A LOGIT-MARKOVIAN MODEL OF CONSUMER PURCHASE BEHAVIOR BASED ON EXPLANATORY VARIABLES: EMPIRICAL EVALUATION AND IMPLICATIONS FOR DECISION MAKING*

This study describes and empirically evaluates an approach to modeling purchase behavior that integrates a Logit-Markov-based multivariate brand-choice model structure with stochastic components. Using actual market data for a frequently purchased consumer product obtained from a consumer purchase panel survey, the paper highlights some marketing applications of the model. Given its general structure, the model can aid marketing managers in defining and evaluating target market segments and in assessing the impact of alternative marketing strategies.     

THE INDUCE VERSUS PURCHASE DECISION: AN EMPIRICAL ANALYSIS OF INDUSTRIAL R&D*

With the recent slowdown in productivity growth within the economy, R&D has come under scrutiny as a policy target variable. If such targeting is to be effective, it must be realized that not all innovations employed within a firm are induced by the firm through its own R&D: many innovations are purchased through technological licensing or in the form of new capital equipment. Here, interfirm differences in this “make” versus “buy” strategy are analyzed within the context of the Utterback-Abernathy production process lifecycle. Our findings suggest that (1) alternative sources to a firm's R&D for stimulating innovation may prove a viable strategy for federal targeting and (2) extrapolating the Utterback-Abernathy model to an industry formulation has empirical validity.     

A Model for Determining Retail Product Category Assortment and Shelf Space Allocation

We develop a category management model to aid retailers in the space constrained decisions of which products to stock (assortment) and how much shelf space to allocate to those products. The model is formulated as a constrained optimization problem with two basic decision variables: assortment and allocation of space to the items in the assortment. The non-linearities in the objective function and the zero-one decision variables disallow a closed form solution. We develop a heuristic solution procedure based on simulated annealing and test it on a problem with a known optimum. We also apply the technique to a larger problem without a known optimum. Finally, the solution found by simulated annealing is compared against a solution produced using a shelf allocation rule based on share of sales.     

AN EMPIRICAL ASSESSMENT OF COMPUTER-ASSISTED DECISION ANALYSIS*

Decision analysis tools often are used in semistructured and ill-structured situations. While some researchers have argued that computerized decision analysis programs may improve decision quality in such situations, research support for this assertion is weak. In this laboratory study, business students used a computerized decision-analysis program with short strategic-management cases to prepare decision reports. Independent raters' perceptions of aided and unaided decision performance were contrasted, attitudes of students toward the program were assessed, individual differences were correlated with attitudes, and the program's impact on students' perceptions of their decision processes and performance was examined. Student attitudes toward the computerized aid were favorable, and individual differences in reactions generally were as predicted. However, the program did not improve the independent ratings of students' decision reports and recommendations. These findings suggest that computerized decision aids should be adopted cautiously. If such aids result in positive user affect and heightened decision confidence without corresponding improvements in decision quality, they may be dysfunctional. Rigorous research methodologies which incorporate independent evaluations of analyses and decisions are recommended for use in future research on computerized decision-analysis programs.     

A Comparison of Due Date Setting,Resource Assignment,and Job Preemption Heuristics for the Multiproject Scheduling Problem*

This research examines a heuristic, rule-based approach for setting due dates in a multiproject, multijob, or assembly shop. Due date estimation is a challenging problem because the operating environment is capacitated, involves the allocation of multiple resources, and allows for the preemption of resources from one project or job to another. The dynamic, continuous arrival of new jobs or orders frequently results in the preemption of resources through the application of managerially determined priority policies. These preemption policies have a significant impact on the ultimate completion time of a job or a project. A three-factor, full-factorial computer simulation experiment is used to assess the relative effectiveness of combinations of four due date setting heuristics, five resource assignment heuristics, and three resource preemption heuristics. Recommendations are made for the selection of due date and resource assignment heuristic combinations under the three preemption policies examined.     

