ASSESSING THE UNACQUAINTED: INFERRED REVIEWER PERSONALITY AND REVIEW HELPFULNESS

• 成果类型: Review
• 署名作者: Liu, Angela Xia; Li, Yilin; Xu, Sean Xin
• 署名单位: University of North Carolina; University of North Carolina Charlotte; Tsinghua University
• 刊物名称: MIS QUARTERLY
• ISSN/ISSBN: 0276-7783
• DOI: 10.25300/MISQ/2021/14375
• 发表日期: 2021
• 页码: 1113-1148
• 关键词: word-of-mouth; random forest classifier; online consumer reviews; logistic-regression; Opinion leadership; instrumental variables; individual-differences; EMPIRICAL-EXAMINATION; perceived usefulness; feature-selection

摘要

This work examines the question of who is more likely to provide future helpful reviews in the context of online product reviews by synergistically using personality theories and data analytics. It trains a deep learning model to infer a reviewer's personality traits. This enables analyses to reveal the role of personality traits in review helpfulness among a large population of reviewers. We develop hypotheses on how personality traits are associated with review helpfulness, followed by hypotheses testing that confirms that higher review helpfulness is related to higher openness, conscientiousness, extraversion, and agreeableness and to lower emotional stability. These results suggest the appropriateness of using these five personality traits as inputs for developing a model for predicting future review helpfulness. Based on an ensemble model using supervised classification algorithms, we develop a predictive model and demonstrate its superior performance. Theoretical and practical implications are discussed.