Retaining employees is a significant factor in organisations’ talent management strategies to reduce turnover costs (Narayanan, Rajithakumar, & Menon, 2019). For HRM policies and practices to be effective, they should take account of the ER factors. This study supports Gani et al.’s (2020, p. 144) definition of retention factors: ‘Retention practices are those organisational context factors that influence an employee’s choice to continue to work for an organisation or to leave the organisation’.

Although a variety of theories relating to social identity, work motivation, employee equity and expectancy contribute to explaining ER (Hom et al., 2017), the social exchange theory has been widely used more than others to gain an understanding of social behaviour and interactions (Al-Emadi et al., 2015). An important finding has been that employers’ actions in respect of valued retention factors reinforce employees’ intention to stay or leave (Al-Emadi et al., 2015).

Next, we present a brief discussion of each of the attitudinal antecedents of voluntary turnover (turnover intention, job satisfaction, affective commitment) and the ER factors included in this study, and we refer to our hypotheses statements.

Turnover intention is defined as ‘a conscious and deliberate wilfulness to leave the organization’ (Matz, Woo, & Kim, 2014, p. 234). Compared to other turnover antecedents (e.g. job satisfaction, affective commitment), turnover intention is the most significant predictor of actual turnover (typically sharing 25% of the turnover variance; Rubenstein et al., 2017). Researchers suggest that job satisfaction and organisational commitment are linked and are both essential attitudinal determinants of turnover intention (Al-Emadi et al., 2015; Joseph, Ng, Koh, & Ang, 2007; Kim & Kao, 2014; Larkin, Brantley-Dias, & Lokey-Vega, 2016; Luna-Arocas & Camps, 2007; Nouri & Parker, 2020; Price & Mueller, 1981; Tett & Meyer, 1993).

Job satisfaction manifests in employees’ feelings about their jobs, which are the result of subjective evaluations of job characteristics (Agho, Price, & Mueller, 1992). Job satisfaction may indicate positive feelings about specific aspects of a job or about a job in general. This study defines job satisfaction as a positive feeling about a job in general—a feeling that (Weiss, Dawis, England, & Lofquist, as cited in Larkin et al., 2016):

The negative relationship between job satisfaction and turnover intention has been well-documented (Guzeller & Celiker, 2019).

According to Allen and Meyer (1990), organisational commitment refers to an employee’s wish to stay with an organisation because of its characteristics, goals and values. These authors identify three facets of organisational commitment: affective commitment or emotional attachment, continued commitment that takes the cost of leaving the job into consideration and normative commitment that considers contractual obligations to the organisation. The focus of the present study is on affective commitment. Research evidence suggests that the relationship between turnover intention and affective commitment overshadows the relationship between turnover intention and the other forms of commitment (Allen & Meyer, 1990). As such, the former relationship determines employees’ attitudes to turnover intention and retention (Meyer & Herscovitch, 2001; Nouri & Parker, 2020; Solinger, Van Olffen, & Roe, 2008).

The literature provides evidence relating to the directional relationships between ER factors and attitudinal determinants of voluntary turnover, and we deal with this aspect next.

In our discussion of the research on the direct relationship between ER factors and the attitudinal antecedents of voluntary turnover (i.e. turnover intention, job satisfaction and affective commitment), we refer to the measurement model of Döckel (2003) which includes the constructs of compensation, job characteristics, training and development opportunities, supervisor support, CO and work–life balance. Döckel’s (2003) measurement model is based on well-known and often-cited ER factor measures developed, validated and cross-validated by other researchers in the field. In our discussion of meta-analyses studies, we also included the mean effect sizes (r¯) (Cohen, 1987) reported, because of their high generalisability.

