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Tourism Review
Measuring the components of visitor experience on a mountain attraction: the case of the Nordkette,
Tyrol, Austria
Ady Milman, Anita Zehrer, Asli Tasci,
Article information:
To cite this document:
Ady Milman, Anita Zehrer, Asli Tasci, "Measuring the components of visitor experience on a mountain attraction: the case of
the Nordkette, Tyrol, Austria", Tourism Review, https://doi.org/10.1108/TR-03-2017-0060
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Measuring the Components of Visitor Experience with a Mountain Attraction:
The Case of the Nordkette, Tyrol, Austria
Ady Milman, Anita Zehrer, Asli Tasci
Dr. Ady Milman
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Rosen College of Hospitality Management, University of Central Florida, 9907 Universal
Blvd., Orlando, Florida 32819, United States, Ady.Milman@ucf.edu
Ady Milman is a professor of tourism and hospitality management in the Department of
Tourism, Events & Attractions in the Rosen College of Hospitality Management at the
University of Central Florida. His area of research includes tourism planning and
development, theme park and attraction management, consumer behavior and consumer
experience.
Dr. Anita Zehrer
Family Business Center, MCI Management Center Innsbruck, Universitätsstrasse 15, 6020
Innsbruck, Austria, anita.zehrer@mci.edu
Anita Zehrer is professor, head of the Family Business Center and Deputy Head of the
Academic Council at the Management Center Innsbruck (MCI), as well as Adjunct Professor
at the University of Notre Dame in Sydney, Australia. She serves as Vice-President of the
German Association for Tourism Research, is member of the Tourism Advisory Board of the
Federal Ministry of Foreign Affairs and Energy, Germany, and tourism expert at the
Committee of Regions at the European Union.
Dr. Asli Tasci
Rosen College of Hospitality Management, University of Central Florida, 9907 Universal
Blvd., Orlando, Florida 32819, United States, Asli.Tasci@ucf.edu
Asli Tasci is an associate professor of tourism and hospitality marketing in the Department of
Tourism, Events & Attractions at the Rosen College of Hospitality Management at the
University of Central Florida. Her research interests include tourism and hospitality
marketing, particularly consumer behavior. She completed a number of studies measuring
destination image and branding with a cross-cultural perspective.
Measuring the components of visitor experience on a mountain attraction: the case of the
Nordkette, Tyrol, Austria
Abstract
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Purpose:
Previous mountain tourism research addressed economic, environmental, social, and political
impacts. Since limited studies evaluated visitors’ perception of their experience, this study
examines the tangible and intangible visitor experience in a Tyrolean Alpine tourist attraction.
Design/Methodology Approach
The study adopted Klaus and Maklan’s (2012) customer experience model, suggesting that
customers base their experience perception on the quality of product experience, outcome focus,
moments of truth, and peace-of-mind. Their model was used to validate the impact on overall
customer experience quality at the mountain attraction through conducting a structured survey
with 207 face-to-face on-site interviews.
Findings
The results of the Confirmatory Factor Analysis did not confirm the four-dimensional structure,
probably due to the differences between mountain tourism experience and the original mortgage
lending experience. Instead, Principal Component Analysis suggested a different dimensional
structure of components that were arbitrarily named as functional, social, comparative, and
normative aspects of the visitors’ experience.
Research limitations/implications
The results are based on a sample in a given period of time, using convenience sampling. While
the sample size satisfied the data analysis requirements, confirmatory factor analysis would
benefit from a larger sample size.
Practical implications
Consumer experience dimensions while visiting a mountain attraction may not be concrete or
objective, and consequently, may yield different types of attributes that influence behavior and
behavioral intentions.
Social Implications
Social exchange theory could explain relationships between visitors and service providers and its
consequences. Attraction managers should increase benefits for visitors and service providers to
enhance their relationships and thus experience.
Originality/value:
The study explored the applicability of an existing experiential consumption model in a mountain
attraction context. The findings introduce a revised model that may be applicable in other tourist
attractions.
1. Introduction
Consumer experience quality is an outcome of rational, emotional, sensorial, physical, and
spiritual signals (Gentile et al, 2007), and is generated through a time extended process of
organization-customer interaction. Although a number of studies addressed the concept of service
quality, most of the contributions were conceptual and primarily addressed the supplier’s side
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(Fuchs & Weiermair, 2001; Grissemann et al., 2013; Zehrer et al., 2014). While recent studies
suggested expanding the body of knowledge on consumer management (Leibold et al., 2002;
Zehrer et al., 2014), very few studies addressed the factors that impact experience (Brida et al.,
2014; Grissemann et al., 2013).
The development of mountains as tourist and leisure destinations has been discussed in the
academic literature. The majority of the studies addressed the environmental, social and political
impacts of mountain developments for tourism and leisure purposes (Schamel and Job, 2017;
Brida et al., 2014; Bätzing et al., 1996; Debarbieux et al., 2015; Mayer et al., 2011; Grissemann
et al., 2013; Rothwell, 1999). Several studies were contributed from a geographical perspective
(Smethurst, 2000; Zimmerer et al., 2017; Weston, 2014).
Recent studies addressed consumer perceptions in the context of mountain tourism. Silva et al.
(2013), for example, adopted a holistic approach to analyzing the relationships between tourism
impacts, tourists’ destination image, and their place attachment in mountain destinations. Other
studies addressed risk perceptions while visiting alpine tourism destinations (Eitzinger &
Wiedemann, 2007; Schusterschitz et al., 2010; De Urioste-Stone et al., 2016; Pröbstl-Haider et
al., 2016; van Riper et al., 2016), participating in specific visitor activities like events (Pettersson
& Getz, 2009), mountain biking (Walker & Shafer, 2011; Hagen et al., 2016; Newsome et al.,
2016; Pickering et al., 2016), voluntary environmental programs (Needham & Little, 2013), hut
accommodation experience (Duglio & Beltramo, 2014), or vacation styles of winter tourists
(Dolnicar & Leisch, 2003).
