Article Per Year (5 Year)
p-Index From 2021 - 2026
1.182
P-Index
This Author published in this journals
All Journal Journal of Tecnologia Quantica Techno Agriculturae Studium of Research Journal of Biomedical and Techno Nanomaterials Journal Emerging Technologies in Education Journal of Social Science Utilizing Technology
Sevda Kara
Hacettepe University
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Physics

Published : 7 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search
Journal : journal of social science utilizing technology

 1 
Leveraging Big Data Analytics for Talent Management and Prediction in Human Resources Ahmad Hariri; Rachmat Prasetio; Abdullah Al-Shammari; Sevda Kara
Journal of Social Science Utilizing Technology Vol. 2 No. 4 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jssut.v2i4.1780

Abstract

Background. The increasing complexity of workforce management in modern organizations has driven the adoption of innovative tools such as Big Data Analytics (BDA) in human resources (HR). Talent management, encompassing recruitment, retention, and performance evaluation, has become a critical focus for organizations aiming to maintain competitiveness. Big Data Analytics enables HR professionals to identify patterns, predict trends, and make data-driven decisions, enhancing talent management processes. Despite its potential, the application of BDA in HR faces challenges, including data integration, privacy concerns, and skill gaps. Purpose. This study explores the role of Big Data Analytics in improving talent management and prediction, focusing on its impact on decision-making and organizational outcomes. Method. A mixed-method research design was employed, incorporating quantitative analysis of HR metrics and qualitative insights from interviews with HR professionals. Data were collected from 15 organizations across diverse industries, analyzing employee performance, recruitment patterns, and turnover rates. Predictive models were developed using machine learning algorithms to forecast talent trends and inform HR strategies. Results. The findings revealed that BDA significantly improved talent acquisition and retention processes, with a 25% increase in recruitment efficiency and a 30% reduction in turnover rates. Predictive models accurately identified high-potential candidates and flagged at-risk employees, enabling proactive interventions. Challenges related to data privacy and technical expertise were highlighted as areas for improvement. Conclusion. The study concludes that leveraging Big Data Analytics transforms talent management by enabling evidence-based decision-making and predictive insights. Addressing implementation challenges and investing in skill development will maximize its potential in HR practices.
The Impact of Ride-Hailing Platforms on the Livelihoods and Social Welfare of Gig Economy Workers in Indonesia Ratu Agung Bagus Ngurah Putra; Nandar Hlaing; Sevda Kara
Journal of Social Science Utilizing Technology Vol. 3 No. 6 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jssut.v3i6.2834

Abstract

The rise of ride-hailing platforms has significantly transformed the labor market in Indonesia, especially within the gig economy. As a rapidly growing sector, these platforms offer flexible work opportunities, attracting millions of workers. However, the impact of ride-hailing on the livelihoods and social welfare of gig economy workers remains understudied. This research aims to explore how ride-hailing platforms influence the income stability, social security, and overall well-being of gig workers in Indonesia. A mixed-methods approach was used for this study, combining surveys and in-depth interviews with 300 gig workers from major cities like Jakarta, Surabaya, and Yogyakarta. The survey collected quantitative data on income patterns, job satisfaction, and access to social benefits, while qualitative interviews provided deeper insights into the social and psychological effects of gig work. The findings indicate that while ride-hailing platforms offer a source of income and flexible working hours, they also expose workers to income instability, lack of social protection, and job insecurity. Only 15% of workers reported having access to formal social security benefits, with many relying on informal networks for support. Additionally, mental health concerns were prevalent due to the unpredictable nature of gig work and the absence of a supportive work environment. The study concludes that while ride-hailing platforms offer significant economic opportunities, there is an urgent need for policy reforms that address workers' rights, social security, and job security to ensure the sustainability of their livelihoods and well-being.
Co-Authors Abdullah Al-Shammari Ahmad Hariri Ahmet Demir Almira Ulimaz Baran Akbulut Cemil Kaya Emine Yildiz Javlonbek Khamraev Mohammad Ali Murat Arslan Nandar Hlaing Rachmat Prasetio Ratu Agung Bagus Ngurah Putra Zeynep Toprak