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Effective analysis and utilization of big data is a key factor for success in many business and service domains (Shukla & Mathur, 2020). In a context of scarce resources and profound change in customer needs, companies and individuals are faced with an abundance of decision possibilities (Kreuzer, Röglinger, & Rupprecht, 2020). Recommendation engines, filtering systems, prioritization and personalization algorithms have been tried to help individuals make better decisions and reduce their indecisiveness. Business analytics (BA) are increasingly being adopted in practice and emerging as an urgent challenge to improve personal and company performance, as evidence-based decision-making seems both desirable and rational (Beer, 2017; Holsapple, Lee-Post, & Pakath, 2014; Power, Cyphert, & Roth, 2019). Companies want to become more data-driven, specifically by taking advantage of real-time BA (Ain, Vaia, DeLone, & Waheed, 2019; Beer, 2017). BA provide a framework to exploit the synergies among fields such as data mining, quantitative methods, operations research, decision support system in a more practical format (Acharjya, Mitra, & Roy, 2019).
The interest of both academics and executives in investigating decision-making processes is longstanding (Ireland & Miller, 2004). The decision-making process needs to be better understood for organizations to create value from the use of BA (Sharma, Mithas, & Kankanhalli, 2014). The skillful use of BA by individual employees along with a culture of data-driven decision-making has the potential to radically improve companies’ performance (Frisk & Bannister, 2017). The rise of smart manufacturing, the core idea behind the fourth industrial revolution (Industry 4.0), is generating more and more data that requires analysis. Recent advancements of several information technologies and manufacturing technologies, such as Internet of Things (IoT), big data, artificial intelligent (AI), cloud computing, cyber-physical systems, digital twins, among others, have leveraged the development and use of business analytics capabilities and an orientation to make decisions based on such data by individuals and organizations (Dhamija, Bedi, & Gupta, 2020; Jagatheesaperumal, Rahouti, Ahmad, Al-Fuqaha, & Guizani, 2021; My, 2021; Rowlands & Milligan, 2021; Sahu, Sahu, & Sahu, 2020).
Information is recognized to play a key role by enhancing and providing insights to improve decision-makers’ performance (Tang & Liao, 2021). There are two main ways for an individual to process information, one being considered intuitive, natural, automatic and experiential and the other logical-conceptual, analytical-rational, explicit, systematic and intentional. Analytical orientation is characterized by an individual’s thinking that is oriented by data, reason and logical connections. The experiential or intuitive orientation, in turn, can be characterized as more holistic, experiential, dissociative, oriented to immediate actions and emotional (Epstein, Pacini, Denes-Raj, & Heier, 1996; Tversky & Kahneman, 1983). Some of the past research argued that much of cognition occurs automatically outside of consciousness and in the realm of intuition (Agor, 1986; Sadler-Smith & Shefy, 2004).