Rohana Ahmad*
| Ahmad Martadha Mohamed | Wan Naqiyah Wan Abdul Majid | Latifah Abdul Ghani | Zahrul Akmal Damin
© 2023 IIETA. This article is published by IIETA and is licensed under the CC BY 4.0 license (http://creativecommons.org/licenses/by/4.0/).
OPEN ACCESS
According to the Congress of Unions of Employees in the Public and Civil Service of Malaysia (CUEPACS), nearly 500,000 Malaysian civil servants have been identified as experiencing work stress. By investigating the effects of work stress on civil servants in Malaysia's federal agencies, the relationship between leadership (LS), team conflict (TC), and work stress (WS) in the Malaysian public sector was examined and the mediating effect of job demands (JD) was tested. The sample comprised 626 respondents who worked in Putrajaya, Kuala Lumpur. Data were collected through survey method and quantitative analysis were done employing the structural equation modeling (SEM) to analyze direct and indirect effect. According to the findings, there was a significant positive impact of TC on WS, TC on JD, and LS on JD. Contrarily, the impact of LS on WS was not significant. The JD constructs was identified as mediator of the aforementioned relationships. The practical implications suggested that organizations should place greater emphasis on cultivating a learning culture to successfully adapt and respond to novel external challenges. Leaders could reduce WS by being honest and empathetic towards the employees and granting them the flexibility to spend precious time with their loved ones. In addition, supervisors and managers should use more transformational approaches in their interactions with subordinates. Team members could stay in touch for social support. The findings extended previous research and strengthened the antecedents of WS, namely LS, TC, and JD, in public organization.
leadership, team conflict, work stress, job demands, civil servants, Malaysia
The coronavirus disease 2019 (COVID-19) pandemic caused global economic and social issues. While there are few impacts of the pandemic, a favorable impact on organizational learning, psychological empowerment, and work stress (WS) was reported [1]. The WS has gained attention in recent years due to its impact on work performance and employees’ attitudes. The WS is a significant negative outcome that may result from relatively bad LS practices, team conflict (TC), or high job demands (JD) [2, 3]. Thus, there is a need for investigations on work engagement literature, specifically regarding WS [4].
Focus has increased on the role of emotions in leadership (LS) since the 1990s, where there was stronger organizational preference for leaders who are more relation-oriented and sensitive to others’ needs as opposed to hierarchically based traditional leaders focused on accomplishing tasks [5]. Several studies investigated the crucial role of feelings in LS theory [6]. Unexpected workplace changes due to COVID-19, such as increased competition, digitalization, and customer demands [7], required organizations to use adaptive and proficient performance models and psychologically empower employees with high engagement [8].
The conservation of resources (COR) theory [9] formed the basis of this study. The COR theory explains that organizations that advance employee proficiency, autonomy, and meaningfulness enhance favorable employee emotions, such as pride and motivation, which help employees build physical and mental reserves for future challenges. Furthermore, the COR theory states that stress develops when a person’s resources are endangered, exhausted, or new investments do not accumulate appropriately [10]. Based on this idea, a good LS and low TC evoke pleasant emotions, such as happiness and love, among employees. Furthermore, the literature suggested that positive emotions assuage JD and WS [11].
In this study, the antecedents of WS considering pandemic were evaluated experimentally to understand how LS affects WS. Furthermore, the mediating functions of JD in the interaction between LS, TC, and WS were analyzed. A novel methodological addition, this is one of the studies that directly analyze and understand the impact of the study variables on WS beyond the pandemic’s impacts.
The findings contribute to WS literature by addressing the importance of employees in volatile circumstances, such as a pandemic, and contribute to knowledge by empirically examining numerous WS antecedents in response to the literature [12]. Given the paucity of research in this area in Putrajaya, Kuala Lumpur, the analysis of the aforementioned interactions within the Malaysian public sector helped shed light on the aforementioned issues in a non-Western context and strengthened the external validity of earlier conclusions established in Western studies.
Therefore, this study will focus on measuring the impact of LS and TC to establish the relationship between these variables (LS and TC) with WS. The analysis will measure whether LS practices and TC increases the civil servants’ WS. Additionally, this study will also look into the direct effect of LS and TC on JD as well as the mediating effect of JD towards the relationship between LS and WS, TC and WS.
2.1 Malaysian public sector
There are three levels of Malaysian governance: federal, state, and local [13]. Public sector employees are indispensable to national growth. The research focus was only the Malaysian federal government public sector. There is a total of 672,737 federal employees in Putrajaya, with many in management positions as managing and administering government agencies is demanding and complex.
The Congress of Unions of Employees in the Public and Civil Services Malaysia (CUEPACS) reported that more than 400,000 Malaysian public sector employees experience various stresses [14]. A series of global lockdowns ensued after the World Health Organization (WHO) declared COVID-19 a pandemic in early 2020, which significantly impacted various service industries, including tourism, hotels, and aviation and ultimately challenging the global economy. Moreover, the pandemic exacerbated the mental anguish of millions of people in economically and politically vulnerable regions. The COVID-19 pandemic affected cultures globally and was predicted to have wide-ranging repercussions [15]. In this study, some of the pandemic repercussions in the Malaysian public sector were analyzed and described.
2.2 The WS
The WS harms all levels of organizational efficiency and effectiveness. Moreover, the WS reduces employee motivation, morale, optimism, satisfaction, and performance, causes high turnover, recurrent sick leave and work accidents, low-quality products and services, poor internal communication, and frequent confrontations and arguments [16]. The COR theory assumes that human motivation involves resource maintenance and build-up. Resources are the valued objects (tools), human traits (emotional stability), conditions (social support), or energy (money) used to obtain or protect additional valued resources [10]. Dubois et al. [17] linked dwindling resources to rising burnout. According to the COR theory [18], stress and burnout occur when people notice the possible or actual loss of resources or fail to gain sufficient resources after a major investment. A stressor may be an initial threat to resources, where sustained losses or threats over time, especially after a considerable commitment in effort, may lead to burnout. Thus, highlighting how people manage work-related stress is crucial.
In this study, the COR theory that employees’ perceptions of the loss of valued resources in the context of change may alter their psychological state and lead to increased stress, persistent anxiety, and depressive symptoms [19] and burnout was verified. Chullen [20] discovered that stress and burnout can spread easily among employees. Employees face a unique combination of job responsibilities and stress where employees who respond similarly to shared events may feel the same emotions. Consequently, work conditions may cause employee burnout. Gong et al. [21] reported that resilience and optimism predicted WS. Many studies examined the psychological impact of WS but more studies are needed to support the influence of LS, TC, and JD on WS [4].
2.3 Theoretical framework
The COR theory has been used in research on WS as a performance indicator, the mediating effect of employee resilience on the relationship between learning culture and affective commitment to change, and the relationship between family and supportive supervisor behaviors, WS, and subjective well-being [22]. Positive emotions (pride and excitement) reduce TC, JD, and WS [11] by boosting employees’ self-confidence and physical and mental resources.
2.4 The LS
Healthy organizations are characterized by employees’ well-being and performance and organizational financial health [23]. The three measures of healthy organizations are mental health, LS, and efficacy [24]. Efficacy refers to the extent to which organizational resources are optimized and is fundamental to organizational health [25]. The LS impacts corporate efficacy directly and personnel performance indirectly [26]. Organizational performance and efficacy must be accomplished without stressing employees. The LS-related elements form the foundation of the aforementioned objectives [27] where a transformational LS style is linked to employee psychological and job well-being.
