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This paper contributes to developing a Method for Creating Persona Templates (MCPT), addressing a significant gap in user-centred design methodologies. Utilising qualitative data collection and analysis techniques, MCPT offers a systematic approach to developing robust and context-oriented persona templates. MCPT was created by applying the Design Science Research (DSR) methodology, and it incorporates multiple iterations for template refinement and validation among project stakeholders; all of the proposed steps of this method were based on theoretical contributions. Furthermore, MCPT was tested and refined within a real-life R&D project focusing on developing a digital platform e-marketplace for short agrifood supply chains in two iteration cycles. MCPT fills a critical void in persona research by providing detailed instructions for each step of template development. By involving the target audience, users, and project stakeholders, MCPT adds rigour to the persona creation process, enhancing the quality and relevance of personae casts. This paper contributes to the body of knowledge by offering an initial proposal of a comprehensive method for creating persona templates within diverse projects and contexts. Further research should explore MCPT’s adaptability to different settings and projects, thus refining its effectiveness and extending its utility in user-centred design practices. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Abstract
Influential nodes are the important nodes that most efficiently control the propagation process throughout the network. Among various structural-based methods, degree centrality, k-shell decomposition, or their combination identify influential nodes with relatively low computational complexity, making them suitable for large-scale network analysis. However, these methods do not necessarily explore nodes' underlying structure and neighboring information, which poses a significant challenge for researchers in developing timely and efficient heuristics considering appropriate network characteristics. In this study, we propose a new method (IC-SNI) to measure the influential capability of the nodes. IC-SNI minimizes the loopholes of the local and global centrality and calculates the topological positional structure by considering the local and global contribution of the neighbors. Exploring the path structural information, we introduce two new measurements (connectivity strength and effective distance) to capture the structural properties among the neighboring nodes. Finally, the influential capability of a node is calculated by aggregating the structural and neighboring information of up to two-hop neighboring nodes. Evaluated on nine benchmark datasets, IC-SNI demonstrates superior performance with the highest average ranking correlation of 0.813 with the SIR simulator and a 34.1% improvement comparing state-of-the-art methods in identifying influential spreaders. The results show that IC-SNI efficiently identifies the influential spreaders in diverse real networks by accurately integrating structural and neighboring information.

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