Examining the Value Co-Creation Model in Motor Racing Events: Moderating Effect of Residents and Tourists
Abstract
:1. Introduction
2. Theoretical Background and Hypothesis Development
2.1. Theoretical Dimensions of Value Co-Creation
2.2. Outcome of the Value Co-Creation: Gratitude, Revisit Intention, and Word of Mouth
2.3. Moderating Role of Travel Patterns Types in the Value Co-Creation
2.4. Hypothesis Development
2.4.1. Co-Production
2.4.2. Value-in-Use
2.4.3. Gratitude, Revisit Intention, and Word-of-Mouth
2.4.4. Moderating Roles of Residents and Tourists
3. Methods: Study 1
3.1. Participants and Data Collection
3.2. Instruments
3.3. Data Analysis
4. Results: Study 1
4.1. Descriptive Statistics
4.2. Reflective Measurement Model
4.3. Formative Measurement Model
4.4. Common Method Variance
5. Methods: Study 2
5.1. Participants and Data Collection
5.2. Instruments
6. Results: Study 2
6.1. Descriptive Statistics
6.2. Measurement Model
6.3. Structural Equation Modeling
6.4. Discriminant Validity
6.5. Multi-Group Analysis
7. Discussion and Implications
7.1. Theoretical Implications
7.2. Practical Implications
8. Limitations and Suggestions for Future Study
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A
Variables | Items | λ of Residents (n = 181) | λ ofTourists (n = 174) |
Knowledge 1 | The event was open to spectators’ ideas and suggestions about its existing services or towards developing a new service. | 0.73 | 0.81 |
Knowledge 2 | The event provided sufficient information (e.g., player information, event schedule, parking, transportation) related to the event to me. | 0.73 | 0.69 |
Knowledge 3 | As a spectator, I felt I willingly spent time and effort to share ideas and suggestions with the event to improve its service and event quality. | 0.71 | 0.83 |
Knowledge 4 | The event provided a suitable platform and opportunity (e.g., feedback button on the event website, on-site customer survey) to spectators to offer suggestions and ideas about the event to event organizers. | 0.73 | 0.78 |
Equity 1 | Event organizers understand spectator preferences toward the event service. | 0.83 | 0.78 |
Equity 2 | The event schedules were aligned with spectators’ requirements (e.g., the way I wish them to be). | 0.79 | 0.80 |
Equity 3 | The event considered spectators’ feedback to be as important as event organizers’ feedback in the event. | 0.81 | 0.83 |
Equity 4 | The event organizers invited spectators in the event design process. | 0.81 | 0.79 |
Interaction 1 | Before, during, and after the event, spectators could conveniently express their specific preferences. | 0.83 | 0.77 |
Interaction 2 | All relevant information about the event was communicated to spectators. | 0.71 | 0.72 |
Interaction 3 | The event encouraged spectator interactions (e.g., developing event services, talking with other fans, assisting other fans, etc.)” | 0.80 | 0.70 |
Interaction 4 | In order to obtain maximum benefits from the event, spectators had to apply their skills, knowledge, and time to the event. | 0.74 | 0.76 |
Experience 1 | The benefits, value, or fun derived from the event depended on the other spectators. | 0.90 | 0.79 |
Experience 2 | Depending on the type/degree of participation of the event activities, spectators’ experiences at the event might be different from other spectators. | 0.78 | 0.77 |
Experience 3 | It was possible for spectators to experiment and try new things (e.g., drinking new beverage, playing new racing video games, upgrade seating experience). | 0.78 | 0.79 |
Personalization 1 | The benefits, value, or fun derived from the event depended on the other spectators. | 0.82 | 0.78 |
Personalization 2 | The event tried to serve the individual needs of its spectators. | 0.78 | 0.81 |
Personalization 3 | Spectators involved themselves differently at the event depending on their knowledge and preferences. | 0.80 | 0.83 |
Personalization 4 | The event provided an overall good personal experience, going beyond monetary value. | 0.58 | 0.51 |
Relationship 1 | The event provided everything necessary for spectators to fully enjoy the event. | 0.82 | 0.79 |
Relationship 2 | Spectators felt an attachment to or relationship with the event. | 0.79 | 0.84 |
Relationship 3 | The event built a community of spectators who really like the event. | 0.78 | 0.79 |
Relationship 4 | The event became well known because spectators shared their positive experiences on social media. | 0.79 | 0.84 |
Gratitude 1 | I strongly identify with the event. | 0.81 | 0.88 |
Gratitude 2 | When someone praises about the event, it feels like a personal compliment. | 0.81 | 0.86 |
Gratitude 3 | I feel attached to this event. | 0.82 | 0.84 |
Revisit Intention 1 | I will make an effort to attend the future Grand Prix in Austin. | 0.88 | 0.87 |
Revisit Intention 2 | I intend to revisit the future Grand Prix in Austin. | 0.84 | 0.92 |
Revisit Intention 3 | I intend to save time and money to revisitthe future Grand Prix in Austin. | 0.78 | 0.87 |
Revisit Intention 4 | I am willing to revisit the future Grand Prix in Austin. | 0.87 | 0.88 |
Word-of-mouse 1 | I will recommend the Grand Prix in Austin to to my family. | 0.84 | 0.89 |
Word-of-mouse 2 | I am likely to recommend the Grand Prix in Austin to my friends. | 0.77 | 0.86 |
Word-of-mouse 3 | I will recommend the U.S. Grand Prix to people interested in racing. | 0.86 | 0.85 |
References
- Ranjan, K.R.; Read, S. Value co-creation: Concept and measurement. J. Acad. Market. Sci. 2016, 44, 290–315. [Google Scholar] [CrossRef]
- Homans, G.C. Social Behavior: Its Elementary Forms; Harcourt, Brace & World: San Diego, CA, USA, 1961. [Google Scholar]
- Aicher, T.J.; Karadakis, K.; Eddosary, M.M. Comparison of sport tourists’ and locals’ motivation to participate in a running event. Int. J. Event Festiv. Manag. 2015, 6, 215–234. [Google Scholar] [CrossRef]
- Galvagno, M.; Dalli, D. Theory of value co-creation: A systematic literature review. Manag. Serv. Qual: An Intern. J. 2014, 24, 643–683. [Google Scholar] [CrossRef]
- Cheung, M.L.; Leung, W.K.; Aw, E.C.X.; Koay, K.Y. “I follow what you post!”: The role of social media influencers’ content characteristics in consumers’ online brand-related activities (COBRAs). J. Retail. Consum. Serv. 2022, 66, 102940. [Google Scholar] [CrossRef]
- Busser, J.A.; Shulga, L.V. Co-created value: Multidimensional scale and nomological network. Tour. Manag. 2018, 65, 69–86. [Google Scholar] [CrossRef]
- Yi, Y.; Gong, T. Customer value co-creation behavior: Scale development and validation. J. Bus. Res. 2013, 66, 1279–1284. [Google Scholar] [CrossRef]
- Zhang, J.C.; Byon, K.K.; Tsuji, Y.; Pedersen, P.M. Co-created value influences residents’ support toward the sporting event through the mediating mechanism of gratitude. Eur. Sport Manag. Q. 2021, 1–23. [Google Scholar] [CrossRef]
- Schmitt, C.D.S.; Petroll, M.D.L.M. A theoretical essay on the influence of Social Exchange Theory and Value Co-creation in Crowdfunding. Intercom Rev. Bras. Ciências Comun. 2021, 44, 247–269. [Google Scholar] [CrossRef]
- Christophersen, T.; Konradt, U. Development and validation of a formative and a reflective measure for the assessment of online store usability. Behav. Inf. Technol. 2012, 31, 839–857. [Google Scholar] [CrossRef]
- Vargo, S.L.; Lusch, R.F. The four service marketing myths: Remnants of a goods-based, manufacturing model. J. Serv. Res. 2004, 6, 324–335. [Google Scholar] [CrossRef] [Green Version]
- Barnes, S.J.; Mattsson, J.; Sørensen, F. Destination brand experience and visitor behavior: Testing a scale in the tourism context. Ann. Tour. Res. 2014, 48, 121–139. [Google Scholar] [CrossRef]
- Neuhofer, B.; Buhalis, D.; Ladkin, A. Smart technologies for personalized experiences: A case study in the hospitality domain. Electron. Mark. 2015, 25, 243–254. [Google Scholar] [CrossRef]
- Chang, M.J.; Kang, J.H.; Ko, Y.J.; Connaughton, D.P. The effects of perceived team performance and social responsibility on pride and word-of-mouth recommendation. Sport Market. Q. 2017, 26, 31–41. [Google Scholar]
- Travel Industry Association of America. Glossary of Travel Terms. 2020. Available online: https://www.travellaw.com/glossary (accessed on 12 September 2020).
- Chen, J.S.; Tsou, H.T.; Ching, R.K. Co-production and its effects on service innovation. Ind. Market. Manag. 2011, 40, 1331–1346. [Google Scholar] [CrossRef]
- Yang, Y.; Wang, S.; Cai, Y.; Zhou, X. How and why does place identity affect residents’ spontaneous culture conservation in ethnic tourism community? A value co-creation perspective. J. Sustain. Tour. 2022, 30, 1344–1363. (in press). [Google Scholar] [CrossRef]
- Tajfel, H.; Turner, J.C.; Austin, W.G.; Worchel, S. An Integrative Theory of Intergroup Conflict; Austin, W., Worchel, S., Eds.; Oxford University Press Inc.: New York, NY, USA, 1979. [Google Scholar]
- Kaplanidou, K.; Vogt, C. The meaning and measurement of a sport event experience among active sport tourists. J. Sport Manag. 2010, 24, 544–566. [Google Scholar] [CrossRef] [Green Version]
- Snelgrove, R.; Taks, M.; Chalip, L.; Green, B.C. How visitors and locals at a sport event differ in motives and identity. J. Sport Tour. 2008, 13, 165–180. [Google Scholar] [CrossRef] [Green Version]
- Woratschek, H.; Horbel, C.; Popp, B. The sport value framework—A new fundamental logic for analyses in sport management. Eur. Sport Manag. Q. 2014, 14, 6–24. [Google Scholar] [CrossRef] [Green Version]
- Mele, C.; Pels, J.; Polese, F. A brief review of systems theories and their managerial applications. Serv. Sci. 2010, 2, 126–135. [Google Scholar] [CrossRef] [Green Version]
- Gerke, A.; Woratschek, H.; Dickson, G. The sport cluster concept as middle-range theory for the sport value framework. Sport Manag. Rev. 2020, 13, 200–214. [Google Scholar] [CrossRef]
- Etgar, M. A descriptive model of the consumer co-production process. J. Acad. Mark. Sci. 2008, 36, 97–108. [Google Scholar] [CrossRef]
- Shilbury, D. A bibliometric analysis of four sport management journals. Sport Manag. Rev. 2011, 14, 434–452. [Google Scholar] [CrossRef]
- Chatterjee, S.; Nguyen, B. Value co-creation and social media at bottom of pyramid (BOP). Bottom Line 2021, 34, 101–123. [Google Scholar] [CrossRef]
- Hoyer, W.D.; Chandy, R.; Dorotic, M.; Krafft, M.; Singh, S.S. Consumer cocreation in new product development. J. Serv. Res. 2010, 13, 283–296. [Google Scholar] [CrossRef]
- Vargo, S.L.; Lusch, R.F. Service-dominant logic: Continuing the evolution. J. Acad. Mark. Sci. 2008, 36, 1–10. [Google Scholar] [CrossRef]
- Gummesson, E.; Mele, C. Marketing as value co-creation through network interaction and resource integration. J. Bus. Mark. Manag. 2010, 4, 181–198. [Google Scholar] [CrossRef]
- Grönroos, C.; Voima, P. Critical service logic: Making sense of value creation and co-creation. J. Acad. Mark. Sci. 2013, 41, 133–150. [Google Scholar] [CrossRef]
- Hedlund, D.P. Creating value through membership and participation in sport fan consumption communities. Eur. Sport Manag. Q. 2014, 14, 50–71. [Google Scholar] [CrossRef]
- Lusch, R.F.; Vargo, S.L. Service-dominant logic: Reactions, reflections and refinements. Mark. Theory 2006, 6, 281–288. [Google Scholar] [CrossRef]
- Horbel, C.; Popp, B.; Woratschek, H.; Wilson, B. How context shapes value co-creation: Spectator experience of sport events. Serv. Ind. J. 2016, 36, 510–531. [Google Scholar] [CrossRef] [Green Version]
- Karpen, I.O.; Bove, L.L.; Lukas, B.A. Linking service-dominant logic and strategic business practice: A conceptual model of a service-dominant orientation. J. Serv. Res. 2012, 15, 21–38. [Google Scholar] [CrossRef]
- Kolyperas, D.; Maglaras, G.; Sparks, L. Sport fans’ roles in value co-creation. Eur. Sport Manag. Q. 2019, 19, 201–220. [Google Scholar] [CrossRef] [Green Version]
- Palminteri, S.; Lebreton, M.; Worbe, Y.; Grabli, D.; Hartmann, A.; Pessiglione, M. Pharmacological modulation of subliminal learning in Parkinson’s and Tourette’s syndromes. Proc. Natl. Acad. Sci. USA 2009, 106, 19179–19184. [Google Scholar] [CrossRef] [Green Version]
- Lawler, E.J. An affect theory of social exchange. Am. J. Sociol. 2001, 107, 321–352. [Google Scholar] [CrossRef] [Green Version]
- Chiu, W.; Won, D.; Bae, J.S. Customer value co-creation behaviour in fitness centres: How does it influence customers’ value, satisfaction, and repatronage intention? Manag. Sport Leis. 2019, 24, 32–44. [Google Scholar] [CrossRef]
- See-To, E.W.; Ho, K.K. Value co-creation and purchase intention in social network sites: The role of electronic Word-of-Mouth and trust–A theoretical analysis. Comput. Hum. Behav. 2014, 31, 182–189. [Google Scholar] [CrossRef]
- Ballouli, K.; Trail, G.T.; Koesters, T.C.; Bernthal, M.J. Differential effects of motives and points of attachment on conative loyalty of Formula 1 US Grand Prix attendees. Sport Market. Q. 2016, 25, 166–181. [Google Scholar]
- Koo, S.K.S.; Byon, K.K.; Baker III, T.A. Integrating Event Image, Satisfaction, and Behavioral Intention: Small-Scale Marathon Event. Sport Market. Q. 2014, 23, 127–137. [Google Scholar]
- Chen, N.; Funk, D.C. Exploring destination image, experience and revisit intention: A comparison of sport and non-sport tourist perceptions. J. Sport Tour. 2010, 15, 239–259. [Google Scholar] [CrossRef]
- Füller, J. Refining virtual co-creation from a consumer perspective. Calif. Manag. Rev. 2010, 52, 98–122. [Google Scholar] [CrossRef]
- Lin, Z.; Chen, Y.; Filieri, R. Resident-tourist value co-creation: The role of residents’ perceived tourism impacts and life satisfaction. Tour. Manag. 2017, 61, 436–442. [Google Scholar] [CrossRef] [Green Version]
- Palmatier, R.W.; Jarvis, C.B.; Bechkoff, J.R.; Kardes, F.R. The role of customer gratitude in relationship marketing. J. Mark. 2009, 73, 1–18. [Google Scholar] [CrossRef] [Green Version]
- Emmons, R.A.; McCullough, M.E. The Psychology of Gratitude; Oxford University Press Inc.: New York, NY, USA, 2004. [Google Scholar]
- Viechtbauer, W.; Smits, L.; Kotz, D.; Budé, L.; Spigt, M.; Serroyen, J.; Crutzen, R. A simple formula for the calculation of sample size in pilot studies. J. Clin. Epidemiol. 2015, 68, 1375–1379. [Google Scholar] [CrossRef] [Green Version]
- Kjellsson, G.; Clarke, P.; Gerdtham, U.G. Forgetting to remember or remembering to forget: A study of the recall period length in health care survey questions. J. Health Econ. 2014, 35, 34–46. [Google Scholar] [CrossRef] [Green Version]
- Bradburn, N.M. Recall Period in Consumer Expenditure Surveys Program [Paper presentation]. In Proceedings of the NORC the Bureau of Labor Statistics Consumer Expenditure Survey Methods Workshop, Chicago, IL, USA; 2010. Available online: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.364.176 (accessed on 14 July 2010).
- Andrew, D.; Pedersen, P.M.; McEvoy, C. Research Methods and Design in Sport Management; Human Kinetics: Champaign, IL, USA, 2019. [Google Scholar]
- Sport Marketing Analytics. Indy Car Attendance Profile. Available online: http://sportsmarketanalytics.com.proxyiub.uits.iu.edu/research.aspx?subrid=558 (accessed on 1 July 2021).
- Komorita, S.S.; Graham, W.K. Number of scale points and the reliability of scales. Edu. Psy. Meas. 1965, 25, 987–995. [Google Scholar] [CrossRef]
- Herche, J.; Engelland, B. Reversed-polarity items and scale unidimensionality. J. Acad. Mark. Sci. 1996, 24, 366–374. [Google Scholar] [CrossRef]
- Kim, K.A.; Byon, K.K. The dark side of spectator behavior: Effects of spectator dysfunctional behavior on anger, rumination, and revisit intention. Sport Market. Q. 2020, 29, 228–240. [Google Scholar] [CrossRef]
- Kim, K.; Byon, K.K.; Baek, W. Customer-to-customer value co-creation and co-destruction in sporting events. Serv. Ind. J. 2020, 40, 633–655. [Google Scholar] [CrossRef]
- Hair, J.F.; Black, W.C.; Babin, B.; Anderson, R.; Tatham, R. Multivariate Data Analysis; Prentice-Hall Inc.: Upper Saddle River, NJ, USA, 2010. [Google Scholar]
- Reinartz, W.; Haenlein, M.; Henseler, J. An empirical comparison of the efficacy of covariance-based and variance-based SEM. Int. J. Res.Mark. 2009, 26, 332–344. [Google Scholar] [CrossRef] [Green Version]
- Garson, G.D. Partial Least Squares: Regression and Structural Equation Models; Statistical Associates Publishers: Asheboro, NC, USA, 2016. [Google Scholar]
- Mishra, P.; Pandey, C.M.; Singh, U.; Gupta, A.; Sahu, C.; Keshri, A. Descriptive statistics and normality tests for statistical data. Ann. Card. Anaesth. 2019, 22, 67–78. Available online: https://www.annals.in/text.asp?2019/22/1/67/250184 (accessed on 1 July 2021).
- Chou, C.P.; Bentler, P.M. Estimates and Tests in Structural Equation Modeling; Hoyle, R.H., Ed.; Sage Publications, Inc.: Thousand Oaks, CA, USA, 1995. [Google Scholar]
- West, S.G.; Finch, J.F.; Curran, P.J. Structural Equation Models with Nonnormal Variables: Problems and Remedies; Hoyle, R.H., Ed.; Sage Publications Inc.: Thousand Oaks, CA, USA, 1995. [Google Scholar]
- Hair Jr, J.F.; Hult, G.T.M.; Ringle, C.M.; Sarstedt, M. A primer on Partial Least Squares Structural Equation Modeling (PLS-SEM); Sage Publications Inc.: Thousand Oaks, CA, USA, 2021. [Google Scholar]
- Hair, J.F.; Risher, J.J.; Sarstedt, M.; Ringle, C.M. When to use and how to report the results of PLS-SEM. Eur. Bus. Rev. 2019, 31, 2–24. [Google Scholar] [CrossRef]
- Fornell, C.; Larcker, D.F. Evaluating structural equation models with unobservable variables and measurement error. J. Mark. Res. 1981, 18, 39–50. [Google Scholar] [CrossRef]
- Henseler, J.; Ringle, C.M.; Sarstedt, M. A new criterion for assessing discriminant validity in variance-based structural equation modeling. J. Acad. Mark. Sci. 2015, 43, 115–135. [Google Scholar] [CrossRef] [Green Version]
- Podsakoff, P.M.; MacKenzie, S.B.; Lee, J.Y.; Podsakoff, N.P. Common method biases in behavioral research: A critical review of the literature and recommended remedies. J. Appl. Psychol. 2003, 88, 879–903. [Google Scholar] [CrossRef]
- Tehseen, S.; Ramayah, T.; Sajilan, S. Testing and controlling for common method variance: A review of available methods. J. Manag. Sci. 2017, 4, 142–168. [Google Scholar] [CrossRef]
- Kock, N. Harman’s single factor test in PLS-SEM: Checking for common method bias. Data Anal. Perspect. J. 2021, 2, 1–6. [Google Scholar]
- Anson, I.G. Taking the time? Explaining effortful participation among low-cost online survey participants. Res. Politics 2018, 5, 1–8. [Google Scholar] [CrossRef] [Green Version]
- Weijters, B.; Geuens, M.; Schillewaert, N. The stability of individual response styles. Psychol. Methods 2010, 15, 96–110. [Google Scholar] [CrossRef] [Green Version]
- Salazar, M.S. The dilemma of combining positive and negative items in scales. Psicothema 2015, 27, 192–199. [Google Scholar] [CrossRef]
- Wong, K.K.K. Partial least squares structural equation modeling (PLS-SEM) techniques using SmartPLS. Mark. Bull. 2013, 24, 1–32. [Google Scholar]
- Hsu, S.H.; Chen, W.H.; Hsieh, M.J. Robustness testing of PLS, LISREL, EQS and ANN-based SEM for measuring customer satisfaction. Total Qual. Manag. Bus. Excell. 2006, 17, 355–372. [Google Scholar] [CrossRef]
- Chin, W.W. Partial Least Squares is to LISREL as principal components analysis is to common factor analysis. Technol. Stud. 1995, 2, 315–319. [Google Scholar]
- Farrell, A.M. Insufficient discriminant validity: A comment on Bove, Pervan, Beatty, and Shiu (2009). J. Bus. Res. 2010, 63, 324–327. [Google Scholar] [CrossRef] [Green Version]
- Cheung, G.W.; Rensvold, R.B. Evaluating goodness-of-fit indexes for testing measurement invariance. Struct. Equ. Model. 2002, 9, 233–255. [Google Scholar] [CrossRef]
- Marsh, H.W.; Hocevar, D. Application of confirmatory factor analysis to the study of self-concept: First-and higher order factor models and their invariance across groups. Psychol. Bull. 1985, 97, 562–582. [Google Scholar] [CrossRef]
- Henseler, J.; Chin, W.W. A comparison of approaches for the analysis of interaction effects between latent variables using partial least squares path modeling. Struct. Equ. Model. 2010, 17, 82–109. [Google Scholar] [CrossRef]
- Gössling, S.; Scott, D.; Hall, C.M. Pandemics, tourism and global change: A rapid assessment of COVID-19. J. Sustain. Tour. 2020, 29, 1–20. [Google Scholar] [CrossRef]
- Jarvis, C.B.; MacKenzie, S.B.; Podsakoff, P.M. A critical review of construct indicators and measurement model misspecification in marketing and consumer research. J. Consum. Res. 2003, 30, 199–218. [Google Scholar] [CrossRef]
- Zhang, J.C.; Byon, K.K.; Williams, A.S.; Huang, H. Effects of the event and its destination image on sport tourists’ attachment and loyalty to a destination: The cases of the Chinese and US Formula One Grand Prix. Asia Pac. J. Tour. Res. 2019, 24, 1169–1185. [Google Scholar] [CrossRef]
- Rihova, I.; Buhalis, D.; Moital, M.; Gouthro, M.B. Conceptualising customer-to-customer value co-creation in tourism. Intern. J. Tour. Res. 2015, 17, 356–363. [Google Scholar] [CrossRef]
- Assiouras, I.; Skourtis, G.; Giannopoulos, A.; Buhalis, D.; Koniordos, M. Value co-creation and customer citizenship behavior. Ann. Tour. Res. 2019, 78, 1–11. [Google Scholar] [CrossRef]
- Jones, C.W.; Byon, K.K.; Huang, H. Service quality, perceived value, and fan engagement: Case of Shanghai Formula One racing. Sport Mark. Q. 2019, 28, 63–76. [Google Scholar] [CrossRef] [Green Version]
- Sarstedt, M.; Hair, J.F.; Ringle, C.M.; Thiele, K.O.; Gudergan, S.P. Estimation issues with PLS and CBSEM: Where the bias lies! J. Bus. Res. 2016, 69, 3998–4010. [Google Scholar] [CrossRef] [Green Version]
- Bagozzi, R.P.; Yi, Y. Multitrait-multimethod matrices in consumer research. J. Consum. Res. 1991, 17, 426–439. [Google Scholar] [CrossRef]
Reflective Model | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
KNLG | EQT | ITCT | EXPC | PSNZ | RLSP | COP | VIU | VCC | α | CR | AVE | |
KNLG | 0.72 | 0.81 | 0.81 | 0.52 | ||||||||
EQT | 0.77 | 0.77 | 0.85 | 0.86 | 0.60 | |||||||
ITCT | 0.68 | 0.83 | 0.74 | 0.83 | 0.83 | 0.55 | ||||||
EXPC | 0.64 | 0.75 | 0.74 | 0.75 | 0.79 | 0.79 | 0.57 | |||||
PSNZ | 0.74 | 0.79 | 0.71 | 0.75 | 0.78 | 0.75 | 0.76 | 0.61 | ||||
RLSP | 0.58 | 0.48 | 0.64 | 0.66 | 0.70 | 0.71 | 0.79 | 0.79 | 0.50 | |||
COP | 0.88 | 0.95 | 0.92 | 0.78 | 0.81 | 0.62 | 0.71 | 0.89 | 0.90 | 0.51 | ||
VIU | 0.74 | 0.76 | 0.79 | 0.90 | 0.90 | 0.86 | 0.83 | 0.71 | 0.83 | 0.87 | 0.50 | |
VCC | 0.85 | 0.91 | 0.90 | 0.87 | 0.89 | 0.75 | 0.97 | 0.67 | 0.71 | 0.83 | 0.86 | 0.50 |
Formative Model | ||||||||||||
KNLG | EQT | ITCT | EXPC | PSNZ | RLSP | COP | VIU | VCC | α | CR | AVE | |
KNLG | 0.72 | 0.81 | 0.81 | 0.52 | ||||||||
EQT | 0.77 | 0.77 | 0.85 | 0.86 | 0.60 | |||||||
ITCT | 0.68 | 0.83 | 0.74 | 0.83 | 0.83 | 0.55 | ||||||
EXPC | 0.64 | 0.75 | 0.74 | 0.75 | 0.79 | 0.79 | 0.57 | |||||
PSNZ | 0.74 | 0.79 | 0.71 | 0.75 | 0.78 | 0.75 | 0.76 | 0.61 | ||||
RLSP | 0.58 | 0.48 | 0.64 | 0.66 | 0.70 | 0.71 | 0.79 | 0.79 | 0.50 | |||
COP | 0.88 | 0.95 | 0.92 | 0.78 | 0.81 | 0.62 | 0.75 | 0.89 | 0.90 | 0.56 | ||
VIU | 0.74 | 0.76 | 0.79 | 0.90 | 0.90 | 0.86 | 0.83 | 0.71 | 0.83 | 0.87 | 0.50 | |
VCC | 0.85 | 0.91 | 0.90 | 0.87 | 0.89 | 0.75 | 0.97 | 0.67 | 0.71 | 0.83 | 0.86 | 0.50 |
Reflective Model | |||||||||
---|---|---|---|---|---|---|---|---|---|
KNLG | EQT | ITCT | EXPC | PSNZ | RLSP | COP | VIU | VCC | |
KNLG | |||||||||
EQT | 0.83 | ||||||||
ITCT | 0.71 | 0.97 | |||||||
EXPC | 0.69 | 0.88 | 0.83 | ||||||
PSNZ | 0.85 | 0.87 | 0.78 | 0.84 | |||||
RLSP | 0.63 | 0.42 | 0.75 | 0.77 | 0.59 | ||||
COP | 1.01 | 1.06 | 1.04 | 0.85 | 0.91 | 0.67 | |||
VIU | 0.77 | 0.81 | 0.86 | 1.06 | 1.10 | 1.05 | 0.89 | ||
VCC | 0.94 | 0.98 | 0.99 | 0.97 | 1.02 | 0.86 | 1.03 | 1.04 | |
Formative Model | |||||||||
KNLG | EQT | ITCT | EXPC | PSNZ | RLSP | COP | VIU | VCC | |
KNLG | |||||||||
EQT | 0.83 | ||||||||
ITCT | 0.71 | 0.97 | |||||||
EXPC | 0.69 | 0.88 | 0.83 | ||||||
PSNZ | 0.85 | 0.87 | 0.78 | 0.84 | |||||
RLSP | 0.63 | 0.42 | 0.75 | 0.77 | 0.59 | ||||
COP | 1.01 | 1.06 | 1.04 | 0.85 | 0.91 | 0.67 | |||
VIU | 0.77 | 0.81 | 0.86 | 1.06 | 1.10 | 1.05 | 0.89 | ||
VCC | 0.94 | 0.98 | 0.99 | 0.97 | 1.02 | 0.86 | 1.03 | 1.04 |
Reflective Model (Formative Model) | |||
---|---|---|---|
Path Coefficient | SE | p-Value | |
Value co-creation → Gratitude | 0.63 (0.63) | 0.07 (0.07) | <0.01 (<0.01) |
Value co-creation → Revisit intentions | 0.45 (0.45) | 0.11 (0.11) | <0.01 (<0.01) |
Value co-creation → Word of mouth | 0.70 (0.71) | 0.05 (0.05) | <0.01 (<0.01) |
R2 | |||
Gratitude | 0.392 (0.395) | ||
Revisit intentions | 0.200 (0.201) | ||
Word of mouth | 0.495 (0.496) |
Weights of the First-Order Constructs on the Designated Second-Order Constructs | |||||||
---|---|---|---|---|---|---|---|
Indianapolis 500 (N = 56) | |||||||
Second-Order Constructs | First-Order Constructs | Weight | t-Value | SD | 95% CI Lower Bound | 95% CI Upper Bound | |
Hypothesis 1a | Co-production | Knowledge | 0.88 ** | 26.42 | 0.03 | 0.82 | 0.95 |
Hypothesis 1b | Co-production | Equity | 0.95 ** | 60.95 | 0.02 | 0.91 | 0.98 |
Hypothesis 1c | Co-production | Interaction | 0.92 ** | 39.95 | 0.02 | 0.87 | 0.96 |
Hypothesis 2a | Value-in-use | Experience | 0.90 ** | 33.68 | 0.03 | 0.85 | 0.95 |
Hypothesis 2b | Value-in-use | Personalization | 0.90 ** | 28.03 | 0.03 | 0.84 | 0.96 |
Hypothesis 2c | Value-in-use | Relationship | 0.86 ** | 12.65 | 0.07 | 0.72 | 0.95 |
Weights of the Second-Order Constructs on the Designated Third-Order Constructs | |||||||
Third-Order Constructs | Second-Order Constructs | Weight | t-Value | SD | 95% CI Lower Bound | 95% CI Upper Bound | |
Hypothesis 1 | Co-creation value | Co-production | 0.97 ** | 71.96 | 0.01 | 0.95 | 0.98 |
Hypothesis 2 | Co-creation value | Value-in-use | 0.94 ** | 67.06 | 0.01 | 0.91 | 0.97 |
VCC | GTT | ReV | WOM | Mean | SD | Skewness | Kurtosis | α | CR | AVE | |
---|---|---|---|---|---|---|---|---|---|---|---|
VCC | 0.78 | 5.56 | 0.83 | −0.65 | 0.08 | 0.76 | 0.85 | 0.61 | |||
GTT | 0.80 | 0.84 | 5.56 | 0.93 | −0.70 | 0.12 | 0.79 | 0.88 | 0.71 | ||
ReV | 0.85 | 0.81 | 0.88 | 5.79 | 1.01 | −1.00 | 0.65 | 0.71 | 0.88 | 0.77 | |
WOM | 0.84 | 0.83 | 0.82 | 0.85 | 5.78 | 0.94 | −0.57 | −0.37 | 0.80 | 0.88 | 0.72 |
Coefficients of the First-Order Constructs on the Designated Second-Order Constructs | |||||||
---|---|---|---|---|---|---|---|
Residents (n = 181) | |||||||
Second-Order Constructs | First-Order Constructs | Weight | t-Value | SD | 95% CI Lower Bound | 95% CI Upper Bound | |
Hypothesis 1a | Co-production | Knowledge | 0.87 ** | 35.42 | 0.02 | 0.84 | 0.90 |
Hypothesis 1b | Co-production | Equity | 0.93 ** | 75.54 | 0.02 | 0.91 | 0.95 |
Hypothesis 1c | Co-production | Interaction | 0.95 ** | 85.45 | 0.01 | 0.92 | 0.96 |
Hypothesis 2a | Value-in-use | Experience | 0.90 ** | 46.30 | 0.02 | 0.87 | 0.93 |
Hypothesis 2b | Value-in-use | Personalization | 0.93 ** | 76.39 | 0.01 | 0.91 | 0.95 |
Hypothesis 2c | Value-in-use | Relationship | 0.94 ** | 83.97 | 0.01 | 0.92 | 0.96 |
Coefficients of the Second-Order Constructs on the Designated Third-Order Constructs | |||||||
N = 355 | |||||||
Third-Order Constructs | Second-Order Constructs | Weight | t-Value | SD | 95% CI Lower Bound | 95% CI Lpper Bound | |
Hypothesis 1 | Co-creation value | Co-production | 0.93 ** | 48.39 | 0.02 | 0.89 | 0.95 |
Hypothesis 2 | Co-creation value | Value-in-use | 0.96 ** | 95.32 | 0.01 | 0.94 | 0.98 |
Hypotheses | Beta | t-Value | 95% CI Lower Bound | 95% CI Upper Bound | Decision |
---|---|---|---|---|---|
H3: VCC → GTT | 0.87 ** | 44.57 | 0.81 | 0.97 | supported |
H4: VCC → ReV | 0.89 ** | 48.45 | 0.82 | 0.98 | supported |
H5: VCC → WOM | 0.82 ** | 41.82 | 0.78 | 0.98 | supported |
First-Order Factor Invariance Tests | |||||||||
---|---|---|---|---|---|---|---|---|---|
Models | χ2 | df | χ2/df | RMSEA | CFI | TLI | ΔCFI | ΔRMSEA | Δχ2 |
Configural invariance | 2060.42 | 796 | 2.59 | 0.057 | 0.925 | 0.882 | / | / | / |
Metric invariance | 2161.08 | 828 | 2.61 | 0.058 | 0.921 | 0.880 | 0.004 | 0.001 | Δχ2 (32) = 100.66, p < 0.01 |
Scalar invariance | 2221.11 | 851 | 2.61 | 0.058 | 0.920 | 0.879 | 0.001 | 0.000 | Δχ2 (23) = 80.86, p < 0.01 |
Third-Order Factor Invariance Tests | |||||||||
Models | χ2 | df | χ2/df | RMSEA | CFI | TLI | ΔCFI | ΔRMSEA | Δχ2 |
Configural invariance | 2188.19 | 842 | 2.60 | 0.057 | 0.914 | 0.881 | / | / | / |
Metric invariance | 2217.86 | 853 | 2.60 | 0.057 | 0.913 | 0.881 | 0.001 | 0.000 | Δχ2 (11) = 100.66, p < 0.01 |
Scalar invariance | 2236.45 | 860 | 2.60 | 0.057 | 0.913 | 0.881 | 0.000 | 0.000 | Δχ2 (7) = 80.86, p < 0.01 |
Path | Residents Path Coefficients | Tourists Path Coefficients | Resident & Tourists Difference | Chi-Square Difference | p-Value (Residents vs. Sport Tourists) | Results | |
---|---|---|---|---|---|---|---|
H6 | COP → Knowledge | 0.90 | 0.86 | 0.04 | Δχ2 (1) = 12.51 | 0.01 | Supported |
H7 | COP → Equity | 0.96 | 0.92 | 0.04 | Δχ2 (1) = 5.58 | 0.02 | Supported |
H8 | COP → Interaction | 0.97 | 0.93 | 0.04 | Δχ2 (1) = 6.01 | 0.01 | Supported |
H9 | VIU → Experience | 0.94 | 0.88 | 0.06 | Δχ2 (1) = 4.26 | 0.04 | Supported |
H10 | VIU → personalization | 0.94 | 0.93 | 0.01 | Δχ2 (1) = 0.06 | 0.80 | Rejected |
H11 | VIU → relationship | 0.96 | 0.93 | 0.02 | Δχ2 (1) = 0.27 | 0.60 | Rejected |
H12 | VCC → GTT | 0.95 | 0.92 | 0.03 | Δχ2 (1) = 2.90 | 0.09 | Rejected |
H13 | VCC → ReV | 0.99 | 0.95 | 0.04 | Δχ2 (1) = 6.57 | 0.01 | Supported |
H14 | VCC → WOM | 0.84 | 0.78 | 0.06 | Δχ2 (1) = 1.87 | 0.17 | Rejected |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Byon, K.K.; Zhang, J.; Jang, W. Examining the Value Co-Creation Model in Motor Racing Events: Moderating Effect of Residents and Tourists. Sustainability 2022, 14, 9648. https://doi.org/10.3390/su14159648
Byon KK, Zhang J, Jang W. Examining the Value Co-Creation Model in Motor Racing Events: Moderating Effect of Residents and Tourists. Sustainability. 2022; 14(15):9648. https://doi.org/10.3390/su14159648
Chicago/Turabian StyleByon, Kevin K., Jingxian (Cecilia) Zhang, and Wooyoung (William) Jang. 2022. "Examining the Value Co-Creation Model in Motor Racing Events: Moderating Effect of Residents and Tourists" Sustainability 14, no. 15: 9648. https://doi.org/10.3390/su14159648