Impact of Teachers’ Career Adaptability and Family on
Professional Learning
Peter McIlveen*1, Harsha N. Perera2, Margaret Baguley1,
Henriette van Rensburg1, Rahul Ganguly1, Anne Jasman1 &
Julijana Veskova1
AUTHOR NOTE
1School of Linguistics, Adult and Specialist Education;
University of Southern Queensland; Toowoomba, Queensland;
Australia.
2 Department of Educational Psychology and Higher Education,
College of Education, University of Nevada, Las Vegas.
*Correspondence concerning this article should be addressed to
Dr Peter McIlveen, School of Linguistics, Adult and Specialist
Education; University of Southern Queensland; Toowoomba,
Queensland, 4350; +61 7 46312375; Australia. Email:
[email protected]
Abstract
Teachers’ engagement in professional learning is vital to the
profession’s sustainability. Their professional learning is
influenced by the demands of balancing work, family, and the strain
of balancing the two. This challenge is addressed through the
notion resilience, operationalized as career adaptability. In a
sample of teachers (N = 193), the present research explored the
relations between career adaptability, family-to-work conflict
(time-based and strain-based), and engagement in professional
learning. Structural equation modelling revealed that time-based
conflict mediated the relation between career adaptability and
strain-based conflict. Strain-based conflict, in turn, negatively
predicted engagement with professional development studies. It is
recommended that strategies for teachers’ professional learning are
inclusive of contextual factors, such as family-to-work conflict,
and focused on enhancing their career adaptability.
Keywords: teacher resilience, career adaptability, work-family
conflict, teacher education, professional learning
Impact of Teachers’ Career Adaptability and Family on
Professional Learning
Better preparing and supporting teachers to stay in the
workforce is a challenge that is recognized by international and
government agencies (e.g., OECD, 2013) and there is a substantive
research literature devoted to solving the problems of retaining
and re-attracting teachers to the workforce (Lindqvist, Nordänger,
& Carlsson, 2014; Mason & Matas, 2015; Özçınar, 2015).
Teachers’ engagement in professional learning is one vital element
of the profession’s sustainability. In the context of the present
research, teachers are obliged to participate in professional
learning under the provisions of the Australian Professional
Standards for Teachers (Australian Institute for Teaching and
School Leadership, 2016). Factors which influence Australian
teachers’ professional learning include the expense, geographical
and professional isolation, and stage of career (Cameron,
Mulholland, & Branson, 2013). In addition to financial
resources for professional learning activities, it is important to
recognize that continuing professional development demands personal
psycho-social resources (e.g., motivation, support from family).
Like other busy professionals, teachers’ engagement in professional
learning is influenced by the demands of balancing family and work,
and their resilience to the negative effects of this ongoing
balancing act. Resilience in teachers can be understood as the
capacity to “bounce back” in the face of adversity (Gu & Day,
2007, p. 1302) and “to withstand or recover quickly” (Day et al.,
2006, p. 50). Teachers’ resilience is germane to their well-being
and longevity in the professional workforce (Arnup & Bowles,
2016; Jensen, Sandoval-Hernandez, Knoll, & Gonzalez, 2012;
Mansfield, Beltman, Broadley, & Weatherby-Fell, 2016; Mason
& Matas, 2015; OECD, 2013). Furthermore, teachers’ resilience
affects their students’ learning and development (Gu & Day,
2007). Resilience in teachers is regarded as so important that it
is the focus of selection for entry into teacher education programs
(e.g., Bowles, Hattie, Dinham, Scull, & Clinton, 2014) and
programs for teacher development (Mansfield et al., 2016).
In the present research, the issue of resilience is explored
through the lens of psycho-social factors that positively and
negatively influence teachers’ engagement in professional learning.
In particular, the research investigates the strain teachers feel
when the psychological effects of family matters (e.g., family
distress/dissatisfaction, family/parental overload, family time
commitments, spouse support) spill-over into the workplace, and how
these effects diminish their engagement in professional
learning.
