A LONGITUDINAL STUDY OF THE EFFECT OF A SERVICE GUARANTEE ON SERVICE QUALITY

This paper develops a theoretical framework that relates a service guarantee to service quality. The framework hypothesizes that a service guarantee can positively affect service quality through its positive effect on both learning through service failure and employee motivation and vision. A longitudinal, empirical study was conducted to test these hypotheses. Surprisingly, the service guarantee was not found to have a direct effect on learning through service failure. However, the service guarantee clearly had a positive effect on service quality primarily through its positive effect on employee motivation and vision. The research strongly supports using a service guarantee to improve service quality.     

THE MARKET SHARE IMPACT OF SERVICE FAILURES

This paper develops a simple but powerful model that relates service satisfaction/dissatisfaction to market share. The model is based on an intuitive service satisfaction framework that relates three service system parameters (service success rate, complaint rate, and service recovery rate) to the percent of satisfied customers. A dynamic model is then posited that relates the defection rate and the addition rate to market share changes. The service satisfaction/market share model yields useful insights into how market share is influenced by these service system parameters. The surprisingly simple model predicts changes in market share due to changes in customer satisfaction.     

EMPLOYEE DEVELOPMENT: AN EXAMINATION OF SERVICE STRATEGY IN A HIGH‐CONTACT SERVICE ENVIRONMENT

A critical component of service strategy in high‐contact environments is service encounter management. Effective service encounters are a result of the quality of employee development, including systems for work and job design, training and development, and attention to employee well being. Results of empirical analysis indicate that service strategies reflecting the dimensions of employee development drive employee outcomes such as productivity and satisfaction. Employee outcomes are significantly associated with customer satisfaction, but only some linkages to financial performance are significant. This study illustrates the importance of employee development in service strategy design for managing service encounters in high contact service environments.     

PIECING TOGETHER SERVICE QUALITY: A FRAMEWORK FOR ROBUST SERVICE*

This paper addresses the issue of service design, specifically that of designing the service encounter for improved quality. We introduce a framework based on the three T's of task, treatment, and tangibles as a means of organizing the application of the diverse and growing body of service quality literature to encounter design. The framework is consistent with how successful service managers disaggregate the design problem. More importantly, we show that mutually supportive interrelationships between the three T's produce an opportunity for designing in a robustness to service failure. The framework is supported by case based evidence.     

Optimizing Service Attributes: The Seller's Utility Problem*

Service designers predict market share and sales for their new designs by estimating consumer utilities. The service's technical features (for example, overnight parcel delivery), its price, and the nature of consumer interactions with the service delivery system influence those utilities. Price and the service's technical features are usually quite objective and readily ascertained by the consumer. However, consumer perceptions about their interactions with the service delivery system are usually far more subjective. Furthermore, service designers can only hope to influence those perceptions indirectly through their decisions about nonlinear processes such as employee recruiting, training, and scheduling policies. Like the service's technical features, these process choices affect quality perceptions, market share, revenues, costs, and profits. We propose a heuristic for the NP‐hard service design problem that integrates realistic service delivery cost models with conjoint analysis. The resulting seller's utility function links expected profits to the intensity of a service's influential attributes and also reveals an ideal setting or level for each service attribute. In tests with simulated service design problems, our proposed configurations compare quite favorably with the designs suggested by other normative service design heuristics.     

SERVICE SYSTEM DESIGN FOR THE PROPERTY AND CASUALTY INSURANCE INDUSTRY

This paper describes the changes that are forcing property and casualty insurance firms to rethink their service system design and in particular their distribution strategies. A set of questions related to distribution that are uppermost in the minds of executives in this industry are presented along with a literature survey of models that can be used to answer some of these questions. Based on the survey, a normative framework for designing the distribution system is proposed. Qualitative and quantitative analysis based on the proposed framework is presented along with empirical data to demonstrate the usefulness of the framework. The paper concludes with an agenda for further research.     

