Tailoring Persuasive and Behaviour Change Systems Based on Stages of Change and Motivation
Bibliographic record
Abstract
Persuasive systems (PS) are effective at motivating behaviour change using various persuasive strategies. Research shows that tailoring these systems increases their effectiveness. However, there is little knowledge on how PS can be tailored to people's Stages of Change (SoC). We conduct a large-scale study of 568 participants to investigate how individuals at different SoC respond to various strategies. We also explore why the strategies motivate behaviour change using the ARCS motivation model. Our results show that people's SoC plays a significant role in the perceived persuasiveness of different strategies and that the strategies motivate for different reasons. For instance, people at the precontemplation stage tend to be strongly motivated by self-monitoring strategy because it raises their consciousness or self-awareness. Our work is the first to link research on the theory of SoC with the theory of motivation and Persuasive Systems Design (PSD) model to develop practical guidelines to inform the tailoring of persuasive systems.
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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 | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".