International Journal of Information Systems and Change Management (5 papers in press)

Regular Issues

• Accounting Information Quality and Information Systems: The Greek Case  
  by Dimitrios Mitskinis, Eftychia Stavrianidou, Nikolaos Antonakis, George Drogalas 
  Abstract: The purpose of this paper is to investigate the variables that affect the quality of accounting information provided by accounting information systems. For the purpose of this study, an electronic questionnaire was created and disseminated among private sector employees who use accounting information systems, forming a final sample of 134 questionnaires. The findings revealed that the quality of accounting information provided by the information system is positively and significantly affected by the use and the contribution of the information system. This research provides valuable insights for businesses, highlighting the critical factors that must be considered to ensure the effective utilisation of accounting information systems, thus guaranteeing the quality and reliability of accounting data. The study underscores the impact of specific technological factors on the quality of accounting information, offering a comprehensive view of the significance of accounting information systems in the modern management of data and decision-making within organisations.
  Keywords: Accounting Information Systems; Quality of Accounting Information; Independent Authority Public Revenue; Greece.
  DOI: 10.1504/IJISCM.2026.10079034
   
  
• Mitigating Cyber Risks in Information Systems through Machine Learning-Based Intrusion Detection against DDoS and Data Exfiltration  
  by Ajit Baral, Bijaya Paikray, Sumati Baral 
  Abstract: As cyber threats become increasingly sophisticated, traditional rule-based intrusion detection systems often fail to identify complex attacks such as Distributed Denial of Service (DDoS) and data exfiltration. This study presents a machine learning-based threat detection approach using the Random Forest algorithm to analyze and classify network traffic. Random Forest was selected for its robustness in handling imbalanced data, resistance to overfitting, and effectiveness in high-dimensional classification tasks. The model was trained and validated on the Verizon Communications dataset, achieving detection accuracies of 98.21% for DDoS attacks and 96.6% for data exfiltration attempts. Results validate the system's effectiveness in strengthening cybersecurity for critical healthcare infrastructure while maintaining adaptability to emerging threat vectors. These findings demonstrate the potential of machine learning, particularly Random Forest, as a scalable and accurate solution for real-time network threat detection and cybersecurity enhancement.
  Keywords: Network Traffic Analysis; Threat Detection; Machine Learning; Distributed Denial of Service (DDoS); Cybersecurity; Anomaly Detection; Network Security.
  DOI: 10.1504/IJISCM.2026.10080051
   
  
• From Culture to Capability: The Role of Leadership in Enabling AI Adoption Through Cultural Readiness  
  by Suryakant Sharma, Sunita Mehta 
  Abstract: In an era of Artificial Intelligence (AI), most organisations are accelerating AI adoption to improve performance with focus on technological aspects. This conceptual study draws on socio-technical theories and examines AI adoption from the social and psychological lens, highlighting the crucial role of leadership and cultural readiness. Existing studies have viewed culture as a broad contextual variable with limited insights into how leadership behaviours enable building cultural readiness for AI adoption. This study draws from transformation leadership, adaptive leadership, distributive leadership, social exchange theory (SET), technology-organisation-environment (TOE) framework, diffusion of innovation theory and proposes a conceptual model to identify specific behaviours that foster cultural readiness in enabling AI adoption. The study further examines how digital maturity and organisational structure moderate leaderships influence on AI adoption. This is an original contribution to the knowledge of AI adoption, organisational culture and leadership.
  Keywords: AI Adoption; Leadership Behaviours; Cultural Readiness; Digital Maturity; Organization Structure; Conceptual Paper.
  DOI: 10.1504/IJISCM.2026.10080053
   
  
• Exploring US Auditors Intention and Implementation of Blockchain: Antecedents and Outcomes  
  by Kada Meghraoui, Lionel Escaffre 
  Abstract: Blockchain technology has transformed auditing by improving transparency, security, and efficiency. This study examines the factors influencing auditors intentions to adopt blockchain and its subsequent implementation and outcomes, using an extended Unified Theory of Acceptance and Use of Technology (UTAUT) model. This study employed a quantitative research approach. Data were collected via a structured questionnaire from 116 US auditors. The theoretical model was analysed using partial least squares structural equation modelling. The results show that facilitating conditions (organisational support and resources) significantly influence adoption intention, while traditional UTAUT factors, such as performance expectancy and effort expectancy, do not. Compatibility issues have a significant negative impact on intention to adopt blockchain. In contrast, risk of fraud, environmental impact, and regulatory compliance do not significantly affect adoption intention. Moreover, the intention to adopt blockchain strongly predicts its implementation, which, in turn, enhances positive outcomes.
  Keywords: Blockchain technology; UTAUT; Auditing; Technology adoption; Facilitating conditions; Regulatory compliance; PLS-SEM; Adoption intention; Compatibility; Auditors.
  DOI: 10.1504/IJISCM.2026.10080701
   
  
• Will Users Keep Using Generative AI? An Integrated Theoretical Approach  
  by Arifur Rahman Khan  
  Abstract: This research investigated the key facilitators and inhibitors shaping users attitudes and continuance intention towards Generative Artificial Intelligence. Drawing on the Elaboration Likelihood Model and Status Quo Bias theory, the study developed an integrated conceptual framework. The proposed model was empirically validated using Partial Least Squares Structural Equation Modeling. The outcomes showed that perceived anthropomorphism, perceived credibility, perceived interaction quality, peer endorsement, and expert endorsement exerted substantial positive effects on continuance intention, whereas perceived inertia, perceived threat, and perceived regret avoidance had significant negative effects. By incorporating both enabling and barrier perspectives, this research extends the current literature on Generative AI continuance and provides managerial insights for platform developers, business organizations, practitioners, and policymakers seeking to foster sustained user engagement of Generative AI.
  Keywords: “Generative Artificial Intelligence” (AI); “Generative AI”; “Continuance Intention to Use”; “AI”.
  DOI: 10.1504/IJISCM.2026.10080766