The Mediating Role of Customer Satisfaction on the Relationship between E-Menus and Customers Behavioral Intentions in the Quick Service Restaurants

Abstract

Considering the significant role of technological developments in food service industry, there is a lack of research studies examining the role of electronic menus (e-menus) on customer satisfaction (CS) and customer behavioral intentions (CBI), especially in the Saudi Arabian food service industry. This research aims to measure customers’ acceptance to use e-menus on CS and CBI in quick-service restaurants (QSRs) in Saudi Arabia. It also examines the mediating role of CS on the link between customers’ acceptance of using e-menus and CBI. The study adopted a quantitative research approach using self-administered surveys distributed and gathered from a random sample of QSRs customers in Al-hasaa. The major findings from 472 valid surveys, examined using a structural equation modeling (SEM) revealed that there is a significant positive effect of customers’ acceptance of using e-menus on CS and on CBI. Moreover, CS has a significant positive effect on CBI. CS has a partial mediation effect in the link between customers’ acceptance of using e-menus and CBI. This reflects that e-menus (i.e., interactivity, media enjoyment, and consumption visions) play a crucial role in enhancing CS and CBI. Several contributions for scholars and practitioners are discussed.

Keywords

• E-Menus
• Customer Satisfaction
• Customer Behavioral Intentions
• Quick Service Restaurants

References

1. Alharbi, S., & Drew, S. (2014). Using the technology acceptance model in understanding academics’ behavioural intention to use learning management systems. International Journal of Advanced Computer Science and Applications, 5 (1), 143-155. http://doi.org/10.14569/IJACSA.2014.050120
2. Al-Zyoud, M. F. (2023). Fresh mindset, hygiene perception, QR code menu, and intention to re-dine among Jordanian consumers. Journal of Foodservice Business Research, 1-16. http://doi.org/10.1080/15378020.2023.2214068
3. Ayad, T. H. (2017). Examining the relationships between visit experience, satisfaction and behavioral intentions among tourists at the Egyptian Museum. Journal of Association of Arab Universities for Tourism and Hospitality, 14 (2), 93-104. http://doi.org/10.21608/JAAUTH.2017.48147
4. Ayad, T. (2022). Tourism Graduates-Are They Employable?. Eurasian Journal of Educational Research, 101, 100-123. http://doi.org/10.14689/ejer.2022.101.007
5. Baba, N., Hanafiah, M. H., Mohd Shahril, A., & Zulkifly, M. I. (2023). Factors Affecting Consumer Acceptance of E-Menu in The Klang Valley Restaurant Sector in Malaysia. International Journal of Academic Research in Business and Social Sciences. 13. http://doi.org/10.1108/JHTT08-2021-0226
6. Bawazir, A. A., Kamal, A. A. B. M., Mee, G., Lean, L. L., Kai, N. S., Nor, S. M., ... & Noordin, A. (2023). Factors Affecting Consumer Acceptance of E-Menu in The Klang Valley Restaurant Sector in Malaysia. International Journal of Academic Research in Business and Social Sciences, 13 (6). http://doi.org/10.6007/IJARBSS/v13-i6/17108
7. Beldona, S., Buchanan, N., & Miller, B. L. (2014). Exploring the promise of e-tablet restaurant menus. International Journal of Contemporary Hospitality Management, 26 (3), 367-382. http://doi.org/10.1108/IJCHM-01-2013-0039
8. Bryman, A., & Cramer, D. (2011). Quantitative data analysis with IBM SPSS 17, 18 and 19: A guide for social scientists. Routledge-Cavendish/Taylor & Francis Group.

