We posit that the frequency of interaction with robots while working (FIR) is positively related to job autonomy. This expected relationship is based on the following arguments. Evidence from research on work design (Hackman & Oldham, 1975; Parker & Knight, 2024) suggests that changes in work design directly influence employees’ perceived autonomy. FIR introduces such changes by transforming the nature of work tasks. As robots take over routine and repetitive tasks, employees can focus on more complex and stimulating activities, enhancing their sense of control over work processes. For instance, as a consequence of the implementation of robots in some companies and the subsequent increase in FIR for some employees, certain jobs were remarkably enriched given that their occupants went from performing physical tasks (e.g., moving boxes and containers) to supervising robots (Wingfield, 2017). Additionally, as FIR increases employees can gain greater flexibility in structuring their work because routine and repetitive tasks are performed by robots. This offers opportunities for independent decision-making, workflow management, and creative problem-solving. The implementation of robots reduces physical job demands (Jonczyk et al., 2025) and creates opportunities for employees to allocate their energy and attention to higher-value activities. This reallocation increases their capacity to manage workloads effectively and structure job tasks, which are critical components of job autonomy. This aligns with Cascio and Montealegre’s (2016) argument that reducing mundane tasks allows employees to engage in activities requiring more creativity and freedom, thereby increasing their perception of autonomy in the workplace. Therefore, we propose the following:

Hypothesis 1: FIR is positively related to job autonomy.

Based on the motivational process embedded within the Job Demands-Resources (JD-R) model (Bakker & Demerouti, 2007), we posit that job autonomy is positively related to work engagement. Work engagement is a “positive, fulfilling, work-related state of mind characterized by vigor, dedication, and absorption” (Schaufeli et al., 2002, p. 74). The motivational process in the JD-R model suggests that job resources, such as autonomy, directly stimulate employees’ intrinsic motivation by fulfilling key psychological needs, ultimately leading to increased work engagement. Job autonomy also empowers employees to manage their tasks in ways that align with their preferences, thereby promoting feelings of competence and self-efficacy, which are central to work engagement (Demerouti et al., 2001). It also allows employees to perceive their tasks as more in line with their personal values, which can deepen their engagement (Bakker & Demerouti, 2007). The more autonomy employees perceive, the more likely they are to experience engagement as they see their work as self-directed and meaningful. Therefore, we propose the following:

Hypothesis 2: Job autonomy is positively related to work engagement.

Furthermore, we posit that job autonomy is directly related to personal growth at work. According to the Self-Determination Theory (SDT; Deci & Ryan, 2000), autonomy is a fundamental psychological need that, when fulfilled, enhances intrinsic motivation and well-being. When employees perceive more autonomy, such as more control over their tasks and decision-making, they feel a sense of ownership and self-direction, both of which are essential conditions for eudaimonic well-being, especially for personal growth (Van den Broeck et al., 2016). Furthermore, according to Schaufeli & Taris (2014), within the JD-R model, job resources, such as job autonomy, play a central role in stimulating personal growth and development. Job autonomy can create an environment that might be more aligned with employees’ personal interests, which encourages them to pursue development opportunities that stimulate and develop their skills. This contributes to their personal growth at work.

Therefore, we propose:

Hypothesis 3: Job autonomy is positively related to personal growth at work.

We further posit that work engagement is positively related to personal growth at work. In the SDT (Deci & Ryan, 2000), work engagement is closely linked to intrinsic motivation. When individuals are intrinsically motivated, they strive toward opportunities for self-development and self-improvement (Schaufeli et al., 2002). This intrinsic motivation drives employees to seek mastery goal orientation, which can foster personal growth. This is in line with the findings of Wallace et al. (2013), who found that employees who are engaged at work develop confidence, capability, and a sense of progress, which are linked to self-improvement and personal growth. Furthermore, engaged workers display high levels of learning (Bakker & Demerouti, 2007) and are more likely to receive positive feedback, which further fuels their personal growth by encouraging them to work on their capabilities (Sonnentag et al., 2010).

Therefore, we propose the following:

Hypothesis 4: Work engagement is positively related to personal growth at work.

Our research further aims to examine the indirect “effects” of FIR on personal growth through different paths. We postulated that FIR is positively related to job autonomy (Hypothesis 1), job autonomy fosters work engagement (Hypothesis 2), and work engagement is positively related to personal growth (Hypothesis 4). Therefore, we hypothesize a positive indirect effect of FIR on personal growth via job autonomy and work engagement.

