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All Journal Priviet Social Sciences Journal Ekopedia: Jurnal Ilmiah Ekonomi Jejak digital: Jurnal Ilmiah Multidisiplin
Chandika Mahendra Widaryo
Mulawarman University
Economics, Econometrics & Finance Education Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Social Sciences Other

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Does financial literacy drive SME success in resource-rich regions? Lusiana Desy Ariswati; Muhammad Ramadhani Kesuma; Rohana Nur Aini; Ellen D. Oktanti Irianto; Chandika Mahendra Widaryo; Margareth Henrika
Priviet Social Sciences Journal Vol. 5 No. 11 (2025): November 2025
Publisher : Privietlab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55942/pssj.v5i11.714

Abstract

This study investigates the relationships among financial literacy, product innovation, risk management, and financial performance in small and medium-sized enterprises (SMEs) located in East Kalimantan, a region in Indonesia characterized by its resource abundance. While prior studies underscore the importance of financial literacy and innovation for SME success, there is limited research focusing on non-urban, commodity-reliant settings. Using a quantitative methodology, this study employs Partial Least Squares Structural Equation Modeling to analyze data gathered from 200 SME owners in non-extractive industries through a structured survey. This study explores whether financial literacy and product innovation directly affect financial performance and whether risk management mediates these dynamics. The findings reveal that financial literacy has a significant positive effect on financial performance, whereas product innovation strengthens risk management but does not directly influence financial outcomes. Additionally, risk management does not mediate these relationships, indicating potential contextual constraints in resource-limited environments. These insights advance the understanding of SME operations in non-urban, resource-dependent regions and highlight the need for customized financial education and innovation. This study provides actionable recommendations for policymakers to enhance SME resilience through targeted strategies, addressing a key gap in the literature on economies tied to natural resources.
Machine Learning in Decision Support Systems: A Bibliometric Study of Intellectual Structures, Thematic Evolution, and Future Research Directions Mohammad Arsyad; Raven Naufal Azka; Muhammad Haiqal Aulia Risian; Muhammad Zainuri; Rifky Fadlian Noor; Chandika Mahendra Widaryo; Muhammad Ramadhani Kesuma
Ekopedia: Jurnal Ilmiah Ekonomi Vol. 2 No. 2 (2026): APRIL-JUNI 2026
Publisher : Indo Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63822/q2ka3a64

Abstract

This study examines the intellectual structure and thematic evolution of research on machine learning (ML) in decision support systems (DSS), with particular attention to the financial management domain, through a bibliometric approach spanning 1993 to 2026. Data were retrieved from the Scopus database and analysed using performance analysis and science mapping methods, supported by VOSviewer to identify publication trends, collaboration networks, and co-occurrence patterns.  The study reveals an annual growth rate of 13.88% in publications, reflecting sustained and accelerating scholarly interest. Collaboration networks remain fragmented, with China, India, and the United States occupying central positions. Thematic analysis indicates a transition from classical ML methods toward advanced integrations encompassing artificial intelligence, big data, and risk analytics.  The findings provide strategic guidance for researchers and practitioners seeking to advance interpretable and ethically grounded ML-based DSS in financial decision-making environments. This study contributes a comprehensive bibliometric mapping of ML in DSS research, identifies persistent intellectual gaps, and proposes a structured agenda for future inquiry integrating explainable AI and cross-disciplinary collaboration.
The Impact of Social Media on Group Decision-Making Processes: A Bibliometric Review Hanifan Ega Pratama; Syahrul Safi’Uddin; Kalfizer Tappang; Reynaldi Reynaldi; Diky Diky; Chandika Mahendra Widaryo; Muhammad Ramadhani Kesuma
Jejak digital: Jurnal Ilmiah Multidisiplin Vol. 2 No. 4 (2026): JUNI-JULI
Publisher : INDO PUBLISHING

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63822/3bzy9d97

Abstract

This study maps and analyses the scholarly literature on the impact of social media on group decision-making processes, with a particular focus on the human resource management (HRM) context, using a bibliometric approach. Data were retrieved from Scopus databases, yielding 116 peer-reviewed articles spanning 2006 to 2026. VOSviewer software was employed to visualise co-authorship networks and keyword co-occurrence patterns, enabling systematic mapping of collaboration structures and thematic evolution. The field exhibits substantial scientific impact, averaging 48.59 citations per document, and is characterised by extensive global collaboration. Thematically, research has evolved from a conceptual stage centred on trust and community interaction toward an applicative and computational stage dominated by big data, artificial intelligence (AI), and consensus model optimisation. Enterprise social media platforms have emerged as strategic infrastructure reinforcing collaboration and inclusivity in hybrid work environments, although challenges related to group bias amplification, polarisation, and echo chamber dynamics remain persistent. Organisations operating in digital and hybrid environments should strategically govern social media platforms to leverage collective intelligence while proactively mitigating algorithmic bias and polarisation risks in group decision-making. This is among the first bibliometric studies to specifically focus on the intersection of social media and group decision-making within an HRM context, identifying AI-driven decision architectures and digital bias mitigation as the most critical frontier for future research.
Co-Authors Diky Diky Ellen D. Oktanti Irianto Hanifan Ega Pratama Kalfizer Tappang Lusiana Desy Ariswati Margareth Henrika Mohammad Arsyad Muhammad Haiqal Aulia Risian Muhammad Ramadhani Kesuma Muhammad Zainuri Raven Naufal Azka Reynaldi Reynaldi Rifky Fadlian Noor Rohana Nur Aini Syahrul Safi’Uddin