Furthering Persuasive Systems Design: Persuasive Technology and Responsible Gambling
Bibliographic record
Abstract
Persuasive systems design (PSD) research focuses on applying principles of attitude and behaviour change from social psychology to computer interfaces with the goal of motivating a user to achieve a target behaviour or goal.An obvious area for implementation is activities that can become addictive: aiding individuals in gaining impulse control and reduce incidence of over-indulgence of a harmful activity or behaviour.Many domains have applied such principles including weight-loss, smoking cessation and alcohol addiction.This thesis extends current research on PSD to recreational gambling.As online gambling carries a greater risk for the development of gambling addiction pathology than offline gambling, examination of how to create effective tools that can reduce this risk are warranted.One such area is usability and PSD, with the goal of aiding individuals to adhere to pre-set monetary limits without reducing the pleasure of gambling.A usercentered design process was employed to improve an existing monetary limit tool with the goal of facilitating responsible gambling.Focus groups served as the preliminary user research, personas and storyboards were developed, heuristic evaluation was employed to refine the monetary limit tool, and finally a controlled lab study was carried out to test the effectiveness of the newly developed tool vs. the current monetary limit tool.Results of the study show that applying usability and PSD principles increased monetary limit adherence and engagement compared with the current monetary limit tool.Importantly, the HCI-inspired monetary limit tool did not interfere with users' fun while gambling.Facilitating responsible gambling is a viable domain for the application of PSD principles.Future research directions are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".