Schedule Recovery: Unplanned Absences in Service Operations*

The U.S. service sector loses 2.3% of all scheduled labor hours to unplanned absences, but in some industries, the total cost of unplanned absences approaches 20% of payroll expense. The principal reasons for unscheduled absences (personal illness and family issues) are unlikely to abate anytime soon. Despite this, most labor scheduling systems continue to assume perfect attendance. This oversight masks an important but rarely addressed issue in services management: how to recover from short‐notice, short‐term reductions in planned capacity. In this article, we model optimal responses to unplanned employee absences in multi‐server queueing systems that provide discrete, pay‐per‐use services for impatient customers. Our goal is to assess the performance of alternate absence recovery strategies under various staffing and scheduling regimes. We accomplish this by first developing optimal labor schedules for hypothetical service environments with unreliable workers. We then simulate unplanned employee absences, apply an absence recovery model, and compute system profits. Our absence recovery model utilizes recovery strategies such as holdover overtime, call‐ins, and temporary workers. We find that holdover overtime is an effective absence recovery strategy provided sufficient reserve capacity (maximum allowable work hours minus scheduled hours) exists. Otherwise, less precise and more costly absence recovery methods such as call‐ins and temporary help service workers may be needed. We also find that choices for initial staffing and scheduling policies, such as planned overtime and absence anticipation, significantly influence the likelihood of successful absence recovery. To predict the effectiveness of absence recovery policies under alternate staffing/scheduling strategies and operating environments, we propose an index based on initial capacity reserves.     

Optimised buffer allocation to construct stable personnel shift rosters

Organisations need to construct stable baseline personnel shift rosters based on forecasts about the future personnel demand and employee availability. However, variability arises in the short-term, which renders these forecasts incorrect and affects the quality of the personnel roster. In this paper, we study how to anticipate this variability by introducing capacity buffers in the personnel shift roster. We propose a new approach by solving an equivalent deterministic formulation of a stochastic personnel shift scheduling problem. In contrast to traditional approaches, the size and position of capacity buffers are not defined in advance but are adequately determined as an endogenous variable by the proposed optimisation model to align the available personnel capacity to the stochastic demand. We propose different strategies to define the anticipated uncertainty and to allocate capacity buffers accordingly. We validate the performance of these strategies through a comparison with a deterministic minimum cost strategy and a more traditional resource buffer strategy based on a three-step methodology. This methodology makes use of simulation and optimisation to mimic the hierarchical personnel planning process.     

A Flexible Evaluative Framework for Order Picking Systems

This paper develops a novel framework to evaluate the integral performance of order picking systems with different combinations of storage and order picking policies. The warehousing literature on order picking mostly considers minimizing either elapsed time or distance as the sole objective, whereas warehouse managers in a supply chain have to look beyond single‐dimensional performance and consider trade‐offs among different criteria. Thus managers still need a unified and efficient framework to select a portfolio of appropriate order picking policies from a multi‐criteria and contextual perspective. Our framework—combining data envelopment analysis, ranking and selection, and multiple comparisons—provides an efficient methodology to simultaneously analyze several interrelated problems in order picking systems with multiple performance attributes, such as service levels and operational costs. We demonstrate our approach through comprehensive evaluations of order picking policies in three low‐level, picker‐to‐parts rectangular warehouses facing demand variations.     

OUTPATIENT SCHEDULING IN HEALTH CARE: A REVIEW OF LITERATURE

This paper provides a comprehensive survey of research on appointment scheduling in outpatient services. Effective scheduling systems have the goal of matching demand with capacity so that resources are better utilized and patient waiting times are minimized. Our goal is to present general problem formulation and modeling considerations, and to provide taxonomy of methodologies used in previous literature. Current literature fails to develop generally applicable guidelines to design appointment systems, as most studies have suggested highly situation‐specific solutions. We identify future research directions that provide opportunities to expand existing knowledge and close the gap between theory and practice.     

SCHEDULING FLEXIBILITY IN THE SERVICE SECTOR: A POSTAL CASE STUDY

This is a case study of workforce scheduling in the U.S. postal system. We use it to analyze the benefits of scheduling flexibility at postal distribution systems, which can come from several sources. We focus on the additional flexibility deriving from increasing the proportion of part-time employees, as well as from increasing the cross-training of part-time employees. These two dimensions of scheduling flexibility are decision points of particular interest to the postal management. We used a large-scale simulation model of the dynamic functioning of the postal distribution system in conjunction with a staff-scheduling model to obtain insights into policy issues of interest. Our results show that the operating environment at a distribution station affects cost and customer service performance, and that gains from greater scheduling flexibility are situationally dependent. The benefit of cross-training part-time workers is modest over the range of levels considered realistic for distribution stations. Our overall recommendation is for postal managers to increase the proportion of part-time employees, allowing more efficient matching of resources with the varying demands for mail delivery on different routes. For the range of conditions we modeled, cost reductions from more part-time workers average over six percent when capacity utilization is low. Customer service improvements are even larger, and range between 20 and 43% when capacity utilization is high. Improvements at the upper end of the range are achieved when demand variability is also at its highest.     

