003 and 2009. In 2010 she was the
UNCG recipient of the UNC Board of Governors Teaching Excellence award. In
xiii
xiv
About the Authors
2012 she received the college Senior Research Excellence award. Most recently, she
was awarded the 2013 Outstanding Mentor Award from The Graduate School of
UNCG for her work advising and mentoring graduate students.
Albert N. Link is the Virginia Batte Phillips Distinguished Professor at the
University of North Carolina at Greensboro (UNCG). He received the B.S. degree
in mathematics from the University of Richmond (Phi Beta Kappa) and the Ph.D.
degree in economics from Tulane University. After receiving the Ph.D., he joined
the economics faculty at Auburn University, was later Scholar-in-Residence at
Syracuse University, and then he joined the economics faculty at UNCG in 1982.
Professor Link’s research focuses on entrepreneurship, technology and innovation policy, the economics of R&D, and policy/program evaluation. He is currently
the Editor-in-Chief of the Journal of Technology Transfer. He is also coeditor of
Foundations and Trends in Entrepreneurship and founder/editor of Annals of
Science and Technology Policy.
Among his more than 50 books, some of the more recent ones are: Handbook for
University Technology Transfer (University of Chicago Press, 2015), Public Sector
Entrepreneurship: U.S. Technology and Innovation Policy (Oxford University Press,
2015), Bending the Arc of Innovation: Public Support of R&D in Small,
Entrepreneurial Firms (Palgrave Macmillan 2013), Valuing an Entrepreneurial
Enterprise (Oxford University Press, 2012), Public Goods, Public Gains (Oxford
University Press, 2011), Employment Growth from Public Support of Innovation in
Small Firms (W. E. Upjohn Institute for Employment Research, 2011), and
Government as Entrepreneur (Oxford University Press, 2009). His other research
consists of more than 180 peer-reviewed journal articles and book chapters, as well
as numerous government reports. His scholarship has appeared in such journals as
the American Economic Review, the Journal of Political Economy, the Review of
Economics and Statistics, Economica, Research Policy, the European Economic
Review, and Small Business Economics.
Professor Link’s public service includes being a member of the National Research
Council’s research team that conducted the 2010 evaluation of the US Small
Business Innovation Research (SBIR) program. Based on that assignment, he later
testified before Congress in April 2011 on the economic benefits associated with the
SBIR program. Link also served from 2007 to 2012 as the US Representative to the
United Nations (Geneva) in the capacity of co-vice chairperson of the Team of
Specialists on Innovation and Competitiveness Policies Initiative for the Economic
Commission for Europe.
Link’s other public service has included being a member of the Advisory Panel
to the National Aeronautics and Space Administration (NASA) on economic development strategies of Low-Earth Orbit and commercialization options for the
International Space Station (2014–2015), a member of the Advisory Committee of
the Canadian Institutes of Health Research (2011–2012), an advisor to National
Governor’s Association on state-university-industry partnership programs (2007–
2008), and a member of the White House Interagency Task Force on Internet
Protocols (2003–2004).
Chapter 1
Setting the Stage
Clothes make the man. Naked people have little or no influence
on society.
Mark Twain
The European Textiles, Clothing, Leather and Footwear
manufacturing sector
is undergoing a renaissance.
European Skills Council
Abstract This chapter sets the stage for the remainder of the book. Herein we
­discuss the meaning of knowledge-intensive entrepreneurship (KIE), and we d­ iscuss
why we have chosen to examine KIE and why we have chosen to emphasize the
European textile and apparel firms in this book. The remaining chapters in the book
are also outlined.
1.1 Introduction
One should be able to discern a lot about a book from its title. Generally, the main
title whets the reader’s appetite, and the subtitle draws his/her attention to the specific topic(s) being emphasized throughout the book. So it is, or so we hope it is,
with this book. The main title is “Knowledge-Intensive Entrepreneurship” and the
subtitle is “An Analysis of European Textile and Apparel Firms.”
Both titles deserve an explanation, and we offer one in the following sections of
this chapter. In Sect. 1.2, we discuss the meaning of knowledge-intensive entrepreneurship (KIE), and in Sect. 1.3 we discuss why we have chosen to examine KIE
and why we have chosen to emphasize the European textile and apparel firms in this
book. In addition to these explanations about our choice for the focus of the book,
we also offer in the sections that follow an explanation for our focus on European
textile and apparel firms under the umbrella of entrepreneurship, in general, and
under the umbrella of KIE, in particular.
© Springer International Publishing AG 2018
N.J. Hodges, A.N. Link, Knowledge-Intensive Entrepreneurship, International
Studies in Entrepreneurship 39, https://doi.org/10.1007/978-3-319-68777-3_1
1
2
1 Setting the Stage
Our focus on the topics of entrepreneurship, textiles, and apparel is academic as
well as pragmatic. From the academic side, we have more than six decades of cumulative research, teaching, and practical experience in the area of entrepreneurship, in
general, and on topics related to textiles and apparel, in particular. This book represents a synthesis of our backgrounds, as well as an opportunity to suggest a future
research agenda for the interested scholar. Thus, this general and specific focus resonates from the perspective of our own scholarship and from our inclination to encourage others to undertake new research in these areas from the perspective of KIE.
Our explanation of KIE in Sect. 1.2 below is more than a cookie-cutter definition of the concept. KIE is a relatively new topic in the entrepreneurship literature—new compared to the intellectual history on and debate about the broader
concept of entrepreneurship—and thus KIE deserves to be placed within the
broader scope of the meaning of entrepreneurship. Correctly placing the concept of
KIE in the broader literature is important from our perspective because it helps one
avoid simply using the term KIE as a popular or topical descriptor. As we note
below when we define KIE, there is a vast and growing literature on the topic,
much of which fails to define precisely the meaning of the concept. In fact, our own
overview of the literature shows no more than a handful of scholars who have taken
the time and effort to bound precisely KIE from a definitional perspective. The
reader will see that we embrace the concept of KIE through our description of the
data that we use in later chapters to describe the characteristics and behaviors of
European textile and apparel firms.1
We discuss the pragmatic reasons, beyond those associated with our academic
backgrounds, for our choice to study the European textile and apparel industries in
Sect. 1.3 below. And, our reasons for our focus on this topic are not mutually exclusive; our reasons are intertwined as might be expected of any study of the topic of
entrepreneurship. First, firms in these two industries occupy a central place among
the European economies. Second, there is a conspicuous void of research that
focuses on those industries, especially at the firm level, within the extant academic
and professional literatures on industry studies. Third, one reason for this industry-­
specific void, or so we conjecture, is that there has previously been a paucity of
empirical information about the textile and apparel industries from which to draw
inferences about either a pattern of firm behavior over time or a pattern of firm
behavior across countries. We are privileged to have at our disposal access to portions of a unique and robust database from which we can discern patterns of behavior of firms in these two industries over time and across countries. Thus, we view
this book as a first step to begin to close this research gap and to gain insight into
these two important European industries. Fourth, there is widespread belief that
innovation and technological change will be drivers of the renaissance of these two
industries throughout the European Union (EU), as we explicitly stated in the
­second epigram at the beginning of this chapter. The European Skills Council (2014)
1
Throughout the book, we will use the acronym KIE as both a noun and an adjective: KIE will
refer to knowledge-intensive entrepreneurship, knowledge-intensive entrepreneurial firms, or a
knowledge-intensive entrepreneur.
1.2 Knowledge-Intensive Entrepreneurship
3
so noted that the increasing pace of technological change within manufacturing
techniques and materials, such as automatic cutting systems, is a driver of the future
growth of the European textiles and apparel firms and thus their industries. And
fifth—and this justification for studying these industries is related to several of the
reasons that we already mentioned—the size and economic performance of the
firms in the US textile and apparel industries have been declining over the past two
decades. A detailed examination of the behavior of European industries, especially
from a KIE perspective, might facilitate our ability to offer policy recommendations
and guidance to begin to reverse these declining trends in the United States. And in
fact, to anticipate our emphasis on US textile and apparel industries in Chap. 10, our
empirical examination of the relationship between the sources of knowledge that
European firms in these two industries rely on and the behavioral growth strategies
that they adopt to achieve purposeful economic performance might provide important background and justification for us to suggest a policy road map applicable to
textile and apparel firms in the United States.
Finally, in Sect. 1.4 below, we reemphasize the purpose of the book and we
briefly outline the remaining chapters.
1.2 Knowledge-Intensive Entrepreneurship
The term entrepreneurship is certainly well known to most readers, although a
­random poll of any ten readers, whether they are academics or not, might well lead
to ten or even more definitions of who an entrepreneur is or what he/she does. That
fact aside, most readers will have a general notion about which the concept of entrepreneurship refers. Below we offer guidance on focusing that concept from entrepreneurship, in general, to knowledge-intensive entrepreneurship, in particular.
The adjective phrase knowledge intensive might cause some to pause after seeing
it in the title of this book, and then it might evoke the urge to ask the logical question: Isn’t all that an entrepreneur does based at least somewhat on the intensity of
his/her knowledge? One might think so. So, the next question the perceptive reader
might ask is: Why are the authors taking the time to clarify and elaborate on the
main title of this book?
The term knowledge-intensive entrepreneurship has a specific meaning to a
­specific group of academics who engage in research in the broader field of entrepreneurship. Generally, at least from our perspective, the acronym KIE emphasizes
sources or bodies of information that an entrepreneur relies on when he/she does
what he/she does; generally, the term entrepreneurship refers to the perception of an
opportunity and subsequent action of an individual to pursue the perceived opportunity. The term entrepreneurship, when used, omits any emphasis on the knowledge
base for his/her ability to perceive opportunities. Stated differently and using academic terms, KIE emphasizes the human capital characteristics of an entrepreneur
that guide his/her perceptive behavior. That said, we discuss below several more
specific definitions of KIE that are present throughout the literature. To anticipate
4
1 Setting the Stage
some of the descriptive information that we provide in later chapters and that we
then analyze in those chapters, we do in fact explore the sources of knowledge that
the entrepreneurs who are involved in the European textile and apparel industries
rely on and some of the economic consequences for doing so.
Before proceeding with a definition or description of KIE, we emphasize our use
of the word explore in the previous paragraph. Much of the remainder of the book
involves empirical analyses, albeit descriptive rather than causal. These empirical
analyses are exploratory in nature, although we do have some a priori hypotheses
guiding our investigation. As such, we are allowing the data to inform our theory of
KIE firms in the textile and apparel industries. Thus, so the reader is fully aware of
our modus operandi, we are exploring new data in an effort to glean an initial understanding of the entrepreneurial behavior of firms in these two industries.
Let us begin with the term entrepreneur and then let us define the term entrepreneurship to refer to what the entrepreneur does. Throughout the intellectual history
about entrepreneurship, the entrepreneur has been thought of in many different
ways, two of which relate to the static versus dynamic nature of the activities in
which he/she is involved. For example, drawing on the writings of Hébert and Link
(1988, 1989, 2006a, b, 2009),2 over the years the entrepreneur has been characterized by some in both the scholarly and professional literatures as the person who
performs static economic roles within a business setting or business ecosystem
(e.g., the person who is an industrial leader, a manager or a superintendent, a person
who supplies financial capital, an employer of factors of production, an owner of an
enterprise, a contractor, an allocator or coordinator of resources among alternative
uses, or even an arbitrageur). Other scholars and practitioners have, over the years,
characterized the entrepreneur as one who takes on a more dynamic role within a
business setting or business ecosystem (e.g., a decision maker, an innovator, or one
who assumes the risk and uncertainty associated with his/her actions). As Hébert
and Link suggest (2009, p. 105):
Entrepreneurial action means creation of opportunity as well as response to existing
­circumstances. Entrepreneurial action also implies that entrepreneurs have the courage to
embrace risks in the face of uncertainty. The failure of perception, nerve, or action renders
the entrepreneur ineffective. For this reason, we must look to these elements for the distinctive nature of the concept, not to the circumstances of action or reaction.
We set forth in Table 1.1 our characterization of the static versus dynamic roles
that have been, over time, attributed to an entrepreneur within the literatures. Of
course, these roles are not perfectly separable. For example, a manager might also
allocate resources and an innovator might also be a risk taker.3
Regardless of the role that one accepts for defining an entrepreneur, he/she is
assumed to act on the basis of some fundamental body of knowledge or information.
2
See, for example, Hébert and Link (1988, 1989, 2006a, b, 2009) history of intellectual thought
about the entrepreneur. Obviously, we have written about entrepreneurs and entrepreneurship
many times, so some duplication of discussions is inevitable.
3
See Audretsch et al. (2016) for a detailed description of static and dynamic entrepreneurship and
for a discussion of the evolution of each concept.
1.2 Knowledge-Intensive Entrepreneurship
5
Table 1.1 Characterization of the static versus dynamic roles of an entrepreneur
Static role of an entrepreneur
An industrial leader
A manager or a superintendent
A person who supplies financial capital
Dynamic role of an entrepreneur
A decision maker
An innovator
One who assumes risk and
uncertainty
An employer of factors of production
An owner of an enterprise
A contractor
An allocator or coordinator of resources among
alternative uses
An arbitrageur
His/her actions are not random or schizophrenic. That body of knowledge that an
entrepreneur embodies—his/her human capital—might only consist of codified
­elements (i.e., simple rules to follow based on, say, one’s experiences), or it might
only be made up of tacit elements (i.e., experiential insight), or it might be comprised of a combination of both codified and tacit elements. Amoroso, Audretsch,
and Link suggest (forthcoming):
The extant literature that is at the crossroads between sources of knowledge and the
­experiential and intellectual base of an entrepreneur (i.e., dimensions of his/her human
capital) suggests that it is through experience and through education that an entrepreneur
obtains knowledge.
With the static roles of an entrepreneur, as we described above with reference to
Table 1.1, no original ideas or directions are necessarily present. Such an entrepreneur simply fulfills a task in a particular manner, or in an established or mandated
manner. Some such entrepreneurs might do their task better than others, but the
actions involved are for the most part predefined, and the outputs from the action are
for the most part predictable. This is not the case with the dynamic roles of an entrepreneur, as we have presented them above in Table 1.1. A dynamic entrepreneur is,
broadly speaking, one who perceives an opportunity and has the ability to act on
that perception. We will refer to perception and action throughout the book, and in
so doing we emphasize that both activities—perception and action—are critical
characteristics of an entrepreneur. The perception of an opportunity might build on
static as well as dynamic concepts, and those dynamic concepts might have evolved
from change. But, perception of an opportunity might involve understanding the
capabilities of an innovation that has tacit elements, where innovation simply refers
to something new; and the responsive action, given perception, is likely sui generis
to his/herself or to his/her firm, and it likely involves accepting the risk and uncertainty that characterizes newness.
Hébert and Link (2009, p. 105) ask: “Does it matter that the entrepreneur is the
person who provokes change or merely adjusts to it?” And, Hébert and Link (2009,
p. 105) answer their own question: “If we rely on the most elemental features of
entrepreneurship—perception, courage, and action—the answer is probably not.”
6
1 Setting the Stage
To repeat, we will return to this characterization of an entrepreneur and of a KIE
individual or firm in terms of perception and action throughout the book.
Schumpeter, who some refer to as the Father of Entrepreneurship, defined the
activities of the entrepreneur in dynamic terms albeit within a specific setting. The
entrepreneur is the person who innovates and who makes new combinations within
a production environment (Schumpeter 1934, p. 78):
Everyone is an entrepreneur only when he[/she] actually carries out new combinations, and
[he/she] loses that characteristic as soon as he has built up his business, when he settles
down to running it as other people run their business.
Is an entrepreneur born or is an entrepreneur made? That question has echoed
through the halls of the ivory tower for decades, and it has even occupied many a
page in the popular presses. However, extending that debate would take us well
beyond the scope of this book. A more subtle, and perhaps more manageable, question for us to ask, and of course attempt to answer with reference to the “K” in KIE
textile and apparel firms, is: What forms the basis on which an entrepreneur perceives an opportunity? Or: Where does an entrepreneur get his/her ideas? And, of
course, a related question is: What forms the basis for how an entrepreneur acts on
his/her perception of an opportunity?
Regarding the first and second questions, the origin of ideas is an important topic
which has been addressed by eminent scholars from multiple disciplines, and then
debated, and then debated yet again. In fact, one might trace the origins of the story
we tell in this book to the hamlet of Wrighton, in the county of Somerset, in southwest England. There, in 1632, John Locke was born. Educated in medicine at the
University of Oxford, Locke soon transcended his formal training to become one of
the most influential philosophers of his time earning, posthumously, the titles of
Founder of British Empiricism and Father of Classical Liberalism. Thus, we turn to
Locke to begin to understand the origin of ideas by reflecting on his following
observation (Locke 1996, p. 59):
All ideas come from sensation or reflection. Let us then suppose the mind to be, as we say,
white paper, void of all characters, without any ideas: How comes it to be furnished?
Whence comes it by that vast store which the busy and boundless fancy of man has painted
on it with an almost endless variety? Whence has it all the materials of reason and knowledge? To this I answer, in one word, from EXPERIENCE.
Similarly, Hume refined Locke’s ideas. He referred to experiences in terms of
impressions, feelings, and sensations (Hume 2007, pp. 7–8):
So we can divide the mind’s perceptions into two classes, on the basis of their different
degrees of force and liveliness. The less forcible and lively are commonly called ‘thoughts’
or ‘ideas’. The others have no name in our language or in most others, presumably because
we don’t need a general label for them except when we are doing philosophy. Let us, then,
take the liberty of calling them ‘impressions’, using that word in a slightly unusual sense.
By the term ‘impression’, then, I mean all our more lively perceptions when we hear or see
or feel or love or hate or desire or will. These are to be distinguished from ideas, which are
the fainter perceptions of which we are conscious when we reflect on our impressions. …
Put in philosophical terminology: all our ideas or more feeble perceptions are copies of our
impressions or more lively ones.
1.2 Knowledge-Intensive Entrepreneurship
7
Schumpeter, implicitly embracing the spirit of Locke and Hume (although we do
not know if Schumpeter realized he was so doing it), offered guidance to the antecedents of dynamic entrepreneurship, and that guidance generally pointed to one’s
experience and one’s leadership. Schumpeter recognized that the knowledge that
kindles an innovation can be new or already existing, but, according to Schumpeter
(1928, p. 378):
It is not the [per se] knowledge that matters, but the successful solution of the task sui
generis of putting an untried method into practice—there may be, and often is, no specific
novelty involved at all, and even if it be involved, this does not make any difference to the
nature of the process.
To return to more contemporary scholars and to return to the knowledge that
underlies entrepreneurial actions, consider the views of Schultz. He bridged the
connection between ideas and entrepreneurship in terms of the connection between
knowledge and education (Schultz 1975, p. 843):
There is enough evidence to give validity to the hypothesis that the ability to deal successfully with economic disequilibria is enhanced by education and that this ability is one of the
major benefits of education accruing to people privately in a modernizing economy.
Schultz (1975, p. 843), for example, was well aware that the connections
between and among knowledge, ideas, and education are neither linear nor smooth;
addressing or acknowledging them is merely “the first step on what appears to be a
long new road.” This new road is sure to contain many potholes and even some
dead ends.
However, Machlup (1980), among other scholars, filled in some of the potholes
and turned the dead ends into detours and the detours into purposeful redirections.
He, for one, argued that formal education is only one source of knowledge; knowledge is also gained experientially and at different rates by different individuals.4
Individuals can accrue knowledge from their day-to-day experiences which “will
normally induce reflection, interpretations, discoveries, and generalizations”
(Machlup 1980, p. 179). Moreover, he wrote (Machlup 1980, p. 179):
Some alert and quick-minded persons, by keeping their eyes and ears open for new facts
and theories, discoveries and opportunities, perceive what normal people of lesser alertness and perceptiveness, would fail to notice. Hence new knowledge is available at little or
no cost to those who are on the lookout, full of curiosity, and bright enough not to miss
their chances.
This background discussion finally brings us to the point of offering a definition
of KIE, and having a precise (or close to precise) definition will allow us to align
ourselves with the extant literature on which the descriptive empirics in the book are
based. It is, however, surprising just how few precise definitions there are of KIE as
we alluded to above. We have summarized a sample of the semantically different yet
conceptually similar definitions that have been offered in the academic literatures in
4
We describe the educational background and the experience background of European textile and
apparel founders in Chap. 5.
8
1 Setting the Stage
Table 1.2, keeping in mind an understanding that knowledge per se is the basis for
all entrepreneurial actions.5
These definitions reflect common ideas. We emphasize that there might not exist
an accurate definition of KIE, either referring to a knowledge-intensive entrepreneur or knowledge-intensive entrepreneurship. The eminent scholars cited in
Table 1.2 are precise about how they [our emphasis] are thinking about the person
or concept, but accuracy might simply be in the eyes of the beholder. Based on these
definitions, it seems to us that entrepreneurship might reasonably be characterized,
as we alluded to above, in terms of the following three points, all of which reflect
the dynamic nature of the entrepreneur or the dynamic nature of what he/she does
and all of which mirror our argument that what defines an entrepreneur is both ­his/
her perception of an opportunity and his/her related and subsequent action on that
perception. Entrepreneurship is characterized:
• As a dynamic activity, rather than as a static one (e.g., a process)
• As a process of perception and action (e.g., one sees an opportunity, develops it
to a concept, and acts on it by bringing it to exploitation)
Table 1.2 Definitions of knowledge-intensive entrepreneurship (KIE)
Author(s)
Groen (2005,
p. 70)
Malerba (2010,
p. 4)
PLANET (2011,
p. 4)
Caloghirou et al.
(2011,
pp. 17–18)
Hirsch-Kreinsen
and Schwinge
(2014, p. 2)
Definition
“Entrepreneurial processes can be defined as processes, in which an
entrepreneur sees a business opportunity (ies), develops it to a business
concept and [then] brings it into exploitation. When these processes are to a
great extent based on relatively new (mostly academically derived) knowledge
or technology, we speak of knowledge intensive entrepreneurial processes”
“Knowledge-intensive entrepreneurship concerns new ventures that
introduce innovations in the economic systems and that intensively use
knowledge. From this broad definition, it follows that knowledge-intensive
entrepreneurship may take place in various ways: through the foundation of
new firms or through the display of entrepreneurial spirit with existing firms
or through the action of single individuals within non-profit organizations
such as universities or public laboratories”
“Knowledge-intensive entrepreneurship [refers to] a core interface between
two interdependent systems: the knowledge generation and diffusion system,
on the one hand, and the productive system, on the other. Both systems
shape and are shaped by the broader social context – including customs,
culture and institutions – thus also pointing at the linkage of
entrepreneurship to that context”
“KIE represents a core interface between two independent systems: the
knowledge generation and knowledge diffusion system on the one hand, and
the productive system on the other”
“KIE is considered an activity dealing with the uncertainties of discovering
and exploiting new opportunities, often driven by individuals but also by
established organizations …”
5
One might think that the definition of KIE is an outgrowth of how scholars thought about
k­ nowledge-intensive firms (KIF). For example, Blackler (1995, p. 1022) wrote: “Knowledgeintensive firms [are] organizations staffed by a high proportion of highly qualified staff who trade
in knowledge itself.” Using KIF as a starting point for KIE is, in our view, not that productive.
1.3 The European Textile and Apparel Industries
9
• As an innovative process characterized by risk and uncertainty (e.g., through actions,
one deals with the uncertainties of discovering and exploiting new opportunities)
A brief statement about risk and uncertainty might be warranted as background
to the third bulleted point above. As Leyden and Link wrote (2015, p. 39)6:
On the one hand, risk signifies a quantity capable of being measured, that is, the objective
probability that an event will happen. Because this kind of risk can be shifted from the
entrepreneur to another party by an insurance contract, it is not an uncertainty in any meaningful sense. On the other hand, risk is often taken to mean a non-measurable eventuality,
because all possible outcomes cannot be specified and/or the probabilities of all possible
outcomes are not known, such as the inability to predict the consumer demand. Knight
dubbed the latter true uncertainty and geared his theories of profit and entrepreneurship to
its magnitude.
In other words, risk has probabilistic outcomes whereas uncertainty does not.
One can indemnify against risk, but not against uncertainty. Innovation or innovative behavior, a characteristic of an entrepreneur, or of a KIE firm, entails both risk
and uncertainty.
1.3 The European Textile and Apparel Industries
One broad justification for our study of firms in the European textile and apparel
industries is, as we previously stated, the economic importance of these firms and
thus industries to the continent of Europe and, through exports, to the rest of the
world (European Commission 2017):
The textile and clothing sector is an important part of the European manufacturing industry,
playing a crucial role in the economy and social well-being in many regions of Europe.
According to data from 2013, there were 185,000 companies in the industry employing 1.7
million people and generating a turnover of EUR 166 billion. The sector accounts for a 3%
share of value added and a 6% share of employment in total manufacturing in Europe.
As we stated above, another justification for our academic focus on the European
textile and apparel industries is that there is a conspicuous void of scholarly research
in the extant literature on either of them, especially at the level of the firm. In fact,
there is even a noticeable absence of even the simplest empirical characterizations
of entrepreneurs or entrepreneurial firms in these two industries. While case studies
dominate a dimension of the existing empirical literature, case studies alone do not
always represent a systemic research-based investigation. Although the systematic
empirical literature on the topic of textiles and apparels is thin, the scholars who
have contributed to it are nonetheless asking interesting, perceptive, and remarkably
similar questions. We revisit these research questions in Chap. 2, and we offer there
a more in-depth review of the related institutional and academic literatures.
The reference to Knight in the quoted passage refers to Frank Knight (1921).
6
10
1 Setting the Stage
Finally, as we also stated above, an in-depth study of the European textile and
apparel industries might offer policy recommendations and even a road map to
reverse the downward trend of the US industries. To elaborate, the European counterparts to firms in the textile and apparel industries in the United States might be
ahead of the so-called resurgence curve in some dimensions. In particular (European
Skills Council 2014, p. 6):
The European Textiles [and] Clothing … sector is undergoing a renaissance. A sector that
has experienced a turbulent recent history [due particularly to the 2008–2009 economic
recession] is now beginning to re-emerge, leaner and more confident of its place in the
world. Driven by creativity and innovation, products manufactured … range from traditionally crafted fashion and textiles goods through to scientifically-led technical items.
Certainly, the decline of the US textile and apparel industries began decades ago
due to, among many other things, relatively less expensive labor costs in Mexico
and Asia—a point that we will emphasize in Chap. 9—but still, we suggest in Chap.
10 there are possibly lessons to be learned by US policy makers from the European
experiences and the so-called renaissance that the European textile and apparel
industries are beginning to realize.
1.4 Overview of the Book
Our book is exploratory and thus descriptive, rather than predictive, in its nature,
focus, and emphasis. It is replete with tables and charts, not to bring about ennui but
for the purpose of completeness. Our primary purpose in the book is to review the
extant institutional and academic literatures that are related to firms in the European
textile and apparel industries, as we do so in Chap. 2, and to reflect on that review
as we explore and as we describe relationships from a new and robust database on
KIE.7
More specifically, in Chap. 2 we overview relevant legislative histories, and we
review the extant literature related to the European textile and apparel industries. To
anticipate, the reader will realize that much of the academic literature related to
those two industries has not examined KIE, or the entrepreneurial behavior of KIE
firms, in general. The lack of a KIE emphasis in the extant literature is not a criticism of the scholarly intent and/or accomplishments of the involved researchers;
7
These data, which are a small portion of all the data in the AEGIS database, are discussed in some
detail beginning in Chap. 4. We thank the AEGIS consortium for providing data of the AEGIS
survey which supported the empirical investigation of knowledge-intensive entrepreneurship in
Europe in different sectoral, country, and socioeconomic contexts. This survey was conducted in
the context of the AEGIS research project (Advancing Knowledge-Intensive Entrepreneurship and
Innovation for Economic Growth and Social Well-being in Europe) co-funded by the European
Commission under Theme 8 “Socio-Economic Sciences and Humanities” of the 7th Framework
Programme for Research and Technological Development. We also thank Professor Yannis
Caloghirou of the National Technical University of Athens and Professor Nicholas Vonortas of the
George Washington University for their assistance in allowing us to use these data.
1.4 Overview of the Book
11
rather, that void motivates, in our opinion, the need to begin a more quantitative
description and analysis of these industries, especially at the firm level.
Our discussion in Chap. 2 motivates three overriding research questions, each of
which is explored in several of the following chapters:
• While there are many small firms that comprise the EU textile and apparel industries, how and to what extent are these firms entrepreneurial and/or innovative in
their behaviors?
• What might KIE, and, in particular, entrepreneurial and innovative behaviors,
mean for firm performance and/or industrial growth?
• What, if anything, do our empirical findings suggest for those small- and
medium-sized firms that comprise the US textile and apparel industries?
In Chap. 3, we illustrate trends in the textile and apparel industries using aggregate data and cross-country disaggregated data related to the number of enterprises
(i.e., firms) in each industry and to the corresponding number of employees. Our
descriptions in this chapter are presented in an effort to emphasize that these two
industries have been declining over time, at least in terms of these two traditional
metrics, but our descriptions also suggest that there are hints of a resurgence, or
what the European Skills Council referred to as a renaissance.8
In Chap. 4, we discuss the AEGIS database from which we are able to extract
firm-specific information to use as an empirical foundation of our analyses in the
other chapters. The AEGIS database was funded by the European Commission
under Theme 8 “Socio-Economic Sciences and Humanities” of the 7th Framework
Programme for Research and Technological Development. A focus of that project
was on dimensions of knowledge-intensive entrepreneurial behavior—behavior
related to both perception and action—under the assumption that KIE is one potential means through which an industry can realize economic growth and thus contribute to societal well-being.
The unit of observation in the AEGIS database is a small, entrepreneurial firm
established between 2002 and 2007, and those firms were drawn from ten European
countries. The countries represented in the database are (alphabetically): Croatia,
Czech Republic, Denmark, France, Germany, Greece, Italy, Portugal, Sweden, and
the United Kingdom. And, across these countries, a number of firms from the high-­
tech, low-tech, and knowledge-intensive business services sectors are represented.
Textile and apparel firms are in the low-tech sector of EU countries.
Our emphasis on KIE might be particularly timely, as we alluded to above, especially if one associates entrepreneurship with innovation, as the intellectual history
on entrepreneurship does.9 The European Union, in the face of “major economic
challenges that require an ambitious economic policy for the 21st century,” set forth
8
We realize that there are many metrics available to describe trends in the textile and apparel
i­ndustries. Those that we have selected were readily accessible and understandable at an intuitive
level and were complementary to the metrics in Chap. 3.
9
See Hébert and Link (1988, 1989, 2006a, b, 2009).
12
1 Setting the Stage
in the Europe 2020 Strategy10 its vision for Europe’s social market economy. This
vision (European Union 2012, p. 8):
Aims at confronting [the EU’s] structural weaknesses through progress in three mutually
reinforcing priorities:
• Smart growth, based on knowledge and innovation [emphasis added];
• Sustainable growth, promoting a more resource efficient, greener and competitive
economy;
• Inclusive growth, fostering a high employment economy delivering economic,
social and territorial cohesion.
Investing more in research, innovation and entrepreneurship is at the heart of
Europe 2020 and a crucial part of Europe’s response to the economic crisis. So is
having a strategic and integrated approach to innovation that maximizes European,
national and regional research and innovation potential.
Human capital may be the link between innovative activity—which itself is
related to technical capital—and individual entrepreneurs. According to the World
Economic Forum (2013: p.3):
A nation’s human capital endowment—the skills and capacities that reside in people and
that are put to productive use—can be a more important determinant of its long term economic success than virtually any other resource. This resource must be invested in and
leveraged efficiently in order for it to generate returns, for the individuals involved as well
as an economy as a whole. Traditionally, human capital has been viewed as a function of
education and experience, the latter reflecting both training and learning by doing. But in
recent years, health (including physical capacities, cognitive function and mental health)
has come to be seen as a fundamental component of human capital.
We present our descriptive analyses in Chaps. 5, 6, 7, 8, and 9. Based on our
knowledge about the academic literature on the textile and apparel industries, which
we summarize in Chap. 2, even the most cryptic descriptive examination of newly
established KIE firms in these industries is a contribution to academic thought. Our
use of the AEGIS database in this regard not only represents an academically centric
step forward from an empirical perspective, but also it conveys insight into the
nature of these firms from which we might glean a policy perspective relevant to the
United States. To the best of our knowledge, our descriptive analyses are the most
complete to date in the literature, and thus they are one small, but perhaps important, building block for future research.
In Chap. 5, we extract firm-specific information from the AEGIS database to
quantify descriptively selected characteristics of KIE firms in the textile and apparel
industries and of their founders. Regarding the former, the firm characteristics that
we focus on are the age of the firm and the numbers of current (as of the data of the
AEGIS survey) full-time and part-time employees. Regarding the latter, the characteristics of founders that we focus on are related to their human capital and financial
capital. That is, we quantify descriptively characteristics of the entrepreneurs themselves. Our descriptive analysis is offered in the aggregate, that is, for firms in the
textile and apparel industries as represented in the AEGIS database without regard
10
See http://ec.europa.eu/europe2020/index_en.htm.
1.4 Overview of the Book
13
to country, and it is cautiously offered on a country-by-country basis for c­ ompleteness,
although we generally refrain from intercountry discussions or c­ omparisons due to
small country sample sizes.
One human capital characteristic that we introduce and discuss in Chap. 5 is the
gender of the founder of the firm. In fact, from our vantage our focus on gender
might be a hallmark characteristic of this book because of the paucity of information, much less research, on that topic from a KIE perspective. We revisit and build
on this gender focus in subsequent chapters as well, and we do so in those chapters
descriptively and not with any other motivation.
In Chap. 6, we explore the sources of knowledge (i.e., information foundations)
used by KIE firms. Our descriptive analyses of sources of knowledge used by firms
for their formation and to develop perceptions for action are segmented again by
industry and then by country within each industry. Viewing sources of knowledge in
terms of information not only reflects on the brief epistemological introductory
theme of this chapter, but also it allows us to think about a conceptual framework on
how the knowledge base of a firm relates to the performance of that firm. This topic
becomes our segue to Chap. 8.
As an intermediate step, we describe the strategic behavior of KIE textile and
apparel firms in Chap. 7. The strategies we emphasize are not independent of each
other, but they are limited by the scope of the AEGIS survey and data. Specifically,
we are forced to focus on strategies that firms pursue to create and sustain the competitive advantage of the company in the market, on strategies related to sensing and
seizing opportunities within the firm, and on participation in strategic alliances.
Our description of the AEGIS data relevant to textile and apparel firms continues
in Chap. 8. There, we explore several performance variables, again limited by the
scope of the AEGIS survey and related data, including the propensity of a KIE firm
to commercialize a new or significantly improved good or service, to realize a
growth in sales, and to realize a growth in employees.
Chapter 9 explores two possible frameworks for understanding the economic
consequences of KIE action. These frameworks are illustrated in the following two
expressions, and we offer these expressions without any a priori bias, much less any
hypotheses, about which is more descriptive from a probative perspective:
Sources of Knowledge → Entrepreneurial Performance (1.1)
Sources of Knowledge → Strategic Behavior
→ Entrepreneurial Performance
(1.2)
We explore in Chap. 9, in a descriptive manner, the relationship between a KIE
firm’s sources of knowledge and how the use of such sources aligns with the firm’s
entrepreneurial performance. As shown in expression (1.1) and in Fig. 1.1 as illustrated by the curved arrow in the figure, sources of knowledge might be directly
related to the firm’s entrepreneurial performance. Or, sources of knowledge might
be indirectly related to the firm’s entrepreneurial performance by working through
the firm’s choice of alternative strategic behaviors, as shown by expression (1.2) and
14
1 Setting the Stage
Sources of Knowledge Strategic Behavior Entrepreneurial Performance
Fig. 1.1 Representation of direct and indirect paths from sources of knowledge to entrepreneurial
performance
by the linear model in Fig. 1.1. Our epistemological discussion above does suggest
that the effect of knowledge on entrepreneurial performance is direct, but of course
that is an empirical issue both in general and among KIE firms in the textile and
apparel industries. Our empirical findings about the relative strength of expressions
(1.1) and (1.2) from the AEGIS data might offer an important stepping stone for
understanding the European textile and apparel industries from what we call a
dynamic KIE perspective.
We briefly overview trends in the US textile and apparel industries in Chap. 10,
and we place that overview in an historical perspective. Given these recent US
trends, we build on our empirical findings from the European textile and apparel
industries to recommend one possible prescription for jump starting domestic
growth of those industries. More specifically, we proffer that the US textile and
apparel industries initiate programs to enhance the ability of firms to access particular sources of knowledge that might be related to entrepreneurial performance. This
policy recommendation follows from our interpretation of statistical relationships
among the AEGIS data and the constructs we develop in Chap. 9.
In Chap. 11, we briefly summarize the findings from our study with specific
reference to the three overriding research questions listed above, and we offer
­concluding remarks especially about directions for possible future research related
to the textile and apparel industries in any country.
Chapter 2
The European Textile and Apparel Industries:
An Institutional and Literature Review
If most of us are ashamed of shabby clothes and shoddy
furniture let us be more ashamed of
shabby ideas and shoddy philosophies.... It would be a sad
situation if the wrapper
were better than the meat wrapped inside it.
—Albert Einstein
How can anyone be silly enough to think himself better than
other people, because
his clothes are made of finer woolen thread than theirs. After
all, those fine clothes were
once worn by a sheep, and they never turned it into anything
better than a sheep.
—Sir Thomas More
Abstract As background for understanding the role of KIE within the EU textile
and apparel industries through the empirical analyses in later chapters, this chapter
summarizes the dynamics of the European textile and apparel industries in the post-­
2005 period. It also discusses the role of small firms in the present-day European
textile and apparel industries, in general, and specifically with respect to innovation.
And, it examines the research literature that explores KIE and, in particular, the literature that is focused on today’s European textile and apparel industries.
2.1 Introduction
Although there has been some research focused on small business and e­ ntrepreneurship
within the EU textile industry as a whole, few studies, if any, have specifically examined KIE as an avenue for firm or sector growth. One possible reason for this lies in
the fact that KIE is most often associated with high-tech industries in or sectors of an
economy. The textile and apparel industries are typically seen as low-tech, and the
firms therein are labor versus knowledge intensive. However, recent studies have
positioned these industries as ones in which KIE can foster growth through
© Springer International Publishing AG 2018
N.J. Hodges, A.N. Link, Knowledge-Intensive Entrepreneurship, International
Studies in Entrepreneurship 39, https://doi.org/10.1007/978-3-319-68777-3_2
15
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2 The European Textile and Apparel Industries: An Institutional and Literature Review
innovation and ones wherein products and processes are often evaluated within a
knowledge-based framework. To address this gap in the literature, we will examine
topics in KIE relevant to the two industries through the AEGIS database.
In order to situate an understanding of the role of KIE within the EU textile and
apparel industries through our empirical analyses, the rest of this chapter is divided
into four main sections: a summary of the dynamics of the textile and apparel industries in the post-2005 period; a discussion of the role of small firms in the present-­
day European textile and apparel industries, in general, and specifically with respect
to innovation; an examination of the research literature that explores KIE and, in
particular, that examines its role within today’s European textile and apparel industries; and an outline of research questions guiding the analysis in the rest of this
book that will be addressed using information from the AEGIS database.
2.2 Industry Dynamics in the European Union
The year 2005 was the year that everything changed for the European textile and
apparel industries. This change had an impact on firms not only in the European
Union but also in the United States and in fact much of the rest of the world. What
began as the Multi-Fiber Arrangement (MFA), in place since 1974 (and later
replaced by the World Trade Organization’s Agreement on Textiles and Clothing
(ATC) in 1995), would be completely phased out by the end of 2004. This phaseout
resulted in the liberalization of restrictions placed on the total number of imports of
textile and apparel products allowed into the European Union and in the United
States. Thus, the end of the ATC meant the removal of certain trade protections that
many in the industry had enjoyed for a very long period of time. Indeed, there is
some indication that the end of the ATC hastened the demise of industrial giants
through consolidation and reorganization in the United States (Gereffi 2000), while,
in the European Union, the phaseout led to larger key players through mergers and
acquisitions (Taplin and Winterton 2004).
Although anxiety was mounting in the period leading up to quota elimination,
the after effects have proven to be somewhat less apocalyptic than predicted (Curran
2009). In fact, prior to 2004, the EU textile and apparel industries had already seen
notable shrinkage as a result of offshore production and concomitant transfer of
low-wage jobs to developing countries (Jones 1997), import penetration combined
with early-stage reductions in quota restrictions (phases 1–3 of the progressive
reduction of ATC quotas), and the modernization of equipment and technology
(Jones and Hayes 2004). This shrinkage was, in part, a response to loosening restrictions that forced European manufacturers to consider developing greater production
efficiency at lower costs (Taplin 2006). Some firms responded by outsourcing low-­
skilled and low-wage labor to countries that they had preferential agreements with,
while others employed restructuring and increased use of Just-in-Time and Quick
Response Technologies (Taplin and Winterton 2004). The latter approach proved
helpful in reducing lead times and competing with retailers that, at the same time,
were increasingly gaining bargaining power with manufacturers (Taplin 2006).
2.2 Industry Dynamics in the European Union
17
Jobs in the textile and apparel industries had been suffering for some time prior
to 2005. According to Taplin (2006), from 1999 to 2002, clothing jobs in the
European Union declined by 18%, and textile jobs declined by 10%. Jones and
Hayes (2004) reported significant declines in industry employment in the United
Kingdom starting as early as 1993, as a reflection of “overall structural trends in the
UK economy” (p. 262). A report by the Organization for Economic Cooperation and
Development (OCED 2004) indicated that there had already been a loss of four million jobs in these industries within developed countries prior to the elimination of
quota restrictions. This is not to say that the loss of employment did not continue into
2005 and beyond because these industries had been experiencing the effects of the
globalization of the economy long before the end of the MFA/ATC. According to
Hines (1997), the European Union was already involved in trade liberalization with
Central and Eastern European countries as well as a bilateral trade agreement with
Turkey. In preparation for quota phaseout, reductions in tariffs began as early as
1995, the year when the MFA was replaced by the ATC (Hines 1997; Taplin 2006).
One of predictions of the end of the MFA/ATC was that the more competitive
developing countries at the time, such as India, Bangladesh, and Pakistan, would
actually benefit by being able to export more textile and apparel products into the
United States and Europe. This would prove to be true for some (Lal and Mohnen
2009). However, by 2008 China held 42% of the European Union’s market share for
exports (Curran 2009). Because the textile and clothing industries are relatively low
cost in terms of equipment and labor, the barriers to entry are low, thereby benefitting developing countries (Taplin and Winterton 2004).
Another industrial shift occurred with regard to jobs, moving from the low-skill-­
level jobs required to cut and sew apparel to the more value-added service segments
of the supply chain, such as design, product development, marketing, and retail
(Jones and Hayes 2004). A focus on technical textiles would also remain limited to
those developed countries that had greater access to technology and support for
innovation (Jones and Hayes 2004).1 Some even predicted that because much of the
industry that would remain during the post-MFA/ATC period would be comprised
of small- or medium-sized firms, this would allow for a stronger focus on innovation
and more agility in responding to subsequent industry changes (OCED 2004).
According to the findings of Lal and Mohnen (2009), national policies related to
technology, along with rising wages and a shift in manufacturing base toward capital- versus labor-intensive sectors, helped to determine a country’s ability to produce and export, along with changes in the WTO. Moreover, those that ultimately
thrived tended to focus on capacity building, including innovative activities that
resulted in better and more efficient use of information technology, upgraded
­equipment, and a focus on high technology, including digitizing processes, to
increase competitiveness (Lal and Mohnen 2009).
In the late 1990s and early 2000s, countries within the European Union were
employing different strategies relative to productivity and capital investment, and
these strategies often differed between apparel and textiles. Key national players at
1
Technical textiles are defined as textile fibers, materials, and support materials that meet technical
requirements rather than aesthetic criteria (European Skills Council 2014).
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2 The European Textile and Apparel Industries: An Institutional and Literature Review
this time included the five European countries with the strongest historical ­traditions
of manufacturing: Italy, the United Kingdom, France, Spain, and Germany and a
core distinction between apparel (southern Europe, including Italy, Portugal, and
Spain) and textile production (northern countries like the United Kingdom,
Germany, and the Netherlands) (Taplin 2006). Retailers were beginning to take on
more power, as they were, and still are, “considerably larger than their suppliers and
in direct contact with the final consumer” (Guercini 2004, p. 333), ultimately taking
over the role that was once occupied by the manufacturer (e.g., vertical integration)
(Jones and Hayes 2004). This shift would lead to faster speed-to-market and
­eventually become known by the term fast fashion.
