Institutional Trust and Affordability on Mobile Banking Adoption in Ghana: A System Dynamic Approach
Bibliographic record
Abstract
Trust between mobile applications and humans is critical for a successful adoption in our society. This study aims to investigate mobile-banking (m-banking) adoption from an institutional trust (ICT performance and Fraud) and affordable mobile broad band point of view and their impact on m-banking adoption in Ghana. In this paper, we extended the Bass diffusion model using system dynamic approach and incorporated fraud, ICT performance and affordability of m-banking services and their effects on m-banking adoption in Ghana. The model is built using system dynamic methodologies (stock and flows), validated to confirm a real-life m-banking adoption behaviour, and simulated to analyse m-banking adoption response under different scenarios. The result shows that improving ICT infrastructure development, preventing cybercrime and reducing the cost of mobile data have a positive impact on m-banking adoption. However, affordability is the primary determinant of m-banking adoption in Ghana, although it can also be enhanced through tax incentives and policy schemes related to mobile communication technologies. The model currently relies on monetary aspects of ICT infrastructure, cybercrime, and broadband data pricing. However, to enhance the model’s reliability, it could be beneficial to expand its scope to include non-monetary factors and other relevant economic variables.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".