Family Ownership, Governance, Management, and the Power Consumption Efficiency in Private Firms
Bibliographic record
Abstract
Environmental sustainability is increasingly prominent in organizational research; however, mainstream discourse often narrows its focus to contexts where organizational activities produce on-site toxic emissions, overlooking the broader impact of remote pollution from inefficient resource use. This oversight creates a critical knowledge gap, as direct pollution is routinely monitored, whereas remote pollution is generally ignored. This study addresses this gap by examining power consumption and the factors influencing firms' (in)efficient energy use. Specifically, we investigate how corporate governance structures in family and non-family firms shape environmental performance in this unregulated area. We focus on the role of employee agency and explore whether family ownership, governance, and management enhance the impact of workforce size on firms' power consumption efficiency. Analyzing 14,959 firm-year observations from 2,908 privately owned firms in Atlantic Canada, we find that family ownership does not significantly affect power consumption efficiency, while family governance and executive involvement improve it, though this advantage quickly diminishes and inverts as firm size increases. We discuss the theoretical and practical implications of these findings.
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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 | 0.001 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".