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Original article
DOI: 10.17323/1999-5431-2022-0-6-53-77
FACTORS INFLUENCING EMPLOYEE ENGAGEMENT IN PUBLIC ADMINISTRATION
Nadezda Jankelová1, Mgr. Zuzana Joniaková2, Diana Puhovichová3
1 2 3 Department of management, Faculty of Business Management, University of Economics in Bratislava, Slovakia; Address: Dolnozemská cesta 1, 852 35 Bratislava, Slovakia
1 Ph.D., Prof. Ing. E -mail: nadezda.jankelova@euba.sk; ORCID: 0000-0002-0045-4737 (Correspondent)
2 Ph.D., Assoc. prof. Ing. Mgr. E-mail: zuzana.joniakova@euba.sk; ORCID: 0000-0002-7706-2977
3 Ing. E-mail: diana.puhovichova@euba.sk; ORCID: 0000-0002-3710-3842
Abstract. The aim of this study is to examine the relationship between the work engagement of state administration employees and managerial support from their superiors. Attention is focused not only on the direct effect of these two variables, but also on the role of perceived fairness and public service motivation in the examined relationship. For data collection, a questionnaire survey was conducted among managers in the state administration in Slovakia (221 respondents). The PLS-SEM method using SmartPLS 3.0 software was used to test the theoretical research model and the proposed hypotheses. The direct correlation between managerial support and employee engagement in state administration was confirmed as significant. Our study showed that even the support from managers can influence work exposure, but the intensity of the effect is enhanced by the engagement of perceived fairness in the work environment and public service motivation of employees. At the same time, women are more sensitive to the effects of the studied variables compared to men. Therefore, it is essential that government management builds a culture of support and fairness that encourages employee engagement.
The contribution of this article is to explore the deeper mechanisms, that influence employee engagement in public administration.
Keywords: perceived fairness, public service motivation, supervisory support, work engagement, state administration.
For citation: Jankelová, N., Joniaková, Z. and Puhovichová, D. (2022) 'Factors influencing employee engagement in public administration, Public Administration Issue, 6 (Special Issue II, electronic edition), pp. 53-77 (in English). DOI: 10.17323/1999-54312022-0-6-53-77
JEL Classification: H70, H83, M12
Acknowledgement. The research was supported by the Scientific Grant Agency of the Ministry of Education of the Slovak Republic and the Slovak Academy of Sciences VEGA Project No. 1/0017/20: Changes in the implementation of management functions in the context of the fourth industrial revolution and adaptation processes in business in Slovakia and also VEGA Project No. 1/0328/21: Post - pandemic enterprise management: identifying temporary and sustainable changes in sequential and parallel management functions in the context of the COVID-19 pandemic.
Introduction
Recently, more and more attention has been paid to the issues of workforce employment in public administration (Cotton, 2012; Lavigna, 2013; Borst, Kruyen and Lako, 2017; Ancarani et al., 2020; Fletcher et al., 2020). In the context of constant change, a disengaged employee becomes costly for the organization. Given the proven contradictions between bureaucracy and engagement in the public sector (Cooke, Brant and Woods, 2018), it is necessary to examine the factors that can strengthen and increase work engagement in this specific space. Hence, the research question of the present paper is what managerial tools can be used to ensure and increase the work engagement of civil servants.
The issue is important for several reasons. The first reason is the already mentioned growing importance of work engagement in the context of achieving the goals of organizations. The dynamics of changes in the external and internal environment is associated with the dynamics of changes in the work environment and the need to focus on WE (Chandrasekar, 2011; Hameduddin, 2021). Based on their meta-analysis of factors influencing work exposure, Bailey, Madden, Alfes and Fletcher (2015) generally state that there is no systematic evidence base related to exposure. The literature is divided into several areas related to the antecedents of work engagement and its consequences in organizations. Such sub-studies are also not numerous in the public administration and need further investigation. The second reason is the adaptation of the principles of NPM (New Public Management) to the management of public administration organizations and at the same time to principles of modern management, successfully applied in the business environment (Anderfuhren-Biget et al., 2010; Stroinska, 2020). The third reason is the very change in the expectations and behavior of citizens and other stakeholders in solving tasks that significantly affect them. Citizens or other entities, such as customers of public administration services, are very sensitive to the way and form of problem solving, which requires a change in organizational behavior in offices. The implementation of these changes can be realized through the engagement of employees.
At the same time, HR managers (Breevaart et al., 2014; Bal and Lange, 2015) call for the need to move away from management orientation in providing quality and at the same time effective services not only to the end customer, but especially to the employee. A new employee-focused HRM (Adamovic, 2017), based on job demands-resources model and research on quality of work life (Grote and Guest, 2017) is supported in recent years mainly by modern human resources manage-
ment, because only a satisfied employee can create added value and contribute to the satisfaction of the citizen, client, and customer.