In spite of these contributions, very few studies examined in detail the factors that impact
customer experience quality while visiting mountain attractions. This study examines the
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tangible and intangible visitor experience in an Alpine tourist and leisure destination. The
purpose of this paper is to test an experiential consumption framework derived from the literature
(Klaus & Maklan, 2011, 2012, 2013), capturing the various stages of customer experience while
visiting a mountain attraction. The theoretical framework was adopted as an assessment tool to
scholars and practitioners and who can use it to create and evaluate customer’s experience. More
specifically, the study addresses the research question: which factors drive customer experience
while visiting a mountain attraction? For the purpose of this study, a mountain attraction is
defined as a place of interest which excursionists and tourists visit, typically for its inherent or
featured natural characteristics, cultural value, or historical significance that offer leisure and
recreational experiences. Mountain attractions are especially important for Alpine tourism, in
particular to the destinations in the vicinity of the mountains.
Following this outline, the paper first undertakes a review of the relevant literature regarding the
concept of customer experience and its measurement and second presents the theoretical
framework of our research. The research methodology and the analysis of the visitor experience
in a mountain attraction setting follows. Practical implications for attraction management are
highlighted in the results and the paper finally discusses the limitations of the study and
suggestions for future research.
2. Literature Review
Since the early 1800s, services in western economies were viewed as a secondary sector
compared to goods. Yet, contemporary thought suggests that customers do not distinguish
between the service and the manufacturing sectors, and goods and services appear together
(Gummesson, 2007, 1991). Since the 1970s, service marketing entered business schools
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(Grönroos, 2000, 1994; Gummesson, 1994) and marketing strategies have shifted from a goodsdominant view with a tangible output to a service-dominant view (Vargo & Lusch, 2004a,
2004b).
The role of the customer as an experience co-producer is an important element of the servicedominant logic (S-D logic) (Prahalad & Ramaswamy, 2004; Vargo &Lusch, 2004a), and
consequently has become an established paradigm in marketing (Cabiddu et al., 2013). The S-D
logic claims that service is the basis of economic activity (Lusch, 2011), and customers do not
only buy products but also benefit from the provider’s services associated with the products
(Lusch et al., 2007). Furthermore, S-D logic focuses on collaboration with customers to cocreate value (Frow & Payne, 2011; Prahalad & Ramaswamy, 2004; Vargo & Lusch, 2004a), and
therefore, the involvement of customers is, in fact, a crucial element for successful service
development (Carbonell et al., 2009). An organization can only provide value propositions,
consisting of physical or technical enablers like signs, symbols, products and the infrastructure
that serve as an input (Bitner, 1992), yet the value realization depends on the customer’s
participation in the service process. Therefore, customers decide whether the value is generated
and this evaluation process becomes beneficiary-specific (Cabiddu et al., 2013; Edvardsson &
Olsson, 1996).
Following the adaptation of customer service management thought, the concept of consumer
experience emerged and implied customer involvement at different stages of the consumption
(Pine and Gilmore, 2011; Gentile et al., 2007). Experiential marketing has recently evolved to
become a marketing discipline, emphasizing the need for businesses to go beyond the provision
of services by offering unique experiences to customers (Nasution et al., 2014; Pine & Gilmore,
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2011). Recent research addressed the interrelationships between service marketing and customer
experience. For example, Nasution et al. (2014) proposed a customer experience framework that
focused further on the customer experiencing the service. Their proposed sequential framework
consisted of five interacting layers: (1) customer values, needs and wants, (2)
experiential marketing strategy, (3) customer experience stages, (4) accumulated
customer experience, and (5) customer behavior change. Tsiotsou (2016) applied a serviceecosystems perspective and identified five factors that influence sports experiences: historical
meaning, tribal logics, rituals and socialization processes, value-in-subcultural-context, and the
co-construction/co-destruction of context. In the hospitality industry, Verbauskiene and
Griesiene (2014) concluded that hospitality service companies should use the experience
marketing tools to create a steady customer since future purchasing decisions depend on his/her
positive experiences.
Generally, customer experience can be associated with rational, emotional, sensorial, physical, or
even spiritual involvement (Pine & Gilmore, 2011; Schmitt, 2003). A customer experience,
therefore, is ‘an interaction between an organization and a customer: It is a blend of an
organization’s physical performance, the senses stimulated, and emotions evoked, each
intuitively measured against customer expectations across all moments of contact’ (Shaw &
Ivens, 2005, p. 51).
There are two customer-oriented measurement approaches of experience quality. The first
methodological approach is the measurement of experience quality by quantitatively assessing
objective criteria such as the nature of the products, the service delivery location, or the
customer’s waiting period (Groonroos, 1994). These objective criteria consider general quality
indicators and overlook the subjective factors such as interactions between the customer and the
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service provider. The second methodological approach embraces the measurement of experience
quality by assessing subjective criteria with qualitative methods such as interviewing or
observing the customer (Edvardsson, 1992). Consequently, marketing research, in the framework
of consumer behavior theory, has developed a range of measurement tools that were empirically
tested in the last two decades (value measurement instruments, observational and physiological
methods, service design methods, self-service technologies, mystery guest data, direct customer
feedback methods). Central to this approach is the quality perception of the single consumer
(Trischler & Zehrer, 2012).
While the literature on consumer or customer experience is extensive, recent contributions by
Klaus & Maklan (2011, 2012, 2013) suggested that customers base their experience perception
on four dimensions: product experience, outcome focus, moments of truth, and peace-of-mind.
Thus, Klaus & Maklan (2012) conceptualized, constructed, and empirically validated a multipleitem scale for measuring customers' experience quality. Lindberg et al. (2014) also discussed the
conceptual understanding of the consumer experience process and suggested four core
dimensions where consumers are situated ontologically in and across (1) time, (2) context, (3)
body, and (4) interaction. In the context of mountain visit experiential consumption, the
experience is produced by the timing of the visit like season, time of the week, and length of stay.
