International Journal of Information Systems and Change Management (4 papers in press)

Regular Issues

• Improving Software Modularity Using Many-Objective NSGA-III Algorithm by Utilising Many Quality Parameters  
  by Naveen Sharma, Randeep Singh, Amit Rathee 
  Abstract: Software modularity aims to ensure high cohesion within modules and low coupling between them. However, prolonged maintenance often leads to modular degradation and increased system complexity. To address this, software re-modularisation is employed, though it remains a challenging task due to conflicting design objectives. This paper presents a novel re-modularisation approach that relocates classes to more appropriate modules using eight carefully formulated design quality criteria, ensuring both structural improvement and semantic preservation. A many-objective metaheuristic, NSGA-III, is applied to optimise these criteria via move class refactoring. The approach is evaluated on seven open-source software systems, demonstrating significant improvements in modular quality with minimal structural changes. Furthermore, the re-modularisation problem is formulated and assessed under single-objective, multi-objective, and many-objective scenarios using Bunch-GA, NSGA-II, and MOEA/D, respectively. The empirical results of the proposed technique proves effective and practical, particularly in maintenance contexts where full re-modularisation is cost- and/or time-prohibitive.
  Keywords: Software re-modularisation; Search-based Software Engineering (SBSE); Many-objective optimisation; Semantic Coherence; Maintenance effort; NSGA-III; Cohesion; and Coupling.
  DOI: 10.1504/IJISCM.2025.10074065
   
  
• Examining the Key Determinants of AI Readiness in Africa  
  by Ibrahim Alhassan, Ibrahim Osman Adam 
  Abstract: Artificial intelligence (AI) is increasingly recognised as a driver of socio-economic development, yet gaps persist regarding its readiness in developing economies. Existing frameworks, primarily developed for high-resource contexts, are ill-suited to Africa’s unique resource-constrained setting. We applied the technologyorganisation- environment (TOE) framework and partial least squares structural equation modelling (PLS-SEM) to assess AI readiness across 54 African countries using data from the Oxford Insights Government AI Readiness Index. Our analysis identified digital capacity and human capital as the most significant determinants of AI readiness, followed by governance, with infrastructure exerting the weakest influence. The validated model confirms the utility of the TOE framework at the national level, and offers strategic insights for policymakers, highlighting the necessity of prioritising investments in digital capacity, human capital, and governance reforms. Notably, this study is the first large-scale, context-specific assessment of African AI readiness employing these methodological approaches.
  Keywords: Artificial intelligence; AI readiness; TOE framework; Digital capacity; Africa.
  DOI: 10.1504/IJISCM.2025.10075301
   
  
• OWDRN: Optimised, Weighted, and Distributed Recurrent Neural Network for Product Recommendation in E-Commerce  
  by Bharati Wukkadada 
  Abstract: Product recommendation suggests the intended and most useful appealing products based on customers' preferences and personalised experience. Based on these characteristics, several studies were conducted and ended with certain disadvantages as scalability, sparse data, cold start issues, computational complexities, and generalisability problems. These aforementioned problems are significantly addressed by proposing a model named Optimized, Weighted, and Distributed Recurrent Neural Network (OWDRN) for product recommendation. Further, the OWDRN model captures the sequential dependencies of user-item interactions using attention layers and embedding that provide better prediction accuracy. Additionally, the model improves performance by Menura honey optimisation (MHO), which reduced the local optima issues and achieved a better convergence rate specifically. Meanwhile, the distributed nature of the model allows the scale efficiency across multiple systems, ensuring robustness and accuracy in various conditions. Thus, the OWDRN model achieves high performance, with an accuracy of 96.84%, f1-score of 96.84%, precision of 97.21%, and recall of 96.48% under TP 90, offering highly personalized product recommendations.
  Keywords: Product Recommendation System; Distributed Neural Network; E-Commerce; Social Networking; Nature-inspired optimisation.
  DOI: 10.1504/IJISCM.2025.10075319
   
  
• How will Communication on Digital Platforms affect Firms' Product innovativeness?  
  by Iwan Koswara, Teddy Kurnia Wirakusumah, Asep Saeful Rohman 
  Abstract: Companies consider it more crucial to utilise digital platforms, especially social media, to enable access to knowledge, receive valuable input, and enhance innovativeness to meet growing market needs. Nevertheless, harnessing social media without incorporating other crucial elements cannot cause effective innovativeness endeavours. Hence, this research examines the capacity of social media communication (SMC) to augment environmentally friendly product innovativeness (EFPI) and the impact of two ecological collaborations on this connection. To investigate the suggested correlation, a structural equation modelling was used to evaluate information obtained from 256 manufacturers in Indonesia. Our findings indicated that SMC did not directly influence EFPI. Besides, the connection between SMC and EFPI is completely mediated by green cooperation within the organization (GCO) and green cooperation with supply partners (GCS). Further analysis revealed a strong correlation between GCO and GCS, indicating that both forms of ecological cooperation are crucial elements in enhancing EFPI.
  Keywords: Digital communication; manufacturing firms; environmentally friendly product innovativeness; ecological knowledge; ecological collaboration; social media.
  DOI: 10.1504/IJISCM.2025.10075477