The Role of Cognition and Emotions in Explaining Innovation Process Performance
Bibliographic record
Abstract
Innovation process performance has been largely understood as resulting from the interaction among strategic, contextual and procedural factors, and a team’s capabilities for decision-making and accomplishing technical tasks under uncertainty. Based on an integrative review of the literature, we propose a theoretical construct for explaining the mediating effects of individual and team’s cognition and emotions on innovation processes performance. We propose understanding innovation as a learning process under risk and uncertainty; where complex, inaccurate and incomplete information becomes available incrementally through selective search. As result, sense-making, and substantive and procedural rationality are effected by cognitive biases, inertia and overload limiting a team’s ability to learn and achieve superior technical results. Our construct explains the role and dynamics among individual and collective cognitive and emotional powers, self-esteem, personal identity, and cognitive load across different milestones and stages common across innovation processes. We propose that, under highly uncertain and fast-pacing environments, technical tasks relying on synthesis capabilities tend to generate higher levels of frustration. Increased frustration tend to increase cognitive overload, thus increasing the frequency of mistakes and diminishing individual’s cognitive load, and affecting a team’s social cognition. We propose that, with appropriate mediating actions, the reinforcing dynamics between cognition and emotions could work both ways in order to positively affect how a team represents the problem at hand, devise and manages the appropriate courses of action (process and methods) for solving it.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".