How exploitation impedes and impels exploration: Theory and evidence
Bibliographic record
Abstract
Research summary : This study suggests that strategy and organizational scholars seeking to analyze the impact of exploitation on exploration would benefit by moving away from the generally assumed unitary perspective on exploitation. Specifically, we propose a multifaceted perspective on exploitation by theoretically and empirically distinguishing between repetitive exploitation versus incremental exploitation. We argue that repetitive exploitation can impede exploration and delay firms' responses to environmental changes, while incremental exploitation can impel exploration and accelerate firms' responses to environmental changes. We test our arguments using extensive longitudinal data from the hard disk drive ( HDD ) industry, and our supportive empirical findings highlight the relevance of our distinction between the two types of exploitation and their very different effects on exploration . Managerial summary : This study offers a solution to the puzzle of why many believe firms cannot excel at advancing existing practices and developing new initiatives, typically described as the trade‐off between exploitation and exploration. We introduce the distinction between repetitive and incremental exploitation and show that only the former type of innovation generates rigidity toward exploration, whereas the latter actually promotes exploration. More specifically, our evidence from the hard disk drive industry shows that those firms emphasizing incremental innovation (as opposed to repetition of their existing practices) were most likely to remain explorative over time, whereas firms emphasizing more repetitive innovation proved less open to changes. We discuss the implications of our findings as suggesting that firms seeking to optimize their innovativeness over the long term should strive to remain active in incrementally innovating their existing practices . Copyright © 2015 John Wiley & Sons, Ltd.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".