Factors Influencing Customers’ Behavioral Intention to Use Restaurant Serving Robot Technology in Bangkok
Keywords:
Technology Acceptance, Service Robot, Restaurant, Users in BangkokAbstract
This study investigates the factors influencing customers’ intention to use food-serving robot technology in restaurants in Bangkok. The examined factors include perceived usefulness, perceived ease of use, trust, perceived risk, and social influence. The target population comprises individuals who have previously interacted with serving robots at one of four selected restaurants: Suki Teenoi, Sizzler, MK Restaurant, and Bar-B-Q Plaza. A total of 400 respondents from Bangkok participated in the study, selected through purposive sampling. Data were collected using a questionnaire. The primary statistical method employed was multiple regression analysis using the Backward Elimination method, which aimed to examine the influence of independent variables on the intention to use serving robot technology.
The results of the regression analysis using the Backward Elimination method partially supported the proposed hypotheses. Three variables were identified as statistically significant predictors of customers’ behavioral intention: (1) perceived ease of use, (2) perceived risk, and (3) social influence. The final model was able to explain 51.9% of the variance in customers’ intention to use serving robot technology (R² = 0.519).
References
ธนเจริญ พิศาล. (2554). ความตระหนักและการยอมรับการนําระบบการจัดการสิ่งแวดล้อม (ISO 14001) มาใช้ในองค์การภาครัฐ: ศึกษากรณีสำนักงานนโยบายและแผนทรัพยากรธรรมชาติและสิ่งแวดล้อม. กรุงเทพฯ: สถาบันบัณฑิตพัฒนบริหารศาสตร์.
ทีมวิเคราะห์ตลาดและเทคโนโลยี. (2566). รายงานตลาดและอุตสาหกรรมหุ่นยนต์บริการ (Service robot) ในประเทศไทย ปี พ.ศ. 2565 และแนวโน้มปี พ.ศ 2566–2567. กรุงเทพฯ: วิเคราะห์ตลาดและเทคโนโลยี.
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 2, 179–212.
Chen, L., Zhang, Y., & Liu, H. (2019). Emergency sensing technologies for service robots in hospitality industry. Robotics and Autonomous Systems, 116, 142–150.
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982–1003. https://doi.org/10.1287/mnsc.35.8.982
Davis, J. H., Schoorman, F. D., Mayer, R. C., & Tan, H. H. (2000). The trusted general manager and business unit performance: Empirical evidence of a competitive advantage. Strategic Management Journal, 21, 563–576.
Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research. Reading.
Jain, N. R. K., Liu-Lastres, B., & Wen, H. (2021). Does robotic service improve restaurant consumer experiences? An application of the value-co-creation framework. Journal of Foodservice Business Research, 26(1), 78-96. doi:10.1080/15378020.20
1991682
McKechnie, S., Winklhofer, H., & Ennew, C. (2006). Applying the technology acceptance model to the online retailing of financial services. International Journal of Retail & Distribution Management, 34(4/5), 388–410. https://doi.org/10.1108/09590550610660297
McKinsey & Company. (2020). Consumer sentiment and behavior continue to
reflect the uncertainty of the COVID-19 crisis. Retrieved from https://www.mckinsey.com/business-functions/
Mick, D. G., & Fournier, S. (1998). Paradoxes of Technology: Consumer Cognizance, Emotions, and Coping Strategies. Journal of Consumer Research, 25(2), 123–143. doi:https://doi.org/10.1086/209531
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of
information technology: Toward a unified view. MIS Quarterly, 27, 425– 478.
Xiang, K., Huang, W.-J., Gao, F., & Lai, Q. (2022). COVID-19 prevention in hotels: Ritualized host–guest interactions. Annals of Tourism Research, 97, Article 103365. https://doi.org/10.1016/j.annals.2022.103365
