Open Access
Issue |
E3S Web of Conf.
Volume 388, 2023
The 4th International Conference of Biospheric Harmony Advanced Research (ICOBAR 2022)
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Article Number | 04017 | |
Number of page(s) | 8 | |
Section | Technological Influence on Society and Applied Social Sciences to Support Sustainable Society | |
DOI | https://doi.org/10.1051/e3sconf/202338804017 | |
Published online | 17 May 2023 |
- Y. K. Dwivedi, N. P. Rana, M. Janssen, B. Lal, M. D. Williams, and M. Clement, “An empirical validation of a unified model of electronic government adoption (UMEGA),” Gov. Inf. Q., vol. 34, no. 2, pp. 211–230, 2017. [CrossRef] [Google Scholar]
- N. Adnan, S. M. Nordin, M. A. Bahruddin, and A. H. Tareq, “A state-of-the-art review on facilitating sustainable agriculture through green fertilizer technology adoption: Assessing farmers behavior,” Trends Food Sci. Technol., vol. 86, pp. 439–452, 2019. [CrossRef] [Google Scholar]
- J. C. Bertot, P. T. Jaeger, and J. M. Grimes, “Promoting transparency and accountability through ICTs, social media, and collaborative egovernment,” Transform. Gov. people, Process policy, 2012. [Google Scholar]
- L. Waller and A. Genius, “Barriers to transforming government in Jamaica: Challenges to implementing initiatives to enhance the efficiency, effectiveness and service delivery of government through ICTs (e-Government),” Transform. Gov. People, Process Policy, 2015. [Google Scholar]
- E. Ruhode, “E-government for development: a thematic analysis of Zimbabwe’s information and communication technology policy documents,” Electron. J. Inf. Syst. Dev. Ctries., vol. 73, no. 1, pp. 1–15, 2016. [CrossRef] [Google Scholar]
- A. Alawneh, H. Al-Refai, and K. Batiha, “Measuring user satisfaction from e- Government services: Lessons from Jordan,” Gov. Inf. Q., vol. 30, no. 3, pp. 277–288, 2013. [CrossRef] [Google Scholar]
- A. Darono and D. Irawati, “Service innovation in the complex environment of tax administration: the Indonesian public sector perspective,” Int. J. Innov. Reg. Dev., vol. 6, no. 1, pp. 102–123, 2015. [Google Scholar]
- A. B. Santoso, Y. Pamungkas, and Y. Ruldeviyani, “Master Data Management Implementation In Distributed Information System Case Study Directorate General Of Tax, Ministry Of Finance Of Republic Of Indonesia,” J. Sist. Inf., vol. 15, no. 1, pp. 18–27, 2019. [Google Scholar]
- Y. Pamungkas, A. B. Santoso, B. Ashari, D. I. Sensuse, M. Mishbah, and R. Meiyanti, “Evaluation of Interoperability Maturity Level: Case Study Indonesian Directorate General of Tax,” Procedia Comput. Sci., vol. 157, pp. 543– 551, 2019. [CrossRef] [Google Scholar]
- H. Devita, “Analisis Sistem e-SPT pada Kantor Pelayanan Pajak KPP Pratama Tuban.” Universitas Islam Negeri Maulana Malik Ibrahim, 2018. [Google Scholar]
- D. Sari, “Konsep dasar perpajakan,” 2013. [Google Scholar]
- S. Mokoagow, G. Nangoy, and J. D. L. Warongan, “Analisis Kepatuhan Pengusaha Kena Pajak (Pkp) Dalam Melaksanakan Kewajiban Perpajakannya Berdasarkan Modernisasi Sistem Administrasi Perpajakan Pada Sektor Pajak Pertambahan Nilai (PPN) Di Manado,” J. Ris. Akunt. DAN Audit. GOODWILL”, vol. 12, no. 2, pp. 179–193, 2021. [Google Scholar]
- Directorate General of Taxes, “Annual Report of the Directorate General of Taxes,” Jakrta, 2007. [Google Scholar]
- Directorate General of Taxes, “Annual Report of the Directorate General of Taxes,” Jakarta, 2015. [Google Scholar]
