Examining the adoption of Big data analytics in supply chain management under competitive pressure: evidence from Saudi Arabia

A systematic literature review towards a conceptual framework for enablers and barriers of an enterprise data science strategy

“…At the individual-level, the tendency of resistance to ‘Data Science’ project execution either by an employee or by a mid-level manager is either due to fear of ‘failure’ or ‘loss of control’ or ‘operational disruption’ (Mikalef et al 2020c ; Shahbaz et al 2019 ). The enablers in the form of developing dynamic capability, such as experience in ‘dealing with complexity’, ‘high tolerance for complexity’ (Gong and Janssen 2021 ; Walker and Brown 2019 ) and ‘Top-management-Team’ support (Alaskar et al 2020 ; Behl et al 2019 ; Chaurasia and Verma 2020 ; Foshay et al 2015 ; Halaweh and Massry 2015 ; Lai et al 2018 ; Lamba and Singh 2018 ; Lautenbach et al 2017 ; Popovič et al 2018 ; Ransbotham et al 2017 ; Verma and Bhattacharyya 2017 ; Walker and Brown 2019 ; Wang et al 2018c ) are a must to address the barriers to considerable extent. Organizational environment for an individual in communicating the benefits of ‘Data Science’ (Chakravorty 2020 ; Gong and Janssen 2021 ; Verma 2017 ) is also a barrier for ‘Data Science’ project success.…”

“…Establishing ‘Data Science’ projects require huge investments on skills and infrastructure (Behl et al 2019 ; De Luca et al 2020 ; Holland et al 2020 ; Lee et al 2017 ; Wu et al 2017 ), which is quite a big challenge for most organizations. Though it may not be avoided completely, infrastructure flexibility in identifying and using of compatible and complementary resources (Alaskar et al 2020 ; Chaurasia and Verma 2020 ; Mikalef and Gupta 2021 ; Moreno et al 2019 ; Shokouhyar et al 2020 ; Verma and Bhattacharyya 2017 ; Walker and Brown 2019 ) already existing in the organization can considerably reduce the burden on new investments. Lack of skills and knowledge (Ahmed et al 2018 ; Behl et al 2019 ; Dubey et al 2019b ; Lamba and Singh 2018 ; Foshay et al 2015 ; GalbRaith 2014 ; Mikalef et al 2020a , 2019b , 2020c ; Rialti et al 2019 ) required to execute the ‘Data Science’ projects can be addressed by setting up ‘Training & Knowledge Management’ capabilities and processes (Calvard 2016 ; Dam et al 2019 ; Ferraris et al 2019 ; Harlow 2018 ; Rialti et al 2020 ).…”

“…Further, there has to be seamless collaboration across the organization (Kache and Seuring 2017 ) to enable desired outcomes. Organizational inertness (Biazzin and Castro-Carvalho 2019 ), resistance to organizational flexibility (Ahmed et al 2018 ; Cabrera-Sánchez and Villarejo-Ramos 2019 ; Dubey et al 2019a ; Mikalef et al 2019a , 2020c ; Wang et al 2018b ) and denial (no support) to experimentation (Alaskar et al 2020 ; Mikalef et al 2020c ; Walker and Brown 2019 ) are other sets of organizational barriers. These challenges need to be addressed with a cultural shift within the enterprise.…”

Study of the Environmental Factors' Effects on Big Data Analytics Adoption in Supply Chain Management

“…Huge volume of valuable data is generated every year within supply chains pushing involved firms to apply BDA "in their supply chain to reduce cycle time, react faster to changes, optimize performance and gain insight into the future" (Feki, 2019). Indeed, "organizations are immersed with data related to their SCM activities" (Alaskar et al, 2020). These data are collected from different sources, such as Web clicks, RFID, tags, sensors, loyalty cards, and barcodes (Al-Qirim et al, 2017;Zhong et al, 2016).…”

Special issue on digital transformation

“…Another paper (Alaskar et al, 2020), which was grounded in the technologyorganisation-environment (TOE) framework, identifies the main factors affecting the intention to adopt big data analytics (BDA) in supply chain management (SCM) by firms based in Saudi Arabia. This study focuses on identifying and analysing the role of competitive pressure as a contextual variable that could moderate the effects of these factors on adoption intention.…”