Working from Home Across National Contexts: Demands, Resources, Engagement, and Productivity Among Italian and Russian Employees

Toscano, F. University of Bologna, Italy

Zappalà, S. Financial University under the Government of the Russian Federation, Moscow, Russia University of Bologna, Italy

Polevaya, M.V. Financial University under the Government of the Russian Federation, Moscow, Russia

Kamneva, E.V. Financial University under the Government of the Russian Federation, Moscow, Russia

Introduction

Remote work has become a major form of work organization, reshaping where, when, and how employees perform their tasks. This transformation is not merely a change in physical location. It also modifies the material environment in which work is carried out, the opportunities for direct supervision and social interaction, and the degree to which employees regulate their own job tasks. While remote work can provide flexibility and reduce some office-based interruptions, it can also introduce new demands, such as inadequate workspaces, household noise, blurred boundaries, and reduced structure in daily work routines (Galanti et al., 2021; Xiao et al., 2021). Accordingly, the effect of working from home on job productivity remains debated. Some studies suggest that remote work can improve performance, whereas others show that productivity may decline under specific individual, organizational, or environmental conditions (Allen et al., 2015; Monteiro et al., 2021; Toscano & Zappalà, 2020a; Troll et al., 2022). These inconsistencies indicate the need for more fine-grained analyses of the demands and resources that shape productivity in home-based work. Although the data were collected during the COVID-19 pandemic, when employees were undergoing an emergency transition to home-based work, the study remains relevant for contemporary remote and hybrid work because it examines psychological mechanisms that continue to shape distributed work arrangements. Recent evidence suggests that remote and hybrid work are neither uniformly beneficial nor detrimental: their outcomes depend on how flexibility, work design, health-related demands, and employees’ self-regulatory capacities are combined (Bloom et al., 2024; Dong et al., 2025; Wells et al., 2023). Therefore, examining how demands and resources operate in home-based work can contribute to current debates on sustainable and effective flexible work design.

This study addresses this issue by adopting the Job Demands–Resources (JD-R) model (Bakker & Demerouti, 2007, 2017) to examine whether specific characteristics of remote work are related to employees’ perceived job productivity. In the JD-R model, job demands are physical, psychological, social or organizational aspects of the job that require sustained effort and are associated with psychological costs; job and personal resources, instead, refer to physical, psychological, social or organizational aspects of the job that help the employee to achieve work goals and stimulate personal growth and learning and development. In the context of work from home, we conceptualize environmental distractions as a “job demand” that may increase stress and undermine productivity. Noise, inadequate furniture, limited work area size, wall colors, and cleanliness of the work environment can become distracting factors (Lee & Brand, 2005) that generate strain (Wineman & Barnes, 2018) and may initiate a health-impairment process. By contrast, job autonomy is conceptualized as a “job resource”, because it can satisfy a primary motivational need for control and self-determination (Deci et al., 2017). Self-leadership, defined as a set of behavioral and cognitive strategies aimed at self-regulation and goal attainment (Stewart et al., 2011), is considered a “personal resource” that may further support productivity in remote work.

The cross-national design of this study is central to its contribution. Data were collected from Italian and Russian remote workers, to examine whether JD-R mechanisms operate similarly across two socio-organizational contexts that differ in work traditions, managerial assumptions, and previous familiarity with autonomous remote work practices. Rather than treating national context merely as a background condition, we use the Italian and Russian samples to provide an exploratory check of whether the proposed psychological mechanisms show a similar pattern across different contexts. This orientation is particularly relevant for research on work psychology and ergonomics. From the activity theory perspective, rooted in the Russian psychological tradition (Maidansky, 2021), job performance depends on how work activity is organized, including task demands, available tools, environmental constraints, methods of action, and self-regulatory processes (Bedny & Karwowski, 2006; Bedny & Meister, 1997). Accordingly, when working from home, employees must organize tasks, regulate attention, manage environmental constraints, and sustain motivation in a setting that is only partially designed for formal work. Thus, the study has two aims: first, to integrate environmental distractions, job autonomy, and self-leadership into a unified JD-R framework on remote work productivity; second, to examine whether the pattern of associations shows convergence among Italian and Russian employees.

Environmental Distractions, Remote Work Stress, and Job Productivity

In this study, we investigate the relationship between environmental distractions experienced when working from home and employees’ perceived job productivity.

Environmental distractions are conceptualized as undesirable stimuli that cause employees irritation and negative experiences (Lee & Brand, 2005). They create attentional conflict by diverting attention from work tasks, thereby impairing cognitive control and increasing the psychological cost of work. Consistent with the JD-R framework, in this study distractions are considered as job demands that consume cognitive and emotional resources and contribute to a health-impairment process.