- L. M. H. Adan, “Analisis Penerapan E-Faktur dan E-Nofa Pada PT. Rajawali Property Jaya,” J. Ilm. Akunt. Manaj., vol. 2, no. 1, 2019. [Google Scholar]
- D. Fatmawati, “Analisis Penerapan Sistem Elektronik Nomor Faktur (E-Nofa) Pajak Terhadap Kepatuhan Pengusaha Kena Pajak Dalam Pelaporan Pajak (Studi Kasus Kantor Pelayanan Pajak Madya Sidoarjo).” Universitas Bhayangkara, 2020. [Google Scholar]
- A. Amri and W. Prihandini, “Sistem Elektronik Nomor Faktur (e-Nofa) Dan Penerbitan Faktur Pajak Fiktif,” J. Akunt. dan Pajak, vol. 20, no. 01, pp. 1–10, 2019. [CrossRef] [Google Scholar]
- A. F. Ratsidyaningtyas, “Analisis Penerimaan E-Faktur melalui pendekatan Technology Acceptance Model (TAM) pada Pengusaha Kena Pajak,” 2016. [Google Scholar]
- M. N. Alhabsyi, D. P. E. Saerang, and N. S. Budiarso, “Analisis Penerapan E-Nofa (Elektronik Nomor Faktur) Pajak Sebagai Upaya Untuk Mencegah Terjadinya Faktur Pajak Fiktif Dan Faktur Pajak Nomor Berganda (Studi Pada Kantor Pelayanan Pajak Pratama Manado),” GOING CONCERN J. Ris. Akunt., vol. 13, no. 04, 2018. [Google Scholar]
- A. Tyasminingsih, “Pengaruh penerapan aplikasi faktur pajak elektronik (e-faktur) terhadap tingkat kepatuhan wajib pajak pada KPP Pratama Surabaya Wonocolo.” Universitas Islam Negeri Maulana Malik Ibrahim, 2016. [Google Scholar]
- S. Dewi, W. Widyasari, and N. Nataherwin, “Pelatihan Dan Pendampingan Sistem Dan Mekanisme E-Faktur Dan E-Nofa Pada Prismagraphia,” Pelatih. DAN PENDAMPINGAN Sist. DAN Mek. E-FAKTUR DAN E-NOFA PADA Prism., 2020. [Google Scholar]
- B. Bavarsad and M. A. Mennatyan, “A Study of the effects of technology acceptance factors on users’ satisfaction of E-government services,” World Appl. Program., vol. 3, no. 5, pp. 190– 199, 2013. [Google Scholar]
- G. T. R. Lin and C. Sun, “Factors influencing satisfaction and loyalty in online shopping: an integrated model,” Online Inf. Rev., 2009. [Google Scholar]
- C. M. Kardina, “Faktor-Faktor Yang Memengaruhi Kepuasan Wajib Pajak Dalam Menggunakan Layanan Pelaporan Spt Berbasis Elektronik (Studi Pada Wajib Pajak Orang Pribadi Kantor Pelayanan Pajak Malang Selatan).” Universitas Brawijaya, 2018. [Google Scholar]
- Y. Hwang, M. Al-Arabiat, and D.-H. Shin, “Understanding technology acceptance in a mandatory environment: A literature review,” Inf. Dev., vol. 32, no. 4, pp. 1266–1283, 2016. [CrossRef] [Google Scholar]
- H.-M. Huang and S.-S. Liaw, “An analysis of learners’ intentions toward virtual reality learning based on constructivist and technology acceptance approaches,” Int. Rev. Res. Open Distrib. Learn., vol. 19, no. 1, 2018. [Google Scholar]
- P. Grover, A. K. Kar, M. Janssen, and P. V. Ilavarasan, “Perceived usefulness, ease of use and user acceptance of blockchain technology for digital transactions–insights from usergenerated content on Twitter,” Enterp. Inf. Syst., vol. 13, no. 6, pp. 771–800, 2019. [CrossRef] [Google Scholar]
- C.-H. Jin, “Adoption of e-book among college students: The perspective of an integrated TAM,” Comput. Human Behav., vol. 41, pp. 471–477, 2014. [CrossRef] [Google Scholar]
- Y. J. Joo, H.-J. So, and N. H. Kim, “Examination of relationships among students’ self-determination, technology acceptance, satisfaction, and continuance intention to use KMOOCs,” Comput. Educ., vol. 122, pp. 260–272, 2018. [CrossRef] [Google Scholar]
