What drives financial performance–resource efficiency or resource slack?
Bibliographic record
Abstract
Abstract Extant research in operations management has revealed divergent insights into the value potential of resource efficiency. While one view relates efficiency with good operations management and asserts that slack resources are a form of waste that should be minimized, the other view suggests that limited resource slack can impose heavy costs on firms by making them brittle. In this research, the authors build on these views to investigate the relationship of inventory, production, and marketing resource efficiency of firms with three metrics of financial performance (i.e., Stock‐Returns, Tobin's Q, and Returns‐on‐Assets). The authors evaluate the theoretical framework using secondary information on all U.S. based publicly‐owned manufacturing firms across the 16‐year time period of 1991–2006. Analysis utilizing a mixed‐model approach reveals that a focus on resource efficiency is positively associated with firm financial performance. However, findings also support the arguments favoring slack, indicating that the financial gains from resource efficiency exhibit diminishing returns.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".