Next Article in Journal
Vulnerability to Sex Trafficking: Adult Women’s Experiences While They Were Adolescents
Previous Article in Journal
From Scientific Journals to Newspapers in Spain: Interest in Disinformation (2000–2023)
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Societies
Volume 14
Issue 4
10.3390/soc14040050
societies-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Enhancing Productivity at Home: The Role of Smart Work and Organizational Support in the Public Sector

by
Barbara Barbieri
1,
Marina Mondo
2,*,
Silvia De Simone
2,
Roberta Pinna
3,
Maura Galletta
4,
Jessica Pileri
5 and
Diego Bellini
1
1
Department of Political and Social Sciences, University of Cagliari, 09123 Cagliari, Italy
2
Department of Pedagogy Psychology and Philosophy, University of Cagliari, 09124 Cagliari, Italy
3
Department of Economics and Business, University of Cagliari, 09123 Cagliari, Italy
4
Department of Medical Sciences and Public Health, University of Cagliari, 09042 Monserrato, Italy
5
Department of Dynamic Clinical Psychology and Health Studies, Sapienza University of Rome, 00185 Roma, Italy
*
Author to whom correspondence should be addressed.
Societies 2024, 14(4), 50; https://doi.org/10.3390/soc14040050
Submission received: 7 March 2024 / Revised: 2 April 2024 / Accepted: 8 April 2024 / Published: 10 April 2024
Browse Figure
Review Reports Versions Notes

Abstract

:
In recent years, Italian Public Administrations (PAs) have swiftly adapted to flexible work arrangements due to the impact of COVID-19. The flexible work has led to addressing new challenges including the need to balance work demands with family commitments, a lack of social support, struggles with inadequate technology, and managing home interruptions that negatively affect home performance. Based on the job demands-resources (JD-R) model, the present study aims at examining the negative impact of home interruptions on smart working performance and the moderating role of two potential job resources: organizational support and perceived quality of the smart working. A convenience sample of 301 Italian public employees engaged in smart working completed an online questionnaire. Multivariate regression analysis results showed that organizational support plays a moderating role in reducing the negative effect of interruptions on home performance. However, the findings did not support a moderating role for the perceived quality of smart working. Overall, these findings highlight the critical role of social factors in buffering the negative effects of smart working, as compared to the quality of the technological arrangements. This information could be valuable for organizations looking to enhance the effectiveness of smart working by focusing on organizational support.

1. Introduction

Modern organizations have undergone significant transformations due to a series of impactful events. Among these, the COVID-19 pandemic has been a catalyst for change. This circumstance has compelled organizations to adapt and embrace smart working arrangements that emphasize autonomy, overcoming the bound of time and space [1,2,3]. In this scenario, the introduction of smart working within the Italian Public Administrations deserves specific attention, which, at least at the beginning, was introduced without extensive support from employees or adaptation to the specific needs of the public sector [4]. Smart working represents a paradigm shift in traditional work models, emphasizing flexibility and autonomy in job execution without stringent constraints on time or location. This approach necessitates a mutual agreement between the employee and employer, underscoring the importance of adaptability in the modern labor market [5,6]. The adoption of smart working practices has surged globally, driven by its potential to reshape workforce dynamics and influence policy development [7]. From this perspective, smart working is conceptualized as a strategic resource, enabling the customization of work processes to align with individual preferences and facilitate attaining professional objectives. Within the Italian context, the discussion on the adoption of flexible forms of working in the public sector began in the last decade, focusing on two main issues: increasing public administration productivity and facilitating the work–life balance of employees [8,9]. The first experiences of flexible working in Italian public administration began in the 1980s with new public management models oriented towards greater efficiency [10]. The first important reform of the Italian public function, in particular Legislative Decree 29/1993, had the objective of harmonizing the rules of the public and private sectors to strengthen the competitiveness and productivity of the public sector, employing flexibility in organizational design [11].
However, these forms of work were used on an experimental basis in the public sector until the COVID-19 health emergency made it necessary to identify new ways of working to balance the continuity of administrative and economic activities with the protection of the health public [12,13]. Nevertheless, previous research has indicated that the shift to smart working can yield both positive and negative outcomes. On the one hand, it has been noted that, under certain conditions, smart working can enhance performance and wellbeing by reducing the conflict between work and family [14,15,16]. On the other hand, this transition has introduced new challenges, including social isolation, difficulties in disconnecting, unsuitable tools for meeting objectives, enforced cohabitation, and diminished organizational support, which also encompasses communication challenges with supervisors and colleagues [17]. Furthermore, it has upset the balance between work and personal life [18,19,20,21,22] and led to increased interruptions [23], particularly at home, thereby undermining wellbeing in the workplace [18,24]. While the dichotomous impact of smart working has been recognized, the literature on the subject is still scarce. For instance, few studies have examined the influence of smart working quality and organizational support on the relationship between interruptions and performance at home, particularly within the public sector [25].
Work interruptions are increasingly common in today’s workplaces due to the use of technology and shared workspaces, whether physical or virtual. Specifically, in a smart working condition, interruptions can arise from the overlap of work and family domains, resulting in a significant loss of working time and decreased performance, with many unfinished tasks [26,27]. In particular, in order to overcome the negative effect of interruptions on employees’ performances, especially at home, attention should be focused on the impact of the quality of technology on home performance, and the perception of receiving support from the organization to carry out the task work remotely in a smart way.
Several studies have highlighted how the use of new technology in smart working [28] without adequate training [29,30], specific competencies [31], and technical support [32] could cause work overload. At the same time, other studies have underlined that when combined with complementary organizational changes, investments in technology lead to improved performance [33,34,35]. Therefore, given these premises, it seems important to examine the quality of technology—specifically, its perceived quality in facilitating smart working—to understand its impact on enhancing remote (home-based) performance. Likewise, attention should be directed towards perceived organizational support, recognized as a vital resource for workers and defined as the degree to which employees believe that their organization values their contribution and cares about their wellbeing [36,37,38,39,40]. Eisenberger [41], for example, highlights how a positive perception of organizational support helps employees achieve goals, improve results, and decrease counterproductive behaviors in the organization, and promotes the acceptance of new technologies by increasing intrinsic motivation and promoting the better performance of tasks. Furthermore, this support is frequently associated with improved performance [42,43,44] and acts as a moderator in numerous studies, mediating the relationship between workplace demands and positive outcomes [45,46,47,48,49].
In light of the above, this study aims to explore the negative impact of home interruptions on smart working performance and the moderating role of two potential job resources: organizational support and perceived quality of smart working. Specifically, we examined these relationships within the job-demands resources (JD-R) model [50,51], which describes how job resources (i.e., in this study, perceived organizational support and perceived quality of smart working) can moderate the negative effect of job demands (i.e., in this study, interruptions) on home performance. The following sections discuss the theoretical background and present the hypothesis and the aims of this study.

2. Theoretical Background and Hypotheses

Flexible work arrangements are increasingly common across Europe and worldwide. A 2022 study by Eurofound [52] revealed that approximately 35% of European workers engage in teleworking or other forms of technology-assisted remote work. In Italy, the nature and execution of work have similarly shifted toward greater flexibility. According to the 2023 report from the Observatory on Smart Working at the Polytechnic University of Milan, the number of remote workers in Italy has slightly increased to 3.585 million, compared to 3.570 million in 2022. This represents an increase of 541% compared to the period before the COVID-19 pandemic. The report also highlighted that nearly all large companies (96%) offer smart working initiatives. Small- and medium-sized enterprises have adopted these practices at 56%, with public administration at a slightly higher rate of 61%. Italian law encourages this shift, with regulations supporting the technological and organizational innovations in work. Law No. 81 of 2017 provides a framework for flexible work methods, introducing “agile working”—also termed “smart working” during the legislative process—as a modality of executing an employment contract without stringent time or location constraints, and potentially incorporating technological tools (Article 18). The shift towards smart working has garnered significant interest among law and organizational scholars, leading to a substantial discourse on the subject. Yet, despite its relevance to work and organizational psychology, the literature on the topic appears limited [53]. However, the psychological impacts of agile working are significant. Working remotely, even if only for one or two days a week, can influence work–life balance, the sense of autonomy over work tasks, and ultimately, the quality of work performance [54,55]. Research has often shown a positive relationship between smart working and job performance, e.g., [56]. Nonetheless, the discussion is far from complete, with conflicting results in the academic literature indicating a need for a nuanced understanding of the effects of context, such as working from home, and the type of work on performance in flexible arrangements. Our study used the job demands-resources (JD-R) model [50,51] as the theoretical lens to investigate the dynamics between the studied constructs. This transactional model posits that working conditions can be categorized into job demands and the resources needed to address them. Job demands, which may be physical, psychological, social, or organizational, represent the necessities imposed by the organization. On the other hand, resources are instrumental in achieving organizational objectives like physical, psychological, social, or organizational demands. Both demands and resources have the potential to elicit positive and negative effects on workers individually, but they also interact with each other, influencing wellbeing and performance [57].
This study considered work interruptions as specific job demands [27,32,58,59,60] related to using technologies in smart working. Implementing smart working can deplete employees’ energy and force them to encounter frequent interruptions and it requires continuous cognitive effort to resume their tasks efficiently. Meanwhile, the perceived organizational support [61,62,63] and perceived quality of smart working [64,65] have been considered resources that can positively influence workers’ tasks such as home performance.