9. Chasanah, N., Indrayanto, A., Krisnaresanti, A., Mustafa, R. M., Restianto, Y. E., Naufalin, L. R., Dinanti, A., & Iskandar, D. (2023). Measuring the customer acceptance of website technology using TAM framework. AIP Conference Proceedings. https://doi.org/10.1063/5.0113161
10. Chin, W. W. (1998). The partial least squares approach for structural equation modeling. In G. A. Marcoulides (Ed.), Modern methods for business research (pp. 295–336). Lawrence Erlbaum Associates Publishers.
11. Chin, W. W. (2010) How to Write Up and Report PLS Analyses. In: Esposito Vinzi, V., Chin, W. W., Henseler, J. and Wang, H. (Eds.), Handbook of Partial Least Squares: Concepts, Methods and Applications (pp. 655-690). Springer, Heidelberg, Dordrecht, London, New York. https://doi.org/10.1007/978-3-540-32827-8_29
12. Cho, M., Bonn, M. A., & Li, J. (2019). Differences in perceptions about food delivery apps between single-person and multi-person households. International Journal of Hospitality Management, 77, 108–116. https://doi.org/10.1016/j.ijhm.2018.06.019
13. Choi, G., & Chung, H. (2013). Applying the technology acceptance model to social networking sites (SNS): Impact of subjective norm and social capital on the acceptance of SNS. International Journal of Human-Computer Interaction, 29 (10), 619-628. http://dx.doi.org/10.1080/10447318.2012.756333
14. Chong, K. L. (2022). Factors affecting the consumers’ embracement of manual self-ordering system (order chit) in restaurants. Journal of Foodservice Business Research, 25 (1), 33-56. https://doi.org/10.1080/15378020.2021.1911565
15. Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum Associates, Publishers, USA.
16. Daradkeh, F. M., Hassan, T. H., Palei, T., Helal, M. Y., Mabrouk, S., Saleh, M. I., Salem, A. E., & Elshawarbi, N. N. (2023). Enhancing Digital Presence for Maximizing Customer Value in Fast-Food Restaurants. Sustainability, 15 (7), 5690. https://doi.org/10.3390/su15075690
17. Dixon, M., Kimes, S. E & Verma, R. (2009). Customer preference for restaurant technology innovations. Cornell Hospitality Report 9 (7), 4- 16.
18. Fornell, C., & Larcker, D. F. (1981). Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. Journal of Marketing Research, 18(1), 39. https://doi.org/10.2307/3151312
19. Feriska, L., Surya, A., Yohanes, M., & Anita, T. L. (2023). Managing Repatronage Intention with Technology Acceptance Model. Proceedings of 2023 International Conference on Digital Applications, Transformation & Economy (ICDATE). https://doi.org/10.1109/ICDATE58146.2023.10248556
20. Gonzalez, R., Gasco, J., & Llopis, J. (2022). Information and communication technologies in food services and restaurants: a systematic review. International Journal of Contemporary Hospitality Management, 34(4), 1423-1447. 10.1108/IJCHM-05-2021-0624
21. Hair, J. F., Hult, G. T. M., Ringle, C. M. and Sarstedt, M. (2017) A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) (2nd Edition). Sage Publications Inc., Thousand Oaks, CA.
22. Hao, F., Guo, Y., Zhang, C., & Chon, K. K. S. K. S. (2024). Blockchain = better food? The adoption of blockchain technology in food supply chain. International Journal of Contemporary Hospitality Management, 36 (10), 3340-3360. https://doi.org/10.1108/IJCHM-06-2023-0752
23. Hossain, M. S., Zhou, X., & Rahman, M. F. (2018). Examining the impact of QR codes on purchase intention and customer satisfaction on the basis of perceived flow. International Journal of Engineering Business Management, 10, 1847979018812323. http://dx.doi.org/10.1177/1847979018812323
24. Hsu, L., & Wu, P. (2013). Electronic-tablet-based menu in a full service restaurant and customer satisfaction - a structural equation model. International Journal of Business, Humanities and Technology, 3 (2), 61-71.
25. Irianto, H. (2015). Consumers' attitude and intention towards organic food purchase: An extension of theory of planned behavior in gender perspective. International journal of management, economics and social sciences, 4 (1), 17-31.
26. Jawabreh, O., Al Jaffal, T., Abdelrazaq, H., & Mahmoud, R. (2018). The impact of menus on the customer satisfaction in restaurants classified in Aqaba special economic zone authority (ASEZA). Journal of Tourism, Hospitality and Sports, 33, 29-39.
27. Jayawardena, C., Ahmad, A., Valeri, M., & Jaharadak, A. A. (2023). Technology acceptance antecedents in digital transformation in hospitality industry. International Journal of Hospitality Management, 108, 103350. http://dx.doi.org/10.1016/j.ijhm.2022.103350
28. Jeong, M., Kim, K., Ma, F., & DiPietro, R. (2022). Key factors driving customers’ restaurant dining behavior during the COVID-19 pandemic. International Journal of Contemporary Hospitality Management, 34 (2), 836-858. https://doi.org/10.1108/IJCHM-07-2021-0831
29. Kazandzhieva, V., Ilieva, G., & Filipova, H. (2017). The impact of technological innovations on hospitality service. Contemporary Tourism-Traditions and Innovations, Sofia University.
30. Kim, J. (2016). An extended technology acceptance model in behavioral intention toward hotel tablet apps with moderating effects of gender and age. International Journal of Contemporary Hospitality Management, 28 (8), 1535–1553. http://dx.doi.org/10.1108/IJCHM-06-2015-0289
31. Kock, N. (2020). Multilevel analyses in PLS-SEM: An anchor-factorial with variation diffusion approach. Data Analysis Perspectives Journal, 1 (2), 1-6.
32. Kurniawan, R., Sutawan, A., & Amalia, R. (2020). Information System Ordering Online Restaurant Menu At Hover Cafe. Aptisi Transactions on Management (ATM), 4 (1), 32-40. https://doi.org/10.33050/atm.v4i1.1082
33. Labus, P., & Jelovac, D. (2022). Restaurants: Applying an Extended Technology Acceptance Model. Acta turistica, 34 (1), 51-82. https://doi.org/10.22598/at/2022.34.1.51
34. Le, T. T., Bui Thi Tuyet, N., Le Anh, T., Dang Thi Kim, N., Trinh Thi Thai, N., & Nguyen Lan, A. (2023). The effects of online restaurant menus on consumer purchase intention: evidence from an emerging economy. British Food Journal, 125 (7), 2663-2679. https://doi.org/10.1108/BFJ-10-2022-0916
35. Lee, W. S., Song, M., Moon, J., & Tang, R. (2023). Application of the technology acceptance model to food delivery apps. British Food Journal, 125 (1), 49-64. http://dx.doi.org/10.1108/BFJ-05-20210574
36. Mullemwar, V., Virdande, V., Bannore, M., Awari, A., & Shriwas, R. (2014). Electronic menu card for restaurants. International Journal of Research in Engineering and Technology, 3 (4), 341-345. http://dx.doi.org/10.15623/ijret.2014.0304061
37. Pagaldiviti, S. R., & Roy, B. K. (2023). The Future of Restaurants. In Advances in environmental engineering and green technologies book series (pp. 63–74). https://doi.org/10.4018/978-16684-9094-5.ch004
38. Pande, S., & Gupta, K. P. (2023). Indian customers’ acceptance of service robots in restaurant services. Behaviour & Information Technology, 42 (12), 1946-1967. https://doi.org/10.1080/0144929X.2022.2103734
39. Preacher, K. J., & Hayes, A. F. (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods, 40 (3), 879–891. https://doi.org/10.3758/BRM.40.3.879
40. Şahin, E. (2020). An evaluation of digital menu types and their advantages. Journal of Tourism & Gastronomy Studies, 8 (4), 2374-2386. https://doi.org/10.21325/jotags.2020.716
41. Saleh, N. S. (2021). Technology Acceptance: Theories and Applications in Digital Tech. Human Sustainability Procedia, 1 (2), 22-30.
42. Sharma, S. K., & Sharma, M. (2019). Examining the role of trust and quality dimensions in the actual usage of mobile banking services: An empirical investigation. International Journal of Information Management, 44, 65–75. https://doi.org/10.1016/j.ijinfomgt.2018.09.013
43. Wang, H. Y., & Wu, S. Y. (2013). Factors influencing behavioural intention to patronise restaurants using iPad as a menu card. Behaviour and Information Technology, 33(4), 395–409. https://doi.org/10.1080/0144929x.2013.810776
44. Veal, A. J. (2006). Research Methods for Leisure and Tourism. 3rd Edition, Prentice Hall, London.
45. 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
46. Venkatesh, N., Thong, N., & Xu, N. (2012). Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology. MIS Quarterly, 36 (1), 157. https://doi.org/10.2307/41410412
47. Wetzels, N., Odekerken-Schröder, N., & Van Oppen, N. (2009). Using PLS Path Modeling for Assessing Hierarchical Construct Models: Guidelines and Empirical Illustration. MIS Quarterly, 33 (1), 177. https://doi.org/10.2307/20650284
48. Wu, H. C. (2013). An empirical study of the effects of service quality, perceived value, corporate image, and customer satisfaction on behavioral intentions in the Taiwan quick service restaurant industry. Journal of Quality Assurance in Hospitality & Tourism, 14 (4), 364-390. https://doi.org/10.1080/1528008X.2013.802581
49. Xi, W., Jin, M., Gong, H., & Wang, Q. (2018). Touch or shake? The interaction effect between hand gesture and reward setting on the enjoyment of gamified marketing. In GamiFIN (pp 100-107).
50. Yeo, V. C. S., Goh, S. K., & Rezaei, S. (2017). Consumer experiences, attitude and behavioral intention toward online food delivery (OFD) services. Journal of Retailing and Consumer Services, 35, 150–162. https://doi.org/10.1016/j.jretconser.2016.12.013
51. Yim, M. Y. C., & Yoo, C. Y. (2020). Are digital menus really better than traditional menus? the mediating role of consumption visions and menu enjoyment. Journal of Interactive Marketing, 50 (1), 65-80. http://dx.doi.org/10.1016/j.intmar.2020.01.001
52. Zaitouni, M. (2019). A Comparison of Self-Service Technologies (SSTs) in the US Restaurant Industry: An Evaluation of Consumer Perceived Value, Satisfaction, and Behavioral Intentions. Electronic Theses and Dissertations. 6596. University of Central Florida.
53. Zaitouni, M., & Murphy, K. S. (2023). Self-Service Technologies (SST) in the US Restaurant industry: An evaluation of consumer perceived value, satisfaction, and continuance intentions. Journal of Foodservice Business Research, 1-32. https://doi.org/10.1080/15378020.2023.2229582