Next Article in Journal
Quantitative Analyses of Chemical Elements in Phragmites australis as Bioindication of Anthropization in Urban Lakes
Next Article in Special Issue
Twenty Years of Research on Millennials at Work: A Structural Review Using Bibliometric and Content Analysis
Previous Article in Journal
Transport Infrastructure and Regional Development: A Survey of Literature on Wider Economic and Spatial Impacts
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Sustainability
Volume 15
Issue 1
10.3390/su15010551
sustainability-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

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

The Indirect Effect of Job Resources on Employees’ Intention to Stay: A Serial Mediation Model with Psychological Capital and Work–Life Balance as the Mediators

by
Mohammed Samroodh
1,
Imran Anwar
2,
Alam Ahmad
3,*,
Samreen Akhtar
3,
Ermal Bino
4 and
Mohammed Ashraf Ali
1
1
Department of Commerce, Aligarh Muslim University, Aligarh 202002, India
2
University Centre for Research and Development, Chandigarh University, Mohali 140413, India
3
College of Administrative and Financial Sciences, Saudi Electronic University, Riyadh 11673, Saudi Arabia
4
College of Business, University of Buraimi, Al Buraimi 512, Oman
*
Author to whom correspondence should be addressed.
Sustainability 2023, 15(1), 551; https://doi.org/10.3390/su15010551
Submission received: 14 November 2022 / Revised: 21 December 2022 / Accepted: 26 December 2022 / Published: 28 December 2022
(This article belongs to the Special Issue Towards Sustainable HRM: Types, Factors, Drivers and Outcomes)
Browse Figure
Versions Notes

Abstract

:
The COVID-19 pandemic has induced a sudden shift from work in an office setting to work from home. The flexibility and job autonomy achieved through telecommuting ought to facilitate positive outcomes among employees. Apart from a few contradicting studies, telecommuting literature predominantly revolves around the positive aspects of working from home. However, the number of employees voluntarily leaving their jobs has increased since “the great resignation” in March 2021. Therefore, building upon the conservation of resource theory and the job demands and resources framework, the current study tests the influence of specific job resources, job autonomy (JA), and perceived organizational support (POS) on employees’ intention to stay (IS) directly and indirectly through a unique serial mediation pathway of psychological capital (PsyCap) and work–life balance (WLB). The results affirmed that JA and POS have a positive association with employees’ IS. Moreover, PsyCap and WLB were also found serially mediating the direct association between JA, POS, and employees’ IS. The current study’s findings offer valuable insights for HR managers on the relevance of specific job resources and the role of psychological capital in controlling attrition rates. The findings of this study could be helpful for HR managers to design measures to reduce attrition rates and foster work–life balance and positive outcomes among employees. This study is among the first to instrument the indirect role (serial mediation) of PsyCap between job resources, WLB, and employees’ IS, thus significantly contributing to the literature.

1. Introduction

The COVID-19 pandemic interrupted the everyday lifestyle of employees worldwide. A large proportion of the workforce was restricted from their daily office commute due to the pandemic, which forced companies to assign their employees to work virtually from their homes. This enforced telecommuting has given a wider recognition to flexible work practices and significance to the work–life balance concept. Along with the disruptive quick technological changes, COVID-19-induced teleworking has significantly gained wider popularity, resulting in a surge in research on telework and its links with work–life balance [1,2,3,4,5]. Employees opted for flexible work practices with a positive perception as it would render them more flexibility [6], less commute stress [7], and better work–life balance [5]. Telework is regarded as a crucial strategy that facilitates work autonomy and work–life balance [8]. Remote working allows employees to remain productive and resilient by maintaining social connections with the assistance of communication technologies [9]. While businesses were coping with the sudden shock of the pandemic by sending their employees to a work-from-home mode [2,3,4,5], another major challenge of increasing “voluntary turnover rates” started taking place in March 2021—“the great resignation”, which posed another major challenge for HR managers globally [10].
According to the US Bureau of Labor Statistics, more than 4.3 million people left their employment voluntarily in December 2021 [11]. This phenomenon is believed to be an aftermath of the pandemic because employees’ emotive, cognitive, and behavioral processes have been drastically transformed due to the COVID-19-induced telework [12]. A recent employee survey conducted in the US showed that 39% of the respondents would contemplate quitting if their employers did not permit them to work remotely during the pandemic [13]. The reasons behind employees’ refusal to revert to traditional fixed-hours office work might be exhausting daily commutes, lack of autonomy and flexibility, and a desirable work–life balance level [14]. Hence, employees’ growing priorities for job autonomy, flexibility, and work–life balance need to be acknowledged by employers.
Employee turnover intention has always been an issue of concern, and these massive turnover rates have severely impacted organizational performance in almost all major industries [10]. Even before the advent of “the great resignation”, employee turnover was an issue of concern in the accounting industry [15,16,17,18]. Prior research had given insights into voluntary telework policies before the COVID-19 outbreak [19,20,21] and confirmed that teleworking had been a common practice in accounting firms in the past [22,23]. Studies focusing on the accounting industry have identified various antecedents for this turnover issue, such as technostress [18,24] and solutions such as resilience as a coping strategy [25]. Peak season stress due to workload compression accompanied by time demands has been a threat to employee well-being in this industry. Recent studies have also discussed the highly demanding work characteristics of this industry [26,27,28] and contended that specific job resources, mainly job autonomy and perceived organizational support, may prove to be crucial in alleviating employees’ attrition rate by enhancing their well-being [4,29].
However, most studies on teleworking employees in the accounting industry have explored job demands and their negative influence on employee well-being aspects such as work–life balance [30,31,32], and the literature lacks studies addressing the issue of employees’ turnover intention through a positive indirect pathway of job resources that might predict employees’ intention to stay via the intervening effect of well-being variables. Therefore, to fill this void, we propose a serial mediation-based conceptual model by drawing upon the job demands and resources model (JD-R) and conservation of resource theory (CRT) (see Figure 1). We put forth that job resources, job autonomy, and perceived organizational support indirectly enhance employees’ intention to stay by enriching their personal resources (i.e., psychological capital) and well-being (i.e., work–life balance). For this purpose, we surveyed the employees of the big-four accounting firms in Bangalore city of India. We examined the indirect influence of job resources in facilitating employees’ intentions to stay through the serial mediation effect of psychological capital and work–life balance. This study is among the first to instrument the serial mediation effect of psychological capital and work–life balance between job resources and employees’ intention to stay, thus substantially contributing to the literature. The insights from this study’s findings will show employers the importance of providing specific job resources to facilitate employee retention. The study findings could also be helpful for HR managers in designing flexible and hybrid work models to ensure talent retention and prevent knowledge exodus due to employee turnover. Coinciding with the aims of this study, we explore the following research questions:
RQ1: Do job autonomy and perceived organizational support improve employees’ psychological capital?
RQ2: Does psychological capital improve employees’ work–life balance?
RQ3: Does improved work–life balance increase employees’ intention to stay?
RQ4: Do job autonomy and perceived organizational support indirectly (via serial mediation of psychological capital and work–life balance) increase employees’ intention to stay?
Sustainability 15 00551 g001 550
Figure 1. Conceptual model.
Figure 1. Conceptual model.
Sustainability 15 00551 g001

2. Theory and Hypotheses

2.1. Conservation of Resource Theory (CRT) and the Job Demands and Resources (JD-R) Framework

The current research model utilizes the conservation of resource theory (CRT) and the job demand resources framework (JD-R) as the theoretical lens to view how job resources lead to the intention to stay via the serial mediation effect of psychological capital (PsyCap) and work–life balance (WLB). The JD-R framework explains how job resources reinforce engagement through a motivational path [33,34]. The CRT postulates that employees attempt to gain and retain valuable resources [35]. Stress happens when they lose these resources or if there is a threat of loss of those resources. The theory assumes that individuals have to gain valuable resources to mitigate and handle stressful circumstances and achieve desirable outcomes. Based on this, PsyCap is considered a crucial personal resource to promote well-being. A basic tenet of the JD-R framework is the interactions between work demands and resources [36]. Some resources (e.g., supervisor support) can help alleviate the negative psychological consequences (e.g., poor work–life balance) that individuals experience. The JD-R framework traditionally discusses the characteristics of job demands and resources. Xanthopoulou et al. [37] also explored the effect of personnel resources in the JD-R model.
Further, drawing upon the CRT, studies have also considered the role of personal resources [38,39,40]. Personal resources are qualities of the self typically associated with personal resources such as resilience and allude to an individual’s perception of their capacity to successfully control and influence their surroundings [39]. Personal resources enable employees to cope with highly demanding work situations. Individuals act as ‘job crafters’ by utilizing personal resources to facilitate positive work-related outcomes [38,41,42]. Grover et al. [43] used PsyCap as the four-dimension construct and found that it directly influences the perceptions of work demands and resources and significantly influences well-being outcomes. This study’s proposed model uses all the above theoretical support to explore the linkages between job resources and intention to stay via mediating effects of PsyCap and WLB.

