Journal Articles
Permanent URI for this collectionhttp://10.0.100.92:4000/handle/123456789/21
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Item Can generative AI motivate management students? The role of perceived value and information literacy(The International Journal of Management Education, 2024-11) Jose, Emily Maria K; Prasanna, Akshara; Kushwaha, Bijay Prasad; Madhumita DasGenerative Artificial Intelligence (GenAI) is a disruptive technology that has started to be used among students in management education. However, the question is whether the utilisation of GenAI in educational settings stimulates students to engage in learning activities and broaden their knowledge base. Hence, this study investigates student motivation and perception of using GenAI (ChatGPT) technology in management education. A random sampling technique was employed to survey 478 students from various educational institutions in the southern region of India. The outcomes revealed that GenAI presents both prospects and hurdles in the domain of management education. The disruptive nature of this technology brings forth numerous opportunities for acquiring knowledge and augmenting one's cognitive capacity. Nonetheless, a cautious and accountable approach is imperative for the successful integration of GenAI into the of management education. Consequently, this study provides pragmatic implications for students, educators, and educational institutions. The effectiveness of GenAI in practical settings can be heightened by arranging interactive training sessions led by AI experts, devising easily accessible online educational modules, embedding AI proficiencies into educators through collaborative endeavors or specialized training programs, and establishing systematic assessment protocols to ensure continual improvement.Item Artificial intelligence an enabler for sustainable engineering decision-making in uncertain environment: a review and future propositions(Journal of Global Operations and Strategic Sourcing, 2024) Wankhede, Vishal Ashok; Agrawal, Rohit; Kumar, Anil; Luthra, Sunil; Pamucar, Dragan; Stević, ŽeljkoPurpose Sustainable development goals (SDGs) are gaining significant importance in the current environment. Many businesses are keen to adopt SDGs to get a competitive edge. There are certain challenges in realigning the present working scenario for sustainable development, which is a primary concern for society. Various firms are adopting sustainable engineering (SE) practices to tackle such issues. Artificial intelligence (AI) is an emerging technology that can help the ineffective adoption of sustainable practices in an uncertain environment. In this regard, there is a need to review the current research practices in the field of SE in AI. The purpose of the present study is to comprehensive review the research trend in the field of SE in AI. Design/methodology/approach This work presents a review of AI applications in SE for decision-making in an uncertain environment. SCOPUS database was considered for shortlisting the articles. Specific keywords on AI, SE and decision-making were given, and a total of 127 articles were shortlisted after implying inclusion and exclusion criteria. Findings Bibliometric study and network analyses were performed to analyse the current research trends and to see the research collaboration between researchers and countries. Emerging research themes were identified by using structural topic modelling (STM) and were discussed further. Research limitations/implications Research propositions corresponding to each research theme were presented for future research directions. Finally, the implications of the study were discussed. Originality/value This work presents a systematic review of articles in the field of AI applications in SE with the help of bibliometric study, network analyses and STM.Item An Evaluation Framework for Machine Learning and Data Science (ML/DS) Based Financial Strategies: A Case Study Driven Decision Model.(IEEE Transactions on Engineering Management, Engineering Management, 2025) Saadatmand, M.; Daim, T. Mena,; C. Yalcin, H.; Bolatan, G.; Chatterjee, MBig data and computational technologies are increasingly important worldwide in asset and investment management. Many investment management firms are adopting these data science (DS) methods and technologies to improve performance across all investment processes. A good question is whether we can make better decisions in developing quantitative strategies. Therefore, the main objective of this research was to develop a multicriteria assessment framework and scoring decision support system to evaluate quantitative investment strategies that apply machine learning (ML) and DS techniques in their research and development. Subject matter experts will assess all framework perspectives from a systematic literature review to approve their reliability. The perspectives consist of economic and financial foundations , data perspective , features perspective , modeling perspective , and performance perspective . The research methodology applied was the hierarchical decision model (HDM) to provide a 360° view of the quantitative investment strategy and improve and generalize the concept to other asset classes and regions. This study accomplished a rigorous integration of an extensive literature review connecting DS, ML, and investment decision-making in developing quantitative investment strategies. As a result, the major contribution of this study is the comprehensive examination, which included identifying and quantifying perspectives and criteria. The results, while limited indicated significant gaps in strategies examined and therefore generated critical knowledge to improve ML/DS-driven investment strategies, which are valuable for financial companies and policymakers.Item Technology Landscape Analysis: Metaverse(IEEE Engineering Management Review, 2025-06) Chatterjee, M; Saadatmand, M; Daim, T.The metaverse is emerging as a focal point of interest for technology enthusiasts, which is seamlessly integrating various technologies to create immersive augmented reality experiences and establish a seamless connection between the physical and virtual worlds. Diverse industries and sectors are increasingly adopting the metaverse, while a growing interest among academic scholars across different fields reflects a strong enthusiasm for exploring its potential. This study examines the metaverse technology landscape to illuminate its trends and active networks involving countries, institutions, researchers, and key domains. By following a methodical procedure, we have drawn a sample of 2451 papers from the Scopus database. Employing social network analysis integrated with domain-specific cluster-based assessments, the study provides a nuanced understanding of the dynamic evolution within metaverse research. Taking a multidisciplinary approach, the literature review offers a comprehensive overview of the latest trends, covering technological advancements and diverse dimensions within social sciences and management studies associated with the evolving metaverse technology landscape. Ultimately, this study serves as a roadmap to comprehend the latest trends in the metaverse landscape that help researchers and practitioners in navigating this evolving digital frontier with insight and clarity.Item Artificial Intelligence-Based Cybersecurity for the Metaverse: Research Challenges and Opportunities(2025-04) Awadallah, Abeer; Eledlebi, Khouloud; Zemerly, Mohamed Jamal; Puthal, Deepak; Damiani, Ernesto; Taha, KamalThe metaverse, known as the next-generation 3D Internet, represents virtual environments that mirror the physical world. It is supported by innovative technologies such as digital twins and extended reality (XR), which elevate user experiences across various fields. However, the metaverse also introduces significant cybersecurity and privacy challenges that remain underexplored. Due to its complex multi-tech infrastructure, the metaverse requires sophisticated, automated, and intelligent cybersecurity measures to mitigate emerging threats effectively. Therefore, this paper is the first to explore Artificial Intelligence (AI)-driven cybersecurity techniques for the metaverse, examining academic and industrial perspectives. First, we provide an overview of the metaverse, presenting a detailed system model, diverse use cases, and insights into its current industrial status. We then present attack models and cybersecurity threats derived from the unique characteristics and technologies of the metaverse. Next, we review AI-driven cybersecurity solutions based on three critical aspects: User authentication, intrusion detection systems (IDS), and the security of digital assets, specifically for Blockchain and Non-fungible Tokens (NFTs). Finally, we highlight challenges and suggest future research opportunities to enhance metaverse security, privacy, and digital asset transactions