Factors Influencing the Acceptance of Industry 4.0 Technologies in Various Sectors: A Systematic Review and Meta-Analysis

Abstract

Adopting Industry 4.0 technologies across sectors is critical for enhancing operational efficiency and competitiveness. However, empirical studies on the determinants of such adoption have yielded inconsistent results. This study conducted a systematic review and meta-analysis based on the Technology Acceptance Model and its extensions. A total of 47 empirical studies were extracted from five academic databases and included in the meta-analysis. The findings confirmed that perceived usefulness (PU), perceived ease of use (PEOU), and social influence (SI) significantly and positively influenced behavioral intention (BI) toward adopting Industry 4.0 technologies. Among them, PU exhibits the strongest correlation with BI (r = 0.528), followed by PEOU (r = 0.469) and SI (r = 0.487). Subgroup analyses based on geographical region, organization size, and sector showed consistent significance in effect sizes, although moderating effects across subgroups were not statistically significant. 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. Practically, the findings provided evidence-based guidance for policymakers, technology developers, and business leaders to tailor adoption strategies and foster digital transformation across sectors.

1. Introduction

Industry 4.0 represents a transformative paradigm shift that integrates advanced information technologies with traditional industrial practices, fundamentally altering how companies across various sectors conceive, design, and produce their goods and services [1]. This revolution is characterized by the convergence of technologies such as the IoT, big data analytics, AI, robotics, cloud computing, and cyber–physical systems, which together enable unprecedented levels of automation, efficiency, and connectivity [2]. In the manufacturing sector, for example, smart factories deploy interconnected devices and systems that facilitate real-time monitoring, predictive maintenance, and adaptive production lines, thereby reducing downtime and optimizing resource allocation [3]. Meanwhile, in the healthcare industry, the incorporation of Industry 4.0 technologies has led to the development of telemedicine services, personalized treatment regimens through data-driven insights, and improved patient management systems, which collectively enhance service quality and operational resilience [4]. Similarly, in sectors such as logistics and supply chain management, enhanced digital infrastructures and real-time data sharing have revolutionized inventory control, routing, and distribution, resulting in more agile and responsive operations [5]. This digital transformation, however, extends beyond the mere adoption of technological assets. This digital transformation also necessitates a profound cultural change within organizations. Companies are increasingly shifting toward a culture of continuous improvement, innovation, and collaborative problem-solving, as employees are encouraged to embrace new digital tools, adapt to evolving skill requirements, and participate actively in cross-functional teams [6]. Furthermore, the decentralization of decision-making processes and the elevation of data literacy among staff members facilitate an environment that rewards creativity, transparency, and rapid adaptation to market dynamics [6]. These cultural shifts are often supported by comprehensive change management strategies, including targeted training programs and leadership initiatives aimed at fostering an organizational mindset that is receptive to technological change and capable of leveraging it to achieve strategic competitive advantages [7]. Thus, Industry 4.0 redefines operational processes and technological infrastructures and transforms the foundational cultural dynamics of organizations, rendering them more agile, resilient, and innovation-driven [8].

The integration of ergonomics within the framework of Industry 4.0 is paramount in fostering an environment where technological innovation and human factors work synergistically to enhance productivity and employee wellbeing. Ergonomics, a discipline focused on optimizing human–system interactions, emerges as a critical element in ensuring that the rapid pace of technological advancement does not compromise the health, safety, and effectiveness of human operators [9]. By systematically assessing and redesigning workspaces to accommodate human capabilities and limitations, ergonomics contributes to the development of adaptive work environments that are resilient in the face of technological disruptions, thereby mitigating risks such as repetitive strain injuries, cognitive overload, and workplace fatigue [10]. Moreover, the implementation of Industry 4.0 technologies facilitates real-time monitoring and data collection on human performance and workplace conditions, which, in turn, enables ergonomists to design more precise interventions that are responsive to the dynamic needs of the workforce [11]. The interrelation between these domains is further exemplified by the adoption of wearable sensors, augmented reality, and advanced simulation models that enhance situational awareness and decision-making and provide ergonomic insights that refine equipment design and workflow configuration. 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 results persist 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 Raj and Jeyaraj [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. The primary objective of this study was to systematically synthesize the existing literature on the adoption of Industry 4.0 technologies, with a specific focus on examining the determinants highlighted within the TAM and its extensions. Specifically, PU, PEOU, and SI were considered significant predictors of technology adoption across various sectors. The motivation stemmed from the critical need to reconcile the contradictory findings that have emerged in previous studies and the lack of a comprehensive meta-analytical evaluation of the effect sizes associated with each construct.

The remainder of this paper is structured as follows. Section 2 presents a comprehensive overview of Industry 4.0 technologies and theories related to technology acceptance. Section 3 discusses the research framework and derives the hypotheses. Section 4 illustrates the methodology and data processing. Section 5 presents the results of the meta-analysis and investigates the moderators. Based on the latter, this paper discusses the findings and draws conclusions in Section 6 and Section 7, respectively.

