MétaCan
Menu
Back to cohort
Record W2285093308 · doi:10.5539/ibr.v9n2p85

Towards a Conceptualization and an Operationalization of the Construct of Employee Engagement

2016· article· en· W2285093308 on OpenAlexvenueno aff
Anuradha Iddagoda, H. H. D. N. P. Opatha, Kennedy D. Gunawardana

Bibliographic record

VenueInternational Business Research · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicJob Satisfaction and Organizational Behavior
Canadian institutionsnot available
Fundersnot available
KeywordsConceptualizationOperationalizationConstruct (python library)Employee engagementOrganizational citizenship behaviorPsychologyMeaning (existential)Job satisfactionConfusionOrganizational commitmentSocial psychologyWork engagementPublic relationsWork (physics)Political scienceEpistemologyComputer science

Abstract

fetched live from OpenAlex

<p>Employees are generally considered as the most important resource needed for an organization to achieve its main goals. Realization of goals achievement heavily depends on the extent to which these employees are engaged in their jobs and their organization. Employee engagement is a factor that contributes positively to employee productivity and then organizational effectiveness. It reveals that a conceptual confusion exists with regard to the meaning of employee engagement owing to that the concept has been defined by different scholars in different ways and also that there are several associated terms such as job satisfaction, job involvement, work involvement, organizational commitment and organizational citizenship behavior which have been used in the literature, either synonymously or non-synonymously. Further a question arises to decide whether employee engagement is an attitude or a behavior. This paper seeks to provide a comprehensive conceptualization of employee engagement that results in formulating a working definition for research purposes involving the construct, and to explore its dimensions and elements for the purpose of measuring the construct.</p>

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.249
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.075
GPT teacher head0.365
Teacher spread0.290 · 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.

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

Citations41
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueInternational Business ResearchSame topicJob Satisfaction and Organizational BehaviorFrench-language works237,207