Identify and Prioritize the Key Success Factors in the Establishment of Crowdsourced Systems
Bibliographic record
Abstract
This study sought to determine the key factors affecting the success of crowdsourcing. In this study, both exploratory and confirmatory examined and finally, structural equation modeling was used to validate the proposed model. In this study, we tried to investigate key elements in the organization, the conceptual framework for the relationship between these factors be addressed. This study aimed to evaluate the identification and prioritization of the key success factors in the establishment of crowdsourcing was used in the automotive industry and purpose of the survey and questionnaires were used. The population in this study, car industry executives. As expressed in the research evaluation experts has been used. To investigate the relationship for each of the relationships shown in the model, the model analysis of Amos Software was used. The findings of this research to the development of crowdsourcing to help organizations, because it has tried to provide empirical evidence and in accordance with the terms of the Iranian executive model, the model organizations in order to be able to managers and planners given the circumstances and the amount of resources and organizational priorities of the development and implementation of projects of crowdsourcing to take better decisions.
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.
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 | 0.013 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".