Review of Professional Management

Why Do Consumers Prefer Online Shopping? An Investigation from the Lens of the Technology Acceptance Model

Tamal Chatterjee¹ and Sudin Bag¹

First Published 15 Jan 2026. https://doi.org/10.1177/09728686251409463

Article Information

Corresponding Author:

Sudin Bag, Department of Business Administration, Vidyasagar University, Midnapore, West Bengal 721102, India.
Email: sudinmba@mail.vidyasagar.ac.in

¹Department of Business Administration, Vidyasagar University, Midnapore, West Bengal, India

Creative Commons Non Commercial CC BY-NC: This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (http://www.creativecommons.org/licenses/by-nc/4.0/) which permits non-Commercial use, reproduction and distribution of the work without further permission provided the original work is attributed.

Abstract

In recent years, the number of online consumers has increased significantly, with individuals dedicating substantial time to online shopping. Familiarity with online platforms has grown, and consumer behaviour has undergone considerable changes over the past two decades. The objective of the present study is to analyse consumer behavioural patterns and identify the factors influencing their current online shopping behaviour. A quantitative research method was employed, involving a sample of 300 respondents. A structured, self-administered research instrument was utilised to predict online consumer behaviour through the technology acceptance model using structural equation modelling. The results indicate that perceived usefulness, perceived ease of use and personal innovativeness significantly influence consumer attitudes, ultimately leading to the utilisation of online platforms for purchasing necessities. Furthermore, trust in online platforms plays a crucial role in shaping consumers’ online shopping decisions. Therefore, the findings of this study are valuable for online marketers aiming to encourage consumers to purchase products and services through online platforms.

Keywords

Perceived ease of use, perceived usefulness, personal innovativeness, attitude, structural equation modelling, TAM

References

Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behaviour. Prentice-Hall.

Al-Nahdi, T. S., Habib, S. A., & Abbaas Albdour, A. (2015). Factors influencing the intention to purchase real estate in Saudi Arabia: Moderating effect of demographic citizenship. IJBM, 10(4), 35–48. http://dx.doi.org/10.5539/ijbm.v10n4p35

Bag, S., Ray, N., & Banerjee, B. (2021). Assessing the effects of experiential quality on behavioural intention of customers in banking services: The moderating role of experiential satisfaction. FIIB Business Review, 14(5), 23197145211052817.

Chauhan, H., Pandey, A., Mishra, S., & Rai, S. K. (2021). Modeling the predictors of consumers’ online purchase intention of green products: The role of personal innovativeness and environmental drive. Environment, Development and Sustainability, 23(11), 16769–16785.

Cohen, J. (1992). Quantitative methods in psychology: A power primer. Psychological Bulletin, 112(1), 155–159.

Cook, A. J., Kerr, G. N., & Moore, K. (2002). Attitudes and intentions towards purchasing GM food. Journal of Economic Psychology, 23(5), 557–572.

Daly, J., Gronow, S., Jenkins, D., & Plimmer, F. (2003). Consumer behaviour in the valuation of residential property: A comparative study in the UK, Ireland and Australia. Property Management, 21(5), 295–314.

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. http://dx.doi.org/ 10.2307/249008

Elamin, A. M., Ali, L., Ahmed, A. Z. E., & Aldabbas, H. (2025). Factors affecting attitudes toward e-shopping in the United Arab Emirates. Cogent Business & Management, 12(1), 2442542.

Faul, F., Erdfelder, E., Buchner, A., & Lang, A.-G. (2009). Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses. Behavior Research Methods, 41(4), 1149–1160.

Fernandes, S., Venkatesh, V. G., Panda, R., & Shi, Y. (2021). Measurement of factors influencing online shopper buying decisions: A scale development and validation. Journal of Retailing and Consumer Services, 59, 102394.

Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.

Gefen, D., & Straub, D. W. (2000). The relative importance of perceived ease of use in IS adoption: A study of e-commerce adoption. Journal of the Association for Information Systems, 1(1), 8.

Gupta, A. S., & Mukherjee, J. (2022). Long-term changes in consumers’ shopping behavior post-pandemic: An exploratory study. International Journal of Retail & Distribution Management, 50(12), 1518–1534.

Hair, J., Hollingsworth, C. L., Randolph, A. B., & Chong, A. Y. L. (2017). An updated and expanded assessment of PLS-SEM in information systems research. Industrial Management & Data Systems, 117(3), 442–458.

Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2019). A primer on partial least squares structural equation modeling (PLS-SEM) (2nd ed.). Sage Publications.

Hair, Jr, J. F., Sarstedt, M., Matthews, L. M., & Ringle, C. M. (2016). Identifying and treating unobserved heterogeneity with FIMIX-PLS: Part I–method. European Business Review, 28(1), 63–76.

Hajli, N., Wang, Y., Tajvidi, M., & Hajli, M. S. (2017). People, technologies, and organizations interactions in a social commerce era. IEEE Transactions on Engineering Management, 64(4), 594–604.

Henseler, J., Ringle, C. M., & Sarstedt, M. (2016). Testing measurement invariance of composites using partial least squares. International Marketing Review, 33(3), 405–431.

Hsiu-Fen, L. (2007). Predicting consumer intentions to shop online: An empirical test of competing theories. Electronic Commerce Research and Applications, 6(2007), 433–442.

Hsu, C. L., Lin, J. C. C., & Chiang, H. S. (2013). The effects of blogger recommendations on customers’ online shopping intentions. Internet Research, 23(1), 69–88.

