Major Assignment 1 (MA1) is a major paper. That means it begins with a cover page and ends with a reference list. There is no Abstract. The paper has three distinct sections: problem Statement, Purpose Statement and Research Question (RQ). That means each section will have a distinct heading.
Your paper must be organized as follows using the headings that are in red:
- The Problem Statement is a total of only 1 to 3 short paragraphs. I want to emphasize short. I only read 3 paragraphs. I suggest using one paragraph per study. Some of the samples I provide have 4 paragraphs. I would like only 3 paragraphs.
- The Problem Statement is a review of three current research studies ATTACHED. For each of the three studies you will report only specific information in the paragraph. Each paragraph must contain only the following information:
- The purpose of the study
- The findings from the study
- What the researcher explicitly stated needs further study
1. The Purpose Statement must come from what the researcher (Gyanchandani, 2017) in the qualitative study you included in the Problem Statement stated needs further study. It is not your opinion of what to study. It comes directly from the qual study in your Problem Statement. We are building on prior research on your topic of interest. If you used more than one qual study then you must select the “gap” that most closely aligns with what you want to study. If a researcher identified several areas that need further study, select one of them.
2. The Purpose Statement is one of the gaps (explicitly stated need for further study) identified in the qualitative study in the Problem Statement.
3. State this as your first sentence: The purpose of this study is to … (citation). You repeat what the researcher said needs further study as the purpose of your study.
4. The Purpose Statement needs to include the in-text citation where the gap was identified. You do not include what the other researchers stated needs further study. This is only from the qual study you reviewed and what the researcher said needs further study.
5. Use what the grading rubric also states is needed in this section. This is important.
1. The RQ has to be written as a qual question. It does not begin with a verb and it does not end in a period. It is an actual question.
- The RQ must use the same language from the Purpose Statement.
Personality and Social
2019, Vol. 45(5) 808 –823
© 2018 by the Society for Personality
and Social Psychology, Inc
Article reuse guidelines:
Partner with the right person because you cannot have a full
career and a full life at home with the children if you are also
doing all the housework and childcare.
—Sheryl Sandberg (2013)
In understanding gender disparities in career advancement,
social psychologists have focused on how stereotypes about
women constrain women’s career decisions (Brown &
Diekman, 2010; Ceci & Williams, 2011; Park, Smith, &
Correll, 2010; Stout, Dasgupta, Hunsinger, & McManus,
2011). But as Facebook COO Sheryl Sandberg suggests,
the dynamics in heterosexual couples can also impact wom-
en’s ability to freely pursue their career. Although there is
an active literature on the gendered distribution of domestic
labor in sociology and economics (England, 2010; Haddock,
Zimmerman, Lyness, & Ziemba, 2006; Kroska, 2004; Offer
& Schneider, 2011), social psychologists have not exam-
ined how expectations about men’s roles constrain wom-
en’s own aspirations to adopt counterstereotypic roles. In
line with field theory (Lewin, 1939), which highlights how
social forces constrain and afford individuals’ behavior, it
stands to reason that women’s expectations of adopting tra-
ditional roles (i.e., becoming a caregiver rather than a
breadwinner) are causally predicted by their perception that
men are becoming more involved in childcare. We tested
this complementarity hypothesis across five experiments
and an internal meta-analysis.
The Division of Domestic Labor and
Asymmetrically Changing Gender
Over the past several decades, gender roles have both
changed and stayed the same. In 1970, almost half of all two
parent households had a mother who stayed at home, whereas
today nearly 70% of families in the United States are com-
prised of dual-earner parents (Pew Research Center, 2015).
Although men generally outearn their partners, women are
increasingly likely to be the primary economic provider in
their families (Pew Research Center, 2013). Despite this evi-
dence of women’s expanding roles, family responsibilities
continue to fall disproportionately to them (Hochschild &
Machung, 2012). In fact, after having children, women are
797294PSPXXX10.1177/0146167218797294Personality and Social Psychology BulletinCroft et al.
1The University of Arizona, Tucson, USA
2The University of British Columbia, Vancouver, Canada
Alyssa Croft, The University of Arizona, 1503 E University Blvd., Tucson,
AZ 85721-0001, USA.
Life in the Balance: Are Women’s
Possible Selves Constrained by Men’s
Alyssa Croft1, Toni Schmader2, and Katharina Block2
Do young women’s expectations about potential romantic partners’ likelihood of adopting caregiving roles in the future
contribute to whether they imagine themselves in nontraditional future roles? Meta-analyzed effect sizes of five experiments
(total N = 645) supported this complementarity hypothesis. Women who were primed with family-focused (vs. career-focused)
male exemplars (Preliminary Study) or information that men are rapidly (vs. slowly) assuming greater caregiving responsibilities
(Studies 1-4) were more likely to envision becoming the primary economic provider and less likely to envision becoming the
primary caregiver of their future families. A meta-analysis across studies revealed that gender role complementarity has a
small-to-medium effect on both women’s abstract expectations of becoming the primary economic provider (d = .27) and
the primary caregiver (d = –.26). These patterns suggest that women’s stereotypes about men’s stagnant or changing gender
roles might subtly constrain women’s own expected work and family roles.
gender roles, possible selves, stereotypes, romantic relationships, work–life balance
Received July 26, 2016; revision accepted August 7, 2018
Croft et al. 809
more likely than men to reduce their work commitment, earn
lower salaries, and advance slowly in their career (Stone,
2007). Many women embrace this choice (Park et al., 2010).
However, twice as many working mothers as fathers report
that parenting responsibilities stand in the way of their career,
particularly among families of highly career-focused men
(Pew Research Center, 2015). Such data suggest that many
women feel their career choices are constrained by men’s
lower caregiving contributions (Croft, Schmader, & Block,
It is not surprising that women, once parents, might make
a rational decision to prioritize family over career. Our ques-
tion is whether women anticipate this trade-off in advance of
negotiating work and family responsibilities with a partner.
Young heterosexual women expect a traditional, gender-
based division of labor in their future relationship (Askari,
Liss, Erchull, Staebell, & Axelson, 2010; Hodges & Park,
2013; Park, Smith, & Correll, 2008). But what if they
believed that men’s interest in childcare was increasing? For
example, although the percentage of stay-at-home fathers is
still low, it has been increasing over the last two decades
(Pew Research Center, 2014), and working couples are
increasingly sharing family responsibilities equally (Pew
Research Center, 2015). Are these, albeit modest, changes in
men’s caregiving roles incorporated into how young women
view their own future?
Schemas of the Self, Others, and
When women envision their future, they imagine the person
they might become (Oyserman & James, 2011). Self-schemas
are people’s cognitive representations of the self, informed
by their past experiences, current context, and future expec-
tations. The self-schemas people have for the person they
could become are called possible selves (Markus & Nurius,
1986; Smith & Oyserman, 2015). Unlike current self-sche-
mas, possible selves are uniquely based on anticipated social
roles and environments people might inhabit. Some past
research has shown that possible selves about being a parent
or provider can be influenced by pragmatic concerns (e.g,
Bloom, Delmore-Ko, Masataka, & Carli, 1999; Lee &
Oyserman, 2007, 2009; Smith, James, Varnum, & Oyserman,
2014). Of greater relevance to the current research is the way
in which possible selves are shaped by gender stereotypes.
Consistent with social role theory (Eagly & Steffen, 1984;
Eagly & Wood, 2013), because young girls see women as
caregivers and men as breadwinners, gender-stereotypic role
expectations are internalized into possible selves. Such ste-
reotypes are especially likely to influence people’s possible
selves when imagining themselves in a distant future that is
necessarily more abstract. For example, a recent study
showed that grade school–aged girls aspire to more gender-
neutral (than female-stereotypic) occupations to the extent
that their fathers exhibit less male-stereotypic behavior by
engaging in domestic tasks (Croft, Schmader, Block, &
Baron, 2014). In addition, there is a notable gender differ-
ence in the family-related possible selves of college students
who imagine their lives in 10 to 15 years, but no such differ-
ence when imagining themselves only 1 year in the future
(Brown & Diekman, 2010). This pattern suggests distant
possible selves are shaped, at least to some degree, by stereo-
Women’s (and men’s) possible selves are not only a
function of the schemas they have about themselves, but
also the schemas about future romantic partners. Aron and
Aron (1986) theorized that the perception of oneself
includes the resources, perspectives, and characteristics of
one’s relationship partner. Importantly, relationship sche-
mas are defined not merely by expectations of the self and
the partner as individuals, but also by expectations about
relationship dynamics (e.g., forecasted division-of-labor).
Heterosexual women’s stereotypical expectations about
their future partner should therefore inform their own pos-
sible selves, but the abstract nature of these future forecasts
makes them susceptible to stereotypes and norms. Thus,
women’s own future selves might be shaped by their beliefs
that men (and therefore future partners) will continue to be
less communal than women (Diekman & Eagly, 2000).
There is some initial support for gender role complemen-
tarity in future selves. In a clever study, men and women who
were randomly assigned to imagine becoming the primary
breadwinner or primary caregiver of their future families
reported preferring a partner with a role complementary to
their own (Eagly, Eastwick, & Johannesen-Schmidt, 2009).
Our research examines the reverse relationship: When
women expect that men’s roles are unchanging (i.e., men
remain more career- than family-focused), are women less
likely to imagine themselves becoming the economic pro-
vider of their family? And if instead women encounter evi-
dence that men are becoming more family-focused, are they
more likely to imagine themselves as a future economic
In addition to women’s anticipated adoption of provider
roles, we also considered their anticipation of becoming the
primary caregiver to their children. On one hand, expecta-
tions that men are becoming more involved in caregiving
might lead women to feel less pressure to take on caregiving
responsibilities themselves. However, we also recognize that
social pressures and individual expectations surrounding
motherhood are quite strong. For example, even when fathers
are involved in childcare, women often find it difficult to
give up the primary caregiver role and still manage how
these tasks are done (Allen & Hawkins, 1999). The role of
primary caregiver might be difficult for women’s to relin-
quish given that it can be a source of power (Williams &
Chen, 2013). Thus, we examined how change in male roles
affects women’s anticipation of becoming the primary eco-
nomic provider and the primary caregiver of their future
families as distinct outcomes.
810 Personality and Social Psychology Bulletin 45(5)
Overview of the Current Research
Five studies (total N = 645) provide a test of the complemen-
tarity hypothesis—stating that the likelihood that heterosex-
ual women anticipate adopting nontraditional gender roles in
their future families (i.e., becoming the primary breadwinner,
and not the primary caregiver) is at least partly contingent
upon their expectations about men’s willingness to adopt non-
traditional roles (i.e., becoming the primary caregiver).
Although parallel complementarity effects could be tested for
men’s future role expectations, we limited our focus to out-
comes for women but will consider the generalizability of
these effects in the general discussion. In the Preliminary
Study, we used counterstereotypical male exemplars to prime
women with thoughts of family-oriented men (vs. career-ori-
ented men) prior to measuring their career- and family-related
possible selves and estimates of the time they will spend on
work and childcare. In Studies 1 to 4, we sought to broaden
the ecological validity of the design by providing participants
with normative messages (like those they might read in the
news) indicating that men are increasingly assuming caregiv-
ing roles (as opposed to staying more career-focused). We
recruited larger sample sizes with each subsequent study and
preregistered hypotheses and analyses for Study 4.
Preliminary Study: Evidence for
In this preliminary study, participants viewed a set of profiles
of men who were either career-oriented, family-oriented, or
career-family balanced. We originally designed this study to
examine how exposure to these profiles might influence
men’s expected gender roles, but the key discovery was that
women primed with more family-oriented (as compared with
career-oriented) male exemplars were more likely to envi-
sion themselves, complementarily, as the primary economic
provider in their future families. Because these initial effects
were used to formulate the complementarity hypothesis, we
focus our presentation on these preliminary findings among
women in the sample. The data for men are summarized in
Supplementary Online Materials (SOM) and footnoted in
results when relevant.
Participants and Design
A sample of 74 heterosexual undergraduate women partici-
pated in this study for course credit (62% East Asian/23%
White). Age data were not collected in this study. Participants
were randomly assigned to one of three male exemplar prime
conditions in a between-subjects design. This study was run
in 2011, and the sample size was planned based on conven-
tions at that time (Simmons, Nelson, & Simonsohn, 2011).
More sample characteristics for each of the studies are pro-
vided in Table 1 and SOM; sensitivity analyses for this and
all studies are detailed alongside key effects for the critical
comparisons in Table 3.
In a two-part study on life narratives, participants were first
asked to rate five similar profiles of men to ostensibly help us
select stimuli for future research. Based on random assign-
ment to condition, these profiles were all either (a) career-
oriented, (b) family-oriented, or (c) career–family balanced.
After viewing each profile, participants completed questions
that included the manipulation checks. During the second
part of the study, participants imagined and made ratings of
their lives 15 years in the future. Measures central to the
complementary hypothesis are reported here, but all addi-
tional measures included in this exploratory study (and each
subsequent study) are listed in SOM.
Materials and Measures
Exemplar primes. The profiles were adapted from Stout et al.
(2011; Study 2). In the family-oriented condition, the men
took time off from their successful careers (as women often
do) to raise small children, whereas in the career condition
the men worked full-time (as men often do). In the balanced
condition, the exemplars were portrayed as having thriving
careers paired with flexible schedules that allowed for some
childcare (see SOM). Across condition, facts about men’s
(former) occupation, children, and wives’ careers were held
Table 1. Sample Characteristics for All Studies.
% who expect
% who expect
Preliminary 74 63 64 $60-70,000 $110-120,000
1 33 67 55 $70-80,000 $150,000 or more
2 121 63 56 $80-90,000 $140-150,000
3 114 61 42 $70-80,000 $140-150,000 35.32 (10.5) 6.90 (1.20)
4 303 71 57 $90-100,000 $150-160,000 39.67 (11.95) 7.25 (1.15)
Note. The first four studies were conducted at a large Canadian university and used CAD for income estimates. Study 4 was conducted at a large
American university and used USD for income estimates. Career ambition was measured on a 1 to 9 scale.
Croft et al. 811
constant. Pilot data on a separate sample of 25 undergradu-
ates (both men and women participated, but no gender data
were collected) revealed that the career-focused exemplars
were rated as significantly more career-oriented (M = 5.96)
than the family-oriented exemplars (M = 2.19), and both
were significantly different from the balanced exemplars
(M = 4.14), all ps < .001 (1 = family-oriented; 4 = bal- anced; 7 = career-oriented).
Ratings of exemplars. Participants’ ratings of each of the five
exemplars’ degree of career–family balance on a 7-point scale
(1 = family; 4 = balanced; 7 = career) were averaged to pro-
vide a manipulation check (α = .84). Participants also rated
the exemplars’ agency (α = .89) and communion (α = .89) on
the 16 item Personal Attribute Questionnaire (PAQ; Spence,
Helmreich, & Stapp, 1975) using a 1 (not at all descriptive) to
5 (very descriptive) scale.
Participants’ future lives. Participants first provided demo-
graphic information for their future life expectations by indi-
cating whether or not (yes/no) and how likely (1 = not at all
likely to 7 = extremely likely) they will be to be married and
have children. They also rated the highest level of education
anticipated for themselves and their spouse, and their pro-
jected annual household and personal income.
Participants rated their abstract future roles as the likeli-
hood of becoming the primary economic provider (“bread-
winner”) and primary caregiver of their future families on
two 7-point scales (0 = not at all likely, 6 = extremely likely).
To assess more concrete task estimates, participants first
allotted a percentage of their total waking hours they would
spend on each of several daily tasks (e.g., work, childcare).
They also completed an adapted Day Reconstruction Method
(DRM; Kahneman, Krueger, Schkade, Schwarz, & Stone,
2004) to forecast a typical Wednesday in their lives 15 years
in the future (see SOM). These anticipated daily schedules
were then manually tallied for the number of hours spent at
work and on childcare. Because these two ways of quantify-
ing time spent working, r(67) = .69, p < .001, and on child- care, r(67) = .36, p = .003, were correlated, the percentage and DRM measures were standardized and averaged to cre- ate two variables of estimated time for work and childcare. Correlations among study variables in this and all studies are summarized in SOM.
Ratings of Exemplars
A one-way analysis of variance (ANOVA) on exemplar rat-
ings revealed the expected effect of condition with all means
differing from one another, all ps < .001, F(2, 68) = 69.42, p < .001, ηp
2 = .67 (see Table 2).1 There were also condition
differences in perceived exemplar agency, F(2, 68) = 18.63,
p < .001, ηp
2 = .35, and communion, F(2, 68) = 9.83, p < .001, ηp
2 = .22. The career-oriented exemplars were rated as
significantly more agentic (M = 4.03, SD = .33) and less
communal (M = 3.15, SD = .52) than both the family-ori-
ented and balanced exemplars, both ps < .001. The family- oriented and balanced exemplars were rated as similarly agentic (Mfamily = 3.22, SD = .57; Mbalance = 3.43, SD = .48) and communal (Mfamily = 3.82, SD = .65; Mbalance = 3.82, SD = .61) to one another, both ps > .12.
The complementarity hypothesis (based on the results of this
study) posits that women’s imagined roles are shaped by
Table 2. Manipulation Check Results for All Studies, Broken Down by Attention Checks (Recall of Manipulation About Men’s Roles)
and Personal Beliefs About Men’s Roles.
Study Conditions n
Check 1 M (SD) Cohen’s d
Check 2 M (SD) Cohen’s d
Belief 1 M (SD) Cohen’s d
Belief 2 M (SD) Cohen’s d
Prelim Family 24 Item A 3.23 (0.55) −3.40***
Career 25 5.17 (0.59) — — — — — — — — —
Balance 24 4.08 (0.55)
1 Rapid 17 Item B 5.82 (1.33) 2.25*** — — — Item D 5.24 (1.20) .85* — — —
Slow 16 2.69 (1.45) 4.00 (1.67)
2 Rapid 36 Item C 2.19 (1.39) −.97*** Item E 3.25 (1.23) −.46*
Slow 40 3.68 (1.66) — — — 3.88 (1.49) — — —
Control 45 — 3.87 (1.67)
3 Rapid 59 Item B 4.95 (1.39) 2.52*** Item C 1.72 (1.25) −.90*** Item E 3.12 (1.27) −.41* — — —
Slow 55 1.85 (1.09) 2.79 (1.12) 3.67 (1.43)
4 Rapid 138 Item B 5.59 (1.39) 1.84*** Item C 1.48 (0.93) −1.27*** Item E 3.43 (1.47) −.17 Item F 4.12 (1.18) .78***
Slow 165 2.72 (1.71) 2.92 (1.30) 3.67 (1.29) 3.41 (1.34)
Note. Text in bold denotes comparison groups for Cohen’s d calculations. All Cohen’s d were calculated using http://www.uccs.edu/~lbecker/. Attention check items: (A) Rate
the individual’s level of balance between family and career: 1 = family-oriented, 4 = balanced, 7 = career-oriented; (B) According to the graphs you saw in today’s study, what is
the rate at which men’s roles in society are changing?: 1 = very slowly, 7 = very rapidly; (C) According to the graphs you saw in today’s study, men are: 1 = increasing their focus
on family, 4 = staying the same, 7 = increasing their focus on career. Personal beliefs items: (D) Please indicate whether or not you agree with the following statement: Men’s roles
in society are changing and will continue to do so in future years: 1 = completely disagree, 7 = completely agree); (E) I personally believe that men are: 1 = increasing their focus on
family, 4 = staying the same, 7 = increasing their focus on career; (F) I personally believe that men’s roles are changing: 1 = very slowly, 7 = very rapidly.
