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Record W4365137104 · doi:10.1002/job.2707

Breaking rules yet helpful for all: Beneficial effects of pro‐customer rule breaking on employee outcomes

2023· article· en· W4365137104 on OpenAlexafffund
Su Kyung Kim, Yujie Zhan

Bibliographic record

VenueJournal of Organizational Behavior · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicJob Satisfaction and Organizational Behavior
Canadian institutionsWilfrid Laurier UniversityUniversity of British Columbia
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsNormativeOrganizational commitmentPsychologyCustomer retentionInefficiencyPsychological contractBusinessService (business)Social psychologyMarketingService qualityEconomicsMicroeconomics

Abstract

fetched live from OpenAlex

Summary Pro‐customer rule breaking refers to employees' breaking of organizational rules with the primary intention of helping customers or providing better customer service. In spite of its prosocial nature, it is unclear whether and how pro‐customer rule breaking also benefits employees who engage in this behavior. Drawing on self‐determination theory, we examine employees' well‐being and voice at work as outcomes of pro‐customer rule breaking. Across a simulation study and a critical incident‐based survey study, we found that pro‐customer rule breaking was positively related to employee psychological need fulfillment, which, in turn, was associated with lower emotional exhaustion, higher job satisfaction, and increased voice. Furthermore, normative conflict with organizational rule moderated the positive relationship between pro‐customer rule breaking and psychological need fulfillment such that employees with high normative conflict with organizational rule (i.e., employee perception that their existing organizational rule results in inefficiency and that their organization could be much better if it changed its practices) benefited more from their pro‐customer rule breaking. We discuss the theoretical and practical implications of these findings and offer directions 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)0.001
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 score0.031
Threshold uncertainty score0.911

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
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 head0.020
GPT teacher head0.281
Teacher spread0.261 · 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

Citations24
Published2023
Admission routes2
Has abstractyes

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