How do pre‐entrants to the industry incubation stage choose between alliances and acquisitions for technical capabilities and specialized complementary assets?
Bibliographic record
Abstract
Abstract Research summary Focusing on the incubation stage of a potential new industry, this article addresses a gap at the intersection of the external sourcing and market entry literatures by examining pre‐entry external sourcing of new resources. Besides drawing on their legacy resources, pre‐entrants during industry incubation commonly use alliances and acquisitions to obtain technical capabilities and complementary assets, thereby creating a portfolio of sourcing modes that collectively shapes the firms' paths to potential market entry. We identify a key pattern at the intersection of type of sourcing mode and type of resource: pre‐entrants to the incubation stage are more likely to use alliances to source technical capabilities, while using acquisitions to source specialized complementary assets. Our empirical context is the agricultural biotechnology industry. Managerial summary Firms typically seek new resources when they begin exploring potential industries, before any products have reached the market, yet the needed investments face substantial uncertainties. This article highlights a pattern in how firms use alliances and acquisitions for technical capabilities and complementary resources during the incubation stage of the agricultural biotechnology industry. We focus on two key features of the external sourcing activity, differing based on the type of resource: developing new core technologies, which often starts early, tends to leverage external alliance partners; by contrast, establishing complementary assets tends to start later through acquisitions. The logic underlying these patterns can help managers make effective decisions about their external sourcing strategies during the incubation stage of a new industry.
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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 | 0.003 | 0.009 |
| 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 | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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 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".