bluuebunny/crossref_metadata_2025_split huggingface dataset:
Integrated Modeling Environments in Organizations: An Empirical Study
Authors: Wright Gordon P. , Chaturvedi Alok R. , Mookerjee Radha V. , Garrod Susan 
Published: October 2008

Considerable attention in the information systems and management science literature has focused on computer-based modeling environments, sometimes called integrated modeling environments or model management systems. This research has been primarily concerned with suggesting features/components of modeling environments such as improved executable modeling languages for model creation, integration, and data representation; specialized database systems for managing model data; and customized model-solver software. However, there has been little (if any) empirical guidance offered in the literature about the specific needs of business and industry for computer-based integrated modeling environments. Using a data set compiled from a national survey of modelers (analysts) and model users (decision makers), we empirically investigate the validity of several of the key assumptions of modeling environment research reported in the literature, and examine the relationships between the modeling factors: data complexity, model complexity, modeling intensity, modeler/user requirements, and need for computer-based integrated modeling environments in organizations. Our empirical analysis of the data set shows that practitioners rank automated access to model data and automated error checking (e.g., model syntax and semantics checking) high as desirable components in modeling environments. We find that users prefer to have modeling environments linked to their current modeling and modeling-support software systems. Our findings further suggest that a high percentage of modelers and users are dissatisfied with the software systems they are currently using to support their modeling activities. Finally, a covariance structure analysis of the modeling environment factors clearly shows that: (a) model complexity has a direct positive effect on modeling intensity; (b) data complexity has an insignificant direct effect on modeling intensity, but has a negative effect on modeler/user requirements; and (c) modeler/user requirements have a direct positive effect on need for computer-based integrated modeling environments in organizations.
Modeling and Testing Structured Markets: A Nested Logit Approach
Authors: Kannan P. K. , Wright Gordon P. 
Published: October 2008

A structured market for a product category is a pair (s, As) consisting of a set of consumers s who have the same decision tree, As, which reflects the process by which the consumers in s evaluate product features when making choices. Using a nested logit approach: (1) we present methods to test for the existence of hypothesized multiple structured markets, and (2) we present empirically determined insights on how brands compete within each structured market through the inclusion of marketing mix variables in our model. A successful empirical application of our methods using a panel data set of coffee purchases obtained from Selling Area Marketing Inc. confirms the existence of at least two distinct structured markets in the ground coffee market. The type-primary market is a segment of “switchers”, highly responsive to changes in market mix variables whereas the brand-primary market is a “loyal” segment, relatively unresponsive to marketing programs.
Multiple Source Procurement Competitions
Authors: Seshadri Sudhindra , Chatterjee Kalyan , Lilien Gary L. 
Published: October 2008

Relationships between buying and selling organizations in business markets are varied and complex. One important relationship is procurement, or the type of alliance that buyers form with sellers to fulfill their purchasing needs. Multiple sourcing is often proposed to prevent a variety of procurement problems. We develop a bidding model to investigate the effect of multiple sourcing on competitive behavior prior to supplier selection. The model treats the number of bidders (and the decision to bid) endogenously. This distinctive feature allows us to show that the distribution of the number of bids is not independent of the competition type. Most previous models have not considered this question at all. Although multiple sourcing does increase participation in a bidding competition, we show that it can lead to strategic pre-award price increases that can mitigate the effects of the advantages stressed by most previous analyses of post-award supplier management.
The Voice of the Customer
Authors: Griffin Abbie , Hauser John R. 
Published: October 2008

In recent years, many U.S. and Japanese firms have adopted Quality Function Deployment (QFD). QFD is a total-quality-management process in which the “voice of the customer” is deployed throughout the R&D, engineering, and manufacturing stages of product development. For example, in the first “house” of QFD, customer needs are linked to design attributes thus encouraging the joint consideration of marketing issues and engineering issues. This paper focuses on the “Voice-of-the-Customer” component of QFD, that is, the tasks of identifying customer needs, structuring customer needs, and providing priorities for customer needs. In the identification stage, we address the questions of (1) how many customers need be interviewed, (2) how many analysts need to read the transcripts, (3) how many customer needs do we miss, and (4) are focus groups or one-on-one interviews superior? In the structuring stage the customer needs are arrayed into a hierarchy of primary, secondary, and tertiary needs. We compare group consensus (affinity) charts, a technique which accounts for most industry applications, with a technique based on customer-sort data. In the stage which provides priorities we present new data in which product concepts were created by product-development experts such that each concept stressed the fulfillment of one primary customer need. Customer interest in and preference for these concepts are compared to measured and estimated importances. We also address the question of whether frequency of mention can be used as a surrogate for importance. Finally, we examine the stated goal of QFD, customer satisfaction. Our data demonstrate a self-selection bias in satisfaction measures that are used commonly for QFD and for corporate incentive programs. We close with a brief application to illustrate how a product-development team used the voice of the customer to create a successful new product.
Composite Dependent Variables and the Market Share Effect
Authors: Jacobson Robert , Aaker David A. 
Published: October 2008

