Blockchain technology holds promise for improving access to financing within supply chains, especially for small and under-financed suppliers. Yet, the specific ways in which blockchain technology mitigates financing frictions and the features that contribute to successful platform implementations remain unclear. Following a theory elaboration approach, we close that gap by studying which financing frictions the BCF solutions aim to address, which blockchain features they use, and the association between the frictions and features. In our analysis, we examine 312 documents with unstructured text describing 11 BCF solutions, both successful and failed. Using AI-based large language models, we identify patterns connecting seven types of financing frictions and three key blockchain features. We find that the transactional friction is the most prominent, despite receiving limited attention in the academic literature, while bankruptcy costs and taxes—frictions that are commonly discussed in the literature—are rarely associated with blockchain features in our sample. Among blockchain features, tokenization is used sparingly; and, unlike other features, it appears in successful BCF solutions only. Moreover, connections between frictions and features are not random: while transactional and hidden actions frictions are linked to all three blockchain features, other frictions are typically associated with only one. Our findings offer a deeper understanding of the mechanisms through which blockchain adds value in supply chain finance, suggesting that aligning blockchain features with specific frictions may enhance the chances of success. We also demonstrate a method for using AI to evaluate large amounts of unstructured data in operations management research.
Sriraman et al. (Thu,) studied this question.
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