Hypothesis 5: There is a positive indirect effect of FIR on personal growth via job autonomy and work engagement, such that FIR is positively related to job autonomy, which, in turn, is positively related to work engagement, which, in turn, is positively related to personal growth at work.

Moreover, based on the positive relationship expected between FIR and job autonomy, and the latter and personal growth at work, we propose:

Hypothesis 6: FIR has a positive indirect effect on personal growth through job autonomy, such that FIR is positively related to job autonomy, which, in turn, is positively related to personal growth.

We posit that job autonomy is negatively related to emotional exhaustion. Emotional exhaustion is generally considered the core symptom of burnout syndrome (e.g., Shirom, 1989) and arises from a depletion of resources when work demands exceed employees’ capabilities (Hobfoll, 1989). Several empirical studies and meta-analyses have provided empirical evidence showing that there are cross-links embedded within the JD-R model so that job resources (e.g., job autonomy) are negatively related to job strains (e.g., emotional exhaustion) (Alarcon, 2011; Crawford et al., 2010; Davcheva et al., 2025; González-Romá et al., 2020; Lesener et al., 2019; Schaufeli & Taris, 2014). Job autonomy can provide employees with tools to prevent emotional exhaustion, such as being able to regulate their workload, adjust their tasks, and decide how and when to perform their work, reducing energy depletion (Demerouti et al., 2001). Autonomy also fosters a sense of control, reducing feelings of helplessness and stress that might contribute to emotional exhaustion (Alarcon, 2011). When employees perceive greater autonomy, they can manage their work in a way that aligns with their capabilities, ultimately reducing exhaustion.

Therefore, we propose the following:

Hypothesis 7: Job autonomy is negatively related to emotional exhaustion.

We posit that emotional exhaustion is negatively related to personal growth at work. Following the SDT (Deci & Ryan, 2000), emotional exhaustion disrupts intrinsic motivation, diminishing opportunities for self-development and self-improvement (Schaufeli et al., 2002), which hinders personal growth. Emotional exhaustion also depletes employees’ resources (Bakker & Demerouti, 2007). When employees are emotionally exhausted and lack key resources, they experience diminished cognitive functioning and motivation (Hobfoll, 1989), which are essential for personal growth. Furthermore, when employees are emotionally exhausted, they are less likely to engage in meaningful, growth-oriented behaviors (Deci & Ryan, 2001) or seek out development opportunities (Maslach & Leiter, 2016), which are essential for eudaimonic well-being, specifically, personal growth. Emotional exhaustion also negatively relates to proactive behaviors (e.g., job crafting) or pursuing challenging goals which have the potential to facilitate personal growth (Sonnentag et al., 2010). Therefore, we propose:

Hypothesis 8: Emotional exhaustion is negatively related to personal growth at work.

Considering that we hypothesized that FIR is positively related to job autonomy (Hypothesis 1), autonomy is negatively related to emotional exhaustion (Hypothesis 7), and emotional exhaustion is negatively related to personal growth at work (Hypothesis 8), we expect to find a positive indirect effect of FIR on personal growth mediated by job autonomy and emotional exhaustion.

Hypothesis 9: FIR has a positive indirect effect on personal growth via job autonomy and emotional exhaustion, such that FIR positively relates to job autonomy, which in turn, negatively relates to emotional exhaustion, which in turn, negatively relates to personal growth.

According to the Technology Acceptance Model (TAM; Davis, 1989), individuals’ attitudes toward technology greatly influence their ability to adapt and respond to technological changes (Hassanein & Head, 2006). We argue that employees with more positive attitudes toward robots are more likely to embrace this technology and integrate it into their work (Frey & Osborne, 2017), potentially discovering ways in which robots can be used to increase their autonomy. When employees see robots as helpful tools rather than threats, they can better adapt to working with them and benefit from potential positive outcomes, such as enhanced autonomy (Hancock et al., 2011). For instance, in a recent study by Nguyen et al. (2025), logistics workers who saw robots as valuable tools for handling repetitive tasks like sorting and lifting were more likely to embrace these technologies and integrate them into their work, enabling them to work more autonomously and focus on other responsibilities. On the other hand, negative perceptions of robots can negatively influence employees’ intention to interact with them (Guan et al., 2021) Similarly, positive attitudes toward robots might reduce resistance to change as employees with a constructive, more positive perspective are better equipped to navigate the challenges of robotization and are more likely to experience greater autonomy in their roles (Hancock et al., 2011). Thus, when employees have favorable attitudes toward robots, they are more likely to see the new technology as a collaborative tool to support their job autonomy rather than a threat that reduces their autonomy.

