Keywords
Ethical Perceptions
Transparency
Fairness
Resource-Advantage Theory
Network Effects
How to Cite
Abstract
AI-driven predictive marketing promises superior targeting, personalization, and decision speed, yet its strategic payoffs depend on how customers and managers judge the ethics of its use. This study examines whether and how capability in AI-powered predictive marketing improves strategic business outcomes by shaping ethical perceptions in privacy and consent, transparency and explainability, and fairness and non-discrimination. Drawing on Resource-Advantage theory, we propose and test a model in firms from Makassar, Indonesia, spanning creative industries, financial services, food and beverage, and technology. Using Partial Least Squares Structural Equation Modeling with higher-order constructs, we assess direct, indirect, and conditional effects, including mediation by governance quality and moderation by perceived manipulation and perceived market concentration or data dominance. The estimates show that stronger AI-PM capability is associated with more favorable ethical perceptions, and these perceptions relate positively to brand trust and credibility, innovation readiness, competitive advantage, and performance. Governance practices, consent management, bias audits across pre-, in-, and post-processing, and explainability routines, act as the primary mechanism strengthening ethical perceptions and outcomes. Conversely, perceived manipulative design weakens capability–outcome links, and perceptions of market concentration reduce the ethical appraisal of personalization efforts. The findings position ethics-by-design as a market-based resource that renders data and algorithmic investments more legitimate and defensible over time. Managerially, firms should pair analytics stacks with governance stacks and invest in complementary IT and organizational readiness, while policymakers can enhance contestability and transparency to preserve choice and fairness in data-intensive markets.
References
Akter, S., Dwivedi, Y., Biswas, K., Michael, K., Bandara, R., & Sajib, S. (2021). Addressing Algorithmic Bias in AI-Driven Customer Management. Journal of Global Information Management, 29, 1–27. https://doi.org/10.4018/JGIM.20211101.OA3
Akter, S., Sultana, S., Mariani, M., Wamba, S., Spanaki, K., & Dwivedi, Y. (2023). Advancing algorithmic bias management capabilities in AI-driven marketing analytics research. Industrial Marketing Management. https://doi.org/10.1016/j.indmarman.2023.08.013
Al., S. (2023). Ethical Considerations in AI-Based Marketing: Balancing Profit and Consumer Trust. Tuijin Jishu/Journal of Propulsion Technology. https://doi.org/10.52783/tjjpt.v44.i3.474
Ali, M., Khan, T., Khattak, M., & Sener, I. (2024). Synergizing AI and business: Maximizing innovation, creativity, decision precision, and operational efficiency in high-tech enterprises. Journal of Open Innovation: Technology, Market, and Complexity. https://doi.org/10.1016/j.joitmc.2024.100352
Arnett, D. (2024). Market segmentation strategy, target markets, and competitors: a resource-advantage theory perspective. Journal of Marketing Management, 40, 1269–1285. https://doi.org/10.1080/0267257X.2024.2391367
Ashok, M., Madan, R., Joha, A., & Sivarajah, U. (2022). Ethical framework for Artificial Intelligence and Digital technologies. International Journal of Information Management, 62, 102433. https://doi.org/10.1016/j.ijinfomgt.2021.102433
Badghish, S., & Soomro, Y. (2024). Artificial Intelligence Adoption by SMEs to Achieve Sustainable Business Performance: Application of Technology–Organization–Environment Framework. Sustainability. https://doi.org/10.3390/su16051864
Bankins, S., & Formosa, P. (2023). The Ethical Implications of Artificial Intelligence (AI) For Meaningful Work. Journal of Business Ethics, 1–16. https://doi.org/10.1007/s10551-023-05339-7
Cacciolatti, L., & Lee, S. (2016). Revisiting the relationship between marketing capabilities and firm performance: The moderating role of market orientation, marketing strategy and organisational power. Journal of Business Research, 69, 5597–5610. https://doi.org/10.1016/J.JBUSRES.2016.03.067
Calvano, E., & Polo, M. (2019). Market Power, Competition and Innovation in Digital Markets: A Survey. Consumer Law eJournal. https://doi.org/10.2139/ssrn.3523611
Campbell, C., Sands, S., Ferraro, C., Tsao, H., & Mavrommatis, A. (2020). From data to action: How marketers can leverage AI. Business Horizons. https://doi.org/10.1016/j.bushor.2019.12.002
Chen, J., & Tajdini, S. (2024). A moderated model of artificial intelligence adoption in firms and its effects on their performance. Information Technology and Management. https://doi.org/10.1007/s10799-024-00422-5
Chen, J., Zhou, W., & Frankwick, G. (2023). Firm AI Adoption Intensity and Marketing Performance. Journal of Computer Information Systems, 65, 172–189. https://doi.org/10.1080/08874417.2023.2277751
Chen, M. (2024). Role of artificial intelligence in marketing automation in China. International Journal of Strategic Marketing Practice.