Finally, as Curran (2009) noted, the first year of no trade restrictions in the
European Union also coincided with the beginning of the global financial crisis, or
what would later be known as the Great Recession, thereby making it difficult to
tease out the true impact of trade liberalization. As Curran (2009, p. 306) wrote:
Some falls in trade may be temporary due to falls in demand, while others are likely to be
permanent shifts in competitiveness which signal more fundamental changes in the
­geography of clothing production.
Likewise, countries within the union that were once only focused on manufacturing,
such as Turkey, have begun to focus more on value-added operations such as design,
product development, and even branding (Kustepeli et al. 2012).
Shifts in the industry appear to be in response to consumer demands and the
attempt by manufacturing firms to maintain competitiveness in an industry where
products are moving through the value chain at faster rates than ever before.
Retailers sought to keep up with the demands of their customers, who, in turn,
expected new products to be available on a regular basis. Apparel retail firms like
Zara, based in Spain, and H & M, a Swedish firm, created models for the lean and
agile supply chain that has come to characterize fast fashion and offered new
approaches to making apparel through innovation in both product design and production. In the case of H & M, the firm keeps its product design very close to the
consumer and seeks a balance between innovation via its own designers and current
trends tracked by in-house forecasters (hm.com). In contrast, Zara’s production is
what is most innovative, as it maintains a supplier network that is relatively small in
scope, doing much of the work in close proximity and outsourcing very little.
Approximately 50% of Zara’s production is located in Europe, unlike its competitors who produce in Asia and South America. Zara’s vertically integrated structure
allows the brand to achieve short lead times and offer products from design to delivery in just 14 days (zara.com).
As increasing a firm’s value through product offerings and brand management becomes more important to firms in countries throughout the European
Union, innovative approaches to information gathering as well as methods for
getting the product to market quickly, such as those used by Zara and H & M,
have become key to competing in an industry that must cater to constant change.
Paradoxically, the biggest challenge to achieving this agility is that this is an
industry that relies heavily on traditional methods of production and has
2.2 Industry Dynamics in the European Union
19
h­ istorically not adjusted quickly to consumer tastes. As consumers are now
connected through the global network of retailers and brands via the Internet
and social media, the industry is under even greater pressure to adapt and meet
the needs of a consumer base that has become more demanding and globally
dispersed than at any other time in history.
2.2.1 The Effects of Globalization
Much like today’s apparel consumer, the industry’s value chain is also global. For
most apparel firms, whether manufactures or retailers, producing a garment can
involve a complex value chain of several, sometimes a dozen or more partners, and
this process typically happens via a network of businesses located all over the world.
As Artschwager et al. (2009, p. 142) explained, the process usually follows this path:
Garment design and development are made in Europe, supported by a worldwide spread of
design offices. Fabrics and other raw materials are sourced in the Far East and sorted in the
central storehouse at headquarters’ site. Assembling is performed in Eastern Europe, and
distribution to shops and wholesalers is conducted also centrally or at distribution centers.
Fabric and garment conditioning (like testing, washing or repair) is executed by quality
checking organizations in Europe or in the Far East. For innovative garments often weaving/
knitting and finishing mills have to be involved directly in the new product development
process. Finally, transport and shipment is carried out by worldwide logistic organizations.
In manufacturing, localization and, particularly, proximity are typically seen as key
factors in competitive advantage (Porter 1985), offering infrastructure, the potential
for skilled labor, and support for entrepreneurship. This was historically the case for
the textile and apparel industries throughout the European Union, wherein close
proximity allowed for industry clusters to form and networks to develop across the
value chain. However, globalization is thought to weaken the tie strength that contributes to the productivity of such clusters, particularly given the trade liberalization experienced in Europe since 2004–2005. Whether or not being a part of a
cluster fosters competitive advantage in the face of global networks and value chains
is a topic of debate within the literature and depends on where in the European
Union one looks.
Puig and Marques (2011) examined the relationship between localization and
proximity and the effect of both on firm performance with a sample of 10,490 Spanish
textile firms. The time period under investigation spans from 2001 (later phases of
the ATC) to 2006 (post-ATC). Industrial districts largely characterize the Spanish
textile industry. However, according to Puig and Marques (2011, p. 1424), industrial
districts differ somewhat from clusters, as the former are characterized by a:
… geographically defined area and centered upon a type of production mainly composed of
a large number of small and medium enterprises (SMEs), a flexible organization of production that allows satisfying a differentiated demand and strong linkages between economic
and non-economic (sociological, cultural, and ethical) factors.
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2 The European Textile and Apparel Industries: An Institutional and Literature Review
These authors set out to investigate whether the strength of these districts had, as
some predicted, suffered with the increasing globalization of the industry. Their
findings indicate that although globalization has changed productivity in these districts (at least in the Spanish industry), as the authors point out, this change may not
be all negative, in that it could lead to greater innovation and a higher degree of
flexibility through collaboration of local firms with “knowledge-intensive multinationals.” That is, “[t]urning globalization and localization into complements and
allowing local firms to move up the value-chain into more knowledge-intensive
sectors” (Puig and Marques 2011, p. 1435), in as much as both localization and
globalization can, when combined to the right degree, actually enhance the competitiveness of firms within the districts.
Puig et al. (2013) then performed a longitudinal analysis to investigate the role of
location alongside structural characteristics of firms (e.g., age, subsector specialization) from 2000 to 2008. The goal was to determine why some firms located in
regions of industry concentration fail. The broader issues underlying the need to
study failure include understanding what it means for the potential decline of the
industry itself as well as the scope of its potential economic impact on regions
defined by industry concentration (e.g., clusters or districts). The authors found that
failure is a function of location and structure, specifically those firms that are
younger, located in geographic areas of medium density, and are part of subsectors
that are more intensive in manpower, that is low (e.g., clothing) versus high tech
(e.g., technical textiles). The findings suggest that the key to staying alive may be to
build in innovative approaches to products as well as production in ways that alleviate the more traditional reliance on labor-intensive practices and, instead, lean
toward more knowledge-intensive ones. We will return to this concept again in later
sections of this chapter.
A study by Hirsch-Kreinsen et al. (2006) suggested that emphasizing the building of networks between companies has, in some ways, helped firms be more globalized yet maintain their local embeddedness. It is often assumed that as a mature
industry, textile firms employ a local orientation, while those within a more high-­
tech industry, such as pharmaceuticals, employ a global one. Their findings suggested that there is not one specific orientation strategy (whether local or global)
that is characteristic of the European Union’s textile and apparel industries.
However, the value chain of the industry has, out of necessity, become more global
than local in orientation, risking loss of the benefits that have historically been
derived from clusters and districts. The authors suggested that (Hirsch-Kreinsen
et al. 2006, p. 19):
Collaboration and networking between companies of different industries at regional,
national and transnational levels are increasingly important determinants of the innovativeness and competitiveness of individual companies.
These authors emphasized the need for EU as well as national and local policies that
strengthen the capabilities of firms to make strategic decisions as to orientation to
be able to be innovative and therefore maintain competitiveness in both the local
and the global context.
2.2 Industry Dynamics in the European Union
21
2.2.2 Location and Knowledge Management
As Lal (2009, p. 10) pointed out, knowledge management is critical to ­competitiveness,
in that:
Within the context of increasing levels of knowledge-intensive production, firms began to
compete not only in terms of prices but also on the basis of their ability to innovate.
In a study of firm-level innovation, Lal examined the value that innovation adds to
the chain, whether through products or through enhanced productivity, faster delivery, and the ability to meet quality standards as well as address environmental and
labor requirements in the production of textile products. The argument that the
author makes is that since the 1970s, all things considered, production in the textile
sector has become increasingly knowledge intensive, in as much as (Lal 2009, p. 10):
… investments in intangibles such as knowledge of soils and farming techniques, research
and development including the production of software, and the application of biotechnology, design capabilities, engineering skills, training, monitoring, marketing and management have come to play a greater role in the production of goods and services.
When considering the broad scope of what is involved in producing a garment, from
fiber to fabric to final destination, there are multiple points where knowledge can be
the key to maintaining competitiveness.
In an attempt to develop a global perspective on innovation research and apply
internationalization theory to innovation specifically within the Spanish textile sector, Vila and Kuster (2007) examined whether there is a correlation between degree
of innovation and level of internationalization within a mature industrial sector that
operates within a highly competitive environment and is comprised primarily of
small-sized firms. These authors stated (p. 17):
In terms of the differentiation strategy, firms start to think about innovation because they want to
offer different things in different markets, and this is the essence of the innovation framework.
Vila and Kuster (2007) found that those firms that were more internationalized
tended to be more innovative (specifically in terms of strategy and process innovation but not necessarily in terms of product or market innovation). They also considered firm age and, in contrast to some of the existing literature, found little difference
between newer and older firms in terms of internationalization. They posited that
this finding may stem from the fact that internationalization is necessary for survival
in the textile and apparel industries, regardless of size.
In a similar vein, Kustepeli et al. (2012) found that though an industry may be the
same within two geographical locations, the regional innovation systems within
these locations may differ, as the two Turkish cities in their particular study did
(Denizli and Adiyaman). This means that innovation activities will not necessarily
be similar in both, and this difference should be taken into account when policy
initiatives are developed. The aim of the authors was to examine differences across
the region in terms of knowledge use and innovativeness, finding that an important
aspect within a successful innovation system is how and the extent to which firms
within the location form networks. As they stated it (Kustepeli et al. 2012, p. 230):
22
2 The European Textile and Apparel Industries: An Institutional and Literature Review
Networks offer competitive advances in innovation, especially in industries that are
­characterized by short product cycles and rapid market changes … Networking relationships affect the generation, diffusion, application and exploitation of knowledge, and therefore enhance a region’s ability to innovate.
Along similar lines, Danskin et al. (2005) examined the role of knowledge management, specifically “acquisition, retention, maintenance, and retrieval of knowledge”
as a means of maintaining competitive advantage through innovation within the
textile products value chain. Based on Porter’s (1985) strategies for competitive
advantage, knowledge management is examined relative to two strategies that the
authors deem important to the textile products industry: cost leadership and differentiation. Strategies that focus on low cost use knowledge to develop shorter lead
times, lower prices, and fewer costs in the manufacturing process. The authors posited two primary types of differentiation strategies useful for the textile and apparel
industries: market based, through product position, and innovation based, through
application of technology relative to consumer needs. The latter is concerned with
developing “entirely new markets” but could potentially be based on a shared network where knowledge is integrated throughout the value chain (raw materials to
fibers to fabrics). Although there are several ways that effective knowledge management can support innovation and provide competitive advantage, as the authors
pointed out (Danskin et al. 2005, p. 98):
Only recently have knowledge management systems as a means of aligning and optimizing
value-chain relationships received attention by textile researchers.
As firms move toward sharing knowledge within a system of smaller, more agile,
and fast-moving networks, questions as to the role of the broader global platform on
which textile products are produced and whether this platform will need to shift
back to being more localized have become increasingly important.
2.3 Small Firms and the EU Industry
Small firms are the norm within the industry, with 60% of the EU workforce
employed by firms with 50 employees or less (Taplin 2006). According to Walter
et al. (2009b), the average apparel firm in the European Union employs 19 people,
and the average textile firm employs 25. The European Skills Council (2014) reports
even smaller average sizes per firm: ten employees for textile firms and eight
employees for apparel firms. However, success among smaller firms has not
occurred equally across the countries that comprise the union.
Guercini (2004) indicated that the small- and medium-sized enterprises (SME)
have fared better in Italy, hence its strong presence within the overall EU industry.
Known as the “Italian specificity factor” (Guercini 2004, p. 320), the industry in
Italy differs in some ways from the EU industry as a whole. For example, instead of
shrinking, as in other EU nations, Italy showed a slight increase in total employment
numbers in 2002 when compared to 1999 (Guercini 2004). Italy also typically
2.3 Small Firms and the EU Industry
23
exports more clothing than the rest of the EU countries, which represents a s­ ignificant
share of the Italian manufacturing industry (Guercini 2004). Moreover, positive
gains for employment continued into 2005 and for exports continued into 2008
(Truett and Truett 2014), and overall, Italy has continued to fare better than other
EU countries in demand and employment (Truett and Truett 2014). Truett and Truett
posited that Italy’s competitive advantage lies in its reputation for outstanding quality and that, as a survival strategy, it is important for the country to maintain this
image. Likewise, when compared to Italy’s retail sector, the rest of the European
Union has seen the virtual disappearance of the independent retailer (e.g., traditional department stores) in the face of general and discount retailers (Walter et al.
2009b). Large retailers like Tesco and Carrefour have shifted the power to the distributor and away from the manufacturing sector, and because the latter remains
fragmented and small in scale, the former is able to maintain control over a supply
chain rooted in low-cost developing countries rather than locations across the
European Union (Walter et al. 2009b).
A 2014 report on European Competitiveness by the European Commission
pointed to a strong need for policy that fosters the growth of firms and particularly
SMEs through internationalization and innovation. The Commission reported
(European Commission 2014, p. 16):
Comprising over 99% of all firms and 60% of total output in the EU, SMEs are central to
efforts to improve long-run competitiveness, particularly in international markets, where
historically they have underperformed as compared with larger firms.
Exporting and foreign direct investment are the two most common modes of foreign
market entry among SMEs in the textile and apparel industries. With respect to
innovation, the report focused on product innovation, suggesting that this type of
innovation “contributes to increasing and to preserving employment in all phases of
the business cycle and in all sectors” (European Commission 2014, p. 18). It seems
that even a low-tech industry sector such as textiles can benefit from incentives that
lead to more efficient, if not innovative, processes and not just those that are based
on new technologies (Schwinge 2015). Indeed, according to Hines (1997, p. 197),
industrial policy in the EU textile sector has historically focused on:
… technological development, training, diversification and conversion, information and
communication … [along with] development and more effective use of existing industrial
networks, know-how and technology transfer.
Despite the changes that occurred with the removal of trade restrictions and shift of
market power to the retailer, the EU textile and apparel industries have found ways
to remain competitive through modernization and reorganization, such as investing
in new technology and innovation capabilities, entering high value-added markets,
and adopting new business models (Walter et al. 2009b). Such efforts have had a
positive impact on the staying power of these industries broadly, in as much as the
160,000 firms that existed across the European Union in 2007 employed 2.5 million
people, helping the European Union to maintain its position as the second largest
exporter of textiles and third largest of clothing globally (Walter et al. 2009b).
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2 The European Textile and Apparel Industries: An Institutional and Literature Review
In textile and apparel production, the value-added parts of the chain are generally
seen as the most knowledge-intensive, such as product design and development (Lal
2009). Moreover, textiles are generally seen as having more potential for benefitting
from knowledge-intensive strategies, in particular textiles that involve more technical end uses, such as automotive fabrics, or smart textiles for apparel that are
designed to assess physiological data of the wearer (e.g., heart rate, blood pressure).
Technical textiles represent about 30% of the EU textile industry. According to the
European Skills Council (2014, p. 13, p. 73):
[The European Union] is now a world leader with Technical Textile production as scientific
advances continue to be made. … As traditional mass textile manufacture moved beyond
European borders, there has been a rise in the development of technical textiles, textiles
created for performance rather than aesthetics requiring a whole new skill set within the
sector. Technical textile development has become a key driver for many producers moving
away from traditional textiles markets where knowledge and innovation are required.
Indeed, in spite of many advances in the making of textiles for clothing, including
spinning, weaving, dyeing, and finishing, manufacturing technology used in cut and
sew appears to be the last frontier in need of technological development. As Walter
et al. (2009b, p. 4) pointed out:
… a number of semi- or fully automated technologies have been introduced on the garment
manufacturing shop floor such as spreading, nesting, marker-making … Despite this, major
parts of handling and joining operations remain highly manual labor intensive making the
whole garment making process uncompetitive in high labour cost countries.
In 2001, the LEAPFROG project was initiated to investigate how the clothing
industry, and specifically its garment production aspects, could be automated in
ways that would rival the textile industry and ultimately make it a “demand-driven,
knowledge-based, high-tech industry” (Walter et al. 2009b). The center of the garment making process is the sewing machine, a nineteenth-century invention that is
still being used in the twenty-first century, and because of this, it is difficult to speed
up the “putting together” of apparel. In other words, the sewing machine is required,
and a person is required to operate it. LEAPFROG researchers considered how
twenty-first-century technology, including nanotechnology, 3D technology, and
robotics, could fundamentally alter how garments are put together. Likewise, the
project proposed new organizational models, networks, and information/communication technologies that could be employed by “extended smart garment organizations (eXGOs)” to facilitate a process that achieves greater efficiency and ultimately
closes the productivity gap (Walter et al. 2009b).
Guided by Euratex, and comprised of 35 partner organizations representing 11
countries, LEAPFROG ran from 2005 to 2009 and cost 25 million Euros, over half
of which were covered by EU funds (Walter et al. 2009b). The guiding principle of
the effort was to recognize the value of networks to the functioning of the industry
and, at the same time, to determine ways to help these networks work better. As
Walter et al. (2009b, p. 166) explained:
The textile and clothing industry has a long tradition of networking. The value chain starting from
fibre production up to garment or technical textile manufacture consists of many steps usually
performed by individual companies, typically SMEs, which need to network with each other.
2.4 Knowledge-Intensive Entrepreneurship in the EU Textile and Apparel Industries
25
Shared networks were deemed critical to the SMEs that normally carry out parts of
the process. However, the LEAPFROG project proposed to take this idea one step
further, creating an extended smart organization (SO) that (Yepes 2009, p. 154):
… develops its business in a network of more or less loosely tied companies that cooperate
as if they were a single virtual company.
According to members of the project team, the term for this approach to network
operations is coopetition, or the blending of “cooperation” with “competition”
(Kartsounis et al. 2009, p. 205; Walter et al. 2009a).
Although the LEAPFROG project largely succeeded in its goals to outline a
­better, more agile production process, full-scale adoption of the models proposed,
including the extended smart organization, is cost prohibitive for an industry that is
already stretched to capacity. It may be that firms which are smaller, leaner, and
“born innovative” would be better able to function within this type of network. Such
possibilities point to the notion of knowledge-intensive entrepreneurship and the
potential benefits it might have for helping the EU industry maintain competitiveness. The concept of KIE and its relationship to the textile and apparel industries is
explored in the next section.
2.4 K
nowledge-Intensive Entrepreneurship in the EU Textile
and Apparel Industries
When exploring the potential for KIE and specifically within the EU textile and
apparel industries, two related debates clearly emerge within the literature. The first
debate centers around what, exactly, KIE means and how it can be applied in research
on firm innovation. The second debate has to do with whether or not the textile and
apparel industries can be examined through the lens of KIE given that it is typically
considered to be a low-tech industrial sector and specifically one that relies on low
manufacturing technology (LMT). As a result of being predominantly LMT, innovation research and policies in the European Union often overlook or dismiss the
growth potential for this sector and others like it (Hirsch-Kreinsen et al. 2006).
With respect to the first debate, research on KIE lacks a common definition of the
concept. Indeed, according to Schwinge (2015), some researchers even use the term
without defining it. An earlier definition includes the entrepreneurial process of
sensing, developing, and exploiting a business opportunity (Malerba 2010). This
view tends to be applied in research focused on new scientific knowledge and technology and particularly relevant to high-tech kinds of entrepreneurship. Thus, with
reference back to Table 1.1 in Chap. 1, Malerba (2010, p. 4) posited a general
­definition of KIE as:
… new ventures that introduce innovations in the economic systems and that intensively
use knowledge.
Yet, as will be discussed relative to the second debate, the textile and apparel
­industries in the European Union are mature industries (Hirsch-Kreinsen 2015).
26
2 The European Textile and Apparel Industries: An Institutional and Literature Review
Thus, Malerba (2010) offered three complementary definitions of KIE: new firms in
sectors that are highly knowledge intensive, academic inventors, or new innovators
in a technology sector. This broader perspective includes new firms as well as established firms active in the process of technological diversification. This perspective,
in turn, positions KIE not as a phenomenon but a condition which allows firms to
display multiple dimensions of knowledge intensity (McKelvey and Lassen 2013).
Hirsch-Kreinsen and Schwinge (2014) and Schwinge (2015) have illustrated that
entrepreneurship and start-up opportunities do exist in LMT industries. Indeed, as
Schwinge explained, KIE in particular occurs in at least two ways within low-tech
and LMT industries: through the creation of a new firm and through entrepreneurial
behaviors at an established firm (2015, p. 31). Moreover, Hirsch-Kreinsen et al.
(2006, p. 3) pointed out that:
As the European Union (EU) evolves into a knowledge society, the competence to generate,
use, diffuse and absorb new knowledge is increasingly viewed as critical for economic
­
success and societal development. Against this background, conventional wisdom views
so-­called high-tech, research-intensive and science-based industries as the key drivers of
future economic prosperity.
In response to this assumption, the authors discussed the findings of a recent research
project funded by the European Union known as PILOT, or Policy and Innovation
in Low-Tech: Knowledge Formation, Employment and Growth Contributions of the
“Old Economy” Industries in Europe. Typically, industries are classified as innovative based on levels of R&D investment or technological intensity (Hirsch-Kreinsen
et al. 2006). As an industry, textiles and apparel are classified as low in intensity and
low in technology. Interestingly, evidence proposes the unique innovative ability
among low-tech industries that operate within high-tech countries, such as those
that comprise much of the European Union’s textile and apparel industries and its
manufacturing heritage (Puig and Marques 2010), including Germany, Italy, the
United Kingdom, and Spain. This is the case not just in terms of technology integration but also in terms of employment and stable growth rates (Hirsch-Kreinsen et al.
2006). For the firms included in the PILOT study, innovativeness as exhibited by
LMTs was not based on recently revealed scientific knowledge but rather on capabilities for configuring different kinds of knowledge (i.e., cognitive capabilities) and
know-how (i.e., organizational capabilities), as well as functions and solutions (i.e.,
design capabilities). Moreover, the LMT is often the customer of the HMT, and this
relationship is one that can ultimately foster the innovative capabilities of both.
Based on the overall findings of the PILOT study, Hirsch-Kreinsen et al. (2006, p. 4)
concluded that:
… the performance of LMT sectors is severely misrepresented by most current indicators
and they contribute very significantly to innovation and growth in advanced economies.
Although a great deal of current research on the two EU industries relies on small
firm data, the current state of research on KIE relevant to the textile and apparel
industries is very limited. Notable exceptions are studies by two German scholars, Hartmut Hirsch-Kreinsen and Isabel Schwinge, who first discussed the
potential for KIE relative to textiles as a low-tech industry in their 2014 book,
2.4 Knowledge-Intensive Entrepreneurship in the EU Textile and Apparel Industries
27
Knowledge-­
Intensive Entrepreneurship in Low-Tech Industries. The authors
­provided a full-­scale analysis of the relevance of knowledge-intensive entrepreneurship for industrial innovation in the context of traditional low-tech industries
and cited textiles as a sector that is frequently overlooked in discussions of innovation and knowledge transfer. In a 2015 follow-up article, Hirsch-Kreinsen went
on to posit that this is because the industry is comprised largely of mature firms
that engage in very little in-house R&D. As a result, the industry is viewed as
lacking in innovation applicability, and therefore, “the economic importance and
specific innovative ability of [it] is overlooked” and particularly within advanced
economies like that of the European Union (p. 67).
Moreover, because research on sector innovation generally begins with the R&D
activity of firms that comprise it, LMT firms, such as those that comprise the textile
sector, are often overlooked. To remedy this gap, Hirsch-Kreinsen (2015) sought to
create a taxonomy of innovative LMT firms based on four “dimensions of knowledge” rather than sector affiliation. When viewed from this perspective, LMT sectors, including textiles and apparel, are seen to employ various types and sources of
knowledge for the purposes of innovation. His argument is that if the term “innovation” is applied to products as well as processes, then LMT firms are in fact innovative and, in turn, as relevant to empirical analyses of KIE as high-tech, HMT firms/
sectors are. Finally, as with the results of the aforementioned PILOT study, the
author argued for innovation policy focused on LMT sectors, as these sectors do in
fact have potential for growth through innovation—even those operating within
advanced economies (Hirsch-Kreinsen 2015).
Based on the above arguments and particularly those put forth by H
­ irsch-­Kreinsen,
Schwinge’s (2015) book, which is her doctoral dissertation, titled The Paradox of
Knowledge-Intensive Entrepreneurship in Low-Tech Industries, presented the first
multidimensional investigation of KIE in the context of low-tech industries.
Schwinge, who was involved in the research activities of the AEGIS project, made
her case via examples taken from the German textile industry, as she posited that it
is an “exemplary low-tech industry” (p. 37). Based on these cases, the author solves
conceptual inconsistencies and develops an alternative perspective on what she
terms the “paradox” of KIE in a low-tech industry. Through case studies of three
German textile firms, Schwinge examined the characteristics of innovation and
knowledge sharing that are specific to low-tech industries in general and specifically
the textile and apparel industries. In doing so, Schwinge’s dissertation provides a
jumping off point for our purposes and in particular the questions that help to shape
the present research study.
2.5 Research Questions Motivated by the Literature
Based on the summary of the trends in thought and research regarding knowledge-­
intensive entrepreneurship, innovation, and the EU textile and apparel industries,
several key issues emerge that help to shape the direction of our analysis of the
28
2 The European Textile and Apparel Industries: An Institutional and Literature Review
AEGIS data. First, to the best of our knowledge, the existing research on KIE and
the EU textile and apparel industries relies on case studies of specific firms rather
than an analysis that presents industry trends in aggregate. One of the main reasons
for this gap comes from the aforementioned lack of cohesive definition for KIE and
the fact that KIE is typically understood within a framework of high-tech firms and
industries. However, recent research provides a foundation for approaching the
topic of KIE as it pertains to low-tech, mature industries, such as textiles and
apparel. We propose that based on this newly formed approach to the topic, clues as
to the various ways that small-sized textile and apparel firms employ innovation and
use knowledge to this end can be found within the AEGIS data set. Thus, the following is the first overriding research question when approaching the data:
• While there are many small firms that comprise the EU textile and apparel industries, how and to what extent are these firms entrepreneurial and/or innovative in
their behaviors?
Although there is little consensus about how KIE is defined, approaching the
textile and apparel industries within the AEGIS data through the lens of KIE can
shed light on the value that entrepreneurial innovation can bring, even to those
industries that are considered minimally innovative from an operational standpoint.
As the emphasis of this study is on the textile and apparel industries in the European
Union, the second question stems from the potential answers to the first:
• What might KIE, and, in particular, entrepreneurial and innovative behaviors,
mean for firm performance and/or industrial growth?
An interesting point to note—and one that will shape some of our discussion in
later chapters—is the noticeable absence of discussion about characteristics of the
owners of small textile and apparel firms. Characteristics such as gender, education,
background, and number of years (nascence) are widely investigated within the
general entrepreneurship literature. Yet they are conspicuously absent within our
review of the literature, with the exception of some mention of them by Schwinge
(2015). We posit that such characteristics, when understood relative to KIE in the
textile and apparel industries, are important to providing the full picture of what
motivates the entrepreneurial and innovative behaviors of a firm.
Third, while there is a great deal of similarity between industry dynamics of the
post-quota EU industries and that of the United States, there has yet to be a comparison made between the two advanced economies based on dimensions of KIE, at
either the firm or sector level. It is our contention that small- and medium-sized
firms in the United States could benefit from findings of the analysis provided here
and particularly with respect to gleaning ways to foster innovation through KIE in
spite of the maturity and global orientation of the sector. Moreover, such an analysis
can be used to strategically position SMEs in the United States in light of new and
existing trade agreements that have been established since the end of the MFA/
ATC. Thus, the third and final question guiding our analysis of the AEGIS data is:
• What, if anything, do our empirical findings suggest for those small- and
medium-sized firms that comprise the US textile and apparel industries?
Chapter 3
Trends in the European Textile and Apparel
Industries
If a man will begin with certainties, he shall end in doubts; but
if he will be content to begin
with doubts, he shall end in certainties.
—Francis Bacon
I never guess. It is a capital mistake to theorize before one has
data. Insensibly one
begins to twist facts to suit theories, instead of theories to suit facts.
—Sherlock Holmes
Abstract Trends in the textile and apparel industries in Europe are described in this
chapter. A case is made that these are industries that were affected by the 2008–
2009 economic and financial crisis in Europe and are just beginning to recover.
3.1 Defining the Industries
From the perspective of the NACE (from the French, Nomenclature statistique des
Activités économiques dans la Communauté Européenne) classification system,1 the
textile and apparel industries are defined in terms of the aggregation of several specific manufacturing activities as shown in Tables 3.1 and 3.2, respectively. However,
it is not uncommon for industrial economists or policy makers to refer collectively
to the TCL industries—textiles, clothing, and leather.
The terms apparel and clothing are used interchangeably in the literature and in
public sector reports, but we prefer the term apparel because it is consistent with the
NACE terminology. Our focus in this book is on textiles and apparel and not on
leather. Our deletion of the leather industry, the “L” from TCL, is by choice, and our
decision not to focus on the leather industry is predicated on four points. First, our
deletion of the leather industry reflects the fact that the textile and apparel industries
dominate TCL industries in the sense that, in 2014, 31% of the employees in the
See, <www.instat.gov.al/media/166724/nace_rev.1.1.pdf>.
1
© Springer International Publishing AG 2018
N.J. Hodges, A.N. Link, Knowledge-Intensive Entrepreneurship, International
Studies in Entrepreneurship 39, https://doi.org/10.1007/978-3-319-68777-3_3
29
30
3 Trends in the European Textile and Apparel Industries
Table 3.1 Taxonomy of the European textile industry
17 Manufacture of textiles
17.1 Preparation and spinning of textile fibers
17.11 Preparation and spinning of cotton-type fibers
17.12 Preparation and spinning of woolen-type fibers
17.13 Preparation and spinning of worsted-type fibers
17.14 Preparation and spinning of flax-type fibers
17.15 Throwing and preparation of silk, including from noils, and throwing and texturing of
synthetic or artificial filament yarns
17.16 Manufacture of sewing threads
17.17 Preparation and spinning of other textile fibers
17.2 Textile weaving
17.21 Cotton-type weaving
17.22 Woolen-type weaving
17.23 Worsted-type weaving
17.24 Silk-type weaving
17.25 Other textile weaving
17.3 Finishing of textiles
17.30 Finishing of textiles
17.4 Manufacture of made-up textile articles, except apparel
17.40 Manufacture of made-up textile articles, except apparel
17.5 Manufacture of other textiles
17.51 Manufacture of carpets and rugs
17.52 Manufacture of cordage, rope, twine, and netting
17.53 Manufacture of nonwovens and articles made from nonwovens, except apparel
17.54 Manufacture of other textiles n.e.c.
17.6 Manufacture of knitted and crocheted fabrics
17.60 Manufacture of knitted and crocheted fabrics
17.7 Manufacture of knitted and crocheted articles
17.71 Manufacture of knitted and crocheted hosiery
17.72 Manufacture of knitted and crocheted pullovers, cardigans, and similar articles
Source: “Classification of Economic Activities, NACE Rev.1.1” <www.instat.gov.al/media/166724/
nace_rev.1.1.pdf>
TCL industries worked in the textile industry and another 51% worked in the apparel
industry. Perhaps more important than the domination of the TCL industry by textiles and apparel workers is the fact that those employees who do work in those two
industries are distributed throughout EU countries; in comparison, the lion’s share
of employees who work in the leather industry are employed in Italy (European
Skills Council 2014). Second, the deletion of the leather industry from our focus
reflects the fact that the textile and apparel industries are relatively more innovation
based than is the leather industry, and thus our focus on KIE is more applicable and
appropriate to the former. Third, to be pragmatic, our research backgrounds align
more closely with the textile and apparel industries than they do to the leather
­industry. And fourth, this is related to our objective to offer policy prescriptions for
the declining US industries based on our AEGIS database-motivated study of EU
3.2 Trends in the EU Textile and Apparel Industries
Table 3.2 Taxonomy of the
European apparel industry
31
18 Manufacture of wearing apparel,
dressing and dyeing of fur
18.1 Manufacture of leather clothes
18.10 Manufacture of leather clothes
18.2 Manufacture of other wearing
apparel and accessories
18.21 Manufacture of workwear
18.22 Manufacture of other outerwear
18.23 Manufacture of underwear
18.24 Manufacture of other wearing
apparel and accessories n.e.c.
18.3 Dressing and dyeing of fur,
manufacture of articles of fur
18.30 Dressing and dyeing of fur,
manufacture of articles of fur
Source: “Classification of Economic
Activities, NACE Rev.1.1” <www.instat.
gov.al/media/166724/nace_rev.1.1.pdf>
industries, the leather industry is global but it includes mostly Italian firms, and thus
generalizations from Italian leather firms to the US leather industry might perhaps
be viewed as incidental or parenthetical.
Innovative activity in the textile industry differs from innovative activity in the
apparel industry. Generally speaking, the value chain goes from textiles (i.e., fabrics)
to apparel (i.e., clothing). Innovative activity in textile firms is often research and
technology based. Research and development (R&D) is appropriately relevant and
important to the development of new fabrics as well as to the attendant production
processes (Hauser 2015). Innovative activity in apparel firms involves more creativity
or novelty in design and marketing, and creativity follows from entrepreneurial insight
more so than from R&D or technologies purchased from others (Landoni et al. 2016).
We offer this value chain generalization from an industry-wide perspective. It
will be, of course, an empirical issue as to the extent to which this generalization
applies to KIE textile and apparel firms, and it will be, of course, an empirical issue
as to the extent to which the AEGIS data are sufficiently granular to allow us to
identify such subtleties about the value chain. Thus, we think it is appropriate to
view these two industries, from a KIE and innovativeness perspective, separately as
we do below and in the following chapters.
3.2 Trends in the EU Textile and Apparel Industries
Figures 3.1, 3.2, 3.3, and 3.4 show growth trends in several economics-based metrics
associated with the textile and apparel industries. In Fig. 3.1, the annual growth rate
in industrial production for the textile industry is shown to be negative in each year
with the exceptions of 2010 and 2013. The greatest decrease in industrial production
32
3 Trends in the European Textile and Apparel Industries
10
5
0
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
-5
-10
-15
-20
Textile Growth Rate
Apparel Growth Rate
Source:
European Commission (2014), Table 7.1.
Fig. 3.1 Annual growth rate in industrial production in the EU textile and apparel industries,
2002–2013 (Source: European Commission 2014, Table 7.1)
0
-2
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
-4
-6
-8
-10
-12
-14
-16
Textile Growth Rate
Apparel Growth Rate
Source:
European Commission (2014), Table 7.2
Fig. 3.2 Annual growth rate in employment in the EU textile and apparel industries, 2002–2013
(Source: European Commission 2014, Table 7.2)
occurred in 2009, the first full year of the financial crisis in Europe and the worst
year of the Great Recession that we emphasized in Chap. 2. However, the sporadic
trend over the 2002–2013 period is similar in both industries. In fact, the correlation
coefficient between the two series in the figure is 0.789. But, given the value chain
relationship between these two industries that we are accepting as a given, we wonder if it might take a resurgence in the EU textile industry to drive a resurgence in the
EU apparel industry; the 2012–2013 period is encouraging to this view.
3.2 Trends in the EU Textile and Apparel Industries
33
2
0
-2
2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
-4
-6
-8
-10
-12
-14
-16
-18
Textile Growth Rate
Apparel Growth Rate
Source:
European Commission (2014), Table 7.3.
Fig. 3.3 Annual growth rate in hours worked in the EU textile and apparel industries, 2002–2013
(Source: European Commission 2014, Table 7.3)
15
10
5
0
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
-5
-10
Texle Growth Rate
Apparel Growth Rate
Source:
European Commission (2014), Table 7.4
Fig. 3.4 Annual growth rate in labor productivity per person employed in the EU textile and
apparel industries, 2002–2013 (Source: European Commission 2014, Table 7.4)
Figure 3.2 shows the trend in the annual growth of employees in both the
t­ extile and apparel industries. Again, the annual growth rates have been less than
zero for all years, but the annual decrease in employees has lessened in the last
few years. As with industrial production, the greatest annual decrease in employees occurred in 2009. The correlation coefficient between the two series in the
figure is 0.855.
34
3 Trends in the European Textile and Apparel Industries
Figure 3.3 shows the trend in the annual growth rate of hours worked in both
industries. As expected, the annual growth rate has been less than zero; it was the
lowest in 2009, nearly a 17% annual decline in hours worked, but that decline has
lessened in the more recent years, and it is approaching a positive value. The
­correlation coefficient between these two series in the figure is 0.783.
Finally, the annual growth rate of labor productivity per person employed is
shown in Fig. 3.4. Labor productivity growth began to decrease in 2006, but the
annual growth rate in labor productivity was highest in the recovery year of 2010.
The correlation coefficient between these two series is 0.621. From 2008 to 2013,
the average annual rate of growth of labor productivity in the textile industry was
2.0%, and it was 0.4% in the apparel industry. As we pointed out above, and as suggested by Fig. 3.4, a resurgence in the textile industry could prompt a resurgence in
the apparel industry.
Data are available on a country-by-country basis for the EU countries on annual
number of enterprises and annual number of employees in each of the two industries. The annual number of enterprises and employees in the EU textile industry, by
country, are reported in Tables 3.3 and 3.4, respectively. Similarly, the annual number of enterprises and employees in the EU apparel industry, by country, are reported
in Tables 3.5 and 3.6, respectfully. We include these tables as a reference for completeness of our discussion, and we highlight selected information from them in an
effort to characterize broadly the industries from a country perspective.
Regarding the textile industry (Tables 3.3 and 3.4), Turkey and Italy dominate
the European Union in terms of both number of enterprises and number of employees. With a specific reference to 2005, Spain, France, and Portugal ranked number
2, 3, and 4 (no data are available for Turkey that year). But, the number of enterprises in Spain declined by 2014, as did the number in Portugal, and the number in
France increased to nearly 7000. However, in 2014, the number of textile establishments in Italy was more than twice that of France. The difference between Italy and
France is likely a reflection of the extent to which Italy has benefited by focusing on
high quality and maintaining its unique brand status within the global luxury goods
market (Truett and Truett 2014).
The cross-country differences in the number of establishments are greater than
the cross-country differences in the number of employees. While the greatest number of textile industry employees is also in Turkey and Italy, Germany ranked third
in 2014 followed by the United Kingdom and Poland. Some of the differences in the
number of employees point to the breadth of activities classified as within the textile
industry in the sense that these aggregate data encompass all stages of the textile
production process, from spinning to weaving and finishing, and firms in all countries are not involved to the same extent in all stages of textile production. Additional
differences may stem from the inclusion of a wide variety of end uses, including
carpets, rugs, nonwovens, hosiery, and apparel (see Table 3.1), which might also
vary in firms across countries.
Regarding the apparel industry (see Tables 3.5 and 3.6), in 2014 Turkey and Italy
dominated the European Union in terms of the number of apparel enterprises. In
terms of the number of employees, Turkey and Italy are the largest employers
Country
Belgium
Bulgaria
Czech Republic
Denmark
Germany (until
1990 former
territory of the
FRG)
Estonia
Ireland
Greece
Spain
France
Croatia
Italy
Cyprus
Latvia
Lithuania
Luxembourg
Hungary
Malta
Netherlands
Austria
Poland
Portugal
2006
1,416
716
2,301
430
:
162
:
:
7,480
4,341
:
19,883
112
258
716
19
1,424
:
1,402
720
3,651
4,065
2005
:
684
2,408
443
3,733
144
:
:
7,800
4,447
:
20,686
114
255
614
21
1,594
:
1,285
748
3,407
4,245
191
159
:
7,213
4,383
:
19,297
113
243
654
19
1,334
:
1,311
703
3,437
4,115
2007
:
718
2,313
435
3,726
170
144
2,712
7,346
4,180
719
18,351
113
240
683
19
1,270
:
1,341
655
3,982
4,033
2008
1,589
649
2,296
420
3,723
191
127
2,206
6,556
4,126
776
17,256
114
278
477
20
1,156
:
1,421
623
3,872
3,811
2009
1,465
673
2,328
396
3,859
Table 3.3 Number of EU textile industry enterprises, by country, 2005–2014
182
122
2,065
6,344
4,975
603
16,504
111
333
500
20
1,146
:
1,642
612
4,524
3,539
2010
1,193
604
2,601
372
3,809
192
134
1,954
6,138
4,612
539
15,799
105
377
567
20
1,113
:
1,693
600
4,609
3,429
2011
1,218
591
2,822
365
4,008
209
107
1,811
6,064
5,042
496
15,291
107
426
649
20
1,091
:
1,823
591
4,211
3,253
2012
1,086
583
3,151
357
3,809
238
110
1,630
5,787
5,324
487
14,767
103
469
751
20
1,065
:
2,012
610
4,491
3,436
2013
1,016
589
2,715
323
3,750
(continued)
234
:
1,721
5,583
6,939
478
14,359
93
482
900
20
1,036
:
2,067
611
4,899
3,383
2014
1,023
589
2,520
317
4,049
3.2 Trends in the EU Textile and Apparel Industries
35
2006
1,902
429
183
877
2,248
4,338
:
:
:
:
:
:
:
2005
1,924
403
:
846
2,147
:
:
:
:
:
:
:
:
Source:Eurostat <appsso.eurostat.ec.europa.eu/>
The symbol : means data are not available
Country
Romania
Slovenia
Slovakia
Finland
Sweden
United Kingdom
Iceland
Liechtenstein
Norway
Switzerland
Former Yugoslav
Republic of
Macedonia, the
Turkey
Bosnia and
Herzegovina
Table 3.3 (continued)
:
:
2007
1,858
415
157
894
2,232
4,262
:
:
528
:
:
:
:
2008
1,770
389
182
892
2,299
4,255
:
:
572
:
:
18,147
:
2009
1,631
380
147
853
2,277
4,068
:
:
557
378
:
:
:
2010
1,499
357
1,184
825
2,338
3,936
:
:
558
377
:
:
117
2011
1,317
363
1,229
811
2,321
3,872
:
:
593
367
202
:
108
2012
1,295
349
1,177
799
2,244
3,780
:
:
588
342
203
22,682
107
2013
1,279
362
1,186
773
2,145
3,847
:
:
586
341
192
20,106
218
2014
1,313
338
1,184
735
2,155
3,880
:
:
567
336
200
36
3 Trends in the European Textile and Apparel Industries
Country
Belgium
Bulgaria
Czech Republic
Denmark
Germany (until
1990 former
territory of the
FRG)
Estonia
Ireland
Greece
Spain
France
Croatia
Italy
Cyprus
Latvia
Lithuania
Luxembourg
Hungary
Malta
Netherlands
Austria
Poland
Portugal
2006
28,919
19,655
39,677
5,646
93,855
8,278
2,504
:
64,786
67,377
:
167,345
450
4,807
14,167
:
13,529
:
12,701
11,715
56,063
57,879
2005
:
20,138
42,353
5,864
95,892
9,255
2,522
:
73,109
75,024
:
175,989
485
5,633
14,388
:
18,732
:
12,607
12,348
57,989
62,442
7,724
2,553
:
58,462
62,863
:
159,713
485
4,476
12,639
:
11,447
:
12,369
11,193
58,498
56,140
2007
28,857
19,768
37,624
5,658
89,439
6,116
2,284
13,291
54,729
53,849
6,228
151,428
470
3,613
9,504
:
10,514
:
12,337
9,805
53,672
52,574
2008
24,964
16,831
33,874
4,973
85,045
4,424
2,448
11,500
43,948
49,002
:
139,297
461
2,456
7,462
:
9,261
:
11,315
8,758
49,688
46,356
2009
23,142
12,771
26,213
3,578
76,676
Table 3.4 Number of EU textile industry employees, by country, 2005–2014
4,390
1,611
8,841
42,315
47,929
5,305
127,848
476
2,325
6,950
:
8,393
:
10,862
8,777
47,920
43,422
2010
21,543
11,992
24,145
3,198
76,064
3,862
1,707
8,010
37,917
43,360
5,033
124,004
448
2,293
7,198
:
8,712
:
11,004
8,832
45,477
41,275
2011
20,617
11,375
24,486
3,853
78,278
3,939
1,187
8,018
36,405
40,793
4,627
116,872
406
2,451
7,523
:
9,454
:
10,874
8,631
41,979
38,011
2012
18,577
11,131
23,991
3,717
75,821
4,014
:
7,207
34,907
38,667
4,473
110,251
403
2,458
7,674
:
8,729
:
10,795
8,433
42,896
38,940
2013
17,786
11,570
23,662
3,604
76,738
(continued)
4,156
:
6,971
34,787
37,599
4,231
106,274
383
2,772
7,951
:
8,828
:
10,652
8,396
45,075
39,966
2014
16,989
11,710
23,455
3,546
75,701
3.2 Trends in the EU Textile and Apparel Industries
37
2006
44,798
7,471
:
4,258
6,247
73,114
:
:
:
:
:
:
:
2005
49,950
8,471
:
4,314
6,516
:
:
:
:
:
:
:
:
:
:
2007
40,238
8,405
:
4,860
6,273
:
:
:
3,348
:
:
Source: Eurostat <appsso.eurostat.ec.europa.eu/>
The symbol: means data are not available
Country
Romania
Slovenia
Slovakia
Finland
Sweden
United Kingdom
Iceland
Liechtenstein
Norway
Switzerland
Former Yugoslav
Republic of
Macedonia, the
Turkey
Bosnia and
Herzegovina
Table 3.4 (continued)
:
:
2008
35,824
6,965
6,809
4,548
6,106
61,852
:
:
3,221
:
:
265,957
:
2009
28,257
6,012
5,266
4,151
5,459
55,250
:
:
2,761
:
:
:
:
2010
27,763
5,126
5,168
3,789
5,625
:
:
:
2,736
:
:
:
6,204
2011
28,862
3,946
5,939
3,631
5,019
53,165
:
:
2,736
:
:
:
5,811
2012
28,756
3,865
5,592
3,308
4,813
51,968
:
:
2,729
:
3,475
396,240
6,381
2013
29,608
3,245
5,438
3,222
4,800
53,887
:
:
2,662
:
:
417,868
6,405
2014
30,860
3,127
5,771
3,020
4,722
60,600
:
:
2,630
:
:
38
3 Trends in the European Textile and Apparel Industries
Country
Belgium
Bulgaria
Czech Republic
Denmark
Germany (until
1990 former
territory of the
FRG)
Estonia
Ireland
Greece
Spain
France
Croatia
Italy
Cyprus
Latvia
Lithuania
Luxembourg
Hungary
Malta
Netherlands
Austria
Poland
Portugal
2006
1,121
4,789
7,936
428
2,984
448
89
:
13,823
11,492
:
39,241
433
1,104
2,361
18
4,580
:
1,285
911
19,254
11,846
2005
1,080
4,750
8,377
473
:
450
92
:
14,231
11,798
:
40,145
:
1,062
2,201
16
5,155
:
1,523
932
19,310
12,541
457
88
:
12,819
11,447
:
38,466
417
961
2,305
17
4,276
:
1,334
891
18,813
11,879
2007
:
4,911
7,825
396
2,771
408
77
11,349
11,751
8,055
1,561
37,449
404
881
2,333
17
4,086
47
1,341
752
17,951
11,643
2008
1,010
4,791
7,918
409
2,612
362
66
10,883
10,483
6,805
1,652
34,657
393
953
1,876
16
3,575
72
1,383
725
15,194
10,688
2009
1,167
4,888
8,375
376
2,932
Table 3.5 Number of EU apparel industry enterprises, by country, 2005–2014
364
62
9,929
9,778
8,895
1,510
32,322
239
944
1,737
15
3,464
74
1,644
729
13,813
9,729
2010
1,039
4,480
9,895
358
2,922
387
68
7,784
9,420
8,256
1,311
32,972
215
916
1,982
14
3,326
62
1,627
718
13,652
9,388
2011
:
4,379
10,326
346
2,943
407
50
7,727
8,966
9,499
1,184
32,376
185
1,027
2,194
13
3,065
54
1,676
721
12,481
8,974
2012
801
4,324
10,784
351
2,614
462
54
6,282
9,027
11,296
1,102
30,662
187
1,078
2,431
15
2,771
36
2,296
724
11,494
8,481
2013
770
4,405
10,789
348
2,764
(continued)
499
:
7,168
8,796
10,996
940
29,442
178
1,105
2,673
12
2,733
39
2,310
712
12,092
8,492
2014
830
4,381
11,280
318
2,910
3.2 Trends in the EU Textile and Apparel Industries
39
2006
6,273
1,075
395
1,192
1,835
:
:
:
:
:
:
:
:
2005
6,408
1,063
:
1,220
1,821
:
:
:
:
:
:
:
:
Source: Eurostat <appsso.eurostat.ec.europa.eu/>
The symbol : means data are not available
Country
Romania
Slovenia
Slovakia
Finland
Sweden
United Kingdom
Iceland
Liechtenstein
Norway
Switzerland
Former Yugoslav
Republic of
Macedonia, the
Turkey
Bosnia and
Herzegovina
Table 3.5 (continued)
:
:
2007
6,197
981
420
1,177
1,838
4,015
:
:
633
:
:
:
:
2008
5,867
927
307
1,169
1,883
3,826
:
:
715
:
:
51,158
:
2009
5,313
890
368
1,132
1,908
3,572
:
:
694
274
:
:
:
2010
4,480
862
4,565
1,087
1,960
3,396
:
:
687
264
:
:
209
2011
4,111
790
4,467
1,075
2,037
3,381
:
:
770
262
968
:
192
2012
4,231
773
4,114
1,031
2,022
3,385
:
:
813
264
943
53,226
191
2013
4,378
744
3,912
1,023
1,978
3,392
:
:
837
269
916
51,579
615
2014
4,584
731
3,467
963
2,000
3,415
:
:
860
258
878
40
3 Trends in the European Textile and Apparel Industries
Country
Belgium
Bulgaria
Czech Republic
Denmark
Germany (until
1990 former
territory of the
FRG)
Estonia
Ireland
Greece
Spain
France
Croatia
Italy
Cyprus
Latvia
Lithuania
Luxembourg
Hungary
Malta
Netherlands
Austria
Poland
Portugal
2006
:
150,051
31,112
2,531
60,478
11,336
2,052
:
94,150
71,323
:
221,834
1,162
15,048
33,692
:
39,409
:
2,873
8,975
138,064
111,436
2005
7,013
150,642
34,517
2,718
:
11,989
2,635
:
97,131
76,738
:
231,137
:
15,852
37,981
:
45,664
:
3,320
9,316
149,961
119,495
10,458
1,795
:
83,700
67,094
:
218,149
1,064
13,317
29,616
:
35,140
:
3,034
8,469
145,417
110,616
2007
6,396
144,276
28,589
2,416
56,203
9,142
1,715
19,702
75,995
55,278
25,963
218,755
925
11,709
25,107
:
32,292
:
2,812
8,315
129,267
106,455
2008
5,851
132,982
25,133
2,033
48,234
7,428
1,389
18,708
62,181
48,701
22,942
199,001
776
9,330
20,632
:
26,046
291
2,524
7,856
109,253
93,380
2009
5,467
114,029
20,672
1,443
43,775
Table 3.6 Number of EU apparel industry employees, by country, 2005–2014
6,668
1,141
15,451
54,530
45,820
21,253
188,312
638
9,606
19,604
:
24,062
310
2,349
7,267
94,518
87,260
2010
4,126
103,162
18,871
1,406
42,894
6,571
974
13,194
48,781
43,471
21,227
182,815
560
10,442
19,888
:
23,250
309
2,173
6,910
90,345
84,235
2011
:
105,861
18,229
1,616
43,629
6,312
676
14,316
42,110
41,039
18,371
181,199
487
9,935
19,531
:
22,339
:
2,021
6,847
82,242
78,668
2012
3,269
103,312
17,117
:
41,890
6,297
:
11,134
39,639
40,039
17,587
167,151
340
10,057
19,111
:
21,480
:
2,675
6,558
77,008
78,844
2013
3,143
101,959
16,303
:
40,398
(continued)
6,236
:
11,995
36,678
39,873
16,140
165,687
292
9,398
18,965
:
20,514
290
2,105
6,056
76,268
82,298
2014
2,741
100,607
16,025
:
42,339
3.2 Trends in the EU Textile and Apparel Industries
41
2006
281,481
11,191
:
4,064
1,720
53,054
:
:
:
:
:
:
:
2005
314,320
12,109
:
4,534
1,792
:
:
:
:
:
:
:
:
Source: Eurostat <appsso.eurostat.ec.europa.eu/
The symbol : means data are not available
Country
Romania
Slovenia
Slovakia
Finland
Sweden
United Kingdom
Iceland
Liechtenstein
Norway
Switzerland
Former Yugoslav
Republic of
Macedonia, the
Turkey
Bosnia and
Herzegovina
Table 3.6 (continued)
:
:
2007
247,584
10,301
:
4,011
1,675
44,430
:
:
1,554
:
:
:
:
2008
207,629
9,616
21,640
3,755
1,625
34,003
:
:
1,525
:
:
329,584
:
2009
167,931
7,875
17,310
2,876
1,454
28,056
:
:
1,400
:
:
:
:
2010
154,547
5,149
16,314
2,425
1,085
36,891
:
:
1,345
:
:
:
9,514
2011
159,784
5,190
15,754
2,276
1,127
28,652
:
:
1,321
:
33,886
:
9,048
2012
159,891
4,019
14,531
2,350
1,070
27,035
:
:
1,255
:
32,844
491,777
9,111
2013
158,034
3,897
14,525
3,528
1,113
32,757
:
:
1,174
:
32,721
481,119
10,491
2014
159,244
3,327
14,813
3,376
1,125
31,825
:
:
1,164
:
33,115
42
3 Trends in the European Textile and Apparel Industries
3.3 Conclusions
43
closely followed by Romania and Bulgaria. These employment numbers support
studies about the industry that discuss the importance of proximity to the industry
(e.g., Puig and Marques 2011; Puig et al. 2013). The latter two countries, in particular, have benefited from the industry dominance of European fast fashion retailers,
such as Zara, that rely on short lead times. By producing garments at low-cost and
in low-skilled EU countries, these retailers can maintain a close proximity to their
manufacturing partners. Romania has also benefited from Italian firms partnering
with businesses in this country to execute the final and more labor-intensive stages
of the manufacturing process (Guercini 2004).
3.3 Conclusions
As our institutional and academic literature review in Chap. 2 suggests, the European
textile and apparel industries have been on the decline for over a decade. The data
in the figures and tables in this chapter not only reinforce this conclusion, but also
they suggest that the industries might indeed be entering a renaissance period (see
the second epigram to Chap. 1) perhaps led by textile firms as evidenced by the
upward trends in production, hours worked, and labor productivity in the 2012–
2013 period. While there would be merit to relating cross-country differences in the
number of enterprises and number of employees in each of these two industries to
national endowments of resources and production-related policies and incentives,
that exercise, while clearly important, would take us outside of the boundaries of
our focus in this book.
As we discussed in Chaps. 1 and 2, we are interested in the remainder of our
book in the three overriding questions. The research questions are:
• While there are many small firms that comprise the EU textile and apparel industries, how and to what extent are these firms entrepreneurial and/or innovative in
their behaviors?
• What might KIE, and in particular, entrepreneurial and innovative behaviors,
mean for firm performance and/or industrial growth?
• What, if anything, do our empirical findings suggest for those small- and
medium-sized firms that comprise the US textile and apparel industries?
Toward that end, we discuss first in Chap. 4, the AEGIS database. Our analysis
of the information therein that is presented and discussed in subsequent chapters
will facilitate our ability to offer suggestive initial answers to these three research
questions and by doing so to possibly influence the direction and scope of future
research related to the textile and apparel industries both in the European Union and
in other countries.
Chapter 4
The AEGIS Database
Not everything that can be counted counts, and not everything
that counts can be counted.
—Albert Einstein
The value of an idea lies in the using of it.
—Thomas Edison
Abstract The AEGIS (advancing knowledge-intensive entrepreneurship and innovation for growth and social well-being in Europe) database is described in this
chapter, including the origin of the data product, the purpose of the data collection
effort, and its completeness relative to other collections of information about small
entrepreneurial firms.
4.1 The AEGIS Project
The AEGIS (advancing knowledge-intensive entrepreneurship and innovation for
growth and social well-being in Europe) project was funded by the European
Commission (EC) under Theme 8 “Socio-Economic Sciences and Humanities” of
the 7th Framework Programme (FP7) for Research and Technological Development.1
FP7 lasted from 2007 until 2013.2 The program, funded at more than €50 billion,
was designed to be a key tool in responding to Europe’s needs in terms of jobs and
competitiveness and for Europe to maintain leadership in the global knowledge
1
In Greek mythology, the word aegis refers to the powerful shield carried by Athena and Zeus.
While the use of the acronym is not explained in EC documents, to the best of our knowledge in
the many other documentations about the AEGIS project, one of which is by Caloghirou et al.
(2011), we opine that the title of AEGIS may imply that the database itself contains powerful information for an understanding of knowledge-intensive entrepreneurship.
2
The following description of FP7 draws directly from https://ec.europa.eu/research/fp7/understanding/fp7inbrief/what-is_en.html (accessed on April 20, 2016). Earlier versions of this description, and other aspects of this chapter, follow discussions in Link and Swann (2016), Boles and
Link (forthcoming), and Amoroso, Audretsch, and Link (forthcoming).
© Springer International Publishing AG 2018
N.J. Hodges, A.N. Link, Knowledge-Intensive Entrepreneurship, International
Studies in Entrepreneurship 39, https://doi.org/10.1007/978-3-319-68777-3_4
45
46
4 The AEGIS Database
economy. FP7 had five major building blocks. They were cooperation, ideas, people, capacities, and nuclear research. The core of FP7 is the cooperation program.
The objective of FP7 cooperation program was to foster collaborative research
across Europe and other partner countries through projects by transnational consortia comprised of both industry and academia members. Research was performed in
ten key thematic areas: health; food, agriculture, and fisheries, and biotechnology;
information and communication technologies; nanosciences, nanotechnologies,
materials, and new production technologies; energy; environment (including climate change); transport (including aeronautics); socioeconomic sciences and the
humanities; space; and security.
The ideas program supported frontier research as was determined on the basis of
scientific excellence. Research was carried out in the areas of science and/or technology, including engineering, socioeconomic sciences, and the humanities. The
people program provided support for researchers to be mobile as they developed
and advanced their careers, and this was to be the case for researchers inside the
European Union as well as for international investigators. The capacities program
strengthened the overall research capabilities that Europe believed are necessary for
its countries to become thriving knowledge-based economies; these capabilities are
to be based on relevant research infrastructures and on research for the specific
benefit of small- and medium-sized enterprises (SMEs). Finally, the nuclear research
program comprised research, technological development, international cooperation,
dissemination of technical information, and exploitation activities, as well as training in a variety of aspects of nuclear activity.
The focus of the AEGIS project within the FP7 was on knowledge-intensive
entrepreneurship (KIE) under the ideas program. The implicit assumption of that
program was that KIE is one potential means through which European countries
could obtain economic growth and, as a result, societal well-being.3 More specifically (PLANET 2011, p. 5):
The AEGIS project has three main objectives relative to an understanding of KIE in
European Union (EU) countries:
• At the micro level, it examines the act of knowledge-intensive entrepreneurship
(KIE), its defining characteristics, boundaries, scope and incentives in various sectors (high and low tech and services). Apart from the supply side, it focuses on the
demand side and the social and cultural dimensions related to KIE.
• At the macro level it examines the link between KIE, economic growth and social
wellbeing. Emphasis is placed on the way the socio-economic environment stokes
“animal spirits” and benefits from them in the context of various shades of capitalism in Europe and elsewhere.
• At the policy level it will try to translate its findings into diagnostic tools for country
or sector specific assessment of KIE and provide operational policy recommendations, by taking into account different national/regional and sectoral systems of
3
Link has written about the AEGIS database several times (e.g., Cunningham and Link 2016; Link
and Swann 2016; Boles and Link 2017; Hodges and Link 2017; Amoroso et al. 2017; Amoroso and
Link 2017). Duplication of text to describe this database is unavoidable.
4.2 Elements of the AEGIS Database
47
innovation within EU and some key large fast growing countries (India, China and
Russia).
According to AEGIS (2012, p. 4):
Knowledge-intensive entrepreneurship is [the] core interface between two interdependent
systems: the knowledge generation and diffusion system, on the one hand, and the productive system, on the other. Both systems shape and are shaped by the broader social context—including customs, culture and institutions—thus also pointing at the linkage of
entrepreneurship to that context.
A background discussion about a definition of KIE follows, and having a precise
(or close to precise) definition of KIE is critical for us to align ourselves with the
extant literature on which the descriptive empirics in this book are based and from
which we offer suggestions for future research. A definition of KIE is also critical
for understanding the focus of the AEGIS database and thus how to interpret the
data therein. One definition of KIE is in the quoted excerpt from AEGIS (2012) just
above, and other definitions were suggested and discussed in detail in Chap. 1 with
reference to the sources in Table 1.2.
4.2 Elements of the AEGIS Database
According Caloghirou et al. (2011, p. 3), the AEGIS survey from which the AEGIS
database was created is itself rather unique4:
… it is different from any other relevant survey (i.e. Community Innovation Survey, Global
Entrepreneurship Monitor, Kauffman, KfW-ZEW, etc.).
As these authors ably explain, the uniqueness of the AEGIS database comes from
the uniqueness of the AEGIS survey that is described herein (Caloghirou et al. 2011,
p. 3):
It is not a crossectoral survey for the production solely of R&D and innovation indicators
(CIS), it is not a general population survey (GEM), it does not cover only one country or
focus mainly on the firm’s competitive environment and financing/capital investment
(KfW-ZEW, Kauffman survey).
Reflected in the development of the AEGIS surveys are important elements that
support the program organizers’ and facilitators’ unique aim for how to construct
the AEGIS survey and database, namely (Caloghirou et al. 2011, p. 3):
[The aim is] to examine the multi-dimensional concept of KIE using many different dimensions (demand, institutional factors, innovation strategies, dynamic capabilities etc.) in
order to identify motives, characteristics and patterns in the creation and growth of new
firms.
4
Within the AEGIS survey and within the explanatory text by Caloghirou et al. (2011), the terms
firm, company, and business appear to be used interchangeably. For purpose of standardization, we
impose our preference for the term firm throughout the text unless specific reference to a survey
question is being made.
48
4 The AEGIS Database
Table 4.1 AEGIS sampling population and survey sample, by country
Country
Croatia
Czech Republic
Denmark
France
Germany
Greece
Italy
Portugal
Sweden
United Kingdom
Total
Sampling population
2,397
3,046
7,890
57,142
37,024
4,180
51,835
5,459
20,886
12,427
202,286
Survey sample
200
200
330
570
557
331
580
331
334
571
4,004
Coverage percent
8.34%
6.57%
4.18%
1.00%
1.50%
7.92%
1.12%
6.06%
1.60%
4.59%
1.98%
Source: Caloghirou et al. (2011)
The firms included in the AEGIS database are not a random sample of European
enterprises. In order to have a large enough sample of firms to study activities and
behaviors in the ten countries, the architects of the database realized, correctly in
our opinion, that firms in smaller countries (e.g., Croatia and the Czech Republic)
needed to be sampled at a higher rate than firms in larger countries (e.g., France and
Germany). The sampling population of firms is shown in Table 4.1, by country. To
account for the nonrandom sampling, we investigated using sampling weights in the
exploratory statistical analyses in subsequent chapters.5,6 However, our findings
were similar when we weighted the data and when we did not. In anticipation of
future scholarly research by others who use the AEGIS database, our descriptive
analyses throughout the book are based on unweighted data for ease of replication.
The AEGIS database contains information on 4,004 firms established between
2001 and 2007 across the ten European countries, also shown in Table 4.1. The
countries represented in the database are (alphabetically) Croatia, the Czech
Republic, Denmark, France, Germany, Greece, Italy, Portugal, Sweden, and the
United Kingdom. And, across these countries, a number of firms from the high-tech
and low-tech sectors and from the knowledge-intensive business services sector are
represented in the database (but sectoral representation did not drive the construction of the database).
5
As described in Caloghirou et al. (2011), the sampling process was challenging due to the desire
to have adequate representation of smaller countries and across industries. The desire to include
new firms, rather than firms that had recently changed legal status, and to impose other restrictions
to ensure that the data included firms in the desired age range further complicated the data collection process. The final sampling frame consisted of 202,286 firms, and the database includes information on 4,004 firms. Caloghirou et al. (2011) provide detailed information on the sampling
process.
6
The sampling weights are, by country, Croatia (11.985), the Czech Republic (15.230), Denmark
(23.909), France (100.249), Germany (66.470), Greece (12.628), Italy (89.371), Portugal (16.492),
Sweden (62.533), and the United Kingdom (21.764).
4.2 Elements of the AEGIS Database
49
Table 4.2 Distribution of AEGIS firms, by country and by sector
Country
Croatia
Czech Republic
Denmark
France
Germany
Greece
Italy
Portugal
Sweden
United Kingdom
Total
Sector
High-techa
35
25
34
68
67
22
57
31
34
47
420
Low-techb
115
92
69
196
160
184
316
170
108
192
1,602
KIBSc
50
83
227
306
330
125
207
130
192
332
1,982
Total
200
200
330
570
557
331
580
331
334
571
4,004
Source: Caloghirou et al. (2011) and the AEGIS database
a
High-tech sector includes aerospace; computers and office machinery; radio-television communication equipment; manufacture of medical, precision, and optical instruments; pharmaceuticals;
manufacturer of electrical machinery and apparatus; manufacturer of machinery and equipment;
and chemical industry
b
Low-tech sector includes paper and printing; textile and clothing; food, beverage, and tobacco;
wood and furniture; basic metals; and fabricated metal products
c
Knowledge-intensive business services (KIBS) include telecommunications, computer and related
activities, research and experimental development, and selected business services activities
The AEGIS survey was conducted from late 2010 to 2011.7 At a minimum, a firm
in the AEGIS sample would have been active for 3 years.
The high-tech sector includes aerospace; computers and office machinery; radio-­
television communication equipment; manufacture of medical, precision, and
optional instruments; pharmaceuticals; manufacturer of electrical machinery and
apparatus; manufacturer of machinery and equipment; and chemical industry. The
low-tech sector, which of course is our focus, includes paper and printing; textiles
and clothing; food, beverage, and tobacco; wood and furniture; basic metals; and
fabricated metal products. Knowledge-intensive business services (KIBS) include
telecommunications, computer and related activities, research and experimental
development, and selected business services activities. See the notes in Table 4.2
and Appendix 4.A for more complete definitions of the industries within each
sector.8
Table 4.3 shows the distribution of firms in the textile and apparel industries, by
country. The number of firms in these two industries is greatest for Italy among
7
The astute reader will note that the survey was completed approximately 5 years before the United
Kingdom voted to leave the European Union (also known as Brexit). Thus, throughout this book,
we have retained the numbers for the UK textile and apparel industries in our presentation and
discussion of the AEGIS database.
8
As the note to Table 4.2 explains, we relied on Caloghirou et al. (2011) for the classification of
industries within the high-tech, low-tech, and knowledge-intensive business services sectors.
50
4 The AEGIS Database
Table 4.3 Distribution of AEGIS firms in the European textile and apparel industries, by country
Croatia
Czech Republic
Denmark
France
Germany
Greece
Italy
Portugal
Sweden
United Kingdom
Total
Industry
Textile
7
4
2
5
5
7
29
13
2
17
91
Apparel
6
3
2
9
3
14
27
11
4
5
84
Total
13
7
4
14
8
21
56
24
6
22
175
Source: AEGIS database
those countries represented in the AEGIS database, as might have been expected
from the number of Italian firms in the aggregate data presented in Chap. 3.
Overall, our analyses in subsequent chapters will be based on 91 textile firms and
on 84 apparel firms; however, not all firms responded to all of the survey questions.
The reader will likely note that even when the two industries are combined, the total
sample of 175 firms is still small in comparison to what others might view as providing an acceptable number of observations for meaningful empirical analyses.
The AEGIS study was designed to collect data on KIE across industries as well
as countries within the European Union, rather than collect in-depth data on just one
or two industries. While we acknowledge this paucity of data for a particular country as a limitation in terms of our ability to explore the data keeping in mind the
purpose of this book, we respectfully suggest that the size of the textile and apparel
samples is acceptable, and it will allow us to offer suggestive answers to the three
overriding research questions that are a motivation for our effort:
• While there are many small firms that comprise the EU textile and apparel industries, how and to what extent are these firms entrepreneurial and/or innovative in
their behaviors?
• What might KIE, and, in particular, entrepreneurial and innovative behaviors,
mean for firm performance and/or industrial growth?
• What, if anything, do our empirical findings suggest for those small- and
medium-sized firms that comprise the US textile and apparel industries?
Appendix 4.A: Industries Defined in Each Sector
51
4.3 Conclusions
Throughout the rest of the book, we will discuss separately the experiences embodied in firms and their founders and the sources of knowledge used by firms and their
founders. And, based on our interpretation about the tables above, we describe and
analyze in an exploratory manner experiences and sources of knowledge in the most
general terms; more importantly we examine them cautiously on a country-by-­
country basis. We also discuss differently those dimensions that lead to entrepreneurial behavior, that is, we discuss them by industry, to account for differences in
the competitive and technical infrastructures which affect entrepreneurial firms and
their founders.
In an effort to approach completeness and to provide an understanding of the
data, we reproduce in the following chapters the AEGIS survey questions that are
relevant to the topics discussed in the chapter. We are aware that there is a lack of
consistency across survey questions in the use of nouns to reference the questions.
Some questions use the word company, others the word firm, and still others the
word business. While our preference would be to standardize on the usage of these
words to describe the unit of observation, we have instead maintained throughout
this book the exact verbiage used in the AEGIS survey, but we employ the word firm
throughout the descriptive text.
Appendix 4.A: Industries Defined in Each Sector
The definitions of the industries that are in the sectoral categories of high tech, low
tech, and KIBS are reported in Caloghirou et al. (2011). A more specific segmentation is in Table 4.4 for information rather than analytical purposes.
52
4 The AEGIS Database
Table 4.4 Segmentation of EU industries, by sector
Selected sectors
High-tech manufacturing sectorsa
Aerospace
Computers and office machinery
Radio-television and communication equipment
Manufacturers of medical, precision, and optical
instruments
Pharmaceuticals
Medium- to high-tech manufacturing sectors
Manufacture of electrical machinery and apparatus
Manufacture of machinery and equipment
Chemical industry (excluding pharmaceuticals)
Medium- to low-tech manufacturing sectors
Basic metals
Fabricated metal products
Low-tech manufacturing sectorsb
Paper and printing
Textile, apparel, and leather
Food, beverages, and tobacco
KIBS sectorsc
Telecommunications
Computer and related activities
Industry code
35.3
39
32
33
24.2
31
29
24 (less 24.4)
27
28
21, 22
17, 18, 19
15, 16
64.2
72
Source: Caloghirou et al. (2011, p. 16)
a
High-tech sector includes aerospace; computers and office machinery; radio-television communication equipment; manufacture of medical, precision, and optical instruments; pharmaceuticals;
manufacturer of electrical machinery and apparatus; manufacturer of machinery and equipment;
and chemical industry.
b
Low-tech sector includes paper and printing; textile and clothing; food, beverage, and tobacco;
wood and furniture; basic metals; and fabricated metal products.
c
Knowledge-intensive business services (KIBS) include telecommunications, computer and related
activities, research and experimental development, and selected business services activities.
Chapter 5
Characteristics of KIE Textile and Apparel
Firms and Founders
Nearly all men can stand adversity, but if you want to test a
man’s character, give him power.
―Abraham Lincoln
Character is like a tree and reputation its shadow. The shadow
is what we think it is and the tree is the real thing.
―Abraham Lincoln
Abstract This chapter relied on the AEGIS database to describe KIE firms in the
textile and apparel industries. Relevant characteristics include the age of the firms,
their number of employees, and characteristics of their founders. The human capital
characteristics of the founders of KIE firms include age, education, and work
experience.
5.1 Introduction
This chapter sets the stage for several of the following chapters both in tone and
structure. Here, we build on the theme of KIE through a characterization of the
entrepreneurial firms and founders that are classified as being in the textile and
apparel industries and that are included in the AEGIS database. The firms and
founder characteristics that we describe and discuss in this chapter are, of course,
limited by the extent to which the AEGIS survey and database contain such information. That said, we are fortunate to know a good deal about the firms and about
their founders, specifically, keeping in mind the KIE focus of this book, about the
human capital and financial characteristics of the firms’ founders.
Focusing on human capital characteristics, such as age (a proxy for experience)
of the founder, his/her education, and his/her experience, ties our analysis in this
and the following chapters to our previous epistemological discussion in Chap. 1
about the sources of knowledge from which entrepreneurial decisions are made.
Focusing on human capital characteristics also ties our analysis to the economics
and management literatures related to firm performance. While our discussion in
© Springer International Publishing AG 2018
N.J. Hodges, A.N. Link, Knowledge-Intensive Entrepreneurship, International
Studies in Entrepreneurship 39, https://doi.org/10.1007/978-3-319-68777-3_5
53
54
5 Characteristics of KIE Textile and Apparel Firms and Founders
Chap. 1 suggests that there is a conceptual relationship between human capital
endowments and performance—be it the individual entrepreneur’s human capital
and performance or the entrepreneur’s human capital and his/her firm’s performance—the academic literature is however mixed on the topic. Shrader and Siegel
(2007, p. 894), for example, wrote:
Many scholars in entrepreneurship have focused on examining relationships between
salient characteristics of entrepreneurs and the performance of new ventures. … These individual attributes can be viewed as aspects of the entrepreneur’s human capital. Unfortunately,
there has been little consistency and no consensus among the findings of this research.
These results have led some entrepreneurship scholars to conclude that characteristics of
entrepreneurs are not important and even to advocate abandoning this line of inquiry. Such
findings and conclusions are puzzling because we might expect that the link between managerial characteristics and performance is stronger for new ventures, given their simpler
structures, lack of organizational inertia, and less complex strategies.
We interpret this viewpoint offered by Shrader and Siegel (2007) as an accurate
interpretation of the extant literature. They cite, as support for their view, the scholarship of Gartner (1988) and Shrader (2001). We also interpret the Shrader and
Siegel viewpoint as a motivation for our analyses in this book because of the robustness of the AEGIS data and because of our industry focus.
Unique to the AEGIS database is information on the founders of each firm, with
emphasis on the “s” in founders. As we discuss below, and to anticipate our subsequent analyses, the mean number of founders in the textile and the apparel industries in the AEGIS database is 1.9 and 2.0, respectively. While we will discuss firm
founders throughout the book, most of the time we will be making a reference to
only the first-listed founder of the firm characterized in the AEGIS database. By
doing so, we are assuming the first-listed founder to be the primary founder.
Shrader and Siegel (2007, p. 894) also pointed out:
Recently, entrepreneurship scholars have begun to study the importance of entrepreneurial
teams, due to the recognition that high potential, high growth firms are typically launched
and grown by teams of entrepreneurs, not individuals. An entrepreneurial team is defined as
a group working together to launch a new business venture. Such a group often resembles
the TMT [top management team] of a more established firm in that it might include several
people with diverse experience and skills in a variety of functional areas. … However, there
is a paucity of research on entrepreneurial teams, strategy, and venture performance.
We interpret the just above-quoted interpretation of the literature by Shrader and
Siegel as being an interpretation that is tantamount to calling for a detailed empirical analyses of founding teams; a topic that has been pursued by scholars in the
management literature has as of yet been ignored in the economics literature; it is
certainly a topic that has been ignored with reference to the scholarly writings about
textile and apparel firms.
The remainder of this chapter, like all of the following chapters in our book, is
exploratory and descriptive in terms of the firms themselves and of their founding
entrepreneurs. We present in the tables that follow characteristics for firms and their
founders not only by industry but also by country. It is important for the reader to
keep in mind that the number of firms represented in the AEGIS database in each
5.1 Introduction
55
Table 5.1 Distribution of AEGIS firms in the European textile and apparel industries, by country
Croatia
Czech Republic
Denmark
France
Germany
Greece
Italy
Portugal
Sweden
United Kingdom
Total
Industry
Textile
7
4
2
5
5
7
29
13
2
17
91
Apparel
6
3
2
9
3
14
27
11
4
5
84
Total
13
7
4
14
8
21
56
24
6
22
175
country is not the same, and the number in many countries is very small. We emphasized this in Chap. 4 with respect to the information in Table 4.3, which we have
reproduced here for reference purposes as Table 5.1. For example, regardless of
industry, the sample size for Italy is more than an order of magnitude greater than
the sample size for Denmark. Even when using weighted averages, we urge the
reader to refrain from generalizing about characteristics or behaviors of textile and
apparel firms in a specific country from samples that contain information on only a
handful of firms. However, we have nevertheless presented country-specific data, by
industry, for completeness (and perhaps to whet the appetite of scholars to pursue
more complete country studies); we do cautiously offer from time to time observations about industry characteristics and behaviors using aggregated data across the
countries represented in the AEGIS database, but we also do point out characteristics of Italian and Portuguese firms, and UK textile firms, in particular because the
number of firms in those countries is relatively greater and thus perhaps more representative of the EU textile and apparel industries in the AEGIS database. Writing
“perhaps more representative” underscores that the AEGIS survey was not constructed to a representative sample of industry firms, by country. Still, we do also
refrain from generalizing about the textile and apparel industries in the EU because
information in the AEGIS database represents only ten countries.
The following sections of this chapter present descriptive statistics about firm
and founder characteristics. We offer no judgment about how these characteristics
relate to other firms in EU industries, and we offer no judgment about whether the
descriptive evidence based on the AEGIS data suggests tâtonnement, that is, we
offer no judgment about whether the firm is in an equilibrium state or not.
The remainder of this chapter is outlined as follows. In Sect. 5.2, we describe two
important characteristics of the firms themselves, namely, their age, measured in
years since being founded, and their size, measured as number of full-time and part-­
time employees at the time of the AEGIS survey. In Sect. 5.3, we discuss human
capital and financial capital characteristics of firms’ founders. In Sect. 5.4, we
56
5 Characteristics of KIE Textile and Apparel Firms and Founders
Table 5.2 Characteristics of textile and apparel firms
Country
Croatia
Czech
Republic
Denmark
France
Germany
Greece
Italy
Portugal
Sweden
United
Kingdom
All
countries
Mean age (in
years) of firms
Mean full-time
employees
Mean part-time
employees
Textile
(n = 91)
8.4
5.8
Apparel
(n = 84)
8.2
8.3
Textile
(n = 91)
11.3
32.8
Apparel
(n = 84)
7.5
6.7
Textile
(n = 91)
2.3
0.5
Apparel
(n = 84)
0.3
1.0
Mean percent of
workers that are
part time
Apparel
Textile
(n = 91) (n = 84)
(%)
(%)
23.8
5.6
0.5
5.6
5.5
7.0
9.2
7.9
6.8
6.1
5.5
7.2
5.0
5.6
6.7
8.1
7.0
7.4
6.0
7.4
0.5
3.8
4.6
13.9
7.4
25.4
1.5
7.2
3.5
5.0
14.3
15.3
8.7
16.4
2.5
4.2
0.5
0.2
8.4
10.1
1.1
0.2
0
3.5
1.0
0.3
2.0
0.8
1.7
0.1
1.0
1.0
50.0
1.9
29.8
10.3
12.6
1.5
0
18.6
25.0
8.0
20.4
6.6
13.9
2.3
37.5
12.5
7.0
7.1
11.3
9.8
2.5
1.0
13.5
10.6
c­ onclude the chapter with a summary of information about founders, and we offer
general descriptive statements about KIE textile and apparel firms and founders as
defined by information from the AEGIS database.
5.2 Age and Size of AEGIS Textile and Apparel Firms
We define the age of each firm as the difference between 2011 and the year the firm
was founded. As previously said, the AEGIS survey was conducted from late 2010
to 2011. Because we do not have exact information when each firm was surveyed
during this time frame, we are uniformly dating all survey responses to have been
collected in 2011. As shown in Table 5.2, the mean age of the firms in the textile
industry is about the same as in the apparel industry, specifically, all of the firms are
about 7 years old. And, this is about the mean age among the Italian firms, recalling
that Italy is the most represented country in terms of number of textile and apparel
firms. Portuguese firms in the textile industry are, on average, slightly younger than
Italian firms and UK firms.
Textile firms are larger than apparel firms based on the mean number of full-time
employees; the mean number of full-time employees is 11.3 for textile firms and 9.8
for apparel firms. See Table 5.2. Sample size by country aside, the variation across
firms in each of these two industries is noticeably smaller for some countries and
noticeably larger for others. In Italy, for example, the difference in mean number of
5.2 Age and Size of AEGIS Textile and Apparel Firms
57
full-time employees is relatively small: the mean number of full-time employees is
7.4 in textile firms (n = 29) and 8.7 in apparel firms (n = 27). In Portugal, the difference in the mean number of full-time employees is relatively large; the mean number of full-time employees is 25.4 in the textile industry (n = 13) and 16.4 in the
apparel industry (n = 11). Even though we point out observed patterns among firms
in Italy and Portugal, and sometimes in the United Kingdom, we again emphasize
caution about making cross-country generalizations.1
Also noticeable from Table 5.2 is that the mean number of part-time employees
in textile firms is more than twice the mean number of part-time employees in
apparel firms. Perhaps a more relevant descriptive statistic, compared to the mean
number of part-time employees among firms in each of the two industries, is the
percent of workers in firms in each industry that are employed on a part-time basis.2
The mean percentage of part-time employees in textile firms is 13.5 compared to
10.6 in apparel firms.3
To gain insight about the relative number of employees by age of firm, we correlated, separately for firms in the textile industry and for firms in the apparel industry, the age of each firm with the number of full-time employees, the number of
part-time employees, and the percent of part-time employees in that firm. Our prior
is that if the number of employees is a proxy for firm size, the correlation coefficients with firm age will be positive, that is, firms grow over time. However, because
the AEGIS data coincide with the immediate post-recession period, it would not be
surprising that many of the correlation coefficients are insignificant. See Table 5.3.
For the textile firms, there is no statistically significant correlation between the age
of the firm and any of the employment metrics. In other words, older firms—keeping in mind that the firms in the AEGIS database range only from 4 to 10 years—are
on average no different than younger firms in terms of the number of full-time or
part-time employees working or in the percent of employees that work part time. A
possible explanation for the lack of employment growth over time—and age of firm
does reflect time—is that the AEGIS data were collected during periods of economic decline as we just noted. In contrast, among apparel firms, the correlations
suggest that older firms are associated with having more full-time workers and a
smaller percent of their workforce that is part time. We only report statistically significant correlation coefficients in this table and in the following tables and in all of
the correlation matrices that follow in this chapter and in subsequent chapters. We
do not report any of the upper off diagonal elements of a matrix.
1
The number of UK firms in the textile industry is greater than the number of Portuguese textile
firms. When we do signal out characteristics of UK firms, we do so only for textile firms because
the number of UK apparel firms is small in comparison to those in Italy and Portugal.
2
The mean percentages in the were calculated as [part ‐ time employees/(full ‐ time employees + part ‐ time employees)].
3
Although beyond the scope of the AEGIS data, a study of changes in the percent of part-time
employees over time is certainly warranted.
***
–
–
–
–
0.569***
1
Part-­time
employees
1
Percent part-­time
employees
significant at 0.01-level, **significant at 0.05-level, *significant at 0.10-level
Age of firm
Full-time
employees
Part-time
employees
Percent
part-time
employees
Textile industry (n = 91)
Age of
Full-­time
firm
employees
1
–
1
–
−0.214*
–
−0.230**
Apparel industry (n = 84)
Full-­time
Age of firm
employees
1
0.226**
1
0.600***
1
Part-­time
employees
1
Percent part -time
employees
Table 5.3 Correlation matrix among firm age, number of full-time employees, number of part-­time employees, and percent of workers that are part-time
employees, by industry
58
5 Characteristics of KIE Textile and Apparel Firms and Founders
5.3 Characteristics of Firm Founders
59
Table 5.4 Characteristics of textile and apparel firm founders
Country
Croatia
Czech
Republic
Denmark
France
Germany
Greece
Italy
Portugal
Sweden
United
Kingdom
All countries
Mean number of
founders
Mean number of
female founders
Mean percent of founders
that are female
Textile
(n = 91)
Apparel
(%)
(n = 84) (%)
39.3
27.8
37.5
22.2
Textile
(n = 91)
1.9
2.8
Apparel
(n = 84)
1.8
1.7
Textile
(n = 91)
0.7
1.3
Apparel
(n = 84)
0.5
0.7
1.0
1.4
1.4
2.7
1.7
2.1
2.0
1.7
2.0
1.6
1.7
1.9
2.7
1.5
1.5
2.0
0
0.2
0.2
0.4
0.6
0.9
0.5
0.5
1.0
0.9
0.7
0.6
1.0
0.6
1.0
1.0
0
6.7
12.5
11.9
33.6
34.6
50.0
31.4
50.0
66.7
33.3
31.4
37.0
50.0
66.7
35.0
1.9
2.0
0.6
0.8
29.4
41.4
5.3 Characteristics of Firm Founders
This section of the chapter is divided into seven subsections. Each subsection
describes unique founder-related characteristics.
5.3.1 Number and Gender of Founders
Several characteristics of firm founders are presented in Table 5.4. The mean number of founders among both textile firms and apparel firms is 1.9 and 2.0, respectively. Among Italian firms, the mean number of founders is greater for apparel
firms, but the reverse is true for firms in Portugal: 2.7 apparel founders compared to
1.7 textile founders in Italy and 2.1 textile founders compared to 1.5 apparel founders in Portugal.
Continuing with reference to Table 5.4, female founders are an exception to the
above pattern of number of founders in general, meaning that there are fewer female
founders than male founders in terms of numbers, both overall and country by country, as shown in Table 5.4. Table 5.4 also reports the percent of founders in the textile and apparel firms that are female, by country. Overall, 29.4% of textile firms
have female founders, while 41.4% of apparel firms have the same. A similar relationship holds among textile and apparel firms in Italy and Portugal meaning that
the percent of firms with female founders is greater among apparel firms than among
textile firms.
60
5 Characteristics of KIE Textile and Apparel Firms and Founders
As we pointed out in Chap. 2, there is a considerable gap in the literature on
characteristics of small firm owners in textile and apparel firms, especially with
regard to gender. Yet, the broader literature on entrepreneurship positions gender as
a key factor in firm performance, especially with respect to access to resources and
financial capital. Because the AEGIS data include the gender of the founding owners, we are able to explore this characteristic in our analyses of firm strategy and
performance in the chapters that follow.
5.3.2 Age of Founders
The AEGIS database does not record the exact age of the firms’ founders. Rather, as
reported in the notes to Table 5.5, the survey question that asked about the founders’
age is structured in terms of decades (age 18–29, age 30–39, and so forth). Imputing
to the first-listed founder of each firm the mean of the age range defined on the
Table 5.5 Human capital and financial capital characteristics of textile and apparel firms
Country
Croatia
Czech
Republic
Denmark
France
Germany
Greece
Italy
Portugal
Sweden
United
Kingdom
All
countries
Mean age (in
years) of foundera
Mean education
level of founderb
Mean years of
experience in the
low-tech sectorc
Textile
(n = 91)
49.3
50.0
Apparel
(n = 84)
46.7
41.7
Textile
(n = 91)
2.4
2.5
Apparel
(n = 84)
2.5
2.7
Textile Apparel
(n = 91) (n = 84)
12.1
13.3
6.0
7.3
Mean percent of
funding from
founder’s own or
family resources
Apparel
Textile
(n = 91) (n = 84)
(%)
(%)
72.9
100
67.5
83.3
45.0
51.0
38.0
46.4
42.2
43.3
40.0
46.1
40.0
46.1
45.0
40.1
44.9
42.1
50.0
49.0
2.0
2.8
2.5
3.0
2.2
2.2
3.0
2.5
3.0
2.3
3.0
2.7
2.3
1.7
3.5
2.4
5.5
20.8
5.8
14.1
12.9
13.9
9.0
12.1
10.0
6.8
22.7
10.0
19.2
8.0
22.8
13.8
100
84.0
34.0
67.1
72.1
96.2
100
78.5
40.0
73.7
56.7
82.9
59.0
71.0
50.0
100
44.5
44.3
2.4
2.4
12.5
13.8
75.8
71.7
Key:
1 = Elementary education 2 = Secondary education 3 = Bachelor degree 4 = Postgraduate degree
5 = Ph.D.
a
Only the first-listed founder is considered. The AEGIS survey asks for the age of the founder in
10-year increments (e.g., 18–29, 30–39, 40–49, >50). The midpoint of each decade was used for
the calculations in this table. If a founder was of an age greater than 50, he/she was treated numerically as being 55 years old.
b
Only the first-listed founder is considered.
c
Only the first-listed founder is considered.
5.3 Characteristics of Firm Founders
61
AEGIS survey to which he/she corresponds, we find that the mean age of first-listed
founders among textile firms is 44.5 years and the mean age of first-listed founders
among apparel firms is 44.3 years. We are implicitly assuming that the first-listed
founder reported in the AEGIS database is the primary founder.4 Pragmatically, this
assumption greatly facilitates our descriptive analyses in this chapter and our statistical analyses in the following chapters.
5.3.3 Educational Level of Founders
As also shown in Table 5.5, the mean levels of education among both first-listed
founders among textile firms and among apparel firms are the same: 2.4 where a
response of 2 refers to a secondary education and a response of 3 refers to a bachelor
degree. See the Key to Table 5.5. The mean education level among both Italian firms
and Portuguese firms is also less than a bachelor degree; in fact, the mean level of
education among Portuguese apparel firm founders is less than a secondary education. The more highly educated founders are, on average, in Swedish firms.
