Consumer Trust in AI-Generated Recommendations: Role of Transparency and Perceived Control
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Abstract
Algorithmic recommenders now mediate much of what consumers see, buy and watch, yet acceptance depends less on accuracy than on whether the system can be understood and influenced. This study examines how transparency, perceived control, recommendation accuracy and privacy concern shape consumer trust in AI-generated recommendations, and whether trust predicts willingness to follow them. Data were collected from 140 respondents using a structured questionnaire on a five-point Likert scale and analysed in IBM SPSS Statistics (Version 27) through reliability analysis, descriptive statistics, Pearson correlation, regression and an independent samples t-test. The model explained 63.9 per cent of the variance in trust. Transparency was the strongest predictor, followed by perceived control and accuracy, while privacy concern exerted a significant negative influence. Trust strongly predicted willingness to act on recommendations, indicating that interpretability and user agency underpin algorithmic acceptance.
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