Encouraging SMEs’ Green Innovation Through Stakeholder Pressure: The Moderating and Mediating Role of Environmental Commitment and Ethics
Bibliographic record
Abstract
This study investigates how stakeholder pressures (SSTPR) prompt SMEs to perform green innovation (GRNI) activities by grounding the analysis exclusively in stakeholder theory. It employs a survey questionnaire to gather information from 141 top- and mid-level executives working in various SME manufacturing firms (listed in DSE, CSE, foreign SMEs) in Bangladesh. The structural equation modeling (SEM) technique is used to analyze data and test hypotheses. The study’s findings reveal that SSTPR, both primary and secondary, have a significant positive impact on the firm’s degree of GRNI. Moreover, it has also been found that environmental commitment (ENVC) has a positive moderating effect on the relation between stakeholder influences and GRNI. On the other hand, environmental ethics (ENVE) has a partial mediation impact on this relationship. The results shed light on the crucial role of stakeholder influence, ENVC, and ENVE in promoting GRNI behavior. These findings will fill knowledge gaps on the factors that drive SMEs’ investments in GRNIs with insightful implications for regulators, managers, and policymakers. This study also assists Bangladesh’s sustainable agenda by bolstering green and sustainable innovation activities.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".