2.1. Work Task Interruptions and Their Negative Effect on Performance

Work interruptions can be seen as demands that can impact employees’ performance. We align with the definition proposed by Puranik [27]: “A work interruption is an unexpected cessation of behavioral performance and/or attentional focus from an ongoing work task” (p. 12). Such interruptions, according to Jett and George [58], disrupt a task’s performance and can hinder or prevent its execution. This phenomenon, and its psychological impacts on worker wellbeing, warrant further study to prevent situations that negatively affect employee wellbeing [66].
The literature indicates that the execution of an ongoing work task can be interrupted in two ways: either by halting the behavioral performance of the task [67] or by diverting attentional focus away from the task [68]. However, scholars do not unanimously agree on differentiating between interrupting work behavior and performing a task with diminished concentration. Some argue that an interruption occurs only when the task is suspended, while others consider it interrupted even if the worker performs it with less attention. Regardless of these differing views, there is a general agreement that interruptions can impact employees’ performance, particularly in a smart working context where such disruptions can occur frequently [69,70,71,72,73]. Interruptions can significantly affect job performance, often leading to decreased productivity and heightened stress [74,75]. The impact of interruptions on work performance may vary based on the work’s nature, the interruptions’ frequency, and individual differences [76]. Interruptions can disrupt concentration and workflow, making it challenging for individuals to focus on tasks [77]. The perception that interruptions are obstacles to effective task performance may result in lower job satisfaction. Work interruptions can contribute to high stress levels, especially when individuals feel pressured to compensate for lost time. Chronic stress can adversely affect mental and physical wellbeing, further impairing work performance [78]. Bailey et al. [79] demonstrate that interruptions have a disruptive effect on both a user’s task performance and emotional state. Prodanova and Kocarev [80] found that interruptions perceived by workers inhibit work effectiveness and performance during smart working among a sample of employees and supervisors, corroborating the findings of Mirhoseini et al. [81]. Addas and Pinsonneault [82] argue that interruptions negatively affect performance, especially when they are incongruent and contain information irrelevant to the primary task. Chen and Karahanna [83] examined how technology-mediated interruptions influence work, concluding that frequent non-work-related interruptions detrimentally affect performance.
Based on the above, we formulate the following hypothesis.
Hypothesis 1:
Work interruptions during smart working are negatively related to home performance.

2.2. The Moderating Effect of Organizational Support between Work Tasks’ Interruptions and Performance

Perceived organizational support (POS) has been defined as the organization’s assurance that help will be available to do one’s job effectively and deal with stressful situations [84,85,86,87]. This study considers perceived organizational support (POS) as a resource that enables workers to navigate numerous interruptions caused by the widespread use of technology in smart working, thereby positively influencing their capacity to handle demands and maintain performance [42,43,44]. Research has consistently pointed out the key role of support in moderating the negative impact of work interruptions on wellbeing factors such as self-efficacy and job satisfaction, marking it as a buffer in high-pressure situations [88]. Moreover, it has been found that support from supervisors can lessen the compound stress resulting from interruptions and the resultant job performance assessment. Benlian [89] has identified the beneficial role of perceived social support in diminishing the stress incurred by interruptions in remote working settings [76,90,91,92].
Research has shown that poor interactions with supervisors and peers can influence how interruptions affect performance, providing a buffer against the negative impact on task completion [93,94]. Gupta and colleagues [95] and Butts and colleagues [96] have observed that interruptions from superiors can be particularly disruptive when compared to other sources. Furthermore, LePine and colleagues [97] have highlighted that the support perceived by supervisors can change how an interruption is viewed, lessening its detrimental effect on performance. This finding was corroborated by a study involving military personnel.
Zappalà and colleagues [25] found that while interruptions can hinder performance during smart working, this adverse effect is mitigated when employees perceive the quality of smart working to be high and when they receive support from supervisors and colleagues. Investigating a smart-working workforce, Golden and Gajendran [98] concluded that a high level of social support can modulate the dynamic between the extent of smart working, including its disruptions, and job performance. In essence, stronger social support correlates with a more positive impact of smart working on job performance.
Such support during challenging times or work breaks can aid in coping with stress and frustration, contributing to a more positive emotional state and potentially improving overall performance [99]. Given the aforementioned studies, organizational support may play a moderating role in acting as a resource in managing interruptions effectively.
In light of the above, we propose the following hypothesis.
Hypothesis 2:
Perceived organizational support moderates the negative relationship between work interruptions and home performance during smart working.

2.3. The Moderating Effect of Quality of Smart Working between Work Task Interruptions and Performance

Smart working has seen considerable growth during the pandemic [21,100], and its adoption post-crisis is tied to workers’ experiences during the lockdown [101]. The favorable perception of smart working has significantly surfaced in recent years, buoyed by technological advancements and evolving workplace dynamics [102,103]. This approach to work enables employees to have more flexible time management, fostering a better integration of work and personal life [14]. The liberty to work from varied locations, not just the traditional office, grants employees greater choice and reduces the costs associated with maintaining a physical workspace [104]. Furthermore, organizations that offer smart working are more likely to attract talent, as they signal a commitment to employee wellbeing; flexibility thus becomes a competitive edge in the job market [105]. Therefore, factors that contribute to a positive perception of smart working include the flexibility of work hours and location, and the capacity to balance work and life concerns [24,106].
In sum, the benefits of smart working are epitomized by schedule and location flexibility, technology that streamlines work, and processes that promote a work–life balance (WLB). As mentioned, smart working can positively influence workers’ performance and, in general, their wellbeing, reducing work–family conflict [14,15,16]. Conversely, its drawbacks are associated with increased work intensity, limited workspaces, technostress, isolation, and an intensified domestic and caregiving workload [22].
Angelici and Profeta [107] assert that workers hold a more favorable perception of smart working due to the considerable autonomy it provides and its aid in balancing work and life demands. Kelliher and Anderson [108] suggest that this autonomy perception strongly enhances employees’ views of smart working quality, especially when control and autonomy are coupled with the ability to manage work and life.
A positive perception of smart working equips workers with the means to manage organizational demands, the burdens of extensive ICT use, and the interruptions stemming from excessive information and requests, as well as familial obligations [109,110].
Building on the foregoing, we present the following hypothesis.
Hypothesis 3:
Perceived quality of smart working moderates the negative relationship between work interruptions and home performance during smart working. The research model is depicted in Figure 1.

3. Materials and Methods

3.1. Participants and Procedure

The research was carried out in Italy, focusing on a sample of 301 employees from the public administration, who were selected using convenience sampling methods. In 2023, data gathering was conducted through an online survey. Participants completed a specially designed ad hoc questionnaire that covered various constructs, and socio-demographic and contextual variables relevant to this study. They were informed of the aim of the study and provided consent to participate in the research. The survey was anonymous. Of the total of participants, 183 were men (60.8%) and 118 were women (39.2%). The participants had an average age of 49 years (SD = 9.67). The tenure was 15.6 years (SD = 10.5). On average, they worked two days per week in smart working (SD = 1.19) and declared having sufficient autonomy at work (M = 3.33; SD = 0.11).

3.2. Socio-Demographic and Control Variables

The online survey included questions about demographic and contextual variables, such as age, gender, job position, sectors of employment, organizational tenure, days per week worked in smart working, and autonomy at work on performance. Previous studies have highlighted the impact of those variables on performance (for a meta-analysis, see [111]). Those variables were considered in statistical analysis to control their potential effect on performance. Age was categorized into three categories (1 = 18–34; 2 = 35–54; 3 ≥ 54), gender was categorized into three categories (1 = male; 2 = female; 3 = other), job position in two categories (1 = manager; 2 = employees), and sectors of employment in eight categories (1 = fincance/banks/insurances; 2 = healthcare; 3 = services; 4 = education; 5 = transport; 6 = industry; ICT = 7; 8 = other). Autonomy was measured with a Likert scale at five points (from “not at all” = 1 to “a lot” = 5) and the frequency of the days per week was measured with a Likert scale at six points (from 1= daily to 6= less than a day a week). Age and organizational tenure were treated as continuous variables in the analysis. Table 1 shows the frequency of each category.

3.3. Measures

Work interruptions at home were evaluated using three items extracted from the work-from-home questionnaire developed by Prodanova and Kocarev [80]. The original version of the Prodanova and Kocarev scale was utilized in previous studies, encompassing both managers and employees engaged in homework across different countries. Employees have indicated their agreement with each item on a 5-point Likert scale, ranging from completely agree (=1) to completely disagree (=5). The items considered were as follows: “My work is always interrupted when working from home”; “I feel my work environment is quite disturbing when working from home”; “My concentration is inhibited when working from home”. Internal consistency was evaluated using Cronbach’s alpha, resulting in a high reliability value of 0.90.
Work performance at home was measured with four items extracted from the smart working questionnaire [112]. The original Mascagna’s scale was used among Italian employees working at home. Employees expressed their agreement with each item on a 5-point Likert scale, ranging from completely agree (=1) to completely disagree (=5). The four items were: “The adoption of this working method is useful to improve your performance within the company”; “The home office helps me achieve my business objectives more efficiently than working from the usual office”; “The adoption of the home office is useful to improve the performance of everyone within the company”; “The adoption of the home office is useful to increase company profits”. The Cronbach’s alpha value was 0.93.
The Italian eight-items version of the Perceived Organizational Support scale (POS) [113] was used in previous research in private and public sectors across different countries. Employees express their level of agreement on a 5-point Likert scale, ranging from completely agree (=1) to completely disagree (=5). An illustrative item is “My organization strongly considers my goals and values”. The Cronbach’s alpha value was 0.90.
The Perceived Quality of Smart Working was assessed through the following two questions: “Do you that smart working can enhance the way you organize your work?”; “Do you think that smart working can improve the quality of your life?”. Employees rated their responses on a 5-point Likert scale, ranging from never (=1) to always (=5). The Cronbach’s alpha value was 0.87.