The mountain visit is also impacted by the activities undertaken by the visitors, the location,
landscape, and panoramic aspect of a mountain experience, and the interaction with staff
members and other guests.
3. Theoretical Framework
To measure customer experience quality, Klaus and Maklan (2011, 2012, 2013) introduced a
measurement scale (EXQ) that incorporated the dimensions of product experience, outcome
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focus, moments-of-truth, and peace-of-mind. Product experience denotes to the importance of
customer’s perception of having choices and the ability to compare them is critical in modeling
consumer behavior (McAlister & Srivastava, 1991). Outcome focus is associated with reducing
customers’ transaction cost like seeking out and qualifying new providers, reflecting the
importance of goal-orientated experiences in consumer behavior (Huffman & Houston, 1993).
Moments-of-truth emphasizes the importance of service recovery (Tax et al., 1998) and
flexibility (Liljander & Strandvik, 1993) in dealing with customers once complications arise.
Peace-of-mind (Klaus & Maklan, 2012) describes the customer’s assessment of all the
interactions with the service provider before, during and after the service purchase. This
dimension includes consumers’ perceptions that are strongly associated with the emotional
aspects of service (Edvardsson, 2005; Klaus & Maklan, 2011, 2012, 2013) (Figure 1). Although
several noticeable projects were published recently (Klaus & Maklan, 2011; Verhoef et al.,
2009), their works focused on specific aspects of the customer experience process.
------------------------------Figure 1 near here
------------------------------Klaus and Maklan’s (2012) development of the service experience concept was based on a twostage approach: initial item generation through qualitative research, purification of their findings
through exploratory factor analysis, and validation through the use of confirmatory factor
analysis and structural equation modeling. They proposed that the antecedents of service
experience are specific concrete attributes, which trigger perceptual attributes. Concrete attributes
are “referred in the literature as the technical aspects of the service experience that influence the
process” (Klaus & Maklan, 2011, p. 10). The perception of service experience attributes is
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believed to “generate a higher-order abstraction which influences behavioral intentions and actual
behavior” (Klaus & Maklan, 2012, P. 11). Their sample consisted of customers who had
purchased one or more mortgages in the previous six months with a major UK bank. The findings
indicated that customers evaluate the service experience at ‘an overall level, a dimensional level
and at attribute level and that each level drives perception on the level above’ (Klaus & Maklan,
2012, p. 21).
Klaus and Maklan’s model (2012) was adopted as a theoretical framework for this study to test
the service experience quality in the tourism and quality context. Klaus and Maklan’s (2012)
Customer Experience Quality (EXQ) constructs were used to validate the impact on the overall
customer experience quality at the Nordkette mountain attraction in Austria. Two items were
omitted in the final questionnaire, due to the tourism and leisure context setting of the study (see
Appendix).
4. Methodology
4.1 Instrument Development
The research instrument was developed in two phases. The first phase was qualitative and aimed
to develop a list of items pertinent to Klaus and Maklan’s (2012). To understand the visitors’
experience quality on the mountain attraction, the authors spent four months observing to
measure the visitor experience in the winter and the beginning of the summer, photographing and
mingling with visitors at the various stopovers of the mountain attraction. Apart from the
observation and informal conversations, photography served as a supplementary data collection
instrument and transcribing the consumers’ experiences observed (Glowski, 2008). The
participant observation process focused on the following elements: Setting (e.g. time of the day,
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weather conditions, spatial aspects of the attraction); Participants (e.g. observation of visitors in
detail like age, clothing, gender); Ends (the apparent purposes and goals of the people observed),
Act Sequence (detailed visitors’ behavior and speech that would allow understanding of
relationships or lack of relationships between visitors and the attraction’s operators as well as
among visitors); and Norms of Interaction between visitors (Daas & McBride, 2014).
Since observation may be perceived as intrusive and threatening to participants, permission was
granted by the mountain attraction’s management that continually received periodic reports to
establish credentials and professional trust (Walshe et al., 2011). Furthermore, although the study
was conducted outside the U.S., the researchers obtained an Institutional Review Board (IRB)
permission from one of the researchers’ home University.
In the second phase, 25 statements were developed based on the qualitative data reflecting the
model’s four constructs (Klaus & Maklan, 2012). The statements’ applicability and
comprehension were tested with 25 subjects in a pilot study conducted at the mountain attraction.
As a result of the pilot study, the number of statements was reduced to 17 statements, and the
research instrument asked the respondents to evaluate the experience quality statements using a
5-point Likert scale, where ‘1’ indicated ‘strongly disagree’ and ‘5’ indicated ‘strongly agree.’
The research instrument also included variables pertaining to place of residence, travel party size,
decision timing to visit the attraction, specific activities experienced while visiting the mountain
attraction, the length of stay at the mountain attraction, sources of influence to visit the attraction,
booking methods, and demographic characteristics.
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4.2 The Research Setting
The Nordkette mountain range is part of Austria’s largest nature park, the Karwendel. The
mountain attraction is overlooking the city of Innsbruck, where visitors can experience panoramic
views of Innsbruck, the Inn River Valley and the surrounding mountain ranges (Province of the
Tirol, 2017). The journey from the city center’s Congress Station (elevation 560 meters), to the
final cable car station of the mountain, takes about 25 minutes. The first stop of the journey takes
8 minutes by funicular to the Hungerburg station (elevation 857 meters), also accessible by car
and public transportation. From there, the journey to the subsequent two mountain vistas is
conducted by modern cable cars. The first stop is Seegrube at an elevation of 1,905 meters; the
second stop is Hafelekar at an elevation of 2,256 meters (Nordkette, 2017), offering excellent
panoramic views and a magnitude of hiking and climbing possibilities.
