(15 papers in press)

Regular Issues

• The social life of large language models: parasocial rupture and the retirement of GPT-4o  
  by Dag Øivind Madsen  
  Abstract: This article develops a conceptual framework for understanding parasocial rupture in large language model (LLM) transitions, focusing on OpenAI’s GPT-4o. Drawing on parasocial relationship theory, affective computing, and technology domestication, it develops the concept of the affective contract to describe users’ implicit expectations of stylistic and relational continuity in conversational AI. The analysis traces the August 2025 GPT-4o to GPT-5 rollout and the February 2026 retirement of GPT-4o from the ChatGPT interface, while preserving API availability. It argues that user backlash cannot be reduced to performance complaints alone; it also reflected perceived persona discontinuity and reduced agency. Petitions, hashtags, and grief-like discourse suggest that LLMs can acquire social lives extending beyond technical function. The paper argues that relational continuity has become a distinct dimension of platform stewardship in model transitions.
  Keywords: large language models; LLM; GPT-4o; parasocial rupture; affective contract; AI governance; model retirement; platform transitions.
  DOI: 10.1504/IJGAIB.2026.10079506
   
  
• Assessing technological and socio-economic barriers to online learning in developing nations: AHP-TOPSIS approach  
  by Yamini Verma, Arishu Kaushik, Doordarshi Singh, Tejinderjeet Singh 
  Abstract: This research highlights the extent to which COVID-19 forced the educational institutions worldwide to shift abruptly from traditional classroom teaching to online instruction mode. The aim of this analysis is to spot the major issues that students experienced in online teaching due to the pandemic situation. Using a structured survey with online learners, data were collected through a specially designed questionnaire to identify major constraints. This study highlights the technological, financial and superfluous barriers encountered in online teaching during the pandemic. AHP-TOPSIS approach has been applied to rank each barrier according to its weight. An explicit idea to overcome the obstacles has also been suggested. The proposed framework enables decision-makers to identify and prioritise critical barriers to online learning and formulate targeted interventions. Its applicability extends beyond India to other developing nations experiencing comparable digital infrastructure and socio-economic constraints.
  Keywords: online learning; educational institutions; online education; barriers; COVID-19; online classes; AHP; TOPSIS.
  DOI: 10.1504/IJGAIB.2026.10080799
   
  
• Human-AI dynamics in the front-end of innovation: generative AI strategies in concept development  
  by Tuomas Hongisto 
  Abstract: This study examines how Generative AI (GenAI) is utilized across the dynamic Front-End of innovation. While GenAI enhances creative problem-solving, its role in strategic innovation processes remains underexplored. Using a multiple case study approach, data was collected from eight high-education student teams during an innovation event through interaction logs, surveys, and project artifacts. Findings show a U-shaped pattern in GenAI engagement: high during initial information gathering and final concept refinement but dropping significantly during problem framing. GenAI effectively accelerates divergent thinking and data synthesis but offers limited support for convergent thinking and strategic decision-making. Furthermore, three collaboration archetypes emerged: Co-Creators, Analysts, and Assistants. Research suggests that while GenAI accelerates execution, it cannot replace human capability in defining complex problems. These insights inform how GenAI can be integrated into early-stage business innovation and management training.
  Keywords: creative problem-solving; front-end of innovation; generative AI; multiple case study; team-based learning.
  DOI: 10.1504/IJGAIB.2026.10080946
   
  
• Cybersecurity Risk Management in Healthcare Organisations: AI Powered Solutions for Safeguarding Patient Data and Medical Infrastructure  
  by Anzir Al Akmam, Paul Reaidy 
  Abstract: Modern healthcare institutions are easy to target by cyber threats due to the sensitivity of patient information and the need for uninterrupted healthcare delivery.This article shows how AI based deception technologies, such as honeypots and honeytokens, can enable better cybersecurity in the healthcare sector. AI, through machine learning and predictive analytics, will physically determine and repel cyber threats to patient data and medical devices. The work carries out a literature review and several case studies in different sectors looking for inspiration and challenges for AI solutions. It reveals how information technology, particularly artificial intelligence, is improving the ability to detect, prevent, and/or respond to real time security threats within healthcare organizations. Implementing AI into current cybersecurity strategies involves high capital investments and improving organizational culture.
  Keywords: Healthcare Cybersecurity; Machine learning; AI deception technology; Cyber threat mitigation; Real time threat detection.
  DOI: 10.1504/IJGAIB.2026.10081063
   
