Interactive monitoring is a way to collect information about subordinates’ work by holding regular meetings with them or inviting individual subordinates to participate in discussions (Liao and Chun, 2016). In the context of remote work, as it is not visible like traditional face-to-face work, employees cannot participate in organizational activities and communicate in the typical organizational behavior, leading to a decrease in direct interactive contact with colleagues and managers (Xiao, 2019), which further increases leaders’ concerns about employees’ work conditions. For example, some studies have found that in remote work, lower visibility may cause superiors to perceive and feel that remote workers are “slacking off” (DeRosa et al., 2004). However, with the development of modern communication technology, leaders have become very convenient and frequent in their interactions with remote employees (Barsness et al., 2005). Compared with face-to-face work, organizations and superiors have more constraints and norms on remote employees. For example, keeping connected anytime and anywhere has become a fundamental norm of remote work (Derks et al., 2015), using interactive monitoring to compensate for the decrease in face-to-face supervision norms. Therefore, interactive monitoring is a necessity and feasible in the context of remote work. As in traditional face-to-face workplaces, interactive monitoring in remote workplaces can even more conveniently provide employees with the following opportunities: (i) to understand the expectations and needs of supervisors; (ii) to explain errors or unsatisfactory performance to supervisors and inform them of their achievements that have not been reported or may be overlooked; (iii) to express personal opinions, concerns, and dissatisfaction (Liao and Chun, 2016). Therefore, through interactive monitoring, supervisors can provide subordinates with specific work resources. On the one hand, through communication and feedback, employees can be provided with resources to solve problems. On the other hand, participating in public discussions can convey good intentions, strengthen constructive working relationships with subordinates, and enhance their emotional resources (Tjosvold, 2008). In the context of organizational isolation of remote work, strengthening interactions with subordinates through interactive monitoring can enhance their psychological resources (Xiao, 2019).

Therefore, in the context of long-term remote work (especially when forced to work remotely), how alleviating employee fatigue and distractions, enhancing work engagement, strengthening identification, and making employees more focused, more energetic, and more willing to contribute is a problem that deserves management attention (Hu and Zhang, 2022). Many studies have confirmed that work resources, including emotional and psychological resources, are the prerequisite for enhancing work engagement (Schaufeli and Bakker, 2004; Bakker et al., 2008; Halbesleben et al., 2014; Rahmadani et al., 2020; Liu and Wen, 2022; Zhan et al., 2022; Zheng et al., 2022). Therefore, this study hypothesizes that the interactive monitoring of superiors to increase subordinates’ work resources in remote work can promote employees’ work engagement.

Workplace deviant behavior is voluntary behavior that violates important organizational norms, either threatening the organization or its members or causing harm to both (Robinson and Bennett, 1995). Its harmful impact on the organization is very concerning, as most employees engage in some level of deviance, causing billions of dollars in productivity and other costs to the organization each year (Mackey et al., 2021). Therefore, it is essential to understand the reasons for deviance and minimize and address it. Previous research has found that in remote workplaces, supervisors cannot intervene in employees’ remote work behavior due to the lack of direct monitoring, which may lead to an increase in employee deviance and a failure to ensure work efficiency (Leslie et al., 2012). Therefore, whether interactive monitoring by supervisors can reduce deviance needs further exploration. Previous research has explained why employees engage in workplace deviance from multiple perspectives, such as emotional event theory (Bordia et al., 2008), stress transaction theory (Mawritz et al., 2014), social learning theory (Mawritz et al., 2012), and personality theory (Meyer et al., 2014). However, scholars have found that these studies have certain consistencies. Furthermore, through meta-analysis, it has been found that the consistencies are reflected in the dominance of social psychology and resource-based theories in exploring the causes of deviance (Mackey et al., 2021). From a resource perspective, it has been found that general work resources hurt employee deviance (Wilson et al., 2015), which means that increasing employees’ work resources can help reduce their deviance. Therefore, this study hypothesizes that interactive monitoring by supervisors in remote workplaces to increase subordinates’ work resources can reduce employee deviance. Therefore, the following hypothesis is proposed:

