Identifying Key Success Factors for Industry 4.0 Implementation: An Empirical Analysis Using SEM and fsQCA

Abstract

Industry 4.0 technologies have been gaining significant momentum in recent years. Despite widespread adoption, many companies struggle with the successful implementation of these technologies. This study aims to identify the critical success factors for implementing Industry 4.0 technologies and to examine the effects of various factor combinations. Using the technology–organization–environment framework and the practice-based view, this paper proposes a comprehensive research model. This study employs a hybrid approach combining structural equation modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA) to analyze survey data collected from 202 Chinese manufacturing firms. The SEM results indicate that top management support, technology competence, supplier support, and pilot projects are significantly associated with successful implementation. However, the fsQCA results reveal that individual factors alone are insufficient for success; rather, it is the combinations of these factors that drive successful implementation. Specifically, three key combinations lead to success: (1) top management support and technology competence; (2) top management support and supplier support; and (3) technology competence, supplier support, and pilot projects. By recognizing these combinations, manufacturing firms can develop more effective implementation strategies for Industry 4.0 technologies.

1. Introduction

Industry 4.0 originally referred to the digitalization of the manufacturing industry, but it has since become virtually synonymous with the “Fourth Industrial Revolution”, encompassing the digital transformation of all industries [1]. This revolution is characterized by the increasing use of new technologies, leading to greater automation of manufacturing and service processes and a deeper interconnection between the physical and virtual worlds [2]. A constellation of emerging technologies forms the backbone of Industry 4.0, including the Internet of Things (IoT), artificial intelligence (AI), cloud computing, big data and analytics, 3D printing, and autonomous robots [3,4,5,6,7]. The opportunities and benefits arising from these technologies are substantial. Beyond creating new value, Industry 4.0 technologies enhance existing management principles and practices [8]. Studies have shown that Industry 4.0 technologies contribute to operational performance [9,10,11], financial performance [12,13], innovation [14], and sustainability [15].

The benefits of Industry 4.0 technologies have motivated an increasing number of firms to adopt them. However, fully realizing these benefits requires successful implementation, which is not guaranteed by mere adoption [16]. According to a survey of smart factory projects in over 1000 manufacturing firms, the success rate was only 14% [12]. This highlights the need for more research on the implementation of Industry 4.0 technologies.

Researchers are calling for more studies on the implementation of Industry 4.0. Ivanov et al. [17] aimed to understand the current state of research in Industry 4.0 across different disciplines, providing insights and opportunities for future research in operations management. Xu et al. [18] surveyed the state of the art in Industry 4.0 as it pertains to various industries. Pozzi et al. [19] highlighted the need for empirical studies, noting that the lack of such research necessitates academic contributions on the critical success factors for Industry 4.0 implementations and their subsequent improvements for manufacturing businesses. Empirical studies on the critical success factors for the implementation of Industry 4.0 technologies are particularly needed. In response to this research gap, our study aims to empirically identify the critical factors for the successful implementation of Industry 4.0 technologies. Therefore, the first research question that this study addresses is as follows: What are the critical success factors for the implementation of Industry 4.0 technologies?

Previous studies have investigated the implementation of Industry 4.0 technologies in terms of critical success factors [19,20], challenges [21], roadblocks/barriers [22,23], and other influencing factors [24,25]. However, little attention has been paid to the interplay or interaction between these factors. The causal combinations of factors for implementation success remain unexplored. From a configurational perspective, multiple combinations of factors could lead to the same successful outcome. Consequently, it is reasonable to assume that different combinations of factors can lead to successful implementation. Therefore, the second research question is the following: What factor combinations lead to the successful implementation of Industry 4.0 technologies?

To answer the research questions, this paper proposes a research model based on the technology–organization–environment (TOE) framework [26] and the practice-based view. SEM and fsQCA are employed to analyze the model using survey data from 202 Chinese manufacturing firms. SEM reveals the net effects of each factor, while fsQCA provides an in-depth understanding of the combinations of factors from a configurational perspective [27,28].

The reasons for choosing Chinese manufacturing firms are as follows. China is a global manufacturing leader with a diverse industrial base, making it ideal for studying Industry 4.0 implementation. Government initiatives such as “Made in China 2025” support digital transformation and smart manufacturing, encouraging widespread technology adoption. Chinese firms’ rapid integration of new technologies offers rich empirical data for analysis. The competitive nature of the sector drives continuous innovation, providing insights into the critical success factors for Industry 4.0. Understanding these implementations in China has far-reaching implications, offering valuable lessons for global manufacturing practices and policies, thereby enhancing the overall understanding and advancement of smart manufacturing worldwide.

This study makes contributions to the literature on the implementation of Industry 4.0 technologies by empirically identifying critical success factors and exploring the factor combinations that lead to successful implementation. By employing a hybrid approach of SEM and fsQCA, this research offers a nuanced understanding of both the individual and combined effects of various factors. Focusing on Chinese manufacturing firms provides valuable insights due to China’s leading role in global manufacturing and rapid technological integration, thereby offering practical implications for both local and international contexts in advancing smart manufacturing practices. The findings indicate that firms do not need to excel in all areas to implement Industry 4.0 technologies successfully. Instead, they can focus on a specific set of factors according to their unique characteristics.

The remainder of the paper is structured as follows: Section 2 presents the literature review. In Section 3, the research model and hypotheses are proposed. Section 4 and Section 5 detail the method and results, respectively. Section 6 provides a discussion of the findings, followed by conclusions in Section 7.

2. Literature Review

2.1. Industry 4.0 Technologies

Technologies such as the IoT, AI, cloud computing, big data, 3D printing, and autonomous robots serve as the foundation of Industry 4.0 [2,29]. They facilitate digital transformation and integration, boosting efficiency and innovation. The IoT enables seamless communication, AI enhances process optimization, and 3D printing facilitates rapid prototyping. Successful implementation of these technologies is crucial for competitiveness, driving the convergence of physical and digital realms in Industry 4.0.

Bartodziej [29] conducted an empirical study to assess the potential of various Industry 4.0 technologies for achieving end-to-end digital integration in production logistics, focusing on their respective functions. Zhou et al. [30] discussed pertinent aspects of Industry 4.0, including strategic planning, key technologies, opportunities, and challenges. Alcácer and Cruz-Machado [31] analyzed the Industry 4.0 landscape, delineating enabling technologies and systems within manufacturing environments. Cifone et al. [32] presented exploratory quantitative research aimed at elucidating how digital technologies can bolster lean practices, enhancing the understanding of leveraging digitalization for operational improvements. Zheng et al. [33] conducted a systematic literature review to address the following research question: What are the applications of Industry 4.0 enabling technologies in the business processes of manufacturing companies?

