The Effect of Perceived Organizational and Supervisory Support on Employee Engagement During COVID-19 Crises: Mediating Effect of Work-Life Balance Policy

Abstract

This article examines how organizational and supervisory supports, directly and indirectly, impact employee engagement in U.S. federal agencies during the COVID-19 pandemic. This study applies two analysis models for two different sample groups: federal workers who were required to be physically present at a worksite during the pandemic and those who were not required to do so. Drawing from the sampling frame comprised of “permanently employed, non-political, non-seasonal, full- or part-time federal employees” in pay status as of October 2019, the findings from the U.S. Federal Employment Viewpoint Survey 2020 indicate that organizational and supervisory supports directly impact employee engagement regardless of employees’ work arrangements. However, work engagement is not increased by organizational and supervisory supports that actively encourage employees to use conventional incentives such as work–life balance policies. At the organizational level, this study suggests the need to redesign strategies to motivate public agents to engage in the agency’s mission because conventional and systemic employee support may be ineffective as currently designed. Overall, this study sheds more light on existing studies of public human resource management by examining employee engagement according to work arrangements during the pandemic.

Keywords

organizational support supervisory support employee engagement work–life policy COVID-19

Introduction

Employee engagement contributes to individual and organizational outcomes. Psychologically present employees are more attentive, focused on their work roles, and satisfied with their jobs and organizations (Kahn, 1990). As for organizational outcomes, employee engagement is associated with higher shareholder returns, profitability, productivity, and customer satisfaction. Also, employee engagement is closely associated with the belief that employees fulfill a role in the organization’s mission and are supported by their supervisors (Saks & Gruman, 2014).

Recent employee engagement research, especially in the private sector, suggests an increased need to study the potential effect of organizational and supervisory support on employee engagement in the current labor market due to deepening employee disengagement (Book et al., 2019; Nguyen & Tran, 2021). Based on the concepts of perceived organizational support (POS) and perceived supervisor support (PSS) derived from the social exchange theory, employees increase work engagement due to the comprehensive support of organizations and managers to employees. Despite the similar decline in employee engagement in the public sector, scholars in public administration have paid little attention to the role of perceived organizational and supervisory support on public employees’ engagement. Unlike the private sector, public employees tend to have fewer performance-based incentives and stronger job protections. Thus, it remains a key issue for government organizations and supervisors to increase public service motivation for their employees (Jin & McDonald, 2017). Consequently, it is important to systematically investigate how the perceived meso-level organizational and micro-level supervisory support helps public employees maintain their engagement with their jobs and organizations.

Based on the understanding that greater employee work engagement leads to increased organizational performance and satisfaction, organizations and supervisors should provide practical benefits and support for their employees. A work–life balance policy is one of the many policy tools used to encourage employees’ engagement in work. It is formal and systemic support that helps employees balance their work and personal life and is considered a critical managerial tool for improving employees’ well-being (McCarthy et al., 2013; Swody & Powell, 2007). Although work-life balance policies are available in organizations, the existing negative perception of this type of policy may prevent employees from actively using it due to shared cultural norms. However, when supportive organizational culture and leadership are available, an employee’s use of a work–life balance policy would exceed work engagement in an organization. Thus, work–life balance policy use mediates the relationship between perceived organizational and supervisory support and employee engagement.

Despite the significance of employee engagement in the public sector, many studies have mostly focused on normal working conditions. However, the COVID-19 pandemic has affected corporate organizational cultures that require concrete relationships between perceived organizational and supervisory support and employee engagement. The impact of the pandemic as a health-based crisis has indicated that “the role of human agents internal and external to [federal agencies] is key in dealing with this abrupt, sensitive event” (Cortez & Johnston, 2020, p. 125). The onset of COVID-19 intensified the reluctance to work on-site, increasing stress levels (Orfei et al., 2022). Overall, the ongoing pandemic raises important questions: how does organizational and supervisory support directly impacts employee engagement in U.S. federal agencies during the COVID-19 pandemic, and how a work–life balance policy (as a formal and systemic tool for support from an organization) mediates the impacts of both organizational and supervisory support on employee engagement?

Using the U.S. Federal Employment Viewpoint Survey (FEVS) data from 2020, the year COVID-19 began, this study examines direct and indirect relationships among organizational and supervisory support, work–life balance policies, and employee engagement. Studying the impact of human resource management strategies on federal employees can offer valuable insights into managing employees effectively in similar settings, including other government agencies or large organizations with complex structures. Particularly, the federal government has made significant efforts to improve workforce engagement by bringing women and racially underrepresented groups into public services. For example, the procedures established by the diversity management efforts such as Executive Order 13583 of August 18, 2011, Executive Order 13988 of January 20, 2021, and Executive Order 14020 of March 8, 2021, identify that the federal Government has continued to advance diversity, equity, and inclusion across the federal workforce (Nelson & Piatak, 2021). Furthermore, it is expected to enhance employee satisfaction and productivity within the government by analyzing human resource management strategies from diverse perspectives.

The remainder of this article proceeds as follows. The following section deals with the concept of engagement and the existing literature that examines the factors that affect employee engagement. Then, the theoretical links that explain the systemic impacts of the perceived supervisory and organizational support on government employees’ engagement are presented. The following section describes the research design and shows the results from the structural equation model (SEM) with multi-order latent constructs. Then, the results section presents how systematic organizational and supervisory support, especially regarding employees’ work and daily tasks, increases government employees’ engagement. Finally, the study’s limitations and potential implications for employee engagement and performance are discussed in the conclusion and discussion sections.

