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Incivility is widespread in the workplace and has been shown to have significant affective and behavioral consequences. However, the authors still have a limited understanding as to whether, how and when discrete incivility events impact team performance. Adopting a resource depletion perspective and focusing on the cognitive implications of such events, the authors introduce a multi-level model linking the adverse effects of such events on team members’ working memory – the “workbench” of the cognitive system where most planning, analyses, and management of goals occur – to team effectiveness. The model which the authors develop proposes that that uncivil interpersonal behavior in general, and rudeness – a central manifestation of incivility – in particular, may place a significant drain on individuals’ working memory capacity, affecting team effectiveness via its effects on individual performance and coordination-related team emergent states and action-phase processes. In the context of this model, the authors offer an overarching framework for making sense of disparate findings regarding how, why and when incivility affects performance outcomes at multiple levels. More specifically, the authors use this framework to: (a) suggest how individual-level cognitive impairment and weakened coordinative team processes may mediate these incivility-based effects, and (b) explain how event, context, and individual difference factors moderators may attenuate or exacerbate these cognition-mediated effects.

I review the burgeoning literature on applications of Markov regime switching models in empirical finance. In particular, distinct attention is devoted to the ability of Markov Switching models to fit the data, filter unknown regimes and states on the basis of the data, to allow a powerful tool to test hypotheses formulated in light of financial theories, and to their forecasting performance with reference to both point and density predictions. The review covers papers concerning a multiplicity of sub-fields in financial economics, ranging from empirical analyses of stock returns, the term structure of default-free interest rates, the dynamics of exchange rates, as well as the joint process of stock and bond returns.

Applied econometric analysis is often performed using data collected from large-scale surveys. These surveys use complex sampling plans in order to reduce costs and increase the estimation efficiency for subgroups of the population. These sampling plans result in unequal inclusion probabilities across units in the population. The purpose of this paper is to derive the asymptotic properties of a design-based nonparametric regression estimator under a combined inference framework. The nonparametric regression estimator considered is the local constant estimator. This work contributes to the literature in two ways. First, it derives the asymptotic properties for the multivariate mixed-data case, including the asymptotic normality of the estimator. Second, I use least squares cross-validation for selecting the bandwidths for both continuous and discrete variables. I run Monte Carlo simulations designed to assess the finite-sample performance of the design-based local constant estimator versus the traditional local constant estimator for three sampling methods, namely, simple random sampling, exogenous stratification and endogenous stratification. Simulation results show that the estimator is consistent and that efficiency gains can be achieved by weighting observations by the inverse of their inclusion probabilities if the sampling is endogenous.

In this chapter, the authors consider the role of time for research in occupational stress and well-being. First, temporal issues in studying occupational health longitudinally, focusing in particular on the role of time lags and their implications for observed results (e.g., effect detectability), analyses (e.g., handling unequal durations between measurement occasions), and interpretation (e.g., result generalizability, theoretical revision) were discussed. Then, time-based assumptions when modeling lagged effects in occupational health research, providing a focused review of how research has handled (or ignored) these assumptions in the past, and the relative benefits and drawbacks of these approaches were discussed. Finally, recommendations for readers, an accessible tutorial (including example data and code), and discussion of a new structural equation modeling technique, continuous time structural equation modeling, that can “handle” time in longitudinal studies of occupational health were provided.

Chapter 3 examines the attributes of an external stimulus, which the brain collects and models to construct a sensation. An important aspect of this process is the sensory system's filtering capacity, which removes extraneous and irrelevant information from the modeled information. The response mechanisms of all five senses are discussed to establish the practice of viewing the discipline (psychophysics) from multiple perspectives (senses). The differences in multiple perspectives on the same data is compiled into a model of the attributes to which the brain attends to engage with a sensation.

I survey applications of Markov switching models to the asset pricing and portfolio choice literatures. In particular, I discuss the potential that Markov switching models have to fit financial time series and at the same time provide powerful tools to test hypotheses formulated in the light of financial theories, and to generate positive economic value, as measured by risk-adjusted performances, in dynamic asset allocation applications. The chapter also reviews the role of Markov switching dynamics in modern asset pricing models in which the no-arbitrage principle is used to characterize the properties of the fundamental pricing measure in the presence of regimes.