On‐Call Overtime for Service Workforce Scheduling when Demand Is Uncertain

This article builds on prior research to develop shift scheduling models that include on‐call overtime for service environments where demand is uncertain. The research is motivated by recent developments in nurse scheduling, such as laws prohibiting mandatory overtime and the popularity of self‐scheduling systems. For single‐period scenarios, models are developed, solution methods are described, and results are explored for a variety of environments. Results show that the use of on‐call overtime can reduce costs slightly, with the amount of savings dependent on characteristics of the scheduling environment. The factor that most significantly affects cost savings is the cost of outside agency workers relative to overtime workers. In addition to lowering costs, on‐call overtime greatly reduces reliance on outside agency workers, which can have important practical implications in terms of quality of service and workforce morale. Results based on single‐period models motivate multiperiod formulations for single‐ and multidepartment scenarios, and solution methods are outlined for those cases. The possibility of using multiperiod models within a rolling horizon framework with forecast updating is discussed. This goes along with an extension of the traditional workforce management hierarchy that separates overtime and regular‐time scheduling, as seen in practice with self‐scheduling and shift‐bidding systems.     

RESOURCE ALLOCATION TO IMPROVE SERVICE QUALITY PERCEPTIONS IN MULTISTAGE SERVICE SYSTEMS

Service quality improvement has become an imperative in today's service firms. In this paper, we present a modeling framework that combines marketing and operations viewpoints for resource allocation. The framework can be used to allocate resources to the different stages of a multistage service system, where the manager's goal is to improve customers' perceptions of service quality, given some budget. Optimal allocation guidelines are provided, and the interplay of three factors on the resulting allocation scheme is captured. These factors are the current level of customers' perceptions of service quality at each stage, the cost of implementing a service quality improvement at each stage, and the importance placed by customers at each stage. Sensitivity analysis to provide additional managerial insights is also performed. We demonstrate the applicability of the modeling framework, using data from a real life health care environment. Model limitations and future research are also discussed.     

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

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 motivation/vision 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 arrive at a better‐fitting, 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.     

Tour Scheduling and Task Assignment of a Heterogeneous Work Force: A Heuristic Approach

The dual problem of work tour scheduling and task assignment involving workers who differ in their times of availability and task qualifications is examined in this paper. The problem is presented in the context of a fast food restaurant, but applies equally well to a diverse set of service operations. Developing a week-long labor schedule is a nontrivial problem, in terms of complexity and importance, which a manager spends as much as a full workday solving. The primary scheduling objective (the manager's concern) is the minimization of overstaffing in the face of significant hourly and daily fluctuations in minimum staffing requirements. The secondary objective (the workers’ concern) is the minimization of the sum of the squared differences between the number of work hours scheduled and the number targeted for each employee. Contributing to scheduling complexity are constraints on the structure of work tours, including minimum and maximum shift lengths and a maximum number of workdays. A goal programming formulation of a representative problem is shown to be too large, for all practical purposes, to be solved optimally. Existing heuristic procedures related to this research possess inherent limitations which render them inadequate for our purposes. Subsequently, we propose and demonstrate a computerized heuristic procedure capable of producing a labor schedule requiring at most minor refinement by a manager.     

