Composite Analysis of Human Resource Change Leadership and Professionalism: A PLS-SEM Application in Vietnamese Sustainable Enterprises
Bibliographic record
Abstract
Scholars assert that human resource management is essential for organizational sustainability and that human resource professionals are institutional entrepreneurs who lead organizational changes toward sustainability. However, human resource (HR) professionals are currently leading sustainability efforts in a limited, piecemeal, and anecdotal manner, which may be caused by a lack of competencies. Therefore, based on the literature review, a model was built to explore the relationship between HR change leadership role and HR professionalism. Qualitative research (in-depth interviews with five experts) was employed to generate and filter the initial items of the scales. Quantitative research with a sample of 1,058 employees working at 24 sustainable enterprises in Vietnam was used to validate the scale and test the hypothesis through exploratory factor analysis and confirmatory composite analysis using the PLS-SEM method. The results demonstrate that the HR change leadership role positively influences HR professionalism. This result means that HR professionals should be capable of being strategic positioners, credible activists, capability builders, technology proponents, and interpersonal leaders to play their change leadership role in a sustainable context. Therefore, the HRM department should redesign the set of sustainable competencies for recruiting and evaluating HR professionals and plan short- and long-term strategies to train and develop sustainable competencies for HR professionals.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".