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Article

Effects of Working from Home on Job Performance: Empirical Evidence in the Saudi Context during the COVID-19 Pandemic

by
Jamel Choukir
1,2,
Munirah Sarhan Alqahtani
1,*,
Essam Khalil
3 and
Elsayed Mohamed
1,4
1
College of Economics and Administrative Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia
2
Business School, University of Sfax, Sfax 3029, Tunisia
3
Department of Business Administration, Higher Technological Institute, Tenth of Ramadan City 44634, Egypt
4
Business Department, Faculty of Commerce, Tanta University, Tanta 31521, Egypt
*
Author to whom correspondence should be addressed.
Sustainability 2022, 14(6), 3216; https://doi.org/10.3390/su14063216
Submission received: 23 January 2022 / Revised: 23 February 2022 / Accepted: 25 February 2022 / Published: 9 March 2022
(This article belongs to the Topic Toward the New Era of Sustainable Design, Manufacturing and Management)
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Abstract

:
This research investigated the mediating role of attitudes and perceptions between working from home (WFH) and employees’ job performance. It also explored the role of gender, education level, and job position in the relationship between specifications and facilities when working from home, as well as attitudes and perceptions. This study is exploratory and capitalizes on novel findings from a questionnaire. Data were collected from 399 employees employed by the principal Saudi businesses. The current study uses structural equation modeling to test the research hypotheses and examines the direct and indirect relationship between working from home and employees’ job performance. The results confirmed the significant direct linkage between WFH and employees’ job performance through the mediating roles of WFH employees’ attitudes and perceptions. Our findings also confirm the significant relationship between WFH employees’ attributes and their job performance, and the significant association between WFH and job performance. However, our results identified the fact that perceptions have an inverse impact on job performance. This study also provides significant theoretical and practical insights for managers who are adopting WFH. It contributes empirically to the literature by informing managers of the factors driving job performance in WFH, helping organizations to cope with the many issues related to a workforce who are working from home. Our research findings also ascertained that WFH seems likely to become a permanent managerial practice in terms of human resources, rather than a simple circumstantial measure. Moreover, this study can be considered as one of the first studies that assess the effect of WFH on employees’ job performance via a mediation role of employee attributes, in the context of Saudi firms.

1. Introduction

Working from home (WFH) has received growing attention from many scholars over recent years [1,2]. In the pandemic context, many studies have recently examined WFH, particularly from the perspective of identifying those jobs that can be performed remotely [3,4,5,6,7]. In [8], the authors explained that the working from home concept is also known as teleworking. The concept of teleworking is used in its current context to indicate working remotely, away from the office. Generally, teleworking is conducted from home, thus it is sometimes called “homeworking” [9]. Telework reemerged over recent years as “a new management practice”, a “policy”, an “arrangement” or a “forced” issue [10,11]. It is not a new phenomenon that has been enabled by IT development [12,13]. There is no globally admitted definition of telework. However, based on its many definitions, telework is viewed as “the organization of work by using ICTs that enable employees and managers to get access to their labor activities from remote locations” ([14] (p. 7).
The author of [9] claims that teleworking uses electronic devices as its principal “tool”, while homeworking might be considered a form of old-fashioned “sweatshop” manufacturing. In addition, the authors of [15] showed a positive move in WFH feasibility that is associated with a growth in average labor income. Still, this potential benefit would not be equally distributed among the firm’s employees. Socio-demagogic variables, such as age, gender, and education have been taken into consideration as the moderating variable between WFH and job performance. The authors of [1] determined several factors that impact different WFH outcomes, such as reduced communication with colleagues, supervisory trust and support, and an appropriate working environment at home. Furthermore, the performance of employees recruited for specific time periods is distinct from those who have consistent employment conditions and contracts [16].
On the one hand, Etheridge and Tang (2020) highlighted the fact that WFH specifications relate specifically to certain socio-economic groups, industries, and occupations [17]. On the other hand, previous scholars [1,18] have pointed out that some demographic variables, such as gender, age, and tenure, act as a moderator concerning job performance. The debate regarding WFH attitudes and perceptions seems to not have been well documented, particularly during the pandemic. Indeed, previous studies have not tackled the link between WFH attitudes and perceptions as mediator variables. Moreover, the adoption stimuli of telework differ pre-COVID-19 and during the pandemic. In the USA, the economic triggers previously seemed to be largely predetermined by a reduction in the expense of maintaining office space [19]. More recently, the COVID-19 pandemic pushed many organizations to adopt WFH. Different distortions that are inherent in human resources management emerged [20,21]. Today, organizations are beginning to view WFH as both an ongoing trend [22] and a regular policy [23].
Unexpectedly, after revising the summaries for the most relevant articles in Table 1 that mix working from home (WFH), employee attitudes (EA), perceptions (PER), job performance (JPRF), we found a scarcity of studies dealing with the integration between the following constructs: WFH, EA, PER and JPRF in a single framework. Moreover, few studies focused on the WFH dimension and its impact on job performance. In addition, there is a scarcity of WFH literature that uses employees’ attitudes and perceptions as mediator variables when studying the link between WFH perspectives and JPRF. This study also investigates the roles of gender, education level, WFH experience, position, department, and sector as control variables in measuring the effect of WFH dimensions on JPRF from the point of view of employees’ attributes.
Knowing the limitations of the previous studies that are discussed above, our study contributes to the literature review in three crucial ways. First, it contributes to the managerial literature by examining the interaction between WFH and job performance. and with the potential mediating role of attitudes and perceptions. This perspective is supported by the authors of [25], who suggested further research exploring the effect of remote working on organization performance. Second, the context of this study is in Saudi Arabia, which suffers from a shortage of WFH studies that analyze the links among the following constructs: WFH, job performance, attitudes, perceptions and attributes. This study empirically brings together the evidence collected regarding WFH configurations and traits in an unfamiliar context. Third, this research proposes a better understanding of how WFH can affect job performance, identifying ways to cultivate appropriate attitudes and perceptions, and to what extent the interaction may be moderated by the control variables of gender, education, WFH experience, position, department, and sector. This research builds on the previous WFH literature by investigating these constructs among a cohort of employees adopting WFH due to the COVID-19 pandemic. Employees working for Saudi firms constitute the entire cohort of this study.
The rest of the article is structured as follows. The following section presents a literature review on the topic of WFH, employees’ attitudes, perceptions, and job performance. Section 3 describes the datasets, defines the relevant variables, and provides descriptive statistics, while Section 4 reports the results and offers a discussion. Section 5 concludes with the managerial implications of WFH, the study’s limitations, and the possible new research directions.

