(7 papers in press)

Regular Issues

• Toward Trustworthy ESG Risk Assessment through XAI: a State-of-the-Art Review  
  by Hossein Habibinejad, Morteza Alaeddini, Paul Reaidy 
  Abstract: As artificial intelligence (AI) becomes increasingly central to environmental, social, and governance (ESG) risk assessment, concerns about model opacity and stakeholder trust have come to the forefront Traditional ESG scoring systems face limitations such as inconsistent data, lack of transparency, and potential bias issues that are often exacerbated by complex, black-box AI models. This paper examines the role of explainable AI (XAI) and responsible AI (RAI) in enhancing the credibility and ethical alignment of ESG assessments. A comprehensive review of the literature highlights critical research gaps, including the absence of standardised explainability metrics, minimal empirical validation in real-world contexts, and the neglect of cultural variability in trust formation. To address these gaps, the paper introduces a theoretical framework that integrates trust determinants, RAI principles, and XAI techniques. The model also incorporates human-centric moderators and feedback loops to ensure adaptability across stakeholder groups. By linking interpretability, ethical safeguards, and user-centred design, the framework offers a path toward more trustworthy and transparent ESG systems. Ultimately, this study contributes to the development of AI-powered tools that support responsible decision-making in sustainable finance while reinforcing stakeholder confidence and accountability.
  Keywords: Explainable Artificial Intelligence (XAI); Responsible Artificial Intelligence (RAI); ESG Risk Assessment; Sustainable Finance; Investment; Trustworthiness; Transparency; Fairness; Accountability.
  DOI: 10.1504/IJGAIB.2025.10073995
   
  
• The Role of Trust in Generative Artificial Intelligence in Enhancing Customer Experience and Engagement in the Healthcare Sector  
  by Hamza Dawood, Sharfuddin Ahmed Khan 
  Abstract: This study investigates how trust shapes adoption of generative AI (Gen AI) in healthcare, examining communication, interaction, intimacy, and empathy as antecedents of trust and their effects on customer experience and engagement. Grounded in computers as social actors (CASA) theory, the paper proposes a conceptual framework and tests it using a quantitative survey of 254 respondents in Pakistan analysed with PLS-SEM. Results show trust in Gen AI positively influences customer experience and engagement; communication, interaction, and empathy significantly enhance trust, while intimacy does not. The study also tests commitment as a moderator between trust and customer outcomes and highlights theoretical contributions by extending CASA to Gen AI chatbots and emphasising anthropomorphic design. Practical implications urge healthcare managers to prioritise user-friendly, trust-building Gen AI systems for both professionals and patients, focusing on effective communication, high-quality interaction, and empathetic interfaces. Limitations include cross-sectional data and a single-country context; future research should pursue longitudinal and cross-cultural studies to validate and extend findings across diverse healthcare environments.
  Keywords: Generative AI; Customer Experience; Customer Engagement; Health sector.
  DOI: 10.1504/IJGAIB.2025.10073996
   
  
• Text-Based Insights on Generative AI Applications in Economics and Business  
  by Morteza Alaeddini, Asgari Alireza, Shahab Ahmadi 
  Abstract: This study provides a thorough bibliometric text-based analysis of generative artificial intelligence (GenAI) research in economics and business. Based on an analysis of 1,613 peer-reviewed articles from Scopus and Web of Science published between 2021 and 2025, the study uses co-occurrence networks, topic modelling and burst analysis to map the intellectual structure of GenAI literature. The key findings reveal GenAI to be a rapidly evolving and increasingly interdisciplinary field, with research hotspots in AI ethics, sentiment analysis, digital transformation, and higher education. Emerging trends include AI-assisted writing, consumer behaviour and strategic management applications. The study highlights the growing integration of GenAI in business functions and educational contexts, while also identifying ethical and methodological challenges. This work offers valuable insights for scholars, educators and practitioners seeking to understand the dynamic landscape and future directions of GenAI in business and economics.
  Keywords: generative artificial intelligence (GenAI); large language model (LLM); bibliometric analysis; thematic analysis; network analysis; research trends; interdisciplinary; management; economics; finance.
  DOI: 10.1504/IJGAIB.2025.10073998
   