Influential LS models, such as charismatic and transformative LS, emerged in the 1980s and have been researched ever since. The LS style and employees’ perceptions of stress have been linked [28] where transformative LS can help overcome WS [29]. Excellent LS is critical to organizational success [30] while transactional LS emphasizes idea exchange among leaders, peers, and members, which entails the leader debating the tasks to be completed with others and rewarding results [31]. According to Syed et al. [32], LS and WS are positively related.
Consistent learning opportunities and development within the organization contribute to decreased work-related stress levels among employees [33]. Based on the COR theory, it is suggested that organizations that encourage employees to become more proficient and empowered via learning will increase good feelings among those employees, such as increased self-confidence. These uplifting emotions will contribute to increased employee commitment, vigor, and absorption levels in their tasks [11]. Thus, the first hypothesis is proposed as follows:
H1: The LS positively impacts WS.
2.5 The TC
The TC has severe repercussions towards employees if not handled quickly and wisely. Vice versa, having teams with great teamwork benefits not only the employees but also the organization as team spirit tends to reduce stress and increase performance [34]. The COVID-19 pandemic contributed to heightened stress levels in practically all nations among both healthcare and non-healthcare employees [35]. Therefore, it is not only essential to lower overall stress levels but also to equip staff with the ability to cope with the stress that is inescapable in such circumstances. Kasparkova et al. [34] stated that one approach is to increase employee loyalty by having employees work in teams. Work-induced tensions within a team appear to have a positive correlation to WS [36, 37]. Therefore, strong levels of team spirit among employees tends to reduce WS, which may result from challenging or unexpected scenarios, such as the pandemic and work environment. Favorable emotions, resiliency, and a strong sense of team spirit among employees are indicators of a positive connection to workplace performance. Recently, most studies focused on interactions among team members and TC as a determining factor of the success level of a team [38].
Humphrey and Aime [39] opined that teams were “a set of interdependent relationships and activities organizing shifting sets or subsets of participants embedded in and relevant to wider resource and institutional environments”. The team should be better understood from the aspect of organizing member activities (teamwork without adequate communication leads to conflict and WS) [40, 41]. Therefore, individuals with a strong sense of team spirit are more resilient in the face of adversity, capable of developing strong social support networks, and have an optimistic worldview. These aforementioned characteristics contribute to better work satisfaction levels. Almost et al. [42] demonstrated that employee TC was significantly related to WS. Therefore, the following hypothesis is proposed:
H2: The TC positively impact WS.
2.6 The JD
Employment requirements can be divided into three categories: physical, social, and organizational [43]. Job requirements involve either physical or mental exertion and their effects might be either physiological or psychological. Physical job requirements or JD includes tasks such as sitting, standing or remain stationary while serving the customers or moving goods across the office for various needs. Schaufeli and Bakker [44] defined the four primary JD as the physical, psychological, social, or organizational aspects of a job that necessitate sustained physical or psychological (cognitive or emotional) effort and are consequently associated with certain physiological or psychological costs. Meanwhile, social JD or job requirements involves skills which includes communication, cooperation, leadership and empathy. Further, one example of organizational job requirements is managing administrator tasks and making judgments under pressure or while worrying about the impact of a particular decision on co-workers. Impact refers to employees’ views of how their behaviors affect job outcomes. Analyses of the relationship between LS, TC, JD and WS have come to the forefront based on the previous work of a few scholars where additional work-related factors contributed to WS and to varying degrees, the three dimensions (LS, TC and JD) allow for WS [45].
There are minimal empirical data on the association between LS, TC and JD; nonetheless, past research presented evidence to support this relationship. In a previous study on JD, job- and individual-related antecedents of LS and TC were examined where the LS and managerial practices that promoted information-sharing and decision-making were linked to WS [46]. Other research demonstrated the good impact of JD on employees’ attitudes [47]. These attitudes included problems with employee changeover, scheduling constraints, and a deficiency of essential resources for good execution [48]. The JD and LS, TC, and WS were positively linked; nevertheless, the term “demands” can refer to a broad range of concerns, which include time pressure, workplace bullying, and work–home life conflict [49]. Accordingly, the following hypotheses are proposed:
H3: The LS positively enhances JD.
H4: The TC positively enhances JD.
2.7 Mediating effect
Chullen [20] observed two ways by which stress and burnout might spread among employees. First, employees may react similarly to common circumstances and feel alike. In this case, employees encounter specific JD and resources, where the workplace may cause burnout. The second way employees share burnout is if their moods coincide [50], where an employee may start to feel emotionally tired at work, individually incompetent on the job, and begin to exhibit pessimism and unsympathetic views toward others. In this research, the effect of high JD on WS was examined.
Social support (LS behaviors) lessens the impact of occupational stress [51]. Dollard et al. [52] proposed that employees seek emotional support from their co-workers and superiors (leaders) when necessary. Theoretically, demanding jobs reduce the mediating effects of LS on WS [53]. Nevertheless, more inputs may overwhelm the leader during difficult and complex tasks [54]. Leaders are under increasing pressure to perform at their peak to meet deadlines and fulfil their responsibilities. Consequently, it was anticipated that leaders would be constrained by their dominant focus on the task assigned to them and their subordinates in the face of high JD. Hence, leaders will regulate existing challenges and resources to complete the organizational mission [55]. Leaders are expected to drive their subordinates to complete work as quickly as possible to meet deadlines. Dollard et al. [52] reported that JD are significantly related to LS and WS; thus, employees are certain to become stressed consequently. Hence, the following hypothesis is proposed:
H5: The JD mediates the relationship between LS and WS.
It is highly likely that an organization will have high employee engagement levels if it cultivates an atmosphere that emphasizes meaningfulness as a psychological condition together with safety and resource availability [56]. The original JD-R (JD-job resources) model proposed by Demerouti et al. [57] demonstrated that the daily workload can lead to chronic overload, thereby resulting in health problems. In such situations, JD cause chronic tiredness and possibly physical health issues (including cardiovascular diseases). Contrastingly, job resources motivate workers [58]. As they are significant and satisfy basic needs (inspiration, dedication, and enthusiasm), employment resources consistently support work activity development [44]. Furthermore, when employees understand that they have lower JD, they will feel more confident and be able to cope with potential TC, such as discrimination and bullying. Nonetheless, employees may be unable to deal with continuous high demands and TC, which may lead to WS over time. Bakar and Salleh [59] reported that JD were significantly related to TC and WS. Based on this information, the following hypothesis is proposed and Figure 1 presents the hypothesized relationships:
H6: The JD mediates the relationship between TC and WS.
Figure 1. The research framework
In this quantitative study, the factors that contributed to the WS antecedents during the pandemic were investigated. The interactions between variables were determined via hypothesis testing. All relevant data were shared with the participants who agreed to complete the survey, which was disseminated to public sector workers who had continuously worked for the duration of the pandemic.
