An earlier systematic literature review study (Jacobs & Roodt,
The purpose of this study is to test two different predictive models that may explain two distal outcomes, namely turnover intention and individual employee performance, in the South African (SA) BPS industry.
There is little, if any, peer-reviewed, empirical research available on the BPS industry that links variables to either proximate or distal outcome variables, such as turnover intention and individual employee performance.
A two-stage, census-based sampling approach was followed that initially targeted 40 organisations within the industry that employ about 13000 employees. Sixteen of these organisations (employing about 6800 individuals) indicated that they wish to voluntarily participate in the study; 821 individuals were targeted to participate in the cross-sectional survey and 487 usable responses were obtained (a 59% response rate). Multivariate data analyses were conducted from an exploratory perspective to retrospectively explain relationships in the structural models.
An overall health promotion process model that predicted the distal outcome, turnover intention, was confirmed within the context of this exploratory study, where human resource management (HRM) practices, job demands (JDs) and job resources (JRs) were related to burnout as the only proximate outcome. On the other hand, an individual performance enhancing process model was also confirmed within the context of this exploratory study by using HRM practices, JRs and JDs, together with proximate variables, such as employee competence and engagement, to explain the distal outcome, individual performance.
The study has implications for executive (strategic) management, human resource (HR) professionals and work unit team leaders in the BPS industry. This study shows which JRs contribute towards the reduction of burnout and turnover intention in the BPS context. On the other hand, it explains how HRM practices, as well as JRs and JDs, in combination with employee competence and engagement, can be used to promote individual performance.
This is the first SA study that uses a range of variables in a multivariate analysis to predict turnover intention and individual performance in the SA BPS industry.
Business Process Services (BPS) is an umbrella term that describes an industry sector that includes a number of different types of business activities, such as contact centre services (CCS) – or more commonly known as call centres, information technology outsourcing (ITO), knowledge process outsourcing (KPO) and shared service centres (SSCs) (Frost & Sullivan Consulting,
In the BPS industry, the increase of adverse consequences, such as decreased customer satisfaction levels, lower contact resolution rates, higher employee attrition and absenteeism has been noted (Dimension Data,
Against this backdrop, Jacobs and Roodt (
The main objective of the study will therefore be to investigate a human capital predictive model of turnover intention and employee performance in the BPS industry. The two research objectives flossing for the main objective are:
to establish if HRM practices, JRs and JDs, in combination with proximate variables (well-being, burnout and engagement), predict the distal outcome, turnover intention
to establish if HRM practices, JRs and JDs, in combination with proximate variables (P–E fit, employee competence and engagement), predict the distal outcome, individual performance.
The importance and relevance of the study are twofold: Firstly, it will identify which variables contribute independently or interactively to proximate outcomes (such as engagement, burnout, well-being, P–E fit and employee competence) and distal outcomes (such as turnover intention or individual performance). Secondly, it will enable executive management, HR managers and unit managers to use this information to effectively manage and improve individual performance in this industry sector.
The theoretical framework, in which this study will be embedded, is the General Systems Theory (GST), because it encapsulates the information age worldview (Dostal, Cloete, & Járos,
In the section below, each of the constructs used in the study and its relationship with other variables in the model will be briefly described. The variables are grouped into three broad categories, namely contextual variables, proximate outcomes and distal outcomes:
In view of the above, it is hypothesised that HRM practices create the foundation for establishing the much needed JRs to counteract JDs within the work context in order to promote individuals’ job performance.
It is argued that HRM practices in combination with JRs and JDs create the context in which employees perform their daily duties. This context can either facilitate or inhibit proximate outcomes.
Minimal competence effectiveness includes ‘… cross-functional awareness, initiative, persuasiveness and understanding practices, ability to listen and to be attentive to detail and information’ (as detailed by Jacobs & Roodt,
… a state of well-being in which every individual realises his or her own potential, can cope with the normal stresses of life, can work productively and fruitfully, and is able to make a contribution to her or his community. (World Health Organisation,
Negative aspects, inclusive of actual illness, and positive aspects, such as contentment, are implied in the definition. Fatigue contributes directly to burnout and the intention to leave, with fatigue-related antecedents lowering employees’ work contentment (Campbell,
… a persistent, negative, work-related state of mind in ‘normal’ individuals that is primarily characterised by exhaustion, which is accompanied by distress, a sense of reduced effectiveness, decreased motivation, and the development of dysfunctional attitudes and behaviours at work. (Schaufeli & Enzmann,
Literature that supported the hypothesis specific to this article
It is argued that the contextual factors in combination with the proximate outcomes (well-being, burnout, P–E fit, employee competence and engagement) will contribute towards distal outcomes, such as turnover intention or employee performance. Two suppositions support this notion: The first supposition is that a negatively perceived context may lead to ill-being and burnout, which in turn may result in turnover. The second supposition is that a positively perceived context may result in greater P–E fit, improved employee competence self-assessments and increased engagement levels. This in turn will result in improved employee performance.
