Using the Job-Demands-Resources model to predict turnover in the information technology workforce – General effects and gender

Abstract

High employee turnover has always been a major issue for Information Technology (IT). In particular, turnover of women is very high. In this study, we used the Job Demand/Resources (JD-R) model to examine the relationship between job demands and job resources, stress/burnout and job satisfaction/commitment, and turnover intention and tested the model for gender differences. Data were collected in five IT companies. A sample of 624 respondents (return rate: 56%; 54% males; mean age: 39.7 years) was available for statistical analyses. Results of our study show that relationships between job demands and turnover intention are mediated by emotional exhaustion (burnout) and relationships between job resources and turnover intention are mediated by job satisfaction. We found noticeable gender differences in these relationships, which can explain differences in turnover intention between male and female employees. The results of our study have consequences for organizational retention strategies to keep men and women in the IT work force.

Full text

DOI: 10.20419/2013.22.373
CC: 3650
UDK: 159.9:331.5
Psihološka obzorja / Horizons of Psychology, 22, 51–65 (2013)
© Društvo psihologov Slovenije, ISSN 2350-5141
Znanstveni raziskovalnoempirični prispevek
*Naslov/Address: Christian Korunka, Faculty of Psychology, University of Vienna, Universitaetsstrasse 7, A-1010 Vienna, Austria,
Tel: +43 1 4277 47342, e-mail: christian.korunka@univie.ac.at
Članek je licenciran pod pogoji Creative Commons Attribution 4.0 International licence. (CC-BY licenca).
The article is licensed under a Creative Commons Attribution 4.0 International License (CC-BY license).
Using the Job-Demands-Resources model to predict turnover
in the information technology workforce – General effects
and gender differences
Peter Hoonakker1, Pascale Carayon1 and Christian Korunka2*
1Department of Industrial and Systems Engineering, University of Wisconsin-Madison, USA
2Faculty of Psychology, University of Vienna, Austria
Abstract: High employee turnover has always been a major issue for Information Technology (IT). In particular, turnover
of women is very high. In this study, we used the Job Demand/Resources (JD-R) model to examine the relationship between
job demands and job resources, stress/burnout and job satisfaction/commitment, and turnover intention and tested the model
for gender differences. Data were collected in five IT companies. A sample of 624 respondents (return rate: 56%; 54% males;
mean age: 39.7 years) was available for statistical analyses. Results of our study show that relationships between job demands
and turnover intention are mediated by emotional exhaustion (burnout) and relationships between job resources and turnover
intention are mediated by job satisfaction. We found noticeable gender differences in these relationships, which can explain
differences in turnover intention between male and female employees. The results of our study have consequences for
organizational retention strategies to keep men and women in the IT work force.
Keywords: job characteristics, organizational characteristics, job demand-resources model, employee turnover, human sex
differences
Uporaba modela delovnih zahtev in virov pri napovedovanju
fluktuacije med zaposlenimi na IT področju – splošni učinki
in razlike po spolu
Peter Hoonakker1, Pascale Carayon1 in Christian Korunka2
1Oddelek za industrijski in sistemski inženiring, Universza v Wisconsin-Madisonu, ZDA
2Fakulteta za psihologijo, Univerza na Dunaju, Avstrija
Povzetek: Visoka fluktuacija zaposlenih predstavlja velik problem na področju informacijskih teh nologij. Fluktuacija na tem
področju je posebno visoka pri ženskah. V pričujoči raziskavi, ki temelji na teoretičnem ozadju modela delovnih zahtev in
virov, smo proučevali povezave delovnih zahtev in resursov s stresom/izgorelostjo, z delovnim zadovoljstvom/pripadnostjo in
z željo po menjavi zaposlitve. Podatki si bili pridobljeni v petih IT podjetjih in temeljijo na vzorcu 624 udeležencev (stopnja
odzivnosti: 56 %; 54 % moških; povprečna starost: 39,7 let). Rezultati so pokazali, da povezavo med delovnimi zahtevami
in željo po menjavi zaposlitve mediira emocionalna izčr panost (dimenzija izgorelosti). Odnos med delovni viri in željo po
menjavi zaposlitve pa mediira delovno zadovoljstvo. Ugotovili smo tudi opazne razlike po spolu v omenjenih povezavah, s
pomočjo katerih lahko razložimo razlike med moškimi in ženskimi zaposlenimi v želji po menjavi zaposlitve. Rezultati imajo
praktične implikacije za strategije ohranjanja moških in ženskih zaposlenih na IT področju.
Ključne besede: značilnosti dela, značilnosti organizacije, job demand-resources model, fluktuacija zaposlenih, razlike med
spoloma

