Modelling Survey Response Data and Determining the Optimal Number of Questionnaires

A modification of Huxley's [3] mail response model is proposed. This new approach retains the simplicity and intuitiveness of Huxley's technique and leads to statistically valid conclusions. Using this model, a procedure is developed to find the optimal number of questionnaires that should be mailed in order to meet some prespecified target.     

A Heuristic Algorithm for the Capacitated Multiple Supplier Inventory Grouping Problem

This paper presents and solves a model for the multiple supplier inventory grouping problem, which involves the minimization of logistics costs for a firm that has multiple suppliers with capacity limitations. The costs included in the model are purchasing, transportation, ordering, and inventory holding, while the firm's objective is to determine the optimal flows and groups of commodities from each supplier. We present an algorithm, which combines subgradient optimization and a primal heuristic, to quickly solve the multiple supplier inventory grouping problem. Our algorithm is tested extensively on problems of various sizes and structures, and its performance is compared to that of OSL, a state-of-the-art integer programming code. The computational results indicate that our approach is extremely efficient for solving the multiple supplier inventory grouping problem.     

A Decision Model for the Robot Selection Problem Using Robust Regression*

Industrial robots are increasingly used by many manufacturing firms. The number of robot manufacturers has also increased with many of these firms now offering a wide range of models. A potential user is thus faced with many options in both performance and cost. This paper proposes a decision model for the robot selection problem. The proposed model uses robust regression to identify, based on manufacturers' specifications, the robots that are the better performers for a given cost. Robust regression is used because it identifies and is resistant to the effects of outlying observations, key components in the proposed model. The robots selected by the model become candidates for testing to verify manufacturers' specifications. The model is tested on a real data set and an example is presented.     

Evaluating Consumer Preferences for Existing Multiattribute Products: A Non-Metric Approach

A model evaluating consumer preferences for multiattribute products is derived. The model possesses the following features: (1) the method works for existing products; (2) the input data require only overall product rankings and attribute rankings; (3) the distribution of part worths (utility) for particular attributes is derived and used to judge the trade-offs among different attributes; (4) the procedure can be implemented with existing software; and (5) the attributes do not need to be quantifiable.     

Estimating the Geographic Extent of Part-Time Labor Markets: U.S. Army Reserves*

Local labor market supportability is becoming an increasingly important issue for the United States Army Reserves. As military bases close and Reserve units are consolidated at fewer Reserve centers, the appropriate reassignments of units to Reserve centers require accurate measures of the ability of local labor markets to support such consolidations. A two-stage random effect model is applied to evaluate the geographical extent of the labor market for Army Reserve centers. In the first stage model, a lognormal distribution is used to describe the commuting distance behavior of the Reserve center members. In the second stage model, we estimate the mean of log transformed commute distance as a function of regional characteristics of the Reserve center. An iterative weighted stepwise selection method is used to find a set of characteristics that adequately predict variation of the mean commute distance over Reserve centers. The resulting model is used as inputs to location and market assessment models to assist the marketing decisions of the Army Recruiting Command.     

A Multiple Objective Embedded Network Model for Human Resource Planning and an Implementation of the Tchebycheff Method

A multiple objective embedded network model is proposed to model a variety of human resource planning problems including executive succession planning, compensation planning, training program design, diversity management and human systems design. The Tchebycheff Method, an interactive multiple objective programming solution procedure developed by Steuer and Choo [32], is implemented using NETSIDE, a computer routine for solving network problems with side constraints developed by Kennington and Whisman [17]. This paper demonstrates how the network structure common to many types of human resource planning problems can be exploited to improve solution efficiency, and how our approach extends the use of network models in human resource planning by including multiple objectives and extranetwork constraints. An illustrative example demonstrating the modeling and solution approach is presented, and the potential applications of these approaches in two specific areas of human resource planning are discussed.     

A Mathematical Model of Service Failure and Recovery Strategies*

Understanding the nature of service failures and their impact on customer responses and designing cost‐effective recovery strategies have been recognized as important issues by both service researchers and practitioners. We first propose a conceptual framework of service failure and recovery strategies. We then transform it into a mathematical model to assist managers in deciding on appropriate resource allocations for outcome and process recovery strategies based on customer risk profiles and the firm's cost structures. Based on this mathematical model we derive optimal recovery strategies, conduct sensitivity analyses of the optimal solutions for different model parameters, and illustrate them through numerical examples. We conclude with a discussion of managerial implications and directions for future research.     