2. A Literature Review

2.1. Industry 4.0 Technologies

Industry 4.0 represents a fundamental paradigm shift in the way industries operate, largely driven by the integration of advanced technologies that reconfigure production processes and business models in unprecedented ways. Industry 4.0 utilizes the convergence of multiple cutting-edge technologies, such as the IoT, AI, robotics, big data analytics, cyber–physical systems, and cloud computing, to create intelligent manufacturing ecosystems that offer enhanced flexibility, efficiency, and responsiveness [27]. These technologies enable real-time data capture, analysis, and decision-making, which revolutionize supply chain dynamics, predictive maintenance, and overall production efficiency [28] while simultaneously reducing downtime and mitigating risks associated with traditional manufacturing systems [29]. Moreover, the digital connectivity afforded by these technologies fosters great integration across various facets of industrial operations, allowing for seamless communication between machines, processes, and human operators, thereby facilitating adaptive and self-optimizing production lines [30]. This technological interconnectivity drives improved operational performance and catalyzes innovation in product development and customization when companies can now leverage data-driven insights to fine-tune processes and tailor products to meet specific market demands [31]. In addition, the adoption of these advanced technologies under the Industry 4.0 framework has a profound impact on workforce dynamics by necessitating the development of new skill sets, promoting a culture of continuous learning and adaptation, and encouraging a more collaborative, interdisciplinary approach to problem-solving [32]. Furthermore, the relevance of technology in Industry 4.0 extends to its ability to enhance sustainability through improved resource management and energy efficiency [33], thereby aligning industrial processes with global environmental and economic goals [34]. Industry 4.0 provides a novel pattern for organizing resources and processes to adapt to the changing environment [35]. Meanwhile, Industry 4.0 is not a solitary concept but consists of a number of related technologies, including the IoT [36,37], blockchain [38], and big data analytics [39,40], among others. Industry 4.0 technologies have attracted much attention from academic circles and industries [37].

Scholars have researched Industry 4.0 technologies in the following aspects. First, the concepts and characteristics of Industry 4.0 technologies have been explored. Introduced in 2011, Industry 4.0 has been determined as a growth engine in Germany and has triggered many societal changes, providing a precedented paradigm for various sectors [41]. The Industry 4.0 environment is filled with complexity from a systematic perspective [42,43]. Continuous input of resources, in-depth interactions among the elements, and dynamic changes in contexts have made Industry 4.0 technologies increasingly complex [41,42,44]. The Industry 4.0 technologies have long been considered a “system of systems” [45,46]. According to Hoyer, Gunawan, and Reaiche [47], Industry 4.0 complexity consists of three dimensions (i.e., implementation, management, and technology), suggesting that further insights into Industry 4.0 technologies are needed to address these complexities.

Second, the determinants of adopting and implementing Industry 4.0 technologies have been widely explored and can be divided into three categories. The first category is associated with SI. The support of governments and the market is essential in Industry 4.0 adoption, as it can determine the amount of attention and effort that can be put into this field [48,49]. Collaborations with other institutions, notably financial institutions and technical organizations, are conducive to obtaining their partners’ production factors (e.g., capital and resources) [48,49,50]. The availability of knowledge [48,51,52], potential cost [48,52], and external pressure [53,54] can also affect the adoption of Industry 4.0 technologies. The second category is related to the internal factors of the organizations. Perceived benefits were investigated by numerous scholars and were affirmed as a critical indicator [41,55,56,57]. Additionally, the organizations’ IT infrastructure conditions and strategies are essential in the acceptance of Industry 4.0 technologies [53,56,58,59,60], which are indispensable in connecting different devices and determining organizations’ future directions. Moreover, the standards of occupational safety and health will be updated, thereby affecting the adoption of Industry 4.0 technologies [56,59]. Finally, the third category is the characteristics of organizations. The organizational sector and organization size are the most widely explored variables associated with the organizations’ characteristics in technology adoption [53,55,61]. The varying characteristics could influence the competitive advantages and challenges faced by organizations.

The third stream of the literature focused on the applications and roles of Industry 4.0 technologies. From an economic point of view, Industry 4.0 technologies can be utilized to optimize production processes [62], boost productivity and efficiency [63,64], and customize advanced products [65]. From the dimension of ecology, these technologies play a crucial role in mitigating gas emissions [66], reducing the usage of resources [67], and reducing the amount of waste [68]. In terms of society, employees’ satisfaction can be enhanced because their efficiency is improved by intelligent and interactive devices [66]. Nevertheless, the reduced job opportunities call for additional attention from governments and managers to tackle the imminent challenges [55,69].

2.2. Adoption of TAM and Its Extensions

Technology is commonly created in a specific social context and can be influenced by human behaviors and institutional background [70]. At this point, human activities can be regarded as a part of technology and, thus, affect the outcomes of technology. To explain the determinants of technology acceptance from the perspective of attitude and behavior, TAM is universally adopted [71]. TAM is an essential theoretical framework underpinning the investigation into the determinants of Industry 4.0 technology adoption. Specifically, this model elucidates how PU and PEOU substantially influence an organization’s decision to integrate advanced technological systems, thereby acting as the conceptual backbone of this meta-analysis. TAM postulates that the intention to adopt the technology is highly influenced by the PEOU and its usefulness [72]. The intention to adopt will work as an intermediary to the actual use of the technology, and the PU will be influenced by the PEOU in TAM [72]. The evolution of the original TAM, TAM2, and TAM3 is a widely used extension. TAM2 considers the determinants of PU [73], and TAM3 includes the determinants of PEOU [74]. The UTAUT model was proposed as a systematic model based on TAM and other relevant theories. The framework of the UTAUT model contains facilitating conditions that can affect behavioral intention [75]. Additionally, it adopts a unique set of terms (i.e., performance expectancy, effort expectancy, and SI) to represent the variables in TAM.

Thus far, several studies have highlighted the benefits of these models’ applications to different technologies and contexts, including healthcare technologies [76], digital technology [77], online mapping technology [78], mobile library applications [79], automated vehicles [80,81], and virtual reality technology [82,83]. TAM and its extensions comprise various factors (e.g., SI) from other models for a better fit in different technologies and contexts [73,84,85]. Notably, the systematic literature review can compare the concepts and results of the prevailing literature and present a holistic picture of the field, but it fails to obtain reliable outcomes due to the different contexts. Therefore, meta-analysis has been widely adopted as a quantitative method. TAM and its extensions were also employed in many meta-analyses in numerous domains, such as e-health application acceptance [86], health information technologies acceptance [87], and e-business acceptance [88]. The above literature indicates that meta-analysis effectively unveils the underlying mechanism of acceptance of Industry 4.0 technologies in various sectors.

