Exploring online consumer review-management response dynamics: A heuristic-systematic perspective

Although the effects of managerial responses (MRs) on subsequent customer reviews (CRs) has been explored, we lack a comprehensive theoretical framework to explain the interdependent relationships between previous and subsequent CRs—specifically the dynamic influences of MRs on future CRs. We draw on emotional contagion and regulation theories to develop a heuristic systematic model to explain CR-MR dynamics in online settings. We propose six systematic processing and three heuristic processing routes to delineate the determination and persuasion effects between previous and subsequent consumers' CRs. The systematic routes describe how current customers' compliments, complaints, and emotions influence their current rating scores. The heuristic processing routes describe how previous customers' rating scores and emotions influence current customers' rating scores and emotions. We suggest MR strategies to regulate these effects. The presence and length of MRs defines the numeric heuristic route while the positive-emotion heuristic route is conceptualized through expressions of thanks, sincerity, interaction, and complimenting customers. Expressions of apology, explanation, empathy, and remedy inform the negative-emotion heuristic route. We collect text from customers' reviews and managers' responses from the TripAdvisor website using text-mining techniques and analyze our hypotheses using Pooled Ordinary Least Squares (pooled OLS) and Generalized Method of Moment (GMM) modeling. Our findings not only enrich the theoretical underpinnings of the CR/MR literature, but also provide managerial guidance on how customers' emotional contagion and rating behaviors might be regulated. •Integrates HSM, emotion contagion, and regulation theories for a comprehensive CR/MR model.•Identifies ten strategic MR components for regulating heuristic processing routes.•Employs text mining and econometric modeling for analyzing longitudinal online data.•Provides practical guidance to firms for effectively managing online customers' emotions.•Connects online CR/MR data with transactional data for potential impact on business performance.