An Odyssey into Virtual Worlds: Exploring the Impacts of Technological and Spatial Environments on Intention to Purchase Virtual Products1
Bibliographic record
Abstract
Although research on three-dimensional virtual environments abounds, little is known about the social and business aspects of virtual worlds. Given the emergence of large-scale social virtual worlds, such as Second Life, and the dramatic growth in sales of virtual goods, it is important to understand the dynamics that govern the purchase of virtual goods in virtual worlds. Employing the stimulus–organism–response (S-O-R) framework, we investigate how technological (interactivity and sociability) and spatial (density and stability) environments in virtual worlds influence the participants’ virtual experiences (telepresence, social presence, and flow), and how experiences subsequently affect their response (intention to purchase virtual goods). The results of our survey of 354 Second Life residents indicate that interactivity, which enhances the interaction with objects, has a significant positive impact on telepresence and flow. Also, sociability, which fosters interactions with participants, is significantly associated with social presence, although no such significant impact was observed on flow. Furthermore, both density and stability are found to significantly influence participants’ virtual experiences; stability helps users to develop strong social bonds, thereby increasing both social presence and flow. However, contrary to our prediction of curvilinear patterns, density is linearly associated with flow and social presence. Interestingly, the results exhibit two opposing effects of density: while it reduces the extent of flow, density increases the amount of social presence. Since social presence is found to increase flow, the net impact of density on flow depends heavily on the relative strength of the associations involving these three constructs. Finally, we find that flow mediates the impacts of technological and spatial environments on intention to purchase virtual products. We conclude the paper with a discussion of the theoretical and practical contributions of our findings.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".