Orientation: The focus of this study was to investigate the relationship between socio-demographic characteristics and the work–home interaction in different occupational groups in South Africa.

Research purpose: The main research aim of the study was to investigate the socio-demographic predictors of negative and positive work–home interaction of South African employees.

Motivation for the study: Little information is known about the prevalence of work–home interaction within groups. This study is aimed at enabling the researcher and organisations to identify those groups that are at risk of negative interference and which are prone to positive interaction, to allow for the development of appropriate strategies and intervention programmes.

Research design, approach and method: A cross-sectional survey design was used in the study. A sample (N = 2040) was taken from four South African industries (i.e. the police service, the earthmoving equipment industry, mining and nursing). A socio-demographic questionnaire and the Survey Work–Home Interaction-Nijmegen (SWING) were used.

Main findings: The results indicated that robust predictors included occupation, gender and language for negative work–home interference; occupation, age and language for positive work–home interference; occupation and language for negative home–work interference; and occupation, age, education and language for positive home–work interference.

Practical/managerial implications: The implications of the study are that negative and positive work–home interaction is uniquely associated with socio-demographic characteristics. Work–life balance initiatives should, therefore, be carefully tailored to address the needs of each socio-demographic group.

Contribution/value-add: The findings of the study suggest answers to the management of the work–home interaction among various socio-demographic groups in organisations.

In modern society, two of the central and salient domains in the life of an employed individual are work and home. The subject of work–home interference (WHI) and home–work interference (HWI) has received widespread publicity and has been subjected to an increasing number of investigations (Eby, Casper, Lockwood, Bordeaux & Brinley, 2005; Jones, Burke & Westman, 2006; Pitt-Catsouphes, Kossek & Sweet, 2006). Many researchers have sought different ways in which to explain the integration of work and home responsibilities, with the integration of the two domains becoming more difficult as a result of the major changes that have taken place, both in the composition of the workforce and in the nature of work (Barnett, 1998; Edwards & Rothbard, 2000). Transformations that have changed the nature of work over the last two decades consist of increased domestic and international competition, restructuring, downsizing, outsourcing, cuts in government funding, changes in management style and structure, lay-offs, mergers, rapidly changing technology, as well as demands for higher quality products and services (Gillespie, Walsh, Winefield, Dua & Stough, 2001). Since the first democratic elections took place in South Africa in April 1994, striking changes and prominent transformations have also occurred in the country. High unemployment rates have become particularly problematic for employees, placing pressure on them to work harder and for longer hours, in response to their feelings of uncertainty about their future. In keeping with international trends, more men and women are working longer hours and are reporting greater demands in their workplaces. Such increase in demand is mainly due to advances that have occurred in the technological and telecommunications fields (in terms of portable computers, mobile phones, etc.), which have made it possible to work longer hours and to perform job-related tasks in a variety of locations (Lewis & Cooper, 2005). Inevitably, such demographic and structural transformations and changes directly affects the workforce, placing more pressure on employees as they struggle with heightened worldwide competition and increasingly demanding customers in an environment that is subject to the demands of speed and cost-effectiveness (Lewis & Cooper, 2005).

Employees may be unaware of the effect that such changes and transformations have on their well-being and functioning in the home environment (Bond, Galinsky & Swanberg, 1998; Ferber, O’Farrell & Allen, 1991; Greenhaus, 1988; Parasuraman & Greenhaus, 1999). When negative interaction is experienced between work and home as a result of such changes, that interaction can have a negative effect on institutional competitiveness, and can be associated with various negative organisational outcomes, which, in turn, can create a problem not only for the employees, but also for the institutions themselves (Duxbury, 2004; Duxbury & Higgins, 2001; Kotzé, 2005; Parasuraman & Greenhaus, 1999). The negative organisational outcomes associated with a poor work–home interaction may include burnout, work-related stress, complaints regarding depression, job turnover, the increased likelihood of mental illness, general health-related stress, low levels of job performance, and the prevalence of accidents (Adams, King & King, 1996; Allen, Herst, Bruck & Sutton, 2000; Geurts & Demerouti, 2003; Kossek & Ozeki, 1998; Parasuraman, Purohit, Godshalk & Beutell, 1996). Work-related stressors could spill over into a person’s home life, which could have a negative effect on the health and well-being of those concerned (Brough, 2003; Strazdins & Broom, 2003). In contrast, a positive interaction between the work–home domains could lead to positive outcomes, such as satisfaction with life and work engagement, for the individuals concerned (Montgomery, Peeters, Schaufeli & Den Ouden, 2003; Mostert, 2006; Mostert, Cronjé & Pienaar, 2006; Voydanoff, 1988).

