Big data analytics (BDA) and degree of internationalization: the interplay between governance of BDA infrastructure and
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Big data analytics (BDA) and degree of internationalization: the interplay between governance of BDA infrastructure and BDA capabilities Alberto Bertello1 · Alberto Ferraris1,2 · Stefano Bresciani1 · Paola De Bernardi1 Accepted: 22 October 2020 © The Author(s) 2020
Abstract In order to face the challenges of internationalization and to cope more efficiently with the uncertainty of foreign expansion, firms are called to analyze an increasing amount of real-time semi-structured and unstructured datasets. In this sense, big data analytics (BDA) can become strategic in stimulating the international growth of small and medium-sized enterprises (SMEs). However, the specific relationship between BDA and internationalization has been analyzed fragmentarily within the mainstream literature. With the purpose of shedding light on this relationship, the authors drew on resource-based view (RBV) and collected data through a questionnaire directed to CEOs of 266 SMEs, receiving 103 responses. A quantitative analysis based on an Ordinary Least Squares (OLS) regression showed that the relationship between governance of BDA infrastructure and the degree of internationalization (DOI) is not significant, while the direct effect of BDA capabilities as well as the interaction term between BDA infrastructure and BDA capabilities are positive and significant. This suggests that the governance of BDA per se is not enough for enhancing internationalization in SMEs. On the contrary, this article points out the relevance of developing specific BDA capabilities and the existence of a positive interplay between governance of BDA infrastructure and BDA capabilities that can exploit the new knowledge coming from BDA in SME international growth. Keywords Big data analytics · Internationalization · Capabilities · Small and medium-sized enterprises
* Alberto Bertello [email protected] Extended author information available on the last page of the article
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1 Introduction In the last years, the increasing amount of data that companies have been called to process and their potential key role in making strategic decisions has attracted the attention of managers and scholars (De Mauro et al. 2018; Erevelles et al. 2016; Gnizy 2018; Lopez-Nicolas and Soto-Acosta 2010; Sivarajah et al. 2017). The conventional practices, based on the exploitation of structured, small, and centralized data, have been recently challenged by the development of innovative information systems able to simultaneously process different semi-structured and unstructured datasets (Bean and Kiron 2013; Kiron et al. 2013; Germann et al. 2014; Grover et al. 2018; Vera-Baquero et al. 2016). The process of extracting, generating, interpreting, and categorizing useful information through the compression of an enormous amount of data is nowadays also known as big data analytics (BDA) (Chen et al. 2013; Davenport 2012). Despite the newness of BDA as a field of studies, several academics started focusing on how and to what extent
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