Hyperpersonalization in Marketing: Opportunities, Risks, and Student Perception — An Experimental Approach

Abstract Book of the 19th International Conference on Modern Research in Management, Economics and Accounting

Year: 2026

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Hyperpersonalization in Marketing: Opportunities, Risks, and Student Perception — An Experimental Approach

Prof. Victor Santos, Rui Miguel Martins Murta da Costa

ABSTRACT:

This study examines hyperpersonalization as an emerging AI-driven marketing practice that enables real-time tailoring of content, offers, and customer experiences through big data analytics, machine learning, and behavioral profiling. Although hyperpersonalization can strengthen engagement, conversion, customer loyalty, and perceived relevance, it also raises significant concerns regarding privacy, algorithmic manipulation, transparency, filter bubbles, and consumer autonomy. The paper addresses this dual nature by combining a conceptual synthesis of the literature with an empirical classroom-based experimental design. Undergraduate and postgraduate students in management and marketing courses are randomly assigned to two survey conditions: an abstract description of hyperpersonalization or concrete examples from digital platforms such as streaming, social media, and e-commerce. The study compares awareness, perceived benefits, perceived risks, and privacy concerns across groups using independent-samples tests and complementary regression analysis. By focusing on digitally native students, the research captures the perceptions of a consumer cohort that is simultaneously highly exposed to algorithmic personalization and increasingly relevant as future professionals in data-driven markets. The expected contribution is threefold: it clarifies the strategic value and ethical risks of hyperpersonalization, provides empirical evidence on how framing influences consumer understanding of AI-mediated marketing practices, and demonstrates the usefulness of A/B experimental survey designs in management education research. The findings are expected to inform marketers, educators, and policymakers on how to balance personalization effectiveness with transparency, responsibility, and consumer trust.

Keywords: A/B Testing; Algorithmic Ethics; Artificial Intelligence; Consumer Perception; Data Privacy