Analyzing Consumer Behavioral Intention to Utilize Food Delivery Services in Ride-Hailing Apps: A Study of Malang’s Culinary MSME

  • Yesiana Ihda Kusnayain Universitas Negeri Malang
  • Naufal Dzakwana Muhammad Universitas Negeri Malang
  • Dediek Tri Kurniawan Universitas Negeri Malang
  • Achmad Fajar Alfian Sodiq Universitas Negeri Malang
Keywords: UTAUT, Behavioral Intention, SME's, Culinary

Abstract

Background: The MSME sector, especially the food and beverage industry, is a critical pillar for national economic stability and employment. However, actual field conditions reveal that technological implementation among small business actors does not always progress smoothly. Moreover, previous empirical findings concerning the effects of UTAUT constructs on technology adoption intentions within small enterprises still yield inconsistent and contradictory results.

Method: This study employed a quantitative approach with explanatory and correlational research design. A sample of 173 Culinary SME respondents was selected using purposive sampling were collected through closed questionnaires with a 1-5 Likert scale. Data analysis utilized Structural Equation Modeling (SEM) based on Partial Least Squares (PLS) using SmartPLS 4.0 software.

Results: The result  showed that the structural model reveals a highly synchronized, positive network of behavioral and infrastructural dependencies. The verification of Effort Expectancy confirms that application simplicity is an essential catalyst in reducing initial resistance to technology.

Conclusion: This study validates the UTAUT model within the supply-side ecosystem of culinary MSMEs in Malang City. Empirical findings show that digital transformation in small food businesses is driven by a synchronized mix of cognitive, social, and structural factors. Specifically, performance expectancy, effort expectancy, social influence, and facilitating conditions shape merchants' intentions to adopt online food delivery platforms. Ultimately, behavioral intention and facilitating conditions successfully translate this willingness into sustained, routine technology usage.

References

Chatterjee, S., Rana, N. P., Tamilmani, K., & Sharma, A. (2021). The effect of AI-based CRM on organization performance and competitive advantage: An empirical analysis in the B2B context. Industrial Marketing Management, 97, 205–219. https://doi.org/10.1016/j.indmarman.2021.07.013

Dwivedi, Y. K., Rana, N. P., Jeyaraj, A., Clement, M., & Williams, M. D. (2019). Re-examining the Unified Theory of Acceptance and Use of Technology (UTAUT): Towards a Revised Theoretical Model. Information Systems Frontiers, 21(3), 719–734. https://doi.org/10.1007/s10796-017-9774-y

Gold, A. H., Malhotra, A., & Segars, A. H. (2001). Knowledge management: An organizational capabilities perspective. Journal of Management Information Systems, 18(1), 185–214. https://doi.org/10.1080/07421222.2001.11045669

Gupta, K., & Arora, N. (2020). Investigating consumer intention to accept mobile payment systems through unified theory of acceptance model: An Indian perspective. South Asian Journal of Business Studies, 9(1), 88–114. https://doi.org/10.1108/SAJBS-03-2019-0037

Hair, J. F. ., Hult, G. T. M. ., Ringle, C. M. ., & Sarstedt, Marko. (2017). A primer on partial least squares structural equation modeling (PLS-SEM). Sage.

Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). MULTIVARIATE DATA ANALYSIS EIGHTH EDITION. www.cengage.com/highered

Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8

Li, Y., & Heath, A. (2020). Persisting disadvantages: a study of labour market dynamics of ethnic unemployment and earnings in the UK (2009–2015). Journal of Ethnic and Migration Studies, 46(5), 857–878. https://doi.org/10.1080/1369183X.2018.1539241

Ray, A., & Bala, P. K. (2021). User generated content for exploring factors affecting intention to use travel and food delivery services. International Journal of Hospitality Management, 92. https://doi.org/10.1016/j.ijhm.2020.102730

Troise, C., O’Driscoll, A., Tani, M., & Prisco, A. (2021). Online food delivery services and behavioural intention – a test of an integrated TAM and TPB framework. British Food Journal, 123(2), 664–683. https://doi.org/10.1108/BFJ-05-2020-0418

Venkatesh, V., Thong, J. Y. L., & Xu, X. (2016). A I S ssociation for nformation ystems Unified Theory of Acceptance and Use of Technology: A Synthesis and the Road Ahead. J Ournal, 17, 328–376.

Published
2026-10-01
How to Cite
Kusnayain, Y. I., Muhammad, N. D., Kurniawan, D. T., & Sodiq , A. F. A. (2026). Analyzing Consumer Behavioral Intention to Utilize Food Delivery Services in Ride-Hailing Apps: A Study of Malang’s Culinary MSME. Sharia Economic and Management Business Journal (SEMBJ), 7(3), 302-312. https://doi.org/10.62159/sembj.v7i3.2606

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