tourist engagement: conceptualization, scale …...measurement model of te scale, including social...
TRANSCRIPT
Tourist Engagement:
Conceptualization, Scale Development and Empirical Validation
By Shuyue Huang
A Thesis
Presented to
The faculty of Graduate Studies
of the University of Guelph
In partial fulfillment of requirements
for the degree of
Doctor of Philosophy
in Management
Guelph, Ontario, Canada
Shuyue Huang, September 2017
ii
ABSTRACT
TOURIST ENGAGEMENT: CONCEPTUALIZATION, SCALE DEVELOPMENT AND
EMPIRICAL VALIDATION
Shuyue Huang Advisor:
University of Guelph, 2017 Professor Hwan-Suk Chris Choi
This thesis continues the recent discussion on the conceptualization, scale development and empirical
studies of engagement for a better understanding of customer experience, especially in the context of
tourism. This study aims to answer a variety of questions raised by the engagement studies in consumer
behavior, including the lack of (1) systematic scholarly inquiry on engagement definition and scope, (2)
consensus regarding its dimensionality, and (3) understanding of engagement’s effect on assessing,
predicting and enhancing customer loyalty, as well as its interaction with various existing loyalty
antecedents.
This study conceptualizes tourist engagement (TE) as a psychological state that occurs by virtue of
interactive, co-creative tourist experiences with a focal agent/object (people/attraction/
activities/encounters) in focal travel experience relationships. By using the three steps of item generation,
scale purification, and scale validation, this study develops a four-dimensional, second-order, 16-item
measurement model of TE scale, including social interaction, interaction with employees, relatedness, and
activity related tourist engagement (with two sub-dimensions, immersed involvement and novelty-
seeking). This scale demonstrates sufficient psychometric properties on uni-dimensionality, reliability,
and validity; the predictive validity test presents a significant effect of TE on behavioral intention,
explaining for 35.4% its variance. Moreover, this study delineates and empirically validates TE’s role in
capturing the quality of tourist experience, focusing on its relationship with perceived service quality,
perceived value, satisfaction, and behavioral intention. Despite the insignificant direct effect between TE
and behavioral intention in an integrated model, TE does exert the largest total effect on behavioral
intention compared to the other three predictors. Also, results show that by improving the engaging
iii
experience, tourist perception on service quality, value, and overall satisfaction can be significantly
enhanced. Meanwhile, a variety of data collection methods (in-depth interviews, focus groups, Amazon
Mechanical Turk survey, and online panel) and analytical techniques (exploratory factor analysis,
confirmatory factor analysis, structural equation modeling, and MANOVA) were used. This study
presents the first endeavor to develop a marketing tool of engagement to measure the quality of travel
experience. It contributes to research on engagement studies, tourist experience and destination service
management.
iv
ACKNOWLEDGEMENT
First and foremost, I would like to thank my supervisor, Dr. Hwan-Suk Chris Choi, for your
tremendous support, ongoing encouragement, and expert advice for pushing me to think outside
of the box and guiding me through my four-year Ph.D. journey. I am very grateful for the time,
and effort, you spent to improve my research. I am fortunate to have you as my mentor and role
model. Without it, I would certainly not be who I am today and could not accomplish my goal of
preparing myself as a prospective educator and researcher.
I would like to express my gratitude to my committee members, Drs. Stephen Smith, Marion
Joppe, and Sunghwan Yi for great suggestions and comments throughout our committee
meetings. I am honored to learn from you. I would like to extend gratefulness to my external
examiners: Drs. Vinay Kanetkar and Hyeyoon (Rebecca) Choi. I appreciate your time in reading
my thesis and providing feedbacks, especially Dr. Hyeyoon (Rebecca) Choi who flew in from
the Ohio University to Guelph. Many thanks to Dr. Statia Elliot for chairing my thesis defense.
Special thanks to Dr. Erna van Duren for giving me opportunities to master a variety of teaching
methods and techniques in the Strategic Management course. I had a very rewarding experience
working with Dr. van Duren. I appreciate you supporting my job application and my personal
improvement. I would also like to thank Dr. WooMi Jo for your guidance and kindness during
my studies. I am thankful for the support from the faculty and staff members in the College of
Business and Economics, namely May Aung, Joe Barth, Diane Dobbins, Joan Flaherty, Jamie
Gruman, Towhidul Islam, Kalinga Jagoda, Elizabeth Kurucz, Bruce McAdams, Chris McKenna,
Brent McKenzie, Miana Plesca and Trent Tucker. Thank you all for creating an inclusive study
environment. I would like to express my appreciation to the administrative staff of the School of
Hospitality, Food and Tourism Management and Department of Marketing and Consumer
Studies, namely Rita Raso, Cori Wells, Domenica Alderton, Barb Piccoli, Brigid Flucker, Kandis
Dyack, Lisa Fodor, and Kim Mitz.
I would like to thank the participants who took part in my in-depth interviews, focus groups and
online surveys. I appreciate your trust and honesty for detailing your perceptions and feelings of
your travel experiences, enabling me to explore tourist engagement in my study. Many thanks for
Dr. Dongkoo Yun from the Centre for Tourism Research in Prince Edward Island, your
generosity in assisting to collect data through the online panel made my study much different.
I would like to extend my sincere thanks to my cohorts and the graduate students in HFTM,
namely Mychal-Ann Hayhoe, Ruben Burga, Joshua LeBlanc, Sina Bahramirad, Ye (Sandy) Shen
Jingen (Lena) Liang and Quan Wan. Thank you all for your support over the last four years. I
have had a wonderful experience with you.
Finally, great appreciation to my parents, my sisters, brother and my lovely friends (Miya, Ting,
Zhen, Hui and Oliver). I could not have completed this Ph.D. without the love and understanding
from all of you. You all have made this achievement possible for me.
v
TABLE OF CONTENTS
ACKNOWLEDGEMENT ........................................................................................................... iv
TABLE OF CONTENTS ............................................................................................................. v
LIST OF TABLES ..................................................................................................................... viii
LIST OF FIGURES ..................................................................................................................... ix
Chapter 1 Introduction................................................................................................................. 1
1.1 Research Background ......................................................................................................................... 1
1.2 Research Rationale .............................................................................................................................. 2
1.3 Research Questions ............................................................................................................................. 5
1.4 Research Setting .................................................................................................................................. 6
1.5 Organization of Dissertation ............................................................................................................... 8
Chapter 2 Conceptualization of Tourist Engagement (TE) ...................................................... 9
2.1 Introduction ......................................................................................................................................... 9
2.2 Literature Review .............................................................................................................................. 11
2.2.1 Engagement in Other Social Science ......................................................................................... 11
2.2.2 Customer Engagement ............................................................................................................... 17
2.3 Conceptualization of Tourist Engagement ........................................................................................ 23
2.3.1 Defining Tourist Engagement .................................................................................................... 23
2.3.2 Theoretical Foundations- Service-Dominant Logic (S-DL) ...................................................... 25
2.3.3 Key Concepts to Understand Tourist Engagement .................................................................... 27
2.4 Differentiating Tourist Engagement from Customer Participation and Involvement ....................... 30
2.4.1 The Differences between Customer Participation and Tourist Engagement ............................. 31
2.4.2 The Difference between Customer Involvement and Tourist Engagement ............................... 33
2.5 Conclusion ........................................................................................................................................ 36
Chapter 3 Scale Development of Tourist Engagement ............................................................ 39
3.1 Introduction ....................................................................................................................................... 39
3.2 Tourist Engagement Dimensions ...................................................................................................... 41
3.3 Scale Development Procedures ......................................................................................................... 42
3.4 Sub-Study 1: Item Generation........................................................................................................... 46
3.4.1 Measurement Items Generated from the Literature ................................................................... 46
3.4.2 Measurement Items Generated from the Qualitative Study ....................................................... 50
3.4.3 Synthesizing Items Generated from Both Sources ..................................................................... 57
vi
3.5 Sub-Study 2: Scale Purification ........................................................................................................ 61
3.5.1 Survey Design ............................................................................................................................ 61
3.5.2 Data Collection .......................................................................................................................... 62
3.5.3 Data Analysis ............................................................................................................................. 63
3.5.4 Study Results ............................................................................................................................. 64
3.6 Sub-Study 3: Scale Validation .......................................................................................................... 72
3.6.1 Survey Design and Data Collection ........................................................................................... 72
3.6.2 Data Analysis ............................................................................................................................. 75
3.6.3 Testing Possible Models and the Hierarchy of Tourist Engagement Scale ............................... 80
3.6.4 Convergent and Discriminant Validity ...................................................................................... 88
3.6.5 Predictive Validity ..................................................................................................................... 92
3.6.6 Comparing the Means of Tourist Engagement Dimensions across Sub-groups ........................ 92
Chapter 4 Empirical validation of Tourist Engagement ......................................................... 96
4.1 Introduction ....................................................................................................................................... 96
4.2 Conceptual Foundations and Hypotheses Development ................................................................... 98
4.2.1 Perceived service quality, perceived value, satisfaction and Behavioral intention .................... 99
4.2.2 The Relationships between Tourist Engagement and Other Constructs .................................. 102
4.3 Methodology ................................................................................................................................... 106
4.3.1 Survey Design .......................................................................................................................... 106
4.3.2 Data Collection ........................................................................................................................ 109
4.3.3 Data Analysis ........................................................................................................................... 109
4.4 Results ............................................................................................................................................. 110
4.4.1 Results of the Measurement Model.......................................................................................... 110
4.4.2 Results of the Structural Model ............................................................................................... 113
Chapter 5 Conclusion ............................................................................................................... 117
5.1 Summary ......................................................................................................................................... 117
5.2 Discussion ....................................................................................................................................... 119
5.3 Theoretical Contribution ................................................................................................................. 122
5.4 Managerial Implication ................................................................................................................... 124
5.5 Limitation and Future Studies ......................................................................................................... 125
Bibliography .............................................................................................................................. 128
Appendices ................................................................................................................................. 163
Appendix 1. The details of items from relevant literature. ................................................................... 164
vii
Appendix 2. Recruitment letter. ............................................................................................................ 169
Appendix 3. Interview and focus group questions. ............................................................................... 170
Appendix 4. The expert survey. ............................................................................................................ 171
Appendix 5. The skewness and kurtosis of the TE items and behavioral intention items. ................... 172
Appendix 6 The skewness and kurtosis of the all the survey items. ..................................................... 173
viii
LIST OF TABLES
Table 2-1. The definitions of engagement in other social sciences. ........................................................... 16
Table 2-2. The definitions and focuses of engagement in marketing literature. ......................................... 18
Table 2-3 The comparison of TE, customer participant and involvement. ................................................. 31
Table 3- 1. Scale development process and analysis methods. ................................................................... 43
Table 3-2. The summary of Chinese respondents. ...................................................................................... 53
Table 3-3. The summary of the North American respondents. ................................................................... 54
Table 3-4. Items used for scale purification. ............................................................................................... 59
Table 3-5. The EFA results of TE scale (N=264). ...................................................................................... 67
Table 3-6. The socio-demographic information of the respondents (N=329)............................................. 74
Table 3-7. The cruise experience of the respondents (N=329). .................................................................. 74
Table 3-8. Descriptive statistics for all measurement items (before CFA) (N=329). ................................. 76
Table 3-9. Descriptive statistics for the remained 16 TE measurement items (after CFA) (N=329). ....... 79
Table 3-10. Goodness-of-fit Indices for three measurement models of TE. ............................................... 86
Table 3-11. The convergent validity of TE scale. ....................................................................................... 90
Table 3-12. The discriminant validity of the four factors of TE. ................................................................ 91
Table 3-13. The final measurement items of TE scale. ............................................................................... 91
Table 3-14. Means of dimensions by socio-demographic characteristics and cruise experience: Results of
one-way MANOVA. ................................................................................................................................... 94
Table 4-1. The measurement items of PSQ, PV, SAT, and BI. ................................................................ 108
Table 4-2. The convergent validity of TE, PSQ, PV, SAT, and BI. ......................................................... 112
Table 4-3. The discriminant validity between TE, PSQ, PV, SAT, and BI. ............................................. 113
Table 4-4. Results on the hypothesized relationships between TE, PSQ, PV, SAT, and BI. ................... 114
Table 4-5. The total effects, direct effects and indirect effects in the hypothesized model. ..................... 115
Table 5-1. Item deletion and adding in the scale development process. ................................................... 120
ix
LIST OF FIGURES
Figure 3-1. The summary of items from relevant literature. ....................................................................... 49
Figure 3-2. The summary of items from the qualitative study. ................................................................... 56
Figure 3-3. The scree plot of EFA. ............................................................................................................. 66
Figure 3-4. Some possible models of TE scale. .......................................................................................... 83
Figure 4-1. The hypothesized relationship between TE, PSQ, PV, SAT and BI. ....................................... 99
Figure 4-2. The tested relationships between TE, PSQ, PV, SAT, and BI ............................................... 114
1
Chapter 1 Introduction
1.1 Research Background
The construct engagement has a foundational element which can be applied to multiple
fields (Flynn, 2012). It has been broadly used in various disciplines since the 1990s (Kahn, 1990).
As a context-dependent construct, engagement takes on various meanings such as consumer
engagement (Brodie, Ilic, Juric, & Hollebeek, 2013; Vivek, 2009) or customer engagement
(Bolton, 2011; Bowden, 2009a, b; Gummerus, Liljander, Weman, & Pihlström, 2012) in
marketing; employee engagement (Kahn, 1990, 1992; Rothbard, 2001; Saks, 2006; Schaufeli,
Salanova, Gonzalez-Roma, & Bakker, 2002) in organizational behavior; student engagement in
educational science (Gunuc & Kuzu, 2015; Hu, 2010; Kuh, 2001, 2003); civic engagement (Delli
Carpini, 2000; Jennings & Stoker, 2004) in sociology; social engagement (Achterberg et al.,
2003; Huo, Binning, & Molina, 2010; Sabbath, Lubben, Goldberg, Zins, & Berkman, 2015) and
task engagement (Bohlmann & Downer, 2016; Matthews et al., 2010) in psychology.
In the last decade, the term “engagement” has become popular and is widely used by
marketing practitioners for a better understanding of customer experience. The difference
between customer experience and engagement is, the former represents as the sum of all
interactions a customer has with a company (Lemon & Verhoef, 2016), while the latter
highlights the interactive, value co-creative process between them (Brodie, Hollebeek, Juric, &
Ilic, 2011; Vargo & Lusch, 2004). Initially used by consulting companies, such as Gallup to
build the emotional connection with customers, the concept of customer engagement (CE) has
triggered profound debates among marketing researchers. Various practitioners and scholars
interpret this term based on their knowledge and experience, although with a lack of clear
boundaries for its definition and scope. Despite these debates on its conceptualization and
2
measurement scale, there is consensus that customer engagement is positively related to
organizational performance, like increase in sales and profits, superior competitive advantage,
customer retention, loyalty, and purchase decisions (Bowden, 2009b; Kumar et al., 2010;
Hollebeek, 2011a, b; Romero & Okazaki, 2015; Patterson, Yu, & de Ruyter, 2006).
Due to the effectiveness of engagement in improving the customer experience and creating
long-term customer value, it is reasonable and beneficial to apply it in the context of tourism
(Chathoth et al., 2014; So, King, & Sparks, 2014; So, King, Sparks, & Wang, 2016; Wei, Miao,
& Huang, 2013), where the engagement construct has not yet been fully discussed. Some
researchers have recently attempted to investigate engagement in tourism, while mainly focus on
consumer engaging behaviors through online platforms in hotel and airline industry (Chathoth et
al., 2014; So et al., 2014; So et al., 2016; Wei et al., 2013). Note that the discussion on how
tourists behave, feel and experience when they are engaged at tourism destination is lacking. The
contextually different nature of engagement requires a newly developed "tourist engagement"
(TE) instead of merely borrowing the current meaning of CE. This is because different from
other types of consumption, what happens in tourism destinations is unpredictable but greatly
affects the tourist engaging experience. A travel experience cannot be fully pre-arranged, and
tourists encounter various unpredictable people and situations on-site (Ryan, 1997), which
contribute to their ultimate overall travel experience. Therefore, tourists’ on-site engagement
needs to be cautiously investigated. The present study explores the engagement concept in
tourism through a systematic research and sheds light on the social and physical interaction and
emotional connection between tourists and destinations/service providers.
1.2 Research Rationale
3
Although CE has been widely used in industrial practices (e.g., Appelbaum, 2001; Bowden,
2009a; Gummerus et al., 2012; Sawhney, Verona, & Prandelli, 2005; Wei et al., 2013), its
academic research is still in its infancy and requires more studies regarding the construct
conceptualization and empirical measurement. The development of a new TE construct demands
a comprehensive conceptualization and measurement to distinguish it from other types of
engagement, as well as to differentiate it from some similar constructs (e.g., participation,
involvement). The following outlines the main issues raised in CE research, which are also
critical for the development of TE.
Current studies on CE lack systematic scholarly inquiry on its definition and scope (Brodie
et al., 2011). Only a few scholars (e.g., Brodie et al., 2011, Hollebeek, 2011b; Patterson et al.,
2006) attempt to conceptualize and provide a clear boundary of CE in marketing. Before it can
be applied to tourism, the meaning of engagement and its theoretical foundations need to be
explored. On the one hand, various sub-forms of engagement referring to different focuses are
simultaneously used in the marketing literature without precise meanings, like customer
engagement, consumer engagement, brand community engagement, brand engagement, customer
brand engagement, and online brand engagement. Arguments also exist concerning whether CE
should include transaction behaviors or not (Bijmolt et al., 2010; Kumar et al., 2010; Van Doorn
et al., 2010; Verhoef, Reinartz, & Krafft, 2010). On the other hand, the current engagement
studies are grounded in different theoretical perspectives, including the interactive and value co-
created experience from service-dominant logic (S-DL) (Vargo & Lusch, 2004) and the
psychological process from psychology (Bowden, 2009b). Researchers are still attempting to
enrich the conceptual discussion of the scope and the theoretical foundations of engagement,
aiming to defend its territory and differentiate it from existing constructs.
4
Furthermore, the existing literature on the dimensionality of customer engagement is still
disputable (B. Little & P. Little, 2006), wherein both single and multi-dimensional approaches
are used. The former focuses mainly on a behavioral dimension (Van Doorn et al., 2011), while
the latter emphasizes the complexity of CE (e.g., Bowden, 2009b; Brodie et al., 2011; Mollen &
Wilson, 2010; Patterson et al., 2006). For instance, using a unidimensional perspective, Van
Doorn et al. (2010) defined CE as “a customer's behavioral manifestation” (p. 254) toward a
brand or firm. However, the majority focuses on the multi-dimensions of CE to include at least
two of cognition, behavior, and emotion. These dimensions are believed to be not exclusive but
to mutually interact with each other to create different levels of engagement. The present TE
study thus aims to provide a thorough discussion on the dimensionality of engagement and
validate it empirically.
Most of the extant studies on CE are qualitative and descriptive in nature (Vivek, 2009). The
lack of empirical studies can be explained by the absence of a validated measurement scale. Only
a few studies have endeavored to develop such a scale (e.g., Hollebeek, Glynn, & Brodie, 2014;
So et al., 2014; Vivek, 2009). Admittedly, the current qualitative approach to CE enables
researchers to explore research problems more flexibly by capturing in-depth information,
understanding complex meanings, and depicting a holistic picture of the phenomena (Creswell,
1998). However, empirical investigations using quantitative methods are also needed when the
model of engagement is built, and the relationships of engagement with other constructs are
proposed. Only by basing their findings on a validated measurement of variables, can researchers
identify the relationships between engagement and other constructs. Thus, the lack of a
measurement scale for engagement hinders its further application in marketing and needs further
investigating.
5
Last but not least, even though CE has been acknowledged to positively relate to
organizational performance, the understanding of CE’s effect on assessing, predicting and
enhancing customer loyalty is lacking; meanwhile, its interaction with various extant loyalty
antecedents is not clear. Various constructs like perceived service quality, perceived value, and
satisfaction have been recognized as important determinants of customer loyalty (Chang, Wang,
& Yang, 2009; Chen, 2008; Cronin, Brady, & Hult, 2000; Yang & Peterson, 2004), but their
relationship with CE has rarely been empirically tested. Even though Brodie et al. (2011)
theoretically proposed some antecedents and consequences of CE to understand customer
iterative engaging processes, few empirical studies have validated CE’s role with these
constructs when predicting customer loyalty. Thus, this study considers the issues of
conceptualization, dimensionality, measurement, and empirical validation when applying
engagement in tourism.
1.3 Research Questions
This study aims to enhance the research of tourist experience and sheds light on the quality
of experience by introducing a new construct, “tourist engagement” (TE), wherein the tourist
experience is recognized as the “central productive activity” (Sternberg, 1997, p. 954) in tourism.
By adopting an experiential lens, TE focuses on the ongoing and value co-creative interactions a
tourist has with other objects in a destination. The measurement of TE provides an important
measurement tool to assess, predict and enhance the quality of the tourist experience. A full
understanding of TE includes its conceptualization, scale development, and empirical validation
of its relationship with other constructs. This thesis attempts to answer the comprehensive
question of “what is TE?”, followed by several sub-questions: (1) how does TE differ from other
6
types of engagement? (2) What are the dimensions of TE? (3) How can TE be measured? (4)
What is the role of TE in an integrated model when working with other constructs (perceived
service quality, perceived value, and satisfaction) as predictors of behavioral intention?
This study contributes to service management scholars' and practitioners' understanding of
engagement in the context of tourism by: (1) conceptualizing the TE construct; (2) identifying
the underlying dimensions of this construct; (3) operationalizing a measure to assess engagement
in tourism; (4) empirically validating the measurement items; (5) developing a model of TE with
some extant antecedents of behavioral intention; (6) empirically testing this proposed; and (7)
exploring the theoretical contributions and managerial implications of the study findings.
A better understanding of TE is beneficial for both tourists and destination managers. By
facilitating the engagement process, tourists experience ongoing and meaningful interactions in
flow (Csikszentmihalyi, 1990), and are more likely to have a high-quality travel experience.
Moreover, the extraordinary travel experiences would spill over into individuals’ daily life,
resulting in an increase in their subjective well-being and happiness (Filep & Deery, 2010; Fritz
& Sonnentag, 2006; Yeh, 2013). For destination managers, an engaged tourist is more likely to
perceive high quality and value of the service and feel satisfied about the travel experience,
which in turns leads to recommendation and revisit intention in the future. These positive
behavioral outcomes create long-term customer value for a destination. To summarize, an
engaged tourist actively participates and becomes involved in tourism activities, and develops
positive word-of-mouth assessment about the destination’s service providers after the trip.
1.4 Research Setting
7
After the conceptualization of TE, this study develops and validates the measurement scale
of TE in cruise tourism, which is among the fast-growing segments of the global tourism
industry (Dowling & Weeden, 2017). The global cruise travelers have experienced an annual
growth of 7% since the 1980s (FCCA, 2012), and reached to 24 million in 2016 (CLIA, 2017).
The reasons for using cruise tourists are twofold. Firstly, the cruise experience is among the
most complex experiences in tourism because of its duration (typically four to six days) and the
combination of multiple destinations and onboard experiences. It is significantly different from
other types of travel activities, which provides 90% all-inclusive products, various of amenities,
activities, and services on board, as well as pre-arranged multiple destinations (Teye & Paris,
2010). If our study could measure the complexity of engagement in cruise tourism, it would
mean the TE scale could be easily adapted to other settings (integrated resorts, theme parks, and
the like). Secondly, cruise tourists are in a complicated social environment and interact
intensively with different actors (employees, fellow tourists, companions, and local
communities). The extant research on cruise tourism has recognized the importance of
interaction on tourist experience, such as customer-to-customer interaction (Huang & Hsu, 2010).
However, a holistic understanding of how different interactions contribute to the value co-
creation in cruise tourists’ experience, and how these interactions influence the emotional
connection between the tourist and a cruise company has not yet been explored. The
investigation of tourist engaging experience in the present study shows an advanced
understanding of the interactions between tourists and other actors, which represent the core
component in tourist engagement, as it is through which tourists add value to their ultimate
cruising experience.
8
1.5 Organization of Dissertation
The dissertation is structured into five chapters. Chapter 1 discusses the research background,
rationale, and questions. Chapter 2 conceptualizes TE through a thorough discussion on its
definition, theoretical foundations and its differences from some similar constructs. This
conceptualization was based on the review of relevant literature in engagement studies, including
engagement in other social studies, and in marketing studies, as well as the theoretical
perspective: S-LD (Vargo & Lush, 2004). Chapter 3 discusses the main components of TE and
presents its scale development process as is recommended by Churchill (1979) and Netemeyer,
Bearden, and Sharma (2003) through three steps: item generation, scale purification, and scale
validation. The item pool was a combination of items generated from the literature and the
qualitative phase of the research. Chapter 4 delineates and empirically tests TE’s relationship
with perceived service quality, perceived value, satisfaction, and behavioral intention. Finally,
Chapter 5 summarizes the theoretical contributions, managerial implications, limitations and
future studies.
9
Chapter 2 Conceptualization of Tourist Engagement (TE)
2.1 Introduction
It is worth noting that the construct engagement shows variations in different
contexts (Malthouse & Calder, 2011), and research on customer engagement (CE)
represents one of its recent applications in the consumer behavior and marketing area.
Recent marketing practices and studies have suggested the positively relationship
between customer engagement and customer loyalty (Bowden, 2009b; Hollebeek, 2011a,
b; Kumar et al., 2010; Romero & Okazaki, 2015; Patterson et al., 2006), which
is a critical concept directly connected to a company’s growth, revenues and profits
(Hallowell, 1996; Innis & La Londe, 1994) in today's competitive marketplace. Note that
a company’s ultimate goal is to retain customers and keep them loyal to it. It is believed
that by investigating the emotional connections between customers and companies, as
suggested by customer engagement, customer loyalty could be more effectively gauged,
predicted, and enhanced (Appelbaum, 2001; Bowden, 2009b; McEwen & Fleming, 2003).
However, to date no known studies have fully explored its application in the context
of tourism, i.e. focusing on tourist experiences in tourism destinations. There are a few
studies that examine CE with tourism brands in the context of social media (Harrigan,
Evers, Miles, & Daly, 2017; Lei, Pratt, & Wang, 2017; So et al., 2014; So et al., 2016),
but not how tourists feel, behave and act when they are engaged in a destination, and how
this engaging experience influences their whole travel experience and post-trip behavior,
such as destination loyalty. Due to engagement’s effectiveness in improving the customer
experience and maintaining customer loyalty, it is reasonable and beneficial to investigate
the concept of engagement in tourism. Thus, this study aims to conceptualize engagement
10
in tourism settings by developing a new construct, i.e. tourist engagement or TE. This
conceptualization endeavor attempts to answer questions like the necessity of developing
a new construct (TE), the definition of TE, the theoretical foundations of TE, the key
concepts to understand TE, and finally how it differs from similar constructs. This
chapter adds to a better understanding of tourist engaging experience through the lens of
value co-creation.
Specifically, the reasons for developing a new construct instead of using the extant
CE are twofold. On the one hand, literature has identified engagement as a very
contextually different construct, which may exert various meanings based on the
subject’s role and the context. Engagement in the context of tourism involves different
features than other types of engagement (e.g., customer engagement, employee
engagement, student engagement, and social engagement) because of the destination
visited. Specifically, a tourism destination contains distinctive complexity and
uncertainty compared to other contexts. It offers both tangible and intangible interactions
to tourists through a variety of objects, including service providers (such as employees),
fellow tourists, companions, local communities, and even attractions and activities. What
happens in tourism destinations is unpredictable but greatly affects their engaging
experience. Travel consumption cannot be fully pre-arranged, and tourists encounter
various unpredictable people and situations on-site (Ryan, 1997), which add to their
ultimate travel experience. Thus, it is imperative to develop the TE construct as no extant
types of engagement fully address the tourism setting.
On the other hand, even though there has been a growing amount the research into
CE, which might have the highest potential to be adapted to the study of TE, current
11
research on CE lacks systematic scholarly inquiry (Brodie et al., 2011): only a few
scholars (e.g., Brodie et al., 2011, Hollebeek, 2011a, b; Patterson et al., 2006) have
attempted to conceptualize it and set clear boundaries in marketing. Various sub-forms of
engagement referring to different focuses are simultaneously used in the marketing
literature without clear differentiation of their meanings, like customer engagement,
consumer engagement, brand community engagement, brand engagement, customer
brand engagement, and online brand engagement. Furthermore, these CE studies are
grounded in different theoretical perspectives, including the interactive and value co-
created experience from S-DL (Vargo & Lusch, 2004) and psychological process from
psychology (Bowden, 2009b). Researchers are still attempting to enrich the conceptual
discussion of the scope and the theoretical foundations of engagement, aiming to defend
its territory and differentiate it from existing constructs. Finally, the majority of this
research focuses on customers’ experience in online brand communities through the use
of social media (e.g., Dessart, Veloutsou, & Morgan-Thomas, 2015; Lei et al., 2017),
which is different from the on-site experience at a destination. Thus, a clear
conceptualization of the meanings and theoretical foundations of engagement need to be
established before TE can be applied in a tourism context. This conceptualization enables
a better understanding of the on-site experience of tourists and establishes the theoretical
foundations for the further measurement of TE.
2.2 Literature Review
2.2.1 Engagement in Other Social Science
12
Engagement is a complex construct that contains a variety of meanings. The verb “to
engage” can be interpreted as “to occupy or attract”, “to participate or become involved
in”, “to arrange”, “to employ or hire”, “to move into position”, “to come into operation”,
and “to bring (weapons) together in preparation for fighting” (http://tinyurl.com/zs6qrga).
These meanings indicate that the term has a behavioral focus with an objective. While the
noun "engagement" emphasizes a connection between a subject and an object
(http://tinyurl.com/j2sja8h), its meaning differs based on the context, which is determined
primarily by the role that a subject performs. Engagement highlights the focus on
duration, action, and arrangement. One example is in the marriage context, where
engagement indicates a formal agreement between two parties from the proposal to the
marriage.
Social science research has demonstrated the application of engagement in various
contexts (see Table 2-1). Specifically, Kahn (1990) first conceptualized (personal)
engagement as a situation where "people employ and express themselves physically,
cognitively, and emotionally during role performances" (p. 694) in an organizational
behavior context. When studying employee engagement, researchers focus on
distinguishing engaged vs. disengaged employees, and its influence on improving
employee satisfaction, maintaining retention and generating positive business outcomes
(Harter, Schmidt, & Hayes, 2002; Markos & Sridevi, 2010; Saks, 2006). In the
educational literature, engagement is associated with the effort, energy or time invested
by students in learning activities (Hu, 2010; Kuh, 2001) and is recognized as an effective
method for achieving successful academic outcomes (Appleton, Christenson, Kim, &
Reschly, 2006; Gan, Yang, Zhou, & Zhang, 2007; Gunuc & Kuzu, 2015). Meanwhile,
13
engagement has also been a popular topic for researchers studying community
governance and is considered to be essential to the success of sustainable community
building, development or revitalization projects (Shandas & Messer, 2008).