Job productivity, while complex to define, can be understood as the ratio between the value of output produced and the psychological and physical costs of input (Murphy, 1990). In organizational settings, productivity refers to the successful completion of work tasks and responsibilities. In this study, we adopt a subjective measure of job productivity, capturing employees’ perceptions of the quality of their output when working remotely compared to their usual productivity when working in-person.

Previous research has documented that environmental distractions (such as family interruptions, inadequate workspace, or household noise) reduce perceived control, increase effort, and heighten employee feelings of frustration and inefficacy (Keller et al., 2020; Xiao et al., 2021). Moreover, frequent interruptions may require employees to switch tasks or change physical locations (e.g., working at a dinner table that must be cleared), increasing time pressure and cognitive load (Baethge & Rigotti, 2013) that are typical causes of job stress.

Job stress is a condition that develops when the interaction between individuals and their work environment deviates from its normal functioning, resulting in the accumulation of negative emotions and cognitive strains generated by work demands (Weinert et al., 2015). Accordingly, we propose that environmental distractions are negatively related to job productivity, and that remote work stress, defined as the stress experienced because of the challenges of working remotely (Toscano & Zappalà, 2020b), acts as a mediator. In fact, the JD-R model suggests that job demands, when ineffectively managed, may initiate a health-impairment process that ultimately impacts job performance. Thus, we hypothesize:

H1a: Environmental distractions are negatively related to job productivity.

H1b: Remote work stress mediates the relationship between environmental distractions and job productivity.

Job Autonomy, Self-Leadership, Remote Work Engagement, and Job Productivity

In parallel with the consideration of the health-impairment process, this study also investigates the effect of job and personal resources on job productivity, considering the motivational process postulated in the JD-R theory (Bakker & Demerouti, 2007, 2017). We focus on two resources, job autonomy and self-leadership, and their capacity to enhance remote work engagement defined as a positive, fulfilling, work-related state of mind experienced during remote work (Memon et al., 2021; Toscano & Zappalà, 2021). 

Job autonomy refers to the extent to which employees can make decisions on how to complete their job tasks or how to modify or influence their own workplaces (Morgeson & Humphrey, 2006). Job autonomy has been long thought to be an advantage of telework, since it allows employees to choose where, when, and how to work, providing them with independence, discretion, and freedom in scheduling work and determining the procedures to be used (Allen et al., 2015). Furthermore, job autonomy leads to an increase of confidence in the job performance in a specific task (Saragih, 2011), another element supporting the existence of a positive relationship between job autonomy and job productivity. Hence, we posit that:

H2a: Job autonomy is positively related to job productivity.

The JD-R model suggests that job resources promote work engagement, which in turn leads to higher performance (Bakker & Demerouti, 2007, 2017). Tasks that allow greater autonomy tend to foster engagement by satisfying employees’ psychological needs for competence and control (Sonnentag, 2017). Work engagement itself is characterized by vigor, dedication, and absorption (Bakker & Demerouti, 2024; Memon et al., 2021) and has been consistently associated with higher job performance (Bakker & Demerouti, 2024). Therefore, we posit:

H2b: Remote work engagement mediates the relationship between job autonomy and job productivity.

Alongside job autonomy, self-leadership is here investigated as a personal resource. Self-leadership is defined as the process through which individuals influence and motivate themselves to achieve work goals using behavioral and cognitive strategies (Stewart et al., 2011), which in the present study are goal setting and self-monitoring.

Goal setting is the practice of defining specific and challenging goals that provide direction and motivation for task accomplishment (Locke & Latham, 2013). From a motivational perspective, goal setting acts as an action plan that influences the amount of effort, direction, and persistence of a certain action. Goals represent what people aim to accomplish, so they help employees to direct their energies in order to move, step by step, towards task completion (Troll et al., 2022).

Self-monitoring is defined as the active observation of one’s own work, and such strategy leads to an awareness of when and why employees engage in behaviors that are functional to their goals (Stewart et al., 2011). Previous research found a positive correlation between self-monitoring behaviors and job performance (Moser & Galais, 2007). Furthermore, previous studies found that self-leadership provides an increased focus on cognitive processes, leading to enhanced job performance (Stewart et al., 2011), and prevents negative functional states, sustaining work efficiency under demanding conditions (Leonova, 2010). For these reasons, we posit that:

H3a: Self-leadership is positively associated with job productivity.