Many researchers have examined the consequences, prevalence, antecedents and outcomes of work–home interaction (Barnett, 1998; Frone, Russell & Cooper, 1992; Greenhaus & Parasuraman, 1994; Perry-Jenkins, Repetti & Crouter, 2000; Voydanoff, 2005). In South Africa, several studies have addressed measurement issues (e.g. Marais, Mostert, Geurts & Taris, 2009; Pieterse & Mostert, 2005; Rost & Mostert, 2007), as well as the correlates of negative and positive interaction between work and home (e.g. De Villiers & Kotze, 2003; Koekemoer & Mostert, 2006; Mostert, 2006; Mostert et al., 2006; Mostert & Oosthuizen, 2006; Oldfield & Mostert, 2007; Whitehead & Kotze, 2003). However, in South Africa, contrasting findings have been reported with regard to the relationship between socio-demographic characteristics and the work–home interaction. In an attempt to address such findings, the aim of the current study is to investigate the socio-demographic predictors of negative and positive work–home interaction with a sample of 2040 South African employees. Such an investigation should enable researchers and organisations to identify the relevant risk groups and to develop appropriate strategies and intervention programmes that will effectively support the workers’ integration of their work and personal lives. The socio-demographic characteristics in the current study include occupation, age, marital status, parental status, education, gender and language.

Work–home interaction

Geurts et al. (2005) investigated the total spectrum of work–home interaction, defining it as not only a process of interaction between both work and home, but also, more specifically, as an interactive process in which a worker’s functioning (behaviour) in one domain is influenced by either negative or positive load effects that have built up in the other domain. This definition suggests that any interference that occurs between the two domains can occur in both directions, so that they might influence each other in either a negative or a positive way. Work–home interaction, therefore, comprises four dimensions, namely (1) negative WHI, which refers to a situation in which negative load reactions build up at work, hampering a person’s functioning at home, (2) negative HWI, referring to those negative load reactions which develop at home that fetter a person’s functioning at work, (3) positive WHI, which is defined as positive load reactions built up at work that facilitate functioning at home, and (4) positive HWI, which occurs when positive load reactions developed at home facilitate functioning at work (Geurts et al., 2005).

Socio-demographic characteristics and work–home interaction

Occupation
Police work has been identified as one of society’s most stressful occupations (Alexander, 1999; Anshel, 2000; Paton & Violanti, 1999). Some studies that have been undertaken in South Africa show that police workers are exposed to such elements as demanding work characteristics (e.g. shift work, unplanned overtime, exposure to suffering and death, etc.), relatively low levels of decision latitude and support at work, organisational transformations, irregular working hours the lack of resources, exposure to violence, job pressure, and emotional stressors (Biggam, Power, MacDonald, Carcary & Moodie, 1997; Marais, 2006; Sekwena, Mostert & Wentzel, 2007; Swanepoel & Pienaar, 2004). Such stressors tend to spill over to police officials’ home life (Emslie, Hunt & Macintyre, 2004; Frone et al., 1992) and ultimately have a negative impact on their personal well-being (Brough, 2003; Strazdins & Broom, 2003).

