Choosing Technology: An Entrepreneurial Strategy Approach
Bibliographic record
Abstract
A central premise of research in the strategic management of innovation is that start-ups are able to leverage emerging technological trajectories as a source of competitive advantage.But, if the potential for a technology is given by the fundamental character of a given technological trajectory, then why does entrepreneurial strategy matter?Or, put another way, if the evolution of technology is largely shaped by the strategic choices entrepreneurs make, then why do technological trajectories exhibit systematic patterns such as the Technology S-curve?Taking a choice-based perspective, this paper illuminates the choices confronting a start-up choosing their technology by resolving the paradox of the Technology S-curve through a reformulation of the foundations of the Technology S-curve.Specifically, we reconceptualize the Technology S-curve not as a technological given but as an envelope of potential outcomes reflecting differing strategic choices by the entrepreneur in exploration versus exploitation.Taking this lens, we are able to clarify the role of technological uncertainty on start-up strategy, the impact of constraints on technological evolution, and how technology choice is shaped by the possibility of imitation.Our findings suggest that staged exploration may stall innovation as a result of the replacement effect, increasing the strategic importance of commitment.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".