Article Information

Authors:
Marissa de Klerk1
Karina Mostert1

Affiliations:
1WorkWell: Research Unit for Economic and Management Sciences, School of Human Resource Sciences, North-West University, Potchefstroom campus, South Africa

Correspondence to:
Karina Mostert

email:
Karina.Mostert@nwu.ac.za

Postal address:
Economic and Management Sciences, North-West University, Potchefstroom campus, Private Bag X6001, Potchefstroom 2520, South Africa

Keywords:
work–home interference; home–work interference; socio-demographic characteristics; occupation; age; marital status; parental status; education; gender; language

Dates:
Received: 16 Mar. 2009
Accepted: 21 Sept. 2009
Published: 15 Apr. 2010

How to cite this article:
De Klerk, M., & Mostert, K. (2010). Work–home interference: Examining socio-demographic predictors in the South African context. SA Journal of Human Resource Management/ SA Tydskrif vir Menslikehulpbronbestuur, 8(1), Art. #203, 10 pages. DOI: 10.4102/sajhrm.v8i1.203

Copyright Notice:
© 2010. The Authors. Licensee: OpenJournals Publishing. This work is licensed under the Creative Commons Attribution License.

ISSN: 1683-7584 (print)
ISSN: 2071-078X (online)

Work–home interference: Examining socio-demographic predictors in the South African context
In This Original Research...
Open Access
Abstract
Introduction
   • Workhome interaction
   • socio-demographic characteristics and workhome interaction
      • Occupation
      • Age
      • Marital status
      • Parental status
      • Education
      • Gender
      • Language
Research Design
   • Research approach
   • Research method
      • Research participants
      • Measuring instruments
      • Research procedure
      • Statistical analysis
Results
   • Descriptive statistics
   • Multiple regression analysis
Discussion
   • Occupation
   • Age
   • Marital status
   • Parental status
   • Education
   • Gender
   • Language
   • Limitations of the study
   • Recommendations
Acknowledgements
References
Abstract

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.

Introduction

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

The most widely cited definition of work–family conflict is that of Greenhaus and Beutell (1985, p. 77), who define the concept as ‘a form of inter-role conflict in which role pressures from the work and family domai ns are mutually incompatible’. In other words, participation in the work (family) role is made more difficult by virtue of participation in the family (work) role. However, the definition focuses almost exclusively one-sidedly on the negative impact of work on the home domain (i.e. on the work–family conflict), and does not consider the fact that the interaction between work and home can also be positive. Recently, researchers have come to realise that the work–home interface is a broad concept, which also encompasses a positive side. For example, fulfilling multiple roles in both the work and home domains may produce resources (e.g. energy mobilisation; skill acquisition; greater self-esteem), which could facilitate the functioning in both life spheres positively (Grzywacz & Marks, 2000).

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

The current study focuses on seven socio-demographic characteristics, namely: occupation (police officers, construction workers, mine workers, or nurses), age, marital status (married vs unmarried), parental status (parents vs non-parents), education (individuals with a school education vs individuals with a postgraduate education), gender (male vs female), and language (English, Afrikaans or a vernacular African language).

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 design

Research approach

A cross-sectional survey design was used in the current study. In terms of such a design, one group of people is observed at one point in time, over a short period, such as a day or a few weeks (Du Plooy, 2001). The design is used to assess interrelationships among variables within a particular population (Struwig & Stead, 2001).

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.

Results

Descriptive statistics

Table 1 shows the means, standard deviations, Cronbach’s alpha coefficients and the correlation coefficients between the work–home interaction scales.

Table 1: Mean, standard deviations (s.d.), Cronbachs alpha () and correlations between the workhome interaction variables

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

To determine which socio-demographic characteristic predicts each work–home interaction variable (i.e. negative WHI, positive WHI, negative HWI and positive HWI), four standard multiple regression analyses, using the forward method with the first group and the enter method with the second group, were performed. The results are reported in Tables 2, 3, 4, 5 and 6.

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.

Table 2: Multiple regression analysis with negative WHI as dependent variable

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.

Table 3: Multiple regression analysis with positive WHI as dependent variable

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.

Table 4: Multiple regression analysis with negative HWI as dependent variable

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.

Table 5: Multiple regression analysis with positive HWI as dependent variable

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.

