Persuasive interaction for collectivist cultures
Bibliographic record
Abstract
Persuasive technology is defined as any interactive product designed to change attitudes or behaviours by making desired outcomes easier to achieve. It can take the form of interactive web applications, hand held devices, and games. To date there has been limited research into persuasive technology outside of America. Cross-cultural research shows that in order for persuasion to be most effective, it is often necessary to draw upon important cultural themes of the target audience. Applying this insight to persuasive technology, we claim that the set of persuasive technology strategies as described by B J Fogg caters to a largely individualist audience. Drawing upon cross-cultural psychology and sociology findings about patterns of behaviour commonly seen in collectivists, we present a principled set of collectivism-focused persuasive technology strategies. These strategies are: group opinion, group surveillance, deviation monitoring,disapproval conditioning, and group customisation. We also demonstrate how application of the strategies can support the design of a collectivist, persuasive game.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".