2. Literature Review

2.1. Working from Home (WFH)

Academic interest in WFH is not necessarily restricted to the pandemic; this issue has been investigated numerous times in the past decade and even before [8,26,27]. Teleworking and WFH were conceived as a substitute for office-based work [13], an alternative [28] or an option [12], or as a flexible work form [29]. During the pandemic, it was redefined as a “policy” or as a “forced” process [30]. Quantitatively speaking, the number of employees working from home is increasing around the world and represents a growing trend [22]. Several scholars, such as the authors of [31,32], have mentioned that the percentage of the labor force working from home has increased from about 5% to over 40%, and even rose to 50% of the US workforce during the lockdown in the COVID-19 pandemic. A recent study has pointed out that the COVID-19 pandemic has accelerated the percentage of employees in the USA who work from home, from 16% pre-pandemic to 65% during the pandemic [25]. The UK is one of the few countries that widely adopted WFH before COVID-19, but still, the percentage of employees who were working from home had already increased by 5% at the beginning of the pandemic in April 2020.
In the same vein, Italy was considered to be the European country with the lowest percentage of teleworkers before the COVID-19 crisis; as a consequence of the pandemic, it faced an intensive process of WFH adoption that led to an increased WFH percentage in a very short time, without the provision of either adequate or appropriate legislation regarding labor and human resources policies [33]. In Egypt, one example of eastern countries, fifty percent of employees were working in accounting and IT, or in the academic field, and supported teleworking [33]. In 2017, the Kingdom of Saudi Arabia (KSA) was not sharing fully in the WFH trend; a remote working program was created in January 2016 by the Ministry of Labor and Social Development and the Human Resources Development Fund. The Saudi Telecommunication Company started the shift early in 2017 and adopted WFH as a pillar of its HR policies. Recent data from the General Authority for Statistics (GAS) in 2021 showed that the percentage of employees WFH in telecommunications, banking and education exceeded 70% IT, banking and insurance industries represented the highest percentage of WFH employees in many different countries [17]. Qualitatively, the literature revealed no strong evidence for the suitability of WFH for all jobs [3,4,8,34,35]. Moreover, WFH is most appropriate for employees who have a degree of autonomy and for jobs that have few face-to-face contacts and knowledge-based tasks [36].
From the perspective of appropriate design, WFH had been studied via minimum/maximum days, full/part-time work, job specifications, or requirements [37]. As has been mentioned in previous studies, not all occupations or individuals are ideally suited for WFH. As a result, not all employees who would prefer WFH will be able to do so. A report from the World Economic Forum revealed that 78% of business leaders believe that the hybrid model and WFH would have a negative impact on productivity. In contrast, 84.5% of businesses intend to offer WFH as an option [25], while some companies like Twitter have instituted permanent remote-working policies that achieved positive outcomes, and asserted that appropriate teleworking hours increase labor productivity, while longer hours have the opposite effect [38].
Despite several studies [8,31,39] asserting that face-to-face meetings are crucial for creativity and innovation and for keeping staff motivated and focused on their jobs, they also mentioned some concerns about this kind of work, such as having a “slump”, that should be taken into consideration. A longitudinal survey at the Trip.com group Chinese office reported that among 1000 employees offered the choice of working from home, only half of them volunteered to do so. The rest preferred to continue working in their offices. After nine months, this company asked the original volunteers whether they preferred to continue working remotely or to come back to the office. Half of them asked to return to the office, despite the fact that their daily average commute took approximately 40 min for each trip. There are many reasons that may explain this desire to return to the office, such as feelings of isolation, depression, extended periods of working, or mental health crises [8].
Working from home, as a “new management practice”, claims more than just connectivity and overall productivity. For efficient and effective outcomes when staff are working from home, organizations must strive to extend all the available facilities at the office to the employee’s home. There are several ways for organizations to help stem any lack of productivity, such as regular check-ins between managers and their teams, schedules separating the work process from family life, and collaborating with colleagues via video calls rather than phone calls [8]. These organizational factors also constitute an essential component in developing WFH [40] and are essential for WFH configurations [31,41]. These factors include supporting employees, the costs of facilities related to WFH, training programs on how to employ an organizational communication system and building a culture of trust [9,41]. Many factors could be considered as WFH enablers, such as decreasing the cost of information and communication technologies (ICT), which is associated with a significant increase in the number of employees that work from home [42]. Several studies such as [12,25,30] reported that WFH also requires an appropriate working environment, such as reducing noise and improving air quality, natural light, space, privacy, comfort, safety, and relationships with co-workers/supervisors. According to previous research, significant factors are not only technical or financial issues but also taking into consideration human and managerial requirements.
Beyond the debate on WFH specifications and facilities, WFH as a way of working can create opportunities or obstacles for both employee and employer [43,44]. From an employee’s perspective, the main advantages of WFH are autonomy and flexibility over their work schedule, the elimination/reduction of commuting time, less money spent on commuting and parking, a lack of work attire, higher morale, job satisfaction and avoidance of office politics. However, the drawbacks of WFH are that it is difficult to separate work time from home time, feelings of isolation from the workplace social network, malfunctioning equipment or a lack of technical support, the implications of limited interaction with a supervisor in terms of career prospects, and feelings of hostility/resentment from colleagues.
On the other hand, from the organization’s perspective, the main advantages of WFH are enhancing employee productivity, lower employee absenteeism rates, increased employee retention, a larger pool of talent from which to recruit/select, and a reduction in overheads and facility costs. However, the obstacles to WFH are difficulties in monitoring employees’ performance and in measuring employee productivity, the fact that changes force organizations outside their comfort zone, possible negative effects on the workplace’s social network. and difficulties in fostering team synergy. In the COVID-19 context, according to several studies [31,45,46], the importance of WFH increased since it allowed employees to continue working and, thus, to receive a salary. It also allowed employers to maintain the production of services and revenue; overall, it limited the pandemic’s recessive impact, and this crisis has led to many changes in organizational and managerial practices [20,21,23].

