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All Journal JURNAL PENGABDIAN KEPADA MASYARAKAT Jurnal Ilmiah Universitas Batanghari Jambi Premiere Educandum: Jurnal Pendidikan Dasar dan Pembelajaran Reswara: Jurnal Pengabdian Kepada Masyarakat Syntax: Journal of Software Engineering, Computer Science and Information Technology Jurnal SAINTIKOM (Jurnal Sains Manajemen Informatika dan Komputer) Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) DEVICE : JOURNAL OF INFORMATION SYSTEM, COMPUTER SCIENCE AND INFORMATION TECHNOLOGY Proceedings of The International Conference on Computer Science, Engineering, Social Sciences, and Multidisciplinary Studies
M. Arif Rahman
Rekayasa Perangkat Lunak / Universitas Dharmawangsa
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Social Sciences

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Collaborative AI Governance for Digital Innovation and Knowledge Integration Performance Umar Hamdan Nasution; Cut Zahri; M. Arif Rahman
Proceedings of The International Conference on Computer Science, Engineering, Social Science, and Multi-Disciplinary Studies Vol. 1 (2025)
Publisher : CV Raskha Media Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64803/cessmuds.v1.11

Abstract

 The rapid development of Artificial Intelligence (AI) has significantly transformed organizational strategies in managing knowledge resources, decision-making processes, and innovation activities within digital ecosystems. AI enables organizations to process large-scale data, generate predictive insights, and improve strategic responsiveness in increasingly dynamic environments. However, the successful implementation of AI does not rely solely on technological readiness, but also requires governance mechanisms that ensure transparency, accountability, fairness, and ethical use of data. Without appropriate governance structures, AI adoption may generate risks related to algorithmic bias, data misuse, lack of explainability, and reduced organizational trust. This study aims to develop an empirical model explaining the relationship between collaborative AI governance, knowledge integration capability, digital innovation capability, and innovation performance. The research is grounded in Resource-Based View (RBV) and Dynamic Capability Theory, which emphasize that organizational capabilities play an important role in creating sustainable competitive advantage. AI governance is conceptualized as a strategic capability that enables organizations to integrate knowledge resources and support innovation processes. Knowledge integration capability reflects the organizational ability to combine data, expertise, and digital resources into new value creation mechanisms, while digital innovation capability represents the ability to transform technological knowledge into innovative products, services, and business models. This study adopts a quantitative research approach using survey data collected from 210 respondents consisting of managers, IT professionals, and digital transformation specialists from organizations implementing AI-based technologies. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to evaluate measurement validity, reliability, and structural relationships among variables. The findings indicate that collaborative AI governance has a significant positive effect on knowledge integration capability, which subsequently enhances digital innovation capability and innovation performance. The results highlight the importance of governance mechanisms in ensuring responsible AI adoption and strengthening organizational innovation outcomes. This study contributes to the literature by integrating AI governance and knowledge integration capability into a unified empirical framework explaining innovation performance in digital ecosystems. The findings also provide practical implications for organizations seeking to improve innovation capability through responsible AI governance strategies 
Co-Authors Cut Zahri Eddy Iskandar Ega Evinda Putri Elyas, Ananda Hadi Hendra Alsa Fahmi Ibnu Rusydi Jovi Antares Listya Devi Junaidi Nita Sari Repelita Situmeang Nur Hidayah randika farike bania Rani Rahim Rijal Sabri Sabrina Hayuni Sahnan Rangkuti Sartika Siregar Suhariyanti Suhendri Tharina Ainaya Umar Hamdan Nasution Welnof Satria Zainal Arifin Zulham Zulham