South Africa, being a multicultural society, is faced with unique and unusual circumstances
that can influence the interaction between their work and personal lives. However, countries
can vary noticeably in cultural norms, values and gender-role beliefs, which can lead to the
different experience of work-life interaction. Because of these differences, South African
workers could experience the interaction between work and home in different ways, and this
interaction may manifest differently in various socio-demographic groups. This makes it
difficult to develop strategies and intervention programmes that will help workers integrate
their work and personal lives more effectively.
The general objective of this study was to investigate the relationship between socio-demographic
characteristics and four dimensions of work-home interaction and to establish
which socio-demographic characteristics best predict work-home interaction amongst South
African employees. A sample (n = 2040) was taken from four industries in South Africa (i.e.
police service, the earthmoving equipment industry, mining and nursing). A socio-demographic
questionnaire and the 'Survey Work-Home Interaction - Nijmegen' (SWING)
were used. Descriptive statistics, Cronbach alpha coefficients, Pearson product-moment
correlation and multiple regression analyses were used to analyse the data. The results
indicated that robust predictors included occupation, gender and language for negative work-home
interference (WHI), occupation, language and age for positive WHI, language and
occupation for negative home-work interference (HWI) and language, occupation, age and
education for positive HWI.
Recommendations were made for organisations and for future research. / Thesis (M.A. (Industrial Psychology))--North-West University, Potchefstroom Campus, 2008.
| Identifer | oai:union.ndltd.org:NWUBOLOKA1/oai:dspace.nwu.ac.za:10394/732 |
| Date | January 2007 |
| Creators | De Klerk, Marissa |
| Publisher | North-West University |
| Source Sets | North-West University |
| Detected Language | English |
| Type | Thesis |
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