2.2. WFH and Job Performance

Performance has rarely been discussed in telecommuting literature because performance cannot be assessed in the same way when monitoring telecommuters and office workers [47]. The swift adjustment from office to home may change the way in which a job is performed [35]. Furthermore, there is no strong evidence supporting the argument that telework increases job satisfaction and productivity [36]. Job performance—as an outcome of completing many core tasks and activities—is typically identified in job analyses [48]. Performance could be assessed from an organizational perspective in relation to telework [29], organizational tenure [49], work-role ambiguity, and job satisfaction [44]. Job performance is also affected by demographic moderator variables, such as gender, age, and tenure [18]. In addition, firm performance is positively correlated with the intensity of telework adoption, functional flexibility, and internal numerical flexibility. It is also negatively correlated with external numerical flexibility [29]. Previous studies have explored the relationship between WFH and job performance in terms of employee well-being [30,50,51] or productivity [17].
Previous scholars linked WFH performance or its effectiveness to various determinants [52], such as occupation and type of industry [34]. Based on their findings, the authors confirmed that WFH is not the panacea for the pandemic since some jobs are easier to perform from home than others. According to the authors of [34] (p. 6), “A move from lockdown to the optimal policy reduces the exposure of low-wage workers and the wage loss of the high-wage workers the most, although everyone’s wage losses become smaller”. The percentage of people changing to working from home can be predicted by the incidence of COVID-19 and the desire of younger people to shift toward remote working [32]. Furthermore, some states in the USA that have a higher rate of employment in information technology jobs, including management, professional and related jobs, were more likely to move toward working from home and laid off fewer employees or were not forced to drastically reduce their workforce.
WFH increases employees’ productivity when they are performing creative tasks, while boring WFH tasks have negative effects on employee productivity [53]. In the same vein, the authors of [8] examined the relationship between performance and employee satisfaction for those who had agreed to be part of a randomized control trial of 994 call-center operators. The results showed that the level of job satisfaction of home workers rose, and job turnover fell, compared to workplace-bound workers. They also found that, overall, adopting WFH increases employees’ performance because of the quieter working atmosphere and the greater effort exerted by employees. Moreover, a survey conducted by Stanford University found that employee turnover or attrition was decreased by 50% when employees had the option to change to remote working [25]. The authors of [54] also reached the previous conclusion, with an emphasis on work intensification.
The consequences of remote working on work effort, work-life balance, and wellbeing have been explored in other works [22]. This switching from fixed workplaces to a home office is not related to circumstantial factors but to dynamic organizational factors, such as organizational commitment, job satisfaction, and job-related wellbeing. However, a previous study [39] revealed that the productivity of WFH could also be a source of pitfalls in the COVID-19 context. Several scholars investigated the impact of WFH on job performance [8,31] whereas others examined the effect of WFH on employees’ attitudes and perceptions [35,40]. Based on the preceding discussion, the following hypotheses are suggested:
Hypothesis 1 (H1).
WFH specifications positively affect employees’ attitudes toward WFH.
Hypothesis 2 (H2).
WFH specifications positively affect employees’ perceptions toward WFH.
Hypothesis 3 (H3).
WFH specifications positively affect employees’ job performance.
Hypothesis 4 (H4).
WFH facilities positively affect employees’ attitudes toward WFH.
Hypothesis 5 (H5).
WFH facilities positively affect employees’ perceptions regarding WFH.
Hypothesis 6 (H6).
WFH facilities positively affect employees’ job performance.