The attraction is open year-round and provides panoramic terraces, food services, a gift shop, as
well as opportunities for summer and winter outdoor activities, including small-scale expertsonly skiing and snowboarding during the winter season, as well as biking and hiking during the
summer months. The Nordkette attraction offers continuous seasonal events like summer solstice
celebration, the Sunday of the Sacred Heart of Jesus, New Year’s Eve, and more (Nordkette,
2017). In 2015, the Nordkette hosted 600,000 guests of whom 250,000 were Tyroleans and
350,000 international guests. Skiers made only up to 10% of all visitors (Nordkette, 2017).
Overall, 4,300 guests per day visit the Nordkette on peak days. The city of Innsbruck ranges on
place two (winter place 4, summer place 1) compared to all tourism destinations of the Tyrol. The
direct value added of tourism amounts to € 4.5 Billion in the Tyrol (Province of the Tirol, 2017).
Innsbruck, which ranges among the biggest alpine cities (e.g. Grenoble, Trento, Bolzano, and
Klagenfurt), represents a dynamic and prosperous economic area and contributes 25% to the
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Tyrolean overall value added (Province of the Tirol, 2017).
4.3 Data Collection and Analysis
The research tool was a structured face-to-face interview questionnaire. Although most of the
participants communicated well in English, the research instrument was also made available in
German. Personal interviews were conducted by experienced graduate research assistants who
were trained to interview visitors in the Innsbruck area. Interviews took place in the vicinity of
the four Nordkette’s stations (Figure 2). While the first two stations could have been passed over
by some visitors due to the option of arrival by public buses or private cars, the mountain
attraction’s third and fourth stations were experienced by all visitors. The survey was conducted
daily, at different weather conditions, and at different times of the day during the March-June
2014 period. A total of 300 subjects were approached, 82 refused to participate and 11 subjects
did not complete the interview, to yield 207 surveys or a 69% response rate.
Data were analyzed using SPSS and AMOS version 24. To test if Klaus and Maklan’s (2012)
customer experience factors also apply in a mountain experience context, Confirmatory Factor
Analysis (CFA) procedures of AMOS were applied on the 17 experience items. Cases with
missing values were eliminated from the data before proceeding to conduct the CFA, to yield a
total of 145 cases with complete information on the scale items. The structure of the data on the
remaining cases was analyzed with reliability tests, inter-item correlations, the Kaiser-MeyerOlkin (KMO) measure of sampling adequacy, and Bartlett’s test of sphericity.
While applying CFA to test the fit of Klaus and Maklan’s (2012) model in the mountain
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experience context, model modifications were used to improve the fit according to Bentler’s
(2006) suggestions. The validity of the model was assessed by using the most commonly
accepted model fit indices. These included the Chi-square test, relative chi-square (χ2/df < 5), the
root mean square error of approximation (RMSEA < .08), PCLOSE test or probability (p-value >
.05) of close fit, the goodness-of-fit statistic (GFI > .9), the adjusted goodness-of-fit statistic
(AGFI > .8), the normed fit index (NFI > .9) the non-normed fit index (NNFI > .9), also known
as the Tucker-Lewis index (TLI > .9), and the comparative fit index (CFI > .9) (Bentler &
Bonnet, 1980; Byrne, 1998; Hooper et al., 2008; Hu & Bentler, 1999; Kline, 2005; McDonald &
Ho, 2002; Tabachnick & Fidell, 2007; Wheaton et al., 1977). In addition, Hoelter's critical N
(>200) was also used for the adequacy of the sample size to yield an adequate model fit for a χ2
test (Hoelter, 1983). After exhausting all model re-specifications on the CFA, the potential
differences in the factor structure of the data were explored using the Principal Component
Analysis (PCA) procedures of SPSS.
------------------------------Figure 2 near here
-------------------------------
5. Findings
5.1 General Profile of the Respondents
The sample consisted of 207 visitors, 56.5% of which were out-of-the-area domestic and
international tourists who spent at least one night in Innsbruck, and 43.5% were local and
Austrian regional residents, primarily residing in the province of Tyrol. Of the 56% of those
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overnight visitors, about 63% was a mix of nationalities from 24 different countries. Tourists
spent an average of 11.6 nights and a median of 7 nights on their current trip, of which, they
spent an average of 5.08 nights and a median of 3 nights in the Innsbruck area, defined within 50
kilometers of the city center. Tourists were primarily from Germany (22.9%), non-regional
Austria, defined as all areas except the Province of Tyrol (15.7%), U.S. (10%), Great Britain
(8.6%), and Canada (4.3%). In the past year, local and regional residents visited the attraction a
mean of 13.02 times and a median of 5 times. About two third of them (65.6%) had annual
holiday cards that allowed unlimited funicular and cable car ride to the mountain attraction. The
sample’s gender distribution was 49.5% males and 50.5% females. The primary visitors’ age
groups were 21-30 years (26.6%) and 41-50 years (24.2%). The typical Nordkette’s visitor was
part of a visiting group with a mean of 2.78 companions and a median of two companions. The
majority of the visitors were adults, while 10.9% of the visitors were accompanied by children
under the age of 18. The typical visitor spent an average of 3.41 hours and a median of 3 hours
on the mountain attraction.
5.2 Behavior Related to Nordkette Mountain Attraction
About a third of the Nordkette’s visitors decided to visit the mountain attraction on the day of
their visit (33.5%) or the day prior to the visit (33.0%). The remainder of the visitors decided to
visit the attraction within a week prior to their visit (14.1%), a month prior (3.4%) or several
months prior to their visit (16%). An analysis of the data indicated the visitors had multiple
sources of influence to visit the Nordkette attraction. The most dominant sources of influence
were: advice by a travel agent (75.2%), Innsbruck tourist office (16%), Austrian or Tirol tourist
board (67.0%), Innsbruck website (66.1%), social media (64.2%), and magazines or newspapers
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(63.3%).