3. Research Framework and Hypothesis

This paper conducted a systematic literature review and meta-analysis to obtain a synthetic result of the determinants of Industry 4.0 technologies (i.e., PU, PEOU, and SI) and behavior intention to adopt Industry 4.0 technologies (BI). Figure 1 illustrates the research framework of this paper.

PU can be used to evaluate if the performance can be improved by adopting these technologies [71]; such adaptability is an indispensable factor in technology adoption [41,71]. Nevertheless, heterogeneity has been detected in various contexts [89]. Reexamining the effects of PU on the adoption of Industry 4.0 technologies is essential.

H1. 

PU significantly and positively affects the BI adoption of Industry 4.0 technologies in various sectors.

PEOU can reflect the degree of ease in accessing and utilizing technology [71]. Meanwhile, effort expectancy (in UTAUT) can be defined as the degree of ease related to adopting a technology [75]. However, mastering an emerging technology will inevitably cost much time and effort because it can inhibit adoption. Some scholars found that the role of PEOU in Industry 4.0 technology acceptance varies significantly [89,90]. In this case, investigating the moderating effects of PEOU on Industry 4.0 technology adoption is essential.

H2. 

PEOU has a significant and positive effect on the BI adoption of Industry 4.0 technologies in various sectors.

SI is a primary component that directly influences BI in the UTAUT framework [75] and has been investigated in numerous studies. Accordingly, SI is an essential factor to consider in exploring the determinants of Industry 4.0 technology adoption. However, their correlation coefficients vary significantly in different studies [91,92,93]. Thus, systematically investigating the effects of SI and BI on the adoption of Industry 4.0 technologies is needed.

H3. 

SI significantly and positively affects the BI adoption of Industry 4.0 technologies in various sectors.

4. Research Design

4.1. Methodology

Systematic literature reviews and meta-analyses are two common ways to comprehensively understand unfamiliar fields. Figure 2 shows the methodological steps of this meta-analysis, including sampling, data extraction, and data analysis. This systematic review involves identifying the topic, assessing the situation, and summarizing the interpretations [94,95], which can provide a holistic review of current research on a particular topic and compare results conducted by different researchers. Meta-analysis is a robust statistical technique that quantitatively aggregates and synthesizes results from multiple empirical studies to derive overall conclusions and assess the magnitude of specific effects [96]. Therefore, this method can enhance the statistical power and resolve inconsistencies among individual studies [97]. This approach involves calculating standardized effect sizes from each selected study, which are combined using rigorous statistical models to produce a weighted average effect size. By systematically addressing variations in sample sizes, methodologies, and measurement instruments across studies, meta-analysis improves the precision of estimated effects and allows for the identification of potential moderators and mediators that may influence the observed relationships [98]. Furthermore, this method provides a transparent and replicable framework for detecting publication bias and assessing the overall quality and robustness of the evidence base [99]. In the Industry 4.0 technology adoption research context, meta-analysis plays a critical role in reconciling divergent findings by integrating data from diverse settings and methodological approaches, ultimately contributing to a comprehensive and evidence-based understanding of the factors driving technology uptake and implementation. Meta-analysis was initially employed and promoted in the medical field, and it plays an increasingly essential role in other fields (e.g., the behavioral sciences) [100,101]. In comparison with the systematic review, meta-analysis possesses many virtues [102,103]. For example, it can quantitatively summarize and calculate the estimation of the effects, which is beneficial for exploring the causes of different effects and dealing with complex problems [104]. Methodologically, meta-analysis can be seen as a formalized and systematic review method [14]. Therefore, this study first conducted a systematic review to identify the influencing factors of Industry 4.0 technology adoption and then adopted meta-analysis to examine the determinants of Industry 4.0 technology acceptance. To avoid inconsistencies in the prevailing literature and derive accurate results, this study attempted to elucidate the moderators that are densely associated with various contexts. As a result, comprehensive insights will be generated into the adoption of Industry 4.0 technologies, and relevant studies and theories will be enriched.

4.2. Sampling

Meta-analysis requires scanning the existing literature, including published journals, theses, and conference proceedings, to obtain systematic conclusions. To evaluate if studies fit the standards of meta-analysis in this paper, the following criteria should be checked: (1) the papers should empirically investigate the adoption of Industry 4.0 technologies, (2) the papers should examine the TAM and its extensions theories to investigate Industry 4.0 technology adoption in various sectors, (3) the papers should provide the sample size and correlation coefficients (or other coefficients that can be transformed into correlation coefficients), (4) the papers are written in English, and (5) the dataset of the paper should be unique. If the same dataset were employed to conduct several trials, only one trial could be selected for the sample.

Figure 3 shows the procedures for searching and selecting the literature. This study searched the literature from five databases, including ScienceDirect, Web of Science, ProQuest, Wiley, and Scopus. The retrieve statement is “ALL [(‘Industry 4.0’ OR ‘Fourth Industrial Revolution’) AND (‘big data’ OR ‘Internet of Things’ OR ‘artificial intelligence’) AND (‘TAM’ OR ‘acceptance’ OR ‘adoption’ OR ‘UTAUT’)]”. This research also manually searched Industry 4.0 technologies in other databases (e.g., Google Scholar) to avoid the possible ignorance of some studies. This study found 26,902 records of Industry 4.0 technology adoption in various sectors in the initial search round from 2003 to 2024. A total of 26,785 papers were excluded after reading the abstracts and full texts. In addition, 74 papers were excluded for some reasons (provided in Figure 3). Ultimately, this paper identified 47 eligible papers included in this meta-analysis.

