THE RELATION BETWEEN JOB CHARACTERISTICS AND QUALITY OF WORKING LIFE: THE ROLE OF TASK IDENTITY TO EXPLAIN GENDER AND JOB TYPE DIFFERENCES Peter Hoonakker +, Alexandre Marian +* and Pascale Carayon +* + Center for Quality and Productivity Improvement * Department of Industrial Engineering University of Wisconsin-Madison 61 Walnut St., 5 WARF Madison, WI 53726 Women are largely underrepresented in the Information Technology (IT) workforce. Our study examines the factors related to the work environment that may contribute to the high turnover of women in the IT workforce. The literature links Quality of Working Life (QWL) to turnover intention, and turnover intention to turnover. In this study, we conducted secondary data analysis of questionnaire data collected from a sample of 1,11 employees of a single organization. We examined the impact of gender and job type (i.e. IT job versus non-it job) on various indicators of QWL, as well as on the relationship between job factors and QWL. The results show that, specifically for women in IT jobs, task identity is highly associated with QWL. INTRODUCTION Turnover in the IT workforce There is an exceptionally high level of turnover in the IT workforce (Moore & Burke, 22; Network, 2). Determining the causes of turnover and controlling it through human resource practices and work system redesign is imperative for organizations that employ IT workers (Igbaria & Siegel, 1992). Female scientists and engineers in industry are more likely to leave their technical occupations and the workforce than women in other fields. Attrition data on female scientists and engineers shows that their exit rates is double that of men (% versus 12%), and are much higher than those of women in other employment sectors (CAWMSET, 2). The relation between job characteristics, Quality of Working Life (QWL) and turnover intention Many factors can influence an employee s commitment to the organization and satisfaction with his or her job. Igbaria and Greenhaus (1992) tested a model of turnover intention among 464 MIS employees. Results indicated that two workrelated attitudes, job satisfaction and organizational commitment, had the strongest and most direct influence on turnover intention. Organizational commitment had a strong, negative effect on turnover intention, but job satisfaction had stronger than organizational commitment on turnover intention (Igbaria & Greenhaus, 1992). This study confirms that job factors can influence attitudes, which in turn, can influence turnover intention. A variety of job and organizational factors can contribute to QWL and turnover (Carayon & Smith, 2). Jansen et al. (1999) used the Hackman and Oldham job diagnostic model to determine the relationship between intrinsic work motivation, burnout and turnover intentions among nurses. They found that intrinsic work motivation is primarily determined by elements of the job that make the work challenging and worthwhile. In this study we are interested in the role of gender and type of job (IT versus non-it job) in the relationship between job and organizational factors and QWL. Effects of gender and working in the IT workforce A previous analysis of the impact of gender and job type (IT versus non-it) on the job characteristics-qwl relation in the IT workforce showed that there were unique job and organizational characteristics that affected job satisfaction and job strain of female IT workers (Carayon, Hoonakker, Marchand & Schwarz, 23). Among IT workers, high work pressure was related to low job satisfaction for women, but not for men. Furthermore, female IT workers who reported high task significance also reported high job strain. This relationship between task significance and job strain seems to be unique for female IT workers. Our research examines the role of the work environment in influencing QWL and turnover and how employers can better design the culture, organization 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). Job/organizational design has been suggested as an important solution for improving QWL and reducing turnover.
Figure 1 Conceptual Framework Gender α 1 Job/Organizational Design: - Feedback - Autonomy - Skill Variety - Task Significance - Task Identity - Work Pressure γ 1 γ 2 β Quality of Working Life: - Job Satisfaction - Job Strain - Organizational Identification - Organization Involvement IT vs. non IT Jobs α 2 Research questions In this paper, we explore the following research questions: Do women in IT jobs report different QWL than men in IT jobs or women and men in non-it jobs? Are there different pathways that link job factors to QWL for women and men in IT jobs versus non-it jobs? Figure 1 represents the conceptual framework tested in this study. Sample METHOD In order to identify the role of gender and job type in the relation between job and organizational factors and QWL, we conducted a secondary analysis of data collected in an earlier study. In a study of organizational change (i.e. Total Quality Management), job characteristics and QWL, data was collected in a Midwestern state agency. Out of the 1,11 respondents, 219 were eliminated because of insufficient job title information. Table 1 summarizes the characteristics of the study sample. We identified IT