Respondents were asked to report on their completed activities or intended activities while
visiting the attraction. The multiple response analysis indicated that the most popular activities
were viewing the scenery, eating or drinking at the restaurants, and hiking the mountain trails
(Table 1). While research about visitor motivation is not available from the Nordkette’s
management, these activities reported by the respondents in Table 1 may light some shade over
the motivation. Respondents’ ratings of their satisfaction (4.35) and likelihood to visit again in
the future (3.85) were both above the mid-point of the 5-point scale.
Respondents were also asked to agree or disagree with a variety of the modified statements
associated with Klaus and Maklan’s (2012) conceptual model of customer experience quality,
including product experience, outcome focus, moments-of-truth, and peace-of-mind. The
respondents’ strongest agreement was with the statement ‘if things went wrong, Nordkette’s
employees dealt with their concerns appropriately’ (moments-of-truth; mean=4.80). Other
strong agreement was associated with the fact that the attraction was convenient to visit because
of past experience (peace-of-mind, mean=4.49), and the fact that the Nordkette’s visit made it
easy to have a mountain experience (outcome focus, mean=4.65) (Table 2).
------------------------------Table 1 near here
------------------------------------------------------------Table 2 near here
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-------------------------------
5.3 Mountain Experience Components
The computation for internal stability revealed a high value of Cronbach’s alpha coefficient for
the scales (α =.841). Bivariate correlation analysis revealed mostly significant correlations at .01
and .05 levels (see Table 3). The Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy was
.769. KMO scores close to or above .7 are considered a good indication that correlation patterns
are relatively compact. Bartlett’s test of sphericity revealed that the approximate of Chi-square is
857.579 with 136 degrees of freedom, which is significant at <.01 level. Based on these
indicators, it was concluded that there was sufficient structure in the data to conduct CFA for
identifying meaningful structures.
------------------------------Table 3 near here
-------------------------------
Despite the sufficient structure, CFA for the initial model test revealed an unacceptable fit to the
data according to the suggested thresholds listed in the methodology section (χ2=293.813,
df=113, p=.000, χ2/df=2.600, GFI=.812, AGFI=.746, CFI=.763, NFI=.673, NNFI (TLI)=.714,
RMSEA=.105, PCLOSE=.000, HOELTER=74 (.01)). Therefore, a posteriori theory trimming
was applied to see if the model fit will improve. Gradual model re-specifications improved the
model fit to an acceptable level; however, the Product Experience factor still showed problematic
convergent validity issues with factor loadings less than .6 (Bagozzi & Kimmel, 1995; Fornell &
Larcker, 1981; Gaskin, 2017; Hair et al., 2010). Further trimming of this factor resulted in its
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disintegration altogether, which worsened the model’s fit indices again. Besides, the remaining
three factors had only three items left, one in each having factor loadings less than .6. When these
items were deleted from the model, the model fit did not improve either. In other words, all items
and the pertinent factors were eliminated from the model one by one. Since the model was not
stabilized, further reliability and validity measures were not assessed.
This gradual disintegration of the model, despite the sufficient structure observed through
correlations and KMO measure, led the authors to suspect that maybe Klaus and Maklan’s (2012)
items measured in a mountain attraction experience have a different relationship with their
corresponding factors. Therefore, the assumptions on the factor structure and their pertinent items
were abandoned and Principal Component Analysis (PCA) was utilized to explore the free
loading of items to the four factors.
The data were not normally distributed; both Kolmogorov-Smirnov and Shapiro-Wilk tests of
normality revealed significant statistics. This is not unique to the current study; skewed data
distribution is a common finding for survey scales (Kim & Kim, 2012). This poses a potential
issue for analysis of data using PCA. The multivariate normality of data is an assumption for
PCA to reveal factors “guaranteed to be independent and uncorrelated… If the normality
assumption does not hold, components are guaranteed to be uncorrelated, but not independent. If
the independence assumption is violated, each component cannot be uniquely interpreted because
of contamination by other components.” (Kim & Kim, 2012, p. 1239). However, it is also
commonly known that the normality assumption is not very strict since PCA is a purely
geometrical technique, without any statistical hypothesis testing driven by a p-value (Hair et al.
1998). Therefore, violation of this assumption does not prohibit from interpreting the findings
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especially if the findings are not used for subsequent gradient analyses or regression. The
normality assumption is critical when findings are used for subsequent gradient analyses or
regression. Due to this relaxed nature of PCA, many studies addressing tourism experience
applied PCA in their analysis (e.g. Lee and Lee, 2015; Mikulić, et al., 2015).
Factors with eigenvalues exceeding one and items with substantial loadings, equal to or greater
than .50 were kept (Hair et al., 2010; Kim & Mueller, 1978). The internal reliability of each
factor was measured using Cronbach’s alpha. Factors with an alpha higher than .69 were
considered to have a high internal reliability (Churchill, 1979). The results of the analysis with
substantial loadings and no substantial cross-loadings are Presented in Table 4. Interestingly, all
items loaded with substantial loadings and no cross-loadings onto four factors and consequently,
no item was deleted. The extracted factors explain 58% of the variance of the original variables.
The computation for internal stability revealed high values of Cronbach’s alpha coefficient:
α=.78 for Factor I, α=.75 for Factor II, α=.70 for Factor III, and α=.65 for Factor IV. Only Factor
IV’s internal stability was slightly lower than the accepted threshold. All other factors’ high
coefficients indicate that factors are stable with substantially high internal consistencies.
------------------------------Table 4 near here
-------------------------------
Individual items showed good correlation with the extracted factors and were all interpretable.
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Since the items grouped in a manner different from the original factors of Klaus and Maklan
(2012), the factors are renamed arbitrarily. Factor I is named Functional Aspects since it includes
items related to functional dimensions of a mountain attraction experience. Factor II is named
Social Aspects since it includes items that require host-guest relations and interactions among
visitors. Factor III is named Comparative Aspects since it includes comparison and choice items
and Factor IV is named Normative Aspects since it includes items related to benchmarks from
past experience. The grand means are 4.26 for Factor I, 4.43 for Factor II, 3.59 for Factor III, and
3.97 for Factor IV, indicating their level of importance.