Although Industry 4.0 is based on the integration of information and communication technologies (ICT) and industrial technologies [30,31], there is no universally accepted set of Industry 4.0 technologies [32,33]. Different researchers propose varied technology lists, as illustrated in

Table 1. Industry 4.0 technologies in the literature.

		Technologies
No.	Source	A	B	C	D	E	F	G	H	I	J	K
1	Dalenogare et al. [9]	✓		✓	✓			✓
2	Tortorella et al. [34]	✓	✓	✓
3	Yu and Schweisfurth [35]	✓	✓	✓	✓	✓	✓	✓		✓
4	Moeuf et al. [36]	✓	✓	✓		✓		✓	✓	✓
5	Schmidt et al. [37]	✓	✓	✓					✓			✓
6	Stentoft and Rajkumar [38]	✓	✓	✓	✓	✓	✓	✓		✓	✓	✓
7	Rossini et al. [39]	✓	✓	✓	✓	✓	✓				✓	✓
8	Liao et al. [40]	✓		✓	✓			✓			✓
9	Vaidya et al. [41]	✓	✓	✓	✓	✓	✓	✓	✓
10	Zhou et al. [30]	✓	✓	✓					✓			✓
11	Bartodziej [29]	✓	✓	✓	✓	✓			✓	✓		✓
12	Kang et al. [42]	✓	✓	✓	✓				✓
13	Zheng et al. [33]	✓	✓	✓	✓	✓	✓	✓	✓		✓
14	Lu [43]	✓	✓	✓								✓
15	Büchi et al. [3]	✓	✓	✓	✓		✓	✓		✓
16	Wang et al. [44]	✓	✓	✓							✓
17	Da Silva et al. [23]	✓	✓	✓		✓	✓	✓	✓	✓
18	Oztemel and Gursev [5]	✓	✓	✓		✓	✓	✓	✓
19	Xu et al. [18]	✓	✓						✓
20	Pacchini et al. [45]	✓	✓	✓	✓	✓	✓		✓		✓
21	Pollak et al. [46]	✓	✓	✓	✓	✓	✓
22	Ghobakhloo [47]	✓	✓	✓	✓	✓	✓	✓	✓	✓
23	Zhong et al. [48]	✓	✓	✓					✓
24	Tang and Veelenturf [49]		✓		✓	✓					✓
25	Alcácer and Cruz-Machado [31]	✓	✓	✓	✓	✓	✓	✓		✓
26	Nayernia et al. [50]	✓	✓	✓	✓	✓	✓	✓		✓	✓
27	Choi et al. [4]		✓		✓	✓	✓				✓	✓
	Frequency	25	25	24	17	16	14	13	13	9	9	7

Notes: A: Cloud computing; B: IoT; C: Big data and analytics; D: Additive manufacturing; E: Autonomous robots; F: AR; G: Simulation; H: CPS; I: Cybersecurity; J: AI; K: Mobile technology; ✓: Source in the literature.

.

In addition to the 11 technologies listed in Table 1, virtual reality (VR), blockchain, and manufacturing execution system (MES) are also recognized as important Industry 4.0 technologies [4,9,33,51,52]. Hence, the Industry 4.0 technologies included in this study are cloud computing, IoT, big data and analytics, additive manufacturing (3D printing), autonomous robots, augmented reality (AR), simulation, cyber-physical system (CPS), cybersecurity, artificial intelligence (AI), mobile technology, VR, blockchain, and MES. According to Frank et al. [53], Industry 4.0 technologies can be categorized into base and front-end technologies. Front-end technologies further consist of smart supply chain technologies, smart manufacturing technologies, smart working technologies, and smart product technologies. As depicted in Figure 1, base technologies (e.g., AI, big data, and cybersecurity) furnish front-end technologies (e.g., autonomous robots) with connectivity, intelligence, and security.

2.2. TOE Framework and Practice-Based View

The TOE framework is a widely accepted theory explaining the implementation of technological innovations at the organizational level [26,54]. It offers a comprehensive perspective by considering technological, organizational, and environmental contexts simultaneously. Technological context encompasses both existing and emerging technologies relevant to the organization [26,54]. Organizational context refers to the organization’s characteristics and resources, including structure, size, and resources [26,54,55]. Environmental context encompasses industry structure, suppliers, and institutional environment [26,54,56].

The TOE framework, described as “generic”, serves as a broad framework under which various factors can be categorized [54]. Its effectiveness has been confirmed by empirical studies on the adoption and implementation of diverse technologies, such as AI, AR, cloud computing, radio frequency identification (RFID), big data, and e-business [57,58,59,60,61].

In addition to technological, organizational, and environmental contexts, practices also play a crucial role in technology implementation. Industry 4.0 researchers advocate for more studies to explore best practices supporting Industry 4.0 implementation [25,62]. Bromiley and Rau [63] introduced the practice-based view, emphasizing the importance of practices. This perspective suggests that publicly known practices are imitable and transferable, enabling organizations to achieve higher performance. Its focus on practices and their interactions with other firm-level factors makes it well-suited for operations research [64].

2.3. Affecting Factors for the Implementation of Industry 4.0

In Industry 4.0 research, there is often a conflation of ‘implementation’ and ‘adoption’ [65,66]. Technically, adoption precedes implementation in the technology assimilation process [67,68,69,70]. Implementation entails the deployment and utilization of technologies, whereas adoption refers to the decision-making process involved in introducing technologies [56,68,70,71]. Researchers have predominantly focused on factors influencing the adoption of Industry 4.0 technologies, investigating enablers, barriers, and antecedents [10,62,72,73,74,75].

However, as noted by Ivanov et al. [17], while Industry 4.0 has gained momentum in both practice and research, the field of operations management studies in this area remains relatively underdeveloped. Through a combination of survey findings and literature analysis, Ivanov et al. [17] constructed structural and conceptual frameworks aimed at understanding the current state of knowledge and identifying future research opportunities for operations management scholars.

