(13 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
   
  
• Firm Maturity, AI Adoption, and Financial Performance in Finnish Service Firms  
  by Farid Lolo, Marko Torkkeli, Adeel Tariq 
  Abstract: Firm maturity shapes artificial intelligence (AI) adoption and its influence on financial performance in service firms. This research examines the relationship between firm maturity and AI adoption; furthermore, it examines the impact of AI adoption on firm performance. Results finds that firm maturity is linked to pursuing incremental, efficiency-driven AI integration; thus, firm maturity is linked with AI adoption. Additionally, AI adoption is not linked with improved returns on assets (ROA) or equity (ROE). This research adds to the knowledge by examining the relationship between firm maturity and AI adoption, which is novel to the best of the authors' knowledge. It also added to the knowledge by re-examining the relationship between AI adoption and firm performance measures.
  Keywords: Artificial intelligence; AI adoption; firm maturity; organisational life cycle; service firms; Finland.
  DOI: 10.1504/IJGAIB.2025.10074781
   
  
• Bottleneck to Breakthrough: A Cross-Sectoral Case Study on Transforming Knowledge Management with Retrieval-Augmented Generation-AI  
  by M.S. Narassima, Swadhithya V, Rajesh R. Kumar, Rahul S, Jashwanth K. Reddy 
  Abstract: In the current knowledge-oriented world, Subject Matter Experts (SMEs) are essential. However, their occasional unavailability leads to operational obstacles. This research examines the potential of Artificial Intelligence (AI) to bridge this from a socio-technical perspective. This offers a comparative analysis across seven industries for the deployment of a Retrieval-Augmented Generation (RAG) AI to address domain-specific problems. Using a mixed-methods framework, we investigated the effectiveness of RAG-AI compared to human subject matter experts in terms of response times and accuracy. The empirical evidence suggests that the AI mechanism enhances performance, reducing the response duration from 63 to 4 minutes, thereby showcasing remarkable progress in productivity. Conversely, the qualitative assessment indicates that although the AI demonstrates proficiency in retrieving and synthesising documented information, it yields answers that are only partially accurate for tasks necessitating contextual insight. The primary advantage of contemporary AI lies not in supplanting experts but rather in supplementing them.
  Keywords: Human-AI Collaboration; Retrieval-Augmented Generation; Knowledge Management; Socio-Technical Systems.
  DOI: 10.1504/IJGAIB.2025.10074790
   
  
• Generative Artificial Intelligence for Adopting Circular Economy in Supply Chains: Opportunities, Challenges, and Future Pathways  
  by Salim Eray Celik, Zhuowen Chen, Abdullah Y?ld?zbasi, Joseph Sarkis 
  Abstract: The circular economy (CE) and generative artificial intelligence (GenAI) can work together to address supply chain sustainability and sustainability issues generally. We provide a perspective on how GenAIs core capabilities technical intelligence, creative and cognitive augmentation, decision intelligence, and interaction may enable CE implementation using the ReSOLVE CE framework. Regenerative agriculture, corporate logistics, product design, and waste valorisation are some practice areas that will be used to illustrate how GenAI can support narrowing, slowing, and closing resource loops central principles of CE. Critical challenges such as limited GenAI circular awareness, social and environmental concerns, and validation may also constrain GenAI in CE and by extension to sustainable supply chains. Within this context, a forward-looking research agenda is proposed using various theoretical perspectives. This perspective aims to guide scholars, practitioners, and policymakers in leveraging GenAI as a catalyst for regenerative, inclusive, and scalable circular supply chains and a sustainable CE.
  Keywords: Generative artificial intelligence; circular economy; supply chain management; ReSOLVE; sustainability. .
  DOI: 10.1504/IJGAIB.2025.10074843
   
  
• The Dark Side of Artificial Intelligence  
  by Hokey Min 
  Abstract: As a powerful icon of the fourth industrial revolution, artificial intelligence (AI) has begun to transform our quality of life, work habits, and society as a whole. Such transformation brought a great deal of excitement and hope for better living environments in the future. Although AI can be a primary driver for enhancing human capabilities beyond what has been known and accomplished, we should exercise caution regarding AIs hype. Once blindsided by the AI hype, AI can make our lives more miserable than delightful. AI can present a range of concerns and risks, including job displacement, deepfakes, privacy invasion, misinformation, cybersecurity threats, and social manipulation with biased learning outcomes. Considering these concerns and risks, AI adopters should develop strategic action plans to mitigate the potential harms and risks of AI. With that in mind, this paper sheds light on the dark side of AI and proposes viable solutions that help maximise the benefits of AI.
  Keywords: artificial intelligence; strategic action plans; qualitative research; content analysis.
  DOI: 10.1504/IJGAIB.2025.10074973
   
  
• A Generative AI Design Framework for Performant-Influenced Decision Making using Fused Filament Fabrication and an Adaptive Neuro Fuzzy Inference System  
  by Ezekiel Yorke, Boppana Veeraiah Chowdary 
  Abstract: The manufacturing industry has witnessed a plethora of advancements in recent decades, notably in the fused filament fabrication (FFF) process. However, seeking ways of accurately predicting part performance for satisfying decision-making criteria based on manufacturing parameters has remained a challenge due to increased volumes of manufacturing process parameters. This research therefore sought to address this challenge by using generative artificial intelligence (GenAI) built on an adaptive neuro-fuzzy inference system (ANFIS) and a genetic algorithm (GA) approach. A design framework was subsequently applied to explore ways of mitigating business-decision uncertainty by analysing the compatibility between data acquisition, analysis, training and optimisation. Findings revealed benefits in the use of a GenAI-ANFS-GA solution for situations where noise or insufficient fuzziness in data existed. Recommendations for future research were also provided, which inferred the effectiveness of integrating such frameworks into existing workflows and therefore derive better business-oriented strategies toward more reliable output.
  Keywords: ANFIS; Generative artificial intelligence; Genetic algorithm; Fused filament fabrication; fuzzy machine learning.
  DOI: 10.1504/IJGAIB.2025.10074979
   
  
• Enhancing Competitiveness through Generative AI (Gen AI): a Literature Review and Conceptual Framework  
  by Himanshu Joshi, Pallavi Dhyani 
  Abstract: Generative artificial intelligence, also referred to as Gen AI, uses large language models and is trained on massive datasets to generate new content and ideas just like a human being. Gen AI is emerging as a game-changer for industries and organisations as they try this technology to build and enhance their competitiveness. This study employs a systematic literature review to explore the enablers, inhibitors, and various ways in which organisations capture the attainment of competitive advantage through the adoption of Gen AI. Utilising the technology-organisation-environment (TOE) framework, this study reveals: 1) key enablers and drivers for adoption of Gen AI tools, 2) key inhibitors that adversely impact adoption, and 3) the possible outcomes of Gen AI adoption for organisations. Lastly, we discuss the framework to develop an agenda for future research.
  Keywords: Generative artificial intelligence; Gen AI; enablers; inhibitors; competitive advantage.
  DOI: 10.1504/IJGAIB.2025.10075241