References
- Ajzen, I. (1991). The Theory of Planned Behavior. Organizational Behavior and Human Decision Processes, 50(2), pp.179-211. https://doi.org/10.1016/0749-5978(91)90020-T
- Al Amin, M., Arefin, M.S., Hossain, I., Islam, M.R., Sultana, N., & Hossain, M.N. (2022). Evaluating the determinants of customers’ mobile grocery shopping application (MGSA) adoption during COVID-19 pandemic. Journal of Global Marketing, 35(3), 228-247.
- Arpaci, I. (2016). Understanding and predicting students’ intention to use mobile cloud storage services. Computers in Human Behavior, 58, pp. 150-157. https://doi.org/10.1016/j.chb.2015.12.067
- Bagozzi, R.P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the academy of marketing science, 16(1), 74-94. https://doi.org/10.1007/BF02723327
- Barbu, C.M., Florea, D.L., Dabija, D.C., & Barbu, M.C.R. (2021). Customer experience in fintech. Journal of Theoretical and Applied Electronic Commerce Research, 16(5), 1415-1433. https://doi.org/10.3390/jtaer16050080
- WorldPopulationReview.com, 2025. Internet Speeds by Country 2025. Retrieved October 16, 2025, from https://worldpopulationreview.com/country-rankings/internet-speeds-by-country
- Cao, L., Liu, X., Trinchera, L., & Touzani, M. (2024). Exploring mobile commerce activities’ impact on retail firm performance. International Journal of Retail & Distribution Management, 52(10/11), 1108-1124.
- Chi, T. (2018). Understanding Chinese consumer adoption of apparel mobile commerce: An extended TAM approach. Journal of Retailing and Consumer Services, 44, 274-284. https://doi.org/10.1016/j.jretconser.2018.07.019
- Chong, A.Y.-L. (2013). A two-staged SEM-neural network approach for understanding and predicting the determinants of m-commerce adoption. Expert Systems with Applications, 40(4), 1240–1247. https://doi.org/10.1016/j.eswa.2012.08.067
- Collier, J.E. (2020). Applied structural equation modeling using AMOS: Basic to advanced techniques. Routledge.
- Cronbach, L.J. (1970). Essentials of psychological testing; Harper and Row: New York, USA.
- Dash, G., & Paul, J. (2021). CB-SEM vs PLS-SEM methods for research in social sciences and technology forecasting. Technological Forecasting and Social Change, 173, 121092. https://doi.org/10.1016/j.techfore.2021.121092
- DataReportal.com (2025). Global Overview Report. Retrieved October 26, 2025, from https://datareportal.com/reports/digital-2025-global-overview-report
- Davis, F.D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 319-340. https://doi.org/10.2307/249008
- Davis, F.D., Bagozzi, R.P., & Warshaw, P.R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management science, 35(8), 982-1003.
- Eurostat (2024). Digital economy and society statistics - households and individuals. Retrieved 22 October 2025 from https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Digital_economy_and_society_statistics_-_households_and_individuals#Ordering_or_buying_goods_and_services
- Fishbein, M., & Ajzen, I. (1975). Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research. Reading, MA: Addison-Wesley.
- 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
- Gefen, D. (2003). TAM or just plain habit: A look at experienced online shoppers. Journal of Organizational and End User Computing (JOEUC), 15(3), 1-13.
- Ghazali, E.M., Mutum, D.S., Chong, J.H., & Nguyen, B. (2018). Do consumers want mobile commerce? A closer look at M-shopping and technology adoption in Malaysia. Asia Pacific Journal of Marketing and Logistics, 30(4), 1064-1086. https://doi.org/10.1108/apjml-05-2017-0093
- Grewal, D., Roggeveen, A.L., & Nordfält, J. (2017). The Future of Retailing. Journal of Retailing, 93(1), 1–6. https://doi.org/10.1016/j.jretai.2016.12.008
- Hair, J.F., Black, W.C., Babin, B.J., Anderson, R.E., & Tatham, R.L (2010). Multivariate data analysis. 7th ed. Prentice Hall: Upper Saddle River, USA.
