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The purpose of this paper is to compare the importance currently placed on meaningful work (MFW), and determine the frequency by which it is experienced in blue-, pink-, and white-collar occupations.

Using the comprehensive meaningful work scale (Lips-Wiersma and Wright, 2012) with 1,683 workers across two studies, ANOVAs were conducted to examine differences in dimensions of MFW.

While unity with others and developing the inner self were regarded as equally important for white-, blue-, and pink-collar workers, the authors data suggest that white-collar workers placed more importance on expressing full potential and serving others than blue-collar workers. The frequency of experiencing MFW differed across the three groups with white-collar workers experiencing higher levels of unity with others, expressing full potential, and serving others; however no mean differences were found for developing the inner self.

This study is the first to empirically investigate an oft-discussed but previously untested question: does the experience of MFW differ across white-, blue-, and pink-collar jobs?

This study sought to examine the attitudes of blue-collar workers toward job automation. The study examined the relations between job automation, fear of job loss and self-actualization.

Using mixed methods (qualitative and quantitative analysis) with 539 participants overall, we examined employees' attitudes toward job automation through two separate studies conducted in a large public organization that employs blue-, white- and pink-collar employees. The blue-collar workers who participated consisted of waste collectors, gardeners and parking supervisors whose work is at risk of job automation.

We found that 74% of the blue-collar employees described technology as a “replacer” that simplifies and reduces human work activities, while only 3% perceived it as an “enabler” that could enrich their jobs and expand human potential. Fifty-three percent of the employees in the white-collar professions described technology as a “replacer,” and 36% perceived it as an “enabler.” Among pink-collar workers, 51% perceived technology as an “enabler,” while only 14% perceived it as a “replacer.” A positive relationship between job automation and self-actualization was evident for pink- and white-collar workers, but not for blue-collar workers.

This study sheds light on how employees in different types of jobs perceive technological advancements at work. A classification of the perception of technology as an “enabler” vs a “replacer” is presented. The relationships between job automation and self-actualization in different job types are explored.

Most employee attitudes and behaviors are determined by both personal and situational characteristics. Studies on person–organization fit (POF), which is defined as the congruence between individual and organizational values, also support this assumption. Employees who perceive high POF have high positive work attitudes and low intention to leave. However, this study assumes that the relationship between perceived POF and work attitudes may be different with respect to employees’ status and aims to investigate how perceived POF may differ in consequences among blue-collar and white-collar employees.

Multiple group analysis of structural equation modeling (SEM) was conducted to test the moderation effect of employee status on the relationship between perceived POF and work attitudes.

Results indicated that the relationship between perceived POF and organizational commitment, job satisfaction, organizational identification and intention to leave differ with respect to individual’s status (blue-collar–white collar). As the status of the individuals increases, the relationship between POF and work attitudes (organizational commitment, job satisfaction and organization identification) becomes weaker.

Rather than just focusing results of POF, this study focuses on moderating variables that differentiate the relationship between POF and outcomes by considering individual differences caused by different motivation and abilities.

A recent research project on white collar quality circles in manufacturing industry was carried out because previous research at UMIST indicated that white collar circles were more difficult to set up and sustain. The study involved interviews in eleven companies which had or had had white collar circles, and questionnaire surveys of manufacturing companies and quality circle consultants. The findings indicate that white collar circles are no more difficult to initiate than blue collar circles but they can be harder to sustain. There was little evidence that companies were aware of the benefits of increasing white collar productivity or the part that circles can play in this and white collar workers are often sceptical about the relevance of circles to them. It was also found that white collar circles can face difficulties such as problem choice, over‐complex projects, organising meetings and a tendency to form multi‐disciplinary groups. Companies intending to start white collar circles need to be aware of the pitfalls but these should not deter them from setting them up.

Existing research concerning the impact of unions on relative wages provides evidence for the existence of significant union/non‐union wage differentials. However, union practices are deemed to have a more pervasive effect on the overall distribution of wages, reducing wage differentials across and within establishments. Attempts to explore union effects on wage dispersion in the context of the Italian labour market. Several indicators of wage dispersion are computed, using both industry and establishment level data, in the attempt to ascertain the different routes through which union presence affects the structure of wages. The empirical evidence shows that Italian trade unions have pursued “egalitarian” objectives and have succeeded in shaping pay policies which, through central and local negotiations, raise low wages and reduce wage differentials both among skill categories and across establishments.

Given South Africa's apartheid history, studies have primarily focused on racial discrimination in employment outcomes, with lesser attention paid to gender and context. The purpose of this paper is to fill an important gap by examining the combined effect of macro- and micro-level factors on occupational sex segregation in post-apartheid South Africa. Intersections by race are also explored.

A multilevel multinomial logistic regression is used to examine the influence of various supply and demand variables on women's placement in white- and blue-collar male-dominated occupations. Data from the 2001 Census and other published sources are used, with women nested in magisterial districts.

Demand-side results indicate that service sector specialization augments differentiation by increasing women's opportunities in both white-collar male- and female-dominated occupations. Contrary to expectations, urban residence does not influence women's, particularly African women's, placement in any male-type positions, although Whites (white-collar) and Coloureds (blue-collar) fare better. Supply side human capital models are supported in general with African women receiving higher returns from education relative to others, although theories of “maternal incompatibility” are partially disproved. Finally, among all racial groups, African women are least likely to be employed in any male-dominated occupations, highlighting their marginalization and sustained discrimination in the labour market.

An analysis of women's placement in white- and blue-collar male-dominated occupations by race provides practical information to design equitable work policies by gender and race.

Sex-typing of occupations has deleterious consequences such as lower security, wage differentials, and fewer prospects for promotion, that in turn increase labour market rigidity, reduce economic efficiency, and bar women from reaching their full potential.

Very few empirical studies have examined occupational sex segregation (using detailed three-digit data) in developing countries, including South Africa. Methodologically, the paper uses multilevel techniques to correctly estimate ways in which context influences individual outcomes. Finally, it contributes to the literature on intersectionality by examining how gender and race sustain systems of inequality.

A study carried out at UMIST of the operation of white‐collar circles in manufacturing firms is reported. Despite the problems associated with initiating and sustaining white‐collar circles, it is not suggested that they should be avoided.

The main purpose of the study is to examine the intellectual structure of academic studies on blue-collar workers. In this context, (1) productivity and evolution of the field, (2) conceptual dimensions, (3) citation density and (4) structure of academic networks related to blue-collar workers were analyzed. The findings of the study produced results covering all fields of science, not just management and organization. As a result of the study, blue-collar studies by years, the most cited blue-collar studies, the researchers who published the most frequently about blue-collar workers, the researchers who conducted the longest research on blue-collar workers, the most frequently used keywords and academic networks in blue-collar studies are determined. The most important finding is that the blue-collar issue is the common intersection point of different fields of science such as health, medicine, finance, management and organization, marketing, psychology and sociology.

We report three findings in a comprehensive study of hourly wage differences between women and men working in same occupation and establishment in Sweden in 1970–1990. (1) Within same occupation and establishment in 1990, women on average earn 1.4% less than men among blue-collar workers, 5.0% less among white-collar employees. This occupation-establishment level wage gap declined strongly from 1970 to 1978. (2) For white-collar employees, occupational segregation accounts for much of the wage gap, establishment segregation for little. For blue-collar workers both types of segregation are important. (3) The within-occupation gaps are small, below 4% and 7% for blue- and white-collar workers.