Extending the application of stakeholder influence strategies to environmental disclosures
Bibliographic record
Abstract
Purpose This paper aims to provide insights into stakeholder expectations regarding the types of disclosures a firm should make, and if dissatisfied with the disclosure policy, whether it will use different intervention strategies in an attempt to induce the desired disclosure outcome. Design/methodology/approach An inductive qualitative framework is used in the study. In‐depth interviews, triangulated against relevant web site and media releases, are used to identify the salient stakeholders and the major environmental issues in Malaysia. Then an experimental approach is used based on role‐playing whereby experienced participants are introduced to hypothetical vignettes that relate to environmental issues identified. Findings The results indicate that the preferred form of disclosure is for the firms to “defend” the reasons behind the environmental event and/or explain what has been done to rectify the situation. With relatively few exceptions, the preferred strategies chosen by various participants align well with the influence strategies identified by Frooman. The findings confirm that although Frooman's model is useful in predicting stakeholder influence strategies, its effectiveness is tempered by the level of significance placed on the event by the stakeholders. Research limitations/implications Although based on a small sample, the results suggest that stakeholder theory has much to offer in terms of understanding management/stakeholder behaviour and corporate environmental disclosures. Originality/value The paper extends the application of stakeholder influence strategies in the “environmental reporting” domain. Likewise, it attempts to address the scarcity of literature taking the view of a wide array of stakeholders and how they choose to influence the firm. Finally, it confirms that stakeholder theory can be extended to aid the understanding of events in non‐western developing economies such as Malaysia.
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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 | 0.033 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".