The scientific periodical journal published by the Faculty of Management and Economics of Tomas Bata University in Zlin offers results of basic and applied economic research of domestic and international authors in the English language.

Purpose: Digital transformation in supply chains (SCs) has emerged as one of the most effective ways to minimize SC disruption risks. Given the unprecedented impact of the COVID-19 pandemic on global SCs, this study aims to identify and provide empirical evidence about the drivers of digital SC transformation, considering the interactivity between environmental dynamism, technology, and organizational capabilities during the pandemic era.Design/methodology/approach: Using partial least squares structural equation modeling (PLS-SEM), this study examines 923 firms in Vietnam to ascertain the drivers of digital SC transformation between small- and medium-sized enterprises (SMEs) and large enterprises, based on the technology–organization–environment (TOE) as an overarching framework.Findings: This study finds that greater digital SC transformation adoption could be achieved under the interactivity between the TOE components of firms' technological competencies, learning capabilities, and disruptions in socioeconomic environments due to the COVID-19 pandemic. Moreover, a multigroup analysis shows that the drivers of digital SC transformation differ between SMEs and large enterprises. SMEs were found to be more motivated by the COVID-19 disruption risk when adopting digital SC models.Originality/value: This study represents an original and novel contribution from Vietnam as an emerging market to the literature on the impact of COVID-19 on the global value chain. Apart from the unique dataset at the firm level, the analysis of interactions between external and internal drivers of digital SC transformation could provide crucial managerial implications for SMEs to survive major disruptions, such as those caused by the COVID-19 pandemic.

This paper introduces novel data on public sentiment towards economic sanctions based on nearly 1 million social media posts in 108 countries during the Russia–Ukraine war by using machine learning. We show the geographical heterogeneity between government stances and public sentiment. Finally, we show how political regimes, trading relationships and political instability can predict how people perceive this war.