Given the significant failure rate observed in many Industry 4.0 projects [12,76], identifying critical success factors (CSFs) becomes imperative. As illustrated in Table 2, researchers have explored various dimensions of Industry 4.0 implementation, encompassing critical success factors [19,20], risks [77], challenges [21], roadblocks/barriers [22,23], and other influential factors [24,25]. These concepts exhibit some degree of overlap, contributing to a substantial number of related factors. Researchers have predominantly relied on expert-opinion-based methodologies such as the Delphi method, best–worst method (BWM), decision making trial and evaluation laboratory (DEMATEL), and interpretative structural modeling (ISM), with empirical studies being relatively scarce [19,21,77,78].

Furthermore, existing research has predominantly focused on examining the individual effects of factors, overlooking the combined effects of factor configurations. Mithas et al. [6] advocated for a configurational perspective in Industry 4.0 research, suggesting that multiple pathways to achieving a desired outcome exist. Specifically, Misangyi et al. [27] identified four fundamental elements characterizing this emerging neo-configurational perspective: (a) conceptualizing cases as set-theoretic configurations, (b) calibrating cases’ memberships into sets, (c) viewing causality through necessity and sufficiency relations between sets, and (d) conducting counterfactual analysis of unobserved configurations. Leveraging data from high-technology firms, Fiss [79] empirically investigated configurations based on the Miles and Snow typology using fsQCA. The findings demonstrated how this theoretical perspective enables a nuanced analysis of causal core, periphery, and asymmetry, thereby shifting the focus towards midrange theories of causal processes.

In essence, the plethora of Industry 4.0 implementation outcomes underscore the multifaceted nature of success factors, highlighting the importance of considering various factor combinations to achieve implementation success.

Table 2. Studies on the factors affecting the implementation of Industry 4.0.

Study	Method	Factors Affecting the Implementation of Industry 4.0
Pozzi et al. [19]	Case study	CSFs: Continuous improvement/lean culture, top management leadership, inter-functional teams, pilot projects, etc.
Moeuf et al. [80]	Delphi method	CSFs: Employee training, prior study, regular use of available data, stronger presence of a manager, etc.
Sony et al. [20]	Survey	CSFs: Top management support, sustainability, alignment with organizational strategy, change management, etc.
Sony and Naik [78]	Review	CSFs: Alignment with organizational strategy, top management support, employee skills, change management, cybersecurity, etc.
Hoyer et al. [81]	Review	Key factors: Political support, cooperation, IT infrastructure, knowledge and skills, lean manufacturing, etc.
Ghadimi et al. [77]	BWM	Risks: Lack of standards and methodical approach, capital risk, insufficient talent, potential delay to manufacturing, etc.
Virmani et al. [22]	GTMA	Roadblocks: Management roadblock, operational roadblock, human resource roadblock, procedural roadblock, behavioral roadblock.
Ghobakhloo [24]	ISM	Determinants: Perceived benefits, financial resources, management support, strategic road mapping, employees’ qualification, etc.
Karadayi-Usta [66]	ISM	Challenges: Workers’ resistance, lack of perseverance, lack of cooperation with suppliers, finding qualified personnel, etc.
Bakhtari et al. [82]	ISM	Challenges: Lack of education and skills training, lack of skilled workforce, data security, lack of technology integration and compatibility, etc.
James et al. [21]	BWM and DEMATEL	HRM challenges: Lack of awareness of employees, inadequate technical skills, employee confidence, training, etc.
Jena and Patel [83]	Fuzzy technique	Employee flexibility, physical and cyber system integration, real-time data sharing and monitoring, lack of standards, etc.
Mueller [84]	Case study	Barriers: Employee acceptance, lack of competency, lack of cooperation, lack of strategy and targets, etc.
Veile et al. [25]	Case study	Corporate culture and communication, personnel, organization structure, safety and security, preparation, etc.
Genest and Gamache [85]	Review	Prerequisites: Knowledge, business strategies, lean, financial capacity, data accessibility, agility in manufacturing, etc.
Da Silva et al. [23]	Review	Barriers: Technology complexity, poor infrastructure, lack of suppliers, lack of skilled labor, internal resistance, etc.

Table 2. Studies on the factors affecting the implementation of Industry 4.0.

Study	Method	Factors Affecting the Implementation of Industry 4.0
Pozzi et al. [19]	Case study	CSFs: Continuous improvement/lean culture, top management leadership, inter-functional teams, pilot projects, etc.
Moeuf et al. [80]	Delphi method	CSFs: Employee training, prior study, regular use of available data, stronger presence of a manager, etc.
Sony et al. [20]	Survey	CSFs: Top management support, sustainability, alignment with organizational strategy, change management, etc.
Sony and Naik [78]	Review	CSFs: Alignment with organizational strategy, top management support, employee skills, change management, cybersecurity, etc.
Hoyer et al. [81]	Review	Key factors: Political support, cooperation, IT infrastructure, knowledge and skills, lean manufacturing, etc.
Ghadimi et al. [77]	BWM	Risks: Lack of standards and methodical approach, capital risk, insufficient talent, potential delay to manufacturing, etc.
Virmani et al. [22]	GTMA	Roadblocks: Management roadblock, operational roadblock, human resource roadblock, procedural roadblock, behavioral roadblock.
Ghobakhloo [24]	ISM	Determinants: Perceived benefits, financial resources, management support, strategic road mapping, employees’ qualification, etc.
Karadayi-Usta [66]	ISM	Challenges: Workers’ resistance, lack of perseverance, lack of cooperation with suppliers, finding qualified personnel, etc.
Bakhtari et al. [82]	ISM	Challenges: Lack of education and skills training, lack of skilled workforce, data security, lack of technology integration and compatibility, etc.
James et al. [21]	BWM and DEMATEL	HRM challenges: Lack of awareness of employees, inadequate technical skills, employee confidence, training, etc.
Jena and Patel [83]	Fuzzy technique	Employee flexibility, physical and cyber system integration, real-time data sharing and monitoring, lack of standards, etc.
Mueller [84]	Case study	Barriers: Employee acceptance, lack of competency, lack of cooperation, lack of strategy and targets, etc.
Veile et al. [25]	Case study	Corporate culture and communication, personnel, organization structure, safety and security, preparation, etc.
Genest and Gamache [85]	Review	Prerequisites: Knowledge, business strategies, lean, financial capacity, data accessibility, agility in manufacturing, etc.
Da Silva et al. [23]	Review	Barriers: Technology complexity, poor infrastructure, lack of suppliers, lack of skilled labor, internal resistance, etc.

The factors listed in Table 2 are numerous and varied. To identify the most relevant success factors for investigation, further research was conducted on the implementation of advanced manufacturing technology (AMT) and information technology/system (IT/IS). Industry 4.0, being a form of AMT, is fundamentally based on information technologies [31]. As illustrated in

Table 3. Critical success factors and sources.