We consider self-leadership as a personal resource and recognize the existence of a growing body of evidence highlighting the positive impact of self-leadership on work engagement (Breevaart et al., 2016). Working with clear goals and monitoring each step toward them streamlines work processes and informs workers about their progress, fueling employee engagement (Stewart et al., 2011). Thus, self-leadership is expected to be positively related to job productivity via a motivational pathway. Working with clearly defined goals and monitoring progress can energize employees and foster a deeper sense of purpose and involvement in the work (Breevaart et al., 2016). Accordingly, we propose the following, last hypothesis:

H3b: Remote work engagement mediates the relationship between self-leadership and job productivity.

According to the theoretical framework presented above, Figure 1 shows the research model of this study.

Figure 1. Conceptual research model with hypothesized associations.

Note: To improve readability, correlations between JA and SL, and between RWS and RWE are not shown here, although they were freely estimated in the model (see Figure 2).

Methods

Procedure 

An online self-report questionnaire was administered through the Qualtrics platform between April and July 2020, during the large-scale transition to home-based work. Participants were recruited through social media and professional networks in Italy and Russia. To be eligible, respondents had to be adult employees, working from home full-time at the time of data collection, and able to complete either the Italian or Russian version of the questionnaire. Participation was voluntary, anonymous, and not compensated. Before accessing the questionnaire, respondents provided informed consent. The study was approved by the Ethics Committee of the university of the corresponding author. The study was conducted in accordance with the ethical standards for psychological and social research.

Participants

From a total of 578 participants, we retained 493 respondents who provided complete responses on the indicators included in the measurement and structural models. This is the final group of respondents used in all the subsequent analyses. 

The study sample was composed of 247 Italian respondents and 246 Russian respondents. In the Italian subsample, 89 participants were men, 157 were women, and one identified as non-binary. In the Russian subsample, 72 participants were men and 174 were women. The Italian subsample was younger, with the largest age group being 26–35 years old (43.3%), followed by 36–45 years old (20.6%) and 46–55 years old (17.4%). In Russia, the largest age group was 36–45 years old (30.5%), followed by 46–55 years old (25.6%) and 26–35 years old (17.5%). Most participants worked in the private or public tertiary sector. In Italy, 118 participants worked in the private tertiary sector and 81 in the public tertiary sector; 44 worked in the secondary sector and 3 in the primary sector, with one missing value on sector. In Russia, 114 participants worked in the private tertiary sector and 105 in the public tertiary sector; 25 worked in the secondary sector and 2 in the primary sector. Mean organizational tenure was 7.45 years in the Italian subsample (SD = 9.40) and 7.30 years in the Russian subsample (SD = 6.76). The sample was heterogeneous but not representative, as it was based on convenience recruitment during the emergency transition to home-based work.

Measures

All multi-item measures were based on previously validated instruments. Shortened versions of the scales were used to reduce participant burden and to capture the focal dimensions included in the research model. The English item pool was translated into Italian and Russian following a translation and back-translation procedure (Brislin, 1986). Discrepancies between the original and back-translated versions were discussed by the research team and resolved through consensus. An independent reviewer for each language then checked the final version for linguistic clarity and consistency with the intended construct meaning. Where necessary, item wording was adapted to address specifically working from home. The Russian item wordings are reported in the Appendix [the full Italian and Russian wordings are available in a Supplementary Appendix]. All measures were administered using a harmonized five-point agreement scale in both language versions, ranging from 1 = completely disagree to 5 = completely agree. When original instruments used a different response format, the item content was retained or adapted to the remote-work context, while the response scale was harmonized to maintain consistency across the questionnaire.

Environmental distractions (ED). Environmental distractions were measured with three items derived from Lee and Brand’s (2005) work on perceived workspace conditions. The items captured acoustic distractions, lack of privacy, and visual distractions in the remote working area. Because the original items referred to workspace characteristics, their wording was contextualized to the home-based work setting (e.g., “In my working area, I experience acoustic distractions”). Higher scores indicated greater perceived environmental distractions.

Job autonomy (JA). Job autonomy was measured with four items from Morgeson and Humphrey’s (2006) Work Design Questionnaire. The items assessed the extent to which employees could make decisions about how to schedule, organize, and carry out their work activities. A sample item is: “My job allows me to make my own decisions about how to schedule my work”. Higher scores indicated greater perceived job autonomy.

Self-leadership (SL). Self-leadership was assessed using a shorter set of items based on the Revised Self-Leadership Questionnaire (Houghton & Neck, 2002). The selected items (e.g., “When I work, I always keep my tasks in mind”) captured two self-regulatory strategies that were theoretically relevant to remote work: goal setting and self-monitoring. Thus, the measure should be interpreted as an indicator of the goal-oriented and self-monitoring aspects of self-leadership, rather than as a full assessment of the multidimensional self-leadership construct. Higher scores indicated greater self-leadership.

