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All Journal Jurnal Ilmiah Manajemen Kesatuan Jurnal Ilmiah Pariwisata Kesatuan Jurnal Abdimas Dedikasi Kesatuan Jurnal Aplikasi Bisnis Kesatuan Jurnal Informatika Kesatuan
Marwan Effendy
Institut Bisnis dan Informatika Kesatuan
Religion Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Environmental Science Languange, Linguistic, Communication & Media Library & Information Science Public Health Social Sciences Other

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Artificial Intelligence Adoption In SMEs: A Technology–Organization–Environment Framework For Improving Efficiency And Business Performance Suci Sri Utami Sutjipto; Ade Mulyana; Marwan Effendy
Jurnal Aplikasi Bisnis Kesatuan Vol. 5 No. 3 (2025): JABKES Edisi Desember 2025
Publisher : Program Vokasi dan LPPM IBI Kesatuan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37641/jabkes.v5i3.1946

Abstract

Artificial intelligence (AI) is becoming increasingly accessible to small and medium-sized enterprises (SMEs) through cloud platforms, generative AI, machine learning services, intelligent automation, and AI-enabled business applications. Yet technology availability does not guarantee organizational adoption or measurable business value. This study develops an integrative Technology–Organization–Environment (TOE) framework explaining how SMEs can convert AI adoption into operational efficiency and business performance. The study uses an integrative literature review combining foundational technology-adoption theory with recent peer-reviewed research on AI adoption and use in SMEs. The synthesis identifies five technological determinants—relative advantage, compatibility, technological readiness, data readiness, and security/integration; four organizational determinants—top management support, AI and digital capability, financial resources, and data-driven culture; and four environmental determinants—competitive pressure, customer demand, external ecosystem support, and policy/institutional support. The findings indicate that AI creates value not through adoption alone but through integration into business processes such as automation, analytics, prediction, personalization, and decision support. Organizational capability emerges as a critical conversion mechanism linking technological potential to performance outcomes. The study further argues that responsible AI governance, including human oversight, privacy protection, cybersecurity, transparency, and accountability, should be treated as a cross-cutting implementation condition. The resulting framework contributes to the SME digital-transformation literature by linking adoption antecedents with value-realization mechanisms and provides a practical roadmap for phased AI implementation in resource-constrained firms. Keywords: artificial intelligence; SMEs; technology adoption; TOE framework; operational efficiency; business performance
Generative AI in Managerial Decision-Making: A Systematic Literature Review of Trust, AI Literacy, and Decision Quality Isnan Mulia; Tri Marlina; Marwan Effendy
Jurnal Aplikasi Bisnis Kesatuan Vol. 6 No. 2 (2026): JABKES Edisi Agustus 2026
Publisher : Program Vokasi dan LPPM IBI Kesatuan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37641/jabkes.v6i2.5764

Abstract

Generative artificial intelligence (GenAI) is rapidly entering managerial decision processes, yet evidence remains fragmented regarding when AI-generated advice should be trusted, how managers should evaluate it, and how decision quality should be assessed. This study systematically reviews GenAI-supported managerial decision-making by integrating PRISMA 2020 screening, bibliometric mapping, author-keyword co-occurrence analysis, thematic synthesis, and the Theory–Context–Characteristics–Methodology (TCCM) framework. A Scopus export containing 3,500 records was screened using temporal, language, document-type, topical, and contextual criteria, yielding 99 studies published between 2024 and 2026. The final corpus comprises 342 unique authors across 94 publication sources. Four themes dominate: decision augmentation and analytics; trust, governance, and explainability; human–AI collaboration and capability; and decision quality and organizational outcomes. Trust is more often represented through governance, transparency, explainability, calibration, and human oversight than as an explicitly measured construct, while AI literacy appears explicitly in only one study. Decision quality is likewise rarely conceptualized directly and is commonly proxied by accuracy, efficiency, or predictive performance. The review proposes an integrative model in which GenAI decision-support capabilities influence managerial decision quality through calibrated trust and human–AI collaboration, with AI literacy functioning as a critical capability for verification and appropriate reliance. The study concludes with a TCCM-based research agenda for human-centered GenAI decision research. Keywords: generative AI; managerial decision-making; trust in AI; AI literacy; decision quality
Co-Authors Aang Munawar Ade Mulyana Anggita Pramesti Ardhieva Wiraldhy Ruslanie Cecilia Valentina Srihadi Suryanti Edi Nurachmad El Syifa Lutfiah Filani Zikri Hassan Gen Gen Gendalasari Githa Ariyanty Harry Roestiono Hesti Wiraatmaja Iis Wahyuni Isnan Mulia Iswandi Sukartaatmadja Muanas Muanas Muhamad Fahmi Muhamad Tofan Neni Nurisnaini Nusa Muktiadji Pingky Dezar Zulkarnain Rahmadayanti Gumilar Suci Sri Utami Sutjipto Suharmiati Suharmiati Sujana Sujana Tri Marlina Wahyu Indra Satria Yoyon Supriadi