Existing studies, focusing on the factors influencing WE in public administration, bring some findings, that point to the need for employee autonomy, support for their cooperation, but also to the correct selection of employees with motivation for public service (Borst, Kruyen and Lako, 2017). However, there is a lack an integrated approach that combines both organizational sources of work engagement, which is the basis for the construction of our model. The starting point of our study is both the context of the job demands-resources (JD-R) framework (Schaufeli, 2013), social exchange theory (Alfes et al., 2013), but also broad-en-and-build theory (Fredrickson, 2001) in the form of variables supervisory support, public service motivation and perceived fairness.
1. Theory and development of hypotheses
1.1 Work engagement (WE)
Work engagement, defined as a positive, satisfying state of mind related to work characterized by energy, devotion and absorption (Schaufeli et al., 2002), has become an actual research topic in management literature (Saks and Gruman, 2014; Albrecht et al., 2015). According to Kahn (1990), employees are engaged when they are simultaneously physically, emotionally and cognitively present while performing a task. Studies show that employees who experience high levels of work engagement are physically healthier, experience greater satisfaction of their psychological needs and are more satisfied than employees with low workloads (Ryff, 1989; Barrett-Cheetham, Williams and Bednall, 2016). However, Mann and Harter (2016) speak of a global crisis of employee engagement, with up to 87% of employees not being engaged as according to the Gallup Institute.
Work engagement has also been the subject of research in the environment of public organizations (Jansen, Kole and Brink, 2010; Kernaghan, 2011; Cotton, 2012; Lavigna, 2013), but there is still a lack of research examining work engagement in public administration (Kernaghan, 2011; Vigoda-Gadot, Eldor and Scho-hat, 2012; Tummers et al., 2016).
One such research is the study by Bors, Kruyen and Lako (2017), who examined the JD-R model of work engagement in public administration organizations. The results of the study show that work engagement mediates the relationship between JD-R and work outcomes. Public organizations can potentially increase work engagement and performance by increasing work-related resources (autonomy, collaboration with colleagues) and the selecting employees with proactive personalities and a high level of motivation for public service.
1.2 Supervisory support (SS)
Supervisory behavior and leadership style are considered important determinants of job satisfaction and engagement of subordinates in different work environments (Yukl, 1989; Durham, Knight and Locke, 1997; Griffin, Patterson and West, 2001). Supportive behavior of a supervisor who trusts subordinates, rewards
performance, strengthens the position of subordinates, cares for their needs, as well as demonstrates the leader's integrity and abilities is a prerequisite for greater satisfaction of subordinates with their work and organization (Fernandez, 2008).
Based on organizational support theory, SS is understood as a construct of social exchange in which employees perceive the extent to which supervisors value their contributions and care about their well-being (Eisenberger and Stinglham-ber, 2011). When employees perceive support from a supervisor, they feel attached to the organization and feel obligated to "return the favor" to their supervisor by staying in the organization (Cropanzano and Mitchell, 2005). The relationship with the supervisor is considered one of the main elements of the work environment, where feedback from the supervisor and constructive communication can improve the skills of employees (van der Heijden et al., 2010).
The results of a study by Jin, McDonald (2016), carried out in the environment of state administration and self-government bodies, show that SS influences employee engagement directly and indirectly through its influence on perceived organizational support. The path connecting SS with organizational support is moderated by learning opportunities, so positive relationships are revived by individuals who have stated that they have opportunities for learning and growth at work.
Hypothesis 1: We assume that SS is positively related to WE.
1.3 Mediation effects of PF and PSM
Perceived fairness (PF)
Perceived fairness is another component that affects job satisfaction and organizational performance. Most members of society consider justice to be an important aspect in various contexts, assessing the justice of a particular situation or event (Cropanzano et al., 2001). The assessment of fairness is therefore linked to the rules and social norms governing the way in which results are distributed (so-called distributional justice), the procedures used to make such distribution decisions (so-called procedural justice), the ways in which people are treated (interpersonal justice) and with how the information is provided during the process (information justice). These four forms of justice are interconnected aspects of perceived fairness that affect important human response (Peiró, Martínez-Tur and Moliner, 2014). Research shows that PF is positively and negatively associated with mental well-being and mental distress (Fondocaro, Dunke and Pathak, 1998) and is considered a crucial psychosocial risk factor for health.
Fairness, both distributional and procedural, therefore influences employee behavior and may subsequently affect performance in the work unit due to changes in their attitudes (Cho and Sai, 2013). Several studies have confirmed that PF is directly related to high levels of organizational performance (Cohen-Charash and Spector, 2001; Rubin, 2009) and high levels of confidence in management (Fulk, Brief and Barr, 1985; Reinke, 2003; Harrington and Lee, 2015; Ryu and Hong, 2020). It is therefore important whether employees in the organization perceive the fairness of organizational procedures and policies and whether management can ensure such fairness by enhancing trust and setting certain standards.
Hypothesis 2: We assume that the relationship between SS and WE is mediated by PF.
