Technology acceptance model in e-commerce segment

Open access

Abstract

Consumer behaviour analysis is a key aspect for the success of e-business. The main objective of the study is to analyse the impact of selected user experience factors on e-commerce web site visiting (technology). The objective of the study is to create a model that will explain the impact of each major factor on the user experience and the re-visit of the e-shop. To explain the use of e-commerce technology, in the second part we have modified the original technology acceptance model (TAM) with other constructs. Specifically, there are modern technologies such as social networks or mobile apps that affect the use of e-shops. The TAM model is one of the most used models of what the system uses to identify the perceived usefulness and perceived simplicity of use from the user’ side. For the main advantage of our study, we consider that we have highlighted the importance of the factor of modern technology and therefore of social networks, mobile applications and contextual advertising. This factor, along with the other two factors, has been incorporated into our model and has shown that modern technologies have a direct impact and are therefore directly related to the frequency using the e-commerce websites.

Barnes, N. G. (2014). “Social commerce emerges as big brands position themselves to turn “follows”, “likes” and “pins” into sales”, American Journal of Management, Vol. 14, No. 4, pp. 11-18.

Davis, F. D., Bagozzi, R.P. Warshaw, P. R. (1989), “User Acceptance of Computer Technology”, available at http://home.business.utah.edu/actme/7410/DavisBagozzi.pdf, (acessed October 19, 2018).

Davis, F. D. (1989), “Perceived usefulness, perceived ease of use and user acceptable of information technology”, MIS Quarterly, Vol. 13, No. 3.

Eroglu, S. A., Machleit, K. A., & Davis, L. M. (2003). “Empirical testing of a model of online store atmospherics and shopper responses.” Psychology and Marketing, Vol. 20, No. 2, pp. 139–150.

Gavurova, B., Bacik, R., Fedorko, R., Nastisin, L. (2018). “The Customer’s Brand Experience in the Light of Selected Performance Indicators in the Social Media Environment.” Journal of Competitiveness, Vol. 10, Issue 2, pp. 72–84.

Gefen, D., Karahanna, E., Straub D. (2003). “Inexperience and experience with online stores: the importance of TAM and trust”, IEEE Trans. Eng. Manage, Vol. 50, No. 3, pp. 307.

Gefen, D., Karahanna, E., Straub D. (2003). “Trust and TAM in Online shopping: An Integfated model”, MIS Quarterly, Vol. 27, pp. 51-93.

Hassenzhal, M. (2007). “To do or not to do: differences in user experience and retrospective judgments depending on the presence or absence of instrumental goals” Interacting with comuputers, Vol. 19, pp. 429-437.

Hawley, M. (2012). “Differentiating Your Design: A Visual Approach to Competitive”, available at http://semanticstudios.com/publications/semantics/000029.php, (acessed October 19, 2018).

IDC (2015), Mobile Internet Users to Top 2 Billion Worldwide in 2016, International Data Corporation (IDC), available at: www.idc.com/getdoc.jsp?containerId=prUS40855515 (accessed 1 July 2017).

Jieun Yu, Imsook Ha, Munkee Choi, and Jaejeung Rho. (2005). “Extending the TAM for a t-commerce.” Inf. Manage, Vol. 42, No. 7, pp. 965-976.

Kim, S., & Park, H. (2013). “Effects of various characteristics of social commerce (s-commerce) on consumers’ trust and trust performance.” International Journal of Information Management, Vol. 33, No. 2, pp. 318-332.

Lu, B., Fan, W., Zhou, M. (2015). “Social presence, trust, and social commerce purchase intention: An empirical research” Computers in Human Behavior, Vol. 56, pp. 225-237.

Momani A. M., Jamous, M. (2017). “The Evolution of Technology Acceptance Theories” International Journal of Contemporary Computer Research (IJCCR), Vol. 1, No. 1, pp. 51-58.

Mosteller, J., Donthu, N., Eroglu, S. (2014). “The fluent online shopping experience”, Journal of Business Research, Vol. 67, No. 11, pp. 2486-2493.

Pappas, I. O. (2018). “User experience in personalized online shopping: a fuzzy-set analysis”, European Journal of Marketing.

Reber, R., Schwarz, N., & Winkielman, P. (2004). “Processing fluency and aesthetic pleasure: Is beauty in the perceiver’s processing experience?” Personality and Social Psychology Review, Vol. 8, No. 4, pp. 364–382.

Salonen, V., Karjaluoto, H. (2016). “Web personalization: the state of the art and future avenues for research and practice.” Telematics Inform. Vol. 33, No. 4, pp. 1088-1104.

Salovaara, A., Tamminen, S. (2009). “Accept or appropriate? A design-oriented critique on technology acceptance models”, available at http://www.academia.edu/3314912/Acceptance_or_appropriation_A_design-oriented_critique_of_technology_acceptance_models, (acessed October 19, 2018).

Sauro, J. (2013). “How to Benchmark Website Usability”, available at https://www.measuringu.com/blog/benchmark-website-usability.php, (acessed October 19, 2018).

Stefko, R., Fedorko, R., Bacik, R. (2016). “Website content quality in terms of perceived image of higher education institution”, Polish Journal Of Management Studies, Vol. 13, No. 2, pp. 153-163.

Ungerman, O., Dedkova, J., Gurinova, K. (2018). “The Impact of Marketing Innovation on the Competitiveness of Enterprises in the Context of Industry 4.0.” Journal of Competitiveness, Vol. 10, Issue 2, pp. 132-148.

Venkatesh, V., and Davis, F. D. (2000). “The Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Studies,” Management Science, Vol. 46, No. 2, pp. 186-204.

Venkatesh, V., Moms, M. G., Davis, G. B. and Davis, F. D. (2003). “User Acceptance of Information Technology: Toward a Unified View,” MIS Quarterly, Vol. 27, No. 3, pp. 425-478.

Wang, C., & Zhang, P. (2012). “The evolution of social commerce: the people, management, technology, and information dimensions” Communications of the Association for Information Systems, Vol. 31, No. 5, pp. 105-127.

Journal Information


CiteScore 2017: 0.45

SCImago Journal Rank (SJR) 2017: 0.169
Source Normalized Impact per Paper (SNIP) 2017: 0.436

Metrics

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 271 271 89
PDF Downloads 234 234 70