Generative and degenerative interactions: positive and negative dynamics of open, user‐centric innovation in technology and engineering consultancies
Bibliographic record
Abstract
The related concepts of open innovation and user‐centric innovation are currently popular in the literature on technology and innovation management. In this paper, we attempt to address two shortcomings to their practical application. First, the precise mechanisms supporting open and user innovation in different industrial contexts are poorly specified. Second, it is not clear under what circumstances they might become dysfunctional. We identify how the interaction of meso‐ and micro‐level mechanisms contribute to project‐based user‐centric innovation, based on a detailed characterization of the business activities of eight technology and engineering consultancies working across a range of sectors. We develop and illustrate the notion ofgenerative interaction, which describes a series of mechanisms that produce a self‐re‐enforcing ecology, which favours innovation and profitability. At the same time, we observe the opposite dynamics of self‐reinforcingdegenerative interactionlikely to produce a cycle of declining innovation and profitability. In the specific context of project‐based firms, we show that user‐centric, open innovation can affect performance negatively, and we discuss the consequences (positive and negative) of different patterns of interaction with clients.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| 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".