Cultural pathways to intention toward augmented reality applications: the mediating roles of habit and resistance

Abstract

This study investigates the antecedents of consumers’ behavioral intention to use augmented reality (AR) applications Data were collected from 432 users in Türkiye through face-to-face surveys. Structural equation modeling (SEM) was employed to test the proposed research model and hypotheses. The results indicate that the model explains 63.1% of the variance in behavioral intention to use AR. The study specifically examines the effects of habit and resistance on AR behavioral intention, as well as the direct and indirect effects of cultural values. The findings reveal that both habit and resistance have significant direct effects on behavioral intention. Moreover, all cultural dimensions exert a significant indirect effect through habit, demonstrating the mediating role of routinized behavior in AR adoption. However, resistance does not play a mediating role in the relationship between cultural values and behavioral intention. The study offers suggestions for firms seeking to enhance user engagement with AR applications.

Keywords

• cultural values
• augmented reality
• technology habit
• technology resistance
• technology adoption

Ethical Statement

This study involved human participants and was conducted in accordance with the Declaration of Helsinki and the ethical guidelines of the Committee on Publication Ethics (COPE). This study was reviewed by the Scientific Research and Publication Ethics Committee of Bahçeşehir University at its meeting dated 03.12.2025 (Decision No. 2025/10) and was found to be in compliance with the principles of Scientific Research and Publication Ethics. Written informed consent was obtained from all participants prior to enrolment and before data collection commenced. Participation was voluntary, and all data were collected anonymously, with no personally identifiable information recorded.

References

1. Agarwal, C., & Thakur, N. (2014). The evolution and future scope of augmented reality. International Journal of Computer Science Issues (IJCSI), 11(6), 59–66. https://www.proquest.com/scholarly-journals/evolution-future-scope-augmented-reality/docview/1640566386/se-2
2. Alkhwaldi, A. F., Al-Qudah, A. A., Al-Hattami, H. M., Al-Okaily, M., Al-Adwan, S. S., & Abu-Salih, B. (2023). Uncertainty avoidance and acceptance of the digital payment systems: A partial least squares–structural equation modeling (PLS-SEM) approach. Global Knowledge, Memory and Communication, 73(8–9), 1119–1139. https://doi.org/10.1108/gkmc-07-2022-0161
3. Al-Okaily, M., Lutfi, A., Alsaad, A., Taamneh, A., & Alsyouf, A. (2020). The determinants of digital payment systems’ acceptance under cultural orientation differences: The case of uncertainty avoidance. Technology in Society, 63, Article 101367. https://doi.org/10.1016/j.techsoc.2020.101367
4. Alshallaqi, M., Al Halbusi, H., Abbas, M., & Alhaidan, H. (2022). Resistance to innovation in low-income populations: The case of university students’ resistance to using digital productivity applications. Frontiers in Psychology, 13, Article 961589. https://doi.org/10.3389/fpsyg.2022.961589
5. Antonakis, J., Bendahan, S., Jacquart, P., & Lalive, R. (2010). On making causal claims: A review and recommendations. The leadership quarterly, 21(6), 1086-1120. https://doi.org/10.1016/j.leaqua.2010.10.010
6. Antonakis, J., Bendahan, S., Jacquart, P., & Lalive, R. (2014). 6 Causality and Endogeneity: Problems. The Oxford handbook of leadership and organizations, 2014, 93-117. https://doi.org/10.1093/oxfordhb/9780199755615.013.007
7. Arghashi, V., & Yuksel, C. A. (2022). Interactivity, inspiration, and perceived usefulness! How retailers’ AR-apps improve consumer engagement through flow. Journal of Retailing and Consumer Services, 64, Article 102756. https://doi.org/10.1016/j.jretconser.2021.102756