Researchers identify job satisfaction as an antecedent to organisational commitment, and organisational commitment as a mediator of the relationship between job satisfaction and turnover intention (Farrell & Rusbult, 1981). However, research findings differ as to the direction and nature of the relationship between job satisfaction and organisational commitment (Farkas & Tetrick, 1989). These findings include models indicating organisational commitment to causally affect job satisfaction, and models indicating a reciprocal relationship between job satisfaction and organisational commitment (Farkas & Tetrick, 1989). More recent studies on ER tend to adopt models with paths running from job satisfaction to organisational commitment (Al-Emadi et al., 2015; Ćulibrk, Delić, Mitrović, & Ćulibrk, 2018; Valaei & Rezaei, 2016). Both organisational commitment and job satisfaction have been shown to contribute uniquely to turnover intention (Tett & Meyer, 1993), suggesting that organisational commitment may not fully mediate the path between job satisfaction and turnover intention.

The aforegoing theoretical discussion supports our study’s second hypothesis:

As the direct and indirect relationships between ER factors and attitudinal antecedents of voluntary turnover were relevant to our study, we reviewed the applicable literature in this regard.

Research suggests that ER factors may be distal determinants of turnover intention and that their effects are mediated by job satisfaction and affective commitment as proximal attitudinal determinants of turnover intention (Duarte et al., 2015; Hom, Mitchell, Lee, & Griffith, 2012; Joseph et al., 2007; Tett & Meyer, 1993). Exchange theory – an expansion of the interdependency theory (Homans, 1961; Kelley & Thibaut, 1978) – supports the direction of the paths of a model according to which job satisfaction depends on the specific and immediate rewards and costs related to the job (e.g. job characteristics). Furthermore, affective commitment to the organisation – a broader concept with a longer-term perspective – depends on job satisfaction and other rewards and costs related to the organisation in general (e.g. compensation and benefits; Farrell & Rusbult, 1981). We hypothesised an unfolding model where ER factors indirectly affected the turnover intention via job satisfaction and affective commitment, and we assumed that affective commitment partially mediated the path between job satisfaction and turnover intention.

The above theoretical discussion supports the study’s third hypothesis:

Our study’s purpose was to gain a more comprehensive understanding of the effect of multiple ER factors on the attitudinal antecedents of voluntary turnover (affective commitment, job satisfaction and turnover intention) through the testing of a multivariate SEM model and the use of extended SEM techniques.

We used a quantitative approach and a cross-sectional survey design to study the relationships between ER factors and the attitudinal antecedents of voluntary turnover.

A convenience sample of 272 skilled, highly skilled or professional employees from various public and private organisations in South Africa was obtained. The response rate was 47%. The majority of the sample consisted of white people (65%), English speaking (78%) and women (64%). These participants had obtained a tertiary qualification (76%) and had been employed in their respective companies for a period of less than 5 years (64%).

We used a number of instruments, each based on a Likert-type scale. To measure the distal determinants of attitudinal antecedents of voluntary turnover, we used the Retention Factor Scale (RFS; Döckel, 003) consisting of the subscales of compensation satisfaction (C), job characteristics (J), training and development (T), supervisor support (S), career opportunities (CO) and work–life balance (W). As measures of attitudinal antecedents of voluntary turnover, we used the Job Satisfaction Scale (JSS; Hellgren, Sjöberg, & Sverke, 1997), the Affective Commitment Scale (ACS; Meyer & Allen, 1991) and the Turnover Intention Scale (TIS; Sjöberg & Sverke, 1996). A biographical questionnaire was administered to the respondents. The measures we used (for a detailed summary, see Table 1) are well known and have been extensively used and frequently cited (Döckel et al., 2006).

This study used two extended CFA techniques to overcome the limitations of the overly restrictive conventional CFA model, which penalises for trivial non-target cross loadings. Firstly, ESEM, nested in CFA (EwC), was used to test the RFS being a complex and multidimensional model. Secondly, PVs were used as factor scores to accurately represent the latent variables of the RFS model, which obviated the need to include the full measurement model in the SEM analysis. We used the MPlus Statistical Software Version 7.3 and the maximum likelihood (ML) estimation method with robust standard errors (MLR) to conduct the SEM analyses (Muthén & Muthén, 2012). We relied on the full-information MLR estimator to adequately account for missing data (Muthén & Muthén, 2012). Overall, 2.1% of the data out of the total dataset was missing at random.