2.3. Perceptions and Attitudes toward WFH

Although the issue of attitudes is studied frequently in the organizational behavior literature, the relationship between WFH and attitudes is rarely investigated [8,12]. The attitudes toward WFH depend on contextual factors that encompass constraint and facilitation [55], as well as individual characteristics such as gender [12]. In spite of the many advantages mentioned concerning WFH [19], it is not without inconvenience [5,6] or drawbacks [23]. From the advantages of WFH, its correlations with a higher level of organizational commitment, job satisfaction, and job-related well-being. They add that these advantages are due to work intensification [22].
Some scholars [2,9,50] have explored the benefits and pitfalls of WFH. They mentioned that the “abrupt” shift to WFH represented benefits to employees in terms of saving time when commuting and offering more flexibility to take care of their families. They also add that numerous negative aspects of full-time WFH were mentioned, such as reducing the opportunity for socialization with colleagues, extended hours of screen exposure due to full-time computer work, leading to fatigue, tiredness, social isolation, depression, stress, and feelings of anxiety. Indeed, the attitudes toward WFH depend on telework specifications and facilities. The relationship between telework and perceptions was rarely captured before or during the pandemic [35], where the promotion of telework was basically for social and economic reasons.
A survey conducted by [56] revealed that employees would be less likely to leave their company when telework was proposed as an option. Alternatively, “the authors of” [57] reported that telework affects how employees were evaluated by their managers and that this may reduce their chances of career advancement. Employees also perceive a low level of social interaction due to telework. Indeed, a negative perception associated with teleworking will affect both the individual and organizational outcomes. Recently, as can be seen from the literature review, WFH attitudes and perceptions have received more analytical attention for many reasons. Firstly, the lack of sufficient evidence about WFH perceptions encourages many scholars to develop more in-depth research in this area. Secondly, there is a need to establish and develop a measurement of the attitudes and perceptions toward WFH. In this study, measuring employees’ WFH perceptions was achieved via statements developed by [8,37,58] that focused on the benefits and problems associated with WFH. Regarding the earlier argument, the subsequent hypotheses are proposed:
Hypothesis 7 (H7).
Attitudes toward WFH positively affect WFH perceptions.
Hypothesis 8 (H8).
Attitudes toward WFH positively affect employees’ job performance.
Hypothesis 9 (H9).
WFH perceptions positively affect employees’ job performance.
Hypothesis 10 (H10).
Attitudes toward WFH and WFH perceptions mediate the relationship between WFH specifications and employees’ job performance.
Hypothesis 11 (H11).
Attitudes toward WFH and WFH perceptions mediate the relationship between WFH facilities and employees’ job performance.

2.4. Employees’ Attributes and WFH

The above literature review discusses a variety of factors that affect WFH. The relationship between WFH and both demographic and social variables has not previously been fully investigated. Some researchers, such as [12,36], explored the links between WFH and sociodemographic variables within the pandemic context. For example, age, skills, and job categories have been examined by the authors of [12] in order to determine if these variables have an impact on employees’ decisions regarding WFH or not. This research showed a significant difference between telecommuters and non-telecommuters, based on gender and job categories. The authors of [19] explored the motivation issue relating to telework, wherein they suggest that male professionals and female clerical staff represent those who have more of a propensity for telework [36]. In the same vein, [1] showed that telework was more attractive to older people, as they had fewer ambitions regarding career prospects [59]. However, younger people may also appreciate telework as they prefer the freedom to plan their time and to work autonomously [60]. Furthermore, the authors of [56] examined the influence of marital status on the evaluation of telework. Gender, marital status, and parenthood were used as control variables in examining the role of the life stage of employees when they assessed telework [61]. Their main results showed that having children plays a vital role in telework behavior and that males are more likely than females to telework.
Most of the previous studies on the attitudes toward WFH were conducted before the pandemic [8,12], while this study has investigated the impact of attitudes toward WFH during the pandemic. However, perceptions as an issue in WFH have been underexplored in a pandemic context. Our study attempts to bridge the gap in the existing literature by investigating the relationship between WFH and employees’ job performance in the IT, banking, telecommunications, and education sectors in the context of the KSA. This study chose these sectors among the main sectors of business activities because they are considered as key growth factors of the KSA economy [62,63]. Hence, to deepen our understanding of employees’ job performance in WFH, the purpose of this study is to develop a theoretical model that explains how WFH influences the level of job performance in the context of high uncertainty during COVID-19. Organizations that are devoted to WFH need to develop a better image among their workers by establishing fair HR systems that motivate, develop, and compensate employees, based on their performance.
The model as defined in the Figure 1 proposes that WFH is particularly related to job performance through staff attitudes and perceptions. It also suggests that WFH is positively related to job performance through attitudes and perceptions.

3. Methods

3.1. Measures

Because of the lack of available measurements for WFH, the present research survey items were derived from more than one source. All research variables in our statistical analysis were evaluated through self-reporting measures that depend basically on multi-item scales. We have built the survey questionnaire, using questions based on the five-point Likert scale and with nominal and ordering questions (see Appendix A).

3.2. Data Collections

We collected our research data via a survey that was circulated among employees in the central Saudi business sectors and focused on employees working in various positions and sectors. This survey covered 565 employees from January 2021 to May 2021. A screening of the questionnaire was carried out to guarantee the accuracy of our survey data and to exclude any incomplete questionnaires [64]. We obtained 399 valid questionnaires, with a response rate of 70.6%. We used the back-translation method, whereby the original survey instrument was written in English and translated from English to Arabic. This method confirms the accuracy and suitability of the language used and the safeguarding of each item’s meaning via the translation procedure [65]. Therefore, our questionnaire was revised two times by three academics.
Later, we used a pilot study asking 20 chosen employees who were not from our final sample to ensure that all research statements in the questionnaire were clear and easily understood. We modified and adjusted some statements, depending on the feedback of pilot-test employees. The study used an online questionnaire posed to employees in IT, retailing, banking and insurance, and the learning sectors in the KSA context. Respondents were recruited through emails, social media platforms and newsletters. Suitable participants were recognized by a preliminary screening question that asked if they spent part of their time WFH due to COVID-19. We also “conducted univariate analysis (e.g., independent samples t-test, analysis of one variance (ANOVA) to investigate the non-response bias and responding sample representativeness” [66] (p. 24). The analyses include running these tests; no statistically significant differences at a p-value of 5% occur for any research items. Our findings did not verify any problematic issue of non-response bias in our research data. Additionally, the internal consistency examination of identified factors introduces overall acceptable Cronbach’s alpha values that ranged from 0.70 to 0.91, with inter-item correlations higher than 0.4 [67].