2.2. Job Autonomy (JA) and Psychological Capital (PsyCap)

Job autonomy (JA) is the amount to which a particular job may give a high degree of flexibility, independence, and choice in work scheduling and determining the processes to be employed to perform them [44]. It refers to an employee’s level of control over their job tasks [45]. Typically, discretion on the job has been considered in terms of JA, which represents the degree to which a job provides the flexibility, independence, and discretion to plan work, make decisions, and choose the techniques utilized to execute tasks. There is enough literature to support the positive outcomes of JA in both the work and life domains. In the work domain, higher job demands can lead to various positive outcomes such as increased job performance, work efficiency, and motivation. JA enables them to define their roles with greater flexibility since they will have greater choices in determining how to carry out their duties [3]. Wang and Netemyer [46] found that JA positively affects job performance. Escalated JA is also said to facilitate increased confidence in the performance of certain tasks [44].
Psychological capital (PsyCap) has risen to prominence as a higher-order concept composed of “hope”, “resilience”, “self-efficacy”, and “optimism” [47]. All the dimensions of PsyCap have linkages with job autonomy. Employees with higher levels of “self-efficacy” may have a better probability of completing the complicated process of creating work procedures and schedules [48], which reduces their likelihood of experiencing stress at work. Optimistic employees may use JA to reduce workplace stress, and optimism interacts with the curvilinear relationship between JA and job stress [49]. Employees with a greater degree of resilience may be better able to manage job stress due to their increased JA. Resilience is seen as a helpful resource for mitigating the negative consequences of a demanding work environment [50]. Hence, the presence of higher job autonomy leads to the building up of the dimensions of PsyCap to enable the individual to cope with higher demands and work to foster positive outcomes.
According to the self-determination theory (SDT), feeling contented and being well can arise from three psychological demands: autonomy, competence, and relatedness [51]. A person’s PsyCap—hope, efficacy, resilience, and optimism—will satisfy the demands of autonomy, competence, and relatedness and increase employees’ career commitment. Hackman and Oldham [52] argued that autonomy leads to a crucial psychological state characterized by “experienced responsibility for outcomes of work”, resulting in increased work efficiency and intrinsic motivation. JA is expected to boost PsyCap since it gives individuals more influence over their professional success and failure [52]. Hence, by taking support from positive work environment literature and JD-R model-based studies, we hypothesize as follows;
Hypothesis 1 (H1).
Job autonomy improves employees’ psychological capital.

2.3. Perceived Organizational Support (POS) and Psychological Capital (PsyCap)

The employee’s perception of the extent to which their organization values their contribution and cares about their well-being is perceived as organizational support (POS) [53]. Generally, employees care more about the organization’s commitment to them. POS is connected with three primary types of favorable employee treatment: fairness, supervisor support, organizational rewards, and favorable employment arrangements [54]. Getting valued by the organization may result in rewards such as approbation and respect, income and career advancement, data access, and other resources needed to perform one’s work more effectively. The principle of reciprocity permits employees and employers to reconcile these divergent perspectives and derive positive outcomes for both. Workplace support is a critical job resource that enables employees to achieve positive outcomes such as work–life balance [55,56,57] and organizational commitment [58].
Psychological capital (PsyCap) is an employee’s positive psychological state of development with sub-dimensions of self-efficacy, hope, resilience, and optimism. It is among the prominent constructs in positive psychology related to individuals’ positive aspects and leading to positive outcomes. POS enhances employees’ PsyCap and further improves their performance and organizational commitment [59]. The sub-dimensions of POS and PsyCap have linkages, and outcomes of both constructs are said to facilitate positive employee outcomes. POS was found to have a positive direct association with PsyCap, and its dimensions, mainly optimism and resilience, partially mediate between POS and depressive symptoms [60]. Huo, Gong, Xing, Tam and Kuai [32] and Wang et al. [61] found a direct positive association between POS and four dimensions of PsyCap (viz. hope, optimism, self-confidence, and optimism). Hence, we formulate the following hypothesis:
Hypothesis 2 (H2).
POS improves employees’ psychological capital.

2.4. Psychological Capital (PsyCap) and Work–Life Balance (WLB)

Psychological capital (PsyCap) is referred to an employee’s positive psychological state [62] and a personal resource that represents an individual’s “optimistic assessment of conditions and likelihood of success based on motivated efforts and tenacity” [63]. There is enough literature support for stating that various personal characteristics are linked with work–life balance (WLB). A meta-analysis by Vaziri et al. [64] reports that PsyCap, with attributes such as hope, optimism, self-efficacy, and resilience, is a strong predictor of WLB. JD-R framework literature supports that job resources improve employees’ PsyCap [64], leading to better employee WLB. This indirect effect can be justified with the JD-R model [65]. JD-R model suggests the need for job resources to deal with high-pressure work demands [43,66]. Job autonomy and organizational support are two main job resources that lead to many positive outcomes such as work–life balance by enriching psychological attributes such as hope, optimism, self-efficacy, and resilience [55,56,57,67]. Grover, Teo, Pick, Roche and Newton [43] also explored the indirect association between job resources, PsyCap, and WLB. Studies related to PsyCap show that it induces positive attitudes in individuals about their work environment [47,68]. Development of these positive attitudes related to the work environment can increase better work–life balance perceptions; thus, we hypothesize as follows;
Hypothesis 3 (H3).
Psychological capital improves employees’ work–life balance.

2.5. Work–Life Balance (WLB) and Intention to Stay (IS)

Work–life balance (WLB) refers to an individual’s skill to maintain a proper balance between work and life domains. There exist a plethora of studies showing the significance of WLB in fostering positive outcomes such as job performance [44], job satisfaction [69,70], organizational commitment [55], and work engagement [71]. All the positive outcomes arising from better work–life balance are supposed to facilitate employees’ intention to stay (IS) as they feel more contended and satisfied in their current position. Across different industries, the perceived level of WLB has been found significantly influence one’s IS in their current position [72]. Similar findings have been reported in the context of accounting professionals as well. Surienty et al. [73] reported that WLB and turnover intentions are negatively related among employees of accounting firms.
Strategies such as family-friendly policies to improve WLB have effectively reduced turnover rates, absenteeism, and stress [74]. Deery [75] has also asserted the role of WLB in reducing employees’ attrition rate. Lindfelt, Ip, Gómez and Barnett [72] discussed the role of managers in formulating WLB policies such as flexible work scheduling and better work support to foster WLB and increase employees’ intention to stay. Hence, in the case of accounting professionals engaged in telecommuting, better work flexibility and organizational support are plausible to facilitate better WLB and thus may eventually enhance employees’ intentions to stay. Therefore, we formulate the below hypothesis:
Hypothesis 4 (H4).
Work–life balance increases employees’ intention to stay.

