Artificial intelligence (AI)-powered personalization is reshaping how consumers in emerging digital economies encounter, evaluate, and commit to market offerings, yet prevailing scholarship continues to treat personalization as a uniformly trust-enhancing stimulus. This study challenges that assumption by advancing a capability-trust contingency model in which the effect of AI-powered personalization on purchase intention is neither direct nor uniform but routed through two analytically distinct trust pathways and conditioned by consumer beliefs about machine competence and data exposure. Drawing on initial trust theory and the personalization-privacy paradox, the model specifies cognitive and affective trust in the AI agent as parallel mediators, with perceived AI capability moderating the personalization-to-cognitive-trust path and privacy risk perception moderating the personalization-to-affective-trust path. The framework is examined in Nigeria, Africa’s largest consumer market and a context where rapid mobile-commerce diffusion coexists with weak institutional data-protection enforcement, conditions under which Western trust assumptions cannot be presumed to transfer. Using survey data from 412 active online shoppers and a moderated-mediation estimation strategy, the analysis demonstrates that personalization elevates purchase intention primarily by building cognitive trust, that this indirect effect strengthens markedly as perceived AI capability rises, and that privacy risk perception attenuates the affective-trust pathway. The contribution is threefold: it decomposes a previously monolithic trust mechanism, it identifies the boundary conditions that govern when personalization helps or backfires, and it extends consumer AI theory to an under-studied institutional setting. Implications for adaptive personalization strategy, transparency signalling, and trust calibration in low-institutional-trust markets are discussed.
Cite this paper
Oluoma, N. U. G. and Yi, Y. (2026). Artificial Intelligence and Consumer Decision-Making: AI-Powered Personalization, Trust Pathways, and Purchase Intentions in Nigeria. Open Access Library Journal, 13, e15727. doi: http://dx.doi.org/10.4236/oalib.1115727.
Davenport, T., Guha, A., Grewal, D. and Bressgott, T. (2020) How Artificial Intelligence Will Change the Future of Marketing. <i>Journal of the Academy of Marketing Science</i>, 48, 24-42. <br>https://doi.org/10.1007/s11747-019-00696-0
De Bruyn, A., Viswanathan, V., Beh, Y.S., Brock, J.K. and Von Wangenheim, F. (2020) Artificial Intelligence and Marketing: Pitfalls and Opportunities. <i>Journal of Interactive Marketing</i>, 51, 91-105. <br>https://doi.org/10.1016/j.intmar.2020.04.007
Huang, M.-H. and Rust, R.T. (2021) A Strategic Framework for Artificial Intelligence in Marketing. <i>Journal of the Academy of Marketing Science</i>, 49, 30-50. <br>https://doi.org/10.1007/s11747-020-00749-9
Verhoef, P.C., Broekhuizen, T., Bart, Y., Bhattacharya, A., Qi Dong, J., Fabian, N., <i>et al</i>. (2021) Digital Transformation: A Multidisciplinary Reflection and Research Agenda. <i>Journal of Business Research</i>, 122, 889-901. <br>https://doi.org/10.1016/j.jbusres.2019.09.022
Grewal, D., Hulland, J., Kopalle, P.K. and Karahanna, E. (2020) The Future of Technology and Marketing: A Multidisciplinary Perspective. <i>Journal of the Academy of Marketing Science</i>, 48, 1-8. <br>https://doi.org/10.1007/s11747-019-00711-4
Mariani, M.M., Perez‐Vega, R. and Wirtz, J. (2022) AI in Marketing, Consumer Research and Psychology: A Systematic Literature Review and Research Agenda. <i>Psychology & Marketing</i>, 39, 755-776. <br>https://www.researchgate.net/publication/334487491_Task-Dependent_Algorithm_Aversion
Vlačić, B., Corbo, L., Costa e Silva, S. and Dabić, M. (2021) The Evolving Role of Artificial Intelligence in Marketing: A Review and Research Agenda. <i>Journal of Business Research</i>, 128, 187-203. <br>https://doi.org/10.1016/j.jbusres.2021.01.055
Dwivedi, Y.K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., <i>et al</i>. (2021) Artificial Intelligence (AI): Multidisciplinary Perspectives on Emerging Challenges, Opportunities, and Agenda for Research, Practice and Policy. <i>International Journal of Information Management</i>, 57, Article 101994. <br>https://doi.org/10.1016/j.ijinfomgt.2019.08.002
