What Is Sustainability in Business? A Discrete Choice Model Experiment
Bibliographic record
Abstract
Through recent years, much has been said about the importance of sustainability in corporate strategy, and multinational corporations abiding by the United Nations (UN) Sustainable Development Goals (SDG’s). More and more corporations try to integrate sustainability principles, otherwise known as ESG (Environmental, Social, and Governance) into their marketing, corporate strategy, and business models. There’s often a clash between non-governmental organizations (NGO’s), civil society, governments, and the private sector on what ESG best practices are, with some calling “greenwashing” some environmental-related actions companies are taking. This paper tries to tackle the first question in this challenge: what is sustainability in business? How individuals in different parts of the world are defining sustainability and how that may help corporations better address their ESG strategy to the market needs. The question is addressed using a discrete choice model, to understand the utility functions of sustainability parameters. Those parameters were defined by a meta-analysis of 200 scientific papers on sustainability. The survey for the discrete choice model was made available online, in seven languages, and it was completed by 501 individuals across 41 countries. Among the parameters researched, sustainable development goals adoption, followed by positive economic impact had the highest utility values. The lowest utility values were attributed to donations and racial equality. A disparity between what one would expect from stated preferences is seen in racial equality, as it ranked 4th in terms of preferences. On the same token, donations ranked last, in line with the utility value.
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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 | 0.018 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".