Abstract
Industry 4.0, a collection of emerging intelligent and digital technologies, has been the main interest of both researchers and practitioners in operations management (OM) in recent years. Despite its proclaimed effectiveness in supply chain (SC) management, empirical studies examining the effects of Industry 4.0 adoption on SC resilience have been underrepresented in the current OM literature. In our study, we explore the effects of 16 Industry 4.0 technologies and IT advancement concerning SC resilience through the mediating roles of SC capabilities with respect to SC collaboration and SC visibility. Following the dynamic resource-based view (RBV), we regard Industry 4.0 adoption and IT advancement as two important IT resources with heterogeneity, SC collaboration and SC visibility as essential SC dynamic capabilities, and SC resilience as competitive advantages. We suggest the combination and evolution of IT resources and dynamic SC capabilities helps firms obtain the competitive advantage regarding SC resilience. Using data from a survey of 408 Chinese manufacturing firms, we reveal Industry 4.0 adoption is positively related to IT advancement and that Industry 4.0 has a nonsignificant impact on SC capabilities, whereas IT advancement has a positive impact on SC capabilities. Additionally, both SC collaboration and visibility positively influence SC resilience and significantly mediate the impacts of Industry 4.0 and IT advancement on SC resilience. Our study offers an enhanced understanding of the specific flows between Industry 4.0 and SC resilience and provides nuanced insights for both literature and practice.
| Original language | English |
|---|---|
| Article number | 108913 |
| Pages (from-to) | 1-19 |
| Number of pages | 19 |
| Journal | International Journal of Production Economics |
| Volume | 262 |
| Early online date | 15 May 2023 |
| DOIs | |
| Publication status | Published (in print/issue) - 1 Aug 2023 |
Bibliographical note
Funding Information:We employ a dynamic resource-based view (RBV) as our theoretical foundation, which combines concepts pertinent to RBV and dynamic capability view (DCV; (Helfat and Peteraf, 2003). Dynamic RBV proposes a dynamic influence path covering the framework of resources-capability-competitive advantage (Barney, 1991; Helfat and Peteraf, 2003; Li et al., 2022; Teece et al., 1997). A fundamental assumption of dynamic RBV is resources and capabilities are heterogeneous across firms (Barney, 1991). Resource heterogeneity derives from two sources: size advantages and first-mover advantages (Peteraf, 1993). We regard Industry 4.0 and IT advancement as two typical types of resource heterogeneity. Specifically, The adoption of Industry 4.0 technologies represent size advantages derived from the scale bases of resources (Ghemawat, 1986). IT advancement corresponds to first-mover advantages of resources, which reflects a firm's strategic focus on adopting and being the first mover of advanced technology (Tigga et al., 2021; Wu et al., 2006; Yeniyurt et al., 2019). Additionally, dynamic RBV maintains there exist unique evolution paths within different types of resources in firms (Helfat and Peteraf, 2003). We thus argue the adoption of Industry 4.0 technologies can be evolved to support IT advancement so as to be further ahead of competitors.However, there have not yet been widely recognized dimensions of various technologies under the complex technology architecture of Industry 4.0. Scholars have studied and elaborated on the dimensions of various Industry 4.0 technologies from different perspectives (Frank et al., 2019; Liao et al., 2017b). For example, Frank et al. (2019) summarized a framework containing two layers for an Industry 4.0 adoption pattern: the base technology layer (i.e., CC, IoT, and BDA) and the front-end technology layer (i.e., ERP, robotics, sensors, AI). Ghobakhloo (2019) categorized Industry 4.0 technologies into five groups: human–machine interaction, sensors and data acquisition technologies, operations technologies, computing technologies, and information and communication technologies. Núñez-Merino et al. (2020) classification was based on the technology life cycle theory: obsolete, mature, emerging, and general approaches to information systems. Bai et al. (2020) categorized Industry 4.0 technologies into physical/manufacturing technologies (e.g., additive manufacturing) and digital/information and communication technologies (e.g., CC, blockchain, BDA, and simulation). Ruel et al. (2023) divided 15 digital technologies into two categories based on the decision-oriented way: operational technologies (i.e., self-driving vehicles, robotics, sensors, collaborative technologies, mobile devices, RFID, 3DP, and CC) and support technologies (i.e., AI, machine learning, BDA, blockchain, VR, and IoT). Based on the literature and firm practices, we categorize 16 technologies into three dimensions according to their applications and typical functions: computing, digitalizing, and integrating technologies (Frank et al., 2019; Ghobakhloo, 2019). First, computing technologies include CC, BDA, and AI (Ghobakhloo, 2019). The common attributes for computing technologies are their powerful computing capability to analyze modeling and predict processing in SC networks (Roden et al., 2017; Toorajipour et al., 2021). Computing technologies can provide cost-effective, smart solutions in complex global SC operations as well as upstream and downstream partnerships (Ghobakhloo, 2019; Subramanian and Abdulrahman, 2017). Second, digitalizing technologies include BI, AV, ERP, simulation, IoT, robotics, and blockchain (Frank et al., 2019; Ghobakhloo, 2019). The common attributes for digitalizing technologies are the digital capability to achieve transformation and digitalization connectivity in SC networking (Manavalan and Jayakrishna, 2019). These digitalizing technologies benefit firms by providing mobilization, real-time data analytics, decision support, high traceability, and collaborative