The role of top management commitment in enhancing competitive advantage: The mediating role of green innovation, supplier, and customer integration
Bibliographic record
Abstract
Recently, environmental protection is emerging as global warming is becoming an increasing issue of concern worldwide. In addition, the world society has shifted its preference from the conventional product to the environmental-friendly product. But, on the contrary, the manufacturing industry management is still in doubt whether the green practices benefit the company, given that it requires a lot of capital investment and culture change. This study examines the impact of top management commitment on competitive advantage with the mediating role of supplier integration, customer integration, and green innovation. The data collection used a questionnaire designed with a five-point Likert scale to measure the objective opinion of the respondents against the item statement by choosing the predetermined measurement scale starting with 1: strongly disagree up to 5: strongly agree. Questionnaires were created in Google Form link and distributed to 600 respondents via email, WhatsApp groups, and Facebook. This survey received 285 responses (response rate 47.50%) considered valid for further analysis. Furthermore, the data were analyzed using the partial least square (PLS) technique using SmartPLS software version 3.0. The result revealed that top management commitment directly influences supplier integration, green innovation, customer integration. Top management commitment did not directly affect competitive advantage. Instead, competitive advantages were affected by supplier integration, green innovation, and customer integration. Similarly, green innovation was supported by supplier integration and customer integration. In addition, top management commitment indirectly affects competitive advantage through supplier integration, green innovation, and customer integration. This result implies that top management commitment plays a vital role and has multiple effects in enhancing competitive advantage through establishing strategy and policies. This result provides a practical contribution on how the manufacturing companies could improve competitive advantage and at the same time concerns the environmental protection through green innovation. This study could also enrich and extend the acceptance of the recent research in the context of the manufacturing industry. Finally, this work has some limitations, particularly regarding the population and the variables involved. Further studies on the current topic are suggested to apply the variables such as digital and technology capability as these issues are currently in the growing stage. Future research is also suggested to cover a broader population such as the service and health 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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.003 |
| 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 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".