The construction industry has grown substantially over the past fifty years (Singh, 1997). According to Applebaum (1981), the construction industry includes the construction of: schools, buildings, shopping centres, dams, bridges, sewer treatment facilities, industrial plants, roads and highways. Employees in the industry are exposed to: relatively long working hours, increased job demands, a dangerous working environment, reduced organisational commitment and the likelihood of injuries and accidents. All such factors create feelings of uncertainty and stress (Lingard, 2003; Lingard & Sublet, 2002; Pieterse & Mostert, 2005).

The mining occupation may be regarded as an environment in which many people’s lives are put at risk due to the nature of the job and the challenging physical environment that is associated with work in processing plants or underground (Singer, 2002; Wynn, 2001). Mineworkers are faced with various forms of stressors, including having to maintain a competitive advantage in complying with the demands of change, the harsh working conditions (e.g. mining underground at temperatures in excess of 28 °C long working hours, sometimes unsafe working conditions, and highly unionised environments), pressure to perform, role conflict and the need to produce quickly within a limited amount of time (Calitz, 2004).

The nursing profession is regarded as very stressful and emotionally demanding (Coffey & Coleman, 2001; Fagin, Brown, Bartlett, Leary & Carson, 1995; Hodson, 2001; Peter, Macfarlane & O’Brien-Pallas, 2004; Snellgrove, 1998). In South Africa, nurses are faced with such stressors as: pressure to improve the quality of service, budget constraints, overcrowded hospitals, high patient loads and exposure to HIV/Aids-infected patients (Gmeiner & Poggenpoel, 1996; Hall, 2004).

It is generally agreed that stressful job characteristics (e.g. unfavourable working time schedules, work overload, job pressures, mental demands, social support, excessive job control or restriction on decision-making and insufficient career opportunities) play a central role in preceding negative interaction between work and home. Stressful aspects of, and a lack of resources in, the workplace could hinder positive interaction from taking place (e.g. Grzywacz & Marks, 2000; Mostert & Oosthuizen, 2006).

Age
With regard to the relationship between age and work–home interaction, some studies have found no relationship between different age groups (Pieterse & Mostert, 2005; Van Tonder, 2005). However, the study conducted by Grzywacz and Marks (2000) found that younger men tended to report more negative spill-over between work and home (as well as between home and work) and less positive spill-over from family to work, than did older men. They also found that younger women tended to report more positive spill-over from work to family and more negative spill-over from family to work than did older women. Furthermore, Grandey and Cropanzano (1999) found age to be negatively related to work–family conflict. A South African survey conducted among construction workers reported that, in general, younger employees tended to experience higher negative and lower positive interference between work and home than did older employees (Rost, 2006). Mostert and Oldfield (2009) reported that older employees experienced higher positive WHI than did younger employees.

Marital status
The relationship between marital status and work–home interaction has not yet been thoroughly investigated, because most studies have included a large percentage of married employees in their study sample. Rost (2006), Van Tonder (2005), and Pieterse and Mostert (2005) reported finding no significant differences between married and unmarried employees. Grzywacz and Marks (2000) reported that being unmarried was associated with less negative spill-over from work to family and with less spill-over from family to work, compared to that of married counterparts. In a sample of 685 police officers, Marais and Mostert (2008) reported that unmarried employees experienced significantly higher levels of positive WHI than did married employees. Mostert and Oldfield (2009) support such findings, but also report that married employees tend to experience higher levels of negative WHI.

Parental status
Both the age and the number of children living at home have been found to have a bidirectional influence on WHI (Grandey & Cropanzano, 1999; Grzywacz & Marks, 2000). Grandey and Cropanzano (1999) found that the number of children living at home related positively to family–work conflict. Grzywacz and Marks (2000) reported that men who had children (with an oldest child under 5 years of age) living at home tended to experience more positive spill-over from work to home than did men without children. The researchers also found that women with (young) children living at home tended to experience more negative interaction between work and family, in comparison with women without children. Furthermore, Demerouti, Geurts & Kompier (2004) found that, compared to women without children, women with children reported experiencing more positive than negative influences from the home domain. In their South African studies, Pieterse and Mostert (2005), Rost (2006) and Van Tonder (2005) found no significant differences with regard to the relationship between parental status and work–home interaction. However, Marais and Mostert (2008) reported that those police officers with children in their sample reported experiencing higher levels of positive HWI than did those police officers without children. Mostert and Oldfield (2009) reported that parents experienced higher negative WHI, as well as higher positive HWI, compared to childless individuals.