3.4. Data Analysis

Preliminarily, we examined the variables under investigation and verified the distribution of the data. To verify the factorial structure of all the constructs, taking into account in this study, an exploratory factor analysis (EFA) was performed. Subsequently, based on the EFA results, the total score was computed for each construct by summing the rating of individual respondents where a higher score indicated a higher level for each construct. Further, we examined the measurement model, and both convergent and discriminant validity by analyzing the composite reliability (CR) and average variance extracted values (AVE) of each construct within the measurement model. CR values higher than 0.7 [114] and AVE values greater than 0.5 [115] indicate acceptable reliability, while root values of the AVE exceeding the correlation between other constructs denote adequate discriminant validity [116]. Additionally, we evaluated the reliability of the scale under consideration by calculating Cronbach’s alpha. A value greater than 0.80 [117] indicates good reliability. Furthermore, we examined an assessment of the Common Method Bias (CMB) through the Harmans’ single factor test [118]. All the construct items for the four factors under consideration in the present study were entered in the exploratory factor analysis to analyze the potential presence of bias. CMB is a concern if a single factor explains the majority of the total variance, specifically exceeding 50% of the total variance.
Bravais–Pearson’s Correlation was used to examine the bivariate correlations among study variables. Following this, a hierarchical regression analysis with four steps was performed to test our hypothesis, regarding the relationship between work interruptions at home and home performance. In the first step, we included age, gender, organizational tenure, days per week worked in smart working, job position, sectors of employment, and autonomy to control for confounding effects. In the second step, the construct variables were entered. In the third and fourth steps, we analyze the potential joint moderation effect of perceived organizational support and perceived quality of smart working on the relationship between work interruptions at home and home performance. In the third step, the interaction between interruptions and perceived quality of smart working was included while in the fourth step the interaction between interruptions and perceived organizational support was entered. The predictors have been standardized before calculating the interaction terms.
Data analysis was performed using IBM SPSS version 20 and Process Macro for SPSS.

4. Results

4.1. Descriptive Statistics and Correlations

As reported in Table 2, a significant and positive relationship was found between organizational tenure, days per week worked in smart working, autonomy, and home performance. Further, work interruptions at home significantly negatively correlated with the quality of smart working and home performance. However, work interruptions did not correlate with perceived organizational support. Furthermore, perceived organizational support and perceived quality of smart working were significantly positively related with home performance.

4.2. Hypothesis Test and Common Method Bias

The measurement model showed an acceptable fit (χ2 = 174.748 df = 80, p = 0.000, χ2/df = 3.023; CFI = 972; TLI = 963; RMSEA = 0.063; SRMR = 0.051). As shown in Table 3, the average variance extracted values were above 0.50, the composite reliability values were higher than 0.60, and the Cronbach’s Alpha values were higher than 0.8. Further, as reported (in bold) in the Table 3, the AVE square was greater than its correlation with the other constructs. The results of the CMB analysis indicated that the first factor accounts for 37% of variance, which is less than 50%. As shown in Table 4, in model 1, the results of the hierarchical regression analysis showed a significant negative relationship between days per week worked in smart working and home performance (p < 0.01) and between autonomy and home performance (and p < 0.001), but did not show a significant relationship between age, gender, organizational tenure, job position and sectors of employment, and home performance. In model 2, the results showed that interruptions at home were significantly negatively related with home performance (p < 0.01). In model 3, the results did not show the moderating effect of perceived quality of smart working between interruption at home and home performance. In model 4, the findings showed the moderating effect of perceived organizational support between work interruptions at home and home performance (p < 0.05). Further, to examine the condition effect of interruptions at home on performance at home for different levels values of the significant moderator, the simple slope test was performed. Results of the simple slope test showed that for a lower level of perceived organizational support, the relationship between interruptions at home on performance at home was stronger (β = −0.51; CI = −0.657 to 0.378) as compared to that for the low level (β = −0.38; CI = −0.477 to 0.295) and at the mean level (β = −0.25; CI = −0.356 to 0.154). These results mean that with the enhancing of the perceived organizational support, the negative effect of interruptions decreased.
H1 and H2 were supported but the results did not confirm H3.

5. Discussion and Practical Implication

Grounded in the JD-R model [50,51], this study explored the adverse effect of interruptions, recognized as organizational demands, on home performance during smart working. Additionally, we examined the potential buffering role of perceived quality of smart working and organizational support, as job resources, in reducing this negative relationship.
Our research findings, aligned with our hypothesis (H1), underscore the considerable negative impact that interruptions can have on home performance. These results are consistent with previous studies that indicate the negative effects of interruptions on workers’ performance [79,81,119,120] and psychological consequences, impacting general wellbeing [66]. Persistent interruptions, whether emerging from technological sources within a smart working context or from domestic environments, can break concentration, diminish productivity, and result in errors or incomplete tasks [74,75,78,80,121].
Additional findings from our study highlight the pivotal role of a supportive organizational environment in mitigating the negative impact of work interruptions on performance, thus confirming our second hypothesis (H2). These results reinforce what has been observed in previous studies [25,89], which identified organizational support as a resource that enables employees to carry out their jobs with greater effectiveness and efficiency. Such support is essential for creating a work environment that fosters productivity, job satisfaction, and overall wellbeing. Therefore, organizational support is a resource in work organization and plays a fundamental role in promoting work engagement [122], job satisfaction [123], increasing job performance, and reducing turnover intentions [124]. By investing in this resource, organizations can cultivate a positive workplace culture where employees feel valued, motivated, and empowered, enabling them to perform at their optimal performance.
Contrary to our third hypothesis (H3), it was found that the perceived quality of smart working does not act as a moderator between work interruptions and performance. This unexpected finding may depend on individual, contextual, and environmental factors. Research has shown that the extensive use of technologies entails constant interruptions stemming from excessive information and requests, as well as familial obligations [109,110], which affect performance.
Upon analyzing the use of smart working and observing the correlations between the perceived quality of smart working and the number of days worked weakly in smart working, we noted a negative correlation between the number of days worked in smart working during the week and the perceived quality of smart working (r = −0.163). These results suggest that the workload due to smart working can be related to the resources of employees. In other words, the more days employees work, the less they can maintain attentional focus away from the task [68] and recover from the organizational demands, thereby reducing the perception of smart working quality. This negative effect might also depend on personal factors such as concentration ability, stress management, and flexibility, which can affect an employee’s ability to manage downtime in a smart working mode without compromising performance. As shown by Tudu and Singh [125] work-from-home performance can be moderated by dispositional or individual characteristics, and motivational and social factors. Furthermore, prior research revealed how the ability of employees to adapt quickly to interruptions and maintain concentration on core activities depends, for example, on environmental and family atmosphere [126], and office arrangements [127].
In addition, a positive correlation has been found between perceived autonomy and perceived quality of smart working (r = 0.190). This implies that the more autonomy employees perceive in carrying out their tasks, the more likely they are to attribute a higher quality to their work in smart working. This finding marks the role of organizational job design. Individuals who have more autonomy tend to hold a more favorable perception of smart working [107]. Autonomy supports them in balancing work and life demands. Thus, given these promising findings, public organizations should support workers by granting them the desired degree of autonomy to enhance the perceived quality of smart working. However, in this study, employees’ autonomy is not particularly high (M = 3.33; SD = 0.11), consequently impacting the perceived quality of smart working.
Considering that research on the subject is limited, our study first contributes to the literature on smart working’s impact on employee performance, albeit without claiming to fill the existing gap. The results clearly show that work interruptions related to technology use and new work demands negatively affect performance when working from home. However, the resources we considered, such as perceived organizational support, help lessen the detrimental effects of these interruptions on home-based performance. Secondly, they allow us to reflect on the practical implications that organizations need to consider. The use of this method of carrying out the employment relationship has advantages and disadvantages that are important to investigate further to allow for the organization to achieve its objectives and the employees to perform in conditions of wellbeing [128,129].
When organizations invest in their employees’ wellbeing and address their needs, they foster positive performance outcomes, even amid technology-related disruptions. Our study proposes that organizations should strengthen employee support perception by enhancing technological competencies and fostering autonomy in disruption management rather than merely attempting to reduce interruptions [58,76,130,131].
Although smart working is recognized for its benefits, it is widely believed that it introduces several challenges, particularly in the field of human resource management, which traditionally focuses on office-based staff [132,133,134,135]. HR strategies must evolve from those used for conventional workplaces [61]. Current research has yet to definitively determine the full impact of smart working on performance and wellbeing [136], also considering the importance of allowing employees to choose smart working voluntarily and to work autonomously, as highlighted by previous studies [137]. Furthermore, the transition to smart working goes beyond simply updating organizational infrastructure and process capabilities; it requires a fundamental acceptance of change by the workforce [136,138].