Based on these arguments, we propose the following hypothesis:

Hypothesis 10: The positive relationship between FIR and job autonomy is moderated by attitude toward robots so that this positive relationship increases as attitude toward robots increases.

Finally, considering the indirect effect hypotheses proposed above (Hypotheses 5, 6, and 9) and the moderator hypothesis just presented, we hypothesize the following conditional indirect effects:

Hypothesis 11: The positive indirect effect of FIR on personal growth via job autonomy and work engagement is moderated by attitude toward robots so that the positive indirect effect increases as attitude toward robots increases.

Hypothesis 12: The positive indirect effect of FIR on personal growth via job autonomy is moderated by attitude toward robots so that the positive indirect effect increases as attitude toward robots increases.

Hypothesis 13: The positive indirect effect of FIR on personal growth via job autonomy and emotional exhaustion is moderated by attitude toward robots so that the positive indirect effect increases as attitude toward robots increases.

The sample consisted of 299 employees working in the production lines of a manufacturing company focused on the management of reusable containers for supermarkets. The company rents, repairs, cleans, and distributes reusable packaging, and implements various types of automation technologies, such as industrial robots. Their robots lift and stack packages at the end of the working lines. Employees working in these lines oversee the automated processes and ensure quality control.

Initial contact with the company was established through members of the research institute. The participation was voluntary, and employees were assured that data would be treated confidentially, as described in detail in the informed consent. In total, 308 employees completed the survey, of which nine participants (2.9%) were excluded because they failed two attention check items (e.g., “To ensure the quality of the survey responses, please select the option strongly disagree.”). The final employee sample (N = 299) consisted of 52.5% women and 44.5% men (3% of participants indicated “Other”). Most participants (41.5%) were aged 26 to 35 years old, 39.5% were between the ages of 36 and 45, 9.4% were aged 18 to 25, and 8.7% were between the ages of 46 and 55. One percent were aged 56 years or older. Regarding education level, 38.5% had completed secondary education, 21.1% a vocational training degree, 30.4% high school, and 8.3% had a university degree. Regarding tenure in the current job, 53.5% reported more than 5 years, 7.7% reported 3 to 5 years, 19.4% between 1 to 3 years, and 19.4% reported less than one year of tenure.

Data collection took place in the last quarter of 2023. The study followed a cross-sectional design. The participants responded to an online questionnaire available through a computer during working hours in a room, provided by the company, away from the production lines.

We estimated the hypothesized relationships among observed variables with structural equation modeling (SEM) as implemented in Mplus (Muthén & Muthén, 2017). The hypothesized indirect and moderation effects were tested by means of bootstrapping with 5,000 samples.

Given that our hypotheses specified directional relationships based on theoretical foundations, we utilized one-tailed hypothesis tests with an alpha level of .05 (e.g., Cho & Abe, 2013). This approach is suitable in mediation studies (Preacher et al., 2010). To maintain consistency, we reported 90% bias-corrected confidence intervals (CI).

Regarding the estimated relationships, the path from FIR to job autonomy was positive and statistically significant (β = .094, p < .05), supporting Hypothesis 1. Job autonomy was positively associated with work engagement (β = .375, p < .01), and personal growth (β = .314, p < .01), supporting Hypothesis 2 and 3, respectively. Work engagement was positively related to personal growth (β = .494, p < .01), supporting Hypothesis 4.

The unstandardized indirect effect of FIR on personal growth through autonomy and work engagement was statistically significant (.017, SE = .009, 90% CI = [.005, .035]). This result supports Hypothesis 5. The unstandardized indirect effect of FIR on personal growth through autonomy was statistically significant (.029, SE = .014, 90% CI = [.009, .056]). This result supports Hypothesis 6. We estimated the corresponding effect sizes by computing the completely standardized indirect effect. The results obtained (.022 and .038, respectively) indicated small effect sizes. Thus, personal growth increased by .022 standard deviations (SDs) for every one-SD increase in FIR via autonomy and work engagement. Similarly, personal growth increased by .038 SDs for every one-SD increase in FIR via autonomy. The total completely standardized indirect effect (.022 + .038 = .06) indicated that personal growth at work increased by 0.06 SDs for every one-SD increase in FIR via the two mentioned mediators.

Job autonomy was negatively associated with emotional exhaustion (β = -.128, p < .05), supporting Hypothesis 7. Emotional exhaustion had a negative relationship with personal growth (β = -.126, p < .05), supporting Hypothesis 8.