Education
Some studies investigating WHI differences based on educational level reported finding no significant differences between the possession of different educational levels and the experiencing of WHI (Pieterse & Mostert, 2005; Rost, 2006). However, Grzywacz and Marks (2000) found that having a lower level of education was robustly associated with experiencing a lower level of positive spill-over from work to home. Van Tonder (2005) and Marais and Mostert (2008) found that employees with a higher level of education tended to experience more negative interference from work to home. Mostert and Oldfield (2009) reported that those employees with a higher level of education tended to experience significantly higher levels of negative WHI and HWI, though also higher levels of positive HWI.

Gender
Across a variety of samples, international studies almost consistently revealed that there were hardly any differences between how men and women experienced negative or positive interaction between work and home bidirectionally (Demerouti et al., 2004; Eagle, Miles & Icenogle, 1997; Frone et al., 1992; Grandey & Cropanzano, 1999). However, Grzywacz and Marks (2000) found that women reported experiencing more positive WHI compared with the amount which was reported as being experienced by men. However, they also found that men and women did not differ in how much negative WHI and HWI they reported experiencing. Some South African studies (Marais & Mostert, 2008; Mostert & Oldfield, 2009; Pieterse & Mostert, 2005; Rost, 2006) have found that men tend to report experiencing a significantly higher level of negative WHI when compared with the amount reported as being experienced by women. Mostert and Oldfield (2009) also reported that men tended to report experiencing more positive WHI and more negative HWI than did women.

Language
With the exception of Pieterse and Mostert (2005), all South African studies found differences between the language and ethnic groups. Van Tonder (2005) reported that Black nurses were found to experience statistically lower levels of negative HWI and higher levels of positive HWI in comparison with White nurses. Mostert and Oldfield (2009) found that, compared with white mineworkers, Black mineworkers reported significantly experiencing higher levels of positive WHI/HWI and negative HWI. Marais and Mostert (2008) found that Afrikaans-speaking participants reported experiencing statistically significant lower levels on all four dimensions of work–home interaction, compared with English- and Setswana-speaking participants. Rost (2006) also reported finding differences between White, Black, Coloured and Indian earthmoving workers in terms of the experiencing of negative WHI and positive WHI/HWI.

Research approach

Research method

Research participants
For the purposes of the current study, selected employees from the following four occupational groups were used (N = 2 040): the South African Police Service (n = 685), the construction industry (n = 528), the nursing profession, including registered, as well as auxiliary, nurses (n = 507) and the mining industry (n = 320). The participants were equally distributed with regard to gender (consisting of 50.9% male and 49.1% female). Of the participants, 33.6% were between the ages of 27 and 36 years and 33.4% were between the ages of 37 and 46 years. Most (65.2%) of the participants were married, with 64% having children. Those participants who were in possession of a school education comprised 54.6%, with 40.6% possessing a postgraduate qualification. In total, 53.8% of the participants were Afrikaans-speaking, 15.1% were English-speaking and 29.3% of the sample consisted of various vernacular language-speaking participants.

Measuring instruments

Socio-demographic characteristics: Questions on the following socio-demographic characteristics were included in the booklets used in the study: occupation, age, marital status, parental status, level of education, gender and the participant’s language.

