FairPlay: Detecting and Deterring Online Customer Misbehavior
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, imp...
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Veröffentlicht in: | Information systems research 2021-12, Vol.32 (4), p.1323-1346 |
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Sprache: | eng |
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Zusammenfassung: | 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.
Customer misbehavior is a serious and pervasive problem in firm-sponsored social media, yet prior studies provide limited insight into how firms should detect and manage it. To address this gap, we first develop a data science approach to detect customer misbehavior on social media and then devise intervention strategies to deter it. Specifically, we build on natural language processing and deep learning techniques to automatically detect customer misbehavior by mining customers’ social media activities in collaboration with a leading apparel firm. The results show that our algorithmic solution achieves superior performance, improving detection by 7%–9% compared with traditional methods. We then implement two types of intervention policies based on the focus theory of normative conduct that advocates the use of injunctive norms (i.e., a punishment policy) and descriptive norms (i.e., a common identity policy) to restrain customer misbehavior. We conduct field experiments with the firm to validate these policies. The experimental results 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. Interestingly, our results show th |
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ISSN: | 1047-7047 1526-5536 |
DOI: | 10.1287/isre.2021.1035 |