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 [
25,
36,
37,
38]. Nonetheless, BDA’s effect on the environment and its future influence on sustainability is an area for future research [
39,
40]. Hao 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].
In addition to BDAC, innovation capabilities (IC) are of essential importance in enhancing SCA, where IC indicates a firm’s ability to introduce and define innovative ideas and deploy them in designing new products or enhancing the current products [
42]. Satupa et al. [
42] affirmed that innovation is a key factor in securing competitive advantage. Moreover, many firms consider big data to be a valuable resource that promotes innovation [
37,
42] which, in turn, leads to competitive advantage [
43,
44]. Recent literature has posited that creating sustainable new capabilities, like BDA, is necessary for the innovation process because doing so has the potential to support the positive impact of big data on innovation sustainability [
41]. Many studies have investigated the role of innovation in firms’ performance and competitive advantage [
24,
42]. Nonetheless, until now, the literature has lacked sufficient evidence clarifying the relationship between BDA, innovative performance and firm performance as an index for its competitive advantage [
42].
Most existing BDA studies have focused on its technological aspects; however, there is a lack of studies on BDA in strategic management [
21]. Indeed, the gap in the BDA area is multifaceted since the topic is recent and the first publication referring to BDA appeared in 2012 [
21]. BDA studies lack multidisciplinary synthesis in exploring the potential value of BDA in the context of strategic management [
45]. The importance and role of BDA in an organization’s performance lacks sufficient study and the link requires further exploration [
19]. In addition, theoretical and empirical research on BDA’s effect on firms’ performance, considering sustainability, is still lacking [
39,
46]. Moreover, there is insufficient empirical research on the value extracted from big data for sustainable innovation and firm improvement [
41].
Drawing on these identified research gaps, this study aims to propose a strategic conceptual model that considers the effect of DA on BDA and the effect of BDA and IC on SCA. This study also attempts to benefit practitioners by providing an explanation and statistical validation for the proposed model. This study makes many contributions to the field. Firstly, it considers different topics and investigates their roles in attaining SCA; secondly, it provides empirical evidence supporting the extant literature; and thirdly, it considers three diverse aspects of sustainability in one model. Consequently, this study aims to address the following research questions: (1) How does DA affect BDAC? (2) How does BDAC contribute to a firm’s IC? (3) How does BDAC enhance a firm’s SCA? (4) What is the relationship between IC and a firm’s SCA?
5. Discussion
The purpose of this study was to investigate the influence of BDA and innovation as dynamic capabilities on firms’ SCA. The empirical results demonstrate that BDAC has a significant effect on IC and that IC enhances SCA but BDA capabilities do not. The outcome of this study makes several contributions to the existing body of knowledge in this field. Specifically, this study is the first to explore the effect of DA level on BDAC and the role of BDAC and IC in attaining SCA.
Our findings prove that an increase in DA level positively affects BDAC; the results of the conceptual model support H1. Indeed, when a firm’s system is integrated with BDA—all components are connected to IoT, and backup system is effective—the availability of data will increase, which, in turn, will enhance BDAC. However, the BDAC will not be enhanced if the DA in the manufacturing companies is not supported with IoT technology enablers.
In addition, an increase in BDAC has a positive effect on IC, so the results of the model support H2. Therefore, firms implementing BDA are more innovative than others that do not [
60]. This result is consistent with the findings of Mikalef [
66] and Lozada [
83]. Moreover, an increase in IC has a positive effect on SCA, so the results of this study support H4. This result confirms the findings of many similar studies [
14,
42,
84]. In practice, these results are obtained when a firm continuously utilizes new and up-to-date technologies in processes, introduces new products, uses the latest technologies in its products or adopts innovativeness in marketing and promotion. Additionally, the results emphasize that BDAC has a key role in enhancing IC by prompting a firm to adopt the latest technologies in products and processes, introduce new products and innovate in marketing and promotion. In this regard, if the manufacturing companies did not support the BDAC variables, the ability of the companies to cope with the rapid advancements in technologies and market changes will be reduced. Furthermore, whilst reviewing the related literature, we observed that very few studies investigated the BDAC effect on firm SCA. The only study found, to the extent of our review, was a study published in 2020 by Kamble et al. [
36], which was conducted at the supply chain level in the agriculture industry and concluded that BDAC is an enabler of sustainable performance. Moreover, no other study has previously investigated the role of DA in reinforcing BDAC. Therefore, this study is the first in conceptualizing the DA construct and synthesizing the SCA construct at the firm level.
