Accounting Performance and Capacity Investment Decisions: Evidence from California Hospitals

Capacity decisions involve trade‐offs between the cost of capacity and the opportunity costs of lost sales. Accounting researchers posit that accounting performance provides sufficient information about these trade‐offs and thus can be used to formulate simple rules to assist capacity decisions. Empirical research has not examined the role of accounting information in capacity investment decisions at the department level in a multiproduct firm in the presence of social costs. Empirical analyses using department‐level data from California hospitals for the period 1998–2005 show that hospitals are more likely to make capacity investments in departments with high accounting performance. However, in the presence of demand variability, the association between accounting performance and capacity investment is attenuated because of the resulting increase in noise in accounting performance measures. Thus, the weight on accounting performance as a decision tool for capital investments reduces when there is demand variability. Another factor that reduces the weight on accounting performance is capacity utilization. Higher capacity utilization can lead to turning away or rerouting of patients to other hospitals and negatively impacts reputation and quality of care, which increases the hospital's social costs. Hence, hospitals do not require high accounting performance before investing in a department with high capacity utilization. This empirical evidence of the role of accounting performance in capacity investment decisions fills a gap in the capacity investment literature and furthers our understanding of the interactions between accounting performance and the operational determinants of firms’ capacity investment behavior.     

Data and Model Management in a Generalized MCDM-DSS*

Drawing upon the choice models developed in the multiple criteria decision making (MCDM) area, this paper proposes an architecture for designing an intelligent decision support system (DSS) that is intended to aid in making choices among multiple alternatives along multiple dimensions. It argues that effective support can be provided to the decision maker when the knowledge-based DSS is capable of dynamically selecting choice models appropriate to the domain and context of a particular problem being specified by the decision maker, and of properly applying them to the problem solution. Development of a prototype intended to partially represent application of the architecture is described. The paper concludes with suggestions for research extensions.     

An Investigation of Localization as an Element of Cognitive Fit in Accounting Model Representations

Cognitive fit, a correspondence between task and data representation format, has been demonstrated to lead to superior task performance by individual users and has been posited as an explanation for performance differences among users of various problem representations such as tables, graphs, maps, and schematic faces. The current study extends cognitive fit to accounting models and integrates cognitive fit theory with the concept of localization to provide additional evidence for how cognitive fit works. Two accounting model representations are compared in this study, the traditional DCA (Debit‐Credit‐Account) accounting model and the REA (Resources‐Events‐Agents) accounting model. Results indicate that the localization of relevant objects or linkages is important in establishing cognitive fit.     

Evidence on the Superiority of Analysts Quarterly Earnings Forecasts for Small Capitalization Firms*

Financial analysts provide information to support investment analysis and decisions for an ever increasing number of firms. As part of their services they also produce earnings forecasts for covered firms. While there has been much research investigating the determinants of financial analyst earnings forecast superiority for large, widely-followed firms, little research has focused on smaller firms. Until recently, these smaller firms have been largely ignored. This study focuses exclusively on small firms and provides evidence of differing behavior for such firms compared to results previously reported for large firms. Errors in quarterly earnings per share forecasts of small firms obtained from a univariate time-series model are also examined. Regression results indicate that time-series model parameters possess information content with respect to forecast accuracy for analyst-covered firms only. These results are obtained after controlling for firm size, model adequacy, and industry, quarter, and year effects. This suggests that analysts are more likely to cover small firms for which they are able to decipher information correlated with that impounded in the “shocks” in the quarterly earnings time series as captured by the time-series model parameters.     

Heuristic Optimization of Physical Data Bases: Using a Generic and Abstract Design Model*

Designing efficient physical data bases is a complex activity, involving the consideration of a large number of factors. Mathematical programming-based optimization models for physical design make many simplifying assumptions; thus, their applicability is limited. In this article, we show that heuristic algorithms can be successfully used in the development of very good, physical data base designs. Two heuristic optimization algorithms are proposed in the context of a generic and abstract model for physical design. One algorithm is based on generic principles of heuristic optimization. The other is based on capturing and using problem-specific information in the heuristics. The goodness of the algorithms is demonstrated over a wide range of problems and factor values.     

