QUALITY OF WORKING LIFE AMONG WOMEN AND MEN IN THE INFORMATION TECHNOLOGY WORKFORCE Jen Schoepke*+, Peter L. T. Hoonakker+ and Pascale Carayon*+ * Department of Industrial Engineering + Center for Quality and Productivity Improvement University of Wisconsin-Madison 610 Walnut Street 575 WARF Madison, WI 53726 Tel: +1-608-263-2520 Fax: +1-608-263-1425 E-mail: [email protected], [email protected], [email protected] In this paper, we examine quality of working life (QWL) and evaluate and compare the predictors of QWL among 624 women and men in a variety of information technology (IT) jobs in five companies. The following QWL factors were examined: job satisfaction, fatigue, tension, organizational involvement, and burnout. The following predictors of QWL were studied: IT demands, role ambiguity, decision control, challenge and demographics (age, marital status, parental status, and education). Analysis shows that in our sample women in IT jobs do not report poorer QWL than men in IT jobs. On the contrary, women report greater organizational involvement than men. key barriers to the entrance and retention of women and underrepresented minorities in the IT workforce (CAWMSET, 2000; ITAA, 2000). Barriers include lack of role models and mentors, exclusion from informal networks, stereotyping and discrimination, unequal pay scales and inadequate work/family balance (CAWMSET, 2000; ITAA, 2000). Igbaria and Greenhaus (1992) tested a model of turnover intentions and QWL among 464 management information systems (MIS) employees using employee questionnaires. The model consisted of five sets of variables: 1) demographic variables; 2) role stressors; 3) career experiences; 4) workrelated attitudes or QWL; and 5) turnover intentions. Results indicated that two measures of QWL, job satisfaction and organizational commitment, had the strongest and most direct influence on turnover intentions, and the impact of other variables on turnover intentions was primarily mediated by these two variables. Education was the only demographic variable that had a direct effect on turnover intention. Higher educated employees had higher turnover intention and lower job and career satisfaction. Employees with low salaries and those who perceived limited career advancement opportunities tended to hold stronger turnover intention than those with higher salaries and more career advancement opportunities, through both direct and indirect effects. Role stressors had a positive, indirect effect on turnover intentions through low job and career satisfaction and organization commitment. Organizational commitment had a strong, negative effect on turnover intention, but inconsistent with prior research, job satisfaction had stronger effects than organizational commitment on turnover intention (Igbaria & Greenhaus, 1992). This study confirms that a range of job and organizational factors can influence QWL that, in turn, can influence turnover intention among IT workers.

INTRODUCTION In this paper, we examine quality of working life (QWL) and evaluate and compare the predictors of QWL among women and men in a variety of information technology (IT) jobs in five companies. The following QWL factors were examined: job satisfaction, fatigue, tension, organizational involvement, and burnout. The following predictors of QWL were studied: IT demands, role ambiguity, decision control, challenge and demographics (age, marital status, parental status, and education). BACKGROUND Projections from the US Bureau of Labor Statistics estimate that between 2000 and 2010 2.5 million new IT jobs will be available, which result from the growth in IT occupations (2.2 million) and the need to replace those leaving the workforce (331,000) (U.S. Department of Commerce, 2003). Thus, a high demand for skilled IT workers is predicted for the next decade (U.S. Department of Commerce, 2003). A large subset of this demand could be fulfilled if women and people of minority status are represented in the IT workforce as they are in the total workforce. For instance, according to statistics from the Bureau of Labor Statistics (Bureau of Labor Statistics (BLS), 2003), women represent 47% of the total workforce, but only 35% of the IT workforce. Under representation may be caused by insufficient women entering the IT workforce as well as too many of them leaving the IT workforce. Female scientists and engineers in industry are more likely to leave their technical occupations and the workforce altogether than women in other fields. Attrition data on female scientists and engineers show that their exit rates are not only double those of men (25% versus 12%), but they are also much higher than those of women in other employment sectors (CAWMSET, 2000). Some preliminary work has been done to identify the