- Hajiheydari, N., & Ashkani, M. (2018). Mobile application user behavior in the developing countries: A survey in Iran. Information Systems, 77, 22-33. https://doi.org/10.1016/j.is.2018.05.004
- Hew, J.-J., Leong, L.-Y., Tan, G. W.-H., Lee, V.-H., & Ooi, K.-B. (2018). Mobile social tourism shopping: A dual-stage analysis of a multi-mediation model. Tourism Management, 66, 121–139. https://doi.org/10.1016/j.tourman.2017.10.005
- Hu, L.T., & Bentler, P.M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural equation modeling: a multidisciplinary journal, 6(1), 1-55. https://doi.org/10.1080/10705519909540118
- Huang, D.H., & Chueh, H.E. (2022). Usage intention model of mobile apps in membership application. Journal of Business Research, 139, 1255-1260. https://doi.org/10.1016/j.jbusres.2021.10.062
- Hubert, M., Blut, M., Brock, C., Backhaus, C., & Eberhardt, T. (2017). Acceptance of Smartphone-Based Mobile Shopping: Mobile Benefits, Customer Characteristics, Perceived Risks, and the Impact of Application Context. Psychology & Marketing, 34(2), 175–194. https://doi.org/10.1002/mar.20982
- Kalinić, Z., Marinković, V., Djordjevic, A., & Liébana-Cabanillas, F. (2019a). What drives customer satisfaction and word of mouth in mobile commerce services? A UTAUT2-based analytical approach. Journal of Enterprise Information Management, 33(1), 71–94. https://doi.org/10.1108/jeim-05-2019-0136
- Kalinić, Z., Marinković, V., Kalinić, L., & Liébana-Cabanillas, F. (2021). Neural network modeling of consumer satisfaction in mobile commerce: An empirical analysis. Expert Systems with Applications, 175, 114803. https://doi.org/10.1016/j.eswa.2021.114803
- Kalinic, Z., Marinkovic, V., Molinillo, S., & Liébana-Cabanillas, F. (2019b). A multi-analytical approach to peer-to-peer mobile payment acceptance prediction. Journal of Retailing and Consumer Services, 49, 143–153. https://doi.org/10.1016/j.jretconser.2019.03.016
- Kao, W. K. (2024). What has changed us? Investigating consumers’ behaviors for m-commerce: Comparing the pre-and post-pandemic eras. Journal of Marketing Communications, 1-23.
- Kao, W.K., & L’Huillier, E.A. (2022). The moderating role of social distancing in mobile commerce adoption. Electronic Commerce Research and Applications, 52, 101116. https://doi.org/10.1016/j.elerap.2021.101116
- Kaushik, A.K., Mohan, G., & Kumar, V. (2019). Examining the Antecedents and Consequences of Customers’ Trust Toward Mobile Retail Apps in India. Journal of Internet Commerce, 1–31. https://doi.org/10.1080/15332861.2019.1686333
- Khaw, K.W., Alnoor, A., Al-Abrrow, H., Chew, X., Sadaa, A.M., Abbas, S., & Khattak, Z. Z. (2022). Modelling and evaluating trust in mobile commerce: a hybrid three stage Fuzzy Delphi, structural equation modeling, and neural network approach. International Journal of Human–Computer Interaction, 1-17.
- Lee, E.-Y., Lee, S.-B., & Jeon, Y.J.J. (2017). Factors influencing the behavioral intention to use food delivery apps. Social Behavior and Personality: An International Journal, 45(9), 1461–1473. https://doi.org/10.2224/sbp.6185
- Lee, V.-H., Hew, J.-J., Leong, L.-Y., Wei-Han Tan, G., & Ooi, K.-B. (2020). Wearable payment: A deep learning-based dual-stage SEM-ANN analysis. Expert Systems with Applications, 113477. https://doi.org/10.1016/j.eswa.2020.113477
- Leong, L.-Y., Hew, T.-S., Ooi, K.-B., & Chong, A. Y.-L. (2020). Predicting the antecedents of trust in social commerce – A hybrid structural equation modeling with neural network approach. Journal of Business Research, 110, 24–40. https://doi.org/10.1016/j.jbusres.2019.11.056
- Li, J., Cowan, K., Yazdanparast, A., & Ansell, J. (2024). Vibrotactile feedback in m-commerce: Stimulating perceived control and perceived ownership to increase anticipated satisfaction. Psychology & Marketing, 41(8), 1748-1768.
