Big Data Analytics Capability and Governmental Performance: An Empirical Examination

Introduction

Propelled by increased accessible infrastructure and computing power, and the acquisition of more volumes of data accumulate into big data it is thought to be one of the most valuable strategic business sources in the coming years (McAfee & Brynjolfsson, 2012). This impact of big data analytics is potentially noticeable in a wide variety of sectors. Many scholars stipulate the future importance and value creation of big data analytics in hospitality (Horng, Lio, Chou, Yu, & Hu, 2022), healthcare (Yu, Zhao, Liu, & Song, 2021), retail (Santoro, Fiano, Bertoldi, & Ciampi, 2019), circular economy (Kristoffersen, Mikalef, Blomsma, & Li, 2021), food industry (Chakraborty, Rana, Khorana, Singu, & Luthra, 2022), and supply chain (Gopal, Rana, Krishna, & Ramkumar, 2022). The same rule applies to the public sector. Big data analytics have potentially many advantages, in terms of smart services, intelligent adaptive forms and predictive service delivery to its citizen, if they are used efficiently and effectively (Merhi & Bregu, 2020), also for smaller governments like municipalities (Milakovich, 2012). For this reason, governments are investing heavily in (big) data analytics (Gartner, 2019).

To reap the benefits of big data analytics it is imperative to gain an understanding of how organizations build big data analytics capabilities. This is important since we know from previous research, that adopted the theoretical lens of the resource-based theory (RBT), that organizations achieve competitive advantage by building capabilities, which in turn are created by combining and deploying several resources. Based on the RBT, Gupta and George (2016) suggests that organizations should focus on creating a big data capability to achieve sustainable competitive advantage by integrating its tangible resources (e.g., data), human resources (e.g., technical skills) and intangibles resources (e.g., data-driven culture). They juxtaposed these three resources that together build a big data analytics capability. Studies found empirical evidence that these resources contribute to the organization’s performance (Wamba, et al., 2017; Ferraris, Devalle, & Couturier, 2019; Mikalef, Krogstie, Pappas, & Pavlou, 2020). A big data analytics capability has thus been shown to positively impact business performance in studies on business organizations.

Unfortunately, how big data analytics capability creates value for the public sector is not sufficiently empirically assessed in the extant literature. Most reports on the value of big data to date have been from consultancy firms (e.g., EY, 2021), and conceptual studies (e.g., Merhi & Bregu, 2020) that lack empirical theoretical insight. As a result, there is limited understanding of how organizations should approach their big data initiatives and scarce empirical support to back-up the claim that these investments result in any measurable administrative value. This study extends the stream of research on big data analytics capability and organizational performance by examining factors that contribute to improved governmental performance because of investments in big data analytics. More specifically, the study aims to examine the following research question: