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Abstract

BACKGROUND:

Fear of losing psychological resources can lead to stress, impacting psychological health and behavioral outcomes like burnout, absenteeism, service sabotage, and turnover.

OBJECTIVE:

The study examined the impact of job stressors (time pressure, role ambiguity, role conflict) on employee well-being and turnover intentions. The study also investigated the mediating role of employee well-being between job stressors and turnover intention based on the conservation of resources (COR) theory.

METHODS:

Data from 396 IT executives in Malaysian IT firms were analyzed using the Partial Least Squares - Structural Equation Modeling (PLS-SEM) technique.

RESULTS:

Results confirmed a significant negative correlation between time pressure (–0.296), role ambiguity (–0.423), role conflict (–0.104), and employee well-being. Similarly, employee well-being showed a significant negative relationship with turnover intentions (–0.410). The mediation analysis revealed that employee well-being mediates the relationship between time pressure (0.121), role ambiguity (0.173), role conflict (0.043), and turnover intentions.

CONCLUSION:

This paper aims to manifest the importance of designing employee well-being policies by firms to retain employees. Findings reflect the role of the managerial approach towards ensuring employee well-being for employee retention, thereby reducing recruitment and re-training costs.

Keywords

Time pressure role ambiguity role conflict employee satisfaction

1 Introduction

The rapidly evolving IT industry faces uncertainty related to the business environment, technological changes, remote work, and talent shortage [1]. Considering the global war for talent, organizations need to compete to retain their talent, or else they have to bear high employee turnover [2]. Retaining professionals in the IT industry has been a prevalent issue as attracting, developing, and retaining qualified professionals is costly, and employee turnover is a potential loss for any organization [3]. Although voluntary turnover has decreased, given the COVID-19 pandemic, Employee Movement and Retention report (2021) notes that 61% of Malaysian workers intend to leave their current job seeking new ones [4]. Turnover intention is “the reported willingness of an employee to leave the organization within a given period” [5]. The intention to leave may arise due to various reasons (dissatisfaction, role ambiguity, low level of engagement at work, poor supervisory support, etc.) [6, 7]. According to Zainal et al. [8], the reasons associated with employee turnover often relate to work-related stressors; however, the explanatory mechanism for the stressor-turnover intention link among IT workers’ demands further scholarly attention [9].

Past studies measured employee well-being using scales of life satisfaction, job satisfaction, work engagement, and work-life balance [10]; however, none of them have specifically focused on the broader sense of employee well-being (EWB) [11]. EWB is a multidimensional construct comprising life well-being (LWB), work well-being (WWB), and psychological well-being (PWB). LWB covers employees’ emotions and family-related issues. WWB entails job-related elements, for instance, management style, compensation and benefits, work arrangements, and labor protection. Lastly, PWB emphasizes self-actualization, learning, growth, and work achievement [11]. Overall, our study examines the effects of job stressors (time pressure, role ambiguity, role conflict) on employee well-being and turnover intention among IT employees based on the principle of resource loss. Additionally, this study examines the mediating role of employee well-being between job stressors and turnover intention while using COR theory’s second tenet (resource investment) [12], especially in the context of Malaysian IT executives working in software companies.

The COR theory states that people strive for and protect the resources they value [13]. Stress has remained a notable factor in COR-related studies [13, 14]. When people fear losing psychological resources, they are prone to experiencing stress, with implications for their psychological health or behavioral outcomes such as burnout, absenteeism, service sabotage, turnover, etc. This study considers the job stressors of time pressure, role ambiguity, and role conflict, which negatively affect the well-being of IT executives. Under constant time pressures and workloads, employees experience resource loss in the form of exhaustion and burnout [15]. Such events may also affect employees’ health, increasing the risk of developing illness [16]. Previous studies also reported a negative association between time pressure and job satisfaction and a positive association between time pressure and fatigue [17]. Gärling et al. [18] and Huhtala et al. [19] also found a negative relationship between time pressure and employee well-being.

Role ambiguity arises due to unclear job-related information regarding job responsibilities and work goals [20]. Employees facing role ambiguity may misidentify their responsibilities and what is expected from them, resulting in poor performance and high levels of stress [21]. According to the COR principle, employees facing role ambiguity will use their cognitive resources to deal with it, which results in emotional exhaustion and reduced well-being [22]. Studies by Barling et al. and Pecino et al. [21, 23] also reported a negative association between role ambiguity and employee well-being. Role conflicts are incompatible demands faced by employees at work [21]. Role conflicts in terms of pressure to perform tasks other than job requirements lead to anxiety and fatigue [24]. According to COR theory, role conflicts result in resource depletion as they are an additional burden on the employees, negatively affecting their well-being and performance [25]. A study by Nambisan et al. [26] found a positive association between role conflicts and stress.

Fig. 1

Theoretical framework.

Consequently, resource loss drives turnover intentions [27]. When IT executives work under conditions of time pressure, role ambiguity, and role conflict, they are likely to experience impaired well-being in terms of life well-being (LWB), work well-being (WWB), and psychological well-being (PWB). Wang et al. [28] argued that stressors at the workplace results in resource loss, reducing employee well-being and triggering turnover intentions. In this situation, an individual will avoid further resource loss and start searching for alternative employment, which forms the core of turnover intentions. Belkin et al. [29] and Xue et al. [30] also noted that well-being reduces turnover intentions of employees. According to Halbesleben et al. [31], another principle of the COR theory is termed resource investment, covered only in a few studies. With resource investment, scholars note that individuals invest in building their resources to save against resource loss and expand the resources [13]. The present study also intends to apply the resource investment principle through employee well-being to examine its mediating role between job stressors and turnover intention to protect IT executives from the resource loss they had due to the job stressors.