Literature Review

Employee Engagement and Core Concepts

Kahn’s (1990) foundational concept of employee engagement describes it as a psychological presence that enables one to take part in organizational activities. Kahn (1990) also defined engagement as “the harnessing of organization members’ selves to their work roles; in engagement, people employ and express themselves physically, cognitively, and emotionally during role performances” (p. 694). In contrast to being engaged, those who are disengaged physically, cognitively, or emotionally withdraw from their roles at work (Kahn, 1990). Based on Kahn’s conceptualization of engagement, Saks and Gruman (2014) and Christian et al. (2011) suggest that highly engaged employees exert themselves cognitively, emotionally, and physically, leading to improved performance to achieve organizational goals. Linking to individuals’ behavioral implications, Rich et al. (2010) state that engagement better represents individual job satisfaction and organization involvement. Story et al. (2008) suggest that engagement combines employees’ positive attitudes and behaviors, leading to increased job performance in favor of the organization’s mission. Thus, when employees are fully engaged in their roles, they perform best, but when they are disengaged, they dissociate themselves from their work roles (Saks & Gruman, 2014).

Scholars address individuals’ engagement as a psychological condition motivated by behavioral implications from work situations that provide psychological meaningfulness, safety, and availability (Crawford et al., 2013; May et al., 2004; Saks, 2006). In this vein, Schaufeli et al. (2002) state that works engagement is not momentary and specific but rather a more consistent and pervasive individual perception that is not controlled by any particular object or event. While dominant studies deal with the perspective of engagement in terms of work engagement (Fletcher et al., 2020), it is indistinguishable regardless of whether it is viewed from the perspectives of employee engagement or work engagement. Indeed, Saks and Gruman (2014) conceptualize engagement as a “relatively enduring state of mind referring to the simultaneous investment of personal energies in the experience of work” (p. 159). Thus, this study uses the concept of employee engagement and work engagement interchangeably.

Using Saks’s (2006) emphasis on distinguishing employee engagement in one’s job and organization, this study also differentiates the two when conceptualizing employee engagement. Given Kahn’s (1990) definition of work engagement is that employees are psychologically present in their “work roles,” Saks (2006) emphasizes that employees take on multiple roles within organizations; two of which are “their work role and their role as a member of an organization” (p. 604). Thus, Saks (2006) urges to examine separately how employees are psychologically present in their roles as performing their job as well as members of organizations.

Employee Engagement in Government Organizations

Employee engagement studies in the private sector have frequently focused on examining the potential impact of engaged employees on organizational success (e.g., Bates, 2004; Chanana & Sangeeta, 2021; Crawford et al., 2010; Kwon & Kim, 2020). These studies have suggested that employee engagement predicts profitability, productivity, employee outcomes, financial performance, and customer satisfaction (Harter et al., 2002; Musgrove et al., 2014; Saks & Gruman, 2014). Given the importance of employee engagement on organizational performance, studies on private sector management have found the positive effects of leadership (e.g., Book et al., 2019; Christian et al., 2011), communication (e.g., Bedarkar & Pandita, 2014; Vercic & Vokic, 2017), job satisfaction (e.g., Abraham, 2012; Garg et al., 2017), and work–life balance (e.g., Jiang & Men, 2017; Parkes & Langford, 2008) as the key managerial antecedents of employee engagement.

The rationale for emphasizing the causal linkage between employee engagement and organizational outcomes is also important in the public sector because organizations with highly engaged public administrators can increase individual service motivation, organizational performance, and citizen satisfaction (Borst, 2018; Borst et al., 2020; Jin & McDonald, 2017). Based on the organizational support theory, scholars examine organizational support’s direct and indirect impact on work engagement. Focusing on the organizational factors, Kiefer et al. (2015) indicate that organizations’ innovative changes positively affect employee engagement in the public sector. Analyzing the Belgian Federal Public Service data, Caesens et al. (2016) found that perceived organizational support promotes the level of proactive behavior toward the organization by increasing work engagement. Using data from a sample of 1,251 employees from state and local government agencies, Jin and McDonald (2017) found that perceived organizational support is key in leading supervisor support to positively affect the degree of work engagement.

Organizational support takes place through human resource management strategies, including enhancing employee autonomy (e.g., Van den Broeck et al., 2017), self-efficacy, and public service motivation (e.g., Borst et al., 2019). For example, Van den Broeck et al. (2017) indicate that work engagement is improved by increasing job autonomy, organizational support, and opportunities for skill utilization. Borst (2018) finds that job autonomy positively impacts work engagement. Similarly, based on the theoretical perspective of the self-determination theory, Tummers et al. (2018) also suggest that the increased employees’ job autonomy and the leader’s communication on tasks intrinsically enhance work engagement. Using a survey of public employees in Ireland, Conway et al. (2016) suggested that giving employees a voice enhances engagement. Focusing on the case of Ethiopian public service, Tensay and Singh (2020) suggest that effective human resource management systems practices indirectly increase organizational performance through enhanced work engagement.

Organizational support can also occur through interpersonal aspects among public service employees. Kroll et al. (2019) argue that individual perceptions of organization-wide social capital positively affect employee engagement shaped through social interactions with team members. Eldor (2018) suggests that when public workers receive compassion from their supervisors, their work engagement is enhanced. Eldor (2018) also indicates that individual compassion can buffer the negative impact of commonly stressful conditions caused by demanding citizens and administrative workload on employee engagement. Levitats and Vigoda-Gadot (2020) suggest that employees’ emotional intelligence is another critical factor expected to promote civil servants’ engagement. In addition, Breaugh (2021) argues that work stress due to hindering demands reduces employee engagement, but basic needs satisfaction fosters engagement. Dealing with management communication factors, Reissner and Pagan (2013) suggest that the intercommunicating culture promoted by managers increases engagement activities.

