Knowledge Sources and Operational Problems: Less Now, More Later
Bibliographic record
Abstract
Unlike problems requiring new-to-the-world solutions that combine knowledge from multiple sources, operational problems can often be solved by repurposing existing knowledge from other contexts into new-to-the-firm solutions. Firms that seek new-to-the-firm solutions to operational problems face a cost-benefit tradeoff when deciding how many knowledge sources to use. With less need for knowledge recombination than for new-to-the-world solutions, greater knowledge breadth incurs greater screening and implementation costs without concomitant benefits. We study how U.S. manufacturing facilities from 1991 to 2005 improve operational performance by reducing their rate of annual output of toxic chemical waste (i.e., improvements to operational effectiveness). Results show that search involving fewer knowledge sources in a given year is associated with greater improvements in operational performance (greater waste reduction). At the same time, however, using multiple knowledge sources over time helps improve operational performance, suggesting that avoiding satiation from a single source and learning across sources play temporal roles in toxic chemical waste reduction. Overall, the results suggest that the greatest improvements in operational performance arise with a focused search for new-to-the-firm solutions within periods while also exploring multiple sources over time.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.012 | 0.019 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".