The Effects of Work-Family Conflict
There is a substantial body of research into family-related
stress and work-related stress, and balancing these two domains
(Frone, Russell, & Cooper, 1992; Frone, Yardley, & Markel,
1997; Greenhaus & Beutell, 1985). There are two directions for
the spill-over from one domain to another: work interference with
family, and family interference with work (FIW). Stress in the work
domain has a negative effect on the family domain (Byron, 2005;
Ford, Heinen, & Langkamer, 2007) and other personal domains
such as leisure, health, and relationships, particularly for women
(Keeney, Boyd, Sinha, Westring, & Ryan, 2013). Given that women
are the majority of teachers, there may be compounding effects for
female with families who sacrifice other important parts of their
lives to manage family responsibilities. With respect to FIW,
stress in the family domain is associated with depression (Frone et
al., 1992), reduced jobs satisfaction (Ford et al., 2007) and
performance at work (Hoobler, Hu, & Wilson, 2010; Whiston &
Cinamon, 2015), and salary and career satisfaction (Hoobler et al.,
2010). Family stress and family conflict (Byron, 2005; Ford et al.,
2007) and, more specifically, role conflict and role ambiguity
within the family (Michel, Mitchelson, Pichler, & Cullen, 2010)
predict family-to-work conflict. Conversely, family social support
is negatively related to family role conflict and role ambiguity
(Haar, Russo, Suñe, & Ollier-Malaterre, 2014; Michel et al.,
2010); that is, greater levels of support are associated with lower
levels of conflict and ambiguity. As for gender differences, a
large meta-analytic study (N > 250,000) found a practically
negligible overall difference between males and females for FIW
(Shockley, Shen, DeNunzio, Arvan, & Knudsen, 2017); however,
the extra hours that women put into family work and the
psychological boundaries they establish between family and work may
be better indicators of gender differences in FIW (Shockley et al.,
2017).
Teachers’ Experience of Work-Family Conflict
The professional relational nature of teachers’ work means that
there are ethical obligations to ensure that their personal
family-related stress does not spill-over into the school where
they maintain other relationships with students and colleagues.
Consider, for example, a teacher who departs from home to work
having moments before had an argument with a member of the family,
or a teacher whose family has endured a long-term unresolved
problem. Whatever is happening at home, a teacher must try to be
emotionally composed, on-task, and ready to perform in class. There
is, however, insufficient research into the influence of
family-related stress on teachers’ work life (Cinamon, Rich, &
Westman, 2007; Özçınar, 2015), and this is despite the need to
investigate teachers’ resilience with regard to work and family
life balance because that balance (or lack of) is a source of
burnout (Cinamon & Rich, 2009) and job dissatisfaction
(McKenzie, Weldon, Rowley, Murphy, & McMillan, 2014).
On the basis of a longitudinal study of 300 teachers, in 100
schools, over 4 years, in the United Kingdom, Day et al. (2006)
reported that work and non-work dimensions interacted with one
another to influence teachers’ sense of professional identity, and
that teachers’ personal identity (i.e., life outside the school)
may be positive or negative in its effects. Furthermore, Day et al.
found that achieving a balance between work-related pressures and
personal pressures (i.e., non-work context) was important, which
resonates with broader research that affirms the positive effects
of work-life balance, internationally and cross-culturally (Haar et
al., 2014). Day et al. found that vulnerable teachers coped by
accepting imbalance, subjugating an aspect of life, tolerating
changes in circumstances, or resisting. Although these avoidance
coping mechanisms may be effective in the short-term in certain
circumstances, research suggests that they may not be as effective
as approach-oriented coping (Taylor & Stanton, 2007).
Years of professional experience may be associated with
different levels of family-to-work conflict; however, we did not
collect data on years of professional experience. In a study of
female teachers, Cinamon and Rich (2005b) found that novice
teachers (with 1–5 years of experience) experienced significantly
higher levels of family-to-work conflict than two groupings of more
experienced teachers (6–15 years, and 16+ years); there was no
difference across the type of school (i.e., elementary, junior
high, senior high). Conversely, Palmer, Rose, Sanders, and Randle
(2012) found that family-related conflict was predicted by longer
years in the profession. Thus, years of experience is relevant but
the nature of its relation with work-family conflict is
unclear.
