https://publish.thescienceinsight.com/index.php/msbd/issue/feed 2025-12-28T00:00:00+00:00 Iqra Javed manager@thescienceinsight.com Open Journal Systems <div id="bannerR"> <h2>Why to publish in MSBD?</h2> <p><em>Management Science and Business Decisions</em> (ISSN 2767-6528; eISSN 2767-3316) is an international journal devoted to advancing the theory and practice of management sciences and business decision-making. It encourages deploying new decision-making techniques to objectively solve old and new problems currently being faced by managers and organizations in different industries and economies. Both positive and negative results are welcomed, provided they have been obtained from scientific methods. New and innovative ideas that have the potential to create a debate are particularly welcomed. The journal seeks to foster exchange between young and seasoned scholars and between scholars and practitioners with a view to aid decision-makers and policy-makers in creating a better world for our future generations. MSBD is an open access double-blind peer-reviewed fast journal that does not charge any fee from the authors. MSBD is published by <a href="https://thescienceinsight.com/">Science Insight</a> (USA) bi-annually.</p> </div> https://publish.thescienceinsight.com/index.php/msbd/article/view/111 2025-12-26T07:35:19+00:00 Dewi Shinta dewiishintaa@gmail.com Khalil Nasir Khan khalilnasir9161@gmail.com Muhammad Nadeem nadeem16501@gmail.com

<p>The current study introduces a systematic framework to address the critical challenge of prioritizing Generative Artificial Intelligence (GenAI) initiatives within Human Resources (HR) Management. Confronted with multiple high-potential yet resource-intensive options, HR leaders require an objective method for strategic investment. The study employs a Multi-Criteria Decision-Making (MCDM) methodology, integrating the Analytical Ordinal Priority Approach (AOPA) and the Dynamic Grey Relational Analysis (DGRA). Ten distinct GenAI use cases are identified and evaluated against eleven strategic criteria—spanning impact, feasibility, risk, and organizational momentum—based on the judgments of a diverse panel of experts from HR, Information Technology, Finance, Legal, and Operations. The results yield a validated, consolidated ranking of initiatives. The Employee Sentiment &amp; Trend Analyzer emerges as the highest-priority initiative, followed by the Intelligent HR Helpdesk Chatbot and the Automated Recruitment Coordinator, while the Interactive Leadership Training Simulator is consistently ranked lowest. The study provides HR leaders with a transparent, data-driven framework for phased implementation, advocating for initial investments in initiatives that balance strategic value, strong return on investment, and manageable risk to build organizational confidence and momentum in the adoption of transformative AI technologies.</p> <p> </p>

2025-12-28T00:00:00+00:00 Copyright (c) 2025 Science Insight https://publish.thescienceinsight.com/index.php/msbd/article/view/118 2025-11-05T14:09:29+00:00 Beenish Ramzan beenishnawaz033@gmail.com

<p>This study aims to evaluate and prioritize the critical factors influencing the adoption of AI-enhanced Enterprise Resource Planning (ERP) systems within China’s logistics sector. A hybrid multi-criteria decision-making (MCDM) methodology is employed, integrating the Dynamic Grey Relational Analysis (DGRA) and the Analytical Ordinal Priority Approach (AOPA). Data were collected from 223 logistics professionals via a structured questionnaire, and the factors were ranked based on their distance to an ideal reference and their ordinally derived importance weights. We found Data Security &amp; Privacy to be the most critical factor based on both models. We also found the strong convergence between DGRA and AOPA results confirms the robustness of the ranking. This study provides the first empirically validated, multi-model approach specifically designed to prioritize AI-enhanced ERP factors for the logistics industry.</p> <p> </p>

2025-12-28T00:00:00+00:00 Copyright (c) 2025 Science Insight