4. Discussion

4.1. Sample and Descriptive Statistics

Table 2 illustrates the descriptive statistics of the WFH employees’ demographics characteristics (gender, educational level,) and the respondents’ WFH experience, position, department, working for business activities or sector) and WFH respondents’ attitudes toward WFH. In total, 238 (59.6%) of the survey are male, against 40.4% who are female. In all, 94.7% of the respondents hold a bachelor’s degree, and 86% of the sample occupied an administrative position, compared with 6.9% in technical and professional roles. Out of the 399 respondents who were interviewed, 88.2% had at least one year or more of experience of WFH, but only (1.5%) of the respondents had more than 4 years’ experience of WFH. Around 96% of the respondents are working in IT or the banking, insurance, or retail sectors. Finally, 53.6% of the respondents were employed in the marketing and customer service departments. In all, 286 of the respondents were working totally from home; 72% are male, against 28% who are female. Interestingly, 113 of the respondents who were partially working from home are female. The percentage of women in the Saudi context who are partially working remotely is higher than for male employees. In total, 394 of the respondents desired to continue working from home and recommended it to all other employees.

4.2. Confirmatory Factor Analysis (CFA)

CFA “was performed to assess overall model fit with the data and measure the unidimensionality of research constructs” [67] (p. 10.) To measure “the goodness of the CFA model fit, several researchers suggested that (x2/df) must be lower than 3, all goodness fit indices”, to wit: CFI, TLI and NFI must surpass 0.9, while the RMSEA index must be ≤0.05, as suggested by [68] (p. 415). The results of our CFA indices presented acceptable values, as revealed in Table 3 These findings also identified a one-dimensional scale for overall WFH specifications and facilities, employees’ attributes (attitudes and perception) and job performance, as illustrated in Table 3. Concerning the convergent validity assessment for our research constructs, all research variable AVE values are greater than 0.5 and, for the construct reliability values, exceed 0.7 as recommended by [68]. Moreover, the assessment of discriminant validity in Table 4 asserted that the diagonal elements and Cronbach’s α values are greater than off-diagonal elements, which confirms the discriminant validity as recommended by [68,69].