8. Attié, E., & Meyer-Waarden, L. (2022). The acceptance and usage of smart connected objects according to adoption stages: An enhanced technology acceptance model integrating the diffusion of innovation, uses and gratification and privacy calculus theories. Technological Forecasting and Social Change, 176, Article 121485. https://doi.org/10.1016/j.techfore.2022.121485

9. Balakrishnan, J., Dwivedi, Y. K., Hughes, L., & Boy, F. (2021). Enablers and inhibitors of AI-powered voice assistants: A dual-factor approach by integrating the status quo bias and technology acceptance model. Information Systems Frontiers, 26(3), 921–942. https://doi.org/10.1007/s10796-021-10203-y
10. Balta-Ozkan, N., Davidson, R., Bicket, M., & Whitmarsh, L. (2013). Social barriers to the adoption of smart homes. Energy Policy, 63, 363–374. https://doi.org/10.1016/j.enpol.2013.08.043
11. Baptista, G., & Oliveira, T. (2015). Understanding mobile banking: The unified theory of acceptance and use of technology combined with cultural moderators. Computers in Human Behavior, 50, 418–430. https://doi.org/10.1016/j.chb.2015.04.024
12. Bearden, W. O., Money, R. B., & Nevins, J. L. (2006). A measure of long-term orientation: Development and validation. Journal of the Academy of Marketing Science, 34(3), 456–467. https://doi.org/10.1177/0092070306286706
13. Bhattacherjee, A., & Hikmet, N. (2007). Physicians’ resistance toward healthcare information technology: A theoretical model and empirical test. European Journal of Information Systems, 16(6), 725–737. https://doi.org/10.1057/palgrave.ejis.3000717
14. Bisandu, D. B., Datiri, D. D., Onokpasa, E., Mammuam, T. A., Thomas, G. A., Gurumdimma, N. Y., & Madugu, J. M. (2019). A framework for the adoption of knowledge management system (KMS) in University of Jos, Nigeria. International Journal of Research and Innovation in Applied Science (IJRIAS), 4(7), 137–154.
15. Brosnan, M. J. (1998). The impact of computer anxiety and self-efficacy upon performance. Journal of Computer Assisted Learning, 14(3), 223–234. https://doi.org/10.1046/j.1365-2729.1998.143059.x
16. Cenfetelli, R. T. (2004). Inhibitors and enablers as dual factor concepts in technology usage. Journal of the Association for Information Systems, 5(11), 472–492. https://doi.org/10.17705/1jais.00059
17. Cenfetelli, R. T., & Schwarz, A. (2011). Identifying and testing the inhibitors of technology usage intentions. Information Systems Research, 22(4), 808–823. https://doi.org/10.1287/isre.1100.0295
18. Cham, T. H., Cheah, J. H., Cheng, BL., & Lim, X. J. (2022). I am too old for this! Barriers contributing to the non-adoption of mobile payment. International Journal of Bank Marketing, 40(5), 1017–1050. https://doi.org/10.1108/ijbm-06-2021-0283
19. Cham, T. H., Tan, G. W. H., Aw, E. C. X., Ooi, K. B., Jee, T. W., & Pek, C. C. (2023). Virtual reality in tourism: Adoption scepticism and resistance. Tourism Review, 79(2), 337–354. https://doi.org/10.1108/tr-10-2022-0479
20. Chopdar, P. K., & Sivakumar, V. J. (2019). Understanding continuance usage of mobile shopping applications in India: The role of espoused cultural values and perceived risk. Behaviour & Information Technology, 38(1), 42–64. https://doi.org/10.1080/0144929x.2018.1513563
21. Dacko, S. G. (2017). Enabling smart retail settings via mobile augmented reality shopping apps. Technological forecasting and social change, 124, 243-256. https://doi.org/10.1016/j.techfore.2016.09.032
22. Davis, F. D. (1985). A technology acceptance model for empirically testing new end-user information systems: Theory and results (Doctoral dissertation, Massachusetts Institute of Technology).
23. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
24. Elsotouhy, M. M., Khashan, M. A., Thabet, M. Z., Galal, H. M., & Ghonim, M. A. (2024). Do technological anxiety, privacy and physical risks matter in retail customers’ adoption of AR apps? An extended UTAUT2 approach. EuroMed Journal of Business. Advance online publication. https://doi.org/10.1108/emjb-05-2024-0104
25. Fang, T. (2003). A critique of Hofstede’s fifth national culture dimension. International Journal of Cross Cultural Management, 3(3), 347–368. https://doi.org/10.1177/1470595803003003006
26. Farooq, M. S., Salam, M., Jaafar, N., Fayolle, A., Ayupp, K., Radovic-Markovic, M., & Sajid, A. (2017). Acceptance and use of lecture capture system (LCS) in executive business studies: Extending UTAUT2. Interactive Technology and Smart Education, 14(4), 329–348. https://doi.org/10.1108/itse-06-2016-0015
27. Fleischmann, C., Cardon, P. W., & Aritz, J. (2020). Smart collaboration in global virtual teams: The influence of culture on technology acceptance and communication effectiveness. In Proceedings of the Hawaii International Conference on System Sciences (HICSS) (pp. 1–11). https://doi.org/10.24251/hicss.2020.045
28. Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.1177/002224378101800104
29. Garad, A., Al-Ansi, A. M., & Qamari, I. N. (2021). The role of e-learning infrastructure and cognitive competence in distance learning effectiveness during the COVID-19 pandemic. Jurnal Cakrawala Pendidikan, 40(1), 81–91. https://doi.org/10.21831/cp.v40i1.33474
30. García de Blanes Sebastián, M., Sarmiento Guede, J. R., Azuara Grande, A., & Filipe, A. F. (2024). UTAUT-2 predictors and satisfaction: Implications for mobile-learning adoption among university students. Education and Information Technologies, 30(3), 3201–3237. https://doi.org/10.1007/s10639-024-12927-1
31. Gardner, B. (2015). A review and analysis of the use of ‘habit’ in understanding, predicting and influencing health-related behaviour. Health Psychology Review, 9(3), 277–295. https://doi.org/10.1080/17437199.2013.876238
32. Gaskin, J., & Lim, J. (2018). CFA tool. Gaskination’s StatWiki. AMOS plugin.
33. Goularte, A. D. C., & Zilber, S. N. (2019). The moderating role of cultural factors in the adoption of mobile banking in Brazil. International Journal of Innovation Science, 11(1), 63–81. https://doi.org/10.1108/ijis-11-2017-0119
34. Gregory, G. D., & Munch, J. M. (1996). Reconceptualizing individualism–collectivism in consumer behavior. Advances in Consumer Research, 23, 104–110.
35. Guo, X., Sun, Y., Wang, N., Peng, Z., & Yan, Z. (2013). The dark side of elderly acceptance of preventive mobile health services in China. Electronic Markets, 23, 49–61. https://doi.org/10.1007/s12525-012-0112-4
36. Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis (7th ed.). Pearson Prentice Hall.
37. Hayes, A., Khatit, K., & Perez, Y. (2024). Augmented reality (AR): Definition, examples, and uses. Investopedia. Retrieved March 1, 2026, from https://www.investopedia.com/terms/a/augmented-reality.asp
38. Heidenreich, S., & Handrich, M. (2015). What about passive innovation resistance? Investigating adoption-related behavior from a resistance perspective. Journal of Product Innovation Management, 32(6), 878–903. https://doi.org/10.1111/jpim.12161
39. 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
40. Herzberg, F., Mausner, B., & Snyderman, B. B. (1959). The motivation to work. Wiley & Sons.
41. Herzberg, F., Mausner, B., & Snyderman, B. B. (2011). The motivation to work. Transaction Publishers.
42. Hew, J. J., Leong, L. Y., Tan, G. W. H., Ooi, K. B., & Lee, V. H. (2019). The age of mobile social commerce: An artificial neural network analysis on its resistances. Technological Forecasting and Social Change, 144, 311–324. https://doi.org/10.1016/j.techfore.2017.10.007
43. Heydari, A., Laroche, M., Paulin, M., & Richard, M. O. (2021). Hofstede’s individual-level indulgence dimension: Scale development and validation. Journal of Retailing and Consumer Services, 62, Article 102640. https://doi.org/10.1016/j.jretconser.2021.102640
44. Heydari, A., Laroche, M., Paulin, M., & Richard, M. O. (2025). Shaping hedonic–utilitarian attitudes and consumer intentions: The determinant role of the indulgence vs. restraint culture dimension. Journal of Retailing and Consumer Services, 86, Article 104344. https://doi.org/10.1016/j.jretconser.2025.104344
45. Hide, T., & Pena, F. A. (2020). Retail innovations: The role of familiarity and cultural values ( Master's thesis, NTNU).