Against the background of the four hypotheses, two specified models (namely a turnover intention model and an individual performance model) will be tested in this study and they are graphically displayed in
Specified Model 1 – Predictors of turnover intention.
Specified Model 2 – Predictors of individual performance.
The research design of this exploratory study followed the quantitative research tradition and used a cross-sectional, census-based survey design. Multivariate statistical techniques were used to analyse the data and to establish and explain relationships in an
The research method followed in this study will be discussed under the following four sub-headings.
A multistage census-based sampling approach was followed for purposes of this study, at two levels, namely the organisation and the individual employee. The target population of this study was employees who were employed in BPS organisations in South Africa. Jones (
It is evident from
Biographic and demographic details of the sample.
Variable | Category | Frequency | Valid percentage |
---|---|---|---|
Gender | Male | 142 | 30.1 |
Female | 329 | 69.9 | |
Missing | 16 | - | |
Total | 487 | 100 | |
Age | <20 | 25 | 5.9 |
21–30 | 250 | 59.4 | |
31–40 | 107 | 25.4 | |
41–50 | 31 | 7.3 | |
>51 | 8 | 1.9 | |
Missing | 66 | - | |
Total | 487 | 100 | |
Education | Lower than Grade 12 | 15 | 3.2 |
Grade 12 | 244 | 51.9 | |
First degree or diploma | 164 | 34.9 | |
Hons, Master’s or doctor | 47 | 10 | |
Missing | 17 | - | |
Total | 487 | 100 | |
Marital status | Unmarried | 293 | 62.4 |
Married or cohabitating | 149 | 31.7 | |
Divorced | 25 | 5.3 | |
Widowed | 3 | 0.6 | |
Missing | 17 | - | |
Total | 487 | 100 | |
Language | Afrikaans | 99 | 21.1 |
English | 144 | 30.6 | |
African Language | 166 | 35.3 | |
Other | 61 | 13 | |
Missing | 17 | - | |
Total | 487 | 100 | |
Race | African | 239 | 51.4 |
White | 87 | 18.7 | |
Mixed race | 112 | 24.1 | |
Indian or Asian | 27 | 5.8 | |
Missing | 22 | - | |
Total | 487 | 100 | |
Tenure | <1 | 152 | 36.8 |
2–5 | 176 | 42.6 | |
6–9 | 59 | 14.3 | |
>10 | 26 | 6.3 | |
Missing | 74 | - | |
Total | 487 | 100 |
The measuring instruments used in the study were based on the following conceptualisations of the constructs (summarised in
A summary of the measures and their use in the study.
Measure | Authors | Variable category | Independent or dependent | Number items | Reliability (alpha) |
---|---|---|---|---|---|
HRM Practices | Jacobs ( |
Contextual | Independent | 17 | 0.945 |
Job demands: | Bakker et al. ( |
Contextual | Independent | 12 | 0.827 |
JD1 – Workload / Work Pace | - | Contextual | Independent | - | 0.792/0.775 |
JD2 – Organisation Systems Change | - | Contextual | Independent | - | 0.819 |
Job resources: | Bakker et al. ( |
Contextual | Independent | 19 | 0.815 |
JR1 – Social support or Pay and benefits | - | Contextual | Both | - | 0.765/0.905 |
JR2 – Personal resources | - | Contextual | Both | - | 0.836 |
Employee competence | Jacobs ( |
Proximate | Both | 8 | 0.912 |
P–E fit | Jacobs ( |
Proximate | Independent | 5 | 0.819 |
Work engagement | Schaufeli et al. ( |
Proximate | Both | 9 | 0.927 |
Well-being | Jacobs ( |
Proximate | Both | 9 | 0.884 |
Burnout | Korunka et al. ( |
Proximate | Both | 6 | 0.893 |
Turnover intention | Roodt ( |
Distal | Dependent | 6 | 0.708 |
Employee performance | Jacobs ( |
Distal | Dependent | 11 | 0.853 |
JD, job demands; JR, job resources.
