Factors Influencing Technology Acceptance of Service Robots Affecting Investment Decisions for Sustainable Development among Restaurant Entrepreneurs in Bangkok Metropolitan Area

Authors

  • Benjatha Wattanakul Lecturer, Faculty of Business Administration, Bangkok Thonburi University
  • Chanidapha Deesukanan Lecturer, Faculty of Business Administration, Bangkok Thonburi University
  • Phongthep Srisopachit Lecturer, Faculty of Business Administration, Bangkok Thonburi University
  • Napat Chandhrarome Lecturer, Faculty of Business Administration, Bangkok Thonburi University
  • Jaroon Chamnanprai Lecturer, Faculty of Business Administration, Bangkok Thonburi University
  • Wit Mekavarakul Lecturer, Faculty of Business Administration, Bangkok Thonburi University

Abstract

This study examines the factors influencing technology acceptance of service robots affecting investment decisions among restaurant entrepreneurs in the Bangkok Metropolitan Area and its vicinity. The research applies the Technology Acceptance Model (TAM) together with social influence and facilitating conditions from the Unified Theory of Acceptance and Use of Technology (UTAUT), with attitude as a mediating variable. Data were collected from 400 restaurant entrepreneurs or senior managers with investment-decision authority (one respondent per restaurant), selected through purposive sampling based on pre-specified inclusion criteria and quotas by restaurant type, using online and field-administered questionnaires. Data were analyzed using correlation analysis, hierarchical multiple regression, and tests of indirect effects using the Sobel test and bootstrapping. The findings show that (1) perceived usefulness and perceived ease of use positively influence attitude (β = 0.474 and 0.266, respectively, p < .001); (2) attitude positively influences investment decisions (β = 0.312, p < .001) and partially mediates both pathways; (3) social influence and facilitating conditions directly and positively influence investment decisions; and (4) perceived usefulness has a significantly stronger influence on investment decisions than perceived ease of use. All predictors jointly explained 50.7% of the variance in investment decisions. The findings point to the policy-relevant potential of linking service-robot adoption to the Sustainable Development Goals (SDGs), particularly SDG 8, 9, and 12, although sustainability indicators were not directly measured. This research helps fill a knowledge gap regarding technology acceptance at the organizational level in the Thai food-service industry and offers practical guidance for entrepreneurs and policymakers promoting digital transformation among Thai SMEs.

References

ณัฐิวุฒิ สินสอนวรภัทร. (2565). ปัจจัยที่ส่งผลต่อการยอมรับนวัตกรรมหุ่นยนต์ดูแลผู้สูงอายุในกรุงเทพมหานคร (วิทยานิพนธ์ปริญญามหาบัณฑิต ไม่ได้ตีพิมพ์). มหาวิทยาลัยธรรมศาสตร์.

บุญทวี จันทสุวรรณ. (2566). การยอมรับนวัตกรรมโดยใช้การประเมินคุณภาพบริการของหุ่นยนต์บริการในโรงแรม 5 ดาวในเขตกรุงเทพมหานคร (วิทยานิพนธ์ปริญญามหาบัณฑิต ไม่ได้ตีพิมพ์). มหาวิทยาลัยศิลปากร.

พฤกษชาติ ชินประสาทศักดิ์, ธีรศักดิ์ กัญจนพงศ์, และพีรภาว์ ทวีสุข. (2568). การยอมรับกระบวนการทำงานหุ่นยนต์อัตโนมัติในงานบัญชี. วารสารวิชาการพระจอมเกล้าพระนครเหนือ (PACT), 38(2), 498–514. https://doi.org/10.55164/pactj.v38i2.276890

ศศิธร จุฑมาตยางกูร และณัฐพล พันธุ์ภักดี. (2569). ปัจจัยที่มีอิทธิพลต่อความตั้งใจใช้งานเทคโนโลยีหุ่นยนต์เสิร์ฟอาหารของลูกค้าร้านอาหารในกรุงเทพมหานคร. วารสารสันตยาภิวัฒน์ วัดหนองนกกด, 4(2), 40–55.

