On the verge of Industry 5.0: a socio-technical perspective on non-knowledge workers and workplace well-being Open Access

The Fourth Industrial Revolution, or Industry 4.0, has brought radical changes to the organization of work, particularly in manufacturing, by integrating advanced digital technologies (Agostini and Filippini, 2019; Masood and Sonntag, 2020; Longo et al., 2022; Cimino et al., 2023a, b; Schulze and Dallasega, 2023). Key paradigms such as Digital Twin (Tao et al., 2018), Extended Reality (Cimino et al., 2024), the Internet of Things (Pivoto et al., 2021), and Human-Robot Collaboration (Karuppiah et al., 2023) are increasingly crucial for enhancing the competitiveness and sustainability of businesses (Ghobakhloo, 2019, 2020). These technologies have driven substantial gains in productivity, flexibility, and operational efficiency (Cimini et al., 2021; Ghobakhloo, 2019, 2020). Industry 5.0 is the human-centric industrial evolution from the fourth industrial revolution (Cimino et al., 2023a, b; Olsson et al., 2025). With the emergence of Industry 5.0, a new vision takes shape, which does not replace but complements the digital optimization of Industry 4.0 with a deliberate focus on human well-being. This shift reframes the role of technology, making well-being a measurable and strategic dimension of industrial development, alongside efficiency, sustainability, and resilience (Leng et al., 2022).

Well-being itself is a multi-dimensional construct, encompassing psychological, physical, social, and professional aspects of working life, and it can be directly affected, either positively or negatively, by the technologies that workers use on a daily basis (Guest et al., 2022). A growing body of research has explored the impact of digital technologies on the well-being of knowledge workers. For instance, Konuk et al. (2023) examine how factors related to artificial intelligence (AI) affect the well-being of middle and senior managers in call centers. Similarly, Gull et al. (2023) investigate the role of AI in shaping the work experiences of knowledge workers in Pakistan. Nguyen et al. (2023) focus on the effects of technology adoption on the well-being of highly educated employees. Lastly, Kaaria (2024) analyzes the broader implications of smart technologies, AI, robotics, and algorithms on employee well-being and workplace dynamics, drawing insights from human resource specialists, futurists, and business executives.

While existing research has predominantly focused on high-skilled, knowledge-intensive roles that often shape and co-design digital systems (Veile et al., 2019), a significant theoretical gap remains regarding the impact of digital transformation on non-knowledge workers, who typically perform routine, manual tasks and have limited agency in the implementation of new technologies (Paton, 2013; Avis, 2018).

For these workers, the adoption of Industry 4.0 technologies is often perceived rather than shaped; they are recipients rather than participants in the digital transformation. As such, the impact can be ambivalent. On one hand, smart machines may alleviate physical strain, reduce errors, or increase efficiency; on the other hand, they may also exacerbate monotony, increase surveillance, reduce autonomy, or introduce unfamiliar systems that lead to frustration and stress. This raises a fundamental issue that this study seeks to address: (1) whether the use of Industry 4.0 machines truly impacts the well-being of non-knowledge workers, and (2) if so, whether this impact is positive or negative.

By leveraging Socio-Technical Systems (STS) Theory (Trist and Bamforth, 1951), which emphasizes the joint optimization of social and technical components, and aligning with the principles of Industry 5.0, a theoretical model designed to capture this complex relationship has been developed and empirically tested. Specifically, the study introduces a theoretical model which includes three key antecedents: System Quality (SQ), Experience with Technology (ET) and Technology Use (TU), capturing both the technical aspects of Industry 4.0 machines and the social dynamics of worker-technology interaction. Workplace Well-Being (WWB) serves as the dependent variable operationalizing the concept of working life quality for non-knowledge workers, capturing the human-centric principles of Industry 5.0.

Furthermore, the study seeks to contribute to the scientific literature in two keyways. First, while previous research has often considered how technology enhances productivity or customer satisfaction (Magno and Dossena, 2022; Al-Emran et al., 2024), few empirical studies have addressed how non-knowledge workers experience Industry 4.0 technologies on an emotional, cognitive, and practical level, and how those experiences impact their overall work satisfaction and well-being. Second, this research expands the discussion on the impact of Industry 4.0 technologies beyond firm performance (Pacchini et al., 2019; Ghobakhloo, 2019, 2020; Belhadi et al., 2021; Nayal et al., 2021; Saetta and Caldarelli, 2023; Wamba, 2022; Abrokwah-Larbi and Awuku-Larbi, 2023; Pozzi et al., 2023; Necula et al., 2024) by empirically validating the direct link between TU, both functional and hedonic, and WWB. By recognizing non-knowledge workers as critical stakeholders in the digital evolution of manufacturing, this study underscores the necessity of inclusive and human-centered innovation, which is an essential principle of Industry 5.0.