In this study, public sector employees in Putrajaya, Kuala Lumpur, were included through stratified random sampling. It is one of probability sampling that is based on the process of random selection, which means that each individual with similar characteristics in the population has an equal chance of being selected as a subject in the sample. Stratified random sampling is often used when there is a great deal of variation within a population. The respondents were chosen from Grade 11 until Grade 41/44. Stratified random sampling enables researcher to make generalizations (i.e., statistical inferences) from the sample to the population [60]. The stratified sampling gives more reliable and detailed information about the sample [61]. Data were collected through survey method and quantitative analysis were done by employing the structural equation modeling (SEM) to analyze direct and indirect effect. The survey method was used in this quantitative research due to the reason that it is useful in describing the characteristics of a large population and it ensures a more accurate results for researcher to draw conclusions and make decisions [61]. Of 800 questionnaires disseminated, 702 responses were received, of which 46 were incomplete and therefore excluded from the analysis. The final responses were from 656 middle- and lower-level management staff in seven organizations. All four variables (LS, TC, JD and WS) were scored on a five-point Likert scale and comprised the following ordered response options: 1 (strongly disagree) to 5 (strongly agree). Data were gathered through direct submission from the respondents. The data were analyzed with a structural equation model (SEM) with partial least squares (PLS-SEM).
As SmartPLS was used as an analysis tool in this study, the minimum sample size was based on the analysis power, which was based on model complexity [62]. Model complexity is calculated based on the maximum number of arrows pointing at any endogenous variable. Based on the study model, four arrows pointed at both exogenous variables of the study. Gefen et al. [63] proposed that social science studies should feature 80% power with medium effect size. According to the table developed by Green [64], four predictors in the model, and following the specifications of Gefen et al. [63], the minimum sample size to test the research model in this study was 84. Thus, with the 656 samples, the number of respondents met the minimum sample size requirements proposed by Hair et al. [62].
4.1 Respondents’ demographics
The respondents were aged between 21 and 25 years (5.9%), 26 and 30 years (15.4%), 31 and 35 years (23.8%), 36 and 40 years (32.3%), 41 and 45 years (13.7%), 46 and 50 years (5.5%), and 51 and 60 years (3.4%). Academically, 3.5% of the respondents had a Master’s degree, 29.4% had a Bachelor’s degree, 34.0% had a diploma, 3.7% had a certificate, 7.9% had a Malaysian Higher Certificate of Education (STPM), 20.9% had a Malaysian Certificate of Education (SPM) and 0.6% had a Lower Secondary Assessment (PMR). While 8.4% of respondents had > 21 years of experience, 19.2% had between 16 and 20 years of experience, 34.3% had between 11 and 15 years of experience, and 38.1% had between five and 10 years of experience.
4.2 Descriptive statistics
Table 1 presents the means, standard deviations (SDs), and correlations of the variables. The TC was positively related to JD and WS. The JD were strongly related to WS.
Table 1. Means, SDs, and correlations
|
Mean |
SD |
LS |
TC |
JD |
WS |
|
|
LS |
52.89 |
9.17 |
- |
|
|
|
|
TC |
18.54 |
6.75 |
-0.184** |
- |
|
|
|
JD |
52.35 |
9.90 |
-0.082 |
0.297** |
- |
|
|
WS |
63.89 |
22.04 |
-0.161** |
0.409** |
0.592** |
- |
**p < 0.01.
4.3 Exploratory factor analysis (EFA)
Reliability was checked with an EFA with varimax rotation. Factor loadings > 0.6 were deemed “high” and those < 0.4 were categorized as “low” [65]. Therefore, the cut-off point in this study was 0.5 to obtain factor loadings > 0.4. The remaining items from the adopted questionnaire after setting the cut-off were as follows: all items for LS and TC, 12 items for JD, and 26 items for WS had factor loadings > 0.5. The results revealed a significant Bartlett’s test of sphericity at 0.001 and Kaiser-Meyer-Olkin (KMO) of 0.955, which indicated that the sample was adequate (a common KMO cut-off score is ≥ 0.70) [66]. The item loadings ranged between 0.611 and 0.862 for LS, 0.774 and 0.875 for TC, 0.523 and 0.695 for JD, and 0.642 and 0.845 for WS, which matched strongly with the main constructs of the survey design.
4.4 Convergent validity
Convergent validity is checked before discrimination validity is tested to assess whether a model is appropriate [67]. After the measurement model was established, the study hypotheses were analyzed with the structural model. Hair et al. [62] recommended testing convergent validity with factor loading, average variance extracted (AVE), and composite reliability (CR). Table 2 demonstrates that most factor loadings were > 0.7, with a few between 0.4 and 0.7. However, item JD1, JD7 and JD 17 were deleted due to the low factor loadings that was below than 0.4. The AVE was > 0.5 and all CR values were > 0.7, which demonstrated that the constructs had adequate convergent validity [68].
Table 2. Convergent validity
|
Construct |
Item |
Loading |
CR |
AVE |
|
Transformational LS |
TF_L1 |
0.617 |
0.955 |
0.606 |
|
|
TF_L2 |
0.759 |
|
|
|
|
TF_L3 |
0.557 |
|
|
|
|
TF_L4 |
0.805 |
|
|
|
|
TF_L5 |
0.811 |
|
|
|
|
TF_L6 |
0.732 |
|
|
|
|
TF_L7 |
0.770 |
|
|
|
Transactional LS |
TS_L1 |
0.545 |
|
|
|
|
TS_L2 |
0.786 |
|
|
|
|
TS_L3 |
0.877 |
|
|
|
|
TS_L4 |
0.874 |
|
|
|
|
TS_L5 |
0.877 |
|
|
|
|
TS_L6 |
0.877 |
|
|
|
|
TS_L7 |
0.896 |
|
|
|
TC |
TC1 |
0.824 |
0.960 |
0.776 |
|
|
TC2 |
0.910 |
|
|
|
|
TC3 |
0.887 |
|
|
|
|
TC4 |
0.871 |
|
|
|
|
TC5 |
0.902 |
|
|
|
|
TC6 |
0.894 |
|
|
|
|
TC7 |
0.874 |
|
|
|
JD |
JD2 |
0.734 |
0.936 |
0.514 |
|
|
JD3 |
0.786 |
|
|
|
|
JD4 |
0.653 |
|
|
|
|
JD5 |
0.644 |
|
|
|
|
JD6 |
0.612 |
|
|
|
|
JD8 |
0.762 |
|
|
|
|
JD9 |
0.852 |
|
|
|
|
JD10 |
0.842 |
||
|
|
JD11 |
0.830 |
||
|
|
JD12 |
0.729 |
||
|
|
JD13 |
0.788 |
||
|
|
JD14 |
0.532 |
|
|
|
|
JD15 |
0.638 |
|
|
|
|
JD16 |
0.531 |
|
|
|
WS |
WS1 |
0.726 |
0.976 |
0.593 |
|
|
WS2 |
0.778 |
|
|
|
|
WS3 |
0.773 |
|
|
|
|
WS4 |
0.850 |
|
|
|
|
WS5 |
0.842 |
|
|
|
|
WS6 |
0.826 |
|
|
|
|
WS7 |
0.782 |
|
|
|
|
WS8 |
0.835 |
|
|
|
|
WS9 |
0.835 |
|
|
|
|
WS10 |
0.829 |
||
|
|
WS11 |
0.820 |
||
|
|
WS12 |
0.489 |
||
|
|
WS13 |
0.770 |
||
|
|
WS14 |
0.853 |
||
|
|
WS15 |
0.847 |
||
|
|
WS16 |
0.609 |
||
|
|
WS17 |
0.820 |
||
|
|
WS18 |
0.761 |
||
|
|
WS19 |
0.768 |
||
|
|
WS20 |
0.531 |
||
|
|
WS21 |
0.702 |
||
|
|
WS22 |
0.799 |
||
|
|
WS23 |
0.743 |
||
|
|
WS24 |
0.782 |
||
|
|
WS25 |
0.808 |
||
|
|
WS26 |
0.801 |
||
|
|
WS27 |
0.757 |
||
|
|
WS28 |
0.690 |
4.5 Discriminant validity
Following the guidelines of Gholami et al. [69], discriminant validity was assessed by counting the indicators that characterise just one component in the data set. To ensure that the components are statistically distinct from other constructs, discriminant validity must be assessed in detail [70]. Thus, the HTMT (heterotrait-monotrait ratio of correlations) was used as proposed by Henseler et al. [71], who advised that the HTMT threshold value should be < 0.90 to ensure discriminant validity. A discriminant validity was established as depicted in Table 3.