Theoretical Linkages and Hypotheses

This study uses social exchange theory (SET) to explain the effects of organizational and supervisory support on employee engagement. SET posits that the “positive, beneficial actions directed at employees by the organization and its representatives contribute to establishing high-quality exchange relationships that create obligations for employees to reciprocate in positive, beneficial ways” (Settoon et al., 1996, p. 219). SET suggests that discretionary benefits in the workplace show that an organization cares about the development and well-being of its employees, resulting in greater employee engagement (Eisenberger et al., 1990). In response to organizational care and support, employees may feel obligated to return the benefit by showing higher engagement to their organizations and contributing to pursuing the organization’s goals (Blau, 2017; Eisenberger et al., 2002).

Organizational and Supervisory Supports and Employee Engagement

Given SET’s description of the exchange of organizational inducement and employees’ contributions to the organization, this study also uses organizational support theory (OST), concepts of POS, and PSS to explain how reciprocity works and what is exchanged. OST proposes that employees develop a perception of being cared for based on social support and interpersonal transactions which signal that their organization values their contributions and cares about their well-being (Eisenberger et al., 2016; Eisenberger & Stinglhamber, 2011). Therefore, employees feel more motivated and are more likely to contribute to their organizations when they receive support from organizations and their supervisors (Chiang & Hsieh, 2012).

POS and PSS are concepts derived from the social exchange theory to explain the development of employees’ engagement with their organizations and supervisors. POS refers to an employee’s sense that an organization values and cares about their contributions (Rhodes & Eisenberger, 2002). PSS refers to an employee’s perception of the supervisor’s concern for their general well-being (Kottke & Sharafinski, 1988). POS and PSS capture the exchange of social support provided to employees and their workplace contributions. Thus, employees expect both tangible and intangible benefits and rewards in exchange for their effort and engagement with their supervisors and organizations.

PSS and POS distinguish the subjects that provide employee support rather than the types of support. These subjects focus on whether an organization offers structured human resource practices or whether supervisors implement these practices and provide instrumental and emotional support based on interpersonal relationships.

Studies have presented empirical evidence showing the significant positive impacts of POS and PSS on employees’ jobs and daily tasks. Research has focused on employee engagement, such as addressing employees’ needs for career growth (e.g., training opportunities; Bartlett, 2001; Gillet et al., 2013; Laschinger & Finegan, 2005; Williams et al., 2010), organizational rewards, and physical work environments (Christian et al., 2011). Studies have also discussed that psychosocial support (e.g., motivation and care for well-being) is positively associated with employee engagement (Christian et al., 2011; Crawford et al., 2010; Jin & McDonald, 2017; Saks, 2006; Schaufeli & Bakker, 2004). Taking together the discussions of SET, OST, POS, and PSS on the positive impacts of employee support on employee motivation and engagement, the following hypotheses are presented:

Hypothesis 1: Organizational support will have a positive impact on employee engagement.

Hypothesis 2: Supervisory support will have a positive impact on employee engagement.

The Indirect Impacts of Organizational and Supervisory Support on the Work–Life Balance Policy

In addition to the effects of both organizational and managerial support on employee engagement, this study proposes the impacts of employee support (i.e., POS and PSS) on policy use. In turn, the indirect effects of employee support on employee engagement through policy use are developed.

A work–life balance policy is a widely used practice by organizations to improve employees’ ability to integrate the needs of work and personal life. This study defines work–life balance policies in a broader sense than traditional family-friendly policies (Greenhaus et al., 2012; Trefalt, 2013). As defined by the Office of Personnel Management (OPM; 2018) and the U.S. Department of Labor (n.d.), this study defines this policy as covering child and adult dependent care, domestic partner benefits, leave policies (including the Family and Medical Leave Act (FMLA) and sick or personal leave), and promotes flexible work arrangements such as alternative work schedules, a compressed working week, flextime, and teleworking. However, offering such policies may not suffice as employees often avoid using work–life balance policies due to the flexibility stigma. In this vein, studies maintain that work–life balance policy use is a matter of managerial support (Kim & Mullins, 2016; OPM, 2018). It underlines the effects of employee support on work–life balance policy use and serves as a rationale for the relationship between the two.

Studies presented empirical evidence showing the positive impacts of employee support on work-life balance policy use. Thompson et al. (1999) and McCarthy et al. (2013) demonstrate that supervisory and organizational support, constituting work-life-friendly environments, are the most critical variables in influencing employee decisions regarding family-friendly benefits and programs. In addition, Blair-Loy and Wharton’s (2002) study finds that supervisor support is more crucial to employees’ use of work-family policies than the fact that employees are with family caregiving obligations. Thus, when employees perceive that organizations or supervisors support their work, they will actively use work–life balance policies, thereby increasing the level of engagement. This study presents the following hypothesis:

Hypothesis 3: Organizational support will positively impact employee engagement through work–life balance policy use.

Hypothesis 4: Supervisory support will positively impact employee engagement through work-life balance policy use.

Use of Work–Life Balance Policy and Employee Engagement

This study also uses SET and OST to explore the direct effects of work-life balance policies on employee engagement. As a form of formal and systemic employee support, organizations offering work–life balance policies may be perceived as valuing their employees (Swody & Powell, 2007), which enables employees to feel an “obligation to care about the organization’s welfare and help the organization reach its objectives” (Crawford et al., 2013, p. 63).

