Research Article
Research Article
Impact of work engagement on turnover intention: moderation by psychological capital in India
expand article infoManish Gupta, Musarrat Shaheen
‡ IFHE University, Hyderabad, India
Open Access


With increased number of employment opportunities in India, employers are increasingly finding it difficult to control employee turnover. Nonetheless, positive psychologists argue that one of the ways to face this challenge is by understanding the positive factors such as, work engagement and personal resources that negatively affect employees’ turnover intention. Therefore, the objective of this study is to examine the moderating role of psychological capital in the work engagement – employee turnover intention relationship. Hierarchical regression was used to analyze a sample of 228 employees working in diverse industries. The findings indicate that psychological capital moderates the relationship between work engagement and intention to turnover. The findings augment the theory of self and role by identifying moderating role of personal resources in strengthening the negative relationship between work engagement and turnover intention. Managers may take steps to enhance the employee-co-worker and employee-supervisor relationship either by promoting team related activities or by enabling their employees to work independently. Also, in order to save the cost of hiring a new candidate and losing an experienced employee, managers may create mechanisms for measuring work engagement of at least their key employees or a regular basis. This paper fulfils an identified need to study how psychological capital plays a key role in affecting the work engagement–employee turnover intention relationship in Indian context.


hierarchical regression, India, turnover intention, psychological capital, work engagement

JEL Classification



Organizations in the modern era compete to retain talent and explore possible ways by which the quality and quantity of the attachment of employees with their can be improved. Whereas qualitative attachment enhances the efficiency of the employee, the quantitative attachment increases the time of association of employees with their work. Work engagement, the discretionary attachment of oneself with one’s role, represents quality of attachment in terms of three components namely vigor, dedication and absorption and quantity of attachment by the value its components hold (Harju et al. 2016, Schaufeli et al. 2002). While scholars and practitioners agree that engaging employees has positive consequences for the employees and the employer, the average percentage of engaged employees across the globe is at an alarmingly low level of 13% which is reflected in Gallup’s survey 2011–12 (Crabtree 2013). Given the grim situation of engagement in the world and the ability of engaged employees to help their organization achieve its goals, it is important to explore the factors that affect and are affected by work engagement.

It is suggested in the engagement literature that managers can help employees engage better if they know the extent to which employees are willing to invest their personal resources at their work (Schaufeli and Bakker 2004). These personal resources such as ability of facing adverse situations and failures confidently that individuals possess are together termed as Psychological capital (PsyCap). Though, researchers in the past have indicated significant relationship of (1) work engagement with intention to turnover (Huang et al. 2016, Van Schalkwyk et al. 2010), (2) PsyCap with intention to turnover (Avey et al. 2009, Olaniyan and Hystad 2016), and (3) different dimensions of PsyCap such as, self – efficacy, resilience, hope, and optimism with work engagement (Joo et al. 2016, Ouweneel et al. 2013), more research is required to understand how PsyCap can influence the relationship between work engagement and intention to turnover. In particular, there is a paucity of research that examines the moderating effect of psychological capital between work engagement and intention to turnover. Therefore, the objective of this study is two-fold: to investigate the relationship between work engagement and intention to turnover in the present context and to examine the moderating role of PsyCap between work engagement and intention to turnover.

To achieve the aforesaid objectives, the first section describes the constructs under investigation and develops hypotheses. The second section describes methodology including participant characteristics, data collection procedure, and data analysis. The third section lists findings and interpretation of the values resulted from the data analysis. The fourth section discusses the theoretical contributions of this study, practical implications of the findings, limitations of the present study, and direction for future research. Finally, conclusions are drawn to summarize the present study.

1. Theory and hypotheses

1.1. Intention to turnover and work engagement

Workforce stability is a powerful competitive strategy that is expected to become increasingly important in the foreseeable future and employee turnover continues to be a topic of interest among management researchers. Highlighting intention to turnover as a key element in the modeling of employee turnover behavior, Egan, Yang, and Bartlett (2004) stated that scholars have determined that behavioral intentions are the single best predictor of actual turnover. Overall, intention to turnover has emerged as the strongest precursor to turnover. Van Schalkwyk et al. (2010) in their study stated that intention to leave is related to actual turnover. Intention to turnover pertains to thoughts of voluntarily leaving an organization. A literature review by Bluedorn (1982) cited 23 studies that reported significant positive relationships between leaving intentions and actual leaving behavior. Employees create efficiency and effectiveness by nurturing stable organizational relationships, they will then stay long enough to become familiar with their customers, suppliers and colleagues, and they will work to become more stable.