We conducted ESEM and target rotation as precursors to the EwC analyses. Exploratory structural equation modelling, which integrates conventional CFA and exploratory factor analysis (EFA), does not penalise for trivial non-target cross loadings. Our decision to set to zero all item loadings not expected to relate to a latent variable was based on theoretical and empirical grounds. We used the EFA solution of Döckel (2003, p. 160) to set the target loadings. The results of Döckel’s EFAs of the RFS show small-item cross loadings with orthogonal rotation and significant inter-correlations between variables.

Asparouhov and Muthén (2009) suggest that, in the case of a study (such as ours) that uses a relatively small sample, conducting an EwC nested in an ESEM and fixing all statistically insignificant cross loadings to zero, reduces the number of parameters that need to be estimated and results in more precise estimates. We used the EwC model as the measurement model in the SEM analysis to determine the relationships between RFS and the criterion variables (Crt) of ACS, JSS and TIS. For the EwC, we applied the procedure of Morin, Marsh and Nagengast (2013) in using all parameter estimates in the ESEM solution as starting values and, except for placing m² restrictions (m = number of factors), freely estimating them in the EwC model. After confirming that all parameter estimates and fit indices’ values of the EwC model replicated those of the ESEM model, we restricted all insignificant cross-loading estimates for factor indicators to ‘0’ as proposed by Asparouhov and Muthén (2009) and tested the restricted EwC model depicted in Figure 1.

In determining the quality of the RFS measurement model, given sample size (N = 272; Muthén & Muthén, 2002), we performed a Monte Carlo (MC) simulation analysis using MPlus. We used 10 000 random sample estimates to determine the 95% coverage and statistical power of the model estimates (> 0.80), parameter estimate and standard error bias (< 5%).

Using CFA, we tested the discriminate and structural validity of the measures of attitudinal antecedents of voluntary turnover (ACS, JSS and TIS), also referred to as the Crt measurement model.

To test Hypotheses 1, 2 and 3, we used PVs for W, CO, S, T, J and C instead of including the full RFS measurement model in the SEM structural model (Figure 2) because such use in a secondary SEM analysis imposes fewer computational and sample-size demands. Figure 2 depicts the SEM path model for the PVs of the RFS (EwC model) on the attitudinal antecedents of voluntary turnover (Crt model), hereafter referred to as EwC-on-Crt^1&2. Only the black regression paths in Figure 2 (and not the red regression paths running between the latent variables JSS, ACS and TIS) reflect Crt¹ model, whereas the black and red regression paths reflect the Crt² model. We used the MLR estimator to test the EwC-on-Crt^1&2 models, which includes both the Crt¹ and the Crt² models. Using the delta method, we estimated the standard errors and calculated the statistical significance of indirect paths or effects. According to Muthén and Muthén (2012), the MLR estimation procedure calculates the standard error estimates that are robust to non-normal data and is considered similar to the bootstrap technique for estimating standard errors.

In estimating the PV data sets, we used multiple imputation procedures (which are commonly used for handling missing data) and the Bayes estimator (Asparouhov & Muthén, 2010). To avoid biased parameter estimates, we included in the multiple imputation model all the items and latent variables used in the EwC and Crt models. (see Asparouhov and Muthén [2010, p. 2] for details on the imputation method they call ‘H0 imputation’.) More specifically, the imputation model included the RFS parameters fixed to the values obtained in the EwC model, and, using starter values from the CFA model, we specified the Crt model parameters to be freely estimated. Following the recommendations of Lüdtke, Robitzsch and Trautwein (2018), we imputed 30 PV data sets using MPlus Markov chain MC Bayesian estimation utilities with Gibbs sampler (PX1) and 100 000 iterations (Muthén & Muthén, 2012). We monitored convergence (which should be below 1.05) with the potential scale reduction indicator and trace plots. For all estimates, we adopted the MPlus default settings for diffuse priors, and we combined the data sets using Rubin’s (1987) method.

