Analisis Minat Penggunaan Dompet Digital Dengan Pendekatan Technology Acceptance Model (TAM) Pada Pengguna di Kota Surabaya
Bibliographic record
Abstract
Penelitian ini bertujuan untuk mengembangkan dan menguji sebuah model konseptual mengenai tingkat penerimaan dalam menggunakan cashless payment systems - dompet digital di Kota Surabaya. Metode penelitian yang digunakan adalah metode kuantitatif. Populasi dalam penelitian ini adalah pengguna dompet digital di Kota Surabaya. Jumlah sampel yang digunakan adalah sebanyak 100 responden dengan menggunakan metode purposive sampling. Metode pengujian data yang digunakan yaitu uji validitas dan reliabilitas. Perhitungan data menggunakan software Smart PLS 3. Temuan dalam penelitian ini menunjukkan bahwa kepercayaan dan persepsi risiko tidak mempengaruhi minat pengguna dalam menggunakan dompet digital. Selain itu, hasil penelitian ini konsisten mendukung beberapa penelitian terdahulu terkait dengan TAM di mana persepsi kegunaan dan persepsi kemudahan penggunaan berperan baik secara langsung maupun tidak langsung terhadap minat penggunaan dompet digital. Persepsi kegunaan menjadi variabel yang berpengaruh dominan terhadap minat penggunaan dompet digital pada pengguna di Kota Surabaya. Kata kunci: Kepercayaan; Persepsi Risiko; Persepsi Kegunaan; Persepsi Kemudahan Penggunaan; Minat Penggunaan ABSTRACT This study aims to develop and test a conceptual model about the level of acceptance in using cashless payment systems – e-wallet in Surabaya city. The research method used is a quantitative method. The population in this study were e-wallet users in the Surabaya city. The number of samples used was 100 respondents using purposive sampling method. The data testing method used are validity and reliability test. Calculation of data using Smart PLS 3 software. The research findings show that trust and perceived risk doesn’t affect users’ intention in using e-wallet. In addition, the results of this study consistently support several previous studies related to TAM where perceived ease of use and perceived usefulness play a role both directly and indirectly in intention to use e-wallet. Perceived usefulness is the variable that has the most dominant influence on the intention to use e-wallet on users in Surabaya city. Keywords: Trust; Perceived Risk; Perceived Usefulness; Perceived Ease of Use; Intention to Use
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".