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Record W2114443108 · doi:10.5539/ass.v9n7p41

The Antecedents Affecting Employee Engagement and Organizational Performance

2013· article· en· W2114443108 on OpenAlexvenueno aff
Alaà Nimer Abukhalifeh, Ahmad Puad Mat Som

Bibliographic record

VenueAsian Social Science · 2013
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicJob Satisfaction and Organizational Behavior
Canadian institutionsnot available
FundersUniversiti Sains Malaysia
KeywordsEmployee engagementBusinessEmployee researchAbsenteeismTurnoverLoyaltyProductivityMarketingEmployee resource groupsService (business)Economic shortageEmployee retentionEmployee developmentPublic relationsPsychologyManagementSocial psychologyPolitical scienceEconomics

Abstract

fetched live from OpenAlex

Employee engagement becomes an important issue as employee turnover rises due to the demand and shortage of skilful employees. Though organizational performances of many organizations have deteriorated due to high turnover rates and related issues such as high absenteeism, low loyalty and productivity, there is still a lack of academic research that addresses the antecedents required for high employee engagement in the food and beverage departments in the service industry. This paper focuses on the antecedents that influence employee engagement in food and beverage service departments, and literature reviewed indicates that there is a significant relationship between employee communication, employee development, rewards and recognition, and extended employee care. Among the antecedents, employee development forms the most significant contributor.

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)0.009
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 score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.012
GPT teacher head0.239
Teacher spread0.227 · 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

Citations78
Published2013
Admission routes1
Has abstractyes

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