The indirect effect of FIR on personal growth through autonomy and emotional exhaustion was not significant (.002, SE = .001, 90% CI = [.000, .006]). This result did not support Hypothesis 9.

The model including the moderator (i.e., attitude toward robots) showed a good fit to data, χ²(9) = 25.357, p = .003, RMSEA = .078, 90% CI = [.043, .115], CFI = .95, SRMR = .056. The results indicated that employees’ attitudes toward robots significantly and positively moderate the relationship between FIR and autonomy (β = .083, SE = .047, p = .038). This suggests a significant conditional direct effect (CDE), meaning that the relationship between FIR and job autonomy strengthens as attitudes toward robots improve. To help interpret this moderator effect, we computed the corresponding simple slopes for three values of the moderator. The results showed that when attitudes toward robots were low (-1SD) the relationship was not significant (.015, BC 90% CI = [-.081, .116]). However, when the moderator value was medium (Mean; .088, BC 90% CI = [.021, .155]) and high (+ 1SD; .162, BC 90% CI = [.074, .258]) the FIR-autonomy relationship was stronger and statistically significant. We also illustrated the relationship between FIR and autonomy with the moderator (see Figure 2). This figure shows that the FIR-Autonomy relationship increases as the moderator increases. These results supported Hypothesis 10.

To test Hypotheses 11, 12, and 13, and the associated conditional indirect effects, we computed the corresponding indices of moderated mediation (IMM; Hayes, 2015). The results obtained showed that employees’ attitude toward robots moderated the following indirect effects: FIR – Job Autonomy – Work Engagement – Personal Growth (IMM = .015, BC 90% CI = [.002, .032]), and FIR – Job Autonomy – Personal Growth (IMM = .026, BC 90% CI = [.003, 0.055]; Hypotheses 11 and 12, respectively). The IMM for the indirect effect involving emotional exhaustion (FIR – Job Autonomy – Emotional Exhaustion – Personal Growth) was not statistically significant (IMM = .001, BC 90% CI = [.000, 0.006]; Hypothesis 13).

To interpret the significant conditional indirect effects (CIE) we computed the corresponding values at different levels of the moderator (see Table 2). The results obtained showed that in both cases the indirect effect was not statistically significant at low values (-1SD) of the moderator, being significant at medium (mean) and high (+ 1 SD) moderator values. Additionally, the value of the indirect effect increased as the moderator increased. Finally, we illustrated the relationship between each indirect effect and the moderator (see Figures 3 and 4). These figures demonstrate that both indirect effects increased as the moderator increased. These results supported Hypotheses 11 and 12 but did not support Hypothesis 13.

Our results have several theoretical implications. They contribute to extending job characteristics and work design models (Hackman & Oldham, 1975; Morgeson & Humphrey, 2006; Parker & Knight, 2024) in several ways. Firstly, our study shows that FIR is a correlate of a key work characteristic: job autonomy. Thus, job characteristics and work design models should consider FIR as a potential antecedent of job autonomy and other key work characteristics. Future studies should test whether the relationship observed here between FIR and job autonomy is also observed when other work characteristics are examined.

Secondly, by showing that attitudes toward robots play an important moderating role in the relationship between FIR and job autonomy, our results provide empirical support for the integration of the Technology Acceptance Model (TAM; Davis, 1989) and work design theories. Our results emphasize the importance of attitudes toward robots for boosting the positive relationship between FIR and job autonomy. This provides a more nuanced knowledge about the conditions under which the functional outcomes of FIR can be expected.

Thirdly, by choosing personal growth at work as a work outcome in our research model, our study contributes to extending work characteristics models through the incorporation of indicators of eudaimonic well-being. Research about work characteristics outcomes has been mainly focused on indicators of hedonic well-being (e.g., job satisfaction; Humphrey et al., 2007; Parker & Knight, 2024). Our results show that work characteristics models should also include eudaimonic well-being indicators. Doing so will provide a more comprehensive perspective on how work design is related to the outcomes of work.

Fourthly, our moderated mediation model provides a detailed and nuanced perspective about the mechanisms through which FIR is related to a key eudaimonic well-being dimension. Our results help us understand “why” working with robots is related to personal growth at work, “what” variables are involved in this indirect relationship, and under “what conditions” this relationship is enhanced. This nuanced knowledge contributes to developing our discipline in a relatively new area (i.e., the relationship between working with robots and work characteristics and outcomes) in which the number of empirical studies is still scarce (Jonczyk et al., 2025). In this regard, it is worth mentioning that “empirically substantiating the boundary conditions of one variable’s [relationship with] another and the mechanism(s) by which that [relationship] operates are recognized as markers of deeper understanding than merely establishing that X affects Y” (Hayes, 2015, p. 1).

Finally, our study shows that FIR matters, as it may influence a key work characteristic (job autonomy), relevant psychological states (work engagement and emotional exhaustion), and an important work outcome (personal growth at work). Thus, our promising results should foster research and theory development in this relatively new area of research.

Our results also have some practical implications. Firstly, our findings suggest that integrating robots into the workplace and fostering FIR can empower employees by giving them more autonomy over their tasks and work processes. This aligns with previous research indicating that robotization can enhance job roles by taking on routine tasks and allowing employees to focus on more complex and meaningful work that involves decision-making (Barrera, González-Roma et al., 2025). Additionally, robots can provide precise information, further increasing employees’ control over their tasks (Fréour et al. 2021). Thus, robotization and fostering FIR can be a way to enrich jobs. To maximize these benefits, organizations should promote positive attitudes toward robots given that our results show that such attitudes can strengthen the positive relationship between FIR and job autonomy. Practical strategies to foster positive attitudes toward robots could include communication initiatives that highlight the advantages of robotization for employees’ quality of working life (i.e., reduced physical demands and musculoskeletal problems; Barrera, González et al., 2025) and training programs that demonstrate how to work with robots and navigate the transition process. Additionally, continuous feedback practices could be established to ensure that employees are actively involved in the implementation of robots, allowing them to share their ideas and suggestions on how robotization affects their work and what measures could be taken to improve it.

Secondly, when implementing robots, it is crucial to make sure that job autonomy is expanded considering the existing resources and constraints rather than diminished. For instance, the time saved by assigning robots the more physically demanding tasks should allow employees to devote time to supervising robots and making firsthand decisions when problems appear. Our results suggest that these improvements in job autonomy can translate into more work engagement, less emotional exhaustion, and more personal growth at work.

Our study has some limitations that suggest directions for future research. Firstly, given that we used a cross-sectional design, we cannot infer causal relationships among the study variables. Longitudinal and quasi-experimental studies are necessary to examine how the implementation of robots is related to job characteristics and employee well-being. For instance, detecting companies willing to implement robots could offer an opportunity to design a study in which the consequences of robotization in some production lines could be compared with a control group in which robots are planned to be implemented later on.

Secondly, our data came from a single organization within a specific industry, which limits the generalizability of our findings. Future studies should replicate our findings in other sectors and focus on different types of robots (i.e., collaborative robots or cobots). In the particular organizational context we studied, robot implementation was positively associated with job autonomy. As we explained above, this suggests that robot implementation allowed employees to focus on the performance of more complex tasks (e.g., supervision of robots) that gave them more control over their work. However, this outcome might not be universal and might depend on the organizational context. Berkers et al.’s (2023) qualitative study showed that the warehouse workers who were consulted in the process of robot implementation had more control over how tasks were carried out and how to interact with the new technology. For them, job autonomy increased. However, in most of the warehouses they studied, where robots were introduced through a top-down, efficiency-driven approach, workers lost autonomy. These findings suggest that the positive relationship between FIR and autonomy that we observed may not be a direct outcome of robotization and may depend on the strategy of implementation followed by companies. This idea opens an avenue for future research.

Thirdly, our data came from a single source, which might have inflated the relationships between the study variables due to common-method bias. However, the fact that some correlations between the study variables were very close to zero (e.g., .01) suggests that this was not a serious problem in our study (Spector, 2006). Last, we only measured one key work characteristic (i.e., job autonomy). Future research should consider other work characteristics that might be affected by the implementation of robots (e.g., skill variety and workload) given that the empirical research on the relationships between robotization and job characteristics and outcomes is still very scarce (Jonczyk et al., 2025). This will help increase our understanding of how robotization is shaping jobs in contemporary organizations.

Fourthly, the scales we used to measure the study variables were composed of few (four or less) items. We did so to shorten the questionnaire length and prevent respondent tiredness. The scales we used in some cases were the original versions [e.g., job autonomy (Morgeson & Humphrey, 2006), work engagement (Schaufeli et al., 2029)], but in other cases we used abridged versions of the original scales (e.g., emotional exhaustion, personal growth). Although all the multi-item scales we used showed a satisfactory reliability, we acknowledge that short scales as ours may not adequately cover the content domain of the involved construct. Thus, future studies should replicate our finding using longer scales.