The rationale for studying the mediating role of employee well-being is, first, that employee well-being is a primary resource that can have reciprocal effects on work-related outcomes [32], such as turnover intention. Second, the COR theory proposes that a lost resource can be reserved by another resource [13]. On this basis, this study aims to study the role of employee well-being to protect the resource loss faced by employees due to job stressors such as role ambiguity and role conflict. Thirdly, prior studies have discussed that employee well-being is an essential factor that works as a retention strategy for any organization [33, 34]. Studies show that well-being is essential for positive work behaviors such as high job performance, whereas reduced well-being associated with job insecurity explains turnover intentions [35]. Wright et al. [36] and Xue et al. [30] also noted that employee well-being helps reduce employee turnover.

1.1 Research hypotheses

H1: Time pressure is negatively related to employee well-being

H2: Role ambiguity is negatively related to employee well-being

H3: Role conflict is negatively related to employee well-being

H4: Employee well-being is negatively related to turnover intention

H5: Employee well-being mediates the relationship between time pressure and turnover intention

H6: Employee well-being mediates the relationship between role ambiguity and turnover intention

H7: Employee well-being mediates the relationship between role conflict and turnover intention

2 Methods

2.1 Study design

This is a cross-sectional study.

2.2 Procedure and participants

This paper uses a multi-stage sampling method, enabling sample selection in stages [37] to collect data from IT executives in Malaysia’s ICT sector. According to Department of Statistics Malaysia, it is one of the fastest-growing industries in Malaysia with a growth rate of 10.4% in 2022 [38] and will continue to be among top-five paying industry [39]. The data were collected through self-administered surveys, distributed among 600 respondents in 40 companies (a minimum of 10 IT executives in each). Companies selected for this study were registered in PIKOM, The National Tech Association of Malaysia. The random selection was made based on weightage (55 companies from Kuala Lumpur and assigned 20% weightage to each state company) (55×20% = 11 companies from Kuala Lumpur, 10 Cyberjaya, 7 Selangor, 6 Penang, and 6 Perak). The selected companies were located in Kuala Lumpur, Selangor, and Putrajaya- representing 66% of the organizations [39]. The questionnaires were distributed from February to May 2021. From 600 questionnaires distributed, we received 396 complete surveys. All respondents have voluntarily contributed and given consent of participation. The final sample size was n = 383, IT executives. We analyzed non-response bias by comparing the early and later responses using t-tests [40]. The analysis indicates a non-significant difference indicating, no evidence of non-response bias [40].

2.3 Demographic profile of respondents

The demographic profile of the respondents is depicted in Table 1, where males represent (53%) and females (47%) of the study population. Respondents’ age falls between (25–30) and (31–35) representing 41.9% and 30.3%, respectively. Majority of the respondents hold a bachelor’s degree (60.3%), followed by master’s degree (21.7%), diploma holders (19.4%), and lastly, STPM (Sijil Tinggi Persekolahan Malaysia: Malaysian Higher School Certificate) (1.3%). The nature of the job of most respondents was system engineer (24%), programmer (22.5%), web designer (16.4%), and software engineer (15.4%). Most IT executives were single (215), representing 54.3%, while the married (181) showed 47.7%.

Table 1
Sociodemographic profile of workers

Profile Frequency Percent

Gender

Male 210 53.0

Female 186 47.0

Age

Less than 25 74 18.7

25–30 166 41.9

31–35 120 30.3

36–40 31 7.8

More than 40 5 1.3

Education

Master 84 21.2

Bachelor 239 60.3

Diploma 68 17.2

STPM 5 1.3

Work Experience

Less than 1 year 46 11.6

1–2 years 95 24.0

3–4 years 173 43.7

More than 4 years 82 20.7

Nature of Job

Programmer 89 22.5

System Engineer 95 24.0

Web Designer 65 16.4

Design Engineer 35 8.8

Analog Design Engineer 30 7.6

IC Design Engineer 12 3.0

Software Engineer 61 15.4

Network Engineer 9 2.3

Marital Status

Single 215 54.3

Married 181 45.7

Profile	Frequency	Percent
Gender
Male	210	53.0
Female	186	47.0
Age
Less than 25	74	18.7
25–30	166	41.9
31–35	120	30.3
36–40	31	7.8
More than 40	5	1.3
Education
Master	84	21.2
Bachelor	239	60.3
Diploma	68	17.2
STPM	5	1.3
Work Experience
Less than 1 year	46	11.6
1–2 years	95	24.0
3–4 years	173	43.7
More than 4 years	82	20.7
Nature of Job
Programmer	89	22.5
System Engineer	95	24.0
Web Designer	65	16.4
Design Engineer	35	8.8
Analog Design Engineer	30	7.6
IC Design Engineer	12	3.0
Software Engineer	61	15.4
Network Engineer	9	2.3
Marital Status
Single	215	54.3
Married	181	45.7

2.4 Variables and instrumentation

Independent variables included time pressure, role ambiguity, and role conflict. Time pressure was measured using nine-item scale by Roxburgh et al. [41]. Respondents were asked about their lives over the previous 12 months period. An example of an item is “I never seem to have enough time to get everything done”. Role ambiguity was measured with an established scale comprising five items [42]. An example of role ambiguity item is “Explanation is not clear of what has to be done". Role conflict was measured using seven-item scale by Rizzo et al. [42]. E.g. “I work under incompatible policies and guidelines". The mediating variable, employee well-being, was measured using eighteen-item scale by Zheng et al. [11]. The scale assesses three dimensions of employee well-being: life well-being (LWB), work well-being (WWB), and psychological well-being (PWB). An example of an LWB item is “I feel satisfied with my life.” An example of a WWB item is “I find real enjoyment in my work.” An example of a PWB item is “I handle daily affairs well”. Dependent variable for the study, turnover intention, was measured with five-item scale adopted from Ganesan et al. [43]. E.g. “I do not think I will spend my entire career with this organization”. All the variables for this study were measured using a five-point Likert scale (1 = “strongly disagree” to 5 = “strongly agree”).

2.5 Statistical analysis

All statistical analyses were performed using Partial Least Squares - Structural Equation Modeling (PLS-SEM). In SEM, model is divided into: 1) measurement model, and 2) structural model [44].

3 Results

3.1 Measurement model assessment

The measurement model results (Table 2) showed that all items’ loadings in the final model were at or above the accepted threshold of 0.70 [44]. Three items were deleted from the study because of the low factor loading, one from role conflict (RC3) and two from time pressure (TP5, TP7). The values of AVE were higher than 0.50 [45]. The composite reliability (CR) of all the constructs was above the accepted threshold of 0.70, indicating that high internal consistency exists in the scale. Table 3 shows that the Heterotrait-Monotrait ratio of correlations (HTMT) did not exceed the 0.85 threshold [46], thus establishing discriminant validity [47].

Table 2
Measurement model assessment

Construct Scale Items Convergent Validity CR Cronbach’s Alpha

Loadings AVE

Role Conflict RC1 0.640 0.512 0.862 0.811

RC2 0.721

RC4 0.742

RC5 0.649

RC6 0.768

RC7 0.762

Role Ambiguity RA1 0.730 0.517 0.842 0.798

RA2 0.589

RA3 0.743

RA4 0.728

RA5 0.790

Time Pressure TP1 0.798 0.92 0.623

TP2 0.838

TP3 0.796

TP4 0.756

TP6 0.842

TP8 0.790

TP9 0.695

Life Well-Being LWB1 0.668 0.878 0.636

LWB2 0.834

LWB3 0.689

LWB4 0.824

LWB5 0.604

LWB6 0.795

Workplace Well-Being WWB1 0.797 0.913 0.636

WWB2 0.831

WWB3 0.830

WWB4 0.800

WWB5 0.771

WWB6 0.749

Psychological Well-Being PWB1 0.817 0.926 0.676

PWB2 0.826

PWB3 0.823

PWB4 0.863

PWB5 0.862

PWB6 0.735

Turnover Intention TOI1 0.698 0.898 0.639

TOI2 0.856

TOI3 0.856

TOI4 0.850

TOI5 0.721

Note: RC3, TP5 and TP7 were deleted due to low loadings.

Table 3

Discriminant validity (HTMT)

Constructs	LWB	PWB	RA	RC	TP	TOI	WWB
Life Well-Being
Psychological Well-Being	0.492
Role Ambiguity_	0.521	0.567
Role Conflict	0.148	0.173	0.153
Time Pressure	0.145	0.203	0.192	0.519
Turnover Intention	0.354	0.371	0.271	0.331	0.387
Workplace Well-Being	0.279	0.208	0.288	0.185	0.218	0.272

3.2 Second-order construct assessment

Two stages of formative second-order constructs were tested [48]. First-order constructs (LWB, WWB, PWB) of employee well-being were examined through correlations among constructs. Table 4 shows that correlations of employee well-being first order-constructs range from 0.185 to 0.427. The correlation results indicate that employee well-being is a formative second-order construct. A reflective second-order construct shows an extremely high correlation among its lower-order constructs (≥0.8) [49]. Furthermore, the variance inflation factor (VIF) for the first-order construct of employee well-being was analyzed to evaluate multi-collinearity [49]. As depicted in Table 4, the values of VIF for first-order constructs are 1.264, 1.070, and 1.237. All three values are under the 3.3 threshold [49], indicating no multi-collinearity concern exists between the first-order constructs of employee well-being. Lastly, the significance of the relationships between employee well-being and its first-order dimensions was found to be significant at 1% [48].

Table 4
Assessment of measurement model after generating second-order constructs

Correlation amongst the first-order constructs Weights of the first-order constructs on employee well-being

Constructs LWB WWB PWB Weights t-value VIF

LWB 1 0.290 5.062 1.264

WWB 0.242 1 0.180 4.587 1.070

PWB 0.427 0.185 1 0.782 9.547** 1.237

	Correlation amongst the first-order constructs	Weights of the first-order constructs on employee well-being
LWB	1			0.290	5.062**	1.264
WWB	0.242	1		0.180	4.587**	1.070
PWB	0.427	0.185	1	0.782	9.547**	1.237

3.3 Goodness of Fit (GoF)

The goodness of fit (GoF) index is recommended by Tenenhaus et al. [50]. The following threshold values are used to assess the (GoF) results in SmartPLS: small (0.10), medium (0.25), and large (0.35) [50]. The results of GoF predict how well data fit the proposed model. Additionally, Henseler et al. [51] recommended using the standardized root mean square residual (SRMR) for the model fit criterion. The SRMR value of (0) generally shows a perfect model fit, while less than (0.08) is considered acceptable for PLS models. The SRMR value in this study is (0.066), which indicates an adequate model fit.

3.4 Structural model assessment

The viability of the structural model is assessed by R2, path coefficients (beta values), and the t-values. A bootstrapping method was applied to determine the t-values to confirm the statistical significance [44]. In addition to these basic measures, researchers also reported the effect sizes (f2) and predictive relevance (Q2) [45].

In this paper, we first analyze the relationship of job stressors (time pressure, role ambiguity, role conflict) on employee well-being and turnover intention. Secondly, we analyze the mediating relationship of employee well-being between job stressors and turnover intention. The first, second, and third hypothesis of this study is supported. Time pressure (β= –0.296, p < 0.01), role ambiguity (β= –0.423, p < 0.01), and role conflict (β= –0.104, p < 0.01) are negatively and significantly related to employee well-being. Employee well-being showed a negative relationship to turnover intention (β= –0.410, p < 0.01) and explains 43% and 16.8% of the variance in employee well-being and turnover intention, respectively. Thus, supporting H4. The R2 value of employee well-being is greater than the value of 0.35, as Henseler et al. [52] suggested, indicating a strong model, while the turnover intention R2 value is less than 0.25, indicating a moderate model. We also evaluated the effect sizes (f2), as emphasized by Hair et al. [44], “While a P-value can inform the reader whether an effect exists, the P-value will not reveal the size of the effect, while reporting and interpreting studies, both the substantive significance (effect size) and statistical significance (P-value) are essential results to be reported”.

To assess the effect size, the threshold values recommended by Henseler et al. [52], which are 0.02, 0.15, and 0.35, show small, medium, and large effects, respectively. The effect size (f2) values given in Table 5 showed that all the relationships have small and moderate (0.129, 0.264, 0.016, and 0.202) effect sizes, respectively. Moreover, to assess predictive relevance blindfolding technique was employed. The Q2 value should be larger than 0 for a particular endogenous construct [53]. In Table 5, the Q2 values are 0.203 for employee well-being and 0.103 for turnover intention. Thus, this indicates that the predictive relevance of the model is acceptable. As recommended by Preacher et al. [54], a bootstrapping procedure was employed to test the indirect effects of employee well-being between job stressors and turnover intentions. Table 5 presents the results of mediation analysis, where hypothesis H5 (b = 0.129, t = 4.567, p < 0.05), 95% Boot CI: [LL = 0.075, UL = 0.178] shows a significant supported relationship of employee well-being as a mediator between time pressure and turnover intention. Hypothesis H6 (b = 0.173, t = 6.761, p < 0.05), 95% Boot CI: [LL = 0.124, UL = 0.224] indicates a significant supported relationship of employee well-being between role ambiguity and turnover intention. Last hypothesis H7 (b = 0.043, t = 2.331, p < 0.05), 95% Boot CI: [LL = 0.001, UL = 0.073] shows a significant supported relationship of employee well-being between role conflict and turnover intention. Thus, H5, H6, and H7 of mediation were statistically supported.

Table 5
Structural model assessment

Structural Model Assessment (Independent relationships with Mediating variable)

Hypotheses Constructs path Std Beta Std Error T-values R² f² Q² VIF Result

H1 TP ->EWB –0.296 0.050 5.887 0.431 0.129 0.203 Supported

H2 RA ->EWB –0.423 0.051 8.248 0.264 Supported

H3 RC ->EWB –0.104 0.043 2.424 0.016 Supported

Structural Model Assessment (Mediating variable relationships with Dependent variable)

H4 EWB ->TOI –0.410 0.043 9.629 0.168 0.202 0.103 1.380 Supported

Structural Model Assessment (Indirect Analysis)

Hypotheses Constructs path Std Beta Std Error T-values IC-LL IC-UU Result

H5 TP ->TOI 0.121 0.027 4.567 0.075 0.178 Supported

H6 RA ->TOI 0.173 0.026 6.761 0.124 0.224 Supported

H7 RC ->TOI 0.043 0.019 2.331 0.001 0.073 Supported

Structural Model Assessment (Independent relationships with Mediating variable)
Hypotheses	Constructs path	Std Beta	Std Error	T-values	R²	f²	Q²	VIF	Result
H1	TP ->EWB	–0.296	0.050	5.887	0.431	0.129	0.203		Supported
H2	RA ->EWB	–0.423	0.051	8.248		0.264			Supported
H3	RC ->EWB	–0.104	0.043	2.424		0.016			Supported
Structural Model Assessment (Mediating variable relationships with Dependent variable)
H4	EWB ->TOI	–0.410	0.043	9.629	0.168	0.202	0.103	1.380	Supported
Structural Model Assessment (Indirect Analysis)
Hypotheses	Constructs path	Std Beta	Std Error	T-values	IC-LL	IC-UU	Result
H5	TP ->TOI	0.121	0.027	4.567	0.075	0.178	Supported
H6	RA ->TOI	0.173	0.026	6.761	0.124	0.224	Supported
H7	RC ->TOI	0.043	0.019	2.331	0.001	0.073	Supported

4 Discussion

Our findings are similar to previous studies demonstrating a negative relationship between time pressure and employee well-being [17 , 55]. According to the resource scarcity argument, within-domain work demands such as time pressures limit the ability of employees to manage their non-work domain responsibilities and lead to adverse psychological and behavioral consequences, affecting psychological well-being [56]. The negative relationship between role ambiguity and employee well-being is consistent with the previous studies that show a similar relationship between both constructs. Role ambiguity among IT professionals might result from a lack of role and job information, various work tasks demanding attention, and focus on completing IT work, etc. According to Maden-Eyiusta [57], role ambiguity makes employees uncertain about their roles, job objectives, and associated responsibilities. Role conflict (H7) showed a significant negative impact on employee well-being. The findings are consistent with previous studies showing that role conflict can affect the well-being of employees [25, 26]. Employee well-being negatively predicted turnover intention among Malaysian IT Executives, and results are in line with the previous studies [29, 30].

