Serena Wee
University of Western Australia
22 Papers
55 Citations
Serena Wee is an academic researcher from University of Western Australia. The author has contributed to research in topics: Machiavellianism & Regret. The author has an hindex of 9, co-authored 22 publications. Previous affiliations of Serena Wee include University of Illinois at Urbana–Champaign & National University of Singapore.
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Papers
Competition, autonomy, and prestige: Mechanisms through which the Dark Triad predict job satisfaction ☆
TL;DR: In this paper, the authors examined how the Dark Triad traits predispose individuals to perceive situations as competitive, prestigious, and comprised of restrictions (i.e., autonomy) which differentially predict job satisfaction.
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Occupational niches and the Dark Triad traits
TL;DR: In this paper, the authors focused on the vocational interests correlated with the Dark Triad traits (i.e., narcissism, psychopathy, and Machiavellianism) by understanding how these traits facilitate the structuring of one's environment.
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Defining Employee Engagement for Productive Research and Practice
TL;DR: In addition, this paper pointed out that Macey and Schneider (2008) may inadvertently have contributed to the muddiness of the construct space of employee engagement by conceptualizing the psychological state of engagement and using the term engagement as a rubric that encapsulates not only cognitive-affective but also dispositional and behavioral constructs.
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Did the COVID-19 Lock-Down Make Us Better at Working in Virtual Teams?
TL;DR: The authors examined whether virtual teams showed enhanced processes in later stages of the COVID-19 pandemic, drawing on event system theory, and found that the enhanced processes were more robust to later stages.
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Making Sense of Model Generalizability: A Tutorial on Cross-Validation in R and Shiny:
Q. Chelsea Song,Chen Tang,Serena Wee +2 more
- 23 Mar 2021
TL;DR: In this paper, the authors explain model generalizability through the statistical c... and explain how well the findings from a sample are applicable to other samples in the population, using a probabilistic model.
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