*p < .05. ***p < .001.
812 Personality and Social Psychology Bulletin 45(5)
their perceptions of men’s childcare engagement. A one-way
ANOVA on expectations of becoming the primary economic
provider yielded a significant effect of condition, F(2, 70) =
3.61, p = .03, ηp
2 = .09 (see Figure 1). As expected, women
who viewed family-oriented men anticipated becoming the
primary provider more than those who viewed either career-
oriented, d = .64, p = .03, or balanced men, d = .73, p = .02
(see Table 3). The manipulation had no significant effect on
becoming the primary caregiver, F(2, 70) = 1.41, p = .25,
2 = .04, and ratings of these two roles were uncorrelated,
r = –.08, p = .52.2 Additional analyses in this and all studies
directly comparing the provider to the caregiver ratings can
be found in SOM.
One-way ANOVAs of the concrete task measures revealed
no effects on estimated time on work, F(2, 71) = .92, p =
2 = .03, or childcare, F(2, 71) = .22, p = .80, ηp
.01 (see Table 4). Interestingly, these concrete time estimates
Table 3. Descriptive Statistics, Estimates of Effect Size, and Sensitivity Analyses for Future Role Measures in All Studies.
Study Conditions n
Prelim Family 24 d = .82 3.25 (1.29) .64* [0.06, 1.21] 3.58 (1.71) −.33 [–0.89, 0.24]
Career 25 2.36 (1.50) 4.12 (1.48)
Balanced 24 2.25 (1.45) 3.33 (1.83)
1 Rapid change 17 d = .89 3.71 (0.85) .90* [0.18, 1.61] 4.29 (0.59) −.24 [–0.92, 0.45]
Slow change 16 2.75 (1.24) 4.50 (1.10)
2 Rapid change 36 d = .58 3.28 (0.88) .58* [0.12, 1.04] 4.08 (0.87) −.44 [–0.89, 0.02]
Slow change 40 2.65 (1.27) 4.55 (1.22)
Control 45 2.91 (1.17) 3.96 (1.22)
3 Rapid change 59 d = .47 2.93 (1.29) .13 [–0.24, .50] 4.07 (1.19) −.13 [–0.50, 0.24]
Slow change 55 2.76 (1.26) 4.22 (1.05)
Same primary provider measure used in
Same primary caregiver measure used in
4 Rapid change 138 d = .29 3.29 (1.19) .13 [–0.15, 0.41] 3.79 (1.28) −.25* [–0.23, 0.03]
Slow change 165 3.13 (1.25) 4.11 (1.33)
New relative provider measure New relative caregiver measure
Rapid change 138 3.90 (0.91) .28* [0.05, 0.51] 4.30 (0.74) −.32* [–0.55, –0.09]
Slow change 165 3.64 (0.95) 4.57 (0.93)
Note. Text in bold denotes comparison groups for Cohen’s d calculations and sensitivity analyses (α = .05, 1 – β = .80, two-tailed for preliminary study,
one-tailed for Studies 1-4). All Cohen’s d calculated using http://www.uccs.edu/~lbecker/. Studies 1 to 4 excluded participants who are not heterosexual
or do not anticipate having a partner and/or children. CI = confidence interval.
*p < .05.
Figure 1. Preliminary study: Women’s expected likelihood of becoming the primary economic provider and primary caregiver for their
families, 15 years in the future.
Note. Error bars represent standard errors.
Croft et al. 813
were generally unrelated to women’s abstract roles expecta-
tions (see Table 4).
The patterns from this exploratory study suggested that
women’s abstract possible selves (but not their concrete task
estimates) might be contingent upon the extent to which they
perceive men as interested in childcare. Interestingly, these
effects were specific to the economic provider and not the
caregiver role, which led us to formulate the complementar-
ity hypothesis, whereby a prime of men’s caregiving behav-
ior would have a complementary effect on women’s imagined
provider role in their future family.
Because the Preliminary Study had not been specifically
designed to test the complementary hypothesis, we devel-
oped a more focused test for Study 1. Out of a concern that
extreme exemplars would be subtyped and treated as
“exceptions to the rule,” rather than seen as indicative of
broader norms (Weber & Crocker, 1983), we developed a
new manipulation. Specifically, in all further studies women
viewed graphs suggesting that men are either rapidly or
slowly taking on more caregiving roles before completing
the same dependent measures from the Preliminary Study.
We hypothesized that when women were led to believe that
men’s roles are changing rapidly (vs. slowly), they would be
more likely to imagine becoming the primary economic pro-
vider in their future family. We again tested for parallel
effects on becoming the primary caregiver and other con-
crete task estimates.
Participants. A sample of 37 heterosexual undergraduate
women below age 25 (Mage = 19.44, SD = 1.27) participated
for course credit (44% East Asian/25% White). Because the
complementarity hypothesis should only apply to women
who expect to have a male partner and children, four partici-
pants were excluded for not meeting these criteria.3 Data
were collected in 2013, and we had aimed to collect 20 par-
ticipants randomly assigned to each condition (Simmons
et al., 2011), but stopped data collection when the term
ended. We recognize that this is a small sample by today’s
conventions, a limitation we address with the later meta-
analysis and discussion of the sensitivity analyses.
Procedure. The procedure was similar to the Preliminary
Study, except that normative trend primes replaced the exem-
plar primes in Part 1 of the session. As part of a study of how
changing trends affect people’s own life narratives, partici-
pants spent 5 min studying graphs on food consumption,
weather changes, smoking rates, and stay-at-home fathers,
before completing the same primary measures used in the
Preliminary Study.4 This fourth graph varied by condition to
manipulate changing norms.
Materials and measures
Graph primes. The focal graph depicted data on stay-at-
home fathers in Canada between 1986 and 2010 (Statistics
Canada, 2010). However, the graph and figure caption were
manipulated to show rapid or slow change (see SOM). In
the slow change condition, the y-axis ranged from 0% to
100% and the figure caption described that the percentage of
stay-at-home fathers is projected to remain relatively low in
the coming years. In the rapid change condition, the y-axis
was condensed to create a steep positive slope, and the fig-
ure caption emphasized projected increases in stay-at-home
fathers in the coming years.
Manipulation checks. Following this manipulation, par-
ticipants rated the speed at which the graph depicted men’s
gender roles as changing (1 = very slowly, 7 = very rapidly)
and their personal beliefs that men’s roles are changing rap-
idly (1 = strongly disagree, 7 = strongly agree).
Table 4. Descriptive Statistics and Estimates of Effect Size for Concrete Task Measures in All Studies.
Study Conditions n
Prelim Family 24 −.21 (0.98) −.39 −.01 (0.94) −.12
Career 25 .16 (0.90) .10 (0.89)
Balanced 24 −.07 (1.04) −.05 (0.65)
1 Rapid change 17 .02 (0.81) .12 −.20 (0.79) −.64
Slow change 16 −.08 (0.85) .33 (0.86)
2 Rapid change 36 .20 (0.71) .64** −.04 (0.80) −.20
Slow change 40 −.35 (0.98) .12 (0.76)
Control 45 .15 (0.75) −.09 (0.73)
3 Rapid change 59 .002 (1.06) .01 −.12 (0.91) −.27
Slow change 55 −.003 (0.94) .14 (1.08)
Note. All mean values are standardized; higher numbers indicate above average anticipated time spent working or caregiving. Text in bold denotes
comparison groups for Cohen’s d calculations. All Cohen’s d calculated using http://www.uccs.edu/~lbecker/. Study 2 to 5 excluded participants who are
not heterosexual or do not anticipate having a partner and/or children.
814 Personality and Social Psychology Bulletin 45(5)
Dependent measures. The same measures of future gender
roles and concrete task estimates used in Study 1 were again
assessed in Study 2. We again combined measures of esti-
mated time doing work (same as Study 1), r(31) = .30, p =
.10, and childcare, r(29) = .37, p = .05. Other measures and
descriptive statistics are provided in SOM.
Results and Discussion
Manipulation checks. Independent samples t tests revealed that
women in the rapid (vs. slow) change condition recalled the
graph showing more rapid change of men’s roles, t(31) = 6.48,
p < .001, d = 2.25 (see Table 2), and were also more likely to agree that men’s roles are changing rapidly, t(31) = 2.45, p = .02, d = .85.
Future roles. Independent samples t tests yielded significant
condition differences on women’s provider expectancies,
t(31) = 2.60, p = .01, d = .90, but their caregiver expectan-
cies did not reach statistical significance despite a small-to-
moderate effect size between conditions, t < 1, d = –.24 (see Table 3; Figure 2). Again, women’s ratings of these two roles were uncorrelated, r = –.01, p = .98.
Concrete tasks. Parallel t tests on concrete tasks revealed no
significant differences and a weak effect of condition on
women’s work time estimates, t(31) = .36, p = .72, d = .12,
whereas women’s childcare estimates showed a nonsignifi-
cant trend of being reduced in the rapid compared with the
slow change condition, t(31) = −1.84, p = .08, d = –.64 (see
Study 1 provided further evidence that women’s expecta-
tion of becoming primary providers in the future might be
complementary to their perceptions about men’s changing
roles. Again, the suggestion of rapidly changing roles did not
significantly affect women’s anticipated role as the primary
caregiver, although it did produce a meaningful effect size
estimate on this measure, and somewhat diminished their
estimates of time spent on childcare. Though these findings
are intriguing and provide a conceptual replication of the
Preliminary Study, Study 2 was carried out as a direct repli-
cation of Study 1 with the inclusion of a control condition
and a larger sample size.
Participants and procedure. Participants were 136 heterosexual
undergraduate women under age 25 (Mage = 20.16, SD = 1.89)
who completed the study for either course credit or payment
(47% East Asian, 23% white). Women who planned to be sin-
gle (n = 3) or childless (n = 12) were excluded, leaving a final
sample of 121 women.5 This study was run in Spring 2014, and
the sample size was planned to double the number of partici-
pants in each condition compared with Study 1.
Procedures and measures were the same as in Study 1,
except that a third of participants were randomly assigned to
a third, no information, control condition that included only
the three filler graphs. The manipulation check was modified
so that participants indicated the degree to which the graphs
showed that men are 1 (becoming more family-oriented), 4
(staying the same), to 7 (becoming more career-oriented).
We again assessed women’s future providing and caregiving
roles. As in the prior studies, concrete time estimates were
aggregated across the percentage and day reconstruction task
measures for time spent on work, r(119) = .47, p < .001, and childcare, r(117) = .15, p = .12.
Figure 2. Study 1: Women’s expected likelihood of becoming the primary economic provider and primary caregiver for their families,
15 years in the future.
Note. Error bars represent standard errors.
Croft et al. 815
Results and Discussion
Manipulation checks. An independent samples t test con-
firmed that women were more likely to recall the graph as
depicting men becoming more family-oriented in the rapid
as compared with the slow change condition (the control
condition was excluded given the absence of the fourth
graph), t(73) = −4.19, p < .001, d = –.97 (see Table 2). One sample t tests also confirmed that men were seen as becom- ing significantly more family-oriented compared with the scale midpoint in the rapid change condition, t(35) = −7.79, p < .001, but statistically similar to the midpoint in the slow change condition, t(38) = −1.21, p = .23.
A one-way ANOVA on participants’ personal beliefs
yielded the expected effect of condition, F(2, 118) = .12,
p < .001, ηp
2 = .04 (see Table 2). Women in the rapid
change condition displayed a nonsignificant trend toward
being more likely than women in either the slow change or
control conditions to believe that men are becoming more
family-oriented, both ps = .07, the planned comparison of
rapid to slow change was significant, t(74) = −1.99, p =
.05, d = –.46. Thus, women accurately perceived the
manipulation, and internalized it to some extent, though
perhaps not as strongly as in the prior study, an issue we
return to in Study 3.
Future roles. A one-way ANOVA on anticipation of becom-
ing an economic provider replicated earlier studies, now with
a larger sample, F(2, 118) = 3.05, p = .05, ηp
2 = .05 (see
Table 3; see Figure 3). Women anticipated becoming the pri-
mary economic provider more when primed with rapid as
compared with slow change in men’s roles (d = .58). Ratings
of women in the control condition fell between the two treat-
ment conditions but did not differ significantly from either
(control vs. rapid, p = .09, d = .43; control vs. slow, p = .43,
d = –.16).
In contrast to previous studies with lower power, there
was also a significant effect of condition on anticipation of
being the primary caregiver, F(2, 118) = 3.16, p = .05 ηp
.05. Women were more likely to expect becoming the pri-
mary caregiver in the slow change condition as compared
with both the control, p = .02, d = .48, and rapid change
conditions, p = .08, d = –.44. The control and rapid change
conditions did not differ from one another, p = .61, d = .11.
The rated likelihoods of these two roles were negatively cor-
related in this sample, r = –.25, p < .01.
Concrete Task Estimates. In this larger sample, there was a
significant effect of condition on women’s work time esti-
mates, F(2, 118) = 5.38, p = .01, ηp
2 = .08 (see Table 4),
consistent with the complementarity hypothesis. Women
anticipated working less in the slow change as compared
with the rapid change (d = .64, p = .004) or control condi-
tion (d = .57, p = .01). There were no significant condition
effects on estimated childcare time, F(2, 118) = .84, p = .44,
2 = .01 (drapid vs. slow change = –.20, dcontrol vs. rapid change = –.07,
dcontrol vs. slow change = –.28).
These findings provide further evidence for the comple-
mentarity hypothesis and also suggest that effects might be
driven more by the effect of perceived slow or stagnant
change constraining women’s future likelihood of becoming
an economic provider compared with a no-information con-
trol. Interestingly, across these first three studies, we did not
observe a clear inverse relationship between an increase in
envisioning oneself as the primary provider and a decrease in
envisioning oneself as the primary caregiver, an issue we
return to in the meta-analysis of all five studies. In this study,
the effect sizes on women’s providing expectations (d = .58)
and anticipated work time (d = .64) were consistently larger
than effects on primary caregiver expectations (d = –.44)
and anticipated caregiving time (d = – .20), though all effects
are interpretable as significant in this larger sample.
Figure 3. Study 2: Women’s expected likelihood of becoming the primary economic provider and primary caregiver for their families,
15 years in the future.
Note. Error bars represent standard errors.
816 Personality and Social Psychology Bulletin 45(5)
Our prior three studies suggested that when women perceive
men’s roles as becoming less traditional (vs. remaining sta-
ble), women are, complementarily, more likely to envision
themselves enacting less traditional roles. The goal of Study
3 was to again replicate the previous studies and potentially
strengthen the effects by requiring participants to actively
reflect upon the graphical information about men’s changing
roles during a brief writing exercise.6
Participants. Participants in this study were 116 heterosexual
undergraduate women below age 25 (Mage = 20.06, SD =
1.63; 42% White, 36% East Asian) who were only eligible if
they indicated during prescreening that they expected to have
a male partner and children (thus no data were excluded
based on these criteria). Data from two participants were
excluded due to technical problems (final N = 114). Data
collection occurred in Fall 2014 and we had planned to col-
lect a minimum of 50 participants in each condition but con-
tinued data collection through the end of the term.
Procedure. Study 3 followed the same procedure as Studies 1
and 2, with one modification intended to foster internaliza-
tion of the normative information. Participants studied the
graphs for 2 min and then were given 3 min to answer the
following question about an ostensibly randomly selected
graph (which always depicted men’s changing roles): “In
your own words, what is this graph saying about current
trends and their predicted patterns for the future?” and then
immediately answered a manipulation check question (“I
personally believe that men are: 1 = increasing their focus
on family; 4 = staying the same; 7 = increasing their focus
on career”). After this writing period, participants completed
the same life narrative survey from the prior studies, although
the day reconstruction task was omitted to save time (mean-
ing that concrete task estimates for this study are based only
on the percentage of time participants expected to spend
working and taking care of children). New to this study, mea-
sures of career ambition and traditional gender role beliefs
(counterbalanced in order)7 were included as potential mod-
erators after this survey to mask our explicit interest in gen-
der roles when primary outcomes were assessed. Finally,
participants were asked the attention check questions from
Study 1 (did the graph show rapid vs. slow change in men’s
roles?) and Study 2 (did the graph portray men as becoming
family- vs. career-oriented?).
Manipulation checks. Independent samples t tests confirmed
that women were more likely to recall the graph depicting
rapidly changing male roles in the rapid as compared with
slow change condition, t(107) = 12.85, p < .001, d = 2.52. They also recalled the graph depicting men becoming more family-oriented in the rapid change compared with the slow change condition, t(108) = −4.74, p < .001, d = –.90 (see Table 2). Finally, women seemed to internalize this informa- tion immediately after reading and writing about it, as they reported a stronger belief that men are becoming more fam- ily-oriented in the rapid compared with the slow change con- dition, t(112) = −2.19, p = .03, d = –.41.
Future roles. Despite the above evidence that the manipula-
tion was accurately perceived and internalized, independent
samples t tests revealed no significant condition differences
between the rapid and slow change groups in this sample on
either the provider, d = .13, or caregiver variables, d = –.13,
both ts < 1 (see Table 3), though both effects were still in the predicted direction.
Concrete tasks. Similar to the results for future roles, inde-
pendent samples t tests comparing participants’ concrete task
estimates revealed no significant condition differences on
either the time spent working, t < 1, d = .01, or enacting childcare responsibilities, t(112) = 1.47, p = .15, d = –.27 (see Table 4).
In sum, Study 3 yielded no support for the complementarity
hypothesis and yielded smaller observed effect sizes com-
pared with the previous three studies. One possibility is that,
although women initially reported a condition difference in
beliefs about the change in men’s roles after writing about this
trend (the manipulation check question), putting them in this
more deliberative mind-set during the manipulation might
have undermined the effectiveness of this kind of priming on
future roles, and perhaps even caused reactance among some
participants (e.g., Brehm, 1966; Molden, 2014). Therefore, in
Study 4 we returned to the same manipulation used in Study
1 and carried out a final preregistered replication of the pre-
dicted complementarity effects on primary provider and care-
giver ratings in a larger American sample (preregistration
c2). Because we were also interested in assessing not only
beliefs about taking on the primary role of provider or care-
giver, but also in whether women imagine sharing these roles
equally, we also included new measures that allowed partici-
pants to rate the relative contribution in their future relation-
ships to both breadwinning and caregiving.