Note: The Cronbach’s alpha reliabilities for two JDs and two JRs are presented in the shaded rows. Please see the full reference list of the article, Jacobs, C.T.G. & Roodt, G. (2019). Predictive performance models in the South African Business Process Services industry.
An example reads: ‘How often have you considered leaving your job?’, with the items scored by employing a seven-point intensity scale with the extremes poles anchored (i.e.
A preliminary or pilot investigation was carried out to establish the face validity of the instrument in a two-pronged approach (Garson,
Survey data were collected over a three-and-a-half-month period by providing paper surveys to 821 employees in 16 participating BPS organisations that met the sampling criteria and had volunteered to participate in the research.
The statistical analyses were conducted in a particular order. Descriptive statistical analyses were conducted first to scrutinise the measures’ item distribution properties.
In the multivariate analysis stage of the data, exploratory factor analyses (EFAs) were first conducted to establish if original theoretical constructs could be replicated. This was necessitated because of the fact that the researcher had developed five of the ten scales and a number of items, as well as because of the exploratory nature of the study. Secondly, item and iterative reliability analyses were then conducted on those established variables. Thirdly, all the variables were then subjected to a confirmatory factor analysis (CFA) to test the fit of the measurement models and the suitability of their use in the structural models. At this stage, some items were omitted to improve the model fit. Finally, structural equation models (SEMs) were then tested for structural model fit. Incremental and absolute fit statistics were used to report on model fit. IBM’s SPSS (Version 22) was used to analyse the data with EQS (Version 6) for SEM, as EQS is robust in handling non-normal data (Bentler,
The summary results of the CFAs are presented and discussed per variable in terms of the absolute fit measures and incremental fit measures. Absolute fit measures are the chi-square (
This study was more exploratory in nature and an exploratory approach was thus followed in the multi-variate testing of goodness-of-fit (GOF) models, with the conclusions being closer to the exploratory side of the confirmatory–exploratory dimension (Boomsma,
All institutional research ethic protocols were observed and adhered to during the execution of this research project.
Before proceeding with the CFAs, all measures were first subjected to EFAs (or dimensionality analysis) as briefly discussed below. The latter procedure involves conducting an item inter-correlation matrix on each measure. A Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy (MSA), as well as a Bartlett’s test of sphericity chi-square coefficient was calculated for each of these matrices to establish the factorability of the matrices. All these MSA values and chi-square coefficients were exceeding the specified criteria and suggested that all the matrices could be factor analysed. The number of eigenvalues larger than unity, as well as scree plots was used to determine how many factors should be extracted. In the case of both JDs and JRs, two factors were extracted. CFAs were subsequently conducted on these extracted factors to test their measurement model fit.
The measurement scales that yielded a single factor, as indicated by eigenvalues larger than unity and reiterated by the scree plot, included the HRM Practices Scale, the Maslach Burnout Index (General Survey) Scale, the Employee Competence Scale, the P–E Fit Scale, the Utrecht Work Engagement Scale and the Turnover Intention Scale. The HRM Practices Scale obtained a KMO sampling adequacy value of 0.951, with a significant Bartlett’s test of sphericity chi-square value (χ² = 4928.806;
