Emotions play a profound role in the workplace, especially in the human service profession. Service agents, for example educators, are expected to express socially desired emotions in a service interaction with learners. This direct face-to-face contact with learners requires a lot of emotions and in order to advance educational goals, teachers perform Emotion Work. Factors like the individual factor Emotional Intelligence and organisational factors like Job Autonomy, Supervisor- and Co-worker Support have a profound impact on how Emotion Work is experienced. Emotion Work has an influence on the experience of Well-Being. The objective of this research is to determine the relationship between Emotion Work, Emotional Intelligence, Organisational Factors and Well-Being within secondary schools in South Africa. The research method consists of a literature review and an empirical study. A cross-sectional survey design was used to collect the data. A non-probability convenience sample was taken from 257 educators in high schools in the Gauteng Province. The Schutte Emotional Intelligence Scale (SEIS), The Utrecht Work Engagement Scale (UWES), Oldenburg Burnout Inventory (English version) (OBLI). Scale from the Frankfurt Emotion Work Scales (FEWS) and Organisational Factor Scale were used as measuring instruments. The statistical analysis was carried out with the SPSS-programme. The statistical methods utilised in the article consisted of descriptive statistics, Cronbach alpha coefficients, factor analysis (using a principle components analysis), Pearson product-moment correlation coefficients and multiple regression analyses were used to analyse the data. A factor analysis confirmed two factors for Burnout, consisting of Exhaustion and Mental Distance; Emotion Work also consists of two factors namely Positive Emotion Work and Negative Emotion Work, Emotional Intelligence (four factors) consisting of Mood Regulation/Optimism, Emotion Management/Social Skills, Emotion Appraisal and Emotion Detachment. The OF (Organisational Factors) and UWES both showed acceptable internal consistencies. The analysis of Pearson correlations in this study showed that Exhaustion is negatively correlated with Job Autonomy, Supervisory Support and Engagement, while positively correlated with Negative Emotion Work and Mental Distance. Mental Distance is negatively correlated with Job Autonomy, Supervisory Support and Engagement and positively correlated with Negative Emotion Work. Engagement is positively correlated to Mood Regulation/Optimism, Emotion Management/Social Skills, Co-worker Support and Supervisory Support. Emotion Management/Social Skills is positively correlated to Emotion Appraisal and lastly Supervisor Support is positively correlated to Co-worker Support. A regression analysis with Engagement as dependent variable indicated that Positive Emotion Work, Negative Emotion Work, Mood Regulation/Optimisrn and Supervisor Support in an educator environment were the best predictors of Engagement. With Exhaustion as the dependent variable, Negative Emotion Work, Job Autonomy and Supervisor Support were the best predictors of Exhaustion and with Mental Distance as the dependent variable, Negative Emotion Work, Job Autonomy and Supervisor Support were the best predictors of Mental Distance. Recommendations are made for the educators' profession and for future research purposes. / Thesis (M.Com. (Industrial Psychology))--North-West University, Potchefstroom Campus, 2007

Human Resource personnel as part of their daily jobs provide a service to other employees within a mining industry. These service workers may experience dissonance between their actual feelings and the feelings they are expected to display. For these service workers to be more engaged at work, emotional intelligence and social support is vital. If these factors are not in place, their well-being may be in jeopardy. The objective of this research was to determine the relationship between Emotion Work, Emotional Intelligence, Well-being and Social Support of service workers in a human resource field within a mining industry. A cross-sectional survey design was used. The study population (n = 229) consisted of human resource personnel in the Limpopo and North West Province. The Greek Emotional Intelligence Scale (GEIS), Frankfurt Emotion Work Scales, Utrecht Work Engagement Scale, Oldenburg Burnout Inventory and Social Support Scale, as well as a biographical questionnaire, were used as measuring instruments. Cronbach alpha coefficients, factor analysis, inter-item correlation coefficients, Pearson product moment correlation coefficient and stepwise multiple regression analysis were used to analyse the data. An analysis of the data indicated that correlations between the