In practice, this model is applicable and very efficient in highly customized and technologically advanced production environments where massive amounts of data are generated. Therefore, this model is not useful when it is employed in traditional mass production systems where no highly customized products are needed and the big data will not benefit the producers compared with amount of investment in this filed. In addition to this, the model is not applicable when firms do not have an IT system or enough flexibility to adapt to a wide range of market requirements or to respond to all customer requirements. In such cases, the BD and BDAC may not impact the IC and thus the SCA will not be enhanced. The same can be considered for companies of low competition or market monopoly.
Finally, this study examines DA issues, e.g., network requirements, as a prerequisite for BDA. In addition, this study assists managers in deciding where to invest and allocate resources, focusing on dynamic capabilities as essential enablers for SCA at the firm level in the manufacturing industry.
7. Conclusions
In answering the research questions introduced by this study, the results confirm that DA has a significant influence on BDAC, which, in turn, has a significant role in enhancing the relationship with IC. On the other hand, BDAC does not have a significant role in enhancing a firm’s SCA. In addition, the results indicate that IC is a significant mediator in the BDAC and SCA relationship. Thus, managers can enhance a firm’s SCA by investing in innovation; this might put the firm at the top in rivalries. In addition, they also should be aware of the prerequisites of and issues with BDA implementation.
A firm can make decisions based on the findings of each hypothesis. For example, in H1, since DA has a significant and positive effect on BDAC, firms must digitize all firm levels, i.e., enterprise, operational and manufacturing, to enhance data availability for BDA. At the manufacturing level, firms can digitalize their manufacturing systems by connecting smart entities such as machines, products, tools and devices to the IoT system, which in turn provides a stream of data to BDA. Moreover, firms will lose the benefits of horizontal and vertical connectivity if they do not employ a proper backup system for their generated BD.
H2 confirms that BDAC have a significant and positive role in enhancing firms’ IC since continuous examination of the generated BD allows firms to enhance the main pillars of the innovation process, which are product, processes, market and organization innovation. Thus, BDAC maintains the firms’ awareness of new technologies to which they must adapt in order to enhance their processes and products and thus be able to distinguish themselves among other competitors in the market. It is concluded that BDAC will enhance innovation management capabilities in manufacturing companies.
In H3, it is found that BDAC as a dynamic capability has positive effect on firms’ SCA. Our results have not shown a significant effect of BDAC on firms’ SCA. This result is justifiable; BDAC do not ensure that firms will implement the analyzed data and the available reports in the three aspects of sustainability.
As a dynamic capability, IC in H4 has a positive and significant effect on firms’ SCA. This encourages the decision-makers to invest in technologies where the innovation capabilities are enhanced. This will reinforce the competitive edge of the firm.
Clearly, using BDA as an advanced analytical tool would be supportive of firms’ managerial decision-making, as posited by [
85]. The adoption of BDAC in a firm offers several opportunities for improving the IC of firms in the manufacturing industry.
This study proposes a beneficial framework that handles many topics such as DA, BDAC, IC and their roles in reinforcing the SCA of manufacturing firms. However, there is still room for further improvement; for example, the approach of this study limits the generalization of findings to the service sector. Therefore, a possible future work could be a sector-based study approach (i.e., either industrial or service sectors), where more specific variables can be added to the research questions to address specific variables in each sector and thus more accurate results could be obtained and sector differentiation could be precisely analyzed.