Many governments worldwide are engaging into digital transformation initiatives to improve efficiency, effectiveness, cost, and transparency. Collaborative e-government processes offer a way to overcome the typical integration and interoperability issues of existing isolated e-government solutions. A study was conducted to help e-government modelers and architects to know current approaches to modeling collaborative e-government processes that consider integration and interoperability. The research questions are: Which kind of representations (architecture, framework, ontology, meta-model, model or process) are used to model these processes? Which concerns (cost, value, citizen, technology, organization) do they focus on? How do they address collaborative processes concepts (interoperability and collaboration)? This article describes the design, execution and results of a Systematic Literature Review (SLR) that gathered primary studies from well-known scientific literature databases, and organized them with a novel literature classification schema consisting of model type, model focus, collaboration scheme, and interoperability level. The initial search found 326 publications, of which duplicates removal and exclusion criteria application left only 52 for detailed analysis. Key findings are: literature for this topic proposes Frameworks and (general) Models, but not metamodels or ontologies; most addressed focus has shifted from Technology and Organization, towards Citizen; collaboration studies have shifted from Open Participation towards Data Transparency; and most work that addresses interoperability remains focused on Technical aspects with a smattering of Semantics and Organizational aspects. These findings reinforce the need for proposals that address the problem of collaborative e-government processes as something that lives at the junction of e-government, software architecture description, collaborative work, and interoperability.

As software systems increase in scale and complexity, architectural decisions must be transparent, traceable, and understandable to diverse stakeholders. However, traditional documentation approaches—such as standard Architectural Decision Records (ADRs)—often lack the structured rationale and contextual detail necessary to support informed analysis and long-term architectural stewardship. This paper presents the Software Architecture Explainability Framework (SAEF), a structured approach for enabling explainable architectural decision-making. Central to the framework is the Explainable Architectural Decision Record (ADR-E), which extends traditional ADRs with explicit rationale, structured stakeholder-oriented explanations, rejected alternatives, and traceability links grounded in explainability principles inspired by AI. SAEF was evaluated through two industrial case studies: the selection of Azure Kubernetes Service for container orchestration and the adoption of an enterprise-grade observability platform. Using a mixed-methods design combining workshops, scenario-based simulations, surveys, interviews, and operational metrics, the study found that ADR-E substantially improved transparency, traceability, and stakeholder alignment. Both cases reported a 30% reduction in mean time to resolution (MTTR) and transparency scores above 4.6/5. Overall, SAEF provides a practical and theoretically grounded foundation for explainable architectural decision-making. Future work will focus on tool support, graphical notations, and longitudinal assessments to enhance adoption and scalability.