4.3. Data Extraction

After the literature search and selection, this paper proposed a coding scheme to extract data. The study characteristics, such as sample size, geographical region, organization size, and organization sector, should be extracted. Meanwhile, this paper collected statistics on links between TAM constructs and the adoption of Industry 4.0 technologies (e.g., correlation coefficient). Some studies reported that other coefficients that can be transformed into correlation coefficients were included to ensure reliability. For instance, standardized β coefficients can be transformed by employing the conversion formula offered by Peterson and Brown [105].

4.4. Data Analysis

This paper adopted the Comprehensive Meta-Analysis (CMA) 3.0 software for statistical analysis [106]. CMA has been widely employed to perform a meta-analysis, which can be adopted to report the critical results and different models [107]. This paper employed the random effects model to investigate the determinants of Industry 4.0 technology adoption due to the heterogeneity among different studies [108]. In addition, this paper adopted funnel plots, the Begg test, and Fail-safe N statistics to examine the existence of publication bias [109]. Meanwhile, subgroup analysis and meta-regressions [110] were employed to explore the moderators in the three pairwise relationships in Figure 1. In the meta-regression analysis, cases with missing data for potential moderating variables were systematically removed to preserve the results’ analytical integrity and statistical validity. By excluding the missing data, the meta-regression is conducted on a dataset with complete moderator profiles, enhancing the estimated coefficients’ reliability and providing clear insights into the moderating effects on the relationships between PU and BI, PEOU and BI, and SI and BI across different contexts.

5. Results

5.1. Study Characteristics

Table 1. Characteristics of the primary studies.

References	Sample Size	Type	Geographic Region	Organization Size	Organization Sector	Gender (M/All Participants)	Age (Years)
[111]	208	Journal	Asia	NA	Service	0.43	27.95
[112]	71	Journal	Asia	NA	Service	0.32	33.3943
[113]	228	Journal	Asia	NA	Service	NA	NA
[114]	418	Journal	Asia	NA	NA	0.31	22.2
[115]	1641	Journal	Europe	NA	Service	0.41	NA
[116]	499	Journal	NA	NA	Multisector	NA	NA
[117]	263	Journal	Asia	NA	Service	0.45	32.7
[118]	170	Journal	Asia	Small–medium	Service	NA	NA
[119]	226	Journal	Europe	NA	Service	0.52	48.6
[120]	110	Journal	Africa	NA	Industry	0.73	NA
[121]	293	Journal	Asia	NA	NA	0.42	20
[16]	207	Journal	Asia	Small–medium	Industry	NA	NA
[122]	108	Journal	Europe	Small–medium	NA	NA	NA
[123]	470	Journal	Asia	Large	Service	0.26	24.4
[124]	241	Journal	NA	NA	Industry	0.69	39.5
[125]	202	Journal	Asia	Small–medium	Multisector	NA	NA
[126]	349	Journal	Africa	NA	Industry	NA	37.6
[127]	388	Journal	Asia	NA	Service	0.61	35.6
[128]	138	Journal	South America	Small–medium	Multisector	NA	NA
[129]	381	Journal	Asia	NA	Service	0.74	36.8
[89]	117	Journal	Europe	Large	Industry	NA	NA
[130]	247	Journal	Asia	NA	Service	0.62	29.8956
[131]	340	Journal	Asia	Large	Industry	NA	NA
[17]	685	Journal	Asia	Large	Industry	0.82	32.8
[132]	170	Thesis	North America	NA	Service	0.85	38
[133]	342	Thesis	NA	Large	Multisector	NA	NA
[134]	216	Journal	Asia	Small–medium	Multisector	NA	NA
[135]	172	Journal	Asia	NA	Service	0.88	41.9
[18]	114	Journal	Asia	Small–medium	Multisector	0.36	30.1
[136]	199	Journal	Europe	Large	Service	NA	NA
[91]	282	Journal	Asia	NA	Industry	0.78	33.8
[137]	282	Journal	Asia	Small–medium	NA	NA	NA
[19]	158	Journal	Asia	Small–medium	Agriculture	NA	NA
[138]	258	Journal	Asia	Large	Industry	NA	NA
[139]	181	Journal	Asia	NA	Multisector	NA	NA
[140]	53	Conference	Asia	NA	Service	0.26	32.3
[141]	37	Conference	Africa	NA	Service	0.78	41.8
[142]	138	Thesis	North America	Small–medium	Industry	0.54	40.9
[143]	500	Journal	Asia	Small–medium	Industry	0.64	NA
[93]	150	Journal	Asia	Large	NA	NA	NA
[20]	72	Journal	Asia	Small–medium	NA	0.82	34.4
[92]	90	Journal	Asia	NA	Service	0.52	38.8
[64]	168	Journal	Asia	NA	Service	NA	NA
[144]	43	Thesis	NA	Small–medium	Industry	NA	NA
[145]	311	Journal	Asia	NA	NA	0.6	33.2
[146]	280	Journal	Asia	Large	Multisector	NA	NA
[147]	293	Conference	Europe	Small–medium	Multisector	0.52	33.4

Note: NA means data not available from the source study.