46. Hofstede, G. (1980). Culture’s consequences: International differences in work-related values. Sage.
47. Hofstede, G. (1994). Values survey module 1994 manual. Institute for Research on Intercultural Cooperation.
48. Hofstede, G. (2001). Culture’s consequences (2nd ed.). Sage.
49. Hofstede, G., Hofstede, G. J., & Minkov, M. (2010). Cultures and organizations: Software of the mind (revised and expanded 3rd ed.). McGraw-Hill.
50. Hofstede, G. (2011). Dimensionalizing cultures: The Hofstede model in context. Online readings in psychology and culture, 2(1), 1-26. https://doi.org/10.9707/2307-0919.1014
51. Hsieh, P. J., & Lin, W. S. (2018). Explaining resistance to system usage in the PharmaCloud: A view of the dual-factor model. Information & Management, 55(1), 51–63. https://doi.org/10.1016/j.im.2017.03.008
52. Hsu, H. C., Wang, P. Y., Lin, L. H., Shih, W. M., & Lin, M. H. (2015). Exploring the relationship between professional commitment and job satisfaction among nurses. Workplace Health & Safety, 63(9), 392–398. https://doi.org/10.1177/2165079915591400
53. Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1–55. https://doi.org/10.1080/10705519909540118
54. İncioğlu, C. (2023). Tüketicilerin online alışveriş davranışlarının Hofstede kültür boyutları ve UTAUT-2 modeli ile incelenmesi: Almanya Türkiye örneği (Doctoral dissertation, Aydın Adnan Menderes Üniversitesi).
55. Jacobs-Basadien, M., Pather, S., & Petersen, F. (2024). The role of culture in the adoption of mobile applications for the self-management of diabetes in low resourced urban communities. Universal Access in the Information Society, 23(2), 743–763. https://doi.org/10.1007/s10209-022-00951-2
56. Jalali, M. S., Kaiser, J. P., Siegel, M., & Madnick, S. (2019). The internet of things promises new benefits and risks: A systematic analysis of adoption dynamics of IoT products. IEEE Security & Privacy, 17(2), 39–48. https://doi.org/10.1109/msec.2018.2888780
57. Jan, J., Alshare, K. A., & Lane, P. L. (2022). Hofstede’s cultural dimensions in technology acceptance models: A meta-analysis. Universal Access in the Information Society, 23(2), 717–741. https://doi.org/10.1007/s10209-022-00930-7
58. Jayaswal, P., & Parida, B. (2023). The role of augmented reality in redefining e-tailing: A review and research agenda. Journal of Business Research, 160, Article 113765. https://doi.org/10.1016/j.jbusres.2023.113765
59. Jung, TH., Lee, H., Chung, N., & tom Dieck, M. C. (2018). Cross-cultural differences in adopting mobile augmented reality at cultural heritage tourism sites. International Journal of Contemporary Hospitality Management, 30(3), 1621–1645. https://doi.org/10.1108/ijchm-02-2017-0084
60. Khan, I. U. (2022). How does culture influence digital banking? A comparative study based on the unified model. Technology in Society, 68, Article 101822. https://doi.org/10.1016/j.techsoc.2021.101822
61. Khan, I. U., Hameed, Z., Khan, S. N., Khan, S. U., & Khan, M. T. (2021). Exploring the effects of culture on acceptance of online banking: A comparative study of Pakistan and Turkey by using the extended UTAUT model. Journal of Internet Commerce, 21(2), 183–216. https://doi.org/10.1080/15332861.2021.1882749
62. Khan, I. U., Hameed, Z., & Khan, S. U. (2017). Understanding online banking adoption in a developing country: UTAUT2 with cultural moderators. Journal of Global Information Management, 25(1), 43–65. https://doi.org/10.4018/jgim.2017010103
63. Khan, S. A. R., Tabish, M., & Yu, Z. (2025). The decision analysis of consumer purchasing for refurbished products. Waste Management & Research, 43(10), 1577–1593. https://doi.org/10.1177/0734242x251328890
64. Kim, J., Erdem, M., & Kim, B. (2023). Adoption of in-room voice assistants: A cross-cultural study. Journal of Hospitality and Tourism Insights, 7(4), 1911–1933. https://doi.org/10.1108/jhti-02-2023-0082
65. Kim, J., Kim, S., & Nam, C. (2016). User resistance to acceptance of in-vehicle infotainment (IVI) systems. Telecommunications Policy, 40(9), 919–930. https://doi.org/10.1016/j.telpol.2016.07.006
66. Kleijnen, M., Lee, N., & Wetzels, M. (2009). An exploration of consumer resistance to innovation and its antecedents. Journal of economic psychology, 30(3), 344-357. https://doi.org/10.1016/j.joep.2009.02.004