In the SEM models (EwC-on-Crt^1&2) that we used to test Hypotheses 1, 2 and 3, we cannot strictly claim direct or indirect causal effects (i.e. the directionality of model paths) as we used cross-sectional data in the analysis conducted. However, Kline (2015) contends that directionality assumptions for model paths may be assumed if the rationale and supporting knowledge are strong. In this study, we relied strongly on exchange theory principles, seminal works, review studies, empirical and meta-analytic research evidence to support directionality assumptions and inferences in the SEM model. We relied on covariance statistics to support or rule out the probability of direct or indirect causal effects whilst controlling for multiple covariates.

With the assistance of the human resource management departments of their respective companies, the respondents completed the surveys in either a paper-based or an electronic format. Participation in the study was voluntary, anonymous and confidential. The relevant ethics committee of the University of Pretoria where the study was conducted approved the use of the data for research purposes (reference no. Nr EMS090/18).

This study addressed the need to analyse multiple latent ER variables and attitudinal antecedents of voluntary turnover in tandem. We used extended SEM techniques to gain a more comprehensive understanding of the simultaneous effect of multiple ER factors on attitudinal antecedents of voluntary turnover. As Lee et al. (2018) emphasise, advanced statistical analyses are important in contributing to a full and deep understanding of the multifaceted nature of ER factors and in enhancing ER management in organisations.

This study indicated the RFS measurement model as being a plausible representation of theory and having the required breadth and depth to ensure sufficient construct validity and enhanced measurement precision. Contrary to conventional models of CFA, the restricted EwC model allowed for trivial but statistically significant cross loadings. According to Hsu et al. (2014), ignoring cross loadings above 0.13 in conventional CFA models can lead to substantial overestimation of pattern coefficients, bloated covariances between factors and biases in path coefficient estimates in SEM models.

Our study confirmed previous research findings (Ansari, 2011; Das & Baruah, 2013; Kashyap & Rangnekar, 2014; Mehmood, Ahmad, Irum, & Ashfaq, 2016) that compensation, job characteristics, work–life balance and CO are, as ER factors, unique predictors of attitudinal antecedents of voluntary turnover. These ER factors explained a large proportion of variance on each of the attitudinal antecedents of voluntary turnover (from 44% to 61%). Remarkably similar to the findings of the meta-analysis studies reported earlier, the path coefficients between compensation, job characteristics, work–life balance and the attitudinal antecedents of voluntary turnover were of a medium to large effect size, confirming the generalisability of our findings. The study supported the notion that ER factors might be distal determinants of turnover intention (R² = 0.44) because their effects appeared to be mediated by job satisfaction (R² = 0.61) and affective commitment (R² = 0.68) as proximal attitudinal determinants of turnover intention (Duarte et al., 2015; Tett & Meyer, 1993). The combined effect of ER factors, affective commitment and job satisfaction on turnover intention was large (R² = 0.65), affirming the importance of these variables for ER management.

Similar to other relevant studies (e.g. Luna-Arocas & Camps, 2007; Sager, Griffeth, & Hom, 1998; Samad & Yusuf, 2012), our study found that affective commitment partially mediated the relationship between job satisfaction and turnover intention. Affective commitment had the most significant direct relationship (β = −0.56) with employee turnover intention, followed by job satisfaction (β = −0.27). The size of the reported relationships can likely be attributed to affective commitment relating to the organisation as a whole (a broad concept with a long-term perspective), which in turn partially depends on satisfaction with the job itself (a narrow concept with a short-term perspective) (Farrell & Rusbult, 1981; Kooij, Jansen, Dikkers, & De Lange, 2010). We found that compensation directly related to affective commitment (Meyer & Allen, 1997) and that job satisfaction only partially mediated the relationship between compensation and turnover intention. Thus, the finding emphasises the cardinal importance of compensation in ER management – it was the only ER factor that directly impacted on all the attitudinal antecedents of voluntary turnover (Moncarz et al., 2009; Singh, 2019; Tracey, 2014).

