Understanding Augmented Reality App Loyalty: The Roles of System and Individual Factors and the Moderating Effect of Sustainability among Young Adult Users

The increasing adoption of Augmented Reality (AR) applications in educational environments has highlighted the importance of understanding the factors associated with user satisfaction and loyalty among young adult users. This study investigates the influence of system and individual factors on perceived satisfaction and loyalty towards educational AR applications, with Sustainable Consumption Practices (SCP) acting as a moderating variable. This study integrates the Information Systems Success Model (ISSM), UTAUT3, and sustainability perspectives to develop a comprehensive research framework. A quantitative approach was employed, and data were collected from 400 young adult users in Malaysia. The data were analysed using Partial Least Squares Structural Equation Modelling (PLS-SEM) through SmartPLS 4.0. The findings revealed that service quality, social influence, hedonic motivation, price value, and personal innovativeness were significantly associated with perceived satisfaction towards educational AR applications. Service quality emerged as the strongest predictor of satisfaction. In addition, perceived satisfaction was significantly associated with loyalty towards educational AR applications. The findings also confirmed that Sustainable Consumption Practices significantly moderated the relationship between perceived satisfaction and loyalty. This study contributes to the existing literature by extending AR research beyond technology adoption towards post-adoption loyalty behaviour. The findings provide useful insights for educational institutions, AR developers, and digital marketers in designing sustainable and engaging AR experiences capable of enhancing long-term user loyalty.

Augmented Reality, perceived satisfaction, loyalty, Information Systems Success Model (ISSM), UTAUT3, sustainable consumption practices, educational AR applications, PLS-SEM.

1. DeLone, W. H., & McLean, E. R. (2003). The DeLone and McLean model of information systems success: A ten-year update. Journal of Management Information Systems, 19(4), 9–30. [Google Scholar] [Crossref]

2. Dwivedi, Y. K., Hughes, L., Baabdullah, A. M., Ribeiro-Navarrete, S., Giannakis, M., Al-Debei, M. M., Dennehy, D., Metri, B., Buhalis, D., Cheung, C. M. K., Conboy, K., Doyle, R., Dubey, R., Dutot, V., Felix, R., Goyal, D. P., Gustafsson, A., Hinsch, C., Jebabli, I., … Wamba, S. F. (2023). Metaverse beyond the hype: Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 66, 102542. [Google Scholar] [Crossref]

3. Gefen, D., Straub, D., & Boudreau, M. C. (2000). Structural equation modeling and regression: Guidelines for research practice. Communications of the Association for Information Systems, 4(7), 1–77. [Google Scholar] [Crossref]

4. Gupta, S., Nawaz, N., Alfalah, A. A., & Naveed, R. T. (2024). Sustainable digital consumption behaviour among young consumers: Emerging perspectives in immersive technologies. Sustainability, 16(2), 811. [Google Scholar] [Crossref]

5. Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis (7th ed.). Pearson Education. [Google Scholar] [Crossref]

6. Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. [Google Scholar] [Crossref]

7. Hair, J. F., Howard, M. C., & Nitzl, C. (2020). Assessing measurement model quality in PLS-SEM using confirmatory composite analysis. Journal of Business Research, 109, 101–110. [Google Scholar] [Crossref]

8. Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2022). A primer on partial least squares structural equation modeling (PLS-SEM) (3rd ed.). Sage Publications. [Google Scholar] [Crossref]

9. Henseler, J. (2017). Partial least squares path modeling. In C. Homburg, M. Klarmann, & A. Vomberg (Eds.), Handbook of market research (pp. 1–19). Springer. [Google Scholar] [Crossref]

10. Hollebeek, L. D., Sprott, D. E., Andreassen, T. W., Costley, C., Klaus, P., Kuppelwieser, V., Karahasanovic, A., Taguchi, T., & Rather, R. A. (2023). Customer engagement in evolving technological environments: Synopsis and research agenda. Journal of Service Management, 34(1), 1–30. [Google Scholar] [Crossref]

11. Oliver, R. L. (1980). A cognitive model of the antecedents and consequences of satisfaction decisions. Journal of Marketing Research, 17(4), 460–469. [Google Scholar] [Crossref]

12. Pallant, J. (2020). SPSS survival manual (7th ed.). McGraw-Hill Education. [Google Scholar] [Crossref]

13. Peattie, K., & Peattie, S. (2009). Social marketing: A pathway to consumption reduction? Journal of Business Research, 62(2), 260–268. [Google Scholar] [Crossref]

14. Ramayah, T., Cheah, J., Chuah, F., Ting, H., & Memon, M. A. (2018). Partial least squares structural equation modeling (PLS-SEM) using SmartPLS 3.0. Pearson Malaysia. [Google Scholar] [Crossref]

15. Rauschnabel, P. A., Felix, R., Hinsch, C., Shahab, H., & Alt, F. (2022). What is XR? Towards a framework for augmented and virtual reality. Computers in Human Behavior, 133, 107289. [Google Scholar] [Crossref]

16. Sirgy, M. J. (1985). Using self-congruity and ideal congruity to predict purchase motivation. Journal of Business Research, 13(3), 195–206. [Google Scholar] [Crossref]

17. Tabachnick, B. G., & Fidell, L. S. (2013). Using multivariate statistics (6th ed.). Pearson Education. [Google Scholar] [Crossref]

18. Tam, C., Santos, D., & Oliveira, T. (2023). Exploring the influence of service quality and user experience on loyalty in immersive digital applications. Information Systems Frontiers, 25(4), 1153–1171. [Google Scholar] [Crossref]

19. Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157–178. [Google Scholar] [Crossref]

20. Zeithaml, V. A., Berry, L. L., & Parasuraman, A. (1996). The behavioral consequences of service quality. Journal of Marketing, 60(2), 31–46. [Google Scholar] [Crossref]

• Assessment of the Role of Artificial Intelligence in Repositioning TVET for Economic Development in Nigeria
• Teachers’ Use of Assure Model Instructional Design on Learners’ Problem Solving Efficacy in Secondary Schools in Bungoma County, Kenya
• “E-Booksan Ang Kaalaman”: Development, Validation, and Utilization of Electronic Book in Academic Performance of Grade 9 Students in Social Studies
• Analyzing EFL University Students’ Academic Speaking Skills Through Self-Recorded Video Presentation
• Major Findings of The Study on Total Quality Management in Teachers’ Education Institutions (TEIs) In Assam – An Evaluative Study