Relationships play a pivotal role in managing work-family
conflict. In particular, having a supportive partner may be
protective against the negative effects of work-family conflict
(Ferguson, Carlson, Zivnuska, & Whitten, 2012). Both
work-to-family and family-to-work conflict is related to teachers’
burnout (Cinamon & Rich, 2009; Cinamon et al., 2007). Cinamon
et al. (2007) found that lack of spousal support was the primary
predictor of family-to-work conflict experienced by teachers.
Importantly, family-to-work conflict predicted lack of vigour at
work. Teachers may experience additional emotional exhaustion as a
result of their own children’s behaviours at home (Palmer et al.,
2012). The psychological burden of transitioning from home,
managing a family of children, to work to manage a class of
children and the back to home again, cannot be insignificant. So
important is this cross-over effect that Palmer et al. recommended
supportive development programs for teachers and schools.
Feeling time-pressured is a source of emotional exhaustion and
job dissatisfaction in teachers (Skaalvik & Skaalvik, 2010).
Although family hours predicts family-to-work conflict it has no
direct relationship with job satisfaction (Ford et al., 2007).
Ostensibly, or perhaps stereotypically considered, teaching may be
attractive to individuals as a profession because ordinary work
hours allow for relatively more family time. Yet, it is axiomatic
that teachers commit time to preparing lessons in the evenings and
engaging in school activities that are held out-of-hours and on
weekends, and other such professional activities that consume time
that would have otherwise been for personal and family time (cf.,
Cinamon & Rich, 2005b; Cinamon et al., 2007). Indeed, research
indicates that female teachers depart the profession more than
males because of family-related reasons such as raising children
(Guarino, Santibañez, & Daley, 2006).
Career Adaptability as Resilience Resource
Resilience is a multidimensional construct inclusive of
psychological, interpersonal, social, and environmental factors
(MASKED AUTHOR CITATION, 2018; Beltman, Mansfield, & Price,
2011; Day et al., 2006; Gu & Day, 2007; Mason & Matas,
2015) and can be conceptualized as a dimension of positive
psychological capital necessary for teachers’ retention (Mason
& Matas, 2015). Resilience varies across domains of activity;
it is situational (e.g., home, school) and it varies according to
time (Gu & Day, 2007). For the present research, career
adaptability (Rottinghaus, Day, & Borgen, 2005), is used as an
indicator of positive psychological capital and resilience in
teachers. Career adaptability is a person’s capability to maintain
a satisfying career via self-regulative psychological resources of
preparedness to cope with predictable and unpredictable tasks and
conditions in work roles (Savickas, 1997) and to fit into work
roles (Savickas, 2005). Similarly, Rottinghaus et al. (2005)
conceptualised career adaptability as the psychological capacity to
cope with change and recover from circumstances that affect career
plans, and comfort with work responsibilities. Thus, career
adaptability may be formulated as a psychological resource—as a
dimension of resilience—which protects teachers from the effects of
stressful circumstances. Research into teachers’ career
adaptability is suggestive of its role in teachers’ engagement and
enterprising behaviour at work (van Dam, Schipper, & Runhaar,
2010). Career adaptability is positively associated with teachers’
self-efficacy (MASKED AUTHOR CITATION), which, in turn, influences
teachers’ professional optimism (MASKED AUTHOR CITATION), and,
moreover, their effectiveness as a teacher (Zee & Koomen, 2016)
and intentions to stay or leave the profession (Wang, Hall, &
Rahimi, 2015).
The Present Research
We sought to examine the relationships among career adaptability
and teachers’ experience of time- and strained-based conflict in
the direction of family-to-work interference (i.e., FIW).