  
• Measuring the Incremental Value of LLM-Enabled Prescriptive Maintenance: A Resource-Constrained Attribution Framework  
  by Fatima Khadija Benzine, Samia Beldjoudi, Iskander Zouaghi, Hassina Seridi 
  Abstract: Generative artificial intelligence and large language models are increasingly used to transform predictive maintenance outputs into maintenance recommendations. However, the economic contribution of this recommendation layer remains difficult to establish, because observed performance improvements may also result from sensor coverage, prediction accuracy, or workflow integration. This paper develops RAVE, a Resource-Aware Value Evaluation framework for assessing LLM-enabled prescriptive maintenance recommendations. The framework evaluates recommendations through four sequential stages: operational quality, resource feasibility, expected net value, and component-level attribution. It combines expert-based recommendation assessment, operational resource constraints, counterfactual comparison with operator baseline decisions, and attribution across sensors, prediction models, recommendation generation, and workflow integration. The framework is demonstrated through maintenance-event cases comparing operator baseline decisions, unconstrained LLM recommendations, and RAVE-filtered recommendations. The study contributes to prescriptive maintenance and AI-enabled decision-support research by providing a structured method for assessing whether LLM-generated recommendations add measurable value beyond existing predictive maintenance capabilities.
  Keywords: Large Language Models; Prescriptive Maintenance; Predictive Maintenance; Business Value; Resource Constraints; Counterfactual Evaluation; Value Attribution.
  DOI: 10.1504/IJGAIB.2026.10081190
   
  
• Reframing Entrepreneurship in the Age of Generative AI: Process Transformation, Startup Lifecycle Dynamics, and Future Research Directions  
  by Alka T. A, M. Suresh 
  Abstract: This research systematically reviews how generative artificial intelligence (GenAI) is transforming entrepreneurial processes and the startup lifecycle, and identifies existing gaps to guide future research. The study reveals that GenAI reconfigures opportunity recognition, experimentation, and business model development, and venture growth by knowledge creation, creative augmentation and simulation, and intuition-based process transition to improve efficiency, innovation, and strategic agility. GenAI supports startups development, such as ideation, validation, and growth, by minimising resource constraints, enabling experimentation, and scalable business models. The focus on this multi-level, process-oriented, and stage-based framework comprises micro-level (human-AI collaboration), meso-level (performance and capability development), and macro-ecosystem-level (policy and governance) dimensions that contribute to policymakers for the development of transparent platform governance standards and contributes to theory by providing a lifecycle view of how GenAI can be adopted in the entire value chain, with opportunities and challenges such as privacy concerns, ethical issues at the same time.
  Keywords: Generative AI; Entrepreneurship; Startup Lifecycle Dynamics; Business model; Innovation.
  DOI: 10.1504/IJGAIB.2026.10081315
   
  
• Generative AI-Driven Business Transformation:: a Multi-Industry Empirical Analysis of Adoption Patterns, Performance, Outcomes and Governance Challenges  
  by Harsimran Singh Sodhi  
  Abstract: The rapid adoption of generative artificial intelligence (GenAI) is transforming how organisations create and capture value, yet its business impact remains inconsistent. This study examines how GenAI adoption intensity, configuration choices, and governance maturity influence organisational performance across five industry sectors. Using an explanatory sequential mixed-methods design, data were collected from 250 organisations and complemented by 30 semi-structured executive interviews. Structural equation modelling revealed that GenAI adoption significantly enhances operational efficiency, innovation performance, and competitive advantage. Among configuration approaches, augmentation produced the strongest innovation outcomes, while automation delivered superior operational efficiency. Governance maturity significantly strengthened the relationship between GenAI adoption and business value. Qualitative findings further identified humanAI collaboration, configuration fit, and data readiness as critical success factors. The study extends the Resource-Based View and Dynamic Capabilities Theory by establishing AI governance capability as a key driver of sustainable GenAI-enabled business value.
  Keywords: Generative AI; business transformation; empirical analysis; AI governance; competitive advantage; human-AI collaboration; dynamic capabilities.
  DOI: 10.1504/IJGAIB.2026.10081543
   
  
• Integrating artificial intelligence in medical assistant programs: a systematic review  
  by Manish Das, Dola Sarkar, Purnendu Mandal 
  Abstract: Rapid integration of artificial intelligence (AI) into healthcare settings has created an urgent need for medical assistant training programs to evolve beyond traditional competency frameworks. This systematic review synthesises evidence from 20 peer-reviewed studies published between 2020 and 2026 to evaluate the range, quality, and effectiveness of AI-integrated training tools and educational interventions for medical assistants. A comprehensive search of major academic databases was conducted, covering publications from January 2015 to March 2026. Studies from several countries were selected and appraised using PRISMA 2020 framework and ROBINS-I risk-of-bias tool. Key instruments identified include AI competency self-assessment scale, comprehensive ai competency questionnaire, and AI training scale, all demonstrating satisfactory reliability and validity in their respective contexts. Findings from recent 2024-2026 studies further confirm that faculty unpreparedness, inadequate standardised curricula, and infrastructure inequities remain primary barriers to AI integration. Ethical AI education and longitudinal assessment frameworks remain critically underdeveloped.
  Keywords: artificial intelligence in healthcare education; medical assistant training; AI competency assessment; clinical simulation; AI-integrated curriculum; healthcare workforce readiness; PRISMA systematic review.
  DOI: 10.1504/IJGAIB.2026.10081629
   
