Enhancing SME Green Performance: The Role of Environmental and Social Responsibility Programs and Environmental Dynamism
Bibliographic record
Abstract
Green performance assesses how well an entity is performing in terms of minimizing its negative impact on the environment while also striving to promote ecological and sustainable practices.It refers to the measurement and evaluation of environmental sustainability and responsible practices within an organization, industry, or system.State-owned enterprises (SOEs) are companies whose majority ownership is owned by the government, which should play a significant role in achieving sustainable development goals, which is the government's primary responsibility.This study investigates the effect of the Corporate Social Responsibility (CSR) programs of SOEs on the green performance of micro-enterprises that receive assistance from SOEs as one of the CSR programs.This study also analyzes the role of environmental dynamism in moderating the effect of CSR programs on micro-enterprises performance.Respondents include 106 micro-enterprises in Central Sulawesi Province, Indonesia.Data collection techniques were carried out through questionnaires and interviews.The data analysis method used was Partial Least Square (PLS-SEM).The result shows that the CSR programs carried out by SOEs for micro-enterprises have improved micro-enterprises performance.Nevertheless, environmental dynamism does not moderate the relationship between the CSR program and the micro-enterprises performance.This study reveals that the micro-enterprises' performance is more sensitive to market turbulence than the competition and technological turbulence as they typically have limited financial, human, and technological resources.This makes them less resilient to sudden market shifts as they may lack the resources to adapt quickly or invest in new technologies.This finding underscores the significance of CSR initiatives in promoting environmentally responsible practices among smaller businesses.It highlights that SOEs, as government-owned entities, can play a crucial role in fostering sustainability within their communities and industries.
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.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".