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

Models

Modeling the Marketing Dynamics: Vector Autoregressive (VAR) Model

Estimating Parameters: Bayesian Method

Modeling Short or Long Decay and Buildup Intensity: IRF

Data and Measures

Data Stationarity and Granger Causality Tests

Data Setting

Measures

Customer satisfaction.

Quality improvement

Apology

Communications

Controls to Rule Out Alternative Explanations to the Results

Research Implications and Conclusions

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

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

Required Service Recovery Efforts to Regain Customer Satisfaction to 95%

Sources of Regained Customer Satisfaction

Results

VAR Model Results

Results on Buildup and Decay

Dynamic Effects of Service Recovery on Customer Satisfaction

Service Recovery Strategies

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

Hypotheses on the Dynamic Effects of Service Recovery

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

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

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

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.

LUIS CARLOS MUTIS MORA