Timing Legitimacy: Identifying the Optimal Moment to Launch Technology in the Market
Bibliographic record
Abstract
How do managers time the launch of new technologies? Without actionable frameworks to ensure consumers and other stakeholders are ready, innovation releases remain a risky endeavor. Previous work on legitimacy has focused on stages following a product launch. However, launch timing involves shared expectations of when actions should occur prior to launch. This conceptual article evaluates the alignment between firm and stakeholder expectations regarding launch timing. It proposes that the market timing of new technology launches is structured by two dimensions: firm-led coordination and stakeholders’ willingness to change. Combining these dimensions, the authors map four types of market timing situations managers can encounter: antagonistic, synergistic, flexible, and inflexible timing. Temporal legitimacy is achieved when a firm and its key stakeholders share timing norms about the ideal moments when activities should occur in a market process. The authors conceptualize proto-markets as prefacing the well-known market legitimacy stages. This article concludes with a detailed managerial decision tree on how to create the optimal technology product launch moment and avenues of future research on market timing beyond technology launches.
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 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 | 0.004 | 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 | 0.000 | 0.000 |
| 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".