Stakeholder Engagement Disclosures in Sustainability Reports as a Legitimation Mechanism for Image Restoration in Critical Incidents
Bibliographic record
Abstract
The study investigates stakeholder engagement as a legitimation mechanism in sustainability reporting, with a focus on how firms employ discursive image restoration following critical incidents. This project examines how companies respond rhetorically to heightened stakeholder skepticism regarding their commitment to corporate social responsibility, particularly after operational crises that affect multiple stakeholder groups. Using a qualitative research methodology and a multiple case study design, the research analyses stakeholder-engagement disclosures related to critical incidents in sustainability reports from two sectors - oil and gas, and pharmaceutical - between 2020 and 2022. These cases include oil spills and product recalls across seven incidents in Nigeria, US and Canada. Furthermore, the thesis employs media coverage analysis in order to examine how news framing interacts with corporate sustainability disclosures to dynamically construct restorative narratives, addressing a gap in prior research that has largely focussed on greenwashing or media effects on reputation rather than on stakeholder engagement as a legitimation mechanism following critical incidents. The findings highlight the central and critical role of external stakeholders in shaping corporate legitimacy in the aftermath of critical incidents. The study identifies two dominant stakeholder engagement approaches in the sustainability reports and post critical incidents that influence legitimation as 'inform' and 'engage'. The research also identifies four discursive legitimation strategies adopted in stakeholder engagement disclosures: 'avoidance'; 'admission'; 'image enhancement'; and 'authorisation' discursive legitimation. Furthermore, the study demonstrates a link between media framing of critical incidents and the corporate deployment of stakeholders' engagement in the sustainability reports. It identifies three media frames: diagnostic frames, motivational frames and prognostic frames that recur across news media coverage published following critical incidents. Overall, the findings show that firms often resort to discursive legitimation strategies as a form of symbolic responsibility instead of enacting substantive change embracing full responsibility. It depicts that media coverage functions as an enabler of these symbolic responses by helping to reshape stakeholder perceptions of corporate social responsibility. The study identifies three pathways that firms pursue in their attempt at image restoration following critical incidents. These three image restoration pathways are deliberate, proactive, and internally-enforced. The research contributes to social, environmental and accounting literature and to the theories of legitimacy and image restoration by particularly emphasising the importance of communicative performative legitimacy, where symbolic stakeholder engagement substitutes for meaningful action, potentially exposing firms to reputational backlash. It further argues that while discursive strategies can temporarily bolster legitimacy, their long-term effectiveness depends on transparency and genuine corporate behaviour. In conclusion, sustainability disclosures have become a critical but underexplored link between legitimation and stakeholder engagement. The continued prevalence of limited engagement and the raging greenwashing indicates that firms frequently adopt adaptive, short-term legitimation strategies for damage control rather than for transparent or collaborative accountability.
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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 | 0.019 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".