The long‐term benefits of organizational resilience through sustainable business practices
Bibliographic record
Abstract
Research summary : Prior work on the benefits of business sustainability often applies short‐term causal logic and data analysis. In this article, we argue that the social and the environmental practices ( SEPs ) associated with business sustainability not only contribute to short‐term outcomes, but also to organizational resilience, which we define as the firm's ability to sense and correct maladaptive tendencies and cope positively with unexpected situations. Because organizational resilience is a latent, path‐dependent construct, we assess it through the long‐term outcomes, including improved financial volatility, sales growth, and survival rates. We tested these hypotheses with data from 121 U.S.‐based matched‐pairs (242 individual firms) over a 15‐year period. We also tested, but did not find support for, the relationship between SEPs and short‐term financial performance . Managerial summary : Most managers look for short‐term financial benefits to justify socially responsible or sustainable practices. In this article, we argue that such practices also help firms become more resilient, which helps them avoid crises and bounce back from shocks. However, it is difficult to measure the avoidance of shocks, so we analyzed long‐term outcomes. We show that firms that adopt responsible social and environmental practices, relative to a carefully matched control group, have lower financial volatility, higher sales growth, and higher chances of survival over a 15‐year period; yet, we were unable to find any differences in short‐term profits. We hope this research provides good reasons for firms to practice sustainability beyond the pursuit of short‐term profits . Copyright © 2015 John Wiley & Sons, Ltd.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".