Work–home interaction: The Survey Work–Home Interference-Nijmegen (SWING) was used to measure the amount of work–home interaction experienced (Geurts et al., 2005). The SWING is a 22-item WHI measure which measures four types of WHI, namely: (1) the negative interference of work on home (negative WHI), which refers to the negative impact of the work situation on one’s functioning at home (in the form of eight items, e.g. ‘How often does it happen that your work schedule makes it difficult to fulfil domestic obligations?’), (2) the negative interference of home on work (negative HWI), which refers to the negative impact of the home situation on one’s job performance (in the form of four items, e.g. ‘How often does it happen that you have difficulty concentrating on your work, because you are preoccupied with domestic matters?’), (3) the positive interference of work on home (positive WHI), which refers to the positive influence of the work situation on one’s functioning at home (in the form of five items, e.g. ‘How often does it happen that you come home cheerfully after a successful day at work, positively affecting the atmosphere at home?’) and (4) the positive interference of home on work (positive HWI), which refers to the positive impact of one’s home situation on one’s job performance (in the form of five items, e.g. ‘How often does it happen that you are better able to interact with your colleague/supervisor as a result of the environment at home?’). All items were scored on a four-point frequency rating scale, ranging from 0 (‘never’) to 3 (‘always’). Geurts et al. (2005) report Cronbach’s alpha coefficients of 0.84 for negative WHI and 0.75 for positive WHI, as well as 0.75 for negative HWI and 0.81 for positive HWI. In a South African sample, Marais et al. (2009) provided evidence of the construct validity, construct equivalence and reliability of the scale. They reported the following Cronbach’s alpha coefficients for the SWING: negative WHI = 0.90, positive WHI = 0.84, negative HWI = 0.87 and positive HWI = 0.82.

Research procedure
The measuring battery was first compiled. After the appropriate permission had been obtained from the management bodies of the various occupational groups, a letter requesting their participation was handed to each individual prior to the administration of the battery. Ethical aspects relating to, and the motivation regarding, the research were discussed with the participants before the questionnaires were handed out. A letter explaining the purpose and importance of the research accompanied the questionnaires, which the participants in the study were required to complete. Participation in the study was voluntary. The participants, who were assured of the confidentiality and anonymity of their answers, were given two to three weeks in which to complete the questionnaires, after which the latter were personally collected on a set date.

Statistical analysis
The statistical analysis was carried out using the SPSS program (SPSS Inc., 2005). Multiple regression analyses were carried out to determine the significant predictors of the four work–home interaction dimensions. To determine whether the model fit the actual outcomes, goodness-of-fit statistics were used. Such statistics are used to assess the fit of a multiple model against the actual data (Field, 2005). R correlation coefficients were used to determine the overall fit of the regression model and R² was used to obtain a good estimation of the substantive size of the relationship, which, in turn, is the amount of variation in the outcome variable that is accounted for by the model. The F-ratio was used to determine how much the model has improved the prediction of the outcome, compared with the level of inaccuracy of the model.

The predictors of the regression analysis of the sample were based on the assumptions made, enabling findings to be generalised in respect of the wider population. Prior to the analysis, the assumptions for the predictors were checked. These assumptions included the following:

•The predictors must be quantitative or categorical.
•The predictors should have some variation in value.
•The predictors should not correlate too highly in terms of multi-colinearity.
•No external variables should correlate with the predictors included in the regression model.
•There should be homogeneity between the groups.
•At each level of the predictors’ variable(s), the variance of the residual terms should be constant.

For the categorical variables with more than two categories, ‘dummy coding’ was used (Field, 2005). A baseline group (the majority group) was created, and was coded with a value of 0 (e.g. in the case of occupation, the SAPS participants were chosen to be the baseline group for the study). Secondly, the dummy group was created and coded with a value of 1 (in the case of occupation, the group might have consisted of construction workers, nurses or miners). To determine whether any differences existed between the baseline and the dummy group, the beta value was used. Such a value shows the change in outcomes due to a unit change in the predictor, and tells the relative difference between each group and the group which has been chosen as the baseline group.