Kumar, V., Rajan, B., Venkatesan, R. and Lecinski, J. (2019) Understanding the Role of Artificial Intelligence in Personalized Engagement Marketing. <i>California Management Review</i>, 61, 135-155. <br>https://doi.org/10.1177/0008125619859317
Shankar, V. (2018) How Artificial Intelligence (AI) Is Reshaping Retailing. <i>Journal of Retailing</i>, 94, 6-11. <br>https://doi.org/10.1016/s0022-4359(18)30076-9
Aguirre, E., Mahr, D., Grewal, D., de Ruyter, K. and Wetzels, M. (2015) Unraveling the Personalization Paradox: The Effect of Information Collection and Trust-Building Strategies on Online Advertisement Effectiveness. <i>Journal of Retailing</i>, 91, 34-49. <br>https://doi.org/10.1016/j.jretai.2014.09.005
Bleier, A. and Eisenbeiss, M. (2015) The Importance of Trust for Personalized Online Advertising. <i>Journal of Retailing</i>, 91, 390-409. <br>https://doi.org/10.1016/j.jretai.2015.04.001
Aguirre, E., Roggeveen, A.L., Grewal, D. and Wetzels, M. (2016) The Personalization-Privacy Paradox: Implications for New Media. <i>Journal of Consumer Marketing</i>, 33, 98-110. <br>https://doi.org/10.1108/jcm-06-2015-1458
Puntoni, S., Reczek, R.W., Giesler, M. and Botti, S. (2020) Consumers and Artificial Intelligence: An Experiential Perspective. <i>Journal of Marketing</i>, 85, 131-151. <br>https://doi.org/10.1177/0022242920953847
Harrison McKnight, D.H., Choudhury, V. and Kacmar, C. (2002) The Impact of Initial Consumer Trust on Intentions to Transact with a Web Site: A Trust Building Mode. <i>The Journal of Strategic Information Systems</i>, 11, 297-323. <br>https://doi.org/10.1016/s0963-8687(02)00020-3
McKnight, D.H., Choudhury, V. and Kacmar, C. (2002) Developing and Validating Trust Measures for E-Commerce: An Integrative Typology. <i>Information Systems Research</i>, 13, 334-359. <br>https://doi.org/10.1287/isre.13.3.334.81
Mayer, R.C., Davis, J.H. and Schoorman, F.D. (1995) An Integrative Model of Organizational Trust. <i>The Academy of Management Review</i>, 20, 709-734. <br>https://doi.org/10.2307/258792
Johnson, D. and Grayson, K. (2005) Cognitive and Affective Trust in Service Relationships. <i>Journal of Business Research</i>, 58, 500-507. <br>https://doi.org/10.1016/s0148-2963(03)00140-1
Logg, J.M., Minson, J.A. and Moore, D.A. (2019) Algorithm Appreciation: People Prefer Algorithmic to Human Judgment. <i>Organizational Behavior and Human Deci</i><i>sion Processes</i>, 151, 90-103. <br>https://doi.org/10.1016/j.obhdp.2018.12.005
Dietvorst, B.J., Simmons, J.P. and Massey, C. (2015) Algorithm Aversion: People Erroneously Avoid Algorithms after Seeing Them Err. <i>Journal of Experimental Psychology</i>:<i> General</i>, 144, 114-126. <br>https://doi.org/10.1037/xge0000033
Longoni, C., Bonezzi, A. and Morewedge, C.K. (2019) Resistance to Medical Artificial Intelligence. <i>Journal of Consumer Research</i>, 46, 629-650. <br>https://doi.org/10.1093/jcr/ucz013
Sheth, J.N. (2020) Borderless Media: Rethinking International Marketing. <i>Journal of International Marketing</i>, 28, 3-12. <br>https://doi.org/10.1177/1069031x19897044
Hoffman, D.L. and Novak, T.P. (2018) Consumer and Object Experience in the Internet of Things: An Assemblage Theory Approach. <i>Journal of Consumer Research</i>, 44, 1178-1204. <br>https://doi.org/10.1093/jcr/ucx105
Xiao, B. and Benbasat, I. (2007) E-Commerce Product Recommendation Agents: Use, Characteristics, and Impact. <i>MIS Quarterly</i>, 31, 137-209. <br>https://doi.org/10.2307/25148784
Yim, M.Y.C., Chu, S.C. and Sauer, P.L. (2017) Is Augmented Reality Technology an Effective Tool for E-Commerce? An Interactivity and Vividness Perspective. <i>Journal of Interactive Marketing</i>, 39, 89-103. <br>https://doi.org/10.1016/j.intmar.2017.04.001
Gefen, D., Karahanna, E. and Straub, D.W. (2003) Trust and TAM in Online Shopping: An Integrated Model. <i>MIS Quarterly</i>, 27, 51-90. <br>https://doi.org/10.2307/30036519
Komiak, S.Y.X. and Benbasat, I. (2006) The Effects of Personalization and Familiarity on Trust and Adoption of Recommendation Agents. <i>MIS Quarterly</i>, 30, 941-960. <br>https://doi.org/10.2307/25148760
Pavlou, P.A. (2003) Consumer Acceptance of Electronic Commerce: Integrating Trust and Risk with the Technology Acceptance Mode. <i>International Journal of Electronic Commerce</i>, 7, 101-134.