Table 6: Multiple regression summary table

Discussion

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

The results showed that occupation was a significant predictor of all four work–home interaction dimensions in both the experimental and control groups. More specifically, participants in the police group experienced statistically significantly higher levels of negative WHI, positive WHI, negative HWI and positive HWI, compared to the participants in the other three occupational groups. A possible explanation of such high levels might be that the participants from the police generally experienced more stressful circumstances compared with the participants from the other occupational groups. Police officials are often exposed to stressful elements, such as demanding work characteristics (e.g. shift work, unplanned overtime, exposure to suffering and death, etc.), lower levels of decision latitude and support at work, organisational transformations, irregular working hours, the lack of resources, job pressures and emotional stressors, which might influence the interaction between the work and home domains (Biggam et al., 1997; Sekwena et al., 2007). However, the presence of resources (e.g. energy mobilisation, skills acquisition, the boosting of self-esteem, domestic help, domestic appliances the provision of babysitters, etc.) and support (e.g. that of spouse, family, friends colleagues, etc.) might enable police officers to deal more effectively with the demanding aspects of their job (Demerouti et al., 2004; Geurts & Demerouti, 2003; Grzywacz & Marks, 2000). Rewarding aspects of the job and the home environment might facilitate a positive interaction between the different domains. However, further research is needed to explore which factors might help to explain such findings.

Age
Age seems to have been a robust predictor of both positive WHI and positive HWI (with it being a significant predictor of negative WHI/HWI interaction only in the experimental group). However, the effect of age was relatively small (with β-values ranging between 0.07 and 0.13). The younger participants appeared to experience statistically significantly higher levels of negative WHI/HWI, while the older participants experienced statistically significantly higher levels of positive WHI/HWI. Such results conflict with the findings of Pieterse and Mostert (2005) and Van Tonder (2005), whereas they give support to other studies (e.g. those of Grandey & Cropanzano, 1999; Grzywacz & Marks, 2000; Mostert & Oldfield, 2009; Rost, 2006). A possible explanation could be that younger participants, who are at an early stage in their careers, tend to invest a large amount of time and energy in their work in order to establish themselves firmly on their career path, resulting in higher levels of negative interaction in both domains. Younger participants might also not yet have acquired the necessary skills for dealing with the integration of their personal life and work, as compared to older participants, who tend to be more experienced. The latter might have, by dint of their greater experience, learned the necessary skills to separate work and home and, therefore, be able to better manage the integration of personal life and work.

Marital status

Marital status was found not to be a predictor of the work–home interaction for either the experimental or the control group. Such a finding was congruent with that of Rost (2006), who found no significant difference between participants in terms of their marital status. It might be assumed that the impact of such status (as it affects the household situation) requires future investigation.

Parental status

Parental status was identified as a significant predictor of both negative and positive HWI, but only in the experimental group. The results indicated that participants with children experience not only higher levels of negative HWI, but also higher levels of positive HWI compared with the findings made in relation to childless participants. Such results are similar to previous findings that have been made in this regard (Demerouti et al., 2004; Marais & Mostert, 2008; Mostert & Oldfield, 2009). Having to fill multiple roles (e.g. the roles of worker, parent and homemaker) might evoke both a negative and a positive process. Tingey, Kiger and Riley (1996) shows that working parents (specifically mothers) might be subject to stress, largely as a result of having to shoulder the heavy responsibilities that are associated with having to perform both household tasks and child care in combination with their job demands. As a result, the rewards and sense of independence that can result from employment might be negated by role overload and family–work spill-over. In contrast, several studies point to the fact that it might be beneficial to be required to fulfil multiple roles. For example, Crosby (1982) showed that married working mothers are more satisfied than are childless women (see also Bersoff & Crosby, 1984). Hoffman (2000) states that working mothers tend to feel less depressed and more empowered, with their morale being higher than that of their stay-at-home counterparts. Such a finding is in line with the ‘role enhancement hypothesis’, which assumes that managing multiple roles might boost energy levels and help to provide extra resources, which contribute to positive spill-over from the home to the workplace (cf. Geurts & Demerouti, 2003).

Education

The results of the study described in this article showed that education was a significant predictor of only one dimension, namely of positive HWI. Participants with a postgraduate education were found to experience statistically significantly higher levels of positive HWI compared with the levels obtained by participants with only a school education. Such findings contradict previous findings, in terms of which no significant difference was found between educational levels and WHI (Pieterse & Mostert, 2005). Participants with a postgraduate qualification might be assumed to have more home resources (such as financial stability, domestic help or the services of a babysitter) and support at home. It is also assumed that they might have learned to become more skilled in prioritising their home and work lives, facilitating their concentration on other obligations.