- Liébana-Cabanillas, F., Marinković, V., & Kalinić, Z. (2017). A SEM-neural network approach for predicting antecedents of m-commerce acceptance. International Journal of Information Management, 37(2), 14–24. https://doi.org/10.1016/j.ijinfomgt.2016.10.008
- Liébana-Cabanillas, F., Sánchez-Fernández, J., & Muñoz-Leiva, F. (2014). Antecedents of the adoption of the new mobile payment systems: The moderating effect of age. Computers in Human Behavior, 35, 464-478. https://doi.org/10.1016/j.chb.2014.03.022
- Lin, S. W., Lo, L. Y. S., & Chen, Y. J. (2025). Unpacking Mobile Website Aesthetics and Its Effect: A Case of M-commerce Website Offering Search and Experience Goods. Journal of Organizational Computing and Electronic Commerce, 35(1), 1-32.
- Lissitsa, S., & Kol, O. (2021). Four generational cohorts and hedonic m-shopping: association between personality traits and purchase intention. Electronic Commerce Research, 21(2), 545-570. https://doi.org/10.1007/s10660-019-09381-4
- MacKenzie, S.B., & Podsakoff, P.M. (2012). Common method bias in marketing: Causes, mechanisms, and procedural remedies. Journal of retailing, 88(4), 542-555. https://doi.org/10.1016/j.jretai.2012.08.001
- Malhotra, N. (2020). Marketing Research: An Applied Orientation, 7th ed.; Pearson Education: Harlow, UK.
- Manchanda, M., & Deb, M. (2020). On m-Commerce Adoption and Augmented Reality: A Study on Apparel Buying Using m-Commerce in Indian Context. Journal of Internet Commerce, 20(1), 84–112. https://doi.org/10.1080/15332861.2020.1863023
- Mason, M.C., Zamparo, G., Marini, A., & Ameen, N. (2022). Glued to your phone? Generation Z’s smartphone addiction and online compulsive buying. Computers in Human Behavior, 136, 107404. https://doi.org/10.1016/j.chb.2022.107404
- McLean, G., & Wilson, A. (2019). Shopping in the digital world: Examining customer engagement through augmented reality mobile applications. Computers in Human Behavior, 101, 210-224. https://doi.org/10.1016/j.chb.2019.07.002
- McLean, G., Osei-Frimpong, K., Al-Nabhani, K., & Marriott, H. (2020). Examining consumer attitudes towards retailers’ m-commerce mobile applications–An initial adoption vs. continuous use perspective. Journal of Business Research, 106, 139-157. https://doi.org/10.1016/j.jbusres.2019.08.032
- Meghisan-Toma, G. M., Puiu, S., Florea, N. M., Meghisan, F., & Doran, D. (2021). Generation Z’young adults and M-commerce use in Romania. Journal of Theoretical and Applied Electronic Commerce Research, 16(5), 1458-1471. https://doi.org/10.3390/jtaer16050082
- Min, S., So, K.K.F., & Jeong, M. (2019). Consumer adoption of the Uber mobile application: Insights from diffusion of innovation theory and technology acceptance model. Journal of Travel & Tourism Marketing, 36(7), 770-783. https://doi.org/10.1080/10548408.2018.1507866
- Molinillo, S., Aguilar-Illescas, R., Anaya-Sánchez, R., & Carvajal-Trujillo, E. (2022). The customer retail app experience: Implications for customer loyalty. Journal of Retailing and Consumer Services, 65, 102842. https://doi.org/10.1016/j.jretconser.2021.102842
- Muñoz-Leiva, F., Climent-Climent, S., & Liébana -Cabanillas, F. (2017). Determinants of intention to use the mobile banking apps: An extension of the classic TAM model. Spanish Journal of Marketing, 21(1), 25–38. https://doi.org/10.1016/j.jretconser.2018.07.019
- Ng, F.Z.X., Yap, H.Y., Tan, G.W.H., Lo, P.S., & Ooi, K.B. (2022). Fashion shopping on the go: A Dual-stage predictive-analytics SEM-ANN analysis on usage behaviour, experience response and cross-category usage. Journal of Retailing and Consumer Services, 65, 102851. https://doi.org/10.1016/j.jretconser.2021.102851
- Ngubelanga, A., & Duffett, R. (2021). Modeling mobile commerce applications’ antecedents of customer satisfaction among millennials: An extended tam perspective. Sustainability, 13(11), 5973. https://doi.org/10.3390/su13115973
- Ooi, K.B., & Tan, G.W.H. (2016). Mobile technology acceptance model: An investigation using mobile users to explore smartphone credit card. Expert Systems with Applications, 59, 33-46. https://doi.org/10.1016/j.eswa.2016.04.015
- Ooi, K.B., Hew, J.J., & Lin, B. (2018). Unfolding the privacy paradox among mobile social commerce users: a multi-mediation approach. Behaviour & Information Technology, 37(6), 575-595. https://doi.org/10.1080/0144929X.2018.1465997
- Parker, C. J., & Kuo, H. Y. (2022). What drives generation-y women to buy fashion items online?. Journal of Marketing Theory and Practice, 30(3), 279-294.