Public service motivation (PSM)
PSM is considered a "key psychological resource" (Bakker, 2015), that is expected to have a high impact on engagement rates (Lavigna, 2015). However, the actual impact of PSM on work exposure has not been sufficiently investigated. PSM is related to the predisposition of individuals to serve the public interest (Perry and Hondeghem, 2008). It is a personality trait of individuals who are willing to act for the good of citizens without reciprocal benefits for themselves (Perry and Vandena-beele, 2015). PSM is therefore a relatively stable, individual variable of a higher level, which is subject to slow changes and helps civil servants to perform their work with full energy and work engagement (Bakker, 2015). However, this effect may depend on the extent to which employees feel that a particular organizational environment has enabled them to fulfill their motives for public service (Bright, 2007). High degree of agreement between the employee's PSM and the organization therefore supports the achievement of high levels of work engagement. PSM can be considered as a property that gives public employees energy, on the basis of which it is likely to positively affect their work engagement (Bakker, 2015).
The results of studies by Borst, Kruyen and Lako (2017) and Cooke, Brant and Woods (2018) show that PSM has a positive effect on the employment of civil servants. Cooke, Brant and Woods (2018) also examined PSM as a moderator and showed that it mitigates the negative relationship between the perceived level of bureaucracy and employee engagement. Employees who have a higher PSM can therefore be more involved in a bureaucratic environment.
Hypothesis 3: We assume that the relationship between SS and WE is mediated by PSM.
Given that both variables can also operate in organizations simultaneously, we also examined their common indirect effect.
Hypothesis 4: We assume that the relationship between SS and WE is mediated by PSM and PF.
The individual relations are shown in the theoretical model in Figure 1.
H1=c' H2=a1b1 H3=a2b2 H=a1b1+a2b2
Public service motivation (PSM)
Figure 1. Theoretical model of the study
2. Methods
2.1 Sample and data collection
All data were collected in the form of a questionnaire survey, which took place February - March 2021 among employees of ministries as state administrative bodies in Slovakia. In agreement with selected representatives of the ministries, of which there are 14 in Slovakia, the questionnaires were sent electronically on the intranet to employees. The mail also contained an address and a request to participate in the survey, explaining its meaning and purpose and stating that by sending a questionnaire, the respondents consent to the data processing. Since the questionnaires were sent out indirectly, we cannot determine the exact value of the sent applications and thus the response rate of the questionnaires. After evaluating all completed questionnaires, a research sample was formed, consisting of 620 employees from various ministries. The structure of employees was as follows. Average age 42.96 years (min. = 22, max. = 65, SD = 11,369), average experience 18.65 years (min. = 1 year, max. = 41 years, SD = 10.50), sex (65% women and 35% men), education (86% university 2nd degree, 10% university 1st degree, 4% secondary education).
2.2 Measurements
The survey was conducted in the conditions of Slovakia. As the measurement tools we used are not available in the Slovak language, we applied some best practices for verifying the validity and methodological soundness of the constructs used, presented by Schaffer and Riordan (2003) for solving cross-cultural complexities. Some of the recommendations that were not feasible in our research area were listed in the research restrictions. For establishing semantic equivalence, we used back-translation before administering an instrument. Bilingual experts translate the instrument from English to Slovak and then back again to English and subsequently, in case of inconsistencies, the individual items were reworded to establish compliance meaning. At the same time, we tried to use short, simple sentences and repeat nouns instead of using pronouns. (A 5-point Likert-type scale (1 = strongly disagree; 5 = strongly agree; 1 = never; 5 = very frequently) was used.
Measures
Each study variable was measured using items from established measures.
Supervisory supports (SS). The evaluation of SS is based on a tool developed by Choi (2012), who based his design on the principles of instrument development by Eisenberg et al. (1986) and validated in many other studies (Hay-ton, Carnabuci and Eisenberger, 2012; Shoss et al., 2013; Neves and Eisenberger, 2014). Unlike other constructs, it focuses on the area of public administration. It contains 4 items, while the internal consistency statistics measured by the author using Cronbach's alpha was higher than 0.7, the factor loadings range were between 0.826 and 0.913 and the initial eigenvalue of the scale was 3.028.
The SS variable is operationalized as a score, created based on employees' responses to 4 items, which are scaled using 5-point Likert-type scales (1 = strongly disagree; 5 = strongly agree).
Work engagement (WE). Workload was measured using a 9-point scale, which is an abbreviated version of the original 17-point Utrecht Work Engagement Scale (UWES), which has excellent psychometric properties (Schaufeli, Bakker and Salanova, 2006). Because the three basic dimensions of work engagement (energy, determination, and absorption) are usually highly correlated, the 9-item scale provides a good indicator of work engagement (Schaufeli, Bakker and Salanova, 2006). Respondents rated how often they had experience with each of the nine items on a 5-point scale from 1 ("never") to 5 ("always"), for example, "I feel energized at work," "I'm proud of the work I do, "and" I get carried away while I work."