following constructs are statistically and practically significant. The results show that Positive Display is statistically and positively practically significantly related (medium effect) to Interaction Control. Caring/Empathy is positively practically significantly related to Positive Display (medium effect). Furthermore the Control of Emotions (medium effect) and Emotion Management (large effect) are both positively practically significantly related to Caring/Empathy. Emotional Resilience however is negatively practically significantly related to Caring and Empathy (medium effect). Emotion Expression Recognition is positively practically significantly related to Control of Emotion (medium effect). However, both Exhaustion (medium effect) and Emotional Resilience (medium effect) are negatively practically significantly related to Control of Emotions. Engagement is positively practically significant (medium effect) to Emotion Management. Emotion Resilience (medium effect) positively correlates with Exhaustion while Engagement (medium effect) negatively correlates with Exhaustion. Engagement positively practically correlates with Resilience (medium effect). Social Support of both supervisor and co-workers positively relates to engagement to a medium effect. Principal component analysis performed on the GEIS resulted in a four-factor solution. The first factor was Caring and Empathy, which includes the willingness of an individual to help other people and understand others' feelings. The second factor was Control of Emotion, which is the ability of the individual to control and regulate emotions within themselves and others. Emotion Expression/Recognition, which is the ability of the individual to express and recognise his or her own emotional reactions, was the third factor, and the fourth was Emotion Management, which is the ability of an individual to process emotional information with regard to perception, assimilation, understanding and management of emotions. All four factors correlate with that of the GEIS originally developed by Tsaousis (2007) and accounted for 31% of the total variance in emotional intelligence. A Multiple Regression Analysis with Exhaustion as dependent variable was carried out. The results show that Emotion Work factors accounted for 2% of the total variance and Emotional Intelligence factors for 12% of the total variance. More specifically it seems that the lack of Caring and Empathy and Emotion Management predicted Exhaustion in this regard. However, when Emotional Intelligence factors were entered into the model, an increase of 10% variance was shown of the variance explained in Exhaustion. Emotion Work, Emotional Intelligence and Social Support predicted 14% of the variance explained in the level of Exhaustion by participants. A Multiple Regression analysis with Emotional Resilience as dependent variable was carried out. The results show that Emotion Work factors accounted for 6% of the total variance. More specifically; it seems that Dissonance predicted the level of Emotional Resilience. When Emotional Intelligence factors were entered into the model, an increase of 15% was shown. Caring and Empathy and Control of Emotions predicted Emotional Intelligence the best. Lastly, when Social Support factors were entered into the regression analysis, the variance explained showed an increase of 5%. Support of Family and Others predicted Emotional Resilience the best. In total, Emotion Work, Emotional Intelligence and Social Support factors explained 20% of the variance in Emotional Resilience. A Multiple Regression Analysis with Engagement as dependent variable with Emotion Work factors, Emotional Intelligence factors and Social Support as predictors of Engagement was done. Entry of Emotion Work factors at the first step of the regression analysis did not produce a statistically significant model and only accounted for 1% of the variance. However, when Emotional Intelligence factors were entered in the second step of the analysis, it accounted for approximately 7% of the variance. More specifically, it seems that Caring and Empathy predicted Engagement. When Social Support factors were entered into the third step of the analysis, an increase of 27% was found. All the Social Support factors (Social Support of Family and Others, Supervisors and Co-workers) accounted for 27% of the variance explained in Engagement. Emotion Work, Emotional Intelligence and Social Support predicted 33% of the total variance explained in the level of Engagement. Limitations within the study were identified, and recommendations were made for human resource personnel in a mining industry, as well as for future research. / Thesis (M.A. (Industrial Psychology))--North-West University, Potchefstroom Campus, 2009.

Human Resource personnel as part of their daily jobs provide a service to other employees within a mining industry. These service workers may experience dissonance between their actual feelings and the feelings they are expected to display. For these service workers to be more engaged at work, emotional intelligence and social support is vital. If these factors are not in place, their well-being may be in jeopardy. The objective of this research was to determine the relationship between Emotion Work, Emotional Intelligence, Well-being and Social Support of service workers in a human resource field within a mining industry. A cross-sectional survey design was used. The study population (n = 229) consisted of human resource personnel in the Limpopo and North West Province. The Greek Emotional Intelligence Scale (GEIS), Frankfurt Emotion Work Scales, Utrecht Work Engagement Scale, Oldenburg Burnout Inventory and Social Support Scale, as well as a biographical questionnaire, were used as measuring instruments. Cronbach alpha coefficients, factor analysis, inter-item correlation coefficients, Pearson product moment correlation coefficient and stepwise multiple regression analysis were used to analyse the data. An analysis of the data indicated that correlations between the following constructs are statistically and practically significant. The results show that Positive Display is statistically and positively practically significantly related (medium effect) to Interaction Control. Caring/Empathy is positively practically significantly related to Positive Display (medium effect). Furthermore the Control of Emotions (medium effect) and Emotion Management (large effect) are both positively practically significantly related to Caring/Empathy. Emotional Resilience however is negatively practically significantly related to Caring and Empathy (medium effect). Emotion Expression Recognition is positively practically significantly related to Control of Emotion (medium effect). However, both Exhaustion (medium effect) and Emotional Resilience (medium effect) are negatively practically significantly related to Control of Emotions. Engagement is positively practically significant (medium effect) to Emotion Management. Emotion Resilience (medium effect) positively correlates with Exhaustion while Engagement (medium effect) negatively correlates with Exhaustion. Engagement positively practically correlates with Resilience (medium effect). Social Support of both supervisor and co-workers positively relates to engagement to a medium effect. Principal component analysis performed on the GEIS resulted in a four-factor solution. The first factor was Caring and Empathy, which includes the willingness of an individual to help other people and understand others' feelings. The second factor was Control of Emotion, which is the ability of the individual to control and regulate emotions within themselves and others. Emotion Expression/Recognition, which is the ability of the individual to express and recognise his or her own emotional reactions, was the third factor, and the fourth was Emotion Management, which is the ability of an individual to process emotional information with regard to perception, assimilation, understanding and management of emotions. All four factors correlate with that of the GEIS originally developed by Tsaousis (2007) and accounted for 31% of the total variance in emotional intelligence. A Multiple Regression Analysis with Exhaustion as dependent variable was carried out. The results show that Emotion Work factors accounted for 2% of the total variance and Emotional Intelligence factors for 12% of the total variance. More specifically it seems that the lack of Caring and Empathy and Emotion Management predicted Exhaustion in this regard. However, when Emotional Intelligence factors were entered into the model, an increase of 10% variance was shown of the variance explained in Exhaustion. Emotion Work, Emotional Intelligence and Social Support predicted 14% of the variance explained in the level of Exhaustion by participants. A Multiple Regression analysis with Emotional Resilience as dependent variable was carried out. The results show that Emotion Work factors accounted for 6% of the total variance. More specifically; it seems that Dissonance predicted the level of Emotional Resilience. When Emotional Intelligence factors were entered into the model, an increase of 15% was shown. Caring and Empathy and Control of Emotions predicted Emotional Intelligence the best. Lastly, when Social Support factors were entered into the regression analysis, the variance explained showed an increase of 5%. Support of Family and Others predicted Emotional Resilience the best. In total, Emotion Work, Emotional Intelligence and Social Support factors explained 20% of the variance in Emotional Resilience. A Multiple Regression Analysis with Engagement as dependent variable with Emotion Work factors, Emotional Intelligence factors and Social Support as predictors of Engagement was done. Entry of Emotion Work factors at the first step of the regression analysis did not produce a statistically significant model and only accounted for 1% of the variance. However, when Emotional Intelligence factors were entered in the second step of the analysis, it accounted for approximately 7% of the variance. More specifically, it seems that Caring and Empathy predicted Engagement. When Social Support factors were entered into the third step of the analysis, an increase of 27% was found. All the Social Support factors (Social Support of Family and Others, Supervisors and Co-workers) accounted for 27% of the variance explained in Engagement. Emotion Work, Emotional Intelligence and Social Support predicted 33% of the total variance explained in the level of Engagement. Limitations within the study were identified, and recommendations were made for human resource personnel in a mining industry, as well as for future research. / Thesis (M.A. (Industrial Psychology))--North-West University, Potchefstroom Campus, 2009.

Thesis (M.A. (Human Resource Management))--North-West University, Potchefstroom Campus, 2009.