The Job Demands Scale, the Job Resources Scale, the Employee Well-being Scale and the Employee Performance Scale constituted two factors each. The Job Demands Scale obtained a KMO sampling adequacy value of 0.550, with a significant Bartlett’s test of sphericity chi-square value (χ² = 203.816;
Mardia’s (
Confirmatory factor analysis (goodness-of-fit statistics for the measurement models of the different variables,
Number models | Multivariate Kurtosis | NFI | NNFI | CFI | RMSEA | Confidence interval | |||
---|---|---|---|---|---|---|---|---|---|
3. HRM practices | 45.970 | 581.767 | 119 | 4.889 | 0.903 | 0.909 | 0.921 | 0.089 | 0.082 – 0.097 |
1. Job demands | 30.849 | 136.950 | 50 | 2.739 | 0.920 | 0.930 | 0.947 | 0.060 | 0.048 – 0.072 |
1. Job resources | 31.114 | 229.332 | 61 | 3.760 | 0.914 | 0.917 | 0.935 | 0.065 | 0.065 – 0.086 |
1. Employee Competence | 51.024 | 178.628 | 20 | 8.931 | 0.890 | 0.860 | 0.900 | 0.128 | 0.111 – 0.146 |
2. P–E fit | 26.837 | 131.862 | 2 | 65.93 | 0.766 | 0.301 | 0.767 | 0.367 | 0.314 – 0.420 |
1. Work engagement | 49.169 | 254.584 | 27 | 9.429 | 0.889 | 0.865 | 0.899 | 0.132 | 0.117 – 0.147 |
3. Well-being | 10.790 | 96.830 | 26 | 5.684 | 0.930 | 0.892 | 0.935 | 0.142 | 0.117 – 0.168 |
1. Burnout | 31.042 | 98.588 | 9 | 10.95 | 0.933 | 0.897 | 0.938 | 0.144 | 0.118 – 0.169 |
2. Turnover intention | 2.034 | 5.183 | 2 | 2.592 | 0.988 | 0.978 | 0.993 | 0.057 | 0.000 – 0.120 |
3. Employee perform | 37.952 | 62.212 | 14 | 4.444 | 0.930 | 0.916 | 0.944 | 0.085 | 0.064 – 0.106 |
NFI, Normed Fit Index; NNFI, Non-Normed Fit Index; CFI, Comparative Fit Index; RMSEA, root mean square error of approximation;
Note: Mardia’s coefficient of multivariate kurtosis is reported. All indices are at
It is evident from
Two structural models (refer to specified Models 1 and 2 in
The specified Model 1 had to be modified by following a systematic process to remove items because of low
Tested and modified Model 1 - Prediction of Turnover Intention.
Goodness-of-fit (Yuan–Bentler correction) indices for Structural Model 1 predicting turnover intention (
Number model | Multivariate Kurtosis | NFI | NNFI | CFI | RMSEA | Confidence interval | |||
---|---|---|---|---|---|---|---|---|---|
5th iteration of Model 1 | 336.66 | 1786.76 | 733 | 2.43 | 0.824 | 0.884 | 0.891 | 0.053 | 0.050 – 0.056 |
NFI, Normed Fit Index; NNFI, Non-Normed Fit Index; CFI, Comparative Fit Index; RMSEA, root mean square error of approximation;
Note: Mardia’s coefficient of multivariate kurtosis is reported. All indices are at
It is evident from
The overall variance of
The specified Model 2 had to be modified to obtain good model fit statistics as displayed in
Tested and modified Model 2 - Prediction of Individual Performance.
Goodness-of-Fit (Yuan–Bentler correction) indices for Structural Model 2 predicting individual performance (
Number models | Multivariate Kurtosis | NFI | NNFI | CFI | RMSEA | Confidence interval | |||
---|---|---|---|---|---|---|---|---|---|
3rd iteration of Model 2 | 534.97 | 2268.47 | 1107 | 2.05 | 0.834 | 0.901 | 0.907 | 0.045 | 0.043 – 0.048 |
NFI, Normed Fit Index; NNFI, Non-Normed Fit Index; CFI, Comparative Fit Index; RMSEA, root mean square error of approximation;
Note: Mardia’s coefficient of multivariate Kurtosis is reported; Yuan, Lambert and Fouladi’s coefficient of multivariate kurtosis is reported. All indices are at
It is evident from
The research problem investigated in this study can be traced back to a systematic literature review study conducted earlier by Jacobs and Roodt (
The main contribution of the study lies therein that it operationalised and tested a multivariate model of employee performance in the South African BPS industry.