Remote work stress (RWS). Remote work stress was measured with four items from Weinert et al. (2015). The items assessed exhaustion, fatigue, and strain associated with working from home. A sample item is: “I feel exhausted from working from home”. Higher scores indicated greater remote work stress.

Remote work engagement (RWE). Remote work engagement was measured with the three-item version of the Utrecht Work Engagement Scale (Schaufeli et al., 2019), with item wording referring specifically to working from home. The items assessed energy, enthusiasm, and immersion while working remotely. A sample item is: “When I work from home, I feel full of energy”. Higher scores indicated greater remote work engagement.

Perceived job productivity (JP). Perceived job productivity was measured with a single item asking respondents to compare their productivity while working remotely with their usual productivity in the traditional office: “When I work remotely, I am more productive”. The use of a single-item measure was based on prior evidence supporting the validity of single-item assessments of work-related outcomes, including job performance and job satisfaction (Ang & Eisend, 2018; Hartner-Tiefenthaler et al., 2023). This item has also been used in previous studies of remote work, showing meaningful associations with theoretically related constructs. For example, Galanti et al. (2021) reported correlations between perceived productivity and, respectively, work-family conflict (r =− .40) and job autonomy (r = .18). Similarly, Toscano and Zappalà (2020b) reported a positive association between perceived productivity and remote work satisfaction, r = .65. Nonetheless, while this measure offers practical advantages and evidence of nomological validity, it does not capture the full multidimensional nature of job productivity. Therefore, findings involving this outcome should be interpreted with caution.

Data Analysis

Before testing the research model, we examined the possibility of common method bias, using Harman’s single-factor test, consisting of an exploratory factor analysis (EFA) with the principal axis method (e.g., Zappalà et al., 2022). Because Harman’s test is only a preliminary diagnostic, we also evaluated the structural distinctiveness of the study variables by comparing a six-factor confirmatory factor analysis (CFA) model with a one-factor model. This comparison was used to assess whether the measures reflected distinguishable constructs rather than a single undifferentiated response factor. Given the cross-national design, we then tested measurement invariance across the Italian and Russian subsamples. Reliability was tested by computing Cronbach’s alpha and McDonald’s omega, while convergent validity was examined by computing Average Variance Extracted values for each multi-item measure. After computing descriptive statistics and correlations, a structural equation modeling (SEM) was used to test the hypothesized model. The diagonally weighted least squares estimator (DWLS) was used in all SEM-based analyses of this study, because the focal indicators were measured with ordered five-point response categories and therefore needed to be treated as ordinal indicators, rather than on aggregated scale scores that may more closely approximate continuous variables. This decision was also supported by the size of the sample, with complete data on all the indicators used in this study, which was adequate for categorical least-squares estimation, and by departures from normality in several indicators, such as autonomy_3 (skewness = −1.41, kurtosis = 1.82), goalsetting_1 (skewness = −1.45, kurtosis = 2.58), and selfmonitoring_2 (skewness = −1.62, kurtosis = 3.86). This choice is consistent with methodological and simulation evidence showing that categorical least-squares estimators are appropriate for ordinal indicators and can provide less biased and more accurate estimates under many ordinal-data conditions (Li, 2016; Rhemtulla et al., 2012).

Perceived job productivity was included as an observed single-item variable. For theoretical reasons (Bakker & Demerouti, 2007, 2017), the relationships between job autonomy and self-leadership, and between remote work stress and remote work engagement, were modeled as correlated. Finally, a multigroup SEM was conducted to explore whether the structural paths showed similar directions and magnitudes across the Italian and Russian subsamples. Given the cross-national design, measurement invariance across the Italian and Russian subsamples was examined through a sequence of increasingly constrained multigroup models. Configural, metric, and scalar/threshold invariance were evaluated. Changes in CFI not exceeding .010 were taken as evidence that the more constrained model did not substantially worsen model fit. All analyses were performed using Jamovi 2.3, integrating the semlj module for structural equation modeling (Gallucci & Jentschke, 2024), and were conducted on the sample.