6. Limitations and Future Research

The present study is subject to certain limitations stemming from its research design, which leave several questions open for future exploration. Firstly, the cross-sectional nature of this study precludes analyzing the cause-and-effect relationships between the variables. Secondly, our use of a convenient sample was limited, excluding various contexts and cultures during data collection that could be pertinent for extending the findings. Thirdly, the variables we investigated included self-report measures, potentially affected by the common method bias. Incorporating objective measures to assess the quality of smart working, social support, and home performance would be beneficial. Lastly, it is critical to monitor how the relationship among the variables in this study evolves through longitudinal follow-up measurements.
Future research on work interruptions could explore additional dimensions to assess their positive and negative impacts and to develop enhancement strategies [27]. Future studies could include wellbeing dimensions to explore how interruptions might adversely affect stress levels, job satisfaction, and workers’ mental health in both the short and long term [78]. A specific focus should be on how interruptions may reduce workplace safety, particularly in high-activity settings or those requiring meticulous attention to detail. The investigation into the technologies and organizational processes employed, especially for smart working designed to mitigate the impact of disruptions, is equally significant [32].
Furthermore, although previous research has generally pointed out that work interruptions harm workers’ performance and wellbeing [27], not all interruptions are detrimental. For instance, family interruptions can have negative effects by hindering work and positive effects by contributing to achieving family goals [139]. Additionally, breaks, whether planned or spontaneous, can positively affect performance and wellbeing by preventing the impairing effects of accumulated strain [140]. These effects could vary significantly depending on numerous variables that have the potential to alleviate the negative effect of interruptions. Future studies could consider individual, social, and environmental factors as mediators or moderators in the relationship between interruptions and different negative outcomes. Studies could also consider individual differences, such as personality traits, cognitive and coping styles, and job crafting abilities [141], to understand how individuals respond to interruptions and strategies to counteract their negative effects. For instance, researchers could examine the role of leadership styles combined with cultural and organizational elements that can foster a conducive atmosphere. It is important to include multidimensional measures of interruptions to understand which ones negatively and which ones positively affect performance. Lastly, qualitative studies could explore which specific home activities interfere with employees’ performance.

7. Conclusions

This research focused on smart working within public administrations, which involves a flexible work mode and the use of technology for remote operations. This study highlighted the critical role of perceived social support in mitigating the impact of work interruptions and performance. The advantages of organizational support are evident to both employees and the entire organization [142]. When employees perceive adequate organizational support, they feel valued and recognized, leading to increased motivation. Organizational support enables workers to handle even technologically demanding requests without stress, balance life and work—especially in smart working environments—and develop greater confidence in their process management skills with autonomy and in achieving their objectives.
Employees who feel supported by their organization are more inclined to remain in the organization over the long term, which can lower the costs associated with absenteeism and employee turnover and promote resilience [143]. Public organizations that provide strong organizational support are able to attract top talent and retain them over the long term in a market characterized by open borders, thanks to the dematerialization of the workplace. Lastly, when employees feel supported and respected by their organization, they are more likely to feel free to share ideas and propose innovative solutions.

Author Contributions

Conceptualization, B.B., M.M., S.D.S., R.P. and M.G.; methodology, B.B., M.M., S.D.S., R.P., M.G. and D.B.; formal analysis, D.B. and B.B.; writing—original draft preparation, M.M., B.B., D.B. and S.D.S.; writing—review and editing, R.P., M.G. and J.P. All authors have read and agreed to the published version of the manuscript.

Funding

This paper has been supported by the Fondazione di Sardegna—Research Funding 2021—University of Cagliari. Title of the funded project: “SEWED—Smart Engaging Work Environment Design”—CUP F73C22001370007.