A Generalized Loss Function for Process Optimization

The use of loss functions for measuring the undesirability associated with failure to achieve target values for manufacturing product characteristics is widespread in industry. This study presents a generalization of one of the most commonly used loss functions and compares the two functions based on five theoretical criteria. A simulation involving multiple probability distributions for a single product characteristic is used to compare the performance of the loss functions. An example from the manufacture of helicopter transmission gears is used to empirically demonstrate the superiority of the generalized loss function over the conventional loss function.     

On Sampling Error in Market Share Estimates*

The relative error in the usual estimator of a brand's market share is reformulated in terms of marketing parameters. Such error is shown to be influenced in an important way by market penetration, as well as by variation in brand and product category volume. Of particular interest is the result that the relative error does not depend on the actual share level. Using data from a marketing research firm that supplies share estimates to the health products industry, we find that the relative error may be substantial even when a large sample is available. An upper bound on this relative error is obtained using marketing parameters that can frequently be measured using industry data and a company's internal records, thus reducing the level of judgmental input required in the planning of sample surveys.     

Optimal and Heuristic Decision Strategies for Integrated Production and Marketing Planning

In this study we examine the effects of integrating production and marketing decisions for a short- to medium-range planning horizon in a profit maximizing firm. We formulate two models for determining price, marketing expenditure, demand or production volume, and lot size for a single product with stable demand when economies of scale are present. The full integration (FI) model simultaneously determines all the decisions involved, while the partial integration (PI) model separates the lot sizing decision from the others, as happens frequently in practice. Geometric programming (GP) techniques and marginal analysis are used to compare FI and PI, and obtain important managerial implications regarding the two models.     

Optimal Cost Sampling for Decision Making with Multiple Regression Models

This paper develops an explicit relationship between sample size, sampling error, and related costs for the application of multiple regression models in observational studies. Graphs and formulas for determining optimal sample sizes and related factors are provided to facilitate the application of the derived models. These graphs reveal that, in most cases, the imprecision of estimates and minimum total cost are relatively insensitive to increases in sample size beyond n=20. Because of the intrinsic variation of the regression model, even if larger samples are optimal, the relative change in the total cost function is small when the cost of imprecision is a quadratic function. A model-utility approach, however, may impose a lower bound on sample size that requires the sample size be larger than indicated by the estimation or cost-minimization approaches. Graphs are provided to illustrate lower-bound conditions on sample size. Optimal sample size in view of all considerations is obtained by the maximin criterion, the maximum of the minimum sample size for all approaches.     

A Computerized Approach to Decision Process Tracing for Decision Support System Design*

We propose the use of computerized process tracing (CPT) tools as an appropriate approach for monitoring the information acquisition and evaluation phase of specific decision processes. CPT tools are unobtrusive and seem particularly relevant for evaluating certain decision tasks that may be supported by decision support systems (DSS). CPT tools can be an important component of DSS development. An information systems research taxonomy developed by previous researchers [29] [36] is used to position research work involving the methodology of CPT. Using a critique suggested by Libby [28], CPT tools are evaluated and compared to alternative process tracing tools. A brief empirical example using CPT is provided, and future uses relative to DSS are suggested. The appendix includes an example of a specific CPT tool.     

Determining Order Quantity and Selling Price by Geometric Programming: Optimal Solution,Bounds, and Sensitivity*

This paper presents a geometric programming (GP) approach to finding a profit-maximizing selling price and order quantity for a retailer. Demand is treated as a nonlinear function of price with a constant elasticity. The proposed GP approach finds optimal solutions for both no-quantity discounts and continuous quantity discounts cases. This approach is superior to the traditional approaches of solving a system of nonlinear equations. Since the profit function is not concave, the traditional approaches may require an exhaustive search, especially for the continuous discounts schedule case. By applying readily available theories in GP, we easily can find global optimal solutions for both cases. More importantly, the GP approach provides lower and upper bounds on the optimal profit level and sensitivity results which are unavailable from the traditional approaches. These bounding and sensitivity results are further utilized to provide additional important managerial implications on pricing and lot-sizing policies.     

