Revisiting the Miles and Snow strategic framework: uncovering interrelationships between strategic types,capabilities, environmental uncertainty,and firm performance

The Miles and Snow strategic type framework is re‐examined with respect to interrelationships with several theoretically relevant batteries of variables, including SBU strategic capabilities, environmental uncertainty, and performance. A newly developed constrained, multi‐objective, classification methodology is modified to empirically derive an alternative quantitative typology using survey data obtained from 709 firms in three countries (China, Japan, United States). We compare the Miles and Snow typology to the classification empirically derived utilizing this combinatorial optimization clustering procedure. With respect to both variable battery associations and objective statistical criteria, we show that the empirically derived solution clearly dominates the traditional P‐A‐D‐R typology of Miles and Snow. Implications and directions for future research are provided. Copyright © 2004 John Wiley & Sons, Ltd.     

Deriving joint space positioning maps from consumer preference ratings

Joint space multidimensional scaling maps are often utilized for positioning analyses and are estimated on survey samples of consumer preferences, choices, considerations, or intentions so as to provide a concise spatial depiction of the competitive landscape including relevant dimensions or attributes, competing brands, and consumers in the same joint space representation. Care has to be given concerning the underlying scale properties of such survey data so as not to distort the resulting joint space positioning map. We present a new joint space multidimensional scaling procedure for positioning analyses for displaying the structure in such survey data when such common ordered successive category measurement scales such as Likert, Edwards, semantic differential, etc., are employed. We present the technical details of this stochastic ordered preference multidimensional scaling vector model as well as the maximum likelihood estimation-based algorithm devised for parameter estimation. Favorable comparisons are made with several existent multidimensional scaling methods in representing the internal structure for such data in marketing positioning studies. An actual commercial positioning application concerning large sports utility vehicles consideration to buy judgments is presented with predictive validation comparisons with other multidimensional scaling joint space procedures.     

Dynamic strategic groups: deriving spatial evolutionary paths

Recent theoretical developments in the domain of strategic groups, specifically those related to cognitive groups and strategic group identity, seem to suggest that strategic group membership is likely to be relatively stable over time and that firms in a strategic group co‐evolve. Yet appropriate data analytic approaches that use information about firms over time to identify stable strategic groups and their evolutionary paths have been lacking. To overcome such limitations, this research proposes a new clusterwise bilinear multidimensional scaling model that can simultaneously identify (1) the number of strategic groups, (2) the dimensions on which the strategic groups are based, and (3) the evolution of the strategy of these groups over time. Our discussion encompasses various alternative model specifications, together with model selection heuristics based on statistical information criteria. An illustration of the proposed methodology using data pertaining to strategic variables for a sample of public banks in the tristate area of New York, Ohio, and Pennsylvania across three time periods (1995, 1999, and 2003) identifies two underlying dimensions with five strategic groups that display very different evolutionary paths over time. Post hoc analysis shows pronounced differences in firm performance across the five derived strategic groups. This article concludes with a discussion of the implications of the findings, as well as potential future research directions. John Wiley & Sons, Ltd. Copyright © 2009 John Wiley & Sons, Ltd.     

Modeling strategic group dynamics: A hidden Markov approach

With competition playing a critical role in market-based strategic planning and implementation, identifying and understanding competiton and competitive dynamics has become critical. In this vein, the strategic groups perspective has emerged as a powerful means to understand such competitive phenomena. Empirical approaches to model competitive dynamics within the strategic groups framework, however, have been piece meal as researchers typically resort to distinct sequential analysis by time period. To overcome the limitations of these simplistic approaches, we develop a hidden Markov model to study strategic group (competitive) dynamics. In this approach, we explicitly account for competitive dynamics over time by modeling strategic group memberships as latent states that follow a first-order Markov process. Thus, we explictly model the notion that firms adopt their strategy for the next time period based on their current strategy and respective outcomes. We illustrate the model with longitudinal data from COMPUSTAT on 63 public banks from the tri-state region of NY-OH-PA. The results show the proposed model to be superior to a number of viable alternative approaches that have been suggested in the literature. We find the existence of three strategic groups: the leveraged group has low current assets compared to current liabilities, high debt to equity, and high total borrowing to assets. The lending group consists of the largest banks that focus on lending with high ratios of gross loans to securities and gross loans to deposits. The balanced group has the largest number of banks where the values of the financial and product ratios are intermediate compared to the leveraged and lending groups. The asymmetries in the switching probabilites are also evident as there seems to be a higher probability of switching into the balanced group than switching out of this group. The switching probabilites are symmetric between the the leveraged and lending groups.     