Multiple perspectives of engagement research contribute to a better understanding of
this concept. Engagement demonstrates its complexity across contexts, while
unfortunately, this raises the issue of its application in the context of tourism. That is to
say, no extant types of engagement can fit into a tourism setting without proper
refinement, especially on the two most important issues: definition and dimensions. The
following part discusses the underpinning principles of engagement based literature
review on engagement studies see Table 2-1).
To start with, when defining engagement, it is worth noting that engagement
indicates motivationally driven interactions between subjects and objects. Motivation has
been considered as a critical element in engagement studies (Appleton et al., 2006;
Matthews et al., 2010). Especially, cognitive engagement is associated with a subject’s
motivation of achieving certain outcomes. For instance, a student engages in academic
activities can be motivated by learning goals, and positive learning outcomes (Gunuc &
Kuzu, 2015; London, Downey, & Mace, 2007; Martin & Dowson, 2009). An individual
can be motivated to participate or be involved in an interaction by certain "stimuli"
(product or services) (Achterberg et al., 2003; Kahn, 1990). Besides, engagement is
embedded in the interactions a subject has with objects. For instance, when studying
retirees’ transition life, Sabbath et al. (2015) define social engagement based on the
“number, frequency, and quality of an individual’s connection” to others (e.g.
14
organizations, family members and close friends), indicating that their engagement level
decreases or increases as determined by the number of interactions.
The meaning of engagement also varies depending on the context. Firstly, engaged
subjects take on various roles (e.g., students, customers, citizens, and employees) in
different contexts (e.g., Gunuc & Kuzu, 2015; Hollebeek, 2011a, b; Jennings & Stoker,
2004; Saks, 2006). For instance, research on employee engagement emphasizes an
individual's work role in an organization (Kahn, 1990), student engagement focuses on
how to increase the engagement level to realize an educational achievement (Gunuc &
Kuzu, 2015; Hu, 2010; Kuh, 2001), while customer engagement highlights customers’
co-creation value for a company’s product or service (Sawhney et al., 2005). Secondly,
an individual's engagement is scientifically influenced by context-related factors. For
example, the level of student engagement is considered to be influenced by family, peer
and school contexts (Appleton et al., 2006). Research also shows that students become
less engaged in school activities when they progress from elementary to middle to high
school (Marks, 2000). Thus, the context in which subjects find themselves should be
taken into serious consideration when defining the concept of engagement.
Another issue is the dimensionality of engagement. Widely defined as a
multidimensional construct across contexts, it is believed that engagement contains
elements from behavioral, emotional and cognitive dimensions (Kahn, 1990), but the
specific expression of these dimensions can be different across contexts. For instance,
Maslach and Leiter (1997) identified engagement as a three-dimensional construct
including energy, involvement, and efficacy; these dimensions present exactly the
opposite of the three dimensions associated with burnout: exhaustion, cynicism, and lack
15
of professional efficacy. According to Schaufeli and colleagues (Schaufeli & Bakker,
2004; Schaufeli et al., 2002), employee engagement can be measured by vigor, dedication,
and absorption, while for student engagement, Appleton et al. (2006) proposed four
subtypes of engagement: academic, behavioral, cognitive, and psychological. This
multidimensional approach is popularly adopted to precisely capture the complexity of
engagement.
16
Table 2-1. The definitions of engagement in other social sciences.
Disciplines Constructs Definitions
Psychology
Social
engagement
“a high sense of initiative and involvement and can respond adequately to social stimuli in the social environment—
participate in social activities and interact with other residents and staff” (Achterberg et al., 2003, p. 213).
“the individuals’ identification with and commitment to the group’s goals and welfare” (Huo et al., 2010, p. 202).
“number, frequency, and quality of an individual’s connection to those in his or her social environment” (Sabbath et
al., 2015, p. 311).
Task engagement
“the classroom-embedded behavioral manifestation of a child’s ability to self-regulate that ultimately enables the child
to take full advantage of the resources available to him or her in the classroom” (Bohlmann & Downer, 2016, p. 20).
“high energetic arousal, task motivation, and concentration” (Matthews et al., 2010, p. 189).
Occupational
engagement
"a lifestyle characteristic (Christiansen, 2005) and involves occupational performance, the dynamic interplay among
personal, occupational, and environmental factors (Law et al., 1996)" (cf. Bejerholm & Eklund, 2007, p. 21).
Educational
Science
Student
engagement
“the quality and quantity of students’ psychological, cognitive, emotional and behavioural reactions to the learning
process, as well as to in-class/out-of-class academic and social activities, to achieve successful learning outcomes”
(Gunuc & Kuzu, 2015, p. 588).
“the quality of effort students themselves devote to educationally purposeful activities that contribute directly to
desired outcomes” (Hu & Kuh, 2002, p. 555).
Organizational
Behaviour
Personal
engagement
“the harnessing of organization members' selves to their work roles; in engagement, people employ and express
themselves physically, cognitively, and emotionally during role performances” (Kahn, 1990, p. 694).
Employee
engagement
“Engagement in a role refers to one's psychological presence in or focus on role activities and may be an important
ingredient for effective role performance (Kahn, 1990, 1992)” (Rothbard, 2001, p. 656).
“as a positive, fulfilling, work-related state of mind that is characterized by vigor, dedication, and absorption.”
(Schaufeli et al., 2002, p. 74).
“the amount of cognitive, emotional, and physical resources that an individual is prepared to devote in the
performance of one’s work roles” (Saks, 2006, p.603).
Sociology Civic engagement
“involvement in voluntary organizations and the performance of volunteer work, both of which facilitate the
development of social networks.” (Jennings & Stoker, 2004, p. 39).
“being able to influence choices in collective action” (Camino & Zeldin, 2002, p. 214).
17
2.2.2 Customer Engagement
Engagement, as a relatively new topic, is gaining increasing attention in the fields of
marketing and consumer behaviour (Bolton, 2011; Brodie et al., 2013; Gummerus et al., 2012).
Since the late 2000s, there has been booming discussion on this concept, not only from
academics but also industry professionals.
Initially proposed by consulting companies (e.g., Gallup), customer engagement (CE) was
developed under the assumption that satisfaction on its own fails to predict customer loyalty, and
it is imperative to shift the attention to the emotional connection with the customer, that is
customer engagement (Appelbaum, 2001; McEwen & Fleming, 2003). The need for more
research-based knowledge was ignited by the Marketing Science Institute (MSI) (2010) which
identified engagement studies as one of the top eight priorities in the coming years; it also
recommended that research on the way customers engage in their consumption experiences is
needed. MSI (2010) defines CE as "customers' behavioral manifestation toward a brand or firm
beyond purchase, which results from motivational drivers including: word-of mouth activity,
recommendations, customer-to-customer interactions, blogging, writing reviews, and other
similar activities" (p. 4). At the same time, academic researchers attempted to conceptualize it
(e.g., Bowden, 2009b; Brodie et al., 2011; van Doorn et al., 2010). Specifically, the marketing
literature on engagement has expanded significantly as researchers use it with various definitions
to describe different focuses of engagement (see Table 2-2).
18
Table 2-2. The definitions and focuses of engagement in marketing literature.
Constructs Terms and definitions Comments Customer/ consumer
engagement • Customer engagement: “a psychological process that models the
underlying mechanisms by which customer loyalty forms for new customers
of a service brand as well as the mechanisms by which loyalty may be
maintained for repeat purchase customers of a service brand” (Bowden,
2009b, p. 65).
• Customer engagement: “Customer engagement (CE) is a psychological
state that occurs by virtue of interactive, cocreative customer experiences
with a focal agent/object (e.g., a brand) in focal service relationships. It
occurs under a specific set of context dependent conditions generating
differing CE levels; and exists as a dynamic, iterative process within service
relationships that cocreate value. CE plays a central role in a nomological
network governing service relationships in which other relational concepts
(e.g., involvement, loyalty) are antecedents and/or consequences in iterative
CE processes. It is a multidimensional concept subject to a context- and/or
stakeholder-specific expression of relevant cognitive, emotional and/or
behavioral dimensions” (Brodie et al., 2011, p. 260).
• Consumer engagement: “Engagement is a state of being involved,
occupied, fully absorbed, or engrossed in something—sustained attention”
(Higgins & Scholer, 2009, p.6).
• Customer engagement: the level of a customer’s various “presence”
(including cognitive, emotional and physical aspects) in their relationship
with the organization (Patterson et al., 2006).
• Customer engagement: “a customers’ personal connection to a brand as
manifested in cognitive, affective, and behavioral actions outside of the
purchase situation” (So et al., 2014, p. 310)
• Customer engagement: “a behavioral manifestation toward the brand or
firm that goes beyond transactions” (Verhoef et al., 2010, p. 247).
• Customer engagement: “the intensity of an individual’s participation in
and connection with an organization’s offerings and/or organizational
activities, which either the customer or the organization initiate” (Vivek
Beatty, & Morgan, 2012, p.127).
Customer engagement (CE) is the most
frequency used terminology, and
consumer engagement does not indicate
much difference. CE can be recognized as
a psychological state (Brodie et al., 2011)
or psychological process (Bowden, 2009b)
of multiple levels of customer physical,
cognitive, emotional and behavioral
presence. Customers are engaged with the
offerings or activities (Vivek, 2009) in
different levels, like absorption,
dedication, and vigor/interaction
(Patterson et al., 2006).
Brand (community)
engagement or online
engagement
• Community engagement: “community engagement’ refers to the positive
influences of identifying with the brand community, which are defined as
the consumer's intrinsic motivation to interact and cooperate with
community members” (Algesheimer, Dholakia, &Hermann, 2005, p.21).
• Customer brand engagement: “the level of a customer’s cognitive,
emotional and behavioral investment in specific brand interactions”
One of the important focuses on CE
research is in the online/virtual
environment, especially under the
development of social platform (Cheung,
Lee, & Jin, 2011), like Facebook, Twitter,
blog. It is believed that the dynamic,
19
(Hollebeek, 2011b. p.555).
• Online engagement: “Online engagement is a cognitive and affective
commitment to an active relationship with the brand as personified by the
website or other computer-mediated entities designed to communicate brand
value. It is characterized by the dimensions of dynamic and sustained
cognitive processing and the satisfying of instrumental value (utility and
relevance) and experiential value (emotional congruence with the narrative
schema encountered in computer-mediated entities)” (Mollen & Wilson,
2010, p. 923).
• Virtual customer engagement: "customer-centric", "active", "two-way",
and "continuous", focuses on "social and experiential knowledge", and has
"direct as well as mediated interactions with prospects and potential
customers". (Sawhney et al., 2005, p.7)
• Brand engagement in self-concept (BESC): “an individual difference
representing consumers’ propensity to include important brands as part of
how they view themselves” (Sprott, Czellar, & Spangenberg, 2009, p. 92)
iterative engagement processes (Jahn &
Kunz, 2012; Mollen & Wilson, 2010) of
engagement can be strengthened by the
technology used in the online brand
community (Brodie et al., 2013).
Customer engagement
behavior • Customer engagement behavior: “a formative construct consisting of
word-of-mouth, loyalty program participation, customer interaction, and co-
creation, which are determined by relationship quality, rewards, self-
enhancement, learning, social integration, and company identification”
(Romero & Okazaki, 2015, p.1).
• Customer engagement behavior: “go beyond transactions, and may be
specifically defined as a customer’s behavioral manifestations that have a
brand or firm focus, beyond purchase, resulting from motivational drivers”
(van Doorn et al., 2010, p. 254).
It focuses on a non-transactional side of
consumer behavior, which is also similarly
discussed by Bijmolt et al. (2010), and
Verhoef et al. (2010). Customer
engagement behaviors contain a wide
range of behaviors: blogging,
recommendations, helping other
customers, word-of-mouth activity,
writing reviews, and even engaging in
legal action (van Doorn et al., 2010)
Customer
engagement value • The customer engagement value is constituted by of four different types of
values: customer lifetime value, customer referral value, customer influencer
value, and customer knowledge value (Kumar et al., 2010).
Customer engagement value is considered
as an overarching new value metric which
incorporates the value of both
transactional and non-transactional
behaviors.
20
Various sub-forms of engagement have been proposed to understand the experience between
a customer and a company, which covers all the interactions with stimuli (i.e. product or service)
during the consumer buying process, including pre-purchase and post-purchase stages (Lemon &
Verhoef, 2016). It is believed that customers who perceive their consumption experience as
memorable have a higher chance of being fully satisfied, tend to spread positive word of mouth,
and are more likely to pay a price premium (Berry, Carbone, & Haeckel, 2002; Mascarenhas,
Kesavan, & Bernacchi, 2006). Although the extant literature has sufficient understanding of
customer experience on pre-purchase to post-purchase stages focusing on repeated transactions,
it lacks investigation on the value co-creation interactions between the two parties (Venkatesan,
2017). These interactions representing customer engagement beyond transactions are extremely
important as they allow customers to develop advocacy behaviors or offer information for a
better development of new products/services for a company (Romero, 2017; van Doorn et al.,
2010). Thus, ensuring customers have positive experiences by actively engaging them is critical
for the success of a company/brand.
Marketing research has adopted two main perspectives to understand CE: a psychological
perspective and a behavioral perspective (Venkatesan, 2017). The former adopts a
multidimensional manner and argues that CE incorporates emotional, cognitive, and behavioral
elements (e.g., Brodie et al., 2011; Hollebeek, 2011b; Patterson et al., 2006; So et al., 2014). Due
to the complexity and a lack understanding of the multidimensional CE, current research starts
with a qualitative approach, but only a few studies have attempted its operationalization. For
instance, Vivek (2009) conducted an initial research to develop a three-dimensional
measurement scale of CE including enthusiasm (cognitive-affective), conscious participation
21
(behavioral), and social interaction (social). Herein, CE is considered as a reflective construct
with different expression of dimensions in different contexts.
The latter (i.e. behavioral perspective) predominantly focuses on non-transactional customer
behaviors which might influence the company. As a formative construct, CE is exhibited through
word-of-mouth, recommendations, loyalty programs, helping other customers, writing reviews,
customer interactions, blogging, and engaging in legal action (Jaakkola & Alexander, 2014;
Romero & Okazaki, 2015; Van Doorn et al., 2010; Verhoef et al., 2010; Wei, Miao & Huang,
2013). Empirical studies have been conducted to understand consumers' engagement behaviors
with brands, especially through the online community. For instance, Lei et al. (2017) measured
CE behaviors through three dimensions, including the number of likes, comments, and shares.
Gummerus et al. (2012) divided CE into "community engagement behaviors" and "transactional
engagement behaviors" and measured CE through the "frequency of brand community visits,
content liking, commenting, and news reading, as well as frequency of playing, and money spent
on the internet gaming site" (p.863). However, it is believed that behavioral participation alone
might not be adequate for CE, as customers can be motivated just by gaining information or
mitigating risks (Brodie et al., 2013). Thus, researchers are more inclined to favor a
multidimensional approach that incorporates both customers' enduring psychological connection
and behavioral participation with a company/brand (Brodie et al., 2011; So et al., 2016;
Hollebeek, 2011b).
Greater understanding of CE has led to a fundamental change in understanding value.
Traditionally, value is embedded in the product a company provides and is considered as trade-
off between benefits (what customers get) and costs (what customers give up) (Zeithaml, 1988).
In other words, companies deliver the value created within the organization to customers in a
22
one-way interaction in a company-centric approach. However, recent marketing focuses on
blurring the strict lines between companies and customers, and CE especially presents various
ways how customers can interact and co-create value with companies (Vargo & Lusch, 2004).
Meanwhile, value co-creation goes beyond transactions, during which customers can influence
and add value to company product innovation by using their knowledge, expertise and
experience (Michel, Brown, & Gallan, 2008). Examples can be found in activities like providing
feedbacks or suggestions on product/service design, sharing knowledge or information with a
company or its customers, recommending offerings to other customers, or even actively
engaging in the consumption process of a product/service (Hoyer, Chandy, Dorotic, Krafft, &
Singh, 2010; Kumar et al. 2010; Libai et al. 2010; Ranjan & Read, 2016; Romero, 2017; Tang,
Fang, & Wang, 2014). This new value perspective influenced by CE highlights a customer-
centric (McAlexander, Schouten, & Koening, 2002) two-way interaction that includes customers
in the value creation process.
Engaged customers are actively involved in the customer-company relationship, with a
feeling that they possess a personal stake in the brand (Harris, 2006). Customers bring in their
experiences and knowledge in product innovation (Sawhney et al., 2005) and new product
development (Hoyer et al., 2010). This change has more phenomenally emerged in online brand
communities with the development of internet technology and social media, because these
techniques and platforms enable firms to engage more customers more frequently and
persistently. Supported by the development of the internet and social networks (e.g., Twitter,
LinkedIn, Facebook, and YouTube), the quality and quantity of CE have been hugely advanced.
Meanwhile, customers are provided with the opportunity to express their feelings, share their
knowledge, and tell their stories instantly. As a result, companies employ the internet as a
23
platform to adopt a collaborative process, wherein customers are included in the value co-
creation process (Lusch & Vargo, 2006a; Vargo & Lusch, 2004).
2.3 Conceptualization of Tourist Engagement
2.3.1 Defining Tourist Engagement
Through the discussion of engagement in social science in general and in marketing more
narrowly, a few features of engagement should be highlighted when defining tourist engagement
(TE). Firstly, engagement can be defined differently in various contexts, indicating that TE
cannot be isolated from the destination and the actors involved, like employees, local community,
fellow tourists and people from companion groups. Secondly, engagement goes beyond the
transaction process and is embedded in the value co-creation interactions between the subject
and objects. The engaged subject is motivated to become involved or participate in the value co-
creation process by using the resources available to him/her. Thirdly, even though a single
dimension of engagement is used in some studies (such as customer engagement behavior), the
majority focuses on its complexity by using a multidimensional approach, which incorporates
cognition, emotion and behaviour. Such an approach was adopted in TE, as both enduring
psychological connection and behavioral participation are critical elements for maintaining
engagement.
TE in this study is defined as tourists' psychological state that occurs by virtue of interactive,
co-creative tourist experiences with a focal agent/object (people/attraction/ activities /encounters)
in focal travel experience relationships. This definition was adapted from Brodie et al. (2011),
which is the most comprehensive definition of CE in current marketing literature. We modified
their definition to include “people/attraction/activities/encounters” as the focal agent/object to
24
ensure that it fits into the context of tourism. Also, we highlighted the subject as “tourists”, and
the context as “travel experience relationships.”
The "interactive" and "co-creative" terms describe the relationship between the subject (e.g.,
a customer or in the present case, a tourist) and the object (e.g., a brand or in the present case,
people or an attraction/activity/encounter), emphasizing a two-way interaction, customer-centric
approach (Sheth, Sisodia, & Sharma, 2000). Slightly different from the interacting and co-
creating process as described in CE literature, the engagement in tourism is realized instantly
through daily face-to-face, verbal, social and physical interactions between tourists and other
actors encountered during travel. For instance, behaviors like providing feedback or suggestions
to others online in CE studies becomes enjoying short conversations or sharing travel
information with fellow tourists. All these interactions add to the tourist experience of the whole
trip.
In addition, TE aims to shed light on the personalized tourist-centric experience. During
travel, engagement occurs within the two-way interactions between the tourist and the object
when the interactive and co-creative experience happens. Specifically, "interactive" highlights
the importance of the dynamic, on-going experiences between the subject (the tourist) and the
object. "Co-creative" explains the tourist's role in the value-creating process, within which the
destination or companies as a system provide resources for value creation, while the tourist is
always a co-creator of value.
The word "customer" has been changed to "tourist" to emphasize the importance of the role
taken by the engagement subject. It has been acknowledged that the meaning of "engage" or
"engagement" significantly relies on the different roles (e.g., employee, student, customer, and
tourist) the subject assumes in various contexts. The understanding of TE cannot be isolated
25
from tourist experiences because it is rooted in their dynamics. TE is also the manifestation of
tourist experiences, through which they can be better "observed." As stated by Malthouse and
Calder (2011), “engagement is based on experiences, making it a different kind of psychological
state, one that must be studied jointly with experiences" (p.277).
The last modification is to change the "service relationships" to "travel experience
relationships," which includes tourists' interaction with service providers and other actors. Even
though travel is part of the service category, the present study highlights the difference between a
generic relationship with this more specific one. Besides sharing the same four features
(intangibility, inseparability, heterogeneity, and perishability) of services, traveling also brings in
some other experiences. It is impossible to physically experience the actual services before
tourists arrive at the destination; once there, they are "stuck" and need to spend a comparatively
longer time to go through their travel experience relationships. The experience is also much
longer than a typical service encounter, beginning the minute someone leaves "their usual
residence" and not ending until they return to it. This travel experience can also find its
theoretical supports from the concept of the "experience economy" (Pine & Gilmore, 1999),
indicating that tourists are seeking memorable experiences of unique occurrences. TE can be
used to enhance the understanding of the tourist experience through a strong, enduring
psychological connection and behavioral interaction with the objects in travel experience
relationships. This change also highlights the context-dependent feature as mentioned by Brodie
et al. (2011).
2.3.2 Theoretical Foundations- Service-Dominant Logic (S-DL)
Since its first mention in 2004 by Vargo and Lusch, "service-dominant logic" (S-DL) has
triggered substantial academic conversations on the paradigm shift from the traditional "goods-
26
dominant logic" in service management. S-DL suggests the understanding of customer value-
creating activities as the starting point of all business strategies (Lusch & Vargo, 2006a; Vargo &
Lusch, 2004). Some critical concepts have been used to understand the process of value creation.
Resource-advantage theory (Hunt & Morgan, 1995) is crucial to understand S-DL. This
theory is among the essential theoretical frameworks supporting S-DL. Accordingly,
heterogeneous and imperfectly mobile resources generate values to accommodate different
demands in the market. Specifically, two types of resources exist: operand and operant resources.
The former, which contains land, plants, animals, and minerals (Vargo & Lusch, 2004) plays
essential role in the traditional goods-dominant logic. They are featured as "inhuman," "machine-
like," "basic," "functional," "physical," or "tangible" (cf. Campbell, O'Driscoll, & Saren, 2013).
However, the latter operant resources are "dynamic" and "infinite" compared to the "static" and
"finite" features in operand resources (Vargo & Lusch, 2004). They are typically characterized as
"human", "organizational", "informational", and "relational" (Hunt, 2004, p.22). One of the
important things to understand in S-DL is the concept of “resource integration” (Vargo, Maglio,
& Akaka, 2008), delineating the value co-creating process through the integration of these two
types of resources by actors in the service network.
Vargo and Lusch have developed, revised, and summarized the foundational premises of S-
DL (Lusch& Vargo, 2006b; Vargo & Lusch, 2004, 2008, 2014). some of which support the
understanding of TE. Firstly, "the customer is always a co-creator of value" (Vargo & Lusch,
2014, p. 240), implying the subject (a tourist) is an operant resource which is a significant
contributor in value co-creation with the object. Also, the interactive value co-creation process
could not be ended unilaterally by the object. Secondly, in the statement "a service-centered view
is inherently customer-oriented and relational" (Vargo & Lusch, 2008, p. 7), the word
27
"inherently" emphasizes the priority of customer orientation, indicating that it is customers as the
beneficiary of services/experience determine the value they perceive in the case of engagement.
Thirdly, it is suggested that “all economic and social actors are resource integrators” (Vargo &
Lusch, 2014, p. 240). This highlights the collaboration in service systems, where the
customers/tourists, employees, companies, destinations, local communities, and maybe other
stakeholders can all be involved as operant resources (Vargo & Lusch, 2008) to facilitate the
value creation process collaboratively. Also, the engagement of a subject can be influenced by
numerous actors in the service relationship. Fourthly, the authors argue that “value is always
uniquely and phenomenologically determined by the beneficiary” (Vargo & Lusch, 2014, p. 240),
demonstrating that customer experience of value is substantially subjective and influenced by the
context in which they find themselves.
2.3.3 Key Concepts to Understand Tourist Engagement
2.3.3.1 Tourists as Operant Resources
As operant resources, tourists are a set of skills, knowledge, and expertise (Baron & Harris,
2008). They engage in destinations to cope with different situations and co-create value with
destination marketing organizations, companies, local communities, fellow tourists add value to
their experience (Prebensen & Foss, 2011). By engaging, tourists become an important part of
adding value to their experiences. They can use their knowledge and experience to interact with
different people and activities, work through various encounters, collaborate with service
providers, and satisfy themselves by interpreting what happens in the service setting. Engaged
tourists are more likely to have extraordinary and memorable experiences, which is positively
related to their satisfaction and behavioral intentions (Prebensen & Foss, 2011).
2.3.3.2 Tourism Companies and Destinations as Resources Providers
28
S-DL is different from the traditional goods-dominant logic, because it expands the notion of
resources for value creation (Lusch & Vargo, 2006b). Traditionally, resources are limited in the
scope of the company; a firm-centric perspective was used wherein a tourism offering is
predefined within the company by using these recourses (Sørensen & Jensen, 2015). However,
S-DL suggests that the tourist experience should be understood in a service system wherein both
operand and operant resources are used for value creation. This service system includes not only
tourism companies but also other stakeholders, as noted above.
Instead of offering service attributes that create value for tourists, tourism companies and
destinations in a service system identify, develop, and provide value propositions for tourists. As
Grönroos (2006) argued, the service provider generates opportunities in tourist value-creating
processes. The value propositions are employed to engage tourists in the three different
dimensions. Furthermore, employees and other stakeholders are also operant resources that add
to the process of value co-creation (Edensor, 2001). It is imperative for service providers to
change from the traditional linear and one-way communication method to a new two-way
communication approach, wherein tourists are communicated with.
2.3.3.3 Co-created Value
Value used to be considered as the trade-offs between what customers receive and what they
pay (Zeithaml, 1988). Many relevant concepts have been developed related to this construct, like
perceived value, value-in-context, value creation, and co-created value. Herein, co-created value
refers to “the level of perceived value created in the customer’s mind arising from interactive,
joint, and/or personalized activities for and with stakeholders” (Hollebeek, 2011b, p.556). Value
co-creation thus refers to the resource exchange process, wherein actors exchange resources
through mutually beneficial interactions, and value is determined by the beneficiary (Vargo &
29
Lusch, 2008; Vargo et al., 2008). In addition, collaboration between different actors leads to
successful resource integration in service systems (Lusch, Vargo, & Wessels, 2008),
predominantly by the exchanges of operant resources during dialogue, interaction, and also
coordinated communication (Tynan & McKechnie, 2009).
2.3.3.4 The Importance of Value-In-Context TE
Value-in-context highlights "the importance of time and place dimensions and network
relationships as key variables in the creation and determination of value" (Vargo & Akaka, 2009,
p.39). It is considered as the value driver in S-DL (Vargo et al., 2008; Vargo & Lusch, 2008),
indicating that value derives from the context customers are in and emerges when customers
experience offerings in this context. Note that value-in-use rather than value-in-context was
mentioned in the early research on S-DL (Vargo & Lusch, 2004, 2008); the former is more
closely related to goods-dominant logic, while the latter which is more S-DL friendly and has
therefore become more popular term (Chandler & Vargo, 2011; Vargo, Lusch, Akaka, & He,
2010).
Value-in-context is critical, because the value of goods or services is not inherent, but is
shaped according to a customer’s perception in different contexts (Woodruff & Flint, 2006).
Therefore, it is imperative to highlight the natural, physical and social environment that travelers
are exposed to. For instance, Bitner (1992) argued that the physical surroundings and atmosphere
affect the interaction between customers and employees, which influence consumer perception
and satisfaction. Value is also added in a social environment, wherein tourists interact and share
experiences with their companion and fellow tourists (Rihova, Buhalis, Moital, & Gouthro,
2015). Consequently, tourists meet and mingle with a variety of people in the natural, physical
and social environment during the trip, and exert their role in adding values to their ultimate
30
travel experience (Prebensen & Foss, 2011). Therefore, destination managers should not only
look into the internal elements within service systems (e.g., tourists, employees, service
companies), but also considering the effects of external but uncontrollable factors (e.g., weather,
cultures, regulations) on tourist experience (e.g., time, weather, laws) (Vargo, Lusch, & Akaka,
2010).
2.4 Differentiating Tourist Engagement from Customer Participation and Involvement
Scholars have attempted to delineate CE’s relationship with other constructs, like
involvement, participation, brand loyalty, commitment, co-created value, emotional brand
attachment, flow, interactivity, connection, perceived quality, rapport, satisfaction, self-brand
connection, and trust (Bowden, 2009b; Brakus, Schmitt, & Zarantonello, 2009; Brodie et al.,
2011; Hollebeek, 2011b; Patterson et al., 2006; Vivek et al., 2012). Among these constructs,
participant and involvement have repeatedly been mentioned as similar relational constructs to
CE, and need to be differentiated from engagement in consumer behavior studies (Brodie et al.,
2011; Hollebeek, 2011b; Vivek et al., 2012). Herein, we answer the question how TE is
conceptually different from these two constructs (see Table 2-3 for the summary of TE, customer
participant and involvement).
31
Table 2-3 The comparison of TE, customer participant and involvement.
Construct Definition Process Interaction Dimensions
Tourist
engagement
the interactive and
co-creative process
between tourists and
objects in focal travel
experience
relationships
during the
consumption
experience
outside the scope of
service companies;
tourists interact with
people/attraction/
activities /encounters
cognitive,
emotional
and
behavioral
Customer
participation
the extent to which
customer is involved
in product/service
design and delivery
mainly before
the consumption
experience or
during the
service delivery
within the scope of
service companies;
customers interact
mainly with
employees, also
customers are
considered partially as
employees
behavioral
Customer
involvement
tourist' perceived
relevancy or interests
in offerings and their
emotional responses
to the offerings
mainly before
the consumption
experience with
behavioral
outcomes
within the scope of
service companies;
customers’ cognitive
and emotional
interaction with
activities
cognitive
and
emotional
2.4.1 The Differences between Customer Participation and Tourist Engagement
Customer participation is "the degree to which the customer is involved in producing and
delivering the service" (Dabholkar, 1990, p. 484). It highlights a customer’s contribution to the
product/service design and delivery. In this case, customers act partially as employees to create
new product value through a variety of activities, like information sharing, and customer–
supplier coordination (Fang, Palmatier, & Evans, 2008). In addition, Dong, Evans, and Zou
(2008) have summarized three main themes in the research on customer participation: (1)
focusing on the economic benefits brought by customer participation and explaining why
participation is critical in producing product and delivering service; (2) considering customers as
partial employees and how to manage them effectively; and (3) focusing on how to motivate
customers in the participation of the service provision process.