Participants. We preregistered a target sample size of 302
needed not only to detect the main effects of our manipulation
Croft et al. 817
on key outcomes but also to test whether a measure of career
ambition significantly moderated these effects (see Note 7
and preregistration), estimated using G*power with f 2 = .03,
three predictors, α = .05, and 1 – β = .80. Anticipating exclu-
sions, we recruited 364 undergraduates from a large univer-
sity in the Southwestern United States to participate in this lab
study for either course credit or payment. As preregistered,
we excluded those who did not self-identify as female (n = 4)
or heterosexual (n = 32), who were older than 25 (n = 1), and
who did not anticipate having a spouse/partner (n = 12) or
children (n = 12) in the future. The final, usable sample was
303 heterosexual women (Mage = 18.76, SD = 1.17; 57%
Caucasian). Analyses without these exclusions can be found
in the SOM.
Procedure and measures. The procedure was adapted from
Study 1, wherein women saw graphs that depicted either
rapid or slow change in men’s roles before reporting their
expected future roles. Measures were the same with the fol-
lowing exceptions. First, we included two additional items to
assess expected caregiver and breadwinner roles relative to
participants’ expected partners: (a) “When it comes to earn-
ing money and contributing financially to my future house-
hold, I expect that”: 1 = My partner will definitely be the
primary economic provider for our family; 4 = My partner
and I will make equal economic contributions for our family;
7 = I will definitely be the primary economic provider for
our family; (b) “When it comes caring for our future children
(e.g., feeding, cleaning, coordinating schedules, activities,
transportation, etc.), I expect that: 1 = My partner will defi-
nitely be the primary caregiver for our children; 4 = My
partner and I will make equal contributions to childcare; 7 =
I will definitely be the primary caregiver for our children.
These measures of relative economic provider and caregiver
were significantly correlated with the original primary pro-
vider, r = .40, p < .001, and caregiver items, r = .57, p < .001, respectively. In addition to this key change, we also included exploratory measures of mechanism at the end of the study (see SOM), but excluded measures of gender role beliefs and concrete daily activities. Manipulation and atten- tion check questions, as well as current demographics, were asked at the very end of the survey.
Results and Discussion
Manipulation checks. Independent samples t tests confirmed
that participants accurately recalled the graphs as depicting
men’s roles as changing faster in the rapid than the slow
change condition, t(301) = −15.84, p < .001, d = 1.84. They also reported personally believing that men’s roles are chang- ing faster in the rapid than in the slow change condition, t(301) = −4.86, p < .001, d = .78.
Women also correctly recalled that the graph showed men
becoming more family-oriented (i.e., scores closer to 1) in
the rapid than in the slow change condition, t(301) = 10.84,
p < .001, d = −1.27. However, unlike in previous studies, their personal belief about men’s family orientation was not significantly different between condition, t(301) =1.47, p = .14, d = –.17.
Future roles. Independent samples t tests on the future roles
measures yielded some support for the complementarity
hypothesis. See Table 3 for means and standard deviations.
Although there was no significant main effect of condition
predicting women’s anticipated likelihood of being the pri-
mary economic provider, t(301) = −1.15, p = .25, d = .13,
this effect was significant on the newly added relative eco-
nomic provider measure, t(301) = −2.39, p = .02, d = .28.
Women in the rapid change condition were significantly
more likely to envision making equal economic contribu-
tions with their partners (scores close to 4), compared with
women in the slow change condition.
In addition, consistent with our complementarity hypothe-
sis, women in the rapid change condition were less likely than
women in the slow change condition to expect that they will be
the primary caregivers for their future children, t(301) = 2.15,
p = .04, d = –.25. Similarly, women in the rapid change condi-
tion envisioned sharing more equal caregiving contributions
with their future partners, relative to the women in the slow
change condition, t(301) = 2.72, p = .01, d = –.32.
Meta-Analysis Across Studies
One limitation of these studies is that several were run before
recent discussions surrounding the need for larger samples. It
has also been noted that in multistudy papers of true effects,
it is highly likely to observe some nonsignificant effects
(Lakens & Etz, 2017). Thus, to gain a more precise estimate
of the complementarity effect, we meta-analyzed effects on
future roles and concrete task estimates using Cumming’s
(2013) meta-analysis module in the Exploratory Software for
Confidence Intervals and recommendations by Goh, Hall,
and Rosenthal (2016), using a random effects model (as sug-
gested by Lakens, 2015). The total number of participants
across the five samples in the slow change/career and rapid
change/family conditions was N = 575 (nrapid = 274 and
nslow = 301; see Table 2). As can be seen in Table 3, sensitiv-
ity analyses using G*Power with α = .05 and 1 – β = .80
suggested that our earlier studies were underpowered to
detect small or moderate effects, but the combined sample
provides sufficient power to detect effects of at least d = .21.
These meta-analyses yielded a significant average esti-
mated effect of d = .27, 95% confidence interval (CI) [.11,
.44] for the likelihood of becoming the primary economic pro-
vider (see Figure 4). The estimated effect size for the likeli-
hood of becoming the primary caregiver was quite similar and
significant, d = –.26, 95% CI [–.42, –.09] (see Figure 5).
These are considered small- to medium-sized effects and are
meaningful both in conceptual guidelines (Cohen, 1988) and
in past quantitative summaries of effects in social psychology
818 Personality and Social Psychology Bulletin 45(5)
(Richard, Bond, & Stokes-Zoota, 2003). Of note, both of these
effects are reduced but remain statistically significant when
including participants who were excluded for not wanting a
male partner and/or children, dprovider= .26, 95% CI [.10, .42],
dcaregiver = –.24, 95% CI [–.40, –.08]. We suspect these are
important criteria for the hypothesized effect, but the effects
observed are not contingent upon their exclusion.
Meta-analyses of the concrete task measures from the ear-
lier studies (not assessed in Study 4) yielded a nonsignificant
average estimated effect for women’s estimated time spent at
work, d = .11, 95% CI [–.31, .52] (see Figure 6), but a sig-
nificant effect for estimated time on childcare-related tasks,
d = –.26, 95% CI [–.50, –.03] (see Figure 7). Taken together,
these findings suggest that, across five samples using differ-
ent methods varying in strength of manipulation, providing
women with information about the degree to which men’s
roles are changing rapidly versus slowly leads to a small to
moderate difference in women’s own imagined economic
providing (and to a lesser extent, caregiving) roles for the
future. In addition, women primed to believe that men’s roles
are changing rapidly might feel some relief on the time spent
caregiving, though this did not seem to translate into expect-
ing to work more hours.
These five studies tested the hypothesis that young hetero-
sexual women’s expectations of their future roles are com-
plementarily tied to their expectations of men’s changing (or
unchanging) roles. Findings suggested that women primed
Figure 4. Meta-analysis: Average estimated effect size of anticipated likelihood of becoming the primary economic provider as a
function of men’s roles.
Figure 5. Meta-analysis: Average estimated effect size of anticipated likelihood of becoming the primary caregiver as a function of men’s
Croft et al. 819
(either through exemplars or normative data) to expect that
men will increasingly take on more childcare, more readily
envision becoming breadwinners and, in parallel, are less
likely to envision becoming the primary caregivers of their
future families. Conversely, and still consistent with a com-
plementarity explanation, if primed to believe that men are
slow to take on childcare, women themselves were more
likely to anticipate being the primary caregiver and less
likely to be the primary economic provider of their future
family. It is worth noting that these effects were found with
samples of women in which the majority expected to earn
graduate degrees and work full-time. Taken together, these
studies provide the first causal evidence that women’s
expected future roles, and especially their involvement in
economic providing, are complementary to what they believe
men’s roles will be. Such findings are novel given that prior
research on the barriers to women’s adoption of nontradi-
tional gender roles has emphasized stereotypes about wom-
en’s own traits or abilities, rather than considering the
complementary barrier represented by women’s expectations
about a hypothetical division of domestic labor with their
Another novel contribution of the present research was to
establish the reliability and effect size estimates of these
complementarity effects on women’s possible selves. Future
research will need to disentangle the explanation for these
effects. One possibility is that if women see more men want-
ing to become primary caregivers, that women then imagine
Figure 7. Meta-analysis: Average estimated effect size of anticipated time spent doing childcare tasks as a function of men’s roles.
Figure 6. Meta-analysis: Average estimated effect size of anticipated time spent working as a function of men’s roles.
820 Personality and Social Psychology Bulletin 45(5)
feeling more constrained to make larger economic contribu-
tion to their families. A second possibility is that men’s
increased contributions to childcare allow women to feel
enabled to take on more demanding careers that would earn
a larger paycheck then their partner. A third possibility is that
evidence of men’s changing gender roles might generally
signal less restrictive gender norms that cue women to imag-
ine less stereotypical future selves. In Study 4, we made
efforts to measure these three mechanisms but found no evi-
dence that the manipulation influenced women’s self-
reported beliefs on any of these three variables (see SOM for
details). Perhaps complementarity is a process that is cued
much more automatically, rather than through any of these
more conscious and rational considerations. It is worth not-
ing that the manipulation used in these studies was quite
subtle—framing the same statistical information in two dif-
ferent ways. Thus, although men’s changing norms seems to
influence women’s own role expectations, women might be
unaware that this is happening or why. That said, perhaps a
stronger and more explicit manipulation of men’s stated
intrinsic motivation to share childcare responsibilities (rather
than data restricted to stay at home dads) might have a much
stronger effect on these explicitly measured mediating
Although our primary focus was on women’s more
abstract vision of their future roles, the first four studies also
assessed their concrete estimates of time spent on tasks
related to those roles. We suspected that perceptions of men’s
roles might have stronger effects on abstract notions of pos-
sible selves than concrete estimates of time, consistent with
the idea that stereotypes might have a stronger effect in shap-
ing abstract estimates for the future (Brown & Diekman,
2010). Although effects on these time estimates varied from
study to study, the meta-analysis suggested some interesting
patterns. Overall, when women believed men’s roles were
changing rapidly, their greater likelihood of becoming the
economic provider was not paralleled by anticipating longer
working longer hours. However, the effect size of men’s
changing roles on reducing women’s likelihood of becoming
the primary caregiver was similar to the reduced amount of
time they imagine spending on caregiving tasks. Taken
together, this pattern might suggest that men’s changing gen-
der roles could potentially expand women’s possible selves
to provide greater balance between economic providing and
caregiving roles and reduce working women’s expectations
of shouldering a disproportionate amount of childcare
Broader Implications of Complementarity
Our findings are particularly interesting in light of the tendency
for some women to “leave before they leave”—to opt out of
demanding career tracks before there is the realistic need to
do some because of family (Sandberg, 2013; Stone, 2007).
The anticipatory complementarity processes documented here
might contribute to women’s underrepresentation in leadership
and management positions. Recent research suggests, for
example, that women rate a promotion as less desirable than do
men to the degree that they expect more career–family conflict
(Gino, Wilmuth, & Brooks, 2015). Our findings indicate that
undergraduate women are affected by complementary stereo-
types about men and women when envisioning their futures,
long before there is a practical need to negotiate the trade-offs
of career and home life with a partner. Perceptions that men’s
roles remain stagnant and traditional led our highly educated
career-focused female samples to expect that their role would
be as the primary caregiver rather than economic provider in
their future family.
One open question that remains in light of the findings
reported here (for women) is whether or not men would dis-
play similar complementary patterns in their expected future
roles if faced with information about women’s changing
roles. Specifically, we could imagine competing possibilities
with respect to men’s outcomes. On one hand, we might pre-
dict parallel effects of complementarity among men who
receive information about women’s changing roles. If men
believe that women are increasingly interested in adopting
breadwinning roles in a future relationship, men might expe-
rience a welcome sense of relief from a (real or imagined)
pressure to provide for their families, placed on them by a
masculine gender role. On the other hand, from a status value
perspective (Ridgeway, 2014), the observed patterns of com-
plementarity might be unique to women envisioning future
roles in response to men’s role change. Given men’s status as
the cultural default and the “higher ranking” partner in het-
erosexual romantic relationships, it might be less likely that
they would observe or respond to a need to adapt their own
role expectations to that of a future wife’s changing roles.
Given these alternative predictions, it will be important for
future research to examine whether these effects of comple-
mentarity generalize to men.
Another important question for future research is to
understand whether these processes are specific to relative
role trade-offs negotiated within romantic couples or if
they extend to women’s other career-related choices and
behaviors. For instance, does the expectation that men’s
roles with respect to childcare are changing have any effect
on the career trajectory women set for themselves prior to
having children or even a long-term partner? Do the expec-
tations of each partner in emerging romantic couples lead
one or both of them to adjust their motives or behavior in
anticipation of negotiating the division of labor? We sus-
pect that perhaps complementarity processes are unique to
dyadically defined roles, given the consistent lack of con-
dition main effects on women’s concrete and nonrelative
outcomes measured across studies (e.g., anticipated time
spent working, career ambition). Thus, it is possible that
this phenomenon is linked to relative roles shared within a
romantic couple and the trade-offs that are negotiated with
one’s partner, rather than one’s own interest and ambition
Croft et al. 821
in pursuing a particular career. That said, once these hypo-
thetical relative role choices are set in motion, the actual
decisions women make could ultimately curtail their career
choices and engagement, even if during their college years
women are unable to anticipate those effects. Future stud-
ies using longitudinal designs or dyadic data from emerg-
ing romantic couples could explore these possibilities
Although complementarity effects on women’s economic
providing and caregiving expectations appeared robust when
aggregating effects across studies, we acknowledge that gaps
remain in our understanding of these effects. One limitation
is that we manipulated changing roles by focusing on men
becoming stay-at-home fathers, a role that is still quite rare.
Sharing caregiving equally is becoming increasingly com-
mon among dual earning couples (Askari et al., 2010; Pew
Research Center, 2015), and in such relationships, there is no
“primary” caregiver or economic provider. Study 4 begins to
address this limitation with the inclusion of a new measure
that allowed women to indicate that they expect to share
breadwinning and caregiving responsibilities equally with
their partners. Effects on these measures supported the com-
plementarity hypothesis—Women who were primed with
men’s roles changing rapidly envisioned sharing both bread-
winning and caregiving more equally with their partner (i.e.,
their ratings were closer to a 50/50 split) than women who
were primed with men’s roles changing slowly. It will be
important for follow-up studies to investigate how young
women’s and men’s expectations of equality in a couple
(e.g., division of childcare time) affect their possible selves
for the future. In addition, future work could use alternative
manipulations of gender role change, such as priming incre-
mental or entity theories of changing gender roles (see Kray,
Howland, Russell, & Jackman, 2017), in an effort to better
define the parameters of these effects.
A second limitation of these complementarity studies is
that our samples consisted of particularly ambitious and
highly educated women (see Table 1), and additional research
is needed to test generalizability to other populations. Women
from lower socioeconomic backgrounds also contend with
the second shift, but our restricted sample demographics pre-
vent us from concluding that these effects would be as read-
ily observed among noncollege educated women. In fact, the
results of Study 3 (see SOM) provide some suggestion that
complementarity effects might be particularly relevant for
career ambitious women, but this is not to say nonprofes-
sional women do not feel constrained by men’s lack of
involvement in childcare. Future studies should examine
these questions more directly.
Finally, a third limitation is that some of our studies were
run prior to current recommendations for using increasingly
large samples, and perhaps as a result, effect sizes vary from
sample to sample. In carrying out this research, our emphasis
has been to replicate these effects to estimate the effect sizes
more accurately. However, we hope that by providing all rel-
evant study materials along with precise estimates of these
effects, other researchers will be inspired to replicate and
extend these findings to better understand the moderators,
mediators, and downstream consequences of these effects.
In conclusion, these studies provide initial evidence for
the novel complementarity hypothesis—the proposition that
the rigidity (or flexibility) of men’s caregiving roles place
constraints on women’s freedom to step into nontraditional
roles within heterosexual couples. These patterns might sug-
gest that women’s stereotypical expectations about men’s
roles in the future may constrain women’s beliefs about
themselves, with the potential to impact their choices in the
present. To extent that women believe that men continue to
prefer traditional provider roles, they may feel constrained
from considering such roles for themselves. At the same
time, our data also suggest that the change we are beginning
to see in men’s roles might empower some women to take on
provider roles that have traditionally been reserved for men,
and ultimately relieve women of bearing the burden of the
“second shift” at home.
The authors thank Jessica Beauchesne, Mojeed Fale, Cindy
Galinsky, Melissa Gaudette, Kyle Gooderham, Javier Granados-
Samayoa, Patrick Irvine, Megan McPherson, Negah Mortazavi,
Pegah Mortazavi, Natalie Nunez, Helen Schweitzer, Sean Thayer,
and Joanne Zhou for their help with data collection.
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect
to the research, authorship, and/or publication of this article.
The author(s) disclosed receipt of the following financial support
for the research, authorship, and/or publication of this article: This
research received support from the Foundation for Personality and
Social Psychology and the Society for the Psychology of Women
(APA Division 35) to the first author and by a SSHRC Insight Grant
to the second author.
1. Degrees of freedom vary due to missing data for three participants.
2. When data from men are included, a 2 (gender) x 3 (condition)
ANOVA on economic provider yielded a significant interaction,
F(2, 130) = 5.35, p = .006, ηp
= .08. Men reported a lower
likelihood of becoming the economic provider in the family
compared with balanced condition. This was the only significant
interaction with gender. See SOM for details.
3. Results are unchanged when all participants are included in
analyses. This is addressed in the meta-analysis and in SOM.
4. An initial pilot test of these new stimuli that did not instruct
women to relate the graphs to their own life revealed no effects
on anticipated roles. Our failure to include a manipulation check
822 Personality and Social Psychology Bulletin 45(5)
prevented us from evaluating whether women had internalized
the graphical information as intended. Details are available from
the first author.
5. Patterns of effects are similar but weaker for the complete sam-
ple. See meta-analysis for more information.
6. Note that we ran a previous iteration of Study 3 but discovered
after data collection was complete that half of the sample had
considerably less time to view the graphs due to experimenter
error. Perhaps as a result, the manipulation had no effect on par-
ticipants’ internalized beliefs about changing gender roles. Thus,
Study 3 was a repeat of this study.
7. We tested but found no evidence that traditional gender roles
moderated effects. Career ambition showed a nonsignificant
trend moderating the effect of the manipulation effects on the
provider ratings. Because this effect did not replicate in Study 4,
we only describe it in the SOM.
Supplementary material is available online with this article.
