Quantifying the Dynamic Effects of Service Recovery on Customer Satisfaction: Evidence From Chinese Mobile Phone Markets

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Quantifying the Dynamic Effects of Service Recovery on Customer Satisfaction: Evidence From Chinese Mobile Phone Markets

Dynamic Effects of Service Recovery on Customer Satisfaction

Hypotheses on the Dynamic Effects of Service Recovery

Hypothesis 1: After service failures, the time-varying impact of service recovery strategies such as quality improve- ment, compensation, and marketing commutations on cus- tomer satisfaction has a long decay, while that of apology on customer satisfaction has a short decay.

Hypothesis 2c: The time-varying impact of communications on customer satisfaction has a higher and faster buildup than that of compensation and apology.

Hypothesis 2b: The time-varying impact of compensation on customer satisfaction has a higher and faster buildup than that of apology and communications.

Hypothesis 2a: The time-varying impact of quality improvement on customer satisfaction has the highest buildup compared to that of compensation, apology, and communications.

Service Recovery Strategies

This study extends the services marketing literature by tracking the dynamic effects of service recovery strategies on customer satisfaction.

Results

Results on Buildup and Decay

VAR Model Results

Managerial Implications for the Role of Service Recovery in Regaining Customer Satisfaction

Sources of Regained Customer Satisfaction

Required Service Recovery Efforts to Regain Customer Satisfaction to 95%

Research Implications and Conclusions

This study quantifies the dynamic role of service recovery stra- tegies for salvaging customer satisfaction after a major service failure.

Data and Measures

Controls to Rule Out Alternative Explanations to the Results

Measures

Communications

Apology

Quality improvement

Customer satisfaction.

Data Setting

Data Stationarity and Granger Causality Tests

Models

Modeling Short or Long Decay and Buildup Intensity: IRF

Estimating Parameters: Bayesian Method

Modeling the Marketing Dynamics: Vector Autoregressive (VAR) Model