Innovation success: an empirical study of software development projects in the context of the open source paradigm
Bibliographic record
Abstract
New product development (NPD) performance is a relevant subject within operations management, particularly in the software industry. Though project managers have long tried to improve the software NPD process, a new paradigm has recently attracted their attention: the Open Source Software (OSS) model. OSS is developed collaboratively—mainly by dispersed teams of volunteers—under licensing terms that make the complete source code publicly available and allow redistribution of modified versions. In contrast to prevalent norms, OSS thrives not by enclosing intellectual property (IP) but rather by opening it through the project's license. The first research question is therefore: How do OSS licenses impact development performance? Secondly, OSS enables the collaboration of users as co-developers, prompting the question: How does the community-level structure of an OSS development project impact development performance? Finally, a developer is the locus of key decisions for the project, leading to the third question: How does the collaboration structure of the core development team impact development performance? This is an empirical field study of working OSS projects using archival data from electronic sources. NPD performance was measured using project productivity, product quality, and product popularity. Social network analysis was used for the measurement of the collaboration structure of core developer teams. The analysis was done using regression and time series techniques. Besides improving over extant work from the research design standpoint, this dissertation offers several contributions. The impact on performance of the project's IP policy in the form of license choice was confirmed and an association between the relative size of the external community and performance was found. Also, this study showed that product quality suffers when NPD projects are crowded, but that the amount of boundary spanning activity was positively associated with product quality. This is also the first study to operationalize and test the concept of temporal dispersion in distributed teams. Other contributions refer to the practical design of NPD project teams and their task allocation policy. Keywords: New Product Development, Innovation, Software Projects, Open Source Software, Productivity, Quality, Social Network Analysis Linear Regression, Time Series, Empirical Research, Archival Data Analysis.
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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 | 0.014 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".