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1 – 10 of over 1000Alexandra L. Ferrentino, Meghan L. Maliga, Richard A. Bernardi and Susan M. Bosco
This research provides accounting-ethics authors and administrators with a benchmark for accounting-ethics research. While Bernardi and Bean (2010) considered publications in…
Abstract
This research provides accounting-ethics authors and administrators with a benchmark for accounting-ethics research. While Bernardi and Bean (2010) considered publications in business-ethics and accounting’s top-40 journals this study considers research in eight accounting-ethics and public-interest journals, as well as, 34 business-ethics journals. We analyzed the contents of our 42 journals for the 25-year period between 1991 through 2015. This research documents the continued growth (Bernardi & Bean, 2007) of accounting-ethics research in both accounting-ethics and business-ethics journals. We provide data on the top-10 ethics authors in each doctoral year group, the top-50 ethics authors over the most recent 10, 20, and 25 years, and a distribution among ethics scholars for these periods. For the 25-year timeframe, our data indicate that only 665 (274) of the 5,125 accounting PhDs/DBAs (13.0% and 5.4% respectively) in Canada and the United States had authored or co-authored one (more than one) ethics article.
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Roman Liesenfeld, Jean-François Richard and Jan Vogler
We propose a generic algorithm for numerically accurate likelihood evaluation of a broad class of spatial models characterized by a high-dimensional latent Gaussian process and…
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We propose a generic algorithm for numerically accurate likelihood evaluation of a broad class of spatial models characterized by a high-dimensional latent Gaussian process and non-Gaussian response variables. The class of models under consideration includes specifications for discrete choices, event counts and limited-dependent variables (truncation, censoring, and sample selection) among others. Our algorithm relies upon a novel implementation of efficient importance sampling (EIS) specifically designed to exploit typical sparsity of high-dimensional spatial precision (or covariance) matrices. It is numerically very accurate and computationally feasible even for very high-dimensional latent processes. Thus, maximum likelihood (ML) estimation of high-dimensional non-Gaussian spatial models, hitherto considered to be computationally prohibitive, becomes feasible. We illustrate our approach with ML estimation of a spatial probit for US presidential voting decisions and spatial count data models (Poisson and Negbin) for firm location choices.
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Neal M. Ashkanasy, Ashlea C. Troth, Sandra A. Lawrence and Peter J. Jordan
Scholars and practitioners in the OB literature nowadays appreciate that emotions and emotional regulation constitute an inseparable part of work life, but the HRM literature has…
Abstract
Scholars and practitioners in the OB literature nowadays appreciate that emotions and emotional regulation constitute an inseparable part of work life, but the HRM literature has lagged in addressing the emotional dimensions of life at work. In this chapter therefore, beginning with a multi-level perspective taken from the OB literature, we introduce the roles played by emotions and emotional regulation in the workplace and discuss their implications for HRM. We do so by considering five levels of analysis: (1) within-person temporal variations, (2) between persons (individual differences), (3) interpersonal processes; (4) groups and teams, and (5) the organization as a whole. We focus especially on processes of emotional regulation in both self and others, including discussion of emotional labor and emotional intelligence. In the opening sections of the chapter, we discuss the nature of emotions and emotional regulation from an OB perspective by introducing the five-level model, and explaining in particular how emotions and emotional regulation play a role at each of the levels. We then apply these ideas to four major domains of concern to HR managers: (1) recruitment, selection, and socialization; (2) performance management; (3) training and development; and (4) compensation and benefits. In concluding, we stress the interconnectedness of emotions and emotional regulation across the five levels of the model, arguing that emotions and emotional regulation at each level can influence effects at other levels, ultimately culminating in the organization’s affective climate.
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