“Facing” leaders: Facial expression and leadership perception

This experimental study investigated the effect of a leader's expression of happy versus nervous emotions on subsequent perceptions of leadership and ratings of traits associated with implicit leadership theories (ILTs). Being fast and universally understood, emotions are ideal stimuli for investigating the dynamic effects of ILTs, which were understood in this study in terms of the constraints that expressed emotions impose on the connectionist networks that activate ILTs. The experimental design contrasted videotaped and still frame presentations of a leadership event; however, this methodological factor had no significant effects and analyses were thus collapsed across this factor. Key findings were that the expression of a happy versus nervous emotion at the end of a problem-solving sequence had multiple effects: happy emotions resulted in higher leadership ratings, higher trait ratings, greater correlations among trait ratings, and greater dependence of trait ratings on leadership perceptions. An exploratory model suggested that leadership impressions mediated the effects of facial emotions on trait ratings. The discussion further links the study findings with interpretations in terms of ILTs and many types of constraints on these cognitive structures. It also suggests ways to integrate these ideas with advances in neuroscience research.     

Marginal zero-inflated regression models for count data

Data sets with excess zeroes are frequently analyzed in many disciplines. A common framework used to analyze such data is the zero-inflated (ZI) regression model. It mixes a degenerate distribution with point mass at zero with a non-degenerate distribution. The estimates from ZI models quantify the effects of covariates on the means of latent random variables, which are often not the quantities of primary interest. Recently, marginal zero-inflated Poisson (MZIP; Long et al. [A marginalized zero-inflated Poisson regression model with overall exposure effects. Stat. Med. 33 (2014), pp. 5151–5165]) and negative binomial (MZINB; Preisser et al., 2016) models have been introduced that model the mean response directly. These models yield covariate effects that have simple interpretations that are, for many applications, more appealing than those available from ZI regression. This paper outlines a general framework for marginal zero-inflated models where the latent distribution is a member of the exponential dispersion family, focusing on common distributions for count data. In particular, our discussion includes the marginal zero-inflated binomial (MZIB) model, which has not been discussed previously. The details of maximum likelihood estimation via the EM algorithm are presented and the properties of the estimators as well as Wald and likelihood ratio-based inference are examined via simulation. Two examples presented illustrate the advantages of MZIP, MZINB, and MZIB models for practical data analysis.     

Toward a More Complex View of Career Exploration

This article examines the concept of career exploration in adult populations. In contrast to the prevailing positive view of career exploration, the authors present a more complex and balanced perspective of this process, addressing some of the barriers to career exploration and the applicability of this concept to different populations. They examine differences between voluntary exploration and forced or chance exploration, discuss how relationships may be barriers to exploration, consider various outcomes of career explorations, and call for a more holistic view of the individual in career counseling.     

The Effects of Multitasking on Operations Scheduling

This study considers a typical scheduling environment that is influenced by the behavioral phenomenon of multitasking. Under multitasking, the processing of a selected job suffers from interruption by other jobs that are available but unfinished. This situation arises in a wide variety of applications; for example, administration, manufacturing, and process and project management. Several classical solution methods for scheduling problems no longer apply in the presence of multitasking. The solvability of any scheduling problem under multitasking is no easier than that of the corresponding classical problem. We develop optimal algorithms for some fundamental and practical single machine scheduling problems with multitasking. For other problems, we show that they are computationally intractable, even though in some cases the corresponding problem in classical scheduling is efficiently solvable. We also study the cost increase and value gained due to multitasking. This analysis informs companies about how much it would be worthwhile to invest in measures to reduce or encourage multitasking.     

College and university compliance with a required meningococcal vaccination law

OBJECTIVE: Maryland became the first state to pass a vaccination law requiring college and university students living on campus to obtain a meningococcal vaccination or to sign a waiver refusing vaccination because college students are at increased risk for disease. The authors sought to identify how Maryland colleges addressed the law and determine whether schools were in full compliance. PARTICIPANTS: The authors surveyed 32 college/university administrators via a self-administered questionnaire. METHODS: The authors calculated vaccination and waiver rates and assessed compliance with the law overall and with specific law components. RESULTS: Among 28 participating schools, annual vaccination rates and waiver rates among students during 2000-2004 ranged from 66%-76% and 12%-17%, respectively. Two (7%) schools were compliant with all components of the law. CONCLUSIONS: Mandatory vaccination laws do not ensure compliance at the college and university level. Mandatory reporting, increased education, and collaboration between colleges and universities and public health agencies are needed.     