Bart, Y., Shankar, V., Sultan, F. and Urban, G.L. (2005) Are the Drivers and Role of Online Trust the Same for All Web Sites and Consumers? A Large-Scale Exploratory Empirical Study. <i>Journal of Marketing</i>, 69, 133-152. <br>https://doi.org/10.1509/jmkg.2005.69.4.133
Wirtz, J., Patterson, P.G., Kunz, W.H., Gruber, T., Lu, V.N., Paluch, S., <i>et al</i>. (2018) Brave New World: Service Robots in the Frontline. <i>Journal of Service Management</i>, 29, 907-931. <br>https://doi.org/10.1108/josm-04-2018-0119
Ostrom, A.L., Fotheringham, D. and Bitner, M.J. (2019) Customer Acceptance of AI in Service Encounters: Understanding Antecedents and Consequences. In: Maglio, P.P., Kieliszewski, C.A., Spohrer, J.C., Lyons, K., Patrício, L. and Sawatani, Y., Eds., <i>Service Science</i>:<i> Research and Innovations in the Service Economy</i>, Springer International Publishing, 77-103. <br>https://doi.org/10.1007/978-3-319-98512-1_5
Belanche, D., Casaló, L.V., Flavián, C. and Schepers, J. (2020) Service Robot Implementation: A Theoretical Framework and Research Agenda. <i>The Service Industries Journal</i>, 40, 203-225. <br>https://doi.org/10.1080/02642069.2019.1672666
Chatterjee, S., Rana, N.P., Dwivedi, Y.K. and Baabdullah, A.M. (2021) Understanding AI Adoption in Manufacturing and Production Firms Using an Integrated TAM-TOE Model. <i>Technological Forecasting and Social Change</i>, 170, Article 120880. <br>https://doi.org/10.1016/j.techfore.2021.120880
Hermann, E. (2021) Leveraging Artificial Intelligence in Marketing for Social Good—An Ethical Perspective. <i>Journal of Business Ethics</i>, 179, 43-61. <br>https://doi.org/10.1007/s10551-021-04843-y
Dinev, T. and Hart, P. (2006) An Extended Privacy Calculus Model for E-Commerce Transactions. <i>Information Systems Research</i>, 17, 61-80. <br>https://doi.org/10.1287/isre.1060.0080
Martin, K.D., Borah, A. and Palmatier, R.W. (2017) Data Privacy: Effects on Customer and Firm Performance. <i>Journal of Marketing</i>, 81, 36-58. <br>https://doi.org/10.1509/jm.15.0497
Lou, C. and Yuan, S. (2019) Influencer Marketing: How Message Value and Credibility Affect Consumer Trust of Branded Content on Social Media. <i>Journal of Interactive Advertising</i>, 19, 58-73. <br>https://doi.org/10.1080/15252019.2018.1533501
Venkatesh, V., Thong, J.Y.L. and Xu, X. (2012) Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology. <i>MIS Quarterly</i>, 36, 157-178. <br>https://doi.org/10.2307/41410412
Fornell, C. and Larcker, D.F. (1981) Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. <i>Journal of Marketing Research</i>, 18, 39-50. <br>https://doi.org/10.1177/002224378101800104
Henseler, J., Ringle, C.M. and Sarstedt, M. (2015) A New Criterion for Assessing Discriminant Validity in Variance-Based Structural Equation Modeling. <i>Journal of the Academy of Marketing Science</i>, 43, 115-135. <br>https://doi.org/10.1007/s11747-014-0403-8
Podsakoff, P.M., MacKenzie, S.B. and Podsakoff, N.P. (2012) Sources of Method Bias in Social Science Research and Recommendations on How to Control It. <i>Annual Review of Psychology</i>, 63, 539-569. <br>https://doi.org/10.1146/annurev-psych-120710-100452