2.6. Psychological Capital (PsyCap) and Work–Life Balance (WLB) as the Mediators between Job Autonomy (JA), Perceived Organizational Support (POS), and Intention to Stay (IS)

Ensuring well-being at work and managing occupational stress has been a concern for HR managers as both have a linkage with the intention to stay among employees. Work resources reinforce engagement through a motivational path as per the JD-R model [33,34,76]. Job resources (e.g., autonomy and organizational support) can lessen the detrimental psychological effects (e.g., poor work–life balance) that individuals go through. Seminal studies have also addressed the role of personal resources (e.g., psychological capital) in the JD-R model [38,39,40]. PsyCap assists employees in mitigating the detrimental effects arising from a highly demanding work environment.
Conversely, the effect of job demands and resources on outcomes may be mediated by personal resources PsyCap and WLB. Grover, Teo, Pick, Roche and Newton [43] employed PsyCap (as a personal resource) and discovered that it strongly impacts the outcomes related to well-being and directly impacts how people perceive the demands and resources of their employment. Hence, it is plausible to assume that PsyCap being a personal resource, can have a mediating effect between job resources and outcomes. According to Vaziri, Wayne, Casper, Lapierre, Greenhaus, Amirkamali and Li [64], psychological capital is a significant predictor of WLB, which facilitates positive outcomes such as job satisfaction [77], job performance [44], organizational commitment [55], intention to stay [78]. Hence, it is plausible to postulate that psychological capital and work–life balance serially mediate the relationship between job resources and intentions to stay such that JA and POS indirectly enhance employees’ intention to stay, and we hypothesize as follows;
Hypothesis 5 (H5).
Work–life balance and psychological capital serially mediate the direct relationship between job autonomy and employees’ intention to stay.
Hypothesis 6 (H6).
Work–life balance and psychological capital serially mediate the direct relationship between perceived organizational support and employees’ intention to stay.

3. Materials and Methods

This study develops a serial mediation model proposing the positive indirect role of two job resources, job autonomy (JA) and perceived organizational support (POS), on employees’ intention to stay (IS) via the serial mediation of psychological capital (PsyCap) (second-order variable having four sub-dimensions, efficacy, hope, resilience, and optimism), and work–life balance (WLB). For this purpose, we surveyed accounting practitioners from big-four accounting companies, Deloitte, PwC, Ernst & Young, and KPMG in an Indian city, Bangalore. Employees at the analyst, associate, and senior associate levels at an off-shore center providing auditing, tax, and assurance advice services were included in the research.

3.1. Survey Measures

The survey instrument consisted of five latent variables (being measured by multi-item measurement scales adapted from published studies) on a 7-point Likert-type scale spread from 1 (strongly disagree) to 7 (strongly agree). A three-item scale was adapted from Langfred [45] for assessing job autonomy, while for perceived organizational support, we took a four-item scale from Eisenberger, Huntington, Hutchison and Sowa [54]. Further, to encapsulate psychological capital (a second-order construct with four sub-dimensions, efficacy, hope, resilience, and optimism), we used the PsyCap Questionnaire developed by Luthans, Youssef and Avolio [63]. We adopted three items for each sub-dimension of PsyCap. Further, we cited Geurts et al. [79] for the work–life balance measurement scale. Lastly, the intention to stay was measured by taking a three-item scale from the MOA questionnaire developed by [80]. Scale items with their sources are given in the appendix (see Appendix A).

3.2. Questionnaire Design and Pilot Testing

The survey was divided into two portions. The first half was supposed to elicit respondents’ demographic characteristics, including age, gender, marital status, dependent children, and designation, while the second section was intended to assess five latent variables included in this study’s postulated research framework. Before proceeding with the main survey, we followed a two-fold piloting method to affirm the survey instrument’s face and content reliability [81]. Initially, we approached four employees (one from each sampled accounting company) and three researchers/academics from HR/OB and organizational psychology research to screen and validate the survey instrument. Corresponding to the suggestions received, we amended scale items linguistically and subjectively. Lastly, we carried out a pilot survey taking responses from 68 employees of sampled companies to ensure internal consistency of the scales.

3.3. Main Survey

We adopted a network sampling strategy for data collection since this study’s target sample consisted of off-shore center workers from the big-four accounting firms in Bangalore working under strict time constraints [4,82]. Because one of the authors had previously worked in one of these organizations and telecommuted voluntarily, we favored the network sample approach. We used his connections to reach out to the responders. The respondents were contacted in July 2021 and requested to complete the survey using the company’s internal mailing system. We sent the questionnaires to a total of 715 workers, but only 341 of them replied to the survey.

3.4. Control Variables

We also controlled for age, gender, marital status, dependent children (1 = yes; 2 = no), and designation (1 = analyst; 2 = associate; 3 = senior associate). Controlling for these demographic characteristics is based on the assumption that unique work demands may have a discernible effect on an employee of a given age, gender, marital status, parenting status, and designation [83].

3.5. Data Screening

Before beginning statistical validation analyses, we examined the raw data for missing, incorrect, and outlier responses. Following an inquiry, 16 questionnaires were discovered to have missing responses and were therefore removed from the sample. Furthermore, the authors looked for improper responses and discovered seven; hence, we discarded them as well. Finally, the researchers used Cook’s distance to detect statistical outliers. Cook’s distance findings revealed that 11 responses had Cook’s statistics over the threshold of 1 [84]; therefore, we removed these response from the dataset, and this study acquired its final sample of 307 responses (see Table 1 for demographic profiles).

3.6. Dealing with Method Bias

The research used a full-collinearity test [85] to determine if it suffers from method bias while adhering to the qualitative checks and measures recommended by Podsakoff et al. (2012) during the development of the survey instrument. To confirm that data are free from method bias, Kock suggested to make study’s each latent variable an outcome variable sequentially and compute VIFs which should not exceed 3.3 in any model. Accordingly, we ran five regression models making each latent variable the dependent variable, and computed VIFs. None of the models produced VIFs above the recommended threshold of 3.3, thus confirming that this study is not affected by method bias [85].

4. Results

4.1. Measurement Model Assessment

We drew and ran a measurement model in AMOS v23.0 to assess the model’s goodness of fit, validity (convergence and divergence), and reliability [86,87]. Subsequently, we ran the structural model testing the hypothesized direct and indirect (serial mediation) relationships among the latent variables. The results confirmed that both models’ (measurement and structural) global fit indices were found well within the threshold limits (see Table 2).
The aim of the assessment of the measurement is also to affirm whether the data meet the convergent and divergent validity criteria and the measurement scales hold internal consistency (Kline, 2015). The convergent validity of the measurement model is affirmed by accounting for AVE values and CFA loadings. When the average CFA loading for each latent variable is not less than 0.708 and the AVE values are above 0.50, the model has adequate convergence [92,93]. As shown in Table 3, the average CFA loading and AVE values for each latent construct are substantially above the threshold limits, and this study’s measurement models meet the convergence criterion. We also computed Cronbach’s alpha (α) and composite reliability (CR) statistics to assess the internal consistency of the measurement scales. CR and α values for each latent variable were measured to be above the cut-off limit of 0.70 [92,94], thereby referring to the sufficient reliability of the measurement scales.
In addition to affirming the model’s convergence, we also assessed divergence among the latent variables Fornell and Larcker [95] and HTMT ratio criteria [96]. According to Fornell and Larcker [95], a latent variable is adequately divergent from other latent variables if the squared root value of its AVE (bold diagonal values in Table 4) exceeds its correlations with other latent variables (values below the diagonals in Table 4). The measurement model of the current study conforms with the criteria affirming that bold diagonal values (the squared root of AVEs) exceed the below diagonal correlation coefficients, thus meeting the assumption of divergent validity.
Furthermore, we demonstrated divergent validity using the HTMT criteria, where validity is proved using HTMT ratios. If the HTMT ratios between the constructs are less than 0.85, the measurement model has divergent validity [96]. Table 5 shows that the HTMT ratios among the constructs are less than 0.85, indicating the establishment of divergent validity.

