Exploring the Gamification Affordances in Online Shopping with the Heterogeneity Examination through REBUS-PLS
Bibliographic record
Abstract
This study investigates, from the perspective of affordance theory, how the implementation of gamification features and mechanisms in online-shopping platforms enable consumers to enjoy immersive shopping experiences and make subsequent shopping decisions. Importantly, the technique of REBUS-PLS is applied to unveil the nature of heterogeneity in perceived affordances and ensure the robustness of structural-model results. The research model is tested using cross-sectional data. Our results not only confirm the effects of different types of gamification affordances on immersive experience and subsequent behavior but also reveal the existence of different consumer groups within the overall sample with respect to their behavior patterns. Apart from social connectiveness, rewardability, playfulness, and novelty all exert significant effects on the immersive experience. In addition, this study identified three distinct groups, namely, “no novelty” users, “no playfulness” users, and “no connective” users.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".