52
High employee turnover has been a major problem for
the Infor mation Technology (IT) se ctor since the very early
days of computing and continues to be a problem (Dubie,
2009; Hoon & Jing, 2011; Moore, 2000; Niedermann &
Sumner, 2003). IT personnel frequently change employers,
and have shown this tendency ever since statistics have
been kept. Studies on turnover in the IT work force
conducted in the late 60’s and early 70’s (e.g., Stone,
1972; Willoughby, 1977) showed that annual turnover
ranged between 15 percent and 20 percent in this period
(Willoughby, 1977). I n th e late 1970s, t urnove r ra n as hig h
as 28 percent annually (McLaughlin, 1979) and up to 20
percent in the early 1980s. By the 1990s, the turnover rate
reached 25 percent to 33 percent per year (Jiang & Klein,
2002), and even Fortune 500 firms have similarly high
turnover rates among their IT personnel (Hayes, 1998). As
compared to the average all-industry voluntary turnover
rates of about 10% (2009: 8%, 2010: 13%, 2011: 9%;
source: SHRM Human Capital Benchmarking database),
turnover in the IT sector is considerable higher. Although
women have comprised nearly 48% of the total American
work force throughout the last 10 years, the percentage of
women in the IT workforce fell from 41% to 25% between
1996 and 2008 (Trauth, Quesenberry, & Huang, 2006;
National Center for Woman & Information Technology,
2009). 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). For instance,
results of a study by Lyness & Judiesch (2001) showed
a 16.5% turnover rate for female managers and 17% for
male managers in financial service organizations. Results
of a more recent study by Becker-Blease, Elkinawy, and
Stater (2010) on turnover among nearly 18,000 executives
using data from Standard & Poor’s 1500 firms, showed
that 7.2% of women and 3.8 percent of men left their job
in the year of the study.
Turnover of highly skilled employees can be very
expensive and disruptive for firms (Reichheld, 1996;
Thatcher, Stepina, & Boyle, 2003). Losing highly skilled
staff members leads companies to incur substantial costs
associated with recruiting and re-skilling, and hidden
costs associated with difficulties completing projects
and disruptions in team-based work environments
(Niedermann & Sumner, 2003; Thatcher et al., 2003).
Determining the causes of turnover within the IT
workforce and controlling it through human resource
practices and work system design is imperative for
organizations (Igbaria & Siegel, 1992).
This study hast the following objectives: (1) To
determine the causes of turnover in IT. The Job Demands-
Resources (JD-R) model (e.g., Bakker, 2007), a currently
widely used stress model was used as theoretical
background and adapted the model for predicting turnover
in the IT work force. The model explains the relationships
between job demands and job stress on the one hand, and
job resources and job satisfaction/commitment on the
other hand, and turnover intention. (2) To test the model
for gender differences. Possible direct and moderator
effects of gender on the relationships in the adapted JD-
R model are examined. Especially moderator effects
of gender with regard to turnover in the IT workforce
were suggested (e.g., Baroudi & Igbaria, 1995), but not
empirically tested so far.
Development of the research model
One of the basic assumptions of the JD-R model is
that – independent of a particular work context - work
environments can be characterized by two dimensions:
job demands and job resources.
Job demands are physical, psychological, social
and organizational characteristics of a job, requiring
physical and psychological effort and energy from an
employee, which in turn are related to physiological and
psychological costs (e.g., Bakker, 2007). Although job
demands are not necessarily negative, they may turn into
job stressors, when meeting those demands requires high
effort (Bakker, Demerouti, & Schaufeli, 2003). Stress and
burnout (i.e. emotional exhaustion) can result in lower
quality and performance, and in increased absenteeism
and turnover.
Job resources are physical, psychological, social
and organizational characteristics of a job, which are
instrumental in achieving work goals, reducing job
demands, and the associated costs with them, and
stimulate learning. Job resources are not only functional
in achieving work goals; they also stimulate personal
growth and development (Bakker, 2007).
A second assumption of the JD-R model is that job
demands and resources are related to well-being and
attitudinal outcomes. The JD-R model postulates that
the two sets of working conditions (i.e., demands and
resources) may each evoke a different process. High
job demands are likely to result in strain reactions (e.g.,
stress, burnout), which in turn may lead to an increase in
absenteeism and turnover. The pathway from job demands
to absenteeism and turnover via emotional exhaustion is
also known as the energetic pathway. On the other hand,
resources (e.g., decision latitude, social support) are likely
to foster goal accomplishment, which in turn can lead to
positive job attitudes (job satisfaction and organizational
commitment) and reduced withdrawal behavior (reduced
absenteeism and turnover). This pathway is also known as
the motivational pathway (e.g., Bakker, 2007).
A third assumption of the JD-R model is that the
relations between job demands and resources and
outcomes are mediated by strain reactions (i.e., burnout,
emotional exhaustion) on the one hand, and motivational
processes (i.e., job satisfaction and commitment) on the
other (Bakker, Demerouti, & Verbeke, 2004; Lewig,
Xanthopoulou, Bakker, Dollard, & Metzer, 2007). Thus,
strain reactions and motivational processes are postulated
P. Hoonakker, P. Carayon and C. Korunka

