Service Loyalty: An Integrative Model and Examination across Service Contexts

Marketing academics and practitioners generally agree that customer loyalty is vital to business success. There is less agreement on the factors that determine customer loyalty, particularly in service contexts. Research on the determinants of service loyalty has taken three distinct paths: 1) quality/value/satisfaction; 2) relationship quality; and, 3) relational benefits. In this research, the authors coalesce these paths to derive a model that links dimensions of customer loyalty (cognitive, affective, intention, and behavioral) with a system of determinants. The model is tested with data from varied services (airlines, banks, beauty salons, hospitals, hotels, and mobile telephone) and 3,500 customers in China. Results are consistent across contexts and support a multidimensional view of customer loyalty. Key loyalty determinants are customer satisfaction, commitment, service fairness, service quality, trust, and a construct new to service loyalty models—commercial friendship. The research contributes to the literature by providing a more complete, integrated view of customer loyalty and its determinants in services contexts.

An Examination of Customers’ Attitudes about Table Top Technology in Full-Service Restaurants

This paper examines the influence of customer-facing technology in full-service restaurants. As a new addition to the service experience, tabletop devices offer the customer more control over the dining experience, and also increase customer participation in the service process, which has the potential to upset the traditional exchange between service providers and customers in restaurants. To examine how customers react to the use of tabletop devices, this study examines 1,343 point-of-sales transactions from 20 units of a full-service casual dining restaurant chain and matches customer in-restaurant transactions to their reactions to tabletop devices used during their meals. Results show that over 70% of the customers who used tabletop devices reported positive affect toward the device, with approximately 79% of customers reporting that the device improved their experience, citing convenience, ease of use, and credit card security as some benefits of using the technology. Approximately 80% of the customers who used the device reported that they would return to the restaurant because of the positive affect. The results also indicate that likeability of the device and tip percentage were positively and significantly connected to customer reports of the devices having a positive effect on experience and on desire to return. In addition, when customers reported increased return intentions, likeability of the device was higher regardless of reports of the device improving restaurant experience, showing that the introduction of tabletop devices had a positive effect for most—but not all—customers.

Accounting for the Multi-Period Impact of Service When Determining Employee Requirements for Labor Scheduling

Providing good customer service, inexpensively, is a problem commonly faced by managers of service operations. To tackle this problem, managers must do four tasks: forecast customer demand for the service; translate these forecasts into employee requirements; develop a labor schedule that provides appropriate numbers of employees at appropriate times; and control the delivery of the service in real-time. This paper focuses upon the translation of forecasts of customer demand into employee requirements. Specifically, it presents and evaluates two methods for determining desired staffing levels. One of these methods is a traditional approach to the task, while the other, by using modified customer arrival rates, offers a better means of accounting for the multi-period impact of customer service. To calculate the modified arrival rates, the latter method reduces (increases) the actual customer arrival rate for a period to account for customers who arrived in the period (in earlier periods) but have some of their service performed in subsequent periods (in the period). In an experiment simulating 13824 service delivery environments, the new method demonstrated its superiority by serving 2.74% more customers within the specified waiting time limit while using 7.57% fewer labor hours.