Consumer intentions to use collaborative economy platforms: A meta‐analysis
Bibliographic record
Abstract
Abstract Collaborative economy platforms (CEP) have been investigated from various disciplines, theoretical frameworks and methodological approaches. Subsequently, numerous models emerged to explain the cognitive process underlying intentions to use CEP. Yet, their findings are fragmented and diverse, impeding thereby theory development and management practice. This article addresses this deficiency by a meta‐analysis of psychosocial determinants of collaborative economy platforms (CEP) use intentions. Based on information from a total of 27 independent samples, we find support for the relation between psychosocial determinants and CEP use intentions, as well as willingness to pay a premium price for CEP. The findings show that (1) emotional and flexibility utility exert the strongest influence on use intentions; (2) functional and social utility exert more influence on willingness to pay a premium price; (3) CEP are primarily used for enjoyment and practical purposes; and (4) hedonism does not strongly lead to an increased willingness to pay.
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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 | 0.023 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.026 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".