A Model for Planning Resource Requirements in Health Care Organizations

In this paper we present a general model and solution methodology for planning resource requirements (i.e., capacity) in health care organizations. To illustrate the general model, we consider two specific applications: a blood bank and a health maintenance organization (HMO). The blood bank capacity planning problem involves determining the number of donor beds required and determining the size of the nursing and support staff necessary. Capacity must be sufficient to handle the expected number of blood donors without causing excessive donor waiting times. Similar staff, equipment, and service level decisions arise in the HMO capacity planning problem. To determine resource requirements, we develop an optimization/queueing network model that minimizes capacity costs while controlling customer service by enforcing a set of performance constraints, such as setting an upper limit on the expected time a patient spends in the system. The queueing network model allows us to capture the stochastic behavior of health care systems and to measure customer service levels within the optimization framework.     

Health Care Supply Chain Design: Toward Linking the Development and Delivery of Care Globally*

This article is motivated by the gap between the growing demand and available supply of high‐quality, cost‐effective, and timely health care, a problem faced not only by developing and underdeveloped countries but also by developed countries. The significance of this problem is heightened when the economy is in recession. In an attempt to address the problem, in this article, first, we conceptualize care as a bundle of goods, services, and experiences—including diet and exercise, drugs, devices, invasive procedures, new biologics, travel and lodging, and payment and reimbursement. We then adopt a macro, end‐to‐end, supply chain–centric view of the health care sector to link the development of care with the delivery of care. This macro, supply chain–centric view sheds light on the interdependencies between key industries from the upstream to the downstream of the health care supply chain. We propose a framework, the 3A‐framework, that is founded on three constructs—affordability, access, and awareness—to inform the design of supply chain for the health care sector. We present an illustrative example of the framework toward designing the supply chain for implantable device–based care for cardiovascular diseases in developing countries. Specifically, the framework provides a lens for identifying an integrated system of continuous improvement and innovation initiatives relevant to bridging the gap between the demand and supply for high‐quality, cost‐effective, and timely care. Finally, we delineate directions of future research that are anchored in and follow from the developments documented in the article.     

The Representation and Impact of Information System Reliability on Cost Variance Decisions*

Management does not directly observe cost variance processes from the production system; instead, management depends on a report from the cost variance information system as to whether a process is in control or out of control. Unfortunately, cost variance information systems are not always perfectly reliable. Accordingly, this paper extends the cost variance analysis model to include reliability of the cost variance information system. Reliability is introduced by distinguishing between the report from the information system and the actual cost variance. Although previous cost variance models do not include reliability, management is still likely to be aware of the lack of reliability in cost variance systems. To account for this lack of reliability, management is likely to use any of a number of heuristics. This paper demonstrates that ad hoc heuristics are unlikely to result in the same solutions suggested by the analytic model. In addition, it is shown analytically that not including reliability in cost variance models can lead to examining variances that do not require investigation and not examining variances that do require investigation.     

Data Envelopment Analysis for Managerial Control and Diagnosis*

Data envelopment analysis (DEA) is a linear programming-based technique that converts multiple input and output measures into a single comprehensive measure of productive efficiency. This is accomplished via the construction of an empirically based production frontier and by the identification of peer groups. Each unit is evaluated by comparison against a composite unit that is constructed as a convex combination of other units in its peer group. DEA has now been applied in a variety of managerial contexts. In this paper we draw on theories of decision making, measurement and control, the mathematical properties of DEA, prior reported applications, and our own experience, to assess the potential of DEA as a general management tool. We first make the distinction between managerial diagnosis and control. We show how measurement requirements differ for these two managerial decision contexts, and argue that DEA has the potential to provide support in each context. Measurement and decision support criteria for each activity are then developed by reference to the literature on diagnosis and control. Based on its mathematical definition and properties, the relevant attributes of DEA are then derived. The technique is evaluated in each capacity by comparison to the appropriate set of criteria. This evaluation is supported with evidence from our experience with DEA in a large public-sector organization. We argue that the structural properties of DEA, critical managerial choices in its application, and situationally specific factors, interact to determine the strengths and limitations of DEA in each decision context. Implications for research and practice are discussed.     