Allen, S., & Hawkins, A. (1999). Maternal gatekeeping: Mothers’
beliefs and behaviors that inhibit greater father involve-
ment in family work. Journal of Marriage and the Family,
61, 199-212. Retrieved from http://www.jstor.org/stable/
Aron, A., & Aron, E. (1986). Love and the expansion of self:
Understanding attraction and satisfaction. New York, NY:
Askari, S. F., Liss, M., Erchull, M. J., Staebell, S. E., & Axelson,
S. J. (2010). Men want equality, but women don’t expect it:
Young adults’ expectations for participation in household and
child care chores. Psychology of Women Quarterly, 34, 243-
Bloom, K., Delmore-Ko, P., Masataka, N., & Carli, L. (1999).
Possible self as parent in Canadian, Italian, and Japanese
young adults. Canadian Journal of Behavioural Science/Revue
Canadienne des Sciences du Comportement, 31, 198-207.
Brehm, J. W. (1966). A theory of psychological reactance. Oxford,
UK: Academic Press.
Brown, E. R., & Diekman, A. B. (2010). What will I be? Exploring
gender differences in near and distant possible selves. Sex
Roles, 63, 568-579. doi:1007/s11199-010-9827-x
Ceci, S. J., & Williams, W. M. (2011). Understanding current causes
of women’s underrepresentation in science. Proceedings of the
National Academy of Sciences of the United States of America,
108, 3157-3162. doi:10.1073/pnas.1014871108
Cohen, J. (1988). Statistical power analysis for the behavioral sci-
ences (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum.
Croft, A., Schmader, T., & Block, K. (2015). An underexamined
inequality: Cultural and psychological barriers to men’s engage-
ment with communal roles. Personality and Social Psychology
Review, 19, 343-370. doi:10.1177/1088868314564789
Croft, A., Schmader, T., Block, K., & Baron, A. S. (2014). The sec-
ond shift reflected in the second generation: Do parents’ gen-
der roles at home predict children’s aspirations? Psychological
Science, 25, 1418-1428. doi:10.1177/0956797614533968
Cumming, G. (2013). Understanding the new statistics: Effect sizes, con-
fidence intervals, and meta-analysis. New York, NY: Routledge.
Diekman, A. B., & Eagly, A. H. (2000). Stereotypes as dynamic
constructs: Women and men of the past, present, and future.
Personality and Social Psychology Bulletin, 26, 1171-1188.
Eagly, A. H., Eastwick, P. W., & Johannesen-Schmidt, M. (2009).
Possible selves in marital roles: The impact of the anticipated
division of labor on the mate preferences of women and men.
Personality and Social Psychology Bulletin, 35, 403-414.
Eagly, A. H., & Steffen, V. J. (1984). Gender stereotypes stem
from the distribution of women and men into social roles.
Journal of Personality and Social Psychology, 46, 735-754.
Eagly, A. H., & Wood, W. (2013). The nature–nurture debates:
25 years of challenges in understanding the psychology of
gender. Perspectives on Psychological Science, 8, 340-357.
England, P. (2010). The gender revolution: Uneven and stalled. Gender
& Society, 24, 149-166. doi:10.1177/0891243210361475
Gino, F., Wilmuth, C. A., & Brooks, A. W. (2015). Compared to
men, women view professional advancement as equally attain-
able, but less desirable. Proceedings of the National Academy
of Sciences, 112, 12354-12359.
Goh, J. X., Hall, J. A., & Rosenthal, R. (2016). Mini meta-analysis
of your own studies: Some arguments on why and a primer
on how. Social and Personality Psychology Compass, 10, 535-
Haddock, S. A., Zimmerman, T. S., Lyness, K. P., & Ziemba, S. J.
(2006). Practices of dual earner couples successfully balancing
work and family. Journal of Family and Economic Issues, 27,
Hochschild, A. R., & Machung, A. (2012). The second shift:
Working parents and the revolution at home (Rev. ed.).
London, England: Penguin Books.
Hodges, A. J., & Park, B. (2013). Oppositional identities:
Dissimilarities in how women and men experience parent
versus professional roles. Journal of Personality and Social
Psychology, 105, 193-216. doi:10.1037/a0032681
Kahneman, D., Krueger, A. B., Schkade, D. A., Schwarz, N., &
Stone, A. A. (2004). A survey method for characterizing daily
life experience: The day reconstruction method. Science, 306,
Kray, L. J., Howland, L., Russell, A. G., & Jackman, L. M. (2017).
The effects of implicit gender role theories on gender system
justification: Fixed beliefs strengthen masculinity to preserve
the status quo. Journal of Personality and Social Psychology,
Kroska, A. (2004). Divisions of domestic work: Revising and
expanding the theoretical explanations. Journal of Family
Issues, 25, 900-932. doi:10.1177/0192513X04267149
Lakens, D. (2015, October 11). Practicing meta-analytic think-
ing through simulations [Web log comment]. Retrieved from
Lakens, D., & Etz, A. J. (2017). Too true to be bad: When sets
of studies with significant and non-significant findings are
probably true. Social Psychological and Personality Science.
Lee, S. J., & Oyserman, D. (2007). Reaching for the future: The
education-focused possible selves of low-income mothers. New
Directions for Adult and Continuing Education, 114, 39-49.
Croft et al. 823
Lee, S. J., & Oyserman, D. (2009). Expecting to work, fearing
homelessness: The possible selves of low-income mothers.
Journal of Applied Social Psychology, 39, 1334-1355.
Lewin, L. (1939). Field theory and experiment in social psychol-
ogy: Concepts and methods. American Journal of Sociology,
44, 868-896. doi:10.1086/218177
Markus, H., & Nurius, P. (1986). Possible selves. American
Psychologist, 41, 954-969. doi:10.1037//0003-066X.41.9.954
Molden, D. C. (2014). Understanding priming effects in social psy-
chology: What is “social priming” and how does it occur? In
D. C. Molden (Eds.), Understanding priming effects in social
psychology (pp. 3-13). New York, NY: Guilford Press.
Offer, S., & Schneider, B. (2011). Revisiting the gender gap in
time-use patterns: Multitasking and well-being among moth-
ers and fathers in dual-earner families. American Sociological
Review, 76, 809-833. doi:10.1177/0003122411425170
Oyserman, D., & James, L. (2011). Possible identities. In S. J.
Schwartz, K. Luyckx, & V. L. Vignoles (Eds.), Handbook of
identity theory and research (pp. 117-145). New York, NY:
Park, B., Smith, J. A., & Correll, J. (2008). “Having it all” or “doing
it all?” Perceived trait attributes and behavioral obligations as
a function of workload, parenthood, and gender. European
Journal of Social Psychology, 38, 1156-1164. doi:10.1002/
Park, B., Smith, J. A., & Correll, J. (2010). The persistence of
implicit behavioral associations for moms and dads. Journal of
Experimental Social Psychology, 46, 809-815. doi:10.1016/j.
Pew Research Center. (2013). Breadwinner moms: Mothers are the
sole or primary provider in four-in-ten households with children;
public conflicted about the growing trend. Retrieved from http://
Pew Research Center. (2014). Growing number of dads home with
the kids: Biggest increase among those caring for family.
Retrieved from http://www.pewsocialtrends.org/2014/06/05/
Pew Research Center. (2015). Raising kids and running a house-
hold: How working parents share the load. Retrieved from
Richard, F. D., Bond, C. F., & Stokes-Zoota, J. J. (2003). One
hundred years of social psychology quantitatively described.
Review of General Psychology, 7, 331-363. doi:10.1037/1089-
Ridgeway, C. L. (2014). Why status matters for inequality.
American Sociological Review, 79, 1-16.
Sandberg, S. (2013). Lean in: Women, work, and the will to lead.
New York, NY: Alfred A. Knopf.
Simmons, J. P., Nelson, L. D., & Simonsohn, U. (2011). False-positive
psychology: Undisclosed flexibility in data collection and anal-
ysis allows presenting anything as significant. Psychological
Science, 22, 1359-1366. doi:10.1177/0956797611417632
Smith, G. C., James, L. E., Varnum, M. E. W., & Oyserman, D.
(2014). Give up or get going? Productive uncertainty in uncer-
tain times. Self and Identity, 13, 681-700. doi:10.1080/152988
Smith, G. C., & Oyserman, D. (2015). Just not worth my time:
Experienced difficulty and time investment. Social Cognition,
Spence, J. T., Helmreich, R., & Stapp, J. (1975). Ratings of self
and peers on sex role attributes and their relation to self-esteem
and conceptions of masculinity and femininity. Journal of
Personality and Social Psychology, 32, 29-39.
Statistics Canada. (2010). [Graph illustration of the growing num-
ber of Canadian stay-at-home-dads]. Stay at Home Dads on
the Increase. Retrieved from http://www.vancouversun.com/
Stone, P. (2007). Opting-out? Why women really quit careers and
head home. Berkley: The University of California Press.
Stout, J. G., Dasgupta, N., Hunsinger, M., & McManus, M. A.
(2011). STEMing the tide: Using ingroup experts to inoculate
women’s self-concept in science, technology, engineering,
and mathematics (STEM). Journal of Personality and Social
Psychology, 100, 255-270. doi:10.1037/a0021385
Weber, R., & Crocker, J. (1983). Cognitive processes in the revi-
sion of stereotypic beliefs. Journal of Personality and Social
Psychology, 45, 961-977. doi:10.1037/0022-35188.8.131.521
Williams, M. J., & Chen, S. (2013). When “mom’s the boss”:
Control over domestic decision making reduces women’s
interest in workplace power. Group Processes & Intergroup
Relations, 17, 436-452. doi:10.1177/1368430213497065
53A Qualitative Study on Work-Life Balance of Software Professionals© 2017 IUP. All Rights Reserved.
A Qualitative Study on Work-Life Balance
of Software Professionals
* Assistant Professor, Sinhgad Institute of Management and Computer Application, Narhe, Pune 411041,
Maharashtra, India. E-mail: firstname.lastname@example.org
Work-life balance is characterized by a condition of balance in which the demands
of both a man’s occupation and individual life are equivalent. It involves contributing
equivalent measures of time and vitality between work and individual life. The
transformation of information and communication technologies and its usage has
affected individuals work and family lives positively or negatively. The objective of
this study is to explore the work-life balance among select employees (N=30). The
study employs thematic analysis through six themes: social need, personal need,
time management, team work, compensation and benefits, and work. The outcomes
suggests that many employees relinquish their own time keeping in mind the end
goal to strike a balance between work and life. Employees, particularly women, have
a great deal of role clash as moms and other family members. Men nowadays need
to take up family duties. A considerable measure of adapting procedures that the
workers used have been talked about in the present study.
The conventional wisdom indicates that employees will never feel truly satisfied with work
until they are satisfied with life. But in this new age it seems that organizations have
failed to comprehend that work-life balance is an important aspect for the individual as
well as for the organization. The survival of any enterprise today is not only dependent
on its own ability to innovate and systemize its activities but also on the happy workers
and it can be achieved by maintaining the balance between work and personal life. So
the biggest challenge for human resource professionals is recruiting, training and retaining
the people by keeping in mind the cost involved in all and this it is very important that
organizations cultivate the culture that provides for balance between the professional and
non-professional life of employees. Work-life balance is the term used to describe those
practices at workplace that acknowledge and aim to support the needs of employees
in achieving a balance between the demands of their family life and work lives (Agarwal,
The IUP Journal of Organizational Behavior, Vol. XVI, No. 4, 201754
2009). According to Kofodimos (1993), work-life balance alludes to “a fulfilling, sound,
and beneficial life that incorporates work, play and love”. Work culture ought to provide
great environment to an individual and his/her family. Thus, the work-life balance is about
overseeing internal pressure from one’s own particular cravings and setting sensible
objectives which do not impinge on family commitments. Work-life balance can be defined
as a state of equilibrium in which sufficient amount of time should be given to personal/
family interests and organizational interests. Those who achieve this balance tend to
achieve higher level of job satisfaction, organizational commitment, as well as lower level
of stress and turnover. In sum, proof proposes a work technique of “running yourself worn
out” which has costs both for meeting performance objectives and be pleased about life;
the employee and the organization gain most when specialists experience extraordinary
equivalence between what they do on and off the clock.
The paper explores the work-life balance among select employees explaining thematic
analysis through six themes: social need, personal need, time management, team work,
compensation and benefits, and work.
Work/Life Balance Defined and Explored
Work-life balance is a challenging issue for the organizations and has attracted the
attention of many researchers. Work-life balance was initially utilized in the 1970s to
describe the balance between an individual’s work and personal life (Newman and
Mathews, 1999). Work-family conflict is characterized by the incongruence between
obligations at home and workplace, which are observed to be commonly inconsistent
(Greenhaus and Beutell, 1985). Work ought to give great environment to an individual
and his family. Hence, work-life balance is about managing internal pressure from one’s
own desires and setting reasonable objectives which do not perpetrate on family
obligations. The absence of inadmissible level of contentions among work and non-work
demands may bring about lower organizational performance.
Work-life balance is described as the sum of practices of individuals who control and
oversee both life and career with accomplishment and fulfillment. It is the term used to
describe those practices at workplace that recognize and intend to support the efforts
of employees in accomplishing a balance between demands of their family and work-
life. Work-life balance implies conforming the pattern of work so that the employee can
benefit from a better fit between their work and zones of their own life and in the long
run would accomplish feasible improvement and profitability.
Theories of Work-Life Balance
A great deal of speculation has been encircled on work-life balance which have been bound
as a singular outline work not recognized all around (Pitt-Catsouphes et al., 2006).
55A Qualitative Study on Work-Life Balance of Software Professionals
A few frameworks on work-life balance incorporate spillover, segmentation, compensation,
congruence, enrichment, inter role conflict, border and boundary theory (Zedeck and
Mosier, 1990; Frone et al., 1992; Clark, 2000; Edwards and Rothbard, 2000; Frone, 2003;
and Greenhaus and Powell, 2006). The theories which are prominent in work-life balance
are as follows:
• Spillover Theory: Spill-over is a process whereby experiences in one role affect
experiences in the other, rendering the roles more alike. Research has
examined the spill-over of mood, values, skills and behaviors from one role to
another (Edwards and Rothbard, 2000). A considerable research work has been
done on spillover theory (Zedeck and Mosier, 1990). Researchers have quite
a while ago perceived that work and family are most certainly not ‘isolate
circles’, yet are related areas or parts with ‘permeable’ limits (Kanter, 1977;
and Pleck, 1977). Spillover can be both positive or negative and if an employee
is feeling stressed in one domain, he/she may feel dissatisfied with other domain
also. On the other hand, positive spillover is when the employee is satisfied
with one domain of his life either work or family, he will feel satisfied and happy
with the other domain as well. This theory supports work-life balance theory
taking into account that distressing occasions and issues in one space has
an impact on how workers see their fulfillment in the other space.
• Segmentation Theory: Work and family were considered two separate areas
and independent of each other (Edwards and Rothbard, 2000). Segmentation
theory has been used to define that work and life are two different areas and
do not impact each other. This theory has been used for the study as it states
that if employee wants to feel satisfied, he can maintain the balance between
work and personal life by disconnecting himself with one of the domains, i.e.,
either work or family.
• Compensation Theory: It considered work and family to have a place with
two different spaces and the negative experience of one space could be repaid
with the positive experience of other space. In other words, work and family
display alter relationship (Clark, 2000).
• Congruence Theory: According to this theory, additional factors such as
knowledge, identity, hereditary compel or level of education could positively impact
both work and family domains evenly, however they are not identified with work
and family influence (Zedeck, 1992; and Edwards and Rothbard, 2000).
• Inter-Role Conflict Theory: It implies that taking care of a demand in one
area (work) makes it hard to meet the demands in other space (family)
(Greenhaus and Beutell, 1985). For instance, role conflict arises when an
employee has to do overtime due to work pressure and at the same time faces
family pressure to come home.
The IUP Journal of Organizational Behavior, Vol. XVI, No. 4, 201756
• Enrichment Theory: Enrichment theory refers to how encounters from
instrumental sources (aptitudes, capacities, and values) or affective sources
(inclination, fulfillment) improve the nature of the other area (Morris and Madsen,
• Work Family Border Theory: Work-family border theory is devoted only to
work and family domains. The result of enthusiasm in this theory is work-family
balance, which refers to satisfaction and good functioning at work and at home,
with a minimum of role conflict (Clark, 2000).
• Boundary Theory: Boundary theory is a general cognitive theory of social
classification (Zerubavel, 1991) which concentrates on results, for example, the
implications individuals allot to home and work and the straightforwardness and
recurrence of transitioning between roles (Ashforth et al., 2000). Boundary and
border theory connected to a scope of work family themes like adaptable
calendars working with family and so on. This theory lead to further analysis of
nature of borders, their permeability, the ease with which they can manage and
move on. In the analysis of work-life balance, the analysis of borders can help
to decide how far an individual can control issues determining work-life balance.
According to Tomazevic et al. (2014) the meaning of work-life balance is to adequately
combine professional life with personal commitments and make a concordance between
these two viewpoints. It can be characterized as the nonappearance of contention among
organizational and individual life.
Kumar and Khyser Mohd (2014) emphasize that work-life balance is about individuals
having a measure of control over when, where and how they work. The authors identified
two main variables, time and stress. The manager should be able to distinguish issue,
and discover an answer with cooperation of others. Organization must incorporate
work-life balance as a HR approach. The investigation primarily concentrates on the
results of imbalanced work-life confronted in the everyday life and the role of the
organization in accomplishing work-life balance.
Felicity Asiedu-Appiah (2013) study presumed that work-life balance is critical in
improving employee performance at work and home. The authors identified that gender
difference exists in work-life balance needs since work and non-work duties are different
for men and women. Same study demonstrated that women exhibited greater necessity
for work-life balance when compared with men. An individual derives satisfaction in life
from work and family domains.
According to Lingard et al. (2012) work-life strategies present the importance of the
issues of creating positive feelings among employees, directing work-life balance and
adaptation of participants. Communication channels should function very well and the
cultural conditions of the country where the organization is located, should be taken into
57A Qualitative Study on Work-Life Balance of Software Professionals
account to realize work-life strategies and applications used in organizations (Lingard
et al. 2012). Kalliath and Brough (2008) found that “work-life balance is the individual
discernment that works and non-work exercises are perfect and advance development
as per an individual’s present life priorities”.
Jang (2009) examined the relationship between work-life balance and the well-being
of working parents. The objective of this study was to identify how working parents cope
with the demands of work and life. The study considered 27 parents with either ill or
disabled children in New Jersey. The author used both qualitative and quantitative
techniques. The outcome discussed the impact of formal and casual work environments
in improving the wellbeing of employees with kids in general and those with a sick or
handicapped child in particular.
Reddy et al. (2010) researched work-life balance among married women
The study took various factors into consideration that lead to work-family conflict and
family-work conflict among married women employees. Work-family conflict and family-
work conflict surveys were conducted on 90 married working ladies aged between 20 to
50 years. The discoveries of the review underscored the need for mediation by the
management of work-family conflicts at organizational level as these affect occupational
satisfaction and employee performance.
Margo et al. (2008) carried out in-depth interviews of 18 teleworking mothers working
in a Canadian financial corporation. The questions asked were related to their work,
leisure, and their perception of work-life balance. The outcome of their study suggested
that the mothers’ viewed teleworking positively because of the flexible schedule that can
go with the rhythm of their children’s school and holiday.
Matjasko and Feldmen (2006) investigated how intrinsic work motivation, work hours,
and taking time for oneself influenced the interplay between the emotional climates of
work and home. The authors examined day-to-day emotional transmission between work
and home (spillover) for 143 families using the experience sampling method and interview
data from the Sloan Center’s 500 family study. They focused on getting work home in
expanded natural setting and help the workers in devoting time for themselves in the midst
of everyday demands between work and home. Confirmations from the review demonstrate
how bringing back work home can influence mothers’ satisfaction, tension and fathers’
nervousness. Among fathers there is an increased intrinsic work motivation and a more
prominent general tension at home. The ramifications of the review suggested women’s
efficiency and wellbeing in two working-parents families.
• To understand the theory of work-life balance;
• To gain knowledge about how workers manage balance between work and
individual commitments and roles;
The IUP Journal of Organizational Behavior, Vol. XVI, No. 4, 201758
• To comprehend the work-related issues and difficulties confronted by the
• To comprehend the family-related issues and difficulties confronted by the
Data and Methodology
The study was conducted on software professionals working in Pune. A conceptual
framework based on a model of Pareek and Purohit (2010) connecting work and personal
life reflects the questions and provides a broad architecture for the literature review and
a thematic framework for an aspect of the data analysis. The elements of the study include
features such as a measure of work/life balance like social need, personal need, time
management, team work, compensation and benefits, and work to underpin the analysis
of work/life balance of software professionals.
A semi-structured interview schedule was developed by the author to study the work-
life adjustment of the members and how function, family and self-related issues are
interconnected in empowering them to adjust between individual and professional
responsibilities and duties. It was administrated to 30 employees working in IT companies
in HR and specialized employments. The study used qualitative techniques, which helped
the author in gaining deeper insight into participants’ experiences. The study investigates
the gender differences and contrasts the variables studied in the study. A number of past
studies have also utilized phenomenological gender and work-life balance to study the
phenomenon of work-life balance by exploring the lived experiences of women (Lewis,
2003; Millward, 2006; and Woodward, 2007).
The author developed semi-structure interview schedule and used it to understand the
inside and outside of the work-life adjustment difficulties, issues and adapting procedures
utilized by the employees. These questions helped the author in comprehending the work-
life-balance which is generally inaccessible in quantitative information and furthermore to
comprehend their coping strategies. The questionnaire (see Appendix) consists of 19
open-ended questions which are divided into six categories:
• Social Need
• Personal Need
• Time Management
• Team Work
• Compensation and Benefits
Based on the scale developed by Pareek and Purohit (2010), the author arrived at
the above six categories for measuring work-life balance.
59A Qualitative Study on Work-Life Balance of Software Professionals
• The selection technique used was purposive sampling. The sample for the
quantitative analysis consisted of 30 employees from IT companies. The sample
selected was a conscious choice comprising of dynamic women employees
with family duties. For this review, face-to-face semi-organized interviews were
conducted. Every member was given the option to withdraw from the study at
any time. Only employees working in the IT sector were selected for the study.
IT sector has been chosen because it is the technology which made it possible
to be in constant touch with employees both during the day and at night. To
a large extent in the IT sector, an employee is expected to be engaged on the
job almost at all times and it creates work-life imbalance. In the IT sector five
companies had been selected for the study.
• Employees with working spouses (full-time) were selected for the study. This
is because Women’s Liberation Development was an impetus to enable women
continuing a profession while having a family. These changes have posed new
difficulties for families such as the division of tasks at home and child care.
Now mother and father both are equally responsible and this created author’s
interest in selecting employees with working spouses for the study.
• Employees with at least one child were selected for the study. The reason
behind this selection is that the individual’s participation inside the work
constraints has expanded as both parents are working. So the author decided
to identify employees who have children and are working to ascertain their
experiences of work-life balance.
Results and Discussion
Thematic Analysis was utilized in the current study: The information obtained in the
present study was dissected by arranging the items/questions in the semi-structured
interview into themes and the reactions of the members was dissected under those topics.
This area of the study talks about the subjective results obtained from the semi-structured
interview which was conducted on 30 employees working in the IT sector. The analysis
was done by using thematic analysis as qualitative approaches are extraordinarily
different, complex and nuanced (Holloway and Todres, 2003) and thematic analysis ought
to be viewed as a foundational strategy for qualitative analysis, and also identified that
‘thematizing meanings’ as one of a few shared nonspecific abilities crosswise over
qualitative analysis. For this reason, Boyatzis (1998) describes it not as a particular
strategy but rather as a device to use crosswise over various strategies. Similarly, Ryan
and Bernard (2000) find thematic coding as a procedure performed inside ‘major’
systematic conventions, (for example, grounded hypothesis), as opposed to a particular
approach in its own privilege. We contend thematic analysis should be viewed as a
strategy in its own particular right. From the answers obtained from the participants
following results have been revealed:
The IUP Journal of Organizational Behavior, Vol. XVI, No. 4, 201760
• Social Need: Social needs include love, belonging, acceptance and safety.
Satisfaction of these needs is important in order to feel supported and accepted.
Having one’s social needs met also helps prevent problems such as loneliness,
depression and anxiety. When a person develops an emotional connection with
other people, he/she can more easily cope with depressing situations and can
find strength through interacting with other people. When participants were
asked about fluffing their social needs they report that they are not able to
maintain connections with others such as friends, family and team members.
But fulfillment of this need is very important in order to avoid problems such
as anxiety, depression or loneliness as we all need to feel accepted and
supported by others.
A few of the reactions by the employees related to the social needs
• I find it difficult to take leave at the time of social emergencies.
• I do not find enough time to spend with my friends.
• I find it difficult to attend and enjoy the parties.
• Personal Need: The personal need of the employee is related to the need of
spending time with family, time for personal interest and so on. When
respondents were asked about the challenges they face to fulfill the personal
need, few men reported that their working wives were not happy with the time
they spent on household task. The other challenges which were reported by
employees were getting children ready for school before office, not able to spend
time with children, and inability to listen to children’s stories about their school,
friends and teachers, and travel to school. A few of them also said that meeting
teacher or going for parent-teacher meeting is also a challenging task for them
and also they do not find time for themselves which they want to utilize for
their hobbies, recreation, health, me-time and so on.
A few of the reactions by the employees related to the personal needs are:
• I am not able to give time for my personal interest as it is difficult to maintain
the balance between role of an employee and a parent.
• I do not find me-time.
• Time Management: On being approached about what they accomplish during
their ‘personal time’, the employees complain that they do not have time for
themselves by any means. They say that they need to do a great deal for their
wellness and leisure activities for which they do not find time by any stretch
of the imagination.
A few of the reactions by the employees related to time management are:
• I do not get time for my sick partner/child/parent.
61A Qualitative Study on Work-Life Balance of Software Professionals
• Organization emphasizes on time more than on task and it creates problem
in managing time.
• I cannot adjust my working schedule to attend my family priorities.
• I do not get time to invite my friends for a party at home.
• Work: When participants were asked about the work-related questions, many
employees complained about unstructured work schedule. The employees also
complained that because of heavy work load, lunch also gets delayed and also
they do not get leisure time. The respondents also stated that emphasis should
be given on task completion than on the time to stay in the
A few of the reactions by the employees related to the work are:
• When my spouse and kids have vacation I cannot make plan of outing with
them as I do not know when, what important work has been scheduled.
• There is ambiguity in role and the task I am supposed to perform in the
• A few women have responded that they are not able to give sufficient time
to their kids and in-laws.
• Team Work: When respondents were asked about the questions related to
the team work, many employees said that they cannot rely on their team as
they are also heavily loaded with work that teammates are not able to help
each other and thus feel dissatisfied.
A few of the reactions by the employees related to team work are:
• I experience work pressure while doing a group task.
• A few respondents said that they can share their task with their colleagues
whenever needed and enjoy working in teams as their teammates are
• Compensation and Benefits: Monetary satisfaction is one of the important
factors which helps in maintaining work-life balance of individuals. It also
includes the benefits provided by the organization. Compensation for extra work
gives satisfaction. When respondents were asked about the extra benefits and
compensation, they made it clear that they do not get additional payment for
the overtime as the organization considers it as part of their duties
A few of the reactions by the employees related to the compensation and benefits
• I am able to meet the basic requirement of my family.
• I enjoy the privileges offered by the organization and also able to enjoy
holiday with my family.
The IUP Journal of Organizational Behavior, Vol. XVI, No. 4, 201762
• A few participants responded that they do not get compensated for putting
in extra effort in the organization.
The issues talked about in the study can help in determining and outlining mediation/
preparing programs and other employee-friendly arrangements by organizations. The
study talks about the issues confronted by a greater proportion of the employees on the
whole – work, life, self, and other issues that may influence a representative’s harmony
among among organizational and individual responsibilities and individual responsibilities
and their influences on execution at work. Organizations can use the present study to
realize strategies that support and encourage the representatives to adapt to these issues.
Accomplishing a decent balance between work and family duties is a growing worry
for contemporary employees and organizations. There is currently mounting proof
connecting work-life awkwardness to diminished wellbeing and prosperity among
individuals and families. It is definitely not surprising then that there is growing enthusiasm
among organizational stakeholders for preparing work-life policies in their organizations.
Work-life balance policies are most likely to be effectively mainstreamed in organizations
which have an unmistakable comprehension of their business reason and which regard
the significance of work-life balance for all employees. Whatever the course, it is hoped
that the study can be used as a framework and offers a premise for reflection and open
deliberation on work-life balance issues in the IT industry in Pune city.
Limitations of the Study:
• The first limitation of qualitative study is that the quality of the study depends
greatly on the individual researcher. Because the researcher designs the type
of questions he/she will ask and can inadvertently influence the results due to
his/her own personal beliefs.
• The other limitation which the author faced was long and tedious process of
applying qualitative models of analysis to quantitative or numerical data. The
author carefully pondered over the data in detail while crafting the analysis.
• It is difficult to analyze the qualitative data as compared to quantitative data
as the latter does not fit neatly in a standard category.
• The presence of the author in the process of data gathering is unavoidable and
can therefore affect or influence the responses of subjects.
1. Agarwal T (2009), Strategic Human Resource Management, pp. 1-20, Oxford
University Press, New Delhi, India.
63A Qualitative Study on Work-Life Balance of Software Professionals
2. Ashforth B E, Kreiner G E and Fugate M (2000), “All in a Day’s Work: Boundaries
and Micro Role Transitions”, Academy of Management Review, Vol. 25, pp. 472-491.
3. Boyatzis R E (1998), Transforming Qualitative Information: Thematic Analysis and
Code Development, Vol. 18, No. 3, pp. 304-323, Thousand Oaks, Sage, CA.
4. Clark C (2000), “Work/Family Border Theory: A New Theory of Work/Family Balance”,
Human Relations, Vol. 53, No. 6, pp. 747-770.
5. Edwards J R and Rothbard N P (2000), “Mechanisms Linking Work and Family:
Clarifying the Relationship Between Work and Family Constructs”, Academy of
Management Review, Vol. 25, pp. 178-199.
6. Felicity Asiedu-Appiah I D M (2013), “Work-Life Balance as a Tool for Stress
Management in Selected Banking Institutions in Ghana”, Global Advanced Research
Journal of Management and Business Studies, pp. 1-21.
7. Frone M R (2003), “Work-Family Balance”, in J C Quick and L E Tetrick (Eds.),
Handbook of Occupational Health Psychology, American Psychological Association,
pp. 143-162, Washington, DC.
8. Frone M R, Russell M and Cooper M L (1992), “Antecedents and Outcomes of Work
Family Conflict: Testing a Model of the Family-Work Interface”, Journal of Applied
Psychology, Vol. 77, pp. 65-78.
9. Greenhaus J H and Beutell N J (1985), “Sources of Conflict Between Work and Family
Roles”, Academy of Management Review, Vol. 10, pp. 76-88.
10. Greenhaus J H and Powell G N (2006), “When Work and Family are Allies: A Theory
of Work-Family Enrichment”, Academy of Management Review, Vol. 31, pp. 72-79.
11. Holloway I and Todres L (2003), “The Status of Method: Flexibility, Consistency and
Coherence”, Qualitative Research, Vol. 3, No. 3, pp. 345-357.
12. Jang S (2009), “The Relationships of Flexible Work Schedules, Workplace Support,
Supervisory Support, Work-Life Balance, and the Well-Being of Working Parents”,
Journal of Social Service Research, Vol. 35, No. 2, pp. 93-104.
13. Kalliath T and Brough P (2008), “Work-Life Balance: A Review of the Meaning of the
Balance Construct”, Journal of Management & Organization, Vol. 14, No. 3,
14. Kanter R M (1997), “Work and Family in the United States: A Critical Review and
Agenda for Research and Policy”, Russell Sage Foundation, New York, Vol. 2,
15. Kofodimos J R (1993), Balancing Act, pp. 57-63, Jossey-Bass, San Francisco.
The IUP Journal of Organizational Behavior, Vol. XVI, No. 4, 201764
16. Kumar and Khyser Mohd (2014), “Work Life Balance: The HR Perspective”, Asia
Pacific Journal of Research, Vol. I, No. XIV.
17. Lewis S (2003), “The Integration of Paid Work and the Rest of Life: Is Post-Industrial
Work the New Leisure?”, Leisure Studies, Vol. 22, No. 4, pp. 343-345.
18. Lingard H, Francis V and Turner M (2012), “Work-Life Strategies in the Australian
Construction Industry: Implementation Issues in a Dynamic Project-Based Work
Environment”, International Journal of Project Management, Vol. 30, pp. 282-295.
19. Margo, Shaw, Laura and Andrey (2008), “‘I’m Home for the Kids’: Contradictory
Implications for Work-Life-Balance of Teleworking Mothers”, Gender Work and
Organisation, Vol. 15, No. 5, pp. 454-476.
20. Matjasko and Feldmen (2006), “Bring Work Home: The Emotional Experience of
Mothers and Fathers”, Journal of Family Psychology, Vol. 20, No. 1, pp. 47-55.
21. Millward L J (2006), “The Transition to Motherhood in an Organizational Context: An
Interpretative Phenomenological Analysis”, Journal of Occupational and Organizational
Psychology, Vol. 79, pp. 315-333.
22. Morris M L and Madsen S R (2007), “Issue Overview: Advancing Work-Life Interaction
In individuals, Organizations and Communities”, Advances in Developing Human
Resources, Vol. 9, No. 4, pp. 439-454.
23. Newman M and Mathews K (1999), “Federal Family-Friendly Workplace Policies”,
Review of Public Personnel Administration, Vol. 19, No. 3, pp. 34-58.
24. Pareek Udai and Purohit Surabhi (2010), Training Instruments in HRD and OD,
3rd Edition, pp. 286-289, Tata McGraw-Hill, New Delhi.
25. Pitt-Catsouphes M, Kossek E and Sweet S (Eds.) (2006), The Work and Family
Handbook: Multi-Disciplinary Perspectives and Approaches, pp. 1-16, Erlbaum,
26. Pleck J (1977), “The Work-Family Role System”, Social Problems, Vol. 24,
27. Reddy, Vranda, Ahmed, Nirmala B P and Siddaraju (2010), “Work-Life- Balance
Among Married Women Employees”, Indian J. Psychol Med., Vol. 32, No. 2,
28. Ryan G W and Bernard H R (2000), “Data Management and Analysis Methods”, in
N K Denzin and Y S Lincoln (Eds.), Handbook of Qualitative Research, 2nd Edition,
pp. 769-802, Thousand Oaks, Sage, CA.
65A Qualitative Study on Work-Life Balance of Software Professionals
29. Tomazevic N, Kozjek T and Stare J (2014), “The Consequences of a Work-Family
(Im) Balance: From the Point of View of Employers and Employees”, International
Business Research, Vol. 7, pp. 83-100.
30. Woodward D (2007), “Work-Life Balancing Strategies Used by Women Managers
in British ‘Modern’ Universities”, Equal Opportunities International, Vol. 26, No. 1,
31. Zedeck S (1992), Work, Families, and Organizations, Jossey-Bass, San Francisco.
32. Zedeck S and Mosier K (1990), “Work in the Family and Employing Organization”,
American Psychologist, Vol. 45, pp. 240-251.
33. Zerubavel E (1991), The Fine Line: Making Distinctions in Everyday Life,
pp. 223-226, The University of Chicago Press, Chicago, IL.
The IUP Journal of Organizational Behavior, Vol. XVI, No. 4, 201766
Is your partner employed?:
Do you have children?:
If yes, number of children:
1. How many days in a week do you normally work? Are you satisfied with the working
hours of the organization?
2. Do you get enough time for your family post the working hours?
3. Do you feel that you are able to balance your work-life?
4. How often do you think or worry about work (when you are not actually at work
or traveling to work)?
5. Does the organization take initiatives to manage work-life of its employees? If yes,
can you tell some of them?
6. Who helps you to take care of your children?
7. Do you regularly meet your child/children teachers to know how your child is
8. Do you have more pressure of work in the organization or is it evenly distributed
and how do you feel about the amount of time you spend at work?
9. Do you ever feel tired or depressed because of work? If yes how do you manage
stress arising from your work?
10. Does your organization provide you with yearly Master health checkup?
11. Does your organization encourage the involvement of your family members in work-
achievement reward functions? If yes, specify the name of such program
12. How do you rate the leave policy of the company?
13. Do you suffer from any stress-related disease?
67A Qualitative Study on Work-Life Balance of Software Professionals
Reference # 06J-2017-10-03-01
14. Are you able to spend quality time with your friends, family?
15. Do you get sufficient time for your sick partner/child/parent? In other words do
you feel that you can adjust your working schedule to attend to your family
16. How do you meet your household requirements?
17. Do you find it difficult to meet the expectations of your senior or subordinates?
18. Do you enjoy your job?
19. Do you think that if employees have good work-life balance the organization will
be more effective and successful? If yes how?
Copyright of IUP Journal of Organizational Behavior is the property of IUP Publications and
its content may not be copied or emailed to multiple sites or posted to a listserv without the
copyright holder’s express written permission. However, users may print, download, or email
articles for individual use.