Specifically, the present study aimed to test an integrative model
of work-family conflict in which career adaptability is expected to
lead to lower strain-based FIW, both directly and indirectly via
time-based FIW. This model reflects the view that teachers who
evince higher career adaptability suffer lower levels of
strain-based FIW, at least in part because they experience less
time-based FIW. In addition to testing this target parametric
structure, we specified an alternative model in which the direct
path from career adaptability to strain-based FIW was constrained
to zero to test the tenability of complete mediation of the links
via time-based interference. The participants in this study were
teachers engaged in professional development activities, as
postgraduate degrees in education. Given the emphasis on
professional development for teachers, it is likely that this group
is particularly sensitive to interference with their professional
learning. Accordingly, we also modelled the positive effects of
career adaptability and negative effects of FIW on their engagement
with their professional development studies.
MethodParticipants
Participants (N = 193) were in full-time or part-time employment
as educators. Participants’ ages were collected in range
categories: 21–29 years (n = 27, 13.7%), 30–39 (n = 63, 32.0%),
40–49 (n = 61, 31.0%), 50–59 (n = 39, 19.8%), 60 or older (n = 7,
3.6%). There were 134 females (68.0%) and 63 males (32.0 %), which
approximates the overall national gender imbalance in the primary
teacher workforce (McKenzie et al., 2014; Weldon, 2015). English
was the predominant, first language spoken at home (n = 175, 88.8
%) with the remainder reporting a mix of English and another 22
other languages, and n = 125 (65%) declared their country of
residence to be Australia. All were enrolled in postgraduate
degrees in specialist education studies offered by a medium-sized
university Australian university, of which 75% take degrees in the
distance mode, and none were full-time students. Participation in
the study was voluntary, not-for-credit, and with the incentive of
entering a prize draw for vouchers. The Human Research Ethics
Committee of the university approved the study.
Measures
Career adaptability. Participants completed a short-form of the
Career Adaptability subscale drawn from the Career Futures
Inventory (Rottinghaus et al., 2005)(MASKED AUTHOR CITATION, 2013).
Career adaptability is “a tendency affecting the way an individual
views his or her capacity to plan and adjust to changing career
plans and work responsibilities, especially in the face of
unforeseen events” (Rottinghaus et al., 2005, p. 5). The modified
3-item scale comprises a rating scale of 1 (strongly disagree) to 5
(strongly agree). An example item is “I will adjust to shifting
demands at work”. The internal consistency of the original
short-form was Cronbach’s α = .82, and α = .76 in the current
study.
Conflict. Participants completed the time-based and strain-based
FIW subscales in the Work-Family Conflict Scale (Carlson, Kacmar,
& Williams, 2000). Possible responses to items range from 1
(strongly disagree) to 5 (strongly agree). There are three items
each for time-based FIW (original α = .79; current α = .86) and
strain-based FIW (original α = .87; current α = .92). Example items
are as follows: for time-based FIW, “The time I spend on family
responsibilities often interferes with my work responsibilities”;
and, strain-based FIW, “Due to stress at home, I am often
preoccupied with family matters at work”. The behaviour-based
interference subscale was not used in the present investigation
because the original study by Carlson et al. raised doubts about
its validity.
Engagement in Professional Learning. The Academic Major
Satisfaction Scale (Nauta, 2007) was used to measure participants’
engagement with their professional development activities. This
six-item measure uses a rating scale of 1 (strongly disagree) to 5
(strongly agree). A sample item is “I often wish I hadn’t gotten
into this major.” The internal consistency of the original scale in
its two validations studies were α = 94 and α = .90. In the current
study α = .847.
Data Analysis
The statistical analysis in this study involved confirmatory
factor analysis (CFA) and structural equation modeling (SEM)
performed in accordance with the two-step modeling methodology
recommended by Anderson and Gerbing (1988). First, a four-factor
CFA was specified to test the proposed measurement structure
underlying the data. For the indicators of Academic Major
Satisfaction, two correlated residuals were specified to account
for the presumed method effects generated by highly similar items
wordings representing systematic item covariance not captured by
the Academic Major Satisfaction factor (e.g., “Overall, I am happy
with the major I’ve chosen”, “I feel good about the major I’ve
chosen”). Second, the structural models were specified and tested.