53
to be mediators between job demands/resources and
turnover. Recent studies have further enhanced the JD-R
model by adding work-to-family conf lict as a job demand
that depletes an employee’s resources (Mauno, Kinnunen,
& Ruokolainen, 2006).
The JD-R model has been confirmed for a wide range
of jobs and working conditions (see, for instance, a special
issue 2011 of the SA Journal of Industrial Psychology), but
never tested to predict turnover intention in the IT work
force, which is the main goal of our study. To achieve this
goal, the specific demands and resources relevant for
turnover in the IT work force have to be defined.
There are two bodies of literature that have examined
job demands and job resources related to turnover
intention and turnover. The first body of literature is the
job and organizational design and job stress literature,
which focuses on job and organizational characteristics
that may cause people to leave their jobs. The second body
is the human resource management (HRM) literature.
The HRM literature focuses on practices that help an
organization to meet its strategic goals by attracting and
maintaining employees and managing them effectively.
Both bodies of literature are relevant for the JD-R model,
because the job design/job stress literature focusses on
job demands, and the HRM literature focusses on job
resources.
The job and organizational design literature has shown
that job demands such as workload (Janssen, De Jonge,
& Bakker, 1999; Houkes, Janssen, De Jonge, & Bakker,
2003), role stressors such as role ambiguity (Baroudi
& Igbaria, 1995; Igbaria & Greenhaus, 1992; Igbaria &
Guimaraes, 1999), lack of challenge (Mathieu & Zajac,
1990) including task repetitiveness (Cotton & Tuttle,
1986; Mobley, 1977) and routinization (Griffeth, Hom, &
Gaertner, 2000), are positively related to turnover.
Work-family conflict can be considered as a job
demand, which has also been negatively linked to
several organizational outcomes such as job satisfaction,
organizational commitment, job stress and turnover
(Frone, Russell, & Copper, 1992; Hoonakker, Carayon, &
Schoepke, 2005; Ahuja, 2002). In many IT-related jobs,
employees are expected to work late, be on-call to solve
technical problems and travel extensively; all of these
factors can result in conf licts between working and family
life. Work-family conflict has been defined as “a form of
inter-role conf lict that occur s when the demand s of work and
family are mutually incompatible” (Greenhaus & Beutell,
1985). The two components of work-family-conflict,
family matters that conflict with working life (FWC) and
work fa ct or s that conflict with family life (WFC), can add
to the psychological demands placed upon workers and
therefore affect their well-being, stress and depression
(Googins, 1991), physical ailments (Frone, Yardley, &
Markel, 1997), life satisfaction (Higgins & Duxbury,
1992) and turnover (Armstrong, Riemenschneider, Allen,
& Reid, 2007). This is particularly true for women (Grant-
Vallone & Donaldson, 2001).
On the other hand, the job and organizational design
literature shows that job resources such as decision
latitude (Beehr, Glaser, Canali, &Wallwey, 2001; Kim &
Stoner, 2008), social support (both supervisory support
and support from colleagues (Jawahar & Hemmasi, 2006;
Lee, 2004; Mobley, Griffeth, Hand & Meglino, 1979;
Rhoades & Eisenberger, 2002)), and Person-Organization-
fit (Verquer, Beehr, & Wagner, 2003) may instigate a
motivational process leading to job-related learning, work
engagement and organizational commitment (Blau &
Boal, 1987) and a propensity to stay in the organization.
Stress/burnout has also been found to be significantly
correlated to tur nover intention (Moore, 2000). Emotional
exhaustion (a core dimension of stress/burnout) is linked
to reduced job satisfaction (e.g., Burke & Greenglass,
1995; Maslach & Jackson, 1984), reduced organizational
commitment (e.g., Leiter, 1991; Sethi, Barrier, & King,
1999), and high turnover and turnover intention (e.g., Firth
& Britton, 1989; Jackson, Turner, & Brief, 1986; Moore,
2000; Pines, Aronson, & Kafry, 1981)). Technology
professionals are particular vulnerable to work exhaustion
(Kalimo & Toppinen, 1995; Moore & Burke, 2002). Work
overload, role ambiguity, role conflict, lack of autonomy
and lack of rewards can be considered as risk factors for
burnout among IT professionals (Moore, 2000; Nelson,
1996; Sonnentag, Brodbeck, Heinbokel, & Stolte, 2001).
Human Resource Management (HRM) literature
focuses on practices (i.e. resources), which help an
organization to meet its strategic goals by attracting,
maintaining and effectively managing employees. One
of the basic assumptions of HRM is that employee
involvement and commitment can have a positive effect on
organizational performance, such as higher productivity
and quality of products and services, and reduced
absenteeism and turnover (Lawler, 1986, 1992, 1996). The
HRM literature stresses the importance of job resources
su ch as t raini ng (ava ilabi lit y and sa tisfa ction w ith trai ning
opportunities at the company), developmental (e.g.,
management development programs, coaching from peers
and supervisors, mentorship) and career advancement
opportunities (promotional opportunities), as well as a
fair reward system.
Review studies have shown that the use of practices
such as extensive recruitment, selection and training
procedures, formal information sharing, and systems that
recogni ze and reward employee merit such as perfor mance
appraisal, promotion and incentive compensation are
linked to organizational outcomes, including reduced
tur nover (McEvoy, & Cascio, 1985), increased product ivity
(Guzzo, Jette, & Katzell, 1985), and improved financial
performance (Borman, 1991; Gerhart & Milkovich, 1992).
These practices are also known as “high performance
work practices” or “high involvement work processes”
(U.S. Department of Labor, 1993). Several studies have
shown that high involvement work practices such as
work design, incentive practices, flexibility, training
opportunities promotion criteria, information sharing and
direction setting and high involvement work processes
such as power, information, rewards and knowledge
are related to turnover (Huselid, 1995; Vanderberg,
Richardson, & Eastman, 1999). Agarwal and Ferrat (2002)
Job-Demands-Resources model and turnover in the IT workforce

