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The purpose of the current study was to examine (a) the mediating effect of negotiation on the relationship between fans' motivations and constraints to consume sport and to (b) investigate the moderating role of team identification in a sport consumption model of motivation, constraints, and negotiation. Using two datasets from various college athletics stakeholders (i.e., students, alumni, and non-college associated fans) at a large university in the southeastern United States. Our analyses using structural equation modeling show that negotiation mediates the relationship between motivation and sport consumption for both diehard and fair-weather fans. We also find that negotiation mediates the relationship between constraints and sport consumption with the effect being significant for fair-weather fans only. Our study sheds light on sport consumption as it relates to fans' motivation to negotiate constraints in order to consume sport. Specifically, we extend current understanding on how different fans negotiate constraints to consume sport. Marketers can use our findings to design campaigns that focus on providing different fans with a variety of incentives to consume sport. Keywords: sport consumption, constraints, motivation, team identification, negotiation, Consider an individual who is a die-hard fan of a sport team--e.g., someone with great interest in the team in both bad and good times (Wann & Branscombe, 1993)--who is [...]

We develop a framework to model the shopping and consumption decisions of forward-looking consumers. Assuming that the consumer's future utility for each product alternative can be characterized by a standard random utility model, we use dynamic programming to determine the optimal consumption policy and the maximum expected value of consuming any n substitutable products selected while shopping (an n-pack). We propose two models. In the first (canonical) model, we assume that an alternative is consumed on each successive consumption occasion and obtain a closed-form optimal policy and a closed-form value function. Given a consumer's preferences for the product alternatives in an assortment, we then show how to identify that consumer's optimal n-pack using a simple swapping algorithm that converges in at most n swaps. In the second (generalized) model, we introduce an outside option so that a product alternative need not be consumed on each consumption occasion. We obtain a closed-form value function for the generalized model and show that its optimal n-pack is related to that of the canonical model using a special type of majorization. Additional structural properties and implications of each model are explored, as are other applications. History: Accepted by Vishal Gaur, operations management. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2017.2729. Keywords: dynamic programming * decision analysis * utility/preference applications * multinomial logit * assortment optimization, 1. Introduction A great deal of research has focused on two key consumer decisions: (i) the shopping, or purchase, decision, which is usually made in store; and (ii) the consumption [...]

We show that appropriate dynamic pricing strategies can be used to draw benefits from the presence of consumers who strategically time their purchase even if the arrival process is not known. In our model, a seller sells a stock of objects to a stream of randomly arriving long-lived agents. Agents are privately informed about their values, and about their arrival time to the market. The seller needs to learn about future demand from past arrivals. We characterize the revenue-maximizing direct mechanism. While the optimal mechanism cannot be reduced to posted prices (and requires personalized prices), we also present a simple, 'learn and then sell' mechanism that is able to extract a large fraction of the maximal revenue. In this mechanism, the seller first charges a relatively low price that allows learning about the arrival process, and in a second stage, the seller charges the optimal posted price given the previously obtained information. History: Accepted by Serguei Netessine, operations management. Funding: B. Moldovanu is grateful to the European Research Council [Project DMD] and to the German Science Foundation for financial support. A. Gershkov is grateful to the Google Inter-University Center for Electronic Markets and Auctions, the Israel Science Foundation [Grant 83/12], and the German-Israeli Foundation [Grant 0371098]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2017.2724. Keywords: revenue management * strategic consumer behavior * name your own price * Markov arrival process, 1. Introduction Revenue management--broadly speaking, the study of the dynamic allocation of capacity and its pricing under uncertain, fluctuating demand--was pioneered on an industrial scale by airline companies in the [...]

In retail operations, customer choices may be affected by stockout and promotion events. Given panel data with the transaction history of customers, and product availability and promotion data, our goal is to predict future individual purchases. We use a general nonparametric framework in which we represent customers by partial orders of preferences. In each store visit, each customer samples a full preference list of the products consistent with her partial order, forms a consideration set, and then chooses to purchase the most preferred product among the considered ones. Our approach involves: (a) defining behavioral models to build consideration sets as subsets of the products on offer, (b) proposing a clustering algorithm for determining customer segments, and (c) deriving marginal distributions for partial preferences under the multinomial logit model. Numerical experiments on real-world panel data show that our approach allows more accurate, fine-grained predictions for individual purchase behavior compared to state-of-the-art alternative methods. History: Accepted by Vishal Gaur, operations management. Funding: This research was supported in part by the National Science Foundation [Grant CMMI 1454310], Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2016.2683. Keywords: nonparametric choice models * inertia in choice * brand loyalty * panel data * partial-orders * customized promotions * personalized predictions, 1. Introduction Demand estimates are key inputs for inventory control and price optimization models used in retail operations and revenue management (RM). (1) In the last decade, there has been [...]

Behavioral pricing and revenue management aim to incorporate realistic consumer behavior into firms' pricing and inventory models. The key input to these models is market demand, which is often assumed to inherit the characteristics of consumer behavior--as when, for example, one assumes that a market consisting of loss- averse consumers is more responsive to losses than to gains. Yet market demand and consumer behavior need not be related, and so, we argue, that approach to modeling market demand is misguided. This paper proposes an approach that accounts for the heterogeneity in consumer valuation: we aggregate the demand of individual loss-averse consumers to obtain market demand and show that market demand may be more responsive to gains or losses, or it may be equally responsive. Our results have profound implications not only for how best to characterize the market demand of behaviorally biased consumers but also for determining the firm's optimal pricing policy. History: Accepted by Serguei Netessine, operations management. Funding: This research was partially funded by two research grants at the Hong Kong University of Science and Technology [Project DAG12BM05S] and National University of Singapore [Project R-314-000-105-133]. Keywords: behavioral biases * loss aversion * gain seeking * consumer heterogeneity * aggregate demand, 1. Introduction The focus of behavioral pricing and revenue management is incorporating realistic consumer behavior theories when designing pricing and inventory policies. These theories depart from strict economic rationality--the cornerstone [...]

Recent technological advances in online and mobile communications have enabled collaborative consumption or product sharing among consumers on a massive scale. Collaborative consumption has emerged as a major trend as the global economic recession and social concerns about consumption sustainability lead consumers and society as a whole to explore more efficient use of resources and products. We develop an analytical framework to examine the strategic and economic impact of product sharing among consumers. A consumer who purchased a firm's product can derive different usage values across different usage periods. In a period with low self-use value, the consumer may generate some income by renting out her purchased product through a third- party sharing platform as long as the rental fee net of transaction costs exceeds her own self-use value. Our analysis shows that transaction costs in the sharing market have a nonmonotonic effect on the firm's profits, consumer surplus, and social welfare. We find that when the firm strategically chooses its retail price, consumers' sharing of products with high marginal costs is a win-win situation for the firm and the consumers, whereas their sharing of products with low marginal costs can be a lose-lose situation. Furthermore, in the presence of the sharing market, the firm will find it optimal to strategically increase its quality, leading to higher profits but lower consumer surplus. History: Accepted by J. Miguel Villas-Boas, marketing. Funding: This research is supported in part by the National Natural Science Foundation of China [Grants 71531005, 71272015, and 71402030], Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2016.2647. Keywords: collaborative consumption * product sharing * pricing . sharing economy * peer to peer . quality, 1. Introduction Consumers often buy or own products but do not fully utilize them. Product sharing among consumers, collaborative consumption, has emerged as a major trend in recent years as [...]

This paper empirically investigates the interplay between buyer search behavior and firm pricing strategy in a commodity market where many firms compete in price to sell homogeneous products. We use the heterogeneity in buyer search cost to rationalize why a firm offers periodic price discounts and enjoys a high profit in such a market. An estimation method is proposed to recover the nonparametric distribution of buyer search cost from the price and transaction volume data. With both data, we show that the search cost distribution can be estimated nonparametrically in a sequential search model, and estimation results from a nonsequential search model are robust to the assumption on the maximum number of searches. We also show that the commonly observed highlow pricing strategy can be an optimal strategy for firms when buyers search for price information sequentially. As an empirical application, we use the proposed method to estimate the two search models using data on a commodity product sold by a firm in a business-to-business market. The models predict a high profit margin for the firm, which is consistent with the data, but the sequential search model fits with the observed price distributions and supply costs better than the nonsequential search model. History: Accepted by Matthew Shum, marketing. Funding: This research was supported in part by the National Natural Science Foundation of China [Grants 71402030 and 71531005]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2016.2641. Keywords: price variation * price competition * search cost * mixed pricing strategy * nonparametric empirical likelihood method, 1. Introduction Over the past few decades there has been an increasing interest in studying the interplay between buyer search behavior and firm pricing strategy in academic research (e.g., Stigler [...]

