What Accelerates the Choice of Mobile Banking for Digital Banks in Indonesia?
Bibliographic record
Abstract
The recent COVID-19 pandemic has led to changes in business, technology, and social interactions, creating a new normal that has important implications for the role of technology, including mobile banking services that offer safer and more hygienic payment methods than cash. Innovations in mobile banking services have been considered to have the ability to provide unbanked customers with better access in growing markets such as Indonesia, which still has a huge unbanked population of 100 million people. This study evaluates the driving factors of mobile banking adoption by 1441 banking customers among major digital banks in Indonesia. Data collected between September 2022 and March 2023 were examined using PLS-SEM with Smart PLS 4.0.9.6. This study extends the Decomposed Theory of Planned Behavior (DTPB) framework by including Disease Risk, Trust, Firm Reputation, Perceived Risk, Performance Risk, Privacy Risk, Financial Risk, Psychological Risk, Time Risk, and Disease Risk. The findings show that Trust, Attitude, Perceived Behavior Control, Perceived Risk, Psychological Risk, and Disease Risk play a significant role in respondents’ intention to adopt mobile banking services. In contrast, Subjective Norm, Firm Reputation, Performance Risk, Privacy Risk, Financial Risk, and Time Risk had lower impacts. The findings suggest that users choose mobile banking over cash as a safety measure. As a result, banks must prioritize their mobile banking innovations tailored to personalized user experience to deepen engagement, with easy-to-use navigation that fits the lifestyles, values, and needs of banking customers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".