Human resource management (HRM) practices: The term human resources emphasises employees as organisational resources or assets (Liu, Combs, Ketchen, & Ireland, 2007). Jacobs and Roodt (2011) incorporated Liu et al.’s (2007) three HRM categories (competence, commitment and context) that entail nine practices (selection, compensation level, training, incentive compensation, internal promotion, participation programmes, flexitime, grievance procedures and employment security) into the model, as these optimally impact organisational performance. Liu et al.’s (2007) model provides a well-substantiated taxonomy of HRM practices in the context of the organisation and is based on a meta-analysis of 92 scientific investigations that included data from over 19 000 organisations. Liu et al.’s (2007) model is therefore an ideal model to use in the BPS environment, as it provides a framework for investigating the effects of HRM practices on performance at an organisational level, a team level and an individual level (Robbins, Judge, Odendaal, & Roodt, 2009). The three key broad HRM practices groupings that impact performance at an organisational level (Liu et al., 2007) are discussed next:

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.

The Job Demands–Resources (JD-R) Model (Bakker & Demerouti, 2016; Demerouti & Bakker, 2011) is an empirical model that elucidates how JRs produce engagement (and employee retention) and wellness issues (starting with a lack of energy, which could eventually lead to health problems) are expedited by JDs, as defined by Bakker, Demerouti, and Schaufeli (2003) and Dwyer and Fox (2006). JDs are related negatively to individual performance, and JRs act as moderators (or buffers) (Dwyer & Fox, 2006). Absenteeism will decrease when JRs increase, which will positively impact employee performance (Bakker et al., 2003; Swart, 2006). JRs increase engagement through an increase in energy, involvement and efficacy, which will enhance individual performance (Demerouti, Bakker, Nachreiner, & Schaufeli, 2001). The interaction between JDs and JRs, as well as their respective relationships with HRM practices, with the ultimate impact on individual performance, is unknown in a multivariate model in the BPS context. These relationships will be hypothesised and tested in the proposed study:

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.

Employee competence is the combination of technical, social or behavioural knowledge, skills and abilities (KSAs); personality attributes; and principles that are essential in executing organisational duties (Grobbelaar, Roodt, & Venter, 2004). Bagnara and Marti (2001, p. 227) summarised BPS knowledge workers’ related core competency as ‘…understanding clients’ requests and finding, accessing and manipulating knowledge in the organisation and in the cognitive artefacts’, alongside having content knowledge, service knowledge and product knowledge.

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, 2011, p. 9, based on Grobbelaar et al., 2004; White & Roos, 2005). The ability to perceive the customer’s perspective and expectations (Axtell, Parker, Holman, & Totterdell, 2007; Burgers, De Ruyter, Keen, & Streukens, 2000) constitutes social competence. Emotional intelligence (Higgs, 2004; Nel & De Villiers, 2004) and personality features were reported to be contributors to individual performance (Moller, Crous, & Schepers, 2004; Nicholls, 2006; Ojha & Kasturi, 2005; Van der Linde, 2005). It is not known how employee competence interacts with JDs and JRs and HRM practices to impact employee performance in a multivariate model. These relationships will be hypothesised and tested in this study.

P–E fit is defined as the employee–work environment congruency that manifests when the individual qualities are complementary to the work situation (Cox et al., 2000), where incongruity could result in stress (Le Fevre, 2003). The five aspects of P–E fit (Billsberry, Marsh, & Moss-Jones, 2004) are as follows: (1) person–organisation (P–O) fit, where the employee’s values match those of the organisation; (2) person–vocation (P–V) fit, which implies the employee’s self-concepts–occupation congruence; (3) person–job (P–J) fit, where the employee’s KSAs and the occupation’s requirements are aligned; (4) person–group (P–G) fit, a measure of the employee–team congruence; and (5) person–people (P–P) fit, relating to congruence between the employee’s culture and that of colleagues. It is known that the P–E fit dimension has an inverse relationship with turnover, with a limited impact on individual performance (Arthur, Bell, Doverspike, & Villado, 2006; McCulloch & Turban, 2007; Yang, Che, & Spector, 2008). The P–J fit dimension also correlates with selection (an HRM practice) and the competence of employees (Werbel & Gilliland, 1999). Some studies allude to the P–E fit and work engagement link through job crafting or the mediating role of the needs-supply sub-dimension of P–J fit between job crafting, burnout and work engagement (Lu, Wang, Lu, Du, & Bakker, 2014; Travaglian, Babic, & Hansez, 2016). What is therefore not known is the relationship of P–E fit with employee engagement in conjunction with employee competence, in affecting the overall individual performance in a multivariate model. This relationship will be hypothesised and tested in the proposed study.

