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This paper aims to explore how the expansion in cryptocurrency users affects the country's research and development (R&D) investments.

The analysis covers 53 countries over the period 2019–2022. The empirical methodology implements an instrumental variable approach to overcome endogeneity and omitted variable bias issues. In particular, this study introduces novel instruments developed from Google data, specifically related to trends observed in the cryptocurrency markets.

The results show that macroeconomic and institutional factors, as well as technology infrastructure affect the country's R&D expenditure, as previously noted in the literature. The country's R&D spending significantly responds to cryptocurrency expansions. Conspicuously, each 10% increase in cryptocurrency market users boosts the ratio of R&D to GDP by 1.35%. The countries that have imposed an adequate taxation policy on cryptocurrency gains experienced more improvement in their R&D budget, in contrast to those that forced aggressive tax brackets or did not apply adequate policies. Cryptocurrency dynamics affect both religiously regulated and nonreligious countries.

This study has three main contributions. First, it introduces the role of the cryptocurrency market as one of the leading global trends to affect the countries’ R&D budget. Second, this paper documents the importance of forcing an adequate tax policy on cryptocurrency capital gains. Third, the results of this paper serve as guidelines for governments to face the challenges raised by the cryptocurrency market.

The paper aims to propose an innovative two-stage decision model to address the sustainable-resilient supplier selection and order allocation (SSOA) problem in the single-valued neutrosophic (SVN) environment.

First, the sustainable and resilient performances of suppliers are evaluated by the proposed integrated SVN-base-criterion method (BCM)-an acronym in Portuguese of interactive and multi-criteria decision-making (TODIM) method, with consideration of the uncertainty in the decision-making process. Then, a novel multi-objective optimization model is formulated, and the best sustainable-resilient order allocation solution is found using the U-NSGA-III algorithm and TOPSIS method. Finally, based on a real-life case in the automotive manufacturing industry, experiments are conducted to demonstrate the application of the proposed two-stage decision model.

The paper provides an effective decision tool for the SSOA process in an uncertain environment. The proposed SVN-BCM-TODIM approach can effectively handle the uncertainties from the decision-maker’s confidence degree and incomplete decision information and evaluate suppliers’ performance in different dimensions while avoiding the compensatory effect between criteria. Moreover, the proposed order allocation model proposes an original way to improve sustainable-resilient procurement values.

The paper provides a supplier selection process that can effectively integrate sustainability and resilience evaluation in an uncertain environment and develops a sustainable-resilient procurement optimization model.