The remainder of the paper is organized as follows: Section 2 presents the theoretical framework of the study, including the theoretical background, the proposed theoretical model, and the formulation of hypotheses which guide the research. Section 3 introduces the employed methodology along with the empirical analysis conducted. In Section 4, the authors present the main results of the study, while Section 5 reports the discussion of the main findings. The main implications of the research work are reported in Section 6 and finally, in Section 7, the conclusions drawn from the study are outlined, including main limitations along with the potential challenges for future research.

To investigate how the use of Industry 4.0 machines affects the WWB of non-knowledge workers, this study builds on a robust theoretical foundation and develops a model supported by literature-based hypotheses. The following sections outline the conceptual background, the theoretical framework guiding the research, and the development of specific hypotheses aimed at addressing the identified research gap.

This study is grounded in the principles of STS theory (Trist and Bamforth, 1951), which underscores the need for the simultaneous optimization of both social and technical systems to achieve organizational or work unit objectives. STS theory examines the interconnections between an organization’s social elements, such as individuals and their interactions, and its technical elements, including technology and equipment (Appelbaum, 1997; Pasmore et al., 2019; Mϋnch et al., 2022; Thomas, 2024). Over the years, STS has been widely applied as a theoretical framework in various industrial sectors, particularly in manufacturing (Soliman et al., 2018).

Building on this perspective, several studies have analyzed Industry 4.0 technologies adoption and use through the lens of STS theory, highlighting the interdependence between human and technological factors. Vereycken et al. (2021) suggested that involving employees in the implementation of Industry 4.0 technologies can help reduce resistance to change while improving their engagement in ongoing transformation processes. Cimini et al. (2021) explored the impact of Industry 4.0 on job creation and organizational structures, revealing that companies adopting these technologies often undergo significant structural reorganization, including a reduction in hierarchical levels. Furthermore, Cimino et al. (2024) conducted interviews with 12 operators to gain a deeper understanding of the effectiveness of an Industry 4.0-ready platform in supporting real-time monitoring and control of complex industrial systems. Additionally, Veile et al. (2019) examined the key competencies that workers need to thrive in Industry 4.0 environments, highlighting the importance of interdisciplinary knowledge, information technology (IT) training, and adaptability. Several other studies have emphasized the importance of adopting a STS perspective when implementing Industry 4.0 (Davies et al., 2017; Avis, 2018).

However, the emergence of Industry 5.0 shifts the focus from pure automation and efficiency to a more human-centric, resilient, and sustainable approach, reinforcing STS principles. Industry 5.0 recognizes that the well-being of employees should not be a secondary outcome but a fundamental goal of technological integration (Leng et al., 2022). The sociotechnical approach to the use and adoption of Industry 4.0 machines has already been recognized for its impact on job enrichment, workers’ needs, and job satisfaction (Oudhuis and Tengblad, 2020), and it is also closely tied to the quality of working life (Guest et al., 2022). Cherns (1976, 1987) highlighted improvements in quality of working life as one of the goals of STS. In alignment with Industry 5.0, which emphasizes enhancing worker well-being alongside technological progress, this study applies STS principles to examine the impact of Industry 4.0 implementation on WWB, an increasingly relevant organizational objective.

Specifically, the focus of this research is on the use of Industry 4.0 machines by non-knowledge workers, whose roles are often characterized by repetitive and monotonous tasks (Paton, 2013). By applying STS principles within the context of Industry 5.0, the aim is to understand how the socio-technical dimensions of these machines can be leveraged to enhance WWB for this segment of the workforce. In proposed theoretical model, which will be presented in Section 2.2, TU and ET are identified as social dimensions, as they reflect how individuals interact with and perceive Industry 4.0 machines. Meanwhile, SQ represents the technical dimension, encompassing factors such as usability, reliability, and adaptability. Additionally, WWB operationalizes the human-centric principles of Industry 5.0 by measuring the quality of working life for non-knowledge workers. This classification aligns with the core principles of both STS theory and Industry 5.0, which consider the simultaneous integration of social and technical elements as essential for reaching organizations objectives, one of which is the improvement of WWB for the employees.