54
examined the relationship between HRM systems and
retention in a study of 350 IT professionals in ten different
organizations. Results of this study showed that employee
perceptions of the fairness of HRM systems are related to
turnover intention.
Reviews on employee turnover in both bodies of
literature conclude that – in concordance with the JD-R
model - stress/burnout on one hand and job satisfaction/
commitment on the other hand are good predictors of
turnover and turnover intention (Cotton & Tuttle, 1986;
Griffeth, Hom, & Gaertner, 2000). Low job satisfaction
was found to be a significant predictor of turnover
intention and turnover also in the turnover intention model
of Mobley, Horner, and Hollingsworth (1978), which
was at least partly confirmed in follow-up studies (e.g.,
Bannister & Griffeth, 1986; Hom, Caranikas-Walker,
Prussia, & Griffeth, 1992). For example, the meta-analysis
of Griffeth et al. (2000) confirmed the important role of
job satisfaction for turnover intention and turnover.
Organizational commitment also plays an important
role in the turnover process (Baroudi, 1985; Blau & Boal,
1987; Sjoberg & Sverke, 2000). However, study results
are inconsistent. In some studies it has been reported that
organizational commitment is more strongly related to
turnover intention than job satisfaction (Baroudi, 1985).
On the other hand, Igbaria and Greenhaus (1992) showed
that job satisfaction has a stronger direct effect on turnover
intention than organizational commitment.
Demographic characteristics
Research has also shown that personal characteristics
are associated with turnover intention. Personal variables
have direct effects on work-related attitudes (Arnold &
Feldman, 1982; Bluedorn, 1982; Compton, 1987; Igbaria
& Greenhaus, 1992; Cotton & Tuttle, 1986; Mobley et
al., 1979). Age and organizational tenure are positively
related to job satisfaction and organizational commitment
(Arnold & Feldman, 1982; Cotton & Tuttle, 1986; Igbaria
& Greenhaus, 1992) and negatively related to turnover
(Cotton & Tuttle, 1986). Education has been found to be
negatively related to job satisfaction (Igbar ia & Greenhaus,
1992), and organizational commitment (e.g., Mottaz,
1988) and positively related to turnover (Cotton & Tuttle,
1986). Salary has been found to be negatively related to
turnover in the meta-analytic study by Cotton and Tuttle
(1986). Prior research suggests that demographic variables
have direct effects on turnover intention over and above
their indirect effects on turnover intention through
satisfaction and involvement (Igbaria & Greenhaus, 1992;
Parasuraman, 1982). Therefore, we control for personal
variables in our model.
Conceptual and empirical models of turnover provide
strong support for the proposition that a behavioral
intention to turnover constitutes the most immediate
determinant of actual turnover behavior (Baroudi &
Igbaria, 1995; Bluedorn, 1982; Cotton & Tuttle, 1986;
Fishbein & Ajzen, 1975; Igbaria & Greenhaus, 1992;
Mobley, 1977; Parasuraman, 1982; Thatcher et al., 2003).
Thus, we included turnover intention as an outcome
variable in our study based on the JD-R model.
Gender
As mentioned earlier, gender plays a specific role in the
tur nover process. Baroud i and Igbaria (1995) examined the
role gender plays in career success within IT occupations.
They found that, even when demographic variables (i.e.
education, knowledge and skills) were controlled for,
women in IT hold lower level positions than men, receive
lower salaries and have fewer opportunities to interact
with peers. There were no significant differences between
men and women in job satisfaction and organizational
commit ment, but there was a sign ificant dif ference in ter ms
of intention to stay. Contrary to predictions, women were
more likely to estimate longer continuing employment.
Baroudi and Igbaria (1995) concluded that there is a need
to look for gender bias in hiring, salary, promotion and
personnel practices and to examine additional factors,
such as family constraints, in order to explain the gender
differences in turnover (Baroudi & Igbaria, 1995; Truman
& Baroudi, 1994). Results of the study by Thatcher et
al. (2003) also showed gender differences (higher rates
of turnover intention in women), and they conclude that
future studies should examine gender’s implications for
IT workers’ attitudes and behavior. However, relatively
little research has examined the range of job demands and
job resources that can affect women in IT. The research
conducted by Igbaria and Greenhouse (1992) provides
a useful foundation, but does not provide a systematic
test of gender differences in the relationships between
job demands, job resources, stress, job satisfaction,
organizational commitment and turnover intention.
Conceptually, there are two possibilities for higher
turnover intention rates in women. Either there is a direct
effect of gender on job demands, job resources, stress,
job satisfaction, commitment and turnover intention or
gender moderates the relationships between these factors.
In the first case, women are more often employed in lower
status jobs, experience lower quality of working life and
therefore have a higher propensity to leave their jobs. In
the second case, men and women experience the same
job demands and job resources, but women have different
attitudinal and behavioral reactions to the similar
organizational experiences and practices. Although
suggested by Baroudi and Igbaria (1995), the moderating
effect of gender has not been tested in previous research
in the IT work force.
Hypotheses
The review of the research literature has highlighted
the importance of the following job demands and job
resources with regard to turnover intention: job demands,
role ambiguity, work-family conflict, decision latitude,
work-related challenges, supervisory support, person-
organization fit, training, developmental and career
advancement opportunities, and fairness of rewards.
P. Hoonakker, P. Carayon and C. Korunka

55
Figure 1 shows the adapted JD-R model aiming to explain
turnover in the IT work force.
The first two hypotheses are based on the core
assumptions of the JD-R model; the next two hypotheses
are formulated to test direct and/or moderating effects of
gender (figure 1).
Hypothesis 1 (H1): Job demands are positively related
to job stress/burnout and turnover intention. Job
resources are positively related to job satisfaction
and commitment and negatively related to turnover
intention.
Hypothesis 2 (H2): The job demands – turnover intention
pathway is mediated by stress/burnout. The job
resources – turnover intention pathway is mediated by
job satisfaction/commitment.
Hypothesis 3 (H3): Gender has a direct effect on job
demands and job resources, stress, job satisfaction,
commitment and turnover intention. As female
employees are more likely to be exposed to ‘negative’
job demands and /or can benefit less from job resources,
they will report more stress and lower job satisfaction
and commitment and higher intention to turnover
(Figure 1).
Hypothesis 4 (H4): Gender moderates the relationships
between job demands and job resources and stress, job
satisfaction a nd commitment, a s well as the relat ionship
between these factors of quality of working life and
turnover intention (Figure 1).
Method
Participants
A total of five Information Technology companies
participated in the study. Company #1 is the IT department
of a large medical group in the Western United States. It
is a large department and employs about 900 employees
(response rate: 55%). Company #2 is an IT system
integrat ion service company in t he Midwest. It is a medium-
sized company and employs 190 employees (response
rate: 66%). Company #3 is a company in the South West,
providing technical, management, and administrative
support services. It is a small company and employs less
than 50 employees (response rate: 36%). Company #4 is
located in the Eastern US and is focused on using different
technologies and f inding new and in novative ways of using
that tech nology in order to increa se business productivit y.
It is a small company and employs less than 50 employees
(response rate: 31%). Company #5 provides a full range of
reliable, cost effective IT services, infrastructure support,
and state-of-the-art security solutions in the Eastern US.
It is a small company and employs less than 50 employees
(response rate: 18%). Overall, we collected data from 624
respondents (response rate: 56%).
Fifty-four percent of the respondents are male. Twenty-
six respondents chose not to reveal their gender and were
excluded from the analysis. Respondents vary in age from
20 to 68 years (M=39.7 years). The majority of the sample
Figure 1. Conceptual model and hypotheses.
Job-Demands-Resources model and turnover in the IT workforce

56
is married (61%), while 9% are living with a partner, 9%
are single, and 8% are separated, divorced or widowed.
Fifty-six percent of the respondents have children, and the
vast majority of these (83%) have children living at home.
Forty-three percent of the respondents have children
younger than 7 years. There are no gender differences in
marital status, number of children, number of children
that still live at home, or number of child ren younger than
seven years (table 2).
Table 1 shows the distribution of men and women
in the IT job categories. Results show that jobs are not
equally distributed among men and women (χ2= 91.6,
p<0.001). Men hold technical positions more often:
nearly 90% of the technicians (technical support/field
technician) and more than two-thirds of the developers
(application software developers a nd system progr ammer/
network software developer) are male. Women hold
analyst positions more often. Nearly two-thirds of the
analysts (application analyst; business analyst/consultant;
database analyst/database architect and systems analyst/
system architect/applications engineer) are female. A
greater proportion of women in our sample hold positions
in telecommunications and training, and more women
hold managerial jobs than men (see table 1).
Instrument
The dimensions and variables identified in the
literature review were incorporated in the first version of a
questionnaire, which was tested in 13 interviews with key
IT personnel. Based on the interviews, changes were made
to the question naire, to addre ss the specific problems of the
I T w or k f or ce (se e C ar ay on , Sc hoe pke, Ho ona kk er, Ha i ms ,
& Brunette, 2006). The final version of the questionnaire
consists of five sections: (1) demographic characteristics:
age, education and organizational tenure; (2) job and
organizational characteristics; (3) HRM practices; (4)
quality of working life; and (5) turnover intention. To
measure job and organizational characteristics, HRM
practices and quality of working life 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).
Detailed information about the psychometric properties
of the scales used in the study is provided in table 3.
Procedure
We used a web-based sur vey to collect the questionnai re
data. For a detailed description of the web-based survey
system, see Barrios (2003). The participating companies
in our study sent out e-mails to notify their employees
of the survey. 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 informed consent procedure was an integrated part of
the web based survey management system.
Analysis
A visual check confirmed that all variables used in
the statistical analyses were normally distributed (a
correlation matrix of all variables is available from the
authors on request).
Table 1. Job categories in the sample in numbers (and percentages) by gender
Job Position Male Female Total
Application software developer 13 (4.0%) 2 (0.7%) 15 (2.5%)
Business analyst/consultant 12 (3.7%) 17 (6.2%) 29 (4.9%)
Database administrator/manager/security 10 (3.1%) 6 (2.2%) 16 (2.7%)
Database analyst / Database architect 2 (0.6%) 5 (1.8%) 7 (1.2%)
Data Center Operator 8 (2.5%) 6 (2.2%) 14 (2.4%)
Documentation / Technical writer 2 (0.6%) 3 (1.1%) 5 (0.8%)
Network administrator/ manager 14 (4.3%) 10 (3.7%) 24 (4.0%)
Network engineer/technician/PC technician 41 (12.7%) 5 (1.8%) 46 (7.7%)
Project/Program/Applications/Operations manager 61 (18.9%) 81 (29.8%) 142 (23.9%)
Quality assurance/Testing engineer 0 (0.0%) 2 (0.7%) 2 (0.3%)
Server Engineer 8 (2.5%) 1 (0.4%) 9 (1.5%)
Software engineer/Software life cycle management 36 (11.2%) 22 (8.1%) 58 (9.8%)
Systems administrator/Systems security 9 (2.8%) 0 (0.0%) 9 (1.5%)
Systems analyst/Systems architect/Application engineer 35 (10.9%) 48 (17.6%) 83 (14.0%)
Systems programmer/Network software developer 12 (3.7%) 6 (2.2%) 18 (3.0%)
Technical support/Field technician 24 (7.5%) 3 (1.1%) 27 (4.5%)
Telecommunications 5 (1.6%) 16 (5.9%) 21 (3.5%)
Training 0 (0.0%) 5 (1.8%) 5 (0.8%)
Webmaster/Web site developer 2 (0.6%) 2 (0.7%) 4 (0.7%)
Other 28 (8.7%) 31 (11.4%) 59 (10.1%)
Total 322 (100%) 272 (100%) 594 (100%)
P. Hoonakker, P. Carayon and C. Korunka

