New Development in Self-Control Theory and its Applications in Organizational Research
Bibliographic record
Abstract
Self-control is one of the most important functions that human beings possess. However, current research on how it affects organizational management is limited. Self-control theory perspectives suggest that although individuals differ in their self-control ability (i.e., trait self-control), engagement in self-control also depletes one’s self-control resources, resulting in diminished state self-control ability (i.e., ego depletion) and subsequent self-control failure. The four empirical papers in the symposium consider the role of both trait and state self-control in affecting various organizational phenomenon. The symposium contributes to our understanding of how self-control works in organizational management by examining: (1) organizational behaviors that deplete one’s self-control resources (e.g., ethical leadership and surface acting when interacting with customers); (2) the interactive effects of factors depleting self-control resources and self-control capacity (e.g., surface acting and trait self-control; status loss and demonstration of self-control ability; hostility and mindfulness); (3) mediating mechanisms that explain how individual differences in self-control play a role (e.g., mindfulness leads to reduced surface acting); (4) consequences of self-control success or failure (i.e., abusive supervision, aggression, protecting the legitimacy of authorities after status loss). The papers included in this symposium draw on the self-control literature, use strong methodological approaches (e.g. experimental designs, multi-source designs, multi-wave data collections, multiple studies) to test their hypotheses, and propose novel perspectives on self-control (e.g., mindfulness as an indicator of self-control capacity, the benefits of demonstrating self-control ability). This symposium will contribute to the 2015 Academy of Management Conference by adding new perspectives to self-control theories and their applications in explaining various organizational behaviors, and by evoking interesting discussions among management researchers. After the Fall: Demonstrating Self-Control Protects the Legitimacy of Authorities After Status Loss Presenter: Jennifer Carson Marr; Georgia Institute of Technology Presenter: Stefan Thau; INSEAD Presenter: Nate Pettit; New York U. A Self-Control Perspective on the Link Between Surface Acting and Abusive Supervision Presenter: Kai Chi Yam; National U. of Singapore Presenter: Ryan Fehr; U. of Washington, Seattle Presenter: Fong T. Keng; U. of Washington Presenter: Anthony Klotz; Oregon State U. The Mechanisms of Mindfulness in Regulating Aggressive Behaviors Presenter: Lindie Hanyu Liang; U. of Waterloo Presenter: Huiwen Lian; Hong Kong U. of Science and Technology Presenter: Samuel Ian Hanig; U. of Waterloo Presenter: Douglas J. Brown; U. of Waterloo Presenter: Lance Ferris; Pennsylvania State U. Presenter: Lisa M. Keeping; Wilfrid Laurier U. When Ethical Leadership Turns Abusive: Role of Ego Depletion and Moral Licensing Presenter: Szu-Han Lin; Michigan State U. Presenter: Jingjing Ma; Michigan State U. Presenter: Russell E. Johnson; Michigan State U.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".