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Record W4323545691 · doi:10.1027/1866-5888/a000318

Agreeable Supervisors Promoting the Organization – Implications for Employee Commitment and Retention

2023· article· en· W4323545691 on OpenAlexaff
Alexandra Panaccio, Wei‐Gang Tang, Christian Vandenberghe

Bibliographic record

VenueJournal of Personnel Psychology · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicJob Satisfaction and Organizational Behavior
Canadian institutionsHEC MontréalConcordia University
Fundersnot available
KeywordsSupervisorPsychologyOrganizational commitmentAgreeablenessPromotion (chess)Social psychologyEmployee researchTurnoverAffective events theoryJob satisfactionPersonalityBusiness administrationJob performanceBig Five personality traitsManagementBusinessJob attitudeExtraversion and introversion

Abstract

fetched live from OpenAlex

Abstract. Through two studies, we examine the importance of supervisor promotion of the organization (SPO) for employee commitment and retention. Study 1 ( N = 910) found SPO to be distinguishable from supervisor’s organizational embodiment, perceived supervisor support, perceived organizational support, and supervisor vision and to interact with perceived supervisor agreeableness to predict employee affective commitment. Using time-lagged data, Study 2 ( N = 272) found SPO to be indirectly related to turnover via employee affective commitment and this relation to be stronger at high perceived supervisor agreeableness. These findings indicate that SPO can help reduce employee turnover but that organizations would be well advised to encourage supervisors to act agreeably with subordinates or hire and promote supervisors who do so.

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)0.005
metaresearch head score (Gemma)0.023
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 score0.005
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.023
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.056
GPT teacher head0.307
Teacher spread0.251 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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