Uncovering the Motives for the Continuous Use of Social Virtual Worlds
Bibliographic record
Abstract
Social virtual worlds (SVWs) have become increasingly important environments for social interaction, especially for the younger generations. For SVWs to be economically sustainable, attracting new users and retaining the existing ones existing users is a paramount issue. This calls for understanding of the reasons why people engage in social virtual worlds. This study investigates the motives for continuously engagement in SVWs and develops a research model grounded on the decomposed theory of planned behavior. The model is empirically tested with a data collected from Canadian active Habbo goers using PLS. Surprisingly, perceived behavioral control and subjective norm were found more important determinants of continuous use intention than attitude. The results indicated that hedonic motives were the main determinant of attitude. However, altogether only 21.9 % of attitude was explained by utilitarian, hedonic and social outcomes. As a result, the study revealed that rather relying on generic items in measuring attitude and the beliefs regarding the utilitarian and social outcomes, the characteristics of SVW context should be reflected in the operationalisations of the constructs.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".