An Investigation of Competitive Preference Structures and Posterior Performance Through a Bayesian Decision-Theoretic Approach

In this paper we analyze competitive decision-making situations in terms of their preference structures and posterior performance, through a Bayesian decision-theoretic framework. The setting is that of a two-by-two, two-person, non-zero-sum and noncooperative game which is repeated over time. The dynamic behavior of the competitors for different classes of games, as identified by their preference structures, is examined and a classification scheme is proposed for the purpose of unification. The competitors\u27 dynamic behavior and posterior performance for some general classes of games is then derived, and the relationship to the results implied from game-theoretic considerations is discussed. Illustrative examples are given, too

The Film Exhibition Business: Critical Issues, Practice, and Research

The supply chain for movies released for theatrical exhibition consists of the distributor, exhibitor, and the audience, as shown in Figure 5.1. The audience has opportunities to watch moveis in a number of distribution outlets: domestic theaters, foreign theaters, home video, and cable and network TV, where the time lags between the releases of the movies in successive outlets differ but are typically measured in months (Figure 5.2). Despite the availability of these multiple release windows, the theatrical performance of films in the United States has been considered by practitioners to be a critical success driver. Theatrical exhibition is the major factor in persuading the public what they want to see, even if that public never sets foot inside a motion picture theater. And how well and how long a picture plays in theaters has everything to do with its value in other markets (Daniels, Leedy, and Sills 1998, p. 34). the main reasons as to why the theatrical experience is believed to have such a significant impact on the performance of the movie in its other distribution channels are the buzz created by the studios prior to and during the theatrical release dates, generated through high advertising spending, and the attention given by the media to box-office performance and figures

The Motion Picture Industry: Critical Issues in Practice, Current Research, and New Research Directions

The motion picture industry has provided a fruitful research domain for scholars in marketing and other disciplines. The industry has high economic importance and is appealing to researchers because it offers both rich data that cover the entire product lifecycle for many new products and because it provides many unsolved “puzzles.” Although the amount of scholarly research in this area is rapidly growing, its impact on practice has not been as significant as in other industries (e.g., consumer packaged goods). In this article, we discuss critical practical issues for the motion picture industry, review existing knowledge on those issues, and outline promising research directions. Our review is organized around the three key stages in the value chain for theatrical motion pictures: production, distribution, and exhibition. Focusing on what we believe are critical managerial issues, we propose various conjectures—framed either as research challenges or specific research hypotheses—related to each stage in the value chain and often involved in understanding consumer movie-going behavior

The Consumer\u27s Rent vs. Buy Decision in the Rentailer

In this paper, we focus on the perspective and business model of the rentailer — a retail outlet that rents and sells new and used home video titles. This requires predicting the consumer\u27s decision to rent or buy a particular title, segmenting its customer base, and pricing new and used titles. We develop a new model based on a simple heuristic found in the behavioral marketing literature of how people predict their own usage of a service. We estimate the model using a unique panel dataset obtained from a large rentailer, and find it provides a good fit to the data. Using the model estimates we obtain a metric indicating a latent customer tendency to buy at full price (compared to buying at a lower price or renting). Other diagnostic information from the model may help convert renters into buyers. First, expected viewing may be pitched to the consumer in order to persuade consumers that the movie will be well utilized. Secondly, we use the model to generate customized new and used title prices