This thesis explores women's learning in unpaid household work through the lenses of impairment and disability. Informal learning from this standpoint is a perspective that is not yet integrated into the adult learning literature. The impetus for the study came from dissatisfaction with the social undervaluing of unpaid housework and carework, and the largely unrecognized learning behind the work, which is predominantly done by women. Disability and impairment provide unique lenses for making visible what people learn and how they learn in this context. Those who have or acquire impairment in adulthood need to learn how to do things differently. For this study I have taken a segment of data from a 4-year, 4-phase project on Unpaid Housework and Lifelong Learning in which I participated. The participants in this segment are women and men with disabilities who took part in 2 focus groups (11 women), an on-line focus group (20 women), and individual interviews (10 women and 5 men). Learning is explored through three different themes: first, learning related to self-care; second, learning to accept the impaired body; and third, strategies and resources used in the learning process. Analysis of the data shows that the learning that happens through unpaid household work is multidimensional, fluid, and diverse. Learning is accomplished through a complex 4-dimensional process involving a blend of the body, mind, emotions, and the spiritual self. Furthermore, what participants learned and how they learned is influenced by the sociocultural context in which it takes place. Learning, when seen as a 4-dimensional process, provides a framework for challenging traditional Western cultural beliefs about what counts as learning and knowledge. Such beliefs have cultivated the viewpoint that learning is individualistic, cognitive, and based on reason. I contest these beliefs by disrupting the binaries that support them (e.g., mind vs. body, reason vs. emotion). Participants used both sides of the binaries in their learning processes, negating the oppositional and hierarchical categories they establish. The concepts in the binaries still exist but the relationship between them is not oppositional, nor is one concept privileged over another, either within or across binaries.

This thesis explores women's learning in unpaid household work through the lenses of impairment and disability. Informal learning from this standpoint is a perspective that is not yet integrated into the adult learning literature. The impetus for the study came from dissatisfaction with the social undervaluing of unpaid housework and carework, and the largely unrecognized learning behind the work, which is predominantly done by women. Disability and impairment provide unique lenses for making visible what people learn and how they learn in this context. Those who have or acquire impairment in adulthood need to learn how to do things differently. For this study I have taken a segment of data from a 4-year, 4-phase project on Unpaid Housework and Lifelong Learning in which I participated. The participants in this segment are women and men with disabilities who took part in 2 focus groups (11 women), an on-line focus group (20 women), and individual interviews (10 women and 5 men). Learning is explored through three different themes: first, learning related to self-care; second, learning to accept the impaired body; and third, strategies and resources used in the learning process. Analysis of the data shows that the learning that happens through unpaid household work is multidimensional, fluid, and diverse. Learning is accomplished through a complex 4-dimensional process involving a blend of the body, mind, emotions, and the spiritual self. Furthermore, what participants learned and how they learned is influenced by the sociocultural context in which it takes place. Learning, when seen as a 4-dimensional process, provides a framework for challenging traditional Western cultural beliefs about what counts as learning and knowledge. Such beliefs have cultivated the viewpoint that learning is individualistic, cognitive, and based on reason. I contest these beliefs by disrupting the binaries that support them (e.g., mind vs. body, reason vs. emotion). Participants used both sides of the binaries in their learning processes, negating the oppositional and hierarchical categories they establish. The concepts in the binaries still exist but the relationship between them is not oppositional, nor is one concept privileged over another, either within or across binaries.

No description available.

Familjeutredare på akut- och utredningshem är en yrkesgrupp som arbetar med utredning av föräldrars omsorgsförmåga. I arbetet träffar familjeutredaren dagligen människor som befinner sig i svåra livsskeenden. Detta ställer krav på familjeutredarens förmåga att hantera emotioner. Studiens syfte var att bidra till en djupare förståelse för familjeutredares emotionella lönearbete, vilka emotionella krav som ställs och hur de hanterar det. Studien utgick från Grundad teori (GT) och datainsamling innefattade sju semistrukturerade intervjuer. Studien resulterade i en teoretisk modell där utgångspunkten var ett emotionellt lönearbete som präglas av en moralisk konflikt. Det centrala i modellen är att arbetet kräver å ena sidan en balansering av förväntningarna på ett moraliskt upprätthållande arbete och å andra sidan det moraliska ifrågasättande som arbetet väcker. För att balansera denna konflikt använder sig familjeutredarna av emotionsstrategier. / Child protection investigators are an occupational group who investigates parentsability to take care of their children. In their everyday work child protection investigators meet parents who find themselves in a difficult life situation. The investigator must therefore have the ability to manage emotions. The aim of this study was to contribute to a deeper understanding of child protection investigators emotional labor, their emotional demands and how they handle them. The methodology used in this study was Grounded theory (GT). Data were collected from seven informants. The findings show that child protection investigators' emotional labor is characterized by a moral conflict. This conflict arises when child protection investigators start questioning the bases of their work. To balance this moral conflict they use emotion strategies.

