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Record W4220880287 · doi:10.1111/ijsa.12381

Affective organizational commitment, self‐concept, and work performance: A social comparison perspective

2022· article· en· W4220880287 on OpenAlexafffund
Wei‐Gang Tang, Christian Vandenberghe

Bibliographic record

VenueInternational Journal of Selection and Assessment · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicJob Satisfaction and Organizational Behavior
Canadian institutionsHEC Montréal
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPsychologyOptimal distinctiveness theoryPerspective (graphical)Social psychologySample (material)Task (project management)Management

Abstract

fetched live from OpenAlex

Abstract Using two studies and four samples, we introduce new forms of assimilative and contrastive affective organizational commitment (AOC) from a social comparison perspective and examine their distinctiveness from traditional AOC. We further explore the interplay among assimilative and contrastive AOCs and the self‐concept in predicting specific performance outcomes. Study 1 used two samples ( N s = 181 and 655) aimed at developing the measures of assimilative and contrastive AOC and found them to be distinct from traditional AC. Study 2 tested the idea that the self‐concept (collective vs. individual) would influence how strongly assimilative versus contrastive AOC would predict helping behavior versus task proficiency as performance outcomes. Using two samples (Sample 1 N = 192; Sample 2 N = 246) with multisource data from subordinates and supervisors, Study 2 found that controlling for traditional AOC, the relationship between assimilative AOC and employee helping behavior was stronger and positive at high levels of the collective self‐concept (Sample 1) and that the relationship between contrastive AOC and employee task proficiency was marginally stronger and positive at high levels of the individual self‐concept (Sample 2). Our findings bear implications for theory and practice and present openings for future research.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.054
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.013
GPT teacher head0.297
Teacher spread0.284 · 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 teacher head, not a consensus.

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

Citations4
Published2022
Admission routes2
Has abstractyes

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