Farris, Parry and Ailawadi (1992; hereafter denoted FPA) demonstrate that bias can arise in a regression involving a composite dependent variable where a subset of components of the dependent variable are used as explanatory factors. They correctly observe that the Jacobson and Aaker (1985; hereafter denoted JA) model has explanatory factors that are also components of the ROI dependent variable and, as such, is subject to “composite variable bias.” FPA note that another way of viewing composite variable bias is that the coefficients in the model reflect not their impact on the dependent variable but rather their impact on the dependent variable less the elements of the components included as explanatory factors. As such, additional effects (analogous to indirect effects) may be present to the extent strategic factors influence the included components. FPA conclude that such bias explains the low estimate of the market share effect reported in JA. However, FPA's attempt to replicate our analysis and assess composite variable bias is flawed by a mistake in their analysis, i.e., their disaggregate models do not follow from the JA aggregate specification. The purpose of this note is to correctly assess the extent to which the JA estimate of the market share effect is affected by composite variable bias and to suggest approaches for modeling a composite dependent variable in the presence of unobservable factors. In particular, we (i) show that the disaggregate specifications of FPA do not follow from JA, (ii) look at specifications not subject to composite variable bias to investigate the magnitude of the composite variable bias in JA, and (iii) provide a disaggregate modeling framework that controls for unobservable effects.
Warranty Policy and Extended Service Contracts: Theory and an Application to Automobiles
Authors: Padmanabhan V. , Rao Ram C. 
Published: October 2008

This paper characterizes the manufacturer warranty policy and its effect on consumer behavior under the following conditions: consumers are heterogeneous in risk-preferences, consumer actions affecting the probability of warranty redemption are unobservable to the manufacturer, and the product reliability is known. We obtain the “menu” of warranty contracts, and then make connections with its institutional counterpart: the extended service contract. The model's implications for consumer behavior are examined using data obtained from a sample of recent buyers of new cars. The role of risk in consumer behavior with respect to choice of extended service contracts, and the allocation of effort for maintenance are found to be consistent with the model's predictions. The empirical analysis permits quantifying the demand for extended service contracts as a function of the extent of manufacturer warranty. The estimates show that for our sample of buyers a manufacturer warranty of three years is optimal in the sense of overcoming the role of risk-aversion in the choice of extended service contracts.
Analyzing Duration Times in Marketing: Evidence for the Effectiveness of Hazard Rate Models
Authors: Helsen Kristiaan , Schmittlein David C. 
Published: October 2008

Some statistical methods developed recently in the biometrics and econometrics literature show great promise for improving the analysis of duration times in marketing. They incorporate the right censoring that is prevalent in duration times data, and can be used to make a wide variety of useful predictions. Both of these features make these methods preferable to the regression, logit, and discriminant analyses that marketers have typically used in analyzing durations. This paper is intended to fulfill three objectives. First, we demonstrate how decision situations that involve durations differ from other marketing phenomena. Second, we show how standard modeling approaches to handle duration times can break down because of the peculiarities inherent in durations. It has been suggested in recent marketing articles that an alternative to these conventional procedures, i.e., hazard rate models and proportional hazard regression, can more effectively handle duration type data. Third, to investigate whether these proposed benefits are in fact delivered for marketing durations data, we estimate and validate both conventional and hazard rate models for household interpurchase times of saltine crackers. Our findings indicate the superiority of proportional hazard regression methods vis-à-vis common procedures in terms of stability and face validity of the estimates and in predictive accuracy.
Durable-Goods Monopolists, Rational Consumers, and Improving Products
Authors: Dhebar Anirudh 
Published: October 2008

