Self- vs. Peer-Directed Search Responses to Organizational Performance Feedback
Bibliographic record
Abstract
Organizational performance feedback theory (PFT) explains when and how organizations search, proposing that performing below an aspiration level is problematic and organizations increase search to solve this problem. Thus, organizational search is problemistic in nature which takes place in the vicinity of an organization’s own prior strategic actions that neighbor the problem. In this study, we expand organizational search within PFT with a competitive perspective by relaxing the assumption that search is only self-directed. We argue that search can also be peer-directed, i.e., firms search in the competitive space of their peers. Integrating competition to organizational search also raises another related and important question: do organizations move towards or away from the competition? Using a dataset of 9191 high-growth firms and a novel topic-modeling methodology, we find a match between an organization’s performance feedback input and search location: performance below self-based (historical) aspirations influences self-directed search and performance below peer-based (social) aspirations influences peer-directed search. Moreover, we find that when performance is above aspirations, both self- and peer-based aspirations influence self-directed search. We also identify which performance feedback inputs motivate organizations to move towards or away from peers. Our findings contribute to the BTOF, PFT and competitive strategy discourses.
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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 | 0.006 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".