4.3. Structural Equations Modeling (SEM) Results

Our SEM findings in Table 5 and Figure 2 revealed that specifications have a positive impact on attitudes and job performance (β1 = +0.33, p ≤ 0.001; β3 = +0.35, p ≤ 0.001), which supported H1 and H3. The facilities also have a positive effect on attitudes, perceptions, and job performance (β4 = +0.14, p ≤ 0.001, β5 = +0.20, p ≤ 0.001, β6 = 0.41, p ≤ 0.001) which confirmed H4, H5 and H6. The attitudes have a positive impact on perceptions and job performance (β7 = 0.15, p ≤ 0.001, β8 = 0.27, p ≤ 0.001) which verified H7 and H8. However, H2 was not supported since specifications had no significant effect on perceptions (β2 = 0.02, p ≥ 0.05).
Finally, the relationship between WFH perceptions and job performance was significant but showed an inverse association (β9 = −0.18, p ≤ 0.00) that confirmed H9.
According to our results as shown in Figure 2, facilities contribute strongly to enhancing JPRF and specifications came second, while attitudes came last. Specifications also have the biggest effect on attitudes where facilities came second. Therefore, specifications and attitudes represent the main contributors in enhancing job performance in WFH in the IT, banking, telecommunications and education sectors in the context of the KSA. Our results also suggested that attitudes affect perceptions more strongly than the facilities.
Regarding the indirect effect of WFH dimensions on job performance via employees’ attitudes and perceptions, as presented in Table 6, our findings showed that specifications have an indirect effect on job performance via employee attitudes and perceptions (β10 = +0.13) which increased the total effect from +0.35 to +0.48; this result confirmed that employee attributes mediate the relationship between specifications and job performance, which supports H10. In the same way, the result of H11 confirmed the mediation role of employee attributes between facilities and job performance (β11 = +0.07) where the total effect increases from +0.41 to +0.48. These results verify the important role of employees’ attributes (attitudes and perceptions) on the connection between WFH and job performance. Therefore, according to the indirect relationship results in our model, we find that the indirect effect of specifications is greater than the effect of facilities in enhancing job performance in KSA organizations. Therefore, the organization should focus on the importance of interaction between specifications and employees’ attitudes and perceptions in enhancing job performance.
The relationship among WFH, job performance and gender, education, position, WFH experience, departments, and business activities was not totally significant. Applying the chi-square and Pearson tests for each link to the examples regarding gender for those who are WFH and job performance, and for all attributes linked to job performance, we obtained a value that exceeded 0.05 percent, which confirmed the significance of the relationship. To answer the question “Does gender, education, position, experience, department and sector affect the relationship between WFH and job performance?”, we ran a hierarchical regression linear analysis. The results showed that the association between WFH and job performance is sturdier for female than for male employees, or for educated employees. Therefore, our analysis showed the existence of the moderating effect of gender.
The ANOVA showed that the link between all attributes and job performance is stronger, with an F value = 77.32, the second rank is the relationship between attitudes toward WFH and attributes, with an F value = 60.94. The relationship between attributes and perceptions is represented with F value = 50.02. However, the F value for specifications and facilities linked to attributes are 22.25 and 13.72.
Table 7 reports the impact of control variables on the connection between job performance and attitudes and perceptions (intermediate variables). All control variables had no significant effect regarding the facilities. Gender as a control variable has a negative significant effect on specifications, perceptions, and attitudes, with a double negative effect for females than males. The second control variable—educational level—has no significant effect on job performance via specifications, perceptions, and attitudes.
Meanwhile, position has a negative significant effect on specifications, perceptions, and attitudes with regard to the relationship with job performance. The position has a negative effect on specifications, by −26.7%. It also decreases perceptions by 21.7% and reduces attitudes by 34.7%. On the other hand, experience has a positive effect on job performance, via perceptions and attitudes. The relationship is statistically significant, wherein any increase in employees’ experience will lead to an increase in perceptions by 36.8% and in employees’ attitudes by 31.9%. The sector also had a significant positive effect via perceptions and attitudes. Differentiation in sectors increased perceptions by 42.4% and increased attitudes by 21.6%.
Our results showed that WFH facilities have no significant effect on attitudes. All control variables also had no significant effect on facilities. This result seems to be expected for many reasons. Firstly, from the motivational perspective, the facilities could only play a role in stimulating employees into accepting WFH. Secondly, attitudes are more related to intrinsic variables than to extrinsic variables, such as the facilities. Finally, in line with the study by [29], the analogy of firm performance is positively related to internal numerical flexibility and is negatively related to external numerical flexibility.
A major finding in our research results was regarding the effect of perceptions on job performance, which is significant, with an inverse relationship. This result suggests that when employees perceive WFH in a negative way, their job performance will be enhanced, while if the employees positively perceive WFH, their job performance will decrease. This result may be for many reasons. Firstly, in the pandemic context, WFH was imposed upon all employees due to the emergency or “abrupt” shift; no one was prepared to cope with these atypical circumstances. Secondly, employees can expect the pitfalls of WFH, such as social isolation, depression, stress, the difficulty of separating work and home time, unsuitable equipment, or lack of technical support, to have implications from a limited interaction with a supervisor for career advancement to feelings of hostility/resentment from colleagues. Thirdly, WFH was perceived as a provisional situation. The shift from office to home has been achieved without setting up the appropriate support. The authors of [73] asserted that 33% of leaders believe in creating a sense of belonging and a community with employees, to minimize the negative effect of social isolation. Moreover, the study by [74] has shown that indoor environment quality affects employees’ productivity. Indeed, WFH was perceived negatively, yet, paradoxically, the job performance increased. Finally, after the shock of the pandemic, WFH has risen to a saturation level and, consequently, job performance has decreased. Employees initially perceived WFH as simply a provisional and emergency situation.
Negative perceptions of WFH also could be explained by factors in the family work environment (gender, marital status, number of children, age, and distance from house to office) and the level of organizational support or facilities (technical, monetary, human and professional). Our study showed a significant relationship between organization facilities and employees’ perceptions. This situation will not necessarily lead to negative job performance, which is determined by targets and objectives that are enabled by IT. Ironically, a positive perception of WFH could lead to negative job performance. Positive perceptions, as shown by the literature review, were a corollary, particularly to the balance between work and family [19,22] and flexibility [43,44]. However, negative perceptions regarding WFH were related mainly to work intensification [54], isolation [8,50], and fewer opportunities for career advancement [57,75].
Our results also showed that WFH experience and sector type have a positive effect on job performance, via perceptions and attitudes. The relationship is statistically significant; each step up we take, regarding experience and sector, increases perceptions and attitudes. Gender and position have a negative significant effect on specifications, perceptions, and attitudes, in terms of the relationship with job performance. Our findings are also consistent with the results of recent studies [76] that confirmed the strong effect of positive home life on job performance than a negative one. Recently, a study by [17] revealed that the productivity of employees during the pandemic crisis could increase or decrease, depending on their state of well-being. Conversely, our findings diverge from some prior research on certain points, such as WFH efficiency. For example, the authors of [2] asserted that WFH during the pandemic decreased work efficiency.

5. Managerial Implications, Limitations, and New Research Directions

Theoretical and Managerial Implications

This study introduces some theoretical and practical contributions to the literature. Firstly, this research will contribute to developing organization theory by filling the gap in the literature exploring the relationship between WFH and job performance, using employees’ attitudes and perceptions as mediator variables. Secondly, the literature was dominated by research related to this issue in a western context, whereas this study contributes to the literature by providing more empirical evidence on previous constructs links in the Saudi context as one of the many countries with an eastern culture. Thirdly, this research will contribute to the management literature with a more in-depth understanding of the impact of organizational and individual factors on enhancing job performance in the pandemic context. Fourthly, HR managers need to understand how to deal with HR in critical and extenuating circumstances (e.g., the COVID-19 pandemic). Fifthly, our study was designed to assess the effects of WFH on responding to job performance challenges, especially since the workforce inside the organization is segmented according to different criteria, such as age, gender, or education level. Therefore, HR managers must deal with two challenges: (a) responding to the specificity of each segment, and (b) giving a guarantee of higher performance.
Sixthly, our empirical evidence provides an appropriate argument for revisiting HR strategies with respect to WFH specifications and configurations. Finally, because assessing employees’ job performance is an essential part of managing the workforce in any organization, it is important that workers’ skills should be used effectively in these organizations; several insights were suggested regarding the formulation of HR strategies, in the COVID-19 context, for stimulating job performance.

