Organizational Ingenuity: Concept, Processes and Strategies
Bibliographic record
Abstract
In this introduction to the special issue we explore the main features of ‘organizational ingenuity’, defined as ‘the ability to create innovative solutions within structural constraints using limited resources and imaginative problem solving’. We begin by looking at the changing views of the importance of ingenuity for economic and social development. We next analyse the nature of ingenious solutions. This is followed by a discussion of structural, resource and temporal constraints that face problem solvers. We next turn our attention to creative problem solving under constraints. We contrast ‘induced’ and ‘autonomous’ problem solving. The first arises when external stakeholders or top managers impose tasks that define problems for the individuals and groups that must solve them; the second arises when these individuals and groups recognize and define the problems for themselves. We argue that in both induced and autonomous problem solving, individuals and groups that wish to act creatively confront two types of constraint. The first are ‘product constraints’ that define the features and functionalities that are necessary for a successful solution. The second are ‘process constraints’ that stand in the way of creative problem solving in a given organizational context. We argue that both types of constraints can lead to organizational ingenuity, but that dealing with process constraints is crucial for organizational ingenuity, and hence for sustaining organizational ingenuity more generally. We provide an overview summary of the articles in the special issue, and conclude with suggestions for future 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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".