Electronic monitoring refers to a form of supervision that utilizes modern computer technology to continuously collect data or information on employees, which may involve the use of surveillance cameras, computer, and telephone email monitoring systems, as well as wearable devices or mobile phone applications with global positioning system (GPS) tracking applications (APPs) (such as DingTalk) (Holt et al., 2017). Electronic monitoring occurs without direct input from the subordinate during information collection; it is a typical form of observation monitoring (Tomczak et al., 2018), the only observation monitoring that can be achieved in remote workplaces. Regarding electronic monitoring, researchers believe it is like a work discipline, where the supervised person feels the constant authority of supervision and manages their behavior. Whether electronic monitoring is overt or covert, the mere feeling that an individual may be monitored, even if it does not occur, can be a powerful management tool and potentially have profound implications for individuals (Holland et al., 2015). Therefore, electronic monitoring puts pressure to perform work tasks as required, which is a job demand (Zhou, 2003).

From the perspective of employees’ work engagement, research on the impact of job demands on reducing employees’ work engagement is relatively mature. Specifically, by studying different occupational groups, it has been found that job demands significantly negatively impact work engagement. For example, Liu and Wen (2022) studied primary and secondary school teachers and found that job demands significantly inhibited their work engagement. Chen et al. (2019) studied nurses and found that the higher the job demands of nurses, the less satisfactory their work engagement. Chen and Fellenz (2020) surveyed service industry employees and confirmed that personal job demands reduce employees’ work engagement. Therefore, this study inferred that electronic monitoring would give employees a certain pressure and requirement as an observation supervision form for employees in remote workplaces, thereby reducing remote employees’ work engagement. In summary, this study proposes the following hypothesis:

From the perspective of employees’ deviant behavior, previous studies have confirmed that the pressure caused by job demands can lead to deviant behavior (Roberts, 2012; Mawritz et al., 2014; Bazzy and Woehr, 2017). As face-to-face supervision is impossible in remote workplaces, electronic monitoring is mainly limited to the “cyber” level. Therefore, it may reduce employees’ cyber-deviant behavior, but the increased sense of pressure caused by job demands makes employees engage in deviant behavior to recover (Fan et al., 2021). Furthermore, previous studies have found that continuously increasing job demands can impose significant psychological pressure on employees, and employees’ deviant behavior results from the complex interaction of environmental stressors (Cui et al., 2021). Specifically, as electronic monitoring in remote work is a type of pressure that requires tasks to be performed, it can affect employees’ evaluation of environmental cognition (Cui et al., 2021), leading to deviant behavior. Therefore, this study hypothesizes that electronic monitoring will lead to more pronounced traditional deviant behavior. Thus, this study proposes the following hypothesis:

Self-efficacy refers to an individual’s belief in their ability to organize and execute specific achievements, and an individual’s self-efficacy in a particular domain can generalize to other domains. It is the overall self-confidence and sense of competence that individuals face environmental demands or new environments (Schwarzer et al., 1997), and it is also a personal resource (An et al., 2021). Previous studies have shown that self-efficacy can mediate between external environmental factors and individual status (Huang and Chen, 2012). Therefore, when facing the new working environment of remote work, it is worth further exploring whether external monitoring can affect individual employees through the mediating role of self-efficacy.

The mediating role of self-efficacy in the relationship between supervisors’ interactive monitoring and employee engagement and deviant behavior in remote workplaces.

In remote workplaces, because they are invisible, employees may worry that their leaders do not know their performance and doubt whether they will be fairly evaluated (Niehoff and Moorman, 1993). Supervisors interactively monitor their subordinates and obtain information by communicating with them. In this case, on the one hand, employees have a fair opportunity to introduce the details of their work progress (Mishra and Ghosh, 2020). On the other hand, employees can express their concerns and opinions, making them more likely to feel that they are being treated fairly (Wu and Wang, 2020). This sense of fairness from interactive monitoring helps to enhance employees’ self-efficacy (Fang, 2014). At the same time, interactive monitoring can help remote employees overcome and control environmental influences and feel capable during interactions (Khan et al., 2020). Additionally, in interactive monitoring, on the one hand, subordinates feel that their work is being observed by their superiors, which promotes their sense of competence. On the other hand, regular interactive discussions as a monitoring method provide subordinates with constructive problem-solving channels from their leaders, enhancing their ability to complete tasks (Mishra and Ghosh, 2020). In summary, supervisors’ interactive monitoring in remote workplaces serves as a work resource that helps to enhance employees’ self-efficacy, an individual psychological resource. Employees with high self-efficacy exhibit more positive states and fewer adverse behaviors (Kim and Beehr, 2017). Therefore, this study hypothesizes that self-efficacy can mediate between supervisors’ interactive monitoring and employee engagement and deviant behavior in remote workplaces.