A Measurement Error Approach for Modeling Consumer Risk Preference

Von Neumann-Morgenstern (vN-M) utility theory is the dominant theoretical model of risk preference. Recently, market researchers have adapted vN-M theory to model consumer risk preference. But, most applications assess utility functions by asking just n questions to specify n parameters. However, any questioning format, especially under market research conditions, introduces measurement error. This paper explores the implications of measurement error on the estimation of the unknown parameters in vN-M utility functions and provides procedures to deal with measurement error. We assume that the functional form of the utility function, but not its parameters, can be determined a priori through qualitative questioning. We then model measurement error as if question format and other influences cause the consumer to choose the unknown “risk parameter” from a probability distribution and to make his decisions accordingly. We provide procedures to estimate the unknown parameters when the measurement error is either (a) Normal or (b) Exponential. Uncertainty in risk parameters induces uncertainty in utility and expected utility, and hence uncertainty in choice outcomes. Thus, we derive the induced probability distributions of the consumer\u27s utility and the estimators for the implied probability that an alternative is chosen. Results are obtained for both the standard decision analysis “preference indifference” question format and for a “revealed preference” format in which the consumer is asked simply to choose between two risky alternatives. Since uniattribute functions illustrate the essential risk preference properties of vN-M functions, we emphasize uniattribute results. We also provide multiattribute estimation procedures. Numerical examples illustrate the analytical results

Risk Sharing and Group Decision Making

In a decision-making problem where a group will receive an uncertain payoff which must be divided among the members of the group, the ultimate payoff of interest is the vector of individual payoffs received by the members of the group. In this paper, preferences are quantified in terms of cardinal utility functions for such vectors of payoffs. These utility functions can represent preferences concerning “equitable” and “inequitable” vectors of payoffs as well as attitudes toward risk. The individual utility functions are aggregated to form a group utility function for the vector of payoffs, and this latter function is, in turn, used to generate a group utility function for the overall group payoff and a sharing rule for dividing the group payoff into individual payoffs. The resulting group decisions are Pareto optimal in utility space. Properties of the sharing rule and the group utility function are investigated for additive and multilinear group utility functions

Diffusion of New Products in Heterogeneous Populations: Incorporating Stochastic Coefficients

Diffusion models have had a major impact on the literature and practice of marketing science. Following the pioneering work of Bass (1969), which suggested a deterministic model for homogeneous populations, the basic diffusion model has been extended to incorporate: changes in the market potential over time (Mahajan & Peterson 1978); complimentarity, substitutability, contigent & independent relations of the new product with other brands in the market place (Peterson & Mahajan 1978); spatial diffusion pattern (Mahajan & Peterson 1979); varying word-of-mouth effects (Easingwood, Mahajan & Muller 1983); various marketing mix effects including the effect of price on both innovation and imitation coefficients (Robinson and Lakhani 1975) or advertising effect on the innovation coefficient (Horsky and Simon 1983). competitive effects (Eliashberg & Jeuland 1982, Fershtman, Mahajan and Muller 1983

Emotional Bidders — An Analytical and Experimental Examination of Consumers\u27 Behavior in Reverse Action

E-commerce has proved to be fertile ground for new business models, which may be patented (for up to 20 years) and have potentially far-reaching impact on the e-commerce landscape. One such electronic market is the reverse-auction model popularized by Priceline.com. There is still uncertainty surrounding the survival of such new electronic markets currently available on the Internet. Understanding user behavior is necessary for better assessment of these sites\u27 survival. This paper adds to economic analysis a formal representation of the emotions evoked by the auction process, specifically, the excitement of winning if a bid is accepted, and the frustration of losing if it is not. We generate and empirically test a number of insights related to (1) the impact of expected excitement at winning, and frustration at losing, on bids across consumers and biddings scenarios; and (2) the dynamic nature of the bidding behavior—that is, how winning and losing in previous bids influence their future bidding behavior

Minimizing Technological Oversights: A Marketing Research Perspective

Technological advances provide vast opportunities for new product development. Some technologies are transformed into successful new products; others are not. In this paper we investigate the role that marketing research methods as currently conceived can play in aligning marketplace needs with technological potential. We discuss the types of opportunities that new technologies present to the marketplace and why the existing set of market research methods are insufficient to assess the potential for all of these new technologies. We then discuss some emerging, non-traditional marketing research methods and assess their potential for addressing the technological oversights problem. We conclude with implications for academics and for practitioners