Much like gender, research that examines the educational levels among firm
founders or owners in these two industries is sparse. The same can be said for
research that examines education levels of firm founders, especially KIE founders,
in other industries. Educational levels are an important characteristic, in as much as
education is a key element of human capital and one that has been shown through
the human capital literature to have an impact on firm behavior with respect to
­innovation. We make reference to the nexus of education and innovation to reflect
on the broader human capital literature and to reflect on the following opinion of the
European Skills Council (2014, p. 6) from Chap. 1:
The European Textiles [and] Clothing … sector is undergoing a renaissance [and it] is now
beginning to re-emerge, leaner and more confident of its place in the world. Driven by creativity and innovation, products manufactured … range from traditionally crafted fashion
and textiles goods through to scientifically-led technical items.
For example, in one of the few studies from Chap. 2 to include educational level
as a firm characteristic, levels were found to be slightly higher among employees of
Turkish textile firms that exhibited greater tendencies to engage in innovative activity (Kustepeli et al. 2012). This finding among Turkish textile firms echoes the point
we mentioned in Chap. 1 that was attributable to Schultz (1975, p. 843):
There is enough evidence to give validity to the hypothesis that the ability to deal successfully with economic disequilibria is enhanced by education and that this ability is one of the
major benefits of education accruing to people privately in a modernizing economy.
4
As a robustness check on the implications of this simplifying assumption, we also calculated the
average age of all founders within each firm. Those results are virtually indistinguishable from the
descriptive information in Table 5.10.
62
5 Characteristics of KIE Textile and Apparel Firms and Founders
To speculate, to the extent that educational levels are correlated with age and to
the extent that age reflects experience, then this finding among Turkish firms also
echoes the point by Locke (1996, p. 59) from Chap. 1:
All ideas come from sensation or reflection. Let us then suppose the mind to be, as we say,
white paper, void of all characters, without any ideas: How comes it to be furnished?
Whence comes it by that vast store which the busy and boundless fancy of man has painted
on it with an almost endless variety? Whence has it all the materials of reason and knowledge? To this I answer, in one word, from EXPERIENCE.
We explore the relationship between age, education, and experience below and
then again in Chap. 8 where we introduce innovation-related measures from the
AEGIS database.
We examined the relationship between the age of a firm’s founder and characteristics of his/her firm. We found no statistically significant correlation between the
educational level of textile firm founders or of apparel firm founders and any of the
firms’ characteristics discussed above in Table 5.4 in Sect. 5.2.5 The relationship
between founders’ age and their experience is considered below.
5.3.4 Work Experience of Founders
Two metrics associated with the work experience of founders are available in the
AEGIS database. The first metric is the years of experience in the low-tech sector.
Recall from Table 4.2 in Chap. 4 that the AEGIS data were drawn from three major
sectors: high-tech, low-tech, and knowledge-intensive business services. Also recall
from Table 4.4 in the appendix to Chap. 4 that the textile and apparel industries are
classified within the low-tech sector.
Table 5.5 also shows that the mean years of experience in the low-tech sector of
the first-listed founder are 12.5 years for textile firm founders and 13.8 years for
apparel firm founders. Again, a comparison between firm founders in Italy and in
Portugal is interesting. In Italy, apparel firm founders have about 6 years more sectoral experience than do textile firm founders: a mean of 19.2 years compared to a
mean of 12.9 years, respectively. However, in Portugal just the reverse is observed.
Portuguese textile firm founders have a mean of 13.9 years of sectoral experience
compared to only 8.0 years for apparel firm founders. To our knowledge, there is no
literature that offers an investigation of firm founder experience relative to either
industry in the EU or elsewhere. Yet, this characteristic is likely important to characterizing KIE within these low-tech industries and perhaps even more so than educational level (Machlup 1980).
The second metric associated with work experience is related to the most recent
occupation of the first-listed founder. The distribution of textile firm founders’ most
recent occupation is in Table 5.6, and the distribution of apparel firm founders’ most
5
This conclusion holds even when the average of the age or education of all of the founders in firms
is used rather than the age or education of the first-listed founder.
5.3 Characteristics of Firm Founders
63
Table 5.6 Percent of textile founders by most recent occupational experience, by country (n = 91)
Croatia
Czech Republic
Denmark
France
Germany
Greece
Italy
Portugal
Sweden
United Kingdom
All countries
Last occupation of founder before establishing this firm
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(%)
(%)
(%)
(%)
(%)
(%)
(%)
14.3
0
42.9
28.6
0
0
14.3
0
0
25.0
50.0
25.0
0
0
0
0
50.0
0
50.0
0
0
0
0
20.0
80.0
0
0
0
0
0
40.0
20.0
0
0
0
0
0
85.7
0
0
0
0
13.8
31.0
20.7
3.4
10.3
0
0
15.4
7.7
38.5
7.7
7.7
0
0
0
0
50.0
50.0
0
0
0
11.8
0
47.0
29.4
0
0
0
9.9
11.0
37.4
18.7
6.6
0
1.1
(8)
(%)
0
0
0
0
0
0
0
7.7
0
11.8
3.3
(9)
(%)
0
0
0
0
20.0
14.3
20.7
15.4
0
0
11.0
Only the first-listed founder is considered in this table. The sum of percentages, by country, might
not equal to 100 due to rounding.
Key:
1 = Owner of a firm still in existence 2 = Owner of a firm that has ceased operations 3 = Employee
of a firm in the same industry 4 = Employee of a firm in a different industry 5 = Self-employed
6 = University or research institute employee 7 = Government employee 8 = Unemployed 9 = This
is first job
recent occupation is in Table 5.7. With respect to textile firm founders in Table 5.6,
37.4% of all founders’ most recent occupation was in the textile industry. The same
is slightly higher for apparel firm founders, a mean of 40.5%. See the Key to the
table.
For both industries, the second most recent occupational experience characterizing these KIE founders was as an employee of a firm in a different industry—18.7%
for textile firms and 22.6% for apparel firms. See the Key to the table. The point to
emphasize from these percentages is that well over one-half of the KIE founders in
these two industries were previously employees in other firms rather than being the
owners of other firms. For the textile industry, the relevant percent is 56.1 and for
the apparel industry it is 63.1. We conjecture that the KIE founders in these two
industries were employees leaving their jobs to become founding owners of new
firms. It would be interesting to know if these founding owners worked for their
family firms prior to starting their own, but that information is not available in the
AEGIS database.
We find it interesting too that 11% of the textile firm founders and 4.8% of the
apparel firm founders responded on the AEGIS survey that they had no recent occupational experience and that this current venture was their first entrée into the market. See the Key to the table. Thus, these individuals are truly nascent entrepreneurs,
where nascent refers to “one who starts a new endeavor such as a business or an
organization” (Gicheva and Link 2016, p. 110). We explore descriptively the characteristics of these nascent entrepreneurs below.
64
5 Characteristics of KIE Textile and Apparel Firms and Founders
Table 5.7 Percent of apparel founders by most recent occupational experience, by country
(n = 84)
Croatia
Czech Republic
Denmark
France
Germany
Greece
Italy
Portugal
Sweden
United Kingdom
All countries
Last occupation of founder before establishing this firm
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(%)
(%)
(%)
(%)
(%)
(%)
(%)
16.7
0
16.7
50.0
0
0
0
0
0
66.7
0
33.3
0
0
0
0
50.0
50.0
0
0
0
0
22.2
22.2
22.2
0
0
11.1
0
0
33.3
33.3
33.3
0
0
0
14.3
50.0
14.3
0
0
0
14.8
11.1
33.3
14.8
22.2
0
0
0
0
63.6
27.3
9.1
0
0
0
0
25.0
50.0
0
25.0
0
0
20.0
60.0
20.0
0
0
0
6.0
9.5
40.5
22.6
10.7
1.2
1.2
(8)
(%)
16.7
0
0
11.1
0
0
0
0
0
0
2.4
(9)
(%)
0
0
0
11.1
0
14.3
3.7
0
0
0
4.8
Only the first-listed founder is considered in this table. The sum of percentages, by country, might
not equal to 100 due to rounding.
Key:
1 = Owner of a firm still in existence 2 = Owner of a firm that has ceased operations 3 = Employee
of a firm in the same industry 4 = Employee of a firm in a different industry 5 = Self-employed
6 = University or research institute employee 7 = Government employee 8 = Unemployed 9 = This
is first job
5.3.5 Resources Used to Establish the Firm
There is a growing literature on the resources that entrepreneurs rely on for firm
growth, but that literature has generally been limited to the question of the resources
used for the initial creation of a firm. Much of the extant literature has addressed
entrepreneurs’ access to financial capital. We offer in Appendix 5.A to this chapter
an annotated review of the academic literature related to the use of financial capital
by entrepreneurs not only for purpose of completeness but also to spotlight the
unique contribution that our analysis in this book provides to the extant literature for
describing the resources used to establish KIE firms.6
Our annotated review focuses on the use of financial capital from a gender perspective. To anticipate the following sections of this chapter, our discussion does
focus on gender differences between KIE founders of firms in the textile and apparel
industries. Some of the more relevant studies within the summary will be used to
contextualize the discussions provided in subsequent chapters. Thus, so focusing
our summary of the academic literature seems appropriate.
We, following the extant literature, focus on the two primary sources of funding
that the founders of the textile and apparel firms relied on for founding their firm,
namely, own resources and family resources. The final columns in Table 5.5 show
An earlier version of the material in this appendix appeared in Link and Strong (2016).
6
65
5.3 Characteristics of Firm Founders
that the mean percent of total funding used to establish the textile firms was 75.8
from own resources and family resources, and the mean percent used to establish
the apparel firms was 71.7% from the same two sources. Both Italian and Portuguese
firms in the textile industry relied relatively more on these two sources than did
firms in the apparel industry. For Italy, the respective percentages were 72.1 and
59.0, and for Portugal, the respective percentages were 96.2 and 71.0.
5.3.6 F
irm and Founder Characteristics by Gender
of the Founder, by Industry
We revisit the issue of gender in this subsection by reporting in Table 5.9 the mean
values of all of the firm and founder characteristics for textile and apparel firms discussed above, but we have segmented them by the gender of the first-listed founder.
Before discussing firm and founder characteristics by gender, we present in
Table 5.8 the correlation coefficients among age, education, and experience of
founders, by industry. As mentioned above, we revisit these relationships in Chap.
8 with reference to the innovative behavior of the firm. The only statistically significant correlation is between age and experience. Perhaps the lack of a significant
correlation between education and either age or experience is due to the categorical
way that education was measured in the AEGIS database.
Regarding information about the textile firms in Table 5.9 and comparing maleand female-founded firms in terms of mean values of the listed characteristic, we
find that:
• The age of male and female founders’ firms is about the same.
• Male-founded firms are larger based on the mean number of full-time employees
and on the mean number of part-time employees.
• The mean age and mean educational levels of male- and female-founded firms
are about the same, with female founders being only slightly older.
• Male founders have about 2 years more of low-tech sector experience compared
to female founders.
• Slightly more female founders had occupational experience as an employee in an
industry other than the textile industry (occupation 4).
• More female founders had occupational experience as a self-employed individual (occupation 5).
Table 5.8 Correlation matrix among founder age, education, and experience, by industry
Age
Education
Experience
***
Textile industry (n = 91)
Age
Education Experience
1
–
1
0.473***
–
1
significant at 0.01-level
Apparel industry (n = 84)
Age
Education Experience
1
–
1
0.514***
–
1
66
5 Characteristics of KIE Textile and Apparel Firms and Founders
Table 5.9 Firm founder characteristics in the textile and apparel industries, by gender
Firm age
Full-time employees
Part-time employees
Percent of employees that are part time
Agea
Educationb
Years of low-tech sector experience
Percent with experience in occupation 1c
Percent with experience in occupation 2c
Percent with experience in occupation 3c
Percent with experience in occupation 4c
Percent with experience in occupation 5c
Percent with experience in occupation 6c
Percent with experience in occupation 7c
Percent with experience in occupation 8c
Percent with experience in occupation 9c
Percent of own or family resources used to
establish the firm
Textile firms
Female
Male
founded
founded
(n = 19)
(n = 72)
7.0
6.8
12.5
6.1
2.8
1.1
13.7%
12.4%
44.6
46.6
2.4
2.4
13.0
10.7
9.7%
10.5%
11.1%
10.5%
37.5%
36.8%
18.1%
21.1%
5.6%
10.5%
0
0
1.4%
0
4.2%
0
11.1%
10.5%
74.4%
81.1%
Apparel firms
Female
Male
founded
founded
(n = 30)
(n = 54)
7.1
7.1
12.1
5.5
1.2
0.6
1.0%
12.2%
45.4
42.3
2.5
2.3
14.5
12.4
9.3%
0
11.1%
6.7%
37.0%
46.7%
22.2%
23.3%
13.0%
6.7%
1.9%
0
1.9%
0
0
6.7%
1.9%
10.0%
74.8%
66.5%
Only the first-listed founder is considered in this table.
The AEGIS survey asks for the age of the founder in 10-year increments (e.g., 18–29, 30–39,
40–49, >50). The midpoint of each decade was used for the calculations in this table. If a founder
was of an age greater than 50, he/she was counted as being 55 years old.
b
Key:
1 = Elementary education 2 = Secondary education 3 = Bachelor degree 4 = Postgraduate degree
5 = Ph.D.
c
Key:
1 = Owner of a firm still in existence 2 = Owner of a firm that has ceased operations 3 = Employee
of a firm in the same industry 4 = Employee of a firm in a different industry 5 = Self-employed
6 = University or research institute employee 7 = Government employee 8 = Unemployed 9 = This
is first job
a
• No female founders had occupational experience as either university or research
institute employees or as government employees (occupations 6 and 7).
• Female founders relied to a greater extent on own and family resources to establish their firm than did male founders.
Regarding the apparel firms in Table 5.9 and comparing male- and female-­
founded firms in terms of mean values of the noted characteristic, we find that:
• The age of male and female founders is about the same.
• Male-founded firms are larger based on the number of full-time employees and
on the number of part-time employees.
5.3 Characteristics of Firm Founders
67
• Male founders have about 2 years more of low-tech sector experience compared
to female founders.
• The mean age and mean educational level of male- and female-founded firms are
about the same, with male founders being older by about 3 years.
• More male founders had occupational experience as owners of a firm that is still
in existence (occupation 1).
• More male founders had occupational experience as owners of a firm that has
ceased operations (occupation 2).
• More female founders had occupational experience as an employee of a firm in
the apparel industry (occupation 3).
• More male founders had occupational experience as a self-employed individual
(occupation 5).
• No female founders had occupational experience as either university or research
institute employees or as government employees (occupation 6 and 7).
• More female founders were unemployed prior to establishing their firm (occupation 8).
• Establishing this firm was the first job for more female founders (occupation 9).
• Male founders relied to a greater extent on own and family resources to establish
their firm.
5.3.7 Nascent Entrepreneurial Founders
There is a burgeoning academic literature on nascent entrepreneurs, but it is narrow
in its scope because of data limitations. An annotated review of that literature is in
Appendix 5.B to this chapter not only for completeness but also to bridge the literature on nascency with that of KIE.7 Our annotated review focuses on nascent entrepreneurs from a gender perspective. Our previous discussion about firm and founder
characteristics has focused on gender differences between KIE founders of firms in
the textile and apparel industries. Thus, so focusing our summary of the academic
literature seems appropriate. While it is beyond the scope of this book to explore all
of the covariates associated with the initial decision for one to become an entrepreneur, it seems fitting to us to extend our descriptive analysis of KIE firms and founders into this area.
Table 5.10 compares the age, education, and gender of nascent entrepreneurs to
established entrepreneurs. The reader should keep in mind that while the percent of
firms in the textile and apparel industries founded by nascent entrepreneurs is not
insignificant, the absolute number of nascent entrepreneurs is small, and thus generalizations from our comparative discussion of Table 5.18 should only be made
with caution. Based on the mean values shown in the table:
• Nascent entrepreneurs are younger.
An earlier version of the material in this appendix appeared in Link and Strong (2016).
7
68
5 Characteristics of KIE Textile and Apparel Firms and Founders
Table 5.10 Characteristics of nascent entrepreneurs and established entrepreneurs in firms in the
textile and apparel industries
Founder characteristic
Age of founder
Education of foundera
Percent of founders who are female
Percent of workers that are part-time
employees
Percent of total funding to establish the
firm that came from own resources or
family resources
Firms founded by a
nascent entrepreneur
(n = 14)
35.0
2.31
35.7%
30.6%
Firms founded by an
established entrepreneur
(n = 161)
45.2
2.42
27.3%
10.8%
83.8%
73.0%
There are too few nascent entrepreneurs for a meaningful segmentation by industry.
The definition of a nascent entrepreneur refers to the first-listed founder.
a
Key:
1 = Elementary education 2 = Secondary education 3 = Bachelor degree 4 = Postgraduate degree
5 = Ph.D.
• Nascent entrepreneurs have slightly less education although the mean level of
education for nascent and established entrepreneurs is less than a bachelor
degree.
• A larger percentage of nascent firm founders are female.
• A larger percentage of the workers in firms founded by a nascent entrepreneur
are part-time employees.
• Nascent entrepreneurs rely more on own and family resources to establish their
firms.
5.4 Conclusions
In this chapter, we explored the AEGIS database to describe selected human capital
and financial capital characteristics of the founders of knowledge-intensive entrepreneurial firms in the European textile and apparel industries. This is the first step
in the direction of our analysis of entrepreneurial activity among EU represented
textile and apparel firms in the AEGIS database, and it is a step that has not yet been
described in the literature about these industries or about KIE firms.
To assess the extent to which human capital variables such as education and
experience motivate innovative behaviors of the textile and apparel firms within the
AEGIS sample, characteristics of founders, including age, education, and work
experience, are investigated descriptively. Based on the initial analysis of firm
founder characteristics presented here, three factors emerge that are important to
5.4 Conclusions
69
addressing the first of our overriding research questions: While there are many small
textile and apparel firms that comprise the EU industries, how and to what extent are
they entrepreneurial and/or innovative in their behaviors? The factors are gender of
the founding owner as well as his/her work experience in the sector and whether the
founder is a nascent or an established entrepreneur. Although some of the literature
on small textile and apparel firms has examined these characteristics, very few firm-­
based studies assess strategy, innovation, or performance in light of them.
Education level of founders was perhaps the most surprising dimension revealed
from the information in the AEGIS database. Notably, most founders across all
countries and within both industries indicate having less than a bachelor degree and
at most a secondary education. On the one hand, this finding may not be so surprising given that textiles and apparel are low-tech industries; on the other hand, it is
interesting to consider that we discovered a sizable percentage of textile firm founders and a smaller percentage of apparel firm founders that were nascent entrepreneurs, having had no prior occupational experience—11% compared to 4.8%,
respectively.
With regard to financial capital, resources used to establish the firm were predominantly a founder’s own and his/her family resources within both industries.
Marked similarities were found to exist across the two industries, namely, number of founding owners—an average of two per firm—and age of first-listed founder,
which was just over 44 years old. Although the average age of the textile firms was
comparable to that of the average among apparel firms, about 7 years old, some differences were found between firms across the two industries. Two of the more
noticeable differences include number of part-time versus full-time employees and
age of the firm (more than twice the number of part-time employees in textile firms
compared to apparel firms and fewer part-time employees as apparel firm age), as
well as years of sectoral experience of the founder. As reported in Table 5.5, the latter differences emerged as particularly noticeable between Italy and Portugal. Such
differences will be examined in further detail within our analysis and discussion of
firm strategies and their performance in the following chapters.
Despite the classification of both industries as being in the low-tech sector, the
data presented in this chapter suggest that human capital is an important first step in
deciphering elements of KIE and perhaps of innovative behavior among small textile and apparel firms in the AEGIS sample. We will return to those characteristics
that merit further investigation—the gender and sectoral experience of the founding
owner and whether he/she is a nascent entrepreneur—in our analyses of strategic
behavior and of economic performance within the next four chapters.
70
5 Characteristics of KIE Textile and Apparel Firms and Founders
ppendix 5.A Annotated Academic Literature Reviews
A
(Table 5.11)
Table 5.11 Annotated literature review related to financial capital
Author(s)
Abbasian
and
Yazdanfar
(2013)
Research question
How does ethnicity
impact external capital
acquisition among
women-owned
businesses at start-up?
Alsos and
Ljunggren
(2013)
How does gender affect
entrepreneur-investor
relationships?
Alsos et al.
(2006)
Is there a funding gap
between women-owned
and men-owned new
businesses?
Amatucci
and Crawley
(2011)
Are there gender
differences in attitudes
toward financial
management?
Amatucci
and Swartz
(2011)
Are there gender
differences in
negotiation styles of
entrepreneurs seeking
finance?
Becker-­
Blease and
Sohl (2007)
Do women-owned
businesses have equal
access to angel capital?
Becker-­
Blease and
Sohl (2011)
How does gender
diversity influence angel
group investment?
Finding(s)
Immigrant women-owned
firms are more likely to
rely on loans from family
members than banks and
have fewer financial
resources
Gender plays a role in the
signals that are
communicated in an
investor-entrepreneur
relationship prior to
funding, which may
influence investment
decisions
Women obtain
significantly less financial
capital than men, but there
are no discernible gender
differences between
perceptions and behaviors
Lack of confidence and
anxiety only partially
explain financial
self-efficacy. Age and race
are significantly related to
financial self-efficacy
Gender-related effects
should be examined
within the social context
of the negotiation event
and should focus on real
rather than apparent
challenges
Women are less likely to
seek angel capital than
men but are equally likely
to receive investment
The proportion of women
in an angel group has a
negative, nonlinear effect
on investment likelihood. A
greater number of women
angel investors can support
women entrepreneurship
through access to
early-stage capital
Data description
Interview data from
Sweden
Case study from
Norway
Survey data from
Norway
Survey data from the
United States
Survey and interview
data from the United
States
Survey data from the
United States
Survey data from the
United States
(continued)
Appendix 5.A Annotated Academic Literature Reviews
71
Table 5.11 (continued)
Author(s)
Brana
(2013)
Bruhn and
Love (2011)
Research question
Does microcredit close
the gender gap in
financing?
Are there gender
differences in the impact
of banking services for
low-income individuals?
Brush et al.
(2007)
What are the financing
strategies of women
entrepreneurs?
Buttner and
Rosen
(1988)
Do bank loan officers
discriminate against
women?
Buttner and
Rosen
(1989)
Do bank loan officers
discriminate against
women?
Buttner and
Rosen
(1992)
Are there gender
differences in the
perceptions and
intentions of
entrepreneurs with
respect to rejection in
the loan application
process?
Caputo and
Dolinsky
(1998)
How does the financial
and human capital of
household members
influence the decision
for a woman to become
an entrepreneur?
Carter et al.
(2007)
What is the role of
gender in bank lending
decisions?
Finding(s)
No, microfinance
institutions seem to
reinforce the gender gap
Yes, increasing bank
services led to an increase
in owning informal
businesses for men and an
increase in wage-earning
opportunities for women
88% of women
entrepreneurs use personal
savings, 58% use personal
credit cards, and 51% use
business credit cards
Yes, traits of successful
entrepreneurs are more
commonly associated with
men than women
Gender, presentation
format, and participation
status interact to influence
the decisions of loan
officers
No, there are no
significant gender
differences. However,
women are more likely
than men to pursue
venture capital and put
their entrepreneurial plans
on hold after a loan
rejection
Higher levels of husbands’
earnings from self-­
employment and
husbands’ knowledge of
business positively
influence the likelihood of
a woman becoming an
entrepreneur
Similar criteria to assess
men and women
entrepreneurs, but male
and female lending
officers use different
processes
Data description
Microcredit application
data from France
Bank data from Mexico
Interview data from the
United States
Survey data from
Southeastern United
States
Experimental data from
the Southeastern United
States
Survey data from the
Eastern United States
National Longitudinal
Study of Labor Market
Experience
Interview data from the
United Kingdom
(continued)
72
5 Characteristics of KIE Textile and Apparel Firms and Founders
Table 5.11 (continued)
Author(s)
Cole and
Mehran
(2009)
Coleman
(2000)
Research question
What is the role of
gender in access to
credit for privately held
firms?
Are there gender
differences in the access
to capital for small
businesses?
Coleman
(2002a)
What are the constraints
faced by women
business owners?
Coleman
(2002b)
What are the borrowing
behaviors of small,
women-owned
businesses?
Coleman
(2004)
How do educational
attainment, gender, and
race impact access to
debt capital?
Coleman
and Carsky
(1996)
What are the banking
relationships of
women-owned
businesses?
Coleman
and Robb
(2009)
Are there gender
differences in new firm
financing?
Finding(s)
Gender differences are
insignificant when
controlling for firm and
owner characteristics
Women-owned firms are
less likely to use external
financing, but there is no
evidence that lenders
discriminate against
women
Characteristics of
women-owned businesses,
as opposed to gender bias,
explain the reduced
likelihood of obtaining
debt capital
Women-owned firms are
less likely to apply for a
loan, but if they do then
they are no less likely to
receive a loan than men
Controlling for
educational attainment,
many gender differences
go away, but black men
are significantly less likely
to be approved for loans
Women are more likely to
use one bank than men.
The majority of women
switch banks, primarily
due to poor customer
service and a
condescending attitude
Women start businesses
with lower capital than
men and subsequently
raise lower amounts of
debt and equity in the next
years than men
Data description
Surveys of small
business finances (the
United States)
National survey of
small business finances
(the United States)
National survey of
small business finances
(the United States)
Survey of small
business finances (the
United States)
Survey of small
business finances (the
United States)
Survey data from
Connecticut
Kauffman Firm Survey
(continued)
Appendix 5.A Annotated Academic Literature Reviews
73
Table 5.11 (continued)
Author(s)
Coleman
and Robb
(2012a)
Research question
What roles do financial
capital and motivations
play in gender-based
firm performance in the
Unites States?
de Bruin and
Flint-Hartle
(2005)
What are the finance-­
related issues of women
entrepreneurs?
Eriksson
et al. (2009)
Does gender impact the
sources of finance for
SMEs?
Fabowale
et al. (1995)
Do terms of bank credit
differ between men and
women business
owners?
Fay and
Williams
(1993)
Are women unfairly
discriminated against
when seeking loans?
Gicheva and
Link (2013)
Are female-owned firms
less likely to attract
private investment to
fund the development of
new technology?
Gicheva and
Link (2015)
Are female-owned firms
less likely to attract
private investment to
fund the development of
new technology?
Finding(s)
Female entrepreneurs are
less likely to use external
sources of financing,
leading to
underperformance after
4 years of operation. This
underperformance does
not impact their
satisfaction level given
female owners prefer
marginal growth
Absence of gender
discrimination in the
venture capital market.
Desire for independence
stymies acceptance of
venture capital funding
Women-owned SMEs are
more likely to use equity
investments, but there are
no gender differences in
firm size or profitability
No, there are no
differences if the
differences in men and
women businesses are
taken into account,
although women perceive
that they are treated
disrespectfully
Yes, there is
discrimination based on
the interaction between
gender and educational
attainment
Yes, female-owned firms
are on average 16
percentage points less
likely to attract private
investment compared to
male-owned firms
Yes, female-owned firms
are disadvantaged in their
access to private
investment, although the
extent varies by region
Data description
Survey data from the
United States
In-depth interview data
from New Zealand
Survey data from
Eastern Finland
Survey data from
Canada
Experimental data from
New Zealand
US SBIR-funded
project data
US SBIR-funded
project data
(continued)
74
5 Characteristics of KIE Textile and Apparel Firms and Founders
Table 5.11 (continued)
Author(s)
Greene et al.
(2001)
Research question
Are there gender
differences in the pattern
of venture capital
funding?
Haines et al.
(1999)
What is the impact of
gender on bank lenders
and small business
borrowers?
Do women investors in
the business angel
market behave or
perform differently from
men?
What is the debt
structure of small
businesses owned by
women?
Harrison
and Mason
(2007)
Haynes and
Haynes
(1999)
Hussain
et al. (2010)
Are there gender
differences in the impact
of financial capital on
firms’ growth?
Kickul et al.
(2007)
What is the impact of
social capital and
training of women
entrepreneurs on
accessing financial
resources for growth?
Koper
(1993)
How are women
entrepreneurs treated by
banks?
Finding(s)
Yes, a gender gap exists,
which may stem from
structural barriers, human
capital, and/or strategic
choices
No support for gender
bias in bank loan decision
process
Data description
NVCA survey data
No, very little differences.
Women investors are
marginally more likely to
invest in female-owned
firms
Women-owned businesses
are more likely to borrow
from family friends but
over time have gained
similar access as men to
credit from commercial
banks
Women are no more
disadvantaged than men in
obtaining finance. Firms
are stronger after having
access to finance than at
start-up. Most
entrepreneurs use
networks to access finance
Women entrepreneurs
with high growth
resources are more likely
to have formal social
networks and need
training in strategic
planning
Women have more
obstacles than men in
accessing credit, which
stems in part from the
types of businesses
pursued. Perceptions of
bank treatment depend on
outcome
UK sample of business
angels identified
through business angel
networks
Bank loan data from
Canada
National survey of
small business finance
Survey data from China
Survey data from New
Hampshire
Survey data from the
Netherlands
(continued)
Appendix 5.A Annotated Academic Literature Reviews
75
Table 5.11 (continued)
Author(s)
Kwong et al.
(2012)
Mahmood
et al. (2014)
Research question
Does being female
increase the probability
that an individual
perceives barriers to
accessing finance?
Is there an optimal
microfinance loan size
for female entrepreneurs
and poverty reduction?
Marlow and
Patton
(2005)
How does gender affect
access to credit?
Neeley and
van Auken
(2010)
Are there gender
differences in
entrepreneurs’ use of
bootstrap financing?
Nelson et al.
(2009)
Why do women
entrepreneurs access
only a small percentage
of venture capital?
Orhan
(2001)
How do French women
entrepreneurs manage
financing?
Orser and
Foster
(1994)
Are there gender
differences in lending
practices?
Orser et al.
(2006)
Are there gender
differences in SMEs
seeking external
financing?
Finding(s)
Women are more likely to
be constrained by
financial barriers than men
Data description
Global entrepreneurship
monitor
Yes, loans should be large
enough to make a
significant impact but not
too large such that the
debt burden is too great
Gender, and associated
negative stereotypes,
shapes the experiences of
entrepreneurship
Some differences exist.
Women have a greater
number of factors than
men that influence their
decision to use bootstrap
financing
Women vary in the degree
to which they identify the
gendered landscape they
navigate as well as the
level of attention and care
needed to manage this
landscape
Women begin and run
small businesses, so banks
do not express great
interest in them. Women
are demanded a higher
collateral requirement
than men
Traditional lending is
biased against small
business, and female
entrepreneurs
predominantly run small
businesses
No, there is not much
difference. Women
entrepreneurs are less
likely to seek external
equity capital.
Interview data from
Pakistan
Theoretical paper
Survey data from
Illinois
Interview data from the
United States
Survey data from
France
Case study from
Canada
Survey of financing of
small- and medium-­
sized enterprises
(Statistics Canada)
(continued)
76
5 Characteristics of KIE Textile and Apparel Firms and Founders
Table 5.11 (continued)
Author(s)
Riding and
Swift (1990)
Research question
Are there gender
differences in accessing
credit?
Robb (2012)
Does the capital
structure of woman-­
owned firms differ from
male-owned firms?
Minority-owned firms
from non-minority
owned firms?
Are there gender
differences in the
financing strategies of
new technology-based
firms?
Robb and
Coleman
(2010)
Robb and
Wolken
(2002)
Roper and
Scott (2009)
Are there gender
differences in the
financing patterns of
small business owners?
How does the perception
of financial barriers
affect the decision to
start-up for potential
women entrepreneurs?
Sandhu
et al. (2012)
What are the financial
barriers faced by women
entrepreneurs?
Saparito
et al. (2012)
Are there gender
differences in the
perceptions of bank-firm
relationships?
Finding(s)
Financing conditions are
less favorable to women,
but this stems from the
nature of the businesses
pursued by women and
not necessarily gender
bias
Yes, woman- and
minority-owned firms rely
more on owner equity
Data description
Survey data from
Canada
Yes, women raise
significantly less financial
capital in the start-up year
and subsequent years.
Women use a higher level
of external debt and a
lower level of external
equity during the start-up
year
Yes, gender differences in
firm characteristics lead to
different financing
patterns
Women are 7.4% more
likely to perceive financial
barriers than men, and
perceived financial
barriers make it less likely
to start-up
Gender prejudice exists in
the male-dominated
banking sector. Loan
rejection rates for female
entrepreneurs are
significantly greater than
those for male
entrepreneurs
Male-male pairs of
owners and lenders have
the highest levels of
satisfaction, whereas
female-female pairs have
the lowest levels of
satisfaction
Kauffman firm survey
Kauffman firm survey
Survey of small
business finances
UK data from global
entrepreneurship
monitor
Survey data from
Punjab, India
Survey data from the
United States
(continued)
Appendix 5.A Annotated Academic Literature Reviews
77
Table 5.11 (continued)
Author(s)
Sauer and
Wilson
(2016)
Research question
What role do liquidity
constraints play in
female
entrepreneurship?
Sena et al.
(2012)
Are there gender
differences in the
borrowing patterns of
the self-employed?
Storey
(2004)
Does gender
discrimination exist in
the microfirms credit
market?
What role does gender
play in venture capital
decision-making?
Tinkler et al.
(2015)
Verheul and
Thurik
(2001)
Does gender affect the
size and composition of
start-up capital?
Watson et al.
(2009)
What explains the
gender gap in SME
finance?
Wu and
Chua (2012)
Are there second-order
gender effects in small
business borrowing?
Finding(s)
A £1000 increase in
liquidity for single women
increases the probability
of starting a new business
by 8.5% compared to the
sample mean
Women are less likely
than men to seek external
finance, and these gender
differences are adversely
affecting the transition to
self-employment
No, when appropriate
controls are accounted for
gender differences
disappear
Gender matters most
when the person, and not
the venture, is the target of
evaluation. Technical
qualifications moderate
the influence of gender
Women have a smaller
amount of start-up capital
compared to men, but men
and women have similar
types of start-up capital
There is no evidence that
a supply-side finance gap
exists or that women are
discourage to apply for
loans. Demand-side
factors like a desire to
maintain control play a
more important role in
finance decisions
Yes, women entrepreneurs
are charged an average of
73 basis points more than
men
An earlier version of this table appeared in Link and Strong (2016).
Data description
UK Wealth and Assets
Survey
English household
survey of
entrepreneurship
Bank loan data from
Trinidad and Tobago
Experimental data
Survey data from
Holland
Survey data from
Australia
National survey of
small business finances
78
5 Characteristics of KIE Textile and Apparel Firms and Founders
ppendix 5.B Annotated Academic Literature Reviews
A
(Table 5.12)
Table 5.12 Annotated literature review related to nascent entrepreneurs
Author(s)
Aldrich et al.
(2002)
Alsos and
Ljunggren
(1998)
Research question
How does gender
impact the composition
of nascent
entrepreneurs’ start-up
teams?
Does the business
start-up process differ
by gender?
Armstrong
(2011)
Does gender influence
new venture planning?
Balachandra
et al. (2013)
Does gender affect
entrepreneur pitch
success?
Bönte and
Piegeler (2013)
What explains the
gender gap in latent and
nascent
entrepreneurship?
Does gender affect
nascent entrepreneurs’
risk orientation with
respect to debt
financing?
Carter (2002)
Carter et al.
(2003)
Are there gender
differences in the career
reasons of nascent
entrepreneurs?
Finding(s)
Homophily with respect
to sex significantly
influences
entrepreneurial team
composition
There are some gender
differences, but they do
not lead to lower
start-up probabilities for
women
Men have higher levels
of entrepreneurial
self-efficacy, but women
have a higher sense of
ownership stemming
from higher levels of
involvement with
business planning
Sex alone does not
prevent pitch success.
However, gendered
expectations are
important because
women who display
masculine behaviors are
more likely to succeed
than those who do not
Men are more likely to
be competitive and
therefore become
self-employed
Nonfinancial resources
mediate the effect of sex
on risk propensity but
not risk perception.
Women have lower
expectations of debt
financing
Yes, men rate financial
success and innovation
higher than do women
Data description
Panel study of
entrepreneurial
dynamics
Interview data from
Norway
Survey data from the
Northeastern United
States
Elevator pitch
competition video data
from the United States
Flash eurobarometer
entrepreneurship
Panel study of
entrepreneurial
dynamics
Panel study of
entrepreneurial
dynamics
(continued)
Appendix 5.B Annotated Academic Literature Reviews
79
Table 5.12 (continued)
Author(s)
Dalborg et al.
(2015)
Research question
Do nascent women
entrepreneurs perceive
more risk than men?
Figueroa-­
Armijos and
Johnson (2013)
How does rurality and
gender affect nascent
entrepreneurship?
Gicheva and
Link (2016)
How do nascent firms
compared to established
firms differ in the
probability that they
will commercialize
from their research?
Why are there gender
differences in the
growth rates of nascent
entrepreneurs?
Manolova et al.
(2012)
Menzies et al.
(2006)
Are there gender
differences among
nascent entrepreneurs?
Reynolds et al.
(2004)
What is the state of
nascent
entrepreneurship in the
United States?
Finding(s)
Yes, nascent women
entrepreneurs perceive
more risks than men,
risk perception
influences start-up
decisions, and risk
preferences partial out
passion on start-up
decisions
Men and women living
in rural areas increase
the likelihood of
engaging in opportunity
entrepreneurship. Low
income among women
and part-time
employment among men
increase the likelihood
of necessity
entrepreneurship
Nascent firms that do
not fail in their research
efforts are more likely to
commercialize
Data description
Survey data from
Sweden
Men grow their
businesses for financial
success, whereas women
have other non-­
economic goals
Women who are
members of a start-up
team are six times more
likely to achieve an
operating business
Introduces and describes
the PSED. Some gender
and ethnic differences
exist in comparing
nascent entrepreneurs
with non-entrepreneurs
Panel study of
entrepreneurial
dynamics (the United
States)
US data from global
entrepreneurship
monitor
US SBIR-funded
project data
Survey data from
Canada
Panel study of
entrepreneurial
dynamics
(continued)
80
5 Characteristics of KIE Textile and Apparel Firms and Founders
Table 5.12 (continued)
Author(s)
Rodríguez and
Santos (2009)
Research question
Are there gender
differences in the
process of firm
creation?
Finding(s)
Males have more
developed promoter
behaviors than females,
and motivations for
female entrepreneurs are
primarily discriminatory
practices in the labor
market. Females receive
less social support than
males
An earlier version of this table appeared in Link and Strong (2016).
Data description
Survey data from
Seville, Spain
Chapter 6
Sources of Knowledge Used by KIE Textile
and Apparel Firms
Reading furnishes the mind only with materials of knowledge; it
is thinking that makes what we read ours.
—John Locke
Bodily exercise, when compulsory, does no harm to the body;
but knowledge which is acquired under compulsion obtains no
hold on the mind.
—Plato
Abstract A fundamental hypothesis in this book is that the sources of knowledge
used by KIE firms influence their strategic entrepreneurial and innovative behavior
and that behavior affects their economic performance. The various sources of
knowledge that KIE firms use address factors that influenced the formation of the
firm and factors for exploring new business opportunities.
6.1 Introduction
We alluded to the importance of knowledge in KIE firms in Chap. 1 with reference
to our discussion of some of the pioneers in both the fields of epistemology and
entrepreneurship. We pointed out that Locke (1996), for example, made reference
early on to one’s experience being a [our emphasis], not the [our emphasis], source
of knowledge. And to build on our discussion of Locke, and also Hume, we quoted
from Schumpeter (1928), whose writings reflected that he was perceptive to realize
that the knowledge that kindles an innovation can be new or already existing.
Schumpeter emphasized that what is important is how that knowledge is used or put
into practice. It was Schultz (1975) and Machlup (1980) who later associated education with how one uses one’s knowledge. We concluded our brief discussion about
knowledge in Chap. 1 with the statement that knowledge per se, according to the
literature, is an antecedent for all entrepreneurial actions and thus entrepreneurial
performance.
© Springer International Publishing AG 2018
N.J. Hodges, A.N. Link, Knowledge-Intensive Entrepreneurship, International
Studies in Entrepreneurship 39, https://doi.org/10.1007/978-3-319-68777-3_6
81
82
6 Sources of Knowledge Used by KIE Textile and Apparel Firms
We will focus our attention on specific metrics associated with entrepreneurial
actions (e.g., strategy and performance) in later chapters, but as a precursor to those
chapters, we discuss in this chapter the sources of knowledge that KIE firms in the
European textile and apparel industries rely on—that is, that they view as relatively
important—for both the formation of their company and for exploring new business
opportunities. And, as should be expected, we will explore the relationship between
the use of specific sources and the founders’ age, education, and experience, that is,
the founders’ human capital. Recall from Table 5.8 in Chap. 5 that age and experience are positively and significantly correlated.
Ultimately, however, the relationship that we seek to explore is the one between
the use of alternative sources of knowledge and entrepreneurial performance. And,
as we suggested in Chap. 1, if such a relationship exists in the AEGIS data, it might
be a direct relationship as in expression (6.1), or it might be an indirect relationship
wherein sources of knowledge affect strategic behavior and strategic behavior in
turn affects entrepreneurial performance as in expression (6.2).
Sources of Knowledge → Entrepreneurial Performance
(6.1)
Sources of Knowledge → Strategic Behavior → Entrepreneurial Performance (6.2)
If we find that either one or both of these relationships exist in the data, and our
discussion in Chap. 1 and our summary just above suggest that one or both might
exist, then we will have possibly identified sources of knowledge as a target variable
relevant to policy recommendations aimed at growing the European textile and
apparel industries, and perhaps those policy recommendations can also be aimed at
these same industries in other nations such as the United States. A focus on the
United States is our emphasis in Chap. 10.
6.2 AEGIS Survey Questions About Sources of Knowledge
6.2.1 Survey Questions About Sources of Knowledge
Two survey questions about sources of knowledge are on the AEGIS survey, and
they are as follows. The first question relates to knowledge relevant to the formation
of the company, and the second question relates to a different use of knowledge,
namely, for exploring new business opportunities. Both survey questions are listed
below, and mean responses to both survey questions are discussed in this chapter,
although we do conclude the chapter with an explanation as to why our final emphasis is only on sources of knowledge for exploring new business opportunities. Our
focus on the question related to exploring new business opportunities over the question related to the formation of the company reflects our priors that new business
opportunities are more closely related to strategic behavior and entrepreneurial performance, and thus, it might be a relevant antecedent to explore in Chaps. 7 and 8.
6.2 AEGIS Survey Questions About Sources of Knowledge
83
The two AEGIS survey questions about sources of knowledge are:
Please indicate the importance of the following factors for the formation of the company on
a 5-point scale, where 5 is extremely important and 1 is not important:
1.
2.
3.
4.
5.
6.
7.
8.
9.
Design knowledge
Knowledge of the market
Networks built during previous career
Availability of finance
Opportunities in a public procurement initiative
Existence of a large enough customer
Opportunity deriving from technological change
Opportunity deriving from a new market need
Opportunity deriving from new regulations or institutional requirements
Please evaluate the importance of the following sources of knowledge for exploring new
business opportunities on a 5-point scale, where 5 is extremely important and 1 is not
important:
1. Clients or customers
2.Suppliers
3.Competitors
4. Public research institutes
5.Universities
6. External commercial labs/R&D firms/technical institutes
7. In-house know-how (R&D laboratories in your firm)
8. Trade fairs, conferences, and exhibitions
9. Scientific journals and other trade or technical publications
10. Participation in nationally funded research programs
11. Participation in EU-funded research programs (Framework Programmes)
It is important to reemphasize the way these questions are phrased on the AEGIS
survey (as well as to underscore that we did not develop the AEGIS survey instrument). First, the former question uses the term factor, and we are interpreting a
factor as a source of knowledge. Second, both of the questions are asking about the
importance of alternative sources of knowledge for two very specific, distinct, and
important purposes, namely, “for the formation of the company” and “for exploring
new business opportunities.” But, the formation of the company is, from a KIE perspective, the result of the perception of an opportunity, and exploring new business
opportunities is an entrepreneurial action in response to the initial perception to
form a company. Exploring new business opportunities is certainly not the only
action that a firm owner will undertake once his/her firm is formed, but it is one
action that might eventually lead to the choice of strategies to leverage new business
opportunities (hence our specific emphasis on that question below). Of course, it is
an empirical question as to whether the AEGIS data will reflect what our KIE-based
logic suggests.