Results

Validity of the Measurement Model and Reliability and Validity of the Measures

A single-factor EFA was performed as part of Harman’s test to examine whether a dominant single-factor structure characterized the data for the variables involved. The single extracted factor accounted for 39% of the total variance, below the commonly used 50% threshold reported in prior research. This result does not suggest the presence of a common method factor, although it should be interpreted as a preliminary diagnostic rather than a definitive test of the absence of a common method bias. To test the structural independence of the six measures included in our model, we conducted two CFAs, comparing a one-factor model with a six-factor model. The hypothesized six-factor measurement model showed good fit to the data, χ²(138) = 346, p < .001, CFI = .995, TLI = .994, RMSEA = .055, SRMR = .056. To examine whether the study constructs were empirically distinguishable, we compared this model with a one-factor model in which all indicators loaded onto a single latent factor. The one-factor model showed poor fit, χ²(152) = 6830, p < .001, CFI = .841, TLI = .821, RMSEA = .299, SRMR = .238. These results supported the distinctiveness of the six constructs. Table 1 reports reliability and convergent validity estimates for the multi-item measures. For JP, reliability could not be estimated directly because the construct was assessed with a single item. Its use was supported by prior evidence on single-item assessments of work-related outcomes (Ang & Eisend, 2018; Hartner-Tiefenthaler et al., 2023) and by previous remote work studies showing meaningful associations between this item and theoretically related constructs (Galanti et al., 2021; Toscano & Zappalà, 2020b).

Table 1

Reliability and Convergent Validity of the Adopted Multi-Item Measures

	Cronbach’s α	McDonald’s ω	AVE	√AVE
1. Environmental distractions	.76	.79	.63	.79
2. Job autonomy	.87	.89	.76	.87
3. Self-leadership	.77	.79	.60	.78
4. Remote work stress	.92	.94	.85	.92
5. Remote work engagement	.74	.77	.58	.76

Note: AVE = Average variance extracted.

Given the cross-national design, measurement invariance across the Italian and Russian subsamples was examined through a sequence of increasingly constrained multigroup models focused on the measurement part of the model. The configural model showed good fit, χ²(276) = 414, p < .001, CFI = .997, TLI = .997, RMSEA = .045, SRMR = .062. Constraining factor loadings to equality across the Italian and Russian groups produced negligible deterioration in fit, ΔCFI = −.001, ΔRMSEA = .009, ΔSRMR = .001, supporting metric invariance. The scalar/threshold model also showed negligible change relative to the metric model, ΔCFI = −.001, ΔRMSEA = −.001, ΔSRMR = −.001. However, because the scalar/threshold model produced a technical warning, evidence for scalar/threshold invariance was interpreted cautiously.

Descriptive Statistics

Table 2 shows descriptive statistics and correlations among the study variables in our sample. All variables were significantly correlated in the expected direction, except for the association between self-leadership and remote work stress, which was not significant. The correlation between job autonomy and job productivity was positive and statistically significant, although small in magnitude.

Table 2

Descriptive Statistics and Correlations Among Study Variables

Variable	M	SD	1	2	3	4	5
1. Environmental distractions	2.50	1.16
2. Job autonomy	4.02	.88	−.13**
3. Self-leadership	4.19	.70	−.10*	.33**
4. Remote work stress	2.69	1.29	.35**	−.10*	−.07
5. Remote work engagement	3.24	.90	−.30**	.18**	.26**	−.42**
6. Job productivity	3.18	1.22	−.27**	.09*	.19**	−.42**	.63**

Note. N = 493; *p < .05, **p < .01.

Model Results

To test our hypotheses, we ran SEM on the full cross-national sample (N = 493). The tested model showed acceptable-to-good fit, with excellent incremental fit indices and acceptable absolute fit indices, χ²(141) = 509, p < .001, CFI = .991, TLI = .989, RMSEA = .073, SRMR = .070. Its results, shown in Figure 2, indicated a negative relationship between environmental distractions and job productivity (B = −.51; SE = .14; β = −.35; p < .001; H1a supported). Environmental distractions were also positively related to remote work stress (B = .50; SE = .06; β = .44; p < .001), while remote work stress was not significantly related to job productivity (B = .18; SE = .13; β = .14; p = .12). The indirect path between environmental distractions and job productivity through remote work stress was not significant (unstandardized point estimate = .09; SE = .07; standardized point estimate = .06; p = .12), which does not support H1b.

With regard to the relationships of job and personal resources with job productivity, the results showed that job autonomy was negatively and significantly associated with job productivity (B = − .19; SE =  .07; β = −.14; p = .003; H2a not supported). The direct association between self-leadership and job productivity was negative, but only marginally significant (B = − .16; SE = .08; β = −.12; p = .051), and therefore H3a was not supported. In addition, both resources were positively related to remote work engagement: job autonomy was positively associated with remote work engagement (B = .15; SE = .05; β = .15; p < .001), and self-leadership was also positively associated with remote work engagement (B = .29; SE = .05; β = .30; p < .001). Remote work engagement, in turn, was strongly and positively associated with job productivity (B = 1.17; SE =  .14; β = .81; p < .001).