32
Regarding the outcomes of participation, effective customer participation is more likely to
ensure that customer needs are met and lead to a greater likelihood of satisfaction with the
service. However, even though some previous studies suggested a positive relationship between
these two constructs (Van Raaij & Pruyn, 1998), Wu (2011) found that customer participation
does not necessarily predict customer satisfaction. It is because if customers invest more effort
and time than expected in the participation process, their participation may not lead to a
satisfactory result. Empirical research has also support the positive effect of customer
participation on customer loyalty (Auh, Bell, McLeod, & Shih, 2007; Eisingerich & Bell, 2006;
Rosenbaum, Ostrom, & Kuntze, 2005). For instance, in the context of financial services,
Eisingerich and Bell (2006) found customer participation exerts a significantly positive effect on
loyalty, suggesting that when a customer actively participates and gets involved in the process of
service provision, he/she tends to assume responsibility for the outcomes, stay with the current
organization, and exhibit less switching behaviours.
Considering the measurement of customer participation in a service context, Wang, Wang,
and Zhao (2007) used a four-dimensional approach in hotel context; the dimensions include
information collection, participation in service design and standard build-up, interaction with the
employees, and "word of mouth" communication. Wu's (2011) study explored customer
participation in theme parks, where the author used customer cooperation in service delivery and
customer attentiveness to communication to represent their participating behaviors. This
contrasts with Eisingerich and Bell's (2006) study, where it was seen as customers' willingness to
provide constructive suggestions, and measured by a four-item scale using a single dimension
approach.
33
Several differences can be identified between TE and customer participation. First, the
former focuses on tourist experiences, especially the interactive and co-creative process between
the subject (i.e. tourists) and objects in a focal travel experience relationship. It adopts a
customer-centric perspective by investigating how tourists perform and act when engaged. TE
requires a multidimensional approach which considers both tourists' enduring psychological
connection and their behavioral manifestation towards service providers. Customer participation
investigates the degree to which customers contribute to the process of service provision. It
adopts a company-centric perspective and emphasizes how companies can benefit from
customers' knowledge and skills by considering them as partial employees. This interaction
happens within the relationship between a company and a customer, and the predominant focus
is on the behavioral aspect. Thus, participation is distinct from engagement “based on the
existence of focal interactive customer experiences with specific engagement objects” (Brodie et
al., 2011, p.257). Engagement is more than participation which can be achieved by just showing
up, while engagement needs value co-creation, indicating a more active and innovative effort to
impact the final results of travel experiences (Prebensen & Foss, 2011).
2.4.2 The Difference between Customer Involvement and Tourist Engagement
Research interest in involvement dates back more than 30 years in consumer behavior
studies (Lesschaeve & Bruwer, 2010). Customer involvement is believed to significantly
influence individual' attitudes, values and decision-making (Josiam, Smeaton, & Clements,
1999;). In consumer behavior study, Zaichkowsky (1986) summarized three research themes of
involvement, including involvement with advertisements, products and purchase decision.
In the context of tourism research, tourist involvement refers to “the interest or motivational
intensity towards a vacation place, with behavioral consequences” (Lehto, O’Leary, & Morrison,
34
2004, p. 805). It can also be defined as tourist perceived relevance of the activities and their
motivational state (Havitz & Dimanche, 1990). Thus, tourist involvement can be viewed as the
extent to which individuals are interested in, and their emotional responses to, activities/
destinations. This involvement is especially delineated in the pre-purchase (Foxall, & Pallister,
1998) or pre-trip decision making process (Cai, Feng, & Breiter, 2004). For instance, Cai et al.’s
(2004) study focused on tourists’ purchase decision involvement, and suggested it to be
significantly related to individuals’ information searching behaviors. In addition, tourist
involvement continues its influences on on-trip and post trip behaviours (Brown, Havitz, & Getz,
2007; Prayag, & Ryan, 2012; Prebensen, Woo, Chen, & Uysal, 2013), For instance, in nature-
based tourism, Jamrozy, Backman, and Backman (1996) found highly involved tourists were
more likely to employ a variety of information sources, actively share their information and
experience, and take more trips compared to non-nature-based travelers. Furthermore, tourist
involvement also plays a critical role in tourist experience (Prebensen et al., 2013), such as
tourist satisfaction (Hwang, Lee, & Chen, 2005; Lu, Chi, & Liu, 2015) and behavioral intention
(Lee & Beeler, 2009).
The difference between participation and involvement is that the former focuses more on the
behavioral aspect (Wang et al., 2007) while the latter focuses on emotional and cognitive aspects
(Kim, 2008; Smith & Godbey, 1991). Besides, the concepts also differ in their duration. Highly
involved individuals are more likely to have intense attitudes which influence their future
behavior (Sherif, Kelly, Rodgers, Sarup, & Tittler, 1973), indicating an enduring influence
compared to mere participation. Regarding the measurement methods, a multidimensional
approach is adopted by various studies (e.g., Gursoy & Gavcar, 2003; Kim, 2008; Kyle & Chick,
2004; Zaichkowsky, 1987). Laurent and Kapferer (1985) were among the first to develop the
35
underlying dimension of involvement by advancing a consumer involvement profile; five
dimensions were identified, including interest, pleasure, sign/symbolic value, risk importance,
and risk probability. When applied in a tourism context, Gursoy and Gavcar (2003) developed
three dimensions of customer involvement among international leisure tourists. Specifically,
these widely used dimensions include pleasure/interest (the pleasure value/meaning a tourist
attaches to a vacation), risk probability (the perceived risk associated with purchasing a vacation),
and risk importance (the perceived importance of negative outcomes caused by the unfavorable
purchase of a vacation).
There are some significant differences between involvement and TE. First, these two states
happen in different stages of consumption experience. Involvement refers to individuals’ interest
or relevancy triggered by the simulate of offerings (e.g., an advertisement, product, or
activity/destination in tourism). It happens in the pre-trip stage and has considerable influence on
tourist decision-making behaviors. In addition, its enduring effect could continue to on-trip and
post-trip experiences. However, TE focuses on tourists’ interactive, co-creative tourist
experiences with other objects when they are engaged on destination. Also, as suggested by
Hollebeek (2011a), involvement can be considered as an antecedent of customer engagement,
indicating that highly involved customers are more likely to get engaged in their relationship
with companies (in the present study, tourism destinations/companies). Another difference
between these two concepts is concerned with their underlying dimensions: while the former
emphasizes emotional and cognitive aspects, the latter represents a combination of behavioral,
emotional and cognitive perspectives. In TE, by coping with the situations and co-creating values
during the trip, tourists can interact with people/attractions/activities/encounters to add value to
their travel experience. In this case, the tourist experience is greatly influenced by his/her
36
relational network, and is also a result of how a tourist exerts skills and knowledge in
collaboration with others in the service system.
2.5 Conclusion
Practitioners and researchers in marketing have recently shifted their interest in customer
loyalty by focusing also on an emotional connection, i.e. customer engagement (CE).
engagement has been investigated in several contexts (e.g., employee engagement, student
engagement, social engagement), its application in marketing is still in its infancy and lacks
empirical studies. Due to the positive outcomes associated with CE, such as customer retention
and organization performance, marketers have considered CE as a promising research topic in
the coming years. Specifically, CE goes beyond mere transaction behavior. Researchers tend to
consider a customer’s enduring psychological connection and behavioral participation with a
company/brand when investigating CE (Brodie et al., 2011; Hollebeek, 2011b; So et al., 2016).
However, the application of engagement in tourism to understand the quality of the travel
experience has not yet been fully discussed. Our study is among first endeavor to conceptualize
engagement which is applicable in the context of tourism. After conducting a systematic
literature review of engagement in social science in general and marketing specifically,
engagement is featured as to be contextually dependent and multidimensional, representing the
motivational-driven interactions between a subject and an object. By including the notion of
value co-creation from S-DL, TE highlights tourist interactive, value co-creative on-site
experience with an object (people/attractions/activities/encounters) in focal travel relationships.
The interactions in TE can be expanded to a variety of objects (e.g., a company, employees,
social media, fellow customers, services/products/activities, and physical/natural environment) if
37
the value co-creation process is facilitated. Based on this understanding, it is reasonable and
beneficial to apply engagement in tourism research to understand tourist experience better.
This conceptualization contributes to the application of engagement in tourism for capturing
the quality of tourist experience. In TE, tourists as operant resources use their skills, knowledge,
and expertise to interact and co-create value with other actors during the trip, and tourism
companies and DMOs are considered as resources providers. While previous consumer studies
mainly looked at online engagement, this TE study explores tourist experience from an offline,
on-site tourist perspective, where tourism destinations provide tourists with both tangible and
intangible interactions with the servicescape, service providers, and fellow tourists.
Even though existing literature has identified several types of engagement, no specific
construct of "tourist engagement" has been proposed. Thus, this study also contributes to
expanding the application of engagement in a difference context. It responds to the call by MSI
to advance the understanding of "engagement" in academic marketing literature. It also provides
an answer to Shaw Bailey and Williams’ (2011) call for empirical studies to enhance tourist
experience research through engagement platforms.
Besides, the conceptualization of TE provides significant implications for service providers
in the context of tourism. It shows a better understanding of how tourists perform and act when
they are engaged. This study sheds light on creating the interactive, co-creative tourist
experiences which can occur between tourists and a focal object. These experiences can be
expanded to tourists' interaction with people/attractions/activities/encounters, indicating that
service providers need to incorporate many actors to facilitate the process of TE.
It is worth noting that the conceptualization of TE is a starting point of applying the notion
of value co-creation in tourist experiences. This conceptualization delineates what TE is and the
38
roles of key actors in TE. It aims to facilitate the understanding of engagement by presenting its
main features, theoretical foundations, key concepts and differences with similar constructs. This
discussion brings in research interests in engagement in the study of consumer behaviour,
especially in tourism. Future research should focus on the operationalization and empirical
studies of TE, to better understand such questions as how to measure TE and how TE influences
other key constructs in consumer behaviour studies.
39
Chapter 3 Scale Development of Tourist Engagement
3.1 Introduction
Tourism is among the leading industries in the experience economy (Pine & Gilmore,
1999). Sternberg (1997) explicitly stated that "tourism's central productive activity [is]
the creation of the touristic experience" (p. 954), indicating that the goods and services in
this industry are produced to yield experiences. Tourists seek out extraordinary and
memorable experiences that “dazzle their senses, touch their hearts, and stimulate their
minds” (Schmitt, 1999, p. 57). More importantly, those who have memorable experiences
are more likely to be more satisfied with the goods and services, becoming loyal
consumers and pay more for those them (Ali, Hussain, & Ragavan, 2014; Berry et al.,
2002). Therefore, in an increasingly competitive industry, companies and destinations
must provide tourists with memorable experiences that translate into long-term
relationships and revenue sources.
Although the tourism industry recognizes the importance of experience,
understanding it and applying that understanding in tourism research has appeared only
recently (Quan & Wang, 2004; Yuan & Wu, 2008). Academic research on the
conceptualizing and measuring of the tourism experience remains limited (Andersson,
2007) probably because the term “experience” is too neutral, too vague, and a tourism
experience can be loosely interpreted as including all interactions a tourist has before,
during, and after a trip (Aho, 2001). The experience may start with deciding where to go
(e.g., Prebensen et al., 2013; Vogt, Fesenmaier, & MacKay, 1994), continue with on-site
interactions with different actors (e.g., Huang & Hsu, 2010; Kastenholz, Carneiro,
Eusébio, & Figueiredo, 2013; Keung, 2000), and move on to sharing the experience
40
afterward (e.g., Kim & Fesenmaier, 2017; Tse & Zhang, 2013). However, this broad
understanding of experience causes difficulties for service providers in identifying how a
meaningful experience is created during the process, i.e., how value is created in tourism
experiences. Fortunately, researchers have started examining the value co-creation
process as part of a better understanding of the tourist experience (Andersson, 2007;
Prahalad & Ramaswamy, 2004).
The conceptualization of tourist engagement (TE) is among the many efforts to
capture the quality of tourist experiences. It explicates value co-creation of tourists and
other participants (i.e., employees, fellow tourists, companions, and local communities).
TE focuses on strong, enduring psychological connections and behavioral interactions of
tourists with other actors, which go beyond the actual purchase behavior. However, as a
newly conceptualized construct, TE has not yet been measured. This shortcoming hinders
its operationalization in marketing and any empirical testing with other constructs like
satisfaction and behavioral intention. Thus, this study developed a reliable and valid
measurement scale for TE to enhance our understanding and improve recommendations
for implementing TE in managing tourist destinations.
This chapter answers two research questions: (1) what are the dimensions of TE? and
(2) how do we measure TE? Our goal was to develop and examine a hierarchical
measurement model of TE and extract and validate those factors that TE comprises. The
following sections are organized as bellows. We first discuss the dimensionality of TE.
Then we introduce the scale development procedures. After this, we present three sub-
studies three sub-studies on scale development; note that each study has its own detailed
methods for data collection, data analysis techniques and results. In sub-study 1, we
41
generated items from the extant literature and qualitative studies (from Chinese and North
American cruise tourists). Sub-study 2 describes item purification and how the TE scale
was refined using an online panel and Amazon Mechanical Turk (MTurk). Sub-study 3
validated the factor structure of TE scale using responses from North American cruise
tourists.
3.2 Tourist Engagement Dimensions
Our study proposed that TE is a multidimensional construct containing cognitive,
emotional, and behavioral components, which is supported by previous studies
(Hollebeek et al., 2014; Kahn, 1990; So et al., 2014; Vivek, 2009). Besides, the
expression of these three components varies based on the different contexts (e.g., online
community, social media, and tourism destination). Instead of pre-defining the
dimensions of TE, we used an exploratory approach that allowed the dimensions to
emerge from empirical studies. We did not want to rule out any possibilities in
measurement at this early stage nor measure the scale development of engagement
constructs like employee engagement (Rich, Lepine, & Crawford, 2010) and customer
engagement (e.g., Hollebeek et al., 2014; So et al., 2014; Vivek, 2009).
Moreover, the literature also suggests a wide use of a reflective approach when
developing the CE scale. Herein, TE is also conceptualized and would be measured as a
higher-order (second-order) construct. Specifically, the measurement of TE comprises
three different levels of a reflective model: an indicator level, a dimensional level, and an
overall level. Note that in a reflective model, indicators are manifestations of the latent
constructs and caused by the latent constructs, while in a formative model, indicators
42
define features of the latent constructs and cause the latent construct (Jarvis, Mackenzie,
& Podsakoff, 2003). Similar to these previous studies, our study used a reflective model
when developing the measurement scale of TE.
3.3 Scale Development Procedures
Scale development followed the guidelines recommended by Netemeyer et al. (2003)
and Churchill (1979): item generation (sub-study 1), scale purification (sub-study 2) and
scale validation (sub-study 3). Both quantitative and qualitative methods were adopted
for scale development, which also aligns with the exploratory design used in the
marketing literature, considered useful “for exploring relationships when study variables
are unknown; developing new instruments, based on initial qualitative analysis;
generalizing qualitative findings; and refining or testing a developing theory” (Harrison
& Reilly, 2011, p.15). We used this approach because engagement remained
underexplored as a construct in service industries, especially tourism. As well, the
relationships between engagement and other constructs were unknown and required
further investigation. Table 3-1 displays the scale development process and analysis
methods used in our study.
43
Table 3- 1. Scale development process and analysis methods.
Study Purpose Method Analytical tool Criteria Expected
outcomes
1 Item
generation
Literature review
Qualitative study:
• Selection criterion: tourists who have had at least
one cruise experience in the past five years
• Data collection: in-depth interviews and focus
groups
• Participants: 10 Chinese and 9 Caucasian
Nvivo 10.0 • Content validity
• Readability Item pool
2 Scale
purification
Quantitative study:
• Selection criterion: tourists who have had at least
one cruise experience in the past two years.
• Data collection: online survey
• Participants: 264 cruise tourists from an online
panel and Amazon Mechanical Turk
SPSS 24.0
(EFA)
• Dimensionality
• Construct reliability Factor model
3 Scale
validation
Quantitative:
• Selection criterion: tourists who have had at least
one cruise experience in the last 12 months
• Data collection: online survey
• Participants: 329 cruise tourists from Amazon
Mechanical Turk
SPSS 24.0 and
AMOS 24.0
(CFA and SEM)
• Convergent validity
• Discriminant
validity
• Predictive validity
Measurement
scale
Note: EFA= exploratory factor analysis, CFA= confirmatory factor analysis, SEM=Structural equation modeling.
44
In sub-study 1, we generated initial measurement items by combining deductive and
inductive approaches. Specifically, a deductive approach generates items from extant
literature and existing scales while an inductive method develops items from the
viewpoint of the target individuals (Kapuscinski & Masters, 2010). The former requires a
clear understanding of the literature and is appropriate when a relevant theory exists
(Hinkin, 1995). Although engagement has not yet been measured in the tourism
destination context, the extant literature provided some examples of measuring CE.
Inductive methods are widely used to explore unfamiliar phenomena (Hinkin, Tracey, &
Enz, 1997), and frequently used methods include interviews, focus groups, and expert
panels (Kapuscinski & Masters 2010). This study used both approaches not only because
we would benefit from relevant literature in CE research, but also to gather opinions from
the target population to explore engagement in tourism.
A comprehensive literature review combined with qualitative studies like interviews
and focus groups should provide sufficient information for item generation. This
combined approach is also common in scale development research in social sciences (e.g.,
Kim, Ritchie, & McCormick, 2011; Lankford & Howard, 1994). This was confirmed in a
recent literature review on scale development studies in Scopus, PsycINFO, and Web of
Science, which found that most (56.2%) used a combination of deductive and inductive
strategies (Morgado, Meireles, Neves, Amaral, & Ferreira, 2017).
To begin, we selected the initial items from the literature on both CE and constructs
like participation and involvement. Items from CE studies constituted an important
component for the final item pool. Items generated from the qualitative study and that
overlapped with the CE literature were retained only after modifying the wording.
45
However, we used participation and involvement to ensure that we did not measure
similar constructs. For instance, if items generated from the qualitative study did not
reflect TE but measured aspects of similar constructs, they were excluded from the item
pool. However, items from research on participation and involvement were not included
in the final item pool. In addition, for the qualitative study using interviews and focus
groups, participants were recruited from among North American (a mature cruise market)
and Chinese cruise tourists (an emerging cruise market) for the generalizability of
measurement scale. After combining items from the literature and the qualitative study,
the initial items were presented to an expert panel for deletion and modification based on
the definition of TE. Sub-study 1 examined the conceptual consistency of each item to
generate a manageable number of measurement items (53 items) for scale purification in
sub-study 2.
Sub-study 2, item purification, identified the underlying dimensionality of TE scale
using exploratory factor analysis (EFA) (Yong & Pearce, 2013). The rationale lays in its
ability to group items into meaningful subsets measuring different factors (Worthington
& Whittaker, 2006). This allowed us to eliminate items that did not measure an intended
factor or that measured more than one factor at a time. This process indicated the
reliability of the measurement scale for “consistency, stability and repeatability of results”
(Twycross & Shields, 2004, p. 36).
Sub-study 3 adopted confirmatory factor analysis to check construct validity, which
refers to “the degree to which a measure assesses the construct it is purported to assess”
(Peter, 1981, p. 134). Construct validity was examined using both convergent and
discriminant validity. Besides, structural equation modeling (SEM) was used for the
46
validity test examining TE’s relationship with tourist behavioral intention. CFA verified
the TE factor structure found through EFA and also checked for any significant
modifications needed before SEM. North American cruise tourists were used as samples
in these two steps.
3.4 Sub-Study 1: Item Generation
3.4.1 Measurement Items Generated from the Literature
The goal of this process was to clearly identify how extant scales of relevant
constructs could be used to develop a TE scale. The extant literature does provide a few
empirical studies of engagement measurement in consumer behavior. For instance, by
asking consumers to associate their engagement with some activity like shopping,
makeup, eating breakfast, or listening to music with some brands, Vivek (2009)
developed a three-dimensional, 10-item measurement of CE that included enthusiasm,
conscious participation, and social interaction. Specifically, enthusiasm indicates strong
excitement or zeal, which includes both cognitive and affective elements (Vivek, 2009).
Conscious participation focuses on the relationship between the activity consumers are
engaged in and their awareness level. Social interaction (with others) shows that
customer engagement level might differ if consumers interact with others during their
activities.
Hollebeek et al. (2014) also developed a three-dimensional, 10-item measurement of
CBE but in social media settings, and included cognitive processing, affection, and
activation. Dimension 1, cognitive processing refers to "a consumer's level of brand-
related thought processing and elaboration in a particular consumer/brand interaction"
47
(Hollebeek et al., 2014, p.154). The term emphasizes what customers think as they use
the brand, and this “cognitive processing” happens because customers are interested
enough to get involved. Affection was defined as “a consumer’s degree of positive brand
related affect in a particular consumer/brand interaction” (p.154). The last dimension,
activation, was first proposed by Hollebeek (2011b) based on in-depth interviews and
focus groups, and then empirically tested in Hollebeek et al. (2014). Activation happens
when customers become engaged in the co-production process (Etgar, 2008), when
customers choose the levels of activities in which they want to participate.
Another scale development study of CE was conducted by So et al. (2014), using
hospitality and airline brands and resulting in a five-dimensional, 25-item measurement:
attention, absorption, enthusiasm, interaction, and identification. Attention refers to the
invisible material resource (like time or cognitive availability) that the subject (tourist)
can assign to or focus on the object in multiple ways in a period (Rothbard, 2001; So et
al., 2014). Absorption in So et al. (2014) has been studied both in employee engagement
and marketing. Specifically, in employee engagement, absorption refers to “being fully
concentrated and deeply engrossed in one’s work, whereby time passes quickly and one
has difficulties with detaching oneself from work” (Schaufeli et al., 2002, p. 75). In
marketing, absorption is recognized as a pleasant state when consumers are fully
concentrated and happy, feeling time fly during interaction with a brand (Patterson et al.,
2006). Enthusiasm was measured using similar items in Vivek (2009). Enthusiasm
emphasizes a positive emotion caused by engagement at a specific time, or a feeling of
short duration. It refers to “an individual’s strong level of excitement and interest
regarding the focus of engagement” (So et al., 2014, p. 5). Interaction indicates
48
fundamental engagement between the subject and the object, defined as "a customer’s
online and off-line participation with the brand or other customers outside of purchase”
(So et al., 2014, p. 6). Different types of interactions facilitate customers’ sharing and
exchanging behaviors concerning their ideas feelings and experiences towards a brand
(Vivek, 2009) and constitute a critical part of customer engagement behavior. The final
dimension, identification, is a customer identifying with a brand (So et al., 2014). This
identification is theoretically part of the social identity theory; in CE, it indicates that
customers connect self-image with brand image.
In considering the literature on customer participation (Wang et al., 2007; Wu, 2010)
and involvement (Gursoy & Gavcar, 2003; Kyle, Absher, Hammitt, & Cavin, 2006), we
identified an initial pool of 25 sub-dimensions and 86 items after deleting overlapping
items: (1) 11 sub-dimensions and 37 items from CE literature and (2) 14 sub-dimensions
and 49 items from the literature on customer participation and involvement (see Figure 3-
1 and Appendix 1).
49
Figure 3-1. The summary of items from relevant literature.
Note: Some of the measurement items overlapped in different dimensions.
50
3.4.2 Measurement Items Generated from the Qualitative Study
Our qualitative study obtained opinions from the target population, exploring how
they perceived engagement during their trip. We then added key themes or content to TE
scale. A qualitative approach allows more flexible investigation of research problems
through revealing in-depth information, explaining complex meanings, and depicting
holistic pictures of the phenomena in the inquiry (Creswell, 1998). The techniques
widely used in marketing include, in descending order, qualitative questionnaires, case
studies, focus groups, observation, content analysis and secondary data (Hanson &
Grimmer, 2007). In this study, we used in-depth interviews and focus groups.
All participant recruitment was approved by the Research Ethics Board of the
University of Guelph, including recruitment channels, incentives, and confidentiality (see
the recruitment letter in Appendix 2). Participants were recruited from both Chinese and
North American cruise tourists using convenience sampling and snowball sampling
methods. On the one hand, we used personal networks to seek respondents and asked
them to recommend friends or family who might participate in the study. We also posted
recruitment advertisements on social media platforms like Wechat and Facebook and on
websites like Kijiji.ca, www.craigslist.ca, and www.qyer.com, ctrip.com. (See the
recruitment letter in Appendix 2)
The same open-ended questions (see details in Appendix 3) were asked in interviews
of focus group participants, although we did formulate additional questions to obtain and
confirm participant answers during data collection. This process lasted from May 2016 to
August 2016. The target participants were at least 25 years old who have had at least one
51
cruise experience in the last five years, who are defined as current-cruisers by Park and
Petrick (2009).
Specifically, each focus group lasted for 120 to 150 minutes, and each interview was
about 45 to 60 minutes. Each participant was offered 20 dollars as compensation.
Questions were developed based on the key components of TE, focused on understanding
tourist experiences with value co-creation during the cruise. Participants were asked to
describe their last travel experience, their motivations, activities in which they
participated on-site, and perceptions of interactions with the employees, fellow tourists,
and their companions. Specifically, questions focused on understanding how these
tourists behaved, acted, and performed when interacting with others, their memorable
moments, motivations, emotions, and their reflections. In addition, they were asked to
share three to five pictures from the cruise, which represent their memorable experiences
(e.g., impressive or happy moments), telling the story behind each picture. Note that the
method of photo-elicitation has been considered as a useful approach to trigger
participants’ memories, values, and beliefs in tourism research (Cederholm, 2004; Scarles,
2010; Schwartz, 1989). This process allowed us to investigate one or two scenarios when
participants felt most engaged during the trip. If other people appeared in any of the
pictures, participants were requested to ask for permission from the subjects to use the
photographs. Participants were assured that their personal information and pictures would
not be published. All participants were assigned ID numbers to ensure data remained
confidential. North American participants were interviewed in English, while Chinese
participants were interviewed in Mandarin (questions were translated into Chinese).
52
We stopped recruitment when no new information emerged from either interviews or
focus groups. In the end, a total of 19 tourists participated in the qualitative study: 10
Chinese (five in-depth interviews and one focus-group of five participants) and nine
North Americans (five in-depth interviews and one focus-group of five participants).
Note that one North American participant was interviewed and invited to the focus group
because of her rich experience in cruise tourism (23 cruise trips). Tables 3-2 and 3-3
display some basic information on the respondents.
53
Table 3-2. The summary of the Chinese respondents (N=10).
Data
collection ID Gender Last cruise Cruise company Days Destinations
People in
group
Expenditure
(CAD $)
Cruise
experience
In-depth
interview
1 F August 2016 Princess Cruises 5 1 5 600 1
2 F August 2016 Princess Cruises 5 1 5 660 1
3 F August 2016 Royal Caribbean 5 2 9 / 1
4 F May 2016 Royal Caribbean 5 1 5 1200 1
5 M January 2014 Norwegian Cruise 5 1 7 / 2
Focus
Group
6 F December 2015 Royal Caribbean 15 6 2 5000 5
8 M May 2016 Royal Caribbean 5 1 2 1200 1
9 M July 2016 Royal Caribbean 5 1 6 1000 1
10 F June 2016 Royal Caribbean 5 1 9 800 1
11 F October 2015 Royal Caribbean 5 2 4 1100 1
Note: Respondent # 7 quitted during the focus group. F=Female, M=Male.
54
Table 3-3. The summary of the North American respondents (N=9).
Data
collection ID Gender Last cruise Cruise company Days Destinations
People
in group
Expenditure
(CAD $)
Cruise
experience
Interview
1* F April 2016 Princess Cruises 17 5 2 1675 20-25
2 F July 2016 Carnival Cruises 8 5 2 1150 1
3 F June 2016 Carnival cruises 11 6 3 / 2
4 F August 2011 Royal Caribbean 10 / 15 / 4
5 F December 2013 Royal Caribbean 5 2 3 / 2
Focus
group
6* F April 2016 Princess Cruises 17 5 2 1675 20-25
7 M April 2016 Princess Cruises 17 5 2 / 6
8 M August 2011 Carnival Cruises 5 2 2 / 3
9 F August 2011 Carnival Cruises 5 2 2 / 3
10 M November 2015 Royal Caribbean 6 2 2 / /
Note: * One respondent participated in both interview and focus group: ID No. 1 and 6. This participant was included because of her rich experience in cruise
tourism. F=Female, M=Male.
55
The interview audio was manually transcribed. Content analysis, which focuses on
communication contents in written, verbal, or visual formats (Cole, 1988), was adopted to
generate potential items. Considering engagement as an underexplored phenomenon in
tourism, an inductive content analysis approach was used wherein coding categories
emerged from the data text directly (Vaismoradi, Turunen, & Bondas, 2013). As
suggested by Elo and Kyngäs (2008), the inductive approach includes a preparation phase;
deciding the unit of analysis; interpreting the data by open coding, coding sheets,
grouping, categorization, and abstraction; and then proposing a model, conceptual system,
and finally categories or conceptual map.
Specifically, Nvivo (version 10.0), a qualitative data analysis software was used for
data analysis. Nvivo is useful for writing memos on particular parts of documents;
creating codes using research objectives; and drawing text to code, link text easily to the
original documents (Welsh, 2002). Baralt (2012) provides a very good example of coding
qualitative data with Nvivo using the following steps: (1) first iteration followed by open
coding; (2) coding all data and developing themes; (3) establishing relationships; (4)
establishing patterns; and finally (5) interpreting the findings. Following the similar steps,
six main themes containing 20 sub-dimensions and 100 items were generated from the
qualitative study. Figure 3-2 shows the final results of qualitative data analysis.
56
Figure 3-2. The summary of items from the qualitative study.
57
3.4.3 Synthesizing Items Generated from Both Sources
By combining the items generated from the literature review with those from the
interviews and focus groups, this study developed an initial item pool of 137 items for
measuring TE: 37 came from the CE literature and 100 from the qualitative study. We
then synthesized the items from both sources to reduce the number of items aligned with
the definition of TE, thus showing it was distinct from customer participation and
involvement. The synthesis allowed themes to emerge. For instance, if an item from the
qualitative study had the same meaning as an item from the literature although expressed
differently, the item was retained after modification to fit this tourism context. If an item
emerged from the qualitative study that measured similar constructs and was not aligned
with the definition of TE, that item was dropped. This resulted in 62 preliminary items.
The next step was expert validation with a goal of developing items that adequately
represent TE. An expert review survey (see Appendix 4) was sent out to four faculty
members and three Ph.D. students. A clear definition of TE was presented to the
academic audience at the beginning of the survey to avoid any misunderstandings of the
concept. The academic audience was asked to comment on readability and
representativeness, providing suggestions for item refinement. The readability check
ensured the use of clear, unambiguous language, minimal offensive information and bias,
and avoided double-barreled statements (Gehlbach & Brinkworth, 2011). Readability was
rated using a five-point Likert scale: 1 = poor, 5 = excellent. The representativeness
criterion ensured that items reflected the meaning of the research construct, thus
providing a content validity check (Richins, 2004). The expert panel rated 1 = not very
58
representative of TE and 5 = very representative of TE. A final list of 53 items was used
for purification in study two (see Table 3-4).
59
Table 3-4. Items used for scale purification.
1 This destination(s) I visited during this cruise trip was among my favorite
destinations when I am traveling.