presents the details of the 47 primary studies, including the study’s first author, publication year, sample size, geographic region, organization size, organization sector, participants gender ratios, and participants’ age. The participant’s gender ratios and participant’s age refer specifically to the demographic information of the research participants included in the reviewed studies rather than to data about the authors of the publications. These details were part of the original sample descriptions provided in the publications used for the meta-analysis. These primary studies were distributed across varied regions, with the majority conducted in Asia (65%), followed by Europe (13%), Africa (6%), North America (4%), and South America (2%). Moreover, the number of male participants in each survey was larger than that of females in over 80% of the primary studies. In addition, of the 23 primary studies reporting the average age of participants, the majority reported the sample age by providing age groups and their percentages. To calculate the average age of the primary study, this paper multiplied the percentage and median value of each age group of the study sample. Additionally, 23 studies reported the organization size. Most studies did not provide the exact number of employees in organizations. Accordingly, the present study employed the median value of the number of employees in primary studies to represent this indicator. If the calculated value was over 200, this paper classified it as a “large organization”; otherwise, it was categorized as a “small–medium organization”. The organizations were classified into four categories: agriculture, industry, service, and multisector, based on specific characteristics derived from the included studies. These classifications were determined by examining the organizations’ primary economic activities and market focus. For instance, agricultural organizations are typified by their focus on primary production, while industrial organizations are defined by their emphasis on manufacturing. On the other hand, service organizations are characterized by a strong reliance on human capital, client interaction, and service delivery systems. In contrast, multisector organizations exhibit diversified operations across more than one of the aforementioned sectors. This categorization facilitates targeted comparative analyses and allows for nuanced insights into how sector-specific factors influence decision-making in the context of Industry 4.0 technology adoption.

Table 2. Summary of the study characteristics of 47 primary studies.

Characteristics	Statistical Results	Characteristics	Statistical Results
Total sample size	12,509	Organization size
Average age (years)	34.16832917	Small–medium	14
Gender (M/all participants)		Large	11
M/all participants > 0.5	17	NA	22
M/all participants < 0.5	9	Organization sector
NA	21	Agriculture	1
Geographical region		Industry	13
Asia	32	Service	18
Europe	6	Multisector	8
South America	1	NA	7
North America	2
Africa	3
NA	4

NA: Data not available from the source study.

presents a holistic summary of the 47 primary studies. “NA” represents that the previous studies did not provide relevant information. Thus, such information cannot be extracted.

Table 3. Pairwise correlations in primary studies.

Direct Effects	Number of Studies	Cumulative Sample Size	Sample Size (Min–Max)	Correlations (Min–Max)	Q	df	I2 (%)
PU–BI	43	9753	37–1641	−0.655–0.892	2064.869 ***	42	97.97
PEOU–BI	43	9845	37–500	−0.067–0.817	704.616 ***	42	94.04
SI–BI	12	4810	71–1641	0.067–0.725	277.447 ***	11	96.04

Note: *** represents the significance at the 1% level.

summarizes the correlation coefficients of the three pairwise relationships (i.e., PU–BI, PEOU–BI, and SI–BI). Meanwhile, the values of I² are above 90%, indicating that apparent heterogeneity exists among the primary studies.

5.2. Overall Effect Sizes

The meta-analysis results were derived from the random effects models due to the significant variations in the effect sizes across these primary studies.

Table 4. Results of random effects and fixed effects models.

	Random Effects Model			Fixed Effects Model
Direct effects	PU–BI	PEOU–BI	SI–BI	PU–BI	PEOU–BI	SI–BI
No. of studies	43	43	12	43	43	12
Total of sample studies	9753	9845	4810	9753	9845	4810
Effect size	0.528 ***	0.469 ***	0.487 ***	0.475 ***	0.444 ***	0.467 ***
95% CI	(0.420, 0.621)	(0.402, 0.531)	(0.363, 0.594)	(0.460, 0.490)	(0.429, 0.461)	(0.445, 0.489)
Z-value	8.256	12.049	6.851	51.323	47.133	34.982
Direct effects	PU–BI	PEOU–BI	SI–BI	PU–BI	PEOU–BI	SI–BI

Note: *** represents the significance at the 1% level.

summarizes the estimated overall effect size of the size-weighted zero-order correlations of PU–BI (0.528, 95% CI 0.420–0.621), PEOU–BI (0.469, 95% CI 0.402–0.531), and SI–BI (0.487, 95% CI 0.363–0.594). The results reveal that all three correlations were positively associated with the adoption of Industry 4.0 technologies in various sectors at a 0.01 significance level. The results suggest that when organizations know the intrinsic benefits of Industry 4.0 technologies and think these technologies are user-friendly, the organizations’ workforce is more likely to experience a higher propensity to engage with and adopt these technologies. Also, when organizations have supportive environments where peer and managerial endorsement is evident, they tend to accept these technologies. Ultimately, by leveraging these insights, organizations can make informed strategic decisions that minimize resistance, optimize resource allocation, and accelerate the integration of cutting-edge technologies, thereby enhancing productivity and competitive advantage in an increasingly digital marketplace.

The quantitative results have demonstrated the effects of the three factors on Industry 4.0 technology adoption, eliminating the controversial results in the previous studies. The positive correlation between PU and BI (H1) was supported, indicating that the acceptance of Industry 4.0 technologies was based on the users’ perceptions of how useful these technologies are in improving their working efficiency and the organization’s productivity. In addition, the positive association between PEOU and BI (H2) was confirmed, suggesting that the acceptance of Industry 4.0 technologies heavily relied on PEOU. Finally, the results revealed that SI should be paid attention to when adopting Industry 4.0 technologies in various sectors (H3). Interestingly, the effect sizes in these three pairwise relationships varied significantly. The effect size of PU–BI was the highest, whereas the effect size of SI–BI was the lowest. A possible reason is that most organizations are economy-oriented; thus, they pay much attention to determining a cost-effective way to improve their productivity and increase their profits. Accordingly, PU can generate a direct impetus for technology adoption, which is the most critical factor to be considered.

