bluuebunny/crossref_metadata_2025_split huggingface dataset:
Information Acquisition and Mental Models: An Investigation into the Relationship Between Behaviour and Learning
Authors: Vandenbosch Betty , Higgins Chris 
Published: October 2008

A cognitive learning perspective is used to develop and test a model of the relationship between information acquisition and learning in the executive support systems (ESS) context. The model proposes two types of learning: mental model maintenance in which new information fits into existing mental models and confirms them; and mental model building in which mental models are changed to accommodate new information. It also proposes that information acquisition objectives determine the type of learning that is possible. When ESS are used to answer specific questions or solve well-defined problems, they help to fine-tune operations and verify assumptions—in other words, they help to maintain current mental models. However, ESS may be able to challenge fundamental assumptions and help to build new mental models if executives scan through them to help formulate problems and foster creativity. Thirty-six interviews with executive ESS users at seven organizations and a survey of 361 users at 18 additional organizations are used to develop scales to measure the model's constructs and provide support for its relationships. These results support the model's prediction that mental model building is more likely with scanning than with focused search. ESS also appear to contribute to mental model maintenance much more often than they do to mental model building. Without a clear focus on mental model building, it seems that business as usual is the more likely outcome.
Inductive Model Analysis Systems: Enhancing Model Analysis in Decision Support Systems
Authors: Sharda Ramesh , Steiger David M. 
Published: October 2008

After building and validating a decision support model, the decision maker frequently solves (often many times) different instances of the model. That is, by changing various input parameters and rerunning different model instances, the decision maker develops insight(s) into the workings and tradeoffs of the complex system represented by the model. The purpose of this paper is to explore inductive model analysis as a means of enhancing the decision maker's capabilities to develop insight(s) into the business environment represented by the model. The justification and foundation for inductive model analysis is based on three distinct literatures: 1) the cognitive science (theory of learning) literature, 2) the decision support system literature, and 3) the model management system literature. We also propose the integration of several technologies that might help the modeler gain insight(s) from the analysis of multiple model instances. Then we report on preliminary tests of a prototype built using the architecture proposed in this paper. The paper concludes with a discussion of several research questions. Much of the previous MIS/DSS and management science research has focused on model formulation and solution. This paper posits that it is time to give more attention to enhancing model analysis.
Interorganizational Cooperation: The Role of Information Technology an Empirical Comparison of U.S. and Japanese Supplier Relations
Authors: Bensaou M. 
Published: October 2008

This paper explores the comparative and cumulative influence of a number of factors on the perceived level of cooperation in a dyadic relationship. Drawing upon the transaction costs economics, organization theory and information systems literatures, we hypothese three sets of key influences: (1) factors exogenous to the relationship, i.e., the characteristics of the environment within which the relationship operates, and factors endogeneous to the relationship including (2) economic and behavioral characteristics of the relationship, and (3) interorganizational information technology applications. These factors have been independently examined in separate research streams. A key contribution of this study is therefore to conceptually and empirically capture their collective influence on cooperation. We empirically test the five hypotheses we develop within the context of buyer-supplier relationships in the U.S. and Japanese automobile industries. Multiple regressions conducted on a data set of 447 distinct relationships indicate that the relational characteristics (i.e., the behavioral climate of the relationship) are the most robust predictor of cooperation in both countries when compared with other structural (e.g., asset specificity) or technological factors (use of EDI—electronic data interchange). Environmental uncertainty (i.e., technological unpredictability) is positively associated with cooperation in Japanese supplier relations, which suggests that cooperation can act as an uncertainty absorption mechanism. Governance structure is a strong and significant predictor of cooperation in both samples, but with the opposite sign. Similarly, information technology (IT) does not play the same predictive role in the two country samples. Significant only in Japan it reflects an ‘electronic partnership’ approach to the use of IT in supplier relations.
Maximum Likelihood Estimation for an Innovation Diffusion Model of New Product Acceptance
Authors: Schmittlein David C. , Mahajan Vijay 
Published: October 2008

A maximum likelihood approach is proposed for estimating an innovation diffusion model of new product acceptance originally considered by Bass (Bass, F. M. 1969. A new product growth model for consumer durables. Management Sci. 15 (January) 215–227.). The suggested approach allows: (1) computation of approximate standard errors for the diffusion model parameters, and (2) determination of the required sample size for forecasting the adoption level to any desired degree of accuracy. Using histograms from eight different product innovations, the maximum likelihood estimates are shown to outperform estimates from a model calibrated using ordinary least squares, in terms of both goodness of fit measures and one-step ahead forecasts. However, these advantages are not obtained without cost. The coefficients of innovation and imitation are easily interpreted in terms of the expected adoption pattern, but individual adoption times must be assumed to represent independent draws from this distribution. In addition, instead of using standard linear regression, another (simple) program must be employed to estimate the model. Thus, tradeoffs between the maximum likelihood and least squares approaches are also discussed.
Effects of Usage and Name on Perceptions of New Products
Authors: Moore William L. , Lehmann Donald R. 
Published: October 2008

