Using Gamification To Influence User Success In Personal Finance Applications
Bibliographic record
Abstract
Research Problem & Objectives: This thesis explores the current research and prior literature surrounding financial literacy’s use as a tool for consumer empowerment. It seeks to understand the current market for financial literacy digital media in the form of mobile budgeting applications. In particular, to understand where weaknesses exist and opportunities lie. Methods: That will entail combining the existing literature and best practices with persuasive technology (gamification) to create a minimum viable product that may address deficiencies in the market. This proposed application will be designed to be an intervention to increase the end user’s level of financial literacy and financial well being. Key Results: This study resulted in the development of a new mobile personal finance budgeting application uniquely designed to make financial literacy information more accessible and available to users, and to help users improve their financial habits & behaviours. This was accomplished through the implementation of gamification techniques. Conclusion: This thesis concluded having created a tool that Canadians might use to increase their level of financial literacy. The mobile budgeting app developed filled a gap in the Canadian mobile personal finance application market by employing research-backed gamification techniques, multiple engagement loops and behavioural change strategies.
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 | 0.007 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".