Exploring Correlates of Product Launch in Collaborative Ventures: An Empirical Investigation of Pharmaceutical Alliances
Bibliographic record
Abstract
This paper examines collaborative ventures leading toward the launch of new products in the pharmaceutical industry. These collaborative ventures are one of the most underresearched areas in the new product literature, yet the preponderance of these collaborative ventures makes it an area of great importance for scholars and practitioners alike. As such, the purpose of the study is to examine why some collaborative projects produce a favorable outcome (the launch of a product) whereas others do not. That is, what characteristics of partner firms in the collaborative ventures and what characteristics of the partnership lead to a successful launch of a new product in the pharmaceutical industry? Secondary data from the pharmaceutical industry are employed in a multinomial logit model. Data from 128 collaborative ventures from 1980 to 2004 are used in the analysis. The partner firms in the collaborative ventures are from various industries ranging from malt beverages to pharmaceutical preparations to electronic and other equipment among others. Of the 128 collaborative ventures, 66 were successful in leading to a new product launch, whereas 62 did not result in the launch of a new product. The results from the multinomial logit analysis suggest that combined marketing resources of parent companies, combined technological intensity of parent companies, and combined asset bases of parent companies contribute to the likelihood of an eventual product launch in a collaborative venture. However, the results of the analysis show that contrary to expectations, technological complementarity of partners in the collaborative venture is not a significant predictor of successful new product launch. The results of the study suggest certain aspects for managers to consider when establishing collaborative ventures. To maximize the possibilities of the collaborative venture leading to the successful launching of a new product, managers should be concerned with the resources potentially available to partners in the collaborative venture from parent firms. These resources are not only of financial nature but also of technological nature. The existence of these resources does not ensure provision of resources to the collaborative venture; however, without the possibility of these resources it appears that successful launch of a product is less likely.
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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 | 0.007 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".