Prior studies including Alfes et al. (2013) have indicated that engaged employees tend to have less intention to turnover. Although, several prior studies have measured constructs carrying engagement label, operational definitions are not always consistent with the theory. Because majority of studies drew on Kahn’s (1990) conceptual foundation (Ashforth and Humphrey 1995, Schaufeli et al. 2002, Saks 2006, May et al. 2004, Rich et al. 2010), taking Kahn’s work as starting point for organizing the literature is important. Kahn (1990) proposed that personal engagement represents a state in which people bring – in their personal selves during their role performances, invest personal energy and experience an emotional connection with their work. Subsequently, Schaufeli et al. (2002) defined work engagement as a positive state of mind in which employees work vigorously and dedicatedly. They immerse themselves in their work. This definition differs from consultant’s definition of employee engagement because work engagement focuses on experience of working rather than attitude toward job characteristics. Therefore, Gallup Workplace Audit (GWA; Harter et al. 2002) scale which measures employee engagement involving job characteristics items neither conforms to Schaufeli et al.’s (2002) conceptualization of work engagement nor adheres to Kahn’s (1990) original conceptualization of personal engagement.

According to Saks (2006), work engagement can be conceptualized as an antecedent of intention to turnover. Engaged employees are so much occupied with positive energy that they actively and persistently immerse themselves in their work leaving little time and space for negative thoughts such as, leaving the organization. As per the engagement theory, it is the work that decides the stay of employees in an organization. Therefore, if the work is engaging, employees would not think of leaving their current organizations.

Empirically, a number of research studies have found work engagement to be positively associated with intent to remain with one’s organization (Harter et al. 2002, Schaufeli and Bakker 2004). More recently, De Lange et al. (2008) in a two – wave (16 – month lag) Belgian panel study tested hypothesis on the relationship between work engagement and actual turnover across time. They found that low work engagement predicted actual transfer to another company. This result was reaffirmed in the Van Schalkwyk et al.’s (2010) study. Thus, it is stated that:

Hypothesis 1: Work engagement negatively influences intention to turnover.

1.2. Moderating role of psychological capital

Luthans (2002) was the first to conceptualize psychological capital as an individual’s positive psychological state of development characterized by self – efficacy, optimism, hope, and resilience. In that, self – efficacy is the confidence to take – on and put – in the necessary efforts to succeed in achieving goals. Optimism is a positive attribution about succeeding now and in the future. Hope is persevering toward goals and, when necessary, redirecting paths to goals (hope) in order to succeed. Resilience is bouncing back and performing better than expectations when beset by problems and adversity to attain success.

Prior empirical studies provide evidence that Psychological capital fosters positive employee outcomes. Psychological capital is the personal resource which is advocated as a precursor of employees’ work engagement. Swetman and Luthans proposed Psychological capital as a fundamental resource which develops fulfilling and productive lives at work (Bakker and Leiter 2010). The authors argued that Psychological capital dimensions foster vigor, dedication, and absorption among employees. Self – efficacy is employees’ firm belief in their work they do and the efforts they make to overcome hurdles. Confidence leverages employees to apply their personal selves freely to their work and which is a reason of their personal growth thereby making them happy at individual level. The employees who believe in their capabilities to mobilize their energies for meeting situational demands have the motivation to immerse themselves into their work (Xanthopoulou et al. 2007). Instead of considering difficult tasks as burden, employees high in self – efficacy, treat them as challenges which results in a higher embracement of their ‘selves’ with their roles. Optimistic and hopeful employees see positive side of the situation that makes them attach themselves cognitively and physically with their work roles. Employees, who work with dedication, keep themselves vigorous and completely absorbed in their work roles. Employees use resiliency for bouncing back. Their bouncing back ability makes them apply their cognitive, physical, and emotional energy to the fullest in their work.

Psychological capital has been suggested as a composite core construct of four positive resources, viz., self – efficacy, hope, optimism, and resilience (Luthans et al. 2007). In job demand resource model (Schaufeli and Taris 2014) Psychological capital has been proposed as employees’ personal psychological resource that has positive influence on several workplace outcomes, such as engagement and both in – role and extra – role performance and negative association with job demands such as work pressure, mental, physical and emotional work related demands. Intention to quit is a negative attitude that arises from workplace stress and demands. Studies on stress, appraisal, and coping (Lazarus 1984) posited that people suffer stress and develop negative attitude when they believe they lack the resources to deal with difficult situations. Psychological capital can be termed as one such personal resource which employees need to combat stress and negative attitude with the the work (Avey et al. 2009). Employees high on Psychological capital can mobilise their personal resources to achieve success, can have positive attribution about several workplace events, and can be resilient to wok pressure and other adversities of the workplace. This leads to a motivated effort towards work and increases the probability of success. It can be said that when individuals are positive and confident about their own success at the work, then those individuals are more engaged and satisfied with their work and tend to have reduced negative attitude (intention to quit) towards their work and organization. Prior studies such as Avey et al. (2009) have validated this assertion and have indicated a negative relationship between psychological capital and intention to quit.