Dimension	Factor	Sources
		Industry 4.0	AMT	IT/IS
Technological	Complexity	Da Silva et al. [23]	Aiman-Smith and Green [86]	Cooper and Zmud [67]
	Compatibility	Bakhtari et al. [82]	Klein and Sorra [87]	Cooper and Zmud [67]
Organizational	Top management support	Ghobakhloo [24], Pozzi et al. [19], Sony et al. [20]	Klein et al. [88], Burcher et al. [89]	Sharma and Yetton [90], Thong et al. [91]
	Technology competence	James et al. [21], Karadayi-Usta [66]	Efstathiades et al. [92], Stornelli et al. [93]	Wei et al. [71], Zhu et al. [56]
Environmental	Supplier support	Da Silva et al. [23], Karadayi-Usta [66]	Efstathiades et al. [94], Voss [70]	Thong et al. [95], Thong et al. [91]
Practice	Pilot project	Pozzi et al. [19], Tortorella et al. [96]	Burcher et al. [89], Lewis and Boyer [97]	Angeles [98]

, critical success factors consistently appearing across these three fields include complexity, compatibility, top management support, technology competence, supplier support, and pilot projects.

2.4. Summary

In summary, the literature review highlights the dearth of empirical studies on CSFs for Industry 4.0 implementation and the limited understanding of their interplay. To address this, the present study combines SEM and fsQCA. SEM quantifies individual factors’ effects, while fsQCA explores their interactions. SEM offers a quantitative framework to analyze the net effects of individual factors on the implementation success of Industry 4.0 technologies. By quantifying the relationships between variables, SEM provides valuable insights into the direct impact of each factor, thus elucidating their relative importance in driving implementation success. However, SEM alone may not capture the nuanced interactions and contingencies among the factors. To address this limitation, fsQCA is employed to delve deeper into the complex relationships between the CSFs. Fuzzy-set QCA adopts a configurational perspective, acknowledging that multiple combinations of factors can lead to implementation success. By analyzing various configurations of CSFs, fsQCA unveils the synergistic effects and contingencies among the factors, offering a more holistic understanding of their combined influence on implementation outcomes. This approach aligns with the call for research to explore the combined effects of factors, as highlighted in the literature review. Ultimately, the study aims to advance understanding of Industry 4.0 implementation complexities, benefiting practitioners and policymakers.

3. Research Model and Hypothesis Development

Building upon the TOE framework and the practice-based view, this paper proposes a research model (as depicted in Figure 2) to investigate the impact of complexity, compatibility, top management support, technology competence, supplier support, and pilot projects on the implementation of Industry 4.0 technologies. The left segment of the model will undergo testing using SEM to assess the individual net effects of each factor. Conversely, the right segment will be explored utilizing fsQCA to unveil the combinations of factors that drive implementation success. This dual-method approach enables a comprehensive examination of both the independent contributions of individual factors and the interactive effects of factor combinations on the successful implementation of Industry 4.0 technologies.

3.1. Technological Factors

This subsection will propose hypotheses for the technological factors, specifically complexity and compatibility.

3.1.1. Complexity

Complexity, as defined in the literature, denotes the degree of difficulty in comprehending and utilizing an innovation [99]. Numerous studies have underscored the significance of technology complexity as a formidable obstacle for firms contemplating the adoption of Industry 4.0 technologies [59,100]. Indeed, concerns have been raised regarding the detrimental effects of technology complexity on implementation success [23]. This complexity poses challenges for organizations in effectively deploying and integrating technologies with one another [101], potentially undermining the synergistic benefits between different technological components. Furthermore, heightened complexity often coincides with increased mental workload and stress among employees [86]. Such conditions can lead to slower learning curves and diminished levels of satisfaction among employees, ultimately impeding their ability to fully harness the capabilities of the new technologies. Consequently, this may curtail the realization of the full potential offered by Industry 4.0. Expanding on this perspective, it is essential for organizations to recognize the nuanced ways in which technology complexity can impact their implementation efforts. By understanding these challenges, organizations can develop tailored strategies to mitigate complexity-related barriers and enhance the likelihood of successful implementation. To further investigate this relationship, this paper proposes the following hypothesis:

Hypothesis 1 (H1).

Complexity negatively influences the implementation success of Industry 4.0 technologies.

This hypothesis suggests that as the complexity of technologies increases, the likelihood of successful implementation decreases.

3.1.2. Compatibility

Compatibility, defined as “the degree to which an innovation is perceived as consistent with the existing values, past experiences, and needs of potential adopters” [99], plays a pivotal role in technology implementation processes. It entails a mutual adaptation process, wherein both the technology and the organization undergo changes to better align with each other [102]. A lack of compatibility can pose a significant challenge to the successful implementation of Industry 4.0 initiatives [82].

The level of compatibility between a new technology and existing technologies, processes, and work styles directly influences the amount of adaptation work required. When a technology seamlessly integrates with the organization’s current setup, it minimizes the need for extensive modifications. Conversely, if a technology is incompatible with the organization’s practices, infrastructure, or culture, it can deter manufacturing firms from adopting smart manufacturing digital technologies [103]. The fear of facing substantial adaptation efforts and the risk of failure often discourages firms from pursuing such implementations.

Supporting this perspective, the study conducted by Masood and Egger [58] highlighted compatibility as a critical success factor for the successful implementation of AR technologies. Based on these observations, this paper proposes the following hypothesis:

Hypothesis 2 (H2).

Compatibility positively influences the implementation success of Industry 4.0 technologies.

3.2. Organizational Factors

This subsection will present hypotheses for the organizational factors, specifically focusing on top management support and technology competence.

3.2.1. Top Management Support

Top management support, encompassing both financial and political backing from the company’s leadership [78], is crucial for the successful implementation of Industry 4.0 initiatives. The adoption of Industry 4.0 technologies often requires substantial resources, both human and physical [24,66]. Top management plays a pivotal role in securing these resources and acting as change agents to facilitate the assimilation of new technologies into the organization [91].

Given that Industry 4.0 emphasizes the utilization of new technologies to enhance vertical, horizontal, and end-to-end integration [104], the involvement of various functional areas is imperative [24]. Top management support becomes essential in mitigating potential conflicts or resistance that may arise during the implementation process, thereby breaking down barriers within and across departments and organizations [105]. Furthermore, the endorsement of top managers signifies their commitment to embracing new technologies, fostering an environment conducive to innovation, and encouraging employees to fully engage in implementation efforts [90].

