THE "EVERYTHING'S DIFFERENT, EVERY TIME" INNOVATION MANAGEMENT PROBLEM: A PROMISING MODEL DEVELOPMENT
Bibliographic record
Abstract
This study reports on the testing of a promising approach for aiding decision-making during innovation. By focusing on the effects of risk/action dyads on success (the Risk/Action/Success (R/A/S) framework), and because perceived risks do appear repeatedly even though they emanate from differing contexts, the model offers an opportunity to learn from what worked best before. Using Artificial Neural Networks, this novel approach allows for generalisation and applicability of specific innovation management actions that are context specific. For academics, the proposed approach contributes to the risk-management literature by proposing a new paradigm for understanding and analysing innovation processes and identification of the most frequently occurring risks as seen by managers directly involved in continuous innovation. In addition, the model offers the capacity to use quantitative techniques to model the overlapping risks and actions during innovation-related decision-making. For practitioners, it can provide specific recommendations in the form of success-sorted lists of actions taken by other innovation managers that faced similar risks. This paper presents the theoretical and practical rationales underpinning this R/A/S framework and reports on the viability of this approach using pilot data.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".