Abstract
The integration of AI in supply chains offers opportunities to enhance efficiency, sustainability, and decision-making. However, effective implementation requires attention to both technical and socio-technical aspects. This study examines AI maturity in the pulp and paper sector using the SC-STAI profiling tool, assessing AI integration across technical, social, human, and organizational domains. Based on nine case studies from Brazil and Portugal, the research identifies key areas for improvement and highlights uneven AI adoption. Findings show that performance and resilience are most impacted, while job role adoption remains the lowest. The study emphasizes the importance of Socio-Technical AI Maturity Models in guiding responsible AI adoption and improving socio-technical alignment in supply chains, contributing to a better understanding of AI readiness in traditional industries and demonstrating the SC-STAI tool's applicability for strategic AI planning.

Abstract
Artificial Intelligence (AI) integration in supply chain systems is growing, and with it grows its potential impact on inter-organisational collaborative networks. We review existing literature on how different AI archetypes (Reflexive, Anticipatory, Supervisory, Prescriptive) could support Collaborative Supply Chain Management (CSCM) activities, and how they impact information sharing, collaborative decision-making, and trust among supply chain partners at different integration levels. Adopting a sociotechnical perspective, we synthesise existing literature and map the archetypes along four levels of AI integration, varying in scope and decision autonomy. The results are conceptual frameworks demonstrating how AI impacts collaboration dynamics as it evolves from a decision-support tool to an autonomous coordination agent. Findings show differentiated effects along archetypes and integration levels, with implications for CSCM governance, transparency, and resilience. We contribute to the discussion on human-AI collaboration in CSCM and offer a baseline for research on the human-centric values of Industry 5.0.

Abstract
Digital Transformation Models (DTM) and Digital Maturity Models (DMM) are two artefacts that guide the planning and implementation of Digital Transformation (DT) initiatives. When used in a combined approach, a DTM-DMM pairing could support DT managers and practitioners, as DTs are holistic and complex initiatives with high failure rates. However, no study has yet systematically addressed the compatibility amongst artefacts. This paper, therefore, aims to analyse the compatibility between academic DTMs and DMMs. Based on architectural compatibility and conceptual similarity, we provide a structured and replicable mixed methods approach to assessing artefact compatibility. To achieve this, we start with a systematic literature review to identify existing academic DTMs and DMMs, analyse the models and group them according to their scope. After, we employ quantitative similarity analysis techniques (Term Frequency-Inverse Document Frequency and Bidirectional Encoder Representations from Transformers combined with Cosine Similarity) and perform a qualitative compatibility analysis to establish ground truth. Based on this analysis, we apply the Receiver Operating Characteristic Curve technique to define threshold values for compatibility assessment. The threshold values were used to suggest compatible DTM-DMM pairings, resulting in nine DTM-DMM binomials for Small and Medium-sized Enterprises. The findings support managers and practitioners in selecting DTM-DMM pairs to guide DT initiatives while offering academics a mixed-methods approach based on the similarity analysis field to evaluate artefact compatibility systematically. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

Abstract
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.