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Record W2940755759 · doi:10.5296/jmr.v11i2.14612

The Employee Engagement Framework: High Impact Drivers and Outcomes

2019· article· en· W2940755759 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Management Research · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCollaboration in agile enterprises
Canadian institutionsSheridan College
Fundersnot available
KeywordsEmployee engagementEmployee resource groupsEmployee researchWork engagementBusinessCustomer engagementPublic relationsDiversity (politics)Human resource managementWork (physics)Quality (philosophy)MarketingKnowledge managementPolitical scienceEngineeringComputer science

Abstract

fetched live from OpenAlex

For the prior two decades, employee engagement has been a subject of interest both in academic research and among managers. Organizations have invested vital resources in promoting employee engagement since employee engagement is identified as a critical driver of organizational performance. Engagement adds distinctly to an organization’s performance, driving to gains in quality, customer satisfaction, and long-term monetary results. In a world that is evolving both regarding the global essence of work and the diversity of the employees, engaged employees may be a core of an ambitious resource. Companies promoting employee engagement will achieve organizational goals effectively. Several employees look for settings where they can be engaged and know that they are participating positively. The paper presents the employee engagement framework enabling organizations to understand how engagement may differ by employee or group and recognize the key drivers that impact engagement at the workplace.

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.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.168
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.035
GPT teacher head0.362
Teacher spread0.326 · 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