Demystifying variance in performance: A longitudinal multilevel perspective
Bibliographic record
Abstract
Research summary : T his study employs longitudinal multilevel modeling to re‐examine the relative importance of business unit, corporation, industry, and year effects on business unit performance. Total variance in performance is partitioned into stable variance and dynamic variance. Sources of these two parts of variance are explored. Empirical results indicate that (1) stable effects of corporation‐industry interaction are substantially important, but were unequally confounded with stable effects of business unit, corporation, and industry in results of previous studies; (2) stable effects of corporation, industry, and corporation‐industry interaction, taken together, are of similar relative magnitude to stable effects of business unit; and (3) random and nonlinear year effects are very important in explaining dynamic variance. These findings extend our theoretical and empirical understanding of performance variability . Managerial summary : W hether stable or changing, business units themselves, corporate‐parents, and industries influence business unit operations. This article investigates the relative effects of these factors on business unit performance. Although the traditional wisdom is that business unit is critical, this research finds that corporate‐parent, industry, and interactions between these, taken together, are as influential as business unit. Specifically, interactions between corporate‐parent and industry are important for over‐time average business unit performance, indicating that a given corporate‐parent unevenly influences its business units in different industries and that a particular industry unevenly influences business units within itself from different corporate‐parents. This study also demonstrates that changes in business unit, corporate‐parent, and industry are important drivers of over‐time volatility of business unit performance and that effects of these changes differ . Copyright © 2016 John Wiley & Sons, Ltd.
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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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".