4.2. Structural Model

We tested the structural model in AMOS v23.0, assessing the direct and indirect (via serial mediation of PsyCap and WLB) influence of job autonomy (JA) and perceived organizational support (POS) on employees’ intention to stay (IS). This study proposed two hypotheses to assess the direct influence of JA (H1) and POS (H2) on PsyCap, followed by assessing the direct influence of PsyCap (H3) on work–life balance (WLB). Hypothesis H4 assessed the direct association between WLB and IS. Lastly, Hypotheses H5 and H6 assessed the indirect influence of JA (H5) and POS (H6) on IS via the serial mediation of PsyCap and WLB. The significance level of serial mediation effects was computed using the bias-corrected percentile method with 5000 bootstraps. Moreover, this study also checked the confounding effect of control variables, age, gender, marital status, and dependent children, and found them insignificant.

4.2.1. Direct Effects

Hypotheses H1 and H2 were meant to assess the direct influence of JA and POS on PsyCap, respectively. The results from Table 6 affirm that JA (β = 0.338; t-value = 4.90; p-value < 0.01) and POS (β = 0.363; t-value = 5.85; p-value < 0.01) have a significant positive association with employees’ PsyCap. PsyCap was also found to improve employees’ WLB significantly (β = 0.467; t-value = 6.58; p-value < 0.01), thus, Hypotheses H1, H2, and H3 were accepted. Lastly, Hypothesis H4 proposed to check the direct association between WLB and employees’ IS, and the results confirmed that WLB substantially enhances employees’ IS (β = 0.485; t-value = 6.47; p-value < 0.01), hence extending support to Hypothesis H4.

4.2.2. Indirect Effects (Serial Mediation Analysis)

Since this study hypothesized the indirect effect of JA (H5) and POS H6) on IS via serial mediation of PsyCap and IS, we tested the indirect effects using the bias-corrected percentile method with 5000 bootstraps [97]. Table 6 evidenced that JA indirectly enhances employees’ intention to stay through the serial mediation of PsyCap and WLB (β = 0.076; BootSE = 0.072; LLCI = 0.061, ULCI = 205). Further, POS was also found to be indirectly enhancing employees’ intention to stay via serial mediation of PsyCap and WLB (β = 0.082; BootSE = 0.077; LLCI = 0.069, ULCI = 233); thus, both Hypotheses H5 and H6 stand accepted.

5. Discussion

Overall, all the hypothesized relationships in the proposed research model were found to be valid. Job resources, job autonomy (JA), and perceived organizational support (POS) have been found to enhance the employees’ psychological capital (PsyCap), directly inferring that higher work autonomy and organizational support help in building employees’ psychological capital, which subsequently enhances employees’ work–life balance. The results align with the findings of a recent meta-analysis by Vaziri, Wayne, Casper, Lapierre, Greenhaus, Amirkamali and Li [64]. With increased JA while teleworking, employees are supposed to have higher work schedule flexibility and freedom (Gajendran et al., 2015; Golder & Veiga, 2005; Jamal, Anwar & Khan, 2021; Jamal, Anwar, Khan et al., 2021). Facilitating higher autonomy has always been treated as a major job resource and the fundamental tenet of psychological well-being fostering work–life balance. Findings of both job resources JA and POS to form PsyCap and induce positive outcomes of work–life balance and intentions to stay conform with work–family spillover theory, plausibly given that individuals may most probably experience positive work-to-family spillover when they are provided with optimum levels of JA and POS. The presence of both resources is also more likely to improve employee morale and satisfaction. The presence of higher JA can also facilitate a smooth transition between work and family roles among individuals, which will enable them to maintain a healthy work–life balance. Additionally, the findings regarding the new serial mediation pathway using work–life balance and PsyCap as mediators were confirmed, adding more evidence to the effects of job resources and positive outcomes. However, apart from the proposed resources, the enhanced PsyCap and WLB perception might be due to the influence of other resources arising from a telework context such as better time management possibility, time saved in daily commute, and flexibility in working. (Wong et al., 2020; Jamal et al., 2021). Prior works have already explored and confirmed the direct positive relationship between telecommuting and work–life balance (Carringal et al., 2022; Jamal et al., 2021). During lockdown times, employees were exposed to enforced telecommuting; hence, the enhanced work–life balance level might be due to the influence of telecommuting as well.
Findings related to the direct role of POS on employees’ PsyCap infer that POS enhances the PsyCap of employees by reducing the detrimental effects of job stressors. POS has the ability to alleviate negative employee outcomes such as stress, depression, and anxiety by facilitating a variety of positive outcomes, thus improving PsyCap. Further, PsyCap was also found to improve employees’ work–life balance significantly, contending that enriching employees’ personal resources such as PsyCap by alleviating the effect of certain job stressors might reduce employees’ burnout which consequently improves well-being factors such as work–life balance. It is plausible that higher PsyCap can make the employees more resilient towards the job stressors and make them more contended in staying within the current organization.
Work–life balance was found to have a direct augmentation effect on employees’ intentions to stay, which aligns with the findings of prior studies [73,98,99]. In the current study context, enhanced job autonomy and better organizational support during enforced telecommuting were believed to facilitate the employees improving their personal resources, i.e., PsyCap, which in turn would improve work–life balance, and ultimately result in an enhancement in intention to stay.
The results of serial mediation analysis affirm that JA and POS indirectly enhance employees’ intention to stay via serial mediation of PsyCap and work–life balance. The findings conform with Huo, Gong, Xing, Tam and Kuai [32] and Ho and Chan [100] and infer that employees with greater autonomy in work and favorable organizational support would have a greater level of PsyCap, and an improved work–life balance; hence, through this serial mediation indirect pathway, employees’ intention to stay with the organization also tends to increase.

6. Implications

Distinct from prior studies, we have explored a unique serial mediation model with two job resources, viz. job autonomy and perceived organizational support (as predictors), one personal resource, i.e., PsyCap (as the first mediator), one well-being variable, i.e., work–life balance (as the second mediator), and the outcome variable intention to stay. Since earlier studies have recommended adding personal resource variables in the JD-R model, the current study addresses this gap by conceptualizing a serial mediation model with a personal resource variable PsyCap, thereby contributing to the JD-R literature. This study’s findings provided insights into the impact of job resources on psychological states and positive outcomes. This study’s overall findings also have a contemporary significance in the context of “the great resignation” by cognizing the intention to stay as the outcome variable [10].
The findings of this study also proffer some valuable suggestions for managerial implications. Since telecommuting has been accelerated during the pandemic times and is expected to continue as the future of the work will be a hybrid work model, the findings from the current study are of some practical use and provide valuable insights to the HR managers to better cope with issues such as “the great resignation” in the future. While designing future work models, employees’ growing quest for higher job autonomy and organizational support should be considered by HR managers. The managers are required to understand the significance of building employees’ PsyCap in order to improve employees’ work–life balance. Management should support teamwork, efficient communication, access to information, providing proper guidelines and feedback, and delivering positive criticism from managers and team leaders to improve employees’ PsyCap levels and enhance their WLB perceptions. Ensuring the enrichment of job resources and improving employees’ PsyCap would improve employees’ perception of work–life balance and further enhance their satisfaction, loyalty, and trustworthiness, making them stay within the firm.

7. Conclusions

Overall, the current model explored the indirect effect of two major job resources, job autonomy and perceived organizational support arising from work-from-home scenarios, on employees’ intentions to stay using the theoretical perspectives of the JDR model and COR theory. Distinct from studies exploring job resources and positive outcomes, the current study has tested a unique serial mediation pathway in this relationship. PsyCap and work–life balance have been found to mediate the relationship between job resources and intentions to stay. Both the job resources were found to enhance the PsyCap of employees, which leads to better work–life balance and enhances intentions to stay among employees. The findings of this study highlight the significance of job autonomy and perceived organizational support in building a resilient and contended workforce with a better retention rate. Insights of this study will be helpful for the managers in designing suitable HR strategies to meet challenges such as “the great resignation.” Apart from its contemporary relevance by discussing voluntary turnover issues in the industry, the present study also expands the literature by filling the methodological gap by exploring a new serial mediation pathway between the relationship of job resources and intention to stay.