jobs based on the definition of Freeman and Aspray (1999). They distinguish between four distinct categories of IT work: conceptualizers, developers, modifiers/extenders and supporters/tenders. This definition was expanded to include new emerging high-end IT jobs, such as web designers and Internet solutions experts. Once the IT job titles were defined, we classified the respondents in two job categories: (1) IT jobs and (2) non-it jobs. Measures We used the Hackman and Oldham s (19) Job Diagnostic Survey to measure the following job characteristics: feedback, autonomy, skill variety, task significance and task identity. Additionally, a short scale measuring work pressure was included (Caplan et al, 19). Four measures of QWL were used in the study: job satisfaction (Quinn et al, 1971), job strain (Reeder et al, 1973) and organizational identification and involvement (Cook & Wall, 198). All scales used in the questionnaire were converted to scores from (lowest) to (highest).the reliability scores (Cronbach s alpha) for the measures were above.7 (from.71 to.89), except for organizational involvement that had a Cronbach s alpha score of.64. Analysis Statistical analyses were performed to examine the impact of gender, job type (IT versus non-it job) and job factors on QWL. To answer the first research question we performed two-way ANOVA s for each QWL measure. To answer the second research question, a series of linear regression analysis was performed as follows (Kahane, 21): - the measures of QWL as the dependent variables (Y i ) - gender (α 1 ), and job type (α 2 ) as dummy independent variables - each job characteristic (β) as independent variables - and the interactions between each job characteristic and gender (γ 1 ), each job characteristic and job type (γ 2 ) and each job characteristic, gender and job type (γ 12 ) as independent variables. We used the following model: Y = µ + β + α + α + γ + γ + (see Figure 1). i 1 2 1 2 γ 12 Table 1 Characteristics of the Study Sample Number of Number of Number of women Number of ITworkers respondents respondents used in the analysis 18 111 (62%) 891 (8%) 476 (54%) 137 (16%) 86 (1%) Number of employees Number of female IT-workers
QWL by gender and job type RESULTS The results of the first analysis did not show significant differences on the measures of QWL by gender. The only QWL variable that shows a significant difference by job type is job strain. Job strain is lower for IT workers than for non-it workers. Table 2 summarizes the results of this first analysis. Table 2 QWL by gender and job type (Mean (Standard deviation)) Male non-it Female non-it Male IT Female IT Job satisfaction 62.64 (22.97) 61.98 (21.7) 6.32 (24.27) 66.2 (19.51) Organizational involvement 58.36 (21.39) 57.1 (21.29) 57.19 (21.15) 59.98 (18.52) Organizational identification 74.2 (14.62).78 (14.76) 72.55 (17.82).92 (14.) Job strain 51.15* (18.9).99* (2.8) 45.83* (23.4) 43.2* (21.73) * Difference between IT and non-it is statistically significant (p<.5). The relation between job characteristics and QWL by gender and job type In order to examine our second research question, we conducted separate regression analyses for each of the independent and dependent variables. Job satisfaction. Table 3 presents the results of the linear regression of job satisfaction by job characteristics, gender and job type and their interaction. Table 3 Job satisfaction by job characteristics, gender and job type (adjusted β, and adjusted ) Main Interaction Feedback.58.19 -.21 23% Autonomy.48 19% Skill variety.47.19.2 -. -.2 12% Task significance.31.36 -.42.15 5%.21 -.84 -.28.97.36 32% Work Pressure -.22.13 4% The results of the regression analysis show that the model with task identity as a predictor of job satisfaction explains the highest amount of variance; it also explains differences by gender and job type. Figure 2 shows the effect of task identity on job satisfaction by gender and job type: women, and more specifically women in IT jobs, with low task identity experience lower job satisfaction than other groups; women who experience high levels of task identity report higher levels of job satisfaction. Job satisfaction Figure 2 Regression slopes of Job satisfaction by Task identity, gender and job type Organizational involvement. There are no significant results with organizational involvement as a dependent variable. Organizational identification. Table 4 presents the results of the linear regression of organizational identification by job characteristics, gender and job type and their interaction. Table 4 Organizational identification by Job characteristics, gender and job type (adjusted β, and adjusted ) Main Interaction Feedback.51 19% Autonomy.21.2 2% Skill variety.3.18 6% Task significance.36. -.33 7%.17 -.89.97 19% Work Pressure The model with task identity as independent variable explains a high amount of variance of organizational identification. The results of the regression analysis also show gender and job type differences. Figure 3 shows these : women, and more specifically women in the IT work force, with low task identity experience lower organizational identification than the other groups; women with high levels of task identity show higher levels of organizational identification.