84
6 Sources of Knowledge Used by KIE Textile and Apparel Firms
6.2.2 I nterindustry Differences in the Importance of Sources
of Knowledge
Mean responses to the survey question above on factors related to the formation of
the company, for both textile firms and apparel firms, are shown in Table 6.1.
Whenever a survey is designed for Likert scale responses, even with an odd number
of response categories, defining a dichotomous division of responses is problematic
and certainly subjective. Of course, one can tell about relative importance from the
mean responses in Table 6.1, that is, one can tell the most relied on or important
factors for the formation of the company from the perspective of textile and apparel
Table 6.1 Mean firm responses about the importance of factors for the formation of the company,
by industry
Textile firms
(n = 91)
3.29
3.99
3.58
3.74
2.19
3.13
2.84
3.33
2.31
Factors
Design knowledge
Knowledge of the market
Networks built during previous career
Availability of finance
Opportunities in a public procurement initiative
Existence of a large enough customer
Opportunity deriving from technological change
Opportunity deriving from a new market need
Opportunity deriving from new regulations or
institutional requirements
Apparel firms
(n = 84)
3.25
4.17
3.69
3.69
1.96
3.51
2.76
3.51
2.43
5 = extremely important and 1 = not important
Opportunity deriving from new regulations or…
Opportunity deriving from a new market need
Opportunity deriving from technological change
Existence of a large enough customer
Opportunities in a public procurement initiative
Availability of finance
Networks built during previous career
Knowledge of the market
Design knowledge
0
Note:
5=extremely important and 1=not important
0.5
Apparel
1
1.5
2
2.5
3
3.5
4
4.5
Textile
Fig. 6.1 Illustration of mean firm responses about the importance of factors for the formation of
the company, by industry (Note: 5 = extremely important and 1 = not important)
6.2 AEGIS Survey Questions About Sources of Knowledge
85
firms. For both textile and apparel firms, the most important factor is knowledge of
the market followed by availability of finance. One can also tell from this table that
the least relied on or least important factor for the formation of the company is
opportunities in a public procurement initiative, followed by opportunities deriving
from new regulations.
Figure 6.1 depicts the mean values from Table 6.1, and the figure is perhaps more
useful for a visual comparison of firms’ responses here and below than the tabular
representation of mean values.
In Table 6.2 and in Fig. 6.2 are the mean firm responses about the importance of
sources of knowledge for exploring new business opportunities. Among both textile
firms and apparel firms, clients or customers are at the top of the list followed by
suppliers. The least important sources are universities and public research institutes.
One might generalize from Table 6.2 and Fig. 6.2 that market-related sources of
knowledge are relatively more important than technical sources of knowledge for
exploring new business opportunities. To reemphasize, this observation is offered as
a subjective observation, and it is also offered cautiously as a generalization. One
might of course disagree about whether in-house know-how is a market-related
source or a technical source; we treat it empirically as a technical source below.
How does one make a statement about any source of knowledge being either
important or not important? For expositional purposes in this book, we will use the
term important when a mean response based on a 5-point Likert scale is greater than
or equal to 3.5, and we will use the term not important when the mean response is
less than 3.5. Placing the subjective nature of this dichotomy aside, among both
textile firms and apparel firms, there are, on average, three important sources of
knowledge for the formation of the company: knowledge of the market, networks
built during previous careers, and availability of finance. There are also three important sources for exploring new business opportunities: clients or customers, suppliers, and competitors (only among textile firms) and marginally in-house know-how
(only among apparel firms). The other factors and sources listed in Table 6.1 and
Fig. 6.1 and in Table 6.2 and Fig. 6.2 are, using this dichotomy, on average not
important.1 That said, even if a source of knowledge is not important, interfirm differences in survey responses may be relevant for understanding interfirm differences in dimensions of KIE. Below, when we suggest indices that characterize
market-based and technical sources of knowledge for exploring new business
opportunities, we rely on all of the survey responses from that survey question
rather than only those sources that are defined to be important.
Following on our proposition is that there might be probative value to understanding dimensions of KIE by accounting for interfirm, intra-industry differences
in the relative importance of all sources of knowledge listed above. That is, some
firms within an industry might view one source of knowledge for exploring new
1
We accept the criticism that our construction of the dichotomy important and not important is
subjective, and thus, the implications from our analyses are accordingly subjective. We respect the
opinion of those scholars who might advocate the use of a 3.0 or greater definition of important or
even a 4.0 or greater definition.
86
6 Sources of Knowledge Used by KIE Textile and Apparel Firms
Table 6.2 Mean firm responses about the importance of alternative sources of knowledge for
exploring new business opportunities, by industry
Source of knowledge
Clients or customers
Suppliers
Competitors
Public research institutes
Universities
External commercial labs/R&D firms/technical
institutes
In-house know-how
Trade fairs, conferences, and exhibitions
Scientific journals and other trade or technical
publications
Participation in nationally funded research programs
Participation in EU-funded research programs
Textile firms
(n = 91)
4.58
4.22
3.59
2.12
1.81
2.11
Apparel firms
(n = 84)
4.48
4.00
3.39
2.06
2.00
2.23
3.31
3.18
2.62
3.48
3.11
2.61
1.80
1.90
1.89
1.99
5 = extremely important and 1 = not important
Parcipaon in EU funded research programmes
Parcipaon in naonally funded research…
Scienfic journals and other trade or technical…
Trade fairs, conferences, and exhibions
In-house know how
External commercial labs / R&D firms / technical…
Universies
Public research instutes
Competors
Suppliers
Clients or customers
0
Apparel Firms
Note:
5=extremely important and 1 = not important
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
Texle Firms
Fig. 6.2 Illustration of mean firm responses about the importance of alternative sources of knowledge for exploring new business opportunities, by industry (Note: 5 = extremely important and
1 = not important)
business opportunities differently than do other firms. One metric associated with
such interfirm differences is the coefficient of variation in responses.2 Calculated
2
The coefficient of variation of a sample of data is the standard deviation of the sample divided by
the mean of the sample, and then that quotient is multiplied by 100.
6.2 AEGIS Survey Questions About Sources of Knowledge
87
values of coefficients of variation suggest that there is the greatest consistency in
responses (i.e., the smaller coefficients of variation) among textile firms and among
apparel firms about knowledge of the market as a factor for the formation of the
company. The greatest consistency among textile firms and apparel firms is about
the importance of clients or customers as a source of knowledge for exploring new
business opportunities.
6.2.3 C
ross-Country Differences in the Importance of Sources
of Knowledge
As we discussed in Chap. 4, with respect to Table 4.3. the AEGIS database pertains
to KIE firms in ten European countries. In Tables 6.3 and 6.4, we show, by country,
the mean responses for both textile firms and apparel firms, respectfully, about the
importance of factors for the formation of the company, and we show similarly in
Tables 6.5 and 6.6 responses about the importance of alternative sources of knowledge for exploring new business opportunities for the two industries, respectfully.
Several patterns can be seen from Tables 6.3 and 6.4. Of course, individual country sample sizes are small; thus, as we have emphasized earlier, generalizations
about patterns from these tables are offered not only for completeness of our discussion of the AEGIS data but also to possibly whet the appetite of researchers to
investigate further inter-country studies of these two industries in the future. This is
a point that we have previously made. However, we do point out some interesting
patterns, especially with reference to Italian and Portuguese firms, that might also
initiate further intra-country studies by other scholars.
Recall that we self-imposed a criterion that a mean response of 3.5 or greater on
the 5-point Likert scale from extremely important (=5) to not important (=1) to the
survey question above about the importance of factors and sources of knowledge. A
response of 3.5 or greater means that a source is important, or, regarding the tables,
a country mean response of 3.5 or greater means that a source of knowledge is, on
average, important to the firms in that country. The mean response about knowledge
of the market being a factor for the formation of the company for Italian textile firms
is 4.17, and it is 3.92 for Portuguese textile firms. For Italian apparel firms, the mean
response is 4.26, and it is 3.64 for Portuguese apparel firms. Looking at Tables 6.3
and 6.4, there is consistency across countries, ignoring sample sizes, about the
importance of knowledge of the market as a factor for formation of both textile and
apparel firms, respectfully.
Looking at Tables 6.5 and 6.6, there is consistency among both textile and
apparel firms that clients or customers are the most important source of knowledge
for exploring new business opportunities. The mean response for Italian textile
firms is 4.52, and it is 4.69 for Portuguese firms; for Italian apparel firms, it is 4.48,
and it is 4.73 for Portuguese apparel firms.
Czech
Republic
(n = 4)
3.00
3.00
2.25
2.25
1.75
3.25
2.50
2.75
2.00
Croatia
(n = 7)
3.29
4.23
3.86
4.71
2.17
3.29
3.57
3.71
2.43
5 = extremely important and 1 = not important
Source of
knowledge
Design knowledge
Knowledge of the
market
Networks built
during previous
career
Availability of
finance
Opportunities in a
public procurement
initiative
Existence of a large
enough customer
Opportunity
deriving from
technological
change
Opportunity
deriving from a
new market need
Opportunity
deriving from new
regulations or
institutional
requirements
2.00
2.50
4.00
2.00
1.00
1.00
4.00
Denmark
(n = 2)
5.00
4.00
1.20
3.00
1.60
3.20
1.40
3.60
3.40
France
(n = 5)
2.00
3.40
1.60
3.60
2.60
3.80
3.25
4.40
3.80
Germany
(n = 5)
2.80
3.80
2.00
2.71
2.57
2.57
2.57
3.71
3.43
Greece
(n = 7)
4.29
3.86
2.76
3.72
3.14
3.17
2.14
3.79
3.72
Italy
(n = 29)
3.24
4.17
2.23
3.15
2.69
2.69
1.69
3.69
3.77
Portugal
(n = 13)
3.15
3.92
Table 6.3 Mean textile firm responses about the importance of factors for the formation of the company, by country (n = 91)
3.00
4.00
1.50
3.00
2.00
4.00
4.50
Sweden
(n = 2)
3.00
4.50
2.24
3.06
2.76
3.47
2.76
3.76
3.29
United
Kingdom
(n = 17)
3.47
4.00
88
6 Sources of Knowledge Used by KIE Textile and Apparel Firms
Czech
Republic
(n = 3)
2.33
4.00
4.33
2.67
1.00
3.33
1.33
3.33
2.33
Croatia
(n = 6)
3.50
4.33
3.50
3.00
1.67
3.67
2.00
3.67
2.00
5 = extremely important and 1 = not important
Source of
knowledge
Design knowledge
Knowledge of the
market
Networks built
during previous
career
Availability of
finance
Opportunities in a
public procurement
initiative
Existence of a large
enough customer
Opportunity
deriving from
technological
change
Opportunity
deriving from a
new market need
Opportunity
deriving from new
regulations or
institutional
requirements
3.00
4.50
3.50
3.50
1.00
3.00
2.50
Denmark
(n = 2)
4.50
5.00
2.22
2.89
2.33
3.33
2.00
3.89
2.89
France
(n = 9)
2.56
3.78
1.67
3.67
3.00
3.00
2.33
4.33
4.67
Germany
(n = 3)
3.33
4.00
2.64
3.00
2.64
3.64
2.64
4.14
3.93
Greece
(n = 14)
4.00
4.36
2.52
3.78
3.04
3.59
1.78
3.70
4.04
Italy
(n = 27)
3.26
4.26
2.90
3.60
3.27
4.00
2.10
3.60
3.18
Portugal
(n = 11)
2.91
3.64
Table 6.4 Mean apparel firm responses about the importance of factors for the formation of the company, by country (n = 84)
2.25
4.25
2.50
2.75
1.75
2.75
3.00
Sweden
(n = 4)
3.00
4.25
1.80
3.20
2.80
4.00
2.00
4.20
4.00
United
Kingdom
(n = 5)
3.00
4.60
6.2 AEGIS Survey Questions About Sources of Knowledge
89
1.25
2.00
2.75
4.00
2.75
1.25
1.00
2.86
3.57
4.43
4.14
4.14
2.29
3.43
5 = extremely important and 1 = not important
Source of knowledge
Clients or customers
Suppliers
Competitors
Public research
institutes
Universities
External commercial
labs/R&D firms/
technical institutes
In-house know-how
Trade fairs,
conferences, and
exhibitions
Scientific journal and
other trade or
technical
publications
Participation in
nationally funded
research programs
Participation in
EU-funded research
programs
Czech
Republic
(n = 4)
4.25
3.00
3.25
1.75
Croatia
(n = 7)
4.86
4.57
3.29
3.00
1.50
1.00
2.50
1.00
4.50
1.00
1.00
Denmark
(n = 2)
4.50
3.50
3.50
1.00
1.20
1.20
2.20
2.60
2.00
1.40
1.40
France
(n = 5)
4.60
4.20
4.00
1.40
1.40
1.20
2.60
2.40
3.40
2.00
2.00
Germany
(n = 5)
4.60
3.40
3.60
2.60
2.86
2.86
3.29
3.29
4.29
2.29
2.43
Greece
(n = 7)
4.00
4.14
4.00
2.29
1.97
2.00
2.72
4.03
2.83
1.69
2.28
Italy
(n = 29)
4.52
4.21
3.38
2.28
2.08
1.62
1.92
3.00
2.31
2.00
2.15
Portugal
(n = 13)
4.69
4.46
4.08
2.15
1.00
1.00
2.50
4.00
4.00
1.00
1.00
Sweden
(n = 2)
5.00
4.50
3.50
1.00
1.35
1.65
2.18
2.65
3.41
1.65
1.59
United
Kingdom
(n = 17)
4.76
4.53
3.53
1.82
Table 6.5 Mean textile firm responses about the importance of alternative sources of knowledge for exploring new business opportunities, by country (n = 91)
90
6 Sources of Knowledge Used by KIE Textile and Apparel Firms
1.67
1.67
4.33
2.67
3.00
1.00
1.00
2.17
2.50
3.83
3.67
3.67
2.33
2.50
5 = extremely important and 1 = not important
Source of knowledge
Clients or customers
Suppliers
Competitors
Public research
institutes
Universities
External commercial
labs/R&D firms/
technical institutes
In-house know-how
Trade fairs,
conferences, and
exhibitions
Scientific journal
and other trade or
technical
publications
Participation in
nationally funded
research programs
Participation in
EU-funded research
programs
Czech
Republic
(n = 3)
5.00
4.33
4.33
2.00
Croatia
(n = 6)
4.50
4.67
3.67
2.83
1.00
1.00
3.00
2.50
4.50
1.50
2.00
Denmark
(n = 2)
4.50
4.00
3.00
1.00
1.67
1.78
2.78
2.89
2.78
1.22
1.67
France
(n = 9)
4.67
4.00
3.33
1.44
1.00
1.00
3.00
3.00
3.00
1.67
1.67
Germany
(n = 3)
3.67
3.67
3.33
2.00
2.36
2.14
2.57
3.07
2.79
2.29
2.29
Greece
(n = 14)
4.07
4.14
3.93
2.36
2.30
2.00
2.30
4.11
3.15
2.19
2.52
Italy
(n = 27)
4.48
3.78
3.30
2.19
2.09
2.36
3.00
2.64
3.36
2.64
2.64
Portugal
(n = 11)
4.73
4.36
3.27
2.36
1.25
1.25
1.50
4.00
3.25
1.50
1.50
Sweden
(n = 4)
4.50
3.00
2.50
1.50
1.20
1.20
2.20
3.40
2.80
1.00
1.60
United
Kingdom
(n = 5)
4.80
4.00
2.80
1.00
Table 6.6 Mean apparel firm responses about the importance of alternative sources of knowledge for exploring new business opportunities, by country
(n = 84)
6.2 AEGIS Survey Questions About Sources of Knowledge
91
92
6 Sources of Knowledge Used by KIE Textile and Apparel Firms
Regarding Table 6.3, knowledge of the market is in general the most important
factor across countries, again ignoring sample sizes, for the formation of the company. Keeping in mind all the caveats associated with interpreting mean values from
countries with a small number of firm respondents, design knowledge is most
important among Denmark’s two textile firms, and that is also the case among the
seven Greek textile firms. The Czech Republic textile firms and the French textile
firms view knowledge of the market as unimportant based on an average mean
response of less than 3.5. However, none of the factors listed in the table are important to the Czech Republic firms, and only the availability of finance is important to
the five French firms. The availability of finance is also the most important factor
among the seven Croatian firms and the five German firms.
In comparison, the responses of the apparel firms in Table 6.4 show that knowledge of the market is the highest ranked factor for the formation of the company
among the 6 Croatian, the 2 Danish, the 4 Greek, the 27 Italian, the 4 Swedish, and
the 5 UK firms. As was the case for the textile firms, availability of finance is important among firms in several countries. It ranks as the second most important factor
among the nine French firms and as the second most important factor among the five
UK firms.
Regarding Table 6.5, it is generally the case that across countries the two most
important sources of knowledge for exploring new business opportunities among
textile firms are clients or customers and suppliers. Clients or customers top the list
in every country except for Greece, although in Greece clients or customers tie for
third behind suppliers (ranks second) and trade fairs, conferences, and exhibitions
(ranks first). In Denmark, trade fairs, conferences, and exhibitions are tied for the
most important source. In fact, trade fairs, conferences, and exhibitions are viewed
as important in Croatia, the Czech Republic, Denmark, Greece, and Sweden.
Overall, the 7 textile firms in Croatia responded that 6 of the 11 sources of knowledge are important, followed by Sweden, Denmark, and Greece.
Regarding Table 6.6, apparel firms across countries also generally ranked clients
or customers and suppliers as the two most important sources of knowledge for
exploring new business opportunities. The only exception is in Sweden where the
four responding firms did not think that suppliers are an important source of knowledge; in-house know-how ranked second. As with the textile firms in Croatia, the
apparel firms in that country also listed 6 of the 11 sources of knowledge as being
important.
The cross-country cross-industry firm differences in the importance of factors for
the formation of the company and of sources of knowledge for exploring new business opportunities might reflect cross-country and cross-industry differences in
characteristics of the firm (e.g., its age or its size, possibly measured in terms of
employees) or the human capital and financial capital of firms’ founders.
Comparing the number of sources of knowledge that are important for exploring
new business opportunities across countries, textile firms generally listed more
sources as being important than did apparel firms. Perhaps, and this is simply conjecture on our part, as production moves closer to the final consumer along the value
6.2 AEGIS Survey Questions About Sources of Knowledge
93
chain, the underlying processes become more specialized in terms of the importance
of existing sources of knowledge for exploring new business opportunities.
We find it interesting that the textile firms in Croatia, Italy, and Sweden responded
to the survey question that in-house know-how is an important source of knowledge
for exploring new business opportunities. The AEGIS survey gives as an example of
in-house know-how to be internal R&D laboratories (and perhaps this is a proxy for
internal innovativeness). Of course, investments in an R&D laboratory can refer to
efforts to create new and improved goods, but it can also refer to efforts to adopt
others’ technologies to make the production process more efficient. Similarly,
apparel firms in these same countries, plus those in the Czech Republic, responded
that in-house know-how is an important source of such knowledge. With an emphasis on “in-house,” it is perhaps noteworthy that, except for the textile firms in
Croatia, no other group of firms in either industry or in any country ranked external
commercial labs/R&D firms/technical institutes as important. Perhaps, and again
this is simply conjecture on our part, what textile and apparel firms are using in-­
house R&D know-how for is to develop their absorptive capacity to know about
goods and services that are present in their value chain.
Up to this point in this chapter, we have viewed the AEGIS survey-defined factors important for the formation of the company and the sources of knowledge
important for exploring new business opportunities individually; that is, one factor
or source is, on average, more or less important than another factor or source. We
now look at correlations among all factors and among all knowledge sources to
explore any collective influences.
For example, the correlation coefficients among factors for the formation of the
company are in Table 6.7 for textile firms and in Table 6.8 for apparel firms. Only
statistically significant correlation coefficients are shown in these tables and, as we
stated in Chap. 5, would be our practice throughout the book. Based on the number
of coefficients in both tables that are statistically significant, we infer that firms rely
on multiple factors and knowledge sources as opposed to only one or two. Textile
firm founders that rely on knowledge of the market for the formation of the company also rely on previously built networks, on the availability of finance, and
opportunity from new regulations or institutional requirements. Apparel firm founders that rely on knowledge of the market rely on a larger bundle of factors including
availability of finance and opportunity deriving from a new market need.
Apparel firm founders that rely on design knowledge for the formation of the
company also rely on several other factors—in fact more other factors than did the
textile firm founders—the most important, based on the size of the correlation coefficient, being knowledge of the market. To our knowledge, there have been no
empirical examinations of the role of knowledge of the market in the formation of
small apparel firms within the European Union; however, Hodges et al. (2015) did
find this to be the case in a study of small apparel firm owners in Russia.
To reflect on our discussion in Chap. 1 about overriding research questions that
are associated with our descriptive analysis throughout this book, we asked: While
there are many small textile and apparel firms that comprise the EU industry, how
and to what extent are they entrepreneurial and/or innovative in their behaviors?
1
0.295***
0.283***
–
–
–
–
0.231**
–
–
–
0.249**
0.358***
0.254***
–
–
0.210**
–
–
0.173*
–
1
0.265**
0.304***
0.198*
0.271***
1
0.341***
Availability
of finance
***Significant at 0.01-level, **significant at 0.05-level, *significant at 0.10-level
Design knowledge
Knowledge of the
market
Networks built during
previous career
Availability of finance
Opportunities in a
public procurement
initiative
Existence of a large
enough customer
Opportunity deriving
from technological
change
Opportunity deriving
from a new market
need
Opportunity deriving
from new regulations
or institutional
requirements
Knowledge
of the
market
Design
knowledge
1
–
Networks
built
during
previous
career
0.398***
0.387***
0.372***
0.438***
1
Opportunities
in a public
procurement
initiative
0.419***
0.484***
0.512***
1
Existence
of a large
enough
customer
0.482***
0.545***
1
Opportunity
deriving from
technological
change
0.423***
1
Opportunity
deriving
from a new
market need
Table 6.7 Correlation matrix between the importance of factors for the formation of the company from the perspective of textile firms (n = 91)
1
Opportunity
deriving from
new regulations
or institutional
requirements
1
0.298***
0.350***
0.196*
0.196*
–
0.340***
0.192*
0.199*
–
0.298***
0.203*
0.209*
0.366***
0.281**
–
–
–
–
0.385***
0.198*
1
0.305***
–
0.380***
0.308***
1
0.393***
Availability
of finance
***Significant at 0.01-level, **significant at 0.05-level, *significant at 0.10-level
Design knowledge
Knowledge of the
market
Networks built during
previous career
Availability of finance
Opportunities in a
public procurement
initiative
Existence of a large
enough customer
Opportunity deriving
from technological
change
Opportunity deriving
from a new market
need
Opportunity deriving
from new regulations
or institutional
requirements
Knowledge
of the
market
Design
knowledge
1
0.520***
Networks
built during
previous
career
0.431***
0.296***
0.493***
0.376***
1
Opportunities
in a public
procurement
initiative
0.345***
0.186*
0.508***
Existence
of a large
enough
customer
0.575***
0.444***
1
Opportunity
deriving from
technological
change
0.472***
1
Opportunity
deriving
from a new
market need
Table 6.8 Correlation matrix between the importance of factors for the formation of the company from the perspective of apparel firms (n = 84)
1
Opportunity
deriving from
new regulations
or institutional
requirements
96
6 Sources of Knowledge Used by KIE Textile and Apparel Firms
To consider this question, we point out in Tables 6.7 and 6.8, among both textile and
apparel firms respectfully, that the correlation coefficients between the existence of
a large enough customer base and opportunity deriving from technological change
are greater than 0.50 and the correlation coefficients are highly significant. This
descriptive finding might suggest that when the company is founded on the existence of meeting the demand of a larger customer base, the firm has also established
itself in the market on the basis of its innovative or technological capabilities perhaps to ensure its own ability to meet demand over time in the face of non-­
technology-­based competition.
Regarding sources of knowledge for exploring new business opportunities, both
textile and apparel firms that rely on clients or customers as an important source
also rely on suppliers. (See Tables 6.9 and 6.10, respectfully.) Those textile firms
that view in-house know-how as an important source for exploring new business
opportunities complement that know how through knowledge from public research
institutes; universities; external commercial labs/R&D firms/technical institutes;
trade fairs, conference, and exhibitions; scientific journals and other trade or technical publications, and participation in national and EU-funded research programs.
Recall that we previously mentioned that we will include in-house know-how as a
technical rather than market-based source. This finding is not inconsistent with our
conjecture that the use of in-house know-how is innovative, namely, to expand the
firm’s absorptive capacity. Among apparel firms, the only significant correlation
coefficient is between in-house know-how and participation in nationally funded
programs, but the magnitude of that correlation coefficient is smaller than for textile
firms. Perhaps, and again this is conjecture on our part, textile firms are more innovative in their exploration of new business opportunities than are apparel firms.
To generalize from the descriptive information in this chapter, textile firms that
rely on clients or customers and suppliers for exploring new business opportunities
tend not to rely on technical knowledge from other sources and vice versa. Apparel
firms that rely on clients or customers and suppliers also rely to some extent on
technical knowledge from other sources. But, textile firms that rely on in-house
know-how also rely on external sources of technical knowledge more so than do
apparel firms.
We emphasized above that the most important factor in the formation of either
textile firms or apparel firms is knowledge of the market. This is not only the case in
general (see Table 6.1), but also it is the case across firms in most countries. Given
that for the most part textile and apparel firms are market driven, it would not be
unreasonable to expect that they rely on market information for exploring new business opportunities. The correlation coefficient for textile firms between knowledge
of the market and clients or customers is 0.188, and it is statistically significant at
the 0.10 level; for apparel firms the correlation coefficient is 0.215, and it is statistically significant at the 0.05 level.
Clients or
customers
Suppliers
Competitors
Public
research
institutes
Universities
External
commercial
labs/R&D
firms/
technical
institutes
In-house
know-how
Trade fairs,
conferences,
and
exhibitions
1
0.259**
0.180*
0.291***
0.219**
–
–
–
–
–
–
Suppliers
0.530***
0.209**
–
1
Clients or
customers
–
–
0.231**
–
1
0.279***
Competitors
–
0.303***
0.749***
0.679***
1
Public
research
institutes
0.214**
0.317***
1
0.680***
Universities
0.205*
0.449***
1
External
commercial
labs/R&D
firms/
technical
institutes
0.317***
1
In-house
know-­
how
1
Trade fairs,
conferences,
and
exhibitions
Scientific
journals
and other
trade or
technical
publications
Participation
in nationally
funded
research
programs
(continued)
Participation
in
EU-funded
research
programs
Table 6.9 Correlation matrix between the importance of alternative sources of knowledge for exploring new business opportunities from the perspective of textile
firms (n = 91)
6.2 AEGIS Survey Questions About Sources of Knowledge
97
–
–
–
–
–
Suppliers
0.181*
0.213**
0.200*
–
Competitors
0.587***
0.512***
0.355***
Public
research
institutes
0.624***
0.481***
0.315***
Universities
0.527***
0.453***
0.403***
External
commercial
labs/R&D
firms/
technical
institutes
***Significant at 0.01-level, **significant at 0.05-level, *significant at 0.10-level
Scientific
journals and
other trade
or technical
publications
Participation
in nationally
funded
research
programs
Participation
in
EU-funded
research
programs
Clients or
customers
Table 6.9 (continued)
0.285***
0.295***
0.398***
In-house
know-­
how
0.227**
0.176*
0.593***
Trade fairs,
conferences,
and
exhibitions
0.459***
0.437***
Scientific
journals
and other
trade or
technical
publications
0.779***
1
Participation
in nationally
funded
research
programs
1
Participation
in
EU-funded
research
programs
98
6 Sources of Knowledge Used by KIE Textile and Apparel Firms
Clients or
customers
Suppliers
Competitors
Public
research
institutes
Universities
External
commercial
labs/R&D
firms/
technical
institutes
In-house
know-how
Trade fairs,
conferences,
and
exhibitions
1
0.206*
–
–
0.182*
–
–
–
0.196*
0.320***
0.183*
Suppliers
0.359***
–
–
1
Clients or
customers
–
0.203*
0.236**
0.224**
1
0.379***
Competitors
0.261**
0.186*
0.784***
0.708***
1
Public
research
institutes
0.218**
0.199*
1
0.830***
Universities
0.307***
0.290***
1
External
commercial
labs/R&D
firms/
technical
institutes
–
1
In-house
knowhow
1
Trade fairs,
conferences,
and
exhibitions
Scientific
journals
and other
trade or
technical
publications
Participation
in nationally
funded
research
programs
(continued)
Participation
in
EU-funded
research
programs
Table 6.10 Correlation matrix between the importance of alternative sources of knowledge for exploring new business opportunities from the perspective of
apparel firms (n = 84)
6.2 AEGIS Survey Questions About Sources of Knowledge
99
–
0.218**
0.189*
–
–
Suppliers
–
0.304***
0.279***
–
Competitors
0.686***
0.719***
0.422***
Public
research
institutes
0.628***
0.736***
0.355***
Universities
0.559***
0.736***
0.432***
External
commercial
labs/R&D
firms/
technical
institutes
***Significant at 0.01-level, **significant at 0.05-level, *significant at 0.10-level
Scientific
journals and
other trade or
technical
publications
Participation
in nationally
funded
research
programs
Participation
in
EU-funded
research
programs
Clients or
customers
Table 6.10 (continued)
–
0.183*
–
In-house
knowhow
0.225**
0.345***
0.511***
Trade fairs,
conferences,
and
exhibitions
0.347***
0.382***
1
Scientific
journals
and other
trade or
technical
publications
0.783***
1
Participation
in nationally
funded
research
programs
1
Participation
in
EU-funded
research
programs
100
6 Sources of Knowledge Used by KIE Textile and Apparel Firms
6.3 Human Capital and the Importance of Sources of Knowledge
101
6.3 H
uman Capital and the Importance of Sources
of Knowledge
We reflect on the relationship between the use of specific sources of knowledge and
the founders’ human capital in this section. To place the focus of this section in a
broader perspective, we ask if differences in founders’ human capital are related to
differences in founders’ perception of the importance of alternative sources of
knowledge. We are not investigating this relationship in an effort to develop a philosophy about how one identifies or uses knowledge for various purposes; such an
effort is not only outside of the scope of this book, but also outside of the sphere of
our training and our research expertise. Rather, we are simply attempting to amplify
some of the themes that we presented in Chap. 1.
Recall that we wrote in Chap. 1 that KIE might reasonably be characterized in
terms of the following three points, all of which reflect the dynamic nature of the
entrepreneur or the dynamic nature of what he/she does:
• As a dynamic activity rather than a static one (e.g., a process)
• As a process of perception and action (e.g., one sees an opportunity, develops it
to a concept, and brings it into exploitation)
• As an innovative process characterized by risk and uncertainty (e.g., through
actions one deals with the uncertainties of discovering and exploiting new
opportunities)
Denoting alternative sources of knowledge differently, and in our case denoting
them as important or not important, sheds some light on how an entrepreneur, that
is, the founder of a KIE firm, perceives the seeds of opportunity. And, again following the themes of Chap. 1, one’s ability to perceive opportunity is a reflection of
one’s experience (e.g., Locke and Hume) and one’s education (e.g., Schultz). We
discuss these relationships with reference to both factors important to the formation
of the company and the importance of sources of knowledge for exploring new business opportunities. And, our discussion directly relates to the overarching research
questions about the extent to which textile and apparel firms are entrepreneurial.
In general, we find the only statistically significant correlations among the human
capital characteristic of founders and both the importance of factors for the formation of the company and sources of knowledge for exploring new business opportunities to be related to the sector experience of the firm’s founder.
More specifically, with reference to factors for the formation of the company,
greater experience in the low-tech sector is positively correlated with design knowledge, knowledge of the market, and networks built during previous careers; all of
these are market-demand factors. With reference to sources of knowledge for
exploring new business opportunities, greater experience in the low-tech sector is
positively correlated with the importance of competitors among textile firms and
with in-house know-how among apparel firms. From a statistical perspective, the
gender of the founder is not correlated with any factors or sources of knowledge.
102
6 Sources of Knowledge Used by KIE Textile and Apparel Firms
All in all, we think it is fair to say that the AEGIS database does not contain sufficient information on founders to support uniformly the assertion that the educational and experience levels of founders of KIE firms in the textile and apparel
industries are in general a covariate with the knowledge sources that the firm uses
that are related to the founding of the firm or for exploring new business opportunities. This does not, of course, mean that the philosophy of Locke and Hume is incorrect; this also does not mean that the AEGIS survey responses are inaccurate. This
conclusion simply means, at least to us, that firms rely on sources of knowledge for
many purposes and the AEGIS database captures only two of them. Exploring new
business opportunities is one and only one purpose for an entrepreneur to rely on
his/her human capital to act on a perception.
6.4 I ndices of Sources of Knowledge for Exploring New
Business Opportunities
The pattern of statistically significant correlation coefficients among themselves in
the matrices in Tables 6.9 and 6.10 regarding sources of knowledge for exploring
new business opportunities suggests, from our vantage, that there are two separate
groups of sources as we alluded to above. One group was referred to as market-­
based sources (clients or customers, suppliers, and competitors), and the other
group was referenced as technical sources (all of the remaining sources).3 Following
this interpretation of the data, we constructed four indices in an effort to collectivize
all of the survey information from above. For textile firms, we constructed a market-­
based and a technical index, and for apparel firms we constructed the same. Our
method for constructing these indices is based on principal components.4 The mean
values of these indices (and standard deviations) for textile firms are:
• Market-based = 3.103 (0.538).
• Technical = 1.584 (0.654).
The mean value of these indices for apparel firms are:
• Market-based = 2.729 (0.511).
• Technical = 1.586 (0.706).
We correlated these indices with the human capital and financial capital measures from Chap. 5 along with whether the firm was nascent or established. The only
3
Amoroso, Audretsch, and Link (forthcoming) considered these sources of knowledge for exploring new business opportunities among all KIE firms in the high-tech sector, as represented in the
AEGIS database. They divided the sources into the following categories: vertical sources, horizontal sources, sources related to research institutes, internal sources, publications and conferences,
and research programs.
4
The correlation coefficient between a principal component index and a simple average of the correlation coefficients is, in all instances, over 0.98, and it is highly significant.
6.5 Conclusions
103
Sources of Knowledge Strategic Behavior Entrepreneurial Performance
Fig. 6.3 Representation of direct and indirect paths from sources of knowledge to entrepreneurial
performance
firm or founder characteristics that are significantly correlated with these indices are
the percent of funding from the founder’s own or family resources (and those correlations are negative) and if the firm is nascent (and those correlations are also
negative). We interpret these findings to imply that the sources of knowledge identified on the AEGIS survey are used for exploring new business opportunities by all
founders, not just those with greater human capital. Related to the epistemological
discussion from Chap. 1, experience is not a dimensional driver of the importance
of knowledge sources used by founders in some firms, that is, more experience is
not related to a greater reliance on knowledge sources. Rather, experience is a driver
of equal proportions to the importance of knowledge in all firms.
Figure 6.3 shows a possible relationship between sources of knowledge relied on
by firms and their strategic behavior and their entrepreneurial performance (refer to
Fig. 1.1). Sources of knowledge in the figure are hypothesized to have both a direct
and an indirect impact on entrepreneurial performance.
6.5 Conclusions
The purpose of this chapter was to explore among KIE firms, in a descriptive manner, the importance of factors for the formation of the company and the importance
of sources of knowledge for exploring new business opportunities. Our understanding of sources of knowledge for exploring new business opportunities from this
chapter and from our constructed indices open the door for both a heuristic and
statistical exploration into the relationship between sources of knowledge and strategic behaviors and entrepreneurial performance.
We expand our understanding about possible precursors to entrepreneurial performance in Chap. 7. There, we examine alternative strategies (i.e., actions) that are
used by textile and apparel firms. In Chap. 8, we investigate alternative metrics that
are associated with entrepreneurial performance, and we explore in Chap. 9 the
relationships between entrepreneurial performance and choices about the use of
alternative sources of knowledge and the adoption of alternative strategies.
Chapter 7
The Strategic Behavior of KIE Textile
and Apparel Firms
The best time to plant a tree was 20 years ago. The second best
time is now.
—Chinese proverb
A person who never made a mistake never tried anything new.
—Albert Einstein
Abstract This chapter explores the sources of knowledge identified in Chap. 6, and
investigates if there is a direct relationship between the importance of the factors
and sources of knowledge and entrepreneurial performance. The ultimate goal of
this chapter is to consider the strength of the first part of the relationship: Sources of
Knowledge→Strategic Behavior→Entrepreneurial Performance. The empirical evidence supports that sources of knowledge are related to strategic behaviors.
7.1 Introduction
We concluded Chap. 6 with the visual schematic from Chap. 1 of the relationship
that we are exploring among the sources of knowledge that are important to textile
and apparel firms, that is, the importance of the sources of knowledge that firms rely
on or use and their strategic behavior and entrepreneurial performance. That schematic is reproduced here as Fig. 7.1.
Sources of Knowledge Strategic Behavior Entrepreneurial Performance
Fig. 7.1 Representation of direct and indirect paths from sources of knowledge to entrepreneurial
performance
© Springer International Publishing AG 2018
N.J. Hodges, A.N. Link, Knowledge-Intensive Entrepreneurship, International
Studies in Entrepreneurship 39, https://doi.org/10.1007/978-3-319-68777-3_7
105
106
7 The Strategic Behavior of KIE Textile and Apparel Firms
The AEGIS survey questions examined in Chap. 6 on what we broadly referred
to as sources of knowledge were very specific in terms of asking about the importance of factors for the formation of the company and the important sources of
knowledge for exploring new business opportunities; as we stated, we view both
under the rubric of sources of knowledge. Only the latter question about exploring
new business opportunities is, however, relevant to Fig. 7.1. We suggested in Chap.
6 the sources of knowledge for exploring new business opportunities listed on the
AEGIS survey can be classified as either market-based sources or technical sources,
the one debatable source being in-house know-how, although we decided to include
it with technical sources. The most important sources of knowledge, based on mean
Likert responses, for exploring new business opportunities by firms in both the textile and apparel industries are generally market-based sources: clients or customers,
suppliers, and competitors.
As we also discussed in Chap. 6, the AEGIS survey does not clarify the meaning
of the survey question’s phrase exploring new business opportunities. From a broad
perspective, we suggest that the importance of any source of knowledge might relate
to the subsequent entrepreneurial performance of a KIE firm, for why else would
such a firm, or any firm for that matter, explore new business opportunities.1 Perhaps
these AEGIS-defined sources of knowledge proxy firm perceptions of opportunity,
and among entrepreneurial firms, perception of opportunity leads to action or, in
terms of the focus of this chapter, to adopting or implementing strategic behaviors.
In this chapter, we explore the sources of knowledge identified in Chap. 6, and
we ask if any of them are related to the strategic behavior of KIE firms in the textile
and apparel industries as we hypothesize through Fig. 7.1. Our prior is that our
schematic has merit, but the extent to which the AEGIS data validate the schematic
is of course an empirical issue. On the one hand, there might only be a direct relationship between the importance of the factors and sources of knowledge and entrepreneurial performance. If that is the case, then the relevant relationship is
represented as Expression (7.1):
Sources of Knowledge → Entrepreneurial Performance
(7.1)
On the other hand, as illustrated in Fig. 7.1, if knowledge guides strategic behavior and strategic behavior results in entrepreneurial performance, then the relationship is represented as Expression (7.2):
Sources of Knowledge → Strategic Behavior → Entrepreneurial Performance (7.2)
The descriptive analyses presented in this chapter take a step forward toward
examining descriptively the first part of the relationship in Expression (7.2). In Sect.
7.2, we describe descriptively two dimensions of the strategic behavior of textile
and apparel firms. Two dimensions of behavior are related specifically to the themes
embedded in two AEGIS survey questions: identifying the contribution of factors in
1
We will define entrepreneurial performance in Chap. 8 in terms of metrics taken directly from the
AEGIS survey.
7.2 Strategic Behavior of Textile and Apparel Firms
107
creating and sustaining the competitive advantage for the company and identifying
statements that describe the sensing and seizing of opportunities within the firm.2
We explore the relationship between the use of specific sources of knowledge
from Chap. 6 or more precisely the market-based and technical indices of sources of
knowledge and these two dimensions of the strategic behavior of KIE firms. In other
words, we are exploring the strength of the relationship in the first part of Expression
(7.2) above. To do so, we formulate strategic behavior indices using principal components as we used in Chap. 6 for sources of knowledge for exploring new business
opportunities, and then we examine the correlation between this index and the four
indices on strategic behavior.
In Sect. 7.3, we proffer and formulate AEGIS-defined innovative strategies relied
on by textile and apparel firms. This index does not come directly from the AEGIS
survey; it is an amalgam of several AEGIS survey responses. As we discussed in
Chap. 1, because of the supply chain relationship between the textile industry and
the apparel industry, we expect there would be different innovative strategies among
the firms in each industry. Again, we examine the correlation between the indices of
sources of knowledge and our own innovation strategy index.
Finally, we summarize our findings in Sect. 7.4, and we introduce the scope of
inquiry in Chap. 8 and its relationship to the findings from this chapter.
7.2 Strategic Behavior of Textile and Apparel Firms
There are two questions on the AEGIS survey that relate specifically to strategic
behavior. The first question emphasizes factors in creating and sustaining the competitive advantage of the company, and the second question emphasizes sensing and
seizing of opportunities within the firm. We discuss the responses to each question
individually in the following two subsections.
7.2.1 C
reating and Sustaining the Competitive Advantage
of the Company
The key question on the AEGIS survey about the strategic behavior of a firm that is
related to how it might create and sustain a competitive advantage is:
Please indicate the contribution of the following factors in creating and sustaining the competitive advantage of the company using a 5-point scale, where 5 is a huge impact and 1 is
no impact:
1. Capability to offer novel products/services
2. Capacity to adapt the products/services to the specific needs of different customers/
market niches
3. Capability to offer expected products/services at low cost
The AEGIS survey questions use the words company and firm as we have here.
2
108
7 The Strategic Behavior of KIE Textile and Apparel Firms
4.
5.
6.
7.
8.
R&D activities
Establishment of alliances/partnerships with other firms
Capability to offer high-quality products/services at a premium price
Networking with scientific research organizations (universities, institutes, etc.)
Marketing and promotion activities
The mean responses to this survey question by both textile and apparel firms are
presented in Table 7.1. As in Chap. 6, with respect to the survey question about
whether a specific alternative source of knowledge is important or not important,
here we define a factor as having an impact if the industry mean response is 3.5 or
greater, and we define it as not having an impact if the mean response is less than
3.5.
Regarding textile firms, one might generalize from the pattern of mean responses
in the table that there are four strategic factors that have, on average, an impact on
firms creating and sustaining a competitive advantage. They are, in order of the
magnitude of the mean responses, capacity to adapt the products/services to the
specific needs of different customers/market niches, capability to offer novel products/services, capability to offer high-quality products/services at a premium price,
and capability to offer expected products/services at low cost. These factors rely on
internal capabilities, and the two most important of them have an entrepreneurial
character in the sense that they deal with novelty and adaptability. We offer the view
that these characteristics reflect on how the textile firms are responding (i.e., acting)
to the perception of an opportunity.
These four important internal factors broadly reflect textile firms’ abilities to
respond to market demand. That is, they are internal demand-side factors. Moreover,
these factors reflect the indigenous ability of KIE firms to respond to an opportunity.
The industry mean responses, however, do not directly address how well or effectively the firms respond to an opportunity, although effectiveness might be implied
from the responses.
Table 7.1 Mean firm responses about the contribution of factors in creating and sustaining the
competitive advantage of the company, by industry
Strategic competitive advantage factors
Capability to offer novel products/services
Capacity to adapt the products/services to the specific needs
of different customers/market niches
Capability to offer expected products/services at low cost
R&D activities
Establishment of alliances/partnerships with other firms
Capability to offer high-quality products/services at a
premium price
Networking with scientific research organizations
Marketing and promotion activities
5 = huge impact and 1 = no impact
Textile firms
(n = 91)
3.91
4.27
Apparel firms
(n = 84)
3.87
4.07
3.65
3.27
3.05
3.67
3.45
3.13
2.64
3.60
2.08
3.24
1.81
3.10
7.2 Strategic Behavior of Textile and Apparel Firms
109
There are three important strategic factors in Table 7.1 for apparel firms that have
an impact on firms creating and sustaining a competitive advantage. They are, in
order of the mean responses, capacity to adapt the products/services to the specific
needs of different customers/market niches, capability to offer novel products/services, and capability to offer high-quality products/services at a premium price.
These three internal factors coincide with those internal factors for textile firms.
Much like the textile firms, the apparel firms have an entrepreneurial nature in the
way in which they create and sustain a competitive advantage in response to
demand-side opportunities through novelty and adaptability.
