## 1 Introduction  
Recently, modern manufacturing systems and advanced technologies have been generating data in greater volumes from a variety of sources in real time, a process referred to as \"big data\" [1, 2]. Businesses today are increasingly utilizing advanced technologies like the Internet of Things (IoT), cyber-physical systems and human-robot collaboration that generate massive amounts of data [3, 4, 5, 6, 7, 8, 9, 10, 11, 12]. However, traditional software is incapable of handling such data properly [13]. This is the role of big data analytics (BDA), extracting the value from these data to enhance a firm's competitive advantage [14]. Simply, BDA is the utilization of diverse analytical approaches to handle a variety of large-scale and complex data to extract usable results that serve a firm's performance [15, 16]. Despite many studies having validated the significant effect of BDA on firm performance [17, 18, 19, 20], many firms are still not certain regarding whether or not to adopt BDA for a variety of reasons. They might lack a sufficient understanding of or capabilities with which to realize its prerequisites or to integrate BDA with their existing processes and systems to extract value [13]. Firms also overlook organizational aspects of BDA integration, leading to ineffective BDA adoption [21]. In addition, studies in this regard are still lacking and their results are not unified [19; 21]. However, global competition and exponential growth in advanced technologies like Internet of Things (IoT), cyber-physical systems and human-robot collaboration [4; 5; 6; 7; 8; 9; 10; 11; 12; 22] have forced many firms to invest in technologies that considerably enhance their competitive advantage among rivals. Although firms have invested resources in BDA, many did not attain the maximum strategic benefit because they focused on technological issues and ignored its requirements and strategic role [23]. Therefore, firms must consider other aspects to pursue a data-driven strategy that captures the value of big data [18]. In this regard, many previous studies have considered its managerial and organizational aspects to assist practitioners in effectively exploiting BDA to gain value [24]. One of the most critical issues to address when firms consider adopting BDA is data availability (DA). DA refers to the continuous availability of the required data when and where they are needed [2]. However, some businesses are more critical to DA than others [25]. Thus, storing data on clouds and servers can contribute to DA issues [26; 27]. In this regard, all data sources (e.g., systems, devices, machines, sensors, etc.) must be integrated with BDA and connected to the IoT to generate data [28; 29; 30; 31]. Moreover, advanced networks, i.e., 5G, are essential in linking data sources with cloud storage systems to ensure real-time DA [32; 33]. Another issue to consider is the link between BDA and sustainability, since today, businesses are compelled to base their practices and operations on sustainability (social, environmental, and economic) [25]--in other words, sustainable competitive advantage (SCA). SCA comprises a firm's assets, capabilities and features which are difficult to imitate by competitors [25]. However, to achieve an SCA, firms must effectively utilize their capabilities, i.e., BDA [34; 35], where big data analytics capabilities (BDAC) indicate the firm's ability to recognize and analyze different data sources to provide valuable insights [34]. Many researchers have studied the effect of BDA on sustainable firm performance [36; 37; 25; 38]. Nonetheless, BDA's effect on the environment and its future influence on sustainability is an area for future research [39; 40]. [PERSON] et al. [41] conclude that firms' sustainable performance has become a critical success factor. In fact, insufficient big data analytics capabilities (BDAC) and lagging behind the current trend in big data technologies are considerable barriers to SCA [37].