This paper utilized funnel plots and fail-safe N statistics to detect publication bias and heterogeneity [109,148]. The X-axis of funnel plots is the converted Fisher Z effect value, and the Y-axis is the standard error, visually revealing the relationship between the two statistics. Small sample studies are commonly distributed at the bottom of the funnel plots, whereas large ones exist at the top. Figure 4, Figure 5 and Figure 6 show funnel plots of the three pairwise relationships. In meta-analysis, a funnel plot is a graphical representation used to assess the presence of publication bias and to visualize the distribution of study effect sizes relative to a measure of precision, such as the standard error. In this plot, individual studies are typically depicted as white circles, with their horizontal position representing the estimated effect size and their vertical position reflecting precision (where studies with greater precision appear higher in the plot). The white diamond, on the other hand, represents the pooled or overall summary effect estimate along with its associated confidence interval. The width of the diamond conveys the precision of this aggregate measure. However, clearly detecting publication bias from the funnel plots can be difficult. To avoid the influence of subjective factors, this paper adopted the Begg test and Fail-safe N to obtain accurate outcomes from the quantitative perspective. According to

Table 5. The analysis of publication bias in primary studies.

Direct Effects	Number of Studies	Fail-Safe N	Begg p-Value
PU–BI	43	31,161	0.125
PEOU–BI	43	24,596	0.173
SI–BI	12	3186	0.392

, the p-values in the three pairwise relationships were non-significant at the 10% level [149,150], revealing that the publication bias will not affect the meta-analysis results. In addition, the values of Fail-safe N represent the number of studies required to refute significant meta-analytic means [151]. A more prominent Fail-safe N value indicates less possibility of publication bias. The formula can calculate the critical value (critical value = 5 × sample size + 10). The fail-safe N values were larger than their corresponding critical values, suggesting that the meta-analysis results were reliable.

5.3. Moderator Analysis

Geographic region, organization size, organization sector, gender, and age were explored as possible moderators. Specifically, continuous variables include age and gender, whereas the geographic region, organization size, and organization sector were categorical variables with dummy coding.

5.3.1. Subgroup Analysis

To analyze the heterogeneity results in Section 5.2, this paper first conducted the subgroup analysis of these links using the random effect models (

Table 6. Subgroup analysis of the relationships by random effects models.

Relationship	Moderators		No. of Studies	Effect Size	95% CI	Z-Value	p-Value
PU–BI	Geographical region	Africa	3	0.669	(0.086, 0.910)	2.193	0.028
		Asia	29	0.524	(0.422, 0.613)	8.651	0.000
		Europe	4	0.595	(0.526, 0.656)	13.399	0.000
		NA	4	0.328	(−0.468, 0.830)	0.787	0.431
		North America	2	0.501	(−0.697, 0.961)	0.764	0.445
		South America	1	0.712	(0.618, 0.786)	10.355	0.000
	Organization size	Large	8	0.598	(0.398, 0.744)	5.038	0.000
		NA	21	0.474	(0.287, 0.626)	4.581	0.000
		Small–medium	14	0.562	(0.397, 0.692)	5.774	0.000
	Organization sector	Agriculture	1	0.214	(0.060, 0.358)	2.706	0.007
		Industry	10	0.577	(0.337, 0.747)	4.195	0.000
		Multisector	10	0.562	(0.196, 0.791)	2.849	0.004
		NA	7	0.522	(0.308, 0.686)	4.342	0.000
		Service	15	0.491	(0.360, 0.603)	6.543	0.000
PEOU–BI	Geographical region	Africa	3	0.562	(0.263, 0.763)	3.398	0.001
		Asia	28	0.448	(0.360, 0.529)	8.969	0.000
		Europe	5	0.543	(0.331, 0.703)	4.502	0.000
		NA	4	0.369	(0.125, 0.571)	2.897	0.004
		North America	2	0.524	(0.016, 0.817)	2.015	0.044
		South America	1	0.629	(0.516, 0.720)	8.595	0.000
	Organization size	Large	8	0.519	(0.340, 0.661)	5.118	0.000
		NA	21	0.463	(0.373, 0.545)	8.918	0.000
		Small–medium	14	0.445	(0.324, 0.551)	6.611	0.000
	Organization sector	Agriculture	1	0.214	(0.060, 0.358)	2.706	0.007
		Industry	10	0.473	(0.298, 0.618)	4.870	0.000
		Multisector	10	0.434	(0.308, 0.545)	6.230	0.000
		NA	7	0.437	(0.220, 0.612)	3.756	0.000
		Service	15	0.518	(0.439, 0.590)	10.949	0.000
SI–BI	Geographical region	Asia	8	0.402	(0.233, 0.548)	4.417	0.000
		Europe	3	0.594	(0.469, 0.695)	7.677	0.000
		North America	1	0.725	(0.635, 0.796)	10.667	0.000
	Organization size	Large	2	0.393	(0.173, 0.575)	3.386	0.001
		NA	8	0.519	(0.401, 0.620)	7.505	0.000
		Small–medium	2	0.454	(−0.332, 0.868)	1.150	0.250
	Organization sector	Industry	4	0.339	(0.084, 0.522)	2.574	0.010
		NA	1	0.650	(0.591, 0.702)	15.794	0.000
		Service	7	0.538	(0.453, 0.614)	10.384	0.000

). The analysis revealed the following results.

PU–BI. Regarding the geographical region, the effect sizes were significant in Asia, Europe, South America, and Latin America at the 1% level. In terms of organization size, the effect sizes were significant both in large organizations and small–medium organizations at the 1% level. Concerning the organization sector, the effect sizes were significant in all sectors except for the agriculture sector.

PEOU–BI. Regarding the geographical region and organization size, the effect sizes were significant in all regions and scales of organizations at the 5% level. Regarding the organization sector, the effect size was not significant in the agriculture sector but was significant in other sectors.

SI–BI. In terms of the geographical region and organization sector, the effect sizes were significant in all regions and sectors. Regarding organization size, the effect sizes of the small–medium organizations were significant at the 5% level, and that of the large organization was significant at the 10% level.

Overall, the effect sizes were nearly significant in all regions, organization sectors, and organization sizes. Some moderators (e.g., agriculture) were non-significant in these relationships because only one sample in agriculture was included in the meta-analysis due to contingencies and uncertainties.

