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THE IMPACT OF WORK-LIFE BALANCE AND FLEXIBLE WORK ARRANGEMENTS ON BURNOUT AND TURNOVER INTENTIONS IN GENERATION Z WORKERS Sujarwadi Sujarwadi; Helmi Azahari; Erna Kusumawati; Muhamad Risal Tawil; Adi Masliardi
Journal Of Human And Education (JAHE) Vol. 6 No. 4 (2026): Journal of Human And Education (JAHE)
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jh.v6i4.2919

Abstract

The phenomenon of high burnout rates and turnover rates among Generation Z workers poses a strategic challenge for human resource management in the era of digital transformation. This study aims to analyze the effect of Work-Life Balance (WLB) and Flexible Work Arrangements (FWA) on burnout and its implications for turnover intentions among Generation Z workers. This study uses a quantitative approach with a causal explanatory design. Data were collected from 210 Generation Z worker respondents in the financial services and technology sectors through a structured questionnaire with a purposive sampling technique. Data analysis was conducted using the Structural Equation Modeling method based on Partial Least Squares (SEM-PLS) with the help of SmartPLS 4.0 software. The results showed that WLB and FWA had a negative and significant effect on burnout. Furthermore, burnout was proven to have a positive and significant effect on turnover intentions. Mediation analysis confirmed that burnout acts as a partial mediating variable in the relationship between WLB and FWA on turnover intentions. Simultaneously, flexibility and work-life balance policy interventions were able to significantly reduce emotional exhaustion and turnover intentions among Generation Z. The implications of this research emphasize the importance of designing adaptive work environments that respect personal life boundaries to foster the retention of young talent. Keywords: Work-Life Balance, Flexible Work Arrangements, Burnout, Turnover Intentions, Generation Z
Analysis of The Influence of Artificial Intelligence on Academic Integrity, Lecturers, Teachers, and Students Asrial; Erna Kusumawati; Gede Erik Aktama; Novita Riani; Hasrul Edyar
Reslaj: Religion Education Social Laa Roiba Journal Vol. 8 No. 8 (2026): RESLAJ: Religion Education Social Laa Roiba Journal
Publisher : Intitut Agama Islam Nasional Laa Roiba Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47467/reslaj.v8i8.13529

Abstract

The rapid expansion and integration of Artificial Intelligence (AI), particularly Generative AI and Large Language Models (LLMs), have fundamentally reshaped the landscape of higher and secondary education globally. This study critically analyzes the multifaceted influence of Artificial Intelligence on academic integrity, lecturers, teachers, and students. Utilizing a comprehensive mixed-methods approach, this research examines both the opportunities and ethical dilemmas introduced by AI tools in contemporary academic environments. The findings reveal that while AI technologies offer unprecedented capabilities in personalized learning, administrative efficiency, and research assistance, they simultaneously introduce severe threats to traditional notions of academic integrity through nuanced forms of digital plagiarism and unauthorized content generation. Lecturers and teachers face a dual burden: adapting pedagogical strategies and assessment models to stay relevant, while actively monitoring and mitigating academic dishonesty. For students, AI acts as a double-edged sword, fostering cognitive enhancement and self-directed learning on one side, while risking cognitive over-reliance, diminished critical thinking, and ethical ambiguity on the other. This study highlights the urgent necessity for educational institutions to establish dynamic policy frameworks, implement robust digital literacy programs, and redesign assessments to emphasize authentic, process-oriented learning over easily automated deliverables. Keywords: Artificial Intelligence, Academic Integrity, Lecturers, Teachers, Students, Generative AI, Higher Education.