8. Limitations and Future Directions

A cross-sectional research design has been adopted in the present study. The responses for all variables were taken at a specific time point, so the time precedence of independent variables cannot be ensured so that a longitudinal study brings better clarity in establishing the causal relationship. The work-from-home practice has been prevailing in the accounting industry even before COVID-19, and it is still going to be in practice in the future. Hence, future studies with a longitudinal design can solve these limitations and give a better picture of the phenomenon. The present study has only considered employees exposed to enforced telecommuting. However, future studies can make comparisons between mandatory telecommuting and hybrid working modes. The current study has been limited to only one city as a geographical location; hence, further studies with different geographical locations can be performed to ensure the generalizability of the findings. Moreover, the current study has focused on only two job resources: job autonomy and perceived organizational support. Future studies could also survey other job resources, viz., supervisor and co-worker support.

Author Contributions

Conceptualization, M.S. and M.A.A.; methodology, I.A.; formal analysis, I.A.; data curation, A.A. and S.A.; writing—original draft, M.S. and I.A.; writing—review and editing, I.A., A.A., S.A., E.B. and M.A.A.; funding acquisition, A.A. and 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

Data were anonymized, and no personal information such as contact numbers or/and email addresses were solicited, so this study was exempted from ethical approval by the Institutional Ethics Committee of Aligarh Muslim University.

Informed Consent Statement

Informed consent was obtained from all individual participants included in this study. Respondents were informed in advance about the theme and aims of this study.

Data Availability Statement

The datasets generated and/or analyzed during the current study will be made available from the corresponding author upon reasonable request.

Conflicts of Interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Appendix A. Items of the Questionnaire with Their Source of Adoption

Construct Name with Items of Measurement
EfficacySource: Luthans, Youssef and Avolio [63]
  • “I feel confident analyzing a long-term problem to find a solution.”
  • “I feel confident in representing my work area in meetings with management.”
  • “I feel confident contributing to discussions about the company’s strategy.”
HopeSource: Luthans, Youssef and Avolio [63]
  • “If I should find myself in a jam at work, I could think of many ways to get out of it.”
  • “There are lots of ways around any problem.”
  • “I can think of many ways to reach my current work goals.”
ResilienceSource: Luthans, Youssef and Avolio [63]
  • “I usually manage difficulties one way or another at work.”
  • “I can get through difficult times at work because I’ve experienced difficulty before.”
  • “I feel I can handle many things at a time at this job.”
OptimismSource: Luthans, Youssef and Avolio [63]
  • “When things are uncertain for me at work, I usually expect the best.”
  • “I always look on the bright side of things regarding my job.”
  • “I’m optimistic about what will happen to me in the future as it pertains to work.”
Perceived Organizational SupportSource: Eisenberger, Huntington, Hutchison and Sowa [54]
  • “The organization values my contribution toward its success.”
  • “The organization really cares about my well-being.”
  • “The organization cares about my general satisfaction at work.”
  • “The organization takes pride in my accomplishments at work.”
Job AutonomySource: Langfred [45]
  • “I have control over pace of my work.”
  • “I have authority in determining tasks to be performed.”
  • “I have authority in determining rules and procedures for my own work.”
Work–Life BalanceSource: Geurts, Taris, Kompier, Dikkers, Van Hooff and Kinnunen [79]
  • “My job makes it easy to maintain the kind of personal life I would like.”
  • “I rarely neglect my personal needs because of the demands of my work.”
  • “My personal life does not suffer because of my work.”
  • “I do not have to miss out on important personal activities due to the amount of time I spend doing work.”
Intention to StaySource: Lawler, Cammann, Nadler and Jenkins [80]
  • “I plan to stay in this job for at least two or three Years.”
  • “I plan to work at my present job as long as possible.”
  • “I feel a strong sense of belonging to this organization.”