An Empirically Derived Model for the Adoption of Customer-based Interorganizational Systems*

Information systems (IS) researchers are now calling for the need to draw from the empirically rich field of organizational innovation. As the impact of strategic systems is increasingly being felt by organizations, the view that these systems are innovations or innovative uses of technology is becoming prevalent. Customer based interorganizational systems (CIOS) represent one of the most prominent types of such systems. This research investigates CIOS adoption. A model is constructed based on significant studies in innovation to identify factors facilitating the adoption decision of a CIOS. Data are gathered from 226 senior executives. Discriminant analysis is used to identify factors that distinguish adopters from nonadopters. Factor analysis of significant variables yielded a parsimonious model of CIOS adoption. The five factor model includes (1) a proactive technological orientation and (2) an internal push for the system as the two most significant sets of facilitators. Implications for research and practice are then discussed.     

A Comprehensive Model for Managing Credit Risk on Home Mortgage Portfolios*

Managing credit risk in financial institutions requires the ability to forecast aggregate losses on existing loans, predict the length of time that loans will be on the books before prepayment or default, analyze the expected performance of particular segments in the existing portfolio, and project payment patterns of new loans. Described in this paper are tools created for these functions in a large California financial institution. A forecasting model with Markovian structure and nonstationary transition probabilities is used to model the life of a mortgage. Logistic and regression models are used to estimate severity of losses. These models are integrated into a system that allows analysts and managers to depict the expected performance of individual loans and portfolio segments under different economic scenarios. With this information, analysts and managers can establish appropriate loss reserves, suggest pricing differentials to compensate for risk, and make strategic lending decisions.     

Strategic Purchases of Bundled Products in a Health Care Supply Chain Environment*

Recent articles in the Wall Street Journal summarize the state of business practice in American hospitals by shedding light on the state of supply chain management practices and foci in today's health care supply chains. In health care, the single largest cost after labor is materials, and it has been documented that health care facilities can reduce the environmental impacts of the products and services they consume before regulatory problems arise or waste disposal costs increase by focusing on their upstream activities. Health care systems around the country consume significant quantities and varieties of products within the health delivery processes. Solving these environmental problems requires a much broader view involving collaborative efforts of professionals from different areas of health care to meet these challenges. The purchasing function bridges the gaps by providing a healthy dialogue on key environmental attributes within the health care supply chain. The concept of bundling new with refurbished products is gaining a lot of attention in the health care supply chain. This research describes a health care purchasing problem for bundling new and refurbished products of the type facing a growing number of large health care providers, and then proposes a methodology for evaluating the complex tradeoffs involved in bundling decisions for refurbished health care products. By exploiting some useful properties of the problem structure, our results provide buyers with useful insights for examining and selecting suppliers who are willing to offer bundles of new and refurbished products.     

A Contingent Claims Model of Corporate Pension Obligations*

This study develops a contingent claims model for valuing the implicit market value of the pension claim associated with defined benefit pension plans. In this model, the firm issues pension, debt, and equity claims. These claims have joint access to two underlying portfolios: corporate and pension. The changes in the market values of these two portfolios are assumed to follow a joint lognormal diffusion process. By imposing terminal boundary conditions implied by Employment Retirement Income Security Act (ERISA) rules and the pension insurance provisions of the Pension Benefit Guaranty Corporation (PBGC) on the partial differential equation, a solution for the pension value is obtained. This quasi-market measure of the value of the pension claim may be represented by a portfolio consisting of four components: (1) a risk-free discount bond with face value equal to promised pension benefits; (2) a short put on pension assets with exercise price equal to pension benefits; (3) a long call on 30 percent of corporate assets with exercise price equal to the face value of secured corporate debt; and (4) a short call on 30 percent of corporate assets with a stochastic exercise price which depends on the terminal value of the pension fund. A numerical example using 1992 and 1993 financial statement data from six major U.S. corporations is provided. This example illustrates the usefulness of the model's prediction and the potential effect of theoretical pension values on corporate debt-equity ratios.     

Discretionary Use of Computers: An Empirically Derived Explanatory Model*

This study identifies variables that explain variations in computer use. Factor analysis of data gathered from 422 business administration faculty reveal eight themes among the independent variables. Seven of these factors significantly discriminate among non, minimal, and high users. These results suggest an empirical model for future studies and provide policy insights for decision makers seeking to stimulate computer usage. Additionally, the study shows that there is much commonality between the previously separate organizational innovation and information systems implementation streams. It also demonstrates that motivation theory may be a valid framework within which to study computer use.     

