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Record W3129294211 · doi:10.1027/1866-5888/a000280

A Cultural Value Congruence Approach to Organizational Embeddedness

2021· article· en· W3129294211 on OpenAlexaff
Emma Lei Jing, Nathaniel C. Lupton, Mahfooz A. Ansari

Bibliographic record

VenueJournal of Personnel Psychology · 2021
Typearticle
Languageen
FieldPsychology
TopicCultural Differences and Values
Canadian institutionsUniversity of LethbridgeUniversity of Alberta
Fundersnot available
KeywordsCollectivismEmbeddednessPsychologyCongruence (geometry)Social psychologyPerceptionValue (mathematics)Organizational commitmentSociologySocial sciencePolitical scienceMathematicsIndividualism

Abstract

fetched live from OpenAlex

Abstract. Drawing on the person–organization fit theory, we investigate how the value congruence between employees’ collectivist values and their perception of organizational collectivism influences organizational embeddedness. Based on a survey of 515 working adults, the polynomial regression and response surface analysis results support that embeddedness is highest in the presence of both high individual and organizational collectivism. Additionally, the smaller the discrepancy between the two perceptions, the more embedded the employees. Our study contributes to the cultural perspectives in the organizational embeddedness research by theorizing and measuring the impact of collectivism at the individual level. The findings also contribute to the person–organization fit theory by identifying a value congruence approach to organizational embeddedness.

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)0.004
metaresearch head score (Gemma)0.018
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0010.003
Scholarly communication0.0030.002
Open science0.0000.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.096
GPT teacher head0.404
Teacher spread0.308 · 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".

Quick stats

Citations9
Published2021
Admission routes1
Has abstractyes

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