Towards a brain-to-society systems model of individual choice

Canonical models of rational choice fail to account for many forms of motivated adaptive behaviors, specifically in domains such as food selections. To describe behavior in such emotion- and reward-laden scenarios, researchers have proposed dual-process models that posit competition between a slower, analytic faculty and a fast, impulsive, emotional faculty. In this paper, we examine the assumptions and limitations of these approaches to modeling motivated choice. We argue that models of this form, though intuitively attractive, are biologically implausible. We describe an approach to motivated choice based on sequential sampling process models that can form a solid theoretical bridge between what is known about brain function and environmental influences upon choice. We further suggest that the complex and dynamic relationships between biology, behavior, and environment affecting choice at the individual level must inform aggregate models of consumer choice. Models using agent-based complex systems may further provide a principled way to relate individual and aggregate consumer choices to the aggregate choices made by businesses and social institutions. We coin the term “brain-to-society systems” choice model for this broad integrative approach.     

A latent structure factor analytic approach for customer satisfaction measurement

The linkage of customer satisfaction, customer retention, and firm profitability has been well established in the marketing literature, and provides ample justification as to why customer satisfaction measurement (CSM) has been a focal point in marketing decision making. Although aggregate market level research on understanding the determinants of customer satisfaction is abundant, CSM decisions at segment level are possible only if the individual or market segment differences in the formation of overall satisfaction judgments and subsequent heterogeneity in the role these various determinants play are understood. Based on expectancy-disconfirmation theory in customer satisfaction, we propose a maximum likelihood based latent structure factor analytic methodology which visually depicts customer heterogeneity regarding the various major determinants of customer satisfaction judgments involving multiple attributes, and provides directions for segment-specific CSM decisions. We first describe the proposed model framework including the technical aspects of the model structure and subsequent maximum likelihood estimation. In an application to a consumer trade show, we then demonstrate how our proposed methodology can be gainfully employed to uncover the nature of such heterogeneity. We also empirically demonstrate the superiority of the proposed model over a number of different model specifications in this application. Finally, limitations and directions for future research are discussed.     

A random coefficients mixture hidden Markov model for marketing research

The hidden Markov model (HMM) provides a framework to model the time-varying effects of marketing mix variables. When employed in a panel data context, it is important to properly account for unobserved heterogeneity across individuals. We propose a new random coefficients mixture HMM (RCMHMM) that allows for flexible patterns of unobserved heterogeneity in both the state-dependent and transition parameters. The RCMHMM nests all HMMs found in the marketing literature. Results of two simulation studies demonstrate that 1) averaging across a large number of different data generating processes, the RCMHMM outperforms all its nested versions using both in-sample and out-of-sample performance and 2) the RCMHMM is more robust than its nested versions when underlying model assumptions are violated. In addition, we apply the RCMHMM to an empirical application where we examine the effectiveness of in-game promotions in increasing the short-term demand for Major League Baseball (MLB) attendance. We find that the effectiveness of four promotional categories varies over the course of the season and across teams and that the RCMHMM performs best.     

A Conjoint-based Product Designing Procedure Incorporating Price Competition

Conjoint analysis has become a major tool in the process of designing and concept testing consumer packaged goods and industrial products. In most applications, however, product concepts are tested against existing sets of competing brands without considering potential competitive reactions. Although many researchers have recognized the need for models to incorporate competitive reactions, few methodological developments have been published thus far. Instead of what-if analysis, which depends heavily on the managers' intuition about the competitors' reactions, S. Chan Choi and Wayne DeSarbo propose a game theoretic approach that models competing firms' reactions in price. This price reaction model is incorporated in the conjoint simulator for evaluating product concepts against competing brands. They illustrate the methodology using a commercial data set previously collected.