Online reviews continue to play a central role in the modern e-commerce systems, but that is viewed as untrustworthy, which is fueled by the bias mechanisms that cause conformity. This paper focuses on the factors that influence the perceptions of consumers in regards to the accuracy and reliability of reviews on the digital platforms using Amazon as the main context. Partial least squares structural equation modelling (PLS-SEM) was used to examine the effects the latent variables which include Social Influence, Rating Distribution and Platform Design have on the Conformity Behavior, therefore, contributing to evaluative judgments of review accuracy and reliability. The empirical research is based on the data gathered with the help of 66 participants who have completed the online questionnaire. PLS-SEM estimates were made in SmartPLS 4, and standard errors and significance tests of 5,000 bootstrap resamples were obtained. The predictive utility of the model was high for both, Review Reliability ( \(R^2 = 0.745\) ) as well as the Review Accuracy ( \(R^2 = 0.546\) ). The path coefficient between Conformity Behavior and Review Accuracy was \(\beta = 0.503\) ( \(p < 0.001\) ) and to Review Reliability was \(\beta = 0.301\) ( \(p = 0.007\) ), which shows that CB had significant effect on both the constructs. Review Reliability had a major direct effect on Rating Distribution ( \(\beta = 0.546\) , \(p < 0.001\) ) and Platform Design ( \(\beta = 0.336\) , \(p = 0.011\) ). On the other hand, the proposed direct impact of Social Influence, Rating Distribution, and Platform Design on Conformity Behavior was not statistically significant, which can be interpreted as the indication that the dynamics behind the previously mentioned factors are more complex and context-specific, which was not initially hypothesized. These findings add significant value to online consumer behavior and can be applied to the development of an online review system that enhances the credibility.