Determinants of success in crowdsourcing software development
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
With the advent of digitization, recent years have witnessed a surge toward collective undertaking of production process different from traditional ways of organizing. In this vein, crowdsourcing has lent itself into a successful emerging mode of organizing and firms are increasingly using it in their value creation activities. However, despite popularity in practice, crowdsourcing has received little attention from IS scholars. Specifically, what the determinants of success in this model are remains an unexplored area of research that we strive to address in this paper. We focus on software development via crowdsourcing and drawing on studies from IS success, OSS and software development, we build a model of success that has three determinants: the characteristics of the project, the composition of the crowd and the relationship among key players. Finally, we describe our research methodology and conclude with potential contributions of our work.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 it