## 5 Discussion and Limitations  
The study presents a new approach to analyzing design methodologies for inclusive user interfaces in low-income countries. ERSA, formally defined as a system of interactive processes, allows for dynamic learning and developing design methodologies for inclusive user interfaces in low-income countries. The ERSA process applied to Orange allowed for a comprehensive examination of the characteristics, patterns, and implications of different design methodologies, contributing to a deeper understanding of design practices in inclusive user interfaces for low-income countries. The results indicate that although design methodologies vary, common principles and practices support effective design processes across different contexts. The presence of both simple one-step methodologies and more elaborate, iterative approaches highlights the need for versatile design strategies to accommodate diverse needs and constraints. The widespread occurrence of single-step methodologies suggests a common tendency to focus on specific aspects of the design process, potentially overlooking broader considerations. The findings indicate that researchers prioritize user-centered design principles, usability testing, or cultural adaptation. The presence of iterative approaches and experimentation in design methodologies reflects a commitment to user-centered and iterative design principles, which, though essential for ensuring the effectiveness and usability of solutions, highlight the challenges in achieving convergence toward optimal solutions. Conceptual and experimental models are central to user-based design methodologies, while mathematical models are poorly represented, and computational models are uniquely used in the physical domain. While computational modeling techniques exist for certain aspects of interface design, they are not utilized in design practices for inclusive user interfaces in low-income countries, neither for the user domain, functional domain, nor processes domain. Surprisingly, computational predictive modeling of user behavior is lacking. The results indicate that designers may be hesitant to adopt computational modeling. While computational modeling can offer numerous benefits, it also comes with its own challenges, including the complexity of developing and implementing models, integrating them into the design process, and interpreting the results. Indeed, computational modeling is complex and technically challenging. This perceived complexity may discourage designers from incorporating computational modeling into design processes, opting for simpler and more familiar methods instead. The rather large absence of mathematical models can also lead to a deficiency in computational modeling within the context of interface design. Mathematical models can enable designers to quantitatively describe and analyze various aspects of interface design, such as user behavior, interaction patterns, and usability metrics. Without these formal mathematical representations, it becomes challenging to systematically analyze and predict the performance of interface designs. In addition, without appropriate mathematical models, designers may lack the necessary tools to systematically evaluate the effectiveness, usability, and user satisfaction of interface designs. Mathematical models enable the integration of advanced techniques, such as machine learning, data analytics, and predictive modeling, into interface design. Without mathematical frameworks for representing and analyzing data, designers may be limited in their ability to utilize these advanced techniques for inclusive user interfaces in low-income countries. Designers often use intuition, creativity, and user feedback to iteratively refine and improve interfaces. Inclusive interface design involves subjective elements related to aesthetics, usability, and human perception, which are challenging to quantify and model computationally.