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Record W2100883741 · doi:10.1037/0021-9010.92.3.840

Self-defeating behaviors in organizations: The relationship between thwarted belonging and interpersonal work behaviors.

2007· article· en· W2100883741 on OpenAlexaff
Stefan Thau, Karl Aquino, P. Marijn Poortvliet

Bibliographic record

VenueJournal of Applied Psychology · 2007
Typearticle
Languageen
FieldPsychology
TopicDeath Anxiety and Social Exclusion
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsBelongingnessPsychologySocial psychologyInterpersonal communicationMultilevel modelInterpersonal relationship

Abstract

fetched live from OpenAlex

This multisource field study applied belongingness theory to examine whether thwarted belonging, defined as the perceived discrepancy between one's desired and actual levels of belonging with respect to one's coworkers, predicts interpersonal work behaviors that are self-defeating. Controlling for demographic variables, job type, justice constructs, and trust in organization in a multilevel regression analysis using data from 130 employees of a clinical chemical laboratory and their supervisors, the authors found that employees who perceive greater levels of desired coworker belonging than actual levels of coworker belonging were more likely to engage in interpersonally harmful and less likely to engage in interpersonally helpful behaviors. Implications for the application of belongingness theory to explain self-defeating behaviors in organizations are discussed.

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)0.003
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.024
GPT teacher head0.350
Teacher spread0.326 · 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 designObservational
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".

Quick stats

Citations226
Published2007
Admission routes1
Has abstractyes

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