Meritocracy a myth? A multilevel perspective of how social inequality accumulates through work

Work plays a crucial role in rising social inequalities, which refer to unequal opportunities and rewards for different social groups. Whereas the conventional view of workplaces as meritocracies suggests that work is a conduit for social equality, we unveil the ways in which workplaces contribute to the accumulation of social inequality. In our cumulative social inequality in workplaces (CSI-W) model, we outline how initial differences in opportunities and rewards shape performance and/or subsequent opportunities and rewards, such that those who receive more initial opportunities and rewards tend to receive even more over time. These cumulative social inequality dynamics take place via nine different mechanisms spanning four different levels (individual, dyadic, network, and organizational). The CSI-W indicates that the mechanisms interact, such that the social inequality dynamics in workplaces tend to (a) exacerbate social inequalities over time, (b) legitimate social inequalities over time, and (c) manifest themselves through everyday occurrences and behaviors.

A Network-Based Approach to Modeling and Predicting Product Coconsideration Relations

Understanding customer preferences in consideration decisions is critical to choice modeling in engineering design. While existing literature has shown that the exogenous effects (e.g., product and customer attributes) are deciding factors in customers’ consideration decisions, it is not clear how the endogenous effects (e.g., the intercompetition among products) would influence such decisions. This paper presents a network-based approach based on Exponential Random Graph Models to study customers’ consideration behaviors according to engineering design. Our proposed approach is capable of modeling the endogenous effects among products through various network structures (e.g., stars and triangles) besides the exogenous effects and predicting whether two products would be conisdered together. To assess the proposed model, we compare it against the dyadic network model that only considers exogenous effects. Using buyer survey data from the China automarket in 2013 and 2014, we evaluate the goodness of fit and the predictive power of the two models. The results show that our model has a better fit and predictive accuracy than the dyadic network model. This underscores the importance of the endogenous effects on customers’ consideration decisions. The insights gained from this research help explain how endogenous effects interact with exogeous effects in affecting customers’ decision-making.

The relationship between universal network perceptions and dyadic network perceptions and their effect on employees’ behavioral reactions to organizational change

Purpose The purpose of this paper is to examine empirically the relationships formed between the universal network quality perceptions and the dyadic network quality perceptions that an individual formulate through social ties at work and their effect on behavioral reaction toward organizational change. Design/methodology/approach The data were collected from 91 full-time hotel employees through a self-report survey. Using regression models and mediation method three hypotheses referring to the relationship between the universal and the dyadic perceptions as well as the indirect effect of the dyadic network perception on behavioral reactions to change, through universal network perceptions, are tested. Findings The results show that universal network perception has a positive association with an individual’s behavior toward change, while the authors’ dyadic network perception hypothesis is not supported. Additional results highlight the indirect effect of dyadic network perception on behavioral reactions to change through universal network perceptions. Research limitations/implications Owing to the nature of the study, the inferences of causality might not be that strong as the authors’ findings are limited to the fact that the outcome variable is the behavioral intention toward a hypothetical organizational change rather than an actual change. Practical implications Although both types of perceptions are needed in affecting behavioral intentions, the universal network perceptions are the ones that need to be considered as indicators of the need for proactive non-conventional management planning with regard to the human element of change management. Originality/value The principal contribution of this study is that it brings greater clarity to how tie quality perceptions are constructed and their impact on employees’ behavior toward organizational change.

Latent Factors in Student–Teacher Interaction Factor Analysis

Classroom interactions between students and teachers form a two-way or dyadic network. Measurements such as days absent, test scores, student ratings, or student grades can indicate the “quality” of the interaction. Together with the underlying bipartite graph, these values create a valued student–teacher dyadic interaction network. To study the broad structure of these values, we propose using interaction factor analysis (IFA), a recently developed statistical technique that can be used to investigate the hidden factors underlying the quality of student–teacher interactions. Our empirical study indicates there are latent teacher (i.e., teaching style) and student (i.e., preference for teaching style) types that influence the quality of interactions. Students and teachers of the same type tend to have more positive interactions, and those of differing types tend to have more negative interactions. IFA has the advantage of traditional factor analysis in that the types are not presupposed; instead, the types are identified by IFA and can be interpreted in post hoc analysis. Whereas traditional factor analysis requires one to observe all interactions, IFA performs well even when only a small fraction of potential interactions are actually observed.

A Model of Potential Encounters in the Workplace: The Relationships of Homophily, Spatial Distance, Organizational Structure, and Perceived Networks

Potential face-to-face encounters are foundational to most workplace social interactions. There is little resolution on the question of what factors are antecedent to these encounters. This study examines the association of potential encounters with homophily, spatial distance, organizational structure, and perceived networks. Real-time, fine-grained data were collected using ultrawide-band location-tracking technology deployed at a knowledge-intensive subunit of a global manufacturing firm. The organization comprised scientists and engineers responsible for environmental policy, and emissions reporting and trading at the parent company. Potential encounters were constructed from the location data and modeled on the factors above using dyadic network regression models. The results show that spatial distance, organizational structure, and perceived network ties are all significantly related to potential encounters. Surprisingly, the homophily variables were nonsignificant. The contributions of this research regarding the relationship between potential face-to-face encounters and homophily, spatial distance, organizational structure, and perceived networks are discussed.