Abstract. We propose a structural model of consumer sequential search under uncertainty about attribute levels of products. Our identification of the search model relies on exclusion restriction variables that separate [...]

A technology firm launches newer generations of a given product over time. At any moment, the firm decides whether to release a new version of the product that captures the current technology level at the expense of a fixed launch cost. Consumers are forward-looking and purchase newer models only when it maximizes their own future discounted surpluses. We start by assuming that consumers have a common valuation for the product and consider two product launch settings. In the first setting, the firm does not announce future release technologies and the equilibrium of the game is to release new versions cyclically with a constant level of technology improvement that is optimal for the firm. In the second setting, the firm is able to precommit to a schedule of technology releases and the optimal policy generally consists of alternating minor and major technology launch cycles. We verify that the difference in profits between the commitment and no-commitment scenarios can be significant, varying from 4% to 12%. Finally, we generalize our model to allow for multiple customer classes with different valuations for the product, demonstrating how to compute equilibria in this case and numerically deriving insights for different market compositions. Keywords: new product introduction; new product development; strategic consumer behavior; technology products; noncooperative game theory History: Received February 25, 2013; accepted November 24, 2014, by Yossi Aviv, operations management. Published online in Articles in Advance October 15, 2015., 1. Introduction Firms continuously strive to improve the products they sell to their consumers, either by enhancing the quality of their products or by incorporating new features into them. Companies [...]

This paper introduces a dynamic model of the stochastic repayment behavior exhibited by delinquent credit-card accounts. Based on this model, we construct a dynamic collectability score (DCS) that estimates the account-specific probability of collecting a given portion of the outstanding debt over any given time horizon. The model integrates a variety of information sources, including historical repayment data, account-specific, and time-varying macroeconomic covariates, as well as scheduled account-treatment actions. Two model-identification methods are examined, based on maximum-likelihood estimation and the generalized method of moments. The latter allows for an operational-statistics approach, combining model estimation and performance optimization by tailoring the estimation error to business-relevant loss functions. The DCS framework is applied to a large set of account-level repayment data. The improvements in classification and prediction performance compared to standard bank-internal scoring methods are found to be significant. Keywords: account valuation; consumer credit; collectability scoring; credit collections; GMM estimation; maximum-likelihood estimation; operational statistics; self-exciting point process History: Received June 6, 2011; accepted February 9, 2015, by Noah Gans, stochastic models and simulation. Published online in Articles in Advance September 28, 2015., 1. Introduction The efficient collection of outstanding debt from defaulted credit-card accounts is mission-critical for many financial institutions. By the end of 2012, the total revolving consumer credit in the [...]

This study proposes a new customer lifetime model: the gamma/Gumpertz distribution (G/G). The advantage .1 of this model relative to the well-known Parch) distribution is twofold: (i) its 'probability density function can exhibit a mode at zero or an interior mode, and (ii) it can be skewed to the right or to the left. We combine the C, A., with a negative binomial distribution (NBD) and obtain the moments of the distribution of the number of trarbactions over (0, T] and (T, T T*]. Out of six data sets, the G/G/NBD model provides a notable improvement in the log-likelihood over the Pareto/N13D model in four data sets. It can indicate substantial differences in expected residual lifetimes compared to the Pareto/NBD and induce a retention rather than acquisition policy. On the average, the G/G/NBD exhibits slightly better forecasts of the mean number of transactions than the Pareto/NBD. Key words: buyer behavior; mixture models; catalog retailing; Gompertz distribution History: Received June 28, 2011; accepted September 7, 2011, by Pradeep Chintagunta, marketing. Published online in Articles in Advance December 2, 2011., Introduction The purpose of this paper is to propose a new customer lifetime model for implementation in the noncontractual setting of customer-base analysis, i.e., when defection times are unknown to [...]