Overtime and Quality of Working Life in Academics and Nonacademics:
The Role of Perceived Work-Life Balance
University of Reading
Simon Easton and Darren Van Laar
University of Portsmouth
While academic jobs generally provide a good degree of flexibility, academics also tend to work extra
hours which can then lead to a poorer work-life balance. In this study, we compare academic versus
nonacademic staff and anticipate that academics will generally report a poorer quality of working life, a
broad conceptualization of the overall work experience of employees. Second, we investigate whether the
negative relationships between being an academic and quality of working life variables are made worse
by working extra hours, and moderated by the perception of having a balanced work-life interface. Our
sample consisted of 1,474 academic and 1,953 nonacademic staff working for 9 higher education
institutions (HEIs) in the United Kingdom. Data were analyzed via structural equation modeling.
showed that academics tend to report a poorer quality of working life than nonacademics within HEIs,
and this is exacerbated by their higher reported number of extra hours worked per week. The work-life
balance of employees was found to moderate the negative relationships between academics (vs.
nonacademics) in variables such as perceived working conditions and employee commitment. We
additionally found curvilinear relationships where employees who worked up to 10 extra hours were
more satisfied with their job and career and had more control at work than those who either did not work
extra hours or worked for a higher number of extra hours. These results extend previous research and
provide new insights on work-life balance among academics and nonacademics, which in turn may be
relevant for the well-being practices of HEIs and wider HE policymaking.
Keywords: quality of working life, academics, working overtime, work-life balance
Academic jobs used to be considered privileged roles associated
with relatively low stress levels in a sense that they provided
flexibility, autonomy and job security after tenure was achieved.
However, this general assumption has been changing over the past
20 years, with increasing productivity demands, not only in terms
of research, but also in terms of teaching and administrative
activities (Kinman, 2014). This relates to institutional reforms that
higher education institutions in many OECD countries have been
experiencing, which have led them to a more market-oriented
perspective (Whitley & Gläser, 2014). The increased productivity
demands have been associated with high reported stress levels
among academics (e.g., Catano et al., 2010; Coetzee & Rothmann,
2005; Kinman, Jones, & Kinman, 2006; Tytherleigh, Webb, Coo-
per, & Ricketts, 2005; Winefield, Boyd, Saebel, & Pignata, 2008),
and there is evidence that academics feel their stress levels are
increasing (Kinman & Wray, 2016). High levels of stress, in
particular distress (e.g., Le Fevre, Matheny, & Kolt, 2003) are an
important element within an individual’s overall quality of work-
ing life. Quality of working life can be defined as the broadest
context in which an employee evaluates their work experience
(Van Laar, Edwards, & Easton, 2007) and comprises multiple
factors. These different factors will be the specific outcome vari-
ables in this study. We will focus on the quality of working life of
academics versus nonacademics in nine British universities as the
overarching outcome in our research model.
First, we anticipate that when compared to nonacademics, aca-
demics would have more demanding jobs because of the diversity
of tasks and the number and quality of expected outputs of their
work (e.g., Kinman, 2014). For this reason, academics are likely to
perceive a poorer quality of working life and in particular to report
higher levels of stress at work (SAW), lower levels of control at
work (CAW), have a less favorable perception of their working
conditions (WCS), have a poorer job and career satisfaction (JCS),
have lower levels of commitment to the organization (ECO) and
have lower levels of general well-being (GWB).
Second, we assess the way in which the reported weekly number
of extra hours worked and individual perceptions about how their
organization promotes their work-life balance can act as modera-
tors in the relationship between role (academic vs. nonacademic)
and SAW, CAW, WCS, JCS, ECO and GWB. In particular, we
assume that a high number of extra hours worked will enhance the
negative relationship between being an academic (vs. nonaca-
demic) and quality of working life outcomes, whereas perceived
promotion of work-life balance by the higher education Institution
(HEI) would buffer these negative relationships.
This study has three important contributions for existing re-
search on academics and nonacademics in HEI:
Rita Fontinha, Henley Business School, University of Reading; Simon
Easton and Darren Van Laar, Department of Psychology, University of
Correspondence concerning this article should be addressed to Rita
Fontinha, Henley Business School, University of Reading, Whiteknights,
Reading, UK RG6 6UD. E-mail: email@example.com
International Journal of Stress Management © 2019 American Psychological Association
2019, Vol. 26, No. 2,
–183 1072-5245/19/$12.00 http://dx.doi.org/10.1037/str0000067
(a) Previous research has compared academics with nonacadem-
ics in relation to a number of areas: stress, commitment to and
from the organization, physical health, psychological health
(Tytherleigh et al., 2005), psychological strain and job satisfaction
(Winefield et al., 2003). We now aim to extend this body of
research by considering a different overarching measure -that of
quality of working life.
–(b) There is an important body of research on working extra
hours (e.g., Coetzee & Rothmann, 2005; Court, 1996; Kinman et
al., 2006; Kinman & Wray, 2013) and on work-life balance (e.g.,
Currie & Eveline, 2011; Doherty & Manfredi, 2006; Kinman &
Jones, 2008; Noor, 2011; Pillay & Abhayawansa, 2014; Pillay,
Kluvers, Abhayawansa, & Vranic, 2013) among academics. How-
ever, we are among the first to consider the way these two
variables might interact with role (academic vs. nonacademic) in
its relationship with the different factors within quality of working
life. This is of particular relevance as it allows us to explore
different patterns of role and working extra hours, and role and
work-life balance, providing a more thorough analysis of the
antecedents of various factors affecting quality of working life.
This represents the second major contribution of our paper.
(c) The third and last contribution of this study relates to the
exploration of the role of working extra hours on the different
factors within quality of working life. In particular, we test curvi-
linear relationships between number of extra hours worked per
week and JCS, WCS, CAW, absence of SAW, ECO and GWB in
order to explain unexpected direct relationships found in our
Academics’ Versus Nonacademics’ Quality of
The broadest context in which a person evaluates or considers
their personal situation has been termed their Quality of Life
(Felce & Perry, 1995). Thus, the quality of working life of an
individual can be conceived of as the broadest context in which an
employee evaluates their work experience (Elizur & Shye, 1990).
While early conceptualizations of quality of working life sought to
identify global definitions and create all-encompassing models,
Taylor, Cooper, and Mumford (1979) were among the first to
suggest that quality of working life might vary between organiza-
tions and employee groups. It was perhaps because researchers
sought to understand quality of working life in various professions,
countries and cultures that an ever-growing list of possible sub-
factors were identified (Van Laar et al., 2007).
The development of models of quality of working life has led to
focused research on factors specific to each theory, but other
researchers have continued to explore the broader concepts of
quality of working life in the applied setting, exploring more
complex relationships between selected factors, mediators and
outcomes (e.g., work by Denvir, Hillage, Cox, Sinclair, & Pear-
main, 2008). A measure of quality of working life used in more
than 30 countries, the ‘Work-Related Quality of Life Scale’
(WRQoL), was used in the present study (Easton & Van Laar,
2012; Fontinha, Van Laar, & Easton, 2016). This scale contains six
factors: individual’s perceptions of whether their organization pro-
vides them with a balanced home-work interface (HWI)—this will
be an independent variable in our model named work-life balance;
perceptions about the physical working conditions available
(WCS); job and career satisfaction (JCS); perceptions regarding
the level of control over decision making at work (CAW); levels of
stress, or its absence, at work (SAW); and general well-being
(GWB). A seventh factor, which assesses level of employee com-
mitment to the organization (ECO) has been used in ongoing
research and development of the WRQoL Scale, and is also used
here (Fontinha et al., 2016). We focus on these dimensions, the
dependent variables in our model, in order to characterize the
quality of working life of academics and nonacademics working in
nine HEIs in the United Kingdom.
Numerous studies have reported that academics consider their
work stressful (e.g., Catano et al., 2010; Coetzee & Rothmann,
2005; Kinman et al., 2006; Tytherleigh et al., 2005; Winefield et
al., 2008), and there is evidence that they feel stress levels are
increasing (Kinman & Wray, 2016) in association with changes in
the University sector (Whitley & Gläser, 2014). This increase in
reported stress appears to be associated with reported distress at
levels which exceed many other occupational groups (Edwards,
Van Laar, Easton, & Kinman, 2009; Winefield et al., 2008). These
high stress levels among academics may be a response to different
work-related aspects, as suggested by the Job Demands-Resources
(JD-R) model (Demerouti, Bakker, Nachreiner, & Schaufeli,
2001). The JD-R model posits that work overload, among other
factors, can adversely affect physical and psychological well-
being, whereas sense of control at work and social support enhance
productivity [by way of improved motivation, according to
Schaufeli and Taris (2014)]. We follow the same rationale in this
study, conceptualizing stress as a response to specific work-related
stimuli (demands). However, we go further by considering multi-
ple factors that compose one’s quality of working life as outcomes
(stress being one factor within quality of working life).
A substantial increase in the number of nonacademic staff
employed by universities across the world has been recently re-
ported (Larkins, 2014). There has been little attention paid to the
working experience of nonacademic staff (Johnsrud, 2002), but
there do appear to be differences between the two staff groups as
regards experience of working in the university sector, as
indicated for example in an Australian study wherein 74% of
nonacademic staff reported overall job satisfaction, but only
61% of academic staff reported overall job satisfaction (Wine-
field et al., 2003). UK academic staff surveys have also increas-
ingly reported increases in teaching loads and fears concerning
job security alongside reductions in job satisfaction for academ-
ics (Metcalf, Rolfe, Stevens, & Weale, 2005; Tytherleigh et al.,
2005). UK academics have high levels of perceived control at
work, but these have been progressively decreasing (Kinman &
These findings suggest that academics generally have a much
lower perceived quality of working life compared to nonacadem-
ics. Accordingly, we hypothesize,
H1: Academics perceive a poorer quality of working life in
terms of WCS, JCS, CAW, SAW, ECO and GWB, when
compared to nonacademics.
Working Extra Hours and Work-Life Balance in
Kinman (2014) suggested that the work of academics has, over
the last 20 years, become more demanding as student numbers
174 FONTINHA, EASTON, AND VAN LAAR
have increased and academics are expected to excel at teaching as
well as research. Furthermore, data from the Annual Survey of
Hours and Earnings provides evidence that teaching and education
professionals in schools, colleges and universities do extra unpaid
work each week, more than any other group of professionals
(Statistical Bulletin, 2013). Kinman and Wray (2013) have re-
ported that over a third of UK academics surveyed stated that they
regularly work more than 10 hours in addition to their contract per
week, which has been linked to adverse consequences in relation
to physical and psychological well-being (Doyle & Hind, 1998;
Gillespie, Walsh, Winefield, & Stough, 2001; Kinman & Jones,
Fein and Skinner (2015) concluded from a survey of 1042
full-time workers in Australia that work-life conflict as a result of
working long hours tended to adversely affect health outcomes. A
study of more than 2,500 academic staff using work diaries re-
vealed an average working week of almost 55 hours during term
time (Court, 1996) and a subsequent report by Kinman (1998)
stated that almost three-quarters of academics indicated that work-
ing during evenings and weekends was commonplace. Long work-
ing hours have been linked to psychological and physical ill-
health, and that association appears to be greater where the average
working week regularly exceeds 48 hours and the individual
perceives little job control (Sparks, Cooper, Fried, & Shirom,
1997). In the HE context, Kinman (1998) reported that academics
who said they worked over 50 hours per week, or who took work
home on a regular basis, tended to score more poorly on assess-
ments of psychological well-being. More recent data shows that
more than three-quarters of academics employed on a full-time
contract (typically 37.5 hours) worked over 40 hours a week, and
more than one-third in excess of 50 hours a week (Kinman &
Wray, 2016). These results lead us to anticipate that while aca-
demics would normally report a poorer quality of working life than
their nonacademic counterparts, this relationship may be exacer-
bated by a high number of extra hours worked per week. Thus, we
H2: A higher number of extra working hours increases the
negative relationship between being an academic (vs. a non-
academic) and elements of quality of working life (WCS, JCS,
CAW, SAW, ECO and GWB).
Work-life balance can be defined as the individual perception
that work and nonwork activities are compatible and promote
growth in accordance with an individual’s current life priorities
(Kalliath & Brough, 2008). Various studies have reported that
balancing of work and home can be difficult for academics (Nete-
meyer, Boles, & McMurrian, 1996; Winefield, Boyd, & Winefield,
2014), particularly due to time-based conflict (time spent working
at the expense of time devoted to family/leisure activities) and
strain-based conflict (job-related strain leads to irritability and
social withdrawal). Menzies and Newson (2007) highlight the
potentially adverse influence of the increase in working from
home, and others, including Boswell and Olson-Buchanan (2007)
and Araújo (2008), have suggested that it is the blurring of bound-
aries between work and home rather than working from home per
se that can be the cause of difficulty, although there is evidence
that a sense of control over working patterns among academics can
be helpful (Kinman & Jones, 2004).
Siegrist (1996) has proposed in the effort-reward imbalance
(ERI) model that the experience of imbalance will be more fre-
quent and more damaging in employees who are excessively
committed to work, where overcommitment is defined as attitudes,
behaviors and emotions that reflect a strong desire for approval
and esteem which can lead to working excessively (Siegrist, 2001).
The ERI model was empirically tested by Kinman and Jones
(2008), who showed that effort-reward imbalance is particularly
damaging for the work-life balance of university workers, who
cope with work demands by overcommitting and working addi-
tional hours over and above their contract. High levels of over-
commitment in academics have been found in a culture where
working long hours and a relatively poor work-life balance can be
more widely accepted (Hogan, Hogan, Hodgins, Kinman, & Bun-
ting, 2014). While enjoyment of and commitment to work can
have health benefits and enhance career success (Kelloway, In-
ness, Barling, Francis, & Turner, 2010), overcommitment has been
reported to increase risk of stress (Avanzi, van Dick, Fraccaroli, &
Sarchielli, 2012; Kinman & Wray, 2016). Furthermore, Greenhaus
and Beutell’s (1985) suggested that role pressures from work and
family settings can be mutually incompatible to a greater or lesser
degree, as workers perceive they have too little time for work and
family commitments, and as they may experience stress, exhaus-
tion and fatigue which adversely affect their psychological and
physical well-being (Greenhaus & Beutell, 1985).
Hobfoll (1989) suggested that employees experience stress
when there is actual or threatened loss of valued resources. Thus,
a balanced work-family interface (also referred to as work-life
balance or home-work interface) has been identified as such a
positive resource for individuals and therefore associated with an
amelioration or absence of stress (Chiang, Birtch, & Kwan, 2010).
Most studies on the outcomes of a balanced work-life interface aim
to understand its implications on stress. In this study we aim to
extend this body of research and consider the way the organiza-
tional context facilitates work-life balance as a relevant resource
that academics can utilize to buffer the negative effects of exces-
sive demands of their roles on their quality of working lives
(including, but not limited to stress). In particular, we hypothesize,
H3: The negative relationship between being an academic (vs.
nonacademic) and elements of quality of working life (WCS,
JCS, CAW, SAW, ECO and GWB) is moderated by one’s
perception of an organizational context facilitating work-life
Figure 1 presents a model with all hypothetical relationships
tested, acknowledging the role of four control variables: age,
gender, tenure and contract type (permanent vs. temporary).
Data Collection and Participants
We contacted a large number of higher education institutions
(HEI) in the UK, asking them to participate in our study. The data
from nine British HEIs were employed in this study, three from the
top third, three from the middle third and three from the bottom
third of UK university league tables (The Complete University
Guide, 2017; The Guardian, 2017). The average position in the
175OVERTIME AND QUALITY OF WORKING LIFE IN ACADEMICS
ranking was calculated considering the two sources of league
tables at the time of data collection (2007–2009). All nine human
resources departments e-mailed all their employees our request to
participate in this study and the link to our Web-based question-
naire. This resulted in a total of 3,771 responses with an average
response rate of 32.54%. We deleted all cases with missing data on
the variables that we were analyzing, which resulted in a total of
3427 usable cases. The total number of academics in our sample
was 1,474 (43%) and the total number of nonacademics was 1,953
(57%). According to data from the Higher Education Statistics
Agency (HESA, 2016), this proportion of academics and nonaca-
demics is consistent with the national average proportion for the
year of data collection, 2007: 46.97% for academics and 53.03%
for nonacademics. Nonacademics predominantly performed
computer-based support tasks. A detailed description of our sam-
ple based on gender, age, tenure (representing the number of years
working for their current higher education institution), number of
extra hours worked per week, contractual time (full-time, part-
time, part-time hourly paid, or no fixed hours) and contract type
(temporary vs. permanent) is presented on Table 1.
We conducted one-way ANOVAs with post hoc Bonferroni
tests in order to investigate whether there were significant differ-
ences between the core characteristics of academics and nonaca-
demics in the nine different HEIs studied. We compared all HEIs
based on the main variables in our study and the most relevant
result was that no significant differences were found between the
nine HEIs regarding the number of extra hours that academics
work per week (F � 1.94; p � .05). However, nonacademics
working for higher-ranked universities worked for more hours than
their counterparts that worked for lower-ranked universities (F �
14.77; p � .001).
All outcome variables in our hypothesized model, as well as
work-life balance were measured with Easton and Van Laar’s
(2012) WRQoL1 (Work-related Quality of Life) Scale. The
WRQoL1 Scale has been used in a wide range of settings and
organizations across the world and has been translated into various
languages (e.g., Blanch, Sahagún, Cantera, & Cervantes, 2010;
Easton & Van Laar, 2013; Vagharseyyedin, Vanaki, & Moham-
madi, 2011). Three items representing employees’ commitment to
the organization were added to the scale and validated in a recent
study (Fontinha et al., 2016). We used this updated 26-item version
of the scale in this study. This scale comprises seven factors:
working conditions (WCS), job and career satisfaction (JCS),
control at work (CAW), employee commitment (ECO), (absence
of) stress at work (SAW), general well-being (GWB), and home-
work interface (HWI). For the purpose of consistency with previ-
ous literature and a clearer understanding of the meaning of the
HWI factor, we have decided to address it as work-life balance in
this study. All items are scored on a 5-point Likert scale from 1
(Strongly disagree) to 5 (Strongly agree) A detailed description of
each factor is presented below.
Work-life balance. This construct was measured using the
three HWI items of the WRQoL1 Scale (Easton & Van Laar, 2012;
Fontinha et al., 2016) and refers to the perceived context provided
by the organization to have a balance between work and personal
life. This factor has a subscale reliability of � � .85 in these data
and picks up on the importance of balancing home and work
demands (Dorsey, Jarjoura, & Rutecki, 2003). One example item
is “My current working hours/patterns suit my personal circum-
Working conditions (WCS). This construct assesses the ex-
tent to which someone is satisfied with their physical working
Figure 1. Relationships between role and quality of working life factors
and interaction effects with additional working hours and work-life bal-
ance. Notes for Figure 1: Observed variables represented in a rectangle;
Latent variables represented in an ellipse; � represents interaction effects
between two variables; for ease of presentation the regression paths be-
tween all observed variables and all latent variables are represented by the
large central arrow.
