Artificial Intelligence in Marketing: Ethical Challenges and Solutions for Consumers and Society

Authors

  • Danyal Mohiuddin Department of Experimental Research, Eye Interaction, London, England, UK
  • Dr. Naureen Farhan Department of Computing and Emerging Technologies, Ravensbourne university London, England, UK https://orcid.org/0000-0001-8370-1125

Keywords:

AI Ethics, Deontological Evaluation, Teleological Evaluation, Trust, Purchase Intention, Marketing, Consumer Behaviour, Scenario-Based Survey

Abstract

This study examines consumers' evaluation of the morality of AI-based marketing and its impact on trust and intention to purchase. The study measures deontological and teleological evaluations, ethical judgment, trust, and purchase intention in three AI marketing situations using a scenario-based survey involving 300 online shoppers. Findings indicate that trust is directly proportional to ethical judgment, and greater purchase intentions are associated with increasing trust. Ethical judgment also mediates the association between rule-based and outcome-based assessments and trust. The reliability test ensures that the entire measurement scale is consistent and accurate. The results of the ANOVA and regression analysis indicate that trust is the most significant factor influencing the relationship between ethics and consumer behaviour. The research finds that the use of AI in marketing requires ethical conduct to encourage purchasing. The results may help brands that want to increase customer trust by implementing responsible policies for intelligent systems.

References

Aleassa, H., Pearson, J. M., & McClurg, S. (2011). Investigating software piracy in Jordan: An extension of the theory of reasoned action. Journal of Business Ethics, 98(4), 663–676. https://doi.org/10.1007/s10551-010-0645-4

Andersch, H., Lindenmeier, J., Liberatore, F., & Tscheulin, D. K. (2018). Resistance against corporate misconduct: An analysis of ethical ideologies’ direct and moderating effects on different forms of active rebellion. Journal of Business Economics, 88(6), 695–730. https://doi.org/10.1007/s11573-017-0876-2

Asif, M., Pasha, M. A., & Shahid, A. (2025). Energy scarcity and economic stagnation in Pakistan. Bahria University Journal of Management & Technology, 8(1), 141–157.

Atzmüller, C., & Steiner, P. M. (2010). Experimental vignette studies in survey research. Methodology: European Journal of Research Methods for the Behavioral and Social Sciences, 6(3), 128–138. https://doi.org/10.1027/1614-2241/a000014

Belk, R. (2021). Ethical issues in service robotics and artificial intelligence. The Service Industries Journal, 41(13–14), 860–876. https://doi.org/10.1080/02642069.2020.1727892

Campbell, C., Sands, S., Ferraro, C., Tsao, H.-Y. (Jody), & Mavrommatis, A. (2020). From data to action: How marketers can leverage AI. Business Horizons, 63(2), 227–243. https://doi.org/10.1016/j.bushor.2019.12.002

Chen, Y.-H., & Barnes, S. (2007). Initial trust and online buyer behaviour. Industrial Management & Data Systems, 107(1), 21–36. https://doi.org/10.1108/02635570710719034

Chintalapati, S., & Pandey, S. K. (2022). Artificial intelligence in marketing: A systematic literature review. International Journal of Market Research, 64(1), 38–68. https://doi.org/10.1177/14707853211018428

Cowls, J. (2021). ‘AI for social good’: Whose good and who’s good? Introduction to the special issue on artificial intelligence for social good. Philosophy & Technology, 34(1), 1–5. https://doi.org/10.1007/s13347-021-00466-3

Cowls, J., Tsamados, A., Taddeo, M., & Floridi, L. (2021). A definition, benchmark and database of AI for social good initiatives (SSRN Scholarly Paper No. 3826465). Social Science Research Network. https://doi.org/10.2139/ssrn.3826465

Darko, A., Chan, A. P. C., Adabre, M. A., Edwards, D. J., Hosseini, M. R., & Ameyaw, E. E. (2020). Artificial intelligence in the AEC industry: Scientometric analysis and visualization of research activities. Automation in Construction, 112, 103081. https://doi.org/10.1016/j.autcon.2020.103081

Dillman, D. A., Smyth, J. D., & Christian, L. M. (2014). Internet, phone, mail, and mixed-mode surveys: The tailored design method (4th ed.). John Wiley & Sons.