As previously discussed (for Hypotheses 3 and 4), organizations provide such policies to improve employees’ abilities to integrate work and personal life needs and enhance their work–life balance. Although work–life balance policies can be regarded as a form of employee support, this study distinguishes between policies and employee support. First, receiving organizational and supervisory support is a matter of perception among employees, whereas using work–life balance policies is a matter of action. Thus, POS and PSS, the theoretical frameworks used for employees’ perceived support, may not be applicable to work–life balance policies. Only half (46%) of public sector employees report that their supervisors are supportive of work-life balance policy use, despite the vast majority (82%) reporting that supervisors are supportive of their overall needs (OPM, 2018). Second, work–life balance policies may not benefit all employees equally. Some may not use the policy due to unavailability or a lack of awareness. Others may be reluctant to use policy benefits because they fear adverse career outcomes, such as wage penalties, reduced promotion opportunities and decreased mobility within and across organizations (i.e., flexibility stigma; Chung, 2020; Drago et al., 2005; Yu, 2022). In this vein, studies identify that employee support (i.e., organizational and supervisory support) and related organizational environment promoting work–life balance are determinants of work-life balance policy use (Blair-Loy & Wharton, 2002; Kim & Mullins, 2016; OPM, 2018).

Studies present empirical evidence that work-life balance policies help employees balance work and family responsibilities and improve employee work outcomes, such as employee retention, productivity, motivation, job satisfaction, and engagement. Halpern (2005) and Swanberg et al. (2011) present that flexible work hours help employees balance work and personal lives and increase employee engagement. Grover and Crooker (1995) and Valcour et al. (2011) show that employees become more committed to their organizations regardless of how much they personally benefit from the policies. These discussions align with the SET argument that organizations can positively affect employees’ contributions and shape their perception of and attitudes toward organizations if employees are provided with policies that reflect this value. Thus, this study presents the fifth hypothesis as follows:

Hypothesis 5: Work-life balance policy use increases employee engagement.

Employee Engagement in the Context of an External Crisis

This study also tests whether a contingency component affects employee engagement when experiencing an external crisis. This section uses the Job Demands-Resources (JD-R) model to explain how the effects of employee support and work-life balance policy use on employee engagement vary by employees’ working arrangements during the pandemic.

The JD-R suggests that the effects of employee support on employee motivation and engagement depend on the degree of physical and psychological challenge of the job (Bakker & Demerouti, 2007). Like SET and OST, JD-R also argues that employee support leads to beneficial outcomes such as increased employee motivation and engagement as job resources buffer the impact of a challenging work environment (Bakker & Demerouti, 2007; Hakanen et al., 2006). JD-R defines job resources as pay, career opportunities, supervisor and coworker support, task significance, and performance feedback at the organizational, interpersonal, and task levels, which keeps employees motivated and engaged (Bakker & Demerouti, 2007; Korunka et al., 2009). Job demands refer to the physical and psychological effort required to perform specific tasks and their associated costs (Demerouti et al., 2001), which may cause stress when employees consistently suffer from the strain of demanding work conditions without having the opportunity to recover from it (Bakker & Demerouti, 2007).

COVID-19 has abruptly and drastically changed workplace conditions. Organizations had to quickly gather the necessary resources and determine their utilization on an as-needed basis to cope with the sudden and substantial changes. Employees were required to work remotely, facing significant pressure from work, an unfavorable working environment, and emotionally challenging interactions with coworkers and customers (Bakker & Demerouti, 2007). Meanwhile, some public employees like border patrol officers, Transportation Security Administration agents, and meat inspectors were required to be present at worksites due to the nature of their work (OPM, 2020a). Although working conditions during the pandemic were challenging, whether experienced on-site or remotely, physically present employees in the workplace faced more significant challenges. The need to commute and be present at worksites may result in considerable job strain and health concerns, causing increased stress. Despite managerial and organizational support in reducing the adverse effects of job demands, the buffer effects differ between the two groups because of the different job demands. Thus, it is plausible to expect that those required to be in the workplace suffered from higher job demands than those who did not have to be on-site, resulting in a different level of employee engagement. Figure 1 presents the empirical model to test the hypotheses.

Hypothesis 6: During the COVID-19 pandemic, both the direct and the indirect impacts of organizational support on employee engagement will be greater among the employees who were required to be present at worksites than those who could work remotely.

Hypothesis 7: During the COVID-19 pandemic, both the direct and the indirect impacts of supervisory support on employee engagement will be greater among the employees who were required to be present at worksites than those who could work remotely.

Figure 1.

An Empirical Model Illustrating the Relationships Between Employee Support, Work–Life Balance Policy Use, and Employee Engagement.

Data and Method

This study used the FEVS administered by the U.S. OPM from September 22 to November 3, 2020. In addition to FEVS’ overall aim to capture employees’ work experiences, work unit, agency, supervisor/team leader, leadership, satisfaction level, work–life programs, and demographics in U.S. federal agencies, FEVS2020 addresses “changes to federal work and workplaces during the pandemic” (OPM, 2020a, p. ii). Among the survey population of 1,555,717, “permanently employed, non-political, non-seasonal, full- or part-time Federal employees’ in pay status as of October 2019” (OPM, 2020a, p. 4), OPM drew the sampling frame comprised of 1,410,610 employees after removing those who were no longer employees of Federal agencies in 2020 (OPM, 2020b). FEVS2020 data obtained a sample of 624,800 federal employees representing 82 federal government agencies ranging from department-level to large and small/independent agencies with a response rate of 44.3% (OPM, 2020a). This study focuses on 464,616 respondents across cabinet-level federal agencies as work arrangements (i.e., whether respondents had to be physically present at a worksite during the pandemic), work–life balance policy use, and other determinants of employee engagement vary by agency levels.¹ Among the final sample, women comprised 44%, 23% are on the supervisory level, 23% are age 40 and under, and 26% are a racial minority. Regarding job tenure in federal agencies, 37% reported that they had worked ten years and less, another 37% said 11 to 20 years, and 25.8% reported 21 years and older. In all, 27.5% are those who were required to be physically present at worksites during the pandemic (i.e., on-site group; see Table 1).

Table 1.

Descriptive Statistics.