processes. Third, integrating technologies include M2M, 3DP, sensors, RFID, VR, and AR. Common attributes for integrating technologies are related to the physical and virtual hardware and software integration in production facility and SC management (Masood and Egger, 2019). Integrating technologies are conducive to improving the efficiency, accuracy, and controllability, as well as the capability of self-diagnosis, real-time tracking, and error-reduction in production (Javaid et al., 2021; Lam et al., 2019; Meng et al., 2017; Stylios, 2019).For decades, dynamic RBV has demonstrated the crucial roles of capabilities in enhancing a firm's competitive advantage (Helfat and Peteraf, 2003; Teece et al., 1997). SC capability refers to firms' ability to interact with their SC partners to support productivity improvement, network visibility, and real-time feedback in all stages of the SCs (Queiroz et al., 2019). With the development of digital technologies, collaboration and end-to-end visibility have been acknowledged as major drivers of future improvements in SCs and the OM domain (Dolgui & Ivanov, 2022).Based on dynamic RBV, we focus on two representative SC capabilities: SC collaboration and SC visibility. SC collaboration represents relational capability that can improve mutual interaction between SC partners and build SC relationships, whereas SC visibility represents technological capability that can improve SC transparency and enhance information sharing (Wang et al., 2013). Specifically, SC collaboration indicates multiple SC partners build closer relationships and make decisions together to create mutual values (Liao et al., 2017a; Scholten and Schilder, 2015). SC collaboration helps the whole SC achieve a competitive advantage through the coordination of relationships, mutual interests, and risk sharing (Benitez et al., 2021; Li et al., 2021; Zhao et al., 2011). By contrast, SC visibility refers to the ability the entire SCs can have real-time access of data to support planning, monitoring, and control decision-making (Ivanov, 2021). SC visibility significantly benefits the whole SC by enhancing SC responsiveness (Srinivasan and Swink, 2018), facilitating operational performance (Swift et al., 2019), and curbing opportunism (Yang et al., 2021).Through hypotheses 2a–b, 3a–b, and 4a–b, we propose Industry 4.0 and IT advancement might indirectly affect SC resilience through SC capabilities, namely SC collaboration and visibility. The indirect effect of Industry 4.0 technologies and IT advancement on SC resilience can be understood based on the resources-capability-performance framework proposed in dynamic RBV (Barney, 1991; Grant, 1991). According to dynamic RBV, when IT resources (i.e., Industry 4.0 and IT advancement) are combined and used, advanced resources create capabilities (i.e., SC collaboration and SC visibility). An SC equipped with SC collaboration and SC visibility is more capable of responding to and recovering from SC disruptions and risks, resulting in stronger SC resilience (Fatorachian and Kazemi, 2021; Queiroz et al., 2019). Industry 4.0 and IT advancement might positively and indirectly affect SC resilience through SC collaboration and visibility. We propose the following hypotheses.Fig. 2 shows SEM results with significant paths and standard coefficients. The impact of the adoption of Industry 4.0 technologies on IT advancement was positive and significant, supporting H1. The results showed implementing Industry 4.0 technologies had a nonsignificant effect on SC collaboration and SC visibility, rejecting H2a and H2b, whereas IT advancement is significantly and positively related to SC collaboration and visibility, supporting H3a and H3b. Both SC collaboration and visibility have significant and positive effects on SC resilience, thus supporting H4a and H4b.All the results shown in Appendix B demonstrated consistency with our previous model. To test H5a-b and H6a-b, we used the bootstrapping method with 5000 resamples and tested the significance of the indirect effects. Results showed Industry 4.0 had a positive indirect effect on SC resilience (total effect = 0.30; t = 6.12, p < 0.001) through SC collaboration (indirect effect = 0.10; t = 4.53, p < 0.001) with a 95% CI not containing zero (95% CI [0.057, 0.143]) and through SC visibility (indirect effect = 0.14; t = 5.12, p < 0.001) with a 95% CI not containing zero (95% CI [0.087, 0.191]), thus supporting H5a and H5b. Results also showed IT advancement still had a positive indirect effect on SC resilience (total effect = 0.53; t = 15.46, p < 0.001) through SC collaboration (indirect effect = 0.17; t = 6.46, p < 0.001) with a 95% CI not containing zero (95% CI [0.124, 0.230]) and through SC visibility (indirect effect = 0.21; t = 7.47, p < 0.001) with a 95% CI not containing zero (95% CI [0.155, 0.261]), thus supporting H6a and H6b. In summary, the results of the alternative measurement of the SC resilience approach were consistent with those of the original model, ensuring the robustness of our findings.Our results showed the adoption of Industry 4.0 technologies positively affects IT advancement, which are partially supported by previous studies (i.e., Ghobakhloo, 2019; Tortorella et al., 2019; Veile et al., 2019). The results indicated a firm consistently takes the lead in adopting or orchestrating diverse advanced Industry 4.0 technologies can generate superior IT advancement in their SC management. The results also corresponded with dynamic RBV, indicating there are patterns and paths through which different kinds of resource heterogeneity can be transformed internally (Peteraf, 1993). Firms’ first-mover advantage (e.g., IT advancement) ahead of industry and competitors can be achieved in case of the size advantage of Industry 4.0 adoption is accumulated to a considerable degree.
Publisher Copyright:
© 2023 The Authors
Keywords
- Industry 4.0
- Digital technologies
- IT advancement
- Supply chain collaboration
- Supply chain visibility
- Supply chain resilience
