Factors of Leadership and Behaviour Towards Organisational Safety Performance: A Predictive Model for Small and Medium Manufacturing Industry
Bibliographic record
Abstract
In Malaysia, small and medium enterprises (SMES) account for the majority of workplace accidents.SMEs encounter challenges in achieving effective safety performance due to limitations in organisational resource in managing occupational safety risks.Scholars have a unified agreement on the substantial factor that contributes towards accident and injury, which is human factor namely unsafe behaviour.On the other hand, scholars advocated that leadership is an effective approach in encouraging safety behaviour at work, especially among SME workers.Through the conceptualization of Transformational-Transactional Leadership Theory and models established by previous studies, this paper proposed to model of both leadership styles in effecting safety behaviour as well as safety performance.A total of 107 responses were collected from Safety and Health / Human Resource personnel who work in the SME (manufacturing) firms in the northern region of Malaysia.In this research, a questionnaire was constructed by adapting items from previous studies.SmartPLS 3.2.9 was used to analyse the data by applying partial least square-structural equation modelling (PLS-SEM) analyses.The results of this research showed that safety behaviour had a substantial effect on safety performance.Furthermore, a significant mediating effect of safety behaviour could be found in the relationship between transformational and transactional leadership on safety performance.This research contributes to the existing body of knowledge by offering an alternative model that has been empirically validated which can be employed as a reference by academia and industry to explain the significant role of transformationaltransactional leadership towards overall safety performance for SME manufacturing industry.
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How this classification was reachedexpand
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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".