An inquiry on managers use of decision-making tools in the core front end of the innovation process
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper focuses on the Core Front End (CFE) activities of the innovation process to say, Opportunity Identification and Opportunity Analysis. In the CFE of innovation, several tools are used to facilitate and optimise decisions. To select them, managers of the product development team have to use several premises to decide which tool is more appropriate to which activity. This paper provides an overview of these mechanisms by looking inside five companies from two different countries. Those mechanisms underline the dimensions influencing the decision process before a specific tool is chosen and how those tools impact specific performance metrics. From the analyses and hypotheses testing performed, it clearly emerges that there is no link between being aware of basic requirements (inputs/outputs) to appropriately use a certain tool and dimensions such as tools’ effectiveness, difficulty in usage, frequency of usage and estimate investment for using them. Also, interesting cross-case patterns emerge.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it