A problem half-solved is a problem well-stated: Increasing the rate of innovation through team problem discovery
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
When turning ideas into innovation, current theories argue that a clear problem is essential throughout the innovation process because it enhances several team dynamics while generating and implementing ideas. However, such clarity can also hinder a team's ability to pivot or adapt their project when needed. To address this tension, we conducted a field study on 579 teams participating in an innovation competition at a Fortune Global 500 company to investigate how the level of problem clarity over time affects idea implementation in teams. Our results show that when teams began with lower levels of problem clarity and then gained higher clarity over time based on prior work developing ideas for the solution, a process we call “team problem discovery,” ∼80 % of these teams completed their respective project in the organization. But when following a more traditional innovation process, in which they began with higher clarity and then maintained it throughout a project, only ∼50 % of teams completed their project. These findings challenge prior assumptions in literature and offer several theoretical insights into the way teams can engage in problem solving and build shared cognition over time to increase the rate of innovation in organizations. • Team problem discovery process (low to high problem clarity): ∼80 % completion rate • Traditional innovation process (high to high problem clarity): ∼50 % completion rate • Exploring ideas followed by exploiting ideas drives the team problem discovery process.
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Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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