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.jbusres.2009.01.018 Byline: David G. Taylor, David Strutton Keywords: Internet; Consumer behavior; Meta-analysis Abstract: E-marketing evolved from a comparatively isolated group of 'dot-com' firms to a mainstream marketing channel activity. This progress has also influenced customers and their 'e-behavior' in the process. This study reviews the literature from information systems and marketing and uses meta-analysis to synthesize key findings. Three important insights emerged. From this exercise, a framework was developed integrating both perspectives, Marketing and IS empirical and conceptual literatures, enhancing the understanding of the customer 'e-behavior' process by which attitudes, perceptions and evaluations interact to influence purchasing intentions. The analysis suggests also that perceived usefulness and perceived ease of use, the primary constructs of the technology acceptance model (TAM), continue to influence purchasing intentions in the post-adoption online context. Thus, the multi-disciplinary nature of online purchasing behavior was demonstrated, underscoring a need for both cross-disciplinary research and a more integrative and collaborative strategy for decision-makers and managers. Author Affiliation: University of North Texas, Denton, TX 76203, United States Article History: Received 1 May 2008; Revised 1 September 2008; Accepted 1 January 2009

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.jbusres.2009.02.027 Byline: Marie-Odile Richard (a), Jean-Charles Chebat (b), Zhiyong Yang (c), Sanjay Putrevu (d) Keywords: Web experience; Web atmospherics; Website exploratory behavior; Website involvement; Modeling; Gender Abstract: This research examined the influence of Internet experience and web atmospherics on consumer online behavior. It developed a model of web navigation behavior where these antecedent variables drove website exploratory behavior and website involvement, which in turn, drove site attitudes and pre-purchase evaluations. These relationships were tested and confirmed in the context of a pharmaceutical website. Further, men and women differed in web navigation behavior, with men engaging in less exploratory behavior and developing less website involvement than women. However, across the two sexes, entertainment, challenge, and effectiveness of information content were the key drivers of website attitudes. The findings provide several guidelines for online communication strategy. Author Affiliation: (a) University of Montreal, Canada (b) Ecole des Hautes Etudes Commerciales, Canada (c) University of Texas at Arlington, United States (d) SUNY at Albany, United States Article History: Received 28 July 2008; Accepted 28 February 2009

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.jbusres.2009.05.004 Byline: Francesco Massara, Sandra S. Liu, Robert D. Melara Abstract: This paper extends current thinking on the relationship between consumers and the retail environment by assessing a theory of consumer-environment interaction that reinterprets arousal and dominance, two dimensions of the PAD model (Mehrabian, Albert, Russel, James A., An approach to environmental psychology. Cambridge, MA: MIT Press, 1974.), as appraisal dimensions (affective expectations). According to the new account, the more specific the task, the less tolerant consumers are about discrepancies between expected and experienced arousal and dominance. The study evaluated the effects of matching or mismatching appraisals on judgments of emotional dimensions as participants shopped within a virtual store environment. Appraisals were manipulated by combining two goal conditions (goal specificity vs. goal ambiguity) with two levels of store arousal (high vs. low) to produce four separate hypothetical states: hedonic fit (ambiguous goal and high arousal), utilitarian fit (specific goal and low arousal), rational control (ambiguous goal and low arousal), and emotional submissiveness (specific goal and high arousal). When perceptual and cognitive appraisals matched (i.e., hedonic or utilitarian fit), participants judged pleasure to be significantly greater than when expectations mismatched (i.e., rational control or emotional submissiveness). Affective expectations concerning arousal and dominance thus are a strong determinant of consumer predisposition toward the environment. Author Affiliation: (a) IULM University, Italy (b) Purdue University, United States (c) City College, City University of New York, United States Article History: Received 1 April 2008; Revised 1 February 2009; Accepted 1 May 2009

