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Angelo J. Kinicki
Researcher at Arizona State University
Publications - 91
Citations - 13166
Angelo J. Kinicki is an academic researcher from Arizona State University. The author has contributed to research in topics: Coping (psychology) & Organizational culture. The author has an hindex of 43, co-authored 90 publications receiving 12066 citations. Previous affiliations of Angelo J. Kinicki include Kent State University.
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Psychological and physical well-being during unemployment: a meta-analytic study.
TL;DR: Unemployed individuals had lower psychological and physical well-being than did their employed counterparts, and work-role centrality, coping resources, cognitive appraisals, and coping strategies displayed stronger relationships with mental health than did human capital or demographic variables.
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Employability: A psycho-social construct, its dimensions, and applications
TL;DR: In this article, the authors argue that employability represents a form of work specific adaptive adaptability that consists of three dimensions: career identity, personal adaptability, and social and human capital.
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Organizational Culture and Organizational Effectiveness: A Meta-Analytic Investigation of the Competing Values Framework's Theoretical Suppositions.
TL;DR: Results indicate that clan, adhocracy, and market cultures are differentially and positively associated with the effectiveness criteria, though not always as hypothesized.
Reference EntryDOI
Organizational Culture and Climate
TL;DR: In this article, the authors focus on organizational culture and climate and the role that these constructs play in understanding individual as well as collective attitudes, behavior, and performance, and highlight the impact of weak emergent processes on individual and organizational outcomes.
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Toward a Greater Understanding of How Dissatisfaction Drives Employee Turnover
Peter W. Hom,Angelo J. Kinicki +1 more
TL;DR: This paper generalized a leading portrayal of how job dissatisfaction progresses into turnover and rigorously tested this model using structural equations modeling and applied it to real-world job turnover data and found that the model was accurate.