Du, P., Li, D., Wang, A., Shen, S., Ma, Z., & Li, X. (2021). A systematic review and meta-analysis of risk factors associated with severity and death in COVID-19 patients. Canadian Journal of Infectious Diseases and Medical Microbiology, 2021, 6660930. https://doi.org/10.1155/2021/6660930

Du, S., & Xie, C. (2021). Paradoxes of artificial intelligence in consumer markets: Ethical challenges and opportunities. Journal of Business Research, 129, 961–974. https://doi.org/10.1016/j.jbusres.2020.08.024

Ferrell, O. C., & Ferrell, L. (2021). Applying the Hunt–Vitell ethics model to artificial intelligence ethics. Journal of Global Scholars of Marketing Science, 31(2), 178–188. https://doi.org/10.1080/21639159.2020.1785918

Ferrell, O. C., & Ferrell, L. (2024). Trailblazing the path for marketing ethics: The profound influence of Shelby Hunt. Journal of Marketing Management, 40(13–14), 1174–1192. https://doi.org/10.1080/0267257X.2023.2295273

Ferrell, O. C., Fraedrich, J., & Ferrell, L. (2021). Business ethics: Ethical decision making and cases (13th ed.). Cengage.

Floridi, L., Cowls, J., King, T. C., & Taddeo, M. (2020). How to design AI for social good: Seven essential factors. Science and Engineering Ethics, 26(3), 1771–1796. https://doi.org/10.1007/s11948-020-00213-5

Gansser, O. A., & Reich, C. S. (2021). A new acceptance model for artificial intelligence with extensions to UTAUT2: An empirical study in three segments of application. Technology in Society, 67, 101535. https://doi.org/10.1016/j.techsoc.2021.101535

Gerlich, M. (2023). Perceptions and acceptance of artificial intelligence: A multi-dimensional study. Social Sciences, 12(9), 502. https://doi.org/10.3390/socsci12090502

Glikson, E., & Woolley, A. W. (2020). Human trust in artificial intelligence: Review of empirical research. Academy of Management Annals, 14(2), 627–660. https://doi.org/10.5465/annals.2018.0057

Hagebölling, M., Segebarth, B., & Woisetschläger, D. M. (2021). Tactical termination of contractual services—An analysis of the phenomenon and its determinants. Journal of Business Research, 137, 170–181. https://doi.org/10.1016/j.jbusres.2021.08.015

Hamilton, J. R., Maxwell, S., & Tee, S. (2024). Useful Excel data validation of qualitative respondent data prior to analysis. In ICEB 2024 Proceedings (Zhuhai, China). https://aisel.aisnet.org/iceb2024/18

Hartwig, B. (2025). Top 11 benefits of artificial intelligence in 2025. Hackr.io. https://hackr.io/blog/benefits-of-artificial-intelligence

Hasija, A., & Esper, T. L. (2022). In artificial intelligence (AI) we trust: A qualitative investigation of AI technology acceptance. Journal of Business Logistics, 43(3), 388–412. https://doi.org/10.1111/jbl.12301

Hildebrand, C. (2019). The machine age of marketing: How artificial intelligence changes the way people think, act, and decide. NIM Marketing Intelligence Review, 11(2), 10–17. https://doi.org/10.2478/nimmir-2019-0010

Hoyer, W. D., Kroschke, M., Schmitt, B., Kraume, K., & Shankar, V. (2020). Transforming the customer experience through new technologies. Journal of Interactive Marketing, 51, 57–71. https://doi.org/10.1016/j.intmar.2020.04.001

Hunt, S. D. (1993). Objectivity in marketing theory and research. Journal of Marketing, 57(2), 76–91. https://doi.org/10.1177/002224299305700206