Hypothesis 3 (H3).

Top management support positively influences the implementation success of Industry 4.0 technologies.

3.2.2. Technology Competence

Technology competence, often referred to as technology readiness, encompasses two critical components: technology infrastructure and employee knowledge and skills [56,105]. The technology infrastructure of a firm includes its existing hardware and software, which form the backbone for the implementation of Industry 4.0 technologies. A well-established technology infrastructure provides a solid foundation, allowing afirm to leverage existing resources and capabilities, thereby saving both time and money. For instance, technologies such asbig data and simulation require extensive data input. If a firm’s current information system can readily provide the necessary data, the implementation process becomes considerably smoother.

In addition to technology infrastructure, the competence of employees is paramount in the successful adoption of Industry 4.0 technologies [21,47,106]. Industry 4.0 demands advanced knowledge and skills from employees to effectively utilize and integrate new technologies into their work processes. Competent employees are better equipped to understand the intricacies of emerging technologies, adapt to changes more swiftly, and collaborate seamlessly with suppliers. Conversely, a lack of competence among employees can hinder the implementation process. Without the requisite knowledge and skills, employees may struggle to grasp the functionalities of new technologies, leading to inefficiencies and potential resistance.

Furthermore, inadequate technology competence among employees can exacerbate fears of job displacement and resistance to change [62]. Employees may perceive new technologies as threats to their livelihoods, resulting in heightened apprehension and reluctance to embrace innovation. Therefore, fostering technology competence among employees is essential for mitigating resistance and ensuring smooth implementation.

Hypothesis 4 (H4).

Technology competence positively affects the implementation success of Industry 4.0 technologies.

3.3. Environmental Factors

This subsection will outline hypotheses for the environmental factors, specifically focusing on supplier support.

Supplier Support

Qualified suppliers play a critical role in supporting firms through the implementation of Industry 4.0 technologies by offering robust technical support and training [95]. Particularly for firms lacking high-level technical competence internally, external suppliers can compensate for this deficiency [107]. Research underscores the importance of close cooperation with suppliers, emphasizing that competent suppliers possess the necessary capabilities and experience in technology implementation, thereby lowering the knowledge and skill barriers for adopting firms [91].

Training is another vital aspect of supplier support. Employees require training in the necessary skills to operate Industry 4.0 technologies effectively [47]. Additionally, training helps employees feel more comfortable with the new technologies, reducing resistance and enhancing the learning process [97]. Well-trained employees are better equipped to implement and utilize new technologies to their fullest potential. Therefore, high-quality training programs and technical competence make a supplier indispensable.

It is evident that the lack of qualified and experienced suppliers poses a significant barrier to the successful implementation of Industry 4.0, particularly for firms in developing economies [23]. Therefore, this paper hypothesizes the following:

Hypothesis 5 (H5).

Supplier support positively affects the implementation success of Industry 4.0 technologies.

3.4. Practices

This subsection will delineate hypotheses for the practices, specifically focusing on pilot projects.

Pilot Project

The use of pilot projects is a prudent approach in the implementation of Industry 4.0 initiatives [96]. These projects typically commence on a small scale, targeting one or a few processes before full deployment, allowing firms to experiment and learn without significant risks [12,96]. Pilot projects serve to reduce uncertainty by providing opportunities for employees to become familiar with new technologies, thereby alleviating fears of the unknown, building confidence, and accumulating valuable experience [12,97].

Moreover, employees involved in pilot projects can play pivotal roles in leading and training others during the subsequent full-scale installation and deployment of the technologies [108]. It is common for firms implementing Industry 4.0 to temporarily pause or reduce production during the installation and testing phases of the technologies [77]. Successful pilot projects pave the way for smoother and faster full-scale implementation [96], minimizing disruptions to production processes and mitigating potential losses. Therefore, this paper hypothesizes the following:

Hypothesis 6 (H6).

Pilot projects positively affect the implementation success of Industry 4.0 technologies.

4. Methodology and Data

4.1. Measurement

A questionnaire comprising three parts was developed to gather data for the study. The first part aimed to collect information about the respondents and their respective companies. The second part focused on determining whether the respondents’ companies had adopted various Industry 4.0 technologies, including cloud computing, IoT, big data and analytics, additive manufacturing, autonomous robots, AR, VR, AI, blockchain, simulation, cybersecurity, CPS, MES, and mobile technology.

In the third part of the questionnaire, implementation success and associated success factors were assessed using five-point scales ranging from 1 to 5 (representing strongly disagree to strongly agree). Following the approach of prior research on the implementation of advanced manufacturing technology [109,110,111], implementation success was operationalized as improvements in operational performance, encompassing aspects such as quality, efficiency, cost, and flexibility following the adoption of Industry 4.0 technologies.

The items pertaining to success factors were primarily adapted from the existing literature on Industry 4.0, IT/IS, and AMT (refer to Appendix A for details). To enhance the clarity and validity of the questionnaire, an expert panel consisting of three scholars was convened to review and refine it. Subsequently, the questionnaire underwent a pre-test phase involving feedback from six senior managers representing manufacturing firms. Their suggestions were carefully considered and incorporated to refine the questionnaire further.

4.2. Data

An online version of the questionnaire was meticulously crafted and distributed within WeChat groups, which served as platforms for senior and middle managers within manufacturing firms. Access to these groups was facilitated by one of the authors, who boasts extensive involvement in the Chinese manufacturing sector spanning decades. The objective was to ensure that respondents possessed the requisite insights into their respective firms, thereby adopting a “key informant” approach to data collection [56,105]. To ascertain the authenticity of the respondents, filter questions were employed, querying respondents about their job titles and tenure within their firms.

To preemptively address common method bias, respondents were assured that the information provided would be strictly utilized for academic purposes and kept anonymous. The survey was conducted between December 2021 and January 2022. After filtering out responses from firms that had not embraced any Industry 4.0 technologies, a total of 202 valid responses were garnered. Demographic details of the sample are delineated in

Table 4. Sample demographics (n = 202).