Power play: the use of space to control and signify power in the workplace

ABSTRACT

The findings of this article emerge from an eight-month study examining career identity practices amongst a select group of fitness professionals in the U.K. We examine how the inter-relationship between physical and social space can denote how power is acquired, displayed, and used by individuals interacting in a shared space. The findings show that power is signified through spatial practices as individuals negotiate through triadic space, creating an identity of space and place for both trainer and client that identifies power, and signifies who has power. Fitness professionals with high levels of symbolic power are able to subvert organizational spatial norms to better serve themselves and their clients, while those with lower levels of symbolic power are forced to, or choose to, negotiate or abdicate space to others. These findings are relevant to a wide range of occupations where self-employment and contract workers interact in shared space.     

The Informal Sector: A Review and Agenda for Management Research

Despite its connotations of non‐compliance, illegality, social exploitation and marginality, the informal sector is a substantial contributor to economic life in developing countries and, increasingly, in more technologically advanced activities. Its prevalence in developed economies has also become more widely recognized. In light of its significance, this paper reviews research on the informal sector from a management and organization scholarship perspective, rather than from an entrepreneurship view, as has been the focus until now. It sets out the atypical management practices that are inherent in the sector, explores the under‐researched relationship between formal and informal firms, and highlights definitional, conceptual and other limitations in extant research. As a step in resolving these issues, the authors present a conceptual model of formality and informality in a three‐dimensional framework that highlights an organizational infrastructure dimension, a view of firms operating along a continuum, and a multi‐level analytical context. Building on this, the authors detail opportunities for enhanced appreciation of in situ management and organizational practices in the informal sector and outline tools for pursuing a management and organization scholarship agenda. Overall, the authors argue that management scholarship has great potential to improve understanding of the informal sector, and that the informal sector provides opportunities to advance management theory, research and practice.     

ESTIMATING A PARAMETER WHEN IT IS KNOWN THAT THE PARAMETER EXCEEDS A GIVEN VALUE

In some statistical problems a degree of explicit, prior information is available about the value taken by the parameter of interest, θ say, although the information is much less than would be needed to place a prior density on the parameter's distribution. Often the prior information takes the form of a simple bound, ‘θ > θ₁ ’ or ‘θ < θ₁ ’, where θ₁ is determined by physical considerations or mathematical theory, such as positivity of a variance. A conventional approach to accommodating the requirement that θ > θ₁ is to replace an estimator, , of θ by the maximum of and θ₁. However, this technique is generally inadequate. For one thing, it does not respect the strictness of the inequality θ > θ₁ , which can be critical in interpreting results. For another, it produces an estimator that does not respond in a natural way to perturbations of the data. In this paper we suggest an alternative approach, in which bootstrap aggregation, or bagging, is used to overcome these difficulties. Bagging gives estimators that, when subjected to the constraint θ > θ₁ , strictly exceed θ₁ except in extreme settings in which the empirical evidence strongly contradicts the constraint. Bagging also reduces estimator variability in the important case for which is close to θ₁, and more generally produces estimators that respect the constraint in a smooth, realistic fashion.     

Deconvolution methods for non-parametric inference in two-level mixed models

Summary.  We develop a general non-parametric approach to the analysis of clustered data via random effects. Assuming only that the link function is known, the regression functions and the distributions of both cluster means and observation errors are treated non-parametrically. Our argument proceeds by viewing the observation error at the cluster mean level as though it were a measurement error in an errors-in-variables problem, and using a deconvolution argument to access the distribution of the cluster mean. A Fourier deconvolution approach could be used if the distribution of the error-in-variables were known. In practice it is unknown, of course, but it can be estimated from repeated measurements, and in this way deconvolution can be achieved in an approximate sense. This argument might be interpreted as implying that large numbers of replicates are necessary for each cluster mean distribution, but that is not so; we avoid this requirement by incorporating statistical smoothing over values of nearby explanatory variables. Empirical rules are developed for the choice of smoothing parameter. Numerical simulations, and an application to real data, demonstrate small sample performance for this package of methodology. We also develop theory establishing statistical consistency.