Fostering Trust and Loyalty in E-Commerce through Ethical Data Practices
Article Sidebar
Main Article Content
This conceptual paper examines how ethical data practices foster consumer trust and loyalty in e-commerce, with particular emphasis on data privacy, data security, and consumer perceived risk. As digital platforms increasingly depend on personal and behavioural data for personalisation, payment processing, targeted marketing, and customer engagement, consumer concerns regarding transparency, informed consent, unauthorised data sharing, cybersecurity, and manipulative data use have become central to online exchange relationships. Drawing on Social Exchange Theory, the paper proposes that consumers are more willing to maintain relationships with e-commerce platforms when the exchange of personal data for convenience and personalised services is perceived as fair, beneficial, secure, and trustworthy. The paper distinguishes ethical data governance from regulatory compliance by arguing that lawful data processing represents only a minimum threshold, whereas ethical governance also requires fairness, proportionality, accountability, respect for autonomy, and consideration of consumer wellbeing. An integrated conceptual framework is developed in which ethical data practices strengthen consumer loyalty through consumer trust, while perceived privacy and security risk weakens the positive relationship between ethical data practices and trust. The discussion also considers emerging governance challenges associated with generative artificial intelligence, algorithmic personalisation, blockchain, and decentralised identity systems. The paper contributes to e-commerce and business ethics literature by explaining how, and under what conditions, responsible data governance supports sustainable digital relationships. Practical implications are presented for managers, marketers, IT teams, policymakers, and consumers, together with measurable constructs and empirical testing directions for future research.
Downloads
References
Ashiq, R., Hussain, A., & Aslam, N. (2023). Exploring the effects of e-service quality and e-trust on consumers' e-satisfaction and e-loyalty: Insights from online shoppers in Pakistan. Journal of Electronic Business & Digital Economics, 3(2), 117-141.
Awad, N. F., & Krishnan, M. S. (2006). The personalization privacy paradox: An empirical evaluation of information transparency and the willingness to be profiled online for personalization. MIS Quarterly, 30(1), 13-28.
Blau, P. M. (1964). Exchange and power in social life. John Wiley & Sons.
Chen, H., Chiang, R. H. L., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS Quarterly, 36(4), 1165-1188.
Cisco. (2024). Cisco 2024 consumer privacy survey. Cisco.
DataReportal. (2025). Digital 2025: Global overview report. DataReportal.
European Parliament & Council of the European Union. (2016). Regulation (EU) 2016/679 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data. Official Journal of the European Union, L119, 1-88.
European Parliament & Council of the European Union. (2024). Regulation (EU) 2024/1689 laying down harmonised rules on artificial intelligence. Official Journal of the European Union, 2024/1689.
Featherman, M. S., & Pavlou, P. A. (2003). Predicting e-services adoption: A perceived risk facets perspective. International Journal of Human-Computer Studies, 59(4), 451-474.
Federal Trade Commission. (2023). FTC and DOJ charge Amazon with violating children's privacy law by keeping kids' Alexa voice recordings forever and undermining parents' deletion requests. Federal Trade Commission.
Federal Trade Commission. (2024). Ring, LLC. Federal Trade Commission.
Ferrell, O. C., Fraedrich, J., & Ferrell, L. (2022). Business ethics: Ethical decision making and cases (13th ed.). Cengage Learning.
Floridi, L., & Taddeo, M. (2016). What is data ethics? Philosophical Transactions of the Royal Society A, 374(2083), 20160360.
Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in online shopping: An integrated model. MIS Quarterly, 27(1), 51-90.
Homans, G. C. (1958). Social behavior as exchange. American Journal of Sociology, 63(6), 597-606.
IBM. (2025). Cost of a data breach report 2025. IBM.
Kim, Y., & Peterson, R. A. (2017). A meta-analysis of online trust relationships in e-commerce. Journal of Interactive Marketing, 38, 44-54.
Kishnani, U., & Das, S. (2024). Dual-technique privacy and security analysis for e-commerce websites through automated and manual implementation. arXiv. https://arxiv.org/abs/2410.14960
Laudon, K. C., & Traver, C. G. (2023). E-commerce 2023-2024: Business, technology, society. Pearson.
Li, W., Cujilema, S., Hu, L., & Xie, G. (2025). How social scene characteristics affect customers' purchase intention: The role of trust and privacy concerns in live streaming commerce. Journal of Theoretical and Applied Electronic Commerce Research, 20(2), 85.
Martin, K. D., & Murphy, P. E. (2017). The role of data privacy in marketing. Journal of the Academy of Marketing Science, 45, 135-155.
Morić, Z., Dakić, V., Đekić, D., & Regvart, D. (2024). Protection of personal data in the context of e-commerce. Journal of Cybersecurity and Privacy, 4(3), 731-761.
National Institute of Standards and Technology. (2024). Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile (NIST AI 600-1). U.S. Department of Commerce. https://doi.org/10.6028/NIST.AI.600-1
Oliver, R. L. (1999). Whence consumer loyalty? Journal of Marketing, 63, 33-44.
Pilakaew, T., Thanitbenjasith, P., & Kamkankaew, P. (2024). The impact of consumer perceptions of privacy and security risks on re-purchasing intention for online shopping in Chiang Mai Province, Thailand. International Journal of Sociologies and Anthropologies Science Reviews, 4(6), 531-542.
PwC. (2024). Voice of the consumer survey 2024. PricewaterhouseCoopers.
Reuters. (2024). Oracle reaches $115 million consumer privacy settlement. Reuters.
Shin, D. (2021). The effects of explainability and causability on perception, trust, and acceptance: Implications for explainable AI. International Journal of Human-Computer Studies, 146, 102551.
Smith, H. J., Dinev, T., & Xu, H. (2011). Information privacy research: An interdisciplinary review. MIS Quarterly, 35(4), 989-1016.
Wang, Y., Chen, X., & Zhang, J. (2024). Understanding consumers' continuous participation willingness in online pre-sale activities: A social exchange theory perspective. Behavioral Sciences, 14(11), 1094.
World Wide Web Consortium. (2022). Decentralized Identifiers (DIDs) v1.0. W3C Recommendation. https://www.w3.org/TR/did-1.0/

This work is licensed under a Creative Commons Attribution 4.0 International License.
All articles published in our journal are licensed under CC-BY 4.0, which permits authors to retain copyright of their work. This license allows for unrestricted use, sharing, and reproduction of the articles, provided that proper credit is given to the original authors and the source.