Blockchain Papers

Alexandre Rigaud

No abstract is available for this record.

Jainish Bhagat

Supply chain finance (SCF) plays a pivotal role in maintaining liquidity and operational continuity across global value networks. However, systemic supply chain disruptions, macroeconomic volatility, and information asymmetry frequently expose SCF programs to severe friction and default risks. While digital transformation is widely touted as a catalyst for supply chain resilience, empirical evidence regarding the explicit mechanisms through which distinct digital transformation capabilities enhance Supply Chain Finance Resilience (SCFR) remains fragmented. Grounded in the Resource-Based View (RBV), Dynamic Capabilities Theory (DCT), and Information Processing Theory (IPT), this study develops and tests an integrated framework evaluating the direct and indirect impacts of Artificial Intelligence Capability (AIC), Blockchain Capability (BC), and Data Analytics Capability (DAC) on SCFR, mediated by Digital Trust in SCF Platforms (DT).Using a computational research simulation methodology, a respondent-level dataset (N=500) representing supply chain, finance, operations, and IT decision-makers across international enterprises was algorithmically generated under a defensible latent-variable covariance structure. Partial Least Squares Structural Equation Modeling (PLS-SEM) with 5,000 bootstrap resamples was executed to evaluate the measurement and structural models. The structural analysis reveals that AIC (β=0.241,p<.001), BC (β=0.312,p<.001), and DAC (β=0.284,p<.001) significantly and positively drive Digital Trust in SCF Platforms, explaining 54.2% of its variance (R^2=0.542). Digital Trust, in turn, exerts a substantial direct effect on SCFR (β=0.385,p<.001). Furthermore, direct effects on SCFR were confirmed for DAC (β=0.218,p<.001) and AIC (β=0.152,p=.002), whereas the direct link from BC to SCFR was non-significant (β=0.071,p=.158). Formal mediation testing using percentile bootstrapping confirmed that Digital Trust fully mediates the relationship between Blockchain Capability and SCFR, while partially mediating the relationships for AIC and DAC. The overall structural model accounts for 58.6% of the variance in Supply Chain Finance Resilience (R^2=0.586,Q_"predict" ^2=0.412).This methodological prototype advances theoretical understanding by unpacking the granular capability configurations necessary to foster digital trust and financial resilience in supply networks. For practitioners and policymakers, the findings highlight that investing in blockchain technology yields minimal resilience benefits unless coupled with platform-wide digital trust mechanisms, whereas AI and analytics offer dual-pathway benefits across operational and relational domains.

José Luis Alberto Delgado, Dilek Demirbaş

This study investigates whether cryptocurrency adoption has affected Argentina’s bilateral trade flows within a gravity-model framework. While blockchain-based technologies are often expected to reduce transaction costs and facilitate international trade, quantitative evidence on their actual impact remains limited. Using panel data on Argentina’s trade with its main partners, the analysis combines standard gravity variables with country-level measures of cryptocurrency activity and estimates fixed effects, random effects, and high-dimensional fixed effects models.The results confirm the continued relevance of traditional trade determinants. Distance shows a robust negative effect on bilateral trade, with an elasticity ranging from −0.54 to −1.65 (p<0.05) across specifications. Country contiguity is associated with a 3.5-fold increase in bilateral trade (coefficient: +1.25, p<0.01). The effect of cryptocurrency adoption, by contrast, varies across specifications: in the random effects model, it is negatively associated with formal trade (−0.049, p<0.01), while in the correctly specified PPML model with origin-destination-year fixed effects, the contemporaneous effect is statistically insignificant. However, when cryptocurrency adoption is lagged one period, it shows a positive and highly significant association with trade (+0.061, p<0.01), suggesting that the trade-facilitating effect of crypto infrastructure may operate with a delay. We also find marginal evidence (p≈0.10) that cryptocurrency adoption attenuates the trade-reducing effect of distance. This counterintuitive result may indicate that cryptocurrency adoption substitutes for formal trade channels or reflects periods of economic instability, including the COVID-19 pandemic. However, this relationship is not robust to more demanding specifications that control for unobserved heterogeneity.Overall, the findings suggest that blockchain-based technologies have not yet translated into measurable trade-facilitating effects, partly due to limited institutional support and legal uncertainty. The paper highlights the gap between the potential benefits of blockchain for international trade and its actual adoption, emphasising the role of coordinated institutional frameworks in enabling technological diffusion.

Houda BenMabrouk, Safa Boukadida, Khaled Guesmi

Purpose The study investigates the effect of investor fear on cryptocurrency crash risk, with emphasis on overall market sentiment and COVID-19-related fear. It also evaluates the relative performance of Google search-based measures compared to the economic policy uncertainty (EPU) index and the volatility indexes (VIX) as benchmark indicators of uncertainty. Design/methodology/approach This study employs a quantitative empirical approach to examine the impact of investor fear on cryptocurrency price crash risk. Investor sentiment is proxied using the FEARS index derived from Google search volumes and the coronavirus fear index. Crash risk is measured using negative conditional skewness of weekly returns and down-to-up volatility. The analysis is based on weekly data for the top 10 cryptocurrencies from August 2010 to October 2021. Regression models are used to examine the relationship between investor fear and crash risk and to compare the explanatory power of Google-based fear indicators with traditional uncertainty measures. Findings The results show that investor fear significantly increases the risk, while COVID-19-related fear further intensifies this effect, highlighting the vulnerability of crypto markets during periods of heightened uncertainty. Moreover, Google-based fear indicators outperform the EPU index and the VIX in explaining and predicting crash risk. Overall, the findings suggest that investor attention and sentiment are more powerful drivers of cryptocurrency crash risk than traditional volatility-based measures. Originality/value This study links investor fear, including COVID-19 sentiment, to cryptocurrency crash risk and finds that Google-based fear indicators outperform traditional measures like the EPU index and the VIX in predicting market downturns.