2 I felt very positive when I was on this cruise trip.
3 I enjoyed socializing with like-minded people during this cruise trip.
4 I actively participated in my favorite activities during this cruise trip.
5 I was excited when I was on this cruise trip.
6 I did not want to miss any moment of this cruise trip.
7 I tend to spend more time in the destination(s) in this cruise trip than other places I
travel.
8 I was enthusiastic each day to make this cruise trip more memorable.
9 I was proud that I traveled to the destination(s) I visited during this cruise trip.
10 I thoroughly enjoyed exchanging small talk with other people during this cruise trip.
11 Time flew by quickly on this cruise trip.
12 I felt happy when I was on this cruise trip.
13 I wanted to learn as much as I could about my destination(s) during this cruise trip.
14 I found my cruise trip experience extremely rewarding.
15 I felt relaxed when I was on this cruise trip.
16 I was quite content with my life while I was on this cruise trip.
17 My life felt amazing when I was on this cruise trip.
18 Even participating in common activities (e.g. walking, swimming and bicycling) made
me fulfilled during this cruise trip.
19 I continue to feel excited about this cruise trip because it was one of the most
memorable experiences of recent years.
20 I enjoyed capturing (e.g. photos, videos) memorable moments during this cruise trip.
21 I found all aspects of the cruise trip appealing.
22 I was completely involved in all of the activities I engaged in during this cruise trip.
23 I spontaneously engaged in many activities during this cruise trip.
24 During my cruise trip, my attention was focused entirely on travel activities.
25 I felt detached from the world around me during this cruise trip.
26 I felt totally immersed in travel activities during this cruise trip.
27 I felt personally connected to employees (e.g. casino staff, pub staff, restaurant staff)
during this cruise trip.
28 I was pleased when my comments or suggestions were answered during this cruise
trip.
29 It was fun to do something unplanned during this cruise trip.
30 I was more than willing to participate in unusual activities during this cruise trip.
31 I felt comfortable approaching employees when I needed help.
32 I was competent in dealing with unexpected problems during this cruise trip.
33 I liked sharing the information (e.g., restaurant, lodging, discount) that I found during
the cruise trip with other tourists.
34 It was fun to participate in spontaneous activities with others during this cruise trip.
35 I enjoyed doing things with others to make my cruise trip more pleasurable.
36 Socially interacting with others made me happy during this cruise trip.
37 I made some friends during this cruise trip.
60
38 I was comfortable approaching people I did not know during my cruise trip.
39 I enjoyed sharing my travel experiences with others during this cruise trip.
40 I was emotionally moved by what employees did for me during this cruise trip.
41 I was impressed by the employees' enthusiasm during this cruise trip.
42 I spent time reflecting on the relationships I made during this cruise trip.
43 I was highly motivated by others to participate in activities during this cruise trip.
44 I participated in activities that I usually do not engage in during this cruise trip.
45 I was curious to learn more about my surroundings when I was on this cruise trip.
46 My time was fully occupied with enjoyable activities during this cruise trip.
47 I felt welcomed by the employees I interacted with during this trip.
48 I loved exploring the destination(s) as much as I could.
49 Traveling with friends or family made my experience more fun.
50 This was an amazing cruise trip because I was able to travel with my friends and/or
family.
51 I encouraged my companions to participate in travel activities during this cruise trip.
52 I learned a lot about my companions during this cruise trip.
53 I grew closer to the people I traveled with during this cruise trip.
61
3.5 Sub-Study 2: Scale Purification
3.5.1 Survey Design
Before conducting a large-scale purification study, we conducted a pre-test using 10
university students and 27 respondents from Amazon Mechanical Turk (MTurk), an
online platform, to check issues like survey logic, format, and typographical errors. For
formal purification, the intention of the questionnaire was to measure participant
experiences with a cruise trip as a whole. In addition to the 53 TE items, the survey
contained measurement items from other key constructs, such as three items for
behavioral intention (Hosany & Witham, 2010; Petrick, 2004). Participants were asked to
recall their most recent cruise experience and, for TE, rate their perception of each item
in a five-point Likert scale anchored with strongly disagree (1) and strongly agree (5);
behavioral intention was assessed in a seven-point Likert scale.
There is some debate about whether using a Likert scale is suitable for performing
factor analysis (based on the assumption of interval data) because Likert scales were
originally designed as ordinal scales. However, as Clason and Dormody (1994) argued,
the Likert scale can be considered interval data if we assume well-constructed and equal
distances between values on the scale. Using a Likert scale also brings up the issue of
whether the midpoint influences measurement reliability and validity (Tsang, 2012).
Much evidence lies on both sides of this debate; with or without midpoints, Likert scales
are acceptable because the midpoints likely have little impact on reliability and validity
(Tsang, 2012). The meaning of the midpoint must be defined and they should be used as
precisely as possible. Midpoints could be stated as "neither agree nor disagree",
"undecided", "don't know", or "no opinion" (Raaijmakers, Hoof, Hart, Verbogt, &
62
Wollebergh, 2000). In this study, we included a midpoint defined in seven-point scales as
4 = “neither agree nor disagree” and in five-point scales as 3 = “neither agree nor
disagree”. The advantage of a midpoint is that it gives respondents space to make a
decision instead of forcing them to choose between agreeing or disagreeing.
3.5.2 Data Collection
Data were collected employing a self-administered questionnaire from one of two
sources: a limit-access online panel and Amazon MTurk. The online panel is owned by
the Centre for Tourism Research in Prince Edward Island (PEI), which has more than
20,000 respondents who have either visited Prince Edward Island or asked for travel
information about it. Using two data sources benefitted our research by providing a more
heterogeneous sample for factor analysis than is possible with a homogeneous sample,
which might decrease variance and factor loadings (Kline, 1994).
The data were collected over a three-week period. Respondents were only eligible to
participate if (1) they were at least 25 years old, (2) the total household income before
taxes in 2016 was more than USD$ 40,000, and (3) they have taken at least one cruise
trip in the last two years. These selection criteria were chosen to fit the profile of North
American cruise travelers (CLIA, 2015). 638 respondents clicked on the survey link
through PEI research center, out of which 202 were eligible and completed the survey.
100 survey tasks were published and completed through Amazon MTurk, while only 62
usable surveys were kept after eliminating invalid responses. As a result, the final dataset
contained 264 observations: 202 from the PEI online panel and 62 from Amazon MTurk.
63
For sample sizes intended for factor analysis, generally the bigger sample the better.
The preference for large samples arises from the fact that scale variance caused by
specific respondents may be eliminated by random effects as sampling size increases
(Tabachnick & Fidell, 2007). Our sample size was adequate for several reasons. First,
previous researchers chose similar sample sizes for initial item purification: 200
respondents for a 97-item instrument (Parasuraman, Zeithaml, & Berry, 1988); 308
respondents for 125 items (Choi & Sirakaya, 2005). Also, the item-to-response ratio in
our study was about 1:5, which is within the recommendation for ratios ranging from 1:3
(Cattell,1978) to 1:10 (Gorsuch, 1983). In addition, other researchers have also suggested
a sample size of 150 participants to be enough if the dataset contains certain high factor
loading scores (> .80) (Guadagnoli & Velicer, 1988) or if item intercorrelations are
strong (Guadagnoli & Velicer, 1988).
3.5.3 Data Analysis
The data were analyzed using SPSS 24.0. The first step was to examine the
distributions of the items, ensuring that the assumptions of multivariate analysis were met.
Results showed that (1) the skewness of TE items ranged from -1.842 to 0.022 and the
kurtosis ranged from -0.781 to 5.82, and (2) the skewness of behavioral intention items
ranged from -1.571 to -1.348 and the kurtosis ranged from 1.575 to 2.734. Both were
within the threshold values (skewness < |2|, and kurtosis < |7|) as suggested by West,
Finch, and Curran (1995). (see the skewness and kurtosis results of the 53 TE items in
Appendix 5). In addition, our study checked the multicollinearity by analyzing all the 53
TE items and three behavioral intention items in the model. The variance of inflation
64
factors (VIFs) results indicated no multicollinearity issue, as all the VIFs were smaller
than the suggested threshold value of 10 (Kline, 2005).
To determine if the sample was suitable for EFA, our study conducted Kaiser-Meyer-
Olkin (KMO) and Bartlett’s Test of Sphericity with all original items. KMO provides
support for sampling adequacy, which ranges from 0 to 1, with a minimum value of 0.60
(Tabachnick & Fidell 2007). Bartlett’s Test determined if sufficient correlations existed
among variables at a significant level (p<0.05) (Hair, Black, Babin, Anderson, & Tatham
2006). This dataset was appropriate for factor analysis: KMO Sampling Adequacy test =
0.933 and Bartlett's test was significant (p<0.001). EFA was thus used for item reduction
in scale purification.
In this study, we used principle components EFA with Varimax rotation. A
correlation matrix was first computed to identify how each TE item correlated to others.
Items with correlations below 0.40 in absolute value were deleted (Hedderson & Fisher,
1993). One item was removed for low correlation. To determine the factor structure and
which items to retain, eigenvalue (>1), factor loading (>0.40), and a scree plot were used
in an iterative process. Items with low factor loading (<0.40) or high cross loading (>0.40)
were eliminated (Hair et al., 2006). However, EFA has no absolute rules for deleting or
retaining items because retained items must also be aligned with the factor on which it
loads.
3.5.4 Study Results
With this recursive process to meet all the criteria and achieve highly explained
variance, this study resulted in a five-factor model with 26 items. Specifically, the scree
plot (see Figure 3-3) shows the eigenvalues of all factors. The number of factors to retain
65
was between 5 and 6 when the curve started to go flat. Because the eigenvalue must be
greater than 1, this study adopted a five-factor model. This factor structure explained 64%
of the total variance, which exceeded the minimum acceptable level of 60% as suggested
by Hinkin et al. (1997). (See details in Table 3-5).
66
Figure 3-3. The scree plot of EFA.
67
Table 3-5. The EFA results of TE scale (N=264).
Factor/item Factor
loading
Eigenvalue Variance
explained
(%)
Corrected
item-to-total
correlation
Communalities Cronbach's
alpha
Factor 1. Social interaction
10.396
39.985
.900
36. Socially interacting with others made me happy
during this cruise trip.
.757
.754 .724
38. I was comfortable approaching people I did not
know during my cruise trip.
.748
.703 .708
37. I made some friends during this cruise trip. .743
.715 .751
3. I enjoyed socializing with like-minded people
during this cruise trip.
.718
.702 .730
10. I thoroughly enjoyed exchanging small talk
with other people during this cruise trip.
.713
.755 . 718
39. I enjoyed sharing my travel experiences with
others during this cruise trip.
.684
.725 .661
33. I liked sharing the information (e.g., restaurant,
lodging, discount) that I found during the cruise
trip with other tourists.
.524
.624 .656
Factor 2. Immersed involvement
2.083
8.010
.860
6. I did not want to miss any moment of this cruise
trip.
.805
.758 .736
4. I actively participated in my favorite activities
during this cruise trip.
.715
.629 .569
22. I was completely involved in all of the activities
I engaged in during this cruise trip.
.660 .681 .610
8. I was enthusiastic each day to make this cruise
trip more memorable.
.624
.638
.568
68
20. I enjoyed capturing (e.g. photos, videos)
memorable moments during this cruise trip.
.586 .583 .475
18. Even participating in common activities (e.g.
walking, swimming and bicycling) made me
fulfilled during this cruise trip.
.563
.636 .550
Factor 3. Novelty-seeking 1.661
6.388
.839
30. I was more than willing to participate in
unusual activities during this cruise trip. .768 .694 .713
29. It was fun to do something unplanned during
this cruise trip. .706 .618 .612
34. It was fun to participate in spontaneous
activities with others during this cruise trip. .641
.702 .721
23. I spontaneously engaged in many activities
during this cruise trip. .546 .630 .573
44. I participated in activities that I usually do not
engage in during this cruise trip. .503
.598
.507
Factor 4. Interaction with employees
1.416
5.447
. .794
40. I was emotionally moved by what employees
did for me during this cruise trip.
.758
.626 .697
41. I was impressed by the employees' enthusiasm
during this cruise trip.
.757
.684 .688
27. I felt personally connected to employees (e.g.
casino staff, pub staff, restaurant staff) during this
cruise trip.
.710
.611
.642
31. I felt comfortable approaching employees when
I needed help.
.455 .479 .486
47. I felt welcomed by the employees I interacted
with during this trip.
.444
.499 .448
69
Factor 5. Relatedness 1.125 4.326
.800
52. I learned a lot about my companions during this
cruise trip.
.802
.729 .750
53. I grew closer to the people I traveled with
during this cruise trip.
.720
.676 .710
42. I spent time reflecting on the relationships I
made during this cruise trip.
.585
.541 .679
Note: KMO Sampling Adequacy test = 0.921; Bartlett's Test of Sphericity: 2 = 3620.810, p <0.001; Total variance explained = 64.16%.
70
Table 3-5 displays the results of EFA for scale purification, including factor loadings,
eigenvalues, variance explained (%) for each factor, corrected item-to-total correlation,
commonalities, and reliability alpha. All factor loadings were higher than 0.40. The
Cronbach’s alpha, ranging from 0.794 to 0.900, met the traditionally acceptable value of
0.70 (Hair et al., 2006). Moreover, all the item-to-total correlations ranged from 0.479 to
0.758, higher than the cut-off point of 0.30, indicating good internal consistency.
In addition, the common method bias was also considered, referring to the extent to
which variance between correlations were the result of the measurement method, not the
constructs being measured (Meade, Watson, & Kroustalis, 2007). Several methods can be
used to avoid the common method bias, such as shuffling the questions during the survey
design, using multiple scales, negatively-worded questions, marker variables, different
data collection times, and Harman’s one-factor test (Podsakoff, MacKenzie, Lee, &
Podsakoff, 2003). We purposefully collected data from two different sources and used
multiple scales during data collection. Also, using Harman's one-factor test, principal
component analysis results showed no one factor (the first factor) that accounted for more
than 50% of the variance (Podsakoff et al., 2003).
The five factors were labeled as Factor 1: Social Interaction (SI); Factor 2: Immersed
Involvement (II); Factor 3: Novelty-seeking (NS); Factor 4: Interaction with Employees
(IE); and Factor 5: Relatedness (RL). See the details of remaining items in Table 3-5. The
first factor, social interaction (seven items, x̄= 3.84, eigenvalue=10.396), explained
39.985% of the variance. It comprised items describing tourist sharing, exchanging, and
socializing during a trip. The second factor, immersed involvement (six items, x̄=4.07,
eigenvalue=2.083), explained 8.010% of the total variance. It contained items describing
71
how immersed and absorbed tourists were in different activities during a memorable
experience. The third factor, novelty-seeking (five items, x̄=3.65, eigenvalue=1.661),
explained 6.388% of the total variance. It incorporated items illustrating how tourists are
attracted to unplanned, unusual, or unexpected encounters/activities. The fourth factor,
interaction with employees (five items, x̄=3.78, eigenvalue=1.416), explained 5.447% of
the total variance. These items described tourist perceptions of employee service and
interaction. The last factor, relatedness (three items, x̄=3.49, eigenvalue=1.125),
explained 4.326% of the total variance. It focused on tourist relationship with
companions (the people with whom they traveled).
To ensure each factor contained sufficient items for CFA in sub-study 3 (Hinkin et
al., 1997), four items from the initial item pool were added to each factor after consulting
with committee members. The newly added items were “when participating in travel
activities, I felt that I was really myself”, “it was joyful to witness how the employees
make other tourists happy”, “I felt a better sense of attachment to my companion(s)”, and
“traveling with a companion made my trip memorable”. This step made it possible to
remove two to four items from each factor while still maintaining a minimum of three
items per construct. Five faculty members reviewed the 30 TE items after some minor
changes in wording. In the end, the validation scale contained 30 items for all five factors:
1. social interaction (seven items); 2. immersed involvement (seven items); 3. novelty-
seeking (five items); 4. interaction with employees (six items); and 5. relatedness (five
items). Some minor changes made the items more succinct. Also, we found it was
possible to collapse two or three factors into one construct in sub-study 3; factors one,
72
four, and five all focus on tourist engagement with a relationship, while factors two and
three are both activity related engagement.
3.6 Sub-Study 3: Scale Validation
3.6.1 Survey Design and Data Collection
As Hinkin et al. (1997) suggested, after using EFA to reduce the set of items in a
questionnaire, researchers could use CFA to examine the convergent and criterion-related
validity of measurement scale using a different sample. The data for CFA was collected
from North American cruise tourists through MTurk. The online survey contained 30 TE
items, a variety of constructs (perceived service quality, perceived value, and tourist
satisfaction, see details in Chapter 4) measuring travel experience and personal
information. We used only behavioral intention in this study for the predictive validity
test. Behavioral intention was measured by three items: (1) “the likelihood that I would
recommend this cruise to friends or family members is…”, (2) “if I were to purchase
another cruise, the probability that the vacation would be with XYZ Cruise Line is…”,
and (3) “the likelihood that I would consider purchasing an XYZ cruise again is…”
(Hosany & Gilbert, 2010; Petrick, 2004). TE items were measured on a five-point Likert
scale, while behavioral intention items were measured using a seven-point Likert scale.
Similar selection criteria for participants were applied; screening questions included
(1) age (>25 years old), (2) total household income in 2016 in USD (>$40,000), (3)
cruise experience in last 12 months (at least one), and (4) cruise line used for the recent
cruise vacation. A pilot study with 13 respondents was conducted. Then 415 survey tasks
were published and completed through Amazon MTurk. After data cleaning, we retained
329 usable surveys (see Tables 3-6 and 3-7).
73
Table 3-6 provides socio-demographic information on the respondents. Female
tourists (62%) outnumbered male tourists (38%). More than 61% were older than 40
years, with a mean age of 44.9 and a median age of 46.0. Most (63%) had an annual
income of more than 60,000 USD in 2016. For educational background, 33% of
respondents graduated from college (including vocational school) and 58% from
university or graduate school. Most of the participants were married (65%) and white
(85%).
Table 3-7 displays the cruise trip experience of the participants. First-time cruise
tourists (42%) and repeat cruise tourists (58%) were numerically fairly balanced. 33%
had been on 1-3 cruise trips in the last 10 years before this trip, and 16% participants had
taken 4-5 trips. For length of the recent cruise trip, 42% took a 3-5 day trip, and 52% took
a 6-8 day trip. Almost all participants traveled with companions (96%), and 87% were
with1 to 5 people. They commonly traveled with spouses (62%), other family members
(22%), friends (21%), and children under 18 (21%).
74
Table 3-6. The socio-demographic information of the respondents (N=329).
Features Variables % Features Variable %
Gender Male 38%
Education
High school or less 9%
Female 62% College, including vocational school 33%
Age
25-29 16% University 35%
30-39 23% Graduate school 23%
40-49 23%
Marriage
Married 65%
50-59 24% Single (never married) 20%
60+ 14% Divorced/Widowed/Separated 13%
Income
$40K-$49K 23% Other 2%
$50K-$59K 14%
Ethnicity
White 85%
$60K-$69K 14% Asian 4%
$70K-$99K 27% Black Canadian/American 7%
$100K-$199K 19% Hispanic or Latino 2%
>$200K 3% Other 2%
Table 3-7. The cruise experience of the respondents (N=329).
Features Variables % Features Variable %
Cruise
experience
First time
cruise tourists 42%
Number of
people you
traveled with
(excluding
yourself)
1-5 87%
Repeat cruise
tourists 58% 6-10
9%
Total number of
cruise in the last
ten years
(excluding this
time)
0 time 42% 11-15 2%
1-3 times 33% 16-20 1%
4-5 times 16% 21 and more 1%
more than 6
times 9%
Travel
companions
Spouse 62%
The length of the
last cruise
3-5 days 42% Companion 16%
6-8 days 52% Children under 18 21%
9-15 days 6% Adult children 9%
16+ days 0% Other family members 22%
Travel with
companions
Yes 96% Friends 21%
No 4% Members of an
organization or group 2%
Note: For the question of travel companions, participants were asked to select all that apply.
75
3.6.2 Data Analysis
The assumptions of multivariate analysis were also examined before CFA. Results
showed no distribution and multicollinearity issues: (1) -1.902 <SkewnessTE< -0.142, -
0.745<KurtosisTE<4.911; -1.691<SkewnessBI<-1.196, 1.286<KurtosisBI<3.688; -0.999
<SkewnessPSQ<-0.852, 0.724<KurtosisPSQ<1.003; -1.088<SkewnessPV<-0.798,
0.228<KurtosisPV<1.207; -1.536<SkewnessSAT<-1.117, 2.020<KurtosisSAT<3.214; and (2)
all the VIFs <10 (Kline, 2005; West et al., 1995). See the skewness and kurtosis results in
Appendix 6.
CFA verified the five-factor structure of TE scale; significant modifications were
made to achieve a satisfactory level of model fit. After that, SEM was used to discover
any causal relationship between TE and behavioral intention for evaluating the predictive
validity of TE. This study used SPSS 24.0 and AMOS 24.0 for data analysis.
A descriptive analysis of the 33 measurement items (30 TE items and three
behavioral intention items) generated a basic picture of the data (See Table 3-8). Of the
TE items, 16 have a mean score greater than 4.0. “Traveling with a companion made my
trip memorable”, in the relatedness factor, had the highest mean value (x̄=4.48). The
other 14 TE items had mean scores ranging from 3.0 to 4.0. The lowest mean score (3.08)
was for "I spent time reflecting on the relationships I made" in the relatedness factor. Of
the five factors, the immersed involvement factor had the highest mean value (x̄=4.19),
followed by relatedness (x̄=3.91), social interaction (x̄=3.89), interaction with employees
(x̄=3.88), and novelty-seeking (x̄=3.88).
76
Table 3-8. Descriptive statistics for all measurement items (before CFA) (N=329).
Factor/construct Item Mean SD
Social interaction:
seven items;
x̄=3.89
SI1 I enjoyed sharing my travel experiences with others. 4.28 0.79
SI2 I enjoyed socializing with like-minded people. 4.15 0.87
SI3 I liked sharing the trip information (e.g. activities, excursion, shopping) with others. 4.18 0.78
SI4 I made some friends. 3.47 1.13
SI5 I thoroughly enjoyed exchanging small talk with other people. 3.72 0.97
SI6 I was comfortable approaching people I did not know. 3.50 1.11
SI7 Socially interacting with others made me happy. 3.95 0.89
Interaction with
employees:
six items; x̄=3.88
IE1 I felt comfortable approaching employees when I needed help. 4.33 0.78
IE2 I felt personally connected to employees (e.g. casino staff, pub staff, restaurant staff). 3.49 1.01
IE3 I felt welcomed by the employees I interacted with during this trip. 4.33 0.73
IE4 I was emotionally moved by what employees did for me. 3.21 0.96
IE5 I was impressed by the employees' enthusiasm. 4.08 0.80
IE6* It was joyful to witness how the employees make other tourists happy. 3.82 0.94
Relatedness:
five items; x̄=3.91
RL1* I felt a better sense of attachment to my companion(s). 4.13 0.77
RL2 I grew closer to the people I traveled with. 4.14 0.84
RL3 I learned a lot about my companion(s). 3.72 0.96
RL4 I spent time reflecting on the relationships I made. 3.08 1.03
RL5* Traveling with a companion made my trip memorable. 4.48 0.77
Immersed
involvement:
seven items,
x̄=4.19
II1 I actively participated in my favorite activities. 4.15 0.87
II2 I did not want to miss any moment of this trip. 4.23 0.88
II3 I enjoyed capturing memorable moments of this trip (e.g. taking photos). 4.35 0.86
II4 I felt fulfilled even when participating in common activities (e.g. walking, swimming,
bicycling).
4.17 0.75
II5 I was completely involved in all of the activities I participated in. 3.99 0.78
II6 I was enthusiastic each day to make this trip memorable. 4.27 0.80
II7* When participating in travel activities, I felt that I was really myself. 4.18 0.77
77
Novelty-seeking:
five items, x̄=3.88
NS1 I participated in activities that I usually do not engage in. 3.76 1.05
NS2 I spontaneously engaged in unexpected activities. 3.79 0.96
NS3 I was more than willing to participate in unusual activities. 3.85 0.88
NS4 It was enjoyable to participate in spontaneous activities with others. 3.90 0.86
NS5 It was fun to do something unplanned. 4.12 0.82
Behavioral
intention:
three items,
x̄=5.97
BI1 The likelihood that I would recommend this cruise to friends or family members is 6.14 1.09
BI2 If I were to purchase another cruise, the probability that the vacation would be with
XYZ Cruise Line is
5.83 1.24
BI3 The likelihood that I would consider purchasing an XYZ cruise again is 5.95 1.22
Note: * were newly added items for study 3. SI=social interaction, IE=interaction with employees, RL=relatedness, II=immersed involvement, NS=novelty-
seeking, BI=behavioral intention.
78
The data was then computed in AMOS for CFA to examine the relationship between
the measurement item and latent factors in TE by using a five-factor, first-order model as
suggested in EFA. Specifically, items with low standardized factor loadings (<0.60) and
high standardized residue (Hair et al., 2006) were deleted. The t-values of factor loadings
for the remained items were all significant (p<0.001), demonstrating a satisfactory level
of convergent validity (Anderson & Gerbing, 1988). After CFA, 16 TE items remained.
Also, a series of modifications improved the model fit. See the descriptive statistics for
the remaining 16 TE measurement items in Table 3-9. Herein, the immersed involvement
factor has the highest mean score (4.19), followed by relatedness (x̄=4.00), novelty-
seeking (x̄=3.92), social interaction (x̄=3.72), and interaction with employees (x̄=3.70).
79
Table 3-9. Descriptive statistics for the remained 16 TE measurement items (after CFA) (N=329).
Factor Item Mean SD
Social interaction:
three items; x̄=3.72
SI5 I thoroughly enjoyed exchanging small talk with other people. 3.72 0.97
SI6 I was comfortable approaching people I did not know. 3.50 1.11
SI7 Socially interacting with others made me happy. 3.95 0.89
Interaction with
employees:
three items; x̄=3.70
IE4 I was emotionally moved by what employees did for me. 3.21 0.96
IE5 I was impressed by the employees' enthusiasm. 4.08 0.80
IE6 It was joyful to witness how the employees make other tourists happy. 3.82 0.94
Relatedness:
three items; x̄=4.00
RL1 I felt a better sense of attachment to my companion(s). 4.13 0.77
RL2 I grew closer to the people I traveled with. 4.14 0.84
RL3 I learned a lot about my companion(s). 3.72 0.96
Immersed
involvement:
three items, x̄=4.19
II5 I was completely involved in all of the activities I participated in. 3.99 0.78
II6 I was enthusiastic each day to make this trip memorable. 4.27 0.80
II7 When participating in travel activities, I felt that I was really myself. 4.18 0.77
Novelty-seeking:
four items, x̄=3.92
NS2 I spontaneously engaged in unexpected activities. 3.79 0.96
NS3 I was more than willing to participate in unusual activities. 3.85 0.88
NS4 It was enjoyable to participate in spontaneous activities with others. 3.90 0.86
NS5 It was fun to do something unplanned. 4.12 0.82
Note: SI=social interaction, IE=interaction with employees, RL=relatedness, II=immersed involvement, NS=novelty-seeking.
80
3.6.3 Testing Possible Models and the Hierarchy of Tourist Engagement Scale
The remaining 16 TE items were further examined to establish a model structure and
the hierarchy of TE scale. CFA is a powerful tool for assessing goodness-of-fit of
competing models, among them are (1) a null model with no correlations among
observed variables and where all items load on independent factors; (2) a one-factor
model where the observed indicators load onto a single factor; and (3) a multi-trait model
where each item loads only on the proper factor (Bollen, 1989; Jöreskog & Sörbom,
1989). Having established the five factors of TE scale in sub-study 2, sub-study 3
explored possible models by testing a one-factor model and a series of multi-trait models.
To choose the multi-trait models to test in this study, we used a partial-
disaggregation method for CFA, which has been a part of other scale development studies
(e.g., Dabholkar, Thorpe, & Rentz, 1995; Sweeney & Soutar, 2001). The partial-
disaggregation approach is a compromise between two extremes: disaggregation and
aggregation. Disaggregation considers measurement items as individual indicators of
factors, while aggregation considers all items aggregate to one composite for a factor
(Bagozzi & Foxall, 1996; Marsh & Hocevar, 1985). Partial-disaggregation allows
researchers to categorize items into composites to reduce random errors while still
maintaining the advantages of structural equations. In this step, we also considered the
possibilities of collapsing factors into one dimension: SI, IE, and RL highlight
relationship-related TE for one construct, and II and NS focus on activity-related TE for
another construct. Thus, we tested a series of models (see Figure 3-4):
• Model 1: A one-factor, first-order model with the observed indicators loading on a
single factor;
81
• Model 2: A five-factor, first-order model with SI, IE, RL, II, and NS as the five
dimensions of TE (based on the result of EFA in study two);
• Model 3: A four-factor, first-order model with measurement items II and NS
collapsed into one dimension, which we called activity-related tourist engagement
(ATE); SI, IE, RL, and ATE thus became the four dimensions of TE;
• Model 4: A four-factor, first-order model with II and NS as sub-dimensions of
activity-related tourist engagement (ATE); SI, IE, RL, and ATE thus became the
four dimensions of TE;
• Model 5: A two-factor, first-order—SI, IE, and RL were sub-dimensions of
relationship-related TE (RTE), II and NS were sub-dimensions of ATE; RTE and
ATE thus became the two dimensions of TE;
• Model 6: A four-factor, second-order model with a factor structure the same as
Model 4, but TE was measured using a second-order structure.
82
Model 1 (one-factor, first-order) Model 2 (five-factor, first-order)
Model 3 (four-factor, first-order) Model 4 (four-factor, first-order)
83
Model 5 (two-factor, first-order) Model 6 (four-factor, second-order)
Figure3-4. Some possible models of TE scale.
Note: SI=social interaction, IE=interaction with employees, RL=relatedness, II=immersed involvement, NS=novelty-seeking, RTE=relationship-related tourist
engagement, ATE=activity-related tourist engagement, TE=tourist engagement.
84
Many parameters have been used to compare the overall fit of measurement models.
These parameters can be divided into three main categories: absolute fit indices,
incremental fit indices, and parsimony fit indices. The absolute fit indices examine the
model fit of a priori models (McDonald & Ho, 2002). Instead of comparing a model to a
baseline model, we use these indices to measure how well a model fits the data without
using comparison (Jöreskog & Sörbom, 1993). The absolute fit indices include the chi-
squared (χ2) test, root mean square error of approximation (RMSEA), goodness-of-fit
index (GFI), adjusted goodness of fit index (AGFI), root mean square residual (RMR),
and standardised root mean square residual (SRMR) (Hooper, Coughlan, & Mullen,
2008). A χ2 test assesses the differences in the covariance matrices of the data sample and
model (Byrne, 1994). Generally, the lower the χ2 value the better, and an insignificant p
value of χ2 shows an adequate model fit, but this is not an absolute criterion because in
most empirical studies, the χ2 value increases for large sample sizes resulting in an
significant p value. An acceptable value of RMSEA has gradually been reduced from
0.10 to 0.08 (Jöreskog & Sörbom, 1996; MacCallum, Browne, & Sugawara, 1996), while
some research even suggests 0.06 as a cut-off value (Hu & Bentler, 1999). The SRMR
ranges from zero to 1.0 and should be less than 0.08 (Hu & Bentler, 1999). GFI and
AGFI are suggested to be higher than 0.90 (Hooper, Coughlan, & Mullen, 2008).
