@article{eickelmann2025quantifying,
  title = {Quantifying Data-Driven SMEs:  Scoring Factors and a Timeline for  Better Decisions},
  author = {Alexander EICKELMANN and Giuseppe STRINA},
  year = 2025,
  url = {https://ibimapublishing.com/articles/JOMS/2025/636737/},
  journal = {Journal of Organizational Management Studies},
  volume = 2025,
  pages = 10,
  doi = 10.5171/2025.636737,
  abstract = {This research builds on our earlier findings regarding the challenges SMEs, particularly in the crafts sector, face when adopting data-driven strategies. While large enterprises successfully utilize AI and data technologies, SMEs often encounter significant barriers to implementation. Building on these insights, this study identifies critical factors in the data-driven domain that can significantly enhance decision-making in German SMEs. The proposed scoring model aims to empower SMEs by enabling them to assess their data capabilities, identify weaknesses, and implement targeted improvements. Through a systematic literature review, the study highlights the most influential factors shaping data-drivenness in SMEs. Using the Design Science Research (DSR) methodology, we developed a refined scoring model and a chronological framework that integrate these factors to guide and improve data utilization. This research ultimately contributes to a deeper understanding of how SMEs can adopt data-driven strategies to foster innovation, optimize operations, and remain competitive in a dynamic market environment.},
  keywords = {data-driven decision-making, scoring model, chronological framework, German SMEs},
  note = Article ID: 636737
}