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.jbusres.2009.05.002 Byline: Josue Martinez-Garmendia Abstract: This paper contributes to the marketing research literature by proposing an application of hedonic modeling to consumer packaged goods using store scanner data. While other studies have applied hedonic price modeling to CPG, the interpretation of their results might have been flawed. This paper, by proposing an extension of the traditional approach to account for sales quantity, makes the hedonic modeling results based on CPG scanner data acquire their intended interpretation. Hedonic modeling provides an additional alternative in the toolkit of the applied marketing researcher to assist revealing markets' relative preferences. To illustrate the use of hedonic modeling in CPG, the paper focuses on Premium Carbonated Soft Drinks. It uses a full year of weekly store scanner data from grocery stores sampled across the U.S. Author Affiliation: Hall and Partners, 711 3rd Avenue, 19th Floor, New York, NY, United States Article History: Received 3 September 2008; Accepted 13 May 2009 Article Note: (footnote) [star]I would like to thank Information Resources, Inc. for providing the data used in the analyses of this paper. The analyses and views expressed in the paper are solely attributable to the author and do not reflect those of Information Resources, Inc. or Hall and Partners.

To authenticate to the full-text of this article, please visit this link: http://dx.doi.org/10.1111/j.1559-1816.2010.00615.x Byline: Shumaila Y. Yousafzai (1,2), Gordon R. Foxall (*), John G. Pallister (*) Abstract: A key objective of information technology (IT) research is to assess the value of technology for users and to understand the factors that determine this value in order to deploy IT resources better. This paper uses structural equation modeling to ascertain the extent to which 3 popular models of users' behavior-theory of reasoned action (TRA), theory of planned behavior (TPB), and technology acceptance model (TAM)-are predictive of consumers' behavior in the context of Internet banking. Unlike other tests of these models, this paper employs independent measures of actual behavior, as well as behavioral intention. The results indicate that TAM is superior to the other models and highlights the importance of trust in understanding Internet banking behavior. Author Affiliation: (*)Cardiff University Article note: (2) Shumaila Yousafzai, Cardiff Business School, Aberconway Building, Colum Drive, Cardiff, CF10 3EU, Wales, UK. E-mail: yousafzais@cf.ac.uk

To authenticate to the full-text of this article, please visit this link: http://dx.doi.org/10.1111/j.1467-9787.2009.00609.x Byline: Gunhak Lee (1), Morton E. O'Kelly (2) Abstract: ABSTRACT. Due to strong local competition in the broadband access market, strategic locational planning is very important for service providers. In this paper, we explore locational equilibria in a competitive broadband market under several situations: with and without customer loyalty and under different initial market conditions. To do this, we propose a competitive broadband location model based on probabilistic patronage behavior, explicitly considering the characteristics of the broadband market and competitive structures. Also, we suggest iterative algorithms to identify spatial equilibria. We show that competing location strategies may lead to a stable spatial configuration of market share. Author Affiliation: (1)Statistics Research Institute, Korea National Statistical Office, 949 Dunsan-dong, Seo-gu, Daejeon, Korea. E-mail:gunhlee@gmail.com (2)Department of Geography, The Ohio State University, 1036 Derby Hall, 154 North Oval Mall, Columbus, OH 43210. E-mail:okelly.1@osu.edu Article History: Received: December 2007; revised: July 2008; accepted: December 2008.

Broadband offers several benefits to consumers over its preceding technology 'narrowband'. Despite it offering such benefits, many countries are still experiencing low levels of adoption of broadband technology by consumers. This study presents an extended technology acceptance model (TAM) that integrates perceived resources, self-efficacy and social influence into the TAM in order to investigate factors determining consumer adoption of broadband. The model was empirically tested employing data collected from a survey of broadband consumers in the United Kingdom. A regression analysis was conducted to evaluate the influence of predictive constructs on behavioural intention to adopt broadband and actual adoption behaviour. Findings of the study indicated that all variables significantly affected consumers' behavioural intention to adopt broadband. The outcomes of the paper will be useful for the stakeholders such as internet service providers and governments interested in encouraging the adoption of broadband. The implications of this work to both researchers and practitioners is discussed. Keywords: adoption; behaviour; broadband; information systems; technology acceptance model