Chintakananda, A., & McIntyre, D. (2014). Market Entry in the Presence of Network Effects. Journal of Management, 40, 1535–1557. https://doi.org/10.1177/0149206311429861
De Bruyn, A., Viswanathan, V., Beh, Y., Brock, J., & Von Wangenheim, F. (2020). Artificial Intelligence and Marketing: Pitfalls and Opportunities. Journal of Interactive Marketing, 51, 91–105. https://doi.org/10.1016/j.intmar.2020.04.007
Dou, G., Wei, K., , L., & Lin, X. (2024). Dynamic competition and market structure for platform-based products: roles of product quality and indirect network effect. International Transactions in Operational Research, 31, 3245–3279. https://doi.org/10.1111/itor.13450
Du, S., & Xie, C. (2020). Paradoxes of artificial intelligence in consumer markets: Ethical challenges and opportunities. Journal of Business Research. https://doi.org/10.1016/j.jbusres.2020.08.024
Fernholz, Y., Ermakova, T., Fabian, B., & Buxmann, P. (2024). User-driven prioritization of ethical principles for artificial intelligence systems. Computers in Human Behavior: Artificial Humans. https://doi.org/10.1016/j.chbah.2024.100055
Figueroa-Armijos, M., Clark, B., & Da Motta Veiga, S. (2022). Ethical Perceptions of AI in Hiring and Organizational Trust: The Role of Performance Expectancy and Social Influence. Journal of Business Ethics, 186, 179–197. https://doi.org/10.1007/s10551-022-05166-2
Gandal, N., & Halaburda, H. (2016). Can We Predict the Winner in a Market with Network Effects? Competition in Cryptocurrency Market. Economics of Networks eJournal. https://doi.org/10.2139/ssrn.2832836
Haftor, D., Costa-Climent, R., & Navarrete, S. (2023). A pathway to bypassing market entry barriers from data network effects: A case study of a start-up’s use of machine learning. Journal of Business Research. https://doi.org/10.1016/j.jbusres.2023.114244
Hair, J. F. Jr., & Sarstedt, M. (2021). Data, measurement, and causal inferences in machine learning: opportunities and challenges for marketing. Journal of Marketing Theory and Practice, 29(1), 65–77.
Huang, M., & Rust, R. (2020). A strategic framework for artificial intelligence in marketing. Journal of the Academy of Marketing Science, 49, 30–50. https://doi.org/10.1007/s11747-020-00749-9
Hunt, S. (1995). The Resource-Advantage Theory of Competition. Journal of Management Inquiry, 4, 317–332. https://doi.org/10.1177/105649269500400403
Hunt, S. (1997). Resource-Advantage Theory: An Evolutionary Theory of Competitive Firm Behavior? Journal of Economic Issues, 31, 59–78. https://doi.org/10.1080/00213624.1997.11505891
Hunt, S. (1999). The strategic imperative and sustainable competitive advantage: Public policy implications of resource-advantage theory. Journal of the Academy of Marketing Science, 27, 144–159. https://doi.org/10.1177/0092070399272003
Hunt, S., & Arnett, D. (2006). Toward a general theory of marketing: resource-advantage theory as an extension of Alderson’s theory of market processes (pp. 453–471).
Hunt, S., & Morgan, R. (1996). The Resource-Advantage Theory of Competition: Dynamics, Path Dependencies, and Evolutionary Dimensions. Journal of Marketing, 60, 107–114. https://doi.org/10.1177/002224299606000410
Jin, K., Zhong, Z., & Zhao, E. (2024). Sustainable Digital Marketing Under Big Data: An AI Random Forest Model Approach. IEEE Transactions on Engineering Management, 71, 3566–3579. https://doi.org/10.1109/TEM.2023.3348991
Joshi, K. (2025). Ethical Digital Marketing in Practice: Consumer Trust, Privacy, and Responsible AI. International Journal of Business and Management Invention. https://doi.org/10.35629/8028-14048087
Kelley, S. (2022). Employee Perceptions of the Effective Adoption of AI Principles. Journal of Business Ethics, 178, 871–893. https://doi.org/10.1007/s10551-022-05051-y
Kopalle, P., Gangwar, M., Kaplan, A., Ramachandran, D., Reinartz, W., & Rindfleisch, A. (2021). Examining Artificial Intelligence (AI) Technologies in Marketing Via a Global Lens: Current Trends and Future Research Opportunities. International Journal of Research in Marketing. https://doi.org/10.1016/j.ijresmar.2021.11.002
Kumar, D., & Suthar, N. (2024). Ethical and legal challenges of AI in marketing: an exploration of solutions. Journal of Information, Communication and Ethics in Society, 22, 124–144. https://doi.org/10.1108/jices-05-2023-0068
Kumar, V., Ashraf, A., & Nadeem, W. (2024). AI-powered marketing: What, where, and how? International Journal of Information Management, 77, 102783. https://doi.org/10.1016/j.ijinfomgt.2024.102783
