The inseparable two: Impact of prior success and failure on new product development project discontinuation
Bibliographic record
Abstract
Abstract New product development (NPD) is a risky and expensive endeavor. Therefore, discontinuing less promising NPD projects, based on their development trajectories, is very important, yet firms face challenges when deciding whether and when to discontinue NPD projects. This article examines how characteristics of a firm's prior success and failure experiences, such as the number of launched and discontinued past projects, their attributes (i.e., the codification and quality of the knowledge underlying these past projects), and their level of relatedness (i.e., similarity) to current projects, influence the risks of continuing and discontinuing NPD projects, thus impacting their future discontinuation decisions. We adopt a mixed method approach, where we formally model and empirically test the influence of prior experiences on future decision‐making. A formal model enables us to offer reasoning that considers the risks of continuing a project versus discontinuing that project as the basis for theoretical arguments for a set of proposed testable relationships. We then empirically test these relationships on 2938 new drug development projects of biopharma firms worldwide. We find that firms with a greater amount of success and failure experiences tend to discontinue less promising NPD projects sooner than firms with less success and failure experiences. Also, we find that while the attributes (particularly knowledge codification) of successful experiences predominantly helped firms decide on timely discontinuation, failure experiences influenced firms' decision on timely discontinuation only when the projects were closely related to their failure experiences.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".