  
• A scientometric review of optimisation strategies in fused filament fabrication: from traditional methods to generative AI approaches  
  by Ezekiel Yorke, Boppana Veeraiah Chowdary 
  Abstract: Generative AI (GenAI) technology along the auspices of machine learning (ML) techniques has ushered in many opportunities for advancing manufacturing applications. However, a gap exists in discerning the critical factors that have been involved in adopting GenAI for smarter decision-making. Hence, a scientometric review was conducted to highlight transitionary trends from conventional to ML-driven techniques in the context of optimisation strategies. Three research questions as well as analytical tools were developed to address the objectives of this study over an analytical period of two decades. Thematic findings highlighted increased emergence in ML applications while showing an inverse in conventional practices. Results further identified evolutionary patterns of these trends, which constituted three epochs, namely the conventional, transition and integrated stages. The study concluded that while these observations reinforced the emergent presence of ML systems for smarter decision-making, managerial steps must be taken to facilitate the integration of more sustainable manufacturing ecosystems.
  Keywords: 3D printing; additive manufacturing; fused filament fabrication; FFF; generative artificial intelligence; GenAI; machine learning.
  DOI: 10.1504/IJGAIB.2026.10081630
   
  
• Generative and agentic artificial intelligence as enablers of circular economy transformation in agri-food supply chains  
  by Sudhanshu Joshi, Tanuja Joshi, Manu Sharma, Gaurav Joshi, Durgesh Pant 
  Abstract: Adopting circular agri-food systems is essential for combating resource depletion, food waste, environmental degradation, and sustainability issues. There are several challenges to implementation, however, such as chain complexity, perishability, uncertainty of demand, fragmented stakeholder coordination, and low decision intelligence. Generative artificial intelligence (Gen AI) and agentic artificial intelligence (agentic AI) provide the ability to predict, make decisions and coordinate. This study provides a systematic analysis of the role these technologies can play in the implementation of circular economy in agri-food supply chains, formulates a conceptual approach on the relationship between the various AI technologies and the circular outcomes in agri-food value chains, and outlines policy implications. Literature review was carried out according to PRISMA guidelines. The five themes that emerged were resource optimisation and sustainable production, optimisation of the circular supply chain, generative AI and intelligence, autonomous AI orchestration, and circular innovation and ecosystem transformation. The framework is aligned with the principles of RESOLVE and illustrates the possibilities for the use of AI in resource management, valorisation of waste, regenerative agriculture, closed-loop integration, resilience, sustainability and regenerative agri-food systems by means of predictive and autonomous functions.
  Keywords: generative artificial intelligence; Gen AI; agentic artificial intelligence; agentic AI; circular economy; closed-loop supply chain; agri-food supply chain; RESOLVE; sustainability.
  DOI: 10.1504/IJGAIB.2026.10081631
   
  
• Developing an AI-enabled Lean Six Sigma 4.0 framework for smart manufacturing systems  
  by Harsimran Singh Sodhi 
  Abstract: The integration of artificial intelligence (AI) with Lean Six Sigma (LSS) marks a significant advancement in manufacturing quality management, though validated frameworks remain scarce, particularly for small and medium enterprises (SMEs). This study proposes an AI-LSS 4.0 model that systematically enhances each phase of the DMAIC cycle using targeted AI technologies: natural language processing for the define phase, internet of things and computer vision for the measure phase, machine learning for the analyse phase, digital twins with reinforcement learning for the improve phase, and predictive maintenance for the control phase. The model is validated through a 12-month longitudinal case study conducted in an automotive machining SME using real-time CNC production data. Results demonstrate substantial performance improvements, with defect rates reduced from 4.2% to 0.3%, cycle time decreased by 38%, and process sigma level improving from 3.8 to 5.6. Statistical tests confirm the model's effectiveness relative to traditional LSS implementations. The findings highlight AI's potential to overcome limitations related to data processing, bias, and scalability, offering a robust and replicable framework for Industry 4.0 applications. The study further examines the emerging role of generative AI in quality management and proposes pathways toward GenAI-LSS 5.0 integration.
  Keywords: artificial intelligence; Lean Six Sigma; LSS; Industry 4.0; DMAIC; manufacturing; case study; quality management; predictive analytics; Industry 5.0; cyber-physical systems; generative AI; Quality 4.0.
  DOI: 10.1504/IJGAIB.2026.10081007
   
  
• AI-powered circular economy by enhancing resource efficiency and waste reduction  
  by S. Catherine Rex, K.P. Sajana, Seema Balan, Babitha Lucin Rani 
  Abstract: The transition to a circular economy necessitates innovative strategies to enhance resource efficiency and minimise waste. This article explores the transformative potential of artificial intelligence (AI) in achieving these goals. By integrating AI technologies into circular economy practices, organisations can optimise resource use and reduce waste through advanced predictive analytics, real-time monitoring, and intelligent automation. This conceptual framework bridges the resource management, waste reduction with AI-driven tools. It also examines the economic and environmental benefits derived from AI integration, highlighting how it contributes to cost savings and improved sustainability performance. To fill the gap in research this article employs a mixed-methods approach, combining quantitative analysis of resource efficiency and waste reduction metrics with qualitative insights from case studies and expert interviews to understand the generative AI's role in enhancing the circular economy. The findings suggest that AI integration significantly improves resource efficiency and waste reduction, offering both economic and environmental advantages. This article aims to offer practical suggestions for practitioners and policymakers to leverage AI in advancing circular economy goals, ultimately contributing to more sustainable and efficient business practices.
  Keywords: artificial intelligence; circular economy; resource efficiency; waste reduction; predictive analytics; sustainability.
  DOI: 10.1504/IJGAIB.2025.10079303
   
  
• The 7Vs of AI slop: a typology of generative waste  
  by Dag Øivind Madsen, Richard W. Puyt 
  Abstract: The rise of generative AI has spawned a new term in digital culture: AI slop. It describes the torrent of low-quality, machine-made content flooding platforms - from bland essays and clickbait to uncanny images, deepfake porn, and synthetic video. This paper proposes a framework of seven dimensions, the 7Vs: volume, velocity, variety, value, verification, visibility and virality. These explain how generative waste proliferates, dominates attention economies, and threatens knowledge systems, creative industries, and democratic discourse. Drawing on theories of platform capitalism, information overload, and epistemic pollution, the paper argues that slop is not a temporary glitch but a structural feature of contemporary media. The 7Vs typology offers a basis for analysing and mitigating AI-generated waste across cultural, academic, commercial, political, and organisational contexts.
  Keywords: AI slop; generative waste; platform capitalism; epistemic pollution; media ecology.
  DOI: 10.1504/IJGAIB.2026.10081546
   
  
• Synthetic dissonance theory, how generative AI use produces creativity devaluation and impostorism  
  by Rashid Shar Baloch 
  Abstract: Through the lens of synthetic dissonance, this study investigates how frequent use of generative artificial intelligence alters creative self-perception. The psychological tension that arises when people depend on algorithmic systems that both support and replace human creativity is known as synthetic dissonance. Building on this framework, the study examines whether the frequency of AI use influences perceived creative agency through two important mechanisms; algorithmic inferiority and AI-induced impostorism. The findings indicate that increased perceptions of algorithmic superiority and stronger impostor syndrome are linked to more frequent use of generative AI. These psychological reactions diminish individuals' perception of creative authorship, ownership, and self-trust. GenAI may improve output, but it diminish creative identity. By theorising synthetic dissonance as a unifying construct, this study deepens understanding of the psychological consequences of human AI interaction and underscores the need to evaluate creativity not only in terms of performance, but also in terms of agency and identity.
  Keywords: synthetic dissonance; GenAI; perceived creativity; AI induced impostorism; algorithmic inferiority; creative agency; human-AI interaction.
  DOI: 10.1504/IJGAIB.2026.10078393
   
  
• Generative AI-enabled business model innovation: pathways to sustainable competitive advantage in the digital economy  
  by Ravinder Rena, Nageswara Rao Aderla 
  Abstract: This study examines how generative artificial intelligence (GenAI) enables business model innovation and contributes to sustainable competitive advantage in the digital economy. It introduces the concept of generative AI strategic capability (GASC), positioning GenAI not as a standalone technological tool but as a higher-order organisational capability, one that reshapes how firms configure resources, generate knowledge, and compete. Drawing on resource-based view, dynamic capability theory, knowledge-based view, and socio-technical systems theory, the study proposes that GenAI capability influences firm performance indirectly, operating through business model innovation and competitive repositioning rather than any direct performance effect. A qualitative, exploratory research design using thematic analysis is employed to develop the conceptual framework. A consistent finding, and one that practitioners may recognise even if they struggle to act on it, is that organisations achieving genuine alignment between GenAI infrastructure, workforce capability, and ethical governance are far more likely to realise long-term performance benefits. The research contributes to strategic management and AI literature by providing an integrated framework that links GenAI capability to enterprise transformation. The findings are intended to offer something practically useful to managers, policymakers, and educators navigating AI-driven change.
  Keywords: generative AI; GenAI; business model innovation; BMI; competitive advantage; digital transformation; digital economy; responsible AI; enterprise innovation.
  DOI: 10.1504/IJGAIB.2026.10080948