Institutional Review Board Statement

Ethical review and approval confirmed by Ethics Committee of the University of Cagliari, code 0077639, in 13 March 2024.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data supporting the conclusions of this article will be made available by the authors on reasonable request from the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Barbieri, B.; Buonomo, I.; Farnese, M.L.; Benevene, P. Organizational capital: A resource for changing and performing in public administrations. Sustainability 2021, 13, 5436. [Google Scholar] [CrossRef]
  2. De Simone, S.; Pileri, J.; Rapp-Ricciardi, M.; Barbieri, B. Gender and entrepreneurship in pandemic time: What demands and what resources? An exploratory study. Front. Psychol. 2021, 12, 668875. [Google Scholar] [CrossRef] [PubMed]
  3. De Vincenzi, C.; Pansini, M.; Ferrara, B.; Buonomo, I.; Benevene, P. Consequences of COVID-19 on Employees in Remote Working: Challenges, Risks and Opportunities an Evidence-Based Literature Review. Int. J. Res. Public Health 2022, 19, 11672. [Google Scholar] [CrossRef]
  4. Rapisarda, S.; Ghersetti, E.; Girardi, D.; De Carlo, N.A.; Dal Corso, L. Smart working and online psychological support during the COVID-19 pandemic: Work-family balance, well-being, and performance. InPACT 2021, 301–306. [Google Scholar] [CrossRef]
  5. Dizaho, E.K.; Salleh, R.; Abdullah, A. Achieveing Work Life Balance Through Flexible Work Schedules and Arrangements. GJMBR 2017, 9, 455–465. [Google Scholar]
  6. Baadel, S.; Kabene, S.; Majeed, A. Work-life conflict costs: A Canadian perspective. Int. J. Hum. Resour. Dev. Manag. 2020, 20, 178–186. [Google Scholar] [CrossRef]
  7. Brewster, C.; Mayne, L.; Tregaskis, O. Flexible working in Europe: A review of the evidence. Int. J. Cross Cult. Manag. 1997, 32, 85–103. [Google Scholar] [CrossRef]
  8. Cellini, M.; Pisacane, L.; Crescimbene, M.; Di Felice, F. Exploring employee perceptions towards smart working during the COVID-19 pandemic: A comparative analysis of two Italian public research organizations. Public Organ. Rev. 2021, 21, 815–833. [Google Scholar] [CrossRef]
  9. Graffi, A.; Parravicini, P. Produttività del lavoro e smart working: Un binomio migliorabile. Labour Law Issues 2022, 8, C-1. [Google Scholar]
  10. Dunleavy, P.; Hood, C. From old public administration to new public management. Public Money Manag. 1994, 14, 9–16. [Google Scholar] [CrossRef]
  11. Poti, U. Lavoro Pubblico e Flessibilità; Rubbettino Editore: Soveria Mannelli, Italy, 2002; p. 210. [Google Scholar]
  12. Langè, V.; Gastaldi, L. Coping Italian emergency COVID-19 through smart working: From necessity to opportunity. J. Mediterr. Knowl. 2020, 5, 163–171. [Google Scholar]
  13. Niersbach, S. How flexible is paid work organized in the public sector before and during the COVID-19 pandemic?: A qualitative study. Int. J. Home Econ. 2021, 14, 88–95. [Google Scholar]
  14. Darouei, M.; Pluut, H. Work from home today for a better tomorrow! How working from home influences work-family conflict and employees’ start of the next workday. Stress Health 2021, 37, 986–999. [Google Scholar] [CrossRef] [PubMed]
  15. Heiden, M.; Widar, L.; Wiitavaara, B.; Boman, E. Telework in academia: Associations with health and well-being among staff. High. Educ. 2021, 81, 707–722. [Google Scholar] [CrossRef]
  16. Standaert, W.; Sophie, T.; Frédéric, S. Virtual meetings and wellbeing: Insights from the COVID-19 pandemic. Inf. Technol. People 2023, 36, 1766–1789. [Google Scholar] [CrossRef]
  17. Bonacini, L.; Gallo, G.; Scicchitano, S. Working from home and income inequality: Risks of a ‘new normal’with COVID-19. J. Popul. Econ. 2021, 34, 303–360. [Google Scholar] [CrossRef] [PubMed]
  18. De Simone, S.; Pileri, J.; Mondo, M.; Rapp-Ricciardi, M.; Barbieri, B. Mea Culpa! The Role of Guilt in the Work-Life Interface and Satisfaction of Women Entrepreneur. Int. J. Res. Public Health 2022, 19, 10781. [Google Scholar] [CrossRef]
  19. Ghislieri, C.; Molino, M.; Dolce, V.; Sanseverino, D.; Presutti, M. Work-family conflict during the COVID-19 pandemic: Teleworking of administrative and technical staff in healthcare. An Italian study. La Med. Del Lav. 2021, 112, 229–240. [Google Scholar]
  20. Palumbo, R. Let me go to the office! An investigation into the side effects of working from home on work-life balance. Int. J. Public Sect. Manag. 2020, 33, 771–790. [Google Scholar] [CrossRef]
  21. Todisco, L.; Tomo, A.; Canonico, P.; Mangia, G. The bright and dark side of smart working in the public sector: Employees’ experiences before and during COVID-19. Manag. Decis. 2023, 61, 85–102. [Google Scholar] [CrossRef]
  22. Shirmohammadi, M.; Au, W.C.; Beigi, M. Remote work and work-life balance: Lessons learned from the COVID-19 pandemic and suggestions for HRD practitioners. Hum. Resour. Dev. Int. 2022, 25, 163–181. [Google Scholar] [CrossRef]
  23. Mascia, M.L.; Agus, M.; Tomczyk, Ł.; Bonfiglio, N.S.; Bellini, D.; Penna, M.P. Smartphone Distraction: Italian Validation of the Smartphone Distraction Scale (SDS). Int. J. Environ. Res. Public Health 2023, 20, 6509. [Google Scholar] [CrossRef] [PubMed]
  24. Morea, D.; Basile, G.; Bonacci, I.; Mazzitelli, A. Smart working as an organisational process or as a social change? An Italian pandemic experience. Empl. Relat. Int. J. 2023, 45, 677–703. [Google Scholar] [CrossRef]
  25. Zappalà, S.; Toscano, F.; Topa, G. The implementation of a remote work program in an italian municipality before COVID-19: Suggestions to hr officers for the post-COVID-19 era. Eur. J. Investig. Health Psychol. Educ. 2021, 11, 866–877. [Google Scholar] [CrossRef] [PubMed]
  26. Perry, S.J.; Carlson, D.S.; Kacmar, K.M.; Wan, M.; Thompson, M.J. Interruptions in remote work: A resource-based model of work and family stress. J. Bus. Psychol. 2022, 38, 1023–1041. [Google Scholar] [CrossRef] [PubMed]
  27. Puranik, H.; Koopman, J.; Vough, H.C. Pardon the interruption: An integrative review and future research agenda for research on work interruptions. J. Manag. 2019, 46, 806–842. [Google Scholar] [CrossRef]
  28. Mondo, M.; Pileri, J.; Barbieri, B.; Bellini, D.; De Simone, S. The Role of Techno-Stress and Psychological Detachment in the Relationship between Workload and Well-Being in a Sample of Italian Smart Workers: A Moderated Mediated Model. Soc. Sci. 2023, 12, 530. [Google Scholar] [CrossRef]
  29. Gabr, H.M.; Soliman, S.S.; Allam, H.K.; Raouf, S.Y.A. Effects of remote virtual work environment during COVID-19 pandemic on technostress among Menoufia University Staff, Egypt: A cross-sectional study. Environ. Sci. Pollut. Res. 2021, 28, 53746–53753. [Google Scholar] [CrossRef]
  30. Rayburn, S.W.; Badrinarayanan, V.; Anderson, S.T.; Gupta, A. Continuous techno-training and business-to-business salesperson success: How boosting techno-efficacy enhances sales effort and performance. J. Bus. Res. 2021, 133, 66–78. [Google Scholar] [CrossRef]
  31. Bellini, D.; Cubico, S.; Favretto, G.; Noventa, S.A.; Ardolino, P.; Gianesini, G.; Ciabuschi, F.; Leitao, J.; Jain, A.K. A metamodel for competence assessment: Co.S.M.O. © competences software management for organizations. Eur. J. Train. Dev. 2021, 45, 603–616. [Google Scholar] [CrossRef]
  32. Pfaffinger, K.F.; Reif, J.A.; Spieß, E. When and why telepressure and technostress creators impair employee well-being. Int. J. Occup. Saf. Ergon. 2022, 28, 958–973. [Google Scholar] [CrossRef]
  33. Decastri, M.; Gagliarducci, F.; Previtali, P.; Scarozza, D. Understanding the use of smart working in public administration: The experience of the presidency of the council of ministers. In Exploring Digital Ecosystems: Organizational and Human Challenges; Lazazzara, A., Ricciardi, F., Za, S., Eds.; Springer International Publishing: New York, NY, USA, 2020; Volume 33, pp. 343–363. [Google Scholar] [CrossRef]
  34. Garicano, L.; Heaton, P. Information technology, organization, and productivity in the public sector: Evidence from police departments. J. Labour Econ. 2010, 28, 167–201. [Google Scholar] [CrossRef]
  35. Scarozza, D.; Previtali, P.; Forte, S. How technology has redefined human resource practices? Understanding the use of smart working. In Human Resource Management and Digitalization, 1st ed.; Cantoni, F., Mangia, G., Eds.; Routledge: Abingdon, UK, 2018; pp. 73–93. [Google Scholar] [CrossRef]
  36. Pinna, R.; De Simone, S.; Cicotto, G.; Malik, A. Beyond organisational support: Exploring the supportive role of co-workers and supervisors in a multi-actor service ecosystem. J. Bus. Res. 2020, 121, 524–534. [Google Scholar] [CrossRef]
  37. Eisenberger, R.; Huntington, R.; Hutchison, S.; Sowa, D. Perceived organizational support. J. Appl. Psychol. 1986, 71, 500–507. [Google Scholar] [CrossRef]
  38. De Simone, S.; Cicotto, G.; Cenciotti, R.; Borgogni, L. Perceptions of social context and intention to quit: The mediating role of work engagement and interpersonal strain. Sustainability 2021, 13, 7554. [Google Scholar] [CrossRef]
  39. Mondo, M.; Pileri, J.; Carta, F.; De Simone, S. Social Support and Self-Efficacy on Turnover Intentions: The Mediating Role of Conflict and Commitment. Soc. Sci. 2022, 11, 437. [Google Scholar] [CrossRef]
