An Agency Perspective on Firm Diversification, Efficiency and Performance: Evidence From Malaysia
Bibliographic record
Abstract
I examine, from the agency perspective, the relationship between three important corporate measures among the Malaysian publicly-listed family-controlled firms: firm diversification, asset utilization efficiency and firm performance. I also explore the role of board independence in moderating the firm diversification-performance relationship. My findings suggest that the greater the extent of firm diversification, the poorer will the asset utilization efficiency be. The poorer efficiency is likely to have caused the equally poorer performance for the firms in my findings. Notably, firm diversification is found to be more detrimental to performance for those firms affiliated to business group compared to firms without group affiliation. The group-affiliated firms which are found to be more diversified than the non-group firms, could have engaged in greater diversification for the self-interest of the controlling family. Specifically, I find that the agency-driven diversification causes the ROA (Tobin’s Q) of the firms to be lowered by 0.354% (0.026) for every additional increase in the number of business segments as the measure of firm diversification. In terms of the moderating effect of board independence, my finding shows that audit committee of board comprises entirely of independent outside directors positively moderate the firm diversification-performance linkage and is capable of reversing the apparently negative linkage between the two.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| 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".