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All Journal International Journal of Economic, Business, Accounting, Agriculture Management and Sharia Administration (IJEBAS) Jurnal Ekonomi, Manajemen, Akuntansi
Ira Nasriani
Institut Kesehatan dan Bisnis St. Fatimah Mamuju
Economics, Econometrics & Finance Social Sciences

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Pengaruh Budaya Kerja, Kompensasi dan Etos Kerja Terhadap Kinerja Karyawan Pada PT. Telkom Mamuju Nur Jaya Baiduri Ruslan; Ira Nasriani; Irma Irma; Andi Kamal M.Sallo; Nur Afra; Ahmad Zulfiqram
EKOMA : Jurnal Ekonomi, Manajemen, Akuntansi Vol. 3 No. 2: Januari 2024
Publisher : CV. Ulil Albab Corp

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56799/ekoma.v3i2.2951

Abstract

This research aims to examine the influence of work culture, compensation and work ethic on employee performance at PT. Telkom Mamuju. The data in this research was obtained from each employee at PT. Telkom Mamuju is willing to be a respondent. This research uses primary data by conducting direct research in the field by giving questionnaires/question sheets to 57 respondents. The data analysis method used is multiple linear regression analysis. The research results show that: partially, the variables of work culture, compensation and work ethic have a positive and significant effect on employee performance.
ARTIFICIAL INTELLIGENCE AND THE TRANSFORMATION OF EARLY TAX NON-COMPLIANCE RISK DETECTION SYSTEMS IN MULTINATIONAL ENTERPRISES Ira Nasriani; Sri Rahayu indah Azhari; Ari Sarwo Indah Safitri; Andi Nurhasanah; Trisnawaty Trisnawaty
International Journal of Economic, Business, Accounting, Agriculture Management and Sharia Administration (IJEBAS) Vol. 6 No. 3 (2026): June
Publisher : CV. Radja Publika

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Abstract

The development of Artificial Intelligence (AI) has transformed tax administration from conventional monitoring systems into more predictive and data-driven risk-based compliance management. However, the complexity of multinational corporations’ activities, including cross-jurisdictional transactions and sophisticated tax planning strategies, has increased the challenges of early detection of tax non-compliance risks. Although previous studies have examined the application of AI in taxation, the existing literature remains fragmented and lacks an integrated understanding of the AI technologies utilized, the factors influencing their effectiveness, and the interrelationships among these factors within tax risk detection systems. This study aims to synthesize the literature on the role of AI in the early detection of tax non-compliance risks among multinational corporations. Using a Systematic Literature Review (SLR) approach based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, 25 articles retrieved from the Scopus and Web of Science databases were analyzed through thematic content analysis. The findings indicate that the dominant AI technologies employed include machine learning, deep learning, natural language processing, predictive analytics, and anomaly detection. Furthermore, the study identifies four key dimensions that determine the effectiveness of AI implementation, namely AI capability, data quality and integration, organizational readiness, and the regulatory and governance environment
Co-Authors Ahmad Zulfiqram Andi Kamal M. Sallo Andi Nurhasanah Ari Sarwo Indah Safitri Irma Irma Nur Afra Nur Jaya Baiduri Ruslan Sri Rahayu Indah Azhari Trisnawaty Trisnawaty