The Collaborative Process of Sustainable Innovations under the Lens of Actor–Network Theory
Bibliographic record
Abstract
The development of sustainable innovation (SI) is complex and risky due to the characteristics and diversity of actors involved in its process. Little is known about the collaborative process underlying this development. The objective of the paper is to explore the collaborative mechanisms and dynamics that influence the process and characteristics of sustainable innovations. The translation approach of the actor–network theory is applied to shed light on the collaborative process of two cases of sustainable innovations within small- and medium-sized enterprises. The sociotechnical graph method is used as a methodology to track the mechanisms and compare the dynamics of their processes. The results reveal that the governance characteristic of sustainable innovations and the moment of mobilization are essential aspects of the collaborative processes. They show that, depending on the intensity and systemic impacts of SI, attraction and retention are important mechanisms in the construction of the governance characteristics of SI. A manager who uses these mechanisms during the mobilization of actors, having resources related to the governance characteristics, succeeds in sustainable innovation development. The paper contributes to the literature on sustainability management by linking the ‘becoming’ of sustainable innovations to their collaborative processes. It also informs managers on how to manage the collaborative process of sustainable innovations by relying on a translation approach.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".