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.