Interestingly, job satisfaction fully mediated the direct path from job characteristics and work–life balance to turnover intention, respectively, suggesting that job satisfaction made a unique contribution to turnover intention and served as a precursor to affective commitment in this context. Moreover, the broader concept of affective commitment may only be influenced indirectly by job characteristics and work–life balance, depending on the job and the associated level of job satisfaction (Farrell & Rusbult, 1981). Hirschfeld (2000) and Lee (2019) have found a direct link between job-related intrinsic motivators (e.g. job characteristics; variety, autonomy, task significance, skill utilisation and self-growth) and the level of job satisfaction, which in turn leads directly to both affective commitment and turnover intention. Similarly, work–life balance directly impacts on intrinsic motivation by allowing for a degree of choice over when, where and how much they work, resulting in increased employee autonomy and job satisfaction, which in turn leads directly to both affective commitment and turnover intention (Mas-Machuca, Berbegal-Mirabent, & Alegre, 2016). This finding is supported by Butts et al. (2013) who show that work–life balance has a more significant direct relationship with job satisfaction than with affective commitment. This finding emphasises the importance of a job-satisfaction-conducive work environment in managing turnover intention directly over the short term and in impacting on affective commitment to the organisation indirectly over the long term. Lee et al. (2018, p. 89) call job satisfaction the ‘cardinal job attitude and prime antecedent to employee turnover’.

This study indicated that training and development, supervisor support and CO did not, as hypothesised, relate substantively to affective commitment and job satisfaction. Therefore, no mediating effects involving these variables were evident. This result is not consistent with previous findings (Gani et al., 2020; Ibidunni et al., 2016; Joāo & Coetzee, 2012; Kraimer et al., 2011; Lee, 2019; Van Dyk & Coetzee, 2012), but it can probably be ascribed to previous studies addressing only one retention factor or a subset of factors (Al-Emadi et al., 2015; De Vos & Meganck, 2009) and not controlling for the effect of multiple ER factors. A statistically significant relationship between two sets of variables may be the product of confounding variables (e.g. compensation and job characteristics), leading to incorrect conclusions about the nature of the relationship.

Our study showed that ER factors might be differently related to attitudinal antecedents of voluntary turnover. Gardner, Wright and Moynihan (2011) made a similar conclusion in their study of voluntary turnover and multiple HR practices, and their multilevel analyses across corporate business units provided valuable insights. They showed that three bundles of HRM practices (skilling, motivation and empowerment) affected voluntary turnover differently and that affective commitment did not mediate the effect of skill-enhancing practices (e.g. training and development) on voluntary turnover. In contrast, affective commitment partially mediated motivational and empowerment-enhancing practices (e.g. compensation, job characteristics and work–life balance). They found that motivational and empowerment-enhancing HRM bundles of practice elicited conventional psychological processes that explained an entire range of outcomes and that skills-related HRM bundles of practice (e.g. training and development) elicited many market processes that might lead to different and even inconsistent outcomes. For example, training and development could improve a person’s employability and marketability within and outside an organisation (Dechawatanapaisal, 2018).

Contrary to the previous findings (Gani et al., 2020; Joāo & Coetzee, 2012; Kraimer et al., 2011; Van Dyk & Coetzee, 2012), our study showed that CO had a unique and direct effect on turnover intention but that affective commitment and job satisfaction did not affect it. We argue that CO may represent a large variety of psychological processes and meanings for different individuals (e.g. relating to career stages, family needs, identity, employment opportunities, market-skill demands, spousal careers) and that employee attitudes (e.g. affective commitment) may be affected differentially (Lee, 2019; Lee et al., 2018).