Hu, Z., Ding, S., Li, S., Chen, L., & Yang, S. (2019). Adoption intention of fintech services for bank users: An empirical examination with an extended technology acceptance model. Symmetry, 11, 340.

Huang, H. M., & Liaw, S. S. (2005). Exploring users’ attitudes and intentions toward the web as a survey tool. Computers in Human Behavior, 21(5), 729–743.

Huang, Z., & Benyoucef, M. (2013). From e-commerce to social commerce: A close look at design features. Electronic Commerce Research and Applications, 12(4), 246–259.

Indarsin, T., & Ali, H. (2017). Attitude toward using m-commerce: The analysis of perceived usefulness perceived ease of use, and perceived trust: Case study in Ikens wholesale trade, Jakarta–Indonesia. Saudi Journal of Business and Management Studies, 2(11), 995–1007.

Jalil, F., Yang, J., Al-Okaily, M., & Rehman, S. U. (2024). E-commerce for a sustainable future: Integrating trust, green supply chain management and online shopping satisfaction. Asia Pacific Journal of Marketing and Logistics, 36(10), 2354–2370.

Jayasubramanian, P., Sivasakthi, D., & Ananthi, P. K. (2015). A study on customer satisfaction towards online shopping. International Journal of Applied Research, 1(8), 489–495.

Khatoon, S., Anwar, I., Shamsi, M. A., & Chaudhary, A. (2024). Consumers’ behavioral intention toward online shopping in the post‐COVID‐19 period. International Journal of Consumer Studies, 48(1), e13001.

Kline, R. B. (2016). Principles and practice of structural equation modeling (4th ed.). The Guilford Press.

Lăzăroiu, G., Neguriţă, O., Grecu, I., Grecu, G., & Mitran, P. C. (2020). Consumers’ decision-making process on social commerce platforms: Online trust, perceived risk, and purchase intentions. Frontiers in Psychology, 11, 890.

Leckie, C., Nyadzayo, M. W., & Johnson, L. W. (2018). Promoting brand engagement behaviors and loyalty through perceived service value and innovativeness. Journal of Service Marketing, 32, 70–82.

Li, X., Chen, Z., & Sun, Y. (2022). A deep learning approach to predicting user behavior in web applications. Computers in Human Behavior, 105, 106–118.

Makmor, N., Aziz Abd, N., & Alam Shah, S. (2019). Social commerce an extended technology acceptance model: The mediating effect of perceived ease of use and perceived usefulness. Malaysian Journal of Consumer and Family Economics, 22, 119–136.

Numraktrakul, P., Ngarmyarn, A., & Panichpathom, S. (2012). Factors affecting green housing purchase. In the 17th International Business Research Conference.

O’Cass, A., & Carlson, J. (2012). An e-retailing assessment of perceived website-service innovativeness: Implications for website quality evaluations, trust, loyalty and word of mouth. Australasian Marketing Journal, 20, 28–36.

O’Dea, M. (2025). Are technology acceptance models still fit for purpose? Journal of University Teaching and Learning Practice, 21(8). https://doi.org/10.53761/1bdbms32

Or, C. (2024). Thirty-five years of the technology acceptance model: Insights from meta-analytic structural equation modelling. OTESSA Journal, 4(3), 1–26. https://doi.org/10.18357/otessaj.2024.4.3.66

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.

Ramayah, T., Yusoff, Y. M., Jamaludin, N., & Ibrahim, A. (2009). Applying the theory of planned behavior (TPB) to predict internet tax filing intentions. International Journal of Management, 26(2), 272.

Rasty, F., Mirghafoori, S. H., Saeida Ardekani, S., & Ajdari, P. (2021). Trust barriers to online shopping: Investigating and prioritizing trust barriers in an intuitionistic fuzzy environment. International Journal of Consumer Studies, 45 1030–1046.

Rens, S. (2001). Supporting the online consumer decision process: Electronic commerce in a small Australian retailer. ACIS 2001 Proceedings, 75. https://aisel.aisnet.org/acis2001/75

Soomro, S. A., & Habeeb, Y. O. (2025). Impact of perceived ease of use on impulsive buying behaviour through mobile commerce with hedonic and utilitarian effects. Asia-Pacific Journal of Business Administration, 17(3), 796–813.

Streukens, S., & Leroi-Werelds, S. (2016). Bootstrapping and PLS-SEM: A step-by-step guide to get more out of your bootstrap results. European Management Journal, 34(6), 618–632.

Suleman, D., & Zuniarti, I. (2019). Consumer decisions toward fashion product shopping in Indonesia: The effects of attitude, perception of ease of use, usefulness, and trust. Management Dynamics in the Knowledge Economy, 7(2/24), 133–146.

Upadhyaya, N. (2024). Artificial intelligence in web development: Enhancing automation, personalization, and decision-making. Artificial Intelligence, 4(1), 12–28.

Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204.

Vhatkar, M. S., Raut, R. D., Gokhale, R., Kumar, M., Akarte, M., & Ghoshal, S. (2024). Leveraging digital technology in retailing business: Unboxing synergy between omnichannel retail adoption and sustainable retail performance. Journal of Retailing and Consumer Services, 81, 104047.

Zhang, L., Chen, L., Wu, Z., Zhang, S., & Song, H. (2018). Investigating young consumers’ purchasing intention of green housing in China. Sustainability, 10, 1044.