ENROLLMENT FORECASTING: USE OF A MULTIPLE-METHOD MODEL FOR PLANNING AND BUDGETING*

A multiple-method approach is presented for making enrollment projections at the departmental, school, and university level. The various methods utilized are based on the application of a cohort model (macro level) and regression techniques (micro level). The model demonstrates its flexibility in selecting one of a number of regression techniques according to a comparison approach using historical and projected results. This multiple-method approach has proved to be highly accurate and thus useful in allocating and redistributing resources to the operating units of a university. Previous efforts have relied almost solely on a single regression technique for projections and allocation purposes rather than a method based on a measure of flexibility, adaptability, and prediction confirmation.     

OPTIMAL ALLOCATION OF RECOVERABLE ITEMS*

Allocation of at most n recoverable items among m demand locations is considered. Items are assumed to be demanded singly and independently and are eventually returned to the demand location. If a stockout occurs, the demand is lost and a penalty corresponding to lost profit is assessed. The allocation that minimizes the sum of expected stockout costs plus holding costs is readily obtained. An example involving allocation of rental cars among outlets is solved in detail.     

EMPIRICAL INVENTORY SIMULATION: A CASE STUDY*

An empirical simulation was used to investigate the economics of minimum-buy policies in the U.S. Army management of spare-parts inventory. An empirical simulation makes use of historical data as direct input to the simulation rather than randomly generated data based on a characterization of the historical data. Empirical simulation alleviates the drawbacks of characterizing historical data with a theoretical distribution; however, because steady-state conditions are not reached, a methodological problem arises in the evaluation of inventory assets at the end of the simulation. Our solution was to minimize this problem by use of a cyclic approach.     

Applications and Implementation DECISION SUPPORT SYSTEMS: AN EMPIRICAL ASSESSMENT FOR DECISION MAKING

A laboratory experiment examined the effects of applying decision support system (DSS) technology to decision making in ill-structured problem environments under varying information conditions. Marketing executives participated in the experiment which investigated the effects of DSS availability, DSS training, and data availability on dependent variables that included: (1) the number of alternatives considered by a subject during decision making, (2) the period of time spent by a subject to complete the decision-making process, (3) the subject's perceived confidence in the decisions he or she had made, (4) the amount of data considered by a subject's during decision making, (5) the individual subject's decision processing, and (6) the subject's performance overall. Our results indicate that all three factors significantly affect the number of alternatives considered by subjects during the decision-making process. We therefore suggest that DSS training be coordinated with decision training in order to realize the potential of DSSs as described in the DSS literature.     

MODELING LEARNING CURVE AND LEARNING COMPLEMENTARITY FOR RESOURCE ALLOCATION AND PRODUCTION SCHEDULING*

Research on learning effects in mathematical programming models for optimum resource allocation has called attention to the difficulty in solving such models in their original nonlinear form. In this paper, systematically varying sizes of linear segments are designed to approximate productivity changes along the learning curve, and a single separable linear programming model is developed. With production complementarity and learning transmission between products, a more realistic resource allocation and production scheduling problem emerges. Two cases of learning transmissions are considered, and the model design process, which defines a decision problem that can be solved by a simplex algorithm, is demonstrated.     

SIMULATING FOOD SHOPPERS' ECONOMIC LOSSES AS A RESULT OF SUPERMARKET UNAVAILABILITY*

This study illustrates a methodology as a first step toward the development of a benefit/cost model for the evaluation of the Federal Trade Commission regulation of the unavailability of advertised specials in food stores. A Monte Carlo simulation was used to estimate economic losses to shoppers from unavailable advertised specials. Product unavailability in the model occurred as a result of alternative managerial decisions about purchase-order quantities for advertised specials and shelf-stocking policies combined with probabilistic purchase decisions by customers. The model generated probabilistic individual customer and management behavioral responses to unavailability during a one-year time period. As a result, the derived economic losses to customers from unavailability were based on alternative assumptions about managerial behavior in conjunction with empirically derived shopper responses to advertised specials. This study thus provides some idea of the amount of damages being caused by the excessive unavailability of advertised specials. Aggregate customer losses were found to vary primarily with respect to management's purchase-order quantity of advertised specials.     