- R. A. S. Al-Maroof and M. Al-Emran, “Students acceptance of Google classroom: An exploratory study using PLS-SEM approach.,” Int. J. Emerg. Technol. Learn., vol. 13, no. 6, 2018. [Google Scholar]
- Y. Sun and F. Gao, “An investigation of the influence of intrinsic motivation on students’ intention to use mobile devices in language learning,” Educ. Technol. Res. Dev., vol. 68, no. 3, pp. 1181–1198, 2020. [CrossRef] [Google Scholar]
- M. A. Al-hawari and S. Mouakket, “The influence of technology acceptance model (TAM) factors on students’e-satisfaction and eretention within the context of UAE e-learning,” Educ. Bus. Soc. Contemp. Middle East. Issues, 2010. [Google Scholar]
- D. R. Compeau and C. A. Higgins, “Computer self-efficacy: Development of a measure and initial test,” MIS Q., pp. 189–211, 1995. [Google Scholar]
- A. Malureanu, G. Panisoara, and I. Lazar, “The relationship between self-confidence, selfefficacy, grit, usefulness, and ease of use of elearning platforms in corporate training during the COVID-19 pandemic,” Sustainability, vol. 13, no. 12, p. 6633, 2021. [CrossRef] [Google Scholar]
- H. Keshavarz, “Web Self-efficacy: A Psychological Prerequisite for Web Literacy.,” Webology, vol. 17, no. 1, 2020. [Google Scholar]
- Q. A. Blanco et al., “Probing on the relationship between students’ self-confidence and selfefficacy while engaging in online learning amidst COVID-19,” J. La Edusci, vol. 1, no. 4, pp. 16–25, 2020. [CrossRef] [Google Scholar]
- S. Arikunto, Metode peneltian. Jakarta: Rineka Cipta, 2010. [Google Scholar]
- I. Ghozali, “Aplikasi Analisis multivariete dengan program IBM SPSS 23 (Edisi 8),” Cetakan ke VIII. Semarang Badan Penerbit Univ. Diponegoro, vol. 96, 2016. [Google Scholar]
- Sugiyono, Metode penelitian pendidikan pendekatan kuantitatif, kualitatif dan R&D. Bandung: Alfabeta, 2013. [Google Scholar]
- U. Sekaran and R. Bougie, Research methods for business a skill building approach. New York: John Wiley & Sons, 2016. [Google Scholar]
- Jogiyanto. Sistem Informasi Keperilakuan. Edisi Revisi Yogyakarta: Andi Offset.2008 [Google Scholar]
- G. Mardiatmoko, “Mardiatmoko, G. (2020). Pentingnya uji asumsi klasik pada analisis regresi linier berganda (studi kasus penyusunan persamaan allometrik kenari muda [canarium indicum l.]),” BAREKENG J. Ilmu Mat. dan Terap., vol. 14, no. 3, pp. 333–342, 2020. [CrossRef] [Google Scholar]
- A. T. N. Tsabiq, S. Subiyanto, and F. J. Amarrohman, “Pembuatan Peta Zona Nilai Ekonomi Kawasan Dan Analisis Nilai Ekonomi Kawasan Melalui Teknik Valuasi Travel Cost Method Dan Contingent Valuation Method,” J. Geod. Undip, vol. 7, no. 2, pp. 1–10, 2018. [Google Scholar]
- Y. Dong, C. Xu, C. S. Chai, and X. Zhai, “Exploring the structural relationship among teachers’ technostress, technological pedagogical content knowledge (TPACK), computer self-efficacy and school support,” Asia-Pacific Educ. Res., vol. 29, no. 2, pp. 147– 157, 2020. [CrossRef] [Google Scholar]
- C. C. Wolverton, B. N. G. Hollier, and P. A. Lanier, “The impact of computer self efficacy on student engagement and group satisfaction in online business courses,” Electron. J. e- Learning, vol. 18, no. 2, pp. pp175–188, 2020. [Google Scholar]
- N. Thongsri, L. Shen, and Y. Bao, “Investigating academic major differences in perception of computer self-efficacy and intention toward e-learning adoption in China,” Innov. Educ. Teach. Int., vol. 57, no. 5, pp. 577–589, 2020. [CrossRef] [Google Scholar]
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