Variables	All (n = 464,616)				On-site (n = 128,085)				Off-site (n = 336,531)
Variables	M	SD	Minimum	Maximum	M	SD	Minimum	Maximum	M	SD	Minimum	Maximum
DV
EE	0.00	1.00	–3.44	1.46	–0.16	1.03	–3.44	1.46	0.08	0.98	–3.44	1.46
IVs
OS	–0.00	1.00	–1.91	2.46	–0.27	1.05	–1.91	2.41	0.08	0.96	–1.91	2.41
SS	0.00	1.00	–3.91	1.01	–.38	1.14	–3.91	1.01	0.16	0.90	–3.91	1.01
MV
WLP	–0.00	1.00	–1.73	2.87	–0.63	0.98	–1.73	2.87	0.22	0.91	–1.73	2.87
CV
Gender	0.44	0.50	0	1	0.29	0.45	0	1	0.51	0.50	0	1
Supervisor	0.23	0.42	0	1	0.25	0.43	0	1	0.23	0.42	0	1
Minority	0.26	0.44	0	1	0.25	0.43	0	1	0.27	0.45	0	1
Job tenure	1.89	0.79	1	3	1.78	0.75	1	3	1.93	0.79	1	3

Note. The averages of DV, IVs, and MV converge to zero because the variables are latent factor indicators without intrinsic scales generated by multiple observed variables (Kenny, 2012; Muthén & Muthén, 2010). Thus, latent variables are usually measured as standardized variables with variance one (Kenny, 2012; Muthén & Muthén, 2010). DV = Dependent variable; EE = Employee engagement; IV = Independent variable; OS =: Organizational support; SS = Supervisory support; MV = Mediating variable; WLP = Work–life balance policy; CV = Control variable. If not specified otherwise, the dependent, independent, and mediating variables presented in this table are latent constructs.

Dependent Variables

Two latent constructs to indicate employee engagement are generated: job engagement (JE), indicating federal workers’ engagement in their job, and organizational engagement (OE), indicating one’s engagement with their agencies. When selecting survey questions (see Online Appendix A) as observed variables to generate the latent constructs, this study drew from Saks’ (2006) scales of employee engagement through job and organizational engagement. Schaufeli and Bakker’s (2004) Utrecht Work Engagement Scale (UWES) and OPM’s Employee Engagement Index (EEI) are also reviewed; however, Saks’ (2006) measurement scales and UWES are not identical to FEVS’ questionnaires, and EEI captures the workforce conditions that support employee engagement. Thus, this study conducted exploratory factor analyses (EFA) and checked sets of goodness-of-fit (GOF) indices to test scale reliability and validity. The Cronbach’s α results for JE is 0.84 and OE is 0.76, which verifies the internal consistency of the measure. EFA yields relatively high standardized factor loadings (ranges from 0.75 to 0.90, see Online Appendix B) with significant positive relationships (p<.001) between observed variables and latent constructs, confirming the convergent validity of the constructs. The measurements establishing the convergent validity and reliability of the factors are an Average Variance Extracted (AVE) value exceeding 0.50 and a Composite Reliability (CR) exceeding 0.70 (Hancock & Mueller, 2001). As obtaining AVE (JE: 0.68, OE: 0.67) and CR values (JE: 0.81, OE: 0.89) above the threshold, it is concluded that the latent constructs well represent the theoretical constructs of job and organizational engagement.

Using JE and OE as the first-order constructs, the second-order constructs representing employee engagement (EE) are used in analyses. Models using second-order constructs are useful when first-order constructs are significantly correlated. Second-order constructs can explain relationships among the first-order constructs generated by multiple survey items (Becker et al., 2012; Chen et al., 2005; Tehseen et al., 2017). GOF results show that EE loads well on JE and OE (see Online Appendix B), indicating a good fit for the two-factor model instead of the single-factor models (Fan et al., 1999), which supports using second-order factor models. It is expected to have greater simplicity in analyzing complex measurement structures in the models by using second-order factor models over first-order ones (Chen et al., 2005).

Independent Variables

To generate the latent construct representing employee support, this study used previous studies’ definitions and organizational and supervisory support measures and conducted EFAs. Five latent constructs representing organizational support for federal employees during the pandemic were generated: (a) expanding flexible work arrangements during COVID (O1), (b) providing information and resource provision regarding COVID (O2), (c) rearranging the physical workspace to implement social distancing (O3), (d) providing support for commuting and work travel (O4), and (e) supporting remote work (O5). Three latent constructs representing supervisory support are generated: (a) immediate supervisors’ support for work-life balance (S1), (b) immediate supervisors’ support for workplace health and safety (S2), and (c) organization’s senior leaders’ support for workplace health and safety (S3).

GOF indices confirm that all eight latent (first-order) constructs for organizational (O1-5) and supervisory support (S1-3) are reliable and valid (see factor loadings in Online Appendices C and D), which means that the latent constructs well represent the theoretical constructs of employee support from organizations and supervisors (Hair et al., 2011, 2017).

This study also utilizes work–life balance policy use (WLP) during the pandemic to mediate between employee support (IVs) and employee engagement (DV). Using the survey items (see survey questionnaires in Online Appendix A), two latent constructs are used: use of flexible work arrangements (W1: Factor loadings>0.70 at 0.001 level, Cronbach’s α: 0.62, AVE: 0.62, CR: 0.83) and caregiver-friendly policies (W2: Factor loadings>0.60 at 0.001 level, Cronbach’s α: 0.63, AVE: 0.54, CR: 0.70).

Analyses include second-order constructs representing organizational support (OS), supervisory support (SS), and WLP. GOF results show that the first-order latent employee support constructs (O1-5 and S1-3) load well on the second-level constructs (OS and SS) representing organizational and supervisory support (see Online Appendices C and D). The results also confirm that the second-order WLP constructs load well on the first-order constructs, W1 and W2 (see factor loadings in Online Appendix E). According to the results, the first-order latent constructs fit well with the second-level ones, supporting the use of second-order constructs rather than multiple first-order ones (Fan et al., 1999).