Employee engagement: Schaufeli and Bakker (2010, p. 13) defined and operationalised work engagement as ‘… a positive, fulfilling, work-related state of mind that is characterised by vigour, dedication, and absorption’. In the words of Saks (2006, p. 602), it is ‘the degree to which an individual is attentive and absorbed in the performance of their roles’. Engagement is characterised by high energy, high involvement and high efficacy, while burnout (the inverse of engagement) is characterised by exhaustion, low energy, cynicism and inefficacy (Simpson, 2008). It is not known how employee engagement interacts with employee competence and P–E fit in the prediction of employee performance. This relationship will be investigated more closely.

Employee well-being is defined as ‘a state of complete physical, mental and social well-being and not merely the absence of disease or infirmity’ (World Health Organisation (WHO), 2006, p. 1). Mental well-being is defined by the WHO as:

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, 2003; Visser & Rothmann, 2008, 2009). Job-related demands explain health issues, work contentment positively correlates with organisational climate, and shoulder and/or arm musculoskeletal distress indicates physical work distress (Bakker et al., 2003; Dladla, 2009; Dwyer & Fox, 2006; Kerstin, 2005). The impact of employee well-being on individual performance in a multivariate model is not known; this relationship will be hypothesised and tested in the proposed study.

Burnout is defined as:

Literature that supported the hypothesis specific to this article included the following: Job demands expedite burnout in the context of the heuristic JD-R Model (Bakker et al., 2003; Dwyer & Fox, 2006). Work burnout is the reduced health or energy processes related to job demands (Demerouti & Bakker, 2011). It has been postulated that job resources moderate (or buffer) job demands’ effect on stress and burnout (Demerouti & Bakker, 2011).

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.

Turnover intention is the intent to resign from current employment (Spies, 2006). For the purpose of this study, the definition of Tett and Meyer (1993, p. 262) is used, who defined turnover intention as ‘… the conscious and deliberate willfulness to leave the organisation’. Burnout (or emotional exhaustion) is positively linked to turnover intention (Spies, 2006). Quit rates are lower when HRM practices such as high relative pay and employment security are present (Batt, 2002). It is not known how HRM practices, JDs, JRs, well-being, burnout and engagement impact turnover intention in a multivariate model; these relationships will be hypothesised and tested in the proposed study.

Employee performance is defined by the South African Bureau of Standards (2008, p. 24) as ‘… individual performance to established targets and performance standards’. Normally, the management of performance concentrates on the employee at an individual level; however, performance at an organisation level should also be considered (Strydom, 2005). Intrinsic motivational levels support operational efficacy (Mahesh & Kasturi, 2006). Work experience (i.e. prior competence) contributes positively to performance (Whitt, 2006), more so than the mere supervision of the individual performance metrics (Du Preez, 2008; Witham, 2009). It is not known how HRM practices, JDs, JRs, P–E fit, engagement and competence impact performance in a multivariate model; these relationships will be hypothesised and tested in the proposed study:

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 Figures 1 and 2.

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 (2008) reported that approximately 1500 BPS centres employed between 150 000 and 175 000 employees in 2008 in South Africa. Koyana (2010) estimated the number of employees at 200 000 in 2010. Of these estimated numbers, the researcher approached approximately 40 organisations employing a total of 13 000 BPS employees, which constituted the target population for this study. At the organisation level, 16 of the 40 organisations that indicated to participate (that employed approximately 6800 employees) with a 40% response rate. At the individual employee level, the final sample consisted of 487 respondents (487 out of 821 surveys distributed) with a 59% response rate.