Structural equation modelling has the advantage of simultaneously
testing the predictive relations among several latent factors,
rather than conducting several separate multiple regressions with
just one criterion (i.e., dependent variable).
Analyses were conducted using Mplus 7.2 (Muthén & Muthén,
1998-2012).Models were estimated using robust diagonal weighted
least squares with a mean-and-variance adjusted test statistic,
operationalized as the WLSMV estimator in Mplus. For tests of model
fit, we relied on the comparative fit index (CFI), Tucker-Lewis
Index (TLI) and Root Mean Square Error of Approximation (RMSEA).
For the CFI and TLI, values > .90 and .95 are indicative of
acceptable and excellent fit, respectively, and for the RMSEA,
values < .08 and .05 are suggestive of reasonable and close fit,
respectively. In addition to reporting these indices, we report the
χ2 test for informational purposes. For nested model comparisons,
the adjusted χ2 difference test (MD χ2 DIFF) appropriate for the
WLSMV estimator was used. Finally, indirect effects were computed
as the product of implicated coefficients with delta method
standard errors—the default under WLSMV estimation in Mplus.
ResultsDescriptive Statistics
We compared females’ and male’s mean levels of Career
Adaptability (females, M = 12.69.63, SD = 1.50; males, M = 12.57,
SD = 1.64), strained-based FIW (females, M = 5.94.63, SD = 2.14;
males, M = 6.03, SD = 2.58), time-based FIW (females, M = 6.91, SD
= 2.17; males, M = 6.80, SD = 2.15), and Academic Major
Satisfaction (females, M = 25.63, SD = 3.88; males, M = 25.08, SD =
4.69). Statistical testing by independent groups t-tests revealed
no significant differences between females and males, which is
consistent with meta-analytic findings in the literature (Shockley
et al., 2017).
The correlations for the 15 questionnaire items used to measure
Career Adaptability, strain-based and time-based FIW, are Academic
Major Satisfaction, are shown in Table 1. The patterns within the
correlations indicate a preponderance of negative relations between
Career Adaptability and the two FIW variables, and negative
relations between Academic Major Satisfaction and the two FIW
variables. In other words, as the scores for Career Adaptability
and Academic Major Satisfaction increased the scores for FIW
decreased, and vice versa.
Measurement Model
For this type of quantitative research, it is necessary to
determine whether the measures’ items appropriately indicate their
respective factors and, conversely, not indicate other factors.
This process assures the items for Career Adaptability, for
example, measure that factor but do not falsely measure another
factor such as strain-based FIW, and vice versa. To test the
measurement structure underlying the observed variables, a
four-factor confirmatory factor analysis (CFA) was performed in
which all latent variable covariances were freely estimated. The
test of this model resulted in an excellent fit to the sample data,
χ2 (82) = 111.001, p < .05, RMSEA = .043 (90% CI = .019, .062),
CFI = .995, TLI = .994. Furthermore, inspection of standardized
residuals and modification indices revealed no areas of substantial
localized strain. As shown in Table 2, all 15 of the observed
indicators loaded substantially and significantly on their
respective factors. Given this appropriate measurement model, we
proceeded to test the target structural model of the relations
among the factors.
INSERT TABLE 1 ABOUT HERE
INSERT TABLE 2 ABOUT HERE
Structural Model
The structural model is shown in Figure 1. The test of this
model resulted in an excellent fit to the data, χ2 (84) = 104.982,
p > .050, RMSEA = .036 (90% CI = .000, .056), CFI = .997, TLI =
.996. The fit of this model was compared to a more parsimonious
alternative structure in which the direct path from career
adaptability to strain-based FIW was constrained to zero. The more
parsimonious model also provided a good fit to the data, χ2 (85) =
112.322, p < .05, RMSEA = .041 (90% CI = .015, .060), CFI =
.996, TLI = .995. However, this model resulted in an appreciable
decrement in fit relative to the more complex target model, MD χ2
DIFF (1) = 4.745, p < .05. On this basis, the fully mediated
model with the direct path freely estimated from Career
Adaptability to strain-based FIW was retained as the preferred
model and is displayed in Figure 1.