According to COR theory, if there is an actual resource loss or a perceived threat of resource loss in the workplace environment, employees will not obtain anticipated returns on investment of resources. In such cases, employees are less likely to put energy into their work to prevent further resource loss [58]. Job stressors (time pressure, role ambiguity, role conflict) threaten and deplete resources, leading to emotional exhaustion and negatively affecting employee well-being [23]. Employee well-being in terms of (LWB), (WWB), and (PWB) mediates the relationship between job stressors and turnover intention among Malaysian IT executives. Low employee well-being precedes turnover intentions, and job stressors precede employee well-being. The results provide empirical evidence for the relationship between job stressors and turnover intentions explained by reduced employee well-being. The findings are consistent with the COR theory, predicting that well-being is a beneficial resource, the loss of which could trigger other resource loss cycles. According to Hobfoll et al. [13], the second principle of COR theory is that people must invest resources to protect against resource loss and gain resources. COR theory states that the prime human motivation is the maintenance and accumulation of resources [31].

Additionally, COR theory suggests that to mitigate or eliminate negative stress responses, an individual is intuitively motivated to protect, retain, and recover these valued resources [59]. According to Siu et al. [60], offsetting the actual loss require people to strive and acquire additional resources. Thus, an employee with job stressors will seek to avoid further resource losses by redirecting the remaining energy towards thinking about alternative employment, which forms the core of turnover intentions. Therefore, when considering a job search, an employee is not only trying to compensate for present job stressors but also directing remaining psychological energy toward weighing up job alternatives. In this case, employee well-being is a resource investment in terms of LWB, WWB, and PWB. Mauno et al. [61] also found the mediating effect of well-being in the job insecurity-turnover intentions relationship. They reported partial mediation of occupational well-being between job insecurity and turnover intention. Another study by Van Der Vaart et al. [34] found the mediating role of employee well-being between psychological contracts and employees’ intention to leave. Earlier studies have shown that employee well-being is among the key factors that work as a retention strategy for any organization [33, 34], while other studies reported employee well-being to be a strong predictor of employee retention [36, 62]. Few other studies have found that employee well-being helps reduce turnover [34, 36]. Therefore, this study includes employee well-being as a mediator because it is a resource that further helps IT executives to mitigate the stressors and turnover intention.

4.1 Contributions

There is a lack of research concerning the mediating role of employee well-being between job stressors (time pressure, role ambiguity, role conflict) and turnover intentions. Thus, the present study investigates the mediating effect of employee well-being (LWB, WWB, PWB) for job stressors and turnover intention relationship. Our findings add to existing literature by providing empirical evidence of employee well-being as a mediator between job stressors and turnover intention of IT executives in Malaysia. This study also used employee well-being as a reflective-formative construct based on our findings, provided in Table 3. The correlation results indicate that employee well-being is a formative second-order construct. A reflective second-order construct shows an extremely high correlation among its lower-order constructs (≥0.8) [49]. The previous study used employee well-being as a reflective-reflective construct, but after analysis, the findings showed that employee well-being is a reflective-formative construct.

4.2 Managerial implications

Stressors are critical reasons for employees’ intention to leave an organization. Role conflict and ambiguity need to be addressed by the organization along with the job responsibilities and objectives at the beginning of the job. Defining job responsibilities and objectives among employees will increase their performance [63]. Employees working under tight deadlines experience pressure which may prevent them from optimally completing their expected work [64]. In this regard, human resource managers need to pay attention to job stressors to minimize the perception of employees’ intention to leave. The results of this study offer factual evidence for HR practitioners to design a well-being oriented program that addresses employees’ life well-being, workplace well-being, and psychological well-being. Employee well-being intervention is in the best interest of employees and employers [65, 66]. Well-being can be accomplished through advocating and providing a formal development environment. Organizations should support employees in the independent pursuit of knowledge for employee growth and development.

4.3 Limitations and future research directions

The first limitation of this study was the cross-sectional approach which can be extended to a longitudinal study to better understand the relationship between the well-being of employees as a mediator between role stressors and turnover intention. Secondly, this study collected data from private software companies among IT executives working in Malaysia. Therefore, the results are not generalizable to other industrial sectors. Thirdly, the design of this study could not rule out the effects of common-method bias since all data used in the study were acquired using the same questionnaire. This procedure might have led to common-method bias that might have inflated or deflated the relationships among factors. However, we conducted a Harman single-factor test for common method variance; it showed that less than 23% of the variance was explained by a single factor, suggesting that common method bias may not be problematic [67]. Future studies can add other job stressors (work-family conflict) to confirm the mediation of employee well-being. Future studies may also focus on the relationship between employees’ perceptions regarding the state of the psychological contract and turnover intention and well-being among IT executives in Malaysia.

