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Drivers of digital supply chain transformation in SMEs and large enterprises – a case of COVID-19 disruption risk
International Journal of Emerging Markets ; 18(6):1355-1377, 2023.
Article in English | ProQuest Central | ID: covidwho-20240497
ABSTRACT
PurposeDigital 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/approachUsing 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 technologyorganizationenvironment (TOE) as an overarching framework.FindingsThis 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/valueThis 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.
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Full text: Available Collection: Databases of international organizations Database: ProQuest Central Type of study: Prognostic study Language: English Journal: International Journal of Emerging Markets Year: 2023 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: ProQuest Central Type of study: Prognostic study Language: English Journal: International Journal of Emerging Markets Year: 2023 Document Type: Article