Gender

With regard to gender, the findings of this study showed that the men experienced significantly higher levels of negative WHI compared with those experienced by the women (as well as compared with the higher levels of negative HWI that were reported as having been experienced by the experimental group). Such results, resembling some of those that had previously been found (e.g. Marais & Mostert, 2008; Mostert & Oldfield, 2009; Pieterse & Mostert, 2005), might mean that men tend to perceive their work activities as more demanding than women do their own such activities. Men might also find it more difficult to separate their work life from their personal life, due to the traditional roles that men once held evolving into more flexible ones. An increasing number of women have come to enter the workforce, resulting in men also having to assist with household chores. Men are also increasingly being expected to become more involved with raising their own children. Men might find it difficult to deal with such demands, thus leading to the tension caused by the conflicting interests of work and home.

Language

Language was found to be a significant predictor of all four work–home interaction dimensions in both the control and experimental groups. Such findings were found to contradict those of previous studies, in which no such difference was found in terms of the different language groups (Pieterse & Mostert, 2005). The results showed that the Afrikaans-speaking participants experienced statistically significantly lower levels of negative WHI/HWI and positive WHI/HWI compared with those participants who spoke one of the languages of the vernacular. Hofstede’s pioneer work regarding cross-cultural research distinguished between individualistic and collectivistic cultures. According to Hofstede (1991), the quality of individualism refers to those societies in which the ties between individuals are loose, with everyone being expected to look after himself or herself and his or her immediate family. In contrast, collectivism refers to those societies in which people from birth onwards are integrated into strong, cohesive in-groups, which, throughout their lifetime, continue to protect them in exchange for unquestioning loyalty to the group as a whole. Language is important as a means of expressing ethnic and cultural identity. The knowledge, beliefs and practices of a particular society are also reflected in its language (Naudé, 2005). Each distinct culture (whether it speak a language of the vernacular or Afrikaans) might, therefore, have distinguishing attributes and backgrounds that affect the way in which situations and circumstances are interpreted. Black culture is a relatively collectivistic culture, in which people are more conscious of groups, and more emotionally dependent on one another, so that it might more often merge the boundaries between work and home. Consequently, such a culture accommodates relatively more interaction between the work and home spheres, which allows them to influence each other more profoundly. In contrast, the Afrikaans culture is a very reserved individualistic culture, in which there tend to be distinct boundaries drawn between work and home. Such boundaries might prevent the work and home spheres from influencing each other significantly.

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 study described in this article had some limitations. The first limitation related to the use of self-report questionnaires to obtain work–home interaction scores, which increased the likelihood that contamination of the reported relationships by means of the common-method variance would occur. Although the strength of such variance cannot be tested, several studies have indicated that it is not as much of an interference factor as one might expect (Semmer, Zapf & Grief, 1996; Spector, 1992). Owing to the fact that South Africa has 11 official languages, a further limitation may be that English is not the respondent’s first language. It is, therefore, recommended that the questionnaires be translated into the other official languages.

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

Research that is conducted in South Africa should focus on those processes that are aimed at helping individuals to develop a balanced lifestyle and an awareness of unhealthy practices. Organisations can assist by investigating staff management practices and by fostering the development of a culture that is conducive to the maintenance of a balanced lifestyle.

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.

Acknowledgements

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.

References

Adams, G.A., King, L.A., & King, D.W. (1996). Relationships of job and family involvement, family social support, and work-family conflict with job and life satisfaction. Journal of Applied Psychology, 81(4), 411– 420.

Alexander, C. (1999). Police psychological burnout and trauma. In J.M. Violanti, & D. Paton (Eds.), Police trauma: Psychological aftermath of civilian combat (pp. 54–64). Springfield: Thomas.

Allen, T.D., Herst, D.E., Bruck, C.S., & Sutton, M. (2000). Consequences associated with work-to-family conflict: A review and agenda for future research. Journal of Occupational Health Psychology, 5, 278–308.

Anshel, M.H. (2000). A conceptual model and implications for coping with stressful events in police work. Criminal Justice and Behaviour, 27, 375–400.

Applebaum, H.A. (1981). Royal Blue: The culture of construction workers. New York: Rinehart & Winston.

Barnett, R.C. (1998). Toward a review and reconceptualization of the work/family literature. Genetic, Social and General Psychology Monographs, 124, 125–182.

