Many companies invest considerable resources in developing Business Analytics (BA) capabilities to improve their performance. BA can affect performance in many different ways. This paper analyses how BA capabilities affect firms’ agility through information quality and innovative capability. Furthermore, it studies the moderating role of environmental turbulence, both technological and in the market. The proposed model was tested using statistical data from 154 firms with two respondents (CEO and CIO) from each firm. The data were analysed using Partial Least Squares (PLS)/Structured Equation Modelling (SEM). Our results indicate that BA capabilities strongly impact a firm’s agility through an increase in information quality and innovative capability. We also discuss that both market and technological turbulence moderate the influence of firms' agility on firms' performance.
Business analytics (BA) are overhauling the way firms are generating and using data (Ramanathan, Philpott, Duan, & Cao, 2017). They attracted increasing attention from both academics and practitioners for their high operational and strategic potential across various industries, including financial services, insurance, retail, healthcare and manufacturing (Dubey, Gunasekaran, Childe, Wamba, & Papadopoulos, 2016; Fosso Wamba, Ngai, Riggins, & Akter, 2017). BA can be defined as a holistic approach to manage, process and analyse data, not only to create actionable insights (adapted from Fosso Wamba, Akter, Edwards, Chopin, and Gnanzou, (2015)) but also to enable organisations to predict changes based on market requirements and respond to them quickly (Işık, Jones, & Sidorova, 2013). BA systems involve the use of capabilities and technologies to collect, transform, analyse and interpret data to support decision-making (Santiago Rivera & Shanks, 2015). BA are known as ‘competitive differentiators (Jeble et al., 2018),’ and both professional press and academic research consistently demonstrate a positive relationship between BA and organisational performance (Ramakrishnan, Jones, & Sidorova, 2012; Viaene & Van den Bunder, 2011). However, the way in which BA influence performance is not entirely clear and calls for further research (Abbasi, Sarker, & Chiang, 2016; Côrte-Real, Oliveira, & Ruivo, 2017; Gunasekaran et al., 2017). Earlier papers on this topic have established a generally positive impact on performance (Gupta & George, 2016; Trkman, McCormack, De Oliveira, & Ladeira, 2010), investigated the availability, quality, and use of information (Popovič, Hackney, Coelho, & Jaklič, 2012) or presented the benefits stemming therefrom (Wang, Kung, & Byrd, 2018) without investigating the path of influence. Thus, while there is substantial evidence that investments in business analytics can create value, the way in which BA lead to value needs deeper analysis (Sharma, Mithas, & Kankanhalli, 2014). In recent years, several attempts have been made to address this issue; Akter, Fosso Wamba, Gunasekaran, Dubey, and Childe, (2016) proposed a three-tier model to investigate the impact of big data analytics on firm performance by taking the moderating role of strategic alignment into account.