57
For testing the hypotheses we used the following
statistical procedures: First, we used a group comparison
test (t-tests and χ2) to test the direct effect of gender
on job demands and job resources, stress/burnout/job
satisfaction/commitment and turnover intention (H3).
Second, we used path analyses with maximum likelihood
estimatio n t o t es t hy pot heses H 1, H 2 a nd H4 (s ee Fi gu re 1) .
Second, we used path analyses to test hypotheses H1, H2
and H4 (see Figure 1). We started with a full path model
(M1), including all variables and pathways, interactions
between demands and job satisfaction/commitment on
one hand, and between resources and stress/burnout on
the other hand for men and women simultaneously. Next,
we improved the path model by removing non-significant
variables and paths. This model (M2) was used for testing
hypotheses H1 and H2. The improved path model was
tested separately for men and women in a multi-group
analysis to test for a moderating effect of gender (H4).
SPSS-AMOS version 17 was used for the analyses.
Results
Results of the path analysis show that the full path
model (M1) shows an insufficient fit (both GFI and AGFI
<0.90; SRMR >0.05; and RSMEA >0.05, see Table 4).
We improved the full model by deleting all insignificant
variables and pathways (for details of the improvement
process, see Hoonakker, Carayon, Schoepke, & Marian
(2004)). In the final path model (M2), almost all relations
between job demands and job resources are mediated by
emotional exhaustion and job satisfaction (see Figure
2). Thus, hypothesis 2 (the relationship between job and
organizational characteristics and turnover intention
is mediated by quality of working life variables) can be
largely confirmed with emotional exhaustion (burnout)
and job satisfaction as mediating variables.
There are three exceptions, i.e., direct paths between
“family life spills over into working life” and turnover
intention, lack of work-related challenges and turnover
Table 2. Demographic characteristics, job demands/resources, stress/burnout, attitudinal outcomes, and turnover
intention by gender
Men Women Total
N324 (54%) 273 (46%) 597 (100%)
Age*38.8 (9.0) 40.9 (9.3) 39.8 (9.2)
Education (ED)*
High school or GED 4.9% 4.4% 4.7%
Some college 25.3% 12.8% 17.4%
Bachelors degree 38.9% 37.4% 38.2%
Some graduate or professional study 12.3% 15.4% 13.7%
Graduate or professional degree 22.5% 30.0% 26.0%
Job type*
Professional 75.5% 66.5% 71.4%
Managerial 21.1% 25.7% 23.2%
Other 3.5% 7.8% 5.5%
Tenure (TE)*4.6 (5.4) 5.4 (5.5) 5.0 (5.4)
Salary (SA) ~$68,000 ~$68.000 ~68,000
IT Job Demands (JD) 55.7 (20.3) 56.9 (19.3) 56.3 (19.8)
Role Ambiguity (RA) 29.9 (18.7) 29.7 (22.4) 29.8 (20.5)
Family-to-Work Conflict (FWC) 44.5 (21.7) 42.3 (24.1) 43.5 (22.8)
Work-to-Family Conflict (WFC) 48.6 (23.7) 49.3 (23.7) 48.9 (23.7)
Challenge (CH) 71.4 (21.2) 71.9 (21.4) 71.6 (21.3)
Decision Latitude (DL) 42.8 (27.9) 41.8 (30.4) 42.3 (29.0)
Supervisory Support (SS)*73.4 (24.4) 69.2 (28.0) 71.5 (26.2)
Support from Colleagues (CS) 68.7 (19.4) 69.2 (21.5) 68.9 (20.4)
Person-Organization Fit (PO) 72.6 (13.9) 71.2 (15.0) 72.0 (14.4)
Training Opportunities (TO) 55.1 (20.8) 57.0 (21.7) 56.0 (21.2)
Developmental Opportunities (DO)*71.8 (22.8) 76.4 (20.0) 73.9 (21.6)
Career Advancement Opportunities (CO) 51.2 (17.8) 53.4 (17.1) 52.2 (16.9)
Fairness of Rewards (RE) 58.1 (18.9) 59.6 (20.1) 58.8 (19.4)
Job Satisfaction (JS) 75.5 (21.9) 74.3 (26.0) 74.9 (23.9)
Organizational commitment (OC)*84.3 (17.5) 87.0 (15.6) 85.5 (16.7)
Burnout (EE) 33.6 (21.9) 35.6 (22.5) 34.5 (22.2)
Turnover Intention (TI) 2.57 (1.81) 2.47 (1.90) 2.52 (1.85)
* Differences between men and women are statistically significant at p < .05
Job-Demands-Resources model and turnover in the IT workforce

58
Table 3. Summary of measures used in the study
Scale name Source
Number
of items
α for sample
and (original
α) reported M (SD)
Min /
Max Example question
IT Demands (JD) Adapted from
(Quinn et al.,
1971)
7 .87
(.81)
71.75
(19.57)
0-100 How often does your job require you to
work very hard?
Role ambiguity
(RA)
(Caplan et al.,
1975) 4
.87
(.91)
68.30
(19.30)
0-100 How often are you clear on what your
job responsibilities are?
Family-to-work
conflict (FWC)
(Grzywacz &
Marks, 2000)
4 .70
(.81)
47.88
(18.13)
0-87 Family matters reduce the time I can
devote to my job.
Work-to-Family
Conflict (WFC)
(Grzywacz &
Marks, 2000)
4 .68
(.84)
53.67
(20.62)
6-100 My job reduces the amount of time I
can spend with my family.
Challenge (CH) (Seashore et al.
1982)
4 .82
(.81)
75.23
(19.55)
17-100 On my job, I seldom get a chance to use
my special skills and abilities.
Decision latitude
(DL)
(McLaney &
Hurrell, 1988) 4
.89
(.90)
40.05
(25.83)
0-100 How much influence do you have over
the decisions as to when things will be
done in your work unit?
Supervisor support
(SS)
(Caplan et al.,
1975)
4 .83
(.90)
63.67
(25.32)
0-100 How much does your immediate
supervisor (boss) go out of his/her way
to do things to make your life easier for
you?
Colleagues support
(CS)
(Caplan et al.,
1975)
4 .79
(.85)
68.27
(19.79)
0-100 How easy is it to talk with other people
at work?
Person-
Organization Fit
(PO)
Adapted from
(Nixon, 1985a)
13 .86
(.88)
74.54
(13.10)
29-100 I understand my company’s principles
and goals and support them.
Training
opportunities (TO)
Developed in pilot
study (Carayon,
Brunette, Schwarz,
Hoonakker, &
Haims, 2003)
12 .92 47.16
(20.59)
0-100 I am given a real opportunity to improve
my skills at this company through
education and training programs.
Development
opportunities (DO)
Adaped from
(Igbaria &
Wormley, 1992)
5 .85
72.69
(21.74)
0-100 Management development: Programs or
activities designed to teach managerial
skills, such as supervision, coaching,
recruiting, management decision
making, strategic policy making.
Career
advancement
opportunities (CO)
Adapted from
(Nixon, 1985b)
10 .81
(.89)
47.89
(15.86)
10-80 My opportunities for advancement in
this company are somewhat limited.
Fairness of rewards
(RE)
Adapted from
(Vandenberg et al.,
1999)
8 .84
(.86)
50.84
(17.95)
4-100 My performance evaluations within the
past few years have been helpful to me
in my professional development.
Job satisfaction
(JS)
(Quinn et al.,
1971)
5 .78
(.80)
72.44
(22.60)
10-100 Knowing what you know now, if you
had to decide all over again whether to
take the job you now have, what would
you decide?
Organizational
commitment (OC)
(Cook & Wall,
1980)
3 .72
(.68)
80.24
(15.43)
17-100 In my work, I like to feel I am making
some effort, not just for myself but for
the organization as well.
Emotional
exhaustion (EE)
(Maslach &
Jackson, 1986) and
(Geurts, Schaufeli,
& De Jonge, 1998)
6 .87 41.04
(22.51)
0-100 I feel emotional drained from my work.
Intention to
turnover (TI)
Michigan
Organizational
Assessment
Scale (MOAQ)
and (Seashore,
Lawler, Mirvis, &
Cammann, 1982)
1 n/a 2.87
(1.83)
1-7 How likely is it that you will actively
look for a new job in the next year?
P. Hoonakker, P. Carayon and C. Korunka