Perceived fairness (PF). PF was measured using 4 items created and validated by Choi (2012). It is a fair resolution of grievances, low tolerance on personal favoritism, prohibited personnel practices, and disclosure of violation of law without fear. The results indicated acceptable values of validity and reliability within internal consistency statistics. Respondents rated items using 5-point Likert-type scales (1 = strongly disagree; 5 = strongly agree).
Public service motivation (PSM). The PSM variable was measured using a revised construct of PSM (Kim and Vandenabeele, 2010; Wright, Christensen and Isett, 2013; Vandenabeele, 2014), validated for use in samples in the US and several other countries (Kim and Vandenabeele, 2010; Wright, Christensen and Isett, 2013; Vandenabeele, 2014). Because the 4 basic dimensions of Attraction to Public Service (APS), Commitment to Public Values (CPV), Compassion (COM), Self-Sacrifice (SS) are usually highly correlated, the 16-item scale provides a good PSM indicator (Kim et al., 2013) Respondents rated items using 5-point Likert-type scales (1 = strongly disagree; 5 = strongly agree).
Control variables were age (in years), education (0 = secondary, 1 = university 1st degree, 2 = university 2nd degree), gender (0 = female, 1 = male), office size (0 = up to 49 employees, 1 = from 50 to 250 employees, 2 = over 250 employees), region (1 = Bratislava region, 2 = other regions), which were chosen due to their possible impact on the examined relationships on the basis of existing studies. Kurtessis et al. (2017) report age and gender as important factors influencing the relationship of supervisory support with various output variables such as overall performance, job satisfaction or engagement, while age reduces the strength of this relationship and women found a stronger relationship between GTC and output than men. Further studies have shown that age, gender (female) and many years of experience have a positive relationship to work engagement (Markovits et al., 2010).
The questionnaire contained a set of 33 indicator variables (Table 1) for the measurement model. As common method bias is a common and serious problem in research, we took several steps to alleviate it. The items in the questionnaire were randomly scattered and shuffled, the scales of some answers were inverted, and at the same time we divided the questionnaire and presented each part in a different context so that the respondents were not affected by their previous answers and their idea of the results. We also used the calculation of the VIF in-
dicator. The occurrence of a VIF greater 3.3 is suggested to indicate pathological collinearity and as an indication that a model may be contaminated by common method bias. Therefore, if all VIFs resulting from a full collinearity test are equal to or lower than 3.3, the model can be considered free of common method bias (Kock, 2015). After realizing collinearity statistics in Smart Pls, we found that the inner VIF values are all lower than 3.3.
Table 1
Latent variable categories and descriptors
Supervisory Supports (SS) Perceived Fairness (PF)
SS1 I have trust and confidence in my supervisor PF1 Complaints, disputes, or grievances are resolved fairly in my work unit
SS2 Overall, how good a job does you feel is being done by your immediate supervisor/team leader? PF2 Arbitrary action, personal favoritism, and coercion for partisan political purposes are not tolerated
SS3 My supervisor supports my need to balance work and other life issues PF3 Prohibited personnel practices (e.g., illegally discriminating for or against any employee/applicant, obstructing a person's right to compete for employment, knowingly violating veterans' preference requirements) are not tolerated
SS4 Supervisors/team leaders in my work unit provide employees with the opportunities to demonstrate their leadership skills PF4 I can disclose a suspected violation of any law, rule or regulation without fear of reprisal
Public Service Motivation Measurement Scale PSM (APS, CPV, COM, SS)
APS1 I admire people who initiate or are involved in activities to aid my community COM1 I feel sympathetic to the plight of the underprivileged
APS2 It is important to contribute to activities that tackle social problems COM2 I empathize with other people who face difficulties
APS3 Meaningful public service is very important to me COM3 I get very upset when I see other people being treated unfairly
APS4 It is important for me to contribute to the common good COM4 Considering the welfare of others is very important
CPV1 I think equal opportunities for citizens are very important SSf1 I am prepared to make sacrifices for the good of society
CPV2 It is important that citizens can rely on the continuous provision of public services SSf2 I believe in putting civic duty before self
CPV3 It is fundamental that the interests of future generations are considered when developing public policies SSf3 I am willing to risk personal loss to help society
CPV4 To act ethically is essential for public servants SSf4 I would agree to a good plan to make a better life for the poor, even if it costs me money
Work engagement (WE)
WE1 At my work, I feel bursting with energy
WE2 At my job, I feel strong and vigorous
WE3 I am enthusiastic about my job
WE4 My job inspires me
WE5 When I get up in the morning, I feel like going to work
WE6 I feel happy when I am working intensely
WE7 I am proud of the work that I do
WE8 I am immersed in my work
WE9 I get carried away when I am working
Sources: Completed by the authors (-hereinafter, unless otherwise noted).