INSERT FIGURE 1 ABOUT HERE
The model shows a negative pathway from Career Adaptability to
time-based FIW and strain-based FIW, and from strain-based FIW to
Academic Major Satisfaction. In other words, the model suggests
that Academic Major Satisfaction will be predicted in a downward
direction by strain-based FIW, which is predicted upwardly by
time-based FIW. On the other hand, Career Adaptability is
downwardly predicting the two FIW factors. The target structural
model reflecting partial mediation of the relations of career
adaptability with strain-based FIW was specified.
As expected, Career Adaptability was negatively and
significantly associated with time-based FIW and strain-based FIW.
Furthermore, consistent with our expectations, time-based FIW was
strong a positive predictor of strain-based FIW. Both postulated
indirect associations were statistically significant. Consistent
with expectations, there was a statistically significant
standardized indirect association between Career Adaptability and
strain-based FIW via time-based FIW (γβ = –.184, p < .001).
There was also a significant standardized indirect association
between Career Adaptability and Academic Major Satisfaction via
time-based FIW and strain-based FIW linked serially (γββ = .037, p
< .05).
Summary of Results
As depicted in Figure 1, the structural equation modelling
revealed Academic Major Satisfaction declines as strain-based FIW
increases. Furthermore, strain-based FIW is associated with
increases in time-based FIW. On the other hand, increases in Career
Adaptability are associated with reductions in time-based and
strain-based interference. Thus, the analyses highlight dynamic
relations that directly influence the participants’ engagement in
the professional studies.
Discussion
The current study yields two important findings. First, this
study adds new evidence to the limited empirical literature on the
effects of family-to-work conflict experienced by teachers. The
data reveal an evident deleterious effect of family interference on
work (both strain-based and time-based) on the participants’
engagement in professional development. Discerning the dynamics of
factors that contribute to teachers’ stress is an important
research objective. Thus, the second finding provides crucial
evidence of a chain of effects between teachers’ career
adaptability, time-based FIW, and strain-based FIW. This chain of
effects demonstrates the impact of FIW on their engagement in
professional development. Teachers’ experience of strain when
family matters spill over to work is not just a function of being
“time-poor”; it is also a function of their career adaptability
which positively contributes to their professional learning.
Therefore, the present evidence supports our suggestion that career
adaptability should be included in models of teachers’ resilience
and the effects of teachers’ family on work.
With a focus on family, the findings of the current study accord
with the conceptualisation of teachers’ career adaptability as
positive psychological capital (Mason & Matas, 2015) that is
related to environmental, social, and personal contextual factors
(Day et al., 2006; Gu & Day, 2007). The present findings are
consistent with those of Day et al. (2006), which indicate a
dynamic relationship between work as a teacher and family-related
responsibilities, and those of Skaalvik and Skaalvik (2010), which
highlight the role of time-pressure. The current study adds to
these previous findings by clarifying the mechanisms underlying
this dynamic relationship; that is, teachers’ family-to-work strain
is affected by career adaptability through time-pressure.
Keeney et al. (2013) argue that the nexus of
work-to-family/family-to-work conflict should be conceptually
extended beyond work-family to a more inclusive set, work-life;
that is, widening the range of factors addressed in the measurement
of non-work aspects of employees’ lives beyond family (e.g.,
friendships, community, leisure). Whilst expanding the view of
work-family conflict is laudable, attention should also be given to
refining the focus on how individual strike a balance among all of
the various influences in their lives. This assertion becomes
important in light of research that demonstrates a disposition to
spill-over (Chen, Powell, & Greenhaus, 2009; Cho, Tay, Allen,
& Stark, 2013). Should such a disposition be evident in
teachers, it may manifest as an inability to manage the many roles
and domains in teachers’ work-life. Furthermore, individuals with
an impulsive/sensation seeking disposition and young children may
be less able to manage family-to-work conflict than those without
such a disposition (Blanch & Aluja, 2009). Thus, a disposition
to spill-over work-family conflict or an impulsive/sensation
seeking disposition may very well be seen as an anti-resilience
factor in teachers because they may manage boundaries between
domains. Determining the relationship between career adaptability
and the disposition to spill-over may contribute further to an
understanding of career adaptability’s positive contribution to
career management and well-being.
