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Record W2171579915 · doi:10.1002/hrm.21699

When Do Employees Cyberloaf? An Interactionist Perspective Examining Personality, Justice, and Empowerment

2015· article· en· W2171579915 on OpenAlexaff
Kwanghyun Kim, María del Carmen Triana, Kwiyoung Chung, Nahyun Oh

Bibliographic record

VenueHuman Resource Management · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicCyberloafing and Workplace Behavior
Canadian institutionsWestern University
FundersKorea University Business School
KeywordsConscientiousnessPsychologySocial psychologyOrganizational justicePersonalityPerspective (graphical)Organizational behaviorBig Five personality traitsExtraversion and introversionOrganizational commitment

Abstract

fetched live from OpenAlex

Cyberloafing—using the Internet for non‐work‐related activities—is a prevalent counterproductive work behavior in the workplace, but researchers have not yet paid sufficient attention to this issue, especially related to the role of personality in cyberloafing. Recognizing such a research gap, and using a trait activation theory framework, this study examines whether conscientiousness and emotional stability negatively relate to cyberloafing. We further investigate how organizational justice perceptions and psychological empowerment moderate the negative relationship between these personality traits and cyberloafing. Based on a sample of 247 employees, we find that those high in conscientiousness cyberloaf less when they perceive greater levels of organizational justice. In addition, highly conscientious individuals cyberloaf less when they have low, rather than high, levels of psychological empowerment. Implications for research and practice as well as future research directions are discussed. © 2015 Wiley Periodicals, Inc.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.004
Scholarly communication0.0050.003
Open science0.0000.002
Research integrity0.0010.001
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 head0.082
GPT teacher head0.372
Teacher spread0.289 · 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

Citations103
Published2015
Admission routes1
Has abstractyes

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