Employee engagement, human resource management practices and competitive advantage
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.
Bibliographic record
Abstract
Purpose – The purpose of this paper is to argue in support of a model that shows how four key HRM practices focused on engagement influence organizational climate, job demands and job resources, the psychological experiences of safety, meaningfulness and availability at work, employee engagement, and individual, group and organizational performance and competitive advantage. Design/methodology/approach – This conceptual review focuses on the research evidence showing interrelationships between organizational context factors, job factors, individual employee psychological and motivational factors, employee outcomes, organizational outcomes and competitive advantage. The proposed model integrates frameworks that have previously run independently in the HR and engagement literatures. Findings – The authors conclude that HRM practitioners need to move beyond the routine administration of annual engagement surveys and need to embed engagement in HRM policies and practices such personnel selection, socialization, performance management, and training and development. Practical implications – The authors offer organizations clear guidelines for how HR practices (i.e. selection, socialization, performance management, training) can be used to facilitate and improve employee engagement and result in positive outcomes that will help organizations achieve a competitive advantage. Originality/value – The authors provide useful new insights for researchers and management professionals wishing to embed engagement within the fabric of HRM policies and practices and employee behaviour, and organizational outcomes.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it