Building a Measurement Model for Market Visioning Competence and Its Proposed Antecedents: Organizational Encouragement of Divergent Thinking, Divergent Thinking Attitudes, and Ideational Behavior
Bibliographic record
Abstract
The overall question that this research seeks to answer is the following: Are there individual and/or organizational resources related to “divergent thinking” that enhance or inhibit the firm's market visioning competence in the case of radical innovation? In order to answer this question, in this paper, the key focus is on the realm of potential divergent thinking‐related resources that the firm may possess and access to aid in the creation and development of an effective market visioning competence. In particular, individual‐level factors related to divergent thinking capabilities and, at the organizational level, encouragement of such capabilities, are investigated. Specifically, we propose that two organizational‐level factors related to organizational encouragement of divergent thinking (“encouraging ideas” and “encouraging diversity”), two individual‐level divergent thinking attitudes factors (“openness” and “ability to move from divergent thinking to convergent thinking efficiently and effectively”), one individual‐level ideational behavior factor (“ability to generate new ideas”), and a further individual cognitive factor (“need for cognition”) have direct impacts on market visioning competence. Scales are then developed and tested for measuring these potential antecedents, and they are included in a measurement model with market visioning competence to enable further research.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".