An inspection of individual items in each factor shows a violation of item structure suggested by
Klaus and Maklan’s (2012) original model. Except for Factor 4- Comparative Aspects, all factors
are a mix of different items from the original four factors envisioned by Klaus and Maklan
(2012). Even though these results may seem counter intuitive at the first look, a deeper analysis
by keeping in mind the complex dynamics of tourism product consumption may reveal otherwise.
For example, one may question “is service recovery more appropriately located in factor 2 or it
would better suit within factor 4?” Considering that service recovery in tourism product
consumption means various social interactions, it makes perfect sense that this item is located in
social aspects factor.
6. Implications and Recommendations
The Confirmatory Factor Analysis did not fit the EXQ model, and therefore, we assume that the
theoretical framework may be more valid in certain experiential consumption situations than
others. Klaus and Maklan’s (2012) model was developed in mortgage purchasing experience
which is different tourism and leisure experience. Their model proposed that dimensions of
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service experience are specific concrete attributes which generate perceptual attributes. Tourist
and leisure activities generate subjective experience perceptions that may vary from one
consumer to another (Edvardsson, 1992).
In the case of the Nordkette mountain attraction, consumer experience dimensions may not be as
concrete or objective due to subjective evaluations and interpretations of every individual’s
subjective experiences. Consequently, the experience dimensions may yield different types of
attributes that result in different types of dimensions that influence behavioral intentions and
actual behavior. For example, items associated with ‘comparison necessity’ or ‘cross-product
comparison’ may not be applicable to some visitors who spend a limited time in the mountain
area. Likewise, items like ‘past experience’ or ‘service recovery’ may not be applicable to the
consumption situation of the mountain attraction’s visitors. Therefore, some items may have
loaded onto different factors in the Principal Component Analysis.
The Nordkette’s mountain experience requires multiple functional aspects or tasks that have to be
performed by the visitors. For example, visitors have the option of reaching the attraction’s
second station (Hungerburg) by a funicular, a bus or a private car, and then take two different
cable car rides to reach the top of the mountain. In addition, visitors have no information prior to
purchasing their tickets about the mountain like bad weather, lack of snow, or poor visibility.
Management should develop operational strategies to ease these perceived risks and provide
information about the funicular and bus timetable to the Hungerburg station, as well as a clear
timetable of the cable car’s operations. As weather conditions and visibility are unknown for
visitors, management should also provide continually updated information about weather
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conditions and visibility to reduce visitors’ risks.
Social aspects also seem appropriate in creating a mountain attraction visiting experience. These
include interaction with the Nordkette’s ticket sales agents, funicular, and cable car operators, as
well as other visitors and accompanying group members. Some visitors may have a better
experience by developing relationships with these stakeholders rather than just transactions.
Consequently, the attraction’s management should enhance employee training and add a touch of
hospitality to the overall visitor experience.
An effective management strategy would be to reduce the costs and increase the benefits for not
only visitors but also service providers. Social media could be used to strengthen the social ties
between service providers and their visitors. Further research is needed to elaborate further on the
usefulness of social exchange theory in a mountain tourism experience context. Social exchange
theory (SET) argues that individuals enter into social relationships with others to experience
valued benefits (e.g., information, advice, friendship) yet incur few costs (e.g., time, effort,
financial) (Homans, 1958; Thibault & Kelley, 1959). This theory is often used to provide a
theoretical underpinning for studies on experiences such as residents’ experiences with tourism
(e.g. Ap, 1992; Hernandez et al., 1996), work experiences (e.g. Rhoades et al., 2001), and
volunteering experiences in leisure settings (e.g. Bang et al., 2009; Doherty, 2009). Mountain
tourism experience, with its social aspects dimension, may be more positive when there are
positive exchange relationships between service providers and visitors, and comparable benefits
for not only visitors but more importantly for service providers (Clark and Mills, 1993; Crosno
and Brown, 2015). These benefits, as well as its consequences, need attention in future research.
Regarding comparative aspects, the Province of Tyrol and the Alps altogether have multiple
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mountain attractions that some visitors may have visited or plan to visit. Therefore, the necessity
for comparison, cross-product evaluation, and freedom of choice between the various mountain
attractions are necessary for marketing and promotional campaigns. By understanding the
underlying factors and causes of mountain experiences, attraction managers might be able to
better cater experiences to their various market segments and thus maximize future reengagement behaviors of their visitors, which might reveal competitive advantages with regard to
their competitors. To differentiate the Nordkette attraction from other mountain attractions in the
greater Innsbruck’s area, a downloadable app from the attraction’s website could be developed.
This social media-compatible app would register visitor activities on the mountain and could be
based on a bonus system, where visitors might earn bonus points every time they visit the
mountain attraction. Encouraging visitors to upload selfies taken on the mountain could also be a
trigger for loyalty and could also be used to earn additional bonus points. Such an app would help
share their mountain experiences with others and be a great marketing and promotion tool for the
attraction. Various status levels like bronze, silver or gold could be awarded when a certain
amount of bonus points is reached. An award like a free ride or similar could be used for
customer relationship management and distinguish the Nordkette from other mountain
attractions.
Normative aspects like reducing transaction costs, past experience influences, time flexibility in
visitation, and stakeholders’ common grounding they share with other visitors and the attraction’s
operators are also important to produce the mountain attraction experience. This is congruent
with the findings of Snepenger et al. (2004), who found by means of a quasi-experimental design
in the spectrum of tourism places, that normative meanings can be utilized to identify core
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benefits or values associated with tourism experiences. Thus, a set of normative criteria might
help to arrange data and information on mountain tourism activities that scholars can enable
building a theoretical framework for mountain tourism, which might facilitate the analysis and
design of future marketing initiatives (Williams, 2002). The app mentioned above could be a
great tool especially for the normative assessment of attraction experiences.
