Quantifying the Dynamic Effects of Service Recovery on Customer Satisfaction

Abstract

This study examines two issues which have challenged prior experimental or survey research: (1) whether the time-varying effects of service recovery on customer satisfaction may follow a long decay or short decay and (2) why and what service recovery efforts have a higher and quicker buildup, with respect to the significance and timing of recovering customer satisfaction losses due to service failures. The authors do so with a real-world data set from China’s mobile phone markets. The authors developed multivariate time-series model to simulate the dynamic service recovery process and implemented Bayesian estimation to resolve overparameterization problem. The empirical results surprisingly reveal that apology-based service recovery efforts are the least effective in salvaging customer satisfaction, with the shortest decay and lowest buildup intensity. In contrast, quality improvement is the most effective, with the highest buildup and longest decay but slowest buildup toward the peak impact point. Compensation has moderate and stable impact overtime. Communications' impact on customer satisfaction builds up the quickest, though with mild endurance and magnitude. Also, the decomposition models enable managers to monitor how many percentages of customer satisfaction gains are originated from which types of service rescue efforts.