The role of self-efficacy in work engagement has been well-validated, and research confirms that work-related self-efficacy is a characteristic of employees who maintain a high level of work engagement during remote work (Mäkikangas et al., 2022). Furthermore, studies have shown that psychological resources such as self-efficacy (Xanthopoulou et al., 2007) and psychological capital (Grover et al., 2018) mediate the positive effect of work resources on work engagement. Therefore, this study hypothesizes that self-efficacy mediates between supervisors’ interactive monitoring and employee work engagement in remote workplaces. Specifically, supervisors’ interactive monitoring in remote workplaces can provide resources such as interactive guidance and encouraging feedback to promote employees’ self-efficacy as a psychological resource (Mäkikangas et al., 2022). This further promotes employees’ work engagement in remote work. Therefore, this study proposes the following hypothesis:

Previous studies have confirmed that self-efficacy can reduce employees’ deviant behavior (Kim and Beehr, 2017; Iqbal et al., 2021). In remote work, supervisors’ interactive monitoring can provide employees with necessary resource support, stimulating their self-efficacy (Kim and Beehr, 2017). This further reduces employees’ engagement in behaviors that harm their self-evaluation, such as deviant behavior (Huck et al., 2017). Therefore, this study proposes the following hypothesis:

The mediating role of self-efficacy in the relationship between supervisors’ electronic monitoring and employee engagement and deviant behavior in remote workplaces.

As a form of observational monitoring, electronic monitoring in remote workplaces can make employees feel constantly supervised, leading to pressure to perform tasks in a required manner and reducing their self-efficacy (Huang and Chen, 2012). Unlike interactive monitoring, electronic monitoring collects information about employees’ performance without participation, reducing their confidence in completing tasks and assessing their performance. This is demonstrated in two ways: (1) subordinates do not have a say in matters related to their performance, weakening their control over outcomes such as performance evaluations; (2) the lack of attention and effort to investigate subordinates’ information deprives them of valuable growth opportunities and may reduce their sense of competence (Mishra and Ghosh, 2020). Specifically, ubiquitous electronic monitoring can reduce employees’ autonomy and sense of self-responsibility, making them unable to arrange and manage their own behavior freely (Niehoff and Moorman, 1993), lowering self-efficacy. The level of self-efficacy affects the degree of effort employees are willing to exert and the duration of their persistence when faced with obstacles. The higher employees’ self-efficacy, the more actively they will respond to obstacles, and vice versa (Yu and Du, 2023). Therefore, electronic monitoring reduces employees’ self-efficacy and further leads to reduced work effort (such as reduced job engagement) and adverse work behaviors (such as deviant behavior) in areas where electronic monitoring is “invisible” (Niehoff and Moorman, 1993). In summary, it is hypothesized that:

The remote work driven by COVID-19 has become the norm for many enterprises, and this study is not limited by factors such as industry and culture; the selection method for research subjects is to find 40 familiar friends and ask them to help find their acquaintances for a questionnaire filling. Referring to existing practices, to ensure the questionnaire’s validity, they must refrain from distributing it in their own company, and only one subject can be found in each enterprise (Luo et al., 2018). With the subjects’ permission, researchers directly contact them through email to collect and distribute the questionnaire. To ensure anonymity, researchers get the unique ID (The last four digits of the phone number + the last four digits of the ID number) and the subjects’ email addresses, but no other information (such as name).

This study collected questionnaire data in two periods to minimize the common method bias. Demographics, supervisor interactions, and electronic monitoring information in remote workplaces were collected in the first stage. This stage involved 40 acquaintances, each recommending 10 subjects, resulting in 400 questionnaires being distributed and 386 valid questionnaires being returned. After 2 months, self-efficacy, work engagement, and deviant behavior questionnaires were completed in the second stage. We distributed it to the 386 valid subjects from the first stage, returning 353 valid questionnaires. As the research context is remote work, all subjects must have had remote work experience. Questionnaires from subjects with no or minimal remote work experience were excluded, and finally, this study collected 299 valid questionnaires with a total effective recovery rate of 74.75%.

