Augmented reality experience: An examination of viewer responses to sports videos
Bibliographic record
Abstract
Abstract Augmented reality (AR) offers a transforming user experience and has been increasingly integrated into entertainment and service contexts. Drawing on experience economy theory and employing a mixed‐methods approach, this research evaluates the antecedents and consequences of four realms of viewer experiences: entertainment, educational, aesthetic, and escapist experiences, in AR‐infused sports videos. A qualitative study of semi‐structured interviews highlights three critical AR features in sports videos (i.e., novelty, vividness, and informativeness) in shaping viewer experiences. Subsequently, a research model is formulated to elucidate the relationships among AR features, four realms of viewer experiences, and behavioral intentions. A quantitative analysis based on survey data reveals that AR features exert varying effects on viewers' entertainment, educational, and aesthetic experiences, yet none significantly affects escapist experience, which is relatively trivial in viewers' overall experience. Entertainment, educational, aesthetic, and escapist experiences have various influences on viewers' intentions to watch again, to recommend, and to pay, except that entertainment and educational experiences do not significantly affect intention to pay. This research stresses the importance of understanding multiple aspects of user experiences in AR research and provides useful guidelines for AR features and viewer experience design in sports videos.
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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 | 0.001 | 0.006 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".