Industry	Number	Percentage	Respondent’s Title	Number	Percentage
Machinery	71	35.1%	CEO/Deputy CEO	37	18.3%
Automobile	35	17.3%	Digitalization/smart manufacturing manager	27	13.4%
Electronics	31	15.3%	Production manager	21	10.4%
Aviation	26	12.9%	Head of production-related department	19	9.4%
Others	39	19.3%	Technology manager	14	6.9%
			Industrial engineering manager	14	6.9%
Years working in the firm			Other middle managers	70	34.7%
<2	9	4.5%	Firm size (number of employees)
2–5	34	16.8%	<500	55	27.2%
5–10	48	23.8%	500–1999	54	26.7%
≥10	111	54.9%	≥2000	93	46.0%

, while implementation statistics concerning Industry 4.0 technologies are depicted in Figure 3.

5. Results

In this study, a hybrid methodology utilizing SEM and fsQCA was adopted to empirically investigate the critical success factors influencing the implementation of Industry 4.0 technologies. SEM, a correlational and symmetrical approach, was employed to examine the overall effects of the influencing factors [28]. Complementing SEM, fsQCA offers a nuanced understanding of factor combinations, thereby enhancing the comprehensiveness of our analysis [27,112,113].

This hybrid approach combining SEM and fsQCA offers comprehensive insights into the complex relationships among variables. SEM enables a thorough examination of individual factors’ effects, while fsQCA explores interactions among these factors, providing a holistic understanding. This synergy enhances the robustness of the findings by corroborating results and identifying areas for further investigation. However, implementing this approach requires expertise in both methodologies and significant resources, making it challenging for researchers lacking familiarity or adequate resources. Additionally, integrating findings from SEM and fsQCA analyses poses interpretation challenges, demanding careful consideration. Despite these complexities, the approach’s benefits in offering nuanced insights and a comprehensive analysis justify its consideration, provided researchers are equipped with necessary skills and resources.

Prior to analysis, Harman’s one-factor test was conducted to assess potential standard method bias [114]. The results indicated that the first extracted factor accounted for only 31.44% of the total variance, falling below the critical threshold of 50%. Consequently, common method bias was deemed negligible, thus ensuring the validity of our findings.

5.1. Results of SEM

Mplus 8.3 and SPSS 26 were utilized for the analysis. Initially, a confirmatory factor analysis (CFA) was conducted to assess the measurement model’s validity. Subsequently, the structural model was evaluated to test the hypotheses.

5.1.1. Results of the Measurement Model

The measurement model analysis was conducted to evaluate reliability and validity, with the results presented in

Table 5. Validity and reliability indicators of the constructs.

Construct	Item	Loading	CR	AVE	Cronbach’s Alpha
Complexity	Complexity1	0.829	0.816	0.598	0.813
	Complexity2	0.798
	Complexity3	0.686
Compatibility	Compty1	0.770	0.812	0.592	0.807
	Compty2	0.856
	Compty3	0.671
Top management support	TMS1	0.845	0.865	0.681	0.861
	TMS2	0.855
	TMS3	0.773
Technology competence	TechComp1	0.854	0.892	0.673	0.862
	TechComp2	0.876
	TechComp3	0.782
	TechComp4	0.765
Supplier support	Supplier1	0.740	0.892	0.675	0.893
	Supplier2	0.778
	Supplier3	0.885
	Supplier4	0.873
Pilot Project	Pilot1	0.638	0.829	0.551	0.824
	Pilot2	0.760
	Pilot3	0.857
	Pilot4	0.696
Implementation success	Success1	0.702	0.800	0.503	0.778
	Success2	0.839
	Success3	0.638
	Success4	0.639

and

Table 6. Correlations and square root AVE of the constructs.

	1	2	3	4	5	6	7
1. Complexity	0.773
2. Compatibility	−0.215	0.769
3. TMS	−0.075	0.200	0.825
4. Technology competence	−0.065	0.403	0.327	0.820
5. Supplier support	−0.176	0.451	0.203	0.489	0.822
6. Pilot project	−0.195	0.362	0.555	0.475	0.554	0.742
7. Implementation success	−0.073	0.404	0.452	0.546	0.586	0.620	0.709

Note: Square root AVE values are on the diagonal.

. All items demonstrated factor loadings above 0.6, indicating satisfactory reliability [115]. Additionally, composite reliability and Cronbach’s alpha values exceeded the recommended threshold of 0.7 for all constructs [116,117], further supporting reliability. Convergent validity was confirmed as average variance extracted (AVE) values surpassed 0.5 for all constructs [116,117]. Discriminant validity was established by ensuring that the square root of the AVE for each construct exceeded its correlation coefficients with other constructs [56,116]. Table 6 presents the square root AVE values and the correlation matrix.

5.1.2. Results of the Structural Model

As shown in

Table 7. Model fit indices.

Indices	${\mathit{\chi }}^2$/df	RMSEA	CFI	TLI	SRMR
Value	1.841	0.065	0.917	0.901	0.057
Threshold	$\le$3	$\le$0.08	$\ge$0.9	$\ge$0.9	$\le$0.08

, the model fit indices conform to the recommended thresholds, with ${\chi }^2/df$ = 1.841, RMSEA = 0.065, CFI = 0.917, TLI = 0.901, and SRMR = 0.057. It is observed that complexity ($\beta$ = 0.071, $p$ > 0.05) and compatibility ($\beta$ = 0.082, $p$ > 0.05) are not significantly related to implementation success. Therefore, H1 and H2 are not supported. Top management support ($\beta$ = 0.177, $p$ < 0.05), technology competence ($\beta$ = 0.199, $p$ < 0.05), supplier support ($\beta$ = 0.289, $p$ < 0.01), and pilot project ($\beta$ = 0.252, $p$ < 0.05), on the other hand, are significantly related to implementation success. Hence, H3, H4, H5, and H6 are supported. A summary of the results is presented in

Table 8. Results of SEM.

Dimension	Construct	Beta	p-Value	Result
Technological	Complexity	0.071	0.316	Not supported
	Compatibility	0.082	0.318	Not supported
Organizational	Top management support	0.177 *	0.041	Supported
	Technology competence	0.199 *	0.019	Supported
Environmental	Supplier support	0.289 **	0.002	Supported
Practice	Pilot project	0.252 *	0.022	Supported

Note: * p < 0.05; ** p < 0.01.

.

5.2. Results of fsQCA

To identify the causal combinations of factors contributing to implementation success, fsQCA 3.0 software was employed, involving data calibration, necessary condition analysis, and sufficient condition analysis.

For data calibration, transformation from the five-point Likert scale to values ranging from 0 to 1 was necessary. Initially, the score for each construct was calculated by averaging its items. Subsequently, the 95%, 50%, and 5% percentiles served as breaking points (full membership: 95%; crossover anchor: 50%; full non-membership: 5%) to calibrate the scores [118].

