Meta-analytical structural modelling of virtual communities: the case of professional and non-professional users
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Currently, the factors that motivate knowledge-sharing process in a virtual community (VC) remain unclear. VCs exist in two different but similar scenarios. The first scenario involves professional virtual communities (PVCs) formed by professionals in similar areas looking to solve common problems. PVCs within a company often work to address similar problems in multiple plants and countries. The second scenario involves non-professional virtual communities (NPVCs). This study discusses some factors that affect knowledge sharing in virtual communities and compares the effects and behaviours between PVCs and NPVCs. We investigate these issues by quantitatively reviewing the available literature using meta-analytical structural equation modelling (MASEM) for both PVCs and NPVCs to evaluate the moderating effects of conventional professional knowledge-sharing methods. Although trust was established as a crucial element in both models, the factors associated with each model differed substantially. The absolute values of the correspondence of trust with self-efficacy and with knowledge sharing were lower for PVCs than for NPVCs. This research revealed the singularities of these different information systems applied to businesses.
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.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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