Competitive Goals and Plant Investment in Environment and Safety Practices: Moderating Effect of National Culture
Bibliographic record
Abstract
ABSTRACT Operations managers clearly play a critical role in targeting plant‐level investments toward environment and safety practices. In principle, a “rational” response would be to align this investment with senior management's competitive goals for operational performance. However, operations managers also are influenced by contingent factors, such as their national culture, thus creating potential tension that might bias investment away from a simple rational response. Using data from 1,453 plants in 24 countries, we test the moderating influence of seven of the national cultural characteristics on investment at the plant level in environment and safety practices. Four of the seven national cultural characteristics from GLOBE (i.e., uncertainty avoidance, in‐group collectivism, future orientation and performance orientation) shifted investment away from an expected “rational” response. Positive bias was evident when the national culture favored consistency and formalized procedures and rewarded performance improvement. In contrast, managers exhibited negative bias when familial groups and local coalitions were powerful, or future outcomes—rather than current actions—were more important. Overall, this study highlights the critical importance of moving beyond a naïve expectation that plant‐level investment will naturally align with corporate competitive goals for environment and safety. Instead, the national culture where the plant is located will influence these investments, and must be taken into account by senior management.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".