5 Conclusion

This study confirms the direct positive relationship between role stressors and turnover intention and the mediating effect of employee well-being for job stressors-turnover intention relationship. This study adds to exiting literature by studying employee well-being as a mediation link between time pressure, role ambiguity, role conflict, and turnover intention. Based on the conservation of resource theory, the findings indicate that employee well-being as a resource can minimize role stressors. Moreover, the intervention in employee well-being could help employers to retain their employees.

Informed consent

A study participation consent was obtained from all participants before the start of the survey. The consent form was attached at the top of the survey.

Conflict of interest

None declared.

Ethical consideration

Not applicable.

Footnotes

Acknowledgments

None to report.

Funding

None to report.

References

Naidoo

. Role stress and turnover intentions among information technology personnel in South Africa: The role of supervisor support. SA J Hum Resour Manag. 2018;16:1–10.

Jaharuddin

, Zainol

. The Impact of Work-Life Balance on Job Engagement and Turnover Intention. South East Asian J Manag. 2019;13(1).

Al-Suraihi

, Samikon

, Al-Suraihi

A-HA

, Ibrahim

. Employee Turnover: Causes, Importance and Retention Strategies. Eur J Bus Manag Res. 2021;6(3):1–10.

Murad

. Has the Great Resignation hit Malaysia. The Star [Internet]. 2021;7-12. Available from: https://www.mef.org.my/MEFITN/SunStar20211128a.pdf

Lazzari

, Alvarez

, Ruggieri

. Predicting and explaining employee turnover intention. Int J Data Sci Anal [Internet]. 2022;14(3):279–92. https://doi.org/10.1007/s41060-022-00329-w

Kaur

, Randhawa

. Supportive supervisor to curtail turnover intentions: do employee engagement and work–life balance play any role? Evidence-based HRM. 2021;9(3):241–57.

Gilani

, Anum

Rabbani

. Role Ambiguity, Goal Orientation and Turnover Intention among Faculty Members of Private Sector Universities. Tech Soc Sci J. 2020;5(March):122–50.

Zainal

NSB

, Wider

, Lajuma

, Ahmad Khadri

MWAB

, Taib

, Joseph

. Employee Retention in the Service Industry in Malaysia. Front Sociol. 2022;7(July):1–12.

Urs

, Balaji Rao

. A Study on Turnover Intentions among Employees working in Software Industry. Int J Appl Eng Res [Internet]. 2019;14(17):3622–32. Available from: http://www.ripublication.com

10.

Bartels

, Peterson

, Reina

. Understanding well-being at work: Development and validation of the eudaimonic workplace well-being scale. PLoS One. 2019;14(4):1–21.

11.

Zheng

, Zhu

, Zhao

, Zhang

. Employee well-being in organizations: Theoretical model, scale development, and cross-cultural validation. J Organ Behav. 2015;36(5):621–44.

12.

Holmgreen

, Tirone

, Gerhart

, Hobfoll

. Conservation of Resources Theory - Resource Caravans and Passageways in Health Contexts. In: Cooper C, Campbell J, editors. The Handbook of Stress and Health: A Guide to Research and Practice. 1st ed. Wiley Online Library; 2017.

13.

Hobfoll

, Halbesleben

, Neveu

J-P

, Westman

. Conservation of Resources in the Organizational Context: The Reality of Resources and Their Consequences. Annu Rev Organ Psychol Organ Behav [Internet]. 2018;5:103–28. https://doi.org/10.1146/annurev-orgpsych-

14.

, Lau

JTF

, Lau

MMC

. Development and validation of the conservation of resources scale for COVID-19 in the Chinese adult general population. Curr Psychol. 2021;

15.

Kronenwett

, Rigotti

. All’s well that ends well!? Moderating effects of goal progress on the relation between challenge and hindrance appraisal and well-being. J Manag Psychol. 2022;37(5):444–66.

16.

Krick

, Felfe

, Pischel Sarah . Health-oriented leadership as a job resource: can staff care buffer the effects of job demands on employee health and job satisfaction? J Manag Psychol. 2021;37(2):139–52.

17.

Skaalvik

, Skaalvik

. Teacher burnout: relations between dimensions of burnout, perceived school context, job satisfaction and motivation for teaching. A longitudinal study. Teach Teach Theory Pract [Internet]. 2020;26(7-8):602–16. https://doi.org/10.1080/13540602.2021.1913404

18.

Gärling

, Krause

, Gamble

, Hartig

. Emotional well-being and time pressure. PsyCh J. 2014;3(2):132–43.

19.

Huhtala

, Geurts

, Mauno

, Feldt

. Intensified job demands in healthcare and their consequences for employee well-being and patient satisfaction: A multilevel approach. J Adv Nurs. 2021;77(9):3718–32.

20.

Zhang

, Hong

, Smith

. The Relationship between Role Ambiguity and Workers’ Creativity during the COVID-19 Pandemic in China. Int J Environ Res Public Health. 2022;19(23).

21.

Barling

, Frone

. If Only my Leader Would just Do Something! Passive Leadership Undermines Employee Well-being Through Role Stressors and Psychological Resource Depletion. Stress Heal. 2017;33(3):211–22.

22.

Shin

, Hur

, Park

, Hwang

. How managers’ job crafting reduces turnover intention: The mediating roles of role ambiguity and emotional exhaustion. Int J Environ Res Public Health. 2020;17(11):1–18.

23.

