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Learning Resource Centre Monthly Bulletin |
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Learning Resource Centre Monthly Bulletin |
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| New Books |
 Ebook: EI 423 Virtual Selling : A Quick-Start Guide to Leveragin By Jeb Blount |  Ebook: EI 424 Reimagining Businesses with AI By Sudhi Sinha and Khaled Al Huraimel |  Ebook: EI 425 Strategy in the Digital Age : Mastering Digital Tr By Michael Lenox |
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| Articles |
| Uncovering Research Trends on Artificial Intelligence Risk Assessment in Businesses: A State-of-the-Art Perspective Using Bibliometric Analysis By Muria-Tarazón, Juan Carlos; Juan Vicente Oltra-Gutiérrez; Oltra-Badenes, Raúl; Escobar-Román, Santiago. Applied Sciences; Basel Vol. 15, Iss. 3, (2025): 1412. DOI:10.3390/app15031412 Abstract :This paper presents a quantitative vision of the study of artificial intelligence risk assessment in business based on a bibliometric analysis of the most relevant publications. The main goal is to determine whether the risk assessment of artificial intelligence systems used in businesses is really a subject of increasing interest and to identify the most influential and productive sources of scientific research in this area. Data were collected from the Web of Science Core Collection, one of the most complete and prestigious databases. Regarding the temporal evolution of publications and citations this study evidences, this research subject shows rapid growth in the number of publications (at a compound annual rate of 31.20% from 2018 to 2024 inclusive), showing its high attraction for researchers, responding to the need to implement systematic risk assessment processes in the organizations using AI to mitigate potential harms, ensure compliance with regulations, and enhance artificial intelligence systems’ trust and adoption. Especially after the surge of large language models like ChatGPT or Gemini, AI is revolutionizing the dynamics of human–computer interaction using natural language, video, and audio. However, as the scientific community initiates rigorous studies on AI risk assessment within organizational contexts, it is imperative to consider critical issues such as data privacy, ethics, bias, and hallucinations to ensure the successful integration and interaction of AI systems with human operators. Furthermore, this paper constitutes a starting point, including for any researcher who wants to be introduced to this topic, indicating new challenges that should be dealt by researchers interested in AI and hot topics, in addition to the most relevant literature, authors, and journals about this research subject. | |||
| Is Artificial Intelligence a Game-Changer in Steering E-Business into the Future? Uncovering Latent Topics with Probabilistic Generative Models By Oprea, Simona-Vasilica; Bâra, Adela. Journal of Theoretical and Applied Electronic Commerce Research; Curicó Vol. 20, Iss. 1, (2025): 16. DOI:10.3390/jtaer20010016 Abstract :Academic publications from the Web of Science Core Collection on “e-business” and “artificial intelligence” (AI) are investigated to reveal the role of AI, extract latent themes and identify potential research topics. The proposed methodology includes relevant graphical representations (trends, co-occurrence networks, Sankey diagrams), sentiment analyses and latent topics identification. A renewed interest in these publications is evident post-2018, with a sharp increase in publications around 2020 that can be attributed to the COVID-19 pandemic. Chinese institutions dominate the collaboration network in e-business and AI. Keywords such as “business transformation”, “business value” and “e-business strategy” are prominent, contributing significantly to areas like “Operations Research & Management Science”. Additionally, the keyword “e-agribusiness” recently appears connected to “Environmental Sciences & Ecology”, indicating the application of e-business principles in sustainable practices. Although three sentiment analysis methods broadly agree on key trends, such as the rise in positive sentiment over time and the dominance of neutral sentiment, they differ in detail and focus. Custom analysis reveals more pronounced fluctuations, whereas VADER and TextBlob present steadier and more subdued patterns. Four well-balanced topics are identified with a coherence score of 0.66 using Latent Dirichlet Allocation, which is a probabilistic generative model designed to uncover hidden topics in large text corpora: Topic 1 (29.8%) highlights data-driven decision-making in e-business, focusing on AI, information sharing and technology-enabled business processes. Topic 2 (28.1%) explores AI and Machine Learning (ML) in web-based business, emphasizing customer service, innovation and workflow optimization. Topic 3 (23.6%) focuses on analytical methods for decision-making, using data modeling to enhance strategies, processes and sustainability. Topic 4 (18.5%) examines the semantic web, leveraging ontologies and knowledge systems to improve intelligent systems and web platforms. New pathways such as voice assistance, augmented reality and dynamic marketplaces could further enhance e-business strategies. | |||
| The Impacts of Artificial Intelligence on Business Innovation: A Comprehensive Review of Applications, Organizational Challenges, and Ethical Considerations By Machucho Ruben Systems; Basel Vol. 13, Iss. 4, (2025): 264. DOI:10.3390/systems13040264 Abstract :This review synthesizes current knowledge on the transformative impacts of artificial intelligence (AI)—computational systems capable of performing tasks requiring human-like reasoning—on business innovation. It addresses the potential of AI to reshape strategies, operations, and value creation across various industries. Key themes include AI-driven business model innovation, human–AI collaboration, ethical governance, operational efficiency, customer experience personalization, organizational capability development, and adoption disparities. AI enables scalable product development, personalized service delivery, and data-driven strategic decisions. Successful implementations hinge on overcoming technical, cultural, and ethical barriers, with ethical AI adoption enhancing consumer trust and competitiveness, positioning responsible innovation as a strategic imperative. For practitioners, this review offers evidence-based frameworks for aligning AI with business objectives. For academics, it identifies research frontiers, including longitudinal impacts, context-specific roadmaps for small- and medium-sized enterprises, and sustainable innovation pathways. This review conceptualizes AI as a driver of systemic organizational transformation, requiring continuous learning, ethical foresight, and strategic ability for competitive advantage. | |||
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| News |
| Big firms rush to acquire AI startups By Financial Express; April 28, 2025 |
| AI-Driven Multimodal Models Reshape Indian Businesses, Deloitte Report By Analytics India Magazine; April 29, 2025 |
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