ALGORITHMIC TRANSPARENCY AND MARKETING EFFECTIVENESS

Abstract

Algorithmic systems increasingly govern digital marketing activities, shaping content visibility, targeted advertising, and personalized recommendations. While prior research has emphasized algorithmic accuracy and performance optimization, limited attention has been given to how algorithmic transparency influences consumer responses. This study examines the role of perceived algorithmic transparency in shaping trust, perceived fairness, and marketing effectiveness outcomes. Drawing on trust theory, fairness heuristic theory, and algorithm aversion literature, we propose and test a conceptual framework linking transparency cues to trust in platforms and brands, fairness perceptions, consumer engagement, and purchase intention. The findings suggest that algorithmic transparency enhances perceived fairness and trust, which in turn positively affect engagement and purchase intention. However, excessive transparency regarding data use practices may activate privacy concerns, attenuating positive effects. The results contribute to emerging research on algorithmic governance in marketing by reframing transparency as both a trust-building mechanism and a strategic trade-off. Managerially, the study highlights the importance of designing transparent algorithmic communication strategies that balance clarity with privacy sensitivity to sustain long-term marketing effectiveness.