Job autonomy showed a positive indirect association with job productivity through remote work engagement (unstandardized point estimate = .17; SE = .07; standardized point estimate = .12; p < .001), as did self-leadership (unstandardized point estimate = .34; SE = .08; standardized point estimate = .25; p < .001). These findings supported H2b and H3b, indicating that remote work engagement mediated the relationships of both job autonomy and self-leadership with perceived job productivity.

However, the pattern differed across the two resources. For job autonomy, the positive indirect effect through engagement was counterbalanced by a negative direct association with productivity, resulting in an overall association close to zero. For self-leadership, the positive indirect effect through engagement was robust, whereas the direct effect on productivity was weak and only marginally significant. This suggests that goal-setting and self-monitoring were positively related to productivity primarily through their association with remote work engagement rather than through a stable direct association with productivity.

Figure 2. Tested model results 

Note: n = 493. Standardized coefficients are reported for direct and indirect effects. Only latent factors are shown; observed indicators are omitted for readability. 

Cross-National Multigroup Analysis

Given the cross-national design of the study, we examined whether the structural paths observed in the full sample showed similar directions and magnitudes across Italian and Russian participants. This analysis was exploratory and focused on the similarity of structural relationships rather than on mean differences. Although scalar/threshold invariance was acceptable according to approximate fit criteria, the multigroup structural results were interpreted cautiously because the scalar/threshold model produced a technical warning and because not all full-sample paths reached statistical significance in both national subsamples. The multigroup model showed acceptable-to-good fit, χ²(282) = 583, p < .001, CFI = .994, TLI = .993, RMSEA = .066, SRMR = .076. The relationships observed in this multigroup comparison are reported in Table 3.

Table 3 

Standardized Structural Paths in the Italian and Russian Subsamples

Path	Direct effects
	Italy	Russia
ED-JP	-.34**	-.30**
ED-RWS	.37**	.49**
RWS-JP	.21	.06
JA-JP	-.14**	-.14
JA-RWE	.08	.22**
SL-JP	-.12	-.07
SL-RWE	.31**	.30**
RWE-JP	.83**	.75**
	Indirect effects
	Italy	Russia
ED-RWS-JP	.08	.03
JA-RWE-JP	.07*	.17**
SL-RWE-JP	.25**	.22**

Note: Country subsample sizes: n = 247 for Italy and n = 246 for Russia.*p < .05, **p < .01; ED = Environmental distractions, JA = Job autonomy, SL = Self-leadership, RWS = Remote work stress, RWE = Remote work engagement, JP= Job productivity.

Table 3 suggests partial convergence between the country-specific models and the full-sample model. Several coefficients showed similar directions in Italy and Russia, especially the paths from environmental distractions to remote work stress and job productivity, from self-leadership to remote work engagement, and from remote work engagement to job productivity. However, not all significant paths observed in the full sample reached statistical significance in both subsamples. Hence, the multigroup findings should be interpreted as exploratory evidence of partial structural convergence rather than as full replication across countries.

The negative association between environmental distractions and job productivity, and the positive association between environmental distractions and remote work stress, were observed in both subsamples. As in the full-sample model, remote work stress was not significantly related to job productivity. The negative relationship between job autonomy and job productivity was similar in magnitude across the two subsamples, although it reached statistical significance only in the Italian subsample. Conversely, the positive relationship between job autonomy and remote work engagement was statistically significant in both subsamples, although it was small in Italy and stronger in Russia.

In the country-specific models, the direct relationship between self-leadership and job productivity was not statistically significant in either subsample. This is consistent with the cautious interpretation of the full-sample direct effect, which was negative but only marginally significant. However, in both national subsamples, the indirect relationship between self-leadership and job productivity through remote work engagement was positive and statistically significant. Overall, the multigroup analysis suggests that some central pathways were directionally similar in Italy and Russia, especially the engagement-based motivational pathway, but the evidence supports only partial convergence rather than broad structural comparability.

Discussion

This study examined working from home as a psychological and organizational reconfiguration of that work activity. Drawing on the JD-R model, we tested whether environmental distractions, job autonomy, and self-leadership were associated with perceived productivity through remote work stress and remote work engagement. We also explored whether these structural associations showed similar patterns across Italian and Russian employees. 

