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All Journal Aptisi Transactions on Management International Transactions on Education Technology (ITEE)
Williams, Jack
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Social Sciences

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Enhancing Circular Economy with Digital Technologies: A PLS-SEM Approach Williams, Jack; Prawiyogi, Anggy Giri; Rodriguez, Miguel; Kovac, Ivan
International Transactions on Education Technology (ITEE) Vol. 2 No. 2 (2024): International Transactions on Education Technology (ITEE)
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/itee.v2i2.590

Abstract

This study investigates the transformative potential of the digital economy in fostering the principles of a circular economy. Utilizing the SmartPLS methodology, we explore key determinants that drive the transition towards a sustainable economic framework and assess their impacts on both environmental sustainability and economic resilience. Our analysis highlights that the integration of digital technologies, such as IoT, blockchain, and AI, within circular economy practices can significantly enhance resource efficiency, reduce waste, and promote sustainable economic growth. These technologies enable better tracking and management of resources, facilitating closed-loop systems that are essential for a circular economy. However, our findings also identify substantial challenges, including concerns over data security, digital divide, and unequal access to advanced technologies, which may hinder the equitable distribution of benefits. The study underscores the importance of an integrated policy approach that combines technological innovation with supportive regulatory frameworks to address these challenges and maximize the benefits of digital integration. Policymakers are encouraged to develop strategies that not only foster technological advancements but also ensure inclusive access and address security issues. This research provides comprehensive insights for stakeholders, including governments, businesses, and academia, in designing effective strategies and policies aimed at promoting a sustainable circular economy in the digital era. By aligning digital advancements with circular economy principles, we can pave the way towards achieving sustainable development goals and creating a resilient economic future.
Leveraging AI-Powered Automation for Enhanced Operational Efficiency in Small and Medium Enterprises (SMEs) Andayani, Dwi; Indiyati, Dian; Mayang Sari, Meri; Yao, Goh; Williams, Jack
APTISI Transactions on Management (ATM) Vol 8 No 3 (2024): ATM (APTISI Transactions on Management: September)
Publisher : Pandawan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/atm.v8i3.2363

Abstract

This study explores the potential of AI-powered automation in enhancing opera- tional efficiency within Small and Medium Enterprises (SMEs). The primary objective is to identify how automation tools driven by artificial intelligence (AI) can streamline business processes, reduce operational costs, and improve productivity. The methodology includes a quantitative analysis of SMEs that have implemented AI-based solutions, supported by qualitative interviews with key stakeholders. The Results indicate significant improvements in operational workflows, particularly in areas such as supply chain management, customer service, and financial operations. The findings demonstrate that SMEs adopting AI technologies experience reduced human error, faster decision-making pro- cesses, and improved customer satisfaction. However, challenges such as initial investment costs and technical expertise remain. The study concludes that with proper implementation and strategic planning, AI-powered automation can be a key driver of success for SMEs in competitive markets.
A Model-Driven Approach to Developing Scalable Educational Software for Adaptive Learning Environments Sutarman, Asep; Williams, Jack; Wilson, Daniel; Ismail, Farid Bin
International Transactions on Education Technology (ITEE) Vol. 3 No. 1 (2024): International Transactions on Education Technology (ITEE)
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/itee.v3i1.663

Abstract

This research presents a model-driven approach to the development of scalable educational software tailored to adaptive learning environments. With the increasing demand for personalized education, adaptive learning systems play a crucial role in meeting diverse student needs by adjusting instructional content dynamically. This paper proposes a software engineering framework that integrates model-driven development (MDD) techniques with scalability principles, allowing for the efficient design and implementation of educational applications that can handle varying workloads and user demands. The framework emphasizes modular architecture, reusability, and flexibility to ensure that software can evolve with emerging educational requirements. Key components include the design of a learning content management system (LCMS) and the application of adaptive algorithms to personalize learning pathways. Additionally, this study explores the integration of cloud technologies to enhance the scalability and performance of educational platforms. A prototype system was developed and tested in a controlled environment, showing significant improvements in scalability, system performance, and student engagement compared to traditional static e-learning platforms. The results indicate that the model-driven approach not only improves software development efficiency but also offers a robust solution for creating adaptive educational systems that can scale to meet the growing needs of learners and institutions. This research contributes to the field of educational software development by providing a systematic methodology for building scalable and adaptive learning environments using advanced software engineering techniques.
Co-Authors Anggy Giri Prawiyogi, Anggy Giri Dian Indiyati Dwi Andayani Ismail, Farid Bin Kovac, Ivan Mayang Sari, Meri Rodriguez, Miguel Sutarman, Asep Wilson, Daniel Yao, Goh