Regarding demographic information, there were 164 women, accounting for 54.8% of the total, and 135 men, accounting for 45.2%. The average age was 32.58 years. A total of 245 people had bachelor’s or higher degrees, accounting for 81.9%. The average number of working years in the current enterprise is 6.83 years. Regarding the nature of the enterprise, state-owned enterprises accounted for 54.5%, private enterprises accounted for 33.8%, foreign enterprises accounted for 0.3%, public institutions accounted for 5.4%, and others accounted for 6%.

We adopted established scales in English to gather data, which were adapted through Back-translation (Richard, 1970) to ensure the validity of it for Chinese interviewees. In addition, we also invited research experts and corporate employees in related fields to conduct testing, and discuss any areas of disagreement until consensus was reached (Cheng et al., 2021). Except for demographic information, a five-point Likert response anchors ranging from 1(strongly disagree) to 5 (strongly agree) were applied. In particular, this study added a question after the demographic variables: “Have you ever experienced remote work?” to determine if the subject is in line with the research context of this study.

Although we carried out a two-wave data collection within 2 months in this study, and tried to dispel the misgivings of the respondents during the survey, yet, there might be a common method variance given that all the data came from respondents’ self-evaluation. Therefore, we employed Harman single-factor method (Harman, 1976) to test whether this variance exists. Thirty-four items from exploratory factor analysis (EFA) were utilized in the test. The results showed that only 6 factors’ eigenvalues exceeded 1 with the first factor referring to autonomy explained 31.41%, which was much lower than 50%. These results clearly demonstrates that common-method variance is not a serious problem in this study (Podsakoff et al., 2003; Malhotra et al., 2006).

Prior to examining the research hypotheses proposed in our model, a multi-level confirmatory factor analysis (CFA) widely adopted and validated by previous literature was carried out to confirm whether our study variables (interactive monitoring, electronic monitoring, self-efficacy, work engagement and deviant behavior) have good discriminant validity. Analysis with AMOS 23 showed that our five-factor model produced a reasonably good fit ( χ 2 d f =1.874, TLI = 0.940, CFI = 0.946, RMSEA = 0.054), and fits better than alternative parsimonious models (Table 1). These results not only suggested that the latent constructs used in this study have acceptable discriminant validity, but also further certified that common-method variance is not a serious problem in our study.

Table 2 presents the means, standard deviations, and correlations of the study variables, which were run by SPSS 23. The results suggest a positive correlation between interactive monitoring and self-efficacy (r = 0.275, p < 0.01), a positive correlation between interactive monitoring and work engagement (r = 0.434, p < 0.01), a negative correlation between electronic monitoring and self-efficacy (r = −0.174, p < 0.01), a positive correlation between electronic monitoring and deviant behavior (r = 0.462, p < 0.01), a positive correlation between self-efficacy and work engagement (r = 0.490, p < 0.01), a negative correlation between self-efficacy and deviant behavior (r = −0.244, p < 0.01), which provides preliminary evidence for subsequent hypothesis testing.

We tested the hypotheses H1a, H1b, H2a, and H2b, meanwhile preliminarily examined the mediating effect via hierarchical regression using SPSS 23. The results are presented in Table 3. Further, referring to the suggestion of Edwards and Lambert (2007), we applied Bootstrap (5,000 times) to verify the mediating effect (H3a, H3b, H4a, and H4b).

Hypothesis 1a proposes that interactive monitoring is positively associated with work engagement. As anticipated, results from Table 3 reports that Hypothesis 1a is supported ( β = 0.398, p < 0.01, Model 2).

Hypothesis 1b proposes that interactive monitoring is negatively associated with deviant behavior. As predicted, results from Table 3 reports that Hypothesis 1b is supported( β = –0.104, p < 0.01, Model 5).

Hypothesis 2a proposes that electronic monitoring is negatively associated with work engagement. The results in Table 3 reports that H2a is not supported( β = 0.007, n.s., Model 2).

Hypothesis 2b proposes that electronic monitoring is positively associated with deviant behavior. As anticipated, results from Table 3 reports that Hypothesis 2b is supported ( β = 0.382, p < 0.01, Model 5).