Table 3. Discriminant validity (HTMT) ratio
|
|
LS |
TC |
JD |
WS |
|
LS |
|
|
|
|
|
TC |
0.186 |
|
|
|
|
JD |
0.184 |
0.283 |
||
|
WS |
0.189 |
0.372 |
0.555 |
4.6 Structural model
The structural model should not have any lateral collinearity issues before hypothesis testing is conducted. Diamantopoulos and Siguaw [72] stated that it is necessary to ensure that the variance inflation factor (VIF) does not exceed 3.3. There were no collinearity issues in this study, as depicted in Table 4, as all VIF values were below the threshold value. The hypotheses were tested with a bootstrap technique that involved resolution resampling (5,000 times) to ensure that the established hypotheses were at t- and p-values and had bias-corrected confidence intervals. The data supported only five of the six hypotheses. Figure 2 presents the bootstrapping outcomes.
4.7 Hypothesis testing
A 95% bias-corrected confidence interval were generated with 5,000 bootstrap samples. In this analysis, surprisingly, LS had a negative insignificant effect on WS and thus H1 is not significant (β = -0.058, t = 1.395: LL = -0.129, UL = 0.025, p > 0.05).
Moreover, the effect of TC on WS was positive and significant (β = 0.207, t = 5.003: lower limit [LL] = 0.124, upper limit [UL] = 0.286, p < 0.05), which supports H2. The LS had a positive significant effect on JD (β = -0.150, t = 3.532: LL = -0.225, UL = -0.057, p < 0.05), which supports H3. The results indicated a positive effect of TC on JD (β = 0.260, t = 6.182, LL = 0.179, UL = 0.346, p < 0.05) and thus H4 is significant.
The JD significantly mediated the relationship between LS and WS (β = -0.075, t = 3.085; LL = -0.123, UL = -0.031, p < 0.05). Thus, H5 is supported. Furthermore, TC positively affected WS through the mediator JD (β = -0.129, t = 4.166: LL = 0.080, UL = 0.194, p < 0.05); thus, JD mediated the relationship between TC and WS and H6 is supported. Table 4 details the findings demonstrating a direct relationship and Table 5 presents the mediation analysis results.
Figure 2. Bootstrapping results
Table 4. Hypothesis testing
|
Hypothesis |
β |
SE |
t |
p |
LL |
UL |
Decision |
VIF |
|
|
HI |
LS -> WS |
-0.058 |
0.042 |
1.395 |
0.164 |
-0.129 |
0.025 |
Not supported |
1.064 |
|
H2 |
TC -> WS |
0.207 |
0.041 |
5.003 |
0.000 |
0.124 |
0.286 |
Supported |
1.114 |
|
H3 |
LS -> JD |
-0.150 |
0.042 |
3.532 |
0.000 |
-0.225 |
-0.057 |
Supported |
1.039 |
|
H4 |
TC -> JD |
0.260 |
0.042 |
6.182 |
0.000 |
0.179 |
0.346 |
Supported |
1.039 |
**$p \leq 0.05$. SE, standard error.
Table 5. Mediating effect
|
Hypothesis |
β |
SE |
T |
p |
LL |
UL |
Result |
|
|
H5 |
LS -> JD -> WS |
-0.075 |
0.024 |
3.085 |
0.002 |
-0.123 |
-0.031 |
Supported |
|
H6 |
TC -> JD -> WS |
0.129 |
0.031 |
4.166 |
0.000 |
0.080 |
0.194 |
Supported |
**$p \leq 0.05$. SE, standard error.
4.8 Coefficient of significance (R2), predictive relevance (Q2), and effect size (f2)
Table 6 depicts the calculation of the R2, f2, and Q2 as predictive variables of WS and JD. The R2 value of 0.369 indicated that LS, TC, and JD represented 36.9% of the total variance of WS while the R2 value of 0.105 indicated that LS and TC explained 10.5% of the overall variance of JD. According to Falk and Miller [73], an R2 value of 36.9% was considered moderate in this study on WS with two independent variables. Moreover, predictive accuracy analysis was performed using Q2 derived from Geisser [74]. The predictive impact of the model was evaluated with a blindfolded technique. Using a distance of seven, the Q2 value demonstrated the importance of the prediction for the definite criterion variable if the Q2 rating > 0 [75]. The Q2 values of the criterion variables WS and JD were 0.214 and 0.047, respectively, representing reasonable predictive relevance. Cohen [76] categorized f2 of 0.35, 0.15, and 0.02 as large, medium, and small, respectively. In this study, LS had a small effect size on JD (f2 = 0.024) while LS did not have an effect size on WS (f2 = 0.005). The TC had a less significant effect size on JD (f2 = 0.072) and WS (f2 = 0.061). Contrarily, JD had a large effect size on WS (f2 = 0.351).
Table 6. The R2, Q2 and f2
|
|
R2 |
Q2 |
f2 |
Decision |
|
WS |
0.369 |
0.214 |
|
|
|
JD |
0.105 |
0.047 |
0.351a |
Large |
|
TC |
|
|
0.072b; 0.061a |
Small; small |
|
LS |
|
|
0.024b; 0.005a |
Small; nil |
aWS; bJD.
In this study, the relationship between LS, TC, JD, and WS in the public sector’s environment was studied. It is crucial to note that in analyzing the results, the authors accounted for work stress by using a well-established scale to determine the psychological impact of a national crisis, such as team conflict, leadership and job demands. This assessment enabled the discovery of the relationship among variables outside the psychological influence of employees’ performance, which reduced statistical bias and increased data dependability. The findings supported the five out of six proposed hypotheses.
Surprisingly, LS styles were negatively linked with WS (H1). This result contradicted past studies that suggested a positive relationship between LS and employee stress [32]. Nevertheless, this finding is consistent with previous studies [77, 78]. Hetland et al. [79] suggested that the link between transformative LS and subordinate burnout is not obvious. The possible reason for this finding could be that employees stress has nothing to do with the leaders but more to do with the nature of the jobs. It could be that employees need to manage their tasks to reduce their work stress. It is opined that LS helped create a better working environment, which in turn helped reduce WS. Employees who believe that their leader is executing their role in creating mutual trust, helpfulness, and friendliness experience less WS. Moreover, leaders who share job expectations clearly with employees can help them avoid WS [80, 81].
Next, it was determined that TC positively influenced WS among public sector employees (H2). Beyond performance effects, TC can be unpleasant and stressful for employees. This finding strongly supported the finding of Jimmieson et al. [82] that TC inhibits goal-directed action, which triggers thoughts and feelings that set the stage for a stress reaction. The TC also provokes unpleasant feelings among team members, which can lead to weakened identity, self-worth, and self-esteem, and similarities and social belonging, thereby leading to physiological and psychological WS [83].
