Current issue, 26

An integrated model of UTAUT to understand digital accounting systems acceptance: A hybrid PLS-SEM-artificial neural network modelling approach Hamza Alqudah, Adel M. Qatawneh, Abdalwali Lutfi, Sajead Mowafaq Alshdaifat, Ahmad Farhan Alshira’h, Hosam Alden Riyadh, Malek Hamed Alshirah, Mohamed Saad, Adi Alsyouf Published February 2026
In the era of the fourth industrial revolution, the digitalisation of small and medium-sized enterprises (SMEs) is imperative for their sustainable growth. Employing digital technologies, including digitisation and digital transformation, is essential for enhancing operational efficiency within enterprises. An adapted model derived from the Unified Theory of Acceptance and Use of Technology (UTAUT) was employed in this study to investigate the behaviour of accountants towards adopting digital accounting systems (DAS). By employing a descriptive cross-sectional survey design, a quantitative study was conducted. The extended UTAUT model functioned as the foundation for the theoretical framework of the study and incorporated the concept of personal innovativeness. A two-step process, combining partial least squares structural equation modelling (PLS-SEM) with artificial neural network (ANN) techniques, was applied to analyse the dataset. The findings demonstrated that the study variables, specifically effort expectancy, social influence, facilitating conditions, and personal innovativeness, positively influence the intention of accountants to adopt DAS. Interestingly, the impact of performance expectancy on this intention was statistically insignificant. Furthermore, personal innovativeness exhibited a significant correlation with the intention to adopt the system, thereby emphasising the necessity of endorsing technologies such as DAS within SMEs. By confirming the impact of personal innovativeness on adoption intention and highlighting the precise measurement framework in Jordanian SMEs, the findings of the study contribute to UTAUT theory. Keywords: behavioural intention; effort expectancy; digital accounting system; personal innovativeness; artificial neural network (ANN); PLS-SEM.
FinTech collaboration networks and the digital transformation of financial services Alisher Mansurov and Marc Pilon Published March 2026
The diffusion of financial technologies (FinTech) in financial services has drawn worldwide attention. However, little is known about how different types of enterprises contribute to the development and transformation of this sector. In this study, we conducted a network analysis of 23,000 FinTech news articles (2008–2022) to map and characterize the global network of FinTech enterprises. We also performed a textual analysis to identify evolving FinTech trends and to differentiate collaboration dynamics between enterprise types. We found that traditional financial services providers are central to the FinTech network, while technology giants, regulatory bodies, and FinTech start-ups have gained increasing prominence. Moreover, ties between similar enterprises are stronger, although these connections have weakened over time. Textual analysis reveals shifting FinTech priorities and distinct collaboration patterns between enterprise groups.
A decision framework for procurement fraud detection: Wisdom from academia and industry Hanchi Gu and Shaoyu Liu Published May 2026
Procurement fraud poses significant financial and reputational risks to organizations, yet existing efforts to address it are fragmented between academia and industry. While academic research has proposed sophisticated fraud detection models using machine learning and data analytics, these solutions often lack practical applicability due to limited guidance for practitioners. In this study, we address this gap by proposing a decision framework for procurement fraud detection, synthesizing insights from existing literature and a real-world data analytics project with a global brewing company. We identify three critical decision problems in developing fraud detection models: (1) constructing fraud indicators, (2) determining the aggregation level, and (3) selecting the model validation method. By evaluating alternatives for each decision, we offer practical solutions that organizations can tailor to their unique procurement processes and risk profiles. The proposed framework combines the knowledge from literature and practical insights, offering actionable guidance for practitioners while bridging gaps between academic research and industry practice. This study contributes to the field by formalizing decision-making challenges in procurement fraud detection and fostering collaboration between academia and industry.
Modelling audit risk with AI and explainability: Cross-country evidence from emerging and mature markets Salah Kayed, Ayman Bader, Abdulhadi Hamid Ramadan, Amer Morshed, Atala Al Qtish, Almotasem Bellah Alhuniti Published June 2026
This study examines how artificial intelligence (AI) compares with traditional econometric models in predicting audit risk across two institutional contexts: the United Arab Emirates (UAE) and the United Kingdom (UK). Using firm-level data from 2017–2024, audit risk is modelled using financial, governance, audit, and market factors. Logistic and probit regressions serve as econometric benchmarks, while Random Forest, XGBoost, and deep neural networks represent AI methods. Explainable AI (XAI) tools—such as SHAP and LIME—enhance interpretability and regulatory transparency. Findings show that AI models consistently outperform econometric ones in accuracy, recall, and AUC across both countries. Yet, key audit risk drivers vary: governance factors like board independence and ownership concentration dominate in the UAE, while financial indicators and Big 4 affiliation are more influential in the UK. Explainability tools clarify predictions, boosting trust and regulatory alignment. Cross-country transferability tests reveal lower accuracy outside the original setting, emphasizing institutional specificity. Overall, the study demonstrates that effective audit risk prediction requires locally adapted, transparent AI models that combine predictive strength with interpretability to enhance auditor and regulator confidence.