Hypothesis 3a proposes that interactive monitoring indirectly affects work engagement via self-efficacy. To compare Model 3 and Model 4, we add monitoring in Model 4 based on Model 3, which still suggests a positive association between self-efficacy and work engagement ( β = 0.598, p < 0.01, Model4). Although the effect turns weaker, it’s still significant. Furthermore, to compare Model 2 and Model 4, we add self-efficacy in Model 4 based on Model 2, which still suggests a positive association between interactive monitoring and work engagement( β = 0.296, p < 0.01, Model 4). The effect is still significant indicating a partial mediation. H3a is supported.

Referring to the suggestions of Edwards and Lambert (2007), Bootstrap (5,000 times) is applied in this study to further verify the indirect effect of self-efficacy in the model. The results are shown in Table 4, demonstrating that this indirect effect as hypothesized is pronounced (estimate = 0.097, 95%CI =0.044, 0.168), which confirms that interactive monitoring indicates a significant mediating effect on work engagement through self-efficacy. And the direct estimate is significant (estimate = 0.284, 95%CI = 0.199,0.369), so self-efficacy partially mediates the relationship between interactive monitoring and work engagement. The above results provide support for hypothesis 3a.

Hypothesis 3b proposes that interactive monitoring indirectly affects deviant behavior via self-efficacy. To compare Model 6 and Model 7, we add monitoring in Model 7 based on Model 6, which still suggests negative association between self-efficacy and deviant behavior( β = –0.196, p < 0.01, Model7). Although the effect turns weaker, it’s still significant. Furthermore, to compare Model 5 and Model 7, we add self-efficacy in Model 7 based on Model 5, which suggests the negative association between interactive monitoring and deviant behavior is not significant( β = –0.070, n.s., Model 7). So the mediating role of self-efficacy is not significant.

Hypothesis 4a proposes that electronic monitoring indirectly affects work engagement via self-efficacy. Because the direct effect between electronic monitoring and work engagement is not significant (Model 2), it’s unnecessary to confirm the indirect effect. H4a is not supported.

Hypothesis 4a proposes that electronic monitoring indirectly affects deviant behavior via self-efficacy. To compare Model 6 and Model 7, we add monitoring in Model 7 based on Model 6, which still suggests a negative association between self-efficacy and deviant behavior ( β = –0.196, p < 0.01, Model 7). Although the effect turns weaker, it’s still significant. Furthermore, to compare Model 5 and Model 7, we add self-efficacy in Model 7 based on Model 5, which still suggests a positive association between electronic monitoring and deviant behavior( β = 0.359, p < 0.01, Model 7). The effect is still significant indicating a partial mediation. H4b is supported.

Referring to the suggestions of Edwards and Lambert (2007), Bootstrap (5,000 times) is applied in this study to further verify the indirect effect of self-efficacy in the model. The results are shown in Table 5, demonstrating that this indirect effect as hypothesized is pronounced (estimate = 0.026, 95%CI = 0.006, 0.054), which confirms that electronic monitoring indicates a significant mediating effect on deviant behavior through self-efficacy. And the direct estimate is significant (estimate = 0.377, 95%CI = 0.289, 0.465), so self-efficacy partially mediates the relationship between electronic monitoring and deviant behavior. The above results provide support for hypothesis 4b.

After the hypothesis tests, the final validated research model is presented in Figure 2.

This study focuses on supervisor monitoring in remote workplaces based on the Job Demands-Resources (JD-R) model. It examines the impact of different monitoring methods on remote employees’ work engagement and deviance via the mediating effect of self-efficacy. Based on 299 valid data collected in two periods, the following research findings are as follows.

The interactive monitoring in remote workplaces has a significant positive impact on employees’ work engagement (H1a is supported), and a significant negative impact on employees’ deviance (H1b is supported). Self-efficacy plays a mediating role between interactive monitoring and work engagement (H3a is supported), but it does not mediate the relationship between interactive monitoring and deviant behavior (H3b is not supported). Electronic monitoring by superiors in remote workplaces has a significant positive impact on employees’ deviant behavior (H2b is supported), but it does not have a significant impact on employees’ work engagement (H2a and H4a are not supported). Self-efficacy mediates the relationship between electronic monitoring and deviant behavior (H4b is supported).

Although some hypotheses in this study were not supported, the results are consistent with existing research. Schaufeli and Bakker, (2004) found that job resources can increase work engagement but not necessarily reduce deviance, while job demands can increase deviance but not necessarily reduce work engagement. Interactive monitoring by superiors in remote workplaces is a type of job resource that enhances work engagement by promoting personal resources such as self-efficacy, but it does not reduce deviance. Electronic monitoring in remote workplaces is a type of job demand that increases employee deviance by depleting personal resources such as self-efficacy, but it does not reduce work engagement.