Hunt, S. D., & Vasquez-Parraga, A. Z. (1993). Organizational consequences, marketing ethics, and salesforce supervision. Journal of Marketing Research, 30(1), 78–90. https://doi.org/10.2307/3172515

Jian, J.-Y., Bisantz, A. M., & Drury, C. G. (2000). Foundations for an empirically determined scale of trust in automated systems. International Journal of Cognitive Ergonomics, 4(1), 53–71. https://doi.org/10.1207/S15327566IJCE0401_04

Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1(9), 389–399. https://doi.org/10.1038/s42256-019-0088-2

Kaya, F., Aydin, F., Schepman, A., Rodway, P., Yetisensoy, O., & Demir Kaya, M. (2024). The roles of personality traits, AI anxiety, and demographic factors in attitudes toward artificial intelligence. International Journal of Human–Computer Interaction, 40(2), 497–514. https://doi.org/10.1080/10447318.2022.2151730

Kelly, S., Kaye, S.-A., & Oviedo-Trespalacios, O. (2023). What factors contribute to the acceptance of artificial intelligence? A systematic review. Telematics and Informatics, 77, 101925. https://doi.org/10.1016/j.tel.2022.101925

Korir, M., Slade, S., Holmes, W., Heliot, Y., & Rienties, B. (2023). Investigating the dimensions of students’ privacy concern in the collection, use and sharing of data for learning analytics. Computers in Human Behavior Reports, 9, 100262. https://doi.org/10.1016/j.chbr.2022.100262

Kumar, D. (2023). Ethical and legal challenges of AI in marketing: An exploration of solutions (SSRN Scholarly Paper No. 4396132). Social Science Research Network. https://doi.org/10.2139/ssrn.4396132

Kumar, V., Rajan, B., Venkatesan, R., & Lecinski, J. (2019). Understanding the role of artificial intelligence in personalized engagement marketing. California Management Review, 61(4), 135–155. https://doi.org/10.1177/0008125619859317

Lambert, S. I., Madi, M., Sopka, S., Lenes, A., Stange, H., Buszello, C.-P., & Stepniewska, A. (2023). An integrative review on the acceptance of artificial intelligence among healthcare professionals in hospitals. NPJ Digital Medicine, 6(1), 111. https://doi.org/10.1038/s41746-023-00852-5

Lee, D., & Hosanagar, K. (2017). How do recommender systems affect sales diversity? A cross-category investigation via randomized field experiment (SSRN Scholarly Paper No. 2603361). Social Science Research Network. https://doi.org/10.2139/ssrn.2603361

Libai, B., Bart, Y., Gensler, S., Hofacker, C. F., Kaplan, A., Kötterheinrich, K., & Kroll, E. B. (2020). Brave new world? On AI and the management of customer relationships. Journal of Interactive Marketing, 51, 44–56. https://doi.org/10.1016/j.intmar.2020.04.002

Lichtenthaler, U. (2019). Extremes of acceptance: Employee attitudes toward artificial intelligence. Journal of Business Strategy, 41(5), 39–45. https://doi.org/10.1108/JBS-12-2018-0204

Love, D. C., et al. (2021). Emerging COVID-19 impacts, responses, and lessons for building resilience in the seafood system. Global Food Security, 28, 100494. https://doi.org/10.1016/j.gfs.2021.100494

Love, E., Salinas, T. C., & Rotman, J. D. (2020). The Ethical Standards of Judgment Questionnaire. Journal of Business Ethics, 161(1), 115–132. https://doi.org/10.1007/s10551-018-3937-8

McKinsey & Company. (2020–2024). AI industry reports. https://www.mckinsey.com

Milano, S., Taddeo, M., & Floridi, L. (2019). Ethical aspects of multi-stakeholder recommendation systems (SSRN Scholarly Paper No. 3493202). Social Science Research Network. https://doi.org/10.2139/ssrn.3493202

