Research on Incentive Mechanism and Evaluation of Gamification Application for Sustainable Consumption in the Context of China
Bibliographic record
Abstract
The gamification of sustainable consumption is receiving more and more attention from both academic and business circles. However, there is still a lack of research on the incentive mechanism and evaluation of gamification design to promote sustainable consumption behavior. Taking the gamified apps that promote sustainable consumption in China as an example, this study attempts to explore the incentive mechanism of gamification application for sustainable consumption by using the “stimulus-organism-response” model. Furthermore, it also constructs an evaluation index system of gamification design for sustainable consumption app and identifies the key factors in the gamification design by using the analytic hierarchy process. The results suggest that gamification apps use game elements and game mechanism frameworks to build a new sustainable consumption context for users, which breaks the boundary between reality and virtuality, and enables users to gain real-life value for their behavior in the virtual world. Moreover, the trust mechanism and socialized contextual experience of the gamified apps further strengthen this sense of connectedness and interaction, and enhance the user's motivation for sustainable consumption. In the gamification design of sustainable consumption app, more attention needs to be paid to the implementation effect behind gamification, that is, to promote the cultivation of public sustainable consumption values and lifestyle. This study advances theoretical and practical understanding of the gamification of sustainable consumption. The results can also be used as a starting base for the development and design of gamified apps in the sustainable consumption field.
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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 | 0.012 | 0.022 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".