Computing and Information Systems - Theses
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ItemCredibility Assessment of Online Consumer Reviews( 2022)In the digital transformation era, online reviews have become an important source of information for decisions about purchasing products and services. Research shows that online reviews influence users’ purchasing behaviours and product sales. However, the overwhelming number of online reviews with unknown reviewers has made it difficult for users to find credible information. Thus, this thesis focuses on the credibility evaluation of online consumer reviews and develops a comprehensive credibility model to help differentiate reviews based on credibility. We, first, outline a baseline model for credibility assessment of online consumer reviews by building upon related literature and using the Heuristic Systematic Model (HSM) of information processing as the theoretical lens. We extend the baseline model by conducting several in-depth semi-structured interviews as a way of understanding how online shoppers assess the credibility of online reviews. Next, we identify important attributes that impact the credibility of online reviews and explore the moderating role of the reader’s perspective in this process through performing a user study on the Amazon Mechanical Turk platform. Finally, we develop a machine learning model to predict the credibility of online reviews and conduct a series of ANOVA analyses (analysis of variance) to differentiate the characteristics of fake and credible reviews. This thesis advances the state-of-the-art studies regarding the credibility of online consumer reviews by making the following key contributions: (1) synthesizing the related literature and providing a taxonomy of the key attributes that impact the credibility of online consumer reviews, (2) extending the HSM model in the area of the credibility of online reviews and explaining how users assess online reviews, (3) confirming important elements in the HSM including concurrency and complex effects of attributes in information processing, and exploring the moderating role of individuals in the credibility evaluation of online consumer reviews, (4) developing a predictive model for the credibility of online consumer reviews, and; (5) identifying different characteristics of fake and credible reviews.