Bersoff, D., & Crosby, F. (1984). Job satisfaction and family status. Personality and Social Psychology Bulletin, 10, 79–83.

Biggam, F.H., Power, K.G., MacDonald, R.R., Carcary, W.B., & Moodie, E. (1997). Self-perceived occupational stress and distress in a Scottish police force. Work & Stress, 11, 118–133.

Bond, J.T., Galinsky, E., & Swanberg, J.E. (1998). The 1997 National Study of the Changing Workforce. New York: Families and Work Institute.

Brough, P. (2003). The influence of perceived control, social support and inter-domain conflict upon psychological wellbeing: A longitudinal investigation. Australian Journal of Psychology, 55, 117.

Calitz, P.L. (2004) The experience of women in the platinum mining industry. Unpublished master’s thesis. North-West University, Potchefstroom, South Africa.

Coffey, M., & Coleman, M. (2001). The relationship between support and stress in forensic community mental health nursing. Journal of Advanced Nursing, 34, 397–407.

Crosby, F. (1982). Relative deprivation and working women. New York: Oxford University Press.

De Villiers, J., & Kotze, E. (2003). Work-life balance: A study in the petroleum industry. South African Journal of Human Resource Management, 1(3), 15–23.

Demerouti, E., Geurts, S.A.E., & Kompier, M.A.J. (2004). Positive and negative work-home interaction: Prevalence and correlates. Equal Opportunities International, 23(1), 6–35.

Du Plooy, G.M. (2001). Communication research: Techniques, methods and applications. (2nd edn.). Pretoria: Juta.

Duxbury, L. (2004). Dealing with work-life issues in the workplace: Standing still is not an option. Don Wood Lecture in Industrial Relations. Kingston: Industrial Relations Centre.

Duxbury, L., & Higgins, C. (2001, October). Work-life balance in the new millennium: Where are we? Where do we need to go? CPRN Discussion Paper. Retrieved September 22, 2006, from http://www.cpm.org

Eagle, B.W., Miles, E.W., & Icenogle, M.L. (1997). Interrole conflicts and the permeability of work and family domains: Are there gender differences? Journal of Vocational Behavior, 50, 168–184.

Eby, L.T., Casper, W.J., Lockwood, A., Bordeaux, C., & Brinley, A. (2005). Work and family research in IO/OB: Content analysis and review of the literature (1980–2002). Journal of Vocational Behavior, 66(1), 124–197.

Edwards, J.R., & Rothbard, N.P. (2000). Mechanisms linking work and family: Clarifying the relationship between work and family constructs. Academy of Management Review, 25, 178–199.

Emslie, C., Hunt, K., & Macintyre, S. (2004). Gender, work-home conflict, and morbidity amongst white-collar bank employees in the United Kingdom. International Journal of Behaviour Medicine, 11, 127–134.

Fagin, L., Brown, D., Bartlett, H., Leary, J., & Carson, J. (1995). The Claybury community psychiatric nurse stress study: Is it more stressful to work in hospital or the community? Journal of Advanced Nursing, 22, 347–358.

Ferber, M.A., O’Farrell, B., & Allen, L.R. (Eds.). (1991). Work and family: Policies for a changing work force. Washington, DC: National Academy Press.

Field, A. (2005). Discovering statistics using SPSS. London: SAGE.

Frone, M.R., Russell, M., & Cooper, M.L. (1992). Prevalence of work-family conflict: Are work and family boundaries asymmetrically permeable? Journal of Organizational Behavior, 13, 723–729.

Geurts, S.A.E., & Demerouti, E. (2003) Work/non-work interface: A review of theories and findings. In M.J. Schabracq, J.A.M. Winnubst, & C.L. Cooper (Eds.), Handbook of work and health psychology (pp. 279–312). Chichester: Wiley.

Geurts, S.A.E., Taris, T.W., Kompier, M.A.J., Dikkers, J.S.E., Van Hooff, M.L.M., & Kinnunen, U.M. (2005). Work–home interaction from a work psychological perspective: Development and validation of a new questionnaire, the SWING. Work & Stress, 19(4), 319–339.

Gillespie, N.A., Walsh, M., Winefield, A.H., Dua, J., & Stough, C. (2001). Occupational stress in universities: Staff perceptions of the causes, consequences and moderators of stress. Work & Stress, 15, 53–72.

Gmeiner, A., & Poggenpoel, M. (1996). Nursing service managers’ views on problems they experience in their everyday lives: Part 1. Curationis, 19(1), 55–60.

