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Assessing the impact of a quality improvement intervention on an organization is particularly difficult in a high contact service operation where the intangible service encounter is the unit of output. Frequently, accounting or financial data must be used to evaluate the effectiveness of the intervention; however, these data may be problematic with respect to sample size and masking effects due to aggregation. Presents a systems model which describes metaphorically how an unstable process can continue to show no performance gains despite continued input of resources into improvement initiatives. A special type of Shewart control chart, known as the X‐chart, is developed as a methodology for assessing process performance after an improvement programme has been implemented. An X‐chart is used to analyse performance data collected in a real service setting where service quality standards were deployed in the front line phase of the operation. Although traditional analysis of variance concluded that there was no significant improvement in performance, the X‐chart indicates that real performance gains were occurring. The X‐chart provides management with an easy‐to‐use decision tool which can help assess the effectiveness of many different types of organizational change initiatives.

This chapter will present a goal programming model which simultaneously generates forecasts for the aggregate level and for lower echelons in a multilevel forecasting context. Data from an actual service firm will be used to illustrate and test the proposed model against a standard forecast technique based on the bottom-up/top-down approach.

This paper aims to propose a service reliability framework for classifying technical reliability tools so that managers can better understand how to use them in practice.

Published research was examined to identify reliability tools that have been used in services. These tools were then categorized using a framework that considered subsystem reliability, system configuration and system reliability.

A number of traditional manufacturing reliability tools have been used in service companies. This paper has categorized those tools within a service reliability framework based on subsystem reliability, configuration and system reliability.

Future research could address the issue of customer perception and customer feedback as part of the reliability appraisal process.

Service managers can use the proposed framework to examine the applicability of these technical tools in service operations and to guide reliability improvement efforts.

The proposed service reliability framework provides an integrated view of subsystems, systems and configuration that is lacking in the service management literature. The framework also emphasizes technical reliability tools that have not received sufficient attention in the service management literature.

The purpose of this paper is to devise a robust statistical process control methodology that will enable service managers to better monitor the performance of correlated service measures.

A residuals control chart methodology based on least absolute value regression (LAV) is developed and its performance is compared to a traditional control chart methodology that is based on ordinary least squares (OLS) regression. Sensitivity analysis from the goal programming formulation of the LAV model is also performed. The methodology is applied in an actual service setting.

The LAV based residuals control chart outperformed the OLS based residuals control chart in identifying out of control observations. The LAV methodology was also less sensitive to outliers than the OLS approach.

The findings from this study suggest that the proposed LAV based approach is a more robust statistical process control method than the OLS approach. In addition, the goal program formulation of the LAV regression model permits sensitivity analysis whereas the OLS approach does not.

This paper shows that compared to the traditional OLS based control chart, the LAV based residuals chart may be better suited to actual service settings in which normality requirements are not met and the amount of data is limited.

This paper is the first study to use a least absolute value regression model to develop a residuals control chart for monitoring service data. The proposed LAV methodology can help service managers to do a better job monitoring related performance metrics as part of a quality improvement program such as six sigma.

The purpose of this paper was to develop a customer‐oriented systems approach to updating a service operation. The approach will allow managers to gather information, make changes in service delivery and then assess the effects of these changes.

A survey was developed and administered at intervals in the same service setting; data from these survey results were used to illustrate the proposed systems‐based approach. Data from the open‐ended survey question were analyzed using content analysis. Based on that analysis, changes were made in service delivery. The impact of these changes was assessed as part of the second survey.

Initial data analysis showed that customer perceptions of the quality of the core service offering, the physical setting and service intangibles differed. Content analysis identified areas for improvement and changes were made in the three areas. Analysis of the second survey showed improvements in customer satisfaction with the service setting and the service intangibles.

The research approach utilizes immediate feedback from customers who experience an actual – rather than hypothesized – service. Measurement of customer satisfaction with service delivery occurs longitudinally – rather than at a single point in time.

The methodology could be used by service managers in an ongoing effort to identify service attributes that may warrant improvement.

This paper provides a structured approach to updating service delivery and illustrates its use in an actual service setting.

The purpose of this study is to examine the relative importance of customer perceptions of waits in a multi‐stage service.

The stages included the wait at the point of service‐entry, the wait during the service stage in which the core service was experienced and the wait at the service‐exit stage as the customer was preparing to leave. Satisfaction with the waits and satisfaction with the core service product, employees' behavior and the physical setting were examined in relation to customers' perceptions of service quality. Four measures of customers' perceptions of service quality were used in this study. These included overall customer satisfaction, willingness to recommend the service to friends, willingness to bring friends to the service and repatronage intentions. A survey was developed based on a review of the literature and in collaboration with the manager of a full‐service restaurant. The survey was administered during the course of the meal by restaurant employees. Multiple regression analysis was used to identify the extent to which satisfaction with each wait affected the four customer perceptions of service quality.

Results showed that the only wait satisfaction that consistently affected customer perceptions of service quality involved the service‐entry wait.

This study is one of the first to empirically examine the effect of service waits at multiple stages of a service operation on perceptions of service quality.

Few states or local school districts mandate a minimum time for lunch. With increasing pressure on schools to maximize instructional time, many US students have witnessed continued reductions in the time allotted to lunch periods and, thus, less time to choose from an increasing number of food options. This study aims to investigate middle and high school students’ preferences regarding the time available for school lunches and whether the amount of time would affect their food choice preferences.

This study investigated students’ self-reported lunchtime constraints and food choice preferences through a paper-and-pencil survey. The categorical and ratio responses were analyzed using ordinal logistic regression.

Students responded that they rarely had enough time to eat school lunch and that the lunch line waiting time strongly or very strongly influenced their food choices. For the students for whom time available for lunch and time in the lunch line influenced what they ate, they were more likely to prefer limited food choices in several categories of the school lunch menu.

Foodservice professionals who wish to actively promote better nutrition might consider practical ways to reduce the foodservice wait time for students. While making healthier default options (e.g. a fruit or fresh vegetable side) could increase service convenience, time required for students to make informed meal choices should not be compromised.

Because lunch line waiting time is related to students’ food choices, schools need to review the number and types of food choices offered in terms of whether they encourage students to make more healthful choices. This study offers a unique perspective on the relationship between time and individual food choices in the school lunch environment and how this relationship affects the quality of children’s diets and their eating behaviors.