MyShoppingBuddy: Exploring Persuasive Strategies and Design Opportunities for Gamifying Real World Shopping Experiences
Bibliographic record
Abstract
Gamified persuasive systems (PS) employ persuasive strategies to facilitate decision making and motivate desired behaviors. These PS have been shown to be effective at promoting behavior change in health, education, and other domains. In the area of shopping, research has focused on how PS can be employed to motivate healthy shopping, however, little is known about the efficacy of PS to promote other equally important shopping related decisions and behaviors such as shopping healthily, within budget and on time. This article presents the results of the evaluation of MyShoppingBuddy, an app prototype tailored to support people to make these main decisions related to shopping: shop healthily, shop within budget, and shopping on time. Specifically, we conducted a survey of 333 participants who interacted with the app, which simulated three shopping decisions and collected their feedback on the app's perceived persuasiveness, usability, and overall user experience. Results from the analysis show that overall, our participants found the app to be persuasive with respect to its ability to motivate them to shop healthily on time, and on budget ( p <. 001). Participants also found the app to be usable. Finally, based on our findings we offer design recommendations for future research.
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How this classification was reachedexpand
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 | 0.002 | 0.001 |
| 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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".