59
intention, and fairness of rewards and turnover intention.
Only one out of 24 possible moderation paths between
job demands and job satisfaction on one hand and job
resources and stress on the other hand is significant
(career opportunities and exhaustion). Thus, hypothesis
1 (there are two pathways to turnover intention) can also
be largely confirmed. The core assumptions of the job
demands-resources model are confirmed in this sample
of IT workers.
Table 2 shows the results of a comparison between
men and women for variables in the model. Women in the
sample are significantly older; they have a significantly
higher level of education; they are more likely to hold a
managerial job type; they have more years of tenure in
the organization and earn about the same salary as men
do; they receive less supervisory support; have more
developmental opportunities and are more committed
to their organization. Except in the case of supervisory
support, H3 (gender has a direct effect on the variables in
the model) can be rejected.
To test hypothesis 4 (gender moderates the relation
between job and organ izational factors and HRM pract ices
and QWL, and also moderates the relationship between
QWL and turnover intention) we conducted a multi-group
analysis, using the f inal model (M2).
As the goodness of fit statistics in Table 2 show,
the model fits the data for men better than for women.
Results of the multi-groups analysis show that that
both the structural weights (Beta-coefficients) and
covariances (correlations between job and organizational
characteristics and HRM practices) differ statistically
significant for men and women (χ2 = 32.7, df=20, p=0.04
and χ2 = 100.9, df=66, p=0.03 respectively). Therefore,
hypothesis 4 is confirmed.
Figure 3 shows the final models for men and women
separately. Results of path analysis confirm the central
role of emotional exhaustion and (lack of) job satisfaction.
Results also show that over half of the relationships
between job and organizational characteristics and
turnover intention in the model for men are mediated by
emotional exhaustion. In the model for women over half of
the relations are mediated by (lack of) job satisfaction. In
the model for men there are direct relationships between
family-to-work conflict and fairness of rewards and
tur nover intention. In the model for women there are direct
relations between “(lack of) work-related challenges” and
“fairness of rewards” and turnover intention. In the male
model, supervisory support and decision latitude do not
play a significant role. In the female model, family life
spills over into work and person-organization fit do not
play a significant role.
Discussion
Turnover has been a problem for IT organizations since
the 1960s. Since then, several studies have been conducted
to shed light on the problem. One particular point of
interest is the difference in tur nover and turnover intention
between men and women. In our study, we adapted the
Job-Demands-Resources (JD-R) Model to examine the
general relationships between job demands, emotional
exhaustion and turnover intention on the one hand and
the relationship between job resources, job satisfaction,
Table 4. Path models: goodness of fit statistics
χ2df p GFI AGFI NFI CFI SRMR RMSEA
Null Model (M0) 3416.8 153 < .001 .45 .39 .00 .00 .000 .190
Full model (M1) 1034.4 89 < .001 .85 .71 .71 .71 .170 .130
Final model (M2), full sample 62.9 35 .003 .99 .96 .98 .99 .030 .036
Final model for men 36.0 35 .420 .99 .95 .98 1.00 .029 .001
Final model for women 55.1 35 .020 .97 .92 .97 .99 .044 .046
Note. GFI = goodness of fit index; AGFI = adjusted goodness of fit index; NFI = normative goodness of fit index; CFI = comparative
goodness of fit index; SRMR = standardized root mean square residual; RMSEA = root mean square error of approximation.
Figure 2. Job demands-resources – turnover intention
path model.
Job-Demands-Resources model and turnover in the IT workforce

60
organizational commitment and turnover intention on the
other hand. We tested the basic assumptions of the model
in a sample of Information Technology (IT) workers, and
also separately for men and women. In general, our results
support the assumptions of the JD-R model. Results show
th at there ind eed se em to be t wo, m ore or less ind epe ndent
pathways to turnover intention. The first (energetic)
pathway follows the relationships between job demands,
job stress, and turnover intention. The second pathway
follows the relationship between job resources, job
satisfaction and turnover intention. Results of our study
also show support for the hypothesis that the relationship
between job demands and turnover intention is mediated
by strain, and that the relationship between job resources
and turnover intention is mediated by job satisfaction.
Not all variables fit the adapted JD-R model perfectly.
First, although most of the relationships were in the
expected directions (job demands → strain → turnover
intention; and job resources → job satisfaction and
organizational commitment → turnover intention),
there is an exception: the variable person-organization
fit is related to both job satisfaction and emotional
exhaustion. Second, not all relationships between job
demands and turnover intention are fully mediated by
emotional exhaustion, and not all relationships between
job resources and turnover intention are fully mediated
by job satisfaction. The energetic pathway is not fully
mediated by emotional exhaustion: there is a direct effect
from family life spills over into work (especially for men).
The motivational pathway is not fully mediated by job
satisfaction: there is a direct effect of fairness of rewards
on tu r nover intention, as well as an indi rect ef fect through
job satisfaction. Last, organizational commitment, one
of the variables that previous research has shown to be
strongly related to turnover intention does not play a
significant role in our model. An explanation could be
that tenure in the IT workforce is too short for employees
to become really committed to the organization they work
for. Tenure of IT personnel is on an average two years.
In general, the energetic pathway (job demands →
strain → turnover intention) seems to play a stronger role
in the model for men than for women. The motivational
pathway (job resources → job satisfaction → turnover
intention) seems to play a stronger role in the model for
women than for men (see Figure 3). The difference bet ween
the model for men and the model for women is statistically
significant. We tested whether gender has a direct effect
on job demands and job resources, strain, job satisfaction
and turnover intention, or whether gender moderates these
relationships. We found very few gender differences in
job demands and job resources, strain, job satisfaction and
turnover intention (see Table 3). The gender differences
Figure 3. Job demands-resources – turnover intention path model (men/women).
P. Hoonakker, P. Carayon and C. Korunka