In order to determine the significant predictors of the four work–home interaction dimensions, the sample was randomly divided into two groups, namely Group 1 (n = 972) (used as the experimental group) and Group 2 (n = 1 068) (used as the control group). For Group 1, a multiple regression analysis using the forward method was used to determine whether there were any statistically significant predictors. To determine whether such predictors were robust, the same model (including only the significant predictors in Group 1) was tested with Group 2, using the enter method. At the statistical level, the test for the successful replication of the model involves using the χ² statistics to determine the difference in statistical fit between Model 1 and Model 2, by determining the difference in R². Non-significant differences between the models indicate statistical support for the hypotheses that are tested.

Descriptive statistics

As can be seen from the results in Table 1, the Cronbach’s alpha coefficients of all the scales were considered acceptable, when they were compared with the guideline of α ≥ 0.70 (Nunnally & Bernstein, 1994), suggesting that all the scales used in the test were reliable. Table 1 also provides the correlation coefficients of the work–home interaction variables.

Multiple regression analysis

Table 2 summarises the regression analysis, using socio-demographic characteristics as predictors of negative WHI. For Group 1, the forward method was used. The model indicated that the statistically significant predictors consisted of employment in construction (β = -0.50; t = -14.80; R² = 0.08; ρ ≤ 0,05), mining (β = -0.38; t = -11.72; R² = 0.13; ρ ≤ 0.05), or nursing (β = -0.18; t = -4.86; R² = 0.23; ρ ≤ 0.05), gender (β = -0.23; t = -6.73; R² = 0.21; ρ ≤ 0.05), age (β = -0.07; t = -2.33; R² = 0.24; ρ ≤ 0.05) and the use of the vernacular as the home language (β = 0.06; t = 2.05; R ² = 0.24; ρ ≤ 0.05), explaining approximately 24% of the variance obtained.

The entry of socio-demographic characteristics for Group 2 produced a statistically significant model (F_(6,997) = 52.48; ρ = 0.00), which accounted for approximately 24% of the variance. Taken together, it seems that significant predictors of negative WHI are employment in construction (β = -0.48; t = -14.78; ρ ≤ 0.05), mining (β = -0.36; t = -11.60; ρ ≤ 0.05), and nursing (β = -0.19; t = -5.06; ρ ≤ 0.05); gender (β = -0.20; t = -5.45; ρ ≤ 0.05); and the use of the vernacular as the home language (β = 0.07; t = 2.19; ρ ≤ 0.05). The difference in R² was 0 and non-significant, indicating that the model obtained in Group 1 appeared as robust in Group 2.

Table 3 summarises the regression analysis with socio-demographic characteristics as the predictors of positive WHI. For Group 1, the forward method was used. The model indicated that the statistically significant predictors were the use of the vernacular (β = 0.33; t = 10.77; R² = 0.12; ρ ≤ 0,05) or of English (β = 0.09; t = 2.89; R² = 0.22; ρ ≤ 0,05) as the home language; employment in mining (β = -0.32; t = -9.87; R² = 0.15; ρ ≤ 0.05), nursing (β = -0.27; t = -8.02; R² = 0.17; ρ ≤ 0.05), and construction (β = -0.27; t = -7.46; R² = 0.21; ρ ≤ 0.05); and age (β = 0.09; t = 3.23; R² = 0.21; ρ ≤ 0.05), explaining approximately 22% of the variance obtained.

The entry of socio-demographic characteristics for Group 2 produced a statistically significant model (F_(6,999) = 53.02; ρ = 0.00), accounting for approximately 24% of the variance obtained. Taken together, it seems that the significant predictors of positive WHI are the use of the vernacular (β = 0.30; t = 9.99; ρ ≤ 0.05) or English (β = 0.07; t = 2.24; ρ ≤ 0.05) as the home language, employment in mining (β = -0.28; t = -9.15; ρ ≤ 0.05), nursing (β = -0.31; t = -9.44; ρ ≤ 0.05), or construction (β = -0.26; t = -7.87; ρ ≤ 0.05) and age (β = 0.11; t = 3.71; ρ ≤ 0.05). The difference in R² was 0 and non-significant, indicating that the model obtained in Group 1 appeared as robust in Group 2.

