Research article – Open access
The Impact of Artificial Intelligence on Competitive Advantage: A Strategic Analysis of AI Adoption in Industries
Nour Marwan Yaseen Bashabsheh
Pages 1 – 19
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Abstract
This study examines how artificial intelligence contributes to competitive advantage across industries such as manufacturing, healthcare, retail, and finance. It highlights AI’s role in improving operational efficiency, supporting innovation, and enhancing decision-making through automation, predictive analytics, and personalized services. The research also discusses major barriers to adoption, including implementation costs, ethical concerns, and skill gaps. It concludes that firms that strategically integrate AI can achieve sustainable competitive advantage when they successfully manage these challenges.
Research article – Open access
The Future of Energy in the Netherlands Towards a Balance between Energy Independence and Environmental Protection
Dr. Ahmed Hassan Soliman
Pages 20 – 37
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Abstract
This research explores the future of energy in the Netherlands by analyzing how the country can balance energy independence with environmental protection. It reviews the Dutch energy transition, focusing on renewable sources such as wind and solar power, the role of technological innovation, and the debate surrounding nuclear energy. The study also examines government policies aimed at carbon neutrality and reducing dependence on fossil fuels. It concludes that achieving a sustainable energy future in the Netherlands requires a balanced strategy that combines clean energy expansion, technological development, and effective policy support.
Research article – Open access
Developing a Framework for Explainable AI in Business Analytics using Machine Learning
Dr. Nour Marwan Yasein Bashabsheh
Pages 38 – 55
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Abstract
This paper proposes a general framework for integrating Explainable Artificial Intelligence (XAI) into business analytics based on machine learning techniques. It addresses the growing need for transparency and interpretability in machine learning models used for business decision-making. The framework incorporates methods such as SHAP, LIME, and other model-agnostic tools to make complex models more understandable and trustworthy. The study concludes that this framework helps organizations balance predictive performance with explainability, thereby improving trust, compliance, and adoption in business environments.
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ISSN: 3050-7618
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