Overall, the findings highlight that environmental distractions were associated with higher remote work stress and lower perceived job productivity, although the indirect effect through remote work stress was not significant. Job autonomy and self-leadership were positively associated with remote work engagement, which in turn was strongly related to perceived job productivity. A theoretically relevant finding concerns the ambivalent role of job autonomy and self-leadership. Both resources showed positive indirect associations with productivity through remote work engagement. However, contrary to our expectations, job autonomy showed a statistically significant negative direct association with productivity, whereas the negative direct association of self-leadership was marginal and should therefore be interpreted more cautiously. These direct coefficients should not be interpreted as simple bivariate relationships. In the zero-order correlations, both job autonomy and self-leadership were positively related to productivity; the negative direct coefficients emerged only after remote work engagement and the other predictors were included in the model. This pattern is compatible with an inconsistent mediation or suppression-like structure, in which autonomy and self-leadership support productivity indirectly through engagement, while their residual direct associations may capture the additional self-regulatory burden of highly autonomous remote work. During the emergency transition to home-based work, employees often had to plan, prioritize, monitor, coordinate, and protect their work boundaries with limited managerial support. Under these conditions, autonomy may have functioned as an ambivalent resource: motivationally beneficial, but also cognitively and organizationally demanding. For self-leadership, however, the evidence points more clearly to an indirect motivational role through engagement than to a stable direct association with productivity. This interpretation is consistent with refinements of the JD-R model suggesting that resources can have context-dependent effects and with the idea that excessive or poorly matched resources may undermine rather than support functioning (Demerouti, 2025; Kubicek et al., 2017; Zhou, 2020).

At the same time, the mediation analysis showed that remote work engagement significantly mediated the relationships of both job autonomy and self-leadership with job productivity, supporting the motivational pathway of the JD-R model and suggesting that engagement remains a key proximal mechanism linking resources to performance-related outcomes in remote work. This pattern was especially clear for self-leadership: goal setting and self-monitoring were positively related to productivity primarily because they were associated with higher remote work engagement. This result suggests that self-regulatory strategies may be especially relevant when employees work with reduced direct supervision and must actively organize their own work activity. Finally, the multigroup analysis extends previous evidence on Russian remote workers (Toscano et al., 2022) by suggesting partial convergence in some central psychosocial mechanisms linking remote work conditions to productivity across Italian and Russian employees. However, these findings should be interpreted as exploratory evidence of partial structural similarity, not as evidence of broad cross-national equivalence.

Theoretical and Practical Implications

Overall, the findings have several theoretical implications. First, they highlight the underexplored role of environmental distractions as a relevant job demand in work-from-home settings. By showing their negative association with perceived productivity, the study extends recent research suggesting that future studies should distinguish more clearly between digital and non-digital distractions (Nakayama & Chen, 2023).

Contrary to expectations, remote work stress was not related to productivity. We did not observe the hypothesized indirect effect of environmental distractions through stress. One possible explanation is that distractions impair productivity directly by reducing concentration and increasing task fragmentation rather than through a general stress mechanism. Alternatively, the stress measure may have captured fatigue and exhaustion but not interruption-related cognitive costs, such as attentional switching and perceived inefficiency. Future research should therefore distinguish more clearly between stress, cognitive load, interruptions, and concentration when examining health-impairment processes in remote work.

Second, the study provides insights into the motivational role of autonomy and self-leadership. Consistent with JD-R theory, both resources were positively associated with engagement, which in turn predicted productivity. However, the direct association between autonomy and productivity was negative, whereas the direct association between self-leadership and productivity was weak and marginal. This pattern highlights the context-dependent nature of resources and supports recent arguments that resources do not always produce uniformly positive effects (Demerouti, 2025). 

Finally, the use of Italian and Russian samples allowed a preliminary examination of the cross-national robustness of the proposed JD-R mechanisms. The partially similar pattern of associations suggests that some JD-R mechanisms in remote work may operate in comparable directions across different national contexts. However, the evidence does not support strong claims of structural equivalence. The multigroup findings should therefore be treated as preliminary and hypothesis-generating.

Our findings extend research on remote work and offer implications for employees, managers, and HR professionals. First, the negative association between environmental distractions and productivity suggests that the home workspace should not be viewed solely as a private matter. Organizations can support concentration by providing guidance on workspace setup, reducing interruptions, and establishing clear norms regarding availability, response times, and meetings. Remote work design should therefore address not only technological access, but also conditions that protect focused work and cognitive well-being.

Second, while self-leadership appears important for maintaining direction and engagement in remote work, it should not be considered a substitute for organizational support. Goal setting and self-monitoring can be strengthened through training, clear performance expectations, regular feedback, and supportive management practices. Likewise, autonomy should be accompanied by sufficient structure, because discretion without clear priorities, feedback, and boundary management support may increase the burden of self-organization. 

