Abstract:

This research builds on our previous work, “Enabling German SMEs and Crafts through Data-Driven Innovation: Developing a Scoring Model and Chronological Framework for Enhanced Decision-Making,” which examined the barriers SMEs, particularly in the crafts sector, face in adopting data-driven strategies (Eickelmann, Tran, & Strina, 2024). In that study, we highlighted how large enterprises have successfully leveraged AI and data-driven technologies, while SMEs struggle with similar implementations. Building on these insights, the current research aims to identify key factors in the data-driven domain that can further enhance decisionmaking in German SMEs. Our proposed scoring model aims to enhance the competitiveness of SMEs by enabling them to assess their data-drivenness, identify weaknesses, and make more informed business decisions. It helps them to improve their overall data-drivenness to make informed, data-driven business decisions. Through a systematic literature review, following the methodology of Tranfield et al. (2003), we identified the most critical factors determining data-drivenness in SMEs. Using the Design Science Research (DSR) approach, we developed a refined scoring model that integrates these factors to assess and enhance data utilization within SMEs.