Finally, the overall mountain experience indirectly influences the image of the Nordkette
mountain attraction and the city Innsbruck due to the given relatedness between visitor
experience and subsequent behavior. Therefore, the role of experience management remains a
critical development strategy for mountain attractions, as it impacts on behavioral intentions.
Thus, knowledge on the perceived experience value of visitors is an important information for
attraction managers in terms of branding and marketing the attraction. Managers should be aware
that some aspects that do not fit the positioning of the attraction might result in a negative image,
which then might prevent regular guests and visitors from revisiting.
7. Conclusions and Limitations
Prior to summarizing the outcome of the study, the authors will cautiously define its limitations.
First, the results are based on a sample of visitors who visited the mountain attraction in a given
period of time, and subjects were selected based on a convenience sampling due to limited access
to potential subjects. While our sample size satisfies the requirements for a sufficient and
thorough data analysis (e.g. Hair et al. 1998), confirmatory factor analysis would benefit from an
even larger sample size. Another limitation is the nature of the model and data processing. The
model was chosen as the authors wanted to find out if Klaus and Maklan’s (2012) model can be
adopted and tested in a tourism and leisure setting. Rewording the items in the research
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instrument to fit in the mountain attraction context may have changed the nature of the
dimensions and their relationships with the construct.
To test the initial question - which factors drive customer experience while visiting a mountain
attraction - it is possible to conclude that the multi-dimensional customer experience model can
be adopted as a new framework to evaluate the overall quality of customer experience. The
model may advance the discussion to an empirical level and will provide a foundation for future
qualitative and quantitative research.
While this research was conducted in a single mountain setting, customer experience
management should be adopted as an important strategic objective for similar attractions where
the experience is not well-focused. To implement this strategy, management should determine
which attributes of the customer experience are the most critical to their marketing efforts as well
as customer satisfaction and loyalty. This will also allow decisions makers’ direct investments to
enhance and manage certain attributes of the customer experience.
Future research should redefine the various experience antecedents that generate the four EXQ
dimensions of product experience, outcome focus, moments of truth, and peace of mind.
Furthermore, the model should be retested by acquiring a larger dataset from mountain
attraction’s visitors. For better reliability, future research should also focus on certain segments of
the mountain attraction’s visitors like local residents, domestic or international tourists. It will
also be interesting to segment the visitors according to their visiting frequency. Finally, linguistic
differences may also hinder the scales’ reliability since respondents with different native
languages may understand the concepts differently; thus a comparison between groups of
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different languages may reveal differences in scale reliability and validity.
Despite these limitations, the study provides an important contribution. The study applies the
customer experience model from Klaus and Maklan (2012) to a mountain tourist attraction in the
Tyrolean Alps, which has not been done before obviously. Even though the model has not been
applied in its original structure, a modified version of it has been revealed for the mountain
experience context. Given the lack of empirical work about visitor experience in the Alps or in
mountain areas in general, this study may initiate further research consideration to the longlasting tourist tradition of these destinations.
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The current study measured only this section of the model
Figure 1: The four dimensions of Klaus and Maklan’s Conceptual Model of Customer
Experience Quality (Source: Klaus & Maklan, 2013, p. 230)
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Figure 2: The Nordkette Mountain Stations
(Source: http://www.nordkette.com/uploads/pics/stationen_nordkette.jpg)
Table 1. Respondents’ Activities While Visiting the Nordkette Mountain attraction (Multiple
Response)
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Which of the following activities, if any, have you done or
will do on this visit to the Nordkette?
Viewing the scenery
Eating or drinking at the restaurants
Hiking around the area
Spend time with my family or friends
Visiting the Alpine Zoo
Shopping for souvenirs
Riding up or down the hill by bike
Other
Total
N
Percent
Percent of
Cases
156
120
90
85
35
23
20
14
543
28.7%
22.1%
16.6%
15.7%
6.4%
4.2%
3.7%
2.6%
100.0%
75.7%
58.3%
43.7%
41.3%
17.0%
11.2%
9.7%
6.8%
263.6%
Table 2. Descriptives and frequencies of variables included in the analyses (N=207)
Klaus and Maklan’s (2012) Factors and Items Mountain Experience Scale Items *
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Product experience (PE)
PE1
Freedom of choice (1)
PE2
Cross-product comparison (2)
PE3
Comparison necessity (3)
PE4
Account management (4)
Outcome focus (OF)
OF1
Inertia (5)
OF2
Result focus (6)
OF3
Past experience influence (7)
OF4
Common grounding (8)
Moments-of-truth (MoT)**
MoT1
Flexibility (9)
MoT3
Risk perception (10)
MoT4
Interpersonal skills (11)
MoT5
Service recovery (12)
Peace of mind (PoM)**
PoM1
Expertise (13)
When visiting NORDKETTE, I want to choose
between different activities on the mountain.
Prior to visiting NORDKETTE, it was necessary
for me to check other possible experiences in the
region
When visiting NORDKETTE, I looked for the
different activity options on the mountain.
When visiting NORDKETTE, I had a positive
experience at least with one employee.
Mean
Std.
Dev.
3.96
1.288
3.55
1.677
3.28
1.691
4.29
1.317
My visit to NORDKETTE makes it easy for me to 4.46
have a mountain experience.
Visiting NORDKETTE gives me what I need and 4.08
want instantly.
I prefer NORDKETTE over other similar mountain 3.73
destinations.
The NORDKETTE experience can relate to my
3.78
mood and feelings.
0.944
During my visit, NORDKETTE’s employees were
flexible and looked after my needs.
NORDKETTE is a safe and risk-free mountain
attraction.
The employees at NORDKETTE demonstrate
good people skills.
When things went wrong, NORDKETTE´s
employees dealt with my concerns appropriately.
4.01
1.292
4.06
1.193
4.21
1.051
4.80
1.273
4.26
1.058
4.35
1.099
4.00
1.498
4.49
1.489
4.36
1.549
I am confident in NORDKETTE’s expertise and
professionalism provides me with a valuable
experience.