67. Kline, R. B. (2016). Principles and practice of structural equation modeling (4th ed.). Guilford Press.
68. Kokash, H. A., Shah Alam, S., Ahmed, S., Khattak, A. N., & Tariq, B. (2024). Using virtual reality in tourism destination decision: A young Saudi tourists’ perspective with habit is a mediator. Journal of Quality Assurance in Hospitality & Tourism, 27(1), 76–109. https://doi.org/10.1080/1528008x.2023.2299762
69. Kumar, P., Chauhan, S., Gupta, P., & Jaiswal, M. P. (2023). A meta-analysis of trust in mobile banking: The moderating role of cultural dimensions. International Journal of Bank Marketing, 41(6), 1207–1238. https://doi.org/10.1108/ijbm-02-2022-0075
70. Lambiase, S., Catolino, G., Palomba, F., Ferrucci, F., & Russo, D. (2025). Investigating the role of cultural values in adopting large language models for software engineering. ACM Transactions on Software Engineering and Methodology, 35(1), 1–43. https://doi.org/10.1145/3725529
71. Laukkanen, T., Sinkkonen, S., Laukkanen, P., & Kivijarvi, M. (2008). Segmenting bank customers by resistance to mobile banking. International Journal of Mobile Communications, 6(3), 309–320. https://doi.org/10.1504/ijmc.2008.017513
72. Lavuri, R., & Thaichon, P. (2025). Webrooming in the luxury market: Role of consumer traits, channel-related factors and shopping motivations. Australasian Marketing Journal. Advance online publication. https://doi.org/10.1177/14413582251333808
73. Lee, L. Y. S. (2016). Hospitality industry web-based self-service technology adoption model: A cross-cultural perspective. Journal of Hospitality & Tourism Research, 40(2), 162–197. https://doi.org/10.1177/1096348013495695
74. Leong, L. Y., Hew, T. S., Ooi, K. B., & Wei, J. (2020). Predicting mobile wallet resistance: A two-staged structural equation modeling-artificial neural network approach. International Journal of Information Management, 51, Article 102047. https://doi.org/10.1016/j.ijinfomgt.2019.102047
75. Leoveanu, V. M., Sandu, M. C., & Coman, A. (2020). Cultural factors as powerful moderators of Romanian students’ adoption of mobile banking in everyday life. In Social computing and social media: Design, ethics, user behavior, and social network analysis (pp. 583–599). Springer International Publishing. https://doi.org/10.1007/978-3-030-49570-1_41
76. Lewin, K. (1947). Frontiers in group dynamics: Concept, method and reality in social science; Social equilibria and social change. Human Relations, 1(1), 5–41. https://doi.org/10.1177/001872674700100103
77. Li, Z., Sha, Y., Song, X., Yang, K., Zhao, K., Jiang, Z., & Zhang, Q. (2020). Impact of risk perception on customer purchase behavior: A meta-analysis. Journal of Business & Industrial Marketing, 35(1), 76–96. https://doi.org/10.1108/jbim-12-2018-0381
78. Limayem, M., Hirt, S. G., & Cheung, C. M. (2007). How habit limits the predictive power of intention: The case of information systems continuance. MIS Quarterly, 31(4), 705–737. https://doi.org/10.2307/25148817
79. Linggawisnu, R. I., Faidah, U. N., & Efranto, R. Y. (2026). Augmented reality acceptance across psychological, technological, and cultural dimensions: A literature review. Theoretical Issues in Ergonomics Science, 1–18. https://doi.org/10.1080/1463922x.2026.2640504
80. Mahfuz, M. A., Khanam, L., & Hu, W. (2016). The influence of culture on m-banking technology adoption: An integrative approaches of UTAUT2 and ITM. In 2016 Portland international conference on management of engineering and technology (PICMET) (pp. 824-835). IEEE. https://doi.org/10.1109/picmet.2016.7806814