Within the context of this study, Industry 4.0 machines are defined as smart, interconnected systems that utilize advanced digital technologies to enhance manufacturing and industrial processes (Morgan et al., 2021). The research aims to examine the impact of Industry 4.0 machines on the working life quality of non-knowledge workers in the manufacturing sector. To achieve this, a theoretical model consisting of four key variables is proposed and validated. Three of these, SQ, TU, and ET, serve as antecedents, capturing both the technical dimension of Industry 4.0 machines (SQ) and the social factors related to workers’ interaction with these technologies (TU and ET). This dual focus is aligned with the STS theory, which serves as the theoretical foundation for this study. The fourth variable, WWB, acts as the dependent variable, operationalizing the concept of working life quality for non-knowledge workers, in alignment with the human-centric principles of Industry 5.0. Specifically, WWB reflects the extent to which a worker is satisfied with various aspects of their job, including work-life safety, employee support, professional growth, workplace facilities, environmental conditions, and overall work climate (Pradhan and Hati, 2019; Zheng et al., 2015). The proposed theoretical model is illustrated in Figure 1.

This section reports the hypotheses regarding the impact of Industry 4.0 machines, assessed through the identified antecedent variables (SQ, ET, and TU), on working life quality of non-knowledge workers, which is operationalized through the variable WWB.

SQ refers to the technical aspects of a system (Delone and McLean, 2003). In this study, it is defined as a composite measure of key technical attributes that impact a system’s usability, reliability, and timeliness. Usability denotes the ease with which users can interact with the system (Magno and Dossena, 2022), reliability reflects its consistency in performance and availability (Al-Emran et al., 2024) and timeliness refers to the system’s ability to deliver responses in prompt manner (Magno and Dossena, 2022). To the best of the authors knowledge, there is a lack of scientific studies directly investigating the impact of this construct on WWB. However, numerous empirical studies in various contexts have explored its influence on user and customer satisfaction. For instance, Magno and Dossena (2022) and Al-Emran et al. (2024) confirmed a positive relationship between SQ and customer satisfaction in the context of Generative Artificial Intelligence. Zhong and Chen (2023) demonstrated that SQ positively mediated customer satisfaction among mobile payment users by directly enhancing the technology’s functional value. Additionally, Mohammad Salameh et al. (2018) identified a positive impact of SQ on customer satisfaction in the domain of mobile commerce. Several other relevant studies also confirmed this positive relationship (Lin, 2007; Kumar and Lata, 2021; Nuryanti et al., 2021). Within the context of this research, WWB is defined as the extent to which non-knowledge workers using Industry 4.0 machines experience satisfaction with their working life, where “satisfaction” reflects their overall positive assessment of various job-related factors (Zheng et al., 2015; Pradhan and Hati, 2019). In this scenario non-knowledge workers can be considered the end users of Industry 4.0 machines, similar to how customers interact with digital technologies in other contexts. Given the established positive relationship between SQ and user satisfaction across various technological domains, this reasoning has been extended to this study. Therefore, it is assumed that higher SQ, characterized by usability, reliability, and timeliness, will positively influence WWB of non-knowledge workers using Industry 4.0 machines within manufacturing sector.

The construct ET is associated with the hedonic goal of using technology, which refers to engaging in an emotional experience (Magno and Dossena, 2022). Previous studies have provided evidence that the adoption and use of certain technologies can generate emotional responses from users (Chang et al., 2014; Partala and Kujala, 2015; Partala and Saari, 2015). These emotional responses can be either positive or negative, depending on individuals’ unique psychological evaluations (Beaudry and Pinsonneault, 2010). In this study, the focus is on the positive emotional experiences associated with TU, particularly the potential for technology to elicit positive emotions such as happiness and pleasant feelings (Lu et al., 2019). Research has consistently demonstrated that positive emotions contribute to increased job satisfaction. Şchiopu (2015) conducted a quantitative daily diary study with 55 professionals from various organizations and occupations, validating the positive relationship between positive emotions and job satisfaction. Fischer (2000) used experience sampling methodology to collect real-time reports of affective experiences at work, revealing that the frequency of positive emotions is a stronger predictor of overall job satisfaction than their intensity. Dreer (2021), analyzing data from 511 German schoolteachers, also confirmed that positive emotions strongly predict job satisfaction. This finding is also supported by Williams et al. (2023). Furthermore, numerous studies have established a strong correlation between job satisfaction and employee well-being. Gordon and Matthew (2011) identified fours domains, including work satisfaction, as key elements of workers’ well-being. Slemp et al. (2015) developed a theoretical model, where WWB is shown as a higher order factor, comprised of positive affect, negative affect, and job satisfaction. Sen and Khandelwal (2017) recognized that organizations can utilize job crafting as a means to enhance job satisfaction, which, in turn, contributes to the overall well-being of employees. Based on these findings, it is hypothesized that the positive emotional responses triggered by the use of Industry 4.0 machines by non-knowledge workers positively influence their satisfaction with their working life (WWB).

Building on the study conducted by Al-Emran et al. (2024), TU is defined as the extent to which individuals engage with a given technology. To the best of the authors' knowledge, the existing literature lacks studies investigating the direct impact of TU on employee well-being at workplace. This gap is also further confirmed by Lozie et al. (2024) within the context of Generative AI technology. However, Fujimoto et al. (2016) found a positive association between technology usage, specifically among Japanese works operating in the mobile sector, and work engagement. Similarly, in the context of AI adoption, Wijayati et al. (2022) confirmed also the strong relationship between TU and work engagement. This latter has been found to be strictly correlated to the employee WWB. Alkahtani et al. (2020) used a cross-sectional quantitative research to discover the significant positive relationship between workplace wellbeing and work engagement among Saudi Arabia employees. Moreover, Damianus et al. (2020) confirmed this relationship by using a descriptive correlational research where respondents were colleges employees. Koon and Ho (2020) also validated this relationship in a study involving 150 full-time employees, further reinforcing the strong connection between well-being and engagement. Finally, Salem et al. (2023) corroborated these findings through a survey of employees and managers in the IT sector in Pakistan, highlighting the robustness of this relationship across different contexts and professional environments.

Furthermore, research has shown that TU plays a significant role in enhancing job satisfaction (Diaz et al., 2012). In this regard, the strong relationship between job satisfaction and employee well-being is well-documented in the scientific literature (Gordon and Matthew, 2011; Slemp et al., 2015; Sen and Khandelwal, 2017). In addition, job satisfaction has been identified as a key determinant of employee well-being, as confirmed by Sironi (2019) in a large-scale study involving 28,283 working individuals.

Building on these findings and aiming to advance the state of the art in this field, it is hypothesized that the use of Industry 4.0 machines by non-knowledge workers within the manufacturing sector positively influences their satisfaction with various aspects of their job (WWB).

To achieve the research objectives, a questionnaire-based survey was conducted from February to March 2025 among a sample of Italian non-knowledge workers operating in the manufacturing sector and using Industry 4.0 machines. The questionnaire was initially developed in English and then translated into Italian using a standard forward and back translation process to ensure linguistic equivalence and cultural adaptability for the Italian version. The questionnaire was structured into three sections, each serving a specific purpose. The first section introduced the study’s aim and scope while providing key definitions to familiarize participants with the concept of Industry 4.0 machines. Given that non-knowledge workers may use Industry 4.0 machines in their daily tasks without necessarily distinguishing them from traditional machines, this section ensured a clear understanding of the topic. This section concluded with a filter question asking whether the respondents actively used Industry 4.0 machines in their daily work, ensuring that only relevant participants were included in the study. The second section collected general demographic and background information, including gender, age, work experience with Industry 4.0 machines, and educational level. These details served as contextual factors to better understand the respondents’ profiles. The third and final section focused on measuring the variables outlined in the proposed research model (see Figure 1). The measurement scales were derived from the scientific literature and, where necessary, slightly adapted to align with the specific research context of this study. Specifically, SQ and ET were measured using four and three items, respectively, adapted from Magno and Dossena (2022). TU was assessed using a three-item scale adapted from Al-Emran et al. (2024). Finally, WWB was measured using a nine-item scale adapted from the study from Zheng et al. (2015) and Pradhan and Hati (2019). The final questionnaire consisted of 19 items, measured on a 7-point Likert scale ranging from “strongly disagree” to “strongly agree”. The Likert scale was selected as it has already been scientifically adopted and validated in the scientific literature for similar constructs and exploratory studies. The complete set of items is provided in Table 1.

Before launching the survey, a testing phase was conducted to assess the questionnaire’s effectiveness and clarity. The pilot study was carried out in person by the authors, involving 10 non-knowledge workers, with the primary goal of ensuring that the survey items were clear and easy to understand. Feedback and recommendations from this phase were carefully reviewed, and necessary modifications were made to enhance clarity. The average completion time for the questionnaire was approximately 10 min, and no incentives were offered to participants. Data was collected using Google Forms; however, the interviews were conducted in person, with the authors directly guiding participants through the survey and recording their responses on the online form. A total of 242 non-knowledge workers from Italian manufacturing companies participated in the study. Of these, 25 respondents were excluded as they did not use Industry 4.0 machines in their daily work, making their feedback irrelevant to the study’s objectives. As a result, 217 structured interviews were considered valid for analysis. Additionally, the data were reviewed for repetitive or incoherent responses, but no issues were identified, confirming all 217 questionnaires as valid. The dataset adheres to the guideline of being at least ten times larger than the maximum number of arrowheads pointing to the dependent variable (WWB) in the model (Hair et al., 2021).

The study first presents the descriptive statistics of the survey participants and then employs Partial Least Squares Structural Equation Modeling (PLS-SEM) to test and validate the proposed research model and hypotheses (Wold, 1975, 1982; Lohmöller, 1989; Bentler and Huang, 2014; Dijkstra, 2014; Dijkstra and Henseler, 2015). PLS-SEM was chosen due to its suitability for small sample sizes (Willaby et al., 2015) and its effectiveness in exploratory research (Hair et al., 2019). The analyses were conducted using SmartPLS 4 software from SmartPLS GmbH (further details on SmartPLS 4 are available at https://www.smartpls.com). The evaluation process followed a two-step approach: first, the measurement model was assessed to ensure the reliability and validity of the constructs used in the study; second, the structural model was analyzed to examine the hypothesized relationships between variables, assess their statistical significance, and evaluate the predictive power of the proposed model.

This section presents the demographic characteristics of survey participants who use Industry 4.0 machines in their daily work. Regarding gender, the majority of respondents were male (72%), followed by female participants (25%), and a small proportion (3%) who preferred not to disclose their gender. This gender imbalance is typical in the Italian manufacturing sector, where male workers generally outnumber female workers (ISTAT, 2022). Regarding age, the largest group of participants fell within the 30–39 years range (29%), followed closely by those in the 40–49 years range (27%). The 20–29 years age group represented 21%, while the 50+ years group comprised 22%. There were no participants under 20 years of age. These findings indicate that the majority of participants are relatively mature in their careers, with substantial experience in the workforce. In terms of educational background, most participants completed high school (52%), with 32% holding a Bachelor’s or Master’s degree, while only 16% had completed middle school education. This suggests a diverse educational background among the respondents, which may lead to varying levels of technological literacy and comfort with using advanced Industry 4.0 machines. Finally, in relation to work experience with Industry 4.0 machines, a majority of the participants had more than one year of experience (56%), while 22% had between 7 and 12 months of experience, and 22% had been using these machines for 1–6 months. This distribution indicates that a substantial portion of the participants are relatively experienced with Industry 4.0 machines, which may positively influence their perceptions of technology and its role in the workplace.

The measurement model was evaluated following the approach proposed by Hair et al. (2019). This process involves four key steps: assessing indicator reliability, evaluating the internal consistency and reliability of the constructs, and examining both convergent and discriminant validity of the model. Indicator reliability was assessed by examining the factor loadings of each item, with the results presented in Table 3. The analysis revealed that all the values met the recommended threshold of 0.708 (Hair et al., 2021), except for two indicators, SQ-3 and WWB-7, which had slightly lower values of 0.693 and 0.699, respectively. However, since these values remain above 0.6, they are still considered acceptable for exploratory studies (Hair et al., 2011, 2021).

Moving forward, to assess internal consistency and reliability, three key measures have been employed: Cronbach’s alpha (Hair et al., 2017), the reliability coefficient rho_A (Dijkstra, 2014; Dijkstra and Henseler, 2015) and the composite reliability coefficient rho_C (Hair et al., 2017). Table 2 presents the values of these measures, all of which meet the recommended thresholds (Hair et al., 2021).

The third step involves assessing convergent validity, which was evaluated using the Average Variance Extracted (AVE) for each construct (Hair et al., 2021). The AVE values for SQ (0.607), ET (0.856), TU (0.742), and WWB (0.644) all exceed the recommended threshold of 0.5 (Hair et al., 2022), confirming the model’s convergent validity.

The final step in assessing the measurement model is evaluating discriminant validity. To achieve this, three key metrics have been used: Cross-loadings (Hair et al., 2017), the Fornell-Larcker criterion (Fornell and Larcker, 1981), and the Heterotrait-Monotrait Ratio (HTMT) (Henseler et al., 2015). Tables 3–5 present the results for each metric, confirming that the model meets the criteria for discriminant validity (Fornell and Larcker, 1981; Henseler et al., 2015; Hair et al., 2017, 2021).

After confirming the reliability and validity of the measurement model, the next step is to evaluate the structural model. First, potential collinearity issues have been assessed by using the Variance Inflation Factor (VIF) (Hair et al., 2021). The results indicate that all VIF values for the independent-dependent relationships are below the recommended threshold of 5, confirming the absence of collinearity concerns (Hair et al., 2011). Specifically, the VIF values for the relationships between SQ and WWB, ET and WWB, and TU and WWB are 2.067, 2.739, and 1.715, respectively. Second, a bootstrapping analysis was conducted with 5,000 sub-samples to test the proposed hypotheses (Hair et al., 2017, 2019). Table 6 presents the main results obtained from the analysis, confirming that each independent construct (SQ, ET, TU) has an impact on WWB. Among them, SQ has the strongest effect (0.429), followed by ET (0.284) and TU (0.183). The statistical significance of these relationships was assessed by calculating the T-values, where a significance level of 5% requires a T-value above 1.96 to confirm statistical significance (Hair et al., 2021). The results support this criterion. Additionally, confidence intervals were also calculated as an alternative method for testing relationship statistical significance. As reported in Table 7, none of the confidence intervals include zero, further reinforcing the statistical significance of the model’s path relationships (Hair et al., 2021).

Finally, the coefficient of determination (R²) has been calculated to assess the model’s explanatory power (Shmueli and Koppius, 2011). The R² value of 0.617 indicates that the model’s explanatory power can be considered between moderate and substantial (Hair et al., 2011). This suggests that a significant portion of the variance in the WWB construct is effectively explained by the predictor variables SQ, ET, and TU.

This study applies the principles of STS theory to develop a model exploring the impact of Industry 4.0 machines on the working life quality of non-knowledge workers in the manufacturing sector. The model includes three antecedent variables (SQ, ET and TU) to capture socio-technical factors that influence WWB. To validate the proposed model, a large-scale data survey was conducted, involving 217 Italian non-knowledge workers in the manufacturing sector, and analyzed using PLS-SEM.

The first set of findings confirms the positive impact of SQ on WWB (H1), emphasizing the critical role that some technical attributes of Industry 4.0 machines play in shaping non-knowledge workers’ well-being at workplace. This relationship can be better understood by examining the key dimensions of SQ, namely usability, reliability, and timeliness. In manufacturing systems, a well-designed and user-friendly Industry 4.0 machine may enhance productivity by allowing workers to perform their tasks efficiently and without unnecessary complexity (Cimino et al., 2023a, b). High usability ensures that Industry 4.0 machines are intuitive and easy to navigate, minimizing both cognitive and operational effort for workers. Similarly, when machines are reliable, consistently performing without frequent breakdowns or errors, they may enhance a sense of trust and stability in technology, reducing uncertainty and workplace stress (Ayyagari et al., 2011). Additionally, timeliness, or the ability of a system to provide prompt and efficient responses, may help to prevent operational bottlenecks and reduces frustration caused by delays or inefficiencies (Badini et al., 2023). All these elements are particularly significant in the manufacturing sector, where non-knowledge workers often have limited digital expertise. Therefore, when Industry 4.0 machines are easy to use, reliable, and responsive, non-knowledge workers are more likely to feel supported in their roles and experience lower stress levels, thus leading to greater satisfaction with their working life. While scientific studies directly investigating the impact of SQ on WWB remain limited, these findings align with existing research that highlights a positive correlation between SQ and systems users’ satisfaction (Lin, 2007; Mohammad Salameh et al., 2018; Kumar and Lata, 2021; Nuryanti et al., 2021; Magno and Dossena, 2022; Zhong and Chen, 2023; Al-Emran et al., 2024). This study serves as a pioneering contribution by extending this relationship further. In fact, by empirically confirming the positive correlation between SQ and WWB in the context of Industry 4.0 machine and non-knowledge workers, this research reinforces the direct and beneficial impact of SQ on WWB in modern manufacturing environments, aligning with the human-centric principles of Industry 5.0.

The second set of findings confirms that ET, intended as hedonic use of technology, positively influences their WWB (H2). This can be explained by several key factors. First, non-knowledge workers in manufacturing often perform routine and repetitive tasks (Paton, 2013) that can lead to boredom and disengagement (Game, 2007). When these workers find the use of Industry 4.0 machines enjoyable, it may provide a break from the monotony, increasing their job satisfaction, which represent a key element contributing to employee well-being at work (Gordon and Matthew, 2011; Slemp et al., 2015; Sen and Khandelwal, 2017). Second, the hedonic aspect of TU can generate positive emotions (Fischer, 2000; Şchiopu, 2015; Dreer, 2021; Williams et al., 2023), which may reduce stress and frustration, leading to improved WWB. Third, when non-knowledge workers enjoy using Industry 4.0 machines, they may feel more competent in managing their tasks increasing their sense of autonomy, a key element to enhance employee well-being (Wheatley, 2017). Fourth, the hedonic use of Industry 4.0 machines extends beyond individual benefits and can contribute to a more positive overall work climate. In fact, these technologies, being smart and interconnected, facilitate the sharing of information among workers. Effective information sharing can enhance mutual trust among colleagues (Lee and Whang, 2000) and in a work environment where trust is high, workers are more likely to collaborate and support each other (Jahansoozi, 2006; De Cremer and Dewitte, 2002), experiencing higher satisfaction with their working life.

Finally, this study confirms the positive correlation between TU and WWB (H3). This relationship can be understood by examining the level of engagement non-knowledge workers experience with Industry 4.0 technologies. Regular interaction with these machines provides opportunities for non-knowledge workers to develop new skills and knowledge. It is important to note that non-knowledge workers often perform repetitive (Paton, 2013), so any improvements in their skill set can have a significant positive impact on their job satisfaction, which in turn leads to higher level of well-being (Gordon and Matthew, 2011; Slemp et al., 2015; Sen and Khandelwal, 2017). Moreover, the use of Industry 4.0 machines can help break the monotony of their work, making it more dynamic and stimulating. By allowing non-knowledge workers to perform tasks in different ways and interact with various technological features, these machines introduce variety and mental stimulation into their daily routines. As a result, workers experience reduced boredom and greater engagement with their tasks. This heightened engagement is often linked to higher satisfaction with their working life (Alkahtani et al., 2020; Damianus et al., 2020). Furthermore, as workers become more proficient in using these technologies, they experience a greater sense of self-efficacy, which leads to increased confidence and competence in their roles. Consequently, they may become less dependent on supervisors for guidance, gain the ability to troubleshoot issues independently, and feel more in control of their work processes. This enhanced sense of capability can also improve their work autonomy, which, as already said, is a key driver of employee well-being at workplace (Wheatley, 2017). Finally, Industry 4.0 technologies are designed to enhance productivity and reduce the burden of physically and mentally demanding tasks (Piccarozzi et al., 2024). As a result, workers who frequently use these technologies may experience less physical strain as machines automate repetitive manual processes, and have a higher sense of accomplishment, from completing tasks more efficiently. These elements lead to lower stress levels, which contribute to WWB enhancement (Patel et al., 2012).

From a theoretical perspective, this study offers several key contributions. First, it extends the application of STS theory by exploring how Industry 4.0 technologies influence WWB for non-knowledge workers. By examining key socio-technical factors such SQ, ET and TU, this research deepens the understanding of how these elements shape worker well-being in technology-driven manufacturing environments.

Second, while prior studies have primarily focused on Industry 4.0 adoption from the perspective of user or customer satisfaction (Magno and Dossena, 2022; Al-Emran et al., 2024), this study shifts the focus to employees by adopting a holistic approach to WWB. It considers multiple dimensions of workers’ experiences, including work-life safety, employee support, professional growth, workplace facilities, environmental conditions, and overall work climate.

Third, this research expands the discussion on the impact of Industry 4.0 technologies beyond firm performance (Pacchini et al., 2019; Ghobakhloo, 2019, 2020; Belhadi et al., 2021; Nayal et al., 2021; Saetta and Caldarelli, 2023; Wamba, 2022; Abrokwah-Larbi and Awuku-Larbi, 2023; Pozzi et al., 2023; Necula et al., 2024) by empirically validating the direct link between TU, both functional and hedonic, and WWB. This underscores the socio-technical benefits of Industry 4.0 technologies, demonstrating that beyond enhancing efficiency, they can also improve the working lives quality of non-knowledge workers, particularly those engaged in repetitive tasks. In this regard, this study is among the first to advance the principles of Industry 5.0 by highlighting the role of technology in improving a more human-centric work environment.

From a practical standpoint, this study offers also several important implications for companies implementing Industry 4.0 technologies in manufacturing environments, particularly for non-knowledge workers. As businesses transition toward the human-centric principles of Industry 5.0, recognizing the influence of socio-technical factors on WWB becomes essential for optimizing both operational efficiency and worker satisfaction with their working life. First, companies should prioritize SQ to ensure that Industry 4.0 machines are intuitive, easy to learn, and require minimal cognitive effort. Given that non-knowledge workers may have limited technological background on Industry 4.0 machines, it is essential to provide clear, step-by-step instructions, visual cues, and user-friendly interfaces that simplify machine operation. Interactive guidance systems, such as touchscreen tutorials or voice-assisted instructions, can help workers navigate new technologies with ease, reducing frustration and stress. Additionally, predictive maintenance should be implemented to enhance machine reliability, minimizing unexpected failures that could disrupt workflows and create uncertainty. Beyond functionality, hedonic use of Industry 4.0 machines plays a crucial role in enhancing WWB among non-knowledge workers aligning with the human-centered vision of Industry 5.0. Companies can improve this aspect by incorporating gamification elements to make interactions with machines more engaging, such as progress tracking or small rewards for completing tasks efficiently. Additionally, allowing non-knowledge workers to personalize some machine settings (with or without the support of technical experts) can help strengthen their sense of autonomy and control over their work environment. Simple customizations, such as adjusting screen layouts or modifying basic operational preferences, can make the interaction with technology more comfortable and engaging. Finally, this study also highlights that the frequency and duration of Industry 4.0 machine use (TU) have a positive impact on WWB. This effect can be attributed to the fact that greater exposure to these technologies allows non-knowledge workers to develop new competencies, increasing their confidence in handling these machines and, in turn, enhancing their overall well-being. To support this process, companies should invest in basic and continuous training programs specifically designed for non-knowledge workers. These training initiatives should be simple and practical focusing on real-world applications while avoiding overly technical explanations. In this way, organizations can support non-knowledge workers in gradually developing their skills, enhancing their adaptability to technological advancements, thus establishing the conditions for more positive emotional experience in the workplace, one of the core principles of Industry 5.0.

The Fourth Industrial Revolution, or Industry 4.0, has transformed the manufacturing sector through the widespread adoption of advanced technologies. These innovations have unlocked unprecedented opportunities to enhance flexibility, efficiency, sustainability, and overall manufacturing performance. Today, the shift from Industry 4.0 to Industry 5.0 represents a move from a purely technology-driven approach to one that prioritizes human well-being within manufacturing systems. In this evolving landscape, it is crucial to assess the impact of Industry 4.0 machines on WWB across various job roles. Understanding these dynamics helps companies to optimize operational efficiency and also to identify key factors that can improve their employee well-being at workplace. Within this research framework, this study applies the principles of STS theory to develop a theoretical model exploring the impact of Industry 4.0 machines on the working lives quality of non-knowledge workers in the manufacturing sector. The model consists of three key antecedent variables (SQ, ET, and TU) which aim to capture socio-technical factors influencing WWB of non-knowledge workers. Three hypotheses were formulated to examine the relationships among these variables. To validate the proposed model and hypotheses, a large-scale survey was conducted using a well-designed questionnaire. The data were collected through in-person interviews, with the authors guiding participants through the survey and recording responses on an online form. A total of 217 structured interviews were considered valid for analysis. The data were analyzed using PLS-SEM. The findings reveal that each independent construct has a significant impact on WWB, with SQ exerting the strongest effect, followed by ET and TU. This study contributes theoretically to advancing knowledge in the research context and, practically, provides implications and guidelines for companies looking to enhance the well-being of non-knowledge workers in the workplace.

Acknowledging the valuable contributions of this research, it is not without limitations. First, this study focuses on manufacturing environments and specifically on non-knowledge workers, which may limit the generalizability of the findings to other industries or worker categories. Future research could extend the analysis to different sectors or organizational settings to validate these results and explore variations in WWB across different job roles. Second, the study is conducted within the Italian context, which may influence the findings due to country-specific cultural factors. As a result, the generalizability of the results to other geographic regions may be limited. Future research could replicate this study in different countries to assess whether the observed relationships hold across diverse cultural contexts, allowing for a more complete understanding of the impact of Industry 4.0 technologies on WWB globally. Furthermore, the study relies on cross-sectional survey data, capturing relationships at a single point in time without accounting for the evolving nature of Industry 4.0 TU and its long-term impact on worker well-being. A longitudinal approach would allow to understand how workers’ experiences and perceptions of Industry 4.0 machines use change over time, particularly as they develop greater familiarity and competence with these systems. Furthermore, the use of a questionnaire as a single data collection method may have limitations in capturing the richness of non-knowledge workers experiences with Industry 4.0 machines. Combining multiple methods, such as interviews or focus groups, should provide a more holistic view. Finally, this study does not explore potential moderating factors that may influence the relationship between Industry 4.0 TU and WWB, such as company size or workers individual differences (i.e. working experience or educational level). These factors may shape how non-knowledge workers interact with technology and perceive its impact on their well-being. Future research could integrate these moderating variables to refine the theoretical model and offer more tailored recommendations for organizations implementing Industry 4.0 technologies.