PoM2
Process ease (14)
The whole process of visiting the NORDKETTE
was easy.
PoM3
Relationship versus transaction (15) During my visit, NORDKETTE´s employees
provided me with a personalized experience.
PoM4
Convenience retention (16)
It is convenient to visit NORDKETTE because of
my past experience there.
PoM5
Familiarity (17)
Since I am familiar with NORDKETTE, I easily
get what I need.
*: 5-point scale. 1=Strongly Disagree, 5=Strongly Agree.
**MoT2-Pro-activity and PoM6-Independent Advice were deleted in the current study.
1.120
1.362
1.367
Mountain Experience Scale Items
PE1 PE2 PE3 PE4 OF1 OF2 OF3 OF4 MoT1
PE1-Freedom of choice (1)
1
PE2-Cross-product comparison (2)
.301** 1
PE3-Comparison necessity (3)
.421** .580** 1
PE4-Account management (4)
.109 .097 .146 1
OF1-Inertia (5)
.246** .121 .118 .208* 1
OF2-Result focus (6)
.304** .125 .170* .143 .539** 1
OF3-Past experience influence (7)
.095 .118 .076 .209* .208* .450** 1
OF4-Common grounding (8)
.244** .280** .381** .264** .303** .301** .405** 1
MoT1-Flexibility (9)
.150 .022 -.009 .413** .278** .294** .320** .313** 1
MoT3-Risk perception (10)
.161 .124 .150 .126 .301** .258** .256** .316** .271**
MoT4-Interpersonal skills (11)
.156 .081 -.027 .212* .366** .411** .259** .077 .557**
MoT5-Service recovery (12)
.310** .022 .042 .068 .253** .379** .092 .166* .306**
PoM1-Expertise (13)
.306** .137 .140 .269** .410** .439** .412** .317** .449**
MoM2-Process ease (14)
.394** .152 .085 .278** .442** .506** .209* .279** .363**
PoM3-Relationship versus transaction .249** .160 .005 .248** .199* .411** .317** .148 .509**
(15)
PoM4-Convenience retention (16)
.301** .150 .018 .199* .173* .322** .212* .152 .379**
PoM5-Familiarity (17)
.186* .163* .117 .023 .167* .129 .161 .134 .258**
*: Correlation is significant at the 0.05 level (2-tailed).
**: Correlation is significant at the 0.01 level (2-tailed).
Listwise N=145
Table 3. Inter-item correlations of mountain experience scale
.328** .402** .383** .216** .439** 1
.190* .310** .225** .083 .315** .484** 1
.138
.069
1
.370** 1
.360** .566** 1
.410** .420** .346** 1
1
.437**
.475**
.397**
.547**
1
.256**
.116
.354**
.471**
.145
MoT3 MoT4 MoT5 PoM1 PoM2 PoM3 PoM4 PoM5
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Table 4. Results of Principal Component Analysis on mountain experience scale items (N=145)
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Mountain Experience Items &
Factors
% of
Cumulative %
Variance
of Variance
Explained
Explained
Factor Loadings
F I F II
F III
Factor I: Functional Aspects
PoM2-Process ease (14)
.799 .138 .077
OF1- Inertia (5)
.696 .081 .095
OF2-Result focus (6)
.690 .245 .105
PoM1-Expertise (13)
.586 .342 .074
MoT3-Risk perception (10)
.567 -.097 .114
Factor II: Social Aspects
PoM4-Convenience retention (16)
.093 .760 .108
PoM5-Familiarity (17)
-.091 .704 .223
PoM3-Relationship versus
.254 .667 -.041
transaction (15)
MoT5-Service recovery (12)
.426 .623 .005
MoT4-Interpersonal skills (11)
.480 .505 -.197
Factor III: Comparative Aspects
PE3-Comparison necessity (3)
.078 -.039 .865
PE2-Cross-product comparison (2) .018 .115 .773
PE1-Freedom of choice (1)
.400 .304 .556
Factor IV: Normative Aspects
PE4-Account management (4)
.089 .089 .063
OF3-Past experience influence (7)
.233 .120 .071
MoT1-Flexibility (9)
.266 .463 -.136
OF4-Common grounding (8)
.268 -.029 .467
Kaiser-Meyer-Olkin Measure of Sampling Adequacy = .769
Bartlett’s test of sphericity = .000
Extraction Method: Principal Component Analysis
Rotation Method: Varimax with Kaiser Normalization.
Factor
Grand
Mean
Cronbach’s
Alpha Value
FIV
31.267
31.267
4.263
.784
11.230
42.497
4.430
.754
8.709
51.206
3.589
.701
7.033
58.239
3.967
.653
.135
.119
.155
.340
.284
.153
.054
.302
-.155
.241
.083
.110
-.163
.668
.643
.599
.539
About the authors
Ady Milman is a professor of tourism and hospitality management in the Department of Tourism,
Events & Attractions in the Rosen College of Hospitality Management at the University of
Central Florida. His area of research includes tourism planning and development, theme park and
attraction management, consumer behavior and consumer experience.
Anita Zehrer is professor, head of the Family Business Center and Deputy Head of the Academic
Council at the Management Center Innsbruck (MCI), as well as Adjunct Professor at the
University of Notre Dame in Sydney, Australia. She serves as Vice-President of the German
Association for Tourism Research, is member of the Tourism Advisory Board of the Federal
Ministry of Foreign Affairs and Energy, Germany, and tourism expert at the Committee of
Regions at the European Union.
Asli Tasci is an associate professor of tourism and hospitality marketing in the Department of
Tourism, Events & Attractions at the Rosen College of Hospitality Management at the University
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of Central Florida. Her research interests include tourism and hospitality marketing, particularly
consumer behavior. She completed a number of studies measuring destination image and
branding with a cross-cultural perspective.
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