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Ranked preference data arise when a set of judges rank, in order of their preference, a set of objects. Such data arise in preferential voting systems and market research surveys. Covariate data associated with the judges are also often recorded. Such covariate data should be used in conjunction with preference data when drawing inferences about judges.

To cluster a population of judges, the population is modeled as a collection of homogeneous groups. The Plackett-Luce model for ranked data is employed to model a judge's ranked preferences within a group. A mixture of Plackett- Luce models is employed to model the population of judges, where each component in the mixture represents a group of judges.

Mixture of experts models provide a framework in which covariates are included in mixture models. Covariates are included through the mixing proportions and the component density parameters. A mixture of experts model for ranked preference data is developed by combining a mixture of experts model and a mixture of Plackett-Luce models. Particular attention is given to the manner in which covariates enter the model. The mixing proportions and group specific parameters are potentially dependent on covariates. Model selection procedures are employed to choose optimal models.

Model parameters are estimated via the ‘EMM algorithm’, a hybrid of the expectation–maximization and the minorization–maximization algorithms. Examples are provided through a menu survey and through Irish election data. Results indicate mixture modeling using covariates is insightful when examining a population of judges who express preferences.

This paper presents a mixture of linear models (or hedonic regressions) for defining housing submarkets. Two different mixture models are considered: the first model allows all the regression coefficients to vary among the clusters (random coefficients); and the second model allows only the intercept term to change (random intercept). The model with a random intercept can be seen as a linear mixed model where the random effects distribution is estimated via non-parametric maximum likelihood (NPML). The models are illustrated using a real data set of 293 properties in Pamplona, Spain. These mixture models provide a classification of the dwellings into homogeneous groups that determine the structure of the submarkets.

Alongside structural equation modeling (SEM), the complementary technique of partial least squares (PLS) path modeling helps researchers understand relations among sets of observed variables. Like SEM, PLS began with an assumption of homogeneity – one population and one model – but has developed techniques for modeling data from heterogeneous populations, consistent with a marketing emphasis on segmentation. Heterogeneity can be expressed through interactions and nonlinear terms. Additionally, researchers can use multiple group analysis and latent class methods. This chapter reviews these techniques for modeling heterogeneous data in PLS, and illustrates key developments in finite mixture modeling in PLS using the SmartPLS 2.0 package.

Research on relational maintenance of long-distance or cross-residential romantic relationships is limited. Moreover, relatively little is known about relational maintenance among non-marital intimate partners in later life, many of whom prefer to live-apart-together (LAT) rather than cohabit. This research paper examines how older adults from the United States maintain their romantic relationships across residences. The authors conducted a grounded theory study drawing on interviews collected from 22 older adults in LAT relationships. The data revealed that older LAT partners engage in a process of safeguarding autonomy to maintain their partnerships and relationship satisfaction. Two broad strategies were identified: upholding separateness and reshaping expectations. While safeguarding autonomy was paramount, participants also emphasized the importance of having a flexible mindset about the physical copresence of their relationships. The findings have implications for practice, suggesting that creating an interdependent couple-identity may undermine, or at least have little bearing on, the relationship stability of older LAT couples. Future research is needed to determine how LAT experiences among racially/ethnically or socioeconomically diverse samples might differ.

While the literature on corporate strategy has typically focused on examining diversification along the industry and geographical market dimensions, this study seeks to supplement previous research by introducing the concept of business model as a new way of thinking about diversification. Specifically, by integrating the literatures on business models, diversification, and acquisition strategy, we provide a conceptual analysis of how business model relatedness may influence performance implications of M&As. When business models among acquirers and targets are related, the sharing and transfer of superior resources may improve post-acquisition performance. In contrast, when business models among acquirers and targets are unrelated, internal and external identity conflicts may harm post-acquisition performance. Moreover, the conceptual framework developed in this study suggests that even if acquirers and targets are related in a product and geographical market sense, dissimilarities across business models may still harm post-acquisition performance. Overall, we suggest that using the recently emerged concept of business model may provide a new step in examining diversification decisions above and beyond the traditionally examined concepts of product and geographical markets, providing a more complete understanding of when and how multibusiness firms can create value.

This chapter discusses key training challenges that organizations need to confront with the objective of building a robust human resource management system. Given the dynamics of the current business environment, training and development has become an indispensable function in global organizations. Building an effective human capital that contributes to continual organizational growth has become the established norm to survive in a competitive business landscape. However, the training and development function is often rendered ineffective, on account of various bottlenecks existing in the organization. Addressing these bottlenecks is quintessential in ensuring the creation of a performance-driven human capital. The goal of this chapter is to draw attention to the training impediments that hinder organizational growth and to diagnose the underlying causes for the same. This chapter concludes with recommendations that organizational decision-makers can leverage in their quest to strengthen the human capital, by utilizing their training and development infrastructure optimally.

Emotional intelligence (EI) has been known to play an important role in teaching for long. Interactions, teaching environment, and emotional responses of students and instructors all have a demonstrable, complex interplay which spills over to behaviour. Particularly predictive and powerfully pattern-inducing in this regard, are emotional responses to events in the external environment. COVID-19 was a critical disruption in the teaching environment on account of its far-reaching effects over the modes and contents of instruction. Thus, there is a clear and present need to connect the emotional responses among students and instructors due to COVID-19 with the practice and interactions occurring during teaching. The authors present a narrative analysis based on qualitative inputs from instructors in a graduate course setting to find the effects of emotional responses to COVID-19 on teaching virtually. The authors bring in the concept of EI to explain the observations made from the analysis. The conclusions drawn are of direct and immediate importance for the future of teaching and learning in times of disruptions such as COVID-19. The study contributes by updating the knowledge base on emotion management in the classroom on the one hand, while adding to newer streams of research on virtual classroom settings and disruption-induced changes in teaching on the other hand. Some significant directions for praxis of business are also included.