OPTIMALISASI KECEPATAN PELAYANAN TERHADAP KEPUASAN DAN LOYALITAS DENGAN KEPERCAYAAN WAJIB PAJAK SEBAGAI VARIABEL INTERVENING (STUDI PADA KANTOR UPPD SAMSAT KABUPATEN KARANGANYAR)
Bibliographic record
Abstract
The purpose of this study was to determine, analyze and prove empirically the effect of the influence of service speed on taxpayer trust and taxpayer satisfaction. The influence of taxpayer trust has a significant effect on taxpayer satisfaction. The effect of service speed on taxpayer loyalty. The influence of taxpayer trust has a significant effect on taxpayer loyalty. The population in this study which became the population was all taxpayers of Samsat UPPD Dipenda Central Java, Karanganyar Regency, amounting to around 2,733 people per week (data taken on March 21, 2020). In this study, a number of 100 samples were taken, so the research was a sample study by means of accidental random sampling. The use of accidental random sampling method is because every day the taxpayer always changes. Accidental random sampling is a technique for obtaining research data with respondents who can be met at the time the research was conducted. Service speed has a significant effect on taxpayer trust. Service speed has a significant effect on taxpayer satisfaction. Trust has a significant effect on taxpayer satisfaction. Service speed has a significant effect on taxpayer loyalty. Trust has a significant effect on taxpayer loyalty.Tujuan penelitian ini adalah untuk mengetahui, menganalisis dan membuktikan secara empiris pengaruh Pengaruh kecepatan pelayanan terhadap kepercayaan wajib pajak dan kepuasan wajib pajak. Pengaruh kepercayaan wajib pajak berpengaruh signifikan terhadap kepuasan wajib pajak. Pengaruh kecepatan pelayanan terhadap loyalitas wajib pajak. Pengaruh kepercayaan wajib pajak berpengaruh signifikan terhadap loyalitas Wajib pajak. Populasi dalam penelitian ini yang menjadi populasi adalah seluruh wajib pajak Samsat UPPD Dipenda Jawa Tengah Kabupaten Karanganyar yang berjumlah sekitar 2.733 orang per minggu (data diambil tanggal 21 Maret 2020). Dalam penelitian ini diambil sejumlah 100 sampel, sehingga penelitiannya merupakan penelitian sampel dengan cara accidental random sampling. Penggunaan metode accidental random sampling dikarenakan setiap hari wajib pajak selalu berganti. Accidental random sampling yaitu suatu teknik untuk mendapatkan data penelitian dengan responden yang dapat ditemui pada saat penelitian dilakukan. Kecepatan pelayanan berpengaruh signifikan terhadap kepercayaan wajib pajak Kecepatan pelayanan berpengaruh signifikan terhadap kepuasan wajib pajak Kepercayaan berpengaruh signifikan terhadap kepuasan wajib pajak Kecepatan pelayanan berpengaruh signifikan terhadap loyalitas wajib pajak. Kepercayaan berpengaruh signifikan terhadap loyalitas wajib pajak.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".