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Record W3162402411 · doi:10.5267/j.msl.2021.5.001

The effect of team value diversity on team performance: The mediating role of relationship conflict and the moderating effects of organization citizenship behavior and leader-member exchange quality

2021· article· en· W3162402411 on OpenAlexvenueno aff
Abul Waleed, Wisal Ahmad, Muhammad Farooq Jan, Sadaqat Ali, Awais Jamal Khattak, Aamir Nadeem

Bibliographic record

VenueManagement Science Letters · 2021
Typearticle
Languageen
FieldComputer Science
TopicOrganizational and Employee Performance
Canadian institutionsnot available
Fundersnot available
KeywordsDiversity (politics)PsychologyTeam effectivenessValue (mathematics)Social psychologyQuality (philosophy)Affect (linguistics)Organizational citizenship behaviorTeam compositionModerationPositive relationshipMultilevel modelKnowledge managementPolitical scienceOrganizational commitmentComputer science

Abstract

fetched live from OpenAlex

This paper aims to investigate the mediating role of relationship conflict and moderating role of organization citizenship behavior and leader-member-exchange quality on the relationship between team value diversity and team performance. Data was collected from 263 employees of the telecom sector addressing the variables of value diversity, team performance, relationship conflict, OCB and LMX. Regression analysis found that team value diversity negatively affects team performance, and relationship conflict significantly mediates this relationship. It was also found that OCB and LMX significantly moderate the relationship, such that in the presence of these two, value diversity doesn’t affect team performance rather team performance is positive when these two are present. The findings of this research confirm that for effective management of team diversity in organizations, team leaders must also wisely manage relationship conflict.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.059
Threshold uncertainty score0.951

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.014
GPT teacher head0.234
Teacher spread0.220 · 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.

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

Citations4
Published2021
Admission routes1
Has abstractyes

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