What Motivates Solvers’ Participation in Crowdsourcing Platforms in China? A Motivational–Cognitive Model
Why this work is in the frame
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Bibliographic record
Abstract
The voluntary participation of individuals is critical to the success of an online crowdsourcing platform (OCP) and acts as a major obstacle for many organizations seeking engagement with solvers. Based on social cognitive theory and motivation theory, an integrative model is proposed to examine solvers’ intrinsic and extrinsic motivations and personal cognition that influences their participation in OCPs, as well as the influences of intrinsic and extrinsic motivations on individuals’ personal cognition. Empirical data from 297 active solvers on a large OCP in China was collected to test the research model using structural equation modeling. Results show that different motivations play varying roles in solvers’ participation in OCPs. Personal cognition (i.e., perceived self-efficacy and personal outcome expectation) can exert significant and positive effects on solvers’ participation, although solvers’ personal cognition has no significant influence on their breadth of participation. Our findings demonstrate that extrinsic motivations (i.e., monetary reward and gain face) and intrinsic motivation (i.e., enjoyment) are positively associated with solvers’ self-efficacy and personal outcome expectation in.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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