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Record W3184240691 · doi:10.1002/job.2549

What if my coworker builds a better LMX? The roles of envy and coworker pride for the relationships of LMX social comparison with learning and undermining

2021· article· en· W3184240691 on OpenAlexaff
Jingzhou Pan, Xiaotong Zheng, Haoying Xu, J. T. Li, Catherine K. Lam

Bibliographic record

VenueJournal of Organizational Behavior · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicJob Satisfaction and Organizational Behavior
Canadian institutionsWilfrid Laurier University
FundersNational Natural Science Foundation of China
KeywordsPridePsychologySocial psychologyExtant taxonSocial comparison theoryPerceptionSocial exchange theory

Abstract

fetched live from OpenAlex

Summary Although the extant literature has demonstrated the benefits of building a higher leader–member exchange (LMX) relationship with a leader, it has overlooked the efforts by lower LMX employees to leverage the difference from higher LMX coworkers. Integrating social comparison theory and EASI theory, we contend that lower LMX social comparison (LMXSC) is associated with positive (self‐improving) and negative (undermining) behavior via different emotional mechanisms and that the focal employee's perceptions of the comparison coworker's pride play a critical role in qualifying the effects of lower LMXSC. The results from a time‐lagged field study and an online experiment reveal that lower LMXSC is associated with both benign and malicious envy, which in turn respectively relate to the focal employee learning and socially undermining the superior coworker. The negative indirect effect of LMXSC on learning behaviors via benign envy is stronger when the coworker compared is perceived to be higher (vs. lower) in authentic pride, whereas the indirect effect of LMXSC on social undermining via malicious envy is stronger when the coworker compared is perceived to be higher (vs. lower) in hubristic pride. We conclude with theoretical and practical implications.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.401

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)0.0000.000

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 head0.026
GPT teacher head0.267
Teacher spread0.241 · 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

Citations72
Published2021
Admission routes1
Has abstractyes

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