References

  1. Bellmann, L.; Hübler, O. Working from home, job satisfaction and work–life balance—robust or heterogeneous links? Int. J. Manpow. 2021, 42, 424–441. [Google Scholar] [CrossRef]
  2. Caringal-Go, J.F.; Teng-Calleja, M.; Bertulfo, D.J.; Manaois, J.O. Work-life balance crafting during COVID-19: Exploring strategies of telecommuting employees in the Philippines. Community Work. Fam. 2022, 25, 112–131. [Google Scholar] [CrossRef]
  3. Jamal, M.T.; Anwar, I.; Khan, N.A.; Saleem, I. Work during COVID-19: Assessing the influence of job demands and resources on practical and psychological outcomes for employees. Asia-Pac. J. Bus. Adm. 2021, 13, 293–319. [Google Scholar] [CrossRef]
  4. Jamal, M.T.; Alalyani, W.R.; Thoudam, P.; Anwar, I.; Bino, E. Telecommuting during COVID 19: A Moderated-Mediation Approach Linking Job Resources to Job Satisfaction. Sustainability 2021, 13, 11449. [Google Scholar] [CrossRef]
  5. Lee, D.-J.; Joseph Sirgy, M. Work-life balance in the digital workplace: The impact of schedule flexibility and telecommuting on work-life balance and overall life satisfaction. In Thriving in Digital Workspaces; Springer: Berlin/Heidelberg, Germany, 2019; pp. 355–384. [Google Scholar]
  6. Kossek, E.E.; Lautsch, B.A.; Eaton, S.C. Telecommuting, control, and boundary management: Correlates of policy use and practice, job control, and work–family effectiveness. J. Vocat. Behav. 2006, 68, 347–367. [Google Scholar] [CrossRef]
  7. Handy, S.L.; Mokhtarian, P.L. Forecasting telecommuting. Transportation 1996, 23, 163–190. [Google Scholar] [CrossRef]
  8. Metselaar, S.A.; den Dulk, L.; Vermeeren, B. Teleworking at Different Locations Outside the Office: Consequences for Perceived Performance and the Mediating Role of Autonomy and Work-Life Balance Satisfaction. Rev. Public Pers. Adm. 2022, 0734371X221087421. [Google Scholar] [CrossRef]
  9. Loh, T.H.; Lara, F. Covid-19 makes the Benefits of Telework Obvious. Available online: https://tinyurl.com/3n7r2t6m (accessed on 10 April 2021).
  10. Serenko, A. The Great Resignation: The great knowledge exodus or the onset of the Great Knowledge Revolution? J. Knowl. Manag. 2022. ahead-of-print. [Google Scholar] [CrossRef]
  11. BLS. Job Openings and Labor Turnover Survey. Available online: https://www.bls.gov/jlt/ (accessed on 15 January 2022).
  12. Malmendier, U. FBBVA Lecture 2020 Exposure, Experience, and Expertise: Why Personal Histories Matter in Economics. J. Eur. Econ. Assoc. 2021, 19, 2857–2894. [Google Scholar] [CrossRef]
  13. Melin, A.; Egkolfopoulou, M. Employees are quitting instead of giving up working from home. Bloomberg 2021. Available online: https://www.bloomberg.com/news/articles/2021-06-01/return-to-office-employees-are-quitting-instead-of-giving-up-work-from-home (accessed on 13 July 2021).
  14. Thompson, D. The Great Resignation Is Accelerating. Atlantic 2021, 15. Available online: https://www.theatlantic.com/ideas/archive/2021/10/great-resignation-accelerating/620382/ (accessed on 12 December 2021).
  15. Nouri, H.; Parker, R.J. Turnover in public accounting firms: A literature review. Manag. Audit. J. 2020, 35, 294–321. [Google Scholar] [CrossRef]
  16. Kohlmeyer, J.M.; Parker, R.J.; Sincich, T. Career-Related Benefits and Turnover Intentions in Accounting Firms: The Roles of Career Growth Opportunities, Trust in Superiors, and Organizational Commitment. In Advances in Accounting Behavioral Research; Emerald Publishing Limited: Bentley, UK, 2017; Volume 20, pp. 1–21. [Google Scholar]
  17. Seyrek, I.H.; Turan, A. Effects of individual characteristics and work related factors on the turnover intention of accounting professionals. Int. J. Acad. Res. Account. Financ. Manag. Sci. 2017, 7, 236–244. [Google Scholar] [CrossRef] [PubMed]
  18. Boyer-Davis, S. Technostress in accounting professionals: A quantitative examination of the differences between managers and non-managers. J. Account. Financ. 2019, 19, 25–41. [Google Scholar]
  19. Greer, T.W.; Payne, S.C. Overcoming telework challenges: Outcomes of successful telework strategies. Psychol. -Manag. J. 2014, 17, 87. [Google Scholar] [CrossRef]
  20. Taylor, G.H.; Blount, Y.; Gloet, M. Aged care, ICT, and working anywhere: An Australian case study. In Anywhere Working and the New Era of Telecommuting; IGI Global: Hershey, PN, USA, 2017; pp. 203–222. [Google Scholar]
  21. Blount, Y.; Gloet, M. Anywhere Working and the New Era of Telecommuting; IGI Global: Hershey, PN, USA, 2017. [Google Scholar]
  22. Almer, E.D.; Kaplan, S.E. The effects of flexible work arrangements on stressors, burnout, and behavioral job outcomes in public accounting. Behav. Res. Account. 2002, 14, 1–34. [Google Scholar] [CrossRef]
  23. Buchheit, S.; Dalton, D.W.; Harp, N.L.; Hollingsworth, C.W. A Contemporary Analysis of Accounting Professionals’ Work-Life Balance. Account. Horiz. 2015, 30, 41–62. [Google Scholar] [CrossRef]
  24. Boyer-Davis, S. Technostress: An antecedent of job turnover intention in the accounting profession. J. Bus. Account. 2019, 12, 49–63. [Google Scholar]
  25. Smith, K.J.; Emerson, D.J.; Boster, C.R.; Everly, G.S., Jr. Resilience as a coping strategy for reducing auditor turnover intentions. Account. Res. J. 2020, 33, 483–498. [Google Scholar] [CrossRef]
  26. Mnif, Y.; Rebai, E. Flexibility and job stress in the accounting profession. Account. Res. J. 2022, 35, 261–275. [Google Scholar] [CrossRef]
  27. Jefferson, D.; Andiola, L.M.; Hurley, P.J. Surviving Busy Season in a Remote Work Environment: Using the Job Demands-Resources Model to Investigate Coping Mechanisms. Available SSRN 2022. [Google Scholar] [CrossRef]
  28. Terry, P.E. Well-Being and Evolving Work Autonomy: The Locus of Control Construct Revisited. Am. J. Health Promot. 2022, 36, 593–596. [Google Scholar] [CrossRef]
  29. Vander Elst, T.; Verhoogen, R.; Sercu, M.; Van den Broeck, A.; Baillien, E.; Godderis, L. Not extent of telecommuting, but job characteristics as proximal predictors of work-related well-being. J. Occup. Environ. Med. 2017, 59, e180–e186. [Google Scholar] [CrossRef] [PubMed]
  30. Bhat, Z.H.; Yousuf, U.; Saba, N. The Implications of Telecommuting on Work-Life Balance: Effects on Work Engagement and Work Exhaustion. 2022. Available online: https://assets.researchsquare.com/files/rs-1642674/v1/8da9a341-5af7-43fc-9e18-04fdffb64ea0.pdf?c=1652284848 (accessed on 25 July 2022).
  31. Bilotta, I.; Cheng, S.; Davenport, M.K.; King, E. Using the job demands-resources model to understand and address employee well-being during the COVID-19 pandemic. Ind. Organ. Psychol. 2021, 14, 267–273. [Google Scholar] [CrossRef]
  32. Huo, W.; Gong, J.; Xing, L.; Tam, K.L.; Kuai, H. Voluntary versus involuntary telecommuting and employee innovative behaviour: A daily diary study. Int. J. Hum. Resour. Manag. 2022, 1–25. [Google Scholar] [CrossRef]
  33. Bakker, A.B.; Demerouti, E. Job demands–resources theory: Taking stock and looking forward. J. Occup. Health Psychol. 2017, 22, 273. [Google Scholar] [CrossRef] [PubMed]
  34. Schaufeli, W.B.; Taris, T.W. A Critical Review of the Job Demands-Resources Model: Implications for Improving Work and Health. In Bridging Occupational, Organizational and Public Health: A Transdisciplinary Approach; Springer: Dordrecht, The Netherlands, 2014; pp. 43–68. [Google Scholar]
  35. Hobfoll, S.E. Social and psychological resources and adaptation. Rev. Gen. Psychol. 2002, 6, 307–324. [Google Scholar] [CrossRef]
  36. Karasek, R.A., Jr. Job demands, job decision latitude, and mental strain: Implications for job redesign. Adm. Sci. Q. 1979, 24, 285–308. [Google Scholar] [CrossRef]
  37. Xanthopoulou, D.; Bakker, A.B.; Demerouti, E.; Schaufeli, W.B. The role of personal resources in the job demands-resources model. Int. J. Stress Manag. 2007, 14, 121. [Google Scholar] [CrossRef] [Green Version]
  38. Bakker, A.B.; Tims, M.; Derks, D. Proactive personality and job performance: The role of job crafting and work engagement. Hum. Relat. 2012, 65, 1359–1378. [Google Scholar] [CrossRef]
  39. Grover, S.L.; Teo, S.T.; Pick, D.; Roche, M. Mindfulness as a personal resource to reduce work stress in the job demands-resources model. Stress Health 2017, 33, 426–436. [Google Scholar] [CrossRef] [Green Version]