As mentioned earlier, psychological capital has a direct positive effect on work engagement. Also, it has a direct negative effect on intention to turnover. Therefore, according to Baron and Kenny (1986) moderation criterion, it is possible that psychological capital moderates the negative relationship of work engagement and intention to turnover. Conceptually, the employees who are high in psychological capital take things positively in their organization. Even if the things do not happen as per their expectations, hope and optimism makes keep them attached with the organization. Even if they perceive that their organization is demanding more work from them and rate them as below average, the resiliency factor in them makes them take these demanding or perhaps over demanding situations as a challenge and bounce back. The authors argue that such positive thoughts do not leave space in their mind for negative thoughts such as, leaving the organization. Thus, the authors expect that psychological capital moderates the work engagement – intention to turnover relationship such that high psychological capital strengthens it and low psychological capital weakens it. To test this assumption the following hypothesis can be stated:

Hypothesis 2: Psychological capital moderates the relationship between work engagement and intention to turnover.

2. Methodology

2.1. Participants and Procedure

Data were collected from employees working with their current organization for at least one year in India. One year with the current organization was a pre – condition to ensure that the participants have gone through at least one appraisal cycle and have reasonably understood their organization’s culture. Data were collected using both, online as well as paper and pencil modes. In order to get genuine response, the participants were assured of confidentiality of their response by making the questionnaire anonymous. The independent and dependent variable were separated by time to control common method bias as suggested by Podsakoff et al. (2003). They suggested that this temporal separation “makes it impossible for the mindset of the source or rater to bias the observed relationship between the predictor and criterion variable, thus eliminating the effects of consistency motifs, implicit theories, social desirability tendencies” (887). First, the participants completed the work engagement and Psychological capital instruments. After a separation of one hour, the participants completed the dependent variable instruments for intention to turnover.

Out of the 274 questionnaires received (response rate = 32.57%), 46 were either incomplete or inappropriately filled. The final sample comprised of 228 working professionals which is sufficient for rigorous analysis including measurement model tests (Bentler 1990). Out of the 228 participants, 54 were women. A total of 139 employees were working in a managerial profile and 164 were post graduates. The mean age of the participants was 37.25 years. Descriptive statistics for the sample have been summarized in Table 1. The employees were working in diverse industries. The study was not restricted to any particular industry as the constructs under investigation are relevant in all the industries. Among them, 45 were in banks, 28 were in information technology enabled services, 30 were in third party insurance claim processing companies, 20 were in logistics, 54 were in academia, and 51 were in insurance.

Descriptive statistics and reliability coefficients (N = 228)

Mean SD 4 5 6
1. TWE (in years) 10.08 6.99
2. WECO (in years) 4.65 4.01
3. Age (in years) 37.25 8.69
4. Psychological capital 5.28 1.12 (.94)
5. Work engagement 5.05 1.21 .77** (.93)
6. Turnover intention 2.91 1.59 –.20** –.30** (.89)

2.2. Measures

Well established, reliable, and valid scales were used to capture respondents’ responses. Responses for all these constructs were captured on seven – point Likert – type scale (strongly disagree = 1 to strongly agree = 7). For the purpose of statistical analysis, the industry type was taken as dummy variable and was controlled because several scholars have indicated contextual nature of work engagement. For example, Rich et al. (2010) suggested that compared to knowledge workers, employees such as, firefighters are expected to score less on cognitive dimension.

Work engagement consisting of vigor, dedication, and absorption was assessed by Schaufeli et al.’s (2006) nine – item UWES – 9 scale (Cronbach’s α > .70). A sample item is: when I get up in the morning, I feel like going to work. Intention to turnover was measured using Lichtenstein et al.’s (2004) three – item scale (Cronbach’s α = .83). A sample item is: I will probably look for a new organization in the next year. Psychological capital was assessed using Luthans et al.’s (2007) 12 – item scale (Cronbach’s α > .70). A sample item is: I always look on the bright side of things regarding my job.

