Sustainable development and stakeholder engagement in the agri‐food sector: Exploring the nexus between biodiversity conservation and information technology
Bibliographic record
Abstract
Abstract Organizations across various industries engage in biodiversity conservation as a way to achieve sustainable development and to manage stakeholder engagement expectations. Although the importance of information and communication technology to promote biodiversity conservation has been recognized, little attention has been devoted to shedding more light on corporate practices in this area. This study explores how organizations do use information technology and reporting practices to influence stakeholders' perceptions on biodiversity initiatives. Data are collected from agri‐food companies listed by the Fortune Global 500. Based on a qualitative content analysis approach, this research found that geospatial technologies and web‐based features support organizations' impression management efforts with regard to their biodiversity conservation practices. More precisely, our findings suggest that organizational impression management tactics of abstraction, selectivity and self‐promotion are used to rationalize corporate actions in this area. The paper develops a better understanding of corporate tactics aimed at influencing stakeholders' perceptions of the reliability and credibility of companies' biodiversity conservation practices. Implications of the results for the stakeholders of business organizations are also discussed. This study offers contributions to the body of literature on biodiversity reporting, communication technology and impression management tactics. Managerial implications and avenues for future research are also described.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 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".