Information Technology Association of America (ITAA),. 1998, 2002; Office ... dropped considerably in recent years (5% alone in 2001, ... tenure; (2) job characteristics: job demands, role ambiguity, decision .... Bachelors degree 38.9%. 37.4%.
Job and Organizational Factors as Predictors of Turnover in the IT Work Force: Differences between Men and Women Peter Hoonakker+, Pascale Carayon+*, Jen Schoepke+* and Alexandre Marian+* +
Center for Quality and Productivity Improvement * Department of Industrial Engineering University of Wisconsin-Madison
Abstract. In this paper we present the results of our study on turnover in the IT workforce and examine job and organizational factors that contribute to turnover, specifically for women. Data was collected using a web based survey. Five companies participated in the survey and 624 employees in different IT jobs participated. The results show that effects of job and organizational characteristics on turnover intention are mediated by quality of working life. The results also show that the pathways to turnover are different for men and women in the IT workforce. Keywords: IT Work force, turnover, job and organizational characteristics, gender 1.
Introduction
There is substantial evidence for a critical shortage of skilled IT workers in the United States (Freeman & Aspray, 1999; Information Technology Association of America (ITAA), 1998, 2002; Office of Technology Policy (OTP), 1997). From 2000 to 2010, the occupation of computer specialists is projected to grow 69 percent, and the occupation of computer and information systems managers is projected to grow 48 percent (Hecker, 2001). Although demand for IT workers dropped considerably in recent years (5% alone in 2001, ITAA 2002), there is still a lack of qualified workers, referred to as the “gap” in IT workers. A large subset of this problem is related to the under representation of women and minorities in the IT workforce. Under representation may be caused by insufficient women and minorities entering the IT workforce as well as 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 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). Turnover of highly skilled employees can be very expensive and disruptive for firms (Reichheld, 1996). Losing highly skilled staff members means that companies incur substantial costs associated with recruiting and re-skilling, and hidden costs associated with difficulties completing projects and disruptions in team-based work environments (Niederman
& Summer, 2003). The “job churning” seems to be related to several factors associated with the digital revolution. Namely, information technologies have short life cycles, requiring continuous hiring of new workers with new skills, as opposed to the more time consuming approach of training current employees (Network, 2000). The demand for management information systems (MIS) employees, for example, is extremely high and MIS professionals have historically displayed very high rates of turnover (Igbaria & Siegel, 1992). Determining the causes of turnover within the IT workforce and controlling it through human resource practices is imperative for organizations (Igbaria & Siegel, 1992). Igbaria and Greenhaus (1992) tested a model of turnover intention among 464 MIS employees using data from a questionnaire survey. The model consisted of five sets of variables: 1) demographic variables; 2) role stressors; 3) career experiences; 4) work-related attitudes; and 5) turnover intention. Results indicated that two work-related attitudes, job satisfaction and organizational commitment had the strongest and most direct influence on turnover intention, and the impact of other variables on turnover intention 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 levels of 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 intention 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
factors can influence attitudes, which in turn, can influence turnover intention. In this paper we present the results of our own study on turnover in the IT workforce and examine the job and organizational factors that contribute to turnover, specifically for women. We will examine the following job and organizational factors: demands, role ambiguity, decision control, challenge, social support, training and developmental activities, career advancement, corporate fit and rewards. 2.
A conceptual model of turnover intention
Figure 1 presents the model of turnover intention examined in this study. The model is comprised of 5 sets of variables: (1) demographic variables: age, education and organizational tenure; (2) job characteristics: job demands, role ambiguity, decision control, challenge in the job, supervisory support and support from colleagues; (3) organizational characteristics: satisfaction with training, career and developmental opportunities, corporate integration; and rewards; (4) quality of working life: organizational involvement, job satisfaction and emotional exhaustion; and (5) turnover intention. All measures used have been found to be reliable and valid. Demographics
Job and Organizational Factors
Quality of Working Life
Turnover intention
Gender
Figure 1 Conceptual model 2.1. The relation between demographic variables, QWL and turnover intention From the literature, we know that demographic variables are expected to have direct effects on work-related attitudes (Arnold & Feldman, 1982; Compton, 1987, Igbaria & Greenhaus, 1992). Prior research reveals that age and organizational tenure are positively related to satisfaction and involvement (Arnold & Feldman, 1982; Cotton & Tuttle, 1986; Igbaria & Greenhaus, 1992). Education has been found to be negatively related to satisfaction (Parasuraman, 1982, Igbaria & Greenhaus, 1992), and organizational involvement (Mottaz, 1988). Moreover, prior research suggests that demographic variables have direct effects on turnover intention over and above their effects on turnover intention through satisfaction and involvement (Parasuraman, 1982, Igbaria & Greenhaus, 1992). 2.2. The relation between job and organizational characteristics and QWL From the job stress and job design literature we know that job and organizational characteristics affect Quality of Working Life (QWL). 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 commitment and perceived stress. The organizational/job design and job stress models highlight the importance of a variety of job and organizational factors as predictors of QWL and turnover (Carayon, Haims & Yang, 2000). The most important job and organizational factors identified in the literature are: job demands, job control, social support, job content, role conflict, and role ambiguity (Carayon-Sainfort, 1992; Karasek, 1979; Theorell & Karasek, 1996). 2.3. The relation between QWL and turnover intention A number of empirical studies confirm the important role of organizational commitment in the turnover process (Baroudi, 1985; Cotton & Tuttle, 1986). It has also been reported that organizational commitment is more strongly related to turnover intention than job satisfaction (Baroudi, 1985). Considerable research has linked job satisfaction to organizational commitment and turnover (Baroudi, 1985). It has been suggested that satisfaction and organizational commitment are related but distinguishable attitudes: commitment is an affective response to the entire organization, whereas job satisfaction represents an affective response to more specific aspects of the job (Porter et al., 1974). However, the results of the study by Igbaria and Greenhaus (1992) showed that job satisfaction has a stronger, direct effect on turnover intention than organizational commitment. Another powerful factor that prior research has repeatedly shown to be significantly correlated to organizational commitment, job satisfaction and turnover intention, is burnout (Moore, 2000). Research has shown that emotional exhaustion (the core dimension of burnout) is linked to reduced job satisfaction (Burke and Greenglass, 1995; Maslach and Jackson, 1984a; Pines et al, 1981; Wolpin et al, 1991); reduced organizational commitment (Jackson et al, 1987; Leiter, 1991; Sehti et al, 1999); and high turnover and turnover intention (Firth and Britton, 1989; Jackson et al, 1986; Moore, 2000; Pines et al, 1981). The research literature suggests that technology professionals are particular vulnerable to work exhaustion (Kalimo and Toppinen, 1995; Moore, 2000). 3.
Method
3.1. Procedure We used a web based survey to collect the questionnaire data. For a detailed description of the web based survey system, see Barrios (2003). The participating company sent out an e-mail to notify their employees of the survey and two days later we sent the employees an e-mail, describing the study, asking for their participation and providing them with a link to our web based survey. An integrated part of the web based survey management system is an informed consent procedure. The total response rate was 56%. 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.
3.2. Sample A total of 5 companies participated in the study: one large company (N>500), one medium sized company (N=200) and three small companies (N
