The Korean Journal of Franchise Management (한국프랜차이즈경영연구)

Korea Society of Franchise Management (한국프랜차이즈경영학회)
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Examining Bandwagon Effects on the Adoption of Kiosks for the Restaurant Owners

외식업체의 무인주문결제 키오스크 도입 의도 : 프랜차이즈 마케팅과 밴드왜건 효과

  • Sung Wook KIM (Department of Digital Convergence Business, Graduate School, Yeungnam University & Eco Safe Zone) ;
  • Sungsoo Hwang (Department of Public Administration, Yeungnam University)
  • Received : 2024.02.21
  • Accepted : 2024.03.07
  • Published : 2024.03.30
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Abstract

Purpose: This study empirically examines the bandwagon effects on the adoption of Kiosks for the restaurants' owners. Utilizing Davis (1989)'s Technology Acceptance Model as a framework, this study contributes to the literature by adding a bandwagon effect variable. Bandwagon effect has been studied extensively on the consumer marketing domain in terms of end-user behavior, but not on the business owners' willingness to invest on the new technology. Research design, data, and methodology: Davis (1989)' Technology Acceptance Model with added a bandwagon effect variable was set as a theoretical model. Data was collected via survey instrument from restaurants' owners who purchased or are considering a Kiosk. Structural Equation Modeling was used to empirically test the proposed model. Results: Results show that bandwagon effect is indirectly affecting to the adoption of Kiosks via perceived usefulness, trustworthiness, and interests. The bandwagon effects are NOT directly affecting the adoption of Kiosks. Conclusion: The findings suggest that buyers of Kiosks as storeowners (not end users) consider buying them after storeowners check perceived interests and trustworthiness from others. Thus, there could be a practical implication that it is important to illustrate perceived interests for the business to the storeowners when marketing new technology.

