AI-driven customer positioning and perception: Strategies, challenges and insights
Article
Burra, R., Wu, T. Y., Chang, K. and Huang, C. F. 2025. AI-driven customer positioning and perception: Strategies, challenges and insights. Digital Technologies Research and Applications. p. In press.
Authors | Burra, R., Wu, T. Y., Chang, K. and Huang, C. F. |
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Abstract | The current research aims to investigate the transformative impact of artificial intelligence (AI) on marketing. AI-driven marketing strategies are widely utilized in practice but their influence on customers is not always clear, leaving a glaring knowledge gap. Drawing on the Technology Acceptance Model, the current research analyses how AI-driven marketing strategies influence customer positioning and perception, with a particular emphasis on customer trust, engagement, and relevant ethical considerations. To maximize the ecological validity of data mining and analysis, we collect multi-layered data from renowned academic platforms, industry reports and consultancy records, in line with the institutional ethical guidelines. Research findings are meaningful in three ways. To begin with, while AI-driven marketing strategies offer promising tools for enhancing customer engagement, they also raise severe concerns related to transparency and ethics, such as threats surrounding data privacy, fairness, as well as the opacity of AI algorithms in implementation. Next, by comparing AI-driven and non-AI-driven marketing strategies, we can uncover the conditions under which AI promotes trust and improves the customer experience. Finally, research findings have advanced knowledge by providing evidence-based insights into the mechanisms through which AI affects customer perception. Overall, new research discoveries have benefited both entrepreneurs and marketing professionals, providing sensible insights to policy making (e.g., AI-driven marketing strategies) and ethical practices (e.g., ethical standards and procedures in marketing). Research limitation and suggestions for future research are discussed. |
Keywords | AI-driven; Customer Perception; Marketing Strategies ; Technology Acceptance Model |
Journal | Digital Technologies Research and Applications |
Journal citation | p. In press |
ISSN | 2754-5687 |
Year | 2025 |
Publisher | UK Scientific Publishing Limited |
Accepted author manuscript | License File Access Level Anyone |
Web address (URL) | https://ojs.ukscip.com/index.php/dtra |
Publication process dates | |
Deposited | 01 Jul 2025 |
Copyright holder | © 2025 The Authors |
Additional information | The first author (Rajitha Burra) is a UEL employee and prospective PhD student. ORCID: 0009-0001-1126-0276. |
https://repository.uel.ac.uk/item/8zwx6
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