Longitudinal changes in perceptions of both new and existing brands in the same product class are studied. Specifically pairwise similarity judgments are collected before and after participation in a 6-occasion choice and usage experiment. Comparisons are made across both the original similarity jugments and the resulting group-level perceptual spaces. Changes are a function of type of brand (old or new) and experimental manipulations. Substantively, the results support the previously untested configural invariance hypothesis, i.e., the perceptions of existing brands are not substantially changed by the introduction of new brands that are relatively different. This suggests this type of scaling technique can be used to predict consumer reactions to new product introductions—even if they are fairly different from current offerings.
An Equilibrium Analysis of Defensive Response to Entry Using a Coupled Response Function Model
Authors: Gruca Thomas S. , Kumar K. Ravi , Sudharshan D. 
Published: October 2008

We determine the optimal response to competitive entry in a market characterized by a market share attraction model. The response of an incumbent is limited to changes in prices, advertising and distribution expenditures; brand positions are assumed to be fixed. We also assume that the entrant's position is chosen exogenously and that the sales potential of the market is constant. After proving the existence of a unique Nash equilibrium, we show that the optimal response depends on the relative market share of the incumbents. The response by nondominant brands (with market shares less than 50%) mirrors the prediction by decoupled response function models—reduce price, advertising and distribution spending. For the dominant brand (having a market share of 50+%), the response is to reduce price and increase marketing spending. This finding explains previous empirical results that could not be addressed by the decoupled models.
The Forecaster's Dilemma
Authors: Ehrman Chaim M. , Shugan Steven M. 
Published: October 2008

Influential forecasts occur when the forecast itself determines whether the forecast is tested. New product sales forecasts are often influential because a low forecast may cause a firm not to launch a new product so that actual sales are never observed. This paper considers a dilemma we face as influential forecasters. Our client requests an unbiased forecast but pressures sometimes exist to provide a bias forecast. From theoretical and empirical perspectives, we discuss the impact of these pressures on the quality of forecasts. We find that: • Noninfluential forecasts, generally, create no pressure for statistically biased forecasts. • As influence increases, the pressures increase. • When our forecasts eliminate alternatives, (e.g., product designs, advertising campaigns), not all forecasts are tested. • Not validating all forecasts causes two effects: Survivor's Curse and Prophet's Fear. • Survivor's Curse makes statistically unbiased forecasts appear optimistic (i.e., overestimate actual sales) because, often, only optimistic forecasts are tested. • Forecasts appearing statistically unbiased or pessimistic might cause concern. Perhaps, some failures are justified. • Prophet's Fear encourages pessimistic forecasts because these forecasts cause hidden opportunity losses while optimistic forecasts cause observable actual losses. • Tested forecasts may appear completely unbiased despite a pessimistic pre-launch bias. • Although no perfect solution exists, clients may lessen bias with experimentation and by seeking more accurate forecasts. Forecasters may lessen bias with forecasts conditioned on launching and by seeking more accurate forecasts.
Attribute Importance Weights Modification in Assessing a Brand's Competitive Potential
Authors: Green Paul E. , Krieger Abba M. 
Published: October 2008

Increasingly, researchers in marketing are recognizing the “lability” of attribute importance weights derived from measurement techniques, such as conjoint analysis. As has been suggested by Simonson and Tversky, attribute importance weights can be sensitive to competitive product context and to purchase situation. This paper describes and applies a procedure for adjusting conjoint importance weights to predict consumers' actual or potential product choices. We discuss the approach from both a descriptive and prescriptive viewpoint. In particular, the latter perspective provides strategic insights into how attribute importance modifications can increase brand share. An industry case, based on real data, is used to illustrate the approach.
The Effect of Package Coupons on Brand Choice: An Epilogue on Profits
Authors: Dhar Sanjay K. , Morrison Donald G. , Raju Jagmohan S. 
Published: October 2008

