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This paper aims to examine the evolution of a competitive market structure over time through the lens of competitive group membership dynamics.

A new hidden Markov modeling approach is devised that accounts for the three sources of competitive heterogeneity involving managerial strategy, corporate performance and the impact of strategy on performance. In addition, some observed “entry” and “exit” states are considered to model firms’ entry into and exit from the market. The proposed model is illustrated with an investigation of the US banking industry based on a data set created from the COMPUSTAT database. This paper estimated the model within the Bayesian framework and devised a reversible jump Markov chain Monte Carlo estimation procedure to determine the number of latent competitive groups and uncover the characteristics of each group.

This paper shows that the US banking industry, contrary to the prior findings of having a relatively stable structure, has, in fact, gone through dramatic changes in the past number of decades.

Contrary to prior work that has primarily focused on managerial strategy to study market evolutions, the competitive groups perspective accounts for all three sources of intra-industry competitive heterogeneity. In addition, unlike prior research, the analysis is not limited to firms remaining in the panel of study for the entire observation period. Such limitation results in missing the various changes that occur in the competitive market structure because of the new entrants or the struggling firms that do not survive in the market.

Customer value has recently become a primary focus among many strategy researchers and practitioners as an essential element of a firm's competitive strategy. Many firms are engaged in some form of customer value analysis (CVA), which involves a structural analysis of the antecedent factors of perceived value (i.e. perceived quality and perceived price) to assess their relative importance in the perceptions of their buyers. Previous CVA research has focused upon using aggregate market or market segment level analyses. The purpose of this paper is to expose the limitations of implementing CVA on either an aggregate or market segment level basis, and propose an alternative individual level approach.

The paper develops an extended hierarchical Bayesian approach for cross‐sectional data with one observation per response unit, which allows for estimation at the individual firm level to make CVA more useful. This paper demonstrates the utility of the proposed Bayesian methodology involving a CVA study conducted for a large electric utility company. It also compares the empirical results from aggregate, market segment, and the proposed individual level analyses, and show how traditional approaches mask underlying price and quality importance.

Marketing and management strategy researchers need to exhibit care when conducting such CVA analyses as underlying heterogeneity can be masked when aggregate market or segment level analyses are conducted.

This paper provides a new hierarchical Bayes recursive simultaneous model formulation for CVA analyses to provide individual level insights with cross‐sectional data.

The purpose of this paper is to integrate aspects of the literature on strategic and performance groups and explicitly derive strategic/performance groups which exhibit differences with respect to both strategy and performance, as well as display associations and potential interrelationships between the two sets of variables.

A two‐way clusterwise bilinear spatial model was formulated (e.g. a scalar products or vector multidimensional scaling model (MDS)) for the analysis of two‐way strategic and performance data which simultaneously performs MDS and cluster analysis. An efficient alternating least‐squares procedure was devised that estimates conditionally globally optimum estimates of the model parameters within each iterate in analytic, closed‐form expressions.

This bilinear MDS methodology was deployed in the context of strategic/performance group estimation using archival data for public banks in the NY‐NJ‐PA tri‐state area. For this illustration, four strategic/performance groups and two underlying dimensions were found.

Consideration of both strategy and performance data should be employed in describing the heterogeneity amongst firms competing in the same industry.

The paper provides a new spatial methodology to derive strategic/performance groups in any given industry to more completely summarize intra‐industry heterogeneity.

The sports industry is one of the fastest growing business sectors in the world today and its primary source of revenue is derived from fans. Yet, little is known about fans' allocation of time, effort, and/or financial expenditures in regard to the sports they care so desperately about. The purpose of this paper is to explore the multidimensional aspects of such manifestations of fan avidity and examine the nature of heterogeneity of such expressions.

Data were collected from a student sample of football fans from a well‐known US university.

In total, 35 different expressions of fan avidity are developed related to how fans follow and support their favorite team. A spatial choice multidimensional scaling model is developed to uncover four latent dimensions of fan avidity expression.