Regarding necessary condition analysis, it is typically conducted prior to sufficient condition analysis, despite the latter being the core of fsQCA [119]. A condition is deemed necessary if the consistency score exceeds 0.9 [120]. However, the consistency scores in

Table 9. Analysis of necessary conditions.

	Implementation Success		Negation of Implementation Success
Condition	Consistency	Coverage	Consistency	Coverage
Complex	0.645	0.649	0.708	0.612
~Complex	0.615	0.710	0.594	0.590
Compatibility	0.745	0.777	0.582	0.522
~Compatibility	0.541	0.601	0.752	0.717
TMS	0.742	0.743	0.573	0.493
~TMS	0.494	0.574	0.702	0.700
Tech competence	0.725	0.826	0.510	0.500
~Tech competence	0.561	0.571	0.823	0.720
Supplier support	0.732	0.832	0.499	0.487
~Supplier support	0.549	0.560	0.828	0.727
Pilot	0.831	0.769	0.600	0.477
~Pilot	0.435	0.559	0.709	0.783

indicate that none of the conditions are necessary for implementation success or failure.

In the sufficient condition analysis, the software generated all possible configurations (2⁶ = 64) to form atruth table. Subsequently, the truth table underwent filtration based on frequency and consistency criteria. Given that our sample size surpasses 150, the frequency threshold was set at three [79,118]. Additionally, the raw consistency threshold and PRI consistency threshold were established at 0.8 and 0.7, respectively [118]. As a result, 167 cases were encompassed within configurations that exceeded the 80% consistency threshold. Following the construction of the truth table, we proceeded with the standard analysis, the results of which are detailed in

Table 10. Configurations for implementation success.

		Solution
		1a	1b	2a	2b	3a	3b
Technological	Complexity	⊗	●	⊗		●
	Compatibility	●	●	●			●
Organizational	TMS	⬤	⬤	⬤	⬤
	Tech competence	⬤	⬤		●	⬤	⬤
Environmental	Supplier support	●		⬤	⬤	⬤	⬤
Practice	Pilot project		●	●	●	⬤	⬤
	Raw coverage	0.334	0.355	0.363	0.482	0.399	0.498
	Unique coverage	0.011	0.0317	0.039	0.012	0.018	0.030
	Consistency	0.959	0.945	0.961	0.938	0.930	0.958
	Solution coverage	0.640
	Solution Consistency	0.924

Notes: ⬤ Presence of a core condition; ● Presence of a peripheral condition; ⊗ Absence of a peripheral condition.

.

Table 10 adheres to the convention of presenting both parsimonious and intermediate solutions [120]. The inclusion of a big black circle signifies the presence of a core condition, present in both intermediate and parsimonious solutions, while a small black circle denotes the presence of a peripheral condition, present only in the intermediate solution. Conversely, big and small crossed circles represent the absence of core and peripheral conditions, respectively. As depicted in Table 10, three core configurations are identified as leading to implementation success. The overall solution consistency stands at 0.924, with an overall solution coverage of 0.640, surpassing the recommended thresholds [118].

Configuration 1 is labeled as top-management-backed, self-sufficient firms. These firms exhibit high-level technology competence, boasting skilled and knowledgeable employees along with robust infrastructure. Additionally, their top management actively supports the implementation of Industry 4.0 technologies. With both top management support and technology competence, these firms achieve implementation success.

Configuration 2 is denoted as top-management-backed, supplier-powered firms. While these firms may not possess inherent technological competence, their top management provides crucial support for the implementation of Industry 4.0 technologies. By collaborating with competent and reliable suppliers, they access technical support and high-quality training, enabling them to achieve implementation success.

Configuration 3 is identified as competent, well-assisted firms. These firms demonstrate technological competence and self-sufficiency. Leveraging the assistance of competent and reliable suppliers, they successfully implement Industry 4.0 technologies without explicit top management support. However, adopting pilot projects is advisable to accumulate experience and prepare employees for full implementation.

6. Discussions

The congruence between the SEM and fsQCA results underscores the consistency in identifying critical success factors for Industry 4.0 implementation, despite differences in underlying assumptions. Both methodologies highlight the importance of top management support, technology competence, supplier support, and pilot projects, aligning with previous research findings.

Top management plays a pivotal role in navigating Industry 4.0 initiatives, taking charge of project oversight and actively participating in implementation efforts. Ghobakhloo [24] underscored the indispensable nature of top management support in advancing smart manufacturing implementation. This support extends beyond mere endorsement, encompassing critical functions such as resource allocation and fostering collaboration among diverse stakeholders. Sony et al. [20] echoed these sentiments, emphasizing top management support as a foundational pillar for the successful adoption of Industry 4.0 technologies. Furthermore, the involvement of top management goes beyond providing financial resources or directives; it often sets the tone for organizational culture and commitment to technological innovation. Effective leadership at the top level not only ensures alignment between strategic goals and implementation efforts but also fosters a culture of adaptability and continuous improvement, which are crucial for successfully navigating the complexities of Industry 4.0 transformations. Additionally, the visible support of top executives can inspire confidence among employees, encouraging greater buy-in and engagement with new technologies and processes.

Technology competence is a multifaceted concept that encompasses both the proficiency of employees in utilizing advanced technologies and the adequacy of the technological infrastructure within an organization [56,105]. Studies have indicated that a lack of qualified employees and insufficient technology infrastructure pose significant barriers to the successful implementation of Industry 4.0 initiatives [23,66]. James et al. [21] further emphasized that the dearth of employee awareness regarding new technologies and the inadequacy of their technical skills represent major impediments to the adoption of Industry 4.0 practices. In light of these challenges, organizations must prioritize initiatives aimed at upskilling their workforce and fostering a culture of continuous learning to equip employees with the requisite knowledge and capabilities to effectively engage with emerging technologies. Through targeted training programs and strategic investments in technological infrastructure, organizations can overcome these barriers and position themselves for success in the rapidly evolving landscape of Industry 4.0.

Supplier support plays a crucial role in the successful implementation of Industry 4.0 initiatives, as highlighted by Karadayi-Usta [66]. Collaborating with suppliers provides firms with access to valuable expertise and experience in technology implementation, thereby mitigating barriers associated with the adoption of new technologies [91]. Given the diverse nature of Industry 4.0 technologies, organizations are encouraged to engage with a range of specialized suppliers to leverage their respective capabilities [23]. However, it is imperative for firms to carefully assess the competence and experience of potential suppliers, as partnerships with inexperienced or incompetent suppliers may impede the implementation process. By forging strategic alliances with knowledgeable and reliable suppliers, organizations can enhance their readiness to embrace Industry 4.0 advancements and navigate the complexities of technological integration more effectively.

