Drawing on the resource-based view and the literature on big data analytics (BDA), information system (IS) success and the business value of information technology (IT), this study proposes a big data analytics capability (BDAC) model. The study extends the above research streams by examining the direct effects of BDAC on firm performance (FPER), as well as the mediating effects of process-oriented dynamic capabilities (PODC) on the relationship between BDAC and FPER. To test our proposed research model, we used an online survey to collect data from 297 Chinese IT managers and business analysts with big data and business analytic experience. The findings confirm the value of the entanglement conceptualization of the hierarchical BDAC model, which has both direct and indirect impacts on FPER. The results also confirm the strong mediating role of PODC in improving insights and enhancing FPER. Finally, implications for practice and research are discussed.
Big data analytics (BDA) is emerging as a hot topic among scholars and practitioners. BDA is defined as a holistic approach to managing, processing and analyzing the 5 V data-related dimensions (i.e., volume, variety, velocity, veracity and value) to create actionable ideas for delivering sustained value, measuring performance and establishing competitive advantages (Fosso, Akter, Edwards, Chopin, & Gnanzou, 2015). Some practitioners and scholars have gone so far as to suggest that BDA is the “fourth paradigm of science” (Strawn, 2012, p.34), a “new paradigm of knowledge assets” (Hagstrom, 2012, p. 2), or “the next frontier for innovation, competition, and productivity” (Manyika et al., 2011, p.1). All these assertions are primarily driven by the ubiquitous adoption and use of BDA-enabled tools, technologies and infrastructure including social media, mobile devices, automatic identification technologies enabling the internet of things, and cloud-enabled platforms for firms' operations to achieve and sustain competitive advantage. For example, BDA allows for improved data-driven decision making and innovative ways to organize, learn and innovate (Yiu, 2012); thus, reinforcing customer relationship management, improving the management of operations risk, and enhancing operational efficiency and overall firm performance (Kiron, 2013). Yet prior studies of the business value derived from information systems (IS) investments have reported mixed results, resulting in the so-called ‘IT productive paradox’. Indeed, some scholars have argued that IS investments do not necessarily lead to improved operational efficiency and effectiveness (Irani, 2010; Roach et al., 1987; Sharif & Irani, 2006; Solow, 1987; Strassmann, 1990), while others identified a positive association between IS investments and firm performance (Barua, Konana, Whinston, & Yin, 2004; Barua, Kriebel, & Mukhopadhyay, 1995; Brynjolfsson & Yang, 1996). Their findings suggest that the absence of a positive link between IS investment and firm performance found by prior studies may be explained by several factors including the unavailability of appropriate data, the existence of time lags between IS investments and the business value generated from these investments, the absence of an assessment of the indirect benefits of IT, and the level of analysis of IS-related benefits (Anand, Fosso, & Sharma, 2013; Brynjolfsson & Hitt, 2000; Brynjolfsson & Yang, 1996; Devaraj & Kohli, 2003; Irani, 2002; Irani, Ghoneim, & Love, 2006). In fact, within this stream of research, eminent scholars argue that the impact of IT on firm performance may be mediated by a number of intermediate variables (Mooney, Gurbaxani, & Kraemer, 1996). Furthermore, they propose applying a broader view of IT resources by integrating a multidimensional perspective into studies of the business value of IT or IT capabilities (Bharadwaj, 2000; Bhatt & Grover, 2005; Santhanam & Hartono, 2003).