Male 606 710
Female 1,347 764
Under 25 107 20
25–44 1,016 741
45–59 731 614
60 or over 99 99
Less than 1 252 141
1 to 2 735 536
3 to 5 392 295
6 to 10 391 342
11 to 20 178 149
More than 20 5 11
Number of extra-hours:
None 536 114
5 or less 794 365
6 to 10 431 509
11 to 20 161 349
More than 20 31 137
Full time 331 182
Part time 1,540 1,254
Part time hourly paid 80 34
No fixed hours 2 4
Temporary 266 470
Permanent 1,687 1,004
176 FONTINHA, EASTON, AND VAN LAAR
environment. Reliability for this subscale was � � .79 and an
example item is: “I work in a safe environment.”
Job and career satisfaction (JCS). This construct was mea-
sured with five items, with a subscale reliability of � � .84 and
includes questions relating to satisfaction with job and career
aspects, such as “I am satisfied with the career opportunities
available for me here.” The job and career satisfaction (JCS) factor
seeks to measure the level to which a respondent feels their
workplace provides sense of achievement, high self-esteem and
fulfilment of potential.
Control at work (CAW). This construct refers to the sense of
control over decision-making at work, which can reflect the op-
portunities of voice and participation in decision making and has
implications for health and well-being (Spector, 2002). This factor
was measured using three items with a subscale reliability of � �
.86, and an example item is “I am involved in decisions that affect
me in my own area of work.”
Stress at work (SAW). This factor assesses the extent to
which an individual perceives they are subject to excessive pres-
sure or experience of SAW. This construct was measured with four
items, an example being “I often feel under pressure at work.” The
items were reversed, meaning that for this construct is presented in
this paper as the Absence of SAW. Subscale reliability of this
factor was � � .84.
General well-being (GWB). This factor assesses an individ-
ual’s sense of psychological well-being and general physical
health. This factor has a subscale reliability of .85 based on six
items. An example of an item is: “I feel well at the moment.”
Other variables. Our hypothesized research model also in-
cluded the variables: role and additional working hours. Role was
operationalized as a dichotomous variable where 1 represented
academics and 0 represented nonacademic staff working in HEI.
Additional working hours per week were self-reported and mea-
sured with a categorical variable where 1 � none; 2 � five or less;
3 � six to 10; 4 � 11 to 20; and 5 � more than 20. Age, gender,
tenure (years at organization) and contract type (1 � permanent;
2 � temporary) were added in our model as control variables.
Data were analyzed via structural equation modeling (SEM)
with v22 of the IBM® SPSS® Amos™ software (Arbuckle, 2012).
We performed our analyses using a two-step approach as recom-
mended by Anderson and Gerbing (1988). First, we tested five
competitive measurement models in order to verify the most
appropriate factorial structure for our variables with this data. Our
hypothesized measurement model (HMM) contained the confir-
matory factor analysis of the 7 factors previously studied for
Quality of working life (HWI—work-life balance, WCS, JCS,
CAW, absence of SAW, ECO, and GWB), (Fontinha et al., 2016),
role (academic vs. nonacademic), number of extra hours worked
per week, as well as age, gender, contract type (permanent vs.
temporary), and tenure as control variables.
The HMM was compared with four alternative models via
chi-squared difference tests. The first alternative measurement
model (AMM1) had a single factor where all items within the
quality of working life scale loaded, as well as all remaining
observable variables. The second alternative measurement model
(AMM2) had two factors: all items within the WRQoL1 Scale
loaded on one and all remaining observable variables loaded on the
other. The third alternative measurement model (AMM3) had all
non-WRQoL1 observable variables set out to be independent (i.e.,
not loading in any factor) and all items within quality of working
life loading on one factor. The fourth alternative measurement
model (AMM4) had all observable variables set out to be inde-
pendent and items from WRQoL1 loading on three factors: this
three factor structure was inspired by previous research (Fontinha,
Van Laar, & Easton, 2016), which probed a model where items
form HWI, WCS, JCS and CAW were antecedents (first factor),
items from ECO and absence of SAW can be mediators (second
factor) and items from GWB can be outcomes within quality of
Second, we tested our hypothesized structural model, depicted
in Figure 1. This model contained two additional variables repre-
senting the interaction effects between role and additional working
hours, and between role and work-life balance.
In order to assess the fit of the models we followed Bollen and
Long’s (1993) and Byrne’s (2001) recommendations and used the
following goodness-of-fit statistics: The comparative fit index
(CFI), the goodness of fit index (GFI), the Tucker-Lewis index
(TLI) also called the non-normed fit index, the root mean square
error of approximation (RMSEA), and the standardized root-mean-
square residual (SRMR). Values for CFI, GFI and TLI indicate an
excellent fit when they equal to or exceed .95. Values above .90
indicate a good fit. Values below .05 for RMSEA and values
below .09 for SRMR indicate excellent fit, while values less than
or equal to .08 and .10, respectively, indicate a good fit. The �2
difference test was used to compare the alternative measurement
Means, standard deviations and correlations between our studied
variables are presented on Table 2. On this table, we are able to
observe that all outcome variables in our hypothesized model
(WCS, JCS, CAW, absence of SAW, ECO, and GWB) are
strongly correlated to each other. The means of WCS, JCS, CAW,
absence of SAW, ECO, and GWB were then compared, and for
academics and nonacademics respectively were: WCS (3.48; 3.70;
t � �9.59, p � .001); JCS (3.24; 3.41; t � �5.90, p � .001);
CAW (3.33; 3.53; t � �6.13; p � .001); ECO (3.27; 3.57;
t � �10.08, p � .001); absence of SAW (2.83; 3.32; t � �16.09;
p � .001); and GWB (3.38; 3.49; t � �3.98, p � .001). The
variables role (academic vs. nonacademic), work-life balance and
number of extra hours worked were observed to correlate strongly
with all remaining variables.
Table 3 presents the fit indices for our hypothesized measure-
ment model (HMM), as well as the fit indices for other competing
models (AMM1, AMM2, AMM3, AMM4). Alternative models
were compared to HMM via chi-squared difference tests and
results showed that HMM has a significantly better fit to our data.
For this reason, HMM’s factor structure was utilized for further
structural analyses. Our hypothesized structural model (see Figure
1) followed HMM’s factorial pattern but two other variables were
added: the variables testing the interactions between role and
additional working hours, and between role and HWI. This model
presented an adequate fit to our data: �2 � 4415.06; df � 430 p �
177OVERTIME AND QUALITY OF WORKING LIFE IN ACADEMICS
.001; GFI � .92; TLI � .91; CFI � .94; RMSEA � .05; SRMR �
The regression weights for the different structural paths and
their significance are presented on Table 4. Data partially sup-
ported our first hypothesis (H1) as being an academic (vs. a
nonacademic in higher education) was significantly related to a
less favorable perception of working conditions (� � �.04; p �
.05), lower perceived control at work (� � �.07; p � .001), lower
levels of commitment to the organization (� � �.11; p � .001),
and to lower rating in terms of absence of stress at work
(� � �.07; p � .001). There were no significant differences
between academics and nonacademics regarding their job and
career satisfaction and we found that academics tend to report
higher levels of general well-being (� � .04; p � .05).
Our second hypothesis anticipated that a higher number of
working hours would exacerbate the negative relationship between
role (academic vs. nonacademic) and all elements of quality of
working life. This hypothesis was partially supported by our data
as we found that the interaction between role and additional
working hours was significantly and negatively related to JCS
(� � �.04; p � .05) and CAW (� � �.08; p � .001). However,
contrary to what was expected this interaction was positively
related to the absence of SAW (� � .07; p � .001).
Our third hypothesis anticipated that the negative relationship
between being an academic (vs. nonacademic) and elements of
quality of working life (WCS, JCS, CAW, SAW, ECO and GWB)
is moderated by one’s perception of work-life balance. We tested
the effects of the interaction term between role (academic 1 vs.
nonacademic 0) and work-life balance and found that they were
positively related to WCS (� � .03; p � .05) and ECO (� � .04;
p � .05). This indicates that academics who perceive they have a
more balanced relationship between work and life will tend to
report better WCS and be more committed to the HEI, partially
supporting H3. The regression paths between the interaction term
and JCS, CAW, SAW and GWB were not significant.
Regarding our control variables, it is relevant to mention that
women reported significantly higher levels of stress at work
(� � �.03; p � .05) but higher levels of WCS (� � .03; p � .05),
JCS (� � .10; p � .001), ECO (� � .10; p � .001) and GWB (� �
.05; p � .001). Older workers reported lower levels of stress (� �
.05; p � .01) and perceived their WCS as poorer (� � �.06; p �
.001). A longer tenure with the HEI is associated with higher levels
of stress (� � �.12; p � .001), with a poorer JCS (� � �.06; p �
.01), a lower ECO (� � �.08; p � .001) and a poorer GWB
(� � �.04; p � .05). Temporary workers reported higher levels of
stress at work (� � �.05; p � .001), but a better GWB (� � .05;
p � .01).
Furthermore, we were particularly surprised with the fact that
the direct relationships between number of extra hours worked and
JCS, WCS, CAW, ECO and GWB were positive. The only ex-
pected relationship was the negative relationship between number
of extra hours worked and absence of stress at work. For these
reasons, we decided to explore these results further and we tested
our data for the existence of curvilinear relationships between the
variables. Results suggest that there is a significant quadratic effect
of the number of extra hours worked in the prediction of JCS
Means, Standard Deviations, and Correlation Matrix
Mean SD 1 2 3 4 5 6 7 8 9 10 11 12
1. Gender (1 � Male; 2 � Female) 1.62 .49 1
2. Age 2.47 .66 �.14��� 1
3. Tenure 2.82 1.20 �.14��� .48��� 1
4. Role (Academic � 1; Non-
Academic � 0) .43 .50 �.17��� .09��� .06��� 1
5. Additional working hours (per
week) 2.53 1.11 �.19��� .15��� .17��� .39��� 1
6. Contract type (Permanent � 1;
Temporary � 0) .79 .41 �.08��� .20��� .30��� �.22��� .04� 1
7. Work-Life Balance 3.55 .92 .07��� �.02 �.10��� �.16��� �.37��� �.06��� 1
8. WCS 3.61 .82 .07��� �.07��� �.10��� �.14��� �.21��� �.03 .59��� 1
9. JCS 3.34 .85 .12��� �.05�� �.11��� �.10��� �.14��� �.03 .54��� .69��� 1
10. CAW 3.45 .96 .04� �.01 �.04� �.10��� �.07��� �.02 .47��� .61��� .75��� 1
11. ECO 3.44 .88 .15��� �.06�� �.15��� �.17��� �.19��� �.05�� .52��� .71��� .69��� .60��� 1
12. (Absence of) SAW 3.11 .93 .10��� �.08�� �.21��� �.27��� �.56��� �.10��� .605��� .48��� .42��� .34��� .44��� 1
13. GWB 3.44 .83 .07��� .01 �.08��� �.07��� �.17��� �.02 .65��� .64��� .65��� .56��� .56��� .49���
� p � .05. �� p � .01. ��� p � .001.
Hypothesized Measurement Model (HMM) Fit, Alternative Measurement Models’ Fit and Comparisons Between Models
�2 df Sig. GFI TLI CFI RMSEA SRMR �2; df; Sig.
HMM 4,319.35 392 p � .001 .93 .92 .94 .05 .04
AMM1 19,912.06 464 p � .001 .64 .66 .68 .11 .12 AMM1 – HMM 15592.71; 72; p � .001
AMM2 18,764.69 463 p � .001 .66 .68 .70 .11 .09 AMM2 – HMM 14445.34; 71; p � .001
AMM3 17,587.20 449 p � .001 .67 .67 .72 .11 .08 AMM3 – HMM 13267.85; 57; p � .001
AMM4 13,527.11 434 p � .001 .73 .75 .78 .09 .07 AMM4 – HMM 9207.76; 42; p � .001
178 FONTINHA, EASTON, AND VAN LAAR
(� � �.15, R2 � .001, p � .05), CAW (� � �.21, R2 � .002;
p � .01) and absence of SAW (� � .23, R2 � .003; p � .001).
This means that there are two reversed U-curves describing the
relationships between number of extra hours worked and both JCS
and CAW, and a regular U-shaped curve describing the relation-
ship between number of extra hours worked and absence of stress
at work. These results are presented on Figure 2 and will be
described in detail in the discussion section.
The main aim of this study was to compare academics and
nonacademics working in higher education regarding their quality
of working lives, relying on the assumption that the former would
have a more demanding role (Tytherleigh et al., 2005; Winefield et
al., 2003), and thus a perceived poorer quality of working life.
Furthermore, we investigated the role of the number of unpaid
extra hours worked per week as a variable that would interact with
role and exacerbate its negative relationship with absence of stress
at work, job and career satisfaction, working conditions, control at
work, commitment to the organization and general well-being. We
additionally aimed to explore the role of perceived work-life
balance as contextual variable. In particular, we explored the way
in which the HEI allowed employees the possibility to have a
balanced work-life interface. This variable would interact with role
and moderate the negative relationship between being an academic
(vs. a nonacademic) and the different factors within quality of
working life. Our results generally support our hypotheses, with
the exception of specific nuances, a detailed account of which is
Consistently with H1, academics are significantly more likely
than nonacademics in higher education to report higher levels of
stress at work. This can be related to their large set of demands at
work (Kinman, 2014) and possibly to the absence of sufficient
resources (Demerouti et al., 2001), leading them to experience a
negative form of stress (Le Fevre et al., 2003). Academics also
report less favorable perception of working conditions, lower
perceived control/participation on decision making at work, and
lower levels of commitment to the organization. This set of find-
ings is consistent with previous research where, compared to
nonacademics in the same organization, academics and researchers
reported higher levels of stress related to work relationships, job
security, resources and communication, pay and benefits (Tyther-
leigh et al., 2005), and psychological strain (Winefield et al.,
2003). However, while Winefield et al. (2003) found that nonaca-
demics were generally more satisfied with their jobs, our study did
not identify significant differences between the two groups regard-
ing the factor job and career satisfaction. We believe this may be
due to the fact that our variable includes a career-focused element
and it might be that although academics are more stressed, they are
satisfied with their jobs and careers because they have much job
autonomy, especially when it comes to research (Darabi, Ma-
caskill, & Reidy, 2016). This may also be the reason to justify our
Detailed Description of the Regression Paths in the Final Hypothesized Structural Equation Model
Outcome Latent Variables
WCS JCS CAW ECO
Role (Academic � 1; Nonacademic � 0) �.04� �.02 �.07��� �.11��� �.07��� .04�
Additional working hours (per week) .10��� .17��� .23��� .13��� �.05��� .05���
Work-Life Balance .74��� .67��� .61��� .62��� .55��� .79���
Role�Additional working hours �.01 �.04� �.08��� �.01 .07��� .02
Role�Work-Life Balance .03� �.00 .02 .04� .01 .03
Gender (1 � Male; 2 � Female) .03� .10��� .02 .10��� �.03� .05���
Age �.06��� �.03 �.02 �.00 .05�� .01
Tenure �.02 �.06�� �.00 �.08��� �.12��� �.04�
Contract type (Permanent � 1; Temporary � 0) .02 .03 .01 .01 �.05��� .05��
Note.WCS � Working Conditions; JCS � Job and Career Satisfaction; CAW � Control at Work; ECO � Employee Commitment to the Organization;
(Absence of) SAW � Absence of Stress at Work; GWB � General Well-Being.
� p � .05. �� p � .01. ��� p � .001.
Figure 2. Additional hours worked in relation to job and career satisfaction, control at work, and absence of
stress at work.
179OVERTIME AND QUALITY OF WORKING LIFE IN ACADEMICS
unexpected finding that academics tend to report higher levels of
general well-being than nonacademics: their individual sense of
achievement with research (Darabi et al., 2016) may potentially be
an important factor for well-being, compared to that of nonaca-
demics, whose jobs are more oriented to the collective functioning
of the HEI that they work for.
Our second hypothesis assumed that the number of extra hours
worked per week exacerbated the differences between academics
and nonacademics postulated on H1. When testing H2, we found
that it was partially supported by our data as the interaction
between role and number of additional hours worked per week was
significantly and negatively related to job and career satisfaction
and to control over decision making at work. This means that
academics who worked longer hours were less satisfied with their
jobs and careers and experienced lower control over decision
making at work, meaning that they perceived fewer opportunities
to voice their opinions and participate in decision making. It could
be argued that for academics, working longer hours is a necessary
condition to cope with the demands of work (Kinman, 2014),
especially when one has not yet achieved a desired job and a career
stage that allows them more voice and participation. However,
further research would be required to examine the impact of career
stage and perceived achievement. More surprisingly and contrary
to expected, the interaction between role and the number of addi-
tional hours worked per week was positively related to the absence
of stress at work. One explanation may be that academics use extra
hours to be able to actually comply with the multiple demands of
their jobs. That is, if working overtime is needed to finish certain
tasks, academics who cannot work for a sufficient number of extra
hours (for diverse reasons, such as family commitments), may find
their work will end up “piling up” and stress levels will increase.
Our third hypothesis was also partially verified. In particular, we
found that if employees perceive to have the conditions for a
balanced work-life interface, then the impact of having an aca-
demic (vs. a nonacademic) role on working conditions and com-
mitment to the organization is reduced. These results suggest that,
as expected, if academics perceive that their HEI provides them the
possibility of having a good work-life balance, then this will
transform their often more negative perceptions of working con-
ditions and commitment to the organization, into favorable per-
ceptions. In other words, academics who perceive a balanced
work-life interface will also have a more favorable opinion of the
working conditions provided by the HEI and reciprocate with a
higher level of commitment to the HEI (Fontinha et al., 2016). Job
and career satisfaction, control at work, absence of stress at work
and general well-being may be variables that are more associated
to academic life itself and not as organization-specific as commit-
ment and working conditions. This may have been the reason why
the first set of factors were not affected by the interaction effect
between conditions for work-life balance provided by the organi-
zation and role.
Although we did not explicitly establish a hypothesis regarding
the relationships between the number of additional hours worked
per week and the different elements within quality of working life,
our structural model presented interesting results. Previous re-
search has suggested a negative effect of hours of work on health
(Sparks et al., 1997). Golden and Wiens-Tuers (2006) found that
overtime work hours were generally associated with increased
work stress, fatigue and work—family interference, which is also
consistent with our results concerning stress at work. However, we
also found significant and positive relationships between working
additional hours and job and career satisfaction, working condi-
tions, control at work, commitment to the organization and general
well-being. Golden and Wiens-Tuers’s (2006) study sheds some
light on the fact that if overtime is mandatory it may be more
harmful compared to when it is nonmandatory. In our particular
sample, overtime is not paid and not mandatory, although specific
role demands may make it feel compulsory.