Further, the finding of this study indicated that LS style significantly influenced JD (H3). This finding is consistent with previous study [53]. Apart from the direct role played by great leaders, a manager’s LS personality is significant in JD. Furthermore, the findings indicated that LS styles could be used as an effective tool in the Malaysian public sector. In this study, it was determined that leaders were a source of confidence and acted as a resource (being attentive, supportive, and considerate) so that the employees could meet their JD independently. Moreover, preliminary findings on organizational leaders were presented in this study, which were consistent with those of a recent US study [84]. The findings demonstrated the potential influence of LS on JD. The favorable influence of LS on JD supported the COR theory [9] on employee attitudes.
Furthermore, the investigation revealed that TC significantly influenced JD (H4). Earlier investigations confirmed these conclusions for TC and JD [85]. Similarly, Costa et al. [86] reported that TC was positively connected to JD. The natural drive of empowerment to take control of one’s work promotes engagement and involvement [87]. Nevertheless, studies on the association between TC and JD are scarce; therefore, this finding is essential for reinforcing the association in different countries. In this analysis, it was proved that the TC-JD association extended beyond the emotional impacts of the work environment on employees. It is pivotal to put in place measures to reduce team conflicts in order to mitigate job demands among employees.
Based on recommendations to research potential mediating factors in the relationship between LS, TC, JD, and WS [88], this analysis determined that JD are significant mediators. The aforementioned studies demonstrated that LS affected WS indirectly through JD (H5). A systematic learning culture that stimulates skilled individuals to develop creative solutions to organizational difficulties should enhance employees’ resilience and work involvement, which correlates with improved working engagement levels [89]. Moreover, the findings revealed that JD is an essential mediator between TC and WS (H6). The LS and TC focus on progressive LS, information exchange, and employee participation [90] to foster WS. The findings indicated that JD will reduce control of employees, which led to WS. In this study, the findings extended those of Schaufeli [91], where role-modelling leaders lessen work expectations for change. Cheong et al. [92] suggested that LS is a crucial antecedent of WS while JD is a necessary element for work outcomes [84].
WS is an important notion in the current demanding work environment. This study contributes to the understanding of employees in government institutions in Malaysia by determining how their job demands and stress can be reduced, in order to foster efficiency in the public sector. Positive emotions can promote approach behaviour and broaden cognitive tasks, which are vital for creating personal resources that contribute to work engagement [93].
Using the COR theory [9], a conceptual model was built to illustrate how the positive state of LS can extend employees’ thought-action repertoires and how positive emotions can help build personal resources which helps dimmish the JD hence reducing WS. This research contributed to the theory of COR theory [9] by demonstrating that leadership support is pivotal for employees to handle job demands and this may help employees manage work related stress. Also, having a clearly defined role among team members could help employees reduce the work stress, because efficient team work would reduce job demands. These data corroborated the expanding part of the COR theory, where the good emotions connected to LS, TC, and JD broadened employees’ thought–action repertoires.
Pandemic has impacted businesses and workers worldwide where the rapidly spreading public health outbreak affected the operations and earnings of numerous firms. Businesses rushed to deal with the pandemic in ways that were devastating to employees’ emotions and involvement, which included compensation cuts, lower benefits, lack of organizational support, abrupt work process changes, and the loss of close colleagues to layoffs or health issues. The findings indicated that LS was a significant organizational resource that could be used to enhance conducive workplace.
One of the most effective strategies for increasing public sector employee motivation and reducing stress is to focus on the LS patterns of supervisors and their management. Supervisors and managers should use more collaborative and transformational approaches in their interactions with subordinates. Cultural and sociopolitical factors have traditionally exerted a significant impact on the Malaysian public sector, which resulted in increased employment demand and higher WS levels among public sector employees [94]. To address the aforementioned issues, the government should concentrate on methods to make public sector work environments less stressful, as Malaysia considers employee well-being critical to its goal of becoming a developed country [95]. As part of a good learning culture, leaders should maintain open communication with all employees and exercise positive transformative LS. This investigation demonstrated that the impact of LS on WS is dependent on the JD. Organizations that can create and improve JD can profit from LS and TC in WS.
Public sector employees experience constant pressure to provide better services to the public and strain to continue [96]. Consequently, they might lose faith in the importance of their work. Employers can improve employees’ emotional resilience by implementing teamwork initiatives that encourage team spirit. Such programs are anticipated to strengthen employees’ sense of belonging and reduce stress, resulting in a better workplace. Furthermore, expanding flexible work arrangements (job sharing) while fostering cooperation might help employees better balance excessive workloads and obtain the necessary peer support from their team members, which would reduce WS and improve JD. Finally, organizations that provide individualized coaching and mentoring at work can assist employees in attaining their job goals more productively while responding to stressful circumstances more effectively.
Organizations can reduce WS by revamping employment and advancing their employees’ careers. Organizations that provide employees more leeway to achieve their job requirements might give them more control, which is vital for growing their sense of work participation and JD. This is especially true when organizations combine job redesign with improved career prospects. Employers who provide employees with information on how to advance their careers and fulfil their potential through training and performance evaluation specifically can make work more meaningful and contribute to employees’ sense of WS.
One study limitation is that the findings had limited generalizability as the sample only comprised Putrajaya public sector employees. As Malaysian public sector employees were the study focus, it is thus necessary to determine the generalizability of the results to other countries as organizational and cultural differences and different work environments may influence the outcomes. Moreover, the data were obtained from the same source (subordinates). Consequently, the findings could have been influenced by common technique bias. Nonetheless, all measures used in this study were thoroughly investigated in previous empirical research investigations.
Based on the CFA and Harman’s single-factor test results, less prevalent technique variance was not a main concern for the research model. Future research should use different sources for different variables to overcome potential issues related to common method variance. Finally, participant characteristics such as experience and age may play a role as control variables but were not investigated in this study. Thus, more research on these factors is required.
This research was supported by Universiti Malaysia Terengganu and funded by Ministry of Higher Education (MOHE), Malaysia, through Fundamental Research Grant Scheme (FRGS-RACER) (RACER/1/2019/SS02/UMT//1).
[1] Blaique, L., Ismail, H.N., Aldabbas, H. (2022). Organizational learning, resilience and psychological empowerment as antecedents of work engagement during COVID-19. International Journal of Productivity and Performance Management. https://doi.org/10.1108/IJPPM-04-2021-0197
[2] Moreland, J.J., Apker, J. (2016). Conflict and stress in hospital nursing: Improving communicative responses to enduring professional challenges. Health Communication, 31(7): 815-823. https://doi.org/10.1080/10410236.2015.1007548
[3] Quick, J.C., Henderson, D.F. (2016). Occupational stress: Preventing suffering, enhancing wellbeing. International Journal of Environmental Research and Public Health, 13(5): 459. https://doi.org/10.3390/ijerph13050459
[4] Malik, P., Garg, P. (2017). Learning organization and work engagement: The mediating role of employee resilience. The International Journal of Human Resource Management, 21(46): 1-24. https://doi.org/10.1080/09585192.2017.1396549
[5] Moin, M.F. (2018). The link between perceptions of leader emotion regulation and followers’ organizational commitment. Journal of Management Development, 37(2): 178-187. https://doi.org/10.1108/JMD-01-2017-0014
[6] Kim, M.S., Koo, D.W. (2017). Linking LMX, engagement, innovative behavior, and job performance in hotel employees. International Journal of Contemporary Hospitality Management, 29(12): 3044-3062. https://doi.org/10.1108/IJCHM-06-2016-0319
[7] Berry, L.L., Danaher, T.S., Aksoy, L., Keiningham, T.L. (2020). Service safety in the pandemic age. Journal of Service Research, 23(4): 391-395. https://doi.org/10.1177/1094670520944608
[8] Lee, A.V., Vargo, J., Seville, E. (2013). Developing a tool to measure and compare organizations’ resilience. Natural Hazards Review, 14(1): 29-41. https://doi.org/10.1061/(ASCE)NH.1527-6996.0000075
[9] Hobfoll, S.E., Vinokur, A.D., Pierce, P.F., Lewandowski-Romps, L. (2012). The combined stress of family life, work, and war in air force men and women: A test of Conservation of Resources Theory. International Journal of Stress Management, 19(3): 217-237. https://doi.org/10.1037/a0029247
[10] Hobfoll, S.E., Tirone, V., Holmgreen, L., Gerhart, J. (2016). Conservation of resources theory applied to major stress. In Stress: Concepts, Cognition, Emotion, and Behavior, Academic Press, 65-71. https://doi.org/10.1016/B978-0-12-800951-2.00007-8
[11] Le Nguyen, K.D., Fredrickson, B.L. (2017). Positive Emotions and Well-Being. In Positive Psychology, 29-45. Routledge.