2.3 Data analysis
To test our research model and the proposed hypotheses and in order to better understand the relationships between the selected constructs, we used the PLS-SEM method (partial least squares structural equation modeling) (Hair et al., 2014). This method makes it possible to test several hypotheses simultaneously within direct and indirect effects in a complex system (Hair, Ringle and Sarstedt, 2011; Ringle, Sarstedt and Straub, 2012; Ringle et al., 2018). We decided to use it for several reasons. The first is the relatively small sample size (221). Other reasons include the complexity of the research model, the focus of the study on predicting dependent variables, and the use of latent variable scores for predictive purposes. We used SmartPLS 3.0 software (Ringle, Sarstedt and Straub, 2012; Roldán and Sánchez-Franco, 2012) for the assessment of both the measurement model and the structural model. The advantage of this software is that it assesses both models simultaneously.
3. Results
3.1 Measurement model
We investigated whether the model meets all common requirements. These are reliability, validity, and internal construct reliability, which verify the quality of the criteria we set. All standardized loadings are greater than 0.70 (possibly slightly lower than 0.7 in two cases) (Chin, 2010). Cronbach's alpha (od 0.721 po 0.931) and composite reliability (CR) (od 0.833 po 0.949) were greater than 0.70 and less than 0.95 (Hair et al., 2017). Rho_A is also satisfactory (range 0.866 to 0.962) and should be between Cronbach's alpha and CR according to theory (Ringle et al., 2018). We measured the convergent validity by calculating the average variance extracted (AVE), which in our models exceeds the level of 0,5 (Chin, 2010) for all constructs, which means that the construct explains an average of at least 50% of its item's variance. Finally, we also subjected our model to discriminant validity analysis using the Fornell-Larcker criterion calculation (Hair, Ringle and Sarstedt, 2011; Henseler, Ringle and Sarstedt, 2015). The table shows
that square-root of AVE for the construct was greater the inter-construct correlation (in two cases, however, problems arose). Discriminant validity was therefore assessed also by heterotrait-monotrait ratio of correlations and since not all are below the threshold of 0.90 (Henseler, Ringle and Sarstedt, 2015), we also performed cross-loading, used in case of problems with discriminant validity (Ringle et al., 2018). Through cross-loading, we verified the loading of factors into parent constructs. We state that discriminant validity is established. We do not provide values in the case of cross-loading due to the large volume of data. Our results (Tables 2 and 3) show that the measurement model meets all requirements.
Table 2
Loadings, reliability and validity
Construct/ indicator Factor loading Composite reliability (CR) rho_A Cronbach's Alpha Average variance extracted (AVE)
SS SS1 0.924
SS2 0.893 0.925 0.895 0.891 0.755
SS3 0.817
SS4 0.837
PF PF1 0.917
PF2 0.878 0.833 0.866 0.721 0.598
PF3 0.865
PF4 0.776
PSM APS1 0.815
APS2 0.812
APS3 0.921
APS4 0.926
CPV1 0.740
CPV2 0.777
CPV3 0.810
CPV4 0.720 0.949 0.962 0.931 0.589
COM1 0.887
COM2 0.822
COM3 0.773
COM4 0.695
SSf1 0.639
SSf2 0.720
SSf3 0.738
SSf4 0.799
Construct/ indicator Factor loading Composite reliability (CR) rho_A Cronbach's Alpha Average variance extracted (AVE)
WE WE1 0,946
WE2 0,817
WE3 0,925
WE4 0,941
WE5 0,894 0.943 0.934 0.931 0.649
WE6 0,840
WE7 0,932
WE8 0,859
WE9 0,853
Table 3
Discriminant validity (Fornell-Lacker criteria)/HTMT Ratio
PF PSM SS WE
PF 0,773/-
PSM 0,902/0.901 0,767/-
SS 0.748/0.904 0.828/0,884 0,869/-
WE 0.750/0,893 0.893/0,917 0.797/0,871 0,806/-
Notes: Diagonal elements (values in Bold Italic) are the square root of variance shared between the constructs and their measures (AVE). Off-diagonal elements are the correlations among constructs. For discriminant validity, the diagonal elements should be larger than the off-diagonal elements. After the slash are the HTMT Ratio results.
3.2 Structural model
The model is evaluated on the basis of R2 and Q2 values, which assess the predictive significance (Hair et al., 2017) and significance of the paths. The goodness of the model is determined by the strength of each structural path determined by R2 value for the dependent variable (Bernal-Conesa, 2017), the value R2 should by equal to or above 0.1 (Falk and Miller, 1992). The results in Table 4 show that all R2 values are above 0.1.
Hence, the predictive capability is established. Further Q2 established the predictive relevance of the endogenous constructs. A Q2 above 0 shows that the model has predictive relevance. The results show that there is significance in the prediction of the constructs (see Table 4). Furthermore, the model fit was assessed using SRMR.
The value of SRMR was 0.100. SRMR values should be less than or equal to 0.100, indicating acceptable model fit (Hair et al., 2017). Table 4 lists all the results obtained, as well as path coefficients and other values (STDev, T statistics, p values).
