Bibliographic record
Abstract
This study examines the role of strategic timing in disruptive innovation. We construct a model where an entrant decides when to enter with a potentially disruptive technology and the incumbent determines whether and when to switch from its existing technology. By accounting for endogenous firm-specific learning alongside exogenous technological improvement, our model highlights the critical role of timing in shaping disruption. First, an entrant’s strategic timing—whether to enter early or delay entry—can cause disruption by deferring the timing of the incumbent’s response or deterring it altogether. Second, we identify conditions where incumbents, even those unable to initially mount a response, can later switch to the disruptive technology and coexist with an entrant. Based on these insights, we propose a typology of five distinct disruption types—Perfect, Postponed, Passing, Phased, and Partial—that expands the traditional understanding of disruption beyond the often-studied “perfect” cases to comprehend transient and mixed cases as well. Our theory of disruptive timing integrates literature on strategic timing and disruptive innovation to offer a new perspective for scholars and actionable insights for practitioners. This paper was accepted by Joshua Gans, business strategy. Funding: This work was supported by the Harvard Business School Division of Research and Faculty Development. Supplemental Material: The online appendices are available at https://doi.org/10.1287/mnsc.2023.01734 .
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".