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Top1. Introduction
One the most prominent information and communication technologies (ICTs) nowadays is big data (BD) and big data analytics (BDA), which refer to the processes and tools used in capturing, storing, distributing, analyzing, and gaining value from massive and diverse data sets obtained from internal and external sources (Gandomi & Haider, 2015, Gangwar, 2018). Firms have begun to see numerous opportunities by introducing BD strategies into their core business and operational functions to enhance the effectiveness of their decision processes and increase their customer satisfaction (Buganza et al., 2020). Firms are also leveraging BD to extend their capabilities into new contexts that exceeds the firm’s boundaries such as supporting the entire supply chain and logistics management (Trabucchi & Buganza, 2019).
The adoption and use of big data analytics in supply chain management (SCM) has attracted the interest of both practitioners and academics (Lin, 2017; Nguyen et al., 2018; Yu et al., 2018). According to Nurmilaakso (2007), the concept of SCM is related to the processes of planning, implementing, and controlling the bidirectional flow of information, products and money between the initial suppliers and final customers through different organizations. According to Feki et al., 2016, firms are adopting BDA in SCM in order to drive decisions and support actions linked to the flows and interactions among supply chain partners.
Simoes et al. (2019) assert that understanding how adoption decisions occur is very important for organizations to improve the outcome of their investments. However, they indicate that much of the current literature assumes that technology adoption is driven primarily by technological and organizational factors, despite the fact that the decision to adopt BDA may have more to do with the external environment in which an organization is located, than rational intraorganizational and technological criteria.
A few studies empirically examined BDA adoption in SCM within the context of developing countries. Moreover, the concepts of BDA and SCM are quite new to the managers within this context compared to the context of developed economies. Hence, top management decisions to adopt BDA in SCM in these firms could be more reliant on environmental factors such as the pressure intensity from competitors, the readiness levels of trading partners, and the degree of support from vendors.
To sum up, despite their crucial role on new technological innovations adoption, very limited research examined the effects of the environmental factors on big data adoption via top management support, particularly within the context of developing economies.
In order to improve our understating of these relationships, the current study combines the institutional theory and the TOE framework to examine the effects of the environmental factors along with the mediating role of top management support on the decision to adopt BDA in SCM in Saudi firms. The review of previous studies focusing on BDA adoption reveals three commonly used environmental factors (Competitive pressure, vendor support and Trading partners’ readiness) in link with intentions. These factors are then considered in the current research.