The lowest ranked factor among both textile and apparel firms is networking
with scientific research organizations. Perhaps this finding reflects the relatively
young age of the KIE firms in the AEGIS database (about 7 years), and perhaps
networking is a practice that becomes more important over time as founders learn
more about external market activities.
Following our pedagogical approach from Chap. 6, the mean responses from
Table 7.1 are illustrated in Fig. 7.2 for both textile firms and apparel firms. It is clear
from a visual inspection of Fig. 7.2, as it was from the means in Table 7.1, that
adaptability and novelty are what confers a competitive advantage to both textile
and apparel firms.
Tables 7.2 and 7.3 show the mean responses to factors in creating and sustaining
the competitive advantage of the company across countries. As in Chap. 6, we caution against generalizations from these tables because the number of firms from
each country is often small. The number of country-specific textile firms varies from
2 (Denmark and Sweden) to 29 (Italy); the number of country-specific apparel firms
varies from 2 (Denmark) to 27 (Italy). Our cautious interpretation, at least among
those countries with relatively more firms represented (i.e., Italian firms, Portuguese
firms, and the UK textile firms), is that the contribution from certain internal strategic factors in creating and sustaining the competitive advantage of the company is
Markeng and promoon acvies
Networking with scienfic research organizaons
Capability to offer high quality products/services…
Establishment of alliances/partnerships with…
R&D acvies
Capability to offer expected products/services at…
Capacity to adapt the products/services to the…
Capability to offer novel products/services
Note:
5=huge impact and 1=no impact
0
0.5
1
Apparel
Texle
1.5
2
2.5
3
3.5
4
4.5
Fig. 7.2 Illustration of mean firm contribution of factors in creating and sustaining the competitive advantage of the company, by industry (Note: 5 = huge impact and 1 = no impact)
Czech
Republic
(n = 4)
4.00
4.50
3.50
3.50
2.50
4.00
1.75
3.75
Croatia
(n = 7)
4.43
4.71
3.71
4.00
3.29
3.14
3.71
4.57
5 = huge impact and 1 = no impact
Strategic competitive
advantage factors
Capability to offer novel
products/services
Capacity to adapt the
products/services to the
specific needs of different
customers/market niches
Capability to offer expected
products/services at low cost
R&D activities
Establishment of alliances/
partnerships with other firms
Capability to offer high-­
quality products/services at a
premium price
Networking with scientific
research organizations
Marketing and promotion
activities
2.00
1.00
4.00
2.50
1.00
2.00
5.00
Denmark
(n = 2)
2.00
1.80
1.80
2.80
2.80
2.00
3.40
3.40
France
(n = 5)
4.00
3.60
2.20
4.80
2.80
3.60
2.60
4.20
Germany
(n = 5)
4.40
3.43
2.29
3.43
2.86
2.57
3.86
4.14
Greece
(n = 7)
4.14
2.97
2.14
3.90
3.72
3.07
3.66
4.28
Italy
(n = 29)
4.10
2.15
2.08
3.31
3.31
3.46
4.08
4.38
Portugal
(n = 13)
3.69
5.00
3.00
3.50
2.50
4.00
2.50
2.50
Sweden
(n = 2)
2.50
4.06
1.35
3.71
2.76
3.24
3.94
4.41
United
Kingdom
(n = 17)
3.65
Table 7.2 Mean textile firm responses about the contribution of factors in creating and sustaining the competitive advantage of the company, by country
(n = 91)
110
7 The Strategic Behavior of KIE Textile and Apparel Firms
Czech
Republic
(n = 3)
4.00
2.67
3.00
1.67
1.00
2.67
1.00
3.33
Croatia
(n = 6)
3.83
4.00
3.33
2.83
2.33
3.67
2.33
3.17
5 = huge impact and 1 = no impact
Strategic competitive
advantage factors
Capability to offer novel
products/services
Capacity to adapt the
products/services to the
specific needs of different
customers/market niches
Capability to offer expected
products/services at low cost
R&D activities
Establishment of alliances/
partnerships with other firms
Capability to offer high-­
quality products/services at a
premium price
Networking with scientific
research organizations
Marketing and promotion
activities
3.00
2.50
3.00
4.00
4.00
4.00
5.00
Denmark
(n = 2)
5.00
2.44
1.11
2.89
3.33
2.78
2.78
4.22
France
(n = 9)
3.56
3.33
1.67
4.67
2.00
3.00
3.00
4.33
Germany
(n = 3)
3.33
3.07
2.00
3.07
2.71
2.71
3.93
4.21
Greece
(n = 14)
3.86
3.37
1.85
4.19
3.67
2.56
3.30
3.96
Italy
(n = 27)
4.19
2.73
2.27
3.45
3.00
2.73
3.73
3.73
Portugal
(n = 11)
3.64
4.25
1.75
4.75
3.25
3.00
2.75
4.75
Sweden
(n = 4)
2.75
2.40
1.00
2.60
2.80
2.80
4.60
4.60
United
Kingdom
(n = 5)
4.00
Table 7.3 Mean apparel firms’ responses about the contribution of factors in creating and sustaining the competitive advantage of the company, by country
(n = 84)
7.2 Strategic Behavior of Textile and Apparel Firms
111
112
7 The Strategic Behavior of KIE Textile and Apparel Firms
greater than from others, but the greater impact appears to be associated with the
same factors that we identified from the mean responses in Table 7.1.
Among the Italian textile firms (n = 29), five factors have a strategic impact, that
is, there is a mean response equal to or greater than 3.5, in creating and sustaining
the competitive advantage of the company. The factor having the greatest impact is
the capacity to adapt products/services to the specific needs of different customers/
market niches. Among the Italian apparel firms (n = 27), four factors have a strategic
impact, but the greatest impact is associated with the firms’ capability to offer novel
products/services and with their capability to offer high-quality products/services at
a premium price.
Tables 7.4 and 7.5 show a correlation matrix among the factors in creating and
sustaining the competitive advantage of the company by textile and apparel firms,
respectively. Regarding the correlation matrix for textile firms in Table 7.4, two patterns appear to be important. The first pattern is the relative magnitude and statistical significance of the correlation coefficient between R&D activity and the two
factors associated with novel and adaptability of products/services. The second pattern is the clustering of significant correlation coefficients among internal factors
and external factors. All of the factors considered in creating and sustaining the
competitive advantage of the company are internal except for the establishment of
alliances/partnerships with other firms and networking with scientific research organizations. R&D activities, which are an internal factor, are correlated with the two
external factors, and again this perhaps reflects the importance of internal R&D
being an investment in the appropriability ability of textile firms.
A similar pattern of significant correlation coefficients is present in the correlation matrix for apparel firms in Table 7.5, although the number of significant coefficients is less than among the factors in Table 7.4 for the textile firms. Again,
internal R&D activities are positively and significantly correlated with firms’ novelty and adaptability of products/services. And, again, we see clustering among
internal and external factors.
Given that the data used herein reflect the behavior of KIE firms, and given that
entrepreneurship in a dynamic sense is associated with innovation, we think that in
addition to the clustering by internal and external factors, a third clustering might be
relevant. That third amalgamated cluster might represent strategic innovative behavior and thus be composed of three factors: capability to offer novel products/services, capacity to adapt the products/services to the specific needs of different
customers/market niches, and R&D activities. We discuss this third clustering
below.
1
0.344***
0.360***
0.239**
0.173*
–
0.261**
0.350***
–
0.464***
–
0.257**
0.329***
–
1
Capacity to adapt the
products/services to
the specific needs of
different customers/
market niches
0.211**
–
–
–
0.174*
1
Capability to
offer expected
products/
services at
low cost
***Significant at 0.01-level, ** significant at 0.05-level, * significant at 0.10-level
Capability to offer novel
products/services
Capacity to adapt the
products/services to the
specific needs of different
customers/market niches
Capability to offer expected
products/services at low cost
R&D activities
Establishment of alliances/
partnerships with other firms
Capability to offer
high-quality products/
services at a premium price
Networking with scientific
research organizations
Marketing and promotion
activities
Capability
to offer
novel
products/
services
–
0.441***
0.216**
1
–
R&D
activities
0.271***
0.235**
–
1
Establishment
of alliances/
partnerships
with other
firms
–
–
1
Capability to
offer highquality products/
services at a
premium price
–
1
Networking
with
scientific
research
organizations
1
Marketing
and
promotion
activities
Table 7.4 Correlation matrix between the textile firm responses to the contribution of factors in creating and sustaining the competitive advantage of the
company (n = 91)
7.2 Strategic Behavior of Textile and Apparel Firms
113
1
0.325***
0.328***
0.250**
–
–
0.197*
0.425***
0.299***
0.535***
0.197*
–
–
0.211**
1
–
–
–
0.222**
–
1
Capability to
offer expected
products/
services at
low cost
***Significant at 0.01-level, **significant at 0.05-level, *significant at 0.10-level
Capability to offer novel
products/services
Capacity to adapt the
products/services to the
specific needs of different
customers/market niches
Capability to offer expected
products/services at low cost
R&D activities
Establishment of alliances/
partnerships with other firms
Capability to offer
high-quality products/
services at a premium price
Networking with scientific
research organizations
Marketing and promotion
activities
Capability to
offer novel
products/
services
Capacity to adapt the
products/services to
the specific needs of
different customers/
market niches
0.185*
0.247**
0.226**
1
0.391***
R&D
activities
–
0.214**
–
1
Establishment
of alliances/
partnerships
with other
firms
0.225**
–
1
Capability to
offer highquality
products/
services at a
premium price
0.319***
1
Networking
with
scientific
research
organizations
1
Marketing
and
promotion
activities
Table 7.5 Correlation matrix between the apparel firms’ responses to the contribution of factors in creating and sustaining the competitive advantage of the
company (n = 84)
114
7 The Strategic Behavior of KIE Textile and Apparel Firms
7.2 Strategic Behavior of Textile and Apparel Firms
115
7.2.2 Sensing and Seizing Opportunities Within the Firm
The key question on the AEGIS survey about the strategic behavior of a firm for
how it senses and seizes opportunities internally is:3
Please indicate to what extent you agree or disagree with the following statements regarding the sensing and seizing of opportunities within your firm using a 5-point scale, where 5
is strongly agree and 1 is strongly disagree:
1. Our firm actively observes and adopts the best practices in our sector.
2. Our firm responds rapidly to competitive moves.
3. We change our practices based on customer feedback.
4. Our firm regularly considers the consequences of changing market demand in terms of new
products and services.
5.Our firm is quick to recognize shifts in our market (e.g., competition, regulation,
demography).
6. We quickly understand new opportunities to better serve our customers.
7. There is a formal R&D department in our firm.
8. There is a formal engineering and technical studies department in our firm.
9. Design activity is important in introducing new products/services to the market.
10. We implement systematic internal and external personnel training.
11. Employees share practical experiences on a frequent basis.
All of the AEGIS-defined statements are focused on internal responses to external opportunities. And, all of the statements reflect perception and action—entrepreneurial traits that we continue to emphasize.
The mean responses to this survey question for the textile and apparel firms are
presented in Table 7.6. Using the criterion of a mean response of 3.5 or greater to
signify agreement and nonagreement to a statement, there is agreement among
firms in the textile and apparel industries on six of the statements about means and
methods for sensing and seizing opportunities. Among textile firms, the strongest
agreement is to the statement “We change our practices based on customer feedback” and to the statement “We quickly understand new opportunities to better
serve our customers.” And, it should be emphasized that these are the only two
AEGIS survey questions that contain the word “customer.”
We stated above (see Table 7.1) regarding the mean responses from textile firms
that the factors that have an impact, versus not having an impact, in creating and
sustaining the competitive advantage of the company are those factors that reflect
firms’ abilities to respond to market demand in an entrepreneurial or innovative
manner. We concluded from our inspection of Table 7.1 for firms in the textile
industry that the pattern of findings reflects the ability of KIE firms to perceive an
3
As an aside, this survey question could be interpreted in terms of ways that KIE textile and apparel
firms enhance their dynamic capabilities. Dynamic capabilities are an organization’s “ability to
integrate, build, and reconfigure internal and external competencies to address rapidly changing
environments” (Teece et al. 1997, p. 516). Dynamic capabilities involve “calibrating opportunities
and diagnosing threats, directing (and redirecting) resources according to a policy or plan of action,
and possibly also reshaping organizational structures and systems so that they create and address
technological opportunities” (Teece 2014, p. 1398). Teece (2014) posits that dynamic capabilities
can be broken into three organizational capacities including sensing, seizing, and continued renewal.
116
7 The Strategic Behavior of KIE Textile and Apparel Firms
Table 7.6 Mean firm agreement to statements regarding the sensing and seizing of opportunities
within the firm, by industry
Statements about sensing and seizing of opportunities within
the firm
Our firm actively observes and adopts the best practices in our
sector
Our firm responds rapidly to competitive moves
We change our practices based on customer feedback
Our firm regularly considers the consequences of changing
market demand in terms of new products and services
Our firm is quick to recognize shifts in our market
We quickly understand new opportunities to better serve our
customers
There is a formal R&D department in our firm
There is a formal engineering and technical studies
department in our firm
Design activity is important in introducing new products/
services to the market
We implement systematic internal and external personnel
training
Employees share practical experiences on a frequent basis
Textile firms
(n = 91)
3.76
Apparel firms
(n = 84)
3.51
3.80
3.87
3.65
3.37
3.58
3.54
3.55
3.84
3.67
3.83
2.05
1.75
2.21
1.82
3.50
3.57
2.64
2.83
3.65
3.26
5 = strongly agree and 1 = strongly disagree
opportunity in the market. We interpret the pattern of findings in Table 7.6 to reflect
that KIE textile firms also agree about acting on that perception. Actively observing
and adopting best practices and changing practices based on customer feedback are
clearly, in our opinion, examples of entrepreneurial action.
Regarding responses by firms in the apparel industry, which are also in Table 7.6,
there are similar patterns that apparel firms agree that demand-side factors are
important for sensing and seizing opportunities. There is agreement about six means
and methods (based on the order listed in the table): observing and adopting best
practices in the sector, changing practices based on customer feedback, considering
the consequences of changes in market demand, recognizing shifts in market
demand, understanding opportunities to better serve customers, and design activity
to introduce new products/services to the market.
Perhaps what is equally as interesting as the similarities between firms in the
textile and apparel industries regarding agreement to statements about sensing and
seizing opportunities are the dissimilarities. On average, firms in both industries
disagree, with respect to sensing and seizing opportunities, to statements about the
presence of a formal R&D department or similar organizational entity being within
the firm. We note that having a formal R&D department in the firm is not the same
as relying on R&D activity to create and sustain a competitive advantage (See
Table 7.1). Bozeman and Link (1991) have shown empirically that many small
firms—not small firms in the textile and apparel industries, however—do not have
formal R&D departments or even any classified R&D scientists, yet the firms and
7.2 Strategic Behavior of Textile and Apparel Firms
117
the scientists conduct R&D. Because of their size, many individuals will participate
in R&D on a to-need basis, but they are not formally considered R&D employees.
There is also unified disagreement to the statement related to personnel training.
Perhaps the founders of these KIE firms realize that perception and action are more
innate characteristics than learned characteristics.
The responses by firms in both the textile and apparel industries are visually
represented in Fig. 7.3. The similarities and dissimilarities in responses by firms in
the two industries are amplified in the figure.
From a cross-country perspective, we again focus on the Italian firms in the textile industry (Table 7.7) and in the apparel industry (Table 7.8) because of sample
sizes. Italian firms in both industries agree with the statement about perception of
opportunity: “We quickly understand new opportunities to better serve our customers.” The mean response among textile firms was 3.93, and among apparel firms it
was 4.33. And, no other mean responses were greater than these, by industry. Tied
for the greatest mean response among textile firms is agreement to the statement,
“Employees share practical experiences on a frequent basis.” Among the apparel
firms, the next highest mean response is, “Our firm actively observes and adopts the
best practices in our sector.” Perhaps the relative agreement to the statement about
textile firm employees sharing practical experiences is indicative of the firm’s ability to perceive opportunity internally as well as externally.
Correlation matrices between the factors related to sensing and seizing opportunities are in Tables 7.9 and 7.10. Whereas clusters of significant coefficients could
readily be seen in Tables 7.4 and 7.5 with respect to the strategic behavior
­characterized in terms of factors in creating and sustaining the competitive advanEmployees share praccal experiences on a…
We implement systemac internal and external…
Design acvity is important in introducing new…
There is a formal engineering and technical studies…
There is a formal R&D department in our firm.
We quickly understand new opportunies to…
Our firm is quick to recognize shis in our market.
Our firm regularly considers the consequences of…
We change our pracces based on customer…
Our firm responds rapidly to compeve moves.
Our firm acvely observes and adopts the best…
Note:
5=strongly agree and 1=strongly disagree
0
Apparel
0.5
1
1.5
2
2.5
3
3.5
4
4.5
Texle
Fig. 7.3 Illustration of mean firm agreement to statements about the sensing and seizing of opportunities within the firm, by industry (Note: 5 = strongly agree and 1 = strongly disagree)
Strategic means and
methods for sensing
and seizing
opportunities
Our firm actively
observes and adopts the
best practices in our
sector
Our firm responds
rapidly to competitive
moves
We change our
practices based on
customer feedback
Our firm regularly
considers the
consequences of
changing market
demand in terms of
new products and
services
Our firm is quick to
recognize shifts in our
market
We quickly understand
new opportunities to
better serve our
customers
There is a formal R&D
department in our firm
Czech
Republic
(n = 4)
4.00
4.75
4.25
4.50
3.75
4.50
1.00
Croatia
(n = 7)
4.57
4.43
4.57
4.29
4.57
4.71
2.57
1.00
3.00
3.00
3.50
4.50
2.50
Denmark
(n = 2)
4.50
2.00
4.00
3.80
3.80
3.60
3.80
France
(n = 5)
3.80
1.80
4.00
3.80
4.00
4.80
4.40
Germany
(n = 5)
4.80
4.14
2.43
1.71
1.29
1.43
2.00
Greece
(n = 7)
1.71
2.21
3.93
3.52
3.76
3.55
3.59
Italy
(n = 29)
3.69
1.85
3.69
3.69
3.46
4.15
3.85
Portugal
(n = 13)
3.62
1.00
3.00
3.00
4.00
4.50
3.50
Sweden
(n = 2)
2.50
Table 7.7 Mean agreement by textile firms to statements about the sensing and seizing of opportunities within the firm, by country (n = 91)
1.47
3.94
3.76
3.94
4.47
4.35
United
Kingdom
(n = 17)
4.18
118
7 The Strategic Behavior of KIE Textile and Apparel Firms
Czech
Republic
(n = 4)
1.00
3.75
3.50
4.00
Croatia
(n = 7)
2.57
3.83
4.14
4.57
5 = strongly agree and 1 = strongly disagree
Strategic means and
methods for sensing
and seizing
opportunities
There is a formal
engineering and
technical studies
department in our firm
Design activity is
important in
introducing new
products/services to the
market
We implement
systematic internal and
external personnel
training
Employees share
practical experiences
on a frequent basis
1.50
2.00
4.50
Denmark
(n = 2)
3.00
3.00
2.20
2.80
France
(n = 5)
1.00
4.20
2.20
3.40
Germany
(n = 5)
2.60
2.43
2.86
2.29
Greece
(n = 7)
3.00
3.93
2.83
3.83
Italy
(n = 29)
1.59
3.46
2.46
3.23
Portugal
(n = 13)
1.46
3.00
1.00
4.00
Sweden
(n = 2)
1.00
3.71
2.06
3.47
United
Kingdom
(n = 17)
1.47
7.2 Strategic Behavior of Textile and Apparel Firms
119
Strategic means and
methods for sensing
and seizing
opportunities
Our firm actively
observes and adopts
the best practices in
our sector
Our firm responds
rapidly to competitive
moves
We change our
practices based on
customer feedback
Our firm regularly
considers the
consequences of
changing market
demand in terms of
new products and
services
Our firm is quick to
recognize shifts in our
market
We quickly understand
new opportunities to
better serve our
customers
There is a formal R&D
department in our firm
Czech
Republic
(n = 3)
4.00
4.00
4.00
4.00
4.00
4.33
1.00
Croatia
(n = 6)
3.67
3.33
3.67
3.17
3.17
3.17
1.50
2.00
5.00
5.00
5.00
4.00
3.50
Denmark
(n = 2)
4.50
2.78
3.89
3.78
3.67
4.22
3.78
France
(n = 9)
3.33
1.00
4.33
4.33
3.67
4.33
4.00
Germany
(n = 3)
3.33
3.00
2.07
2.29
1.86
1.93
1.93
Greece
(n = 14)
1.86
2.44
4.33
3.96
4.22
3.48
3.78
Italy
(n = 27)
4.04
1.91
4.18
3.82
3.55
4.18
3.82
Portugal
(n = 11)
3.64
1.00
4.50
4.75
3.75
5.00
2.00
Sweden
(n = 4)
3.75
Table 7.8 Mean agreement by apparel firms to statements about the sensing and seizing of opportunities within the firm, by country (n = 84)
1.80
4.40
4.00
3.60
4.00
3.80
United
Kingdom
(n = 5)
4.40
120
7 The Strategic Behavior of KIE Textile and Apparel Firms
Czech
Republic
(n = 3)
1.00
3.00
3.67
3.67
Croatia
(n = 6)
1.67
4.00
2.17
4.00
5 = strongly agree and 1 = strongly disagree
Strategic means and
methods for sensing
and seizing
opportunities
There is a formal
engineering and
technical studies
department in our firm
Design activity is
important in
introducing new
products/services to the
market
We implement
systematic internal and
external personnel
training
Employees share
practical experiences
on a frequent basis
4.50
2.50
4.50
Denmark
(n = 2)
1.00
3.44
3.11
4.11
France
(n = 9)
2.11
4.33
3.33
3.67
Germany
(n = 3)
3.33
1.93
3.50
2.36
Greece
(n = 14)
2.71
3.33
2.63
3.85
Italy
(n = 27)
1.67
3.36
2.73
3.36
Portugal
(n = 11)
1.36
2.25
2.00
4.25
Sweden
(n = 4)
1.00
4.60
2.60
3.80
United
Kingdom
(n = 5)
1.40
7.2 Strategic Behavior of Textile and Apparel Firms
121
Our firm actively
observes and adopts
the best practices in
our sector
Our firm responds
rapidly to
competitive moves
We change our
practices based on
customer feedback
Our firm regularly
considers the
consequences of
changing market
demand in terms of
new products and
services
We change our
practices based on
customer feedback
1
0.447***
0.552***
0.522***
0.484***
0.481***
0.395***
Our firm
responds
rapidly to
competitive
moves
0.447***
1
Our firm
actively
observes
and
adopts
the best
practices
in our
sector
0.549***
0.696***
1
We
change
our
practices
based on
customer
feedback
0.750***
1
Our firm
regularly
considers the
consequences
of changing
market
demand in
terms of new
products and
services
1
Our firm
is quick
to
recognize
shifts in
our
market
We quickly
understand
new
opportunities
to better
serve our
customers
There is a
formal
R&D
department
in our firm
There is a
formal
engineering
and
technical
studies
department
in our firm
Design
activity is
important
in
introducing
new
products/
services to
the market
We
implement
systematic
internal
and
external
personnel
training
Table 7.9 Correlation matrix between textile firms’ agreement to statements about the sensing and seizing of opportunities within the firm (n = 91)
Employees
share
practical
experiences
on a
frequent
basis
We quickly
understand new
opportunities to
better serve our
customers
There is a formal
R&D department in
our firm
There is a formal
engineering and
technical studies
department in our
firm
Design activity is
important in
introducing new
products/services to
the market
−0.193**
−0.263***
–
–
–
–
0.347***
0.197*
–
0.530***
0.607***
Our firm
responds
rapidly to
competitive
moves
We
change
our
practices
based on
customer
feedback
0.382***
Our firm
actively
observes
and
adopts
the best
practices
in our
sector
0.287***
–
–
0.607***
Our firm
regularly
considers the
consequences
of changing
market
demand in
terms of new
products and
services
0.218**
–
–
0.608***
Our firm
is quick
to
recognize
shifts in
our
market
0.223**
–
–
1
We quickly
understand
new
opportunities
to better
serve our
customers
–
0.335***
1
There is a
formal
R&D
department
in our firm
–
1
There is a
formal
engineering
and
technical
studies
department
in our firm
1
Design
activity is
important
in
introducing
new
products/
services to
the market
We
implement
systematic
internal
and
external
personnel
training
(continued)
Employees
share
practical
experiences
on a
frequent
basis
0.177*
0.283***
0.207**
0.320***
Our firm
responds
rapidly to
competitive
moves
0.347***
0.181*
We
change
our
practices
based on
customer
feedback
0.410***
0.245**
Our firm
regularly
considers the
consequences
of changing
market
demand in
terms of new
products and
services
0.260**
0.201*
Our firm
is quick
to
recognize
shifts in
our
market
***Significant at 0.01-level, **significant at 0.05-level, *significant at 0.10-level
We implement
systematic internal
and external
personnel training
Employees share
practical
experiences on a
frequent basis
Our firm
actively
observes
and
adopts
the best
practices
in our
sector
Table 7.9 (continued)
0.323***
0.281***
We quickly
understand
new
opportunities
to better
serve our
customers
–
–
There is a
formal
R&D
department
in our firm
–
–
There is a
formal
engineering
and
technical
studies
department
in our firm
–
0.209*
Design
activity is
important
in
introducing
new
products/
services to
the market
0.266***
1
We
implement
systematic
internal
and
external
personnel
training
1
Employees
share
practical
experiences
on a
frequent
basis
Our firm
actively
observes and
adopts the best
practices in our
sector
Our firm
responds rapidly
to competitive
moves
We change our
practices based
on customer
feedback
Our firm
regularly
considers the
consequences of
changing market
demand in terms
of new products
and services
1
0.659***
0.600***
0.589***
0.566***
Our firm
responds
rapidly to
competitive
moves
0.619***
1
Our firm
actively
observes
and adopts
the best
practices
in our
sector
0.587***
1
We change
our
practices
based on
customer
feedback
1
Our firm
regularly
considers the
consequences
of changing
market demand
in terms of new
products and
services
Our firm
is quick
to
recognize
shifts in
our
market
We quickly
understand
new
opportunities
to better
serve our
customers
There is a
formal
R&D
department
in our firm
There is a
formal
engineering
and
technical
studies
department
in our firm
Design
activity is
important
in
introducing
new
products/
services to
the market
We
implement
systematic
internal
and
external
personnel
training
Table 7.10 Correlation matrix between apparel firms’ agreement to statements about the sensing and seizing of opportunities within the firm (n = 84)
(continued)
Employees
share
practical
experiences
on a
frequent
basis
We change our
practices based
on customer
feedback
We quickly
understand new
opportunities to
better serve our
customers
There is a
formal R&D
department in
our firm
There is a
formal
engineering and
technical studies
department in
our firm
0.693***
0.591***
−0.211*
–
0.574***
−0.226**
−0.313***
Our firm
responds
rapidly to
competitive
moves
0.62***
Our firm
actively
observes
and adopts
the best
practices
in our
sector
Table 7.10 (continued)
–
−0.317***
0.610***
0.647***
We change
our
practices
based on
customer
feedback
–
–
0.664***
0.668***
Our firm
regularly
considers the
consequences
of changing
market demand
in terms of new
products and
services
−0.189*
−0.269**
0.740***
1
Our firm
is quick
to
recognize
shifts in
our
market
−0.181*
−0.394***
1
We quickly
understand
new
opportunities
to better
serve our
customers
0.361***
1
There is a
formal
R&D
department
in our firm
1
There is a
formal
engineering
and
technical
studies
department
in our firm
Design
activity is
important
in
introducing
new
products/
services to
the market
We
implement
systematic
internal
and
external
personnel
training
Employees
share
practical
experiences
on a
frequent
basis
0.266**
–
0.478***
0.315***
–
0.418***
Our firm
responds
rapidly to
competitive
moves
0.468***
–
0.402***
We change
our
practices
based on
customer
feedback
0.515***
–
0.334***
Our firm
regularly
considers the
consequences
of changing
market demand
in terms of new
products and
services
0.391***
–
0.349***
Our firm
is quick
to
recognize
shifts in
our
market
***Significant at 0.01-level, **significant at 0.05-level, *significant at 0.10-level
Design activity
is important in
introducing new
products/
services to the
market
We implement
systematic
internal and
external
personnel
training
Employees share
practical
experiences on a
frequent basis
Our firm
actively
observes
and adopts
the best
practices
in our
sector
0.387***
–
0.308***
We quickly
understand
new
opportunities
to better
serve our
customers
–
0.291***
–
There is a
formal
R&D
department
in our firm
–
0.285***
–
There is a
formal
engineering
and
technical
studies
department
in our firm
0.309***
–
1
Design
activity is
important
in
introducing
new
products/
services to
the market
0.222*
1
We
implement
systematic
internal
and
external
personnel
training
1
Employees
share
practical
experiences
on a
frequent
basis
128
7 The Strategic Behavior of KIE Textile and Apparel Firms
tage of the firm, we do not observe a clustering of responses to the statement above
with respect to sensing and seizing of opportunities within the firm.
In the previous two subsections, we described responses to AEGIS survey questions that are related to dimensions of firms’ strategic behavior. The first dimension
relates to the contribution of alternative factors in creating and sustaining the competitive advantage of the company, and the second dimension relates to agreement
to statements regarding the sensing and seizing of opportunities within the firm.
While the structure of these two AEGIS survey questions is different, our interpretation of the sentiment underlying the two questions is similar in the sense that they
both deal with perception of opportunity and the actions to pursue the opportunity.
7.2.3 Strategic Agreements
We introduced Sect. 7.2 by stating that there are two questions on the AEGIS survey
that related specifically to strategic behavior, and those are the two questions discussed in the previous two subsections. Strategic agreements can indeed represent
strategic behavior. Firms can reduce duplication of research and other inventive
activities through agreements, and they can leverage their own resources through
the sharing of resources with others that might have different resources, such as
capital equipment, and through agreements the overall research process can be
shortened. The key question on the AEGIS survey about the firm’s use of strategic
agreements is:
Please indicate to what extent your company has participated in the following types of
agreements using a 5-point scale, where 5 is often and 1 is not at all:
1.
2.
3.
4.
5.
6.
7.
Strategic alliance
R&D agreement
Technical cooperation agreement
Licensing agreement
Subcontracting
Marketing/export promotion
Research contract out
Before describing the mean responses to firm participation in the alternative alliances considered in this survey question, we refer to Table 7.1 in which we showed
that the establishment of alliances/partnerships with other firms did not have an
impact on a firm’s ability to create and sustain the competitive advantage of the
company. The mean response to that factor among textile firms is 3.05, and among
apparel firms, it is 2.64. Regarding the survey question above about agreements, we
note, however, that the survey question does not elaborate on the purpose of participating in various types of agreements.
In general, neither textile firms nor apparel firms participate in any form of strategic agreement, meaning that the responses were less than 3.5 almost across the
board. Perhaps these responses reflect the way the survey question is phrased, or
perhaps they simply complement the related responses in Table 7.1.
7.3 Indices of Strategic Behavior
129
7.3 Indices of Strategic Behavior
In Sect. 7.2, we described responses to two AEGIS survey questions related to strategic behavior, and we identified two components that might represent strategic
innovative behavior. Regarding the survey question about creating and sustaining
the competitive advantage of the company, we suggested from our inspection of the
correlation coefficients in Tables 7.4 and 7.5 that one might group the factors in
those that are internal and those that are external. The internal factors are all of those
listed in the survey question as factors except for the two that are external: establishment of alliances/partnerships with other firms and networking with scientific
research organizations.
We also suggested that a third cluster of factors might have economic significance, namely, a cluster that represents strategic innovative behavior. The innovation cluster of factors would include capability to offer novel products/services,
capacity to adopt the products/services to the specific needs of different customers/
market niches, and R&D activities.
As we did in Chap. 6, we created six indices related to factors associated with
creating and sustaining a competitive advantage—three indices for textile firms and
three indices for apparel firms. Using principal components, the mean values (standard deviations) of these indices are:
• Internal index for textile firms for creating and sustaining the competitive advantage of the company = 2.170 (0.414).
• Internal index for apparel firms for creating and sustaining the competitive
advantage of the company = 2.074 (0.493).
• External index for textile firms for creating and sustaining the competitive advantage of the company = 2.016 (0.825).
• External index for apparel firms for creating and sustaining the competitive
advantage of the company = 1.735 (0.734).
• Innovation index for textile firms = 2.932 (0.667).
• Innovation index for apparel firms = 2.896 (0.816).
We also discussed in Sect. 7.2.2 agreements and disagreements to statements
about the sensing and seizing of opportunities within the firm. (See Table 7.6.)
Based on our inspection of the correlation coefficients in Tables 7.9 and 7.10, we
did not see a clustering of significant coefficients. Thus, it is our conclusion that
only two indices, one for textile firms and one for apparel firms, are appropriate to
characterize responses to the statements about sensing and seizing opportunities
within the firm.
Again, using principal components, the mean values (standard deviations) of
these indices are:
• Index for textile firms for the sensing and seizing of opportunities within the firm
= 2.400 (0.558).
• Index for apparel firms for the sensing and seizing of opportunities within the
firm = 2.378 (0.706).
130
7 The Strategic Behavior of KIE Textile and Apparel Firms
Tables 7.11 and 7.12 show the matrix of correlation coefficients among these
indices.
Clearly, the indices reported in Table 7.11 for textile firms are correlated with
one another. Recall from Chap. 6 that we created two indices based on responses to
the AEGIS survey question about the importance of alternative sources of knowledge for exploring new business opportunities. Our interpretation of the pattern of
correlations among the alternative sources of knowledge was that the sources cluster into two groups. One cluster of responses was related to what we referred to as
market-based sources, and the other cluster was related to what we referred to as
technical sources. As shown in Table 7.11 for textile firms and in Table 7.12 for
apparel firms, there is a mild positive and significant correlation between these two
indices.
In this chapter, we created three indices related to the behavioral strategy of
firms. The first indices are based on responses to the AEGIS survey question about
factors for creating and sustaining the competitive advantage of the company. The
pattern of correlation coefficients among alternative factors is in Tables 7.4 and 7.5.
Our interpretation of the pattern of correlations is that factors are clustered between
being internal and being external. We also constructed what we call an innovative
strategy index based on the three factors (see Table 7.1) related to novelty, adaptability, and R&D activity.
In addition, we also created in this chapter an opportunity index based on firm
responses to the AEGIS survey about agreement to statements regarding the sensing
and seizing of opportunities within the firm. We call this an opportunity index, and
we discuss it below.
As shown in Tables 7.11 and 7.12, for textile firms and for apparel firms, respectively, there is correlational evidence that sources of knowledge do have a positive
effect on strategic behavior as suggested from Expression (7.2) above. From Tables
7.11 and 7.12, it appears that market-based sources of knowledge are positively
related to the internal index for strategic behavior associated with creating and sustaining the competitive advantage of both textile firms and apparel firms. But, the
market-based index of sources of knowledge is not correlated with the external
index for strategic behavior. Among textile firms, the technical index is correlated
with both the internal and external indices of strategic behavior, but among apparel
firms it is only correlated with the external index of strategic behavior.
The correlational pattern in Tables 7.11 and 7.12 regarding the innovation index
of strategic behavior is correlated with both the market-based and the technical
indices of sources of knowledge among textile firms, but only the market-based
index is so constructed among apparel firms. To elaborate on earlier statements that
there is a value chain relationship between the textile industry and the apparel industry and that there is widespread belief that innovation and technological change will
be drivers of the renaissance of the textile and apparel industries, one might infer
from these two tables that the textile industry is guided more so by technology and
innovation and the apparel industry is guided more so by market-driven factors.
Our last strategic behavior index is based on responses to the AEGIS survey
question about agreement to the statement about the sensing and seizing of oppor-
0.235**
0.272***
–
0.484***
0.197*
0.505***
1
0.516***
Technical
sources of
knowledge
index
0.911***
0.395***
0.416***
1
Internal index
for creating and
sustaining a
competitive
advantage
0.408***
–
1
External index
for creating
and sustaining
a competitive
advantage
1
0.350***
Innovation
index
1
Index for sensing and
seizing opportunity
Key:
Market-based = source of knowledge index based on responses to the survey question about the importance of selected market-based sources of knowledge for
exploring new business opportunities
Technical = source of knowledge index based on responses to the survey question about the importance of selected technical sources of knowledge for exploring new business opportunities
Internal = strategic behavior index based on responses to the survey question about the impact of selected internal factors that contributed to creating and sustaining the competitive advantage of the company
External = strategic behavior index based on responses to the survey question about the impact of selected external factors that contributed to creating and
sustaining the competitive advantage of the company
Innovation = strategic behavior index based on responses to the survey question about the impact of selected internal and external factors that contributed to
creating and sustaining the competitive advantage of the company
Opportunity = strategic behavior index based on responses to the survey question about sensing and seizing opportunities with the firm
***Significant at 0.01-level, **significant at 0.05-level, *significant at 0.10-level
Market-based sources of knowledge index
Technical sources of knowledge index
Internal index for creating and sustaining
a competitive advantage
External index for creating and sustaining
a competitive advantage
Innovation index
Index for sensing and seizing opportunity
Market-­
based
sources of
knowledge
index
1
0.228**
0.363***
Table 7.11 Correlation matrix among textile firms’ source of knowledge and strategic behavior indices (n = 91)
7.3 Indices of Strategic Behavior
131
0.235**
–
–
0.190*
0.329***
–
–
–
0.445***
1
Technical sources
of knowledge
index
0.947***
0.219**
0.365***
1
Internal index for
creating and
sustaining a
competitive
advantage
0.363***
–
1
External index
for creating and
sustaining a
competitive
advantage
1
–
Innovation
index
1
Index for
sensing and
seizing
opportunity
Key:
Market-based = source of knowledge index based on responses to the survey question about the importance of selected market-based sources of knowledge for
exploring new business opportunities
Technical = source of knowledge index based on responses to the survey question about the importance of selected technical sources of knowledge for exploring new business opportunities
Internal = strategic behavior index based on responses to the survey question about the impact of selected internal factors that contributed to creating and sustaining the competitive advantage of the company
External = strategic behavior index based on responses to the survey question about the impact of selected external factors that contributed to creating and
sustaining the competitive advantage of the company
Innovation = strategic behavior index based on responses to the survey question about the impact of selected internal and external factors that contributed to
creating and sustaining the competitive advantage of the company
Opportunity = strategic behavior index based on responses to the survey question about sensing and seizing opportunities with the firm
***Significant at 0.01-level, **significant at 0.05-level, *significant at 0.10-level
Market-based sources of knowledge
index
Technical sources of knowledge
index
Internal index for creating and
sustaining a competitive advantage
External index for creating and
sustaining a competitive advantage
Innovation index
Index for sensing and seizing
opportunity
Market-­based
sources of
knowledge
index
1
Table 7.12 Correlation matrix among apparel firms’ sources of knowledge and strategic behavior indices (n = 84)
132
7 The Strategic Behavior of KIE Textile and Apparel Firms
7.4 Summary of Our Findings and Segue to Chap. 8
133
tunities within the firm. This opportunity index is correlated with both the market-­
based and the technical indices of sources of knowledge but only among textile
firms as shown in Tables 7.11 and 7.12.
7.4 Summary of Our Findings and Segue to Chap. 8
From our vantage, the analyses in this chapter suggest that sources of knowledge are
related to strategic behaviors. If one adopts the linear model in Expression (7.2) and
if one cautiously infers a directional relationship from the correlations in Tables
7.11 and 7.12, then our analyses suggest that market-based sources of knowledge
affect more strategic elements of behavior among textile firms than among apparel
firms. That said, one might not be too far off base in inferring a directional relationship from the correlations in Tables 7.11 and 7.12 based on how the AEGIS survey
questions are phrased. Simply put, in our view, a firm is more likely to rely on
sources of knowledge for exploring new [our emphasis] business opportunities
before [our emphasis] it could begin to evaluate the contribution of factors that created or sustained its competitive advantage from those opportunities or before [our
emphasis] it sensed or seized on internal opportunities to perceive activities in the
market.
In Chap. 8, we discuss several measures of the entrepreneurial performance of
the KIE textile and apparel firms. Then, in Chap. 9, we correlate those measures
with the knowledge indices and strategy indices discussed herein.
Chapter 8
The Entrepreneurial Performance of KIE
Textile and Apparel Firms
However beautiful the strategy, you should occasionally look at
the results.
—Sir Winston Churchill
Is it not strange that desire should so many years outlive
performance?
—William Shakespeare
Abstract This chapter uses the AEGIS data to construct measures of entrepreneurial performance so that the second part of the Sources of Knowledge→Strategic
Behavior→Entrepreneurial Performance relationship can be examined empirically.
The performance measures are related to commercialization, sales growth, and
employment growth.
8.1 Introduction
In Chap. 6, we discussed sources of knowledge, in particular alternative sources of
knowledge among textile and apparel firms for exploring new business opportunities. And, we constructed two indices to quantify the importance of such sources.
Our indices considered market-based sources and technical sources of knowledge.
Then, in Chap. 7, we discussed several dimensions of the strategic behavior of textile and apparel firms. Those strategic behavior dimensions included the impact of
internal and external factors for creating and sustaining the competitive advantage
of the company, an innovation index, and an index for the sensing and seizing of
opportunities within the firm. Of course, our ability to construct knowledge source
indices and strategic behavior indices is limited by the breadth of the AEGIS
database.
Our purpose in this book is to explore relationships about textile and apparel KIE
firms. Our exploration is aimed at addressing three overriding research questions
that followed from our institutional and literature reviews in Chap. 2. These research
questions are:
© Springer International Publishing AG 2018
N.J. Hodges, A.N. Link, Knowledge-Intensive Entrepreneurship, International
Studies in Entrepreneurship 39, https://doi.org/10.1007/978-3-319-68777-3_8
135
136
8 The Entrepreneurial Performance of KIE Textile and Apparel Firms
• While there are many small firms that comprise the EU textile and apparel industries, how and to what extent are these firms entrepreneurial and/or innovative in
their behaviors?
• What might KIE and, in particular, entrepreneurial and innovative behaviors
mean for firm performance and/or industrial growth?
• What, if anything, do our empirical findings suggest for those small- and
medium-sized firms that comprise the US textile and apparel industries?
To address these questions, we chose to examine quantitatively competing relationships as represented by Expressions (8.1) and (8.2):
Sources of Knowledge → Entrepreneurial Performance Sources of Knowledge → Strategic Behavior → Entrepreneurial Performance (8.1)
(8.2)
The starting point to address these questions began with the construction of two
knowledge source indices; then we constructed eight strategic behavior indices,
four to represent textile firms and four to represent apparel firms. One of those indices represents strategic innovative behavior (see the first research question above).
We concluded Chap. 7 with the observation that there is quantitative evidence to
suggest that Sources of Knowledge→Strategic Behavior among textile and apparel
firms. Specifically, among textile firms, market-based knowledge sources are positively and statistically correlated with internal strategies for creating and sustaining
the competitive advantage of the company, innovative strategies by the firm, and
strategies for the sensing and seizing of opportunities within the firm. Market-based
sources of knowledge among apparel firms are also correlated with internal and
innovative strategies.
Technical sources of knowledge are correlated with all of the aforementioned
strategies among textile firms but only external strategies for creating and sustaining
the competitive advantage of apparel companies.
To complete our exploration of relationships about textile and apparel KIE firms
as represented by Expressions (8.1) and (8.2), we need to construct measures of
entrepreneurial performance, and that is the focus of Sect. 2.
8.2 Measures of Entrepreneurial Performance
The AEGIS survey questions from which we are able to construct measures of
entrepreneurial performance are:
Did this company introduce new or significantly improved goods or services during the past
3 years? Yes or No.
Please estimate the average increase/decrease in sales between 2009 and 2010.
Please estimate the average increase/decrease in employment between 2009 and 2010.
8.2 Measures of Entrepreneurial Performance
137
Table 8.1 Descriptive data on measures of entrepreneurial performance, by industry
Measure of entrepreneurial
performance
Commercialization
Percent sales increase/decrease
Percent employment increase/decrease
Textile industry (n = 91)
Mean
Range
0.593
0/1
9.36% −70 to 100%
2.81% −80 to 100%
Apparel industry
(n = 84)
Mean
Range
0.607
0/1
5.25% −90 to 100%
0.36% −70 to 70%
Fig. 8.1 Illustration of descriptive data on measures of entrepreneurial performance, by industry
Descriptive data on each of these three entrepreneurial performance measures
are in Table 8.1, and these data are illustrated in Fig. 8.1. In addition to mean values,
which we have shown in the tables in Chaps. 6 and 7 with reference to sources of
knowledge and strategic behavior, we show in Table 8.1 the range of sales increase/
decrease and the range of employment increase/decrease. We add this descriptive
element because of the economic state of the textile and apparel industries during
the time period that the AEGIS data were collected. (See, in particular, the graphical
depictions of the economic state of these industries in Chap. 3.) These graphical
depictions are based on mean values in Table 8.1, and they do not show the ranges
from that table.
From Table 8.1 and from Fig. 8.1, it appears that the mean probability of commercialization is similar between textile firms and apparel firms. About 59% of
textile firms commercialized new or significantly improved goods or services during the past 3 years compared to about 61% of apparel firms.
Recall from Chap. 3, as illustrated in Figs. 3.1 and 3.2, that for the EU industries
as a whole, production increased more than employment did between 2009 and
2010, and the increase was greater in the textile industry than in the apparel industry. In fact, the annual growth rates in both production and employment became
positive in 2010 for both industries, but not for employment in either industry.
Similar, but not identical, cross-industry patterns are evident in Table 8.1 and
Fig. 8.1. For firms in both industries, as represented from the AEGIS data, mean
sales growth is positive between 2009 and 2010, and the growth rate is greater for
138
8 The Entrepreneurial Performance of KIE Textile and Apparel Firms
textile firms (9.36%) than for apparel firms (5.25%). Also, for firms in both industries, mean sales growth is greater than employment growth. Unlike the EU growth
rates, the mean growth rate in employment among the AEGIS database firms is
positive over the 2009–2010 period, but still that rate is greater among textile firms
(2.81%) than among apparel firms (0.36%).
8.3 E
ntrepreneurial Performance and Firm and Founder
Characteristics
The first of the three overriding research questions that we asked, beginning in
Chap. 1, was:
• While there are many small firms that comprise the EU textile and apparel industries, how and to what extent are these firms entrepreneurial and/or innovative in
their behaviors?
In this section, we offer an answer to this question—although we have made
reference to this question in previous chapters—by examining the relationship
between selected firm and founder characteristics and entrepreneurial performance
of firms. We will revisit this research question and the other two research questions
in Chap. 11, but first things first. Specifically, our answer to this question is based
on the correlation between such characteristics and the three entrepreneurial performance variables presented above.
Our answer is that most firm and founder characteristics are not correlated with
our entrepreneurial performance measures. Rather than present a number of correlation matrices with a symbol in most of the cells, we have chosen to summarize our
statistically significant findings below:
• Older textile firms have experienced a slower growth in sales.
• Textile firms with a female founder have experienced a slower growth in employment, but apparel firms with a female founder have experienced a faster growth
in employment.
• Nascent textile firms have experienced a slower growth in employment.
• Apparel firms with more experienced founders have experienced less
commercialization.
Entrepreneurial performance is examined in more detail in Chap. 9.
Chapter 9
The Antecedents of Entrepreneurial
Performance in KIE Textile and Apparel
Firms
If a man will begin with certainties, he shall end in doubts; but
if he will be
content to begin with doubts, he shall end in certainties.
—Francis Bacon
The highest form of efficiency is the spontaneous cooperation of
a free people.
—Woodrow Wilson
Abstract Regarding the relationship Sources of Knowledge→Strategic
Behavior→Entrepreneurial Performance, the empirical evidence suggests that for
textile firms, technical sources of knowledge affect the strategic behavior of firms,
and that behavior in turn affects entrepreneurial behavior as measured by sales
growth. However, for apparel firms, technical sources of knowledge have a direct
rather than an indirect effect on sales growth.
9.1 Introduction
In many respects, this is the first capstone chapter of the book. Herein we describe
our quantitative findings related to the strength of the following competing
relationships:
Sources of Knowledge → Entrepreneurial Performance Sources of Knowledge → Strategic Behavior → Entrepreneurial Performance
(9.1)
(9.2)
The second capstone chapter is Chap. 11, the concluding chapter of our book.
There, we summarize our empirical evidence and emphasize our answers to each of
the three overriding research questions mentioned throughout the book and save our
initial answer to the first research question that we offered in Chap. 8:
© Springer International Publishing AG 2018
N.J. Hodges, A.N. Link, Knowledge-Intensive Entrepreneurship, International
Studies in Entrepreneurship 39, https://doi.org/10.1007/978-3-319-68777-3_9
139
140
9 The Antecedents of Entrepreneurial Performance in KIE Textile and Apparel Firms
• While there are many small firms that comprise the EU textile and apparel industries, how and to what extent are these firms entrepreneurial and/or innovative in
their behaviors?
• What might KIE, and, in particular, entrepreneurial and innovative behaviors,
mean for firm performance and/or industrial growth?
• What, if anything, do our empirical findings suggest for those small- and
medium-sized firms that comprise the US textile and apparel industries?
Our answers depend on our interpretation of expressions (9.1) and (9.2), that is,
on how we interpret the AEGIS data to describe the antecedents of entrepreneurial
performance in KIE textile and apparel firms.
9.2 Interpreting the Empirical Findings
In Tables 9.1 and 9.2, we present the correlation coefficient between and among our
three categories of indices: sources of knowledge indices, strategic behavior indices, and entrepreneurial performance indices. In both of the tables, we are looking
for a correlation pattern that will allow us to make a judgment, albeit a tentative one
based on our exploratory analyses, about the explanatory power of expressions (9.1)
and (9.2).
We concluded Chap. 8 with the observation that there is empirical evidence that
Sources of Knowledge→Strategic Behavior among both textile firms and apparel
firms, but the relationship appears to be more pronounced among textile firms. Of
particular note, our discussion of the role of innovation in the apparent renaissance
of the textile and apparel industries in the European Union and the value chain relationship between the textile industry and the apparel industry is that market-based
knowledge sources are correlated with our innovation index among both textile and
apparel firms, but only among textile firms are the technical sources of knowledge
correlated with our innovation index.
The empirical evidence in both Tables 9.1 and 9.2 is weak regarding the Sources
of Knowledge→Entrepreneurial Performance relationship from expression (9.1).
The importance of technical sources of knowledge are positively and significantly
correlated with commercialization activity among textile firms and with sales
growth among apparel firms, but market-based sources of knowledge are not correlated with any of the entrepreneurial performance measures.
It appears that sources of knowledge affect entrepreneurial performance indirectly by working through its influence on strategic behavior. See Fig. 9.1. The
empirical evidence among textile firms to support the Strategic
Behavior→Entrepreneurial Performance relationship is noticeably stronger than
among apparel firms. Among apparel firms, there is correlational evidence that
firms that pursue an internal strategy for creating and sustaining a competitive
advantage tend to commercialize more than those that did not, and those that pursue
a strategy for sensing and seizing opportunities within also tend to have realized
greater sales growth than those that did not.
1
0.516***
0.505***
0.484***
0.197**
0.192*
–
–
0.228**
0.363***
–
0.235**
0.272***
–
–
–
1
Technical
sources of
knowledge
index
0.390***
0.169*
0.911***
0.395***
0.416***
–
1
–
–
0.256**
0.408***
–
1
Strategic behavior
Internal
External
index for
index for
creating and
creating and
sustaining a
sustaining a
competitive
competitive
advantage
advantage
***Significant at 0.01-level, **significant at 0.05-level, *significant at 0.10-level
Market-based sources
of knowledge index
Technical sources of
knowledge index
Internal index for
creating and
sustaining a
competitive advantage
External index for
creating and
sustaining a
competitive advantage
Innovation index
Index for sensing and
seizing opportunity
Commercialization
Percent sales increase/
decrease
Percent employment
increase/decrease
Market-­
based
sources of
knowledge
index
Sources of knowledge
–
0.387***
–
1
0.350***
Innovation
index
–
–
0.213**
1
Index for
sensing and
seizing
opportunity
–
1
0.183*
Commercialization
1
0.420***
Percent sales
increase/
decrease
Entrepreneurial performance
Table 9.1 Correlation matrix between sources of knowledge, strategic behavior, and entrepreneurial performance for textile firms (n = 91)
1
Percent
employment
increase/decrease
–
0.226**
–
–
–
–
–
–
0.235**
–
–
0.445***
1
–
0.190*
0.329***
1
Technical
sources of
knowledge
index
–
0.214*
–
0.947***
0.219**
0.365***
1
–
–
–
0.363***
–
1
Strategic behavior
Internal
External
index for
index for
creating and
creating and
sustaining a
sustaining a
competitive
competitive
advantage
advantage
***Significant at 0.01-level, **significant at 0.05-level, *significant at 0.10-level
Market-based sources
of knowledge index
Technical sources of
knowledge index
Internal index for
creating and sustaining
a competitive
advantage
External index for
creating and sustaining
a competitive
advantage
Innovation index
Index for sensing and
seizing opportunity
Commercialization
Percent sales increase/
decrease
Percent employment
increase/decrease
Market-­
based
sources of
knowledge
index
Sources of knowledge
–
–
–
1
–
Innovation
index
–
0.258**
0.231**
1
Index for
sensing and
seizing
opportunity
–
1
0.204*
Commercialization
0.500***
1
Percent
sales
increase/
decrease
Entrepreneurial performance
Table 9.2 Correlation matrix between sources of knowledge, strategic behavior, and entrepreneurial performance for apparel firms (n = 84)
1
Percent
employment
increase/
decrease
142
9 The Antecedents of Entrepreneurial Performance in KIE Textile and Apparel Firms
9.2 Interpreting the Empirical Findings
143
Regarding textile firms, the commercialization of a new or significantly improved
good or service is enhanced through both an internal strategy for creating and sustaining a competitive advantage and from innovation. Sales growth is enhanced
through both an internal and an external strategy for creating and sustaining a competitive advantage and through a strategy based on sensing and seizing opportunities within. Stated differently, textile firms that adopt strategies that create and
sustain a competitive advantage and that sense and seize opportunities internally
have been the firms that have increased sales between 2009 and 2010.
Several relationships might be inferred from the pattern of correlation coefficients in Tables 9.1 and 9.2. Among textile firms:
Market based sources of knowledge →
Internal strategies for creating and sustaining a competitive advantage → (9.3)
Increased likelihood of commercialization and greater growth in sales Another relationship among textile firms is
Technical sources of knowledge →
Innovative strategies →
Increased likelihood of commercialization (9.4)
And yet a third relationship among textile firms is
Technical sources of knowledge →
Sensing and seizing opportunity →
Increased growth in sales
(9.5)
A relationship among apparel firms is
Market based sources of knowledge →
Internal strategies for creating and sustaining a competitive advantage → (9.6)
Increased likelihood of commercialization
To anticipate our discussion in Chap. 10 about prescriptions for the growth of US
textile and apparel firms, we focus here on the entrepreneurial performance measure
of sales growth. Working backward, we ask what strategic behavior is associated
with sales growth. Among textile firms, the answer is internal and external strategies
for creating and sustaining a competitive advantage and sensing and seizing opportunity with the firm. For apparel firms, the answer is efforts for sensing and seizing
opportunity within the firm.
Again, working backward, for textile firms, we ask what sources of knowledge
are associated with both the internal and external strategies for creating a competitive advantage and with sensing and seizing opportunity within the firm. The answer
144
9 The Antecedents of Entrepreneurial Performance in KIE Textile and Apparel Firms
Sources of Knowledge Strategic Behavior Entrepreneurial Performance
Fig. 9.1 Representation of the indirect paths from sources of knowledge to entrepreneurial
performance
is technical sources of knowledge. For apparel firms, we ask what sources of knowledge are associated with efforts for the sensing and seizing of opportunities within
the firm, and the answer is none, but unlike with textile firms, technical sources of
knowledge have a direct link to sales growth.
Thus, to anticipate our discussion in Chap. 10, our prescriptions for the growth
of US textile and apparel firms will focus on the public sector’s provision of technical sources of knowledge.
Chapter 10
Prescriptions for Growth for US Textile
and Apparel Firms
Don’t ever take a fence down until you know why it was put up.
—Robert Frost
The difference between style and fashion is quality.
—Giorgio Armani
Abstract Based on the empirical findings from Chap. 9 and an overview of the
institutional history of the US textile and apparel industries, policy prescriptions for
the growth of the US industry are suggested. Namely, we suggest the formation of
a textile extension program (TEP) and/or an apparel extension program (AEP)
might be able to inform firm principals which universities or research institutes have
greater expertise to solve specific manufacturing or production issues. TEP and/or
AEP hubs might also point firm principals to regional or national research programs
that are aligned well with their manufacturing or production needs.
10.1 Introduction
In Chap. 9 we showed that various sources of knowledge are correlated with economic growth metrics associated with firms in the European textile and apparel
industries. In this chapter, we reflect on trends in the US textile and apparel industries, and those trends are similar to the trends that we described in Chap. 3 for the
European textile and apparel industries (see Fig. 3.2 on employment growth in particular). We use our findings from Chap. 9 to suggest prescriptions for the growth of
the US textile and apparel industries.
In the following sections, we describe recent employment and establishment
dynamics in the US textile and apparel industries, and we offer a brief discussion
about the antecedents to these dynamics. We then describe some emerging trends
that have the potential to shape what these industries may look like in the future.
Then, we discuss our findings from the AEGIS database to offer prescriptions for
growth. We draw on the US experience with its Manufacturing Extension Program
© Springer International Publishing AG 2018
N.J. Hodges, A.N. Link, Knowledge-Intensive Entrepreneurship, International
Studies in Entrepreneurship 39, https://doi.org/10.1007/978-3-319-68777-3_10
145
146
10 Prescriptions for Growth for US Textile and Apparel Firms
(MEP) to suggest institutional changes that might be fostered by the US textile and
apparel industries to reverse recent downward trends.
10.2 I llustration of Employment Trends in the US Textile
and Apparel Industries
Data are available for US textile mills, for textile product mills, and for the apparel
industries. These three industrial segments are formally defined in the following
way. Note, however, that in our discussion in previous chapters about the EU textile
industry, it is an amalgam of textile mills and textile product mills (see Table 3.1).
Firms in the textile mills industry subsector group are involved in the transformation of a basic fiber (natural or synthetic) into a product, such as yarn or fabric, that
is further manufactured into usable items, such as apparel, sheets, towels, and textile
bags for individual or industrial consumption. Further manufacturing may be performed in the same firms and classified in this subsector, or it may be performed at
a separate establishment and be classified elsewhere in manufacturing. The main
processes in this subsector include preparation and spinning of fiber, knitting or
weaving of fabric, and the finishing of the textile. The NAICS structure follows and
captures this process flow. Major industries in this flow, such as preparation of
fibers, weaving of fabric, knitting of fabric, and fiber and fabric finishing, are
uniquely identified. Texturizing, throwing, twisting, and winding of yarn contain
aspects of both fiber preparation and fiber finishing and are classified with preparation of fibers rather than with finishing of fibers. See Table 10.1.
Table 10.1 Taxonomy of the
US textile mills industry
313 Textile mills
3131 Fiber, yarn, and thread mills
31311 Fiber, yarn, and thread mills
3132 Fabric mills
31321 Broadwoven fabric mills
31322Narrow fabric mills and
Schiffli machine embroidery
31323 Nonwoven fabric mills
31324 Knit fabric mills
3133 Textile and fabric finishing and
fabric coating mills
31331 Textile and fabric finishing
mills
31332 Fabric coating mills
Source: “North American Industry
Classification System” < https://www.
census.gov/eos/www/naics/>
10.2 Illustration of Employment Trends in the US Textile and Apparel Industries
Table 10.2 Taxonomy of the
US textile product mills
industry
147
314 Textile product mills
3141 Textile furnishings mills
31411 Carpet and rug mills
331412 Curtain and linen mills
3149 Other textile product mills
31491 Textile bag and canvas mills
31499 All other textile product mills
314994 Rope, cordage, twine, tire
cord, and tire fabric mills
314999 All other miscellaneous
textile product mills
Source: “North American Industry
Classification System” < https://www.
census.gov/eos/www/naics/>
Firms in the textile product mills industry subsector group make textile products
(except apparel). With a few exceptions, processes used by these establishments are
generally cut and sew (i.e., purchasing fabric and cutting and sewing to make non-­
apparel textile products, such as sheets and towels). See Table 10.2.
Firms in the apparel industry subsector group operate with two distinct manufacturing processes: (1) cut and sew (i.e., purchasing fabric and cutting and sewing to
make a garment) and (2) the manufacture of garments in establishments that first
knit fabric and then cut and sew the fabric into a garment. The apparel subsector
firms include a diverse range of manufacturing from full lines of ready-to-wear
apparel as well as custom apparel. Custom apparel is manufactured by apparel contractors, performing cutting or sewing operations on materials owned by others;
jobbers, performing entrepreneurial functions involved in apparel manufacturing;
and tailors, manufacturing custom garments for individual clients. Knitting fabric,
when done alone, is classified in the textile mills industry, but when knitting is combined with the production of complete garments, the activity is classified in the
apparel industry. See Table 10.3.
Figures 10.1, 10.2, and 10.3 show the annual growth rates of employment in
these three industries. The pattern of overall decline is similar to that of EU textile
and apparel firms as we discussed in Chap. 3 (see Fig. 3.2).
The decline in employment1 in these industries has been linked to gains in productivity. In fact, productivity increased by approximately 4% per year on average
for the 20-year period between 1980 and 2000 (Gelb 2001). Fewer workers were
needed to produce the same amount of goods. But the decline is not attributed to an
increase in productivity alone. The decline in domestic employment has also been
linked to an increase in imported textiles and apparel. As we point out later in this
chapter, US imports of textiles and apparel substantially increased during 1980–
2000. For example, the amount of apparel imported in 2000 alone was ten times the
Declines in employment that are not related to production decreases.
1
148
10 Prescriptions for Growth for US Textile and Apparel Firms
Table 10.3 Taxonomy of the
US apparel industry
315 Apparel industry
3151Apparel knitting mills
31511 Hosiery and sock mills
31519 Other apparel knitting mills
3152 Cut and sew apparel
manufacturing
31521 Cut and sew apparel
contractors
31522 Men’s and boys’ cut and sew
apparel manufacturing
31524 Women’s, girls’, and infants’
cut and sew apparel manufacturing
31528 Other cut and sew apparel
manufacturing
3159 Apparel accessories and other
apparel manufacturing
31599 Apparel accessories and
other apparel manufacturing
Source: “North American Industry
Classification System” < https://www.
census.gov/eos/www/naics/>
5
0
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
-5
-10
-15
-20
-25
Source:
Bureau of Labor Statistics (2016)
Fig. 10.1 Annual growth rate in US employment in the textile mills industry, 2006–2015 (Source:
Bureau of Labor Statistics 2016)
amount imported in 1980 (Gelb 2001). In 2008, approximately 97% of apparel
products sold in the US market were imported (AAFA 2009, August).
Employment in the textile and apparel industries in the US is expected to continue to decrease into the foreseeable future (Bureau of Labor Statistics 2008).
10.2 Illustration of Employment Trends in the US Textile and Apparel Industries
149
10
5
0
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
-5
-10
-15
-20
Source:
Bureau of Labor Statistics (2016)
Fig. 10.2 Annual growth rate in US employment in the textile product mills industry, 2006–2015
(Source: Bureau of Labor Statistics 2016)
15
10
5
0
-5
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
-10
-15
-20
-25
-30
Source:
Bureau of Labor Statistics (2016)
Fig. 10.3 Annual growth rate in US employment in the apparel industry, 2006–2015 (Source:
Bureau of Labor Statistics 2016)
Nevertheless, these industries have continued to provide substantial employment,
with 497,100 wage and salary workers in 2008 contributing $34 billion to the US
GDP (National Council of Textile Organizations [NCTO] n.d.-a, -b). Although most
apparel and textile establishments are small, and employment is concentrated in
mills employing 50 or more workers, the US apparel manufacturing industry represents about 8000 firms, including major global corporations such as Levi Strauss,
Phillips-Van Heusen, VF Corporation, and Warnaco (Research and Markets 2010).
With so many establishments, one could probably find industry-related employment
150
10 Prescriptions for Growth for US Textile and Apparel Firms
Table 10.4 The largest occupations in the US textile and apparel industries from May 2015
Occupation
Sewing machine operators
Textile winding, twisting, and drawing out machine setters, operators, and
tenders
Textile knitting and weaving machine setters, operators, and tenders
First-line supervisors of production and operating workers
Inspectors, testers, sorters, samplers, and weighers
Textile bleaching and dyeing machine operators and tenders
Textile cutting machine setters, operators, and tenders
Helpers-production workers
Shipping, receiving, and traffic clerks
General and operations managers
Laborers and freight, stock, and material movers, hand
Packers and packagers, hand
Sales representatives, wholesale, and manufacturing, except technical and
scientific products
Textile, apparel, and furnishings workers, all other
Total employed
87,390
24,680
21,860
14,100
11,940
11,250
9,860
8,770
7,900
7,770
7,220
6,840
6,810
6,520
Source: Bureau of Labor Statistics (2016)
in all 50 states. However, as recently as 2008, roughly four out of every ten jobs in
the textile and apparel industries were located in California, North Carolina, and
Georgia (Bureau of Labor Statistics 2008).
According to the NCTO (n.d.-a, -b), about 579,000 Americans were employed in
the textile and apparel industries in 2015. The US textile and apparel industries are
comprised of many different job types. While a high school diploma or GED is sufficient for most entry-level production occupations, administrative and professional
workers often require more technologically advanced education and training to
operate sophisticated machinery. As in most manufacturing industries, the process
of creating finished products is classified into a variety of steps, leading to the generation of a range of jobs. In addition to production level jobs, fashion designers
create original garments based on fashion trends. Table 10.4 includes employment
of wage and salary workers in the largest occupations of those employed in textiles
and apparel (Bureau of Labor Statistics 2016).
At the same time that some may question the quality and stability of the jobs
available in these industries, there are also positive employment growth trends
occurring. For example, on average, hourly wages appear to be increasing in some
areas of manufacturing, and in 2008 the average wage in apparel manufacturing was
as high as $17.41 (Textiles Intelligence Limited 2010). In 2009, textile workers on
average earned 143% more than clothing store workers ($517 a week vs. $213)
(NCTO n.d.-a, -b). Wages in other sectors have also increased, albeit more modestly
(AAFA 2006). The majority of employees in the industry work in production,
roughly two out of every three as of 2008 (Bureau of Labor Statistics 2008). By a
rather large margin, the largest occupational grouping in the US apparel industry is
sewing machine operators. However, while production workers still make up the
10.3 Recent Dynamics in the US Textile and Apparel Industries
151
Table 10.5 Establishments in textile, textile product, and apparel manufacturing (in thousands),
2014
Industry
Total
Textile mills
Textile product mills
Apparel manufacturing
Number of establishments
323.6
104.1
113.1
106.4
Establishments %
100
32.2
34.9
32.8
Source: United States Census Bureau: Business Patterns (2014)
majority of those employed in the industry, the percentage of these workers as compared to total workers has decreased (AAFA 2006). This trend is likely a result of a
loss of production jobs in general and not just specific to the textile and apparel
industries.
Finally, the textile and apparel industries provide an economic stimulus for other
sectors of the economy (US Department of Commerce n.d.) which, in turn, support
employment growth across other industries. A single textile job supports three additional jobs inside and outside of the textile industry. For example, textile products
become major components of products from surgery to aircraft bodies. As a result,
beyond the textile sector, textiles support employment in the chemical, energy, shipping, rail, banking, water, and energy production sectors (NCTO n.d.-a, -b; US
Department of Commerce n.d.).
In Sect. 10.3, we provide a review of recent dynamics, including data on establishments, imports, and exports, to further explain the overall pattern of decline in
growth of these US industries.
10.3 R
ecent Dynamics in the US Textile and Apparel
Industries
According to the American Apparel and Footwear Association (AAFA), in 2013
apparel and footwear contributed a record of $361 billion to the US economy. Based
on the AAFA data, the average amount spent on apparel and footwear during this
period was $1,141 per person. Table 10.5 shows the number of establishments in
textile, textile product, and apparel manufacturing by detailed industry sector in
2014 (United States Census Bureau 2014).
In 2015, American shipments for textile and apparel products totaled $76 billion,
which represented a 14% increase from 2009 (NCTO n.d.-a, -b). However, as
depicted in Fig. 10.4, the US exports significantly more fibers than it imports. For
example, in 2011, the country exported seven times more fibers ($10.8 billion) than
it imported ($1.5 billion). Most of these exports were cotton-related fibers, specifically cotton that was not carded or combed (United Nations Statistical Office 2015).
Eventually, the export of cotton fibers slowly declined, resulting in a total export
value of $6.2 billion in 2015. Compared to exports, the number of US imports
Billion Dollars
152
10 Prescriptions for Growth for US Textile and Apparel Firms
11
10
9
8
7
6
5
4
3
2
1
0
2006
2007
2008
Source:
United Nations Statistical Office
2009
2010
Year
Imports
2011
2012
2013
2014
2015
Exports
Fig. 10.4 Textile fibers (HS codes 50–53, 55, and 63) import and export, 2006–2015 (Source:
United Nations Statistical Office)
70
Billion Dollars
60
50
40
30
20
10
0
1997
Source :
United States Bureau of Census
2002
Year
2007
2012
NAICS 313 Value of Shipments ($1,000)
Fig. 10.5 Textile (NAICS 313) domestic shipment (Production), 1997–2012 (Source: United
States Bureau of Census)
e­ xperienced minimal change. During the period of 2006–2015, the import value
fluctuated between $1 and $1.5 billion.
With respect to textile production, the value of US domestic shipments (textile
production) is displayed in Fig. 10.5, followed by the value of US textile imports
and exports shown in Fig. 10.6. In 2006, the value of textiles produced in the country ($37 billion) exceeded the value of imports ($21 billion) and exports ($11 billion). However, this gap started to close shortly after 2006 with the value of textile
production declining to $36 billion in 2007 and $30 billion in 2012 (see Fig. 10.5).
During this same period, imports saw moderate fluctuations, ranging from $17 billion in 2009 to $26 billion in 2015 (see Fig. 10.6). Exports, on the other hand, saw
10.3 Recent Dynamics in the US Textile and Apparel Industries
153
30
25
Billion Dollars
20
15
10
5
0
2006
2007
2008
Source:
United Nations Statistical Office
2009
2010
Year
2011
Imports
2012
2013
2014
2015
Exports
Fig. 10.6 Textile yarn, fabrics, made-up articles, NES, and retail products (HS codes 50–60, 63,
and 65) import and export, 2006–2015 (Source: United Nations Statistical Office)
80
70
Billion Dollars
60
50
40
30
20
10
0
1997
Source:
United States Bureau of the Census
2002
Year
2007
2012
NAICS 315 Value of Shipments ($1,000)
Fig. 10.7 Apparel (NAICS 315) domestic shipment (production), 1997–2012 (Source: United
States Bureau of the Census)
minor increases, with values ranging from $9 billion in 2009 to $11 billion in 2015
(see Fig. 10.6). The most frequently exported product was nonwovens.
Represented in Fig. 10.7 is the value of US domestic shipments as it relates to
apparel production. Within a 15-year period, the value of apparel production
dropped drastically from $69 billion to $12 billion. The largest drop occurred
between 1997 and 2002 when production decreased $27 billion. This drop was followed by a moderate decrease from $42 billion in 2002 to $21 billion in 2007,
amounting to a loss of $21 billion. From 2007 to 2012, domestic shipments experienced another $10 billion drop. These losses are illustrated in Fig. 10.8, which
shows the disparity between imports and exports of clothing and accessories in the
United States.
As with textile production, exports related to apparel production saw minimal
growth over a 10-year period. From 2006 to 2015, the total value of exports
154
10 Prescriptions for Growth for US Textile and Apparel Firms
90
80
Billion Dollars
70
60
50
40
30
20
10
0
2006
2007
2008
Source:
United Nations Statistical Office
2009
2010
Imports
Year
2011
2012
2013
2014
2015
Exports
Fig. 10.8 Articles of apparel and clothing accessories (HS codes 61, 62, and 65) import and
export, 2006–2015 (Source: United Nations Statistical Office)
­ uctuated between $4 billion and $5 billion. Of these shipments, the majority were
fl
apparel and clothing accessories, both crochet/knitted and not crocheted/knitted. In
fact, the former comprised almost half of the total value of exports. Conversely,
imports saw exponential growth during this period. Starting at $78.6 billion in 2006,
imports ebbed and flowed until they reached $91.5 billion in 2015. Due to this
growth in imports, domestic production of apparel was negatively affected.
10.3.1 Exports
In 2008, three of the top ten exports were cotton fibers and textile products, including cotton (HS5201, not carded or combed),2 cotton yarn (HS5205, not sewing
thread), and woven cotton fabrics (HS5209). Cotton, as a fiber, comprised the
majority of exports with a value of $4.8 billion. Other cotton products had less of an
impact, with cotton yarn producing a value of $0.68 billion and woven cotton fabrics producing a value of $0.71 billion (United Nations Statistical Office 2015).
The remaining products included nonwovens (HS5603) with a value of $1.5 billion, other knitted or crocheted products (HS5603) with a value of $0.92 billion,
carpets and other textile floor coverings (HS5703) with a value of $0.89 billion,
artificial filament tow (HS5502) with a value of $0.86 billion, and synthetic filament
yarn (HS5402) with a value of $0.81 billion. In 2015, two of the top ten exports
were cotton fibers and textile products: cotton, not carded or combed (HS5201) with
a value of $3.9 billion and cotton yarn (HS5205) with a value of $1.1 billion. Other
products that impacted the growth of exports were nonwovens with a value of $1.8
2
HS codes have slowly replaced SITC codes; therefore, we use HS codes to refer to each type of
product classification.
10.4 Past and Present: Historical Foundations and Emerging Trends
155
billion, textile fabrics with a value of $1 billion, and artificial filament tow with a
value of $0.91 billion (United Nations Statistical Office 2015).
10.3.2 Imports
In 2008, six of the top ten imports were apparel and clothing accessories. Among
these apparel and clothing accessories included jerseys, pullovers, cardigans, knitted or crocheted (HS6110) with a value of $14.3 billion; women’s suits, jackets,
dresses, skirts, etc., not knitted or crocheted (HS6204), with a value of $12.4 billion;
men’s suits, jackets, trousers, etc. and shorts, not knitted or crocheted (HS6203),
with a value of $8.3 billion; t-shirts, singlets, and other vests, knitted or crocheted
(HS6109), with a value of $4.7 billion; women’s suits, dresses, skirts, etc. and
shorts, knitted or crocheted (HS6104), with a value of $3.3 billion; and men’s shirts,
not knitted or crocheted (HS6205) with a value of $3.2 billion (United Nations
Statistical Office 2015).
The remaining products included footwear, upper of leather (HS6403), with a
value of $11.9 billion; footwear, outer soles, and uppers of rubber and plastics
(HS6402), with a value of $5.3 billion; bed, table, toilet, and kitchen linens (HS6302)
with a value of $4.6 billion; and made-up articles (HS6307) with a value of $2.8
billion. In 2015, almost all of the same products comprised the top 10, albeit in a
different order. Women’s suits (HS6204) fell from #2 in 2008 to #3 in 2015 with a
value of $10.5 billion. Likewise, t-shirts (HS6109) fell from #6 in 2008 to #7 in
2015 with a value of $6 billion. One of the biggest changes occurred with men’s
shirts, which appeared on the list at #9 in 2008 but did not make the list in 2015
(United Nations Statistical Office 2015).
In the next section, we offer a broader context for these numbers. To provide this
context, we discuss how the US textile and apparel industries have changed over
time and consider trends that are currently emerging that are likely to shape these
industries in the future.
10.4 P
ast and Present: Historical Foundations and Emerging
Trends
The textile and apparel industries have a long history within the United States. This
history encompasses manufacturing as well as product development and retail and,
much like we discussed in Chap. 2 with respect to these industries within the
European Union, includes a shift in orientation from domestic to global.
For decades, the textile and apparel industries represented a major manufacturing base, supporting thousands of jobs and helping to sustain local and regional
economic vitality, particularly in the Southeastern region of the United States
156
10 Prescriptions for Growth for US Textile and Apparel Firms
(Gaventa and Smith 1991). Today, these industries represent a small fraction of the
overall economy in this region. This decline is partly due to difficult economic times
but is also a result of the overall shift to manufacturing products in countries other
than the United States (Carlton and Coclanis 2005). Much like the European Union,
the end of the MFA was a blow to domestic production, particularly for the textile
industry. However, unlike the European Union, many suggest that the United States
was dealt its first truly devastating blow by the North American Free Trade
Agreement (NAFTA), ratified on January 1, 1994. The textile and apparel industries
continue to struggle with whether trade agreements offer more harm than benefit as
the debate continues.
In this section, the historic foundations of the industry are briefly explained. We
conclude with discussion of emerging trends that could potentially shape the industries of the future, including the concept of reshoring and the role of small business
and entrepreneurial innovation.
10.4.1 Historical Foundations
With the invention of the cotton gin and power loom during the late eighteenth century, the textile sector quickly became the largest employer in the United States, as
mills began to emerge first across the Northeast (Delfino and Gillespie 2005).
Entrepreneurs and established business owners quickly turned their attention to the
South, as this region was considered particularly well suited not just for growing
cotton but for building mills to manufacture cotton fabric due to the availability of
water as well as a large population of potential mill workers (Glass 1992). In order
to attract rural workers, mill owners began building employee housing in close
proximity to the mills, forming neighborhoods or “villages,” with a railroad system
linking mills located throughout states in the South, primarily North and South
Carolina, Alabama, Mississippi, and Georgia (Simpson 1948).
Industry growth in the South was most evident during the period of 1885 to 1915,
when, in North Carolina alone the number of textile product mills increased from 60
to 318, and the number of workers reached 51,000 (Glass 1992). This growth continued well into the twentieth century. For example, by 1951, over 1,000 mills
employed approximately 250,000 people, and by 1960, nearly 50% of all manufacturing jobs in North Carolina were in the textile industry (Glass 1992). Apparel
production also increased throughout the South and particularly in North Carolina,
Tennessee, Georgia, and South Carolina, states which accounted for almost 15% of
all goods manufactured in the United States during this period (Zingraff 1991).
The situation began to shift during the 1970s and 1980s with the increasing threat
from imports, as textile imports tripled from 1974 to 1984 and apparel imports grew
to 43% (Cooper 1973; Glass 1992). Despite attempts at trade legislation and
increased capital investment in equipment, imports were being offered at prices that
domestic manufacturing could not compete with (Gaventa and Smith 1991).
Hundreds of mills in the Southeast closed between 1975 and 1985, thereby b­ eginning
10.4 Past and Present: Historical Foundations and Emerging Trends
157
the steady rise in corporate mergers, bankruptcies, and layoffs that continued
through the end of the twentieth century (Zingraff 1991). These losses changed the
face of the industry itself, as one time textile product corporate giants were restructured and in some cases even merged with former competitors (Hodges and Karpova
2008).
By the early 1980s, of the “10 M’s” (Woodruff and McDonald 1982) that were
most important to the textile and apparel industries—manpower, material, machinery, money, mill engineering, mill management, manipulation, maintenance, marketing, and merchandising—only the last three plus money comprised the US
industries (Hodges and Frank 2013). By this time, most of the process of production, including supplies, labor, and supervision, had moved abroad. By 1994, with
the advent of NAFTA, what little remained of apparel production in the United
States appeared to be gone. However, some posit that loss of textile sector jobs
began before NAFTA, as early as the 1970s, and that half of the jobs lost were the
result of new technology used to improve productivity (Zingraff 1991). Much like
in Europe, for the US industry, the end of the MFA in 2004 signaled the completion
of the shift, and globalization became the core manufacturing strategy.
At the end of the twentieth century and into the twenty-first century, overall textile complex employment patterns continued to follow a downward trend, going
from 220,000 jobs in 1997 to 116,300 in 2003, for a total of 103,700 job losses, and
constituting a 47% loss in total workforce (Bureau of Labor Statistics 2003). By
2005, job losses in the textile and apparel industry were estimated to be about
900,000 (U. S. Department of Agriculture 2006). The resulting reality was quite
different from the industry’s domestic “golden age” of the late nineteenth and early
twentieth centuries (Hodges and Karpova 2006; Suggs 2002).
10.4.2 Emerging Trends
Three interrelated trends that have emerged in the past decade are relevant to our
discussion of the textile and apparel industries in the United States: reshoring, innovation, and entrepreneurship. As we will point out in this section, the three trends
appear to be interconnected, primarily through the widespread and rapidly growing
e-commerce opportunities that are available on both the supply and demand sides.
Recently, there have been signs indicating a possible return to manufacturing
textile and apparel in the United States. Referred to as “reshoring,” the scale remains
quite small, however, and much of it focuses on one aspect of the production process being completed domestically. Small, targeted textile and apparel businesses
are also emerging via the Internet, a channel that affords these firms a greater reach,
while at the same time, offers the convenience and quick turnaround that is increasingly being sought by US consumers. Indeed, it appears that the number of American
consumers who are interested in domestically made apparel products is increasing
(Mittica et al. 2012; Nash-Hoff 2014), as is the demand for “Made-in-USA” apparel
in the global market (Qubein 2013). As a result, fashion brands such as Abercrombie
158
10 Prescriptions for Growth for US Textile and Apparel Firms
and Fitch Co. and Levi Strauss and Co. have been attempting to create a “Made-in-­
USA” story by producing parts of their products (e.g., denim fabric) in the United
States (Rowan 2014).
An increasing demand for American-made apparel would undoubtedly have a
positive impact on manufacturing in the United States (Nash-Hoff 2014). For firms,
the advantages of reshoring include shorter delivery times, more control over suppliers, quicker response to fashion changes, and greater simplicity of reorders
(Mittica et al. 2012; Rowan 2014). Perhaps the most important benefit of domestic
production for both the firm and the consumer is the potential that it offers for maintaining a higher level of product quality (Nash-Hoff 2014; Uluskan et al. 2016).
At the core of the benefits offered by reshoring are developments in technology
and investments in the industry infrastructure. Advanced technology enables textile
and apparel manufacturing firms to enhance productivity and ultimately to reduce
production costs. According to the NCTO (n.d.-a, -b), the textile industry is one of
the top industries projected for productivity growth, as indicated by investment in
new facilities and equipment and a particularly notable increase in investment in
textile and textile product mills: from $960 million in 2009 to $1.8 billion in 2014,
reflecting an overall increase of 87% (NCTO n.d.-a, -b).
This investment is positively associated with the development of innovative
products as well as the improvement of production processes (NCTO n.d.-a, -b). Per
Rowan (2014), “niche, high-margin products” can provide the country’s industries
with a competitive edge that will help to reestablish domestic manufacturing (p. 1).
Similarly, Schmidt (2016) indicates that the concept of reshoring is more appropriate for high-end and high-quality products such as luxury goods. Therefore, much
like what we discussed relative to the Italian industry in Chap. 2, innovative products could become the foundation for a competitive advantage and a return to more
domestic manufacturing in the United States (“SelectUSA Summer Forum” 2014).
In a similar vein, to compete with low-cost imports, the US textile and apparel
industries have had to seek change and pursue a course of innovation by developing
new products through advanced technologies (Borneman 2007). For example, high-­
tech fiber technologies have significantly influenced the growth of sales in the US
home textile (Corral 2005) and innerwear industries (Monget 2014). New and innovative fibers and fabrics help to meet the growing consumer demand for differentiated and high-performance products versus their lower-quality, mass produced
imported counterparts (Dockery 2005; McCurry 2008).
Alongside product differentiation, innovation enables greater efficiency in manufacturing processes and can have a positive impact on the design process through
advanced technologies. That is, designers can create novel products using new technologies, which, in turn, encourages greater innovation. Ultimately, unique, high-­
value products are difficult to produce via the use of suppliers in developing
countries.
The most notable motivating force behind the return to domestic production
appears to be entrepreneurship and small business. Small, entrepreneurial firms
have become increasingly more significant to the industries in the United States.
According to the US Small Business Association (SBA 2015), small firms account
10.5 A Proposal to Strengthen the US Textile and Apparel Industries
159
for 98.5% of all US manufacturing firms in the textile and apparel industries (NAICS
313, 314, 315, and 316). Additionally, 64% of the total number of employees in
textile and apparel manufacturing are employed by small businesses (SBA 2015).
Small firms face some rather large challenges, however. For example, the apparel
industry outsources most labor-intensive tasks (e.g., cutting, sewing) to achieve
lower production costs and higher profit margins. Small firms find it difficult to
source abroad as manufacturers tend to deal in very large quantities, thereby making
the cost savings worthwhile for large firms but not feasible for small firms (Doeringer
and Crean 2006). Conversely, the flexibility of US small textile and apparel manufacturers can offer a competitive advantage, particularly when it comes to fast fashion retailers looking to produce small quantities of apparel products quickly
(Doeringer and Crean 2006).
Innovation has the potential to significantly influence competitive advantage
among small, entrepreneurial textile and apparel firms in the United States. In turn,
innovation by these firms will help to bolster the trend in reshoring and rebuilding a
manufacturing infrastructure within the United States. Small batches of high-­
quality, high-value textile and apparel products are more likely to embody innovation in product and process resulting from close collaboration between designers
and manufacturers (Doeringer and Crean 2006). A domestic value chain permits
greater speed and flexibility, enabling these firms to continue producing high-­
quality, high-value products.
As we brought up in Chap. 2, European textile and apparel firms are emphasizing
such factors within strategies designed to strengthen industry capabilities. Again,
we refer to the statement made by the European Skills Council (2014, p. 6):
The European Textiles [and] Clothing … sector is undergoing a renaissance. Driven by
creativity and innovation, products manufactured … range from traditionally crafted fashion and textile goods through to scientifically-led technical items.
Given the overall positive direction that EU KIE textile and apparel firms appear
to be headed in, it is likely that, alongside investments in new equipment and technology (Dockery 2005) and through the widespread availability of the Internet and
e-commerce, the potential for a similar renaissance in the US industries could
become a reality.
The next section offers discussion of how these industries could be further
strengthened in the United States considering the past and the present and based on
our analyses of the role of KIE among EU textile and apparel firms.
10.5 A
Proposal to Strengthen the US Textile and Apparel
Industries
In this section of the chapter, we consider what our findings from the AEGIS database suggest for the strengthening of the US textile and apparel industries. Recall
that we concluded in Chap. 9 that our policy focus would be in the indirect
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10 Prescriptions for Growth for US Textile and Apparel Firms
relationship among textile firms between technical sources of knowledge and
growth in sales and in the direct relationship among apparel firms between technical
sources of knowledge and growth in sales.
To the extent that the educational background of founders represents an informational base on which the firm can draw to take advantage of the tacit and codified
knowledge gained from in-house R&D, a policy question that might be relevant is:
How can the public sector enrich the information base of small and relatively young
firms?3 One possible policy response to this question could be to establish
technology-­
based information centers throughout countries to act as a public
resource for firms to use to enhance their in-house R&D efforts.
In making this recommendation, we reflect on the Hollings Manufacturing
Extension Partnership (MEP) within the US National Institute of Standards and
Technology (NIST).4 MEP’s origins are part of the Manufacturing Technology
Centers Program that began in 1989. This program was a response to the decline of
the United States relative to Japan in the manufacture of high-technology goods.
Located within the National Institute of Standards and Technology (NIST), MEP
has offered technical and business support primarily to the nation’s small- and
medium-sized manufacturers. As reviewed by Schacht (2013, p. 1):
The improved use of technology by small and medium-sized businesses is seen as important to the competitiveness of American manufacturing firms. How a product is designed
and produced often determines costs, quality, and reliability. Lack of attention to process
technologies and techniques may be the result of various factors, including finances,
absence of information, equipment shortages, and/or undervaluation of the benefits of technology. The purpose of the centers program is to address these issues through outreach and
the application of expertise, technologies, and knowledge developed within the manufacturing research activities of the federal government.
One of the economic justifications for MEP is what economists call market failure (Wessner 2013, p. 11):
Small manufacturers often confront failures in information markets due to difficulty in
evaluating what information they need and the quality of the available [sources]. Many
small and medium manufacturers [say] that they cannot attract the interest or afford the fees
of private consultancies given their small scale, limited resources, and remote locations. In
many cases, [these manufacturers cannot obtain] the type of services required for their scale
of operation.
In other words, small manufacturers face a resource or information barrier that
delimits their ability to identify, much less obtain, the knowledge they need to not
only remain competitive but also to grow. MEP is, in a sense, an information hub
where small and medium manufacturers can be put in touch with reliable sources of
knowledge (e.g., information about resource availability such as equipment). MEP
3
Recall from Chap. 5 that the mean size of a firm in our final sample of firms from the AEGIS
database is about 11 employees, and the mean age of a firm is about 7 years.
4
NIST is part of the US Department of Commerce. See: http://www.nist.gov/mep/about/index.
cfm.
10.5 A Proposal to Strengthen the US Textile and Apparel Industries
161
and the personnel in its regional offices are a source of knowledge to help firms
reduce the search time and search costs for accrual and appropriate information.
