A living laboratory for managing the front-end phase of innovation adoption: the case of RFID implementation
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
The recent interest in radio frequency identification (RFID) technologies offers an interesting opportunity for researchers to examine the different phases of the innovation process. Although this technology has improved substantially over the last few years, its adoption by the business community still raises some challenges and unanswered questions for both developers and potential users. This paper provides a detailed description of an actual innovative project to implement RFID. It also provides a strong argument for dedicated organisational settings in which open innovation project management can develop through a living lab. The advent of the living laboratory approach as an innovation platform characterised by 'users as innovators' cooperating in an open and neutral research environment has generated many theoretical and practical findings that have greatly enriched the literature on project fuzzy front-end (FFE). We propose a conceptual framework with four main dimensions that encompasses the complexity of this type of undertaking, in which project success is considered from the standpoint of both the developer and the adopter. This approach proved to be an efficient way to reduce fuzziness at the early project implementation stages.
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.
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 | 0.002 | 0.000 |
| 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 | 0.000 |
| 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