Pilot projects serve as an effective management strategy in the implementation of Industry 4.0 technologies. These initiatives typically commence on a small scale, targeting specific processes or areas prior to full-scale deployment, thereby enabling organizations to experiment and learn from real-world scenarios with minimal risks [96]. Pozzi et al. [19] advocated for the adoption of preparatory measures such as pilot projects as a means to mitigate uncertainties associated with the integration of Industry 4.0 technologies. By conducting pilot projects, firms can incrementally acclimate themselves to novel technologies, gaining practical insights and building confidence in their capabilities before undertaking broader implementation efforts [12]. This iterative approach not only fosters a deeper understanding of technological functionalities but also facilitates the identification and resolution of potential challenges, ultimately enhancing the likelihood of successful implementation across the organization.

While SEM showed that top management support, technology competence, supplier support, and pilot projects are positively related to the implementation success of Industry 4.0 technologies, the results of fsQCA showed that firms do not necessarily have to be well equipped with all these factors. With top management support, a technologically competent firm can achieve implementation success on its own, while a technologically inferior firm needs effective supplier support to obtain success. When top management support is absent, as long as the firm is technologically competent and assisted by reliable suppliers, it is also likely to succeed. In most cases, pilot projects contribute to implementation success.

In summary, the findings from SEM and fsQCA shed light on the nuanced relationship between technology complexity, compatibility, and implementation success within the context of Industry 4.0 adoption. The SEM results indicate that neither technology complexity nor compatibility significantly influences implementation success, aligning with prior research findings in related domains. Studies by Wei et al. [71] and Thong [121] have similarly reported non-significant associations between technology complexity/compatibility and the adoption of RFID and IS, respectively. Moreover, the fsQCA results corroborate these findings by revealing that technology complexity and compatibility are peripheral or negligible conditions in determining implementation success. However, this does not discount the importance of these factors entirely. Although their direct impact may be limited, they still exert some influence on implementation outcomes. Configurations 1a and 2a demonstrate that implementation success is more probable when the technology exhibits lower complexity and greater compatibility with the organizational context. These insights highlight the nuanced nature of technology attributes in the context of Industry 4.0 implementation. While technology complexity and compatibility may not be primary drivers of success, their optimization can still enhance the implementation process. Thus, while not central to success, organizations should strive to minimize complexity and ensure alignment with existing systems and processes to facilitate smoother adoption and integration of Industry 4.0 technologies.

7. Conclusions

This study aimed to investigate the critical success factors for the implementation of Industry 4.0 technologies. Besides the net effects of the individual factors, the effects of their combinations are of particular interest. SEM and fsQCA were employed to analyze survey data collected from 202 Chinese manufacturing firms. The results of SEM showed that top management support, technology competence, supplier support, and pilot projects significantly influence implementation success. The results of fsQCA revealed that three configurations lead to implementation success: top management support and technology competence; top management support and supplier support; and technology competence, supplier support, and pilot projects.

7.1. Theoretical Implications

This study makes two significant contributions to the existing literature. Firstly, it addresses a notable gap by empirically examining the critical success factors for the implementation of Industry 4.0 technologies. Prior research has offered limited empirical evidence on these critical success factors, and this study adds to the body of knowledge by empirically identifying top management support, technology competence, supplier support, and pilot projects as essential drivers for the successful implementation of Industry 4.0 technologies.

Secondly, this study pioneers the application of fsQCA in investigating the causal patterns influencing the implementation success of Industry 4.0 technologies. As a configurational approach grounded in complexity theory, fsQCA complements conventional methods such as SEM by identifying causal combinations that lead to desired outcomes [27]. The findings reveal that while none of the individual factors investigated are individually necessary or sufficient for implementation success, various combinations of these factors are sufficient for achieving successful implementation. This highlights the nuanced nature of Industry 4.0 implementation, indicating that firms need not possess all factors in isolation to succeed. Instead, they can tailor their implementation strategies based on their unique characteristics, leveraging different combinations of critical success factors to achieve their implementation goals.

The methodology employed in this study, combining SEM and fsQCA, holds promise for reproducibility in diverse markets. By adapting the research design to accommodate specific contextual factors, such as regional challenges and organizational characteristics, valuable insights can be gained. Overall, this methodology provides a robust framework for understanding and improving Industry 4.0 implementation globally, facilitating collaborative efforts and cross-market studies to refine and validate findings across different regions and industries.

7.2. Managerial Implications

Top managers embarking on the implementation of Industry 4.0 technologies need not overly concern themselves with technical complexity or compatibility issues. Instead, their focus should be on demonstrating unequivocal support and commitment to these innovations, thereby ensuring that employees have access to the resources and encouragement needed for successful adoption. Moreover, efforts to enhance a firm’s technology competence, encompassing both IT infrastructure and employee knowledge and skills, are paramount. In instances where improving technology competence proves challenging within a short timeframe, the involvement of a reliable supplier becomes indispensable. Leveraging the expertise of an experienced and competent supplier can compensate for any deficiencies in the firm’s technical capabilities, providing essential technical support and training.

Additionally, prior to the full-scale implementation of Industry 4.0 technologies, the implementation of a pilot project is advisable whenever feasible. Pilot projects serve as invaluable preparatory measures, allowing employees to acclimate themselves to the new technologies, bolster their confidence, and accumulate crucial hands-on experience. By conducting pilot projects, firms can mitigate the risks associated with large-scale deployment, ensuring a smoother transition and maximizing the likelihood of implementation success.

7.3. Limitations and Future Research

There are several limitations in this study. Firstly, it was conducted in China, a developing economy where Industry 4.0 is still emerging. Future research could explore more-developed and less-developed economies to compare technological competencies among firms and suppliers. Secondly, the focus was exclusively on the manufacturing industry, potentially overlooking critical success factors relevant to the service industry. A comparative analysis between manufacturing and service sectors would be insightful. Thirdly, the study only examined a limited number of factors, leaving room for exploration of additional variables as Industry 4.0 technologies continue to evolve. Lastly, the amalgamation of diverse research types, periods, and regions in the study’s tables poses a limitation, potentially impacting data consistency and reliability. Further efforts to ensure the coherence and comparability of the findings are necessary to address biases stemming from dataset heterogeneity.