5.3.2. Meta-Regressions

Based on the subgroup analysis, the effect size for the levels of each moderator was found to be significant. However, the difference in the effect size between the levels of each moderator was not explored. Accordingly, this paper investigated the moderating effects via meta-regressions.

Table 7. Moderating effects of these relationships by random effects models (continuous variables).

Relationship	Covariate	Coefficient	Z-Value	2-Sided p-Value
PU–BI	Gender
	Intercept	1.008	3.40	0.0007
	gender	−0.721	−1.57	0.1175
	Age
	Intercept	1.171	3.49	0.0005
	Age	−0.0167	−1.69	0.0907
PEOU–BI	Gender
	Intercept	0.554	2.04	0.0412
	gender	−0.094	−0.22	0.8262
	Age
	Intercept	0.526	1.73	0.0828
	Age	0.001	0.12	0.9017
SI–BI	Gender
	Intercept	1.344	2.54	0.0112
	gender	−1.356	−1.79	0.0738
	Age
	Intercept	0.3189	0.59	0.5521
	Age	0.0071	0.49	0.6261

shows the results of continuous variables for meta-regression with the method of moments for all covariates employing the random effects models.

Regarding the categorical variables, their coefficient represents the marginal effect of the focused category in comparison with the reference category [152]. No significant moderating effects of the three possible moderators were found on the effect size. For example, the moderating effects of the geographical region (Q = 4.24, df = 7, p = 0.7514), organization size (Q = 1.06, df = 2, p = 0.5875), and organization sector (Q = 1.04, df = 4, p = 0.9040) in the link PU–BI were non-significant. Regarding the continuous variables, the moderating effects of gender in the link between SI and BI were significant at the 10% level (Z = −1.79, p = 0.0738), implying that the effects of SI on the adoption of Industry 4.0 technologies varied significantly in different genders. However, the moderating effects of gender and age were non-significant in these pairwise relationships.

6. Discussion

6.1. Direct Associations

This paper employed a systematic literature review and meta-analysis to investigate the determinants of adopting Industry 4.0 technologies in various sectors, enrich the knowledge of Industry 4.0 technology adoption, and reach accurate conclusions beneficial to different sectors. Three pairwise relationships (i.e., PU–BI, PEOU–BI, and SI–BI) were significant and positive. Specifically, this study involved 47 papers that investigated Industry 4.0 technology acceptance to examine the effect sizes of the correlations between PU, PEOU, and SI with BI. The findings of this study were robust and reliable, with more included papers than the work of Raj and Jeyaraj [25], who collected only 12 papers. H1 was supported, with PU showing the strongest effect on BI among the three constructs. This finding underscored that when users perceive Industry 4.0 technologies as useful, they are more likely to adopt them, a finding consistent with prior research [92]. H2 was also supported, indicating that ease of use remains a key driver of adoption. Technologies perceived as user-friendly are more readily accepted, highlighting the importance of usability in implementation strategies. H3 was supported as well, showing that SI significantly affected BI. This finding suggested that peer expectations, organizational norms, and leadership support all play a role in technology uptake. This study also explored the role of contextual moderators in explaining variations in effect sizes. Beyond confirming TAM assumptions, this study made a theoretical contribution by examining contextual moderators, such as sector, organization size, and region, to explain variability in effect sizes. This study highlighted the need to adapt TAM to specific Industry 4.0 contexts, offering a path for refining the model and extending its applicability in complex adoption environments.

In the context of Industry 4.0, the impact of PU on BI is particularly significant, which is likely due to the industrial sector’s high emphasis on the actual benefits and performance improvements brought by technology [130]. Compared to PEOU and SI, PU can more directly reflect the potential of Industry 4.0 technology in improving production efficiency, reducing costs, and optimizing processes, thus becoming a key driving factor for organizations to adopt Industry 4.0 technology decisions [153]. In addition, decision-making in industrial environments tends to be rational, emphasizing investment returns and value creation, which further strengthens the core role of PU in adopting intentions [6].

6.2. Moderator Effects

The results discovered tremendous heterogeneity in effect sizes among the existing literature on the adoption of Industry 4.0 technologies, ranging from −0.655 to 0.892 for the relationship of PU–BI, −0.067 to 0.817 for the relationship of PEOU–BI, and 0.067 to 0.725 for the relationship of SI–BI. This heterogeneity prompted the investigation of moderating variables in these relationships. The results demonstrated that most effect sizes were significant at the 5% level in various geographical regions, organization sizes, and organization sectors in these pairwise relationships. The difference in geographical regions indicates the difference in culture, which is crucial in technology adoption [154]. Meanwhile, the organization’s characteristics are crucial in technology acceptance [155,156]. The observed differences in effect sizes across regions may be attributed not solely to cultural differences but also to differences in the size of the populations. For instance, in comparing Asia to Europe, the larger population size in certain Asian regions can lead to variations in resource allocation, technological exposure, and market dynamics, which in turn may influence the adoption of Industry 4.0 technologies [157].

According to the results in the meta-regressions, the difference between moderator levels was apparently non-significant in most relationships, but at the 10% level, gender showed a moderating effect on the relationship between SI and BI, and age showed a moderating effect on the relationship between PU and BI. Males and females can demonstrate different attitudes and behaviors toward Industry 4.0 technology adoption under SI [158,159]. In parallel, gender differences can lead to uneven distribution of males and females in various occupations, which is commonly observed in society [160]. Compared with the subgroup analysis and meta-regression results, the effect sizes for a specific moderator level (in a specific subgroup) can be generally significant. However, the difference between the levels of each moderator was non-significant.

