Bibliographic record
Abstract
Sustainability is critical to the future success of businesses; those that do not implement sustainability initiatives may lose customers, investors, and/or profits. This study examines barriers to corporate sustainability, measured through the four dimensions of the Prism of Sustainability (environmental, social, economic, and institutional), a framework of sustainability not commonly used in business research. An online survey of sustainability managers from a variety of industries in the United States distributed in the spring of 2021 yielded a total of 361 responses. Results reveal that lack of leadership and lack of governance were the most predominant barriers to corporate sustainability. Surprisingly, the most frequently cited barrier in the literature—resources—was not identified as a significant barrier for U.S. companies. The impact of the pandemic was also qualitatively explored to see if such constraints might have a nuanced effect on corporate sustainability efforts. This research expands the contexts in which the Prism of Sustainability is applied in business studies, highlighting it as a means to assess corporate sustainability. Results provide important managerial implications, highlighting the importance of measures to govern the organization’s sustainability effort and the critical role leadership plays. Sustainable management is a necessity for business, and therefore, addressing barriers to achieving it will be imperative for companies’ futures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".