Online reviews play an important role in consumers’ purchasing decisions. Yet many consumers struggle to recognise fake reviews, even though these are specifically designed to influence opinions and buying behaviour. In her PhD research at the University of Twente, Michelle Walther investigated how consumers evaluate online reviews, which cues they use to identify fake reviews, and how these skills can be improved.
The research shows that consumers primarily look for useful product information while shopping online. Detecting fake reviews is usually not their main objective. To evaluate reviews, consumers rely on various cues, such as the relevance of the review, the credibility of the reviewer, and the trustworthiness of the content.
Consumer Review Evaluation Model
Based on an extensive literature review, observations, and so-called “thinking aloud” sessions, Walther developed the Consumer Review Evaluation Model (CREM model). This model describes how consumers evaluate online reviews step by step and when they decide whether or not to take a review into account in their purchasing decision.
In addition, the researcher developed a training programme based on the CREM model. Experiments showed that participants who completed the training were significantly better at identifying fake reviews.
The dissertation therefore provides new insights into the process of review evaluation and demonstrates how consumers can be better protected against misleading online information. The findings may contribute to more effective consumer education and a more trustworthy online shopping environment.
About the research
The PhD study employed a combination of research methods, including a systematic literature review, grounded theory, and experimental research. The study focused on three central questions: which cues consumers use to recognise fake reviews, how they apply these cues while shopping online, and how their detection skills can be improved. More information: PhD Defence Michelle Walther | Seeing Through The Stars | A Journey Through Human Fake Review Detection
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