Frontline client service workers are central to the service elements of any small and medium enterprise. People who have much customer or client contact are seen to be subject to stronger emotional display rules. These display rules may result in compromising the psychological and/or physical health of workers, because they often lead to a disturbing dissonance between felt emotions and the emotions one must exhibit. It is, therefore, of vital importance for service workers to exhibit Emotional Intelligence, which will enable them to manage both their own emotions and their interactions with other people. Their inability to do so may result in stress as well as physical and emotional exhaustion, also known as Burnout. The objective of this research was to determine the relationship between Emotion Work, Emotional Intelligence, Well-being and Social Support of client service workers within small and medium enterprises, A cross-sectional survey design was used. An availability sample was taken from small and medium enterprises employing client service workers in the Mpumalanga Province (N = 145). The Greek Emotional Intelligence Scale (GEIS), Frankfurt Emotion Work Scales (FEWS), Utrecht Work Engagement Scale (UWES), Oldenburg Burnout Inventory (OLBI) and Social Support Scale, as well as a biographical questionnaire were used as measuring instruments. Cronbach alpha coefficients, factor analysis, inter-item correlation coefficients, Pearson product moment correlation coefficients, stepwise multiple regression analysis, and Multivariate analysis of variance (MANOVA) were used to analyse the data. Principal component analysis resulted in a one-factor solution for Engagement labelled Work Engagement, and a two factor solution for Burnout namely: Disengagement and Emotional Exhaustion. Regarding Social Support, a three factor model was extracted namely; Social Support - Co-worker, Social Support -Supervisor and Social Support - Family. A three factor model was extracted for Emotion Work namely: Emotional Dissonance, Display of Client Care and Extent of Client Interaction. A four-factor solution was extracted for Emotional Intelligence namely: Emotional Expression/Recognition, Use of Emotions to Facilitate Thinking, Control of Emotion as well as Caring and Empathy. An analysis of the data indicated that all of the correlations between the different constructs mentioned below are statistically and practically significant, Disengagement was positively related to Emotional Exhaustion and negatively related to Emotional Expression/Recognition, Emotion Use to Facilitate Thinking and Work Engagement. Emotional Exhaustion was positively related to Emotional Dissonance and negatively related to Emotional Expression/Recognition. Emotional Dissonance was positively related to Display of Client Care, while Display of Client Care was positively related to Extent of Client Interaction, as well as Caring and Empathy. Emotional Expression/Recognition was positively related to both Emotion Use to Facilitate Thinking and Work Engagement. Emotion Control was positively related to Emotion Use to Facilitate Thinking, while it in turn was positively related to Work Engagement. Finally, Social Support from Co-workers was positively related to Social Support from Supervisors and Family, and Social Support from Supervisors was positively related to Social Support from Family. A multiple regression analysis indicated that Emotion Work, Social Support and Emotional Intelligence predicted 29% of the variance in Work Engagement, 30% of the variance explained in Disengagement and 37% of the variance in Emotional Exhaustion. A multivariate analysis of variance (MANOVA) which was used to determine differences between the departmental, age, race, qualification, language and gender groups with regard to Emotion Work, Emotional Intelligence, Well-being and Burnout, indicated no statistical significant differences (p < 0,05). The results indicated a correlation between Emotional Intelligence, Emotion Work and Well-being factors. Emotional Intelligence factors predicted Work Engagement and Emotion Work predicted Emotional Exhaustion. Recommendations were made for the profession of client service work in small and medium enterprises, as well as for future research purposes. / Thesis (M.A. (Industrial Psychology))--North-West University, Potchefstroom Campus, 2008.