Lee, Y., Kim, T., Choi, S., & Kim, W. (2022). When does AI pay off? AI-adoption intensity, complementary investments, and R&D strategy. Technovation. https://doi.org/10.1016/j.technovation.2022.102590
Libai, B., Bart, Y., Gensler, S., Hofacker, C., Kaplan, A., Kötterheinrich, K., & Kroll, E. (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
Liu, Y., & Luo, R. (2022). Network Effects and Multinetwork Sellers’ Dynamic Pricing in the U.S. Smartphone Market. Management Science, 69, 3297–3318. https://doi.org/10.1287/mnsc.2022.4530
Ma, L., & Sun, B. (2020). Machine learning and AI in marketing – Connecting computing power to human insights. International Journal of Research in Marketing. https://doi.org/10.1016/j.ijresmar.2020.04.005
Mariani, M., Machado, I., Magrelli, V., & Dwivedi, Y. (2022). Artificial intelligence in innovation research: A systematic review, conceptual framework, and future research directions. Technovation. https://doi.org/10.1016/j.technovation.2022.102623
Mykhalchyshyn, N. (2024). NETWORK CONCENTRATION AND SOCIAL EFFECTS. Black Sea Economic Studies. https://doi.org/10.32782/bses.87-5
Naz, H., & Kashif, M. (2024). Artificial intelligence and predictive marketing: an ethical framework from managers’ perspective. Spanish Journal of Marketing – ESIC. https://doi.org/10.1108/sjme-06-2023-0154
Oğuz, A. (2024). Consumer Behavior in the Era of AI-Driven Marketing. Human Computer Interaction. https://doi.org/10.62802/h9frxh42
Pandey, S. (2021). Artificial intelligence in marketing: A systematic literature review. International Journal of Market Research, 64, 38–68. https://doi.org/10.1177/14707853211018428
Pliatsidis, A. (2024). Analyzing concentration in the Greek public procurement market: a network theory approach. Journal of Industrial and Business Economics. https://doi.org/10.1007/s40812-023-00291-z
Saatci, E. (2025). AI and Ethics: Scale Development for Measuring Ethical Perceptions of Artificial Intelligence Across Sectors and Countries. International Journal of Economic Behavior and Organization. https://doi.org/10.11648/j.ijebo.20251301.14
Soni, V. (2024). Bias Detection and Mitigation in AI-Driven Target Marketing: Exploring Fairness in Automated Consumer Profiling. International Journal of Innovative Science and Research Technology (IJISRT). https://doi.org/10.38124/ijisrt/ijisrt24may2203
Stylianou, K., Spiegelberg, L., Herlihy, M., & Carter, N. (2021). Cryptocurrency Market Structure and Concentration in the Presence of Network Effects. Economics of Networks eJournal. https://doi.org/10.2139/ssrn.3756480
Varadarajan, R. (2020). Customer information resources advantage, marketing strategy and business performance: A market resources based view. Industrial Marketing Management, 89, 89–97. https://doi.org/10.1016/j.indmarman.2020.03.003
Varadarajan, R. (2023). Resource advantage theory, resource based theory, and theory of multimarket competition: Does multimarket rivalry restrain firms from leveraging resource advantages? Journal of Business Research. https://doi.org/10.1016/j.jbusres.2023.113713
Varadarajan, R. (2024). Resource-advantage theory, resource-based theory and market-based resources advantage: effect of marketing performance on customer information assets stock and information analysis capabilities. Journal of Marketing Management, 40, 1135–1154. https://doi.org/10.1080/0267257X.2024.2331181
Vatankhah, S., Bamshad, V., Arıcı, H., & Duan, Y. (2024). Ethical implementation of artificial intelligence in the service industries. The Service Industries Journal, 44, 661–685. https://doi.org/10.1080/02642069.2024.2359077
Victoria, C., I., Olatoye, F., Awonuga, K., Mhlongo, N., Elufioye, O., & Ndubuisi, N. (2024). AI and ethics in business: A comprehensive review of responsible AI practices and corporate responsibility. International Journal of Science and Research Archive. https://doi.org/10.30574/ijsra.2024.11.1.0235
Wittmann, C. (2024). Resource-advantage theory, market segmentation and competitor analysis. Journal of Marketing Management, 40, 1286–1299. https://doi.org/10.1080/0267257X.2024.2428684
Yaiprasert, C., & Hidayanto, A. (2023). AI-driven ensemble three machine learning to enhance digital marketing strategies in the food delivery business. Intelligent Systems with Applications, 18, 200235. https://doi.org/10.2139/ssrn.4379507
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright (c) 2026 Hasanuddin Economics and Business Review