Table 4 summarises the regression analysis with the socio-demographic characteristics as the predictors of negative HWI. For Group 1, the forward method was used. The model indicated that the statistically significant predictors consisted of the use of the vernacular (β = 0.15; t = 5.00; R² = 0.06; ρ ≤ 0.05) as the home language; employment in construction (β = -0.45; t = -13.12; R² = 0.10; ρ ≤ 0.05), nursing (β = -0.36; t = -9.84; R² = 0.16; ρ ≤ 0,05), or mining (β = -0.35; t = -10,67; R² = 0.24; ρ ≤ 0.05); gender (β = -0.08; t = -2.47; R² = 0.25; ρ ≤ 0.05); age (β = -0.08; t = -2,59; R² = 0.25; ρ ≤ 0.05); and parental status (β = -0.06; t = -2.20; R² = 0.25; ρ ≤ 0.05), which explained approximately 25% of the variance obtained.

The entry of socio-demographic characteristics for Group 2 produced a statistically significant model (F_(7,776) = 44.44; ρ = 0.00), accounting for approximately 29% of the variance obtained. Taken together, it seems that the significant predictors of negative HWI are the use of the vernacular (β = 0.16; t = 4.89; ρ ≤ 0.05) as the home language; employment in construction (β = -0.44; t = -12.02; ρ ≤ 0.05 ), nursing (β = -0.40; t = -9.45; ρ ≤ 0.05), or mining (β = -0.39; t = -11.10; ρ ≤ 0.05). The difference in R² was 0 and non-significant, indicating that the model obtained in Group 1 appeared as robust in Group 2.

Table 5 summarises the regression analysis with socio-demographic characteristics as the predictors of positive HWI. For Group 1, the forward method was used. The model indicated that the statistically significant predictors were employment in mining (β = -0.30; t = -9.08; R² = 0.14; ρ ≤ 0,05); nursing (β = - 0.23; t = -6.64; R² = 0.16; ρ ≤ 0.05), or construction (β = - 0.21; t = -6.04; R² = 0.19; ρ ≤ 0.05), the use of the vernacular language as the home language (β = 0.30; t = 9.91; R² = 0.11; ρ ≤ 0.05), parental status (β = -0.08; t = -2.56; R² = 0.20; ρ ≤ 0.05), age (β = 0.07; t = 2.27; R² = 0.21; ρ ≤ 0.05); and education (β = -0.06 ; t = -2.08; R² = 0.21; ρ ≤ 0.05), explaining approximately 21% of the variance obtained.

Entry of socio-demographic characteristics for Group 2 produced a statistically significant model (F_(7,710) = 25.69; ρ = 0.00), accounting for approximately 20% of the variance obtained. Taken together, it seems that the significant predictors of positive HWI are employment in mining (β = -0.37; t = -9.66; ρ ≤ 0.05 ), nursing (β = -0.29; t = -7.02; ρ ≤ 0.05), or construction (β = -0.26; t = -6.37; ρ ≤ 0.05); age (β = 0.13; t = 3.59; ρ ≤ 0.05); the use of the vernacular language as the home language (β = 0.21; t = 5.78; ρ ≤ 0.05); and education (β = - 0.07; t = -2.03; ρ ≤ 0.05). The difference in R² was 0 and non-significant, indicating that the model obtained in Group 1 appeared as robust in Group 2.

Based on the multiple regressions, a summary of predictors for the four work–home interaction dimensions is displayed in Table 6. The predictors found in both Group 1 and Group 2 are indicated in bold. Such predictors that were only found in Group 1 and not in Group 2, are indicated in normal font.

