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Opening Up: New Theory and Evidence on the Role of Self-Disclosure in Organizations

2023· article· en· W4385223246 on OpenAlexaffabout
Aï Ito, J.A. Harrison, Michelle Bligh, Marie‐Hélène Budworth, Paolo Fragomeni, Hodar Lam, Shuai Yuan, Zhuojun Wang, Mahshid Khademi, Sophie Theresa Schep, Nicola Glumann, Avery Thomson, Emre Yetgin, Quinn Cunningham

Bibliographic record

VenueAcademy of Management Proceedings · 2023
Typearticle
Languageen
FieldDecision Sciences
TopicTechnology Adoption and User Behaviour
Canadian institutionsYork University
Fundersnot available
KeywordsSelf-disclosurePsychologyBusinessSocial psychology

Abstract

fetched live from OpenAlex

Sometimes described as ‘opening-up’, self-disclosure, or the act of sharing personal/relevant information with another party is acknowledged in psychology as an important interpersonal behavior. Despite this evidence on the importance of self-disclosure, research on self-disclosure in organizations is sparse. In recent years there is rising interest in the study of self-disclosure in the workplace. Often viewed as a positive behavior to show goodwill, the effects of self-disclosure on organizational outcomes at various levels have received little empirical and theoretical attention from management scholars. This symposium explores the interpersonal and group effects of self-disclosure on employees at both the individual, interpersonal, and group levels. The symposium consists of four papers—one theory, two field studies, and one experimental study—that explore the effects of self-disclosure and its role in organizations. Specifically, the first paper offers new theoretical insights about the gendered effect of self-disclosure in the context of remote working and what that means for careers. Also fuelled by the use of remote working, the second paper investigates self-disclosure concerning individual leaders’ perceptions of loneliness. The third paper explores the effects of self-disclosing virtually, private medical information about suffering from remote work conditions during the COVID-19 pandemic, and a group of colleagues. It uses experimental vignettes to investigate the potential benefits of virtual self-disclosure to a group of colleagues regarding private medical information. The final paper delves deeper into employees’ position to self-disclose. It examines the role of self-stigmatization in the relationship between employee mental health disease diagnoses and employee decisions to self-disclose. These papers advance our understanding of the effects of self-disclosure at various levels in organizations. We believe the symposium is a step toward uncovering the importance of self- disclosure and will encourage future research. Self-Disclosure in Today’s Remote World of Work: Understanding the Consequences for Women’s Careers Author: Marie-Helene Elizabeth Budworth; York U. Author: Paolo Fragomeni; York U., Toronto Don’t Leave Accountable Leaders Alone: The Role of Self-Disclosure at Work Author: Hodar Lam; U. of Amsterdam Author: Shuai Yuan; U. of Amsterdam Author: Wang Zhuojun; Institute of Psychology, Chinese Academy of Sciences The Effects of Self-Disclosure on In-Group Identification: A COVID-19 Vignette Study Author: Avery Thomson; Epic Author: Emre Yetgin; Rider U. Author: Quinn Cunningham; Rider U. The Role of Self-Stigma in the Disclosure Decision of Employees with Mental Health Disabilities Author: Mahshid Khademi; U. of St. Gallen (HSG) Author: Sophie Theresa Schep; U. of St. Gallen Author: Nicola Glumann; U. of St. Gallen

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)0.016
metaresearch head score (Gemma)0.038
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.016
Threshold uncertainty score0.083

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.038
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.005
Science and technology studies0.0030.023
Scholarly communication0.0110.020
Open science0.0030.005
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.0080.001

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 head0.082
GPT teacher head0.364
Teacher spread0.282 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2023
Admission routes2
Has abstractyes

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