Table 4
Predictive capability, predictive relevance, SRMR and effects results
Original Sample Standard p Sample Mean Deviation T Statistics v l LLCI ULCI Decision (P) (P) (STDEV)
PF -> WE 0.234 0.231 0.043 5.493 0.000 0.150 0.311 Supported
PSM -> WE 0.532 0.534 0.054 9.791 0.000 0.433 0.639 Supported
SS -> PF 0.748 0.749 0.020 36.937 0.000 0.708 0.789 Supported
SS -> PSM 0.828 0.829 0.014 57.258 0.000 0.800 0.857 Supported
SS -> WE (H1) 0.180 0.181 0.040 4.455 0.000 0.105 0.265 Supported
R2 Q2 SRMR=0.100 d_ULS=5.682 d G=5.128 Chi-Square=12009.302 NFI=0.567
PF 0.560 0.330
PSM 0.686 0.396
WE 0.819 0.525
Notes: WE = work engagement, PF = perceived fairness, PSM = public service motivation, SS = supervisory support, LLCI = Lower Limit Confidence Interval, ULCI = Upper Limit Confidence Interval, p < 0,05; R2 = R Square, Q2 = construct cross validated redundancy.
Next, direct relationships were tested. All direct effects are significant. The results revealed that PF has a significant impact on WE (p = 0.234, t = 5.493, p <0,05), that PSM has a significant impact on WE (p = 0.532, t = 9.791, p <0,05), that SS has a significant impact on PF (p = 0.748, t = 36.937, p <0,05), that SS has a significant impact on PSM (p = 0.828, t = 57.258, p <0,05) and that SS has a significant impact on WE (p = 0.180, t = 4.455, p <0,05). Hypothesis 1 proposed that SS is positively associated with WE. We therefore find support for the first hypothesis (H1 is supported).
3.3 Mediating effects
Using the bootstrapping method, we investigated the effect of mediation variables, namely PF and PSM (Bolin, 2014). We developed two sets of hypotheses: H2 for mediation of PF between SS and WE (H2=a1b1) and H3 for mediation of PSM between SS and WE (H3=a2b2), H4 for mediation of PSM and PF between SS and WE. The individual mediations are listed in Tables 5 and 6.
Table 5
Path coefficients, total, direct and indirect effects in the action of the PF mediator
Original Sample Standard T Statistics p Values Sample (ß) Mean (ß) Deviation
PF -> WE 0.576 0.579 0.036 16.100 0.000
SS -> PF 0.748 0.748 0.020 37.858 0.000
SS -> WE (direct effect) 0.366 0.363 0.034 10.671 0.000
Original Sample (ß) Sample Mean (ß) Standard Deviation T Statistics P Values
SS -> WE (total effect) 0.797 0.796 0.016 49.233 0.000
SS -> PF -> WE (indirect effect) 0.431 0.433 0.026 16.814 0.000
Notes: WE = work engagement, PF = perceived fairness, PSM = public service motivation, SS = supervisory support, p < 0,05.
Hypothesis H2 has support. The indirect effect of PF is significant. This is an incomplete mediation as the effect of both direct and indirect effect on the total effect is somewhat equal (54% indirect, 46% direct effect).
Table 6
Path coefficients, total, direct and indirect effects in the action
of the PSM mediator
Original Sample Standard T Statistics P Values Sample (ß) Mean (ß) Deviation
PSM -> WE 0.741 0.745 0.042 17.462 0.000
SS -> PSM 0.828 0.828 0.015 55.707 0.000
SS -> WE (direct effect) 0.183 0.181 0.045 4.076 0.000
SS -> WE (total effect) 0.798 0.797 0.017 46.242 0.000
SS -> PSM -> WE (indirect effect) 0.614 0.616 0.036 17.083 0.000
Notes: WE = work engagement, PF = perceived fairness, PSM = public service motivation, SS = supervisory support, p < 0,05.
Hypothesis H3 has support. The indirect effect of PSM is significant. It is an incomplete mediation. 77% of the total effect is transmitted through PSM. The joint action of mediators is shown in Table 7.
Table 7
Path coefficients, total, direct and indirect effects in the joint action of mediators
Original Sample Standard T Statistics p Values Sample (ß) Mean (ß) Deviation
PF -> WE 0.234 0.231 0.043 5.493 0.000
PSM -> WE 0.532 0.534 0.054 9.791 0.000
SS -> PF 0.748 0.749 0.020 36.937 0.000
SS -> PSM 0.828 0.829 0.014 57.258 0.000
SS -> WE (direct effect) 0.180 0.181 0.040 4.455 0.000
Indirect effect through PF 0.175 0.173 0.031 5.608 0.000
Indirect effect through PSM 0.441 0.443 0.047 9.373 0.000
Total effect SS -> WE 0.797 0.797 0.017 47.631 0.000
Notes: WE = work engagement, PF = perceived fairness, PSM = public service motivation, SS = supervisory support, p < 0,05.