Many factors influence an employee's commitment to the organization and satisfaction with his or her job. One particular powerful factor that prior research has repeatedly shown to be significantly correlated to the job attitudes of interest (namely organizational commitment, job satisfaction and turnover intention) is work exhaustion, or job burnout (Moore, 2000; Moore & Burke, 2002). The research literature in IT and the popular press suggest that technology professionals are particularly vulnerable to work exhaustion and stress (Kalimo & Toppinen, 1995; McGee, 1996). Our project examines the role of the work environment and how employers can better design the culture and environment of the IT workplace to accommodate the needs of underrepresented groups. An Information Week salary survey showed that IT workers ranked "challenge" of their job, "responsibility" and "job atmosphere" as more important than their base salary. QWL, job stability and learning opportunities through job assignments dominated the responses (Meares & Sargent, 1999). QWL has been defined by many researchers in a variety of ways, thus presenting some disagreement on a precise definition; however, there is general consensus of its multidimensional qualities and usefulness as a concept (Baba & Jamal, 1991). For instance, Carayon (1997) defines QWL as the complex interactions of the elements of the work system, namely the individual, the tasks, organizational factors, the environment, and tools and technology. Still others view QWL as the effect of the workplace on job satisfaction, which spills over into satisfaction with non-work domains, and translates into overall satisfaction with life and subjective well being (Sirgy, Efraty, Siegel, & Lee, 2001). Davis (1983) has defined QWL as "the quality of the relationship between employees and the total working environment, with human dimensions added to the usual technical and economic considerations" (p.80). Using this definition, we examine a range of indicators of QWL: job satisfaction, organizational involvement, fatigue, tension, and burnout (emotional exhaustion). The Sociotechnical Systems Theory (STS) (Trist, 1981), the Organizational Health Model (Sauter, Lim, & Murphy, 1996) and the Balance Theory (Smith & Carayon-Sainfort, 1989) provide theoretical perspectives for examining work systems. The STS emphasizes the interrelatedness of the social and technical systems within an organization and integrates job and organizational design perspectives, through linking the job design theories of human relations, job enrichment and participation. The Organizational Health Model asserts that organizational characteristics (e.g., management practices, organizational values) directly influence organizational health i.e. performance outcomes and satisfaction outcomes (Sauter et al., 1996. The Balance Theory is a theoretical framework that examines job and organizational design characteristics within each component of the work system that interact to influence the "stress load" upon an individual (Smith & Carayon-Sainfort, 1989). It identifies sources of occupational stress (stressors or psychosocial work factors) that can influence stress, attitudes and behaviors (e.g., turnover intention).

The organizational/job design and job stress literature highlights the importance of a variety of job and organizational factors as contributors to QWL and turnover (Carayon & Smith, 2000). Some of the most important job and organizational factors identified in this literature are: job demands, job control, job content, and feedback (Carayon & Smith, 2000; Cooper & Marshall, 1976; Karasek, 1979; Theorell & Karasek, 1996). In this study, we examined four job design factors: IT demands, role ambiguity, decision control, and challenge. In addition to these job design factors, we use four demographic characteristics: age, parental status (having children versus not), marital status (type of living condition, e.g., living alone versus not), and education level (highest level of formal education achieved, e.g., high school or G.E.D., some college, Bachelors degree, some graduate/professional study, or graduate/professional degree). RESEARCH OBJECTIVES We first compare QWL among women and men. We then examine whether the relationship between job characteristics/demographics and QWL varies for women and men. METHODOLOGY The data analyzed in this paper is captured from the database of the project on "Paths to Retention and Turnover in the IT Workforce: Understanding the Relationships Between Gender, Minority Status, Job and Organizational Factors" [http//]. Participating companies were obtained via solicitation in the project (Carayon, Brunette, Schwarz, Hoonakker, & Haims, 2003). Participants within the participating companies were identified based on two characteristics: 1) their job was within the information technology workforce, and 2) they have worked in their current job for two months or more. The data collection tool used is a 139-item web-based questionnaire (Carayon, Schoepke, Hoonakker, Haims, & Brunette, 2004). Data collection for this project started in February of 2003 and is still in progress, though for the purposes of this paper, we used data collected up to January 2004. Sample The sample consists of five companies varying of size. Company 1 is a medium-sized Midwestern IT firm with 190 professionals (response rate 66% (n=125)). Company 2 is an eastern health care provider network with 895 IT professionals (response rate 55% (n=489)). Company 3 is a small western IT firm with 11 IT professionals (response rate 36% (n=4)). Companies 4 and 5 are both small eastern IT firms with 9 and 11 IT professionals respectively (response rates 44% (n=4) and 18% (n=2)). Since the large company is not an IT company per se, the sample exemplifies the literature that 92% of IT professionals work in non-IT companies (ITAA, 2002). Table 1 shows the demographics of the sample. The total sample size is 624 with 46% women and 54% men (26 did not