6.3. Theoretical Implications

This paper contributed to the literature on the adoption of Industry 4.0 technologies. First, this paper verified the internal connections of the TAM-related theories and enhanced their validity in the Industry 4.0 technology context by providing cumulative results using meta-analysis. Few papers have employed meta-analysis to investigate the adoption of Industry 4.0 technologies in various sectors. To our knowledge, only one study has attempted to explore the antecedents of Industry 4.0 [25] and could not conduct the moderator analysis. Accordingly, this paper enriched the existing literature by incorporating additional and comprehensive papers on the adoption of Industry 4.0 technologies. Specifically, this paper incorporated a total of 12,509 samples from 47 studies published in the period 2003–2022. To better provide targeted suggestions for governments, developers, and managers, this paper investigated the moderators, which is also in response to the call from Ismagilova et al. [161]. The subgroup analysis and meta-regressions generated fresh insights into the moderators related to the variance across the current TAM-related papers’ relationships on the acceptance of Industry 4.0 technologies, extending the research boundaries of Industry 4.0 technology adoption in various sectors.

6.4. Practical Implications

The findings of the meta-analysis can have important implications for policymakers in governments, developers in R&D departments, and managers in specific sectors. First, considering Industry 4.0 technology adoption can be facilitated owing to the increase in PU, thus improving the basic functions and developing advanced functions in Industry 4.0 technologies. The developers of Industry 4.0 technologies are suggested to exploit functions to adapt to the rapidly changing society [162], while managers in various sectors can introduce the benefits of these technologies to help employees perceive their usefulness. Second, the significant effects of PEOU and Industry 4.0 technology adoption suggest that making Industry 4.0 technologies user-friendly is essential in reducing the challenges of operating them [163]. The government and managers should provide professional guidance and education for participants to equip them to use these technologies [164]. Meanwhile, developers are advised to minimize the process of employing Industry 4.0 technologies for easier use. Finally, the significant relationship between SI and BI indicates the following: on the one hand, the government should create a supportive environment for various sectors to adopt these technologies [42]. On the other hand, managers must pay attention to their peers, friends, and competitors’ attitudes and behaviors toward Industry 4.0 technologies [165].

6.5. Limitations and Future Directions

Despite its contributions, this study has several limitations that warrant consideration and offer directions for future research. These limitations can be grouped into four main categories.

First, the type of Industry 4.0 technologies may act as a potential moderator in the examined relationships. However, due to the relatively recent emergence of many Industry 4.0 technologies and the limited number of related empirical studies, this paper was unable to explore the moderating effects of specific technology types. When more studies become available, future research can investigate these effects to provide sector-specific insights for Industry 4.0 implementation.

Second, additional TAM-related constructs (e.g., facilitating conditions) [166] may also play a significant role in technology adoption. However, due to the scarcity of empirical studies examining these constructs within Industry 4.0 contexts, they could not be included in this meta-analysis. Future studies are encouraged to incorporate these variables to provide more comprehensive and tailored guidance.

Third, some scholars have emphasized that TAM and its extensions should be applied cautiously across different sectors and organizational contexts [167]. Given that the current study is based primarily on TAM-related literature, the generalizability of its conclusions may be limited. Future research could adopt alternative frameworks to validate and extend the current findings.

Fourth, while this study focused on the independent effects of PU, PEOU, and SI on behavioral intention, it did not explore the interrelations among these constructs. For instance, PEOU may indirectly influence BI through PU. Future research should examine these pathways to understand the dynamic mechanisms behind technology adoption better.

7. Conclusions

This study provided a robust synthesis of empirical evidence regarding the adoption of Industry 4.0 technologies by leveraging the TAM and its extensions. Drawing on data from 47 studies across diverse sectors, the meta-analysis confirmed that PU, PEOU, and SI are significant determinants of Industry 4.0 technology adoption, with PU emerging as the most influential factor. While the overall effect sizes remain significant across various subgroups, including geographical region, organization size, and industry sector, the moderating effects of age and gender are non-significant, suggesting a stable applicability of TAM constructs in different settings. These findings reconciled previous contradictory results and offered a theoretically enriched and practically relevant framework for policymakers, technology developers, and organizational managers aiming to facilitate efficient digital transformation. Ultimately, this study underscored the critical role of PU, PEOU, and SI in driving technological change, paving the way for future research to explore further dimensions and contextual variables that can enhance our understanding of Industry 4.0 technology adoption.

In addition to reaffirming the foundational assumptions of the TAM model, this study provided important theoretical insights by identifying and synthesizing moderators that influence the strength of PU, PEOU, and SI on BI in the context of Industry 4.0 technologies. These findings extended the applicability of TAM beyond traditional settings and suggested that future adaptations of TAM should explicitly account for contextual variables such as sector type, regional differences, and organizational scale. Thus, the model’s explanatory power can be improved to better reflect the complex environments of emerging technologies. Furthermore, this study offered actionable implications for public policy and business strategy. For policymakers, the findings underscored the importance of user perceptions when designing digital transformation policies. For industry leaders, the results can guide the prioritization of investment strategies, emphasizing PU and PEOU, as well as initiatives to strengthen SI, particularly in environments resistant to change. The integrated evidence from this meta-analysis supports more nuanced and context-aware decisions for adopting Industry 4.0 technologies.

Future research should build on the findings of this meta-analysis by employing longitudinal study designs to observe the dynamic evolution of user perceptions (e.g., PU, PEOU, and SI) and behavioral intentions over time. This approach would provide deeper insights into the sustainability of technology adoption in Industry 4.0 contexts and capture changes resulting from policy shifts, technological advancements, or organizational learning. Moreover, researchers are encouraged to conduct sector-specific investigations into emerging Industry 4.0 technologies, such as digital twins, edge computing, and human-robot collaboration, because different sectors may face distinct adoption barriers and facilitators.