We consider the case of a monopolist supplying an improving durable product to a population that is heterogeneous in its valuation of product quality. In a two-period framework, we show that if consumers expect the product to improve in “present-value” terms, then intertemporal discrimination might result in the first-period marginal consumer being left with zero surplus and some higher-end consumers postponing purchase. The resulting trajectories for quality and price do not constitute a subgame-perfect equilibrium. One of our conclusions is that the logic of profit maximization in the context of rational consumer choice imposes a demand-side constraint on the rate of product improvement. We also emphasize the disequilibrium consequences of improving a product so rapidly that high-end consumers are tempted to wait for a future new-and-improved version. Finally, the formulation adopted in the paper may be useful to understand observed differences in product improvement rates in different markets.
Gaining More by Stocking Less: A Competitive Analysis of Product Availability
Authors: Balachander Subramanian , Farquhar Peter H. 
Published: October 2008

The price competition between two firms can affect their decisions about product availability. With occasional stockouts a firm loses from foregone sales, yet it may indirectly benefit from the higher price a competitor is able to charge. The more prone customers are to search elsewhere for the product upon encountering a stockout, the greater is the gain from the reduced price competition between firms. This paper establishes conditions whereby the strategic interaction of reduced price competition between firms outweighs the loss from foregone sales. In such cases, a firm can increase its expected profits by choosing to stock less. These results provide a competitive rationale for the high stockout levels observed in some retail situations and have implications for the analysis of product availability in public policy settings.
Customer Base Analysis: An Industrial Purchase Process Application
Authors: Schmittlein David C. , Peterson Robert A. 
Published: October 2008

Customer base analysis is concerned with using the observed past purchase behavior of customers to understand their current and likely future purchase patterns. More specifically, as developed in Schmittlein et al. (1987), customer base analysis uses data on the frequency, timing, and dollar value of each customer's past purchases to infer • the number of customers currently active, • how that number has changed over time, • which individual customers are most likely still active, • how much longer each is likely to remain an active customer, and • how many purchases can be expected from each during any future time period of interest. In this paper we empirically validate the model proposed by Schmittlein et al. In doing so, we provide one of the few applications of stochastic models to industrial purchase processes and industrial marketing decisions. Besides showing that the model does capture key aspects of the purchase process, we also present a more effective parameter estimation method and some results regarding sampling properties of the parameter estimates. Finally, we extend the model to explicitly incorporate dollar volume of past purchases. Our results indicate that this kind of customer base analysis can be both effective in predicting purchase patterns and in generating insights into how key customer groups differ. The link of both these benefits to industrial marketing decision making is also discussed.
A Model of Stochastic Variety-Seeking
Authors: Trivedi Minakshi , Bass Frank M. , Rao Ram C. 
Published: October 2008

In this paper, we propose and test a stochastic model of consumer choice that incorporates attribute-based variety seeking. Our stochastic variety-seeking model (SVS) has nested within it a fixed variety-seeking model, a zero-order model of choice, and a first-order (“pure variety”) model. We compare the SVS model to alternative models. Under stochastic variety seeking, we examine the nature of the variety sought and provide a test of the “satiation” hypothesis. Unlike fixed variety-seeking models, our model allows variety seeking to vary in intensity and consistency over individuals as well as over purchase occasions. We show that the model is equivalent to a random utility model with the features that the attributes of brands relative to those of the brand previously bought influence choice, and the extent of variety seeking is random over choice occasions for a given individual. The model permits a closed-form solution to the conditional switching probabilities parameterized by variety-seeking parameters, and dependent on brand attributes. We apply our model to individual choice data obtained from a field study specifically designed for the purpose at hand.
Customer Satisfaction Incentives
Authors: Hauser John R. , Simester Duncan I. , Wernerfelt Birger 
Published: October 2008

Customer satisfaction incentive schemes are increasingly common in a variety of industries. We offer explanations as to how and when incenting employees on customer satisfaction is profitable and offer several recommendations for improving upon current practice. Faced with employee groups (including managers) who may have shorter time horizons than the firm, such systems enable a firm to use customer reaction to monitor implicitly how employees allocate effort between the short and long terms. These systems can be used to encourage employees to make tradeoffs that are in the best interests of the firm. We derive optimal reward systems for an equilibrium in which the firm maximizes profits, employees maximize their expected utility, and customers choose purchase quantities based on initial reputations, employee efforts (both ephemeral and enduring), and price. The formal model shows how the reliance placed on customer satisfaction in an incentive scheme should depend upon the precision with which customer satisfaction is measured and the extent to which employees focus on the short term. Recommendations for improving upon current practice include: measure customers, former customers, and potential customers; measure satisfaction with competitors' products; disaggregate satisfaction to reflect better the performance of employee groups, and, when different customer segments have different switching costs or they vary in the precision with which their satisfaction can be measured, then measure the segments separately and assign different weights in the incentive plan. Throughout the paper we interpret the formal results based on our experience with actual firms and the current literature. We close with a brief discussion of on-going research at field sites.
Tackling the Retailer Decision Maze: Which Brands to Discount, How Much, When and Why?
Authors: Tellis Gerard J. , Zufryden Fred S. 
Published: October 2008

