Business value of big data analytics: A systems-theoretic approach and empirical test

John Qi Dong, Chia Han Yang

研究成果: Article

1 引文 斯高帕斯(Scopus)

摘要

Although big data analytics have been widely considered a key driver of marketing and innovation processes, whether and how big data analytics create business value has not been fully understood and empirically validated at a large scale. Taking social media analytics as an example, this paper is among the first attempts to theoretically explain and empirically test the market performance impact of big data analytics. Drawing on the systems theory, we explain how and why social media analytics create super-additive value through the synergies in functional complementarity between social media diversity for gathering big data from diverse social media channels and big data analytics for analyzing the gathered big data. Furthermore, we deepen our theorizing by considering the difference between small and medium enterprises (SMEs) and large firms in the required integration effort that enables the synergies of social media diversity and big data analytics. In line with this theorizing, we empirically test the synergistic effect of social media diversity and big data analytics by using a recent large-scale survey data set from 18,816 firms in Italy. We find that social media diversity and big data analytics have a positive interaction effect on market performance, which is more salient for SMEs than for large firms.

原文English
文章編號103124
期刊Information and Management
57
發行號1
DOIs
出版狀態Published - 2020 一月

    指紋

All Science Journal Classification (ASJC) codes

  • Management Information Systems
  • Information Systems
  • Information Systems and Management

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