City Research Online

Abstract

With the growing number of systems that provide user-generated reviews the relationship between users and vendors, particularly unfamiliar vendors, is changing. Users are increasingly using online reviews for assessing vendors’ services prior to purchasing them. However, users might be uncertain how much to trust reviews because most users are unfamiliar with reviewers and reviews might not be credible. Thus, it is becoming increasingly important to understand which reviews are trusted by users when they make purchase decisions and why.

Previous work has suggested that factors of the review and reviewer - perceived review valence, quality, helpfulness, accuracy, perceived reviewer’s expertise and bias - influence user trust. It has also suggested that interface signals, such as the total number of reviews posted by the reviewer, are employed by users when deciding to trust reviews and reviewers as part of their purchase decision-making.

This research aims to advance knowledge regarding user trust in online reviews when making purchase decisions. It first explores how users employ interface signals in their perception of factors of the review and reviewer that influence trust. Second, it clarifies how these factors relate to one another and to trust. It explores the role of new factors - perceived reviewer’s personality and personality similarity to the user - that have not been previously considered in trust in online reviews. Third, it demonstrates how the user’s own background - dispositional trust, past experience and personality - shapes trust in online reviews. To do so, this research involved three empirical studies, two of which were lab-based studies that collected qualitative and quantitative data and one online study that collected quantitative data.

The findings show that there are two categories of interface signals, reviewrelated and reviewer-related that matter in trust. Review-related signals seem more important not only in trust overall, but also are employed by users to perceive factors of both review and reviewer that influence trust more so than reviewer-related signals.

Regarding the interplay between the factors that have been suggested to influence trust, it seems that user perception of these factors are related to one another. The perceived quality and helpfulness of the review seem to be most related to the perceived reviewer’s expertise and the perceived review accuracy seems to be most related to perceived reviewer’s bias. While all these factors relate to trust, factors of the review seem to have a more significant role. The findings also show that the perceived reviewer’s personality relates to trust and factors that can influence trust. For instance, the reviewer’s perceived high conscientiousness is related to high perceived review quality, high perceived reviewer’s expertise and high trust. The perceived reviewer’s personality similarity to the user seems to play a weaker role in trust than the perceived reviewer’s personality.

The user’s own background seems to have a significant role in shaping trust in online reviews. High dispositional trust, extraversion and neuroticism are related to high perceived review quality, accuracy, high perceived reviewer’s expertise and high trust. The user’s positive past experience of using online reviews is related to high willingness of making a purchase based on reviews.

This research makes several theoretical and practical contributions. It builds on previous work on user trust in online reviews and vendors, and the perception of personality. The findings point the way towards a framework of trust relationships in systems that provide user-generated reviews. Also, the findings have design implications because they show which and how interface signals can influence trust.

Publication Type:	Thesis (Doctoral)
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments:	Doctoral Theses School of Science & Technology > School of Science & Technology Doctoral Theses School of Science & Technology > Computer Science

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