Given the unexpected nature of our findings, we decided to run
further analyses and test for curvilinear relationships. We found
that the relationships between number of extra hours worked and
job and career satisfaction and control at work were inverted
U-curves, meaning that employees who worked up to 10 extra
hours were more satisfied with their job and career and felt they
have more control over decision making at work, when compared
to those who either worked a higher number of extra hours or did
not work overtime at all. This might be because workers who do
not work overtime are in less challenging and powerful positions
in the HEI, while those who work extremely long hours are
struggling to achieve career success (e.g., early career academics
and academics with specific high role demands including teaching
and administrative loads), or it might be that the benefits of
working up to 10 extra hours outweigh the costs of doing less or
working inefficiently or too much. We additionally found a regular
U-shaped curve describing the relationship between number of
extra hours worked and absence of stress at work. This helps
explain the unexpected findings on H2. In particular, we may say
that there is an optimal level of extra hours that can be used to cope
with stress and finish pending work, which is of about 5 hours or
less. When employees work for 6 to 20 hours, there is a steep
decrease in the reported absence of stress at work (thus they would
feel significantly more stressed). This decrease becomes less ac-
centuated when employees report to work more than 20 extra
hours, which relates to the smaller difference between working
from 10 to 20 hours and more than 20 hours: the absence of stress
levels tend to stabilize at a very low point for these individuals.
Limitations and Future Research Directions
Despite its relevant contributions, this paper has limitations
which are acknowledged. First, this study has a cross-sectional
design, which makes it impossible to infer causal paths and clearly
attest whether our antecedents “cause” our outcomes. However,
our hypotheses followed previous longitudinal empirical research
(Frone, Russell, & Cooper, 1997) suggesting that our independent
variables would indeed be likely to be antecedents of the different
elements within quality of working life. We would recommend
testing these results longitudinally and analyzing different cross-
lagged paths in order to verify the directionality of our relation-
A second limitation concerns the risk of common method vari-
ance due to using self-reported data. Questionnaires were the
single source of data collection, and variables such as the number
of extra hours worked were self-reported. However, we used
widely validated measures, which were built following Podsakof,
MacKenzie, Lee, and Podsakoff’s (2003) suggestions for question-
naire design to reduce the risk of common method variance (e.g.,
changes in the response format, anonymity, intermixing the items
180 FONTINHA, EASTON, AND VAN LAAR
of different constructs on the questionnaire, instructing participants
that there are no right or wrong answers). Furthermore, also
following Podsakoff et al.’s (2003) suggestions, we used confir-
matory factor analysis and compared several competing models
via chi-squared difference tests, which reassures us that the facto-
rial structure of the model is robust. Nevertheless, future research
should account for the effect of more objective variables that could
influence the number of working hours of academics, such as
overall pay and the specific goals that need to be achieved (e.g.,
number of published papers needed to achieve a permanent posi-
tion). One could anticipate that a higher overall pay could trigger
the perceived need to work extra hours. The need to achieve
publication goals, especially for academics on probation (tenure
track) could additionally lead them to work overtime in order to
achieve these goals and gain a permanent position.
The third limitation of our study refers to the fact that our data
were only collected in HEIs in the United Kingdom. Although
previous evidence suggests that academics work over time in
different parts of the world (Coetzee & Rothmann, 2005; Court,
1996; Kinman et al., 2006; Kinman & Wray, 2013) it could be the
case that contextual elements such as employment legislation
could have influenced our results (the OECD, 2013, provided
evidence that employment legislation tends to be more protective
in Continental Europe, when compared to the UK, but the latter
tends to be more protective than in the United States or in Asian
countries). Further research is needed to test our results in different
Implications for Research and Practice
The results of this study bring important contributions to both
research on the quality of working life of academics and nonaca-
demics in HEI, and practice in terms of policy-making in the
higher education context. First, this study extends existing research
by comparing academics and nonacademics in HEI, drawing upon
an established set of factors from an overarching measure of
quality of working life. Second, we highlight the importance of the
role of overtime in exacerbating the relationship between being an
academic (vs. a nonacademic) and quality of working life, and the
moderating role of a perceived organizational context that pro-
motes work-life balance in this negative relationship. Third, we
found curvilinear relationships between number of extra hours
worked and JCS, CAW and absence of SAW.
The relatively poor reported quality of working life of academ-
ics reinforces previous findings (Tytherleigh et al., 2005; Wine-
field et al., 2003) and is of relevance to HEI policymakers, given
duty of care as regards the health and well-being of their staff.
Furthermore, our results demonstrate that a favorable context that
promotes work-life balance will tend to be associated with a higher
commitment from an academic workforce, thereby potentially
reducing expenses such as those due to staff turnover. These
findings indicate that development of clear policies in relation to
the promotion of maintaining work-life balance, and active mon-
itoring and facilitation of such, should be a key focus for higher
education Institutions. In particular, increasing control over work-
ing hours and helping academics achieve recovery from work
demands could be used by higher education Institutions as inter-
Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modelling
in practice: A review and recommended two-step approach. Psycholog-
ical Bulletin, 103, 411–423. http://dx.doi.org/10.1037/0033-2909.103.3
Araújo, E. R. (2008). Technology, gender and time: A contribution to the
debate. Gender, Work and Organization, 15, 477–503. http://dx.doi.org/
Arbuckle, J. L. (2012). IMB® SPSS® Amos™ 21 User’s Guide. Amos
Avanzi, L. van Dick Fraccaroli, F., & Sarchielli, G. (2012). The downside
of organizational identification: Relations between identification,
workaholism and well-being. Work & Stress, 26, 289–307. http://dx.doi
Blanch, J. M., Sahagún, M., Cantera, L., & Cervantes, G. (2010). Ques-
tionnaire of general labour well-being: Structure and psychometric prop-
erties. Revista de Psicología del Trabajo y de las Organizaciones, 26,
Bollen, K. A., & Long, J. S. (1993). Testing structural equation models.
Newbury Park, CA: Sage.
Boswell, W., & Olson-Buchanan, J. (2007). The use of communication
technologies after hours: The role of attitudes and work-life conflict.
Journal of Management, 33, 592– 610. http://dx.doi.org/10.1177/
Byrne, B. M. (2001). Structural equation modeling with Amos. Basic
concepts, application and programming. Mahwah, NJ: Erlbaum.
Catano, V. M., Francis, L., Haines, T., Kirpalani, H., Shannon, H., Stringer,
B., & Lozanzki, L. (2010). Occupational stress in Canadian universities:
A national survey. International Journal of Stress Management, 17,
Chiang, F. F. T., Birtch, T. A., & Kwan, H. K. (2010). The moderating
roles of job control and work-life balance practices on employee stress
in the hotel and catering industry. International Journal of Hospitality
Management, 29, 25–32. http://dx.doi.org/10.1016/j.ijhm.2009.04.005
Coetzee, S. E., & Rothmann, S. (2005). Occupational stress, organisational
commitment and ill-health of employees at a higher education institution
in South Africa. SA Journal of Industrial Psychology, 31, 47–54. http://
The Complete University Guide. (2017). University League Tables. Re-
Court, S. (1996). The use of time by academic and related staff. Higher
Education Quarterly, 50, 237–260. http://dx.doi.org/10.1111/j.1468-
Currie, J., & Eveline, J. (2011). E-technology and work/life balance for
academics with young children. Higher Education, 62, 533–550. http://
Darabi, M., Macaskill, A., & Reidy, L. (2016). A qualitative study of the
UK academic role: Positive features, negative aspects and associated
stressors in a mainly teaching-focused university. Journal of Further and
Higher Education, 1–15. http://dx.doi.org/10.1080/0309877X.2016
Demerouti, E., Bakker, A. B., Nachreiner, F., & Schaufeli, W. B. (2001).
The job demands-resources model of burnout. Journal of Applied Psy-
chology, 86, 499–512. http://dx.doi.org/10.1037/0021-9010.86.3.499
Denvir, A., Hillage, J., Cox, A., Sinclair, A., & Pearmain, D. (2008).
Quality of working life in the UK (research report 452). Sector Skills
Doherty, L., & Manfredi, S. (2006). Action research to develop work-life
balance in a UK university. Women in Management Review, 21, 241–
Dorsey, E. R., Jarjoura, D., & Rutecki, G. W. (2003). Influence of con-
trollable lifestyle on recent trends in specialty choice by US medical
181OVERTIME AND QUALITY OF WORKING LIFE IN ACADEMICS
students. JAMA: Journal of the American Medical Association, 290,
Doyle, C., & Hind, P. (1998). Occupational stress, burnout and job status
in female academics. Gender, Work and Organization, 5, 67–82. http://
Easton, S., & Van Laar, D. L. (2012). User Manual of the WRQoL scale.
Portsmouth, England: University of Portsmouth.
Easton, S., & Van Laar, D. L. (2013). Evaluation of Outcomes and Quality
of Working Life in the Coaching Setting. The Coaching Psychologist, 9,
71–77. ISSN 1748–1104.
Edwards, J. A., Van Laar, D., Easton, S., & Kinman, G. (2009). The
work-related quality of life scale for higher education employees. Qual-
ity in Higher Education, 15, 207–219. http://dx.doi.org/10.1080/
Elizur, D., & Shye, S. (1990). Quality of work life and its relation to quality
of life. Applied Psychology, 39, 275–291. http://dx.doi.org/10.1111/j
Fein, E. C., & Skinner, N. (2015). Clarifying the effect of work hours on
health through work–life conflict. Asia Pacific Journal of Human Re-
sources, 53, 448–470. http://dx.doi.org/10.1111/1744-7941.12065
Felce, D., & Perry, J. (1995). Quality of life: Its definition and measure-
ment. Research in Developmental Disabilities, 16, 51–74. http://dx.doi
Fontinha, R., Van Laar, D., & Easton, S. (2016). Quality of working life of
academics and researchers in the UK: The roles of contract type, tenure
and university ranking. Studies in Higher Education, 1–18. http://dx.doi
Frone, M. R., Russell, M., & Cooper, M. L. (1997). Relation of work-
family conflict to health outcomes: A four-year longitudinal study of
employed parents. Journal of Occupational and Organizational Psy-
chology, 70, 325–335. http://dx.doi.org/10.1111/j.2044-8325.1997
Gillespie, N. A., Walsh, M., Winefield, A. H., & Stough, C. (2001).
Occupational stress in universities: Staff perceptions of the causes,
consequences and moderators of stress. Work & Stress, 15, 53–72.
Golden, L., & Wiens-Tuers, B. (2006). To your happiness? Extra hours of
labor supply and worker well-being. The Journal of Socio-Economics,
35, 382–397. http://dx.doi.org/10.1016/j.socec.2005.11.039
Greenhaus, J., & Beutell, N. (1985). Sources of conflict between work and
family roles. The Academy of Management Review, 10, 76–88. Re-
trieved from http://www.jstor.org/stable/258214
The Guardian. (2017). University League Tables. Retrieved from https://
HESA. (2016). Staff 2011/2012. Retrieved from https://www.hesa.ac.uk/
Hobfoll, S. E. (1989). Conservation of resources. A new attempt at con-
ceptualizing stress. American Psychologist, 44, 513–524. http://dx.doi
Hogan, V., Hogan, M., Hodgins, M., Kinman, G., & Bunting, B. (2014).
An examination of gender differences in the impact of individual and
organisational factors on work hours, work-life conflict and psycholog-
ical strain in academics. The Irish Journal of Psychology, 35, 133–150.
Johnsrud, L. K. (2002). Measuring the quality of faculty and administrative
worklife: Implications for college and university campuses. Research in
Higher Education, 43, 379 –395. http://dx.doi.org/10.1023/A:10148
Kalliath, T., & Brough, P. (2008). Work-life balance: A review of the
meaning of the balance construct. Journal of Management & Organi-
zation, 14, 323–327.
Kelloway, E. K., Inness, M., Barling, J., Francis, L., & Turner, N. (2010).
Loving one’s job: Construct development and implications for individ-
ual well-being. In P. L. Perrewé & D. C. Ganster (Eds.), New Develop-
ments in Theoretical and Conceptual Approaches to Job Stress (pp.
Kinman, G. (1998). Pressure points: A survey into the causes and conse-
quences of occupational stress in UK academic and related staff. Lon-
don, England: Association of University Teachers.
Kinman, G. (2014). Doing more with less? Work and wellbeing in aca-
demics. Somatechnics, 4, 219–235. http://dx.doi.org/10.3366/soma.2014
Kinman, G., & Jones, F. (2004). Working to the Limit. London, England:
Kinman, G., & Jones, F. (2008). Effort-reward imbalance, over-
commitment and work-life conflict in UK academics. Journal of Man-
agerial Psychology, 23, 236–251. http://dx.doi.org/10.1108/0268394
Kinman, G., Jones, F., & Kinman, R. (2006). The wellbeing of the UK
academy, 1998–2004. Quality in Higher Education, 12, 15–27. http://
Kinman, G., & Wray, S. (2013). Higher stress: A survey of stress and
wellbeing among staff in higher education. London, England: UCU
Kinman, G., & Wray, S. (2016). Work-related wellbeing in UK higher
education. London, England: University and College Union.
Larkins, F. P. (2014). Trends in non-academic staff for Australian univer-
sities 2000–2010. Retrieved from http://www.lhmartininstitute.edu.au/
Le Fevre, M., Matheny, J., & Kolt, G. S. (2003). Eustress, distress, and
interpretation in occupational stress. Journal of Managerial Psychology,
18, 726–744. http://dx.doi.org/10.1108/02683940310502412
Menzies, H., & Newson, J. (2007). No time to think: Academics’ life in the
globally wired university. Time & Society, 16, 83–98. http://dx.doi.org/
Metcalf, H., Rolfe, H., Stevens, P., & Weale, M. (2005). Recruitment and
retention of academic staff in higher education National Institute of
Economic and Social Research (Research Report RR658). London,
Netemeyer, R. G., Boles, J., & McMurrian, R. (1996). Development and
validation of work-family conflict and family-work conflict scales. Jour-
nal of Applied Psychology, 81, 400–410. http://dx.doi.org/10.1037/
Noor, K. M. (2011). Work-life balance and intention to leave among
academics in Malaysian public higher education institutions. Interna-
tional Journal of Business and Social Science, 11, 240–248. Retrieved
OECD. (2013). Detailed description of employment protection legislation–
2012–2013—OECD Countries. Retrieved from http://www.oecd.org/els/
Pillay, S., & Abhayawansa, S. (2014). Work-family balance: Perspectives
from higher education. Higher Education, 68, 669–690. http://dx.doi
Pillay, S., Kluvers, R., Abhayawansa, S., & Vranic, V. (2013). An explor-
atory study into work/family balance within the Australian higher edu-
cation sector. Higher Education Research & Development, 32, 228–243.
Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., & Podsakoff, N. P. (2003).
Common method biases in behavioral research: A critical review of the
literature and recommended remedies. Journal of Applied Psychology,
88, 879–903. http://dx.doi.org/10.1037/0021-9010.88.5.879
Schaufeli, W. B., & Taris, T. W. (2014). A critical review of the job
demands-resources model: Implications for improving work and health.
In G. F. Bauer & O. Hämmig (Eds.), Bridging occupational, organiza-
tional and public health: A transdisciplinary approach (pp. 43–68).
182 FONTINHA, EASTON, AND VAN LAAR
Dordrecht, the Netherlands: Springer Science. http://dx.doi.org/10.1007/
Siegrist, J. (1996). Adverse health effects of high-effort/low-reward con-
ditions. Journal of Occupational Health Psychology, 1, 27–41. http://
Siegrist, J. (2001). A theory of occupational stress. In J. Dunham (Ed.),
Stress in the workplace: Past, present and future. London, England:
Sparks, K., Cooper, C., Fried, Y., & Shirom, A. (1997). The effects of
hours of work on health: A meta-analytic review. Journal of Occupa-
tional and Organizational Psychology, 70, 391–408. http://dx.doi.org/
Spector, P. E. (2002). Employee control and occupational stress. Current
Directions in Psychological Science, 11, 133–136. http://dx.doi.org/10
Statistical Bulletin. (2013). Annual survey of hours and earnings, 2013
provisional results. Retrieved from http://webarchive.nationalarchives
Taylor, J. C., Cooper, C. L., & Mumford, E. (1979). The quality of working
life in Western and Eastern Europe. ABP.
Tytherleigh, M., Webb, C., Cooper, C., & Ricketts, C. (2005). Occupa-
tional stress in UK higher education institutions: A comparative study of
all staff categories. Higher Education Research & Development, 24,
Vagharseyyedin, S. A., Vanaki, Z., & Mohammadi, E. (2011). Quality of
work life: Experiences of Iranian nurses. Nursing & Health Sciences, 13,
Van Laar, D., Edwards, J. A., & Easton, S. (2007). The Work-Related
Quality of Life scale for healthcare workers. Journal of Advanced
Nursing, 60, 325–333. http://dx.doi.org/10.1111/j.1365-2648.2007
Whitley, R., & Gläser, J. (2014). The impact of institutional reforms on the
nature of universities as organisations. Research in the Sociology of
Organizations, 42, 19 – 49. http://dx.doi.org/10.1108/S0733-558X20
Winefield, A., Boyd, C., Saebel, J., & Pignata, S. (2008). Job stress in
university staff: An Australian research study. Bowen Hills, Queensland:
Australian Academic Press.
Winefield, A. H., Gillespie, N., Stough, C., Dua, J., Hapuarachchi, J. R., &
Boyd, C. M. (2003). Occupational stress in Australian university staff:
Results from a national survey. International Journal of Stress Manage-
ment, 10, 51–63. http://dx.doi.org/10.1037/1072-5245.10.1.51
Winefield, H. R., Boyd, C., & Winefield, A. H. (2014). Work-family
conflict and well-being in university employees. The Journal of Psy-
chology, 148, 683– 697. http://dx.doi.org/10.1080/00223980.2013
Received October 13, 2016
Revision received February 15, 2017
Accepted March 6, 2017 �
E-Mail Notification of Your Latest Issue Online!
Would you like to know when the next issue of your favorite APA journal will be available
online? This service is now available to you. Sign up at https://my.apa.org/portal/alerts/ and you will
be notified by e-mail when issues of interest to you become available!
183OVERTIME AND QUALITY OF WORKING LIFE IN ACADEMICS
- Overtime and Quality of Working Life in Academics and Nonacademics: The Role of Perceived Work-L …
Academics’ Versus Nonacademics’ Quality of Working Life
Working Extra Hours and Work-Life Balance in Higher Education
Data Collection and Participants
Working conditions (WCS)
Job and career satisfaction (JCS)
Control at work (CAW)
Stress at work (SAW)
General well-being (GWB)
Limitations and Future Research Directions
Implications for Research and Practice