ASSESSING ENVIRONMENTAL SCANNING AT THE SUBUNIT LEVEL: A MULTITRAIT-MULTIMETHOD ANALYSIS*

Environmental scanning activities of over 400 top management subunits in 108 European manufacturing firms provided the data base for this research. Four traits (constructs) of scanning using three methods (interest, frequency, and time) were examined. A confirmatory factor analysis approach to multitrait-multimethod (MTMM) data was employed. Verification of the reliability as well as convergent and discriminant validity of the scanning scales are reported and discussed. The viability of confirmatory factor analysis in providing a precise analysis of partitioning variance according to trait, method, and error is demonstrated.     

A Two-Period Repeated Game Advertising Investment Model for Oligopolistic Markets with an Application to the Beer Industry*

This paper proposes a simple analytical model of advertising competition in oligopoly markets. The widely used log-log sales response function underlies the model specification. Advertising carryover effects are assumed to persist for one period following the period in which the expenditure occurs. Firms are assumed to be engaged in a repeated competitive game in which in every period advertising levels are set such that they maximize current and next period (i.e., two-period) profits. A Nash equilibrium solution is sought for the game. Compared with previous empirical studies of advertising competition in a game theoretic framework, the proposed model offers the following advantages: (1) oligopoly, not duopoly, markets are analyzed; (2) industry sales is allowed to vary over time as a function of advertising expenditures; (3) non-zero discount rates are used for the players. An empirical application is provided using data from the beer market on sales and advertising expenditures of Anheuser-Busch and Miller Brewing. Comparisons are provided with policies that ignore the dependence of next period profits on current advertising levels, reaction function strategies and spending levels obtained from a market share game. Extension of the model formulation to multiple marketing instruments is briefly discussed.     

THE ANALYSIS OF OUTLYING DATA POINTS USING ROBUST REGRESSION: A MULTIVARIATE PROBLEM-BANK IDENTIFICATION MODEL*

Because the eight largest bank failures in United States history have occurred since 1973 [24], the development of early-warning problem-bank identification models is an important undertaking. It has been shown previously [3] [5] that M-estimator robust regression provides such a model. The present paper develops a similar model for the multivariate case using both a robustified Mahalanobis distance analysis [21] and principal components analysis [10]. In addition to providing a successful presumptive problem-bank identification model, combining the use of the M-estimator robust regression procedure and the robust Mahalanobis distance procedure with principal components analysis is also demonstrated to be a general method of outlier detection. The results from using these procedures are compared to some previously suggested procedures, and general conclusions are drawn.     

Decision Processes for Developing Strategic Applications of Information Systems: A Contingency Approach*

Strategic applications of information systems (IS) are considered by IS researchers to be determined by contextual factors such as environmental uncertainty, and influenced by attributes of the processes preceding them, such as planning and top management support. For better management of the process leading to these applications, it is essential to understand the relationship between process attributes and contextual factors. Utilizing a contingency approach, this article takes a step toward such an understanding. Based on successful strategic IS applications from 81 large companies, it examines the relationship between the context of these applications and the decision-making process leading to them. The results indicate that the external environment is related to whether a rational or political decision-making process is used. The IS function seems to influence the use of the decision process implied by IS researchers, one in which the top management champions the strategic application, while the IS managers contribute by conducting in-depth analysis. However, the organization structure was not related to any decision process attribute. The implications of these findings for future research and practice are also discussed.     

Optimal Offer Strategies in Mergers and Acquisitions*

This paper models the corporate takeover process as a bargaining game under certainty. During the takeover process, an acquirer is generally uncertain about the minimum price the target shareholders will accept. Normally, a takeover is concluded after a sequence of offers have been made. This paper derives optimal offer strategies for the buyer at each stage of this bargaining game under uncertainty. Uncertainty about the target's minimum acceptable price is represented by a probability distribution. Optimal offer strategies depend on the probability distribution of the minimum acceptable price, which can change during the offer process.     

On the Repeated Test-Retest Reliability of the End-User Computing Satisfaction Instrument: A Comment*

This comment extends the test-retest reliability of the end-user computing satisfaction (EUCS) instrument by Torkzadeh and Doll [10]. Whereas Torkzadeh and Doll [10] reportedstability for two hour and two week EUCS test-retest reliability, we investigate the test-retest reliability of the EUCS instrument at two points in time, separated by a two yearinterval. We assess the end user computing satisfaction of personal computer, as well as mainframe, administrative end users in a large public organization. The results of the repeated test-retest using differing application platforms add further support for the reliability of the EUCS measure and highlight some areas of concern for managers of information systems.     

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.