Moving to the Next Strategy Stage: Examining Firms' Awareness, Motivation and Capability Drivers in Environmental Alliances
Bibliographic record
Abstract
Abstract Complementing extant studies on the antecedents of firms' environmental strategy, this article focuses on the trajectories of corporate engagement in proactive environmental alliances. Specifically, we build an awareness–motivation–capability framework and analyze factors that drive the move beyond incremental pollution prevention and facilitate firms' engagement in transformative, sustainable development strategies in their alliances. Based on 212 environmental alliance‐related observations, our test results indicate limited explanatory power of regulatory pressures, but highlight the role of firms' environmental networks to sharpen their awareness to engage in transformative alliances. Further, we elaborate on the nuances and boundary conditions of firms' risk‐taking propensity, industry concentration, financial capacity and especially prior sector‐spanning experiences as motivation and capability drivers. These insights contribute to the discourse on firms' environmental strategy and alliance formation by depicting how and to what extent environment‐specific and more general firm attributes predispose them to engage in transformative rather than incremental environmental projects. Copyright © 2017 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 | 0.002 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.003 |
| 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".