Search

Game attendance resulting from ticket sales is the single largest revenue stream for Major League Baseball (MLB) teams. We propose a general multiple distributed lag framework following the Koyck family of models for estimating MLB attendance drivers and focus specifically on the differential direct and carryover effects of in-game promotions. By setting various model constraints, the proposed framework incorporates different forms of serial correlation and promotion-specific dynamic effects. Using information model-selection heuristics, we select an optimal model of attendance drivers for the Pittsburgh Pirates' 2010–2012 MLB seasons. We demonstrate that our newly proposed model with an unrestricted serial correlation structure performs best. We find that although kids promotions have the highest direct effect on attendance, giveaway and entertainment promotions have substantial carryover effects and the largest total effects. We use our results to optimize the Pirates' promotional schedule and find that a reallocation of resources across promotional categories can increase profits between 39% and 88%. Data, as supplemental material, are available at http://dx.doi.org/10.1287/mnsc.2013.1856 . This paper was accepted by Eric Bradlow, special issue on business analytics.

The resource-based view (RBV) of the firm suggests that strategic deployment of capabilities allows strategic business units (SBUs) to exploit distinctive competencies and create sustainable competitive advantage. Following the RBV, we propose a new predictive methodology for deriving typologies of SBUs that accommodates heterogeneity among SBUs with respect to their strategic capabilities, how effectively they are employed, and performance. Statistically, we devise a constrained finite-mixture structural-equation procedure that simultaneously accounts for firm capabilities, performance outcomes, and the relationships between them. The procedure allows for a comprehensive modeling and grouping of entities, and simultaneously provides a diagnosis of the sources of heterogeneity via the flexibility of estimating a series of nested models. Managerially, our proposed methodology is grounded in the strategic type and RBV literature and can capture the effects of environmental and industry-specific factors. Using data obtained from 216 SBUs in the United States for illustration, the results show that our derived four mixed-type solution dominates the four-group, Prospectors-Analyzers-Defenders-Reactors classification as well as a number of other nested model solutions in terms of objective statistical fit criteria for this data set, suggesting a more contingency-driven strategic stance adopted by these SBUs. We conclude with a discussion of the theoretical and managerial benefits of an improved methodology for empirically deriving strategic typologies.

A common objective of social science and business research is the modeling of the relationship between demographic/psychographic characteristics of individuals and the likelihood of certain behaviors for these same individuals. Frequently, data on actual behavior are unavailable; rather, one has available only the self-reported intentions of the individual. If the reported intentions imperfectly predict actual behavior, then any model of behavior based on the intention data should account for the associated measurement error, or else the resulting predictions will be biased. In this paper, we provide a method for analyzing intentions data that explicitly models the discrepancy between reported intention and behavior, thus facilitating a less biased assessment of the impact of designated covariates on actual behavior. The application examined here relates to modeling relationships between demographic characteristics and actual purchase behavior among consumers. A new Bayesian approach employing the Gibbs sampler is developed and compared to alternative models. We show, through simulated and real data, that, relative to methods that implicitly equate intentions and behavior, the proposed method can increase the accuracy with which purchase response models are estimated.

Choi, DeSarbo and Harker (1990) have recently proposed a numerical methodology for optimal product positioning and pricing under the assumption that the incumbents react only with price changes in the short run. The current note extends this methodology to include incumbents' long-run strategy of competitive product repositioning, in addition to price reactions. This note also introduces the notion of product relocation cost to the equilibrium analysis, and applies the concept of (full and partial) adjustment processes toward an equilibrium to model various strategic behavior of firms. This methodology is illustrated using an existing set of data collected for a major telecommunication equipment manufacturer.

This paper presents a consumer-based methodology for new product pricing and positioning in the face of price competition. The price competition is modelled as a Nash equilibrium for which two complementary approaches are employed: an analytical approach of duopoly provides qualitative insights into the competitive behavior, and a numerical approach of general oligopoly provides quantitative solutions under a wide variety of market scenarios. The optimal product positioning is formulated as a Stackelberg-Nash (leader-followers) equilibrium which can be solved numerically using recent advances in the field of variational inequalities. A small example concerning analgesics is used to illustrate this methodology.

We introduce an integrative tool, called a strategy map, for describing the nature of a given competitive environment. The approach can also suggest specific courses of actions for competing businesses in those environments. The strength of the method is its ability to capture and communicate visually through a map the complex relations among competitive strategy variables and performance simultaneously. This article focuses on theory from the strategy field to develop a basic model that ties together the key elements in the development of any competitive strategy map. This theoretical development is followed with an examination of some of the important methodological developments in the field. The article then goes into the statistical development of the methodology. Then the maps are developed through a methodology which captures the simultaneous, multidimensional, and interrelated nature of business strategy and performance for a group of businesses within an industry. Specifically, we (a) present a multidimensional scaling methodology for describing the competitive nature of a given industry, (b) examine the necessary tradeoffs that a competitor must make in targeting its performance goals, and (c) explore the relative importance the different strategy variables have for achieving these performance goals in the given competitive environment. An empirical application using PIMS data is presented—strategy maps are derived for a mature, industrial supplier to illustrate how the method can be employed as a descriptive tool in competitive strategy. The findings are then validated for a randomly selected business.