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New Development in Self-Control Theory and its Applications in Organizational Research

2015· article· en· W2796148787 on OpenAlexaff
Gary P. Latham

Bibliographic record

VenueAcademy of Management Proceedings · 2015
Typearticle
Languageen
FieldDecision Sciences
TopicTechnology Adoption and User Behaviour
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsEgo depletionMindfulnessSelf-controlPsychologyControl (management)Social psychologyTraitAggressionEmpirical researchComputer sciencePsychotherapist

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.016
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0060.010
Science and technology studies0.0020.011
Scholarly communication0.0060.007
Open science0.0020.003
Research integrity0.0040.008
Insufficient payload (model declined to judge)0.0070.001

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.

Opus teacher head0.137
GPT teacher head0.411
Teacher spread0.274 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

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Citations0
Published2015
Admission routes1
Has abstractyes

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