It is evident from Table 1 that 69.9% of the sample was female, 59.4% was aged between 21 and 30 years, followed by 25.4% aged between 31 and 40 years; 62.4% of the sample was unmarried, while 35.3% of the sample’s home language was an African language, followed by 30.6% who indicated English as their home language. A total of 51.4% of the sample was African people, followed by 24.1% of mixed race people, and 42% of the sample had a tenure between 2 and 5 years.

The measuring instruments used in the study were based on the following conceptualisations of the constructs (summarised in Table 2):

HRM practices: The instrument focuses predominately on HRM practices as perceived by the employees. The construct is based on Liu et al.’s (2007) study and consists of three categories, with nine practices in total: (1) Competence, which includes Selection, Compensation level and Training as constructs; (2) Commitment, which includes Incentive compensation and Internal promotion as constructs; and (3) Context, which includes Participation programmes, Flexitime, Grievance procedures and Employment security as constructs. This measure consists of 17 items and was developed by Jacobs (2014), with the construct validity and reliability therefore previously unknown. An item example, rated on a seven-point intensity response scale with the extremes poles anchored, reads: ‘How supportive is the company’s recruitment strategy in establishing a competent workforce?’ An indication of 1: Not supportive on the scale represents low intensity, and 7: Highly supportive represents high intensity.

Job demands: Changes in processes, procedures and tasks (at an organisational systems level, which filter down to demands at an individual level) are driven by continuous increases in complexity, because of customer demands and operational efficiencies. Work pace demands (at group level) reiterate the organisation’s drive towards operational efficiencies. Workload represents the emphasis on organisational effectiveness and efficiency, at an individual-level demand (Bakker et al., 2003; Dwyer & Fox, 2006; Rothmann, Mostert, & Strydom, 2006; Swart, 2006). The various items from different authors were utilised based on applicability to the BPS environment. This measure included 12 items to capture the different JDs. An example is Item 28: ‘How often do changes in your tasks pose difficulties to you?’ The response scale range from 1: Never (indicating low intensity) to 7: Always (indicating high intensity). Rothmann et al. (2006) reported on reliability for the South African scale they had developed with Cronbach’s alpha coefficients ranging from 0.76 to 0.92. Construct validity was confirmed for two dimensions of JDs (overload and job insecurity).

Job resources: An important resource is social support (at group level) because of emotional labour. At the individual level, pay and benefits, as well as personal resources, act as individual performance enablers (Bakker et al., 2003; Dwyer & Fox, 2006; Rothmann et al., 2006; Swart, 2006). Nineteen items were used to capture the different JRs in this study. An example reads: ‘How often can you ask your colleagues for help if you have too much work?’, with Never (indicating low intensity) and Always (indicating high intensity) being the anchors. Rothmann et al. (2006) noted reliability with Cronbach’s alpha ranging between 0.76 and 0.92 for the South African scale they had developed. Construct (factorial) validity was found for three dimensions of JRs (Growth and opportunities, Advancement and Organisational support).

Employee competence: The items were developed by expanding the competence-related conceptualisations of Grobbelaar et al. (2004) on the employee, team and organisational levels. Eight items developed by Jacobs (2014) were used to measure the employee competence construct in this study, with the construct validity and reliability therefore previously unknown. An example item is: ‘How adequately are employees equipped with appropriate skills to execute the core function of your organisation?’, with the item(s) being scored on a seven-point intensity scale, with the extremes poles anchored (i.e. 1: Not adequate at all, indicating low intensity, and 7: More than adequately, indicating high intensity).

P–E fit: Billsberry et al. (2004) created five P–E fit dimensions: P–O fit, P–V fit, P–J fit, P–G fit and P–P fit. As P–J fit has been incorporated into Employee competence in this study, relevant items based on P–O fit, P–V fit, P–G fit and P–P fit were combined into one construct, for parsimonious purposes, in order to measure the broader P–E fit construct. Five items developed by Jacobs (2014) were used to measure the P–E fit in this study, with the construct validity and reliability therefore previously unknown. An example is: ‘How similar are your own values to those of your organisation?’, with the item(s) being scored on a seven-point intensity scale with the extremes poles anchored (i.e. 1: Not similar at all indicating low intensity, and 7: Highly similar indicating high intensity).