Pecino

, Mañas

, Díaz-Fùnez

, Aguilar-Parra

, Padilla-Góngora

, López-Liria

. Organisational climate, role stress, and public employees’ job satisfaction. Int J Environ Res Public Health. 2019;16(10).

24.

Awan

, Dunnan

, Jamil

, Gul

, Anwar

, Idrees

, et al. Impact of Role Conflict on Intention to Leave Job With the Moderating Role of Job Embeddedness in Banking Sector Employees. Front Psychol. 2021;12(November):1–13.

25.

, Hu

, Zheng

. Role stress, job burnout, and job performance in construction project managers: The moderating role of career calling. Int J Environ Res Public Health. 2019;16(13).

26.

Nambisan

, Baron

. On the costs of digital entrepreneurship: Role conflict, stress, and venture performance in digital platform-based ecosystems. J Bus Res [Internet]. 2021;125(June):520–32. https://doi.org/10.1016/j.jbusres.2019.06.037

27.

Barthauer

, Kaucher

, Spurk

, Kauffeld

. Burnout and career (un)sustainability: Looking into the Blackbox of burnout triggered career turnover intentions. J Vocat Behav [Internet]. 1033;117:34. https://doi.org/10.1016/j.jvb.2019.103334

28.

Wang

, Lu

, Wu

, Luo

. Workplace ostracism and employee wellbeing: A conservation of resource perspective. Front Public Heal. 2023;10(4).

29.

Belkin

, Becker

, Conroy

. The Invisible Leash: The Impact of Organizational Expectations for Email Monitoring After-Hours on Employee Resources, Well-Being, and Turnover Intentions. Gr Organ Manag. 2020;45(5):709–40.

30.

Xue

, Wang

, Chen

, Ding

, Zhu

. Signifying the Relationship Between Psychological Factors and Turnover Intension: The Mediating Role of Work-Related Stress and Moderating Role of Job Satisfaction. Front Psychol. 2022;13(May):1–11.

31.

Halbesleben

JRB

, Neveu

, Paustian-Underdahl

, Westman

. Getting to the “COR”: Understanding the Role of Resources in Conservation of Resources Theory. J Manage. 2014;40(5):1334–64.

32.

Kundi

, Aboramadan

, Elhamalawi

EMI

, Shahid

. Employee psychological well-being and job performance: exploring mediating and moderating mechanisms. Int J Organ Anal. 2020;29(3):736–54.

33.

Page

, Vella-Brodrick

. The Working for Wellness Program: RCT of an Employee Well-Being Intervention. J Happiness Stud. 2013;14(3):1007–31.

34.

Van Der Vaart

, Linde

, Cockeran

. The state of the psychological contract and employees’ intention to leave: The mediating role of employee well-being. South African J Psychol. 2013;43(3):356–69.

35.

DiPietro

, Moreo

, Cain

. Well-being, affective commitment and job satisfaction: influences on turnover intentions in casual dining employees. J Hosp Mark Manag [Internet]. 2020;29(2):139–63. Available from: https://doi.org/10.1080/19368623.2019.1605956

36.

Wright

, Bonett

. Job satisfaction and psychological well-being as nonadditive predictors of workplace turnove. J Manage. 2007;33(2):141–60.

37.

Shimizu

. Multistage Sampling. Wiley StatsRef Stat Ref Online. 2014;1-9.

38.

Department of Statistics Malaysia. Information And Communication Technology Satellite Account 2020. Department of Statistics Malaysia. 2021.

39.

Malaysian Science, Technology and Innovation Indicators Report [Internet]. 2020. Available from: https://mastic.mosti.gov.my/sti/sti-survey/science-technology-and-innovation-indicators-report

40.

Sheikh

, Mattingly

. Investigating non-response bias in mail surveys. J Epidemiol Community Health. 1981;35(4):293–6.

41.

Roxburgh

. Racing Through Life: The Distribution of Time Pressures by Roles and Role Resources Among Full-Time Workers. J Fam Econ Issues. 2002;23(2):121–45.

42.

Rizzo

, House

, Lirtzman

. Role Conflict and Ambiguity in Complex Organizations. Adm Sci Q. 1970;15(2):150.

43.

Ganesan

, Weitz

. The impact of staffing policies on retail buyer job attitudes and behaviors. J Retail. 1996;72(1):31–56.

44.

Hair

, Hult

GTM

, Ringle

, Sarstedt

, Danks

, Ray

. Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R. Springer; 2021.

45.

Hair

JJF

, Hult

GTM

, Ringle

, Sarstedt

, Danks

, Ray

. Partial Least Squares Structural Equation Modeling (PLS-SEM). 2nd ed. SAGE Publications, Inc; 2017. pp. 1-384.

46.

Clark

, Watson

. Constructing validity: Basic issues in objective scale development. Psychol Assess. 1995;7(3):309–19.

47.

Henseler

, Ringle

, Sinkovics

. The use of partial least squares path modeling in international marketing. Adv Int Mark. 2009;20:277–319.

48.

Chin

, Peterson

, Brown

. Structural equation modeling in marketing: Some practical reminders. J Mark Theory Pract. 2008;16(4):287–98.

49.

Peng

, Lai

. Using partial least squares in operations management research: A practical guideline and summary of past research. J Oper Manag.. 2012;30(6):467–80.

50.

Tenenhaus

, Vinzi

, Chatelin

, Lauro

. PLS path modeling. Comput Stat Data Anal.. 2005;48(1):159–205.

51.

Henseler

, Ringle

, Sinkovics

. The use of partial least squares path modeling in international marketing. Adv Int Mark. 2009;20(January):277–319.