81. Malhotra, N. C., & Dash, S. (2011). Marketing research: An applied orientation. Pearson Publishing.
82. Mani, Z., & Chouk, I. (2018). Consumer resistance to innovation in services: challenges and barriers in the internet of things era. Journal of Product Innovation Management, 35(5), 780-807. https://doi.org/10.1111/jpim.12463
83. Migliore, G., Wagner, R., Cechella, F. S., & Liébana-Cabanillas, F. (2022). Antecedents to the adoption of mobile payment in China and Italy: An integration of UTAUT2 and innovation resistance theory. Information Systems Frontiers, 24(6), 2099–2122. https://doi.org/10.1007/s10796-021-10237-2
84. Negara, J. G. P., & Setyohadi, D. B. (2020). Uncertainty avoidance and individualism collectivism on acceptance of smart city mobile applications. In Proceedings of the 2020 4th International Conference on Informatics and Computational Sciences (ICICoS) (pp. 1–6). IEEE. https://doi.org/10.1109/icicos51170.2020.9298974
85. Nunnally, J. C. (1978). Psychometric theory (2nd ed.). McGraw-Hill.
86. Osman, D., Khan, R., Gittens, D., & Lundy, H., Jr. (2022). The relationship between the new technology and the intention of faculty to use in their teaching: The roles of mediators. International Journal of Education Research, 17(1).
87. Oviedo-Trespalacios, O., Briant, O., Kaye, S. A., & King, M. (2020). Assessing driver acceptance of technology that reduces mobile phone use while driving: The case of mobile phone applications. Accident Analysis & Prevention, 135, Article 105348. https://doi.org/10.1016/j.aap.2019.105348
88. Palau-Saumell, R., Forgas-Coll, S., Sánchez-García, J., & Robres, E. (2019). User acceptance of mobile apps for restaurants: An expanded and extended UTAUT-2. Sustainability, 11(4), Article 1210. https://doi.org/10.3390/su11041210
89. Perkin Coie LLM. (2019). 2019 augmented and virtual reality survey report. Retrieved March 1, 2026, from https://perkinscoie.com/sites/default/files/2025-05/2019-VR-AR-Survey-Digital-v1_0.pdf
90. Png, I. P., Tan, B. C., & Wee, K. L. (2001). Dimensions of national culture and corporate adoption of IT infrastructure. IEEE Transactions on Engineering Management, 48(1), 36–45. https://doi.org/10.1109/17.913164
91. Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903. https://doi.org/10.1037/0021-9010.88.5.879
92. Podsakoff, P. M., MacKenzie, S. B., & Podsakoff, N. P. (2012). Sources of method bias in social science research and recommendations on how to control it. Annual review of psychology, 63(1), 539-569. https://doi.org/10.1146/annurev-psych-120710-100452
93. Pozi, N. S. B. M. (2023). Factors influencing acceptance of mobile augmented reality application based on technology acceptance model (TAM) (Master's thesis, Universiti Utara Malaysia).