In Raju, Dhar, and Morrison (1994), a paper that appeared earlier in this journal, we developed an analytical model and conducted empirical analyses to examine the effect of package coupons on market share. In this epilogue, we extend the analytical framework in our earlier paper to study the relative impact of package coupons on profits. We also report findings from five quasi-experiments (including two new quasi-experiments) that provide empirical validity to our model-based predictions. Our results have significance for brand managers in packaged goods firms. Our analysis suggest that while selecting among package coupons, brand managers should carefully define which performance criterion to use: market share impact, redemption rate, or profit impact. Package coupons that lead to the highest market share impact (or redemptions) may not lead to the highest profit impact. We compare the relative profit impact of the following package coupons: peel-off, on-pack, and in-pack. Peel-off coupons must be redeemed on the same purchase occasion on which they are obtained. On-pack coupons are obtained at one purchase occasion but can only be redeemed for a discount on the couponed brand at a future purchase occasion. In-pack coupons are similar to on-pack coupons except that the consumer is unaware of the presence of these coupons when the product is purchased (in-pack coupons are printed or placed inside the package). Consumers with an in-pack or on-pack coupon from a previous purchase occasion will have a higher probability of purchasing the couponed brand even if the brand was not currently offering coupons. Consequently, to understand choice behavior, it is not enough to take into consideration the current choice environment; our model therefore must also keep track of whether or not a consumer has a package coupon that was obtained on an earlier purchase occasion. In other words, since choice is dependent on the purchase environment as well as the state of the consumer, we use a Markov model to represent the choice process. Analytical results are derived based on the long run probabilities of the Markov transition matrix. Our analytical and empirical results suggest that by and large, of the various package coupons examined in our research, on-pack coupons lead to the highest impact on profits. Furthermore, while peel-offs lead to a higher market share than in-packs, because in-packs stimulate repurchase among previous buyers, they lead to higher profits than peel-offs; though only for stronger brands.
The Value of Purchase History Data in Target Marketing
Authors: Rossi Peter E. , McCulloch Robert E. , Allenby Greg M. 
Published: October 2008

An important aspect of marketing practice is the targeting of consumer segments for differential promotional activity. The premise of this activity is that there exist distinct segments of homogeneous consumers who can be identified by readily available demographic information. The increased availability of individual consumer panel data open the possibility of direct targeting of individual households. The goal of this paper is to assess the information content of various information sets available for direct marketing purposes. Information on the consumer is obtained from the current and past purchase history as well as demographic characteristics. We consider the situation in which the marketer may have access to a reasonably long purchase history which includes both the products purchased and information on the causal environment. Short of this complete purchase history, we also consider more limited information sets which consist of only the current purchase occasion or only information on past product choice without causal variables. Proper evaluation of this information requires a flexible model of heterogeneity which can accommodate observable and unobservable heterogeneity as well as produce household level inferences for targeting purposes. We develop new econometric methods to implement a random coefficient choice model in which the heterogeneity distribution is related to observable demographics. We couple this approach to modeling heterogeneity with a target couponing problem in which coupons are customized to specific households on the basis of various information sets. The couponing problem allows us to place a monetary value on the information sets. Our results indicate there exists a tremendous potential for improving the profitability of direct marketing efforts by more fully utilizing household purchase histories. Even rather short purchase histories can produce a net gain in revenue from target couponing which is 2.5 times the gain from blanket couponing. The most popular current electronic couponing trigger strategy uses only one observation to customize the delivery of coupons. Surprisingly, even the information contained in observing one purchase occasion boasts net couponing revenue by 50% more than that which would be gained by the blanket strategy. This result, coupled with increased competitive pressures, will force targeted marketing strategies to become much more prevalent in the future than they are today.
Editorial: A Discussion of Bait and Switch
Authors: Staelin Richard 
Published: October 2008

One of the major responsibilities of the Editor and the Area Editor is to decide on the appropriateness of a submitted paper. As stated in prior editorials (Staelin 1996, 1997), four major criteria are used in making this determination: (1) Will the paper be of interest to our readers? (2) Is it readable? (3) Is it not wrong? (4) Does it make a contribution to the field? Assuming the determination is positive, it is the job of the Editor, with the help of the review team, to specify a course of action that he believes will lead to a publishable paper. What then leads the review team to encourage the publishing of three papers that directly comment on each other's work and, in the process, alter the existing practice of this journal?
Monopolist Pricing with Dynamic Demand and Production Cost
Authors: Kalish Shlomo 
Published: October 2008