[12] Hartmann, S., Weiss, M., Newman, A., Hoegl, M. (2020). Resilience in the workplace: A multilevel review and synthesis. Applied Psychology, 69(3): 913-959. https://doi.org/10.1111/apps.12191
[13] Ilias, A., Zainudin, N.N.B. (2013). Factor affecting the computerized accounting system (CAS) usage in public sector. Journal of Internet Banking and Commerce, 18(1): 1-30.
[14] Bernama. (2019). Cuepacs: Formulate National Stress Management Policy immediately. URL: https://www.malaysiakini.com/news/497108.
[15] Webster, N. (2020). Mental health day: War and Covid-19 strain leave many in Middle East suffering in silence. The National. https://www.thenationalnews.com/world/mena/mentalhealth-day-war-and-covid-19-strain-leave-many-in-middle-east-suffering-in-silence-1.1091291.
[16] Bliese, P.D., Edwards, J.R., Sonnentag, S. (2017). Stress and well-being at work: A century of empirical trends reflecting theoretical and societal influences. Journal of Applied Psychology, 102(3): 389-402. https://doi.org/10.1037/apl0000109
[17] Dubois, C.A., Bentein, K., Ben Mansour, J., Gilbert, F., Bédard, J.L. (2014). Why some employees adopt or resist reorganization of work practices in health care: associations between perceived loss of resources, burnout, and attitudes to change. International Journal of Environmental Research and Public Health, 11(1): 187-201. https://doi.org/10.3390/ijerph110100187
[18] Hobfoll, S.E. (2002). Social and psychological resources and adaptation. Review of General Psychology, 6(4): 307-324. https://doi.org/10.1037/1089-2680.6.4.307
[19] McMillan, K., Perron, A. (2013). Nurses amidst change: The concept of change fatigue offers an alternative perspective on organizational change. Policy, Politics, & Nursing Practice, 14(1): 26-32. https://doi.org/10.1177/1527154413481811
[20] Chullen, C.L. (2014). How does supervisor burnout affect leader-member exchange? A dyadic perspective. International Business and Economics Research Journal (IBER), 13(5): 1113-1126. https://doi.org/10.19030/iber.v13i5.8777
[21] Gong, Z., Li, M., Niu, X. (2021). The role of psychological empowerment in reducing job burnout among police officers: A variable-centered and person-centered approach. SAGE Open, 11(1): 1-8. https://doi.org/10.1177/2158244020983305
[22] Hobfoll, S.E., Halbesleben, J., Neveu, J.P., Westman, M. (2018). Conservation of resources in the organizational context: The reality of resources and their consequences. Annual Review of Organizational Psychology and Organizational Behavior, 5: 103-128. https://doi.org/10.1146/annurev-orgpsych-032117-104640
[23] Di Fabio, A. (2017). Positive Healthy Organizations: Promoting well-being, meaningfulness, and sustainability in organizations. Frontiers in Psychology, 8: 1938. https://doi.org/10.3389/fpsyg.2017.01938
[24] Montano, D., Reeske, A., Franke, F., Hüffmeier, J. (2017). Leadership, followers' mental health and job performance in organizations: A comprehensive meta-analysis from an occupational health perspective. Journal of Organizational Behavior, 38(3): 327-350. https://doi.org/10.1002/job.2124
[25] Albrecht, S., Breidahl, E., Marty, A. (2018). Organizational resources, organizational engagement climate, and employee engagement. Career Development International, 23(1): 67-85. https://doi.org/10.1108/CDI-04-2017-0064
[26] Paais, M., Pattiruhu, J. R. (2020). Effect of motivation, leadership, and organizational culture on satisfaction and employee performance. The Journal of Asian Finance, Economics and Business, 7(8): 577-588. https://doi.org/10.13106/jafeb.2020.vol7.no8.577
[27] Jena, L.K., Pradhan, S., Panigrahy, N.P. (2018). Pursuit of organisational trust: Role of employee engagement, psychological well-being and transformational leadership. Asia Pacific Management Review, 23(3): 227-234. https://doi.org/10.1016/j.apmrv.2017.11.001
[28] Offord, M., Gill, R., Kendal, J. (2016). Leadership between decks: A synthesis and development of engagement and resistance theories of leadership based on evidence from practice in Royal Navy warships. Leadership & Organization Development Journal, 37(2): 289-304. https://doi.org/10.1108/LODJ-07-2014-0119
[29] Khalid, A., Zafar, A., Zafar, M.A., Saqib, L., Mushtaq, R. (2012). Role of supportive leadership as a moderator between job stress and job performance. Information Management and Business Review, 4(9): 487-495. https://doi.org/10.22610/imbr.v4i9.1004
[30] Buchanan, R. (2015). Worlds in the making: design, management, and the reform of organizational culture. She Ji: The Journal of Design, Economics, and Innovation, 1(1): 5-21. https://doi.org/10.1016/j.sheji.2015.09.003
[31] Rohana, A., Mazni, S. (2020). The impact of Malaysian public sector in the relationship between transformational leadership styles and career development. International Journal of Public Administration, 43(3): 203-212. https://doi.org/10.1080/01900692.2019.1627555
[32] Syed, A.R., Rehman, K.U., Kitchlew, N. (2018). Impact of perceived leadership style on employees' work stress: Moderating and mediating role big 5 personality traits. Paradigms, 12(1): 6-15. https://doi.org/10.24312/paradigms120102
[33] Singh, D. (2019). A literature review on employee retention with focus on recent trends. International Journal of Scientific Research in Science and Technology, 6(1): 425-431. https://doi.org/10.32628/IJSRST195463
[34] Kasparkova, L., Vaculik, M., Prochazka, J., Schaufeli, W.B. (2018). Why resilient workers perform better: The roles of job satisfaction and work engagement. Journal of Workplace Behavioural Health, 33(1): 43-62. https://doi.org/10.1080/15555240.2018.1441719
[35] Moretti, A., Menna, F., Aulicino, M., Paoletta, M., Liguori, S., Iolascon, G. (2020). Characterization of home working population during COVID-19 emergency: A cross-sectional analysis. International Journal of Environmental Research and Public Health, 17(17): 6284. https://doi.org/10.3390/ijerph17176284
[36] Hetzel-Riggin, M.D., Swords, B.A., Tuang, H.L., Deck, J.M., Spurgeon, N.S. (2020). Work engagement and resiliency impact the relationship between nursing stress and burnout. Psychological Reports, 123(5): 1835-1853. https://doi.org/10.1177/0033294119876076
[37] Steinkopf, B., Reddin, R.A., Black, R.A., Van Hasselt, V.B., Couwels, J. (2018). Assessment of stress and resiliency in emergency dispatchers. Journal of Police and Criminal Psychology, 33(4): 398-411. https://doi.org/10.1007/s11896-018-9255-3