Research in industrial and organizational psychology demonstrates that the regulation of negative emotions in response to both organizational stressors and interpersonal workplace interactions can result in functional and dysfunctional outcomes (Côté, 2005; Diefendorff, Richard, & Yang, 2008). Research on the regulation of negative emotions has additionally been conducted in social psychology, developmental psychology, neuropsychology, health psychology, and clinical psychology. A close reading of this broader literature, however, reveals that the conceptualization and use of the term “emotion regulation” varies within each research field as well as across these fields. The main focus of our chapter is to make sense of the term “emotion regulation” in the workplace by considering its use across a broad range of psychology disciplines. We then develop an overarching theoretical framework using disambiguating terminology to highlight what we argue are the important constructs involved in the process of intrapersonal emotion generation, emotional experience regulation, and emotional expression regulation in the workplace (e.g., emotional intelligence, emotion regulation strategies, emotion expression displays). We anticipate this chapter will enable researchers and industrial and organizational psychologists to identify the conditions under which functional regulation outcomes are more likely to occur and then build interventions around these findings.

We develop a multilevel model of emotional processes grounded in social identity theory to explore the role of emotion in transformational leadership.

This work is conceptual in nature and develops theory surrounding emotion in organizations by integrating theories on transformational leadership, emotion management, and organizational identity.

Transformational leaders utilize interpersonal emotion management strategies to influence and respond to emotions arising from the self-evaluative processes of organizational members during times of organizational identity change.

The conceptual model detailed provides insight on the intersubjective emotional processes grounded in social identity that influence transformational leadership. Future research into transformational leadership behaviors will benefit from a multilevel perspective which includes both interpersonal emotion management and intrapersonal emotion generation related to social identity at both the within-person and between-person levels.

The proposed model expands on the role of emotions in transformational leadership by theoretically linking the specific transformational behaviors to discrete emotions displayed by followers. While previous empirical research has indicated the positive outcomes of transformational leadership and the role of emotion recognition, work has yet to be presented which explicates the role of discrete emotions in the transformational leadership process.

Although in principle prior information can significantly improve inference, incorporating incorrect prior information will bias the estimates of any inferential analysis. This fact deters many scientists from incorporating prior information into their inferential analyses. In the natural sciences, where experiments are more regularly conducted, and can be combined with other relevant information, prior information is often used in inferential analysis, despite it being sometimes nontrivial to specify what that information is and how to quantify that information. In the social sciences, however, prior information is often hard to come by and very hard to justify or validate. We review a number of ways to construct such information. This information emerges naturally, either from fundamental properties and characteristics of the systems studied or from logical reasoning about the problems being analyzed. Borrowing from concepts and philosophical reasoning used in the natural sciences, and within an info-metrics framework, we discuss three different, yet complimentary, approaches for constructing prior information, with an application to the social sciences.

Dynamic predictions of financial distress of the firms have received less attention in finance literature rather than static prediction, specifically in Malaysia. This study, therefore, investigates dynamic symptoms of the financial distress event a few years before it happened to the firms by using neural network method. Cox Proportional Hazard regression models are used to estimate the survival probabilities of Malaysian PN17 and GN3 listed firms. Forecast accuracy is evaluated using receiver operating characteristics curve. From the findings, it shown that the independent directors’ ownership has negative association with the financial distress likelihood. In addition, this study modeled a mix of corporate financial distress predictors for Malaysian firms. The combination of financial and non-financial ratios which pressure-sensitive institutional ownership, independent director ownership, and Earnings Before Interest and Taxes to Total Asset shown a negative relationship with financial distress likelihood specifically one year before the firms being listed in PN 17 and GN 3 status. However, Retained Earnings to Total Asset, Interest Coverage, and Market Value of Debt have positive relationship with firm financial distress likelihood. These research findings also contribute to the policy implications to the Securities Commission and specifically to Bursa Malaysia. Furthermore, one of the initial goals in introducing the PN17 and GN3 status is to alleviate the information asymmetry between distressed firms, the regulators, and investors. Therefore, the regulator would be able to monitor effectively distressed firms, and investors can protect from imprudent investment.