Firms’ Response to Climate Change: The Interplay of Business Uncertainty and Organizational Capabilities
Bibliographic record
Abstract
Abstract With climate change emerging as one of the most important issues increasing uncertainty in the business circle, firms have shown different reactions. Why do firms differ in adopting and implementing carbon management practices (CMPs) in response to the global warming issue? This paper attempts to explore this question with particular attention to two factors: external business uncertainty and internal organizational capabilities. This study investigated whether business uncertainty, organizational learning and lean production capabilities influenced the adoption and implementation of CMPs as well as examining how organizational capabilities moderate the relationships between business uncertainty and the level of CMPs. The results of a cross‐sectional survey and hierarchical regression analyses indicate that perceived business uncertainty decreases the adoption of CMPs, organizational learning and lean production capabilities strongly facilitate the adoption and implementation of CMPs, and lean production capability positively moderates the impacts of business uncertainty on the adoption of CMPs. This study provides guidance for managers and academics considering how to identify, design and manage the dimensions of a firm's practices in response to the global warming issue within the organization as well as with other organizations. Copyright © 2015 John Wiley & Sons, Ltd and ERP Environment
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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 | 0.004 | 0.017 |
| 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 | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".