## 1 Introduction
When companies transition to smart factories and digitally integrated systems, the principles of ergonomics ensure that the human element remains central, promoting sustainable work practices that align with the objectives of efficiency and innovation [12]. The development and popularization of Industry 4.0 technologies in various sectors must acquire widespread acceptance. Some scholars noted that the acceptance of Industry 4.0 technologies substantially varies in different contexts [13]. Consequently, contextual factors need to be considered when investigating the determinants of Industry 4.0 technology adoption, although this process undoubtedly intensifies the complexity of understanding the acceptance of Industry 4.0 technologies. Despite the increased complexity, a comprehensive review of this field can be obtained through meta-analysis, which combines the results of multiple studies for statistical analysis [14]. The evidence from a meta-analysis approach is reliable owing to the comparison and analysis performed on the results of different studies [15]. In response, a large and growing body of the literature has explored the determinants of Industry 4.0 technology acceptance in various sectors [16; 17; 18; 19; 20]. The characteristics and research gaps of these studies can be concluded as follows. First, inconsistent resultspersist due to the variations in sample size and research contexts, which can confuse and mislead managers, developers, and governments. Second, several factors affecting Industry 4.0 adoption have been neglected in existing studies. For example, compared with large organizations, smaller organizations are less likely to adopt Industry 4.0 technologies because additional capital is required to build relevant infrastructure for their adoption and implementation [21]. Therefore, the determinants of Industry 4.0 technology adoption need in-depth exploration and systematic conclusions. In terms of research methods, several papers have conducted a literature review and meta-analysis on the topic of Industry 4.0 technologies in terms of Industry 4.0 readiness models [22], Ergonomics 4.0 determinants [23], and the role of Industry 4.0 technologies [24]. To our knowledge, only one paper has investigated the adoption of Industry 4.0 technologies that employ the Technology-Organization-Environment framework [25]. There were research gaps in the work of [PERSON] and [PERSON] [25]. First, only 12 papers were included in their work to investigate the antecedents of Industry 4.0 adoption. The small sample size can lead to biased results. Second, their work only focused on the antecedents of Industry 4.0 technologies and failed to explore in-depth influencing mechanisms (e.g., moderators). Therefore, this study aimed to address these research gaps by synthesizing the existing literature on the adoption of Industry 4.0 technologies, with a specific focus on examining the determinants highlighted within the technology acceptance model (TAM) and its extensions. Specifically, (PU), perceived ease of use (PEOU), and social influence (SI) were considered significant predictors of behavioral intention to use Industry 4.0 technologies (BI) across various sectors. Also, this study used meta-regression to explore the moderators influencing the relationships of PU-BI, PEOU-BI, and SI-BI. The findings of this study contributed to the literature with an in-depth understanding of the acceptance of Industry 4.0 technologies in various sectors and how moderators influence the acceptance. The inconsistencies in the current papers can lead to adverse actions in the adoption of Industry 4.0 technologies, ultimately resulting in different outcomes. In practice, neglecting some factors of Industry 4.0 technology adoption or taking incorrect measures due to these inconsistencies can possibly lead to disasters and harm the organizations [26]. This study aimed to assess the relative positive or negative effects of PU, PEOU, and SI on BI.