The results of the study should be interpreted within the framework of the systems theory (Dostal et al.,
Model 1, developed for the prediction of turnover intention, was confirmed in this study. It was established that HRM practices are related to JR1 (pay and benefits) and to JR2 (personal resources), but not to JDs (JD1 – organisational systems change; JD2 – work pace and workload). All JRs and JDs in the model were related to burnout, which in turn is related to turnover intention. Well-being and engagement were omitted to create a more parsimonious model and a better model fit. These findings suggest that HRM practices provide the platform for developing JRs that will reduce the occurrence of burnout and promote the intention to stay. These results are in support of H1 (
Liu et al.’s (
JDs (as identified by Bakker et al.,
Model 2, developed for the prediction of individual performance, was confirmed in this study. It was established that HRM practices are directly and relatively strongly related to employee engagement and to employee competence, but also more weakly related to JR1 (pay and benefits) and to JR2 (personal resources), but not to JDs (JD1 – organisational systems change; JD2 – work pace and workload). Both JDs (JD1 – organisation systems change as well as JD2 – work pace and workload) and P–E fit are related to engagement, but not to employee competence. Owing to the poor measurement fit indices of P–E fit, no further discussion on this aspect will be included here. JRs (JR1 – pay and benefits; JR2 – personal resources) are related to engagement and to employee competence, while JR2 is also directly related to individual performance. JDs and JRs are unrelated. The relationship between engagement and employee competence with individual performance is rather weak, compared to the relationship between personal resources and individual performance. These results are in support of H2 (
Liu et al.’s (
This study also confirms the general research trend that JDs and JRs are related to work engagement (Demerouti & Bakker,
To the knowledge of the authors, this is the first SA study in the BPS industry that operationalised and tested multivariate models for the prediction of employee retention, as well as individual performance. Good fit statistics were obtained for testing both the measurement models (with the exception of P–E fit) and the structural models.
Both the stated ROs of the study were achieved. More specifically, the following conclusions can be drawn from the findings in respect of RO1:
HRM practices are related to JRs (both JR1 and JR2), but not to JDs, and all JRs and JDs (although unrelated) predict the proximate outcome burnout, which is in turn related to the distal outcome, turnover intention. RO1 is hereby achieved and both H1 and H3 are supported. Well-being and engagement were omitted from the model to improve model fit and to provide a more parsimonious model.
In respect of RO2, the study established that HRM practices in combination with JRs predict proximate outcomes, employee competence and work engagement (P–E fit is only related to engagement), but HRM practices have a stronger direct link with engagement and employee competence, compared to JRs and JDs (in the case of engagement) and only JRs (in the case of competence). Engagement and employee competence are in turn related to the distal outcome individual performance, but personal resources are more strongly associated with individual performance, compared to the two first mentioned variables. RO2 is hereby achieved and both H2 and H4 are supported.
The study has important implications for executives, managers and HR practitioners in the BPS industry, as well as for researchers: It can be noted that HRM practices combined with JRs relative to JDs result in reducing burnout in the short term. HRM practices therefore contribute to the development and strengthening of JRs. The occurrence of burnout is in this case related to turnover intention. It seems that more effective HRM practices and/or JRs reduce the occurrence of burnout and ultimately turnover intention.
Executives and managers in the BPS industry can take note of the fact that HRM practices on their own or combined with JRs may create a context that is conducive for the promotion of employee competence and work engagement in the short term, but also employee performance in the longer term. But this will only happen if the HRM practices are perceived as positive and effective. It seems that managers and HR practitioners in the sampled organisations were successful in creating such effective HRM practices and such a conducive HRM context.
The results suggest that HR practitioners and managers in the BPS industry in this study successfully developed HRM practices that are positively perceived with JRs counteracting the impact of JDs; they have succeeded in creating a context that is conducive for the development of employee competence and work engagement in the short term with a clear impact on employee performance in the long term.
The study only used a cross-sectional sample to collect that data for this exploratory study. Individuals were used as the unit of analysis in this study. As a consequence, there may be artificially enhanced levels of common method variance where individuals have completed measures for both the independent and for the dependent variables. A multi-level study design, a longitudinal design, or even a design where an objective individual performance measure is used, may therefore yield different results and should therefore be explored in future studies.
It is proposed that future research (if research ethics principles permit) should make use of multi-level studies where data are captured on different levels. Alternatively, data for the dependent variable can be captured on an objective level. A longitudinal research design may shed light on the cause and effect relationships in different time periods between variables in the model which could not be established by this cross-sectional study.
To conclude, the study set out to develop and test multivariate models of employee turnover intention and individual performance in the SA BPS industry. Two ROs and four hypotheses were formulated in the study. In order to operationalise the ROs and test the hypotheses, a cross-sectional research design was used to collect data and multivariate data analyses were conducted to establish
The authors declare that they have no financial or personal relationships which may have inappropriately influenced them in writing this article.
C.T.G.J. conducted the research for his PhD study. This article is based on a portion of his doctoral thesis. G.R. who was his supervisor for his doctoral study wrote the article.