Motivations of collaborative obtainers and providers in Europe
Bibliographic record
Abstract
The article analyses the motivations for participating in collaborative digital platforms in Europe. From the duality of roles approach, the motivations of European obtainers and providers are studied, with special emphasis on the role played by occupational status. For that purpose, a pan-European sample of 14,050 citizens from 28 countries is investigated and a quantitative data analysis is applied through a system of structural equations. Regarding overall motivations, the research has identified that economic and usefulness motivations predict the obtaining of goods and services through collaborative platforms. In the case of provision, utility motivations are complemented by other pro-social predictors, such as the possibility of non-monetary exchanges. In addition, the occupational status of the individuals significantly determines their key motivations. Self-employed individuals are essentially motivated by price and novelty in explaining when they consider becoming obtainers. In contrast, managers are more motivated by convenience. In addition, self-employed individuals will be more likely to provide resources on collaborative platforms for non-monetary exchange reasons. Managerial implications of these results are also discussed.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".