WHAT FACTORS ATTRACT STUDENTS TO CONTINUOUSLY USE ONLINE FOOD DELIVERY PLATFORMS: AN EMPIRICAL STUDY BASED ON TAM AND SERVICE QUALITY

Authors

  • QingGang Sun (Corresponding Author) School of Innovation and Entrepreneurship, West Yunnan University of Applied Sciences, Dali 671099, Yunnan, China.
  • Jie Zhang School of Foreign Languages, West Yunnan University of Applied Sciences, Dali 671099, Yunnan, China.

Keywords:

Continuance intention, Online food delivery platform, Technology Acceptance Model, Service quality, Structural equation modeling, University students

Abstract

This study aims to identify determinants of college students' continuance intention to use online food delivery (OFD) platforms by integrating the Technology Acceptance Model (TAM) and service quality theory. Data were collected from 163 valid questionnaires via random sampling at university OFD pick-up points. Partial least squares structural equation modeling (PLS-SEM) was employed using SmartPLS 4.1.4 for data analysis. Delivery service quality influences continuance intention indirectly through satisfaction. Platform technical quality and price value perception affect continuance intention via perceived usefulness. Satisfaction and perceived usefulness directly drive continuance intention. Food quality perception and food safety trust show no significant effects. This study extends TAM to O2O food delivery services and validates dual mediation paths of satisfaction and perceived usefulness, offering an operational framework for platform optimization. The sample is limited to one university; future research may enlarge sample scope and adopt longitudinal design. Platforms should prioritize delivery service, technical stability and student-oriented pricing to boost continuance usage.

References

[1] Wang E S T, Lin H C, Tsai M C. Effect of institutional trust on consumers' health and safety perceptions and repurchase intention for traceable fresh food. Foods, 2021, 10(12): 3012.

[2] Davis F D. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 1989, 13(3): 319-340.

[3] Parasuraman A, Zeithaml V A, Berry L L. SERVQUAL: A multiple-item scale for measuring consumer perceptions of service quality. Journal of Retailing, 1988, 64(1): 12-40.

[4] Koay K Y, Cheah C W, Chang Y X. A model of online food delivery service quality, customer satisfaction and customer loyalty: A combination of PLS-SEM and NCA approaches. British Food Journal, 2022, 124(10): 3317-3338.

[5] Tandon S, Kaur S, Qureshi K M, et al. Antecedents for online food delivery platform leading to continuance usage intention via e-word-of-mouth review adoption. PLoS ONE, 2021, 18(8): e0272345.

[6] Efendi A, Sari D F, Sumaryati S. Examining student satisfaction with learning management system-based e-learning: A self-determination theory perspective. Voices of English Language Education Society, 2024.

[7] Huang L, Liu Y, Chen Y. Development of mSERVQUAL: An extension of SERVQUAL for mobile services. Journal of Service Management, 2015, 26(3): 345-367.

[8] Guan X. Analysis of strategy to improve the quality of "the last kilometer" distribution service. Asian Journal of Education and e-Learning, 2021, 9(1): 45-50.

[9] Wu M, Gao J, Hayat N, et al. Modelling the significance of food delivery service quality on customer satisfaction and reuse intention. PLoS ONE, 2024, 19(2): e0298195.

[10] Foroughi B, Yadegaridehkordi E, Iranmanesh M, et al. Determinants of continuance intention to use food delivery apps: Findings from PLS and fsQCA. International Journal of Contemporary Hospitality Management, 2024, 36(4): 1235-1261.

[11] Tran V. Using mobile food delivery applications during the COVID-19 pandemic: Applying the theory of planned behavior to examine continuance behavior. Sustainability, 2021, 13(21): 11936.

[12] Lee S, Han H. Food delivery application quality in customer brand loyalty formation: Identifying its antecedent and outcomes. International Journal of Hospitality Management, 2022, 107: 103292.

[13] Hu C. An analysis of college students' satisfaction with online food delivery (OFD). Information Systems and Economics, 2022, 1(1): 1-12.

[14] Au W T, Baum T. Service quality of online food delivery mobile application: An examination of the spillover effects of mobile app satisfaction. International Journal of Contemporary Hospitality Management, 2023, 35(11): 3237-3260.

[15] Renaldi A, Hati S R H, Ghazali E, et al. The determinants of customer loyalty in the sharing economy: A study of the largest local food delivery apps in Indonesia. Cogent Business & Management, 2024, 11(1): 2364055.

[16] Karahan M O. Factors impacting consumers' continuance intention for online food delivery services in Turkey. International Review of Management and Marketing, 2025, 15(2): 273-284.

[17] Choi J C. User familiarity and satisfaction with food delivery mobile apps. SAGE Open, 2020.

[18] Brislin R W. The wording and translation of research instruments. In: Lonner W J, Berry J W, eds. Field Methods in Cross-Cultural Research. Thousand Oaks: Sage, 1986: 137-164.

[19] Lynn M R. Determination and quantification of content validity. Nursing Research, 1986, 35(6): 382-385.

[20] Polit D F, Beck C T. The content validity index: Are you sure you know what's being reported? Critique and recommendations. Research in Nursing & Health, 2006, 29(5): 489-497.

[21] Hair J F, Risher J J, Sarstedt M, et al. When to use and how to report the results of PLS-SEM. European Business Review, 2019, 31(1): 2-24.

[22] Podsakoff P M, MacKenzie S B, Lee J Y, et al. Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 2003, 88(5): 879-903.

[23] Chin W W. The partial least squares approach to structural equation modeling. In: Marcoulides G A, ed. Modern Methods for Business Research. Mahwah: Lawrence Erlbaum Associates, 1998: 295-336.

[24] Cohen J. Statistical Power Analysis for the Behavioral Sciences. 2nd ed. Hillsdale: Lawrence Erlbaum Associates, 1988.

[25] Heskett J L, Jones T O, Loveman G W, et al. Putting the service-profit chain to work. Harvard Business Review, 1994, 72(2): 164-174.

[26] Muangmee C, Kot S, Meekaewkunchorn N, et al. Factors determining the behavioral intention of using food delivery apps during COVID-19 pandemics. Journal of Theoretical and Applied Electronic Commerce Research, 2021, 16(5): 1297-1310.

Downloads

Published

2026-08-11

Issue

Vol. 3 No. 4 (2026)

Section

Research Article

DOI:

https://doi.org/10.61784/ssm3093

How to Cite

QingGang Sun, Jie Zhang. What Factors Attract Students To Continuously Use Online Food Delivery Platforms: An Empirical Study Based On Tam And Service Quality. Social Science and Management. 2026, 3(4): 40-48. DOI: https://doi.org/10.61784/ssm3093.