Integrating religiosity into a technology acceptance model for the adoption of mobile payment technology
Bibliographic record
Abstract
This research studies the effects of the religiosity on financial technology (fintech) adoption. The study examines religiosity as part of the Technology Acceptance Model (TAM) dimensions for the adoption of mobile payment technology. We explore the role of religiosity in TAM and recommend several policies for related organizations. The study uses professional sample calculation from 113 traditional markets under Perumda Pasar Jaya as a business entity whose capital is wholly or mostly owned by the regional government through regional assets of DKI Jakarta Province, Indonesia, which use mobile payment technology. We obtained 363 respondents from June 2020 to June 2021, coinciding with the Covid-19 pandemic. Hypothesis testing was done employing SmartPLS 3.2.9 software and questionnaires. The study also adapts previous studies to ensure the questionnaires are relevant to the research objects. The research result show that religiosity explained the formation of TAM by small businesses in traditional markets under Perumda Pasar Jaya Management. Religiosity and the adoption of mobile payment technology determined whether a user used fintech or not. As the research period was limited to June 2020 - June 2021, including field research in the traditional markets, newer TAM mobile payment technology development and other TAM mobile payment-based research were not included. This research offers a new TAM development model using religiosity for mobile payment adoption in traditional markets.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".