Searching for knowledge in response to proximate and remote problem sources: Evidence from the U.S. renewable electricity industry
Bibliographic record
Abstract
Abstract Research Summary We consider how different problem sources—proximate versus remote—relate to heterogeneity in search breadth. While studies of search have established the importance of search breadth, and argued that problems trigger search, they have focused on a single problem source instigating search. We extend prior research by considering how search breadth differs in the presence of proximate and remote problem sources. Because of differences in familiarity with each type of problem, and in expectations of their ability to influence the problem source, problems triggered by remote sources associate with greater breadth. Firms' technological capabilities, meanwhile, temper these findings; capable firms exhibit broader search when facing problems raised by proximate sources. Using data describing the U.S. renewable electricity sector, we generate theoretical and empirical implications. Managerial Summary When facing new problems, firms tend to seek knowledge from various sources to better understand the problem and identify relevant solutions. The source of the problem may be an important trigger to the breadth of search activities, particularly whether the source is proximate or remote. To test this notion, we compare U.S. utility firms' search breadth when facing regulations emphasizing increased renewable generation, from both the federal and the state government. We find that firms tend to search for knowledge about renewable technologies more broadly following federal regulatory actions. However, firms that have previously generated renewable electricity search more broadly following state regulatory actions. By exploring firms' choices in the U.S. renewable electricity sector, our research generates important managerial and public policy implications.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".