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All Journal Journal Research of Social Science, Economics, and Management BEAM
Richard Andre Sunarjo
Universitas Pelita Harapan, Indonesia
Environmental Science

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The Effect of Digital Transformational Leadership, Learning Organization, Supportive Work Environment, and Organizational Commitment on Turnover Intention of Gen Z IT Employees at PT. XYZ Puty Sherlyta; Ardi Ardi; Margaretha Pink Berlianto; Richard Andre Sunarjo
Journal Research of Social Science, Economics, and Management Vol. 5 No. 4 (2025): Journal Research of Social Science, Economics, and Management
Publisher : Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59141/jrssem.v5i4.1194

Abstract

This study aims to analyze the influence of Digital Transformational Leadership (DTL), Learning Organization (LO), Supportive Work Environment (SWE), and Organizational Commitment (OC) on Turnover Intention (IT) at PT XYZ. Respondents in this study were employees from Generation Z, worked in the IT division, and had a minimum of one year of service. Data were collected through a questionnaire using the judgemental sampling method, with a sample of 172 respondents out of a minimum calculation of 142 based on the Krejcie & Morgan formula. The collected data was analyzed using the Structural Equation Modeling (SEM) method based on Partial Least Squares (PLS). The results of the study show that Digital Transformational Leadership has a significant negative influence on Turnover Intention, as well as a significant positive influence on Learning Organization and Organizational Commitment. Learning Organization also has a significant negative effect on Turnover Intention and a significant positive effect on Organizational Commitment. Meanwhile, Supportive Work Environment (SWE) has a significant negative effect on Turnover Intention and a significant positive effect on Organizational Commitment. These findings affirm the importance of Digital Transformational leadership, a learning culture, and a supportive work environment in increasing organizational commitment and reducing the desire to change jobs in young employees in the technology field.
Artificial Intelligence Driven Audience Sentiment Analytics for Interactive Digital Broadcasting Platforms Richard Andre Sunarjo; Tessa Handra; Rifqa Nabila Muti; Kamal Arif Al-Farouqi
Bridging of Emerging AI and Media Broadcasting (BEAM) Vol. 1 No. 1 November (2025): Bridging of Emerging AI and Media Broadcasting
Publisher : Sundara Publishing

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Abstract

The rapid growth of interactive digital broadcasting platforms has significantly transformed the way audiences engage with media content through live chats, comments, and social media interactions. However, the massive volume of usergenerated feedback creates challenges for broadcasters in understanding audience sentiment efficiently. This study aims to analyze audience sentiment using Artificial Intelligence (AI) driven analytics to improve the understanding of audience engagement in interactive digital broadcasting platforms. The research applies a quantitative approach using AI based Natural Language Processing (NLP) techniques to process and analyze audience feedback data collected from comments, live chat interactions, and social media responses related to digital broadcast content. The analytical process includes data preprocessing, sentiment classification, and machine learning based modeling to identify patterns of audience emotional responses and engagement. The findings indicate that AI driven sentiment analytics can effectively classify audience opinions and detect real time sentiment trends associated with broadcasted content. The results also demonstrate that AI-based analysis enables broadcasters to gain deeper insights into audience preferences, evaluate content performance, and optimize broadcasting strategies more efficiently compared with conventional manual analysis methods. In conclusion, the integration of AI in audience sentiment analytics offers a valuable approach for enhancing audience understanding and supporting data-driven decision-making in modern digital broadcasting ecosystems while promoting more responsive and personalized media experiences.
Co-Authors Ardi Ardi Berlianto, Margaretha Pink Kamal Arif Al-Farouqi Puty Sherlyta Rifqa Nabila Muti Tessa Handra