report their gender). The average age is 40 years, with women being significantly older than men (t-test; p<0.05). The marital status (e.g. living with someone versus not) is significantly different between women and men (2 test; p<0.05): 65% of the women live with a spouse/partner, compared with 73% of the men. Parental status is not significantly different between women and men. Women in this sample are significantly highly educated than men: women have more graduate/ professional study (15%) and more graduate/professional degrees (30%) than men do, respectively (12%) and (23%). Measures To measure job characteristics and QWL we used existing scales that were found to be valid and reliable in previous research. All scales we used in the questionnaire were converted to scores from 0 (lowest) to 100 (highest). The measures of job characteristics included the following scales: job demands for the IT workforce (adapted from Quinn et al., 1971; = 0.87); role ambiguity (Caplan, Cobb, French, Harrison, & Pinneau, 1975; = 0.87); decision control (McLaney & Hurrell, 1988; = 0.89); and challenge (Seashore, Lawler, Mirvis, & Cammann, 1982; = 0.82). The following QWL factors were measured: job satisfaction (Quinn et al., 1971; = 0.78); organizational involvement (Cook & Wall, 1980; = 0.72); tension (Swanson, 1997, unpublished data; = 0.81); fatigue (Grove & Prapavessis, 1992; = 0.88); and burnout (Leiter & Schaufeli, 1996; Maslach & Jackson, 1985; = 0.91). Analysis Analysis was conducted using the statistical software program SPSS©. To look for significant differences between women and men in the QWL factors, the mean values reported by women and by men were compared using t-test. We then examined the influence of demographic factors on QWL. This step of the analysis helped us identify the demographic variables to enter in the regression analysis of QWL. Regression analysis was then conducted between job satisfaction (dependent variable) and the 4 job characteristics (challenge, role ambiguity, IT demands, and decision control) and demographics (where applicable) (independent variables) for women and then for men. This step was repeated for each of the remaining QWL factors, i.e. organizational involvement, tension, fatigue, and burnout. RESULTS One significant difference in QWL between women and men was found (see Table 2). Women reported higher organizational involvement than men. Differences between women and men on the other four QWL measures (job satisfaction, fatigue, tension and burnout) were not statistically significant. Table 3 shows the results of the regression analysis with the QWL factors as explained by the job characteristics and

demographics for women and men. The percentage of variance of QWL explained by the job design factors and demographics varies from 5% to 36%. With regard to job satisfaction, all the job characteristics, except for role ambiguity among women, were significant predictors. The model with job characteristics, age and parental status explained only 5% and 6% of organizational involvement among women and men respectively. Two job characteristics, role ambiguity and decision control, were statistically significant predictors of organizational involvement. Among women, high fatigue was related to high IT demands, high role ambiguity and low decision control; whereas among men, fatigue was predicted only by IT demands. Among both women and men, high tension was related to high IT demands and low decision control. In addition, among women, role ambiguity and challenge were significant predictors of tension. With regard to burnout, all the job characteristics, except decision control among men, were significant predictors. Across all five QWL measures, there was only one instance where a demographic variable was a significant predictor: men who had kids reported less burnout than men without kids. DISCUSSION Based on the literature, we expected that women in IT jobs would report poorer QWL than men in IT jobs (Baroudi & Igbaria, 1995; Igbaria & Greenhaus, 1992), leading to greater turnover for women in IT. It can be seen that in our sample women in IT jobs do not report poorer QWL than men in IT jobs. On the contrary, women report greater organizational involvement than men. This contradiction is further complicated by the highest level of formal education completed. In our sample, women have a statistically significant higher level of education versus men; however, Igbaria and Greenhaus (1992) noted that higher educated IT employees were more susceptible to turnover. Thus, based on the literature, we expect that lower QWL and higher education for women would lead to higher turnover within the IT workforce (Igbaria & Greenhaus, 1992). Further research needs to be conducted to validate our results on QWL for IT employees. Validation of the results should lead to the exploration of why, in spite of a higher QWL, women have been found to have a higher turnover in the IT workforce. There were some differences between women and men with regard to the job characteristics that influenced QWL. Only for women, role ambiguity is related to fatigue and tension. Only for men, role ambiguity is related to job satisfaction and organizational involvement. For both women and men, role ambiguity is related to burnout. Only for women, decision control is related to organizational involvement and for men, decision control is related to fatigue and burnout. For women, challenge is related to tension. Surprisingly, only for men did having a child (parental status) decrease burnout. Women and men have reported differences in burnout, however, the relationship between burnout and other factors is not always straightforward (Cordes & Dougherty, 1993; Maslach & Jackson, 1985). Further, Maslach & Jackson (1985) found that

