## 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. [PERSON] 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. [PERSON] et al. [18] surveyed the state of the art in Industry 4.0 as it pertains to various industries. [PERSON] 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.