Service Completion Estimates for Cross‐trained Workforce Schedules under Uncertain Attendance and Demand

Although cross‐trained workers offer numerous operational advantages for extended‐hour service businesses, they must first be scheduled for duty. The outcome from those decisions, usually made a week or more in advance, varies with realized service demand, worker attendance, and the way available cross‐trained workers are deployed once the demands for service are known. By ignoring the joint variability of attendance and demand, we show that existing workforce scheduling models tend to overstate expected schedule performance and systematically undervalue the benefits of cross‐training. We propose a two‐stage stochastic program for profit‐oriented cross‐trained workforce scheduling and allocation decisions that is driven by service completion estimates obtained from the convolution of the employee attendance and service demand distributions. Those estimates, reflecting optimal worker allocation decisions over all plausible realizations of attendance and demand, provide the gradient information used to guide workforce scheduling decisions. Comparing the performance of workforce scheduling decisions for hundreds of different hypothetical service environments, we find that solutions based on convolution estimates are more profitable, favor proportionately more cross‐trained workers and fewer specialists, and tend to recommend significantly larger (smaller) staffing levels for services under high (low) contribution margins than workforce schedules developed with independent expectations of attendance and demand.     

Governance Considerations for Seeker–Solver Relationships: A Knowledge‐Based Perspective in Crowdsourcing for Innovation Contests

The need to solve innovation problems and insource knowledge has led to an increasing number of organizations engaging in crowdsourcing activities and subsequently establishing working relationships with winning solution providers. Using a knowledge‐based view and the problem‐solving perspective, we develop a theoretical framework suggesting how specific innovation problem attributes (i.e. the decomposability, formulation and search space of the problem) influence the governance decision (unilateral vs. bilateral) of seekers to manage the relationship with winning solvers. We empirically analyse the framework using 582 challenges broadcast on the NineSigma crowdsourcing platform. Our results indicate that problem attributes – the formulation and search space of the problem – have a positive effect on seekers’ preference towards unilateral governance structures. However, we did not find any empirical confirmation of the effect that the decomposability of the innovation problem has on seekers’ preference towards unilateral governance structures. This study offers several contributions to the crowdsourcing literature, and also has important implications for managers of organizations aiming to insource knowledge through crowdsourcing for innovation contests.     

Identifying Equilibrium Models of Labor Market Sorting

We assess the empirical content of equilibrium models of labor market sorting based on unobserved (to economists) characteristics. In particular, we show theoretically that all parameters of the classic model of sorting based on absolute advantage in Becker, 1973 with search frictions can be nonparametrically identified using only matched employer–employee data on wages and labor market transitions. In particular, these data are sufficient to nonparametrically estimate the output of any individual worker with any given firm. Our identification proof is constructive and we provide computational algorithms that implement our identification strategy given the limitations of the available data sets. Finally, we add on‐the‐job search to the model, extend the identification strategy, and apply it to a large German matched employer–employee data set to describe detailed patterns of sorting and properties of the production function.     

Market‐Implied Spread for Earthquake CAT Bonds: Financial Implications of Engineering Decisions

In the event of natural and man‐made disasters, owners of large‐scale infrastructure facilities (assets) need contingency plans to effectively restore the operations within the acceptable timescales. Traditionally, the insurance sector provides the coverage against potential losses. However, there are many problems associated with this traditional approach to risk transfer including counterparty risk and litigation. Recently, a number of innovative risk mitigation methods, termed alternative risk transfer (ART) methods, have been introduced to address these problems. One of the most important ART methods is catastrophe (CAT) bonds. The objective of this article is to develop an integrative model that links engineering design parameters with financial indicators including spread and bond rating. The developed framework is based on a four‐step structural loss model and transformed survival model to determine expected excess returns. We illustrate the framework for a seismically designed bridge using two unique CAT bond contracts. The results show a nonlinear relationship between engineering design parameters and market‐implied spread.     

Time‐Staged Overtime Staffing for Services with Updated Forecasts and Availabilities

This article develops a framework for staffing in a service environment when multiple opportunities exist for prescheduling overtime prior to the start of a shift. Demand forecasts improve as the shift approaches, while the availability of workers to be scheduled for overtime decreases. First, a single‐shift model is developed and used in computational studies to evaluate the benefits of time‐staged overtime staffing, which include slightly lower costs and significant reductions in unscheduled overtime and outside agents. A multishift model is then developed to consider constraints on consecutive hours worked and minimum rest intervals between shifts. A multishift computational study shows how the benefits of time‐staged overtime staffing depend on problem characteristics when interactions between shifts are considered. The article discusses how single‐shift and multishift models relate to each other and alternative ways the models may be used in practice, including decentralized open shift management and centralized overtime scheduling.