Creative Problem-Solving Process Styles, Cognitive Work Demands, and Organizational Adaptability
Bibliographic record
Abstract
In this theoretical article, organizational adaptability is modeled as a four-stage creative problem-solving process, with each stage involving a different kind of cognitive activity. Individuals have different preferences for each stage and thus are said to have different creative problem-solving process “styles.” The Creative Problem Solving Profile (CPSP) assesses these styles and maps onto and interconnects directly with the four stages of this creative problem-solving process. Field research ( n = 6,091) is presented in which the psychometric properties of the CPSP are established and the distribution of styles in different occupations and at different organizational levels are examined. A concrete blueprint is provided for organizational leaders to follow to (a) increase organizational adaptability, (b) simplify and facilitate change management, and (c) address important organizational effectiveness issues at the individual, team, and organizational levels. Real-world application examples are shared and future research opportunities to expand the CPSP’s usefulness are suggested.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".