Hypothesis 4 has support. It is a mediating effect of two mediators. This means that SS contributes only 22.6% of the total effect of SS on WE (0.797) through its direct effect (0.180). The remaining 77.4% of the total effect passes through PF (0.175), which is 22% of the total effect and 28% of the indirect effect, and through PSM (0.441), which is 55% of the total effect and 72% of the indirect effect. The empirical model is shown in Figure 2.
®
0.828 PSM 0.532
jew ^BDk
^-0.180-> I
SS ' WE
0.748 0.234
PF
Figure 2. Empirical study model
The mediation effects are shown in the Figure 3.
Mediation through PF
Mediation through PSM
Mediation through both together
0.748 0.234
B-0.180->K
0.828 0.532
Figure 3. Models of mediation relations
3.4 Multigroup analysis
The condition for the implementation of multigroup analysis is full invariance, determined using measurement invariance of composite models (MICOM) (Henseler, Ringle and Sarstedt, 2015).
We performed all three necessary steps for testing, namely configuration invariance, compositional invariance, and equality of composite means and variations (Henseler, Ringle and Sarstedt, 2015; Hair et al., 2017). The conditions were met for the criteria of gender, education, and length of practice. Tables 8 and 9 present the multi-group parametric test results according to the segmentation variables.
Table 8
PLS-SEM/multigroup analysis for employees by gender
Path Coefficients-diff T7 , ... i , , . p-Value original p-Value new (female vs male)
PF -> WE -0.164 0.967 0.066
PSM -> WE 0.318 0.004 0.008*
SS -> PF -0.046 0.846 0.308
SS -> PSM 0.049 0.045 0.091
SS -> WE -0.167 0.988 0.024*
Note: ^Significant difference between path coefficients.
In terms of gender, a significant difference was found between women and men for PSM paths - WE a SS - WE in benefit of women.
Table 9
PLS-SEM/multigroup analysis for employees by education
Path Coefficients-diff (higher education p-Value original p-Value new vs other)
PF -> WE -0.050 0.664 0.672
PSM -> WE 0.056 0.338 0.676
SS -> PF 0.115 0.015 0.029*
SS -> PSM 0.027 0.186 0.372
SS -> WE -0.005 0.519 0.962
Note: ^Significant difference between path coefficients.
Differences by education show that significant differences in favor of employees with a university degree were found for the SS-PF path. No significant differences were found for the length of experience criterion.
4. Discussion
As already mentioned, the importance of work engagement in the context of achieving the goals of organizations is a current topic. We agree with the statement of Merchant and Van der Stede (2012) that for organizations it is mainly employees who are crucial for their ability to achieve set goals. The aim of this study is to enrich existing knowledge in the field of employee engagement in public administration in several ways.
The first is to point out the need for managerial support, which directly affects the level of employee engagement. This factor is also an important factor in the state administration environment. The results of our research show its significant direct effect, supporting the growth of engagement. According to Borst et al. (2017), work engagement in public administration mediates the relationship between resources and work results, so it is naturally in the center of attention of managers.
However, our intention was not only to explore the direct relationship between SS and WE, but to expand knowledge about the impact of other variables and the relationship between managers' attitudes and employee engagement, which will significantly support the performance and sustainable development of organizations. The findings show that the perception of PF by employees as well as their SPM plays an important role in this context.
The findings significantly showed that the relationship between SS and WE is mediated by PF, with the share of the indirect effect in the total effect being 54%. Our findings are consistent with the results of studies by Alexander and Ruder-man, (1987), Folger and Konovsky, (1989), Sweeny and McFarlin (1993), according to which the perception of rules and decision-making procedures by employees as fair is positively associated with their organizational engagement, confidence in managers and job satisfaction.
In the case of PSM, its role as a mediator in the relationship between SS and WE was also proven, the share of the indirect effect in the total effect is up to 77% in the case of this factor. If both factors act simultaneously, i.e., the organization employs employees with high PSM and they perceive the environment and processes as fair, PF and PSM contribute significantly to the overall effect of SS on WE (77.4%), it is almost complete mediation with a higher effect of PSM (72%), the effect of PF was quantified at 28%. The share of PF thus decreases in the joint action of mediators compared to its separate mediation action.
Our findings are consistent with the results of Bors et al. (2017), who also point to the importance of choosing employees with a proactive personality and a high level of motivation for public service, which can potentially increase work engagement and employee performance. We agree with Lavigna's (2015) statement that the challenge is to recruit and retain employees who have a high degree of PSM and then build high levels of engagement on that basis. For government organizations, this means that PSM assessment needs to be included in recruitment (e.g., as a selection criterion for interviews). According to Bakker (2015), managers must maximize the factors that maintain or improve PSM and, conversely, eliminate the factors that reduce it. Consistent with the findings of Lavigna (2015) and Bakker (2015), research and practice confirm that this can be done in several ways, such as effective com-
munication, performance management, support for employee autonomy (Borst, Kruyen and Lako, 2017) and building an atmosphere of trust.