  40. Huang, W.; Zhang, S.; Li, H. Effects of person-job fit on occupational commitment among kindergarten teachers: Occupational well-being as mediator and perceived organizational support as moderator. BMC Psychol. 2023, 11, 402. [Google Scholar] [CrossRef] [PubMed]
  41. Eisenberger, R.; Stinglhamber, F.; Vandenberghe, C.; Sucharski, I.L.; Rhoades, L. Perceived organizational support: Why caring about employees counts. Annu. Rev. Organ. Psychol. Organ. Behav. 2020, 7, 101–124. [Google Scholar] [CrossRef]
  42. Ford, M.T.; Wang, Y.; Jin, J.; Eisenberger, R. Chronic and episodic anger and gratitude toward the organization: Relationships with organizational and supervisor supportiveness and extrarole behavior. J. Occup. Health Psychol. 2018, 23, 175–187. [Google Scholar] [CrossRef]
  43. Kurtessis, J.N.; Eisenberger, R.; Ford, M.T.; Buffardi, L.C.; Stewart, K.A.; Adis, C.S. Perceived organizational support: A meta-analytic evaluation of organizational support theory. J. Manag. 2017, 43, 1854–1884. [Google Scholar] [CrossRef]
  44. Ridwan, M.; Mulyani, S.R.; Ali, H. Improving employee performance through perceived organizational support, organizational commitment and organizational citizenship behavior. Syst. Rev. Pharm. 2020, 11, 839–849. [Google Scholar]
  45. Erdogan, B.; Enders, J. Support from the top: Supervisors’ perceived organizational support as a moderator of leader-member exchange to satisfaction and performance relationships. J. Appl. Psychol. 2007, 92, 321–330. [Google Scholar] [CrossRef] [PubMed]
  46. Jain, A.K.; Giga, S.I.; Cooper, C.L. Perceived organizational support as a moderator in the relationship between organizational stressors and organizational citizenship behaviors. Int. J. Organ. Anal. 2013, 21, 313–334. [Google Scholar] [CrossRef]
  47. Malik, S.; Noreen, S. Perceived organizational support as a moderator of affective well-being and occupational stress. Pak. J. Commer. Soc. Sci. 2015, 9, 865–874. [Google Scholar]
  48. Medzo-M’engone, J. Job demands and psychological well-being among Gabonese civil servants: The mediating role of perceived organizational support. J. Workplace Behav. Health 2021, 36, 335–350. [Google Scholar] [CrossRef]
  49. Zahoor, N.; Donbesuur, F.; Christofi, M.; Miri, D. Technological innovation and employee psychological well-being: The moderating role of employee learning orientation and perceived organizational support. Technol. Forecast. Soc. Chang. 2022, 179, 121610. [Google Scholar] [CrossRef]
  50. Bakker, A.B.; Demerouti, E. The job demands-resources model: State of the art. J. Manag. Psychol. 2007, 22, 309–328. [Google Scholar] [CrossRef]
  51. Bakker, A.B.; Demerouti, E. Job demands–resources theory: Taking stock and looking forward. J. Occup. Health Psychol. 2017, 22, 273–285. [Google Scholar] [CrossRef] [PubMed]
  52. Eurofound. Available online: https://www.eurofound.europa.eu (accessed on 28 March 2024).
  53. Toscano, F.; Zappalà, S. Smart working in Italia: Origine, diffusione e possibili esiti. Psicol. Soc. 2020, 15, 203–223. [Google Scholar]
  54. Allen, T.D.; Golden, T.D.; Shockley, K.M. How effective is telecommuting? Assessing the status of our scientific findings. Psychol. Sci. Public Interest 2015, 16, 40–68. [Google Scholar] [CrossRef]
  55. Gajendran, R.S.; Harrison, D.A. The good, the bad, and the unknown about telecommuting: Meta-analysis of psychological mediators and individual consequences. J. Appl. Psychol. 2007, 92, 1524–1541. [Google Scholar] [CrossRef] [PubMed]
  56. Bessa, I.; Tomlinson, J. Established, accelerated and emergent themes in flexible work research. J. Ind. Relat. 2017, 59, 153–169. [Google Scholar] [CrossRef]
  57. Bakker, A.B.; Demerouti, E. Multiple levels in job demands-resources theory: Implications for employee well-being and performance. In Handbook of Well-Being; Diener, E., Oishi, S., Tay, L., Eds.; Noba Scholar: Salt Lake City, UT, USA, 2018; p. 13. [Google Scholar]
  58. Jett, Q.R.; George, J.M. Work interrupted: A closer look at the role of interruptions in organizational life. Acad. Manage. Rev. 2003, 28, 494–507. [Google Scholar] [CrossRef]
  59. Rummel, J.; Wesslein, A.K.; Meiser, T. The role of action coordination for prospective memory: Task-interruption demands affect intention realization. J. Exp. Psychol. Learn. Mem. Cogn. 2017, 43, 717–735. [Google Scholar] [CrossRef]
  60. Wilkes, S.M.; Barber, L.K.; Rogers, A.P. Development and validation of the workplace interruptions measure. Stress Health 2018, 34, 102–114. [Google Scholar] [CrossRef] [PubMed]
  61. Barbieri, B.; Balia, S.; Sulis, I.; Cois, E.; Cabras, C.; Atzara, S.; De Simone, S. Don’t call it smart: Working from home during the pandemic crisis. Front. Psychol. 2021, 12, 741585. [Google Scholar] [CrossRef] [PubMed]
  62. Gerich, J.; Weber, C. The Ambivalent Appraisal of Job Demands and the Moderating Role of Job Control and Social Support for Burnout and Job Satisfaction. Soc. Indic. Res. 2019, 148, 251–280. [Google Scholar] [CrossRef]
  63. Kim, C.Y.; Lee, J.H.; Shin, S.Y. Why are your employees leaving the organization? The interaction effect of role overload, perceived organizational support, and equity sensitivity. Sustainability 2019, 11, 657. [Google Scholar] [CrossRef]
  64. Ko, E.J.; Kim, A.H.; Kim, S.S. Toward the understanding of the appropriation of ICT-based Smart-work and its impact on performance in organizations. Technol. Forecast. Soc. Chang. 2021, 171, 120994. [Google Scholar] [CrossRef]
  65. Malik, A.; Rosenberger, P.J., III; Fitzgerald, M.; Houlcroft, L. Factors affecting smart working: Evidence from Australia. Int. J. Manpow. 2016, 37, 1042–1066. [Google Scholar] [CrossRef]
  66. Feldman, E.; Greenway, D. It’s a matter of time: The role of temporal perceptions in emotional experiences of work interruptions. Group Organ. Manag. 2021, 46, 70–104. [Google Scholar] [CrossRef]
  67. Biron, A.D.; Lavoie-Tremblay, M.; Loiselle, C.G. Characteristics of work interruptions during medication administration. J. Nurs. Scholarsh. 2009, 41, 330–336. [Google Scholar] [CrossRef] [PubMed]
  68. Myers, R.A.; McCarthy, M.C.; Whitlatch, A.; Parikh, P.J. Differentiating between detrimental and beneficial interruptions: A mixed-methods study. BMJ Qual. Saf. 2016, 25, 881–888. [Google Scholar] [CrossRef] [PubMed]
  69. Delanoeije, J.; Verbruggen, M.; Germeys, L. Boundary role transitions: A day-to-day approach to explain the effects of home-based telework on work-to-home conflict and home-to-work conflict. Hum. Relat. 2019, 72, 1843–1868. [Google Scholar] [CrossRef]
  70. İmren, M.; Tekman, H.G. The Relationship Between Media Multitasking, Working Memory and Sustained Attention. Uludağ Üniversitesi Fen-Edeb. Fakültesi Sos. Bilim. Derg. 2019, 20, 1075–1100. [Google Scholar] [CrossRef]
  71. Jacobs, R.L. Knowledge Work and Human Resource Development. Hum. Resour. Dev. Rev. 2017, 16, 176–202. [Google Scholar] [CrossRef]
  72. Li, S.Y.W.; Magrabi, F.; Coiera, E. A systematic review of the psychological literature on interruption and its patient safety implications. J. Am. Med. Inform. Assoc. 2012, 19, 6–12. [Google Scholar] [CrossRef]
  73. Mustajab, D.; Bauw, A.; Rasyid, A.; Irawan, A.; Akbar, M.A.; Hamid, M.A. Working from home phenomenon as an effort to prevent COVID-19 attacks and its impacts on work productivity. Int. J. Appl. Buss. 2020, 4, 13. [Google Scholar] [CrossRef]
  74. Hurbean, L.; Wong, L.H.; Ou, C.X.; Davison, R.M.; Dospinescu, O. Instant Messaging, Interruptions, Stress and Work Performance. Inf. Technol. People, 2023; ahead of print. [Google Scholar] [CrossRef]
  75. Tams, S.; Ahuja, M.; Thatcher, J.; Grover, V. Worker stress in the age of mobile technology: The combined effects of perceived interruption overload and worker control. J. Strateg. Inf. Syst. 2020, 29, 101595. [Google Scholar] [CrossRef]
  76. Mark, G.; Czerwinski, M.; Iqbal, S.T. Effects of individual differences in blocking workplace distractions. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, Montreal, QC, Canada, 21–26 April 2018; pp. 1–12. [Google Scholar] [CrossRef]
  77. Tams, S.; Thatcher, J.B.; Grover, V. Concentration, competence, confidence, and capture: An experimental study of age, interruption-based technostress, and task performance. J. Assoc. Inf. Syst. 2018, 19, 857–908. [Google Scholar] [CrossRef]
  78. Keller, A.C.; Meier, L.L.; Elfering, A.; Semmer, N.K. Please wait until I am done! Longitudinal effects of work interruptions on employee well-being. Work Stress 2020, 34, 148–167. [Google Scholar] [CrossRef]
  79. Bailey, B.P.; Konstan, J.A.; Carlis, J.V. The effects of interruptions on task performance, annoyance, and anxiety in the user interface. In Proceedings of the IFIP TC.13 International Conference on Human–Computer Interaction, Uppsala, Sweden, 24–28 August 2001; IOS Press: Amsterdam, The Netherlands, 2001; pp. 593–601. [Google Scholar]
  80. Prodanova, J.; Kocarev, L. Is job performance conditioned by work-from-home demands and resources? Technol. Soc. 2021, 66, 101672. [Google Scholar] [CrossRef] [PubMed]
  81. Mirhoseini, S.; Hassanein, K.; Head, M.; Watter, S. User performance in the face of IT interruptions: The role of executive functions. In Information Systems and Neuroscience: NeuroIS Retreat 2019; Davis, F.D., Riedl, R., vom Brocke, J., Léger, P.M., Randolph, A.B., Fischer, T., Eds.; Springer: Cham, Switzerland, 2020; Volume 32, pp. 41–51. [Google Scholar]