Mittelstadt, B. (2019). Principles alone cannot guarantee ethical AI (SSRN Scholarly Paper No. 3391293). Social Science Research Network. https://doi.org/10.2139/ssrn.3391293

Nimri, R., Dharmesti, M., Arcodia, C., & Mahshi, R. (2021). UK consumers’ ethical beliefs towards dining at green restaurants. Journal of Hospitality and Tourism Management, 48, 572–581. https://doi.org/10.1016/j.jhtm.2021.08.017

Puntoni, S., Reczek, R. W., Giesler, M., & Botti, S. (2021). Consumers and artificial intelligence: An experiential perspective. Journal of Marketing, 85(1), 131–151. https://doi.org/10.1177/0022242920953847

Rai, A. (2020). Explainable AI: From black box to glass box. Journal of the Academy of Marketing Science, 48(1), 137–141. https://doi.org/10.1007/s11747-019-00710-5

Reidenbach, R. E., & Robin, D. P. (1990). Toward the development of a multidimensional scale for improving evaluations of business ethics. Journal of Business Ethics, 9(8), 639–653. https://doi.org/10.1007/BF00383391

Russell, S., & Norvig, P. (2021). Artificial intelligence: A modern approach (4th ed.). Pearson.

Rust, R. T. (2020). The future of marketing. International Journal of Research in Marketing, 37(1), 15–26. https://doi.org/10.1016/j.ijresmar.2019.08.002

Siau, K., & Yang, Y. (2017). Impact of artificial intelligence, robotics, and machine learning on sales and marketing. In Proceedings of MWAIS 2017.

Smith, A. E., Zlatevska, N., Chowdhury, R. M. M. I., & Belli, A. (2023). A meta-analytical assessment of deontological and teleological evaluations. Journal of Business Ethics, 188(3), 553–588. https://doi.org/10.1007/s10551-022-05311-x

Suh, B., & Han, I. (2003). The impact of customer trust and perception of security control on the acceptance of electronic commerce. International Journal of Electronic Commerce, 7(3), 135–161.

Sweeney, L. (2013). Discrimination in online ad delivery (SSRN Scholarly Paper No. 2208240). Social Science Research Network. https://doi.org/10.2139/ssrn.2208240

Taddeo, M., & Floridi, L. (2018). How AI can be a force for good. Science, 361(6404), 751–752. https://doi.org/10.1126/science.aat5991

Tai, M. C.-T. (2020). The impact of artificial intelligence on human society and bioethics. Tzu Chi Medical Journal, 32(4), 339–343. https://doi.org/10.4103/tcmj.tcmj_71_20

Yin, J., Qian, L., & Singhapakdi, A. (2018). Sharing sustainability. Journal of Business Ethics, 149(2), 313–333. https://doi.org/10.1007/s10551-016-3043-8

YouGov. (2025). Ad-verse reactions: How Americans feel about personalized advertising in 2025. https://commercial.yougov.com/rs/464-VHH-988/images/WP-2025-03-US-personalized-advertising-report.pdf

Author Biographies

Danyal Mohiuddin, Department of Experimental Research, Eye Interaction, London, England, UK

Department of Experimental Research,

Eye Interaction, London, England, UK

Email: danyalmohiuddin294@outlook.com

Dr. Naureen Farhan, Department of Computing and Emerging Technologies, Ravensbourne university London, England, UK

Department of Computing and Emerging Technologies,

Ravensbourne university London, England, UK

Email: n.farhan@rave.ac.uk

ORCID ID: https://orcid.org/0000-0001-8370-1125

Downloads

Published

2025-06-30

How to Cite

Mohiuddin, D., & Farhan, D. N. (2025). Artificial Intelligence in Marketing: Ethical Challenges and Solutions for Consumers and Society. Journal of Business Insight and Innovation, 4(1), 73–87. Retrieved from https://insightfuljournals.com/index.php/JBII/article/view/69

Similar Articles

1 2 3 4 5 6 > >> 

You may also start an advanced similarity search for this article.