  40. Huang, J.; Wang, Y.; You, X. The job demands-resources model and job burnout: The mediating role of personal resources. Curr. Psychol. 2016, 35, 562–569. [Google Scholar] [CrossRef]
  41. Hakanen, J.J.; Seppälä, P.; Peeters, M.C. High job demands, still engaged and not burned out? The role of job crafting. Int. J. Behav. Med. 2017, 24, 619–627. [Google Scholar] [CrossRef] [PubMed]
  42. Petrou, P.; Demerouti, E.; Xanthopoulou, D. Regular versus cutback-related change: The role of employee job crafting in organizational change contexts of different nature. Int. J. Stress Manag. 2017, 24, 62. [Google Scholar] [CrossRef]
  43. Grover, S.L.; Teo, S.T.T.; Pick, D.; Roche, M.; Newton, C.J. Psychological capital as a personal resource in the JD-R model. Pers. Rev. 2018, 47, 968–984. [Google Scholar] [CrossRef]
  44. Johari, J.; Yean Tan, F.; Tjik Zulkarnain, Z.I. Autonomy, workload, work-life balance and job performance among teachers. Int. J. Educ. Manag. 2018, 32, 107–120. [Google Scholar] [CrossRef]
  45. Langfred, C.W. The paradox of self-management: Individual and group autonomy in work groups. J. Organ. Behav. 2000, 21, 563–585. [Google Scholar] [CrossRef]
  46. Wang, G.; Netemyer, R.G. The effects of job autonomy, customer demandingness, and trait competitiveness on salesperson learning, self-efficacy, and performance. J. Acad. Mark. Sci. 2002, 30, 217–228. [Google Scholar] [CrossRef]
  47. Paterson, T.A.; Luthans, F.; Jeung, W. Thriving at work: Impact of psychological capital and supervisor support. J. Organ. Behav. 2014, 35, 434–446. [Google Scholar] [CrossRef]
  48. Avey, J.B.; Luthans, F.; Jensen, S.M. Psychological capital: A positive resource for combating employee stress and turnover. Hum. Resour. Manag. 2009, 48, 677–693. [Google Scholar] [CrossRef]
  49. Babar, S.M. Psychological capital as a moderator in the relationship between job autonomy and job stress: The case of Pakistan financial services firms. Вестник Санкт-Петербургскoгo университета. Менеджмент 2019, 18, 614–633. [Google Scholar]
  50. Kinman, G.; Grant, L. Exploring stress resilience in trainee social workers: The role of emotional and social competencies. Br. J. Soc. Work 2010, 41, 261–275. [Google Scholar] [CrossRef]
  51. Ryan, R.M.; Deci, E.L. Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. Am. Psychol. 2000, 55, 68. [Google Scholar] [CrossRef] [PubMed]
  52. Hackman, J.R.; Oldham, G.R. Motivation through the design of work: Test of a theory. Organ. Behav. Hum. Perform. 1976, 16, 250–279. [Google Scholar] [CrossRef]
  53. Rhoades, L.; Eisenberger, R. Perceived organizational support: A review of the literature. J. Appl. Psychol. 2002, 87, 698. [Google Scholar] [CrossRef] [PubMed]
  54. Eisenberger, R.; Huntington, R.; Hutchison, S.; Sowa, D. Perceived organizational support. J. Appl. Psychol. 1986, 71, 500. [Google Scholar] [CrossRef]
  55. Aryee, S.; Srinivas, E.S.; Tan, H.H. Rhythms of life: Antecedents and outcomes of work-family balance in employed parents. J. Appl. Psychol. 2005, 90, 132. [Google Scholar] [CrossRef] [PubMed]
  56. Ferguson, M.; Carlson, D.; Zivnuska, S.; Whitten, D. Support at work and home: The path to satisfaction through balance. J. Vocat. Behav. 2012, 80, 299–307. [Google Scholar] [CrossRef]
  57. Greenhaus, J.H.; Ziegert, J.C.; Allen, T.D. When family-supportive supervision matters: Relations between multiple sources of support and work–family balance. J. Vocat. Behav. 2012, 80, 266–275. [Google Scholar] [CrossRef]
  58. Aubé, C.; Rousseau, V.; Morin, E.M. Perceived organizational support and organizational commitment. J. Manag. Psychol. 2007, 22, 479–495. [Google Scholar] [CrossRef]
  59. Sihag, P.; Sarikwal, L. Effect of perceived organizational support on psychological capital-A study of IT industries in Indian framework. EJBO: Electron. J. Bus. Ethics Organ. Stud. 2015, 20, 19–26. [Google Scholar]
  60. Liu, L.; Hu, S.; Wang, L.; Sui, G.; Ma, L. Positive resources for combating depressive symptoms among Chinese male correctional officers: Perceived organizational support and psychological capital. BMC Psychiatry 2013, 13, 1–9. [Google Scholar] [CrossRef] [Green Version]
  61. Wang, H.; Sui, Y.; Luthans, F.; Wang, D.; Wu, Y. Impact of authentic leadership on performance: Role of followers’ positive psychological capital and relational processes. J. Organ. Behav. 2014, 35, 5–21. [Google Scholar] [CrossRef] [Green Version]
  62. Luthans, F.; Youssef, C.M.; Avolio, B.J. Psychological Capital and Beyond; Oxford University Press: Oxford, UK, 2015. [Google Scholar]
  63. Luthans, F.; Youssef, C.M.; Avolio, B.J. Psychological Capital: Developing the Human Competitive Edge; Oxford University Press: Oxford, UK, 2007; Volume 198. [Google Scholar]
  64. Vaziri, H.; Wayne, J.H.; Casper, W.J.; Lapierre, L.M.; Greenhaus, J.H.; Amirkamali, F.; Li, Y. A meta-analytic investigation of the personal and work-related antecedents of work–family balance. J. Organ. Behav. 2022, 43, 662–692. [Google Scholar] [CrossRef]
  65. Bakker, A.B.; Hakanen, J.J.; Demerouti, E.; Xanthopoulou, D. Job resources boost work engagement, particularly when job demands are high. J. Educ. Psychol. 2007, 99, 274. [Google Scholar] [CrossRef] [Green Version]
  66. Schaufeli, W.B.; Bakker, A.B. Job demands, job resources, and their relationship with burnout and engagement: A multi-sample study. J. Organ. Behav. Int. J. Ind. Occup. Organ. Psychol. Behav. 2004, 25, 293–315. [Google Scholar] [CrossRef]
  67. Hill, E.J.; Hawkins, A.J.; Ferris, M.; Weitzman, M. Finding an extra day a week: The positive influence of perceived job flexibility on work and family life balance. Fam. Relat. 2001, 50, 49–58. [Google Scholar] [CrossRef]
  68. Laschinger, H.K.S.; Fida, R. New nurses burnout and workplace wellbeing: The influence of authentic leadership and psychological capital. Burn. Res. 2014, 1, 19–28. [Google Scholar] [CrossRef] [Green Version]
  69. Jin, J.F.; Ford, M.T.; Chen, C.C. Asymmetric differences in work–family spillover in North America and China: Results from two heterogeneous samples. J. Bus. Ethics 2013, 113, 1–14. [Google Scholar] [CrossRef]
  70. Lu, L. A Chinese longitudinal study on work/family enrichment. Career Dev. Int. 2011, 16, 385–400. [Google Scholar] [CrossRef]
  71. Tang, Y.; Hornung, S. Work-family enrichment through I-Deals: Evidence from Chinese employees. J. Manag. Psychol. 2015, 30, 940–954. [Google Scholar] [CrossRef]
  72. Lindfelt, T.; Ip, E.J.; Gómez, A.; Barnett, M.J. The impact of work-life balance on intention to stay in academia: Results from a national survey of pharmacy faculty. Res. Soc. Adm. Pharm. 2018, 14, 387–390. [Google Scholar] [CrossRef]
  73. Surienty, L.; Ramayah, T.; Lo, M.-C.; Tarmizi, A.N. Quality of work life and turnover intention: A partial least square (PLS) approach. Soc. Indic. Res. 2014, 119, 405–420. [Google Scholar] [CrossRef]
  74. Wang, P.; Walumbwa, F.O. Family-friendly programs, organizational commitment, and work withdrawal: The moderating role of transformational leadership. Pers. Psychol. 2007, 60, 397–427. [Google Scholar] [CrossRef]
  75. Deery, M. Talent management, work-life balance and retention strategies. Int. J. Contemp. Hosp. Manag. 2008, 20, 792–806. [Google Scholar] [CrossRef]
  76. Demerouti, E.; Bakker, A.B.; Nachreiner, F.; Schaufeli, W.B. The job demands-resources model of burnout. J. Appl. Psychol. 2001, 86, 499. [Google Scholar] [CrossRef] [PubMed]
  77. Haar, J.M.; Russo, M.; Suñe, A.; Ollier-Malaterre, A. Outcomes of work–life balance on job satisfaction, life satisfaction and mental health: A study across seven cultures. J. Vocat. Behav. 2014, 85, 361–373. [Google Scholar] [CrossRef]
  78. Mishra, P.; Gupta, R.; Bhatnagar, J. Grounded theory research. Qual. Res. J. 2014, 14, 289–306. [Google Scholar] [CrossRef]
  79. Geurts, S.A.; Taris, T.W.; Kompier, M.A.; Dikkers, J.S.; Van Hooff, M.L.; Kinnunen, U.M. Work-home interaction from a work psychological perspective: Development and validation of a new questionnaire, the SWING. Work Stress 2005, 19, 319–339. [Google Scholar] [CrossRef]