2.3. Data analyses

Exploratory factor analysis (EFA) using Principal component analysis with Varimax rotation was performed to understand whether the proposed framework is feasible in the present context. To check whether there exists multicollinearity, variance inflation factor (VIF) was calculated among variables under investigation. For each construct, Cronbach’s alpha values which are also known as reliability coefficients were calculated. Bi – variate correlations were also calculated in order to find out whether the constructs are associated with each other or not. After correlation calculation, measurement model was tested to examine relationship between the latent variables and their measures by allowing the latent variables to correlate with each other. In that, the recommended threshold value for comparative fit index (CFI) and Incremental Fit Index (IFI) is greater than or equal to 0.90, for Root Mean Square Error of Approximation (RMSEA) is less than 0.080, and for Chi – square / df is greater than 3.00 (Kenny et al. 2014). Finally, path coefficients were calculated using hierarchical regression analysis to test the hypotheses.

3. Results

In EFA, factor loadings less than 0.55 were suppressed to get a clear view of the structure. The rotation converged in five iterations resulting in the emergence of a three factor structure without any cross – loadings (refer Appendix 1 for particular factor loadings). The multicollinearity analysis revealed that all variance inflated factor (VIF) values were found below the maximum recommended threshold of 3.0 thereby indicating absence of multicollinearity (Gujarati 2012).

In the internal consistency check, all the Cronbach’s alpha values were found greater than 0.88 (refer Table 1). These values are well above the minimum threshold criterion of 0.70. As shown in Table 1, the correlations between work engagement, psychological capital, and intention to turnover are reasonably significant (p < .01). However, the correlation coefficients among industry type and other study variables were not significant. Next, the measurement model indicated a good fit (CFI = .927, IFI = .928, RMSEA = .076, Chi square / df = 2.305).

As shown in Model 1 of Table 2, work engagement explained a significant proportion of variance in intention to turnover, R2 = .09, F (2, 225) = 11.09, p < .01. There was also a significant interaction between work engagement and psychological capital, R2 = .13, F(1, 224) = 11.32, p < .01. The change in R2 value due to moderation by Psychological capital in the work engagement – intention to turnover relationship was also significant (p < .01). Model 2, which allows moderation by Psychological capital, has significant path coefficient for interaction term β = –.27, t (224) = –3.29, p < .01. Figure 1 is the graph drawn based on Aiken et al.’s (1991) suggestion for interpreting interaction effect. The graph for high level of Psychological capital is steepest, indicating that compared to the engaged employees with low Psychological capital level, the engaged employees with high Psychological capital level, are less likely to leave their organizations. The simple slope for low level (t = –4.03, p < .001) and high level (t = –4.14, p < .001) of Psychological capital were found significantly different from zero. Moreover, the non – parallel lines for low and high levels of Psychological capital indicate moderating role of Psychological capital between the negative relationship of work engagement and intention to turnover such that, presence of Psychological capital strengthens the negative relationship between work engagement and intention to turnover.

In this sample, Cronbach’s alpha values for psychological capital, work engagement, and intention to turnover were .94, .94, and .89 respectively. Psychological capital–work engagement, work engagement–intention to turnover, and psychological capital–intention to turnover correlation coefficients were r1(172) = .79, r2(172) = –.27, r3(172) = –.21. Multiple regression analysis shows that work engagement and Psychological capital together explained a significant proportion of variance in intention to turnover, R2 = .07, F (2, 171) = 6.60, p = .002. Hierarchical regression analysis reveals a significant interaction between work engagement and psychological capital, βmoderator = –.22, t (170) = –2.18, p = .03. The change in R2 value due to moderation by psychological capital in the work engagement–intention to turnover relationship was also significant (p = .03).

Hierarchical regression analyses predicting OCB

Predictor Turnover intention
R2 β
Step 1 .09**
Work Engagement –.34**
Psychological capital .06
Step 2 .04**
Psychological capital × Work Engagement –.27**
Total R2 .13**
N 228
Fig. 1.

The interaction effect of psychological capital

4. Discussion

The objective of the study was to understand the catalytic role of psychological capital in the form of moderator between work engagement and intention to turnover. The results of the present study support hypotheses that though work engagement has a negative relationship with intention to turnover, psychological capital strengthens this relationship. The subsequent sections discuss theoretical contributions, managerial implications, and direction for future studies.