Work engagement: Work engagement was defined by Schaufeli, Salanova, González-Romá, and Bakker (2002, p. 74) as ‘… a positive, fulfilling, work-related state of mind that is characterised by vigour, dedication, and absorption’. The three components of the Utrecht Work Engagement Scale (UWES) constitute proposed opposites of burnout (vigour vs. exhaustion; dedication vs. cynicism; absorption vs. ineffectiveness – also see Schaufeli, Bakker, & Salanova, 2006). The shortened nine-item version of the UWES (Schaufeli & Bakker, 2003, 2004) was used in this study. An example is: ‘I am enthusiastic about my job’. The items are scored on seven-point intensity scales with the extremes poles anchored (i.e. 1: Never indicating low intensity, and 7: Always indicating high intensity). Schaufeli et al. (2006) reported reliability of the UWES-9 of a Cronbach’s alpha higher than 0.80 across 10 European countries, including South Africa, with the internal consistencies above the value of 0.70 for the three engagement scales. Acceptable factorial validity of the UWES-9 was determined by Schaufeli et al. (2006) across 10 countries.

Well-being: Employee well-being incorporates both physical health (with the emphasis on overall physical welfare, rather than just the absence of disease or infirmity), as well as an integrated mental and/or social condition of wellness (World Health Organisation, 2006). The well-being scale therefore measures whether the employee is physically and mentally integrated enough to manage normal stresses in order to work in a productive manner. Nine items developed by Jacobs (2014) were used to measure well-being in this study, with the construct validity and reliability therefore previously unknown. An example reads: ‘How good has your health been since you started working at the organisation?’, with the items being scored on a seven-point intensity scale with the extremes poles anchored (i.e. 1: Not good indicating low intensity, and 7: Very good indicating high intensity). 


Burnout: The Maslach Burnout Inventory – General Survey (MBI-GS) (Schaufeli, Leiter, Maslach, & Jackson, 1996) includes three subscales, namely (1) Exhaustion, which refers to fatigue; (2) Cynicism, which refers to an indifference or a distant attitude towards one’s work in general; and (3) Professional efficacy, which includes personal accomplishment, social and non-social accomplishments at work (Korunka, Tement, Zdrehus, & Borza, 2010). Six items from the latter authors were used to measure the burnout construct. An example reads: ‘I feel emotionally drained from my work’ with the items being scored on a seven-point intensity scale with the extremes poles anchored (i.e. 1 Never, and 7: Every day). Storm and Rothmann (2003) reported reliabilities of Cronbach’s alpha as follows in their validation study of the MBI-GS in the South African context: 0.88 for Exhaustion, 0.78 for Cynicism and 0.79 for Professional efficacy. The MBI-GS’s validity was also confirmed as a three-factor model of burnout.

Turnover intention: The TIS-6 was constructed as a ‘conation (intention) to distinguish it from the affective (emotion) and the cognitive (knowledge) components of psychological activities’ (Bothma & Roodt, 2013, p. 2), with the supposition that intention of behaviour is a reliable determinant of the behaviour actually occurring. The TIS-6 of Roodt (2004) was used to measure turnover intention.

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. 1: Never indicating low intensity, and 7: Always indicating high intensity). Reliability was confirmed by Bothma and Roodt (2013) in the South African context with a Cronbach’s alpha of 0.80. Criterion-predictive and differential validity of the instrument was confirmed by Bothma and Roodt (2013).

Employee performance: Employee performance centres on ‘…individual performance to established targets and performance standards’ (South African Bureau of Standards, 2008, p. 24). This measure includes first-call or first-contact resolution, turnaround, customer satisfaction, quality control and wasteful expenditure (including rework because of the fact that first-contact resolution could not be achieved). This measure was used as the dependent variable in the study. Employee performance was measured with 11 items as developed by Jacobs (2014), with the construct validity and reliability therefore previously unknown. An example is Item 100: ‘How often do you manage to complete the number of required tasks per day?’, with the items being scored on a seven-point intensity scale with the extremes poles anchored (i.e. 1: Never indicating low intensity, and 7: Always indicating high intensity).

Table 2 presents a summary of the measures, the number of items each and their use, as well as their reliabilities. The final instrument contained 106 items, with three to four items per construct, as recommended for theoretical and statistical analysis purposes (Hair, Black, Babin, & Anderson, 2010).

