Modeling the Effects of a Service Guarantee on Perceived Service Quality Using Alternating Conditional Expectations (ACE)*

Abstract

This paper addresses the dearth of empirical research on the relationship between service guarantee and perceived service quality (PSQ). In particular, we examine the moderating effects of a service guarantee on PSQ. While a recent study provided empirical evidence that service quality is affected by service guarantee and employee variables such as employee motivationlvision and learning through service failure, the nature and form of the relationships between these variables remain unclear. Knowledge of these relationships can assist service managers to allocate resources more judiciously, avoid pitfalls, and establish more realistic expectations. Data was obtained from employees and customers of a multinational hotel chain that has implemented a service guarantee program in 89 of its hotels in America and Canada. As the employee variables could affect performance in a non-linear fashion, we relaxed the assumption of model linearity by using the Alternating Conditional Expectations (ACE) algorithm to amve at abetterfitting, non-linear regression model for PSQ. Our findings indicate the existence of significant non-linear relationships between PSQ and its determinant variables. The ACE model also revealed that service guarantee interacts with the employee variables to affect PSQ in a non-linear fashion. The non-linear relationships present new insights into the management of service guarantees and PSQ. Explanations and managerial implications of our results are presented and discussed.

An Empirical Examination of the Structural Dimensions of the Service Recovery System*

“…Additionally, specification in terms of structural dimensions should allow researchers to strategically match alternative recovery systems to the conditions under which a firm operates and competes. In a broader sense, these results could be integrated and reconciled with studies presented in the quality management literature to investigate how recovery can be part of the overall total quality management system (Douglas & Fredendall, 2004), to explain how recovery systems might contribute to perceived quality (Babakus, Beinstock, & Van Scotter, 2004), and to further clarify the role and effectiveness of service guarantees in recovery (Sum, Lee, Hays, & Hill, 2002; Baker & Collier, 2005).…”

Determinants of service quality and continuance intention of online services: The case of eTax

“…Research on services has primarily focused on the classification of services (Schmenner, 2004), service design (e.g., Easton & Pullman, 2001), and service quality (e.g., Carr, 2002;Sum, Lee, Hays, & Hill, 2002). In this context, much research has examined the effects of various service attributes (e.g., service quality, service failure, service recovery, recovery strategy) on customer outcomes (Babakus, Bienstock, & Van Scotter, 2004;Baker & Collier, 2005;Easton & Goodale, 2005;De Jong & De Ruyter, 2004;Krishnan, Ramaswamy, Meyer, & Damien, 1999;Ostrom & Iacobucci, 1995;Zhu, Sivakumar, & Parasuraman, 2004).…”

Manufacturing strategy and competitive performance – An ACE analysis

“…The ACE algorithm derives transformations among variables to achieve the best fit, and these transformations are unambiguously defined and estimated without the use of heuristics, restrictive distributional assumptions, or restriction of the transformation to a particular parametric family. The ACE algorithm has been used by previous studies involving soil-water diffusivity (De Veaux and Steele, 1989), engine exhaust emissions (Rodriguez, 1985), material requirements planning (Sum, et al, 1995), the effects of service guarantee (Sum et al, 2002), and estimating optimal transformations for multiple regressions (Wang and Murphy, 2004). Details on the ACE algorithm can be found in (Breiman and Friedman, 1985;De Veaux, 1989).…”