In the strategic management field, dynamic capabilities (DC) such as organizational agility are considered to be paramount in the search for competitive advantage. Recent research claims that IT business value research needs a more dynamic perspective. In particular, the Big Data Analytics (BDA) value chain remains unexplored. To assess BDA value, a conceptual model is proposed based on a knowledge-based view and DC theories. To empirically test this model, the study addresses a survey to a wide range of 500 European firms and their IT and business executives. Results show that BDA can provide business value to several stages of the value chain. BDA can create organizational agility through knowledge management and its impact on process and competitive advantage. Also, this paper demonstrates that agility can partially mediate the effect between knowledge assets and performance (process level and competitive advantage). The model explains 77.8% of the variation in competitive advantage. The current paper also presents theoretical and practical implications of this study, and the study's limitations.
In the era of Big Data, firms in every sector are required to deal with a huge amount of data. Data in vast amounts can offer invaluable insights and competitive advantage if the right technological and organizational resources support them (Morabito, 2015). Recently, several academics and practitioners have stressed the need to understand how, why, and when Big Data Analytics (BDA) applications can be a valuable resource for companies to gain competitive advantage (Abbasi, Sarker, & Chiang, 2016; Agarwal & Dhar, 2014; Corte Real, Oliveira, & Ruivo, 2014; LaValle et al., 2011). Although BDA technologies have been recognized as the “next big thing for innovation” (i.e., a potential source of business value and competitive advantage), the BDA value chain remains relatively unexplored and needs further investigation. No empirical research exists assessing how BDA can bring business value (Abbasi et al., 2016), establishing a linkage between knowledge assets, organizational agility, and performance (process-level and competitive advantage) (Corte Real et al., 2014). Firms that inject BDA in their business operations can surpass their peers by 5% in productivity and 6% in profitability (Barton, 2012). For that reason, European firms are investing heavily in BDA technologies (SAS, 2013; Sharma, Mithas, & Kankanhalli, 2014). Nevertheless, this investment can only be valuable if organizations use the appropriate technology and organizational resources to achieve competitive advantage (Manyika et al., 2011a).
In response to the scarcity of research on this subject, this study examines the impact of BDA on the business value chain in a European context by empirically testing a new theoretical framework that merges two strategic management theories (Knowledge Based View (KBV) and dynamic capabilities (DC)) at firm-level. Not only does this paper extend BDA research by transposing, merging, and examining hypotheses in IT innovations and management fields, but also contributes to DC research by empirically assessing the antecedents and impacts of a specific dynamic capability (organizational agility), when using BDA technologies. This is the first paper that studies the entire BDA value chain at firm-level, linking concepts of knowledge management, agility, and performance (process-level and competitive advantage). To clarify the role of agility on performance, this papers tests if agility is a mediator of knowledge assets on performance (process-level performance and competitive advantage). The study explores the following three research questions (RQs):
RQ1 – What are the BDA enablers for the creation of organizational agility?
RQ2 – What are the impacts of this dynamic capability created by BDA on sustainable competitive advantage?
RQ3 – Is agility a mediator of knowledge assets on performance (process-level performance and competitive advantage)?
This study offers guidance for executives and managers to assess the conditions under which BDA can add business value to organizations. Managers and IT executives can benefit from an evaluation instrument to assess the impact of BDA. Also, this paper provides valuable support to justify BDA investments and initiatives. Firms that have not yet decided to adopt these technologies can obtain a view of potential gains from adopting and effectively using BDA. This research demonstrates how best to leverage the knowledge embedded in BDA systems, acquiring organizational agility capabilities that lead toward competitive advantage.
The remainder of this paper has the following structure: Section 2 provides an introduction to the BDA concept and a theoretical background to assess BDA initiatives; Section 3 presents the conceptual model and the hypotheses; Section 4 outlines the methodology; and Section 5 shows the empirical results. Finally, the paper presents a discussion and the conclusions from the findings.
2.1. Big Data Analytics
Chen, Chiang (Chen, Chiang, & Storey, 2012) coined the term Big Data Analytics (BDA) as a related field of business intelligence & analytics (BI&A), referring to the BI&A technologies that mostly concern data mining and statistical analysis. Authors define BDA as “a new generation of technologies and architectures, designed to economically extract value from very large volumes of a wide variety of data, by enabling high velocity capture, discovery and/or analysis.” (IDC, 2011). BDA technologies allow firms to improve existing applications by offering business-centric practices and methodologies that provide a competitive advantage (Chen et al., 2012; Davenport, 2006). The latest literature indicates that there is much room for further BDA research (Abbasi et al., 2016; Agarwal & Dhar, 2014; Erevelles, Fukawa, & Swayne, 2016). There are already academic studies that reflect the adoption and use of BDA (e.g., (Malladi, 2013; Xu, Frankwick, & Ramirez, 2015; Kwon, Lee, & Shin, 2014)). Regarding value, most BDA academic studies focus on analyzing business value from a data or system perspective (e.g., (LaValle et al., 2011; Kwon et al., 2014)). From the strategic management perspective only one conceptual paper explores how BDA affects several marketing activities (Erevelles et al., 2016). The remaining literature addresses industry primarily (LaValle et al., 2011; Russom, 2011). As firms do not know how to capture business value (Barton, 2012; LaValle et al., 2011), some scholars (Corte Real et al., 2014; Malladi, 2013) argue that BDA value research is scarce and needs to extend beyond post-adoption stages toward competitiveness (Erevelles et al., 2016; Xu et al., 2015). Although numerous approaches assess IT Value at the process and firm levels (see Schryen (Schryen, 2013) for a review), this study extends IT business value research from the strategic management perspective, by empirically assessing the BDA business value chain in European firms.
2.2. Theoretical foundation
Many studies in recent decades investigate IT business value and competitive advantage using the resource-based view (RBV) (Barua, Kriebel, & Mukhopadhyay, 1995; Bharadwaj, 2000; Mata, Fuerst, & Barney, 1995; Melville, Kraemer, & Gurbaxani, 2004; Ruivo, Oliveira, & Neto, 2015; Soh & Markus, 1995; Zhu & Kraemer, 2005). The limitations of RBV encourage the use of other theories such as DC and KBV (Arend & Bromiley, 2009; Wang & Ahmed, 2007). As DC theory constitutes the second foundation that supports knowledge-based thinking (Pettigrew, Thomas, & Whittington, 2001), this study combines these theories. KBV explores a firm's potential to acquire competitiveness in a dynamic market context, but only DC theory can solve the problem of sustaining competitive advantage in turbulent environments (Grant, 1996; Volberda, 1996).
2.2.1. Knowledge Based View theory
KBV states that a firm's knowledge resources are unique and inimitable and that the firm's primary function is to leverage them into productive outcomes (Grant, 1996; Nonaka, 1995). The possession of knowledge resources gives the firm basic foundations to renew or reconfigure its resource base and to build dynamic capabilities (Wu, 2006), such as organizational agility. Companies that have high levels of staff knowledge and involvement can more skillfully identify the need to make changes to existing resources and decide about the actions necessary to implement these changes (Nieves & Haller, 2014). KBV theory can help to conceptualize the performance effects of IT investments (Pavlou et al., 2005). Management studies use this theory (e.g., (Nieves & Haller, 2014)), as do studies in IT fields (e.g., (Sher & Lee, 2004)) to understand the role of knowledge management in the creation of DC. In BDA technologies, Xu, Frankwick (Xu et al., 2015) seek to understand the relationships among traditional marketing analytics, BDA, and new product success. The current paper is the first that empirically tests KBV to understand the role of BDA in the creation of agility.