Idea Generation and Survival in an Organizational Innovation Jam
Bibliographic record
Abstract
This paper aims to contribute to both innovation management theory and ideation practice in firms, by empirically analyzing what factors influence the “life” of an idea within organizations. As the empirical base for our study, we utilize data from a 48-hour IT- based creative session called Ideation Jam within a Swedish multination company. During this session ideas were created, developed, and selected by a large number of employees, something which can be regarded as a live experiment emulating what normally occurs in organizations, though in a much more compressed timeframe. The empirical observations allow us to see how ideas generated by the employees within the organization arise, evolve, and die or are selected over time. In addition, we explore how this process of selection and survival of an idea is influenced by the social networks that are generated around it. The findings indicate that the amount of comments (activity) generated around an idea, and its insertion in the early stages into the Jam (time lag), increase the likelihood that it will eventually be considered a novel and valuable idea, and thus is selected for further development and possible realization. In addition, by employing a core-periphery analysis, we find that the social structure in which the idea is embedded has important implications for its survival. Theoretical and managerial implications that can be drawn from our findings, as well as limitations of our study and directions for future research are discussed.
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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 | 0.005 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".