Sustainability with (out) compromise: how companies perceive, manage, and communicate trade-off decisions in the practice of corporate sustainability
Bibliographic record
Abstract
The practice of corporate sustainability is beset with compromise; it involves inevitable trade-offs across competing objectives and across a range of stakeholders and time horizons. These trade-offs create tension points that present the company with strategic choices that ultimately shape its overall approach to sustainability. Accordingly, trade-offs constitute a material aspect of a company’s sustainability practice, and ought to be disclosed in sustainability reports. The purpose of this research is therefore to understand how companies perceive, manage, and report on these critical trade-off decisions in the practice of sustainability. To achieve this objective, this dissertation conducted a study in three phases. In Phase I, this study conducted a review and content analysis of the trade-off literature through the lens of the natural resource-based view of the firm. Through this process, this study proposed a hierarchical framework for the analysis of trade-offs based on their root tensions, their interconnections, and their connection to sustainability synergies. In Phase II, this study used an organizational cognition perspective to posit that companies perceive and respond to these trade-off decisions in ways that reflect the company’s underlying sustainability logic. To explore this link, this study performed a content analysis of interviews with sustainability managers, as well as archival documents. This study found that companies with an instrumental logic saw trade-offs as binary and resolved them by counterbalancing the ‘lose’ dimension with ‘wins’ elsewhere. In contrast, companies with an integrative logic saw trade-offs as non-binary, and resolved them through an iterative, risk-based approach. Finally, in Phase III, this study used a legitimacy perspective to determine whether companies are disclosing these trade-offs in their sustainability reports. To do so, this study analyzed sustainability reports and interviews with sustainability managers using content analysis. This study found that 92% of all reporting companies had encountered sustainability trade-offs but had not disclosed them in their reports. Evidence of these accounts were nevertheless present in the implicit (or latent) content of the reports. These findings highlight the negative light in which many companies perceive trade-offs, and the legitimacy threat that their disclosure poses.
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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 | 0.017 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.026 |
| Scholarly communication | 0.021 | 0.023 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.006 | 0.005 |
| 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".