Firms’ Response to Climate Change: The Interplay of Business Uncertainty and Organizational Capabilities
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it