individuals with children consistently reported lower levels of burnout. In our study, we found this to be true for men with children; curiously, we did not find the same results for women who have children. Further research needs to be conducted to understand the relationship that children and family play in QWL in the IT workforce. A strength of this study is the range of participating companies. Data was collected from small, medium, and large companies, thus providing a diversity of working conditions and work environments of the IT workforce population. Another strength of this study is the questionnaire used to collect the data. The questionnaire was developed in a systematic process (Carayon, Brunette, Schwarz, Hoonakker, & Haims, 2003; Carayon et al., 2004), thus contributing to the validity of the measures. A limitation of this study is data was collected from five companies, thus limiting the generalizability of the results. In addition, data was collected via a web-based survey at one point in time. ACKNOWLEDGEMENTS Funding for this research is provided by the NSF Information Technology Workforce Program (Project #EIA-0120092, PI: P. Carayon). REFERENCES

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Table 1: Sample characteristics

Women Men Sample size 274 (46%)* 324 (54%)* Age (mean(SD)) 41 (9.3) 39 (9.2) Marital status (live with spouse/partner) 179 (65%) 238 (73%) Parental status (have 145 (54%) children) 184 (57%) Education level: High school or G.E.D. 12 (4%) 16 (5%) Some college 35 (13%) 69 (21%) Bachelors degree 102 (37%) 126 (39%) Some graduate or professional study 42 (15%) 40 (12%) Graduate or professional degree 82 (30%) 73 (23%) * 26 participants did not give their gender Total 624 40 (9.3) 421 (67%) 333 (56%) 28 (5%) 105 (17%) 234 (38%) 87 (14%) 161 (26%)

Table 2: QWL factors for women and men

Women [mean (SD)] Men [mean (SD)] t-test [p-value] Job Satisfaction 74.32 (26.02) 75.45 (21.92) 0.56 Organizational Involvement 82.75 (14.93) 79.58 (16.26) 0.01 Fatigue 31.42 (25.62) 31.16 (26.12) 0.9 Tension 18.81 (21.77) 18.30 (20.43) 0.77 Burnout 35.56 (22.46) 33.64 (21.89) 0.29

Table 3: Regression analysis between QWL and job characteristics and demographic variables

Independent Variables Job Indicators IT Demands Role Ambigutiy Decision Control Challenge Age Demographic Parental Status Marital Status Variables Education Level 2 Adjusted R Job Satisfaction Women Men -0.29*** -0.26*** -0.10 -0.21*** 0.25*** 0.17*** 0.41*** 0.45*** QWL Variables (Dependent Variables) Organizational Involvement Fatigue Tension Women Men Women Men Women Men 0.10 -0.02 0.44*** 0.46*** 0.39*** 0.40*** -0.08 -0.15** 0.16** 0.05 0.15* 0.06 0.05 0.12* -0.20** -0.05 -0.18** -0.13* 0.11 0.11 -0.10 -0.06 -0.14* -0.03 0.09 0.03 0.02 0.09 Burnout Women Men 0.50*** 0.49*** 0.17** 0.18*** -0.16** -0.08 -0.25*** -0.12* -0.10 -0.03 0.05 -0.14**











Note: A high score on job demands means high job demands; a high score on role ambiguity means high role ambiguity; a high score on decision control means high decision control; a high score on challenge means high challenge. *** = p < 0.001 ** = p < 0.01 * = p < 0.05



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