The present study contributes to the existing literature on positive psychology in terms of providing empirical evidence to understand the moderating role of psychological capital between work engagement and intention to turnover and also, generalizing the negative relationship results between work engagement and intention to turnover to Indian context, in particular, the results indicate moderation by psychological capital between work engagement and intention to turnover. As depicted in Table 2, relating to the negative relationship between work engagement and intention to turnover. Also, when Psychological capital is added to the model as a moderator, the interaction effect of work engagement and psychological capital reduced the strength of the work engagement – intention to turnover relationship significantly. This finding supports hypotheses H1 that work engagement negatively influences intention to turnover and H2 that Psychological capital moderates the relationship between work engagement and intention to turnover. These expected outcomes are in line with Ouweneel et al.’s (2013) finding that self–efficacy, which is one of the dimensions of psychological capital, was positively related to work engagement.

The significant and negative relationship between work engagement and intention to turnover augments the findings of Alfes et al. (2013) by generalizing their results in the Indian context. Compared to their study which was confined to the United Kingdom’s service sector organization, the contribution of the present study is in providing support for applicability of their findings to the diverse industries in India.

The results of the present study indicate support for the positive effect of overall psychological capital on work engagement. Unlike Ouweneel et al. (2013), who tested self – efficacy, a dimension of psychological capital, the present study reveals that overall psychological capital also has a positive influence on work engagement. The findings also provide empirical evidence from India for the negative PC – TI relationship. Like Avey et al.’s (2009) study, this study also used a heterogeneous sample and found similar results for the PC – TI relationship. Using a heterogeneous sample of six industries, this study finding is in line with their result thereby generalizing them in the Indian context. Unlike previous studies that tested individual dimensions of Luthan’s Psychological capital as antecedents of work engagement and intention to turnover, this study examined the moderating role of Psychological capital (as a higher order construct) in affecting the WE–TI relationship. It was found that Psychological capital moderates the relationship such that engaged employees who are high in Psychological capital would have low intention to turnover.

Organizations are encouraged to enhance their work culture by taking steps to develop challenging environment, infuse competition spirit among employees, establishing regular and timely performance feedback systems so that employees can get to know about their performance and poor performers get proper environment to bounce back and prove them. While recruiting, organizations may prefer that candidate whose records reflect evidence for high psychological capital in the past such as, significant improvement of marks in aspirant’s academic career. The study findings indicate importance of work engagement in reducing intention to turnover. In order to save the cost of hiring a new candidate and losing an experienced employee, managers may create mechanisms for measuring work engagement of at least their key employees on a regular basis. We believe that this measurement will help organizations identify the current degree of their employees’ engagement followed by corrective actions wherever and whenever required.

Like any other psychological study, the present study is also not free from limitations. First, the study results might have suffered from common method bias due to self – report measures. However, we argue that independent and moderating variables under investigation measure perception of self about other entities which necessitates self – rating. Also, as discussed earlier, Egan et al. (2004) find intention to turnover as a single predictor of actual turnover. Nevertheless, various statistical tools such as EFA, CFA, Cronbach’s coefficient for internal consistency were used to ensure reliability and validity of the responses. Future studies may further strengthen of the results by conducting studies on multiple samples and comparing their means.

Second, due to time and financial constraints, the study was cross – sectional in nature which may not provide evidence for true cause and effect relationships. However, we argue that causal relationship is more a matter of logical reasoning rather than statistical significance. According to Gujarati (2012), no statistical tool, no matter how strong it is can reveal a causal relationship. Therefore, researchers are encouraged to use experimental designs that, to some extent, help in explaining causal relationships.

Third, more research is required to understand the moderating effect of experience with the current organization between work engagement and intention to turnover. Scholars may like to analyze impact of this moderation on the different work engagement dimensions separately. It is because a longer stay in the organization may lead to the feeling of boredom and lack of vigor. Fourth, the objective of our study was to understand the moderating role of psychological capital between work – related variable and intention to turnover limited our focus on job and job type. The future studies are encouraged to involve organization related variables along with work engagement in the present model. It would also help in finding out whether the employees leave because of their work role or the people around them.


Overall, the investigation on understanding the work engagement – intention to turnover relationship resulted in generalizing the prior findings in the present context. However, the results suggest that this relationship is affected significantly with change in the level of psychological capital such that, for psychologically capable employees, the relationship between work engagement and intention to turnover would be stronger. Conversely, for psychologically deficient employees, the relationship between work engagement and intention to turnover would be weaker.


This research study was not funded by any individual or organization.

Disclosure statement

The authors do not they have any competing financial, professional, or personal interests from other parties.


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