The objectives of the study reported in this paper are: (1) to evaluate the adequacy of two data mining techniques, decision tree and neural network in analysing consumer preference for a fast-food franchise and (2) to examine the sufficiency of the criteria selected in understanding this preference. We build decision tree and neural network models to fit data samples collected from 800 respondents in Taiwan to understand the factors that determine their brand preference. Classification rules are generated from these models to differentiate between consumers who prefer the brand and those who do not. The generated rules show that while both decision tree and neural network models can achieve predictive accuracy of more than 80% on the training data samples and more that 70% on the cross-validation data samples, the neural network models compare very favourably to a decision tree model in rule complexity and the numbers of relevant input attributes. doi: 10.1057/palgrave.jors.2602646 Keywords: data mining; decision tree; neural network; consumer brand preference

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.ejor.2008.06.027 Byline: Nicolas Glady (a), Bart Baesens (a)(b)(c), Christophe Croux (a) Keywords: Churn prediction; Classification; Customer lifetime value; Prediction models Abstract: The definition and modeling of customer loyalty have been central issues in customer relationship management since many years. Recent papers propose solutions to detect customers that are becoming less loyal, also called churners. The churner status is then defined as a function of the volume of commercial transactions. In the context of a Belgian retail financial service company, our first contribution is to redefine the notion of customer loyalty by considering it from a customer-centric viewpoint instead of a product-centric one. We hereby use the customer lifetime value (CLV) defined as the discounted value of future marginal earnings, based on the customer's activity. Hence, a churner is defined as someone whose CLV, thus the related marginal profit, is decreasing. As a second contribution, the loss incurred by the CLV decrease is used to appraise the cost to misclassify a customer by introducing a new loss function. In the empirical study, we compare the accuracy of various classification techniques commonly used in the domain of churn prediction, including two cost-sensitive classifiers. Our final conclusion is that since profit is what really matters in a commercial environment, standard statistical accuracy measures for prediction need to be revised and a more profit oriented focus may be desirable. Author Affiliation: (a) Faculty of Business and Economics, K.U. Leuven, Naamsestraat 69, B-3000 Leuven, Belgium (b) School of Management, University of Southampton, SO17 1BJ, UK (c) Vlerick Leuven Ghent Management School, Reep 1, B-9000 Ghent, Belgium Article History: Received 22 November 2006; Accepted 19 June 2008

Mobile data services are entering into all aspects of people's life and are expected to be an important revenue source of telecom carriers. Nevertheless, the acceptance pace of mobile data services is slower than the expected level projected by the telecommunication industry. This research investigated the reason that people use mobile data services in China. Combined with subscribers' experience factors, an extended technology acceptance model (TAM) for mobile data services was presented. The model was tested using data collected from 802 mobile subscribers. The findings of the study indicate that mobile voice service and innovation experience of mobile data services affect subscribers' consumption intention greatly; subscribers' perceived ease-of-use and brand experience influence subscribers' attitude towards mobile data services largely. Promotion of mobile data services is most effective when it is promoted with perfect voice service experience. Additionally, our findings suggest that mobile service carriers need to enhance their investments in mobile data services' R&D. Collaboration among the value chain members of mobile data services will promise cutting-edge new products and services. Using system methodology, a mobile data service infusion system model was constructed to explain the general adoption rules of mobile data services. The adoption of mobile data services in mobile logistics area was also discussed. Keywords mobile data services; technology acceptance model; experience marketing; systems methodology; mobile logistics, INTRODUCTION Mobile data services which are provided by the mobile data net based on the basic mobile network have been developed endlessly by mobile carriers in recent years. Among these [...]

As the spectator sport market has become large and competition for consumers has increased, the need for understanding spectators' motives and points of attachment has become important for developing effective marketing strategies. The purpose of the study was to examine four different models that explain the relationships among motives and points of attachment and determine a model that explains the most variance in the referent variables. A total of 501 college students responded to the Motivation Scale for Sport Consumption (MSSC) and the Points of Attachment Index (PAI). The results showed that motives can be divided into fan motives and spectator motives, and these motives were related to different sets of points of attachment: organizational identification and sport identification., Testing Models of Motives and Points of Attachment among Spectators in College Football Sport spectating is a popular leisure activity in the United States. In fact, attendance and revenues for [...]

Existing causal models measuring the relationship between customer attitudes and loyalty are not useful for market research due to their weaknesses. These models do not represent causal processes properly and do not provide information about the effects of managerial actions.

