Board Capabilities and the Mediating Roles of Absorptive Capacity on Environmental Social and Governance (ESG) Practices
Bibliographic record
Abstract
Public listed companies in Malaysia have been pressured tremendously to accept the engagement of Environment, Social and Governance (ESG), but the engagement is still low based on previous studies. ESG will enhance company financial performance, image as well as the ability to attract and retain the workplace which contributes to the market value in the economy. This shows that ESG engagement improve company brand image and reputation, increase customer loyalty and sales as well as productivity. Corporate governance is seen to be the key role to ensure that companies engage with ESG practices since it can enhance the value creation and improve financial performance. Even the present investors are bound to look for non-financial performance elements like corporate governance and environmental, social and governance (ESG) practices that the company engaged since it is an evidence of effective corporate governance. Based on today’s global and innovation-driven economy which also include social and environmental matters consisting of welfare distribution and growth, it is said that countries need to be more efficient in finding new ways to enhance the environmental policy promoting greater change and dynamics. Thus, they must find new ways to develop an innovation policy to emphasise the knowledge-driven economy on the capacity to adapt and adopt best practices, create, diffuse and transform innovation and knowledge. The absorptive capacity will recognise the ability of the individual and company in adopting the innovation which play an essential part in determining the characteristics of good corporate governance to ensure best ESG practices in the company. This paper examines the relationship between board capabilities and ESG practices through the mediating role of absorptive capacity. Board size, board diversity and board independent are the board capabilities that the paper investigates. Collection of information and data was from company's listed in FTSE4Good Bursa Malaysia from the year 2012 to 2016. The results from the regression analysis show that ESG practices have a significant relationship with board size, board diversity, board independence and absorptive capacity. On top of that, absorptive capacity is perceived to have influence on board diversity and board independence towards ESG practices. The results provide empirical evidence and guidance in identifying areas of problems in the current policy and amend it for a better policy in promoting sustainability.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".