Things develop quickly in today's internet-linked global economy and competition is intense. Speed, cost, and quality are no longer the trade-offs they once were. Today's consumers demand immediate access to high-quality products and services at a reasonable price. Thus, Managers are challenged to speed up the product creation and delivery cycle, while cutting costs and improving quality. Regardless of the size and purpose of the organisation and the technology involved, people are the common denominator when facing this immense challenge. Success or failure depends on the ability to attract, develop, retain, and motivate the employees. The human-resource employee is the drive behind all these voice-to-voice and face-to-face interactions, attempting to represent the emotions, attitudes, and behaviours required by the organisation. The objective of this research study was to determine the relationship between Emotion Work and Well-being among human-resource employees in the chrome industry. The research method consisted of a literature review and an empirical study. A cross-sectional survey design was used to collect the data. A non-probability convenience sample was taken from human-resource employees in the chrome industry. The Utrecht Work Engagement Scale, Oldenburg Burnout Inventory, the Frankfurt Emotion Work Scales, Greek Emotional Intelligence Scale, Emotional Labour Scale, and Social Support Scale were used as measuring instruments. The data was analysed by making use of descriptive statistics, Cronbach alpha coefficients, factor-analysis, Pearson product-moment correlation coefficients, and multiple regression analyses of variance (multivariate analysis of variance and ANOVA), calculated using SPSS. Compared to the the guidelines of a > 0,07 (Nunnally & Bernstein, 1994), all of the scales of the measuring instruments have normal distributions except for Engagement where the kurtosis was positively skewed. Compared to the guidelines of a > 0,07 (Nunnally & Bernstein, 1994), the Cronbach alpha coefficient of all the constructs is considered to be acceptable. A factor analysis confirmed four factors of Emotion Work, consisting of Emotional Dissonance, the Display of Positive/Pleasant Emotions, the Display of Empathy, and the Display of Negative/Unpleasant Emotions. Emotional Intelligence also consists of four factors namely the Use of Emotion, Caring Empathy, the Control of Emotions, and Expression Recognition. Well- being consists of two factors namely Exhaustion and Engagement. Finally, Social Support consists of three factors Co-worker Support, Supervisor Support, and Family Support. An analysis of the data indicated that all of the correlations between the different constructs mentioned below are statistically and practically significant. The Display of Positive/Pleasant Emotions and the Display of Empathy is positively related to Emotional Dissonance. The Display of Empathy is positively related to Emotional Dissonance as well as to the Display of Positive/Pleasant Emotions. The Display of Negative/Unpleasant Emotions is negatively related to the Display of Positive/Pleasant Emotions and the Display of Empathy. Exhaustion is negatively related to Engagement, the Use of Emotions, and the Control of Emotions. Engagement is positively related to Co-worker Support, the Use of Emotion as well as to the Control of Emotions. Family Support is positively related to both Supervisor Support and Co-worker Support. Supervisor Support is positively related to Co-worker Support. The Use of Emotion is positively related to Caring Empathy and the Control of Emotions. Caring Empathy is positively related to the Control of Emotions, the Display of Positive/Pleasant Emotions, and the Display of Empathy, while it in turn is negatively correlated to the Display of Negative/Unpleasant Emotions. The Control of Emotions is negatively related to the Display of Negative/Unpleasant Emotions. Emotional Dissonance is positively related to both the Display of Positive/Pleasant Emotions and the Display of Empathy. Finally, the Display of Positive/Pleasant Emotions is positively related to the Display of Empathy. In a multiple regression analysis with Emotional Dissonance as dependant variable and with demographic variables, as independent variable a statistically significant model was produced. More specifically, 11% of the variance predicted in Emotional Dissonance was explained by gender, age and ethnicity. In a multiple regression analysis indicated that Emotional Intelligence (specifically the Use of Emotion), Emotion Work (specifically Emotional Dissonance), and Social Support (specifically Co-worker Support) predicted 31% of the total variance in Engagement. Emotional Intelligence (specifically the Use of Emotion and the Control of Emotions), Emotion Work (specifically Emotional Dissonance and the Display of Negative/Unpleasant Emotions), and Social Support (specifically Co-worker Support) predicted 43% of the total variance in Exhaustion A multivariate analysis of variance, which was used to determine differences between ethnic, age, and gender groups with regard to Emotion Work, indicated that participants in the African ethnic group experienced higher levels of Emotional Dissonance than participants in the White ethnic group. Female participants display higher levels of Emotional Dissonance, the Display of Positive/Pleasant Emotions, and the Display of Empathy, while male participants experienced higher levels of the Display of Negative/Unpleasant Emotions. The age group of 42 to 51 experienced lower levels of Emotion Work owing to the experience of Emotional Dissonance, than participants in the age groups of 21 to 31, 32 to 41, and 52 to 66. / Thesis (M.A. (Human Resource Management))--North-West University, Potchefstroom Campus, 2009.