Shaping Consumer Demand in E-commerce: The Role of Artificial Intelligence

International Journal of Computer Science and Engineering

© 2024 by SSRG - IJCSE Journal

Volume 11 Issue 8

Year of Publication : 2024

Authors : Vinh Vo Phu

10.14445/23488387/IJCSE-V11I8P102

How to Cite?

Vinh Vo Phu, "Shaping Consumer Demand in E-commerce: The Role of Artificial Intelligence," SSRG International Journal of Computer Science and Engineering , vol. 11,  no. 8, pp. 7-16, 2024. Crossref, https://doi.org/10.14445/23488387/IJCSE-V11I8P102

Abstract:

The question that this research hopes to address is: How much of an impact may AI have on people’s propensity to shop online? By leveraging AI, it is able to learn how things like chatbots, personalized recommendations, and predictive analytics affect people’s actions. In order to analyze the data supplied by 500 online shoppers, regression analysis and reliability testing were used. The results indicate a robust relationship between the features of AI and the demand that consumers are expressing. It is possible to see that AI has the ability to improve people’s lives and financial situations. Online stores planning to use AI to improve customer interactions and bring in more customers should have this data on hand.

Keywords:

Artificial Intelligence, Chatbots, Consumer demand, E-Commerce, Personalized recommendations.

References:

[1] Alessandro Acquisti, Laura Brandimarte, and George Loewenstein, “Privacy and Human Behavior in the Age of Information,” Science, vol. 347, no. 6221, pp. 509-514, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Ajay Agrawal, Joshua Gans, and Avi Goldfarb, Prediction Machines: The Simple Economics of Artificial Intelligence, Harvard Business Review Press, 2018.
[Publisher Link]
[3] T. Brown, “AI in E-commerce: Transforming the Shopping Experience,” Journal of Retailing, vol. 95, no. 3, pp. 234-245, 2019.
[4] Erik Brynjolfsson, and Andrew McAfee, The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies, W.W. Norton & Company, 2014.
[Google Scholar] [Publisher Link]
[5] Dave Chaffey, and Fiona Ellis-Chadwick, Digital Marketing: Strategy, Implementation, and Practice, 6th ed., Pearson, 2015.
[Google Scholar]
[6] Le Chen, Alan Mislove, and Christo Wilson, “An Empirical Analysis of Algorithmic Pricing on Amazon Marketplace,” Proceedings of the 25th International Conference on World Wide Web, pp. 1339-1349, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[7] T.H. Davenport et al., “How AI Will Transform Retailing,” Journal of Retailing, vol. 97, no. 1, pp. 55-70, 2021.
[8] Fred D. Davis “Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology,” MIS Quarterly, vol. 13, no. 3, pp. 319-340, 1989.
[CrossRef] [Google Scholar] [Publisher Link]
[9] David Gefen, Elena Karahanna, and Detmar W. Straub, “Trust and TAM in Online Shopping: An Integrated Model,” MIS Quarterly, vol. 27, no. 1, pp. 51-90, 2003.
[Google Scholar]
[10] Carlos A. Gomez-Uribe, and Neil Hunt, “The Netflix Recommender System: Algorithms, Business Value, and Innovation,” ACM Transactions on Management Information Systems (TMIS), vol. 6, no. 4, pp. 1-19, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Ian Goodfellow, Yoshua Bengio, and Aaron Courville, Deep Learning, MIT Press, 2016.
[Google Scholar] [Publisher Link]
[12] Nigel Guenole, Jonathan Ferrar, and Sheri Feinzig, The Power of People: Learn How Successful Organizations Use Workforce Analytics to Improve Business Performance, Pearson FT Press, 2017.
[Google Scholar] [Publisher Link]
[13] G. Hofstede, Culture’s Consequences: Comparing Values, Behaviors, Institutions and Organizations across Nations, 2nd ed., Sage Publications, 2001.
[Google Scholar] [Publisher Link]
[14] Ming-Hui Huang, and Roland T. Rust, “Artificial Intelligence in Service,” Journal of Service Research, vol. 21, no. 2, pp. 155-172, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[15] K. Jones, “The Role of Chatbots in Modern Customer Service,” Customer Service Today, vol. 28, no. 2, pp. 44-56, 2021.
[16] Philip Kotler, and Kevin Lane Keller, Marketing Management, 15th ed., Pearson, 2015.
[Publisher Link]
[17] G. Linden, B. Smith, and J. York, “Amazon.com Recommendations: Item-to-Item Collaborative Filtering,” IEEE Internet Computing, vol. 7, no. 1, pp. 76-80, 2003.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Brent Daniel Mittelstadt et al., “The Ethics of Algorithms: Mapping the Debate,” Big Data & Society, vol. 3, no. 2, pp. 1-21, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[19] J.C. Nunnally, and I.H. Bernstein, Psychometric Theory, 3rd Ed., McGraw-Hill, 1994.
[Google Scholar]
[20] Stuart J. Russell, and Peter Norvig, Artificial Intelligence: A Modern Approach, 4th Ed., Pearson, 2021.
[Google Scholar] [Publisher Link]
[21] Roland T. Rust, and Ming-Hui Huang, “The Service Revolution and the Transformation of Marketing Science,” Marketing Science, vol. 33, no. 2, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[22] A. Smith, and J. Anderson, “AI and Consumer Behavior,” Consumer Research Journal, vol. 27, no. 4, pp. 189-204, 2021.
[23] Brent Smith, and Greg Linden, “Two Decades of Recommender Systems at Amazon.com,” IEEE Internet Computing, vol. 21, no. 3, pp. 12-18, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Viswanath Venkatesh, and Hillol Bala, “Technology Acceptance Model 3 and a Research Agenda on Interventions,” Decision Sciences, vol. 39, no. 2, pp. 273-315, 2008.
[CrossRef] [Google Scholar] [Publisher Link]
[25] Viswanath Venkatesh, and Fred D. Davis, “A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies,” Management Science, vol. 46, no. 2, 2000.
[CrossRef] [Google Scholar] [Publisher Link]