Understanding factors of disengagement within a virtual community: an exploratory study
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
Social media intelligence is a strategic knowledge source for decision and performance for firms, knowledge from customers, products and the market. From both information systems (IS) and marketing perspectives, an important issue is the understanding of the main factors that lead to disengagement of members in social media platforms or virtual communities. If engagement has been well studied, disengagement has been almost ignored. A literature review shows that so far only two studies have examined disengagement in a virtual community context. Given that such a major aspect of online firms’ success has so far been ignored, the following question is posed: what are the factors of disengagement within a virtual community? In order to answer the research question, we conducted a survey-based empirical study on actual members of virtual communities. We used component-based Partial Least Squares (PLS) method to analyse the 268 answers. Our results show that a lack of individual valorisation and affection influences disengagement within virtual communities. We also identified that a lack of links between the brand and the community members influences disengagement.
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 | 0.012 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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