- Pavlou, P.A. (2003). Consumer acceptance of electronic commerce: Integrating trust and risk with the technology acceptance model. International journal of electronic commerce, 7(3), 101-134. https://doi.org/10.1080/10696679.2021.1934877
- Pitardi, V., & Marriott, H.R. (2021). Alexa, she’s not human but… Unveiling the drivers of consumers’ trust in voice-based artificial intelligence. Psychology & Marketing, 38(4), 626-642. https://doi.org/10.1002/mar.21457
- 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
- Pop, R.A., Dabija, D.C., Pelau, C., Dinu, V. 2022. Usage Intentions, Attitudes, and Behaviours towards Energy-Efficient Applications during the COVID-19 Pandemic. Journal of Business Economics and Management, 23(3), pp.668-689. https://doi.org/10.3846/jbem/2022/16959
- Pop, R.A., Hlédik, E., & Dabija, D.C. (2023). Predicting consumers’ purchase intention through fast fashion mobile apps: The mediating role of attitude and the moderating role of COVID-19. Technological Forecasting and Social Change, 186, 122111. https://doi.org/10.1016/j.techfore.2022.122111
- Rese, A., Baier, D., Geyer-Schulz, A., & Schreiber, S. (2017). How augmented reality apps are accepted by consumers: A comparative analysis using scales and opinions. Technological Forecasting and Social Change, 124, 306-319. https://doi.org/10.1016/j.techfore.2016.10.010
- Sarkar, S., Chauhan, S., & Khare, A. (2020). A meta-analysis of antecedents and consequences of trust in mobile commerce. International Journal of Information Management, 50, 286-301. https://doi.org/10.1016/j.ijinfomgt.2019.08.008
- Sim, J.J., Loh, S.H., Wong, K.L., & Choong, C.K. (2021). Do We Need Trust Transfer Mechanisms? An M-Commerce Adoption Perspective. Journal of Theoretical and Applied Electronic Commerce Research, 16(6), 2241-2262. https://doi.org/10.3390/jtaer16060124
- Siyal, A. W., Chen, H., Shah, S. J., Shahzad, F., & Bano, S. (2024). Customization at a glance: Investigating consumer experiences in mobile commerce applications. Journal of retailing and consumer services, 76, 103602.