94. Ram, S., & Sheth, J. N. (1989). Consumer resistance to innovations: the marketing problem and its solutions. Journal of consumer marketing, 6(2), 5-14. https://doi.org/10.1016/0737-6782(89)90083-0
95. Rashid, S. (2025). Habit predicting higher education EFL students’ intention and use of AI: A nexus of UTAUT-2 model and metacognition theory. Education Sciences, 15(6), Article 756. https://doi.org/10.3390/educsci15060756
96. Rauschnabel, P. A., He, J., & Ro, Y. K. (2018). Antecedents to the adoption of augmented reality smart glasses: A closer look at privacy risks. Journal of Business Research, 92, 374-384. https://doi.org/10.1016/j.jbusres.2018.08.008
97. Raymond-Paul, J. D., & De Sousa, J. (2025). Revolutionizing retail: The role of augmented reality-driven information systems in enhancing customer experience and engagement. Student Papers. Retrieved March 1, 2026, from http://lup.lub.lu.se/student-papers/record/9202639
98. Rogers, E. M. (2003). Diffusion of innovations (5th ed.). Free Press.
99. Roy, S. S., Balaji, M. S., & Nguyen, B. (2020). Consumer-computer interaction and in-store smart technology (IST) in the retail industry: The role of motivation, opportunity, and ability. Journal of Marketing Management, 36(3–4), 299–333. https://doi.org/10.1080/0267257x.2020.1736130
100. Schapsis, C., Chiagouris, L., & Pham, N. C. (2021). Are consumers ready for augmented reality? Factors influencing online footwear purchasing intentions using AR technology. Journal of Marketing Development and Competitiveness, 15(2), 21–36. https://doi.org/10.33423/jmdc.v15i2.4331
101. Schapsis, C., Chiagouris, L., & Wingate, N. (2025). Decoding Generation Z’s habits: The augmented reality shift from gimmick to utility in omni-digital shopping. Journal of Product & Brand Management, 34(1), 119–135. https://doi.org/10.1108/jpbm-12-2023-4879
102. Schein, K. E., & Rauschnabel, P. A. (2023). Augmented reality in manufacturing: Exploring workers' perceptions of barriers. IEEE Transactions on Engineering Management, 70(10), 3344–3357. https://doi.org/10.1109/tem.2021.3093833
103. Sheikh, Z., Islam, T., Rana, S., Hameed, Z., & Saeed, U. (2017). Acceptance of social commerce framework in Saudi Arabia. Telematics and Informatics, 34(8), 1693–1708. https://doi.org/10.1016/j.tele.2017.08.003
104. Spector, P. E. (2006). Method variance in organizational research: truth or urban legend?. Organizational research methods, 9(2), 221-232. https://doi.org/10.1177/1094428105284955
105. Srite, M., & Karahanna, E. (2006). The Role of Espoused National Cultural Values in Technology Acceptance1. MIS quarterly, 30(3), 679-704. https://doi.org/10.2307/25148745
106. Stellar, J. E., Gordon, A., Anderson, C. L., Piff, P. K., McNeil, G. D., & Keltner, D. (2018). Awe and humility. Journal of personality and social psychology, 114(2), 258-269. https://doi.org/10.1037/pspi0000109
107. Sully de Luque, M., & Javidan, M. (2004). Uncertainty avoidance. In Culture, leadership, and organizations: The GLOBE study (Vol. 62, pp. 602–653).
108. Sunny, S., Patrick, L., & Rob, L. (2019). Impact of cultural values on technology acceptance and technology readiness. International Journal of Hospitality Management, 77, 89–96. https://doi.org/10.1016/j.ijhm.2018.06.017
109. Syed, H., & Malik, N. A. (2014). Comparative study of effect of culture on technology adoption in Pakistan and USA. The Business & Management Review, 5(1), 42–51.
110. Tak, P., & Panwar, S. (2017). Using UTAUT 2 model to predict mobile app based shopping: Evidences from India. Journal of Indian Business Research, 9(3), 248–264. https://doi.org/10.1108/jibr-11-2016-0132
111. Talay, M. B., Akdeniz, M. B., Obal, M., & Townsend, J. D. (2019). Stock market reactions to new product launches in international markets: The moderating role of culture. Journal of International Marketing, 27(4), 81–98. https://doi.org/10.1177/1069031x19874789
112. Tandon, U., Kiran, R., & Sah, A. N. (2018). The influence of website functionality, drivers and perceived risk on customer satisfaction in online shopping: An emerging economy case. Information Systems and e-Business Management, 16(1), 57–91. https://doi.org/10.1007/s10257-017-0341-3
113. Tarhini, A., Hone, K., Liu, X., & Tarhini, T. (2016). Examining the moderating effect of individual-level cultural values on users’ acceptance of E-learning in developing countries: a structural equation modeling of an extended technology acceptance model. Interactive Learning Environments, 25(3), 306-328. https://doi.org/10.1080/10494820.2015.1122635