[38] O'Neill, T.A., Mclarnon, M.J. (2018). Optimizing team conflict dynamics for high performance teamwork. Human Resource Management Review, 28(4): 378-394. https://doi.org/10.1016/j.hrmr.2017.06.002
[39] Humphrey, S.E., Aime, F. (2014). Team microdynamics: Toward an organizing approach to teamwork. Academy of Management Annals, 8(1): 443-503. https://doi.org/10.5465/19416520.2014.904140
[40] Schermuly, C.C., Meyer, B. (2016). Good relationships at work: The effects of leader member exchange and team-member exchange on psychological empowerment, emotional exhaustion, and depression. Journal of Organizational Behavior, 37(5): 673-691. https://doi.org/10.1002/job.2060
[41] Humphrey, S.E., Aime, F., Cushenbery, L., Hill, A.D., Fairchild, J. (2017). Team conflict dynamics: Implications of a dyadic view of conflict for team performance. Organizational Behavior and Human Decision Processes, 142: 58-70. https://doi.org/10.1016/j.obhdp.2017.08.002
[42] Almost, J., Wolff, A.C., Stewart-Pyne, A., McCormick, L.G., Strachan, D., D'souza, C. (2016). Managing and mitigating conflict in healthcare teams: An integrative review. Journal of advanced nursing, 72(7): 1490-1505. https://doi.org/10.1111/jan.12903
[43] Bakker, A.B., Demerouti, E., Sanz-Vergel, A.I. (2014). Burnout and work engagement: The JD-R approach. Annual Review of Organizational Psychology and Organizational Behavior, 1(1): 389-411. https://doi.org/10.1146/annurev-orgpsych-031413-091235
[44] Schaufeli, W.B., Bakker, A.B. (2004). Job demands, job resources, and their relationship with burnout and engagement: A multi-sample study. Journal of Organizational Behavior: The International Journal of Industrial, Occupational and Organizational Psychology and Behavior, 25(3): 293-315. https://doi.org/10.1002/job.248
[45] Quiñones, M., Van den Broeck, A., De Witte, H. (2013). Do job resources affect work engagement via psychological empowerment? A mediation analysis. Journal of Work and Organizational Psychology, 29(3): 127-134. https://doi.org/10.5093/tr2013a18
[46] Muduli, A. (2017). Workforce agility: Examining the role of organizational practices and psychological empowerment. Global Business and Organizational Excellence, 36(5): 46-56. https://doi.org/10.1002/joe.21800
[47] Bakker, A.B., Demerouti, E. (2017). Job demands–resources theory: Taking stock and looking forward. Journal of Occupational Health Psychology, 22(3): 273-285. https://doi.org/10.1037/ocp0000056
[48] Dudenhöffer, S., Claus, M., Schöne, K., Letzel, S., Rose, D.M. (2017). Sickness presenteeism of German teachers: Prevalence and influencing factors. Teachers and Teaching, 23(2): 141-152. https://doi.org/10.1080/13540602.2016.1204284
[49] Gosselin, E., Lemyre, L., Corneil, W. (2013). Presenteeism and absenteeism: Differentiated understanding of related phenomena. Journal of Occupational Health Psychology, 18(1): 75-86. https://doi.org/10.1037/a0030932
[50] Cece, V., Duchesne, M., Guillet-Descas, E., Martinent, G. (2020). Self-determined motivation, emotional process and subjective performance among young elite athletes: A longitudinal hierarchical linear modelling approach. European Journal of Sport Science, 20(9): 1255-1267. https://doi.org/10.1080/17461391.2019.1709562
[51] Abbas, J., Aqeel, M., Abbas, J., Shaher, B., Jaffar, A., Sundas, J., Zhang, W. (2019). The moderating role of social support for marital adjustment, depression, anxiety, and stress: Evidence from Pakistani working and nonworking women. Journal of Affective Disorders, 244: 231-238. https://doi.org/10.1016/j.jad.2018.07.071
[52] Dollard, M.F., Tuckey, M.R., Dormann, C. (2012). Psychosocial safety climate moderates the job demand-resource interaction in predicting workgroup distress. Accident Analysis and Prevention, 45: 694-704. https://doi.org/10.1016/j.aap.2011.09.042
[53] Breevaart, K., Bakker, A.B. (2018). Daily job demands and employee work engagement: The role of daily transformational leadership behavior. Journal of Occupational Health Psychology, 23(3): 338-349. https://doi.org/10.1037/ocp0000082
[54] Katou, A.A., Koupkas, M., Triantafillidou, E. (2021). Job demands-resources model, transformational leadership and organizational performance: a multilevel study. International Journal of Productivity and Performance Management, 71(7): 2704-2722. https://doi.org/10.1108/IJPPM-06-2020-0342
[55] Sandhaland, H., Oltedal, H.A., Hystad, S.W., Eid, J. (2017). Effects of leadership style and psychological job demands on situation awareness and the willingness to take a risk: A survey of selected offshore vessels. Safety Science, 93: 178-186. https://doi.org/10.1016/j.ssci.2016.12.004
[56] Chaudhary, R. (2019). Corporate social responsibility perceptions and employee engagement: Role of psychological meaningfulness, safety and availability. Corporate Governance: The International Journal of Business in Society, 19(4): 631-647. https://doi.org/10.1108/CG-06-2018-0207
[57] Demerouti, E., Bakker, A.B., Nachreiner, F., Schaufeli, W.B. (2001). The job demands resources model of burnout. Journal of Applied Psychology, 86(3): 499-512. https://psycnet.apa.org/doi/10.1037/0021-9010.86.3.499
[58] Bakker, A.B., Demerouti, E. (2018). Multiple levels in job demands-resources theory: Implications for employee well-being and performance. Handbook of Well-Being.
[59] Bakar, Z.A., Salleh, R. (2015). Role demands, work-family conflict and motivation: A proposed framework. Global Business and Management Research, 7(2): 78-87.
[60] Sharma, G. (2017). Pros and cons of different sampling techniques. International Journal of Applied Research, 3(7): 749-752.
[61] Christensen, L.B., Johnson, R.B., Turner, L.A. (2015). Research Methods, Design and Analysis. 12th Edition. Pearson Education, Inc.
[62] Hair, J., Hollingsworth, C.L., Randolph, A.B., Chong, A.Y.L. (2017). An updated and expanded assessment of PLS-SEM in information systems research. Industrial Management and Data Systems, 117(3): 442-458. https://doi.org/10.1108/IMDS-04-2016-0130
[63] Gefen, D., Rigdon, E.E., Straub, D. (2011). Editor’s comments: An update and extension to SEM guidelines for administrative and social science research. MIS Quarterly, 35(2): 3-14. https://doi.org/10.2307/23044042
[64] Green, S.B. (1991). How many subjects does it take to do a regression analysis. Multivariate Behavioral Research, 26(3): 499-510. https://doi.org/10.1207/s15327906mbr2603_7
[65] Hair, J.F., Jr, Anderson, R.E., Tatham, R.L., Black, W.C. (1998). Factor Analysis: Multivariate Data Analysis. 5th ed. Prentice-Hall, Upper Saddle River, New Jersey.