Regarding the impact of gender on the relationships examined, a significant difference between women and men was demonstrated for the PSM - WE and SS - WE paths in favor of women. The findings show that women are more sensitive to managerial support, which can be helpful for their engagement. It is also more intensely influenced by their prosocial motivation. Differences by education indicate that significant differences were found in the SS-PF path in favor of employees with a university degree. This category of employees more strongly combines the managerial approach of superiors with the perception of environmental justice. No significant differences were found for the length of the practice criterion.
4. 1 Conclusions
An important aspect of this paper is that it comprehensively clarifies the context of employment of government employees. The study confirmed the existence of significant direct relationships between MS and WE, but also showed that the engagement of other variables in the work environment can significantly increase the intensity of this relationship. The results of our study confirm the role of PF, which has the potential, together with the correct selection of employees, to create optimal conditions for supporting employee engagement. For government organizations, this presents a challenge to implement a culture of support and justice, from which they can benefit significantly by increasing the engagement of their own employees.
Despite their important role in the functioning of society, state administrative organizations remain under-represented in professional management literature and research. The results of our study therefore partially contribute to filling this gap while opening up perspectives for further research in this area.
4.2 Limitations
Our research study also has several limitations. The first is a relatively small sample of respondents (221) and its limitation to Slovak conditions. On the other hand, it includes state administration organizations from the whole territory of Slovakia, thus the results could be generalized for this region. At the same time, given the content of the discourse on the topic of work engagement, we assume that our study can also enrich the scientific discussion within a wider area. Research may also be limited by not using the pilot survey as one of the best practices for verifying the validity and methodological soundness of the constructs used. However, we applied other recommendations that we considered sufficient. Although we used several steps to mitigate common method bias, they did not perform data acquisition from various sources. We obtained data from the managers of the state administration themselves, while we are aware that the collection of data from several sources, i.e. the demand not only of managers but also of employees could increase the objectivity of research. Finally, in addition to the factors concerned in this study, there may be other factors that may affect the examined relationships. Our model worked with sectional rather than longitudinal data, which may be unable to reflect the real causal relationship because of the time-lag effect, and the use of panel data could be the future direction.
Despite these limitations, we believe that the results obtained contribute to the expansion of knowledge in several ways. Our findings broaden our understanding of how PF and PSM fundamentally affect the relationship between MS and WE. Building a culture of support, justice and the right selection of employees appears to be a highly functional strategy for government employee engagement.
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APPENDIXES
Appendix 1
MICOM results for employees by gender
Compositional invariance Equality of composite mean values Equality of composite variance values
Variables Original correlation 5% quantile Mean difference 2,5% 97,5% Log Variance Ratio 2,5% 97,5%
PF 0.997* 0.995 0.090* -0.184 0.179 -0.261* -0.311 0.216
PSM 1.000* 1.000 0.048* -0.167 0.167 -0.203* -0.352 0.214
SS 1.000* 1.000 0.016* -0.162 0.177 -0.175* -0.336 0.189
WE 1.000* 1.000 0.101* -0.160 0.157 -0.222* -0.212 0.181
Note: ^Significant result: score correlations are greater or equal than the 5% quantile of the empirical distribution, and composite mean and variances values are outside the permutation-based interval of confidence.
Appendix 2
MICOM results for employees by education
Compositional invariance Equality of composite mean values Equality of composite variance values
Variables Original correlation 5% quantile Mean difference 2,5% 97,5% Log Variance Ratio 2,5% 97,5%
PF 0.959 0.992 0.008* -0.235 0.226 -0.123* -0.434 0.328
PSM 0.999 0.999 0.084* -0.190 0.260 -0.355* -0.414 0.316
SS 1.000* 1.000 -0.032* -0.214 0.219 -0.149* -0.431 0.262
WE 0.999* 0.999 0.132* -0.219 0.227 -0.192* -0.329 0.256
Note: ^Significant result: score correlations are greater or equal than the 5% quantile of the empirical distribution, and composite mean and variances values are outside the permutation-based interval of confidence.
Appendix 3
MICOM results for employees by tenure
Compositional invariance Equality of composite mean values Equality of composite variance values
Variables Original correlation 5% quantile Mean difference 2,5% 97,5% Log Variance Ratio 2,5% 97,5%
PF 0.998* 0.993 -0.037* -0.253 0.202 -0.273* -0.291 0.264
PSM 1.000* 0.999 0.027* -0.250 0.209 -0.240* -0.262 0.241
SS 1.000* 0.999 0.157* -0.247 0.203 -0.140* -0.328 0.443
WE 1.000* 0.999 0.162* -0.191 0.207 -0.369 -0.290 0.261
Note: ^Significant result: score correlations are greater or equal than the 5% quantile of the empirical distribution, and composite mean and variances values are outside the permutation-based interval of confidence.