This same organizational structure might be relevant for providing information
about dimensions of the R&D process, thus making firm investments in R&D a
more effective source of knowledge, especially for young KIE companies.5 As we
showed in Chap. 9, among both textile firms and apparel firms, our technical source
of knowledge index is directly and/or indirectly correlated with sales growth. The
elements of our technical source of knowledge index are public research institutes;
universities; external commercial labs/R&D firms/technical institutes; in-house
know-how (R&D laboratories in your firm); trade fairs, conferences, and exhibitions; scientific journals and other trade or technical publications; participation in
nationally funded research programs; and participation in EU-funded research programs (Framework Programs).
A textile extension program (TEP) or an apparel extension program (AEP) might
well serve US textile and apparel firms by, for example, identifying for firm principals which universities or research institutes have greater expertise to solve specific
manufacturing or production issues. TEP and/or AEP hubs might also point firm
principals to regional or national research programs that are aligned well with their
manufacturing or production needs.
In proposing a strategy drawing from the MEP foundation, we take into consideration our findings from the AEGIS database as to how small firms operate within
the European Union and frame them relative to the key trends we identified that may
serve to shape these industries in the United States: reshoring, entrepreneurial innovation, and the opportunities afforded by the Internet. KIE involves drawing upon
and/or creating new technologies, as well as relying on sources of knowledge internal and external to the firm to innovate. TEPs and AEPs could be tailored to address
any combination of product knowledge, process knowledge, and/or market knowledge needs on the part of the KIE firm. In addition, TEPs and/or AEPs could offer
access to technologies, including those that are design related or manufacturing
related, through innovation labs linking KIE textile and apparel firms with universities and research institutes.
TEP and AEP hubs might also establish a foundation for a “cluster” (Porter
1985) or “industrial district” (Puig and Marques 2011) wherein groups of small,
agile KIE firms could be networked together. As we discussed in Chap. 2, such hubs
might be connected through “coopetitive” networks of firms referred to as “extended
smart organizations (XSOs)” by the LEAPFROG project (Yepes 2009). Regardless
of terminology used, if such an idea were to be implemented within the United
States, we have provided substantiation here of the extent to which these hubs could
take advantage of existing sources of institutional knowledge. Such knowledge
5
Following Link and Maskin (2016), it can be shown theoretically that the expected net return to a
company’s investment in in-house R&D will be greater if the company receives from an external
source relevant information about the technology being developed. In the Link and Maskin model,
the external source is NASA; in the case relevant to this paper, the external source would be an
MEP-like organization.
162
10 Prescriptions for Growth for US Textile and Apparel Firms
sources would be particularly available in areas of the country that were home to
these industries for decades and still offer jobs in them, such as Georgia, North
Carolina, and California (Bureau of Labor Statistics 2008).
As we pointed out in Chap. 5, identifying the role of human capital in innovative
firm behaviors is key to understanding how elements of KIE operate among small
textile and apparel firms in the AEGIS sample. A similar survey of US small textile
and apparel firms would offer insight into firm founder characteristics, as well as
firm behaviors, related to KIE. Although such characteristics and behaviors may
differ to some degree from those operating within the European Union, it is important to recognize the place of human capital in KIE. Indeed, despite the classification of the textile and apparel industries as low versus high technology, we have
illustrated how growth is achieved through innovation and particularly when innovation is fostered by KIE.
Chapter 11
Concluding Remarks
Twenty years from now, you will be more disappointed by the
things that you didn't do than by
the ones you did do, so throw off the bowlines, sail away from
safe harbor,
catch the trade winds in your sails. Explore, Dream, Discover.
—Mark Twain
You never know what is enough unless you know what is more
than enough.
—William Blake
Abstract This concluding chapter summarizes the findings in the book by answering three overarching research questions: (1) While there are many small firms that
comprise the EU textile and apparel industries, how and to what extent are these
firms entrepreneurial and/or innovative in their behaviors? (2) What might KIE,
and, in particular, entrepreneurial and innovative behaviors, mean for firm performance and/or industrial growth? And (3) what, if anything, do our empirical findings suggest for those small- and medium-sized firms that comprise the US textile
and apparel industries? The book makes the case that (1) textile firms are more
entrepreneurial and/or innovative than apparel firms as measured using the AEGIS
data, (2) entrepreneurial and/or innovative behavior is the statistical driver of firm
performance, and, (3) regarding sales growth for the US industries, extension programs focused on sources of technical knowledge may be an effective growth
policy.
In Chap. 1, we introduced three overriding research questions that would be explored
throughout this book:
• While there are many small firms that comprise the EU textile and apparel industries, how and to what extent are these firms entrepreneurial and/or innovative in
their behaviors?
© Springer International Publishing AG 2018
N.J. Hodges, A.N. Link, Knowledge-Intensive Entrepreneurship, International
Studies in Entrepreneurship 39, https://doi.org/10.1007/978-3-319-68777-3_11
163
164
11 Concluding Remarks
• What might KIE, and, in particular, entrepreneurial and innovative behaviors,
mean for firm performance and/or industrial growth?
• What, if anything, do our empirical findings suggest for those small- and
medium-sized firms that comprise the US textile and apparel industries?
In the ensuing chapters, we touched on these questions and suggested answers to
them in various places throughout the book. Our concluding remarks in this chapter
summarize our responses to these questions. We do this herein not only as a summary of the punchline of the book but also as reinforcement of our belief that these
are very relevant questions. Answers to these questions might well provide the
building blocks for future research about firms in the European textile and apparel
industries and about the same in many other countries.
While there are many small firms that comprise the EU textile and apparel industries, how and to what extent are these firms entrepreneurial and/or innovative in
their behaviors?
In Chap. 7, we constructed three indices which reflect, in our opinion, a dimension of how entrepreneurial and/or innovative textile and apparel firms are in their
behaviors.1 The AEGIS survey question from which these indices are constructed is:
Please indicate the contribution of the following factors in creating and sustaining the competitive advantage of the company using a 5-point scale where 5 is a huge impact and 1 is
no impact:
1 . Capability to offer novel products/services
2. Capacity to adapt the products/services to the specific needs of different customers/market
niches
3. Capability to offer expected products/services at low cost
4. R&D activities
5. Establishment of alliances/partnerships with other firms
6. Capability to offer high-quality products/services at a premium price
7. Networking with scientific research organizations (universities, institutes, etc.)
8. Marketing and promotion activities
We relied on responses 1 through 6 in Chap. 7 to create, using principal components, what we called an internal index for creating and sustaining the competitive
advantage of the firm; responses 7 and 8 were used to create what we called an
external index for creating and sustaining the competitive advantage of the firm; and
responses 1, 2, and 4 were used to create what we called an innovation index for the
firm. We showed in Tables 7.11 and 7.12 that all three of these three indices are correlated with each other.
Drawing inference simply from the numerical values of these three indices, we
conclude that textile firms are more entrepreneurial and/or innovative than apparel
firms in the dimensions so measured:
• Internal index for textile firms for creating and sustaining the competitive advantage of the company = 2.170.
1
We also constructed an index for sensing and seizing opportunity within the firm. We are not
discussing that index here because our ultimate focus, as discussed in Chap. 10, is on our technical
index of knowledge.
11 Concluding Remarks
165
Table 11.1 Correlation matrix between strategic behavior and entrepreneurial performance for
textile firms (n = 91)
Entrepreneurial performance
Commercialization
Percent sales increase/
decrease
Percent employment
increase/decrease
Strategic behavior
Internal index for
creating and
sustaining a
competitive advantage
0.390***
0.169*
External index for
creating and
sustaining a
competitive advantage
–
0.256**
Innovation
index
0.387***
–
–
–
–
***Significant at 0.01-level, **significant at 0.05-level, *significant at 0.10-level
Table 11.2 Correlation matrix between strategic behavior and entrepreneurial performance for
apparel firms (n = 91)
Entrepreneurial performance
Commercialization
Percent sales increase/
decrease
Percent employment
increase/decrease
Strategic behavior
Internal index for
creating and
sustaining a
competitive advantage
0.214*
–
External index for
creating and
sustaining a
competitive advantage
–
–
Innovation
index
–
–
–
–
–
*Significant at 0.10-level
• Internal index for apparel firms for creating and sustaining the competitive
advantage of the company = 2.074.
• External index for textile firms for creating and sustaining the competitive advantage of the company = 2.016.
• External index for apparel firms for creating and sustaining the competitive
advantage of the company = 1.735.
• Innovation index for textile firms = 2.932.
• Innovation index for apparel firms = 2.896.
What might KIE, and, in particular, entrepreneurial and innovative behaviors,
mean for firm performance and/or industrial growth?
In Chap. 9, we addressed this second question by correlating the three indices of
entrepreneurial and/or innovative behavior with three measures of firm performance: commercialization, percent sales increase/decrease, and percent employment increase/decrease. Our findings for textile firms are reproduced in Table 11.1,
and our finding for apparel firms is reproduced in Table 11.2.
From our vantage, it is clear that our internal index of entrepreneurial and/or innovative behavior, or what the AEGIS survey instrument calls strategic behavior and
hence the heading on these tables, is the statistical driver of firm performance. In fact,
166
11 Concluding Remarks
among apparel firms, our internal index is the only significant correlate among all
three of the indices of strategic behavior, and it is only correlated with commercialization. In contrast, among textile firms, the internal index is significantly correlated with
commercialization and with sales growth. The external index is correlated with only
sales growth, and the innovation index is only correlated with commercialization.
None of the three entrepreneurial and/or innovative behavior indices are correlated with employment growth. If sales growth is a prerequisite to observing
employment growth, then the defined time dimension of the AEGIS data might
preclude this relationship being observed.
What, if anything, do our empirical findings suggest for those small- and medium-­
sized firms that comprise the US textile and apparel industries?
We addressed this important question head-on in Chap. 9. We asked in that chapter what strategic behavior is associated with sales growth. Among textile firms, the
answer is internal and external strategies for creating and sustaining a competitive
advantage. For apparel firms, the answer is neither of these two behaviors. We then
asked what sources of knowledge are associated with both the internal and external
strategies for creating a competitive advantage. The answer for textile firms is technical sources of knowledge. For apparel firms, we asked what sources of knowledge
are associated with strategic behaviors, and the answer is none, but unlike textile
firms, technical sources of knowledge have a direct link to sales growth.
We concluded Chap. 9 with an emphasis on technical sources of knowledge
being a relevant target variable for growth policy. Recall that technical sources of
knowledge are public research institutes; universities; external commercial labs/
R&D firms/technical institutes; in-house know-how (R&D laboratories in your
firm); trade fairs, conferences, and exhibitions; scientific journals and other trade or
technical publications; participation in nationally funded research programs; and
participation in EU-funded research programs (Framework Programmes).
In Chap. 10 we built on this finding about the relationship between technical
sources of knowledge and recommended a policy prescription for the US textile and
apparel industries. Policy is limited, especially in the short run, in its ability to affect
market-based sources of knowledge. That prescription was for US policy makers to
draw on lessons learned from the Hollings Manufacturing Extension Partnership
(MEP) within the US National Institute of Standards and Technology (NIST).
Specifically, we recommend in Chap. 10 and again here that a textile extension program (TEP) and an apparel extension program (AEP) be established to serve textile
and apparel firms by identifying relevant technical sources of knowledge that will
leverage firms’ abilities to solve specific manufacturing or production issues.2 TEP
and/or AEP hubs might also point firm principals to regional or national research
programs that are aligned well with their manufacturing or production needs. Such
hubs might also serve as support networks for small businesses seeking to enhance
their entrepreneurial or innovative strategies through collaboration within the textile
and apparel value chain.
2
Technical sources of knowledge among apparel firms directly affect performance rather than
working through entrepreneurial and/or innovative behavior.
References
Abbasian, S., & Yazdanfar, D. (2013). Exploring the financing gap between native born womenand immigrant women-owned firms at the start-up stage: Empirical evidence from Swedish
data. International Journal of Gender and Entrepreneurship, 5(2), 157–173.
AEGIS. (2012). Advancing knowledge-intensive entrepreneurship and innovation for economic
growth and social well-being in Europe: D5.4 Final Report. mimeograph.
Aldrich, H. E., Carter, N. M., & Ruef, M. (2002). With very little help from their friends: Gender and
relational composition of nascent entrepreneurs’ startup teams. Frontiers of Entrepreneurship
Research. http://fusionmx.babson.edu/entrep/fer/Babson2002/VI/VI_P1/VI_P!.htm
Alsos, G. A., & Ljunggren, E. (1998). Does the business start-up process differ by gender? A
longitudinal study of nascent entrepreneurs. Frontiers of Entrepreneurship Research. http://
fusionmx.babson.edu/entrep/fer/papers98/V/V_A/V_A.html
Alsos, G. A., & Ljunggren, E. (2013). The role of gender in entrepreneur-investor relationships: A
signaling theory approach. Frontiers of Entrepreneurship Research, 33(8), 1–15.
Alsos, G. A., Isaksen, E. J., & Ljunggren, E. (2006). New venture financing and subsequent business growth in men- and women-led businesses. Entrepreneurship Theory and Practice, 30(5),
667–686.
Amatucci, F. M., & Crawley, D. C. (2011). Financial self-efficiency among women entrepreneurs.
International Journal of Gender and Entrepreneurship, 3(1), 23–37.
Amatucci, F. M., & Swartz, E. (2011). Through a fractured lens: Women entrepreneurs and the private equity negotiation process. Journal of Developmental Entrepreneurship, 16(3), 333–350.
American Apparel & Footwear Association (AAFA). (2006). American Apparel and Footwear
Association trends: 2005 ed. Virginia: AAFA.
American Apparel & Footwear Association (AAFA). (2009). An annual statistical analysis of
the U.S. Apparel & Footwear Industries annual 2008 edition (pp. 1–11). Arlington: American
Apparel & Footwear Association.
Amoroso, S., & Link, A. N. (2017). Under the AEGIS of knowledge-intensive entrepreneurship: Employment growth and gender of founders among European firms. Small Business
Economics. https://doi.org/10.1007/s11187-017-9920-4.
Amoroso, S., Audretsch, D. B., & Link, A. N. (2017). Sources of knowledge used by entrepreneurial firms in the European high-tech sector. Eurasian Business Review. https://doi.org/10.1007/
s40821-017-0078-4.
Armstrong, C. E. (2011). Thinking and slacking or doing and feeling? Gender and the interplay
of cognition and affect in new venture planning. Journal of Developmental Entrepreneurship,
16(2), 213–226.
Artschwager, A., Fischer, T., & Stellmach, D. (2009). New quality of partnership in the textile world: Concepts and technologies. In L. Walter, G. A. Kartsounis, & S. Carosio (Eds.),
© Springer International Publishing AG 2018
N.J. Hodges, A.N. Link, Knowledge-Intensive Entrepreneurship, International
Studies in Entrepreneurship 39, https://doi.org/10.1007/978-3-319-68777-3
167
168
References
Transforming clothing production into a demand-driven, knowledge-based, high tech industry
(pp. 141–197). London: Springer.
Audretsch, D. B., Kuratko, D. F., & Link, A. N. (2016). Dynamic entrepreneurship and technology-­
based innovation. Journal of Evolutionary Economics, 26(3), 603–620.
Balachandra, L., Briggs, A. R., Eddleston, K., & Brush, C. (2013). Pitch like a man: Gender stereotypes and entrepreneur pitch success. Frontiers of Entrepreneurship Research, 33(8), 1–15.
Becker-Blease, J. R., & Sohl, J. E. (2007). Do women-owned businesses have equal access to angel
capital? Journal of Business Venturing, 22(4), 503–521.
Becker-Blease, J. R., & Sohl, J. E. (2011). The effect of gender diversity on angel group investment. Entrepreneurship Theory and Practice, 35(4), 709–733.
Blackler, F. (1995). Knowledge, knowledge work and organizations: An overview and interpretation. Organization Studies, 16(6), 1021–1046.
Boles, J., & Link, A. N. (2017). On the R&D/marketing interface in knowledge intensive entrepreneurial firms. International Entrepreneurship and Management Journal, 3(3), 943–952.
Bönte, W., & Piegeler, M. (2013). Gender gap in latent and nascent entrepreneurship: Driven by
competitiveness. Small Business Economics, 41(4), 961–987.
Borneman, J. (2007). U.S. textiles: Optimism, change and innovation. Textile World, 157(2).
Retrieved from https://login.libproxy.uncg.edu/login? url=http://search.proquest.com/docview
/195637844?accountid=14604
Bozeman, B., & Link, A. N. (1991). Innovative behavior in small-sized firms. Small Business
Economics, 3(2), 179–184.
Brana, S. (2013). Microcredit: An answer to the gender problem in funding? Small Business
Economics, 40(1), 87–100.
Bruhn, M., & Love, I. (2011). Gender differences in the impact of banking services: Evidence from
Mexico. Small Business Economics, 37(4), 493–512.
Brush, C. G., Carter, N. M., Gatewood, E. J., Greene, P. G., & Hart, M. M. (2007). Enhancing
women’s financial strategies for growth. In N. M. Carter, C. Henry, B. Ó. Cinnéide, &
K. Johnston (Eds.), Female entrepreneurship: Implications for education, training and policy
(pp. 151–167). New York: Routledge.
Bureau of Labor Statistics, U.S. Department of Labor. (2003). Current employment statistics survey (1997–2003). Retrieved from http://www.bls.gov/ces/home.htm
Bureau of Labor Statistics, U.S. Department of Labor. (2008). Textile product, and apparel manufacturing. In Career guide to industries, 2008–09 edition. Retrieved from http://www.bls.gov/
oco/cg/cgs015.htm
Bureau of Labor Statistics, U.S. Department of Labor. (2016). Occuptational employment and wages,
May 2015. Retrieved from https://www.bls.gov/news.release/archives/ocwage_03302016.pdf.
Buttner, E. H., & Rosen, B. (1988). Bank loan officers’ perceptions of the characteristics of men,
women, and successful entrepreneurs. Journal of Business Venturing, 3(3), 249–258.
Buttner, E. H., & Rosen, B. (1989). Funding new business ventures: Are decision makers biased
against women entrepreneurs? Journal of Business Venturing, 4(4), 249–261.
Buttner, E. H., & Rosen, B. (1992). Rejection in the loan application process: Male and female
entrepreneurs’ perceptions and subsequent intentions. Journal of Small Business Management,
30(1), 58–65.
Caloghirou, Y., Protogerou, A., & Tsakanikas, A. (2011). Advancing knowledge-intensive entrepreneurship and innovation: Final report summarizing survey methods and results for economic growth and social well-being in Europe. mimeograph.
Caputo, R. K., & Dolinsky, A. (1998). Women’s choice to pursue self-employment: The role of
financial and human capital of household members. Journal of Small Business Management,
36(3), 8–17.
Carlton, D., & Coclanis, P. (2005). Southern textiles in global context. In S. Delfino & M. Gillespie
(Eds.), Global perspectives on industrial transformation in the American South (pp. 151–174).
Columbia: University of Missouri Press.
References
169
Carter, N. M. (2002). The role of risk orientation on financing expectations in new venture creation: Does sex matter? Frontiers of Entrepreneurship Research. http://fusionmx.babson.edu/
entrep/fer/Babson2002/VI/VI_P2/VI_P2.htm
Carter, N. M., Gartner, W. B., Shaver, K. G., & Gatewood, E. J. (2003). The career reasons of
nascent entrepreneurs. Journal of Business Venturing, 18(1), 13–39.
Carter, S., Shaw, E., Lam, W., & Wilson, F. (2007). Gender, entrepreneurship, and bank lending: The
criteria and processes used by bank loan officers in assessing applications. Entrepreneurship
Theory and Practice, 31(3), 427–444.
Cole, R. A., & Mehran, H. (2009, August). Gender and the availability of credit to privately
held firms: Evidence from the surveys of small business finances. Federal Reserve Bank of
New York Staff Report 383.
Coleman, S. (2000). Access to capital and terms of credit: A comparison of men- and women-­
owned small businesses. Journal of Small Business Management, 38(3), 37–52.
Coleman, S. (2002a). Constraints faced by women small business owners: Evidence from the data.
Journal of Developmental Entrepreneurship, 7(2), 151–174.
Coleman, S. (2002b). Characteristics and borrowing behavior of small, women-owned firms:
Evidence from the 1998 Survey of Small Business Finances. Journal of Business and
Entrepreneurship, 14(2), 151–166.
Coleman, S. (2004). Access to debt capital for women- and minority-owned small firms: Does
educational attainment have an impact? Journal of Developmental Entrepreneurship, 9(2),
127–143.
Coleman, S., & Carsky, M. (1996). Women owned businesses and bank switching: The role of
customer service. Entrepreneurial and Small Business Finance, 5(1), 75–83.
Coleman, S., & Robb, A. (2009). A comparison of new firm financing by gender: Evidence from
the Kauffman Firm Survey data. Small Business Economics, 33(4), 397–411.
Coleman, S., & Robb, A. (2012). Gender-based firm performance differences in the United States:
Examining the roles of financial capital and motivations. In K. Hughes & J. Jennings (Eds.),
Global women’s entrepreneurship research: Diverse settings, questions, and approaches
(pp. 75–94). Cheltenham: Edward Elgar.
Cooper, W. D. (1973). An introduction to the US textile industry of the 70s. North Carolina:
Department of Textiles Extension and Continuing Education.
Corral, C. (2005). Product innovations drive strong bath market. Home Textiles Today, 26(29).
Retrieved from https://login.libproxy.uncg.edu/login? url=http://search.proquest.com/docview
/223054203?accountid=14604
Cunningham, J. A., & Link, A. N. (2016). Exploring the effectiveness of research and innovation
policies among European Union countries. International Entrepreneurship and Management
Journal, 12(2), 415–425.
Curran, L. (2009). The EU clothing market in 2008—opening the floodgates? Journal of Fashion
Marketing and Management, 13(3), 305–310.
Dalborg, C., von Friedrichs, Y., & Wincent, J. (2015). Risk perception matters: Why women’s passion may not lead to a business start-up. International Journal of Gender and Entrepreneurship,
7(1), 87–104.
Danskin, P., Englis, B., Solomon, M., Goldsmith, M., & Davey, J. (2005). Knowledge management as competitive advantage: Lessons from the textile and apparel value chain. Journal of
Knowledge Management, 9(2), 91–102.
de Bruin, A., & Flint-Hartle, S. (2005). Entrepreneurial women and private capital: The New
Zealand perspective. International Journal of Entrepreneurial Behavior & Research, 11(2),
108–128.
Delfino, S., & Gillespie, M. (2005). Global perspectives on industrial transformation in the
American South. Columbia: University of Missouri Press.
Dockery, A. (2005). Mills stress innovation, marketing. Textile World, 155(1), 16–17.
Doeringer, P., & Crean, S. (2006). Can fast fashion save the US apparel industry? Socio-Economic
Review, 4(3), 353–377.
170
References
Eriksson, P., Katila, S., & Niskanen, M. (2009). Gender and sources of finance in Finnish SMEs: A
contextual view. International Journal of Gender and Entrepreneurship, 1(3), 176–191.
European Commission. (2014). Helping firms grow: European competitiveness report, 2014.
Brussels: European Commission.
European Commission. (2017). Textile and clothing in the EU. https://ec.europa.eu/growth/
sectors/fashion/textiles-clothing/eu_en
European Skills Council. (2014). European sector skills council textile clothing leather footwear
report 2014. Brussels: European Skills Council.
European Union. (2012). Guide to research and innovation strategies for smart specialisation (RIS
3). Brussels: European Union.
Fabowale, L., Orser, B., & Riding, A. (1995). Gender, structural factors, and credit terms between
Canadian small businesses and financial institutions. Entrepreneurship Theory and Practice,
19(4), 41–65.
Fay, M., & Williams, L. (1993). Gender bias and the availability of business loans. Journal of
Business Venturing, 8(4), 363–376.
Figueroa-Armijos, M., & Johnson, T. G. (2013). Entrepreneurship in rural America across typologies, gender and motivation. Journal of Developmental Entrepreneurship, 18(2), 1–37.
Gartner, W. B. (1988). Who is an entrepreneur? Is the wrong question. American Journal of Small
Business, 12(4), 11–32.
Gaventa, J., & Smith, B. E. (1991). The deindustrialization of the textile south: A case study. In
J. Leiter, M. Shulman, & R. Zingraff (Eds.), Hanging by a thread: Social change in southern
textiles (pp. 181–198). New York: Cornell University.
Gelb, B. (2001). RS20436: Textile and apparel trade issues. CRS Report for Congress. Retrieved
from http://www.ncseonline.org/nle/crsreports/economics/econ-134.cfm
Gereffi, G. (2000). The transformation of the North American apparel industry: Is NAFTA a curse
or a blessing? Integration and Trade, 4(11), 47–95.
Gicheva, D., & Link, A. N. (2013). Leveraging entrepreneurship through private investments: Does
gender matter? Small Business Economics, 40(2), 199–210.
Gicheva, D., & Link, A. N. (2015). The gender gap in federal and private support of entrepreneurship. Small Business Economics, 45(4), 729–733.
Gicheva, D., & Link, A. N. (2016). On the economic performance of nascent entrepreneurs.
European Economic Review, 86, 109–117.
Glass, B. D. (1992). The textile industry in North Carolina. North Carolina: Division of Archives
and History of the North Carolina Department of Cultural Resources.
Greene, P. G., Brush, C. G., Hart, M. M., & Saparito, P. (2001). Patterns of venture capital funding:
Is gender a factor? Venture Capital, 3(1), 63–83.
Groen, A. J. (2005). Knowledge intensive entrepreneurship in networks: Towards a multi-level/
multi-dimensional approach. Journal of Enterprising Culture, 13, 69–88.
Guercini, S. (2004). International competitive change and strategic behavior of Italian textile firms.
Journal of Fashion Marketing and Management, 8(3), 320–339.
Haines, G. H., Jr., Orser, B. J., & Riding, A. L. (1999). Myths and realities: An empirical study of
banks and the gender of small business clients. Canadian Journal of Administrative Sciences,
16(4), 291–307.
Harrison, R. T., & Mason, C. M. (2007). Does gender matter? Women business angels and the supply of entrepreneurial finance. Entrepreneurship Theory and Practice, 31(3), 445–472.
Hauser, P. J. (2015). Innovations in textile machinery: Wet processing. Textile World, 165(1),
26–28.
Haynes, G. W., & Haynes, D. C. (1999). The debt structure of small businesses owned by women
in 1987 and 1993. Journal of Small Business Management, 37(2), 1–19.
Hébert, R. F., & Link, A. N. (1988). The entrepreneur: Mainstream views and radical critiques.
New York: Praeger.
Hébert, R. F., & Link, A. N. (1989). In search of the meaning of entrepreneurship. Small Business
Economics, 1(1), 39–49.
References
171
Hébert, R. F., & Link, A. N. (2006a). Historical perspectives on the entrepreneur. Foundations and
Trends in Entrepreneurship, 2(4), 261–408.
Hébert, R. F., & Link, A. N. (2006b). The entrepreneur as innovator. Journal of Technology
Transfer, 31(5), 589–597.
Hébert, R. F., & Link, A. N. (2009). A history of entrepreneurship. London: Routledge.
Hines, T. (1997). The competitive nature of the clothing industry in the European Union. Journal
of Fashion Marketing and Management, 2(2), 194–199.
Hirsch-Kreinsen, H. (2015). Patterns of knowledge use in ‘low-tech’ industries. Prometheus,
33(1), 67–82.
Hirsch-Kreinsen, H., & Schwinge, I. (2014). Knowledge-intensive entrepreneurship in low-tech
industries. Cheltenham: Edward Elgar Publishing.
Hirsch-Kreinsen, H., Jacobson, D., & Robertson, P. (2006). ‘Low-tech’ industries: Innovativeness
and development perspectives—a summary of a European research project. Prometheus, 24(1),
3–21.
Hodges, N., & Frank, P. (2013). The case of the disappearing mill village. Textile: A Journal of
Cloth and Culture, 11(1), 38–57.
Hodges, N., & Karpova, E. (2006). Employment in the U.S. textile and apparel industries:
Comparative analysis of regional vs. national trends. Journal of Fashion Marketing and
Management, 10(2), 209–226.
Hodges, N., & Karpova, E. (2008). A tale of two industries: An interpretive analysis of media
reports on textiles and apparel in North Carolina. Clothing and Textiles Research Journal,
26(3), 253–272.
Hodges, N., & Link, A. N. (2017). On the growth of European apparel firms. Journal of the
Knowledge Economy, 8(2), 489–498.
Hodges, N., Watchravesringkan, K., Yurchisin, J., Karpova, E., Marcketti, S., Hegland, J.,
Yan, R.-N., & Childs, M. (2015). Women and small apparel business ownership: A cross-­
cultural exploration of the entrepreneurial experience. International Journal of Gender and
Entrepreneurship, 7(2), 191–213.
Hume, D. (2007). In P. F. Millican (Ed.), An enquiry concerning human understanding. New York:
Oxford University Press.
Hussain, J. G., Scott, J. M., Harrison, R. T., & Millman, C. (2010). ‘Enter the dragoness’: Firm
growth, finance, guanxi, and gender in China. Gender in Management, 25(2), 137–156.
Jones, R. M. (1997). The Swedish clothing industry: Strategies for survival and lessons for the UK.
Journal of Fashion Marketing and Management, 1(4), 372–383.
Jones, R. M., & Hayes, S. G. (2004). The UK industry: Extinction or evolution? Journal of Fashion
Marketing and Management, 8(3), 262–278.
Kartsounis, G.-A., Stellmach, D., & Walter, L. (2009). Conclusions. In L. Walter, G. A. Kartsounis,
& S. Carosio (Eds.), Transforming clothing production into a demand-driven, knowledge-­
based, high tech industry (pp. 201–208). London: Springer.
Kickul, J. R., Gundry, L. K., & Sampson, S. D. (2007). Women entrepreneurs preparing for growth:
The influence of social capital and training on resource acquisition. Journal of Small Business
and Entrepreneurship, 20(2), 169–182.
Knight, F. (1921). Risk, uncertainty, and profit. Boston: Houghton Mifflin.
Koper, G. (1993). Women entrepreneurs and the granting of business credit. In S. Allen &
C. Truman (Eds.), Women and entrepreneurship: Female durability, persistence and intuition
at work (pp. 57–69). New York: Routledge.
Kustepeli, Y., Gulcan, Y., & Akgungor, S. (2012). The innovativeness of the Turkish textile industry within similar knowledge bases across different regional innovation systems. European
Urban and Regional Studies, 20(2), 227–242.
Kwong, C., Jones-Evans, D., & Thompson, P. (2012). Differences in perceptions of access to
finance between potential male and female entrepreneurs: Evidence from the UK. International
Journal of Entrepreneurial Behavior and Research, 18(1), 75–97.
172
References
Lal, K. (2009). The textiles and clothing industry and economic development: A global perspective.
In K. Lal & P. Mohnen (Eds.), Innovation policies and international trade rules (pp. 10–36).
New York: Palgrave.
Lal, K., & Mohnen, P. (2009). Innovation policies and international trade rules. New York:
Palgrave.
Landoni, P., Dell’Era, C., Ferraloro, G., Peradotto, M., Karlsson, H., & Verganti, R. (2016). Design
contribution to the competitive performance of SMEs: The role of design innovation capabilities. Creativity and Innovation Management, 25(4), 484–499.
Leyden, D. P., & Link, A. N. (2015). Public sector entrepreneurship: U.S. technology and innovation policy. New York: Oxford University Press.
Link, A. N., & Maskin, E. S. (2016). Does information about previous projects promote R&D on
the International Space Station? In P. Besha, & A. MacDonald (Eds.), Economic development of
low Earth orbit (pp. 43–59). Washington, DC: National Aeronautics and Space Administration.
Link, A. N., & Strong, D. R. (2016). Gender and entrepreneurship: An annotated bibliography.
Foundations and Trends in Entrepreneurship, 12(4–5), 287–441.
Link, A. N., & Swann, C. A. (2016). R&D as an investment in knowledge based capital. Journal
of Industrial and Business Economics, 43(1), 11–24.
Locke, J. (1996). In K. P. Winkler (Ed.), An essay concerning human understanding. Cambridge:
Hackett Publishing Company.
Machlup, F. (1980). Knowledge and knowledge production. Princeton: Princeton University Press.
Mahmood, S., Hussain, J., & Matlay, H. Z. (2014). Optimal microfinance loan size and poverty
reduction amongst female entrepreneurs in Pakistan. Journal of Small Business and Enterprise
Development, 21(2), 231–249.
Malerba, F. (2010). Knowledge-intensive entrepreneurship and innovation systems: Evidence from
Europe. London: Routledge.
Manolova, T. S., Brush, C. G., Edelman, L. F., & Shaver, K. G. (2012). One size does not fit all:
Entrepreneurial expectancies and growth intentions of US women and men nascent entrepreneurs. Entrepreneurship and Regional Development, 24(1–2), 7–27.
Marlow, S., & Patton, D. (2005). All credit to men? Entrepreneurship, finance, and gender.
Entrepreneurship Theory and Practice, 29(6), 717–735.
McCurry, J. (2008). Increased competition drives more innovation in North America. Technical
Textiles International (TTI), 17(3), 11–14.
McKelvey, M., & Lassen, A. H. (2013). Managing knowledge intensive entrepreneurship.
Cheltenham: Edward Elgar Pub. Ltd..
Menzies, T. V., Diochon, M., Gasse, Y., & Elgie, S. (2006). A longitudinal study of the characteristics, business creation process and outcome differences of Canadian female vs. male nascent
entrepreneurs. International Entrepreneurship and Management Journal, 2(4), 441–453.
Mittica, C. J., Lavenduski, S., & Ruvo, C. (2012). Immigration: The multinational face of apparel
production. Wearables, 56–57. Retrieved from http://www.wearablesmag.com
Monget, K. (2014). Innovations lead growth in innerwear. WWD, 207(17). Retrieved from https://
login.libproxy.uncg.edu/login? url=http://search.proquest.com/docview/1492102705?accoun
tid=14604
Nash-Hoff, M. (2014). How San Diego reflects national manufacturing trends. Industry Week.
Retrieved from https://login.libproxy.uncg.edu/login? url=http://search.proquest.com/docview
/1491340246?accountid=14604
National Council of Textile Organizations (NCTO). (n.d.-a). U.S. textile industry. Retrieved from
http://www.ncto.org/ustextiles/index.asp
National Council of Textile Organizations (NCTO). (n.d.-b). Textile employment and economic
impact. Retrieved from http://www.ncto.org/industryemployment/index.asp
Neeley, L., & van Auken, H. (2010). Differences between female and male entrepreneurs’ use of
bootstrap financing. Journal of Developmental Entrepreneurship, 15(1), 19–34.
Nelson, T., Maxfield, S., & Kolb, D. (2009). Women entrepreneurs and venture capital: Managing
the shadow negotiation. International Journal of Gender and Entrepreneurship, 1(1), 57–76.
References
173
Organization for Economic Co-Operation and Development (OCED). (2004). A new world map in
textiles and clothing: Adjusting to change (pp. 1–7). Paris: OCED Policy Brief.
Orhan, M. (2001). Women business owners in France: The issue of financing discrimination.
Journal of Small Business Management, 39(1), 95–102.
Orser, B. J., & Foster, M. K. (1994). Lending practices and Canadian women in micro-based businesses. Women in Management Review, 9(5), 11–19.
Orser, B. J., Riding, A. L., & Manley, K. (2006). Women entrepreneurs and financial capital.
Entrepreneurship Theory and Practice, 30(5), 643–665.
PLANET. (2011). Advancing knowledge-intensive entrepreneurship and innovation for economic
growth and social well-being in Europe. D5.4 Final Report.
Porter, M. (1985). Competitive advantage. New York: Free Press.
Puig, F., & Marques, H. (2010). Territory, specialization and globalization: Recent impacts on
European traditional manufacturing. London: Routledge.
Puig, F., & Marques, H. (2011). The dynamic evolution of the proximity effect in the textile industry. European Planning Studies, 19(8), 1423–1439.
Puig, F., Garcia-Mora, B., & Santamaria, C. (2013). The influence of geographical concentration and structural characteristics on the survival chance of textile firms. Journal of Fashion
Marketing and Management, 17(1), 6–19.
Qubein, N. (2013). Self-starters. Business, North Carolina, 33(5). Retrieved from https://login.libproxy.uncg.edu/login?url=http://search.proquest.com/docview/1355932878?accountid=14604
Research and Markets. (2010). US apparel manufacturing report: 8000 companies such as Levi
Strauss, Phillips-Van Heusen, VF corporation, and Warnaco with annual revenue of approx.
$20 billion. Retrieved from http://www.businesswire.com/news/home/20100830005736/en/
Research-Markets-Apparel-Manufacturing-Report-8000-Companies
Reynolds, P. D., Carter, N. M., Gartner, W. B., & Greene, P. G. (2004). The prevalence of nascent
entrepreneurs in the United States: Evidence from the panel study of entrepreneurial dynamics.
Small Business Economics, 23(4), 263–284.
Riding, A. L., & Swift, C. S. (1990). Women business owners and terms of credit: Some empirical
findings of the Canadian experience. Journal of Business Venturing, 5(5), 327–340.
Robb, A. (2012). Financing women owned firms: A review of recent literature. In D. Cumming
(Ed.), The Oxford handbook of entrepreneurial finance. New York: Oxford University
Press.
Robb, A. M., & Coleman, S. (2010). Financing strategies of new technology-based firms: A comparison of women-and men-owned firms. Journal of Technology Management & Innovation,
5(1), 30–50.
Robb, A., & Wolken, J. (2002, March). Firm, owner, and financing characteristics: Differences
between female- and male-owned small businesses. FEDS Working Paper 2002–18.
Rodríguez, M. J., & Santos, F. J. (2009). Women nascent entrepreneurs and social capital in the
process of firm creation. International Entrepreneurship and Management Journal, 5(1),
45–64.
Roper, S., & Scott, J. M. (2009). Perceived financial barriers and the start-up decision: An econometric analysis of gender differences using GEM data. International Small Business Journal,
27(2), 149–171.
Rowan, L. (2014). Made in America, maybe: The potential renaissance of domestic apparel manufacturing in the United States. (Master’s thesis). Retrieved from https://repository.library.
georgetown.edu/handle/10822/709794
Sandhu, N., Hussain, J., & Matlay, H. (2012). Barriers to finance experienced by female owner/
managers of marginal farms in India. Journal of Small Business and Enterprise Development,
19(4), 640–655.
Saparito, P., Elam, A., & Brush, C. (2012). Bank-firm relationships: Do perceptions vary by gender? Entrepreneurship Theory and Practice, 37(4), 837–858.
Sauer, R. M., & Wilson, T. (2016). The rise of female entrepreneurs: New evidence on gender differences in liquidity constraints. European Economic Review, 86, 73–86.
174
References
Schacht, W. H. (2013). Manufacturing extension partnership program: An overview. Washington,
DC: Congressional Research Service.
Schmidt, G. (2016). ‘Made in the USA’ turns out to be challenging: ‘Reshoring’ is more suited to
the luxury market. Dayton Daily News. Retrieved from https://login.libproxy.uncg.edu/login?
url=http://search.proquest.com/docview/1807736120?accountid=14604
Schultz, T. W. (1975). The value of the ability to deal with disequilibria. Journal of Economic
Literature, 13(3), 827–846.
Schumpeter, J. A. (1928). The instability of capitalism. Economic Journal, 38(151), 361–386.
Schumpeter, J. A. (1934). The theory of economic development (trans: Opie, R.). Cambridge:
Harvard University Press. [Originally 1912].
Schwinge, I. (2015). The paradox of knowledge-intensive entrepreneurship in low-tech industries:
Evidence from case studies of the German textile industry. Wiesbaden: Springer.
SelectUSA summer forum: Reinvesting in America, creating jobs at home. (2014). Targeted news
service. Retrieved from https://login.libproxy.uncg.edu/login? url=http://search.proquest.com/
docview/1543150067?accountid=14604
Sena, V., Scott, J., & Roper, S. (2012). Gender, borrowing patterns and self-employment: Some
evidence for England. Small Business Economics, 38(4), 467–480.
Shrader, R. (2001). Collaboration and performance in foreign markets: The case of young, high-­
technology manufacturing firms. Academy of Management Journal, 44(1), 45–60.
Shrader, R., & Siegel, D. S. (2007). Assessing the relationship between human capital and firm
performance: Evidence from technology-based new ventures. Entrepreneurship Theory and
Practice, 31(6), 893–908.
Simpson, W. H. (1948). Southern textile communities. Charlotte: American Cotton Manufacturers
Association.
Small Business Administration (SBA). (2015). Database [data file]. Retrieved from https://www.
sba.gov/advocacy/firm-size-data
Storey, D. J. (2004). Racial and gender discrimination in the micro firms credit market? Evidence
from Trinidad and Tobago. Small Business Economics, 23(5), 401–422.
Suggs, G. G. (2002). ‘My world is gone’: Memories of life in a southern cotton mill town. Detroit:
Wayne State University Press.
Taplin, I. (2006). Restructuring and reconfiguration: The EU textile and clothing industry adapts to
change. European Business Review, 18(3), 172–186.
Taplin, I., & Winterton, J. (2004). The European clothing industry: Meeting the competitive challenge. Journal of Fashion Marketing and Management, 8(3), 256–261.
Teece, D. J. (2014). The foundations of enterprise performance: Dynamic capabilities and ordinary
capabilities in an (economic) theory of firms. Academy of Management Perspectives, 28(4),
328–352.
Teece, D. J., Pisano, G., & Shuen, A. (1997). Dynamic capabilities and strategic management.
Strategic Management Journal, 18(7), 509–533.
Textiles Intelligence Limited. (2010). Labour costs in the textile industry, by country, 1980–2008.
Textile Outlook International, 143, 128.
Tinkler, J. E., Whittington, K. B., Ku, M. C., & Davies, A. R. (2015). Gender and venture capital
decision-making: The effects of technical background and social capital on entrepreneurial
evaluations. Social Science Research, 51, 1–16.
Truett, L., & Truett, D. (2014). A ray of hope? Another look at the Italian textile industry. Empirical
Economics, 46(2), 525–542.
U.S. Census Bureau. (2014). Statistics of U.S. businesses. Retrieved from http://www.census.gov/
econ/susb/
U.S. Department of Agriculture. (2006). Cotton: Background. Retrieved from http://www.ers.
usda.gov/briefing/cotton/ustextileapparel.htm
U.S. Department of Commerce. (n.d.). Contribution of the U.S. textile and apparel industries to the
U.S. economy. Retrieved from https://www.bis.doc.gov/defenseindustrialbaseprograms/osies/
defmarketresearchrpts/texreport_ch2.html
References
175
Uluskan, M., Joines, J., & Blanton Godfrey, A. (2016). Comprehensive insight into supplier quality and the impact of quality strategies of suppliers on outsourcing decisions. Supply Chain
Management: An International Journal, 21(1), 92–102.
United Nations Statistical Office. (2015). Yearbook of international trade statistics. Department of
International Economic and Social Affairs, Statistical Office, United Nations. Retrieved from
http://unstats.un.org/unsd/trade
Verheul, I., & Thurik, R. (2001). Start-up capital: “Does gender matter?”. Small Business
Economics, 16(4), 329–345.
Vila, N., & Kuster, I. (2007). The importance of innovation in international textile firms. European
Journal of Marketing, 41(1–2), 17–36.
Walter, L., Kartsounis, G.-A., & Carosio, S. (2009a). Transforming clothing production into a
demand-driven, knowledge-based, high tech industry. London: Springer.
Walter, L., Scalia, M., & Marchi, F. (2009b). The European textile and clothing industry in the
global environment: Reality and challenges. In L. Walter, G. A. Kartsounis, & S. Carosio
(Eds.), Transforming clothing production into a demand-driven, knowledge-based, high tech
industry (pp. 1–8). London: Springer.
Watson, J., Newby, R., & Mahuka, A. (2009). Gender and the SME “finance gap”. International
Journal of Gender and Entrepreneurship, 1(1), 42–56.
Wessner, C. W. (2013). 21st Century manufacturing: The role of the manufacturing extension
partnership program. Washington, DC: National Academy Press.
Woodruff, J. L., & Michael McDonald, J. (Eds.). (1982). Handbook of textile marketing. New York:
Fairchild Publications.
World Economic Forum. (2013). The human capital report. Geneva: World Economic Forum.
Wu, Z., & Chua, J. H. (2012). Second-order gender effects: The case of U.S. small business borrowing cost. Entrepreneurship Theory and Practice, 36(3), 443–463.
Yepes, R. (2009). Engineering value networks in the fashion industry. In L. Walter, G. A.
Kartsounis, & S. Carosio (Eds.), Transforming clothing production into a demand-driven,
knowledge-based, high tech industry (pp. 152–165). London: Springer.
Zingraff, R. (1991). Facing extinction? In J. Leiter, M. Shulman, & R. Zingraff (Eds.), Hanging
by a thread: Social change in southern textiles (pp. 199–216). New York: Cornell University.
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