[66] Kaiser, H. (1974). An index of factorial simplicity. Psychometrika, 39: 31-36. https://doi.org/10.1007/BF02291575
[67] Ngah, A.H., Gabarre, S., Eneizan, B., Asri, N. (2020). Mediated and moderated model of the willingness to pay for halal transportation. Journal of Islamic Marketing, 12(8): 1425-1445. https://doi.org/10.1108/JIMA-10-2019-0199
[68] Fornell, C., Larcker, D.F. (1981). Evaluating structural equation models with unobservable variables and measurements error. Journal of Marketing Research, 18(4): 39-50. https://doi.org/10.1177/002224378101800104
[69] Gholami, R., Sulaiman, A.B., Ramayah, T., Molla, A. (2013). Senior managers’ perception on green information systems (IS) adoption and environmental performance: Results from a field survey. Information & Management, 50(7): 431-438. https://doi.org/10.1016/j.im.2013.01.004
[70] Hair, Joe F, Risher, J.J., Sarstedt, M., Ringle, C.M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1): 2-24. https://doi.org/10.1108/EBR-11-2018-0203
[71] Henseler, J., Ringle, C.M., Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1): 115-135. https://doi.org/10.1007/s11747-014-0403-8
[72] Diamantopoulos, A., Siguaw, J.A. (2006). Formative versus reflective indicators in organizational measure development: A comparison and empirical illustration. British Journal of Management, 17(4): 263-282. https://doi.org/10.1111/j.1467-8551.2006.00500.x
[73] Falk, R., Miller, N. (1992). A Primer for Soft Modeling. Akron, OH: The University of Akron Press.
[74] Geisser, S. (1974). A predictive approach to the random effect model. Biometrika, 61(1): 101-107. https://doi.org/10.1093/biomet/61.1.101
[75] Cha, J. (1994). Partial least squares. In R.P. Bagozzi (Ed.), Advanced Methods of Marketing Research, 52-78, Oxford: Blackwell.
[76] Cohen, J. (1992). Quantitative methods in psychology: A power primer. Psychological Bulletin, 112(1): 155-159.
[77] Lyons, J.B., Schneider, T.R. (2009). The effects of leadership style on stress outcomes. The Leadership Quarterly, 20(5): 737-748. https://doi.org/10.1016/j.leaqua.2009.06.010
[78] Harms, P.D., Credé, M., Tynan, M., Leon, M., Jeung, W. (2017). Leadership and stress: A meta-analytic review. The Leadership Quarterly, 28(1): 178-194. https://doi.org/10.1016/j.leaqua.2016.10.006
[79] Hetland, H., Sandal, G.M., Johnsen, T.B. (2007). Burnout in the information technology sector: Does leadership matter? European Journal of Work and Organizational Psychology, 16(1): 58-75. https://doi.org/10.1080/13594320601084558
[80] Schwepker Jr., C.H., Ingram, T.N. (2016). Ethical leadership in the salesforce: Effects on salesperson customer orientation, commitment to customer value and job stress. Journal of Business & Industrial Marketing, 31(7): 914-927. https://doi.org/10.1108/JBIM-07-2015-0136
[81] Schwepker Jr., C.H., Good, M.C. (2017). Reducing salesperson job stress and unethical intent: The influence of leader-member exchange relationship, socialization and ethical ambiguity. Industrial Marketing Management, 66: 205-218. https://doi.org/10.1016/j.indmarman.2017.08.008
[82] Jimmieson, N.L., Tucker, M.K., Campbell, J.L. (2017). Task conflict leads to relationship conflict when employees are low in trait self-control: Implications for employee strain. Personality and Individual Differences, 113: 209-218. https://doi.org/10.1016/j.paid.2017.03.035
[83] De Dreu, C.K., Van Dierendonck, D., Dijkstra, M.T. (2004). Conflict at work and individual well-being. International Journal of Conflict Management, 15(1): 6-26. https://doi.org/10.1108/eb022905
[84] Choi, I. (2020). Moving beyond mandates: organizational learning culture, empowerment, and performance. International Journal of Public Administration, 43(8): 724-735. https://doi.org/10.1080/01900692.2019.1645690
[85] Hon, A.H., Chan, W.W. (2013). The effects of group conflict and work stress on employee performance. Cornell Hospitality Quarterly, 54(2): 174-184. https://doi.org/10.1177/1938965513476367
[86] Costa, P.L., Passos, A.M., Bakker, A.B. (2015). Direct and contextual influence of team conflict on team resources, team work engagement, and team performance. Negotiation and Conflict Management Research, 8(4): 211-227. https://doi.org/10.1111/ncmr.12061
[87] Thakre, N., Mathew, P. (2020). Psychological empowerment, work engagement, and organizational citizenship behavior among Indian service-sector employees. Global Business and Organizational Excellence, 39(4): 45-52. https://doi.org/10.1002/joe.22003
[88] Ogueyungbo, O.O., Chinonye, L.M., Igbinoba, E., Salau, O., Falola, H., Olokundun, M. (2020). Organisational learning and employee engagement: The mediating role of supervisory support. Cogent Business and Management, 7(1): 1816419. https://doi.org/10.1080/23311975.2020.1816419
[89] Cooke, F.L., Cooper, B., Bartram, T., Wang, J., Mei, H. (2019). Mapping the relationships between high-performance work systems, employee resilience and engagement: A study of the banking industry in China. The International Journal of Human Resource Management, 30(8): 1239-1260. https://doi.org/10.1080/09585192.2015.1137618
[90] Watkins, K.E., Kim, K. (2018). Current status and promising directions for research on the learning organization. Human Resource Development Quarterly, 29(1): 15-29. https://doi.org/10.1002/hrdq.21293
[91] Schaufeli, W.B. (2015). Engaging leadership in the job demands-resources model. Career Development International, 20(5): 446-463. https://doi.org/10.1108/CDI-02-2015-0025
[92] Cheong, M., Spain, S.M., Yammarino, F.J., Yun, S. (2016). Two faces of empowering leadership: Enabling and burdening. The Leadership Quarterly, 27(4): 602-616. https://doi.org/10.1016/j.leaqua.2016.01.006
[93] Salanova, M., Llorens, S., Schaufeli, W.B. (2011). ‘Yes, I can, I feel good, and I just do it!’ On gain cycles and spirals of efficacy beliefs, affect, and engagement. Applied Psychology, 60(2): 255-285. https://doi.org/10.1111/j.1464-0597.2010.00435.x
[94] Bowen, P., Edwards, P., Lingard, H., Cattell, K. (2014). Occupational stress and job demand, control and support factors among construction project consultants. International Journal of Project Management, 32(7): 1273-1284. https://doi.org/10.1016/j.ijproman.2014.01.008
[95] Johari, J., Shamsudin, F.M., Yean, T.F., Yahya, K.K., Adnan, Z. (2019). Job characteristics, employee well-being, and job performance of public sector employees in Malaysia. International Journal of Public Sector Management, 32(1): 102-119. https://doi.org/10.1108/IJPSM-09-2017-0257
[96] Tummers, L.L., Bekkers, V., Vink, E., Musheno, M. (2015). Coping during public service delivery: A conceptualization and systematic review of the literature. Journal of Public Administration Research and Theory, 25(4): 1099-1126. https://doi.org/10.1093/jopart/muu056