Control Variable

This study also uses other factors that influence federal workers’ engagement in their job and organizations, in addition to supervisory and organizational support and WLP. These factors are gender (1=female, 0=male), minority status (1=minority status, 0=not), age (1=40 or older, 0=under 40), job tenure (1=10 years or less, 2=11–20, 3=21 years and more), and supervisory status (1=supervisory status, 0=non-supervisory status; Guglielmi et al., 2016; Jin & McDonald, 2017).

Data Analysis

Mplus software version 8.7 was used for structural equation modeling (SEM). The key characteristic of SEM is its ability to represent and model complex interactive relationships through path diagrams that can capture direct and indirect relationships among variables (Hoyle, 2011). This characteristic of SEM is relevant to the model that hypothesizes integrated effects between supervisory and organizational support, WLP, and employee engagement.

To examine the idea of contingency components of employee support and its outcomes, this study did separate analyses among federal employees according to their working arrangements during the pandemic—those who had to be present at their workplace (“on-site group” hereafter) and others who worked remotely (“off-site group” hereafter). As previous studies have demonstrated, employees’ job content, including detailed functions and responsibilities, determines their engagement with their organization (Saks, 2006). In addition, forced physical presence at the workplace is closely related to employee autonomy in using work–life balance policies (e.g., Choi, 2018; Giauque et al., 2022). The FEVS data, in fact, found that both employee engagement in their job and organization were lower among the on-site group (difference significant at 0.001 level). The use of work–life balance policies, the mediator between PSS and POS and employee engagement in the model, was significantly lower among the on-site group during the pandemic than the off-site one (the difference is significant at 0.001 level, see Table 1). In addition, such required work arrangements may substantially vary employees’ concerns and needs about their work as well as their health and safety and channels of daily communication with their supervisors and colleagues.

This study uses multiple imputation techniques to manage the missing values in the dataset. The missing data rate ranges from 17% (the use of a child care policy) to 1% (job engagement). While there is no consensus on the point at which missing data becomes problematic (Schlomer et al., 2010), Roth (1994) notes that data imputation techniques can be used when <20% of cases are missing data. Based on Allison’s (2012) notes that generated 10 imputed data sets with estimates of 95% efficiency, Graham et al. (2007) recommend generating 20 imputed data sets for 10% to 30% of the missing data. Thus, 20 imputed datasets were used to address missing data in this study. Table 1 presents descriptive statistics of the variables used in the analysis.

Results

The data set contains categorical and ordered variables in which weighted least squares means and variance adjusted estimation is recommended (Brown, 2015; Muthén and Muthén, 1998–2017). The robustness measurement suggested as indicators of a good fit for the SEM model are the significance of the chi-square value at the 0.05 level, comparative fit index and Tucker–Lewis index values exceeding 0.90 (Byrne, 2012; Peterson et al., 2006), and an root mean square error of approximation value <0.05 (Browne & Cudeck, 1992). The test results presented in Table 2 show that the SEM analysis model fits the data well. Table 2 also indicates SEM results explaining employee engagement among on-site and off-site groups.

Table 2.

The Standardized Results of a Structural Equation Model Predicting Employee Engagement (Using Imputed Data).

	Path	All		On-site group		Off-site group
HS	Path	β	SE	β	SE	Β	SE
H1	OS → EE	–.04	.15	.03	.03	–0.16	1.29
H2	SS → EE	.74*	.35	.85***	.01	1.02*	0.44
H3	WLP → EE	–.45	.36	–.14***	.04	–0.28	0.78
H4	OS → WLP → EE	–.00	.19	–.15*	.07	0.03	0.13
H5	SS → WLP → EE	–.17	.02	–.01*	.01	–0.09	0.40
	N	464,616		128,085		336,531
	Chi²	p < .001		p < .001		p < .001
	RMSEA	.052		.043		.042
	CFI	.947		.964		.961
	TLI	.941		.960		.956

Note. HS = Hypotheses; SE = standard error; OS = organizational support; EE = employee engagement; SS = supervisory support; WLP = work-life balance policy; RMSEA = root mean square error of approximation; CFI = comparative fit index; TLI = Tucker–Lewis index.

p < .05. **p < .01. ***p < .001.

Test of Direct Impacts of Antecedents on Engagement

Separate models were run for the on-site and off-site groups, while the full model was used as a reference to assess the effects of employee support and work-life balance policies.

Hypothesis 1, predicting the positive effects of organizational support on employee engagement, is not supported in the on-site or off-site group models. Hypothesis 2 predicts the positive impacts of supervisory support on employee engagement, and it is supported in both the on-site group (β = .85, p<.001) and the off-site group (β = 1.02, p<.05). For one-unit increases in supervisory support to employees, the z-score of employee engagement increases by .85 among on-site groups and by 1.02 among off-site groups. However, as opposed to the expectation, the analyses indicate that work–life balance policy use during the pandemic decreases employee engagement among the on-site group (β = –.14, p<.001), and the effect was not statistically significant among the off-site group. Hypothesis 5, predicting the positive effects of work–life balance policy use on employee engagement during the pandemic, is not supported among both employee groups.

Test of Indirect Impacts of Antecedents on Engagement

For Hypotheses 3 and 4, the indirect effects of employee support on engagement through work-life balance policy use are not supported in the on-site or off-site group. However, the analyses yield significant but counterintuitive findings. Both organizational (H3: β = –.15, p<.05) and supervisory support (H4: β = –.01, p<.05) are found to decrease employee engagement through the policy use only for the on-site group. For the off-site group, who can work remotely, the analyses did not find statistically significant indirect effects.

