Bibliographic record
Abstract
Organizational search is a process by which organizations identify and make specific changes in routines that will improve performance. We develop theory to explain the decision-making processes of organizational search by examining how organizations come to define and commit to changes in specific routines, given a multiplicity of routines involved in accomplishing an organization’s work. We find that organizations did not necessarily commit to those changes in routines that would most improve performance. Instead, organizations committed to changes in routines that were politically feasible. Three principal findings from our research help explain the process and outcome of organizational search. First, individuals, grounded in their professional roles, frame problems and solutions that are meaningful to them given their work. This leads to both fragmented and conflicting frames across professional roles. Second, fragmented and conflicting frames are products of two distinct mechanisms. Fragmented frames are a product of differences in attention across professional roles, while framing conflicts are a product of jurisdictional competition across professional roles. Third, proposed changes in routines differed in their level of political contentiousness. Fragmentation was more easily overcome in organizational search than political conflict. As a result, organizations were less likely to commit to proposed changes in routines that became a source of jurisdictional competition, or led to framing conflict in the organizational search process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 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".