  82. Addas, S.; Pinsonneault, A. E-mail interruptions and individual performance: Is there a silver lining? MIS Q. 2018, 42, 381–406. [Google Scholar] [CrossRef]
  83. Chen, A.; Karahanna, E. Life Interrupted: The Effects of Technology-Mediated Work Interruptions on Work and Nonwork Outcomes. MIS Q. 2018, 42, 1023–1042. [Google Scholar] [CrossRef]
  84. Shanock, L.R.; Eisenberger, R. When supervisors feel supported: Relationships with subordinates’ perceived supervisor support, perceived organizational support, and performance. J. Appl. Psychol. 2006, 91, 689–695. [Google Scholar] [CrossRef]
  85. Eisenberger, R.; Stinglhamber, F.; Vandenberghe, C.; Sucharski, I.; Rhoades, L. Perceived supervisor support: Contributions to organizational support and employee retention. J. Appl. Psychol. 2002, 87, 565–573. [Google Scholar] [CrossRef] [PubMed]
  86. Rhoades, L.; Eisenberger, R.; Armeli, S. Affective commitment to the organization: The contribution to perceived organizational support. J. Appl. Psychol. 2001, 86, 825–836. [Google Scholar] [CrossRef] [PubMed]
  87. Stinglhamber, F.; Vandenberghe, C. Organizations and supervisors as sources of support and targets of commitment: A longitudinal study. J. Organ. Behav. 2003, 24, 251–270. [Google Scholar] [CrossRef]
  88. Stocker, D.; Keller, A.C.; Meier, L.L.; Elfering, A.; Pfister, I.B.; Jacobshagen, N.; Semmer, N.K. Appreciation by supervisors buffers the impact of work interruptions on well-being longitudinally. Int. J. Stress Manag. 2019, 26, 331–343. [Google Scholar] [CrossRef]
  89. Benlian, A. A Daily Field Investigation of Technology-Driven Spillovers from Work to Home. MIS Q. 2020, 44, 1259–1300. [Google Scholar] [CrossRef]
  90. Carayon, P. Stressful jobs and non-stressful jobs: A cluster analysis of office jobs. Ergonomics 1994, 37, 311–323. [Google Scholar] [CrossRef] [PubMed]
  91. Ma, J.Y.; Kerulis, A.M.; Wang, Y.; Sachdev, A.R. Are workflow interruptions a hindrance stressor? The moderating effect of time-management skill. Int. J. Stress Manag. 2020, 27, 252–261. [Google Scholar] [CrossRef]
  92. Stich, J.F.; Tarafdar, M.; Cooper, C.L.; Stacey, P. Workplace stress from actual and desired computer-mediated communication use: A multi-method study. N. Technol. Work. Employ. 2017, 32, 84–100. [Google Scholar] [CrossRef]
  93. Mark, G. Multitasking in the Digital Age; Morgan & Claypool: San Rafael, CA, USA, 2015; pp. 1–113. [Google Scholar]
  94. Rivera-Rodriguez, A.J.; Karsh, B.T. Interruptions and distractions in healthcare: Review and reappraisal. BMJ Qual. Saf. 2010, 19, 304–312. [Google Scholar] [CrossRef]
  95. Gupta, A.; Li, H.; Sharda, R. Should I send this message? Understanding the impact of interruptions, social hierarchy and perceived task complexity on user performance and perceived workload. Decis. Support Syst. 2013, 55, 135–145. [Google Scholar] [CrossRef]
  96. Butts, M.M.; Becker, W.J.; Boswell, W.R. Hot buttons and time sinks: The effects of electronic communication during nonwork time on emotions and work-nonwork conflict. Acad. Manage. J. 2015, 58, 763–788. [Google Scholar] [CrossRef]
  97. LePine, M.A.; Zhang, Y.; Crawford, E.R.; Rich, B.L. Turning their pain to gain: Charismatic leader influence on follower stress appraisal and job performance. Acad. Manag. J. 2016, 59, 1036–1059. [Google Scholar] [CrossRef]
  98. Golden, T.D.; Gajendran, R.S. Unpacking the role of a telecommuter’s job in their performance: Examining job complexity, problem solving, interdependence, and social support. J. Bus. Psychol. 2019, 34, 55–69. [Google Scholar] [CrossRef]
  99. Foy, T.; Dwyer, R.J.; Nafarrete, R.; Hammoud, M.S.S.; Rockett, P. Managing job performance, social support and work-life conflict to reduce workplace stress. Int. J. Product. Perform. Manag. 2019, 68, 1018–1041. [Google Scholar] [CrossRef]
  100. Belzunegui-Eraso, A.; Erro-Garcés, A. Teleworking in the Context of the COVID-19 Crisis. Sustainability 2020, 12, 3662. [Google Scholar] [CrossRef]
  101. Erro-Garcés, A.; Urien, B.; Čyras, G.; Marytė Janušauskienė, V. Telework in baltic countries during the pandemic: Effects on wellbeing, job satisfaction, and work-life balance. Sustainability 2022, 14, 5778. [Google Scholar] [CrossRef]
  102. Bednar, P.M.; Welch, C. Socio-technical perspectives on smart working: Creating meaningful and sustainable systems. Inf. Syst. Front. 2020, 22, 281–298. [Google Scholar] [CrossRef]
  103. Carroll, N.; Conboy, K. Normalising the “new normal”: Changing tech-driven work practices under pandemic time pressure. Int. J. Inform. Manag. 2020, 55, 102186. [Google Scholar] [CrossRef] [PubMed]
  104. Papagiannidis, S.; Marikyan, D. Smart offices: A productivity and well-being perspective. Int. J. Inform. Manag. 2020, 51, 102027. [Google Scholar] [CrossRef]
  105. Onken-Menke, G.; Nüesch, S.; Kröll, C. Are you attracted? Do you remain? Meta-analytic evidence on flexible work practices. Bus. Res. 2018, 11, 239–277. [Google Scholar] [CrossRef]
  106. Dean, A.; Auerbach, A. 96 Percent of Us Professionals Say They Need Flexibility, But Only 47 Percent Have It. Harv. Bus. Rev. 2018. Available online: https://hbr.org/2018/06/96-of-u-s-professionals-say-they-need-flexibility-but-only-47-have-it (accessed on 12 January 2024).
  107. Angelici, M.; Profeta, P. Smart-Working: Work Flexibility without Constraints. Manag. Sci. 2020, 70, 1680–1705. Available online: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3556304 (accessed on 7 January 2024). [CrossRef]
  108. Kelliher, C.; Anderson, D. For better or for worse? An analysis of how flexible working practices influence employees’ perceptions of job quality. Int. J. Hum. Resour. Manag. 2008, 19, 419–431. [Google Scholar] [CrossRef]
  109. Hall, L.; Atkinson, C. Improving working lives: Flexible working and the role of employee control. Empl. Relat. 2006, 28, 374–386. [Google Scholar] [CrossRef]
  110. Putnam, L.L.; Myers, K.K.; Gailliard, B.M. Examining the tensions in workplace flexibility and exploring options for new directions. Hum. Relat. 2014, 67, 413–440. [Google Scholar] [CrossRef]
  111. Guzzo, R.A.; Nalbantian, H.R.; Anderson, N.L. Age, experience, and business Performance: A meta-analysis of work unit-level effects. Work Aging Retire. 2022, 8, 208–223. [Google Scholar] [CrossRef]
  112. Mascagna, F.; Izzo, A.L.; Cozzoli, L.F.; La Torre, G. Smart working: Validation of a questionnaire in the Italian reality. Senses Sci. 2019, 6, 363–376. [Google Scholar] [CrossRef]
  113. Battistelli, A.; Mariani, M.G. Supporto organizzativo: Validazione della versione italiana della survey of perceived organizational support (versione a 8 item). G. Ital. Psicol. 2011, 38, 189–214. [Google Scholar] [CrossRef]
  114. Hair, J.F.; Matthews, L.M.; Matthews, R.L.; Sarstedt, M. PLS-SEM or CB-SEM: Updated guidelines on which method to use. J. Multivar. Anal. 2017, 1, 107–123. [Google Scholar] [CrossRef]
  115. Bagozzi, R.P.; Yi, Y. On the evaluation of structural equation models. J. Acad. Mark. Sci. 1988, 16, 74–94. [Google Scholar] [CrossRef]
  116. Fornell, C.; Larcker, D.F. Structural equation models with unobservable variables and measurement error: Algebra and statics. J. Mark. Res. 1981, 18, 382–388. [Google Scholar] [CrossRef]
  117. Lance, C.E.; Butts, M.M.; Michels, L.C. The sources of four commonly reported cutoff criteria: What did they really say? Organ. Res. Methods 2006, 9, 202–220. [Google Scholar] [CrossRef]
  118. Podsakoff, P.M.; MacKenzie, S.B.; Lee, J.Y.; Podsakoff, N.P. Common method biases in behavioral research: A critical review of the literature and recommended remedies. J. Appl. Psychol. 2003, 88, 879–903. [Google Scholar] [CrossRef]
  119. Gluck, J.; Bunt, A.; McGrenere, J. Matching attentional draw with utility in interruption. Proc. SIGCHI Conf. Hum. Factor Comput. Syst. 2007, 41–50. [Google Scholar] [CrossRef]
  120. Kapista, M.; Blinnikova, I. Task performance under the influence of interruptions. In Operator Functional State: The Assessment and Prediction of Human Performance Degradation in Complex Tasks; Hockey, G.R.J., Gaillard, A.W.K., Burov, O., Eds.; NATO Science Series; IOS Press: Amsterdam, The Netherlands, 2003; Volume 355, pp. 323–329. [Google Scholar]
  121. Sonnentag, S.; Reinecke, L.; Mata, J.; Vorderer, P. Feeling interrupted—Being responsive: How online messages relate to affect at work. J. Organ. Behav. 2018, 39, 369–383. [Google Scholar] [CrossRef]
  122. Meyers, M.C.; Kooij, D.; Kroon, B.; de Reuver, R.; van Woerkom, M. Organizational support for strengths use, work engagement, and contextual performance: The moderating role of age. Appl. Res. Qual. Life 2020, 15, 485–502. [Google Scholar] [CrossRef]
  123. Côté, K.; Lauzier, M.; Stinglhamber, F. The relationship between presenteeism and job satisfaction: A mediated moderation model using work engagement and perceived organizational support. Eur. Manag. J. 2021, 39, 270–278. [Google Scholar] [CrossRef]
  124. Asghar, M.; Tayyab, M.; Gull, N.; Zhijie, S.; Shi, R.; Tao, X. Polychronicity, work engagement, and turnover intention: The moderating role of perceived organizational support in the hotel industry. J. Hosp. Tour. Manag. 2021, 49, 129–139. [Google Scholar] [CrossRef]