Faculty Performance Evaluation: Modeling to Improve Personnel Decisions*

Based on likely future changes in faculty evaluation practices, this paper examines how institutions of higher education might operationalize performance evaluation as related to research, teaching, and service. A model is developed that allows coupling performance evaluation and relevant market considerations with merit pay, tenure, and promotion decisions. The approach is specifically applicable to tenure-track faculty members in colleges and universities.     

AN EMPIRICAL EVALUATION OF INDIVIDUAL ITEM FORECASTING MODELS

A computer simulation experiment was replicated to correct errors in an earlier paper and to compare seven individual item forecasting models across five different demand patterns. Results confirm previous findings that the better forecasting model depends upon the demand pattern and the forecast horizon, as well as the noise level. Nevertheless, exponential double smoothing emerged as the most robust model.     

POLICE PATROL BEAT DESIGN: ALLOCATION OF EFFORT AND EVALUATION OF EXPECTED PERFORMANCE*

This paper describes an iterative process for deploying police patrol force manpower. An application of the process is described as a case study of the St. Louis County Police Department (Missouri). The application is based on the county geographic information system (COGIS) and uses the hypercube queuing model (HQM). The procedures presented illustrate the generation of performance characteristics associated with a specific beat deployment. The application results indicate that the iterative model presented is a reliable and valid instrument for allocating police patrols.     

Generalizing the Announced Price Increase Problem*

This paper generalizes the announced price increase problem to consider a variety of practical concerns arising out of applications ranging from foreign exchange fluctuations to upper limits on the purchase of discounted supermarket items. These include limitations on the amount that can be purchased at the old price and/or on the length of the grace period, within which buyers may take advantage of this lower price. These constraints give rise to 16 possible situations. For each situation purchasing and inventory decision rules are developed. A numerical example is discussed to highlight the main features of the models.     

AN ADAPTIVE DECISION SUPPORT SYSTEM FOR ACADEMIC RESOURCE PLANNING

Institutions of higher learning are growing increasingly interested in the use of model-based approaches to their resource allocation problems. Recent modeling approaches, however, have failed to consider that resource allocation planning is not a well-structured decision process. Additionally, many decision makers are necessarily involved in the academic planning process and may assume dissimilar perspectives on the importance of achieving different goals and objectives. Furthermore, satisfactory allocation solutions can be expected to vary considerably from decision maker to decision maker as the individual's cognitive processes, perceptions, and evaluations are taken into consideration. This paper describes a decision support system (DSS) approach that attempts to adapt to a variety of academic decision makers with differing planning views in an environment of multiple conflicting objectives. This DSS, which was successfully tested on four academic decision makers in a large midwestern university, shows considerable promise for providing decision support to decision makers with varied problem-solving styles.     

PERCEPTION AND PREFERENCE CONGRUENCY: A COLOR REPLICATION*

The work of Doehlert has involved an investigation of automobile-color preferences and their relationship to color perceptions. For the most part, Doehlert's work has pointed to congruency. Other work, dealing with formal congruency testing between unfolded preferences and perceptions, has proven somewhat disappointing. Since the nature of the stimulus set may play a role in a resulting congruency, this study will attempt to replicate and extend the work of Doehlert. Given replication, a framework for stimulus set uniqueness may be obtained. Either way, keynote implications for marketing decision making are to be developed.     

A CANONICAL ANALYSIS OF MANAGERIAL-ENVIRONMENTAL RELATIONSHIPS: IMPLICATIONS FOR METHODOLOGY AND POLICY*

The present study uses canonical analysis to examine the contingency model of organizations in the nursing home field. Data on environmental pressures and managerial practices relevant to the decision-making process are analyzed. Results support the theory that managerial practices are contingent on environmental pressures. The implications of these results for health-care decision makers and for future assessments of managerial-environmental relationships are discussed.