First and foremost, this study contributes to the research on leadership and employee behavior in remote workplaces. With remote work becoming increasingly popular and expected to gain further prevalence, there is a need for more research on how to improve employee engagement and reduce deviant behavior (Huo et al., 2022). This study enhances the understanding of relevant research by examining the impact of different monitoring approaches by superiors on improving employee engagement and reducing deviant behavior in remote workplaces.

Secondly, this study expands the research context of supervisor monitoring. The current research on the effect of supervisors’ monitoring is mainly based on traditional offline workplaces (Zhou, 2003; Liao and Chun, 2016; Son et al., 2017; Mishra and Ghosh, 2020). However, by limiting the research context to remote workplaces, including subjects with remote work experience within the scope of valid data, and specifying remote work scenarios in the survey questions, this study provides strong evidence for the effectiveness of supervisor monitoring in remote workplaces.

Once again, this study enhances the theoretical perspective of the influence mechanism of supervisor monitoring. The current research on the effect of supervisors’ monitoring mainly depended on the perspectives of social exchange theory (Liao and Chun, 2016; Son et al., 2017) and self-determination theory (Zhou, 2003; Mishra and Ghosh, 2020). However, by distinguishing the different effects of interactive and electronic monitoring from supervisors through the lens of job demands-resources, this study highlights that supervisor monitoring in remote workplaces can serve as both a job resource and a job demand, depending on the specific monitoring approach. This further complements the research on the differentiated effects of interactive and observational monitoring by superiors. Additionally, it echoes the varying degrees of influence of job resources and job demands on positive and negative outcomes, as suggested by Schaufeli and Bakker, (2004), where job resources increase work engagement but do not necessarily reduce deviance, while job demands increase deviance but do not necessarily reduce work engagement.

This study focuses on the effectiveness of supervisor monitoring in remote workplaces, which aligns with current management practice requirements. The research findings can provide the following insights to remote work managers.

When managing remote work, supervisors can adopt more interactive monitoring, such as regularly communicating and exchanging with employees, holding online meetings periodically, listening to employees’ self-reported work performance and problems, and providing timely interactive feedback and assistance to address employees’ deficiencies in remote workplaces. This approach can help remote employees increase their work engagement and, to some extent, prevent a decrease in organizational identity caused by organizational isolation in remote work (Xiao, 2019).

It is recommended that organizations reduce the use of electronic monitoring in remote workplaces, as this can further increase employees’ work stress and sense of intrusion in remote workplaces, thereby enhancing their deviant behavior and hindering work efficiency.

Self-efficacy is a positive individual resource for remote work employees. Organizations should focus more on developing employees’ psychological capital and consider enhancing their self-efficacy as an essential construction means. This can increase remote work employees’ engagement and reduce their deviant behavior.

First, although we carried out a two-wave data collection within 2 months in this study, yet there might still be a common method variance given that all the data came from respondents’ self-report. Therefore, future researchers are strongly encouraged to adopt a multi-stage and a multi-source (e.g., employees, leaders, team members, etc.) questionnaire design, in order to obtain more scientific and effective research results.

Second, many studies have confirmed that employees’ work engagement fluctuates significantly daily, so it is recommended to consider using a diary method to measure relevant variables to obtain more effective research data.

Third, this study just tests work engagement and deviant behavior as the results of supervisor monitoring in remote workplaces, not test performance, which is most important to organizations. So, in future research, it is necessary to verify the impact of different leadership supervision methods on employee work performance in remote work situations.

Fourth, regarding the research context, this study only treated remote work as an overall variable measurement context. Future research can make more detailed distinctions. On the one hand, a comparative analysis can examine the effectiveness of supervisor monitoring in remote workplaces and traditional face-to-face workplaces for the same participants. On the other hand, the intensity of remote work can be controlled during the study to enable more effective analysis.

Regarding boundary conditions, as this study primarily aims to discuss the effectiveness of remote workplaces, moderators were not selected for boundary condition verification. Future research can explore factors such as different individual characteristics (e.g., proactiveness, responsibility) or work characteristics (e.g., result-oriented work) for further exploration.