- Statista.com, 2021. Most popular mobile applications accessed in Romania in 2021. Retrieved November 6, 2022, from https://www.statista.com/statistics/1272847/romania-most-popular-mobile-apps-by-type
- Statista.com, 2025a. Number of internet and social media users worldwide as of October 2025. Retrieved October 12, 2025, from https://www.statista.com/statistics/617136/digital-population-worldwide/
- Statista.com, 2025b. Online Food Delivery – Romania. Retrieved October 12, 2025, from https://www.statista.com/outlook/emo/online-food-delivery/romania?srsltid=AfmBOorS2yC4MXdT5WU8T5NlTaazNLdIp5owTrrTYgU3TbtIAXzKC-Z
- Tam, C., Santos, D., & Oliveira, T. (2020). Exploring the influential factors of continuance intention to use mobile Apps: Extending the expectation confirmation model. Information Systems Frontiers, 22(1), 243-257. https://doi.org/10.1007/s10796-018-9864-5
- Tan, G. W. H., Ooi, K. B., Chong, S. C., & Hew, T. S. (2014). NFC mobile credit card: the next frontier of mobile payment?. Telematics and Informatics, 31(2), 292-307. https://doi.org/10.1016/j.tele.2013.06.002
- Tang, A. K. (2019). A systematic literature review and analysis on mobile apps in m-commerce: Implications for future research. Electronic Commerce Research and Applications, 37, 100885. https://doi.org/10.1016/j.elerap.2019.100885
- Tew, H.-T., Tan, G.W.-H., Loh, X.-M., Lee, V.-H., Lim, W.-L., & Ooi, K.-B. (2021). Tapping the Next Purchase: Embracing the Wave of Mobile Payment. Journal of Computer Information Systems, 1–9. https://doi.org/10.1080/08874417.2020.1858731
- Tong, S., Luo, X., & Xu, B. (2019). Personalized mobile marketing strategies. Journal of the Academy of Marketing Science, 48(1), 64–78. https://doi.org/10.1007/s11747-019-00693-3
- Touni, R., Kim, W.G., Choi, H.M., & Ali, M.A. (2020). Antecedents and an outcome of customer engagement with hotel brand community on Facebook. Journal of Hospitality & Tourism Research, 44(2), 278-299. https://doi.org/10.1177/1096348019895555
- Vahdat, A., Alizadeh, A., Quach, S., & Hamelin, N. (2020). Would you like to shop via mobile app technology? The technology acceptance model, social factors and purchase intention. Australasian Marketing Journal. https://doi.org/10.1016/j.ausmj.2020.01.002
- Van Heerde, H.J., Dinner, I.M., & Neslin, S.A. (2019). Engaging the unengaged customer: The value of a retailer mobile app. International Journal of Research in Marketing, 36(3), 420-438. https://doi.org/10.1016/j.ijresmar.2019.03.003
- VanNoort, G., & vanReijmersdal, E.A. (2019). Branded Apps: Explaining Effects of Brands’ Mobile Phone Applications on Brand Responses. Journal of Interactive Marketing, 45, 16–26. https://doi.org/10.1016/j.intmar.2018.05.003
- 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, 157-178. https://doi.org/10.2307/41410412
- Vinerean, S., Budac, C., Baltador, L.A., & Dabija, D.C. (2022). Assessing the Effects of the COVID-19 Pandemic on M-Commerce Adoption: An Adapted UTAUT2 Approach. Electronics, 11(8), 1269. https://doi.org/10.3390/electronics11081269
- Wen, C., Wang, N., Fang, J., & Huang, M. (2022). An Integrated Model of Continued M-Commerce Applications Usage. Journal of Computer Information Systems, 1-16. https://doi.org/10.1080/08874417.2022.2091682
- Williams, M.D. (2021). Social commerce and the mobile platform: Payment and security perceptions of potential users. Computers in Human behavior, 115, 105557. https://doi.org/10.1016/j.chb.2018.06.005
- Wilson, R. D., & Bettis-Outland, H. (2020). Can artificial neural network models be used to improve the analysis of B2B marketing research data?. Journal of Business & Industrial Marketing, 35(3), 495–507.
- Yang, H., (2013). Bon Appétit for Apps: Young American Consumers’ Acceptance of Mobile Applications. Journal of Computer Information Systems, 53(3), 85–96. https://doi.org/10.1080/08874417.2013.11645635
- Yang, K.C. (2005). Exploring factors affecting the adoption of mobile commerce in Singapore. Telematics and informatics, 22(3), 257-277. https://doi.org/10.1016/j.tele.2004.11.003
- Yu, N., & Huang, Y.T. (2022). Why do people play games on mobile commerce platforms? An empirical study on the influence of gamification on purchase intention. Computers in Human Behavior, 126, 106991. https://doi.org/10.1016/j.chb.2021.106991
- Zhao, Y., & Bacao, F. (2020). What factors determining customer continuingly using food delivery apps during 2019 novel coronavirus pandemic period?. International journal of hospitality management, 91, 102683. https://doi.org/10.1016/j.ijhm.2020.102683