Test of Differences in Employee Engagement on the Types of Working Conditions During the Pandemic

Hypotheses 6 and 7 expected that the overall effects of employee support and work–life balance policy on engagement would be greater for the off-site group. This is partially supported; the positive direct effects of supervisory support on employee engagement are greater among the off-site group than the on-site group. For a one-unit increase in supervisory support, the increase in the z-score of employee engagement among the off-site group is 1.02 (p<.05), while it is .85 among the on-site group. Figures 2 and 3 present visual models of the significant paths in the analyses for on-site and off-site employee group models.

Figure 2.

A Visual Model of Significant Model Results Predicting Employee Engagement Using Higher-Order Latent Constructs: On-Site Group Model.

Figure 3.

A Visual Model of Significant Model Results Predicting Employee Engagement Using Higher-Order Latent Constructs: Off-Site Group Model.

Additional Findings

Analyses also found that several control variables not hypothesized in the model have significant impacts on employee engagement. For the on-site group, employees who are women (β = .02, p<.05) and racial minorities (β = .04, p<.001) are found to have higher employee engagement. However, work–related factors, such as job tenure and supervisory status, decrease it. Those who have worked in federal agencies longer (β = –.04, p<.001) and are on the supervisory level (β = –.03, p<.05) show lower employee engagement. For the off-site group, only one control variable significantly impacts employee engagement. Those identifying as racial minorities have higher employee engagement (β = .03, p<.001).

Discussion

COVID-19 has reemphasized understanding human resource management (HRM) theories and practices in the context of external crises. Although the effects of external crises are not new to HRM studies, the pandemic calls attention to more comprehensive perspectives that examine how employee engagement is affected as HRM combines different aspects of crises, including rapid changes in working arrangements, new work demands, and concerns regarding individuals’ health and safety (Caligiuri et al., 2020; Ererdi et al., 2022).

Theoretical Implications

The findings of this study make several theoretical contributions. First, findings help advance the understanding of how social exchange and organizational support theories, including POS and PSS, can be applied during external crises like the pandemic. The results suggest that organizations can respond to crises by devising various support arrangements at organizational and managerial levels (Fazio et al., 2017; Stinglhamber & Vandenberghe, 2003). Specifically, this study found positive effects of supervisory support on employee engagement during the pandemic regardless of federal employees’ work arrangement, but the effect is more noteworthy for remote workers. The findings are aligned with recent studies about employee support and its outcomes during the pandemic. For example, Caligiuri et al. (2020) and Dirani et al. (2020) demonstrated that supervisory supports help employees better cope with uncertainty by assisting them in understanding what they are expected to do amid the pandemic, which resulted in new forms of work, organizational tensions, and widespread health and safety concerns. Findings also support Bakker and Demerouti’s (2017) study demonstrating that the effects of workplace support are particularly substantial when employees are in stressful environments. Thus, supervisory support, based more on interpersonal connections than systemic support from organizations, can be a crucial form of support during the pandemic, as it can help improve employees’ engagement with their job and the organization.

Second, the findings advance the social exchange theory, organizational support theory, and JD-R model by showing that established employee support, such as work–life balance policies, may have contradictory effects when organizations face external crises, depending on work situations among employees. Analyses results found negative direct and indirect effects of employee support and work–life policies on employee engagement for those required to be present at the workplace during the pandemic and no effects among those able to work remotely during the pandemic.

Practical Implications

Although SEM results present no significant effects of organizational support on employee engagement for on-site and off-site employees, this does not mean that organizational support does not affect employee engagement. Such findings enable this study to make a practical contribution by expanding our understanding of employee support depending on employees’ work arrangement. Like previous studies (e.g., Jin & McDonald, 2017; Shams et al., 2020), the results demonstrate the positive effects of supervisory support on employee engagement. In particular, this study emphasizes the importance of supervisory support that relies on interpersonal connections when unexpected work arrangements lead to confusion about managing work and home obligations.

While it is difficult to effectively respond to employees’ demands due to the inherent complexity of implementing policy change at the organizational level (Bolognesi et al., 2021), supervisors at the individual level can quickly respond to their demands due to their better interpersonal communication. By engaging in communication with employees, supervisors can understand their needs, solicit their input, and encourage them to try new strategies. Malik and Sanders’ (2021) recent human resource management study emphasizes that the role of supervisors is vital in human resource management during global crises.

Recently, WLPs have expanded discussions on the providers’ side (i.e., positive aspects of providing such policies and their impacts, such as enhanced work–life balance, job satisfaction, and lower stress levels) to include users’ perspectives and posit that employee support, as a form of work–life balance, may not be equally available or beneficial for all employees. First, regardless of their family circumstances or desire to use such policies, some employees are not eligible to utilize the policies because their job duties and work requirements prevent them from doing this. Second, work-life policies may be more beneficial to certain employee groups, depending on domestic caregiving responsibilities, demographic groups, and occupational status (Kossek & Lautsch, 2018). Third, despite using the policies, it may not be enough for employees to reduce their work-life conflicts because the policies cannot decrease their non-work-related personal responsibilities or alleviate troubles at home (Feeney & Stritch, 2019).

