A Dynamic Model for Adjusting Online Ratings Based on Consumer Distrust Perception

Online reputation systems display aggregated ratings derived from numerical scores and textual reviews of real consumer experiences. These ratings serve as operational estimates of a product or service’s value and are used by consumers and organizations as a direct reference for decision-making. However, when suspicious review patterns emerge, such as repetition, extreme ratings, temporal concentration, or low diversity, the perceived value is systematically altered, and the aggregated score no longer reflects the practical evaluation used by users. This perceptual dimension of reputational value has not been modeled in conventional reputation indices. This paper proposes a soft-computing-based reputation adjustment model that quantifies this perceptual change. The model does not replace or reorder the original reputation index (ORI); instead, it introduces a continuous correction layer operating on the displayed rating, modeling the mapping between the aggregated score and the value internalized by users through entropy-weighted indicators of informational disorder. Experimental validation was conducted on 60 participants’ product evaluations across eight products. Results show that the conventional rating exhibits a systematic upward bias relative to perceived trust (mean absolute error = 1.27), whereas the adjusted index significantly reduces this bias (mean absolute error = 0.12; paired t-test, p < 0.001). The proposed model corrects perceptual overestimation while preserving the original reputation signal, improving alignment between displayed ratings and effective user trust.