61
that are statistically signif icant show that women in
our sample are relatively higher educated, report more
developmental opportunities and a higher organizational
commitment but also that they receive less supervisory
support. We did find that the relationships between job
demands and job resources, strain, job satisfaction and
turnover intention are statistically different for men and
women.
In contrast to these results, several earlier studies
on turnover in the IT workforce, especially the studies
conducted by Igbaria and colleagues (Igbaria & Baroudi,
1995; Igbaria & Chidambaram, 1995; 1997; Igbaria,
Parasuraman, & Greenhaus, 1997) in the 1990s, did show
significant gender differences in job demands and job
resources, strain, job satisfaction and turnover intention.
Igbaria and colleagues explained these gender differences
from a human-capital perspective. According to the
human-capital paradigm continued gender (and racial)
discrimination can be explained because individuals are
rewarded in their jobs for their investment in education
and job training (Blau & Ferber, 1987). The paradigm
suggests that women accumulate less human capital
(knowledge and skills derived from on-the-job training
and continuous work experience) than men do, because
women have lower educational levels, less experience and
fewer skills, particularly in professional and managerial
areas (Igbaria & Chidambaram, 1997). In several of their
studies, Igbaria and colleagues found support for the
human-capital paradigm: women in their studies were less
experienced, lower educated, earned less, and less often
held managerial positions (Igbaria et al., 1997; Igbaria &
Chidambaram, 1997). Historically, women have had lower
levels of educational attainment (Freeman, 2004; National
Center for Education Statistics, 1999), which in turn could
negatively affect their opportunities in the labor market.
However, in the past de cade, this has changed dr amatically.
In general, more women have completed college, and more
women have received Bachelor’s and Master’s degrees
than men. Only in the highest level of education (Ph.D.),
men hold more degrees than women (National Center for
Education Statistics, 1999, 2002). Although women have
made tremendous progress in social sciences, history,
psychology, biological sciences/life sciences, business
management and administrative services where they have
attained relative gender parity or made up the majority
in 2001, other fields such as computer and information
sciences, physical sciences, science technologies, and
engineering continue to have a large r proport ion of males.
However, the percentage of females majoring in those
fields is increasing (Freeman, 2004).
Why do the relationships, based on the JD-R model,
vary across gender? The most striking difference in the
relationship is that for women, job satisfaction plays
a crucial role. In their study on gender differences in
turnover intention, Miller and Wheeler (1992) found that
tur nover intent ion among women was twice as high as men
in comparable occupations. Howeve r, after controlling for
age and job dissatisfaction, the gender effect disappeared.
The researchers also found that meaningfulness of work
was a strong predictor of intention to leave for women. We
found similar results in a study on gender differences in
job and organizational factors as predictors of quality of
working life (Hoonakker, Marian, & Carayon, 2004). For
female employees in the IT department of a large public
organization, task identity was one of the most important
factors explain ing gender differences i n quality of working
life (job strain, job commitment and job satisfaction). Task
identity 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, Szilgyi, & Keller, 1976).
Campbell and Perlman (2006) found similar results. They
found that the only gender differences in job satisfaction
was that women more often than men found failure to see
much meaning in their work a more f requent source of job
dissatisfaction. This can also explain why lack of work-
related challenges has a direct effect on turnover intention
for women and not for men.
The study limitations include the fact that data were
collected from only five companies, therefore limiting the
generalizability of the results. Also, these data are cross-
sectional which limits the interpretation regarding causal
relationships between job demand and resources, strain
and attitudinal factors, and turnover intention. Finally,
this research is focused on turnover intention, not actual
turnover.
The results of our study have implications for
retention of personnel in the IT workforce. To reduce
turnover intention, and associated turnover of personnel,
organizations have two possibilities: (1) ensure that job
demands are not too high, in that way prevent burnout
and consequently turnover intention (especially for men),
and (2) ensure that personnel have enough resources, and
remain satisfied with their job, thereby reducing turnover
intention (especially for women).
Further, for both men and women, there is a direct
relation between fai rness of rewards a nd tur nover intention.
Therefo re, in ord er t o retai n th eir IT pers onn el, comp anies
should create a system that guarantees the fairness of the
reward system. Attention should be paid for example to
the relationship between how well employees perform
and the likelihood of receiving recognition and praise,
receiving a raise in pay and receiving high performance
appraisal ratings.
Finally, our results show that work-family conf lict
also plays an important role. For both men and women in
the study, ‘work-to-family conflict is strongly related to
burnout. Burnout in turn is strongly related to turnover
intention. Therefore, IT companies should examine the
possibility of offering family-friendly practices to help
employees better balance work and family. Results of
analysis on effective Human Resource Management
practices to retain personnel have shown that offering
family-friendly practices, such as telecommuting and
part time work to retain IT personnel, can be effective
(Hoonakker, Carayon, Marian, & Schoepke, 2004). IT
personnel who can make use of these options, who think
Job-Demands-Resources model and turnover in the IT workforce

62
the options are sufficient for their needs and who do not
feel discouraged from taking advantage of these options
are significantly more willing to stay with their company.
Research has shown that employees who can take
advantage of these practices are more than three times
more likely to remain with their company (Hoonakker et
al., 2004).
Acknowledgements. Funding for this research was
provided by the National Science Foundation (NSF)
Information Technology Workforce Program (Project
#EIA-0120092). We would like to thank all of the
participating companies and their employees for agreeing
to be involved in our research project. We would like to
thank Randi Cartmill for reviewing and editing earlier
versions of this manuscript.
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