6. Limitations and Research Perspective

Our research provides important insight regarding WFH as a new management trend in a particular context and its interaction with job performance. Nevertheless, this study is subject to limitations, which also offer opportunities for further research. At least four potential limitations should be underlined and addressed. The first one is related to the cross-sectional design of our research, limiting the causal relationship. Although we have tried to cover this issue by integrating attitudes and perceptions as mediators and the gender, education, position, experience, and department as moderators, the effect of WFH warrants further explanation through a longitudinal approach to monitoring variance after/before pandemics. The second is that all the measurements used in this study were by self-reporting. The findings will inevitably be exposed to a social desirability bias [77]. The third is that Saudi firms are not homogeneous. A comparison between sectors regarding WFH practices seems to be an interesting research direction. Fourth, an extension of the moderator variables in the research model, such as age and marital status, could explain job performance issues more appropriately. Our sampling process was based on a convenience criterion, which could itself be a source of bias. In the context of COVID-19, access to the respondents was considerably long-winded and random. Conducting research on WFH issues, using random sampling in some firms, will present an interesting perspective. Finally, there is a need for more studies that examine the role of culture in the relationship between WFH and job performance. Extended demographic variables (marital status, parenthood, family size) as control variables between WFH and job performance need to be documented.

Author Contributions

Conceptualization, J.C. and M.S.A.; methodology, J.C. and E.M.; software, E.K. and E.M.; validation, J.C., M.S.A. and E.M.; formal analysis, E.K. and E.M.; investigation, J.C., M.S.A., E.M. and E.K.; resources, J.C., M.S.A., E.M. and E.K.; Data curation, E.M. and E.K.; writing—original draft preparation, J.C. and E.M.; writing—review and editing, J.C. and E.M.; visualization, M.S.A., E.M. and E.K.; supervision, J.C., E.M. and M.S.A.; project administration, J.C., E.M. and M.S.A. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not Applicable.

Informed Consent Statement

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

Data Availability Statement

The data used to support the findings of this study are available from the corresponding author upon request.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Table
Table A1. Constructs, statements and references.
Table A1. Constructs, statements and references.
ConstructsItems/StatementsReferences
WFH:
Specifications
  • I am WFH full-time.
  • I would seek employment elsewhere if WFH were not available.
  • The nature of my job requires WFH.
  • I would like to work remotely, at least some of the time, for the rest of my career.
  • My workplace’s stance on remote work is:
    (part of the team, all employees, some days, as needed).
  • I recommend WFH for all employees.
[8,19,37]
Facilities
  • I received technical support (network, login, server problems).
  • I received training and coaching during the shift from office to house.
  • My expenses regarding the internet and laptop were covered by my company.
[40]
Attitudes
  • I like the work flexibility most about WFH.
  • I like the lack of structure least about WFH.
  • I would prefer working in my office rather than WFH.
[37]
Perceptions
  • I feel emotionally drained from my work.
  • I feel used up at the end of the workday, getting up in the morning and having to face another day on the job.
  • I feel burned out from my work.
  • I feel frustrated by my job.
  • I feel I am working too hard on my job.
[8]
Control variables
  • Gender.
  • Education level.
  • Position/function or job title.
  • WFH experience: how many years have you been WFH?
[37]
Job performance
  • I am more efficient WFH than in the office.
  • I am more productive WFH than in the office.
  • I achieve more WFH than in the office.
  • I am more effective when I am WFH than in the office.
  • The quality of the outcomes is better in WFH than in my office.
  • I am more able to resolve problems when WFH than in the office.
[37,78]