  125. Tudu, B.; Singh, S. Conceptualizing the moderating effects between work from home and individual performance–developing a conceptual framework using the self-determination theory. Curr. Psychol. 2023, 42, 29149–29160. [Google Scholar] [CrossRef]
  126. Ruppanner, L.; Huffman, M.L. Blurred boundaries: Gender and work–family interference in cross-national context. Work Occup. 2014, 41, 210–236. [Google Scholar] [CrossRef]
  127. Rigotti, T.; Yang, L.Q.; Jiang, Z.; Newman, A.; De Cuyper, N.; Sekiguchi, T. Work-related psychosocial risk factors and coping resources during the COVID-19 crisis. Appl. Psychol. 2021, 70, 3–15. [Google Scholar] [CrossRef] [PubMed]
  128. Segbenya, M.; Okorley, E.N.A. Effect of teleworking on working conditions of workers: A post-COVID-19 lockdown evaluation. Hum. Behav. Emerg. 2022, 16, 4562263. [Google Scholar] [CrossRef]
  129. Franken, E.; Bentley, T.; Shafaei, A.; Farr-Wharton, B.; Onnis, L.A.; Omari, M. Forced flexibility and remote working: Opportunities and challenges in the new normal. J. Manag. Organ. 2021, 27, 1131–1149. [Google Scholar] [CrossRef]
  130. Leroy, S.; Glomb, T.M. Tasks interrupted: How anticipating time pressure on resumption of an interrupted task causes attention residue and low performance on interrupting tasks and how a “ready-to-resume” plan mitigates the effects. Organ. Sci. 2018, 29, 380–397. [Google Scholar] [CrossRef]
  131. Zide, J.S.; Mills, M.J.; Shahani-Denning, C.; Sweetapple, C. Work interruptions resiliency: Toward an improved understanding of employee efficiency. J. Organ. Eff. People Perform. 2017, 4, 39–58. [Google Scholar] [CrossRef]
  132. Macke, J.; Genari, D. Systematic literature review on sustainable human resource management. J. Clean. Prod. 2019, 208, 806–815. [Google Scholar] [CrossRef]
  133. Manuti, A.; Giancaspro, M.L.; Molino, M.; Ingusci, E.; Russo, V.; Signore, F.; Zito, M.; Cortese, C.G. “Everything will be fine”: A study on the relationship between employees’ perception of sustainable HRM practices and positive organizational behavior during COVID-19. Sustainability 2020, 12, 10216. [Google Scholar] [CrossRef]
  134. Albrecht, S.L.; Bakker, A.B.; Gruman, J.A.; Macey, W.H.; Saks, A.M. Employee engagement, human resource management practices and competitive advantage: An integrated approach. J. Organ. Eff. People Perform. 2015, 2, 7–35. [Google Scholar] [CrossRef]
  135. Metallo, C.; Agrifoglio, R.; Lepore, L.; Landriani, L. Explaing users’ technology acceptance through national cultural values in the hospital context. BMC Health Serv. Res. 2022, 22, 84. [Google Scholar] [CrossRef] [PubMed]
  136. Wang, W.B.; Shieh, C.J.; Al-Khafaji, H.M.R.; Sevbitov, A.; Ismael, A.M.; Chetthamrongchai, P.; Suksatan, W.; Bahrami, P. Predicting Antecedents of Employee Smart Work Adoption Using SEM-Multilayer Perceptron Approach. Hum. Behav. Emerg. 2023, 2023, 7623801. [Google Scholar] [CrossRef]
  137. Bolisani, E.; Scarso, E.; Ipsen, C.; Kirchner, K.; Hansen, J.P. Working from home during COVID-19 pandemic: Lessons learned and issues. Manag. Mark. 2020, 15, 458–476. [Google Scholar] [CrossRef]
  138. Jayawardena, C.; Ahmad, A.; Valeri, M.; Jaharadak, A.A. Technology acceptance antecedents in digital transformation in hospitality industry. Int. J. Hosp. Manag. 2023, 108, 103350. [Google Scholar] [CrossRef]
  139. Hunter, E.M.; Clark, M.A.; Carlson, D.S. Violating work-family boundaries: Reactions to interruptions at work and home. J. Manag. 2019, 45, 1284–1308. [Google Scholar] [CrossRef]
  140. Albulescu, P.; Macsinga, I.; Rusu, A.; Sulea, C.; Bodnaru, A.; Tulbure, B.T. “Give me a break!” A systematic review and meta-analysis on the efficacy of micro-breaks for increasing well-being and performance. PLoS ONE 2022, 17, e0272460. [Google Scholar] [CrossRef]
  141. Ingusci, E.; Signore, F.; Giancaspro, M.L.; Manuti, A.; Molino, M.; Russo, V.; Zito, M.; Cortese, C.G. Workload, techno overload, and behavioral stress during COVID-19 emergency: The role of job crafting in remote workers. Front. Psychol. 2021, 12, 655148. [Google Scholar] [CrossRef]
  142. Demerouti, E.; Bakker, A.B. Job demands-resources theory in times of crises: New propositions. Organ. Psychol. Rev. 2023, 13, 209–236. [Google Scholar] [CrossRef]
  143. Coco, M.; Guerrera, C.S.; Santisi, G.; Riggio, F.; Grasso, R.; Di Corrado, D.; Di Nuovo, S.; Ramaci, T. Psychosocial impact and role of resilience on healthcare workers during COVID-19 pandemic. Sustainability 2021, 13, 7096. [Google Scholar] [CrossRef]
Societies 14 00050 g001
Figure 1. Research model.
Figure 1. Research model.
Societies 14 00050 g001
Table 1. Frequency of variables categories (N = 301).
Table 1. Frequency of variables categories (N = 301).
Frequency
Age
18–3426
35–54177
>5498
Gender
Male183
Female118
Other-
Job Position
Managers182
Employees119
Sectors of Employment
Finance/banks/insurance20
Healthcare13
Services77
Education67
Transports15
Industry3
ICT9
Municipalities77
Other20
Table 2. Study variables: descriptive statistics and bivariate correlations (N = 301).
Table 2. Study variables: descriptive statistics and bivariate correlations (N = 301).
MSD1234567891011
  • Age
48.99.671
2.
Gender
--−0.0461
3.
Organizational Tenure
15.610.50.629 ***0.0161
4.
Days per week worked in SW
4.041.190.0940.0840.113 *1
5.
Job position
--0.016−0.1020.0010.0191
6.
Sector of employment
--0.066−0.0250.0820.089−0.0181
7.
Autonomy
3.330.110.111−0.0200.089−0.0010.166 **−0.0311
8.
Quality of SW
3.691.030.0530.0470.044−0.163 **−0.020−0.0140.190 ***1
9.
Organizational Support
3.930.880.009−0.030−0.046−0.0330.079−0.0780.207 ***0.192 ***1
10.
Interruptions
2.091.02−0.099−0.078−0.1020.067−0.0010.041−0.256 ***−0.359 ***0.1021
11.
Home Performance
3.600.910.1120.0390.134 *−0.134 *0.017−0.0850.273 ***0.594 ***0.350 **−0.3391
* p < 0.05; ** p < 0.01; *** p < 0.001.
Table 3. Values of Cronbach’s alpha, Composite Reliability (CR), Average Extracted Variance (AVE), and AVE root square (in bold) for each construct of the study and correlations.
Table 3. Values of Cronbach’s alpha, Composite Reliability (CR), Average Extracted Variance (AVE), and AVE root square (in bold) for each construct of the study and correlations.
AlphaCRAVE1234
1. Perceived quality of smart working0.8780.8800.7860.886
2. Home performance0.9320.9350.7840.6340.885
3. Home interruptions0.9080.9080.768−0.402−0.3430.876
4. Perceived organizational support0.9010.9010.6050.2200.3920.1180.778
Table 4. The hierarchical regression results of the association of socio-demographic, work variables, and interruptions at work in SW with home performance, and regression analysis of Quality of Smart Working (QSW) and Perceived Organizational Support (POS) as moderators between interruptions at work and home performance relationship (N = 301).
Table 4. The hierarchical regression results of the association of socio-demographic, work variables, and interruptions at work in SW with home performance, and regression analysis of Quality of Smart Working (QSW) and Perceived Organizational Support (POS) as moderators between interruptions at work and home performance relationship (N = 301).
Home Performance
Model 1Model 2Model 3Model 4
Model 1βtpβtpβtpβtp
Age0.0390.5420.588−0.004−0.0720.943−0.004−0.0780.9380.0030.0510.960
Gender0.0520.9390.3480.0150.3520.7250.0160.3640.7160.0120.2740.784
Organizational Tenure0.1071.4870.1380.1152.0490.0410.1162.0550.0410.1041.8390.067
Days per week worked in SW−0.146−2.6210.009−0.050−1.1330.258−0.051−1.1400.255−0.045−1.0190.309
Job position−0.019−0.3990.735−0.007−0.1700.865−0.008−0.1710.8640.0020.0570.955
Sectors of Employment−0.073−1.3130.190−0.054−1.2380.217−0.054−1.2400.216−0.062−1.4320.153
Autonomy0.2614.6480.0000.0801.7910.0870.0791.6900.0920.0731.5570.120
Model 2
Work interruptions −0.164−3.4570.001−0.164−3.3570.001−0.207−3.9550.000
Quality of SW 0.4559.4450.0000.4559.4200.0000.4409.0730.000
Organizational Support 0.2645.7360.0000.2645.7300.0000.2715.9060.000
Model 3
Work interruptions × Quality of SW −0.009−0.2030.839−0.039−0.8690.396
Model 4
Work interruptions × Organizational Support 0.1072.1900.029
Adjusted R20.096 0.448 0.446 0.453
Omnibus test of the regressionF (7292) ≤ 0.001F (10,289) ≤ 0.001F (11,288) ≤ 0.001F (12,287) ≤ 0.001
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Barbieri, B.; Mondo, M.; De Simone, S.; Pinna, R.; Galletta, M.; Pileri, J.; Bellini, D. Enhancing Productivity at Home: The Role of Smart Work and Organizational Support in the Public Sector. Societies 2024, 14, 50. https://doi.org/10.3390/soc14040050

AMA Style

Barbieri B, Mondo M, De Simone S, Pinna R, Galletta M, Pileri J, Bellini D. Enhancing Productivity at Home: The Role of Smart Work and Organizational Support in the Public Sector. Societies. 2024; 14(4):50. https://doi.org/10.3390/soc14040050

Chicago/Turabian Style

Barbieri, Barbara, Marina Mondo, Silvia De Simone, Roberta Pinna, Maura Galletta, Jessica Pileri, and Diego Bellini. 2024. "Enhancing Productivity at Home: The Role of Smart Work and Organizational Support in the Public Sector" Societies 14, no. 4: 50. https://doi.org/10.3390/soc14040050

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop