International Research Journal of Innovations in Engineering and Technology - IRJIET International Open Access, Monthly, Peer Reviewed, Reputed Journal ISSN (online): 2581-3048

Impact Factor (2025): 6.9

DOI Prefix: 10.47001/IRJIET

Changing Restaurant Behavior: Review

Mohammed Jasim Mohammed YaseenDepartment of Computer Science, College of Computer Science and Mathematics, University of Mosul, Mosul, IraqAmmar Thaher Yaseen Al Abd AlazeezDepartment of Computer Science, College of Computer Science and Mathematics, University of Mosul, Mosul, Iraq

Vol 9 No 10 (2025): Volume 9, Issue 10, October 2025 | Pages: 88-95

International Research Journal of Innovations in Engineering and Technology

OPEN ACCESS | Research Article | Published Date: 22-10-2025

doi Logo doi.org/10.47001/IRJIET/2025.910012

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Abstract

Recent behavioral transformations within the restaurant industry have become deeply interconnected with technological advancements, digital analytics, and contemporary marketing methodologies. Customers today exhibit heightened awareness and autonomy in their decision-making processes, extensively relying on digital reviews, social media evaluations, and personal recommendations to guide their dining choices. Consequently, restaurants capable of swiftly adapting to these dynamic market conditions by improving customer experiences, adopting sustainable practices, and effectively utilizing digital data analytics are more likely to achieve sustained success.

This research aims to critically examine recent empirical studies and academic literature that explore behavioral shifts within the restaurant industry. By conducting a systematic analysis of existing research, the study intends to identify the critical factors contributing to restaurants' successes or failures in aligning with evolving consumer expectations. The primary focus will revolve around evaluating the significance of technological integration, sustainability initiatives, and data-driven decision-making practices within restaurants. Ultimately, the findings from this study will provide valuable insights and actionable recommendations for restaurant operators striving to thrive amidst ongoing market transformations.

Keywords

Data Stream, Data Mining, Restaurant Behavior Change


Citation of this Article

Mohammed Jasim Mohammed Yaseen, & Ammar Thaher Yaseen Al Abd Alazeez. (2025). Changing Restaurant Behavior: Review. International Research Journal of Innovations in Engineering and Technology - IRJIET, 9(10), 88-95. Article DOI https://doi.org/10.47001/IRJIET/2025.910012

Licence Copyright (c) 2026 International Research Journal of Innovations in Engineering and Technology creative common licence This work is licensed under Creative common Attribution Non Commercial 4.0 Internation Licence
References
  1. Agbenyegah, A.T., Zogli, L.K.J., Dlamini, B., Mofokeng, N.E.M.D., Kabange, M.M.: Ambient Situation and Customer Satisfaction in Restaurant Businesses: A Management Perspective. African J. Hosp. Tour. Leis. 11, 394–408 (2022). https://doi.org/10.46222/ajhtl.19770720.232.
  2. Liu, Y., Ting, H., Ringle, C.: Appreciation to and Behavior Intention Regarding Upscale Ethnic Restaurants. J. Hosp. Tour. Res. 47, 235–256 (2023). https://doi.org/10.1177/10963480211011544.
  3. Schifferstein, H.N.: Changing food behaviors in a desirable direction, (2020). https://doi.org/10.1016/j.cofs.2019.11.002.
  4. Using behavioural science to promote sustainable diets around the world.
  5. Jahan, S., Bhaumik, A., Abid, S., Tiwari, K.: Consumer Behaviour Towards Mobile Marketing in UAE Restaurants: Application of Technology Acceptance Model. In: Frontiers in Artificial Intelligence and Applications. pp. 249–256. IOS Press BV (2022). https://doi.org/10.3233/FAIA220390.
  6. Fechner, D., Karl, M., Grün, B., Dolnicar, S.: How can restaurants entice patrons to order environmentally sustainable dishes? Testing new approaches based on hedonic psychology and affective forecasting theory. J. Sustain. Tour. (2023). https://doi.org/10.1080/09669582.2023.2274283.
  7. Kim, W., Che, C., Jeong, C.: Restaurant customers’ food leftover reduction intention derived from nature connection and biospheric values: A comparison between men and women. Front. Psychol. 13, (2023). https://doi.org/10.3389/fpsyg.2022.976102.
  8. Mohammad, A., Aldmour, R., Al-Hawary, S.: Drivers of online food delivery orientation. Int. J. Data Netw. Sci. 6, 1619–1624 (2022). https://doi.org/10.5267/j.ijdns.2022.4.016.
  9. Kumolu-Johnson, B.: Improving Service Quality in the Fast-Food Service Industry. J. Serv. Sci. Manag. 17, 55–74 (2024). https://doi.org/10.4236/jssm.2024.171002.
  10. Liu, Y., Ting, H., Ringle, C.: Appreciation to and Behavior Intention Regarding Upscale Ethnic Restaurants. J. Hosp. Tour. Res. 47, 235–256 (2023). https://doi.org/10.1177/10963480211011544.
  11. Rajput, A., Gahfoor, R.Z.: Satisfaction and revisit intentions at fast food restaurants. Futur. Bus. J. 6, (2020). https://doi.org/10.1186/s43093-020-00021-0.
  12. Kaewmahaphinyo, T., Nuangjamnong, C., Dowpiset, K.: Factors Influencing Customer Satisfaction and Behavioral Intention for Fast-Casual Restaurants (A Case Study in a Shopping Center, Bangkok). (2020).
  13. Creating dynamic value for markets, models, and mechanics.
  14. Thanoon, M.Y.S.: Concept Drift Effect in Streaming Database for Computer Science Department Staff. (2023).
  15. Sheet, M.Y., Alazeez, A.T.Y.T.A.A.: The Data Stream Principles, Tools and Applications: A Review. 2022 8th Int. Conf. Contemp. Inf. Technol. Math. ICCITM 2022. 53–58 (2022). https://doi.org/10.1109/ICCITM56309.2022.10031765.
  16. Salih, A.M., Thaher, A., Al, Y., Alazeez, A.: Mining Streaming Database: A Review. (2024).
  17. Al-Khamees, H.A.A., Al-A’araji, N., Al-Shamery, E.S.: Data Stream: Statistics, Challenges, Concept Drift Detector Methods, Applications and Datasets. Int. J. Comput. Digit. Syst. 13, 717–728 (2023). https://doi.org/10.12785/ijcds/130157.
  18. Yousef, A.M.S.: Classifying Lectures in Streaming Database of Computer Science Department A. (2024).
  19. Al Abd Alazeez, A.T.Y.A.A.A.: Data Stream Mining Between Classical and Modern Applications: A Review. J. Educ. Sci. 30, 30–43 (2021). https://doi.org/10.33899/edusj.2021.130093.1158.
  20. No Title, https://studio3t.com/knowledge-base/articles/nosql-database-types, last accessed 2025/01/13.
  21. B.-L.C.,, Shahrim Karim, Sanghyeop Lee 2 and Heesup Han 3, *: Customer Restaurant Choice: An Empirical Analysis of Restaurant Types and Eating-out Occasions.
  22. Zigmond, J.: Behavioral change., (2010).
  23. Alison Schilling, C.D.: The Evolution of Data in Restaurants: Getting to Know Your Guest, (2022).
  24. DISEASES, W.E. 2 O.F.T.P.A.C.O.N.: slide to order a food systems approach to meal delivery apps, (2021).
  25. Grunertetal.2023.pdf.
  26. DigitalTransformationRestaurantIndustryJSTPre-Print.pdf, (2021).
  27. BERJ15324Article3p.257-275.pdf, (2024).
  28. IJIFNSA-D-2-2024.pdf, (2024).
  29. Customer behaviour in restaurants before and during COVID-19: A study in Vietnam, (2021).

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