The managerial aspects of these empirical findings are provided, and the authors suggest several directions for future research.

The resource‐based view (RBV) of the firm has gained much attention in recent years as a means to understand how a strategic business unit obtains a sustainable competitive advantage. In this framework, several research studies have explored the relationships between resources/capabilities and firm performance. This paper seeks to extend this line of research by explicitly modeling the heterogeneity of such relations across firms in various different industries in exploring the interrelationships between capabilities and performance.

A unique latent structure regression model is developed to provide a discrete representation of this heterogeneity in terms of different clusters or groups of firms who employ different paths to achieve firm performance vis‐à‐vis alternative capabilities. An application of the proposed methodology to a sample of 216 US firms were provided.

Finds that the derived four group latent structure regression solution statistically dominates the one aggregate sample regression function. Substantive interpretation for the findings is provided.

The paper contributes to the understanding of the performance effects of investing in capabilities in the RBV framework, which has previously been lacking, especially in the areas of information technology capabilities.

Multidimensional scaling (MDS) represents a family of various geometric models for the multidimensional representation of the structure in data as well as the corresponding set of methods for fitting such spatial models. Its major uses in business include positioning, market segmentation, new product design, consumer preference analysis, etc. The purpose of this paper is to apply a new stochastic constrained MDS vector model to examine the importance of some 45 different leadership attributes as they impact perceptions of effective leadership practice.

The authors present a new stochastic constrained MDS vector model for the analysis of two‐way dominance data.

This constrained vector or scalar products model represents the column objects of the input data matrix by points and row objects by vectors in a T‐dimensional derived joint space. Reparameterization options are available for row and/or column representations so as to constrain or reparameterize such objects as functions of designated features or attributes. An iterative maximum likelihood‐based algorithm is devised for efficient parameter estimation.

The authors present an application to a study conducted to examine the importance of leadership attributes as they impact perceptions of effective leadership practice. Implications for future research and limitations are discussed.

The paper aims to examine the nature of competition within an industry by proposing and examining three separate sources of competitive heterogeneity: the strategies that industry members use, the performance that they obtain, and how effectively the strategies are utilized to obtain such performance results.

To do so, a restricted latent structure finite mixture model is devised that can quantify the contribution of these three potential sources of heterogeneity in the formulation of latent competitive groups within an industry. The paper illustrate this modeling framework with respect to COMPUSTAT strategy and performance data collected for public banks in the USA.

The paper shows how traditional conceptualizations via strategic or performance groups are inadequate to fully represent intra‐industry heterogeneity.

This research paper proposes a new class of restricted finite mixture‐based models, which fit a variety of alternative forms/models of heterogeneity. Information heuristics are developed to indicate “best model.”

Principal components analysis (PCA) is one of the foremost multivariate methods utilized in marketing and business research for data reduction, latent variable modeling, multicollinearity resolution, etc. However, while its optimal properties make PCA solutions unique, interpreting the results of such analyses can be problematic. A plethora of rotation methods are available for such interpretive uses, but there is no theory as to which rotation method should be applied in any given social science problem. In addition, different rotational procedures typically render different interpretive results. The paper aims to introduce restricted PCA (RPCA), which attempts to optimally derive latent components whose coefficients are integer‐constrained (e.g.: {−1,0,1}, {0,1}, etc.).

The paper presents two algorithms for deriving efficient solutions for RPCA: an augmented branch and bound algorithm for sequential extraction, and a combinatorial optimization procedure for simultaneous extraction of these constrained components. The paper then contrasts the traditional PCA‐derived solution with those obtained from both proposed RPCA procedures with respect to a published data set of psychographic variables collected from potential buyers of the Dodge Viper sports car.

This constraint results in solutions which are easily interpretable with no need for rotation. In addition, the proposed procedure can enhance data reduction efforts since fewer raw variables define each derived component.

The paper provides two algorithms for estimating RPCA solutions from empirical data.