We propose a model that seeks the optimal timing and depth of retail discounts with the optimal timing and quantity of the retailer's order over multiple brands and time periods. The model is based on an integration of consumer decisions in purchase incidence, brand choice and quantity with the dynamics of household and retail inventory. The major contribution of the model is that it shows how the optimum depth and timing of discount varies with key demand characteristics such as consumer stockpiling, loyalty, response to the marketing mix, and segmentation. In addition, the optima also vary with key supply characteristics such as retail margins, depth and frequency of manufacturer deals, retail inventory, and retagging costs. The most valuable contribution of the model is that it can provide an optimal discount strategy for multiple brands over multiple time periods. The optimization model runs on a user-friendly personal computer program. An application based on UPC scanner data illustrates the model's uses. Sensitivity analyses of the optimization model under alternative scenarios reveal novel insights as to how optimal discounts vary as a function of the key demand and supply characteristics.
Channel Coordination When Retailers Compete
Authors: Ingene Charles A. , Parry Mark E. 
Published: October 2008

This paper explores channel coordination by a manufacturer that sells through competing retailers and that treats these retailers equally, as required by the Robinson-Patman Act. The authors show that, in general, there exists no single two-part tariff with a constant per-unit charge that will duplicate the behavioral results (i.e., prices, quantities, and channel profits) that are obtained by a vertically integrated system; that is, the channel cannot be coordinated except in the trivial cases of identical or noncompeting retailers. However, an appropriately specified quantity-discount schedule will enable the channel to earn the same profits generated by a vertically integrated system. Conditions are derived under which a manufacturer will prefer to offer various two-part tariffs with constant per-unit charges instead of the channel-coordinating quantity-discount schedule. The authors also establish the existence of a menu of two-part tariffs that mimics all results of a vertically integrated system. However, only under stringent conditions will retailers select the appropriate tariff from the menu. When these conditions are not satisfied, the channel is worse off than in the case of a single, second-best tariff. It is also demonstrated that under a wide range of parametric values the manufacturer will prefer to offer the second-best two-part tariff rather than a menu of two-part tariffs that could maximize channel profits.
Manufacturer's Return Policies and Retail Competition
Authors: Padmanabhan V. , Png I. P. L. 
Published: October 2008

Manufacturers' returns policies are a common feature in the distribution of many products. The obvious rationale for returns policies is insurance. Practitioners, not surprisingly, have a different perspective and view returns as a cost of doing business. In this paper, we study the strategic effect of returns policies on retail competition and highlight its profitability implications for a manufacturer. The setting for our research is the distribution of products with uncertain demand, limited shelf lives, and retail competition. Our objective is to provide a better understanding of when manufacturers should adopt returns policies. The insights are obtained with a model based on the economics of strategy and decision making under uncertainty. We show that when retailing is competitive and there is no uncertainty in demand, a returns policy subtly induces retailers to compete more intensely. The provision of a returns policy reduces retail prices without affecting wholesale prices, thereby reducing retailer margins and improving manufacturer profitability. The intuition is that with a returns policy, Cournot-like levels of retail stocks cannot be sustained. Each retailer will order stocks so that it will not be constrained by stocks, and so, intensifying retail competition. When, however, demand is uncertain and there is a single retailer, a returns policy encourages the retailer to over-stock, and so decreases (upstream) manufacturer profits. While the provision of a returns policy leads to lower retail margins, the optimality of returns policy depends on the overall uncertainty and marginal cost. A returns policy reduces the dispersion in retail prices between the high and low states of demand and the range of retail prices in the returns case is contained within the range of retail prices for the no-returns case. In the general setting, when there are competing retailers and demand is uncertain, there is a trade-off for the manufacturer between the benefits (more intense retail competition) and the costs (excessive stocking) of a returns policy. We find that a manufacturer should accept returns when the marginal production cost is sufficiently low and demand uncertainty is not too great. To validate the results of our theory, we conduct an empirical test with data from a major U.S. retailer. The tests confirm our prediction that a returns policy intensifies retail competition and reduces retailer margins. Our theory and empirical test should interest practitioners and researchers in distribution, especially those concerned with managing retail competition.