A model where some consumers rose to become market leaders whose consumption choices are emulated by uninformed individuals is presented. Follow-on sales induce firms to treat market leaders preferentially which reinforces their learning.

The effects on consumers of life-project framing discourses and culturally pervasive discourses are analyzed. Cultural perspectives are an important complement to cognitive models of consumer persistence.

The testing of a process model to study the effect of affective misforecasting on the evaluation of products by consumers is described.

Probability theory is used to plan and improve marketing strategies. Discussion focuses on the customer probability cube, which helps predict customer purchasing behavior, and ways for estimating the likelihood of purchasing.

A model which tackles four issues on vehicle emission reduction policies is presented. These issues include how abatement issues involve many different agents such as manufacturers and consumers, the different choices that consumers make on their cars, the tax on emissions, and lastly, the costs that come from lost consumer surplus from driving fewer miles.

A behavioral perspective model (BPM) is described that suggests how situational influences on consumer choice might be categorized and employ a specified approach to environmental psychology to test that model in the context of attitude formation and attitude-behavior consistency. The results indicate that attitude responses to consumer environments vary as predicted by the model.

A data shows that retrospective reports about emotional experiences could be useful predictors of attitude when compared to traditional measures of cognitive structure and that prior experience with a behavior could play a major role in moderating these relationships. The appeal of multiattribute attitude model was always their value for predicting and diagnosing motives and preferences.

The affect, feelings and emotion and attitude, evaluative judgment based on brand beliefs streams of research are combined to propose an integrated model of attitude and choice. The predictive validity of the proposal model is tested and compared to several other nested models using a regression and logit framework.

A commentary on some of the salient issues related to attitude and affect is presented. It also inspires more research on understanding the nature of the cognitive and affective constructs and their interaction to influence overall attitude, intention, and behavior.

A conceptual model is developed on whether the organizational buyer's decision-making uncertainty (DMU) could be reduced by the buyer's perception of supplier trust, supplier commitment, and supplier dependence. The results suggest that even though buyer's perception of the supplier's dependence does not significantly increase buyer's trust, it does have a direct effect on DMU.

The analytic asymptotic methods to characterize time-series properties of nonlinear dynamic stochastic models are developed and discussed. It resulted in characterizing the model economy, both in terms of its average behavior and its occasional large cyclical fluctuations.

Byline: Inseong Song (1), Pradeep K. Chintagunta (2) Keywords: choice models; new product adoption; heterogeneity; price expectations Abstract: We develop an empirical model for the adoption process of a new durable product that accounts for consumer heterogeneity as well as consumers' forward-looking behavior. Accounting for heterogeneity is important for two reasons. As the mix of consumers with different preferences and price sensitivities could change over time, firms need to update their marketing strategies. Further, it allows for a variety of shapes for the aggregate adoption process over time. As prices for durable and technology products fall over time with firms continually introducing enhanced products, consumers may anticipate these prices and improvements and delay their purchases in the product category. Forward-looking consumers optimize purchase timing by trading off their utilities from buying the product and their expectations on future prices, quality levels, and brand availability. Such forward-looking behavior will result in price dynamics in the marketplace as price changes today influence future purchases. And it results in different shapes of the new product sales pattern over time by influencing the time to take-off. We show how the parameters of our model can be estimated using aggregate data on the sales, prices, and attributes of brands in a product category. We apply our model to market data from the digital camera category. Our data are consistent with the presence of both heterogeneity and forward looking behavior among consumers. At the product category level, we are able to decompose the effects of the entry of Sony into primary demand expansion and switching from other brands. At the brand level, we find that there exist several segments in the market with different preferences for the brands and different price sensitivities leading to differences in adoption timing and brand choice across segments. For a given brand, we show how the changing customer mix over time has implications for that brand's pricing strategies. We characterize how price effects vary across brands and over time and how price changes in a given time period influence sales in subsequent periods. Model comparison and validation results are also provided. Author Affiliation: (1) Department of Marketing, Hong Kong University of Science and Technology, China (2) Graduate School of Business, The University of Chicago, USA Article History: Registration Date: 11/10/2004