International Journal of Services and Standards (5 papers in press)

Regular Issues

• Empowering humanitarian supply chains: the transformative role of social media in disaster response and relief a systematic literature review  
  by Alka Pandey, Shradha Gupta, Behzad Mosallanezhad 
  Abstract: The purpose of this study is to systematically review the published papers on humanitarian supply chain management and recognise how social media plays a transformative role in disaster response. In this study, various data sources, such as published literature and articles, A systematic literature review was carried out using the PRISMA reporting checklist. Through a sample of 74 published pieces of literature, this study has shown that there is a limited study on the influence of social media on the HSCM. The study has shown the revolutionary impact of social media on humanitarian supply chain management. The incorporation of social media into humanitarian operations is of utmost importance in a world where crises, natural catastrophes, and wars continue to test our ability to provide timely and efficient relief. By facilitating instantaneous updates, volunteer mobilisation, and data-driven decision-making, social media is a potent instrument for communication, coordination, and information sharing.
  Keywords: humanitarian supply chain; social media; disaster relief; PRISMA.
  DOI: 10.1504/IJSS.2025.10076007
   
  

Special Issue on: The Impact of AI Chat GPT on Accounting and Auditing

• Using OWL technology to construct the knowledge framework of IFRS 17  
  by Kuo-hua Chou, Chia-cheng Su 
  Abstract: The effective date of the IFRS 17 Insurance Contract accounting standard has been postponed twice since its publication due to its huge structure and complex contents. To deal with complex accounting standards like IFRS 17, accounting professionals and academia need supplementary learning tools in order to more effectively master its core knowledge. This research uses OWL ontological technology to model the concepts of IFRS 17, and uses HermiT inference engine to infer the implicit knowledge embedded in the ontological framework. The research results show that the use of OWL technology can effectively construct the complex contents of IFRS 17, and the test cases also prove that the Protégé software and HermiT inference engine can perform knowledge inferences on the complex contents of IFRS 17. The results resemble an expert system prototype for IFRS 17.
  Keywords: IFRS 17; insurance contract; OWL; ontology; expert system; knowledge framework.
  DOI: 10.1504/IJSS.2025.10073517
   
  
• AI inconsistencies in solving accounting problems: implications for students and educators  
  by Rixing Lou, Yang Liu 
  Abstract: AI inconsistencies are technically inherent. When users encounter contradicting answers from different AI sources, they are encouraged to evaluate and reflect on the different responses. It is a compelling question to explore AI inconsistencies. We examine AI inconsistencies in an accounting setting, a field reliant on judgment and rule interpretation. We analyse responses from three leading AI chat tools - ChatGPT-4o, Gemini 2.5 Flash, and Claude Sonnet 4 - on 218 randomly selected accounting problems. Our findings reveal widespread AI inconsistencies, particularly in more complex questions, questions that have multiple parts, and questions that require special output formats. Our study contributes to AI and accounting research by shedding light on AI discrepancies and their impact on learning and educational applications.
  Keywords: AI inconsistency; accounting; education.
  DOI: 10.1504/IJSS.2025.10074147
   
  
• Document design and AI-driven data extraction: an exploratory study  
  by Jacob Peng  
  Abstract: Artificial intelligence (AI) has been gaining traction in business, with many ERP and RPA tools incorporating AI capabilities. Traditionally, Optical Character Recognition (OCR) has been used to digitise accounting tasks by scanning source documents and feeding the raw data into the accounting system, but AI now offers the potential to significantly improve this process. This research investigates the impact of document design on the accuracy of AI-driven accounting data extraction. Using invoices as a representative source document, I examine how visual complexity, colour choices, and background characteristics influence the performance of the Gemini AI platform. While AI demonstrates considerable potential in automating accounting tasks, my research underscores the importance of considering source document design factors that facilitate or hinder the automation of data extraction using AI. This study contributes to the ongoing development of AI in accounting and auditing by highlighting the interplay between document design and AI performance.
  Keywords: data extraction; Gemini; artificial intelligence; optical character recognition; OCR; accounting cycle.
  DOI: 10.1504/IJSS.2025.10075103
   
  
• Toward a framework for large action models in accounting: opportunities and challenges in the transition from LLMs to LAMs  
  by Chang-Wei Li, Chengyee Janie Chang 
  Abstract: Recent advances in artificial intelligence (AI) have driven a shift from passive large language models (LLMs) to large action models (LAMs), which integrate decision-making with autonomous execution (Wang et al., 2025). While LLMs excel in text-based tasks, LAMs bridge the gap between cognitive processing and real-world actions. Advancement in LAMs is crucial in business fields such as accounting and auditing, where AI-driven intelligence has the potential to enhance efficiency, improve accuracy, and assist in decision-making. This paper proposes a conceptual framework for LAMs in accounting automation, incorporating domain expertise and robotic process automation (RPA). We outline key architectural strategies and explain how they are integrated into the overall LAM system design. In addition, we present a simulated prototype that demonstrates how LAMs can automate practical accounting tasks. Furthermore, we examine opportunities in financial automation and compliance, as well as risks related to technical limitations, regulatory challenges, and system integration related to LAMs for future research.
  Keywords: large language models; LLMs; large action models; LAMs; robotic process automation; RPA; accounting; agentic AI.
  DOI: 10.1504/IJSS.2025.10075106