  
• Exploring the Role of Generative AI in Entrepreneurial Opportunity Recognition: a TAM-Based Approach  
  by Shakil Sajed, Sayma Mehezabin Bably, S.M. Rifat, Sheikh Tahmid, Md. Noor Un Nabi, Imtiaz Masroor, Hazera Amin Meghla, Md. Nur Alam 
  Abstract: What happens when the next generation of entrepreneurs taps into the power of artificial intelligence to identify opportunities that others would miss? With the further development of Generative Artificial Intelligence (GAI) tools, it will be possible to change the perception of entrepreneurs and how they find a way to exploit opportunities. Although these technologies are gaining popularity, the effects of these technologies on entrepreneurial thinking have not been thoroughly addressed. This study focuses on the effect of the adoption of Generative Artificial Intelligence (GAI) on the capability of university students to detect entrepreneurial opportunities. Adopting the Technology Acceptance Model (TAM) as an analytical tool, the research evaluates the impact of the perceptions of ease of use and usefulness of GAI on its adoption and entrepreneurial opportunity recognition. The study presents fresh insights regarding the emergence of artificial technology and entrepreneurship with relevance to educationists, policymakers, and innovation centres that strive to realise the next generation of entrepreneurs to apprehend their full potential.
  Keywords: Generative Artificial Intelligence; Entrepreneurial Opportunity Recognition; Technology Acceptance Model (TAM); AI Adoption; Entrepreneurial Education.
  DOI: 10.1504/IJGAIB.2025.10074082
   
  
• Bridging Semantic Knowledge and Generative AI: a Modular Framework for Automated Reporting in Digital Commissioning Management  
  by Renato Ramos, Diego Calvetti, Daniel Luiz De Mattos Nascimento, Fernando Bellon 
  Abstract: This paper proposes a framework integrating semantic triple stores with large language models (LLMs) to enhance automated report generation in digital commissioning systems. It addresses the challenge of efficiently extracting and analysing complex industrial data by combining RDF graphs with LLM-based natural language processing. The approach involves: 1) developing an ontology for digital commissioning; 2) structuring data as RDF triples; 3) integrating triplestores with LLMs using LangGraph and LangChain. This enables natural language querying with high semantic accuracy. Using a simulated dataset, the system achieved 100% accuracy in SPARQL query generation across diverse question types, effectively handling entity relationships, hierarchies, and query complexities. The framework bridges structured data and natural language interfaces in industrial contexts, improving efficiency and accuracy in data retrieval and reporting. Future research should explore scalability, heterogeneous datasets, and data quality challenges in real-world implementations.
  Keywords: Generative AI; Knowledge Graphs; Digital Commissioning; Large Language Models; Semantic Web.
  DOI: 10.1504/IJGAIB.2025.10074187
   
  
• Generative AI: game changer for HRM framework for adoption of ChatGPT  
  by Nripendra P. Rana, Brijesh Sivathanu, Rajasshrie Pillai 
  Abstract: The emergence of ChatGPT is impacting HRM functions, and we aim to understand the implications of ChatGPT on various functions of HRM and the adoption of ChatGPT in organisations. We used the grounded theory approach and interviewed 32 HR managers. The qualitative analysis was done by using NVivo 8.0. We proposed a model that discusses ChatGPT adoption and its contribution to improving HR performance. This paper discusses and provides a comprehensive framework for the adoption of ChatGPT for HRM and its influence on HR performance. This manuscript presents the implications of ChatGPT for HRM, which provides insights to CHROs, HR managers, HR business partners, and CTOs of organisations. This is the first paper that discusses the application of ChatGPT for HRM and presents a framework for HR performance. It provides valuable insights to the HR fraternity and technology officers. Furthermore, it provides research directions to scholars to pursue further research in ChatGPT and its implications for HRM.
  Keywords: ChatGPT; HR performance; HRM; S-O-R; adoption.
  DOI: 10.1504/IJGAIB.2025.10074419
   
  
• Business Transformation in the Age of Generative AI: from Strategy to Societal Impact  
  by Iskander Zouaghi, Samuel Fosso Wamba 
  Abstract: Generative Artificial Intelligence (GenAI) is transforming the foundations of business innovation, operations, and strategy. Moving beyond traditional AI’s focus on prediction, GenAI enables the autonomous creation of novel content, designs, and processes across diverse business domains. This paper synthesises the state of research on GenAI's transformative impact, covering strategic innovation, operational excellence, customer engagement, and organisational development. It explores key technical architectures, Transformers, GANs, VAEs, Diffusion, and Multimodal systems, and examines emerging challenges related to ethics, fairness, privacy, and regulation. Drawing from recent literature and practical deployments, the study identifies critical gaps and proposes a future research agenda at the intersection of AI and business. The analysis highlights GenAI's dual role as a catalyst for business model innovation and a driver of systemic change in organizational learning and decision-making. The paper invites scholars and practitioners to engage in shaping this rapidly evolving field with responsibility and foresight.
  Keywords: Generative artificial intelligence; GenAI; Business model innovation; Strategic transformation; Operational excellence; Customer experience; AI architectures; Ethical AI; Supply chain optimization.
  DOI: 10.1504/IJGAIB.2025.10074539