FairPlay: Detecting and Deterring Online Customer Misbehavior
研究开发数据科学方法检测社交媒体上的顾客不当行为，并通过现场实验比较惩罚与共同身份两种干预策略的效果，发现惩罚短期有效但长期衰减，共同身份效果持久且能提升购买频率。
This study examines how firms can detect and manage customer misbehavior in online brand communities. We first develop a data science approach to detect customer misbehavior on social media and devise intervention strategies to deter it. Our design science approach achieves superior performance, improving detection by 7%–9% compared with traditional methods. We then implement two types of intervention policies based on injunctive (i.e., a punishment policy) and descriptive norms (i.e., a common identity policy) to restrain customer misbehavior. The results of field experiments indicate that punishment considerably reduces customer misbehavior in the short term, but this effect decays over time, whereas common identity has a smaller but more persistent effect on misbehavior reduction. In addition, punishing dysfunctional customers decreases their purchase frequency, whereas imposing a common identity increases it. Our results also show that combining the two policies effectively alleviates the detrimental effect of punishment, especially in the long run.