The inequalities in the availability of benefits, and the lack of autonomy in using work-life balance policies, can explain the findings on the negative direct and indirect effects of employee support, work-life balance policy use, and employee engagement among the on-site group. The negative effect of the absence of autonomy in using work–life balance policies on employees’ work outcomes is aligned with Choi’s (2018) findings. During the pandemic, work–life balance policies were not readily available for the on-site group due to their job duties and work requirements; thus, they used the policies less than many other public sector employees (see Table 1). However, they have as much domestic caregiving responsibility as the off-site group. The FEVS2020 data shows no statistically significant differences between on-site and off-site groups in terms of having domestic caregiving responsibilities, such as child care and elder care.² Therefore, it would be reasonable that the on-site group reported greater work-life conflicts. Compared to off-site groups, on-site groups more strongly agree with the statement that school and daycare closures have been disruptive to their ability to work and that COVID-19 disrupted their ability to work more than the off-site group (the difference is statistically significant at 0.001 level). They also show less satisfaction with work–life policies, including teleworking, alternative work schedules, and child care and eldercare programs offered in their organizations than the off-site group.³ As they were required to be present at workplaces during the pandemic, the on-site group may have unaddressed health concerns or needs. Interacting with other people and commuting would increase their chances of being exposed to the virus. In fact, they show less confidence in their organization’s preparedness for the phased return of employees to the worksite (average: 3.72) than the off-site group (average: 4.17; the difference is statistically significant at 0.001 level).

The availability and benefits of a work–life balance policy among employees can also explain the lack of a significant effect of employee support on engagement through policy use among off-site groups. For the off-site group, the policy use may not be critical to enhancing employee engagement because they were required to work from home during the pandemic due to the lockdown, which means they already had work flexibility. Giauque et al. (2022) also found that forced telework during the pandemic was effective in enhancing employees’ work autonomy, while it was ineffective in enhancing employee engagement among public workers in Swiss. The WLP may not reduce their work–life conflicts or non-work-related personal responsibilities when all family members had to stay home during the pandemic.

Limitations

Although this study contributes to existing studies by investigating employee engagement according to different work arrangements during the pandemic, this study acknowledges several limitations that would encourage further research on engagement in the public sector. First, using data from one survey conducted in 2020 may not provide a more comprehensive understanding of engagement. As such, further research could more rigorously examine how the findings are distinctive between the pandemic and non-pandemic periods by comparing two different sets of survey data before and during the pandemic.

The second limitation of this study is the possibility of omitted variables. Although there might be additional factors determining employees’ perceptions of organizational and supervisory support and engagement, especially while undergoing turbulence and existential threats like the pandemic, limited variables are used in this study. For example, employees’ health status during the pandemic, supervisors’ ability to use technology to facilitate virtual collaboration among employees, and organizational readiness in online management (Elyousfi et al., 2021) may affect organizational outcomes.

Third, this study utilized the best available proxy for employee engagement measures to capture one’s engagement in their job and organization, but it is still not free from validity concerns. Individual assessments of work engagement might be more subjective than objective; employees’ subjective perception based on their work experiences, judgments, and expectations may determine their levels of engagement.

Finally, the variables of organizational and supervisory support and employee engagement are possibly endogenous. It means that reverse causation could also be true (i.e., the increased employee engagement could motivate organizations or supervisors to support employees more). Since the current survey data are observational, a possible alternative method would be using experiments that provides more accurate tests of the causal structure of models (Van Ryzin, 2013).

Conclusion

By analyzing the recent FEVS data collected during the global pandemic, this study shows that employee support from both organizational and supervisory levels matters, especially when organizations and employees experience an external crisis. This study suggests the need to revisit strategies to motivate public agents to engage in the agency’s mission because conventional and systemic employee support may be ineffective as designed. During the pandemic, WLP showed direct and indirect negative effects on those required to be present at workplaces. In addition, for those not required to be present at work, work-life balance policy use had no significant impact on their engagement with their job and organization. The findings caution that there is no best way to support federal employees during external organizational crises because employee support is not equally effective for all employees.

This study also provides some implications at the managerial level. Given that supervisory support directly impacts employee engagement regardless of employees’ work arrangement, supervisors and managers must play a critical role in the fast-changing and uncertain environment during crises for both employees and organizations. Their support is necessary for employees to make sense of a new situation (Christianson & Barton, 2021) by informing employees of changing rules and providing information to reduce uncertainty regarding work tasks and roles. Also, managers can understand the diverse needs of employees in different family and work situations. For example, due to the sudden need for homeschooling and home care during the lockdown for those with younger children and older family members, supervisors can help them find the best available arrangement to complete their work in the given situation, such as re-arranging work hours.

Empirical findings of this study show that conventional employee supports, such as WLPs, may not benefit employees as designed, which aligns with Ashforth’s (2020) assertion that external crises may be critical in evaluating their organization. Despite this, the extreme context of external crises that severely affect employees’ responsibilities at work and home remains an understudied area in WLP studies. Therefore, the findings confirm the influence of work-life balance policies during external organizational crises and suggest that employees’ work arrangements affect how they respond to organizational support.

Supplemental Material

sj-docx-1-ppm-10.1177_00910260231171395 – Supplemental material for The Effect of Perceived Organizational and Supervisory Support on Employee Engagement During COVID-19 Crises: Mediating Effect of Work-Life Balance Policy

Supplemental material, sj-docx-1-ppm-10.1177_00910260231171395 for The Effect of Perceived Organizational and Supervisory Support on Employee Engagement During COVID-19 Crises: Mediating Effect of Work-Life Balance Policy by Sang Eun Lee and Geiguen Shin in Public Personnel Management

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Geiguen Shin

Supplemental Material

Supplemental material for this article is available online.

Notes

Author Biographies

Sang Eun Lee is an assistant teaching professor in the School of Public Affairs at Hainan University-Arizona State University Joint International Tourism College (HAITC) at Arizona State University. Her research interests include human resource management, representative bureaucracy, gender issues, public management and performance, public sector innovation, leadership, and quantitative research methods.

Geiguen Shin is an assistant professor in the Department of Public Administration at Kookmin University, Seoul, South Korea. His research focuses on public management and performance, with a specific application to public school and law enforcement, public sector innovation, fiscal policy, a linkage between redistribution and development, experimental methods, and time-series forecasting.

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