Overall, effective remote work requires a balanced design in which employee autonomy and self-regulatory capacities are supported by organizational structure, guidance, and protection from avoidable distractions.

Limitations

Like all studies, this research has limitations. First, the cross-sectional design reduces the strength of our findings and is not fully consistent with the directional assumptions implied by mediation models. Accordingly, the indirect effects should be interpreted as associations among theoretically ordered variables rather than as evidence of causal processes.

Second, the data were collected in 2020, during an emergency transition to home-based work. The results should therefore be generalized cautiously and should be retested in contemporary remote and hybrid work arrangements that are more planned, institutionalized, and supported by organizational policies.

Third, although the study included Italian and Russian employees, national context was used as a grouping variable rather than as a direct measure of cultural, institutional, or organizational characteristics. Therefore, the study cannot identify the specific cultural, managerial, or policy-related mechanisms responsible for possible country-specific differences. Future studies should include direct measures of cultural values, remote work policies, managerial practices, and organizational support to explain why remote work mechanisms may vary across contexts.

Fourth, although measurement invariance was generally supported according to approximate fit criteria, evidence for scalar/threshold invariance was interpreted cautiously because the scalar/threshold model produced a technical warning. The cross-national findings should also be interpreted cautiously because the country-specific structural models were based on relatively modest subsample sizes and not all paths replicated across both groups.

Finally, perceived job productivity was measured with a single item. Although this item has practical advantages and previous evidence of nomological validity, it does not capture the full multidimensional nature of productivity. Future studies should combine self-reported productivity with supervisor ratings, objective indicators, or task-specific performance measures.

Several procedural remedies were adopted to reduce common method bias, including anonymity, voluntary participation, and the separation of measures within the questionnaire. In addition, Harman’s single-factor test and the comparison between the one-factor and six-factor CFA models suggested that the study variables were not reducible to a single undifferentiated response factor. Nevertheless, because all variables were collected through self-report measures, common method bias cannot be completely ruled out.

Conclusion

This study provided insights into the dynamics of remote work by examining the roles of environmental distractions, job autonomy, and self-leadership as predictors of perceived job productivity, with a particular focus on remote work stress and remote work engagement as mediating mechanisms. Drawing on a cross-national sample from Italy and Russia, the findings support the motivational pathway of the JD-R model: both autonomy and self-leadership were positively associated with engagement, which in turn predicted productivity. At the same time, the findings were less consistent with the health-impairment pathway, because remote work stress did not significantly mediate the association between environmental distractions and productivity. The unexpected negative direct association between job autonomy and productivity, together with the weak and marginal negative direct association between self-leadership and productivity, suggests the need to critically reassess the assumption that resources are always uniformly beneficial in remote work contexts. Under conditions of high self-management and limited structure, autonomy may also increase the self-regulatory burden placed on employees, whereas self-leadership appears to contribute to productivity primarily by sustaining engagement rather than through a stable direct effect on productivity. 

The study provides preliminary evidence of partial convergence in some key psychological mechanisms across the Italian and Russian subsamples, while also indicating that cross-national findings should be interpreted cautiously because not all structural paths were consistently significant across both countries. Future research should retest these mechanisms in contemporary hybrid work arrangements, where remote work is more planned, institutionalized, and supported by organizational policies than during the emergency pandemic transition. Such research should also consider sustainable work design, examining how flexible work can support productivity while protecting employee health, work-life boundaries, inclusion, and long-term organizational functioning. Overall, the findings contribute to the theoretical refinement of the JD-R model in remote work settings and offer guidance for organizations seeking to sustain engagement and productivity in evolving work arrangements

Ethics Statement

This study obtained ethics approval from the University of Bologna, prot. N. 252554. 

Author Contributions

F.T. and S.Z. conceived and designed the study, developed its theoretical framework, and coordinated the research and data collection in Italy. M.V.P. and E.V.K. contributed to the conceptualization of the study, adapted the questionnaires to the Russian context, and coordinated data collection in Russia. F.T. performed the statistical analyses, with input from S.Z., F.T. and S.Z. interpreted the results, and drafted and revised the manuscript. All authors read and approved the final version of the manuscript.

Informed Consent from the Participants 

All the participants filled in the informed consent form before taking part. 

Conflict of Interest 

The authors declare no conflict of interest. 

Acknowledgements

The authors thank Fatimah Anwar Ali for her preliminary work on earlier versions of portions of this study as part of her master’s thesis. Her contribution was limited to those preliminary drafts and did not meet the journal’s criteria for authorship, as documented through the CRediT contributor-role taxonomy.

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