Keywords

Acknowledgement

This work was supported by the 2023 Yeungnam University Research Grant

References

  1. Abrahamson, E. (1991). Managerial fads and fashions: The diffusion and rejection of innovations. Academy of Management Review, 16 (3), 586-612. https://doi.org/10.2307/258919
  2. Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50 (2), 179-211. https://doi.org/10.1016/0749-5978(91)90020-T
  3. An, H.-J. (2020). A study on consumer attitude, satisfaction and behavioral intention of airline technology-based self-service (TBSS): Focusing on Airport Kiosk. Journal of Tourism and Leisure Research, 32 (4), 273-290. https://doi.org/10.31336/JTLR.2020.4.32.4.273
  4. Asch, S. E. (1951). Effect of group pressure upon the modification and Distortion of judgments. Journal of Marketing Research, 16, 394-400.
  5. Baek, M. R., Choi, H. W., & Lee, H. Y. (2015). Age-specific acceptance intention over wearable smart healthcare device. Korean Journal of Business Administration, 28 (12), 3171-3189.
  6. Baek, S. Y., & Park, K. S. (2001). Diffusion of information technology innovations and bandwagon effect: Innovation ambiguity and assessment standard deviation. Asia Pacific Journal of Small Business, 23 (1), 89-103.
  7. Bateson, J. E. G. (1985). Self-service consumer: An exploratory study. Journal of Retailing, 61 (3), 49-76.
  8. Bhattacherjee, A. (2000). Acceptance of e-commerce Services: The case of Electronic Brokerages. IEEE Transactions on Systems Man and Cybernetics, 30 (4), 411-420. https://doi.org/10.1109/3468.852435
  9. Chao, M. Y & You, Y. Y. (2022). The effects of information sources on trust, wom intention, and eWOM intention in the restaurant sector. Korean Journal of Franchise Management, 13 (3), 1-15. https://doi.org/10.21871/KJFM.2022.9.13.3.1
  10. Choi, S. -R., & Jeon, I. O. (2017). The impact of TBSS product attributes and service quality on customer satisfaction - Focusing on customer value of unmanned postal service users-. Korea Logistics Review, 27 (4), 101-114.
  11. Cronbach, L.J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16 (3), 297-334. https://doi.org/10.1007/BF02310555
  12. Cunnungham, L.F., Young, C.E., & Gerlach, J. (2008). A comparison of consumer views of traditional service and self-service technologies. Journal of Service Marketing, 23 (1), 11-23.
  13. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. Management Information Systems Quarterly, 319-340.
  14. EKorea (2019). A Kiosk approach to older digital alienation. (http://www.ekoreanews.co.kr/news/articleView.html?idxno=33794).
  15. Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research. MA: Addison-Wesley.
  16. Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18 (1), 39-50. https://doi.org/10.1177/002224378101800104
  17. Hair, Jr. J. F., Black, W. C., Babin, B.J., Anderson, R. E., & Tatham, R. L. (2006), Multivariate data analysis. NJ: Pearson Prentice Hall.
  18. Han, S.-H., Kim, H.-J., Choi, K.-Y., Han, K.-C. (2011). A study on marketing activation of franchise enterprise utilizing social network service (SNS). Korean Journal of Franchise sManagement, 2 (2), 24-44.
  19. Hong, S. Y., & Choe, J.-H. (2019). A study on the Kiosk UI reflecting the elderly's characteristics. Journal of the Korea Contents Association, 19 (4), 556-563.
  20. Jeon, H. A., KIM, K. A., & Kim S. H. (2018). The effects of customer's relational benefits on continuous usage intention of technology-based self-service: Focusing on types of technology-based self-service. Journal of Marketing Management Research, 23 (1), 27-49.
  21. Jeon, S.-Y., & Ha, K.-S. (2010). Factors of the Acceptance affecting the e-payment system: Focusing on service quality and social influence. Journal of the Korea Academia-Industrial Cooperation Society, 11 (9), 3239-3248. https://doi.org/10.5762/KAIS.2010.11.9.3239
  22. Jeong, S.-J., & Kim, H.-S. (2020). The influence of technology-based self-service (TBSS) attributes on consumer attitude, customer satisfaction. Food Service Industry Journal, 16 (3), 233-246.
  23. JO, M. N., & Cha, J.-B. (2017). Consumer attitudes and behavioral intentions on delivery application quality: Focusing on technology acceptance model (TAM). Journal of Tourism Sciences, 41 (4), 171-184.
  24. Kang, J.-H. (2018). A study on consumer acceptance intention of Unmanned order payment systems of foodservice companies: Targeted on chatbots and digital kiosks. International Journal of Tourism and Hospitality Research, 32 (1), 153-168.
  25. Kang, J.-W., & Nam, K.Y. (2018). Classifying quality attributes of self-service kiosk in the restaurant industry using Kano model. Korean Journal of Hospitality & Tourism, 27 (8), 263-279. https://doi.org/10.24992/KJHT.2018.12.27.08.263.
  26. Khechine, H., Ndjambou, P., & Lakhal, S. (2016). A meta-analysis of the UTAUT model: Eleven years later. Canadian Journal of Administrative Sciences/Revue Canadienne des Sciences de l'Administration, 33 (2), 138-152. https://doi.org/10.1002/cjas.1381
  27. Kim, D. B., & Nam, K. Y. (2019). The impacts of perceived convenience and technical difficulties on perceived value and behavioral intention: Focused on moderating effect of personal innovativeness. Culinary Science & Hospitality Research, 25 (5), 99-111.
  28. Kim, D. U., & Oh, J. S. (2006). A structure on mobile service quality. Journal of Korea Service Management Society, 7 (3), 51-82.
  29. Kim, J. K., Yang, J. J., & Lee, Y.-K. (2023). How do self-service kiosks improve COVID-19 pandemic resilience in the restaurant industry?. Sustainability, 15 (13), 10168.
  30. Kim, H. Y, & Hur, J. (2018). A study on the visual of self-service ordering system in fast food restaurant. Journal of the Table & Food Coordinate, 13 (2), 75-92.
