Business Model and Wearables: What Convergence and Collaboration in the Area of Connected Objects and Clothing?
Bibliographic record
Abstract
The general context of our study is that of the emergence of an ecosystem of innovation and business in relation to IT in a world market in full restructuring. We studied the case of the fashion industry to determine how actors interact and how companies seize new business opportunities. Our qualitative research is based on an exploratory approach; an abductive method allows us to go back and forth between literature review, data analysis and interpretation. Traditional industries are subject to strong global competition, and we ask ourselves the following question: in the context of the 4th Industrial Revolution, how do the clothing and fashion industries remain competitive? Should they increase strategies for innovation, creativity, and the integration of new digital technologies? In short, our paper shows how firms are adopting new business models in a digital context. We found that the new business model is based on the use of various skills, the establishment of intersectoral cooperation (IT, design, textiles, health, etc.) and the search for innovations. These elements are at the heart of the strategy chosen to penetrate a niche market that of intelligent objects and garments. These processes are reflected in a dynamic of intersectoral convergence, which provides new avenues for innovation on the basis of new digital technologies.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.005 | 0.023 |
| Scholarly communication | 0.022 | 0.034 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".