114. Triandis, H. C. (1995). Individualism and collectivism. Westview Press.
115. Trinh, T. T. H., Le, H. B. H., & Nguyen, N. H. (2020). Factors affecting private customers in adopting mobile banking in Vietnam. Management Science Letters, 10(12), 2769–2780. https://doi.org/10.5267/j.msl.2020.4.033
116. Tsai, J. M., Cheng, M. J., Tsai, H. H., Hung, S. W., & Chen, Y. L. (2019). Acceptance and resistance of telehealth: The perspective of dual-factor concepts in technology adoption. International Journal of Information Management, 49, 34–44. https://doi.org/10.1016/j.ijinfomgt.2019.03.003
117. Tu, J. C., & Jia, X. H. (2024). A study on immersion and intention to pay in AR broadcasting: Validating and expanding the hedonic motivation system adoption model. Sustainability, 16(5), Article 2040. https://doi.org/10.3390/su16052040
118. Veiga, J. F., Floyd, S., & Dechant, K. (2001). Towards modelling the effects of national culture on IT implementation and acceptance. Journal of Information Technology, 16(3), 145–158. https://doi.org/10.1080/02683960121858
119. Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273–315. https://doi.org/10.1111/j.1540-5915.2008.00192.x
120. 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
121. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540
122. Venkatesh, V., Thong, J. Y., & 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. https://doi.org/10.2307/41410412
123. Verplanken, B., & Orbell, S. (2022). Attitudes, habits, and behavior change. Annual Review of Psychology, 73, 327–352. https://doi.org/10.1146/annurev-psych-020821-011744
124. Wang, Y., Ko, E., & Wang, H. (2021). Augmented reality (AR) app use in the beauty product industry and consumer purchase intention. Asia Pacific Journal of Marketing and Logistics, 34(1), 110–131. https://doi.org/10.1108/apjml-11-2019-0684
125. Wu, Z., & Liu, Y. (2023). Exploring country differences in the adoption of mobile payment service: The surprising robustness of the UTAUT2 model. International Journal of Bank Marketing, 41(2), 237–268. https://doi.org/10.1108/ijbm-02-2022-0052
126. Xu, W., Ismail, M., Shahruddin, S., Quan, W., & Li, Y. (2025). Exploring tourists’ intentions to adopt augmented reality in cultural heritage museums: Insights from a modified technology acceptance model. SAGE Open, 15(2). https://doi.org/10.1177/21582440251339936
127. Yang Meier, D., Barthelmess, P., Sun, W., & Liberatore, F. (2020). Wearable technology acceptance in health care based on national culture differences: Cross-country analysis between Chinese and Swiss consumers. Journal of Medical Internet Research, 22(10), Article e18801. https://doi.org/10.2196/18801
128. Yim, M. Y. C., Chu, S. C., & Sauer, P. L. (2017). Is augmented reality technology an effective tool for e-commerce? An interactivity and vividness perspective. Journal of Interactive Marketing, 39(1), 89–103. https://doi.org/10.1016/j.intmar.2017.04.001
129. Yuan, M., Khan, I. R., Farbiz, F., Yao, S., Niswar, A., & Foo, M. H. (2013). A mixed reality virtual clothes try-on system. IEEE Transactions on Multimedia, 15(8), 1958–1968. https://doi.org/10.1109/tmm.2013.2280560
130. Zeng, Y., Liu, L., & Xu, R. (2022). The effects of a virtual reality tourism experience on tourist’s cultural dissemination behavior. Tourism and Hospitality, 3(1), 314-329. https://doi.org/10.3390/tourhosp3010021
131. Zoubi, M., Estaitia, H., Morshed, A., Khrais, L. T., Haikal, E., & AlSheikh, M. (2025). Augmented reality and sustainable luxury: transforming fashion retail in the UAE. Technological Sustainability, 4(3), 275–292. https://doi.org/10.1108/techs-03-2025-0068