Idea Generation in Highly Institutionalized Fields:A Dual Process Model of Ideation
Bibliographic record
Abstract
The early phase of innovation processes in highly institutionalized fields relies on the capabilities of actors to generate new ideas that break with the field frame. Informed by a dominant logic, a field frame shapes collective cognition and can thus prevent the generation of new ideas and block innovation. An important question facing innovation research is thus how actors can generate ideas that break with the field frame in highly institutionalized fields? To answer this question, we draw on insights into dual process modeling from cognitive sciences. Dual process modeling emphasizes the different nature of the conscious (deliberate) and subconscious (implicit) systems involved in ideation. We further elaborate on how these two systems relate to four streams of research that management scholars evoke to model microprocesses of generating new ideas, namely metaphors, conceptual blending, bricolage and innovative design reasoning. We identify metaphors and bricolage as central to the subconscious system, which play an important role in maintaining the field frame, whereas conceptual blending and innovative design reasoning can be instrumental in generating ideas in the conscious system, thereby potentially enabling actors to generate innovative ideas that break with the field frame. Hence, this paper clarifies processes that unfold at the individual level of analysis during innovation activities and proposes leverages for actors to innovate within highly institutionalized fields.
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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 | 0.008 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".