52.

Cohen

. Statistical Power Analysis for the Behavioral Sciences. 2nd ed. Routledge; 1988. pp. 1-567.

53.

Vinzi

, Chin

, Henseler

, Wang

. Handbook of Partial Least Squares - Concepts, Methods and Applications. Springer. Springer Berlin Heidelberg; 2010. pp. 1-812.

54.

Preacher

, Hayes

. Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behav Res Methods. 2008;40(3):879–91.

55.

Zacher

, Jimmieson

, Bordia

. Time pressure and coworker support mediate the curvilinear relationship between age and occupational well-being. J Occup Health Psychol. 2014;19(4):462–75.

56.

Obrenovic

, Jianguo

, Khudaykulov

, Khan

MAS

. Work-Family Conflict Impact on Psychological Safety and Psychological Well-Being: A Job Performance Model. Front Psychol. 2020;11(March):1–18.

57.

Maden-Eyiusta

. Role conflict, role ambiguity, and proactive behaviors: does flexible role orientation moderate the mediating impact of engagement? Int J Hum Resour Manag [Internet]. 2021;32(13):2829–55. Available from: https://doi.org/10.1080/09585192.2019.1616590

58.

Mansour

, Nogues

, Tremblay

. Psychosocial safety climate as a mediator between high-performance work practices and service recovery performance: an international study in the airline industry. Int J Hum Resour Manag [Internet]. 2021;1-35. Available from: https://doi.org/10.1080/09585192.2021.1949373

59.

Hobfoll

, Tirone

, Holmgreen

, Gerhart

. Conservation of Resources Theory Applied to Major Stress. Stress: Concepts, Cognition, Emotion, and Behavior: Handbook of Stress. Elsevier Inc.; 2016. pp. 65-71.

60.

Siu

, Cheung

, Lui

. Linking Positive Emotions to Work Well-Being and Turnover Intention Among Hong Kong Police Officers: The Role of Psychological Capital. J Happiness Stud. 2015;16(2):367–80.

61.

Mauno

, De Cuyper

, Tolvanen

, Kinnunen

, Mäkikangas

. Occupational well-being as a mediator between job insecurity and turnover intention: Findings at the individual and work department levels. Eur J Work Organ Psychol. 2014;23(3):381–93.

62.

Ashraf

, Siddiqui

. The Impact of Employee Engagement on Employee Retention: The Role of Psychological Capital, Control at Work, General Well-Being and Job Satisfaction. Hum Resour Res. 2020;4(1):67.

63.

Otoo

FNK

. Human resource management (HRM) practices and organizational performance: The mediating role of employee competencies. Empl Relations. 2019;41(5):949–70.

64.

Meister

, Cheng

, Dael

, Krings

. How to Recover fromWork Stress, According to Science. Harvard Business Review. 2022 Jul 5;

65.

Kelly

, Berkman

, Kubzansky

, Lovejoy

. 7 Strategies to Improve Your Employees’ Health and Well-Being. Harvard Business Review. 2021 Oct 12;

66.

Javaid

, Bano

, Mirza

, Isha

ASN

, Nadeem

, Jawaid

, et al. Connotations of psychological and physiological health in the psychosocial work environment: An industrial context. Work. 2019;551-61.

67.

Roni

. Introduction to SPSS. Edith Cowan University. Joondalup: SOAR Centre; 2014. pp. 1-81.

Construct Scale	Items	Convergent Validity		CR	Cronbach’s Alpha
		Loadings	AVE
Role Conflict	RC1	0.640	0.512	0.862	0.811
	RC2	0.721
	RC4	0.742
	RC5	0.649
	RC6	0.768
	RC7	0.762
Role Ambiguity	RA1	0.730	0.517	0.842	0.798
	RA2	0.589
	RA3	0.743
	RA4	0.728
	RA5	0.790
Time Pressure	TP1	0.798	0.92		0.623
	TP2	0.838
	TP3	0.796
	TP4	0.756
	TP6	0.842
	TP8	0.790
	TP9	0.695
Life Well-Being	LWB1	0.668	0.878		0.636
	LWB2	0.834
	LWB3	0.689
	LWB4	0.824
	LWB5	0.604
	LWB6	0.795
Workplace Well-Being	WWB1	0.797		0.913	0.636
	WWB2	0.831
	WWB3	0.830
	WWB4	0.800
	WWB5	0.771
	WWB6	0.749
Psychological Well-Being	PWB1	0.817	0.926		0.676
	PWB2	0.826
	PWB3	0.823
	PWB4	0.863
	PWB5	0.862
	PWB6	0.735
Turnover Intention	TOI1	0.698		0.898	0.639
	TOI2	0.856
	TOI3	0.856
	TOI4	0.850
	TOI5	0.721

Job stressors and turnover intention of IT executives in Malaysia: The mediating role of employee well-being

Abstract

BACKGROUND:

OBJECTIVE:

METHODS:

RESULTS:

CONCLUSION:

Keywords

1 Introduction

2 Methods

2.1 Study design

2.2 Procedure and participants

2.3 Demographic profile of respondents

2.5 Statistical analysis

3 Results

3.1 Measurement model assessment

3.4 Structural model assessment

4.1 Contributions

4.2 Managerial implications

4.3 Limitations and future research directions

5 Conclusion

Informed consent

Conflict of interest

Ethical consideration

Footnotes

Acknowledgments

Funding

References