This paper deals with pricing of a new product over time by a monopolist who maximizes the discounted profit stream. The interdependency of cost and demand on cumulative production makes the problem inherently dynamic. Cost is assumed to be declining with cumulative production (learning curve effect), while demand is a function of price and cumulative sales, representing word-of-mouth and saturation effects. The paper addresses this problem in a general framework that includes several previous results as special cases, and provides new insights in other situations. While the learning curve and word-of-mouth effect cause prices to be lower than the price that maximizes immediate revenues, the saturation factor has the opposite effect. The price path over time is affected by these factors and the interest rate. We characterize the price path under several different situations and interpret the results for policy guidelines.
Assessing Validity and Test-Retest Reliability for “Pick K of N” Data
Authors: Schmittlein David C. 
Published: October 2008

A mixed binomial error model is proposed which permits the evaluation of validity and test-retest reliability when individuals are asked to pick K of N objects. The approach explicitly incorporates the effect of guessing as well as the respondent's true discrimination ability, and recognizes that that ability will vary across respondents. So, given the choices made by a set of individuals, we are interested in estimating the distribution of true ability over the population. To evaluate the criterion-related validity each person's K choices (of N objects) are compared to an objective standard—the K “correct” choices. For the primary model considered here these data can be summarized by the distribution, over individuals, of the number of “matches” or agreements between a person's choices and the objective criterion. A similar procedure is used for evaluating test-retest reliability. The distribution for the number of matches is again used; but here the matches will refer to agreement between the individual's K choices on this occasion, and the K choices made by that individual on a previous occasion. The use of these reliability and validity results to address specific marketing questions, such as the segmentability of a market or the effect of advertising on perception and attitudes, is also discussed.
Optimal Pricing and Advertising Policies for New Product Oligopoly Models
Authors: Thompson Gerald L. , Teng Jinn-Tsair 
Published: October 2008

In this paper our previous work on monopoly and oligopoly new product models is extended by the addition of pricing as well as advertising control variables. These models contain Bass's demand growth model, and the Vidale-Wolfe and Ozga advertising models, as well as the production learning curve model and an exponential demand function. The problem of characterizing an optimal pricing and advertising policy over time is an important question in the field of marketing as well as in the areas of business policy and competitive economics. These questions are particularly important during the introductory period of a new product, when the effects of the learning curve phenomenon and market saturation are most pronounced. We consider first the monopoly case with linear advertising cost, exponential demand, and three different pricing rules: the optimal variable pricing, the instantaneous marginal pricing, and the optimal constant pricing rules. Several theoretical results are established for these rules including the facts that the instantaneous marginal pricing rule is a myopic version of the optimal pricing rule and the optimal constant pricing rule is a weighted average over time of the instantaneous marginal pricing rule. Another surprising result is that, after the market is at least half saturated, a pulse of advertising must be preceded by a significant drop in price. Numerical solutions of a number of examples are discussed. Oligopolistic models are analyzed as nonzero-sum differential games in the rest of the paper. The state and adjoint equations are easy to write down, but impossible to solve in closed form. Hence we describe how to reformulate these models as discrete differential games, and give a numerical algorithm for finding open loop Nash solutions. The latter was used to solve three triopoly models. In each case it was found that optimal prices and advertising rates start high and steadily decline.
Testing Competitive Market Structures
Authors: Urban Glen L. , Johnson Philip L. , Hauser John R. 
Published: October 2008

An accurate understanding of the structure of competition is important in the formulation of many marketing strategies. For example, in new product launch, product reformulation, or positioning decisions, the strategist wants to know which of his competitors will be most affected and hence most likely to respond. Many marketing science models have been proposed to identify market structure. In this paper we examine the managerial problem and propose a criterion by which to judge an identified market structure. Basically, our criterion is a quantification of the intuitive managerial criterion that a “submarket” is a useful conceptualization if it identifies which products are most likely to be affected by “our” marketing strategies. We formalize this criterion within the structure of classical hypothesis testing so that a marketing scientist can use statistical statements to evaluate a market structure identified by: (1) behavioral hypotheses, (2) managerial intuition, or (3) market structure identification algorithms. Mathematically, our criterion is based on probabilities of switching to products in the situation where an individual's most preferred product is not available. ‘Submarkets’ are said to exist when consumers are statistically more likely to buy again in that ‘submarket’ than would be predicted based on an aggregate “constant ratio” model. For example, product attributes (e.g., brand, form, size), use situations (e.g., coffee in the morning versus coffee at dinner), and user characteristics (e.g., heavy versus light users) are specified as hypotheses for testing alternate competitive structures. Measurement and estimation procedures are described and a convergent approach is illustrated. An application of the methodology to the coffee market is presented and managerial implications of six other applications are described briefly.