The incremental fit indices include the Normed Fit Index (NFI), Non-Normed Fit
Index (NNFI, or the Tucker-Lewis Index), and Comparative Fit Index (CFI). These three
indices should show values ≥ 0.95 although 0.90 is also considered acceptable (Hu &
Bentler, 1999).
85
The parsimony fit indices contain parameters like the Parsimony Goodness-of-Fit
Index, the Parsimonious Normed Fit Index, and the Akaike Information Criterion. The
first two range from 0 to 1, while values close to 1 indicate a perfect model fit although
this is not expected in empirical studies; no threshold values have been recommended for
these indices (Mulaik et al., 1989). Also, no absolute value has been recommended for
the Akaike Information Criterion, although a smaller value may indicate a better fit
(Akaike, 1974). Another parameter in this category is χ2/df. If sample size affects the χ2
value, χ2/df should show a value less than 5. 0 (Wheaton, Muthen, Alwin, & Summers,
1977).
86
Table 3-10. Goodness-of-fit Indices for six measurement models of TE.
Models
Absolute fit indices Incremental fit indices Parsimony fit indices
χ2 P-Value GFI AGFI SRMR RMSEA NFI NNFI CFI PNFI PGFI χ2/df AIC
Model 1 871.21 .000 .71 .62 .10 .15 .68 .66 .70 .59 .54 8.38 935.21
Model 2 182.03 .000 .94 .91 .04 .05 .93 .96 .97 .73 .65 1.94 226.03
Model 3 269.70 .000 .90 .87 .06 .07 .90 .92 .93 .76 .65 2.75 345.70
Model 4 192.87 .000 .93 .91 .05 .06 .93 .95 .96 .75 .67 1.99 270.87
Model 5 216.43 .000 .93 .90 .06 .06 .92 .95 .95 .76 .67 2.19 290.43
Model 6 216.43 .000 .93 .90 .06 .06 .92 .95 .95 .76 .67 2.19 290.43
Acceptable
level N/A <0.05 ≥0.90 ≥0.90 ≤0.08 ≤.0.80 ≥0.90 ≥0.90 ≥0.90 N/A N/A <5.0 N/A
87
As indicated in Table 3-10, other than Model 1, all models fit well with the data,
except for a low AGFI in Model 3. The results indicated that each item loaded on to the
proper factor, not on to a single factor. However, these parameters of model fit cannot
absolutely determine the structure of the TE scale. The choice of TE scale measurement
model should take into account theoretical support and such information as the
discriminant validity of the five factors. To determine the structure of the TE scale, we
subsequently considered two questions: (1) How many factors should be included in the
final model and (2) What is the hierarchical structure of the TE scale.
On factor structure, in this study, we first tested a five-factor model (Model 2) as
proposed in sub-study 2. Despite the superior model fit, we found high correlation (.806)
between II and NS in Model 2; results also indicated insufficient discriminant validity
between these two factors (AVEII=0.51, AVENS=0.58, the square root of AVEs for II and
NS were smaller than the correlation between them). Thus, we did not consider the five-
factor structure of Model 2 adequate for the TE scale.
That high correlation between II and NS suggested that these two factors should
possibly be consolidated into a single factor or be considered sub-dimensions of a higher-
order construct. Both approaches were explored. Model 3 consolidated the items for II
and NS into a single factor, called activity-related tourist engagement (ATE), but Model
3 did not show a superior model fit than others, indicating that consolidating II and NS
would not work properly. On the other hand, when we considered II and NS as two sub-
dimensions of a higher-order construct, ATE, we found superior fit to the data (see Model
4), demonstrating that immersed involvement and novelty-seeking are both
manifestations of a single higher-order construct (ATE). Moreover, Model 5, which
88
considered SI, IE, and RL as sub-dimensions of a higher-order construct (relationship-
related tourist engagement or RTE), was also tested to compare to Model 4. However,
despite acceptable model fit for Model 5, we found an extremely high correlation (.949)
between RTE and ATE. To reduce the complexity of TE scale and avoid any issue with
discriminant validity, in this study, we adopted the four-factor model of TE scale, or
Model 4.
For the hierarchical structure of TE scale, Model 6 was tested by considering as a
second-order construct with four dimensions, one of which had two sub-dimensions
(ATE comprises II and NS). The results for Model 6 also showed good fit to the data.
Even though both models 4 and 6 fit well with the data, in this study, we adopted Model
6 because this hierarchical structure aligned with the literature and the dimensionality
discussed earlier. Higher-order structure of measurement scales is not unusual in the
literature. For instance, Dabholkar et al. (1995) used a hierarchical-factor model to
capture the dimensions of service quality for retail stores; their results suggested the
overall service quality was a five-dimensional, second-order construct, with three of the
five dimensions containing two sub-dimensions.
3.6.4 Convergent and Discriminant Validity
The final TE scale model comprised four dimensions, and TE was structured as a
second-order construct (see Model 6). Furthermore, the convergent and discriminant
validity of TE scale was explored at both the first-order and second-order levels. As
Table 3-11 shows, all factor loadings of the four dimensions in the first-order level were
more than 0.60, ranging from 0.65 to 0.97. All the t-values of the standardized factor
loadings were significant. The composite reliability ranged from 0.80 to 0.90, higher than
89
the cut-off value of 0.70. Also, the average variance extracted (AVE) ranged from 0.58 to
0.82, exceeding the 0.5 threshold (Fornell & Larcker, 1981). Therefore, the study results
showed an acceptable level of convergent validity in the first-order level.
Using the same criteria, the convergent validity of TE in the second-order level was
computed. The loadings of four first-order factors ranged from 0.63 to 0.95, the
composite reliability was 0.86, and the AVE was 0.61. The AVE of a second-order
construct is calculated by "averaging the squared multiple correlations for the first-order
sub-dimensions (or averaging the square of each sub-dimension's completely
standardized loading on the second-order construct)" (MacKenzie, Podsakoff, &
Podsakoff, 2011, p. 313). In this study, we used the standardized loadings of the four
first-order factors to calculate the AVE of TE.
Discriminant validity is traditionally evaluated using Fornell and Larcker’s (1981)
method: the square root of AVEs for each factor should be higher than its correlation
with any other constructs. Table 3-12 shows the correlation matrix and the square root of
the AVEs of all four factors. All the discriminant validities were supported. Table 3-13
shows the final measurement items of TE scale.
90
Table 3-11. The convergent validity of TE scale.
Level Factor/Construct Item
Item reliability Composite
reliability AVE Factor
loadings
Standard
error
Standardized
factor loading t-value
First-order
level
Social interaction
SI5 1 — 0.80 —
0.83 0.62 SI6 1.037 0.079 0.73 13.160***
SI7 0.949 0.064 0.83 14.847***
Interaction with
employees
IE4 1 — 0.65
0.80 0.58 IE5 0.988 0.09 0.76 11.033***
IE6 1.302 0.112 0.86 11.589***
Relatedness
RL1 1 — 0.77
0.84 0.64 RL2 1.289 0.085 0.91 15.212***
RL3 1.168 0.09 0.72 12.992***
Activity-related
tourist engagement
II 1 — 0.97 — 0.90 0.82
NS 1 — 0.83 —
Second-order
level Tourist engagement
SI 1.24 0.129 0.76 9.634***
0.86 0.61 IE 0.956 0.115 0.76 8.341***
RL 0.755 0.091 0.63 8.325***
ATE 1 — 0.95 —
Note: SI=social interaction, IE=interaction with employees, RL=relatedness, II=immersed involvement, NS=novelty-seeking, ATE=activity-related tourist
engagement.
91
Table 3-12. The discriminant validity of the four factors of TE.
Factor SI IE RL ATE
SI 0.79
IE 0.65 0.76
RL 0.36 0.51 0.80
ATE 0.74 0.67 0.65 0.90
Note: SI=social interaction, IE=interaction with employees, RL=relatedness, II=immersed involvement,
NS=novelty-seeking, ATE=activity-related tourist engagement.
The diagonal elements (bolded) are the square root of the AVEs of each factor and should be greater than
its correlation with other factors.
Table 3-13. The final measurement items of TE scale.
Dimensions Items
Social interaction
SI5 I thoroughly enjoyed exchanging small talk with other people.
SI6 I was comfortable approaching people I did not know.
SI7 Socially interacting with others made me happy.
Interaction with
employees
IE4 I was emotionally moved by what employees did for me.
IE5 I was impressed by the employees' enthusiasm.
IE6 It was joyful to witness how the employees make other tourists happy.
Relatedness
RL1 I felt a better sense of attachment to my companion(s).
RL2 I grew closer to the people I traveled with.
RL3 I learned a lot about my companion(s).
Activity-related
tourist engagement
II5 I was completely involved in all of the activities I participated in.
II6 I was enthusiastic each day to make this trip memorable.
II7 When participating in travel activities, I felt that I was really myself.
NS2 I spontaneously engaged in unexpected activities.
NS3 I was more than willing to participate in unusual activities.
NS4 It was enjoyable to participate in spontaneous activities with others.
NS5 It was fun to do something unplanned.
Note: SI=social interaction, IE=interaction with employees, RL=relatedness, II=immersed involvement,
NS=novelty-seeking.
92
3.6.5 Predictive Validity
In this study, we then tested the predictive validity of TE scale by examining the
following relationship: TE → behavioral intention (BI). Predictive validity is "the ability
of a measure to effectively predict some subsequent and temporally ordered criterion”
(Netemeyer et al., 2003, p.76). SEM was used to explore this relationship (TE → BI).
Results showed that TE predicts BI at a significant level (β = 0.60; t = 8.79; p <
0.001). The chi-square was 301.466 (df = 146, χ2/df=2.07, p < 0.001), GFI = 0.91; NFI =
0.92, CFI = 0.96, SRMR=0.053, and RMSEA = 0.06. The results demonstrated TE
explains 35.4% of variance of BI.
3.6.6 Comparing the Means of Tourist Engagement Dimensions across Sub-groups
After the scale validation, we would like to implement each dimension of TE scale to
different sub-groups to see if significant differences exist between groups. Even though
most of the current empirical studies are based on the assumption that the analyzed data
are homogenous, however, in the real-world of marketing practices, this assumption is
unrealistic. Consumers’ heterogeneity is not uncommon regarding their perceptions and
evaluations of latent variables (Sarstedt & Ringle, 2010). For instance, consumer
behaviors can be varied because of their geo-demographic information, lifestyle,
responses to product/services, or a combination of these attributes (Wedel & Kamakura,
1999; Wind, 1978). These attributes include education, income, age, genders,
nationalities, travel experience, motivation, involvement, values, and satisfaction
(Diamantopoulos, Schlegelmilch, Sinkovics, & Bohlen, 2003; Dimanche, Havitz, &
Howard, 1993; Füller & Matzler, 2008; Park & Yoon, 2009; Thurau, Carver, Mangun,
Basman, & Bauer, 2007). Therefore, our study intended to explore whether individual
93
socio-demographic background and travel experience could influence their engagement
experience.
We used multivariate analysis of variance (MANOVA) to examine if any significant
differences in the four dimensions of TE and the whole TE experience exist. In the
MANOVA procedure, the composite means of SI, IE, RL, ATE, and TE were computed
and used as dependent variables. Table 3-14 displays the results of MANOVA, indicating
that most of the independent variables did not have effects on participants’ engagement
level, except gender and education. Specifically, females showed a higher level of tourist
engagement compared to males (p=0.005*, F= 8.138), especially in the interaction with
employees (p=0.013*, F=6.264), relatedness (p=0.020*, F=5.423), and activity- related
tourist engagement (p=0.006*, F=7.601). Also, participants with a university degree
showed a significant higher engagement in social interaction compared to those who have
a graduate degree (p=0.019*, F=3.380). The graduate group was more conservative when
interacting with fellow tourists, with a low mean score of social interaction (x̄=3.48).
Although the composite means of TE and its four dimensions fluctuated across groups, in
most cases, the socio-demographic and cruise experience variables did not have a
significant impact on them. This result is not unexpected, as nowadays cruise companies
have been able to provide a variety of services/activities to keep passengers from all
groups (e.g., age, income, education groups) entertained and stay on the cruise ship. Our
results confirmed that cruise companies are successful in engaging a wide diversity of
tourists. When applying this TE scale to other settings, future studies might find more
significant differences across different sub-groups.
94
Table 3-14. Means of dimensions by socio-demographic characteristics and cruise
experience: Results of one-way MANOVA.
Variables
Means
Social
interaction
Interaction
with
employees
Relatedness
Activity-
related
tourist
engagement
Tourist
Engagement
Gender
(a) Male 3.66 3.57 3.88 3.89 3.79
(b) Female 3.76 3.79 4.07 4.09 3.97
P 0.013* P 0.020* P 0.006* P 0.005*
F 6.264 F 5.423 F 7.601 F 8.138
Age
(a) 25-29 3.58 3.50 4.07 4.06 3.87
(b) 30-39 3.52 3.61 4.07 4.03 3.86
(c) 40-49 3.77 3.74 4.01 4.02 3.92
(d) 50-59 3.87 3.81 3.95 4.00 3.93
(e) 60+ 3.87 3.85 3.85 3.96 3.90
Income
(a) $40K-
$49K
3.78 3.84 4.16 4.10 4.00
(b) $50K-
$59K
4.01 3.78 4.04 4.10 4.01
(c) $60K-
$69K
3.63 3.67 3.93 3.97 3.84
(d) $70K-
$99K
3.67 3.59 3.90 3.97 3.83
(e) $100K-
$199K
3.66 3.74 3.93 3.96 3.85
Education
(a) High
school or less
3.87 3.69 3.93 4.03 3.92
(b) College 3.72 3.72 4.01 4.07 3.93
(c)
University
3.85 3.81 4.08 4.07 3.98
(d) Graduate
school
3.48 3.54 3.88 3.85 3.73
P 0.019*
F 3.380
95
Cruise
experience
(a) First-
timers
3.65 3.66 4.03 3.96 3.86
(b) Repeaters 3.78 3.74 3.97 4.05 3.93
Number of
cruise in last
10 years
(a) 0 time 3.65 3.66 4.03 3.96 3.86
(b) 1-3 times 3.72 3.73 3.88 3.98 3.86
(c) 4-5 times 3.91 3.72 3.98 4.17 4.00
(d) more than
6 times
3.71 3.78 3.99 4.06 3.93
Length of the
last cruise
(a) 3-5 days 3.65 3.69 3.97 3.99 3.87
(b) 6-8 days 3.78 3.73 4.02 4.04 3.93
Note: * Statistically significant at p≤0.05. The underlined means indicate that the differences are
statistically significantly at p≤0.05.
96
Chapter 4 Empirical validation of Tourist Engagement
4.1 Introduction
The extant consumer behavior studies have recognized engagement as a crucial
factor in improving customer experience and maintaining customer loyalty, which is
directly connected to a company growth, revenues and profits (Hallowell, 1996; Innis &
La Londe, 1994). Empirical studies such as So et al. (2014) demonstrated that
engagement exerts a significantly positive effect on behavioral intention, explaining for
30% of its variance. When applying to tourism, tourist engagement (TE) shows a
considerable influence on the prediction of behavioral intention, accounting for 35.4% of
its variance (see predictive validity test in Chapter 3). We suggest TE be an effective
construct to gauge, predict, and enhance tourist behavioral intention, by generating
memorable experience during the trip and sustaining an enduring emotional connection
between tourists and the destination.
Despite its importance in maintaining the long-term relationship between tourists and
the destination, as a newly developed construct, TE has not yet been implemented in
marketing practices. It is imperative to examine TE’s relationships with the extant
constructs for further implementation because marketing practitioners and academia tend
to understand customer behavior holistically. The focus is on TE’s role in the formation
of tourist behavioral intention, which is essential to destination performance (Baloglu,
Pekcan, Chen, & Santos, 2004; Žabkar, Brenčič, & Dmitrović, 2010).
It is worth noting that to empirically validate TE’s role in an integrated model, our
study uses behavioral intention rather than customer loyally as an outcome variable, even
researchers tend to use these two constructs interchangeably. Although engagement has
97
been considered as crucial to assess and predict customer loyalty (e.g., Bowden, 2009a;
Sawhney et al., 2005; Wei et al., 2013), the author wants to clarify the reason for using
behavioral intention. Customer loyalty has been studied both from attitudinal and
behavioral aspects (Oliver, 1999; Zeithaml, 2000), where the former indicates customers’
desire to continue the relationship with a product/service provider, while the latter refers
to repeat patronage (Yang & Peterson, 2004). However, the problem is that intention does
not necessarily turn into action, and repeat patronage may not reflect intentions (Yang &
Peterson, 2004). Even though the actual behavior is ideal, it is difficult to measure. To
compromise, researchers tend to use behavioral intention, which is the last step before
real action (Oliver, 1999), to measure or reflect customer loyalty.
Note that a variety of constructs have been recognized as antecedents to behavioral
intention, like perceived service quality (Lee, Graefe, & Burns, 2004; Zeithaml, Berry, &
Parasuraman, 1996), perceive value (Cronin et al., 2000; Gallarza, & Saura, 2006; Petrick,
2004; Rasidah, Jamal, & Sumarjan, 2014), emotions (Gracia, Bakker, & Grau, 2011;
Hosany & Gilbert, 2010; Prayag, Hosany, & Odeh, 2013), attitudes (Goldsmith, Flynn,
Goldsmith, & Stacey, 2010; Lam & Hsu, 2006), motivation (Huang & Hsu, 2009; Lee,
2009) and satisfaction (Hosany & Witham, 2010; Silvestre, Santos, & Ramalho, 2008;
Williams & Soutar, 2009). Among these antecedents, perceived service quality, perceived
value and satisfaction are the most frequently tested with evidence from many empirical
studies (Baker & Crompton, 2000; Chen & Chen, 2010; Gallarza & Saura, 2006; Lee et
al., 2004; Petrick, 2004; Rasidah et al., 2014; Tam, 2004; Williams & Soutar, 2009).
Meanwhile, these three constructs and behavioral intention are also suggested as key
constructs to be used for empirical validation of customer engagement (Bowden, 2009b;
98
Brodie et al., 2011; Hollebeek, 2011a, b; van Doorn et al. 2010; Vivek et al., 2012).
Therefore, this study intends to (1) empirically test TE’s relationship with theses
constructs, and (2) explore TE’s role in the formation of tourist behavioral intention,
including revisiting intention and recommendation (Alegre & Juaneda, 2006; Opperman,
2000). The ultimate goal is to present how the linkages between these constructs can be
established to enhance long-term tourist relationship with the destination.
Specifically, this chapter develops an integrated model to show the formation of
behavioral intention, by using perceived service quality, perceived value, satisfaction, and
TE as predictors. The reasons are twofold. On the one hand, the first three are among the
most frequently tested antecedents when predicting behavioral intention. By adding TE,
we intend to demonstrate TE’s implementation if it fits well with the widely practiced
marketing tools. We would like to present how TE can be differentiated from the extant
constructs in predicting behavioral intention. On the other hand, it is worth noting that the
direct positive relationship between TE and behavioral intention has been identified in the
predictive validity test, while it is uncertain how TE works if other constructs are added.
We explore the mechanism between TE and behavioral intention to determine if any
mediating effects could be identified. Importantly, the examination of the relationship
between engagement and these constructs is limited in extant research (e.g., Hapsari,
Clemes, & Dean, 2017; So et al., 2014), and has not yet been studied in the context of
tourism. This chapter contributes to the implementation of TE in marketing practices, and
the validation of TE in its relationship with extant constructs.
4.2 Conceptual Foundations and Hypotheses Development
99
Figure 4-1. The hypothesized relationship between TE, PSQ, PV, SAT and BI.
Note: TE=tourist engagement, PSQ=perceived service quality, PV=perceived value, SAT=satisfaction,
BI=behavioral intention.
4.2.1 Perceived service quality, perceived value, satisfaction and Behavioral intention
4.2.1.1 Behavioral intention
We used behavioral intention as an outcome variable, which is one of the best
predictors of consumer behavior (Fishbein & Ajzen, 1975). It is associated with a range
of desirable outcomes, including (1) saying positive things about the products/services, (2)
recommending them to other customers, (3) remaining loyal to them, (4) spending more
on them, and (5) paying price premiums for them (Zeithaml et al., 1996). In tourism,
behavioral intention is typically measured by tourist revisit intention and
recommendation (Baker & Crompton, 2000).
4.2.1.2 Tourist Satisfaction
We selected satisfaction because it is among the essential components to destination
success due to its positive effect on tourist revisit intention and recommendation (Chen &
Chen, 2010; Chi & Qu, 2008; Petrick, 2004; Yoon & Uysal, 2005). Satisfaction refers to
100
the overall assessment concerning customer total buying and consumption experience
with a product/service (Johnson & Fornell, 1991).
Research has substantially identified the direct effect of satisfaction on tourist
behavioral intention in several tourism settings, like sporting events (Ahrholdt, Gudergan,
& Ringle, 2017), wine festival (Yuan & Jang, 2008), heritage tourism (Chen & Chen,
2010), and cruise tourism (Meng, Liang, & Yang, 2011). Specifically, in cruise context,
passengers have a significantly higher satisfaction compared to some other leisure
activities, including satisfaction with the service they receive and the service environment
they are in (Nicholls, Gilbert, & Roslow, 1999). The extant research demonstrates that
satisfied tourists tend to have a series of customer loyalty to the destination (Yuksel,
Yuksel, & Bilim, 2010). Thus, the following hypothesis was put forward:
H1. Tourist satisfaction has a significantly positive effect on behavioral intention.
4.2.1.3 Perceived value
To customers, a value might be perceived in several ways, like affordable prices, or
the more you spend, the more you can get (Desarbo, Jedidi, & Sinha, 2001). In marketing,
the value is perceived based on the trade-off between the cost given and benefits received
by customers (Zeithaml, 1988). Herein, perceived value refers to "consumer's overall
assessment of the utility of a product based on perceptions of what is received and what is
given" (Zeithaml, 1988, pp. 14).
Prior empirical studies have identified perceived value as a strong predictor of
customer satisfaction and behavioral intention among various tourist groups, like heritage
tourists (Chen & Chen, 2010), cruise passengers (Petrick, 2004), adventure tourists
(Williams & Soutar, 2009), resort tourists (Ali, Omar, & Amin, 2013) and university
101
student travelers (Gallarza & Saura, 2006). For instance, when exploring the behavioral
intentions in an airline service context, Chen (2008) found perceived value has
significantly positive direct effects on both satisfaction and behavioral intentions. A
similar relationship was identified in Pandža Bajs's (2015) study on tourist destination of
Dubrovnik, Croatia; perceived value acts as a strong predictor of satisfaction and
behavioral intention. Also, it is found that perceived value not only exerts a direct effect
on behavioral intention but also through the mediation of satisfaction (Hutchinson, Lai, &
Wang, 2009; Kim & Park, 2017; McDougall & Levesque, 2000). If customers receive
more value than what they spent (e.g., time, money, effort), they are more likely to be
satisfied with the service (Kumar, 2002), which in turns leads to positive word-of-mouth
and repurchase intention (Clemes, Gan, & Ren, 2011). In some cases, perceived value is
a stronger predictor than customer satisfaction considering its total effects on behavioral
intention (Lee, Yoon, & Lee, 2007). Therefore, the following hypotheses were proposed:
H2. Perceived value has a significantly positive effect on tourist satisfaction.
H3. Perceived value has a significantly positive effect on behavioral intention.
4.2.1.4 Perceived service Quality
Perceived service quality has been considered as an evaluation outcome based on the
performance of service delivered (Grönroos, 1984). It is recognized as customer
cognitive response to the service they receive. The importance of service quality is
because it is controllable by managers (Baker & Crompton, 2000). By delivering a
prominent level of quality, a company is expected to achieve competitive advantages
over its competitors (Clow & Vorhies, 1993; Johnson & Sirikit, 2002). In tourism,
service quality refers to "the visitor's assessment of the standard of the service delivery
102
process in association with the trip experience" (Chen & Tsai, 2007, p.116). Its
performance is significantly related to the visitations and revenues in tourism destination
(Yuan & Jang, 2008).
Extensive research has confirmed perceived service quality as a robust predictor of
perceived value (Petrick, 2004; Tam, 2004), and customer satisfaction and behavioral
intention (Cole & Illum, 2006; Kouthouris & Alexandris, 2005; Lee et al., 2004; Yuan &
Jang, 2008; Zeithaml et al., 1996). For instance, when using service quality, perceived
value and satisfaction to predict the behavioral intention of cruise passengers, Petrick
(2004) found the quality model fit the data most accurately and concluded quality as the
best predictor of repurchase intention and positive word-of-mouth. Lee et al.’s (2004)
study on forest visitor found significant positive effects of service quality on satisfaction
and behavioral intention when exploring their interrelationships. In some cases, service
quality exerts its effect on behavioral intention through the mediation of perceived value
and satisfaction (Hutchinson et al., 2009; Su, Hsu, & Swanson, 2017; Yuan & Jang,
2008). Hence, the following hypotheses were put forward:
H4. Perceived service quality has a significantly positive effect on perceived value.
H5. Perceived service quality has a significantly positive effect on tourist satisfaction.
H6. Perceived service quality has a significantly positive effect on behavioral
intention.
4.2.2 The Relationships between Tourist Engagement and Other Constructs
4.2.2.1 Tourist Engagement and Behavioral intention
Considering its positive effect on behavioral intention, we consider TE as a valuable
tool for destination managers and tourism companies. In marketing, the positive
103
relationship between engagement and behavioral intention has been discussed
qualitatively and quantitatively. For instance, despite the debates on its conceptualization
and measurement, researchers agree on the effectiveness of customer engagement in
enhancing customer loyalty and maintain a long-term relationship between customers and
a company (Bowden, 2009b; Hollebeek, 2011a, b; Hollebeek et al., 2014; van Doorn et
al., 2010). Empirically, a few studies recently tested and confirmed customer
engagement's positive influence on behavioral intention (So et al., 2014; Hapsari et al.,
2017). Accordingly, the following hypothesis was proposed:
H7. Tourist engagement has a significantly positive effect on behavioral intention.
4.2.2.2 Tourist Engagement and Satisfaction
The extant research presents some discussions on the relationship between
engagement and satisfaction, concerning whether engagement is a necessary compared to
satisfaction in predicting customer behavioral intention. Satisfaction has long been
considered as an important antecedent of behavioral intention, not only because of the
high association between them, but also the powerful influence of satisfaction in
mediating the relationships between other antecedents and behavioral intention. However,
researchers recently argue that conventional constructs, such as satisfaction (Oliver,
1999), are inadequate in explaining consumer behavior due to the lack of emotional
connection, i.e. customer engagement (Hollebeek, 2011a). Through customer
engagement, scholars have started adding the interactive and value co-creative process
between a customer and a company to the mechanism of customer loyalty (Brodie et al.,
2011; Van Doorn et al., 2010). Our study considers TE and satisfaction are both
important in capturing different aspects of behavioral intention. The former highlights the
104
dynamic value co-creation process mainly happening during tourist interaction with other
actors, while the latter measures the overall rational evaluation of the trip.
Moreover, some researchers suggest engagement as an antecedent of satisfaction and
behavioral intention. For instance, Brodie et al., (2013) investigated customer
engagement in an online brand community environment and suggested that customer
engagement could enhance consumer loyalty and satisfaction. Similar relationships were
proposed by Wirtz et al. (2013), claiming that customer engagement is likely to be related
to various outcomes, such as customer satisfaction. More specifically, engaged customers
can gain knowledge and strengthen their social interactions in online communities; when
these interactions meet or exceed their expectations, they are more apt to feel satisfied,
which eventually leads to positive word-of-mouth and repurchase intention (Wirtz et al.,
2013). Therefore, our study proposes TE as an antecedent to satisfaction, which could
also mediate TE’s effect on behavioral intention. The following hypothesis was proposed.
H8: Tourist engagement has a significantly positive effect on satisfaction.
4.2.2.3 Tourist Engagement and Perceived Value
Hollebeek (2011a) proposed co-created value as one of the potential consequences of
customer brand engagement; the value here refers to the level of perceived value created
in consumer mind during their interaction with objects (activities and actors). We
consider tourist engagement as a dynamic and interactive process between a subject and
objects, during which tourists utilize a set of skills, knowledge, and expertise (Baron &
Harris, 2008) to co-create value with different actors. Through TE, tourists add value to
their own travel experiences, as well as to other actors.
105
Specifically, tourists invest time, effort and energy in the value co-creation process,
in which they are more likely to feel responsible for results they receive compared to
what they give up, resulting in a higher level of perceived value. Furthermore, the more
value they perceive, the more likely they stay connected with service providers, like
through recommendation and repurchase intention. Indeed, Vivek’s (2009) study
supports perceived value as an important outcome of engagement: consumer engagement
exerts a significantly positive effect on both extrinsic and intrinsic values. Their study
also identified the partial mediating role of value in the relationship between customer
engagement and marketing outcomes, like the connection with and goodwill towards the
service providers. Therefore, the following hypothesis was developed:
H9. Tourist engagement has a significantly positive effect on perceived value.
4.2.2.4 Tourist Engagement and Perceived Service Quality
When differentiating customer engagement with extant constructs, such as perceived
service quality, Patterson et al. (2006) claimed that the former emphasizes on customer
performance during its interaction with a company, while the latter refers to service
performance assessed by a customer. It is inferred that these two constructs are different
in its stage (during the interaction vs. after the interaction) because perceived service
quality is determined by a customer based on his/her appraisal of service performance
after accumulated value creation process (Grönroos, 1984; Parasuraman et al., 1988).
Considering their relationship, Hollebeek (2011a) suggested perceived service quality as
one of the potential consequences of customer engagement, especially in value co-
creation contexts. If highly engaged, tourists are more likely to influence the result of the
service performance by adding their efforts. A higher perception of service quality will
106
then exert a positive effect on their behavioral intention. Thus, the following hypothesis
was developed:
H10. Tourist engagement has a significantly positive effect on perceived service
quality.
4.3 Methodology
4.3.1 Survey Design
We designed a survey to test the relationships between the above-mentioned
constructs. The survey contained five sections. Section 1 dealt with participant cruising
history and their recent cruise trip. Section 2 measured their engaging experience during
the last cruise using 30 TE items (after scale validation in Chapter 3, the remaining 16
items were used in Chapter 4). Section 3 assessed their perceived service quality and
perceived value towards the trip. Section 4 evaluated their satisfaction and future
intention. Section 5 measured their personal information, like age, gender, education,
marital status, and ethnicity.
