The Influence of Interactivity, Aesthetic, Creativity and Vividness on Consumer Purchase of Virtual Clothing: The Mediating Effect of Satisfaction and Flow
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
With the rapid global growth of digital fashion, virtual clothing has recently become a hot research topic. However, there is still little literature exploring the mutual influence between virtual clothing design features and consumer purchase intentions. This study delves into the dynamic relationship between virtual clothing design attributes and consumer purchase tendencies, seeking to foster a balanced development of the virtual clothing industry. Following a literature review, this study formulated a theoretical model that includes seven key latent factors: Interactivity, Aesthetic, Creativity, Vividness, Satisfaction, Flow experience, and Purchase Intention. Anchored in the Stimulus-Organism-Response (SOR) paradigm, the study conducted offline trials where respondents tried on virtual clothing using AR as a stimulus, carefully examining data from 295 respondents. The analysis shows that while Interactivity and Aesthetics significantly increase the likelihood of consumer purchases, Creativity and Vividness lack a direct and substantial impact on this intent. Satisfaction and Flow experience are important mediating variables that profoundly influence the purchasing decisions for virtual clothing. The findings of this study provide new insights into virtual clothing design and marketing strategies, emphasizing the importance of optimizing user interaction and Aesthetic experiences in enhancing consumer purchase intentions.
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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 | 0.001 |
| 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