Figure 3 Regression slopes of Organizational identification by, gender and job type Figure 4 Regression slopes of Job strain by, gender and job type Organizational Identification Job Strain Job strain. Table 5 presents the results of the linear regression of job strain by job characteristics, gender and job type and their interaction. Table 5 Job strain by job characteristics, gender and job type (adjusted β, and adjusted ) Main Interaction Feedback -.19 5% Autonomy -.32 -.18 95 Skill variety -.24.17 2% Task significance -.29.31 3% -.9.42 -. -.26 1% Work Pressure.57 34% The model with work pressure as independent variable explains the highest amount of variance of job strain. There are no gender or job type. The model with task identity as predictor of job strain shows gender and job type interaction. Figure 4 shows the effect of task identity on job strain: women with low task identity experience higher job strain than the other groups; women who experience high task identity report lower job strain. DISCUSSION Overall, there were little differences in QWL for men and women and for IT and non-it workers. This is not what we expected based on the literature. However, the pathways from job characteristics to QWL differ by gender and by job type (IT versus non-it). The most important result of our study is that task identity seems to play an important role in predicting QWL for women in the IT work force, but not for the other groups. is the extent to which employees do an entire piece of work (instead of small parts) and can clearly identify the results of their effort (Sims et al, 1976). We have found little literature on the role of task identity as a predictor of QWL, and almost no literature examining the role of gender and job type in this relationship between task identity and QWL. According to the job characteristics theory of Hackman & Oldham (19), the job itself should be designed to possess certain characteristics that create conditions for high work motivation, satisfaction and performance. Critical Psychological States (conditions) are by definition internal to persons and therefore, not directly manipulable in managing work; however, they depend on five job characteristics: reasonable objective, measurable and changeable properties of the job that foster the desired psychological states to produce internal work motivation (see Figure 5). One of the limitations of this study is that it was conducted in one public sector organization. Therefore, the results cannot be generalized to other public sector organizations. In addition, because the dynamics in the public sector may be different from the private sector, our results cannot be generalized to private sector organizations. ACKNOWLEDGEMENTS Funding for this research is provided by the NSF Information Technology Workforce Program (Project #EIA- 1292, PI: P. Carayon) http://cqpi2.engr.wisc.edu/itwf/
REFERENCES Caplan, R. D., Cobb, S., French, J. R. P., Harrison, R. V., & Pinneau, S. R. (19). Job demands and worker health. Washington DC: US Government Printing Office. Carayon, P., Hoonakker, P., Marchand, S., & Schwarz J. (23). Job characteristics and quality of working life in the IT workforce: the role of Gender. ACM SIGCPR/SIGMIS 23 conference. Carayon, P., Haims, M. C., & Kraemer, S. (21). Turnover and retention of the Information Technology workforce: The diversity issue. In M. J. Smith & G. Salvendy (Eds.), Systems, Social and Internationalization Design Aspects of Human-Computer Interaction (pp. 67-7). Mahwah, NJ: Lawrence Erlbaum Associates. Carayon, P., & Smith, M. J. (2). Work organization and ergonomics. Applied Ergonomics, 31, 649-662. CAWMSET. (2). Land of Plenty: Diversity as America's Competitive Edge in Science, Engineering and Technology. Washington DC: Congressional Commission on the Advancement of Women and Minorities in Science, Engineering and Technology Development. Cook, J., & Wall, T. D. (198). New work attitudes measures of trust, organizational commitment, and personal need non-fulfillment. Journal of Organizational Psychology, 53, 39-52. Freeman, P., & Aspray, W. (1999). The Supply of Information Technology Workers in the United States. Washington, DC: Computing Research Association. Hackman, J. R., & Oldham, G. R. (19). Development of the Job Diagnostics survey. Journal of Applied Psychology, 6, 159-17. Hackman, J. R. & Oldham, G. R. (198). Work redesign. Reading, MA: Addisson Wesley. Igbaria, M., & Greenhaus, J. H. (1992). Determinants of MIS employees' turnover intentions: A structural equation model. Communications of the ACM, 35(2), 34-51. Igbaria, M., & Siegel, S. R. (1992). The reasons for turnover of information systems personnel. Information and Management, 23, 321-33. Janssen, P. M., de Jonge, J., & Bakker, A. B. (1999). Specific determinants of intrinsic work motivation, burnout and turnover intention: a study among nurses. Journal of Advanced Nursing, 29(6), 136-1369. Kahane, L. H. (21). Regression Basics. Thousand Oaks, CA: Sage Publications. Meares, C. A., & Sargent, J. F. (1999). The digital workforce: Building infotech skills at the speed of innovation. Washington, DC: U.S. Department of Commerce, Technology Administration, Office of Technology Policy. Moore, J., & Burke, L. A. (22). How to turn around 'turnover culture' in IT. Communications of the ACM, 45(2), 73-78. Network, D. D. (2). Meeting workforce demands in the digital economy. Retrieved October 23, 2, from the World Wide Web: http://www.digitaldividenetwork.org/workdemands Quinn, R., Seashore, S., Kahn, R., Mangion, T., Cambell, D., Staines, G., & McCullough, M. (1971). Survey of Working Conditions: Final Report on Univariate and Bivariate Tables. Washington, D.C.: U.S. Government Printing Office, Document No.2916-1. Reeder, L.G., Schrama, P. G., & Dirken, J. M. (1973). Stress and cardiovascular health: An international cooperative study: I. Social Science and Medicine, 7(8), 573-584. Sims, H. P., Szilgyi, A. D. & Keller, R. T. (1976). The measurement of job characteristics. Academy of Management Journal, 19, 195-212.