The measurement items of all the constructs except TE were modified based on
relevant literature to ensure content validity. The survey contained four items for
perceived service quality (Petrick, 2004), four items for perceived value (Duman &
Mattila, 2005), three items for satisfaction (Yüksel & Yüksel, 2007), and three items for
behavioral intention (Hosany & Gilbert, 2010; Petrick, 2004). See the details of
measurement items in Table 4-1. Besides, TE, perceived service quality, perceived value,
and satisfaction were evaluated based on a five-point Likert scale anchored with strongly
disagree (1) and strongly agree (5). The behavioral intention was measured using a seven-
107
point Likert scale. Note that there are three ways of measuring satisfaction: overall
satisfaction, attribute based satisfaction, and importance performance analysis. Our study
used the first measurement type, as we are more interested in the relationship between
satisfaction and TE, rather than satisfaction itself.
108
Table 4-1. The measurement items of PSQ, PV, SAT, and BI.
Construct Literature Item Measurement item
Perceived
service
quality
Petrick (2004) PSQ1 This cruise line is outstanding quality.
PSQ2 This cruise line is very reliable.
PSQ3 This cruise line is very dependable.
PSQ4 This cruise line is very consistent.
Perceived
value
Duman &
Mattila (2005)
PV1 Compared to the price I paid, time and effort I spent, I think I have received good value.
PV2 I feel that my last cruise vacation was worth the money and time I spent.
PV3 Overall, my last cruise vacation was a good buy.
PV4 I value my last cruise vacation because it met my needs and expectations for a reasonable price.
Satisfaction Yüksel &
Yüksel (2007)
SAT1 The cruise experience satisfied my needs and wants.
SAT2 I think I did the right thing when I purchase this cruise trip.
SAT3 This cruise experience is exactly what I needed.
Behavioral
intention
Hosany &
Gilbert (2010);
Petrick (2004)
BI1 The likelihood that I would recommend this cruise to friends or family members is
BI2 If I were to purchase another cruise, the probability that the vacation would be with XYZ Cruise Line is
BI3 The likelihood that I would consider purchasing an XYZ cruise again is
Note: PSQ=perceived service quality, PV=perceived value, SAT=satisfaction, BI=behavioral intention.
109
4.3.2 Data Collection
We collected data from North American cruise tourists through Amazon Mechanical
Turk (MTurk). Similar selection criteria for participants were applied; screening
questions included (1) age (>25 years old), (2) total household income in 2016 in USD
(>$40,000), (3) cruise experience in last 12 months (at least one), and (4) cruise line used
for the recent cruise vacation. A pilot study with 13 respondents was conducted. Then
415 respondents from MTurk were selected to participate in this survey. After data
cleaning, we retained 329 usable surveys.
4.3.3 Data Analysis
We used structural equation modeling (SEM) for model testing. SEM is a powerful
confirmatory technique which presents a series of hypothesized cause–effect
relationships between exogenous and endogenous variables (Worthington & Whittaker,
2006). SEM combines two components: factor analysis and multiple regression (Pugesek,
Tomer, & Von Eye, 2003) and can test multiple relationships among all the constructs
simultaneously. Confirmatory factor analysis (CFA) was performed before SEM to
ensure the reliability and validity of measured constructs. Meanwhile, significant
modifications were made in CFA to achieve a satisfactory level of model fit before SEM.
A variety of parameters were examined to determine whether the proposed model fits
well with the data, including Chi-Squared test, GFI, NFI, CFI, RMSEA, and SRMR
(Hooper et al., 2008; Hu & Bentler, 1999). We used Amos 24.0 for data analysis. Note
that the assumptions of multivariate analysis were examined in the Sub-study 3 of
Chapter 3.
110
4.4 Results
4.4.1 Results of the Measurement Model
Before testing the path coefficients, CFA was conducted on the measurement model
containing TE, perceived service quality, perceived value, satisfaction and behavioral
intention. A series of model modification was carried out to improve the goodness-of-fit
indices. Items with high standardized residue (Hair et al., 2006) were deleted (i.e., BI1
and QUAL1). 28 items were remained for the measurement model, with the indices
x2=632.237, x2/df=1.89, GFI=0.88, NFI=0.91, CFI=0.96, RMSEA=0.05, and
SRMR=0.05. Although GFI was lower than 0.90, it does not necessarily suggest a poor
model fit. Researchers have suggested a more liberal cut-off value to be used if the data
contains a larger number of items (>24) or smaller sample size (Sharma, Mukherjee,
Kumar, & Dillon, 2005). If all the other criteria are reached, a liberal cut-off value of GFI
(close to 0.90) is also allowed in some studies (Chaudhary, 2015; Lin, Hung, & Chiu,
2008; Moghadam, Zabihi, Kargaran, & Hakimzadeh, 2013). Therefore, we considered the
model fit with the data at a satisfactory level.
As shown in Table 4-2, we examined the standardized factor loadings, t-values,
composite reliabilities and average variance extracted (AVE) for convergent validity test
(Hair et al., 2006). All the standardized factor loadings were greater than 0.60 (Chin,
1998), ranging from 0.64 to 0.96. The t-values for all the regression weights were at a
significant level (p<0.001). The composite reliabilities of all latent constructs ranged
from 0.86 to 0.94, exceeding the cutoff value of 0.70 (Hair et al., 2006). Meanwhile, the
AVEs ranged from 0.60 to 0.87, greater than the recommended threshold value of 0.5
111
(Fornell & Larcker, 1981). In addition, Table 4-3 shows that the discriminant validities
were all met according to the Fornell and Larcker’s (1981) method.
112
Table 4-2. The convergent validity of TE, PSQ, PV, SAT, and BI.
Construct Item/factor
Item reliability Composite
reliability AVE Factor
loading
Standard
error
Standardized
factor loading t-value
Tourist
engagement
SI 1.15 0.12 0.72 9.92***
0.86 0.60 IE 0.92 0.11 0.75 8.70***
RL 0.75 0.09 0.64 8.81***
ATE 1.00 — 0.96 —
Perceived
service quality
PSQ2 1.00 — 0.91 —
0.94 0.84 PSQ3 1.07 0.04 0.95 28.59***
PSQ4 1.02 0.04 0.89 24.50***
Perceived
value
PV1 0.93 0.04 0.88 21.79***
0.94 0.80 PV2 0.98 0.04 0.92 23.82***
PV3 0.99 0.04 0.91 23.62***
PV4 1.00 — 0.87 —
Satisfaction
SAT1 1.00 — 0.88 —
0.90 0.75 SAT2 1.07 0.05 0.89 22.34***
SAT3 1.13 0.06 0.83 19.55***
Behavioral
intention
BI2 0.97 0.04 0.92 25.62*** 0.93 0.87
BI3 1.00 — 0.95 —
Note: TE=tourist engagement, PSQ=perceived service quality, PV=perceived value, SAT=satisfaction, BI=behavioral intention.
*** p<0.001.
113
Table 4-3. The discriminant validity between TE, PSQ, PV, SAT, and BI.
TE PSQ PV SAT BI
TE 0.78
PSQ 0.56 0.92
PV 0.55 0.52 0.89
SAT 0.70 0.67 0.77 0.87
BI 0.56 0.62 0.62 0.75 0.94
Note: TE=tourist engagement, PSQ=perceived service quality, PV=perceived value, SAT=satisfaction,
BI=behavioral intention. The diagonal elements (bolded) are the square root of the AVEs of each construct and
should be greater than its correlation with other constructs.
4.4.2 Results of the Structural Model
The structural model fit with the data at a satisfactory level: x2=621.763, x2/df=1.87,
GFI=0.88, NFI=0.91, CFI=0.96, RMSEA=0.05, and SRMR=0.05. This model explained a
significant amount of variance for each construct: behavioral intention (58.7%), satisfaction
(74.7%), perceived value (36.1%), and perceived service quality (29.8%).
Table 4-4 and Figure 4-2 display the hypothesis testing results of the relationships between
five constructs. Among the ten hypotheses on the direct effects, eight hypotheses were supported,
with the p values <0.05. The largest path coefficient was between TE and perceived service
quality (β=.55). Note that the predictive validity test in Chapter 3 revealed a significant effect
between TE and behavioral intention. However, once other constructs were incorporated as
predictors of behavioral intention, the hypothesized relationship between TE and behavioral
intention was not supported (p=.443). This indicates a possibility of indirect effect through other
constructs (Baron & Kenny, 1986). In addition, the hypothesis on the relationship between
perceived value and behavioral intention was also not supported (p=.122).
114
Table 4-4. Results on the hypothesized relationships between TE, PSQ, PV, SAT, and BI.
Hypothesized path Path coefficients C.R. p-value Result
H1. SAT → BI .49 4.974 *** Supported
H2. PV → SAT .48 9.626 *** Supported
H3. PV → BI .11 1.545 .122 Not supported
H4. PSQ → PV .32 5.162 *** Supported
H5. PSQ→ SAT .27 5.721 *** Supported
H6. PSQ → BI .20 3.372 *** Supported
H7. TE→ BI .05 0.768 .443 Not supported
H8. TE→ SAT .28 5.202 *** Supported
H9. TE→ PV .36 5.350 *** Supported
H10. TE→ PSQ .55 8.561 *** Supported
Note: TE=tourist engagement, PSQ=perceived service quality, PV=perceived value, SAT=satisfaction,
BI=behavioral intention. *** statistically significant at p<0.001.
Figure 4-2. The tested relationships between TE, PSQ, PV, SAT, and BI
Note: TE=tourist engagement, PSQ=perceived service quality, PV=perceived value, SAT=satisfaction,
BI=behavioral intention. *** statistically significant at p<0.001.
Note that the purpose of this study was to (1) compare the effect of TE with other predictors
of behavioral intention, and (2) determine TE’s relationships with other constructs. Table 4-5
summarizes the total effects, direct effects and indirect effects among these five constructs.
Results showed that although TE is not directly related to behavioral intention in this model, its
115
total effect exceeds all the other antecedents. Also, TE exerts significant total positive effects on
perceived service quality, serviced value, and satisfaction (p<0.001).
Table 4-5. The total effects, direct effects and indirect effects in the hypothesized model.
Path Standardized
total effects
Standardized
direct effects
Standardized
indirect effects
TE→BI .557 (***) .050 (.439) .507 (***)
PSQ→BI .443 (***) .198 (**) .245 (***)
PV→BI .348 (***) .110 (.314) .238 (**)
SAT→BI .494 (***) .494 (**) .000
TE→SAT .687 (***) .280 (***) .407 (***)
PSQ→SAT .425 (***) .271 (***) .154 (***)
PV→SAT .481 (***) .481 (***) .000
PSQ→PV .319 (***) .319 (***) .000
TE→PV .538 (***) .364 (***) .174 (***)
TE→PSQ .546 (***) .546 (***) .000
Note: ***significant at p<0.001; **significant at p<0.01; *significant at p<0.05.
Even previous studies have recognized engagement's effect in maintaining a long-term
relationship between a customer and a service provider, the transformation mechanism lacks.
Moreover, the effect of TE has not yet been implemented in tourism. Through incorporating TE
in a comprehensive hierarchical model, our study enables the understanding of through which
path TE enhances tourist future intention to the destination.
Specifically, this empirical study shows that TE, perceived service quality, perceived value,
and satisfaction are significant predictors of tourist behavioral intention. By the incorporation of
TE, our study extended the extant traditional model of predicting behavioral intention using the
latter three constructs. In addition, results showed that TE works as a robust predictor compared
to other constructs considering its total effect on behavioral intention. Our study also suggests
TE as a strong predictor of perceived service quality, perceived value, and satisfaction. For
instance, regarding the total effect, TE has more influence on satisfaction compared to perceived
service quality and perceived value. The results indicate that by increasing the engaging
116
experience of tourists, managers can simultaneously influence their experience on various
aspects, like quality, value, satisfaction and even future intention to the destination.
117
Chapter 5 Conclusion
5.1 Summary
In the last decade, the term “engagement” has become popular and is widely used by
marketing practitioners for a better understanding of customer experience and
maintaining customer loyalty. It is believed that the mechanism to customer loyalty can
be further investigated by incorporating the emotional connection and value co-creation
process between customers and a company, i.e. through customer engagement (CE). This
study intends to apply the engagement concept in the context of tourism, where it has not
yet been discussed. The concept of tourist engagement (TE) developed in this study aims
to measure the quality of memorable tourist experience and to sustain a long-term
emotional connection between tourists and the destination.
Chapter 1 discusses the research background, rationale, and questions. The construct
engagement has been applied to multiple fields since the 1990s, like social engagement,
employee engagement, and student engagement. When recently implemented in market
practices, customer engagement is considered to be positively related with company
performance and customer loyalty. Despite the debates on its conceptualization and
measurement scale, the consensus is to consider it as a promising research theme and
advance its studies in marketing. However, the engagement construct has not yet been
discussed in tourism, especially focusing on tourist experience in the destination. Due to
the contextual nature of engagement, this study applies it into tourism with cautious
through three steps: conceptualization, scale development, and empirical validation. This
thesis attempts to answer the comprehensive question of "what is TE?", followed by
several sub-questions: (1) how does TE differ from other types of engagement? (2) What
118
are the dimensions of TE? (3) How can TE be measured? (4) What is the role of TE in an
integrated model when working with other constructs (perceived service quality,
perceived value, and satisfaction) as predictors of behavioral intention?
Chapter 2 conceptualizes TE through a thorough discussion on its definition,
theoretical foundations and its differences from some similar constructs (i.e., customer
participation and involvement). This conceptualization was based on the review of
relevant literature in engagement studies, including engagement in other social studies,
and in marketing, as well as the theoretical perspective: the service-dominant logic
(Vargo & Lush, 2004). Specifically, engagement is viewed as a contextually dependable
and multidimensional construct, representing the motivational-driven interaction between
a subject and an object. In TE, tourists, as operant resources, use their skills, knowledge,
and expertise to interact and co-create value with other actors during a trip; while tourism
companies and destination organizations are resources providers. Herein, TE is defined as
tourists' psychological state that occurs by virtue of interactive, co-creative tourist
experiences with a focal agent/object (people/attraction/ activities /encounters) in focal
travel experience relationships.
Chapter 3 presents its scale development process as was recommended by Netemeyer
et al. (2003) and Churchill (1979) through three sub-studies: item generation, scale
purification, and scale validation. The initial items were generated from relevant
literature (customer engagement) and qualitative studies (in-depth interviews and focus
groups with cruise tourists), resulting in 137 items: 37 from customer engagement
literature and 100 from the qualitative studies. After deleting the overlapped items and
considering comments from the expert review, 53 items were kept for purification. By
119
using an online panel and Amazon Mechanical Turk (MTurk), a total of 264 participants
were used for purification, resulting in a five-factor model of TE. This factor model
included social interaction, immersed involvement, novelty-seeking, interaction with
employees, and relatedness. Another survey with 329 participants was collected for scale
validation. However, after testing a variety of factor models using partial aggregation, we
determined a four-dimensional, second-order, 16-item measurement model of TE scale;
the immersed involvement and novelty-seeking factors constituted a dimension—
activity-related tourist engagement. This scale demonstrates sufficient psychometric
properties on uni-dimensionality, reliability, and validity. Besides, the predictive validity
test presents a significant effect of TE on behavioral intention, explaining for 35.4% its
variance.
Chapter 4 presents the way to implement TE in marketing practices by testing its
relationship with several constructs, including perceived service quality, perceived value,
satisfaction, and behavioral intention. Chapter 4 aims to compare TE's effect on the
behavioral intention with other predictors, as well as explore its relationships with other
constructs. Results showed that TE exerts the largest total effect on behavioral intention,
and also works as antecedents for perceived service quality, perceived value, and overall
satisfaction.
5.2 Discussion
Our study develops and validates the operationalization of TE scale in three steps
(see Table 5-1). The final TE scale contains social interaction (SI), interaction with
employees (IE), relatedness (RL), and activity-related tourist engagement (ATE), which
includes sub-dimensions of immersed involvement (II) and novelty-seeking (NS).
120
Table 5-1. Item deletion and adding in the scale development process.
Study Procedure No. of items
Sub-study 1
(item generation)
• Generation of initial item pool from the literature review
and qualitative studies with 19 interviewees 137
• Item combination and deletion 62
• Representativeness and readability check of measurement
items with four faculties and three Ph.D. students 53
Sub-study 2
(scale
purification)
• EFA for item purification by using 264 cruise tourists 26
• Inclusion of four more items to ensure each factor
contains at least five items for CFA 30
Sub-study 3
(scale validation) • Scale validation using 329 cruise tourists 16
SI, IE, and RL constitute a crucial component of engagement: interaction between
the subject and the object, which focuses on relationship-related engagement. Three types
of interactions in which tourists co-create value with others were identified in this study,
including social interaction with fellow tourists, interaction with employees, and
interaction with companions. This approach of dividing relationship-related engagement
into three components aligns with Appleton et al.’s (2006) study, which demonstrates the
importance of relationship in establishing student engagement (e.g., teacher-student
relationships and peer and family support). The interaction component has also been
examined in consumer studies, like the social interaction in Vivek (2009) and the
interaction dimension in So et al. (2014). However, in TE, the concept of interaction has
been expanded and specified, highlighting face-to-face, intensive interaction with various
actors at the destination. This interaction in travel relationships enables interactive and
value co-creative process, it is through which ideas, thoughts, and feelings about the
travel experience are shared. Furthermore, it enhances tourist experiences from
behavioral, cognitive, emotional, social, and psychological aspects.
121
The SI dimension (emotional and behavioral components) suggests that tourists are
willing to share their experiences with fellow tourists. This sharing generates value and
adds to individual memorable experiences as is described "socially interacting with others
made me happy." Influencing others' experiences and recommending places/services that
worth trying give tourists positive feelings because they share valuable insights on things
to do and places to see. SI goes beyond a simple sharing and enables value co-creation
among different tourists. Note that tourists voluntarily and willingly fulfill their own
needs by investing time, effort, and money on a trip, because they attach value to this
process (Prebensen, Chen, & Uysal, 2014). Service providers could benefit from this
because tourists can cope with a wide variety of situations and interact with other people
on their own (Prebensen & Foss, 2011), bringing positive experiences to others.
It is noteworthy that IE (cognitive and emotional components) is different from
customer participation, which has been studied from the company perspective and the
goal is to improve service performance by integrating customers as partial employees.
However, the IE dimension is different because it focuses on how customers perceive and
react to the interaction and how this influences their travel experiences. As Vargo &
Lusch (2014) suggested in S-DL, customers (tourists) are both beneficiary and determiner
of value during the service process. Therefore, in taking a customer-centric approach, IE
touches upon customer emotional, cognitive, and behavioral responses to an offering.
The RL dimension (cognitive component) highlights individual interactions with
people they travel with. Traveling with companions, tourists feel less isolated from their
home during a trip. This connectedness with close friends, family members, or colleagues
adds to the ultimate experience by integrating individuals' personal life and work life with
122
travel life. Relatedness is the only relationship exists before a trip, extending after the trip,
and has more chance of spilling over into daily life. This component generates value in
travel experience by maintaining social bonds with a sense of attachment, closeness, and
knowledge about companions.
The ATE, containing cognitive, emotional and behavioral components, indicates that
an object (in engagement) does not need to be a person; engagement can also refer to
activities and encounters. Immersed involvement (II) refers to subjective perception and
behavioral participation during the trip. It highlights occasions when tourists are fully
concentrated and absorbed by what they are doing. This process adds value to travel
experiences because tourists enjoy what they are doing and feel positive. II generates a
feeling that "time is flying," which indicates loss of self-consciousness, effortless
concentration, and intrinsic enjoyment known as flow (Csikszentmihalyi, 1990).
Meanwhile, the last component, novelty-seeking (NS), is motivational driven. Although
NS has long been considered an essential element in travel motivation (Crompton, 1979;
Lee & Crompton, 1992), the concept in our study goes past just motivation. In TE, this
motivation is transformed into actual behavior. That is, tourists not only are motivated by
unplanned, unexpected, and unusual activities/encounters but also take spontaneous
action to engage. This type of uncertainty adds value to memorable experiences.
5.3 Theoretical Contribution
Our study contributes to the research on engagement studies, tourist experience, and
destination service management. To start with, it enriches the way researchers and
practitioners use engagement. Although engagement as a construct has been explored in
123
several contexts (e.g., education, organization, and marketing), its application in the
context of tourism, particularly focusing tourist on on-site experience, has not yet been
studied. Our study expands the discussion of engagement in a different context. It is
among the first endeavors to conceptualize a “tourist engagement” (TE) construct, with a
thorough research on its definition, dimensionality, and measurement scale. Our study
responds to the call by MSI to advance the understanding of "engagement" in academic
marketing literature and contributes to the calls for more empirical studies on engagement
(Bolton, 2011; Hollebeek et al., 2014; Shaw et al., 2011).
In addition, we contribute to a better understanding of tourist experience through
value co-creation perspective. By applying the engagement concept and introducing S-
DL (Vargo & Lusch, 2004), we shed light on individuals’ role in adding value to their
own travel experience. Specifically, tourists actively use their skills, knowledge, and
expertise to interact and co-create value with other actors during the trip. They are not
only adding values to their own memories, but also bring in positive experiences to others
through interactions. Herein, tourism companies and DMOs are resources providers
offering tourists with tangible and intangible interactions with the servicescape, service
providers, and fellow tourists.
Moreover, our study offers a robust TE scale which can be implemented in
destination service management. The measurement scale is relatively short, certainly a
manageable size. Moreover, the four dimensions of TE are theoretically consistent with
engagement as it is conceptualized in consumer behavior and tourism. These dimensions
provide destination marketers direction to improve tourist engaging experience. Also, this
study establishes a relationship between TE and behavioral intention, indicating TE is a
124
strong predictor of post-consumption evaluations. It explains how TE, especially the co-
creation of value by tourists in their engagement with others could influence future
actions, including purchase intention and word-of-mouth. Study results also provide an
alternative to evaluating the customer-brand relationship, demonstrating that the
emotional connection between tourists and destination can be enhanced by integrating a
variety of actors into value co-creation. We expect to see TE scale used to generate
knowledge about managing tourist engagement experiences during their trips and
maintaining their emotional connections with destinations as they move into behavioral
intention.
5.4 Managerial Implication
This study provides some important implications for destination marketing practices.
The reliable and valid measurement of TE that can be used by destination managers and
tourism companies in assessing their success. Specifically, it can be used to assess the
level of engagement with a destination through four dimensions. As a result, managers
and tourism companies can launch different strategies to improve or enhance each
dimension of tourist engaging experience. TE scale allows managers to develop a
measurable and reasonable marketing program where they can allocate resources (time,
money, and human resources) for enhancing specific dimensions of TE. The goal is to
retain the loyalty of current tourists and attract new tourists by ensuring the quality of
travel experience.
In addition, this TE scale can work as an effective tool for market segment (e.g., by
using composite means, factor scores of each dimension). For instance, tourists could be
125
segmented into heterogeneous groups based on their engagement levels, like highly
engaged, moderately engaged, and unengaged. Highly engaged tourists would be more
profitable for destinations and companies through revisit intentions and word-of-mouth
effect; marketing strategies could be implemented to retain the loyalty of these highly
engaged tourists. Besides, management could work to improve the satisfaction among
tourists in the other two groups. In addition, it would also be beneficial to identify the
segmented groups through their socio-demographic background, past travel experience,
attitudes, behaviors and etc. Therefore, different marketing strategies and travel packages
could be provided to attract different groups.
Moreover, our study shows how TE works in an integrated model with perceived
service quality, perceived value, and satisfaction to predict behavioral intention. It
provides a holistic picture of understanding tourist behavior from value co-creation to
post-consumption experience. The mechanism shows by increasing tourist engaging
experience, their perception of the service performance, value and overall satisfaction
could be significantly enhanced, which in turns leads to a long-term relationship between
tourists and the destination (revisit intention or positive word-of-mouth).
5.5 Limitation and Future Studies
Our study has some limitations which need to be solved in future studies. First, our
study started with both North American and Chinese cruise tourists during the item
generation process (Sub-study 1), aiming to generalize the measurement scale in a cross-
cultural setting. However, due to the difficulties to reach Chinese tourists when the author
was studying in Canada, our study only tested with North American tourists in the sub-
126
study 2 and 3. The author would like to visit the biggest port of call in China (Shanghai)
in the future and empirically test the developed TE scale with Chinese cruisers using site
intercept survey. This future study plan also aligns with Hinkin et al.’s (1997)
recommendation: use a replication study as the last step of scale development., This
replication wants to explore if TE has different expressions (in dimensions) across
cultures and nationalities. Secondly, our study only collected data from cruise tourists;
aforementioned, if our study could measure the complexity of TE in this complex cruise
tourism, it would mean the TE scale could be easily adapted to other settings. Therefore,
another suggestion for future research is to generalize our study results in other settings
(integrated resorts, theme parks, and the like).
Another direction is to empirically test some other antecedents and consequences of
engagement as proposed in the extant literature. For instance, literature has suggested
participant, flow, interactivity, involvement, commitment, rapport, and trust (Bowden,
2009b; Brodie et al., 2011; Hollebeek, 2011a; Patterson et al., 2006; van Doorn et al.
2010; Vivek, et al., 2012) as antecedents of customer engagement, while involvement,
commitment, rapport, satisfaction, trust, perceived quality, co-created value, brand
experience, and brand loyalty as potential consequences (Bowden 2009b; Brodie et al.,
2011; Hollebeek, 2011a; Patterson. et al., 2006; van Doorn, et al. 2010; Vivek, et al.,
2012). Further research needs to investigate the relationship between TE and the above-
listed constructs. In addition, it is important to figure out what drive tourists to engage;
constructs like travel motivation (Choi, Huang, Khazaei, & Flaherty, 2017; Crompton,
1979), self-concept (Huang & Choi, 2017; Huang, LeBlanc, J., & Choi, 2016), and past
travel experience (Huang & Hsu, 2009) could be examined as possible antecedents of TE.
127
Last but not least, as engagement focuses on the emotional connection between
customers and a company, future studies could also consider using emotional relationship
related constructs as dependent variables, like brand emotional attachment (Malär,
Krohmer, Hoyer, & Nyffenegger, 2011), brand romance (Patwardhan &
Balasubramanian, 2011) and brand love (Carroll & Ahuvia, 2006).
128
Bibliography
Achterberg, W., Pot, A. M., Kerkstra, A., Ooms, M., Muller, M., & Ribbe, M. (2003).
The effect of depression on social engagement in newly admitted Dutch nursing
home residents. The Gerontologist, 43(2), 213-218.
Aho, S. K. (2001). Towards a general theory of touristic experiences: Modelling
experience process in tourism. Tourism Review, 56(3/4), 33-37.
Ahrholdt, D. C., Gudergan, S. P., & Ringle, C. M. (2017). Enhancing service loyalty: The
roles of delight, satisfaction, and service quality. Journal of Travel Research, 56(4),
436-450.
Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions
on Automatic Control, 19(6), 716-723.
Alegre, J., & Juaneda, C. (2006). Destination loyalty: Consumers’ economic
behavior. Annals of Tourism Research, 33(3), 684-706.
Algesheimer., R., Dholakia, U.M., & Hermann, A. (2005). The social influence of brand
community: Evidence from European car clubs. Journal of Marketing, 69(3), 19–34.
Ali, F., Hussain, K., & Ragavan, N. A. (2014). Memorable customer experience:
Examining the effects of customers experience on memories and loyalty in
Malaysian resort hotels. Procedia-Social and Behavioral Sciences, 144, 273-279.
Ali, F., Omar, R., & Amin, M. (2013). An examination of the relationships between
physical environment, perceived value, image and behavioural Intentions: A SEM
approach towards Malaysian resort hotels. Journal of Hotel and Tourism
Management, 27(2), 9-26.
129
Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A
review and recommended two-step approach. Psychological Bulletin, 103 (3),411-23.
Andersson, T. D. (2007). The tourist in the experience economy. Scandinavian Journal of
Hospitality and Tourism, 7(1), 46-58.
Appelbaum, A. (2001). The constant customer. Retrieved from
http://gmj.gallup.com/content/745/constant-customer.aspx
Appleton, J. J., Christenson, S. L., Kim, D., & Reschly, A. L. (2006). Measuring
cognitive and psychological engagement: Validation of the student engagement
instrument. Journal of School Psychology, 44(5), 427-445.
Auh, S., Bell, S. J., McLeod, C. S., & Shih, E. (2007). Co-production and customer
loyalty in financial services. Journal of Retailing, 83(3), 359-370.
Bagozzi, R. P., & Foxall, G. R. (1996). Construct validation of a measure of adaptive-
innovative cognitive styles in consumption. International Journal of Research in
Marketing, 13(3), 201-213.
Baker, D. A., & Crompton, J. L. (2000). Quality, satisfaction and behavioral
intentions. Annals of tourism research, 27(3), 785-804.
Baloglu, S., Pekcan, A., Chen, S. L., & Santos, J. (2004). The relationship between
destination performance, overall satisfaction, and behavioral intention for distinct
segments. Journal of Quality Assurance in Hospitality & Tourism, 4(3-4), 149-165.
Baralt, M. (2012). Coding qualitative data. In A. Mackey & S. M. Gass (Eds.), Research
methods in second language acquisition: A practical guide (pp. 222–244). New York,
NY: Wiley-Blackwell.
130
Baron, S., & Harris, K. (2008). Consumers as resource integrators. Journal of Marketing
Management, 24(1-2), 113-130.
Bejerholm, U., & Eklund, M. (2007). Occupational engagement in persons with
schizophrenia: Relationships to self-related variables, psychopathology, and quality
of life. The American Journal of Occupational Therapy, 61(1), 21-32.
Berry, L. L., Carbone, L. P., &Haeckel, S. H. (2002). Managing the total customer
experience. Sloan Management Review, 43 (Spring), 85-89.
Bijmolt, T., Leeflang, P., Block, F., Eisenbeiss, M., Hardie, B., Lemmens, A., & Staffert,
P. (2010). Analytics for customer engagement. Journal of Service Research, 13 (3),
341-356.
Bitner, M. J. (1992). Servicescapes: The impact of physical surroundings on customers
and employees. The Journal of Marketing, 56(2), 57-71.
Bohlmann, N. L., & Downer, J. T. (2016). Self-regulation and task engagement as
predictors of emergent language and literacy skills. Early Education and
Development, 27(1), 18-37.
Bollen, K. A. (1989). Structural equations with latent variables. New York, NY: John
Wiley and Sons.
Bolton, R. N. (2011). Customer engagement: Opportunities and challenges for
organizations. Journal of Service Research, 14(3), 272-274.
Bowden, J. (2009a). Customer engagement: A framework for assessing customer-brand
relationships: The case of the restaurant industry. Journal of Hospitality Marketing &
Management, 18(6), 574-596.
131
Bowden, J. (2009b). The process of customer engagement: A conceptual framework.
Journal of Marketing Theory and Practice, 17 (1), 63-74.
