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 blockchain-enabled supply chain financing (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.
AkerlofGA (1978) The market for “lemons”: Quality uncertainty and the market mechanism. In: Uncertainty in economics, pp.235–251. Elsevier.
2.
AlbertazziUGambacortaL (2010) Bank profitability and taxation. Journal of Banking & Finance34(11): 2801–2810.
3.
AllenFSantomeroAM (1997) The theory of financial intermediation. Journal of Banking & Finance21(11-12): 1461–1485.
4.
AltmanEI (1984) A further empirical investigation of the bankruptcy cost question. The Journal of Finance39(4): 1067–1089.
5.
AzariaAAzoulayRRechesS (2024) ChatGPT is a remarkable tool – for experts. Data Intelligence6(1): 240–296.
6.
BabichVHilaryG (2019) Blockchain and other distributed ledger technologies in operations. Foundations and Trends® in Technology, Information and Operations Management12(2-3): 152–172.
7.
BabichVHilaryG (2020) OM forum–distributed ledgers and operations: What operations management researchers should know about blockchain technology. M&SOM22(2): 223–240.
8.
BabichVHilaryG (2022) Tutorial on Blockchain Applications in Supply Chains. In: Innovative technology at the interface of finance and operations, Vol. 13, pp.51–72. Springer.
9.
BeckTDemirguc-KuntA (2006) Small and medium-size enterprises: Access to finance as a growth constraint. Journal of Banking & Finance30(11): 2931–2943.
10.
BergstraJBengioY (2012) Random search for hyper-parameter optimization. Journal of Machine Learning Research13(1): 281–305.
ChavaSRobertsMR (2008) How does financing impact investment? The role of debt covenants. The Journal of Finance63(5): 2085–2121.
19.
ChenBZhangZLangrenéN, et al. (2023) Unleashing the potential of prompt engineering in large language models: A comprehensive review. arXiv: 2310.14735, arXiv Preprint. Available at: https://arxiv.org/abs/2310.14735.
20.
ChodJTrichakisNTsoukalasG, et al. (2020) On the financing benefits of supply chain transparency and blockchain adoption. Management Science66(10): 4378–4396.
21.
ChungRFirthMKimJB (2002) Institutional monitoring and opportunistic earnings management. Journal of Corporate Finance8(1): 29–48.
22.
CongLWHeZ (2019) Blockchain disruption and smart contracts. The Review of Financial Studies32(5): 1754–1797.
23.
CreswellJWPothCN (2016) Qualitative Inquiry and Research Design: Choosing Among Five Approaches. Los Angeles, CA: Sage publications.
24.
DavidsonSDe FilippiPPottsJ (2018) Blockchains and the economic institutions of capitalism. Journal of Institutional Economics14(4): 639–658.
25.
de KokT (2025) ChatGPT for textual analysis? How to use generative LLMs in accounting research. Management Science71(9): 7888–7906.
FarrellJShapiroC (1990) Asset ownership and market structure in oligopoly. The RAND Journal of Economics21(2): 275–292.
31.
FisherGAguinisH (2017) Using theory elaboration to make theoretical advancements. Organizational Research Methods20(3): 438–464.
32.
FleissJLLevinBPaikMC (2013) Statistical Methods for Rates and Proportions. Hoboken, NJ: John Wiley & Sons.
33.
GisevNBellJSChenTF (2013) Interrater agreement and interrater reliability: Key concepts, approaches, and applications. Research in Social and Administrative Pharmacy9(3): 330–338.
34.
GururanganSMarasovićASwayamdiptaS, et al. (2020) Don’t Stop Pretraining: Adapt Language Models to Domains and Tasks. arXiv: 2004.10964, arXiv Preprint. Available at: https://arxiv.org/abs/2004.10964.
35.
HabibMAJohnsenDB (1999) The financing and redeployment of specific assets. The Journal of Finance54(2): 693–720.
36.
HackbarthD (2009) Determinants of corporate borrowing: A behavioral perspective. Journal of Corporate Finance15(4): 389–411.
Harvard University (2023) Getting started with prompts for text-based Generative AI tools. https://huit.harvard.edu/news/ai-prompts (accessed on 26 September 2024).
39.
HaugenRASenbetLW (1978) The insignificance of bankruptcy costs to the theory of optimal capital structure. The Journal of Finance33(2): 383–393.
40.
HermalinBEWeisbachMS (2012) Information disclosure and corporate governance. The Journal of Finance67(1): 195–233.
41.
HouJLiangCChenPy (2024) How socially perceived threat shapes preventive behavior in the context of COVID-19. Production and Operations Management35(2): 451–471.
42.
HowardJRuderS (2018) Universal Language Model Fine-tuning for Text Classification. arXiv: 1801.06146, arXiv Preprint. Available at: https://arxiv.org/abs/1801.06146.
43.
JappelliTPaganoM (2002) Information sharing, lending and defaults: Cross-country evidence. Journal of Banking & Finance26(10): 2017–2045.
44.
JavaidMHaleemASinghRP, et al. (2022) A review of blockchain technology applications for financial services. BenchCouncil Transactions on Benchmarks, Standards and Evaluations2(3): 100073.
