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Joint space multidimensional scaling (MDS) maps are often utilized for positioning analyses and are estimated with survey data of consumer preferences, choices, considerations, intentions, etc. so as to provide a parsimonious spatial depiction of the competitive landscape. However, little attention has been given to the possibility that consumers may display heterogeneity in their information usage (Bettman et al., 1998) and the possible impact this may have on the corresponding estimated joint space maps. This paper aims to address this important issue and proposes a new Bayesian multidimensional unfolding model for the analysis of two or three-way dominance (e.g. preference) data. The authors’ new MDS model explicitly accommodates dimension selection and preference heterogeneity simultaneously in a unified framework.

This manuscript introduces a new Bayesian hierarchical spatial MDS model with accompanying Markov chain Monte Carlo algorithm for estimation that explicitly places constraints on a set of scale parameters in such a way as to model a consumer using or not using each latent dimension in forming his/her preferences while at the same time permitting consumers to differentially weigh each utilized latent dimension. In this manner, both preference heterogeneity and dimensionality selection heterogeneity are modeled simultaneously.

The superiority of this model over existing spatial models is demonstrated in both the case of simulated data, where the structure of the data is known in advance, as well as in an empirical application/illustration relating to the positioning of digital cameras. In the empirical application/illustration, the policy implications of accounting for the presence of dimensionality selection heterogeneity is shown to be derived from the Bayesian spatial analyses conducted. The results demonstrate that a model that incorporates dimensionality selection heterogeneity outperforms models that cannot recognize that consumers may be selective in the product information that they choose to process. Such results also show that a marketing manager may encounter biased parameter estimates and distorted market structures if he/she ignores such dimensionality selection heterogeneity.

The proposed Bayesian spatial model provides information regarding how individual consumers utilize each dimension and how the relationship with behavioral variables can help marketers understand the underlying reasons for selective dimensional usage. Further, the proposed approach helps a marketing manager to identify major dimension(s) that could maximize the effect of a change of brand positioning, and thus identify potential opportunities/threats that existing MDS methods cannot provides.

To date, no existent spatial model utilized for brand positioning can accommodate the various forms of heterogeneity exhibited by real consumers mentioned above. The end result can be very inaccurate and biased portrayals of competitive market structure whose strategy implications may be wrong and non-optimal. Given the role of such spatial models in the classical segmentation-targeting-positioning paradigm which forms the basis of all marketing strategy, the value of such research can be dramatic in many marketing applications, as illustrated in the manuscript via analyses of both synthetic and actual data.

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 insurance market has high churn rates because customers’ purchase decision-making process and claims management rely heavily on intermediaries. The purpose of this paper is to investigate the role played by insurers and intermediaries in customer satisfaction, as well as in the preferences of customers regarding the purchase decision-making process.

The first step was to select the most important attributes for Portuguese insurance customers. Three focus groups were conducted (using B2C and B2B markets), and data from Portuguese car insurance customers were gathered through an ad hoc questionnaire. Structural equation models and the multidimensional scaling unfolding model were applied.

Intermediaries play a key role in the retail insurance distribution channels by influencing customer satisfaction, claims management and the purchasing process (premium acceptance).

Because of the influence that intermediaries have on customer satisfaction, insurers should improve their partnerships (back office support) with intermediaries.

The study contributes to the retail distribution literature of the insurance sector by providing empirical evidence of the impact of intermediaries on customers’ satisfaction.

Shows that traditional product mapping methods for product positioning analysis fail in international settings due to the high variability of product preferences within each country and the lack of a common product perception among countries. Shows how explicitly incorporating variation in the product positioning analysis can overcome both of these problems. Uses a comparison of how Japanese and US consumers differ in their perceptions of the gift market for young males to illustrate how the proposed method differs from traditional methods. Indicates that Japanese consumers perceive the gift market much more uniformly than Americans.

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.

Aims to show the potential benefits associated with the application of genetic algorithms (GAs) to the field of marketing management. Describes the background and fundamentals of the technique, and introduces a list of relevant marketing areas to which an optimization technique such as genetic algorithms could be applied. Presents two worked examples (one in site location and the other in market segmentation) to illustrate the power and suitability of using GAs in marketing.

The purpose of this chapter is to provide researchers a summary of some of the latest developments in item response theory (IRT), and to help these groups realize that psychometric tools can now be used for theory testing in addition to the traditional role of improving construct measurement. The author first reviews some of the fundamental tenets of classical test theory to contrast with IRT. He then describes recent advances in goodness-of-fit tests that have helped turn IRT into a model-testing tool. Finally, the author reviews several new test models that provide new flexibilities, summarizing summarize several examples of research that has used these new models in organizational research. At the end of this review, the author provides suggestions to help researchers better use these new IRT tools. Although there have been significant advances in IRT in the past decade, there has not been a systematic review of these developments. This review places those developments in context to provide readers a real appreciation of these breakthroughs.

Multidimensional fit (MDF) has been coined as “elusive” and relevant to an individual’s social identity and self-concept, unfolding over time as individuals assess their fit relative to Person-Organization, Person-Vocation, Person-Job, and Person-Team Fit. In this chapter, the literature as it relates to the refugee employment journey, MDF, and HRM practices that facilitate or inhibit MDF is reviewed. Furthermore, in this study, the process-oriented view of the refuge path highlights the complexity of their experience, noting an array of antecedents as they relate to country, host country and individual differences, interventions through NGOs, refugee resettlement agencies, and organizations, as well as the less explored entrepreneurial path. These diverse paths and the process of finding fit, and the obstacles refugees face, are viewed through the lens of shocks and reassessment of MDF throughout their journey. Finally, the study’s outcomes illustrate individual wellbeing factors, organizational level benefits, as well as community level benefits to MDF.