Brakus, J. J., Schmitt, B. H., & Zarantonello, L. (2009). Brand experience: What is it?
How is it measured? Does it affect loyalty?. Journal of Marketing, 73(3), 52-68.
Brodie, R. J., Hollebeek, L. D., Juric, B., & Ilic, A. (2011). Customer engagement
conceptual domain, fundamental propositions, and implications for research.
Journal of Service Research, 14(3), 252-271.
Brodie, R. J., Ilic, A, Juric, B., & Hollebeek, L. D. (2013). Consumer engagement in a
virtual brand community: An exploratory analysis. Journal of Business Research,
66(1), 105-114.
Brown, G. P., Havitz, M. E., & Getz, D. (2007). Relationship between wine involvement
and wine-related travel. Journal of Travel & Tourism Marketing, 21(1), 31-46.
Cai, L. A., Feng, R., & Breiter, D. (2004). Tourist purchase decision involvement and
information preferences. Journal of Vacation Marketing, 10(2), 138-148.
Camino, L., & Zeldin, S. (2002). From periphery to center: Pathways for youth civic
engagement in the day-to-day life of communities. Applied Developmental
Science, 6(4), 213-220.
Campbell, N., O’Driscoll, A., & Saren, M. (2013). Reconceptualising resources: A
critique of service-dominant logic. Journal of Macromarketing, 33 (4), 306-321.
Carroll, B. A., & Ahuvia, A. C. (2006). Some antecedents and outcomes of brand love.
Marketing Letters, 17(2), 79-89.
Cattell, R. B. (1978). The scientific use of factor analysis. New York, NY: Plenum.
132
Cederholm, E. A. (2004). The use of photo‐elicitation in tourism research-framing the
backpacker experience. Scandinavian Journal of Hospitality and Tourism, 4(3), 225-
241.
Chandler, J. D., & Vargo, S. L. (2011). Contextualization and value-in-context: How
context frames exchange. Marketing Theory, 11(1), 35-49.
Chang, H. H., Wang, Y. H., & Yang, W. Y. (2009). The impact of e-service quality,
customer satisfaction and loyalty on e-marketing: Moderating effect of perceived
value. Total Quality Management, 20(4), 423-443.
Chathoth, P. K., Ungson, G. R., Altinay, L., Chan, E. S., Harrington, R., & Okumus, F.
(2014). Barriers affecting organisational adoption of higher order customer
engagement in tourism service interactions. Tourism Management, 42, 181-193.
Chaudhary, M. (2015). Structural equation modelling of child's role in family
buying. International Journal of Business Innovation and Research, 9(5), 568-582.
Chen, C. F. (2008). Investigating structural relationships between service quality,
perceived value, satisfaction, and behavioral intentions for air passengers: Evidence
from Taiwan. Transportation Research Part A: Policy and Practice, 42(4), 709-717.
Chen, C. F., & Chen, F. S. (2010). Experience quality, perceived value, satisfaction and
behavioral intentions for heritage tourists. Tourism Management, 31(1), 29-35.
Chen, C. F., & Tsai, D. (2007). How destination image and evaluative factors affect
behavioral intentions?. Tourism Management, 28(4), 1115-1122.
Cheung, C. M. K., Lee, M. K. O., & Jin, X-L. (2011). Customer engagement in an online
social platform: A conceptual model and scale development. Paper presented at
Thirty Second International Conference on Information Systems, Shanghai, China.
133
Retrieved from
http://aisel.aisnet.org/cgi/viewcontent.cgi?article=1225&context=icis2011
Chi, C. G. Q., & Qu, H. (2008). Examining the structural relationships of destination
image, tourist satisfaction and destination loyalty: An integrated approach. Tourism
Management, 29(4), 624-636.
Chin, W.W. (1998). The partial least squares approach to structural equation modeling.
Modern Methods for Business Research, 295(2), 295-336.
Choi, H.S.C., Huang, S., Khazaei, A., & Flaherty, J. (2017). Market segmentation for
travel motivation and involvement of winery visitors in the Niagara region.
International Journal of Tourism Sciences. DOI: 10.1080/15980634.2017.1349054
Choi, H. S. C., & Sirakaya, E. (2005). Measuring residents’ attitude toward sustainable
tourism: Development of sustainable tourism attitude scale. Journal of Travel
Research, 43(4), 380-394.
Churchill, G. A. (1979). A paradigm for developing better measures of marketing
construct. Journal of Marketing Research, 16 (February), 64-73.
Clason, D.L. & Dormody, T.J. (1994). Analysing data measured by individual Likert type
items. Journal of Agricultural Education, 35(4), 31-5.
Clemes, M. D., Gan, C., & Ren, M. (2011). Synthesizing the effects of service quality,
value, and customer satisfaction on behavioral intentions in the motel industry: An
empirical analysis. Journal of Hospitality & Tourism Research, 35(4), 530-568.
Clow, K. E., & Vorhies, D. W. (1993). Building a competitive advantage for service
firms: Measurement of consumer expectations of service quality. Journal of Services
Marketing, 7(1), 22-32.
134
Cole F.L. (1988) Content analysis: Process and application. Clinical Nurse Specialist 2(1),
53–57.
Cole, S. T., & Illum, S. F. (2006). Examining the mediating role of festival visitors’
satisfaction in the relationship between service quality and behavioral
intentions. Journal of Vacation Marketing, 12(2), 160-173.
Creswell, J.W. (1998). Qualitative inquiry and research design: Choosing among five
traditions. Thousand Oaks, CA: Sage.
Crompton, J. L. (1979). Motivations for pleasure vacation. Annals of Tourism Research,
6(4), 408-424.
Cronin, J.J., Brady, M.K., & Hult, G.T.M. (2000). Assessing the effects of quality, value,
and customer satisfaction on consumer behavioural intentions in service
environments. Journal of Retailing, 76(2), 193-218.
Cruise Lines International Association (CLIA). (2015). 2014 North American cruise
market profile. Retrieved from http://www.cruising.org/docs/default-
source/research/clia_naconsumerprofile_2014.pdf
Csikszentmihalyi, M. (1990). Flow: The psychology of optimal performance. NY:
Cambridge University Press.
Dabholkar, P. A. (1990). How to improve perceived service quality by improving
customer participation. In B.J. Dunlap (Ed.), Development in marketing science (pp.
483–487). Cullowhee, NC: Academy of Marketing Science.
Dabholkar, P. A., Thorpe, D. I., & Rentz, J. O. (1995). A measure of service quality for
retail stores: Scale development and validation. Journal of the Academy of Marketing
Science, 24(1), 3-16.
135
Delli Carpini, M. X. (2000). Gen. com: Youth, civic engagement, and the new
information environment. Political Communication, 17(4), 341-349.
Desarbo, W. S., Jedidi, K., & Sinha, I. (2001). Customer value analysis in a
heterogeneous market. Strategic Management Journal, 22(9), 845-857.
Dessart, L., Veloutsou, C., & Morgan-Thomas, A. (2015). Consumer engagement in
online brand communities: A social media perspective. Journal of Product & Brand
Management, 24(1), 28-42.
Diamantopoulos, A., Schlegelmilch, B. B., Sinkovics, R. R., & Bohlen, G. M. (2003).
Can socio-demographics still play a role in profiling green consumers? A review of
the evidence and an empirical investigation. Journal of Business research, 56(6),
465-480.
Dimanche, D. F., Havitz, D. M. E., & Howard, D. D. R. (1993). Consumer involvement
profiles as a tourism segmentation tool. Journal of Travel & Tourism Marketing, 1(4),
33-52.
Dong, B., Evans, K. R., & Zou, S. (2008). The effects of customer participation in co-
created service recovery. Journal of the Academy of Marketing Science, 36(1), 123-
137.
Dowling, R. & Weeden., C (2017). Cruise ship tourism (2nd eds.). Oxfordshire, UK:
CABI.
Duman, T., & Mattila, A. S. (2005). The role of affective factors on perceived cruise
vacation value. Tourism Management, 26(3), 311-323.
Edensor, T. (2001). Performing tourism, staging tourism. Tourist Studies, 1(1), 59–81.
136
Eisingerich, A. B., & Bell, S. J. (2006). Relationship marketing in the financial services
industry: The importance of customer education, participation and problem
management for customer loyalty. Journal of Financial Services Marketing, 10(4),
86-97.
Elo, S., & Kyngäs, H. (2008). The qualitative content analysis process. Journal of
Advanced Nursing, 62(1), 107-115.
Etgar, M. (2008). A descriptive model of the consumer co-production process. Journal of
the Academy of Marketing Science, 36 (1), 97-108.
Fang, E., Palmatier, R. W., & Evans, K. R. (2008). Influence of customer participation on
creating and sharing of new product value. Journal of the Academy of Marketing
Science, 36(3), 322-336.
FCCA. (n.d.). Cruise industry overview 2012: State of the cruise industry. Retrieved from
http://www.f-cca.com/downloads/2012-Cruise-Industry-Overview-Statistics.pdf
Filep, S., & Deery, M. (2010). Towards a picture of tourists' happiness. Tourism Analysis,
15(4), 399-410.
Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behaviour: An
introduction to theory and research. Boston, MA: Addison-Wesley.
Flynn, L. M. (2012). An exploration of engagement: A customer perspective.
(Unpublished PhD thesis). Depaul University, Chicago, IL.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with
unobservable variables and measurement error. Journal of Marketing Research,18
(1), 39-50.
137
Foxall, G. R., & Pallister, J. G. (1998). Measuring purchase decision involvement for
financial services: Comparison of the Zaichkowsky and Mittal scales. International
Journal of Bank Marketing, 16(5), 180-194.
Fritz, C., & Sonnentag, S. (2006). Recovery, well-being, and performance-related
outcomes: The role of workload and vacation experiences. Journal of Applied
Psychology, 91(4), 936-945.
Füller, J., & Matzler, K. (2008). Customer delight and market segmentation: An
application of the three-factor theory of customer satisfaction on life style
groups. Tourism Management, 29(1), 116-126.
Gallarza, M. G., & Saura, I. G. (2006). Value dimensions, perceived value, satisfaction
and loyalty: An investigation of university students’ travel behaviour. Tourism
management, 27(3), 437-452.
Gan, Y., Yang, M., Zhou, Y., & Zhang, Y. (2007). The two-factor structure of future-
oriented coping and its mediating role in student engagement. Personality and
Individual Differences, 43(4), 851-863.
Gehlbach, H., & Brinkworth, M. E. (2011). Measure twice, cut down error: A process for
enhancing the validity of survey scales. Review of General Psychology, 15(4). 380–
387.
Goldsmith, R. E., Flynn, L. R., Goldsmith, E., & Stacey, E. C. (2010). Consumer
attitudes and loyalty towards private brands. International Journal of Consumer
Studies, 34(3), 339-348.
Gorsuch, R. L. (1983). Factor analysis (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum.
138
Gracia, E., Bakker, A. B., & Grau, R. M. (2011). Positive emotions: The connection
between customer quality evaluations and loyalty. Cornell Hospitality
Quarterly, 52(4), 458-465.
Grönroos, C. (1984). A service quality model and its marketing implications. European
Journal of marketing, 18(4), 36-44.
Grönroos, C. (2006). Adopting a service logic for marketing. Marketing Theory, 6(3),
317-333.
Guadagnoli, E., & Velicer, W. F. (1988). Relation to sample size to the stability of
component patterns. Psychological Bulletin, 103 (2), 265-275.
Gummerus, J., Liljander, V., Weman, E., & Pihlström, M. (2012). Customer engagement
in a Facebook brand community. Management Research Review, 35(9), 857-877.
Gunuc, S., & Kuzu, A. (2015). Student engagement scale: Development, reliability and
validity. Assessment & Evaluation in Higher Education, 40(4), 587-610.
Gursoy, D., & Gavcar, E. (2003). International leisure tourists’ involvement
profile. Annals of Tourism Research, 30(4), 906-926.
Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006).
Multivariate data analysis. Englewood Cliffs, NJ: Prentice Hall.
Hallowell, R. (1996). The relationships of customer satisfaction, customer loyalty, and
profitability: An empirical study. International Journal of Service Industry
Management, 7(4), 27-42.
Hanson, D., & Grimmer, M. (2007). The mix of qualitative and quantitative research in
major marketing journals, 1993-2002. European Journal of Marketing, 41(1/2), 58-
70.
139
Hapsari, R., Clemes, M., & Dean, D. (2017). The impact of service quality, customer
engagement and selected marketing constructs on airline passenger loyalty.
International Journal of Quality and Service Sciences, 9(1), 21-40. Doi:
10.1108/IJQSS-07-2016-0048.
Harrigan, P., Evers, U., Miles, M., & Daly, T. (2017). Customer engagement with
tourism social media brands. Tourism Management, 59, 597-609.
Harris, J. (2006). Consumer engagement: What does it mean? Retrieved from
http://www.imediaconnection.com/content/9729.imc
Harrison, R.L., & Reilly, T.M. (2011). Mixed methods designs in marketing research.
Qualitative Market Research, 14(1), 7-26.
Harter, J.K., Schmidt, F.L., & Hayes, T.L. (2002). Business-unit-level relationship
between employee satisfaction, employee engagement, and business outcomes: A
meta-analysis. Journal of Applied Psychology, 87(2), 268-79.
Havitz, M. E., & Dimanche, F. (1990). Propositions for testing the involvement construct
in recreational and tourism contexts. Leisure Sciences, 12(2), 179-195.
Hedderson, J., & Fisher, M. (1993). SPSS made simple. Belmont, CA: Wadsworth
Incorporated.
Higgins, E. T., & Scholer, A. A. (2009). Engaging the consumer: The science and art of
the value creation process. Journal of Consumer Psychology, 19(2), 100-114.
Hinkin, T. R. (1995). A review of scale development practices in the study of
organizations. Journal of Management, 21(5), 967-988.
140
Hinkin, T. R., Tracey, J. B., & Enz, C. A. (1997). Scale construction: Developing reliable
and valid measurement instruments. Journal of Hospitality & Tourism
Research, 21(1), 100-120.
Hollebeek, L. (2011a). Demystifying customer brand engagement: Exploring the loyalty
nexus. Journal of Marketing Management, 27(7-8), 785-807.
Hollebeek, L. (2011b). Exploring customer brand engagement: Definition and themes.
Journal of Strategic Marketing, 19(7), 555-573.
Hollebeek, L. D., Glynn, M. S., & Brodie, R. J. (2014). Consumer brand engagement in
social media: Conceptualization, scale development and validation. Journal of
Interactive Marketing, 28(2), 149-165.
Hooper, D., Coughlan, J., & Mullen, M.R., (2008). Structural equation modelling:
Guidelines for determining model fit. Electronic Journal of Business Research
Methods, 6(1), 53–60.
Hosany, S., & Gilbert, D. (2010). Measuring tourists’ emotional experiences toward
hedonic holiday destinations. Journal of Travel Research, 49(4), 513-526.
Hosany, S., & Witham, M. (2010). Dimensions of cruisers’ experiences, satisfaction, and
intention to recommend. Journal of Travel Research, 49(3), 351-364.
Hoyer, W. D., Chandy, R., Dorotic, M., Krafft, M., & Singh, S. S. (2010). Consumer
cocreation in new product development. Journal of Service Research, 13(3), 283-296.
Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure
analysis: Conventional criteria versus new alternatives. Structural Equation
Modeling: A Multidisciplinary Journal, 6(1), 1-55.
141
Hu, S., & Kuh, G. D. (2002). Being (dis) engaged in educationally purposeful activities:
The influences of student and institutional characteristics. Research in Higher
Education, 43(5), 555-575.
Hu, S.P. (2010). Scholarship awards, college choice, and student engagement in college
activities: A study of high-per- forming low-income students of color. Journal of
College Student Development, 51 (2), 150-161.
Huang, J., & Hsu, C. H. (2010). The impact of customer-to-customer interaction on
cruise experience and vacation satisfaction. Journal of Travel Research, 49(1), 79-92.
Huang, S., & Choi, H.S. (2017). Understanding Canadian and US tourists: A self-concept
based segmentation study. European Journal of Tourism Research, 16, 201-213.
Huang, S., & Hsu, C. H. (2009). Effects of travel motivation, past experience, perceived
constraint, and attitude on revisit intention. Journal of Travel Research, 48(1), 29-44.
Huang, S., LeBlanc, J., & Choi, H.S. (2016). How do Chinese tourists differ from
Caucasian tourists? An empirical study from the perspective of tourists’ self-concept.
International Journal of Tourism Sciences, 16(4), 222-237.
Hunt, S. D. (2004). On the service-centered dominant logic for marketing. Invited
commentaries on “Evolving to a new dominant logic for marketing”. Journal of
Marketing, 68 (1), 18-27.
Hunt, S. D., & Morgan, R. M. (1995). The comparative advantage theory of competition.
The Journal of Marketing, 59, 1-15.
Huo, Y. J, Binning, K. R., & Molina, L. E. (2010). Testing an integrative model of
respect: Implications for social engagement and well-being. Personality and Social
Psychology Bulletin, 36(2), 200-212.
142
Hutchinson, J., Lai, F., & Wang, Y. (2009). Understanding the relationships of quality,
value, equity, satisfaction, and behavioral intentions among golf travelers. Tourism
Management, 30(2), 298-308.
Hwang, S. N., Lee, C., & Chen, H. J. (2005). The relationship among tourists’
involvement, place attachment and interpretation satisfaction in Taiwan’s national
parks. Tourism Management, 26(2), 143-156.
Innis, D. E., & La Londe, B. J. (1994). Customer service: The key to customer
satisfaction, customer loyalty, and market share. Journal of Business Logistics, 15(1),
1-27.
Jaakkola, E., & Alexander, M. (2014). The role of customer engagement behavior in
value co-creation: A service system perspective. Journal of Service Research, 17(3),
247-261.
Jahn, B., & Kunz, W. (2012). How to transform consumers into fans of your brand.
Journal of Service Management, 23(3), 322-361.
Jamrozy, U., Backman, S. J., & Backman, K. F. (1996). Involvement and opinion
leadership in tourism. Annals of Tourism Research, 23(4), 908-924.
Jarvis, C.B., Mackenzie, S.B., & Podsakoff, P.M. (2003). A critical review of construct
indicators and measurement model misspecification in marketing and consumer
research. Journal of Consumer Research, 30(2), 199-218.
Jennings, M. K., & Stoker, L. (2004). Social trust and civic engagement across time and
generations. Acta Politica, 39(4), 342-379.
143
Johnson, M.D., & Fornell, C. (1991). A framework for comparing customer satisfaction
across individuals and product categories. Journal of Economic Psychology, 12(2),
267–286.
Johnson, W. C., & Sirikit, A. (2002). Service quality in the Thai telecommunication
industry: A tool for achieving a sustainable competitive advantage. Management
Decision, 40(7), 693-701.
Jöreskog, K. and Sörbom, D. (1993). LISREL 8: Structural equation modeling with the
SIMPLIS command language. Chicago, IL: Scientific Software International Inc.
Jöreskog, K. and Sörbom, D. (1996). LISREL 8: User’s reference guide. Chicago, IL:
Scientific Software International Inc.
Jöreskog, K.G, & Sörbom, D, (1989). LISREL 7: A guide to the program and
applications. Chicago: SPSS.
Josiam, B. M., Smeaton, G., & Clements, C. J. (1999). Involvement: Travel motivation
and destination selection. Journal of Vacation Marketing, 5(2), 167-175.
Kahn, W.A. (1990). Psychological conditions of personal engagement and
disengagement at work. Academy of Management Journal, 33(4), 692-724.
Kahn, W.A. (1992). To be full there: Psychological presence at work. Human Relations,
45(4), 321-349.
Kapuscinski, A. N., & Masters, K. S. (2010). The current status of measures of
spirituality: A critical review of scale development. Psychology of Religion and
Spirituality, 2(4), 191-205.
144
Kastenholz, E., Carneiro, M. J., Eusébio, C., & Figueiredo, E. (2013). Host–guest
relationships in rural tourism: Evidence from two Portuguese
villages. Anatolia, 24(3), 367-380.
Keung, S. W. C. (2000). Tourists’ perceptions of hotel frontline employees’ questionable
job-related behaviour. Tourism Management, 21(2), 121-134.
Kim, J. H., Ritchie, J. R. B., & McCormick, B. (2011). Development of a scale to
measure memorable tourism experiences. Journal of Travel Research, 51(1), 12–25.
Kim, J., & Fesenmaier, D. R. (2017). Sharing tourism experiences: The posttrip
experience. Journal of Travel Research, 56(1), 28-40.
Kim, K. (2008). Analysis of structural equation model for the student pleasure travel
market: Motivation, involvement, satisfaction, and destination loyalty. Journal of
Travel & Tourism Marketing, 24(4), 297-313.
Kim, K. H., & Park, D. B. (2017). Relationships among perceived value, satisfaction, and
loyalty: Community-based ecotourism in Korea. Journal of Travel & Tourism
Marketing, 34(2), 171-191.
Kline, P. (1994). An easy guide to factor analysis. New York, NY: Routledge.
Kline, R. B. (2005). Principles and practice of structural equation modeling (2nd ed.).
New York: The Guilford Press.
Kouthouris, C., & Alexandris, K. (2005). Can service quality predict customer
satisfaction and behavioral intentions in the sport tourism industry? An application of
the SERVQUAL model in an outdoors setting. Journal of Sport & Tourism, 10(2),
101-111.
145
Kuh, G. D. (2001). Assessing what really matters to student learning: Inside the national
survey of student engagement. Change, 33(3), 10-17, 66.
Kuh, G. D. (2003). What we're learning about student engagement from NSSE. Change
35(2), 24-32.
Kumar, P. (2002). The impact of performance, cost, and competitive considerations on
the relationship between satisfaction and repurchase intent in business
markets. Journal of Service Research, 5(1), 55-68.
Kumar, V., Aksoy, L., Donkers, B., Venkatesan, R., Wiesel, T., & Tillmanns, S. (2010).
Undervalued or overvalued customers: Capturing total customer engagement value.
Journal of Service Research, 13(3), 297-310.
Kyle, G. T., Absher, J. D., Hammitt, W. E., & Cavin, J. (2006). An examination of the
motivation—involvement relationship. Leisure Sciences, 28(5), 467-485.
Kyle, G., & Chick, G. (2004). Enduring leisure involvement: The importance of personal
relationships. Leisure Studies, 23(3), 243-266.
Lam, T., & Hsu, C. H. (2006). Predicting behavioral intention of choosing a travel
destination. Tourism Management, 27(4), 589-599.
Lankford, S. V., & Howard, D. R. (1994). Developing a tourism impact attitude scale.
Annals of Tourism Research, 21(1), 121-139.
Laurent, G., & Kapferer, J. N. (1985). Measuring consumer involvement profiles.
Journal of Marketing Research, 22(1), 41-53.
Lee, C. K., Yoon, Y. S., & Lee, S. K. (2007). Investigating the relationships among
perceived value, satisfaction, and recommendations: The case of the Korean
DMZ. Tourism Management, 28(1), 204-214.
146
Lee, J., & Beeler, C. (2009). An investigation of predictors of satisfaction and future
intention: Links to motivation, involvement, and service quality in a local festival.
Event Management, 13(1), 17-29.
Lee, J., Graefe, A. R., & Burns, R. C. (2004). Service quality, satisfaction, and behavioral
intention among forest visitors. Journal of Travel & Tourism Marketing, 17(1), 73-
82.
Lee, T. H. (2009). A structural model to examine how destination image, attitude, and
motivation affect the future behavior of tourists. Leisure Sciences, 31(3), 215-236.
Lee, T. H., & Crompton, J. (1992). Measuring novelty seeking in tourism. Annals of
Tourism Research, 19(4), 732-751.
Lehto, X. Y., O’Leary, J. T., & Morrison, A. M. (2004). The effect of prior experience on
vacation behavior. Annals of Tourism Research, 31(4), 801- 818.
Lei, S. S. I., Pratt, S., & Wang, D. (2017). Factors influencing customer engagement with
branded content in the social network sites of integrated resorts. Asia Pacific Journal
of Tourism Research, 22(3), 316-328.
Lemon, K. N., & Verhoef, P. C. (2016). Understanding customer experience throughout
the customer journey. Journal of Marketing, 80(6), 69-96.
Lesschaeve, I., & Bruwer, J. (2010). The importance of consumer involvement and
implications for new product development. In S.R. Jaeger & H. MacFie (Eds.),
Consumer-driven innovation in food and personal care products (pp. 386-423).
Cambridge, UK: Woodhead Publishing Ltd.
147
Libai, B., Bolton, R., Bügel, M. S., De Ruyter, K., Götz, O., Risselada, H., & Stephen, A.
T. (2010). Customer-to-customer interactions: Broadening the scope of word of
mouth research. Journal of Service Research, 13(3), 267-282.
Lin, C. P., Hung, W. T., & Chiu, C. K. (2008). Being good citizens: Understanding a
mediating mechanism of organizational commitment and social network ties in
OCBs. Journal of Business Ethics, 81(3), 561-578.
Little, B., & Little, P. (2006). Employee engagement: Conceptual issues. Journal of
Organizational Culture, Communications and Conflict, 10(1), 111–120.
London, B., Downey, G., & Mace, S. (2007). Psychological theories of educational
engagement: A multi-method approach to studying individual engagement and
institutional change. Vanderbilt Law Review, 60(2), 455–481.
Lu, L., Chi, C. G., & Liu, Y. (2015). Authenticity, involvement, and image: Evaluating
tourist experiences at historic districts. Tourism Management, 50, 85-96.
Lusch, R. F., & Vargo, S. L. (2006a). Service-dominant logic: Reactions, reflections and
refinements. Marketing Theory, 6(3), 281-288.
Lusch, R. F., & Vargo, S. L. (2006b). The service-dominant logic of marketing: Dialog,
debate, and directions. Armonk, NY: M.E. Sharpe.
Lusch, R.F., Vargo, S.L., & Wessels, G. (2008). Toward a conceptual foundation for
service science: Contributions from service-dominant logic. IBM Systems Journal,
47(1), 5-14.
MacCallum, R. C., Browne, M. W., & Sugawara, H. M. (1996). Power analysis and
determination of sample size for covariance structure modeling. Psychological
Methods, 1(2), 130-149.
148
MacKenzie, S. B., Podsakoff, P. M., & Podsakoff, N. P. (2011). Construct measurement
and validation procedures in MIS and behavioral research: Integrating new and
existing techniques. MIS Quarterly, 35(2), 293-334.
Malär, L., Krohmer, H., Hoyer, W. D., & Nyffenegger, B. (2011). Emotional brand
attachment and brand personality: The relative importance of the actual and the ideal
self. Journal of Marketing, 75(4), 35-52.
Malthouse, E. C., & Calder, B. J. (2011). Engagement and experiences: Comment on
Brodie, Hollebeek, Juric, and Ilic (2011). Journal of Service Research, 14(3), 277-
279.
Markos, S., & Sridevi, M. S. (2010). Employee engagement: The key to improving
performance. International Journal of Business and Management, 5(12), 89-96.
Marks, H. M. (2000). Student engagement in instructional activity: Patterns in the
elementary, middle, and high school years. American Educational Research Journal,
37(1), 153-184.
Marsh, H. W., & Hocevar, D. (1985). Application of confirmatory factor analysis to the
study of self-concept: First-and higher order factor models and their invariance
across groups. Psychological Bulletin, 97(3), 562-582.
Martin, A., & Dowson, M. (2009). Interpersonal relationships, motivation, engagement,
and achievement: Yields for theory, current issues, and educational practice. Review
of Educational Research, 79(1), 327–365.
Mascarenhas, O. A., Kesavan, R., & Bernacchi, M. (2006). Lasting customer loyalty: A
total customer experience approach. Journal of Consumer Marketing, 23(7), 397-405.
149
Maslach, C., & Leiter, M., P. (1997). The truth about burnout. San Francisco, CA:
Jossey-Bass.
Matthews, G., Warm, J. S., Reinerman-Jones, L. E., Langheim, L. K., Washburn, D. A.,
& Tripp, L. (2010). Task engagement, cerebral blood flow velocity, and diagnostic
monitoring for sustained attention. Journal of Experimental Psychology: Applied,
16(2), 187-203.
McAlexander, J. H., Schouten, J.W., & Koening, H.F. (2002). Building brand community.
Journal of Marketing, 66(1), 38-49.
McDonald, R. P., & Ho, M. H. R. (2002). Principles and practice in reporting structural
equation analyses. Psychological Methods, 7(1), 64-82.
McDougall, G. H., & Levesque, T. (2000). Customer satisfaction with services: Putting
perceived value into the equation. Journal of Services Marketing, 14(5), 392-410.
McEwen, W. J., & Fleming, J. H. (2003). Customer satisfaction doesn't count. Retrieved
from http://www.gallup.com/businessjournal/1012/customer-satisfaction-doesnt-
count.aspx
Meade, A. W., Watson, A. M., & Kroustalis, C. M. (2007, April). Assessing common
methods bias in organizational research. Paper presented at the 22nd Annual
Meeting of the Society for Industrial and Organizational Psychology, New York, NY.
Meng, S. M., Liang, G. S., & Yang, S. H. (2011). The relationships of cruise image,
perceived value, satisfaction, and post-purchase behavioral intention on Taiwanese
tourists. African Journal of Business Management, 5(1), 19-29.
Michel, S., Brown, S. W., & Gallan, A. S. (2008). Service-logic innovations: How to
innovate customers, not products. California Management Review, 50(3), 49-65.
150
Moghadam, S. K., Zabihi, M. R., Kargaran, M., & Hakimzadeh, A. (2013). Intellectual
capital and organizational learning capability. Journal of Soft Computing and
Applications, 2013(2013), 1-9.
Mollen, A., & Wilson, H. (2010). Engagement, telepresence and interactivity in online
consumer experience: Reconciling scholastic and managerial perspectives. Journal of
Business Research, 63(9-10), 919-925.
Morgado, F. F. R., Meireles, J. F. F., Neves, C. M., Amaral, A. C. S., & Ferreira, M. E. C.
(2017). Scale development: Ten main limitations and recommendations to improve
future research practices. Psicologia: Reflexão e Crítica, 30(3). DOI
10.1186/s41155-016-0057-1
MSI. (2010). 2010-2012 Research Priorities. Boston, MA: Marketing Science Institute.
Mulaik, S. A., James, L. R., Van Alstine, J., Bennett, N., Lind, S., & Stilwell, C. D.
(1989). Evaluation of goodness-of-fit indices for structural equation models.
Psychological Bulletin, 105(3), 430-435.
Netemeyer, R. G., Bearden, W., & Sharma, S. (2003). Scaling procedures: Issues and
applications. Thousand Oaks, CA: Sage.
Nicholls, J. A. F., Gilbert, G. R., & Roslow, S. (1999). An exploratory comparison of
customer service satisfaction in hospitality-oriented and sports-oriented
businesses. Journal of Hospitality & Leisure Marketing, 6(1), 3-22.
Oliver, R. L. (1999). Whence consumer loyalty? Journal of Marketing, 63 (4), 33-44.