Grandey, A., & Cropanzano, R. (1999). The conservation of resources model applied to work-family conflict and strain. Journal of Vocational Behavior, 54, 350–370.

Greenhaus, J.H. (1988). The intersection of work and family roles: Individual, interpersonal, and organisational issues. Journal of Social Behaviour and Personality, 3, 23–44.

Greenhaus, J.H., & Beutell, N.J. (1985). Sources of conflict between work and family roles. Academy of Management Review, 10, 76–88.

Greenhaus, J.H., & Parasuraman, S. (1994). Work-family conflict, social support and wellbeing. In M.J. Davidson, & R.J. Burke (Eds.), Women in management: Current research issues (pp. 213−229). London: Chapman.

Grzywacz, J.G., & Marks, N.F. (2000). Re-conceptualizing the work-family interface: An ecological perspective on the correlates of positive and negative spillover between work and family. Journal of Occupational Health Psychology, 5, 111–126.

Hall, E.J. (2004). Nursing attrition and the work environment in South African health facilities. Curationis, 27(4), 29–36.

Hodson, C. (2001). Psychology and work. New York: Routledge.

Hoffman, L.W. (2000, 20 April). Positive effects of working mothers. USA Today, p. 4–5.

Hofstede, G. (1991). Cultures and organizations: Software of the mind. New York: McGraw-Hill.

Jones, F., Burke, R.J., & Westman, M. (Eds.). (2006). Work-life balance: A psychological perspective. New York: Psychology Press.

Koekemoer, F.E., & Mostert, K. (2006). Job characteristics, burnout and negative work-home interference. South African Journal of Industrial Psychology, 32(3), 87–97.

Kossek, E.E., & Ozeki, C. (1998). Work-family conflict, policies, and the job-life satisfaction relationship: A review and directions for organizational behaviour – human resources research. Journal of Applied Psychology, 83, 139–149.

Kotzé, T. (2005). The nature and development of the construct ‘quality of work life’. Acta Academica, 37(2), 96–122.

Lewis, S., & Cooper, C. (2005). Work–life integration: Case studies of organisational change. Chichester: Wiley.

Lingard, H. (2003). The impact of individual and job characteristics on ‘burnout’ among civil engineers in Australia and the implications of employee turnover. Construction Management and Economics, 21(1), 69–80.

Lingard, H., & Sublet, A. (2002). The impact of job and organizational demands. Construction Management and Economics, 21(1), 69–80.

Marais, C. (2006). Work-home interaction and wellbeing in the South African Police Service. Unpublished doctoral dissertation, North-West University, Potchefstroom, South Africa.

Marais, C., & Mostert, K. (2008). Work-home interaction of police officers in the North West Province: Examining socio-demographic differences. Acta Criminologica, 21(1), 62–76.

Marais, C., Mostert, K., Geurts, S., & Taris, T. (2009). The psychometric properties of a translated version of the Survey Work-Home Interaction – Nijmegen (SWING) instrument. South African Journal of Psychology, 39(2), 202–219.

Montgomery, A.J., Peeters, M.C.W., Schaufeli, W.B., & Den Ouden, M. (2003). Work-home interference among newspaper managers: Its relationship with burnout and engagement. Anxiety, Stress and Coping, 16(2), 195–211.

Mostert, K. (2006). Work-home interaction as partial mediator between job resources and work engagement. Southern African Business Review, 10(1), 53–74.

Mostert, K., Cronjé, S., & Pienaar, J. (2006). Job resources, work engagement and the mediating role of positive work-home interaction of police officers in the North West Province. Acta Criminologica, 19, 64–87.

Mostert, K., & Oldfield, G.R. (2009). Work-home interaction of employees in the mining industry. South African Journal of Economic and Management Sciences, 12(1), 81–99.

Mostert, K., & Oosthuizen, B. (2006). Job characteristics and coping strategies associated with negative and positive work-home interference in a nursing environment. South African Journal of Economic and Management Sciences, 9(4), 429443.

Naudé, J. (2005). The cultural turn in South African translation: Rehabilitation, subversion and resistance. Acta Academica, 37, 22–25.

Nunnally, J.C., & Bernstein, I.H. (1994). Psychometric theory. (3rd edn.). New York: McGraw-Hill.

Oldfield, G.R., & Mostert, K. (2007). Job characteristics, ill health and negative work-home interference in the mining industry. South African Journal of Industrial Psychology, 33(2), 68–75.