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Sustainability 14 03216 g001 550
Figure 1. Theoretical framework and hypotheses.
Figure 1. Theoretical framework and hypotheses.
Sustainability 14 03216 g001
Sustainability 14 03216 g002 550
Figure 2. Results of the structural model’s tested constructs links.
Figure 2. Results of the structural model’s tested constructs links.
Sustainability 14 03216 g002
Table
Table 1. Summary of database research results.
Table 1. Summary of database research results.
Keywords
Database		WFHWFH and
EA		WFH and PERWFH and
JPRF		WFH and
EA and PER
and JPRF		WFH and EA and PER and JPRF
in KSA
Academic Search Complete EBSCO
* Relevant		61,246 *795 *493 *3428 *None *None *
ScienceDirect
* Relevant		554,230 *32,815 *33,340 *77,093 *None *None *
Source: The authors. Date: 20 September 2021. * Number of articles for this concept in each chosen database. “We reviewed and analyzed the abstracts of the most relevant articles to our study as recommended by the author of” [24] (p. 2). WFH = Work from home, EA = Employee attitudes, PER = Perception, JPRF = Job performance, KSA = Kingdom of Saudi Arabia.
Table
Table 2. Research sample characteristics.
Table 2. Research sample characteristics.
GenderMaleFemaleWFH TotallyWFH Rate of More than 75%
238 (59.6)161 (40.4)214M/72F35M/77F
Educational levelSecondaryUndergraduateGraduates
3 (0.8)378 (94.7)18 (4.5)
PositionAdministrationTechnician and professionalManagersCEO
344 (86)27 (6.9)25 (6.3)3 (0.8)
DepartmentFinance
157 (39.4)		Customer service
155 (38.8)		Human
28 (7.0)		Marketing
59 (14.8)
Experience in WFH1 year to 4Less 6 months6 months to less than one yearMore than 4 years
352 (88.2)25 (6.3)16 (4.0)6 (1.5)
Business activitiesITRetailingBanking and insuranceEducation
165 (41.4)117 (29.3)103 (25.8)14 (3.5)
Table
Table 3. Convergent validity evaluation for relative measures of the research constructs.
Table 3. Convergent validity evaluation for relative measures of the research constructs.
ConstructFactor LoadingsAVEsConstruct Reliability
Specification (SPCF): 0.700.93
SPCF10.85
SPCF20.90
SPCF30.76
SPCF40.89
SPCF50.79
SPCF60.91
Facilities (FACIL): 0.610.82
FACIL10.78
FACIL20.82
FACIL30.73
Perception (PERCP): 0.500.83
PERCP10.72
PERCP20.70
PERCP30.71
PERCP40.71
PERCP50.70
Attitudes (ATTU): 0.500.75
ATTU10.71
ATTU20.70
ATTU30.72
Job performance (JPRF): 0.580.87
JPRF10.70
JPRF20.77
JPRF30.80
JPRF40.72
JPRF50.80
The indices of the goodness of model fit: x2/df = 1.639, CFI = 0.91, TLI = 0.91, NFI = 0.92, RMSEA = 0.044. The acceptable values for: Factor loading ≥ 0.5, AVE ≥ 0.5, Construct reliability ≥ 0.7. Note: All standardized loadings are significant at the 0.01 level or better. x2/df = Chi-square/degree of freedom, CFI = comparative fit index, TLI = Tucker–Lewis index, NFI = normed fit index, RMSEA = root mean square error of approximation.
Table
Table 4. Discriminant validity assessment for the research variables.
Table 4. Discriminant validity assessment for the research variables.
VariablesαSPCFFACILPERCPATTUJPRF
SPCF0.780.84
FACIL0.760.19 **0.78
PERCP0.780.28 **0.19 **0.71
ATTU0.810.31 **0.29 **0.17 **0.71
JPRF0.760.36 **0.35 **0.21 **0.38 **0.76
Note 1: ** Means that Correlation is significant at the 0.01 level (2-tailed), α = Composite Cronbach Alpha, SPCF = Specifications, FACIL = Facilities, PERCP = Perceptions, ATTU = Attitudes, and JPRF = Job Performance. Note 2: “Diagonal elements (in bold) are the square root of the average variance extracted (AVE). Off-diagonal elements are the correlations among constructs. For discriminant validity, diagonal elements should be larger than off-diagonal elements” [70] (p. 538).
Table
Table 5. SEM results for the research hypotheses.
Table 5. SEM results for the research hypotheses.
Predictor VariablesHypothesizedHypothesized
Relationship		Standardized
Coefficient
SpecificationsAttitudesH1→ Support0.33 ***
PerceptionsH2→ Not Support0.02 n/s
Job performanceH3→ Support0.35 ***
FacilitiesAttitudesH4→ Support0.14 **
PerceptionsH5→ Support0.20 ***
Job performanceH6→ Support0.41 ***
AttitudesPerceptionsH7→ Support0.15 ***
Job performanceH8→ Support0.27 ***
PerceptionJob performanceH9→ Support−0.18 ***
** p < 0.01; *** p < 0.00, n/s = not significant. Goodness-of-fit indices: x2/df = 2.91, CFI = 0.98, TLI = 0.98, NFI = 0.98, RMSEA = 0.046.
Table
Table 6. Direct and indirect total effects for our research mode.
Table 6. Direct and indirect total effects for our research mode.
Criterion VariablePredictor VariablesDirect EffectIndirect Effect aTotal Effect b
H10
Job performance (via attitudes and perceptions)		Specifications0.350.130.48
H11
Job performance (via attitudes and perceptions)		Facilities0.410.070.48
Note 1: a = “Indirect effects were computed only for cases in which the relevant structural parameters were statistically significant and insignificant direct effects were not included in the computation of total effect” [71] (p. 99). Note 2: b = total effects reflect the sum of direct and indirect effects among research constructs [72].
Table
Table 7. Results of the hierarchical regression model.
Table 7. Results of the hierarchical regression model.
Dependent VariablesWFH (Facilities) ModelWFHA (Specifications)
Model		WFHP
Perceptions
Model		EATTI
(Attitudes)
Model
Standard βStandard βStandard βStandard β
WFH perf. × Gender−0.012−0.163−0.114−0.191
WFH perf. × Education0.0070.0190.0410.020
WFH perf. × Position−0.039−0.267−0.217−0.347
WFH perf. × Experience0.0350.1900.3680.319
WFH perf. × Department0.0030.128−0.1160.179
WFH perf. × Sector0.0060.0480.4240.216
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Choukir, J.; Alqahtani, M.S.; Khalil, E.; Mohamed, E. Effects of Working from Home on Job Performance: Empirical Evidence in the Saudi Context during the COVID-19 Pandemic. Sustainability 2022, 14, 3216. https://doi.org/10.3390/su14063216

AMA Style

Choukir J, Alqahtani MS, Khalil E, Mohamed E. Effects of Working from Home on Job Performance: Empirical Evidence in the Saudi Context during the COVID-19 Pandemic. Sustainability. 2022; 14(6):3216. https://doi.org/10.3390/su14063216

Chicago/Turabian Style

Choukir, Jamel, Munirah Sarhan Alqahtani, Essam Khalil, and Elsayed Mohamed. 2022. "Effects of Working from Home on Job Performance: Empirical Evidence in the Saudi Context during the COVID-19 Pandemic" Sustainability 14, no. 6: 3216. https://doi.org/10.3390/su14063216

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