  31. Kim, S.W., Cho, H. S. (2017) Big data and consumer satisfaction. Seoul: Kubuk.
  32. Kim, Y.-K. (2012). An integrated theoretical model on the relationships between the related factors of the technology acceptance model. Journal of Industrial Economics and Business, 25 (2), 1639-1670.
  33. Kolodinsky, J. M., Hogarth, J. M., & Hilgert, M. A. (2004). The adoption of electronic banking technologies by US consumers. International Journal of Bank Marketing, 2 (4), 238-259. https://doi.org/10.1108/02652320410542536
  34. LaBarbera, P. A. (1988). The Nouveaux Riches: Conspicuous consumption and the issue of self-fulfillment. Research in Consumer Behavior, 3, 179-210.
  35. Langeard, E. (1981). Services marketing: New insights from consumers and managers. Marketing Science Institute's Report No.8 1-104, Marketing Science Institute, Cambridge, MA.
  36. Lee, I. S., Rha, Y. A., & Yoon, H. H. (2013). The effects of foodservice consumer's perceived technology-based self-service characteristics on satisfaction and purchase intention: With a focus on an online menu ordering system. International Journal of Tourism and Hospitality Research, 27 (4), 85-100.
  37. Lee, J.-W., Lee, S.-H., Lee, S.-H. (2015). Exploratory study for franchise marketing mix. Korean Journal of Franchise Management, 6 (2), 87-104.
  38. Leibenstein, H. (1950). Bandwagon, Snob, and Veblen Effects in the theory of consumers demand. The Quarterly Journal of Economics, 64 (2), 183-207. https://doi.org/10.2307/1882692
  39. Lin, J. S. C., & Hsieh, P. L. (2011). Assessing the self-service technology encounters: Development and validation of SSTQUAL scale. Journal of Retailing, 87 (2), 194-206. https://doi.org/10.1016/j.jretai.2011.02.006
  40. Liu, B. (2012). Sentiment analysis and opinion mining. Synthesis Lectures on Human Language Technologies, 5 (1), 1-167. https://doi.org/10.1007/978-3-031-02145-9
  41. Loiacono, E. T., Watson, R. T., & Goodhue, D. L. (2002). WebQual: A measure of website quality. Marketing Theory and Applications, 13 (3), 432-438.
  42. Long, D. S., Fox, N. R., & York, T. (2007). Calculated futures: Theology, ethics, and economics. Waco:, TX: Baylor University Press.
  43. Meuter, M. L., Ostrom, A. L., Roundtree, R. I., & Bitner, M. J. (2000). Self-service technologies: understanding customer satisfaction with technology-based service encounters. Journal of Marketing, 64 (3), 50-64.
  44. Moon, H.-Y. (2018). A study on customer-based it service in restaurant industry - Focused on technology acceptance model-. Journal of Tourism and Leisure Research, 30 (5), 325-339. https://doi.org/10.31336/JTLR.2018.05.30.5.325
  45. Nadeau, R., Cloutier, E., & Guay, J. H. (1993). New evidence about the existence of a bandwagon effect in the opinion formation process. International Political Science Review, 14 (2), 203-213. https://doi.org/10.1177/019251219301400204
  46. Oliver, R. L. (1980). A cognitive model of the antecedents and consequences of satisfaction decisions. Journal of Marketing Research, 17 (4), 460-469.
  47. Park, I.-S., & Ahn, H.-C. (2012). a study on the user acceptance model of mobile credit card service based on UTAUT. The e-Business Studies, 13 (3), 551-574. https://doi.org/10.15719/GEBA.13.3.201209.551
  48. Rogers, E. M. (1986). Communication technology: The new media in society. New York: The Free Press.
  49. Rogers, E. M. (2003). Diffusion of Innovations, 5th edition. New York: Free Pres.
  50. Shim, H. S., & Han, S. L. (2012). Effects of TR and consumer readiness on sst usage motivation, attitude and intention. ASIA Marketing Journal, 14 (1), 25-48.
  51. Shin, C. H., Lee, H. J. (2012). A study on airlines' self-service technology (SST) quality affecting customer satisfaction and behavioral intention. Journal of Tourism Management Research, 16 (2), 97-117.
  52. SisaON (2017). Order now with AI's Chatbot…The food industries, fourth industrial revolution wind. (http:/www.sisaon.co.kr/news/articleView.html?idxno=5128).
  53. Song, K. S., Yoo, H. J., & Park, Y. J. (2015). A study on the development of ndsl information service quality (NDSLQUAL) model. Journal of Korea Service Management Society, 16 (1), 9-123.
  54. Song, S. G., & Won, J. Y. (2013). The Effects of ERP Systems user's cognition on satisfaction and continuous usage intention: Focused on moderate effects of system interaction and social interaction. Korean International Accounting Review, 50, 239-262. https://doi.org/10.21073/kiar.2013..50.012
  55. Sundar, S. S., Oeldorf-Hirsch, A., & Xu Q. (2008). The Bandwagon effect of collaborative filtering technology. In CH'EA'08 Extended Abstracts on Human Factors in Computing Systems, 3453-3458.
  56. The Seoul Economy (2018). The heyday of the kiosk. McDonald's sparks campaign to increase minimum wage, extends unmanned job-loss (http://www.sedaily.com/NewsView/1RZJ38TQN0).
  57. Vakulenko, Y., Hellstrom, D., & Hjort, K. (2018). What's in the parcel locker? Exploring customer value in e-commerce last mile delivery. Journal of Business Research, 88, 421-427. https://doi.org/10.1016/j.jbusres.2017.11.033
  58. Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39 (2), 273-315. https://doi.org/10.1111/j.1540-5915.2008.00192.x
  59. Venkatesh, V., & Davis, F.D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46 (2), 186-204. https://doi.org/10.1287/mnsc.46.2.186.11926
  60. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. Management Information Systems Quarterly, 425-478.
  61. Venkatesh, V., Thong J. Y., & Xu X. (2012). Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. Management Information Systems Quarterly, 36 (1), 157-178.
  62. Yu, J., Ha, I., Choi, M., & Rho, J. (2005). Extending the TAM for a T-commerce. Information & Management, 42 (7), 965-976.  https://doi.org/10.1016/j.im.2004.11.001