Clinic Overbooking to Improve Patient Access and Increase Provider Productivity*

The problem of patient no‐shows (patients who do not arrive for scheduled appointments) is significant in many health care settings, where no‐show rates can vary widely. No‐shows reduce provider productivity and clinic efficiency, increase health care costs, and limit the ability of a clinic to serve its client population by reducing its effective capacity. In this article, we examine the problem of no‐shows and propose appointment overbooking as one means of reducing the negative impact of no‐shows. We find that patient access and provider productivity are significantly improved with overbooking, but that overbooking causes increases in both patient wait times and provider overtime. We develop a new clinic utility function to capture the trade‐offs between these benefits and costs, and we show that the relative values that a clinic assigns to serving additional patients, minimizing patient waiting times, and minimizing clinic overtime will determine whether overbooking is warranted. From the results of a series of simulation experiments, we determine that overbooking provides greater utility when clinics serve larger numbers of patients, no‐show rates are higher, and service variability is lower. Even with highly variable service times, many clinics will achieve positive net results with overbooking. Our analysis provides valuable guidance to clinic administrators about the use of appointment overbooking to improve patient access, provider productivity, and overall clinic performance.     

Lowering Inventory Systems Costs by Using Regression-Derived Estimators of Demand Variability*

Scientific techniques for inventory management typically are applied to systems containing many items. Such techniques require an estimation of the demand variance (and mean) of each item from historical data. This research demonstrates a significant potential for improvement in system cost performance from using least-squares regression fits of a variance-to-mean functional relation instead of the standard statistical variance estimate. Even when there is a moderate degree of heterogeneity among items and when the form of the variance-to-mean relation is misspecified, substantial cost savings may be realized. The cost of statistical uncertainty may be reduced by half. The research also provides evidence that system cost is fairly insensitive to the number of items used to fit the regression. This paper provides the underlying reason why a regression-derived variance estimator yields lower cost: it is less variable than the usual individual item variance estimator.     

Judgmental Adjustments of Previously Adjusted Forecasts*

Forecasts are important components of information systems. They provide a means for knowledge sharing and thus have significant decision‐making impact. In many organizations, it is quite common for forecast users to receive predictions that have previously been adjusted by providers or other users of forecasts. Current work investigates some of the factors that may influence the size and propensity of further adjustments on already‐adjusted forecasts. Two studies are reported that focus on the potential effects of adjustment framing (Study 1) and the availability of explanations and/or original forecasts alongside the adjusted forecasts (Study 2). Study 1 provides evidence that the interval forecasts that are labeled as “adjusted” are modified less than the so‐called “original/unadjusted” predictions. Study 2 suggests that the provision of original forecasts and the presence of explanations accompanying the adjusted forecasts serve as significant factors shaping the size and propensity of further modifications. Findings of both studies highlight the importance of forecasting format and user perceptions with critical organizational repercussions.     

The Application of Neural Networks and a Qualitative Response Model to the Auditor's Going Concern Uncertainty Decision*

An auditor gives a going concern uncertainty opinion when the client company is at risk of failure or exhibits other signs of distress that threaten its ability to continue as a going concern. The decision to issue a going concern opinion is an unstructured task that requires the use of the auditor's judgment. In cases where judgment is required, the auditor may benefit from the use of statistical analysis or other forms of decision models to support the final decision. This study uses the generalized reduced gradient (GRG2) optimizer for neural network learning, a backpropagation neural network, and a logit model to predict which firms would receive audit reports reflecting a going concern uncertainty modification. The GRG2 optimizer has previously been used as a more efficient optimizer for solving business problems. The neural network model formulated using GRG2 has the highest prediction accuracy of 95 percent. It performs best when tested with a small number of variables on a group of data sets, each containing 70 observations. While the logit procedure fails to converge when using our eight variable model, the GRG2 based neural network analysis provides consistent results using either eight or four variable models. The GRG2 based neural network is proposed as a robust alternative model for auditors to support their assessment of going concern uncertainty affecting the client company.     

Partitioning Work Centers for Group Technology: Insights from an Analytical Model*

This paper develops analytical approximations based on an M/M/c queuing model, for the operating characteristics of job shop work centers. The model is used to analyze the effects of partitioning a work center and introducing setup reduction into the cell components. The results using this model are shown to be consistent with the results of prior simulation-based studies of group technology under certain parameter ranges. A simulation experiment was conducted to verify the effects with multi-item, non-Markovian assumptions, and the general effects predicted by the model were found to apply.