  80. Lawler, E.; Cammann, C.; Nadler, D.; Jenkins, D. Michigan organizational assessment questionnaire. J. Vocat. Behav. 1979. [Google Scholar] [CrossRef]
  81. Saunders, M.; Lewis, P.; Thornhill, A. Research Methods for Business Students, 6th ed.; Pearson Education: Essex, UK, 2016. [Google Scholar]
  82. Urbański, M.; Ul Haque, A. Are you environmentally conscious enough to differentiate between greenwashed and sustainable items? A global consumers perspective. Sustainability 2020, 12, 1786. [Google Scholar] [CrossRef] [Green Version]
  83. Jamal, M.T.; Anwar, I.; Khan, N.A. Voluntary part-time and mandatory full-time telecommuting: A comparative longitudinal analysis of the impact of managerial, work and individual characteristics on job performance. Int. J. Manpow. 2021, 43, 1316–1337. [Google Scholar] [CrossRef]
  84. Stevens, J.P. Applied Multivariate Statistics for the Social Sciences; Routledge: New York, NY, USA, 2012. [Google Scholar]
  85. Kock, N. Common method bias in PLS-SEM: A full collinearity assessment approach. Int. J. E-Collab. (Ijec) 2015, 11, 1–10. [Google Scholar] [CrossRef] [Green Version]
  86. Anderson, J.C.; Gerbing, D.W. Structural equation modeling in practice: A review and recommended two-step approach. Psychol. Bull. 1988, 103, 411. [Google Scholar] [CrossRef]
  87. Kline, R.B. Principles and Practice of Structural Equation Modeling; Guilford Publications: New York, NY, USA, 2015. [Google Scholar]
  88. Wheaton, B.; Muthen, B.; Alwin, D.F.; Summers, G.F. Assessing reliability and stability in panel models. Sociol. Methodol. 1977, 8, 84–136. [Google Scholar] [CrossRef]
  89. Shevlin, M.; Miles, J.N. Effects of sample size, model specification and factor loadings on the GFI in confirmatory factor analysis. Personal. Individ. Differ. 1998, 25, 85–90. [Google Scholar] [CrossRef]
  90. Hu, L.t.; Bentler, P.M. Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Struct. Equ. Model. Multidiscip. J. 1999, 6, 1–55. [Google Scholar] [CrossRef]
  91. MacCallum, R.C.; Browne, M.W.; Sugawara, H.M. Power analysis and determination of sample size for covariance structure modeling. Psychol. Methods 1996, 1, 130. [Google Scholar] [CrossRef]
  92. Hair, J.F.; Black, W.C.; Babin, B.J.; Anderson, R.E. Multivariate data analysis. In Multivariate Data Analysis; Prentice Hall: Hoboken, NJ, USA, 2010; p. 785. [Google Scholar]
  93. Henseler, J.; Ringle, C.M.; Sarstedt, M. Using partial least squares path modeling in advertising research: Basic concepts and recent issues. In Handbook of Research on International Advertising; Edward Elgar Publishing: Cheltenham, UK, 2012. [Google Scholar]
  94. Bagozzi, R.P.; Yi, Y. On the evaluation of structural equation models. J. Acad. Mark. Sci. 1988, 16, 74–94. [Google Scholar] [CrossRef]
  95. Fornell, C.; Larcker, D.F. Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. J. Mark. Res. 1981, 18, 39–50. [Google Scholar] [CrossRef]
  96. Henseler, J.; Ringle, C.M.; Sarstedt, M. A new criterion for assessing discriminant validity in variance-based structural equation modeling. J. Acad. Mark. Sci. 2015, 43, 115–135. [Google Scholar] [CrossRef] [Green Version]
  97. Tofighi, D.; Kelley, K. Indirect effects in sequential mediation models: Evaluating methods for hypothesis testing and confidence interval formation. Multivar. Behav. Res. 2020, 55, 188–210. [Google Scholar] [CrossRef]
  98. Doherty, L. Work-life balance initiatives: Implications for women. Empl. Relat. 2004, 26, 433–452. [Google Scholar] [CrossRef]
  99. Maxwell, G. Checks and balances: The role of managers in work–life balance policies and practices. J. Retail. Consum. Serv. 2005, 12, 179–189. [Google Scholar] [CrossRef]
  100. Ho, H.C.; Chan, Y.C. The impact of psychological capital on well-being of social workers: A mixed-methods investigation. Soc. Work 2022, 67, 228–238. [Google Scholar] [CrossRef] [PubMed]
Table
Table 1. Respondent’s demographic profiles (N = 307).
Table 1. Respondent’s demographic profiles (N = 307).
Demographic VariableFrequencyPercentage (%)
Age
Less than 25 years7022.80
25–39 years15650.80
40 years and above8126.40
Gender
Male19764.00
Female11036.00
Marital status
Single18459.90
Married11537.50
Other82.60
Dependent children?
Yes9430.60
No21369.40
Designation
Analyst11738.10
Associate10433.90
Senior Associate8628.00
Table
Table 2. CFA and SEM model fit indices.
Table 2. CFA and SEM model fit indices.
ModelCMIN/DFGFITLICFIRMSEA
Measurement model (second-order)1.7150.9270.9690.9720.048
Structural model1.9220.9180.9540.9580.051
ThresholdsBetween 1–4≥0.90≥0.95≥0.95<0.07
Wheaton et al. [88]Shevlin and Miles [89]Hu and Bentler [90]Hu and Bentler [90]MacCallum et al. [91]
Table
Table 3. CFA loadings, Cronbach’s alpha, CR, and AVE.
Table 3. CFA loadings, Cronbach’s alpha, CR, and AVE.
Variable NameNo. of ItemsAvg CFA LoadingAlpha (α)CRAVE
Intention to stay30.8140.8910.9030.663
Work–life balance40.7880.8330.8400.621
Psychological capital (second-order)-0.8620.9030.9080.741
Efficacy30.8820.8630.8700.778
Hope30.8040.8350.8420.646
Resilience30.8410.8770.8830.707
Optimism30.7920.8650.8690.627
Job autonomy30.8250.8150.8210.681
Perceived organizational support40.8500.8940.9030.722
Table
Table 4. Correlations, divergent validity, and descriptive statistics.
Table 4. Correlations, divergent validity, and descriptive statistics.
Construct NameMeanSDISWLBPsyCapJAPOS
Intention to stay5.1171.2020.814
Work–life balance5.4081.3400.584 **0.788
Psychological capital4.9811.5510.473 **0.561 **0.862
Job autonomy5.4421.0670.442 **0.446 **0.470 **0.825
Perceived organizational support5.0891.2070.408 **0.397 **0.458 **0.367 **0.850
Note: ** Correlations are significant at 1% level. Values in bold on diagonals represent the squared root of AVE, and off-diagonal values represent correlations. IS = intention to stay; WLB = work–life balance; PsyCap = psychological capital; JA = job autonomy; POS = perceived organizational support.
Table
Table 5. Discriminate validity (HTMT ratio criterion).
Table 5. Discriminate validity (HTMT ratio criterion).
Construct NameISWLBPsyCapJAPOS
Intention to stay
Work–life balance0.565
Psychological capital0.6410.611
Job autonomy0.6630.6540.614
Perceived organizational support0.6800.5090.6490.504
Table
Table 6. Standardized direct and indirect (serial mediation) effects.
Table 6. Standardized direct and indirect (serial mediation) effects.
Independent VariablesDependent VariablesIndirect Effect
PsyCapWLBISOn IS viaPsyCap and WLB
Age––0.049 NS
Gender––−0.044 NS–
Marital Status––0.035 NS–
Dependent children (Yes/No)––0.053 NS–
Job autonomy0.338 ***0.279 ***0.209 ***0.076 **
Perceived organizational support0.363 ***0.218 ***0.233 ***0.082 **
Psychological capital–0.467 ***0.292 ***–
Work–life balance––0.485 ***–
Adjusted R20.3430.3710.385–
Note: Standardized effects are significant at 5% level, i.e., ** p-value < 0.05, and at 1% level, i.e., *** p-value < 0.01. NS = not significant, i.e., p-value > 0.05. IS = intention to stay; WLB = work–life balance; PsyCap = psychological capital.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Samroodh, M.; Anwar, I.; Ahmad, A.; Akhtar, S.; Bino, E.; Ali, M.A. The Indirect Effect of Job Resources on Employees’ Intention to Stay: A Serial Mediation Model with Psychological Capital and Work–Life Balance as the Mediators. Sustainability 2023, 15, 551. https://doi.org/10.3390/su15010551

AMA Style

Samroodh M, Anwar I, Ahmad A, Akhtar S, Bino E, Ali MA. The Indirect Effect of Job Resources on Employees’ Intention to Stay: A Serial Mediation Model with Psychological Capital and Work–Life Balance as the Mediators. Sustainability. 2023; 15(1):551. https://doi.org/10.3390/su15010551

Chicago/Turabian Style

Samroodh, Mohammed, Imran Anwar, Alam Ahmad, Samreen Akhtar, Ermal Bino, and Mohammed Ashraf Ali. 2023. "The Indirect Effect of Job Resources on Employees’ Intention to Stay: A Serial Mediation Model with Psychological Capital and Work–Life Balance as the Mediators" Sustainability 15, no. 1: 551. https://doi.org/10.3390/su15010551

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

Article Metrics

Back to TopTop