45.
JensenMCMecklingWH (1976) Theory of the firm: Managerial behavior, agency costs and ownership structure. Journal of Financial Economics3(4): 305–360.
46.
JinL (2006) Capital gains tax overhang and price pressure. The Journal of Finance61(3): 1399–1431.
47.
JohnK (1987) Risk-shifting incentives and signalling through corporate capital structure. The Journal of Finance42(3): 623–641.
48.
KahnCWintonA (2004) Moral hazard and optimal subsidiary structure for financial institutions. The Journal of Finance59(6): 2531–2575.
49.
KarolyiGATaboadaAG (2015) Regulatory arbitrage and cross-border bank acquisitions. The Journal of Finance70(6): 2395–2450.
50.
KashyapAKRajanRSteinJC (2002) Banks as liquidity providers: An explanation for the coexistence of lending and deposit-taking. The Journal of Finance57(1): 33–73.
51.
KaufmanGG (2014) Too big to fail in banking: What does it mean?Journal of Financial Stability13: 214–223.
52.
KimJH (2018) Asset specificity and firm value: Evidence from mergers. Journal of Corporate Finance48: 375–412.
53.
KouvelisPXuF (2021) A supply chain theory of factoring and reverse factoring. Management Science67(10): 6071–6088.
54.
LambrechtBMMyersSC (2008) Debt and managerial rents in a real-options model of the firm. Journal of Financial Economics89(2): 209–231.
55.
LeeASBaskervilleRL (2003) Generalizing generalizability in information systems research. Information Systems Research14(3): 221–243.
56.
LiuSGaoCLiY (2024) Large Language Model Agent for Hyper-Parameter Optimization. arXiv: 2402.01881, arXiv Preprint. Available at: https://arxiv.org/abs/2402.01881.
PanYPanLChenW, et al. (2023) On the Risk of Misinformation Pollution with Large Language Models. arXiv: 2305.13661, arXiv Preprint. Available at: https://arxiv.org/abs/2305.13661.
63.
RoumeliotisKITselikasND (2023) ChatGPT and open-AI models: A preliminary review. Future Internet15(6): 192.
64.
RuderSPetersMESwayamdiptaS, et al. (2019) Transfer Learning in Natural Language Processing. In: Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: Tutorials, pp.15–18.
65.
SahooPSinghAKSahaS, et al. (2024) A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications. arXiv: 2402.07927, arXiv Preprint. Available at: https://arxiv.org/abs/2402.07927.
66.
SaldanaJ (2021) The Coding Manual for Qualitative Researchers. Thousand Oaks, CA: SAGE publications Ltd.
67.
ShenYHeacockLEliasJ, et al. (2023) ChatGPT and other large language models are double-edged swords. Radiology307(2): 230163.
68.
ShepherdDASuddabyR (2017) Theory building: A review and integration. Journal of Management43(1): 59–86.
69.
ShibuyaYBabichV (2021) Economics of multi-tier supply chain financing. Working paper, Available at SSRN 3787044, https://ssrn.com/abstract=3787044.
70.
SrivastavaMRaoAPariharJS, et al. (2023) What do the AI methods tell us about predicting price volatility of key natural resources: Evidence from hyperparameter tuning. Resour Policy80(0): 103249.
71.
StrunkWWhiteEB (2000) The Elements of Style. 4 ed. New York: Allyn & Bacon.
72.
TangCSYangSAWuJ (2019) Financing suppliers under performance risk. Foundations and Trends® in Technology, Information and Operations Management12(2-3): 135–151.
73.
TanriseverFCetinayHReindorpM, et al. (2015) Value of Reverse Factoring in Multi-stage Supply Chains. Working paper, Available at SSRN 2183991, https://ssrn.com/abstract=2183991.
74.
ThakorAV (1996) Capital requirements, monetary policy, and aggregate bank lending: Theory and empirical evidence. The Journal of Finance51(1): 279–324.
75.
TuckmanBVilaJL (1992) Arbitrage with holding costs: A utility-based approach. The Journal of Finance47(4): 1283–1302.
76.
WarnerJB (1977) Bankruptcy costs: Some evidence. The Journal of Finance32(2): 337–347.
77.
WeiJBosmaMZhaoVY, et al. (2021) Finetuned Language Models Are Zero-Shot Learners. arXiv: 2109.01652, arXiv Preprint. Available at: https://arxiv.org/abs/2109.01652.
78.
WeickKE (1989) Theory construction as disciplined imagination. Academy of Management Review14(4): 516–531.
WuttkeDARosenzweigEDHeeseHS (2019) An empirical analysis of supply chain finance adoption. Journal of Operations Management65(3): 242–261.
82.
XuQHeY (2023) Optimal information disclosure strategies for a retail platform in the blockchain technology era. International Journal of Production Research61(11): 3781–3792.
83.
YermackD (2017) Corporate governance and blockchains. Review of Finance21(1): 7–31.
84.
ZhouYMuresanuAIHanZ, et al. (2023) Large Language Models are Human-Level Prompt Engineers. arXiv: 2211.01910, arXiv Preprint. Available at: https://arxiv.org/abs/2211.01910.
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