Interestingly, after controlling for the effect of all the variables in the model, supervisor support did not show unique or incremental effects on any of the attitudinal antecedents of voluntary turnover. This finding contradicts previous findings (Gani et al., 2020; Ibidunni et al., 2016; Lu, Zhao, & While, 2019; Tett & Meyer, 1993; Van Dyk & Coetzee, 2012), showing that psychological processes and the meaning of supervisor support may differ for individuals, depending on the employment context and the impact of supervisor support on the motivational and empowerment ER bundles. For instance, Chaudhuri, Arora and Roy (2020, p. 11) state that ‘supervisor support was considered to be a relevant factor that helped with work–life balance’. However, further research is needed to clarify the findings on supervisor support and ER.

To conclude, we found that by studying multiple ER factors in tandem and controlling for all covariances between the ER factors in an expanded SEM model gave new insights into the uniqueness of each ER factor in predicting the attitudinal antecedents of voluntary turnover. Our findings suggest that ER factors have a differential effect on the attitudinal antecedents of voluntary turnover, which can partly be ascribed to differences in the underlying characteristics of HRM bundles of practice as described by Gardner et al. (2011). Employee retention management could be enhanced by a deep understanding of the differential effects of each ER factor on attitudinal antecedents of voluntary turnover, and gaining such an understanding requires advanced SEM skills to thoroughly test increasingly complicated path models using latent variables (Hom et al., 2017; Lee et al., 2018).

Our research makes a substantial contribution to existing theory by bringing about a better understanding of the multifaceted interrelations between critical ER factors, job satisfaction, affective commitment and turnover intention through the use of extended SEM techniques. A unique finding we made was that affective commitment and job satisfaction were important mediators that differentially affected the relationship between ER factors and employee turnover intention. The responsibility for the differential effects of ER factors on the attitudinal antecedents of voluntary turnover can probably be attributed to motivational and empowerment-enhancing HRM bundles of practice (i.e. compensation, job characteristics, work–life balance, CO and supervisor support) and skill-related HRM bundles of practice (i.e. training and development). This study has shown that the ER factors of compensation, job characteristics, work–life balance and CO (in the given order of importance) are unique determinants of the attitudinal antecedents of voluntary turnover. Career opportunities, an empowerment-enhancing ER factor, directly affect only the turnover intention, suggesting that individual differences and market forces may affect motivational attitudes (e.g. affective commitment) differently. The fact that training and development and supervisor support were found not to be unique determinants of attitudinal antecedents of voluntary turnover could be the result of employment contexts, individual differences and market processes. To effectively manage ER, management must understand the relative embeddedness of a range of ER factors and prioritise motivational and empowerment-enhancing bundles of practice (e.g. compensation, job characteristics, work–life balance and CO) to impact on attitudinal antecedents of voluntary turnover.

The expanded SEM models tested in this study allowed for increased rigour and accuracy of method in simultaneously controlling for all the effects in the model and in determining the uniqueness and relative contribution (i.e. embeddedness) of each variable. The said models provided a new perspective on the relationship between ER factors and attitudinal antecedents of voluntary turnover. The use of the EwC measurement model was a novel approach to applying ESEM to smaller samples whilst retaining the breadth and depth of the measured constructs. Applying PVs in SEM studies with smaller samples is a new approach that counters a variety of potential estimation problems in SEM models and simplifies a complex model without loss of accuracy.

Future studies should also follow a ‘methodological–substantive synergy’ approach and focus on the complex relationship between ER factors and attitudinal proxy variables of voluntary turnover in more contexts and relate it to actual voluntary turnover.

To conclude, we heeded the call of De Vos and Meganck (2009), Dechawatanapaisal (2018), Lee et al. (2018) and others to demonstrate the value of studying multiple ER factors in tandem to better understand the relative embeddedness of a range of ER practices in retaining employees. In addition, we indicated that the use of extended SEM techniques readily met the need for studying ER factors using more comprehensive latent multivariate approaches and for providing new insights into the relationships between ER factors and attitudinal antecedents of voluntary turnover. The findings of this study have important practical implications for effectively managing ER through prioritising and implementing more effective HR bundles of practice.