Firm Performance and Its Drivers: How Important Are the Industry and Firm-Level Factors?
Bibliographic record
Abstract
This paper provides empirical evidence for the relative importance of industry and firm-level factors as determinants of firm performance. It also shows the relevance of the individual factors at both industry and firm levels. The paper therefore attempts to provide evidence for effects of industry and business-specific factors on firm performance using data from a developing economy. The study uses the financial and other organization-specific data of firms listed on the Nigerian Stock Exchange. The findings show that organization-specific factors are relatively more important than the industry factors, accounting for 66.58 percent of the variation in return on asset with little or no evidence for the effects of industry-level factors on return on asset. Financial leverage, firm size and firm growth rate are shown to be the most relevant firm-level factors. Firm-level factors also accounts for slightly more variance in Tobin’s Q than the industry factors.The results also show that the industry sector of the firm is the most relevant industry-level determinant of firm market performance. There is however little or no evidence for the effects of both industry- and firm-level factors on return on equity.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".