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Record W2947331787 · doi:10.5430/jms.v10n3p48

Employee’s Perception on Workplace Gossip in the South African Public Sectors: The Implication on Job Performance

2019· article· en· W2947331787 on OpenAlexvenueno aff
Dlamini Phakamani Irvine, Mdletshe Bonga Blessing

Bibliographic record

VenueJournal of Management and Strategy · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicEvolutionary Game Theory and Cooperation
Canadian institutionsnot available
Fundersnot available
KeywordsGossipPerceptionPsychologyJob satisfactionDimension (graph theory)Social psychologyPublic relationsPolitical science

Abstract

fetched live from OpenAlex

Gossip is pervasive in any working environment. Sketching upon the existing and scant body of research knowledge surrounding the subject of gossip, the current qualitative study undertaken critically assessed how the employees protrude themselves after being victims of gossip in a workplace. This study examined the influence of workplace gossip on the job performance of employees within selected municipalities in South Africa. The researcher intended to establish the likeliest behaviour of municipality employees towards their job performance in the event of encountering workplace gossip. Interviews with twenty-five office workers were conducted and data documented and analyzed. The heuristic of this study was to equip managers or those in practice with an in-depth understanding about office gossip, by providing a new dimension about the influence workplace gossip on job performance and employees self-efficacy. Moreover, the study necessitated an in-depth understanding of several reactions that emanates from employees behavioral patterns when affected by office gossip. The study uncovered a substantial outcome, such that if gossip is work-related, rather than non-work-related, employees are more likely to improve their performance. However, unremittingly exposure to gossip can have a negative impact on employee’s self-efficacy.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.769
Threshold uncertainty score0.229

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.270
Teacher spread0.244 · 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

Citations3
Published2019
Admission routes1
Has abstractyes

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