A preliminary or pilot investigation was carried out to establish the face validity of the instrument in a two-pronged approach (Garson, 2012). Firstly, subject experts, the study supervisor and a statistical consultant assessed the instrument. Some changes to the items were suggested, which included that some questions were re-worded to improve understanding. Secondly, 15 respondents from a contact centre that facilitates mostly outbound calls participated in the pilot study by completing the survey (these were excluded from the final sample). They were requested to respond on items that were unclear.

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, 2006).

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 (χ²), degrees of freedom (df), the chi-square–degrees of freedom ratio (χ²/df) and the root mean square error of approximation (RMSEA). Incremental fit indices are the Bentler–Bonnett Normed Fit Index (NFI), Non-Normed Fit Index (NNFI) and the Comparative Fit Index (CFI) (Bentler, 2006; Hair et al., 2010). The required limits for fit indices are as follows:

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, 2000). GOF was interpreted in light of the characteristics of the study with considerations to the complexity of the models; Hair et al. (2010) caution in adopting one-size-fits-all approach to GOF, specifically if the model is very complex.

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 (H1 – Perceived HRM practices, JDs and JRs are individually or interactively predictors of proximate outcomes – well-being, burnout and engagement) and H3 (H3 – Perceived contextual factors, as well as proximate outcomes of well-being, burnout and engagement, are related to turnover intention).

Liu et al.’s (2007) model provides a well-substantiated taxonomy of HRM practices in the context of employee retention in the BPS organisation. Career promotion and succession are limited in the BPS industry (Batt, 2002; Gorjup et al., 2008), but internal advancement has a direct influence on commitment levels (or the intention to stay). Participation programmes, flexitime and grievance procedures positively impact retention (Batt, 2002; Cox et al., 2000). Long-term employment reassurance (job security) is essential for commitment (or the intention to stay) (Batt, 2002; Zapf et al., 2003). The research results of this study confirm the said relationships between the contextual factors and burnout, as well as individual’s intention to stay (a.k.a. employee retention).

JDs (as identified by Bakker et al., 2003; Dwyer & Fox, 2006) include changes in tasks, work pace demands and workload that may have negative effects (burnout) if persisting over a long period. JRs (as identified by Bakker et al., 2003; Dwyer & Fox, 2006) include job characteristics, social support, time control, individual performance feedback and personal resources. It is clear that most of these mentioned JDs and JRs are, respectively, positively and negatively related to burnout in this study, which is again linked to the intention to leave or to stay.

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 (H2 – Perceived HRM practices, JDs and JRs are individually or interactively predictors of proximate outcomes – employee competence and work engagement) and of H4 (H4 – Perceived contextual factors, as well as proximate outcomes P–E fit, employee competence and work engagement, are related to individual performance).

Liu et al.’s (2007) model also provides a sound taxonomy of HRM practices in the context of individual performance in the BPS organisation. Several authors (Frenkel, Tam, Korczynski, & Shire, 1998; Houlihan, 2000; Townsend, 2007) are of the opinion that most of the skills development efforts should focus on technical competence and product knowledge, in order to meet individual performance pressures. Pay-for-performance is a common practice in sales-orientated BPS environments (Batt et al., 2009) in order to promote motivation (or commitment) levels. Promoting job security (or long-term employment reassurance) is essential for achieving these said commitment levels (Batt, 2002; Zapf et al., 2003). This study’s results confirm these links between HRM practices and the proximate outcome (engagement), as well as the distal outcome (individual performance).

This study also confirms the general research trend that JDs and JRs are related to work engagement (Demerouti & Bakker, 2011; Demerouti et al., 2001; Schaufeli & Taris, 2014). Only JRs (but not JDs) are related to employee competence in this study. However, HRM practices are more strongly related to work engagement and employee competence compared to JDs and JRs. This surprising finding may be attributed to the fact that HRM practices as a contextual variable contributes more directly to establishing engagement and employee competence when compared to JRs and/or JDs. Work engagement is more strongly related to individual performance, compared to employee competence, but both are weaker linked to individual performance when compared to personal resources, which has the strongest link. This surprising finding can be attributed to the fact that personal resources are more directly linked to individual performance compared to employee competence and work engagement, which are influenced by a range of other variables.