As can be seen in Table 6, occupation acted as the predictor in all four work–home interaction dimensions in both the experimental and control groups. Age also acted as the predictor in all four work–home interaction dimensions, though it was a robust predictor for only the positive interaction dimensions in both groups. Marital status did not serve as a predictor for any of the work–home interaction dimensions, whereas parental status served as a predictor for the negative and positive HWI dimensions in the experimental group. Furthermore, education served as a predictor for the positive HWI dimension. Gender acted as the predictor of the negative WHI dimension in both groups, though of the negative HWI dimension only in the experimental group. Finally, language served to predict all four work–home interaction dimensions.

The aim of the study was to investigate the socio-demographic predictors of the negative and positive work–home interaction of South African employees. Those socio-demographic characteristics that were investigated in this study included occupation, age, marital status, parental status, education, gender and language.

Occupation

Marital status

Parental status

Education

Gender

Language

In conclusion, the results of the study revealed that the different socio-demographic characteristics predicted different dimensions of work–home interaction. The present findings may have important implications for future research and practice. Firstly, in order to promote the work–life balance and to prevent negative interference between work and home, organisations should provide work–family facilities that enable employees to better align both life spheres. Secondly, the focus of such organisations should be on the development of formal policies (allowing for compressed work schedules, flexible starting and finishing times, childcare facilities and parental leave), as well as on improving the informal work environment (Geurts & Demerouti, 2003). Lastly, organisations tend to regard work and the individual as the sole unit of analysis, whereas it is imperative that they examine the cross-over effect of spouses and families on work–home interaction, using the couple or family as the unit of analysis. Doing so may increase our understanding of the complexities relating to the fulfilment of multiple roles in different domains.

Limitations of the study

The results of the study discussed in the current article could also not be generalised to other occupational groups. Future studies should include coverage of other occupational groups in South Africa. By doing so, the generalisation of the findings would be more effective, promoting the in-depth investigation of work–home interaction and various socio-demographic characteristics across the different cultures that are present in South Africa.

In conclusion, the current study focused only on a limited number of socio-demographic characteristics and did not take into account other variables that have been found in the past to relate to work–home interaction (e.g. work and home characteristics, psychological involvement, personality variables, etc.). Future research should, therefore, focus on examining a model with different sets of variables.

Recommendations

By studying the work–home interaction along with other variables, such as the demands of work and home, and the different coping/recovery strategies employed, valuable lessons could be learned about how to manage WHI, as well as about how to enhance the positive spill-over effects between the work and home domains. Furthermore, organisations should not merely provide workrelated training and support to employees, but also provide training and support in how to cope with non-work-related demands (e.g. parental training, role reorientation for couples, opportunities for working from home, childcare facilities, etc.).

The most important recommendation for future research regards the use of longitudinal designs. Such designs might be used to validate the hypothesised causalities of the relationships still further, as well as to examine whether the reported relationships hold true over time. Although longitudinal designs are important, Montgomery et al. (2003) suggest that they be reserved for use in such circumstances as those in which they can be used to maximum advantage, rather than in exploratory investigations into new research domains. Demerouti et al. (2004) suggest that, although the relationship between work and non-work can be seen as such a relatively new domain, there is, nevertheless, the need for longitudinal studies within such a domain.

Additional studies should be carried out in all the provinces of South Africa, as each province has its own inherent characteristics (such as those pertaining to language, culture, crime, etc.). The results obtained in such studies might then be compared with the results obtained in the study currently under review.

Various occupations, job and socio-demographic characteristics, personality traits and family situations should also be investigated. Since the working conditions are unique within the different occupations, though they are still related to the work–non-work interface, the investigation of heterogeneous populations is important. Future research should also be directed towards conducting cross-national comparative studies. Such investigations should not only serve to broaden and strengthen the findings made in the study under review, but also provide valuable information in regards to the development of interventions that are directed at achieving balance between work and home.

The material described in this article is based upon work that was supported by the National Research Foundation, with the reference number TTK2006061200018. The article is based on Marissa de Klerk’s Master’s dissertation.

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