Navigating Sustainable Business Model Transitions Through Tooling, Language, and Context by Intermediaries: A Data Science-Based Longitudinal Case Study

Abstract

This paper describes the first phase of our research into the influence of language construction and context-specific factors on business model tools for sustainable business models within the advisory process of intermediaries. Employing a longitudinal case study, we follow two intermediaries for a year and combine machine learning methods, participatory principles, and qualitative methods to understand this complex process better. Our research focuses explicitly on intermediaries' role in guiding SMEs using business model tools through this transition. We examine how language construction and contextual factors influence the use of business model tools in the advisory process. By analyzing 196 articles and books on Sustainable Business Model Tools (SBMTs) and business models using Latent Dirichlet Allocation (LDA), our preliminary findings provide a basis to uncover insights that contribute to developing a toolkit to improve intermediaries' support for SMEs while also achieving sustainable change. The dataset resulted in 10-15 potential topics covering innovation, sustainability, customer value, digital transformation, and management processes. Further refinement of our search process and algorithms is needed to specify these topics, emphasizing language construction and context-specific factors for understanding how intermediaries can guide SMEs in adopting sustainable practices effectively.