Parasuraman, S., & Greenhaus, J.H. (1999). Integrating work and family: Challenges for a changing world. Westport: Praeger.

Parasuraman, S., Purohit, Y.S., Godshalk, V.M., & Beutell, N.J. (1996). Work and family variables, entrepreneurial career success, and psychological well-being. Journal of Vocational Behavior, 48, 275–300.

Paton, D., & Violanti, J.M. (1999). Trauma stress in policing: Issues for future consideration. In J.M. Violanti, & D. Paton (Eds.), Police trauma: Psychological aftermath of civilian combat (p. 327). Springfield: Thomas.

Perry-Jenkins, M., Repetti, R.L., & Crouter, A.C. (2000). Work and family in the 1990s. Journal of Marriage and Family, 62, 981–998.

Peter, E.H., Macfarlane, A.M., & O’Brien-Pallas, L.L. (2004). The moral habitability of the nursing work environment. Journal of Advanced Nursing, 47(4), 356–367.

Pieterse, M., & Mostert, K. (2005). Measuring the work-home interface: Validation of the Survey Work-Home Interaction – Nijmegen (SWING) Instrument. Management Dynamics, 14(2), 2–15.

Pitt-Catsouphes, M., Kossek, E.E., & Sweet, S. (Eds.). (2006). The work and family handbook: Multidisciplinary perspectives, methods, and approaches. Mahwah: Lawrence Erlbaum Associates.

Rost, I. (2006). Work wellness of employees in the earthmoving equipment industry. Unpublished doctoral dissertation, North-West University, Potchefstroom, South Africa.

Rost, I., & Mostert, K. (2007). The interaction between work and home of employees in the earthmoving industry: Measurement and prevalence. South African Journal of Industrial Psychology, 33(2), 54–61.

Sekwena, E., Mostert, K., & Wentzel, L. (2007). Interaction between work and personal life: Experiences of police officers in the North West Province. Acta Criminologica, 20(4), 37–54.

Semmer, N., Zapf, D., & Grief, S. (1996). Shared job strain: A new approach for assessing the validity of job stress measurements. Journal of Occupational and Organisational Psychology, 69, 293–310.

Singer, R. (2002, 20 May). South African women gain ground below surface. USA Today, 1–2.

Singh, S. (1997). State of the art in automation of earthmoving. Journal of Aerospace Engineering, 10(4), 179–188.

Snellgrove, S.R. (1998). Occupational stress and job satisfaction: A comparative study of health visitors, district nurses and community psychiatric nurses. Journal of Nursing Management, 6, 97–104.

Spector, P.E. (1992). A consideration of the validity of meaning of self-report measures of job conditions. In C.L. Cooper, & L.T. Robertson (Eds.), International Review of Industrial and Organizational Psychology (pp. 281–328). Chichester: Wiley.

SPSS Inc. (2005). SPSS 14.0 for Windows. Chicago: SPSS Inc.

Strazdins, L., & Broom, D.H. (2003). Servicing emotions at work and at home: Implications for stress and wellbeing. Australian Journal of Psychology, 55, 145.

Struwig, F.W., & Stead, G.B. (2001). Planning, designing and reporting research. Cape Town: Pearson.

Swanepoel, C., & Pienaar, J. (2004). Coping, stress and suicide ideation in the South African Police Service in Gauteng Province. Acta Criminologica, 17(2), 17–33.

Tingey, H., Kiger, G., & Riley, P.J. (1996). Juggling multiple roles: Perceptions of working mothers. Social Science Journal, 33, 183–191.

Van Tonder, H.P. (2005). A psychometric analysis of the Survey Work-Home Interaction Nijmegen (SWING) in a nursing environment. Unpublished master’s thesis, North-West University, Potchefstroom, South Africa.

Voydanoff, P. (2005). The effects of community demands, resources, and strategies on the nature and consequences of the work-family interface: An agenda for future research. Family Relations, 54, 583–595.

Voydanoff, P. (1988). Work role characteristics, family structure demands and the work/family conflict. Journal of Marriage and the Family, 50, 749–761.

Whitehead, T., & Kotze, M.E. (2003). Career and life-balance of professional women: A South African study. South African Journal of Human Resource Management, 1(3), 77–84.

Wynn, E.J. (2001, May) Women in the mining industry. Paper delivered at the Aus IMM Youth Congress, Brisbane, Australia.


 

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