ศูนย์วิจัยกสิกรไทย. (21 ธันวาคม 2566). เทรนด์ธุรกิจร้านอาหารปี 2567: มูลค่าตลาดเติบโตแบบชะลอตัว ท่ามกลางต้นทุนที่สูง. https://www.kasikornresearch.com

สำนักงานส่งเสริมวิสาหกิจขนาดกลางและขนาดย่อม. (ม.ป.ป.). รายงานสถานการณ์วิสาหกิจขนาดกลางและขนาดย่อม (MSME) ประจำปี 2566. https://www.sme.go.th

Baron, R. M., & Kenny, D. A. (1986). The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51(6), 1173–1182. https://doi.org/10.1037/0022-3514.51.6.1173

Belanche, D., Casaló, L. V., & Flavián, C. (2021). Frontline robots in tourism and hospitality: Service enhancement or cost reduction? Electronic Markets, 31(3), 477–492. https://doi.org/10.1007/s12525-020-00432-5

Chan, A. P. H., & Tung, V. W. S. (2019). Examining the effects of robotic service on brand experience: The moderating role of hotel segment. Journal of Travel & Tourism Marketing, 36(4), 458–468. https://doi.org/10.1080/10548408.2019.1568953

Cochran, W. G. (1977). Sampling techniques (3rd ed.). John Wiley & Sons.

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008

Etikan, I., Musa, S. A., & Alkassim, R. S. (2016). Comparison of convenience sampling and purposive sampling. American Journal of Theoretical and Applied Statistics, 5(1), 1–4. https://doi.org/10.11648/j.ajtas.20160501.11

Garg, A. (Ed.). (2025). Technological innovations in the food service industry. IGI Global. https://doi.org/10.4018/979-8-3693-7683-6

Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis (8th ed.). Cengage Learning.

Ivanov, S., & Webster, C. (2019). Robots in tourism: A research agenda for tourism economics. Tourism Economics, 25(8), 1167–1187. https://doi.org/10.1177/1354816619879583

Kao, W. K., & Huang, Y. S. (2023). Service robots in full- and limited-service restaurants: Extending technology acceptance model. Journal of Hospitality and Tourism Management, 54, 10–21. https://doi.org/10.1016/j.jhtm.2022.11.006

Marangunić, N., & Granić, A. (2015). Technology acceptance model: A literature review from 1986 to 2013. Universal Access in the Information Society, 14, 81–95. https://doi.org/10.1007/s10209-014-0348-1

Thaker, R. (2021). Service robots in hospitality and tourism: Adoption, challenges, and long-term interaction. International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences, 9(1), 1–7. https://doi.org/10.5281/zenodo.14001439

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540

Wirtz, J., Kunz, W. H., Hartley, N., & Tarbit, J. (2023). Corporate digital responsibility in service firms and their ecosystems. Journal of Service Research, 26(2), 173–190. https://doi.org/10.1177/10946705221130467

World Economic Forum. (2022). The global risks report 2022 (17th ed.). https://www.weforum.org/reports/global-risks-report-2022

Zhong, L., Sun, S., Law, R., & Zhang, X. (2020). Impact of robot hotel service on consumers' purchase intention: A control experiment. Asia Pacific Journal of Tourism Research, 25(5), 1–19. https://doi.org/10.1080/10941665.2020.1726421

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Published

2026-08-16

How to Cite

Wattanakul, B. ., Deesukanan, C. ., Srisopachit, P. ., Chandhrarome, N. ., Chamnanprai, J., & Mekavarakul, W. . (2026). Factors Influencing Technology Acceptance of Service Robots Affecting Investment Decisions for Sustainable Development among Restaurant Entrepreneurs in Bangkok Metropolitan Area. Journal of Business and Society Innovation, 3(8). Retrieved from https://so14.tci-thaijo.org/index.php/J-BSI/article/view/3560

Issue

Vol. 3 No. 8 (2026): May - August

Section

Research Article

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