Pandža Bajs, I. (2015). Tourist perceived value, relationship to satisfaction, and
behavioral intentions: The example of the Croatian tourist destination
Dubrovnik. Journal of Travel Research, 54(1), 122-134.
151
Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1988). Servqual: A multiple-item scale
for measuring consumer perc. Journal of Retailing, 64(1), 12.
Park, S. Y., & Petrick, J. F. (2009). Examining current non-customers: A cruise vacation
case. Journal of Vacation Marketing, 15(3), 275-293.
Park, D. B., & Yoon, Y. S. (2009). Segmentation by motivation in rural tourism: A
Korean case study. Tourism Management, 30(1), 99-108.
Patterson, P., Yu, T., & De Ruyter, K. (2006). Understanding customer engagement in
services. Paper presented at Australia–New Zealand Marketing Academy Conference,
Brisbane, Australia. Retrieved from
http://smib.vuw.ac.nz:8081/WWW/ANZMAC2006/documents/Pattinson_Paul.pdf
Patwardhan, H., & Balasubramanian, S. K. (2011). Brand romance: a complementary
approach to explain emotional attachment toward brands. Journal of Product &
Brand Management, 20(4), 297-308.
Peter, J.P. (1981). Construct validity: A review of basic issues and marketing practices.
Journal of Marketing Research, 18, 133-45.
Petrick, J. F. (2004). The roles of quality, value, and satisfaction in predicting cruise
passengers’ behavioral intentions. Journal of Travel Research, 42(4), 397–407.
Pine, B.J., & Gilmore, J.H. (1999). The experience economy: Work is theatre and every
business a stage. Boston, MA: Harvard Business School Press.
Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., & Podsakoff, N. P. (2003). Common
method biases in behavioral research: A critical review of the literature and
recommended remedies. Journal of Applied Psychology, 88(5), 879–903.
152
Prahalad, C. K., & Ramaswamy, V. (2004). Co-creation experiences: The next practice in
value creation. Journal of Interactive Marketing, 18(3), 5-14.
Prayag, G., Hosany, S., & Odeh, K. (2013). The role of tourists' emotional experiences
and satisfaction in understanding behavioral intentions. Journal of Destination
Marketing & Management, 2(2), 118-127.
Prayag, G., & Ryan, C. (2012). Antecedents of tourists’ loyalty to Mauritius: The role
and influence of destination image, place attachment, personal involvement, and
satisfaction. Journal of Travel Research, 51(3), 342-356.
Prebensen, N. K., & Foss, L. (2011). Coping and co‐creating in tourist
experiences. International Journal of Tourism Research, 13(1), 54-67.
Prebensen, N. K., Woo, E., Chen, J. S., & Uysal, M. (2013). Motivation and involvement
as antecedents of the perceived value of the destination experience. Journal of Travel
Research, 52(2), 253-264.
Prebensen, N., Chen, J., & Uysal, M. (2014). Co-creation of tourist experience: Scope,
definition, and structure. In N. Prebensen, J. Chen, & M. Uysal (Eds.), Creating
experience value in tourism (pp.1-10). London: CABI.
Pugesek, B. H., Tomer, A., & Von Eye, A. (2003). Structural equation modeling:
Applications in ecological and evolutionary biology. Canbrigde, England:
Cambridge University Press.
Quan, S., & Wang, N. (2004). Towards a structural model of the tourist experience: An
illustration from food experiences in tourism. Tourism Management, 25(3), 297-305.
153
Raaijmakers, Q. A. W., Hoof, A. v., Hart, H. t., Verbogt, T. F. M. A., & Wollebergh, W.,
A. M. (2000). Adolescents’ midpoint response on Likert-type scale items: Neutral or
missing values? International Journal of Public Opinion Research, 12(2), 208-216.
Ranjan, K. R., & Read, S. (2016). Value co-creation: Concept and measurement. Journal
of the Academy of Marketing Science, 44(3), 290-315.
Rasidah, H., Jamal, S. A., & Sumarjan, N. (2014). A Conceptual study of perceived value
and behavioral intentions in green hotels. Australian Journal of Basic and Applied
Sciences, 8(5), 254-259.
Rich, B. L., Lepine, J. A., & Crawford, E. R. (2010). Job engagement: Antecedents and
effects on job performance. Academy of Management Journal, 53(3), 617-635.
Richins, M. L. (2004). The material values scale: Measurement properties and
development of a short form. Journal of Consumer Research, 31(1), 209-219.
Rihova, I., Buhalis, D., Moital, M., & Gouthro, M. B. (2015). Conceptualising customer‐
to‐customer value co-creation in tourism. International Journal of Tourism
Research, 17(4), 356-363.
Romero, J. (2017). Customer engagement behaviors in hospitality: Customer-based
antecedents. Journal of Hospitality Marketing & Management, DOI:
10.1080/19368623.2017.1288192
Romero, J., & Okazaki, S. (2015). Exploring customer engagement behavior: construct
proposal and its antecedents. Retrieved from
http://proceedings.utwente.nl/339/1/UsePLS_2015_submission_67.pdf
Rosenbaum, M. S., Ostrom, A. L., & Kuntze, R. (2005). Loyalty programs and a sense of
community. Journal of Services Marketing, 19(4), 222-233.
154
Rothbard, N.P. (2001). Enriching or depleting? The dynamics of engagement in work and
family roles. Administrative Science Quarterly, 46, 655-684.
Ryan, C. (1997). The tourist experience: A new introduction. London: Cassell.
Sabbath, E. L., Lubben, J., Goldberg, M., Zins, M., & Berkman, L. F. (2015). Social
engagement across the retirement transition among “young-old” adults in the French
GAZEL cohort. European Journal of Ageing, 12(4), 311-320.
Saks, A. M. (2006). Antecedents and consequences of employee engagement. Journal of
Managerial Psychology, 21 (7), 600-619.
Sarstedt, M., & Ringle, C. M. (2010). Treating unobserved heterogeneity in PLS path
modelling: A comparison of FIMIX-PLS with different data analysis strategies.
Journal of Applied Statistics, 37(8), 1299–1318.
Sawhney, M., Verona, G., & Prandelli, E. (2005). Collaborating to create: The Internet as
a platform for customer engagement in product innovation. Journal of Interactive
Marketing, 19(4), 4-17.
Scarles, C. (2010). Where words fail, visuals ignite: Opportunities for visual
autoethnography in tourism research. Annals of Tourism Research, 37(4), 905-926.
Schaufeli, W., & Bakker, A. B. (2004). Job demands, job resources, and their relationship
with burnout and engagement: A multi‐sample study. Journal of Organizational
Behavior, 25(3), 293-315.
Schaufeli, W., Salanova, M., Gonzalez-Roma, V., & Bakker, A.B. (2002). The
measurement of engagement and burnout: A two sample confirmatory factor analytic
approach. Journal of Happiness Studies, 3(1), 71-92.
155
Schmitt, B. (1999). Experiential marketing. Journal of Marketing Management, 15(1-3),
53-67.
Schwartz, D. (1989). Visual ethnography: Using photography in qualitative research.
Qualitative Sociology, 12(2).
Shandas, V., & Messer, W. B. (2008). Fostering green communities through civic
engagement: Community-based environmental stewardship in the Portland area.
Journal of the American Planning Association, 74(4), 408-418.
Sharma, S., Mukherjee, S., Kumar, A., & Dillon, W. R. (2005). A simulation study to
investigate the use of cutoff values for assessing model fit in covariance structure
models. Journal of Business Research, 58(7), 935-943.
Shaw, G., Bailey, A., & Williams, A. (2011). Aspects of service-dominant logic and its
implications for tourism management: Examples from the hotel industry. Tourism
Management, 32(2), 207-214.
Sherif, C. W., Kelly, M., Rodgers Jr, H. L., Sarup, G., & Tittler, B. I. (1973). Personal
involvement, social judgment, and action. Journal of personality and social
psychology, 27(3), 311- 328.
Sheth, J. N., Sisodia, R. S., & Sharma, A. (2000). The antecedents and consequences of
customer-centric marketing. Journal of the Academy of Marketing Science, 28(1),
55-66.
Silvestre, A. L., Santos, C. M., & Ramalho, C. (2008). Satisfaction and behavioural
intentions of cruise passengers visiting the Azores. Tourism Economics, 14(1), 169–
184.
156
Smith, S. L., & Godbey, G. C. (1991). Leisure, recreation and tourism. Annals of Tourism
Research, 18(1), 85-100.
So, K. K. F., King, C., & Sparks, B. (2014). Customer engagement with tourism brands
scale development and validation. Journal of Hospitality & Tourism Research, 38(3),
304-329.
So, K. K. F., King, C., Sparks, B., & Wang, Y. (2016). The role of customer engagement
in building consumer loyalty to tourism brands. Journal of Travel Research, 55(1),
64-78.
Sørensen, F., & Jensen, J. F. (2015). Value creation and knowledge development in
tourism experience encounters. Tourism Management, 46, 336–346.
Sprott, D., Czellar, S., & Spangenberg, E. (2009). The importance of a general measure
of brand engagement on market behavior: Development and validation of a
scale. Journal of Marketing Research, 46(1), 92-104.
Sternberg, E. (1997). The iconography of the tourism experience. Annals of Tourism
Research, 24(4), 951–969.
Su, L., Hsu, M. K., & Swanson, S. (2017). The effect of tourist relationship perception on
destination loyalty at a world heritage site in China: The mediating role of overall
destination satisfaction and trust. Journal of Hospitality & Tourism Research, 41(2),
180-210.
Sweeney, J. C., & Soutar, G. N. (2001). Consumer perceived value: The development of
a multiple item scale. Journal of Retailing, 77(2), 203-220.
Tabachnick, B. G., & Fidell, L. S. (2007). Using multivariate statistics (5th ed.). Boston,
MA: Allyn & Bacon.
157
Tam, J. L. (2004). Customer satisfaction, service quality and perceived value: An
integrative model. Journal of Marketing Management, 20(7-8), 897-917.
Tang, T., Fang, E., & Wang, F. (2014). Is neutral really neutral? The effects of neutral
user-generated content on product sales. Journal of Marketing, 78(4), 41-58.
Thurau, B. B., Carver, A. D., Mangun, J. C., Basman, C. M., & Bauer, G. (2007). A
market segmentation analysis of cruise ship tourists visiting the Panama Canal
watershed: Opportunities for ecotourism development. Journal of Ecotourism, 6(1),
1-18.
Tsang, K. K. (2012). The use of midpoint on Likert scale: The implications for
educational research. Hong Kong Teachers’ Centre Journal, 11, 121-130.
Tse, T. S., & Zhang, E. Y. (2013). Analysis of blogs and microblogs: A case study of
Chinese bloggers sharing their Hong Kong travel experiences. Asia Pacific Journal
of Tourism Research, 18(4), 314-329.
Twycross, A., & Shields, L. (2004). Validity and reliability–What’s it all about? Part 2
Reliability in quantitative studies. Paediatric Nursing, 16(10), 36-36.
Tynan, C., & McKechnie, S. (2009). Experience marketing: A review and reassessment.
Journal of Marketing Management, 25(5-6), 501-517.
Vaismoradi, M., Turunen, H., & Bondas, T. (2013). Content analysis and thematic
analysis: Implications for conducting a qualitative descriptive study. Nursing &
Health Sciences, 15(3), 398-405.
Van Doorn, J., Lemon, K., Mittal, V., Nass, S., Pick, D., Pirner, P., & Verhoef, P. C.
(2010). Customer engagement behavior: Theoretical foundations and research
directions. Journal of Service Research, 13 (3), 253-266.
158
Van Raaij, F.W., & Pruyn, A.H. (1998). Customer control and evaluation of service
validity and reliability. Psychology & Marketing, 15(8), 811–832.
Vargo, S. L., & Akaka, M. A. (2009). Service-dominant logic as a foundation for service
science: Clarifications. Service Science, 1(1), 32-41.
Vargo, S. L., & Lusch, R. F. (2004). Evolving to a new dominant logic for marketing.
Journal of Marketing, 68(1), 1-17.
Vargo, S. L., & Lusch, R. F. (2008). Service-dominant logic: Continuing the evolution.
Journal of the Academy of marketing Science, 36(1), 1-10.
Vargo, S. L., & Lusch, R. F. (2014). Inversions of service-dominant logic. Marketing
Theory, 14(3), 239-248
Vargo, S. L., Lusch, R. F., & Akaka, M. A. (2010). Advancing service science with
service-dominant logic. In P. P. Maglio, C.A. Kieliszewski, & J. Spohrer (Eds.),
Handbook of Service Science (pp. 133-156). Berlin, Germany: Springer.
Vargo, S. L., Lusch, R. F., Akaka, M. A., & He, Y. (2010). Service-dominant logic: A
review and assessment. Review of Marketing Research, 6(1), 125-167.
Vargo, S. L., Maglio, P. P., & Akaka, M. A. (2008). On value and value co-creation: A
service systems and service logic perspective. European Management Journal, 26(3),
145-152.
Venkatesan, R. (2017). Executing on a customer engagement strategy. Journal of the
Academy of Marketing Science, 45 (3), 289–293.
Verhoef, P. C., Reinartz, W. J., & Krafft, M. (2010). Customer engagement as a new
perspective in customer management. Journal of Service Research, 13(3), 247-252.
159
Vivek, S. D., Beatty, S. E., & Morgan, R. M. (2012). Customer engagement: Exploring
customer relationships beyond purchase. The Journal of Marketing Theory and
Practice, 20(2), 122-146.
Vivek, S.D. (2009). A scale of consumer engagement (Unpublished PhD thesis).
University of Alabama, Tuscaloosa, AL.
Vogt, C. A., Fesenmaier, D. R., & MacKay, K. (1994). Functional and aesthetic
information needs underlying the pleasure travel experience. Journal of Travel &
Tourism Marketing, 2(2-3), 133-146.
Wang, M., Wang, J., & Zhao, J. (2007). An empirical study of the effect of customer
participation on service quality. Journal of Quality Assurance in Hospitality &
Tourism, 8(1), 49-73.
Wedel, M., & Kamakura, W. A. (1999), Market Segmentation: Concepts and
Methodological Foundations, New York: Springer
Wei, W., Miao, L., & Huang, Z. J. (2013). Customer engagement behaviors and hotel
responses. International Journal of Hospitality Management, 33, 316-330.
Welsh, E. (2002). Dealing with data: Using NVivo in the qualitative data analysis
process. Forum: Qualitative Social Research [Online journal], 3(2). Retrieved from:
http://www.qualitativeresearch.net/index.php/fqs/article/view/865/1881
West, S. G., Finch, J. F., & Curran, P. J. (1995). Structural equation models with
nonnormal variables: Problems and remedies. In R. H. Hoyle (Ed.), Structural
equation modeling (pp. 56–75). Thousand Oaks, CA: Sage Publications.
Wheaton, B., Muthen, B., Alwin, D. F., & Summers, G. F. (1977). Assessing reliability
and stability in panel models. Sociological Methodology, 8, 84-136.
160
Williams, P., & Soutar, G. N. (2009). Value, satisfaction and behavioral intentions in an
adventure tourism context. Annals of Tourism Research, 36(3), 413-438.
Wind, Y. (1978). Issues and advances in segmentation research. Journal of Marketing
Research, 15(3), 317-337.
Wirtz, J., den Ambtman, A., Bloemer, J., Horváth, C., Ramaseshan, B., van de Klundert,
J., Canli, Z.G., & Kandampully, J. (2013). Managing brands and customer
engagement in online brand communities. Journal of Service Management, 24(3),
223-244.
Woodruff, R. B., & Flint, D. J. (2006). Marketing's service-dominant logic and customer
value. In R. F. Lusch & S. L. Vargo (Eds.), The service-dominant logic of marketing:
Dialog, debate and directions (pp. 183-195). Armonk, NY: ME Sharpe.
Worthington, R. L., & Whittaker, T. A. (2006). Scale development research a content
analysis and recommendations for best practices. The Counseling Psychologist, 34(6),
806-838.
Wu, C. H. J. (2011). A re-examination of the antecedents and impact of customer
participation in service. The Service Industries Journal, 31(6), 863-876.
Yang, Z., & Peterson, R. T. (2004). Customer perceived value, satisfaction, and loyalty:
The role of switching costs. Psychology & Marketing, 21(10), 799-822.
Yeh, C. M. (2013). Tourism involvement, work engagement and job satisfaction among
frontline hotel employees. Annals of Tourism Research, 42, 214-239.
Yong, A. G., & Pearce, S. (2013). A beginner’s guide to factor analysis: Focusing on
exploratory factor analysis. Tutorials in quantitative methods for psychology, 9(2),
79-94.
161
Yoon, Y., & Uysal, M. (2005). An examination of the effects of motivation and
satisfaction on destination loyalty: A structural model. Tourism Management, 26(1),
45-56.
Yuan, J., & Jang, S. (2008). The effects of quality and satisfaction on awareness and
behavioral intentions: Exploring the role of a wine festival. Journal of Travel
Research, 46(3), 279-288.
Yuan, Y. H. E., & Wu, C. K. (2008). Relationships among experiential marketing,
experiential value, and customer satisfaction. Journal of Hospitality & Tourism
Research, 32(3), 387-410.
Yüksel, A., & Yüksel, F. (2007). Shopping risk perceptions: Effects on tourists’ emotions,
satisfaction and expressed loyalty intentions. Tourism Management, 28(3), 703-713.
Yuksel, A., Yuksel, F., & Bilim, Y. (2010). Destination attachment: Effects on customer
satisfaction and cognitive, affective and conative loyalty. Tourism
management, 31(2), 274-284.
Žabkar, V., Brenčič, M. M., & Dmitrović, T. (2010). Modelling perceived quality, visitor
satisfaction and behavioural intentions at the destination level. Tourism
Management, 31(4), 537-546.
Zaichkowsky, J. L. (1986). Conceptualizing involvement. Journal of Advertising, 15 (2),
4–14, 34.
Zaichkowsky, J. L. (1987). The emotional aspect of product involvement. Advances in
Consumer Research, 14(1), 32-35
Zeithaml, V. A. (1988). Consumer perceptions of price, quality, and value: A means-end
model and synthesis of evidence. The Journal of Marketing, 52, 2-22.
162
Zeithaml, V. A., Berry, L., & Parasuraman, A. (1996). The behavioural consequences of
service quality. Journal of Marketing, 60, 31–46.
Zeithaml,V. A. (2000). Service quality, profitability, and the economic worth of
customers: What we know and what we need to learn. Journal of the Academy of
Marketing Science, 28, 67–85.
163
Appendices
164
Appendix 1. The details of items from relevant literature.
Constructs Components Measurement Items Literature Contexts
Customer
engagement
enthusiasm
I spend a lot of my discretionary time ____.
I am heavily into _______.
I am passionate about _____.
My days would not be the same without ___.
Vivek
(2009)
various
activities conscious
participation
Anything related to _____ grabs my attention.
I like to learn more about ______.
I pay a lot of attention to anything about ____.
social interaction
I love ______ with my friends.
I enjoy ______ more when I am with others.
__is more fun when other people around me do it too.
Customer
engagement
behavior
cognitive
processing
Using [brand] gets me to think about [brand].
I think about [brand] a lot when I’m using it.
Using [brand] stimulates my interest to learn more about [brand].
Hollebeek
et al.
(2014)
social media
settings affection
I feel very positive when I use [brand].
Using [brand] makes me happy.
I feel good when I use [brand].
I’m proud to use [brand].
activation
I spend a lot of time using [brand], compared to other [category]
brands.
Whenever I’m using [category], I usually use [brand].
[Brand] is one of the brands I usually use when I use [category].
165
Customer
engagement
identification
When someone criticizes this brand, it feels like a personal insult.
When I talk about this brand, I usually say we rather than they.
This brand’s successes are my successes.
When someone praises this brand, it feels like a personal
compliment.
So et al.
(2014)
hotel and
airline brands
enthusiasm
I am heavily into this brand.
I am passionate about this brand.
I am enthusiastic about this brand.
I feel excited about this brand.
I love this brand.
attention
I like to learn more about this brand.
I pay a lot of attention to anything about this brand.
Anything related to this brand grabs my attention.
I concentrate a lot on this brand.
I like learning more about this brand.
absorption
When I am interacting with the brand, I forget everything else
around me.
Time flies when I am interacting with the brand.
When I am interacting with brand, I get carried away.
When interacting with the brand, it is difficult to detach myself.
In my interaction with the brand, I am immersed.
When interacting with the brand intensely, I feel happy.
166
interaction
In general, I like to get involved in brand community discussions.
I am someone who enjoys interacting with like-minded others in the
brand community.
I am someone who likes actively participating in brand community
discussions.
In general, I thoroughly enjoy exchanging ideas with other people in
the brand community.
I often participate in activities of the brand community.
Tourist
involvement
pleasure
/interest
Buying a vacation is like buying a gift for myself.
A vacation is somewhat of a pleasure to me.
I attach great importance to a vacation.
One can say vacation destinations interests me a lot.
Gursoy &
Gavcar
(2003)
international
leisure
tourists risk probability
Whenever one buys a vacation, one never really knows whether it is
the one that should have been bought.
When I face a variety of vacation choices, I always feel a bit at loss
to make my choice.
Choosing a vacation destination is rather complicated.
When one purchases a vacation, one is never certain of one’s choice.
risk importance
It is really annoying to purchase a vacation that is not suitable.
If, after I bought a vacation, my choice proves to be poor, I would
be really upset.
Involvement
attraction
Camping is one of the most enjoyable things I do.
Camping is very important to me.
Camping is one of the most satisfying things I do. Kyle et al.
(2006) camping
centrality
I find a lot of my life is organized around camping.
Camping occupies a central role in my life.
To change my preference for camping to another recreation activity
would require major rethinking.
167
social bonding
I enjoy discussing camping with my friends.
Most of my friends are in some way connected with camping.
Participating in camping provides me with opportunity to be with
friends.
identity affirmation
When I participate in camping, I can really be myself.
I identify with people and image associated with camping.
When I’m camping, I don’t have to be concerned with the way I
look.
Identity Expression
You can tell a lot about a person by seeing them camping.
Participating in camping says a lot about who I am.
When I participate in camping, other see me the way I want them to
see me.
Customer
participation
cooperation
The employees of this theme park got my full cooperation.
I carefully obeyed the rules and policies of this theme park.
I will prepare well before receiving the specific theme park service
to facilitate service delivery.
Wu (2010) theme park
attentive
communication
When I experienced a problem at this theme park, I would tell the
employee the way that they can serve my need.
Whenever I had the chance, I made constructive suggestions to this
theme park on how to improve its service.
If I noticed a problem, I informed an employee of this theme park
even if it does not affect me.
168
Customer
participation
participation in
service design and
standard build-up
I will describe my requirements to the hotel.
I will inquire of the receptionist what services I can get.
I’d like to join in the activity of service designing and standard-
building.
I’d like to design the function and quality standard, and provide time
of the service products according to my needs.
When I am dissatisfied with the service, I will let the hotel or servers
know.
I will tell the server when I am not satisfied with his service.
I’d like to take part in the investigation about customer requirements
of hotel.
active interaction
with employees
I will actively cooperate with the servers during the service process.
I will express my appreciation to the server when I am satisfied with
his service.
I’d like to ask for servers by name.
I will tell the servers what I need.
Wang et al.
(2007) hotel
information
collection
I will research the competitors before deciding which one to check
in.
I will seek the information from media (such as magazines,
advertisements, websites of hotels) about the hotel before deciding
which one to check in.
I’d like to receive mail (or e-mail) ads from the hotel.
I’d like to keep contact with this hotel.
word of mouth
communication
Advice from relatives and friends is very important to me when
choosing a hotel.
I will ask my relatives or friends, if they have been there, about their
impression of this hotel.
Good “word of mouth” from relatives and friends is an important
reason I chose this hotel.
169
Appendix 2. Recruitment letter.
To whom it may concern,
We are conducting a research project to develop a new construct “Tourist Engagement”.
The study results are expected to better understand the meaningful interactions and value
cocreation processes in tourists’ experience, and help managers provide tourists with
memorable experience and build a long-term relationship with tourists.
We are looking for experienced tourists who were at least 25 years old and have had at
least one cruise experience in the last 12 months (first-timer) or who have had at least one
cruise experience in the last 5 years (repeaters). Focus group and in-depth interview will
be conducted. The estimated time for focus group will be 60-90 minutes, and the
estimated time for interview will be 60 minutes. During the focus group or in-depth
interview, you will be required to share 3 to 5 pictures you took during the cruise trip.
The pictures will be used to assist a story telling process, and will not be published. If
there are also other people shown in any of the picture, please inform them this picture
will be used in a focus group or an in-depth interview, but will not be submitted or
published. You can choose to participate in either a focus group or an in-depth interview
and you will receive 20 dollars for your participation. All participants will be assigned an
ID number to ensure data remains confidential.
If you are qualified and interested in participating in this study, please contact the
researcher Shuyue Huang at [email protected] or 1-519-731-5867 and indicate which
study you want to participate in. Shuyue Huang will then arrange a suitable time at
University of Guelph or a public coffee shop in Guelph.
This study has been reviewed and received ethics approval through University of Guelph
Research Ethics Board with the REB16JN008.
Again, your cooperation and contribution to this study are greatly appreciated.
Sincerely,
170
Appendix 3. Interview and focus group questions.
1. Please briefly describe your last cruise trip experience.
(e.g. when, destination, cruise company, cruise line, how many days, with whom, type of
trip)
2. Please show me some pictures that you took during the cruise trip and briefly tell the
story behind the pictures.
(3-5 pictures, pictures you think that are meaningful to share, the pictures represent the
impressive or happy moments during the trip)
3. Could you please recall one scenario where you had (some or all) of the following
experiences?
• fully absorbed by and enthusiastic (e.g. about the activities, people)
• actively searched and used the resources available
• felt a sense of identification and commitment to what you were doing on board
• could not detach yourself from what you were doing
• time flew
• willing to get involved or participate in the activities, interact with people
• willing to spend time, energy and effort to interact and share with people (e.g. help
others, chat with others, share experience etc.)
• fully forgot your daily roles and work
• felt love, adoration, or crazy about what happened on board
4. What motivated you to participate in the scenario mentioned above?
5. Could you share one memorable experience you had with employees, your
companions, or fellow tourists? (one example for each if possible)
6. After the cruise, how did you share this travel experience with family or friends?
(e.g. telling stories or recommending this trip to friends and relatives, sharing pictures on
social media, posting comments online and etc.)
7. If you take another cruise, what would you want to do and what would you want to
avoid?
8. Is there anything interesting or important you want to add?
171
Appendix 4. The expert survey.
Expert Review Survey
Thank you for taking the time to be an expert reviewer for the scale development of tourist engagement. We are interested in
understanding the meaningful interactions and value co-creation processes in tourists’ experience. This should help managers provide
tourists with a memorable experience and build a long-term relationship with tourists. Please read the definition of tourist engagement
and answer the following questions. There are no right or wrong answers.
Definition of Tourist Engagement:
A tourist's psychological state that occurs by virtue of interactive, co-creative tourist experiences with a focal agent/object
(people/attraction/ activities /encounters) in focal travel experience relationships.
Please evaluate each of the 53 items of the tourist engagement scale based on relevancy and readability.
A) Readability: Please rate the readability of the initially developed items below using the following 5-point scale:
Poor (1); Neutral (3); Excellent (5)
B) Representativeness: Please indicate the extent to which you think the following items are representative of tourist engagement
using the following 5-point scale:
Not Very Representative of Tourist Engagement (1); Somewhat
Representative of Tourist Engagement (3); Very Representative of Tourist Engagement (5)
C) Any suggestions or comments for refinement
No. Items Readability Relevancy Suggestion (e.g.
wording)
1 ……
172
Appendix 5. The skewness and kurtosis of the TE items and behavioral intention items.
Construct Item Skewness Kurtosis Item Skewness Kurtosis Item Skewness Kurtosis
TE
1 -0.349 -0.271 19 -0.547 -0.249 37 -0.325 -0.781
2 -1.508 3.058 20 -0.861 0.423 38 -0.621 0.195
3 -0.713 0.171 21 -0.875 0.486 39 -0.673 0.749
4 -0.926 0.918 22 -0.609 0.057 40 -0.081 -0.260
5 -1.297 2.120 23 -0.378 -0.338 41 -0.657 0.474
6 -0.808 0.144 24 -0.314 -0.235 42 -0.209 -0.385
7 0.022 -0.389 25 -0.323 -0.486 43 -0.271 -0.392
8 -0.916 1.469 26 -0.599 0.357 44 -0.288 -0.644
9 -0.775 0.408 27 -0.369 -0.219 45 -0.768 0.624
10 -0.681 0.054 28 -0.084 -0.327 46 -0.900 0.693
11 -1.035 1.037 29 -0.556 0.589 47 -0.798 1.318
12 -1.842 5.821 30 -0.345 -0.297 48 -1.316 2.763
13 -0.879 0.540 31 -0.738 0.418 49 -1.339 1.756
14 -1.214 1.774 32 -0.261 -0.553 50 -1.075 0.899
15 -1.491 3.680 33 -0.637 0.536 51 -0.721 0.381
16 -0.916 0.498 34 -0.123 -0.752 52 -0.371 -0.131
17 -0.721 0.165 35 -0.145 -0.652 53 -0.692 0.249
18 -0.705 0.300 36 -0.481 -0.293
Behavioral
intention 1 -1.571 2.734 2 -1.348 1.575 3 -1.477 1.682
173
Appendix 6 The skewness and kurtosis of the all the survey items.
Constructs Item Skewness Kurtosis Constructs Item Skewness Kurtosis
TE
1 -0.985 0.546
TE
16 -0.420 -0.631
2 -1.208 1.899 17 -0.142 -0.630
3 -1.073 0.734 18 -0.546 -0.244
4 -0.576 -0.284 19 -0.341 -0.745
5 -1.389 1.775 20 -0.558 0.110
6 -1.399 2.933 21 -0.249 -0.131
7 -0.843 0.171 22 -1.064 1.208
8 -0.688 0.855 23 -0.578 0.012
9 -1.359 2.594 24 -0.689 0.327
10 -0.684 0.549 25 -0.707 0.356
11 -0.272 -0.428 26 -0.933 1.055
12 -1.267 2.662 27 -0.549 0.062
13 -0.950 1.145 28 -0.743 0.337
14 -0.318 -0.378 29 -1.902 4.911
15 -1.059 1.884 30 -0.838 0.778
PSQ
1 -0.852 0.724
PV
1 -0.798 0.241
2 -0.999 1.003 2 -1.088 0.915
3 -0.982 0.932 3 -1.057 1.207
4 -0.991 0.732 4 -0.859 0.228
SAT
1 -1.117 2.020
BI
1 -1.691 3.688
2 -1.536 3.214 2 -1.196 1.286
3 -1.444 2.120 3 -1.422 2.073
Note: TE=tourist engagement, PSQ=perceived service quality, PV=perceived value, SAT=satisfaction,
BI=behavioral intention.