International Journal of Services and Standards (9 papers in press)

Regular Issues

• Decoupling economic growth and employment: the applicability of Okun’s law in China  
  by Wu Wen-Jie, Mustafa Rehman Khan, Kay-Hooi Keoy, Ai-Fen Lim 
  Abstract: China, the world’s second-largest economy, has a significant impact on global commerce, investment, and economic policy. China’s economic growth and financial achievements make the relationship between GDP growth and unemployment a topic of interest. Economists often employ ‘Okun’s law’ to investigate this relationship. The primary objective of this paper is to evaluate the validity of Okun’s law in China. This study applies Vector Autoregression method on annual time-series data from 2000 to 2020. The findings reveal that the traditional negative relationship between gross domestic product growth and unemployment does not hold in China, primarily due to structural shifts in the economy. This study highlights the need for adjusting development strategies to effectively address unemployment within China’s evolving economic landscape.
  Keywords: unemployment rate; economic growth rate; Okun’s Law; China.
  DOI: 10.1504/IJSS.2025.10074360
   
  
• Enhancing deforestation risk reduction and management through multi-layer learning: a probabilistic topic modelling of CDP survey responses  
  by Christian N. Madu, Picheng Lee 
  Abstract: This paper applies text mining and multi-layer learning to evaluate how global companies manage forest-related operational, regulatory, and reputational risks as categorised by the Carbon Disclosure Project (CDP). We analyse the responses of these companies to the CDP Forest-Risk Survey to derive tacit knowledge on risk reduction. The methodology adopted in this study offers new insights into sustainable forest management. We find that initiatives aimed at fibre reduction are intrinsically connected to overarching efforts to diminish operational risks. Effective management of regulatory risks requires collaboration across diverse areas. There is shared obligation of businesses to influence policy formulation and compliance. To mitigate reputational risks, companies focus on sustainable sourcing of materials as a response to consumer apprehensions regarding deforestation and environmental repercussions. The study also identifies distinct deforestation priorities tied to various qualitative aspects of operational risks. Through this analysis, certain knowledge, skills, and abilities often implicit can be inferred from the responses provided by participants in the CDP survey.
  Keywords: Carbon Disclosure Project; CDP; deforestation; forest; risk.
  DOI: 10.1504/IJSS.2025.10075102
   
  
• Charting the changing research landscape of ChatGPT: a comprehensive bibliometric analysis on current research and future research directions  
  by V.V. Devi Prasad Kotni  
  Abstract: This work enhances the current state of knowledge in the fields of artificial intelligence and ChatGPT by conducting bibliometric analysis on ChatGPT. The aim of this work is to examine the present patterns in the evolving research environment of ChatGPT by comparing the productive institutions, countries, and sources in ChatGPT research, as well as identifying the most influential keywords, author networks, top citing articles, and top cited articles of ChatGPT research. A total of 19,886 publications from the years 2022 to 2025 were obtained by searching the Scopus database using the term "ChatGPT." These articles were then subjected to bibliometric analysis using VOSviewer software. The findings of the study will be highly valuable for scholars, educators, and other individuals involved in the fields of artificial intelligence, chatbots, and ChatGPT. Further investigations in the field of conversational AI will motivate future researchers based on the research directions outlined in this article.
  Keywords: ChatGPT; chatbot; conversational AI; artificial intelligence; AI; bibliometrics.
  DOI: 10.1504/IJSS.2025.10075104
   
  
• From click to kerb in minutes: unpacking the Q-commerce boom and its impact on consumer wellbeing in India  
  by Pooja Darda, Om Jee Gupta, Sujit Kumar Patra 
  Abstract: Quick commerce (Q-commerce) is revolutionising online shopping in India, by delivering urgently needed goods within 10 to 30 minutes. Researchers have not thoroughly studied the factors influencing consumer acceptance of Q commerce in the Indian context, despite its growing popularity. To fill this void, this qualitative study examines Q-commerce adoption in India by analysing the trends, drivers, and consumer behaviour. We held 15 focus group discussions with 120 participants from varied socio-economic backgrounds and places across India, guided by the uses and gratifications theory (UGT). Our thematic analysis reveals eight key drivers of adoption that answer the question of adoption on a functional level and provide a precise understanding of how consumer behaviour and its long-term effects on consumer welfare affect uptake of Q-commerce in India. The research conclusions also hold serious consequences for scholars, businesses and the governments that have intend to control the rapidly evolving e-commerce market in India. Our study sheds light on the complex nature of factors that mediate the adoption of Q-commerce and provides a foundation for research and practical recommendations to advance the consumer uptake and development of this booming sector.
  Keywords: quick commerce; Q-commerce; rapid delivery; consumer behaviour; online shopping; consumer well-being; digital transformation; hyperlocal delivery.
  DOI: 10.1504/IJSS.2025.10075105
   
  
• Innovative performance enhancement via cross-border mergers and acquisitions: an empirical study based on Chinese acquirers  
  by Wenxin Guo 
  Abstract: One posited mechanism behind the internationalisation of Chinese multinationals is that they have frequently engaged in cross-border acquisitions that explore new capabilities in host nations. However, it remains unclear whether Chinese multinationals can achieve this goal, given their limited advanced knowledge and the complexities of international activities. Accordingly, this study contributes to existing literature by empirically examining the impact of cross-border mergers and acquisition activities on the innovative capability development of Chinese acquirers. Based on data of 329 Chinese firms during 2000-2010, findings suggest that their cross-border mergers and acquisition activities lead to higher innovative performance in subsequent years. In addition, this innovative performance enhancement results more from acquiring foreign targets in high-tech industries than those in low-tech industries, more from acquiring related foreign targets than unrelated foreign targets, and from acquiring foreign targets in developed nations rather than those in developing nations.
  Keywords: international mergers and acquisitions; explorative learning; innovative performance enhancement.
  DOI: 10.1504/IJSS.2025.10075107
   
  

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