Implications for Practice
Engaging in professional learning a Professional Standard
(Australian Institute for Teaching and School Leadership, 2016)
that is crucial to teachers’ engagement in their profession, yet
there is relatively little research into the factors the facilitate
or inhibit teachers’ professional learning (Cameron et al., 2013).
The present research highlights the impact of family-to-work strain
on teachers’ engagement in the professional learning. There is
consistent evidence of the positive effects of a supportive spouse
and co-workers on work-life balance and the positive flow on
effects to work satisfaction (Cinamon et al., 2007; Ferguson et
al., 2012). The model of family-related work decisions (Greenhaus
& Powell, 2012; Powell & Greenhaus, 2012) is suggestive of
how family influences can be most effectively managed to guard
against negative influences between the two domains. For example,
recent research affirms the influence of family-supportive
supervisor behaviour on employees’ wellbeing (Matthews, Mills,
Trout, & English, 2014). Furthermore, the supportive
development program for teachers who are also parents (Palmer et
al., 2012) raises the prospect of a contextualized solution rather
than a purely individualized solution. On the basis of the present
finding, we recommend that any interventions to support teachers’
professional learning take into account their families as a potent
contextual influence that may help or hinder their learning.
With respect to burnout in teachers as a function of work and
family conflict (Cinamon & Rich, 2009; Cinamon et al., 2007),
the present findings lend support to the proposition that enhancing
career adaptability may be another avenue to enhance resilience and
reduce burnout. Cinamon and Rich (2005a) designed a program about
work-family conflict to support teachers and comprised the
following aims:
(a) raise manager awareness of the causes and consequences of
work-family conflict;
(b) enhance manager understanding of family-friendly
organizational policy;
(c) boost at-risk employee understanding of work and family role
identities;
(d) improve at-risk employees’ skills and attitudes enabling
successful blending of roles; and,
(e) increase at-risk employees’ self-efficacy for management of
work-family conflict (p. 95-96).
On the basis of the current research outcomes, the development
of career adaptability should be integrated into such program aims
because it may have a positive impact on engagement in professional
learning activities.
Teachers’ decisions to engage in professional learning is
dependent on their career stage, personal circumstances, isolation,
and their specific professional development needs (Cameron et al.,
2013). Given the negative impact of time-based and strain-based WIF
on engagement in formal studies, some teachers may prefer
modalities of professional learning that are not as rigid as a
postgraduate degree program with attendant time pressures,
deadlines, years of duration, and expenses. There is evidence for
the value and utility of social media (Mercieca & Kelly, 2017)
and video conferencing (Maher & Prescott, 2017) for
collaborative support and professional learning. The benefit of
such models is their relative flexibility and accessibility;
however, professional development activities require commitments of
time, regardless of modality. Local, collaborative inquiry (Hardy,
2016)is another approach to professional learning that
simultaneously facilitates teachers’ management of new workplace
requirements (e.g., new curricula, regulations).
Limitations and Future Research
Approximately 15% of Australian teachers take postgraduate
degrees (McKenzie et al., 2014). Results of this study should be
interpreted in the knowledge that all participants were educators
taking postgraduate degrees. There may be factors associated with
this group of educators that were not included in this study. For
example, it could be reasonably assumed that these may experience
higher levels of perceived demand on their time than teachers who
are not studying (e.g., committing 10 to 20 hours per week to study
and meeting regular deadlines for assessment). Thus, teachers who
feel pressured by study commitments may be more susceptible to
higher levels of FIW if family is distracting their focus on
professional learning. In addition, teachers who take postgraduate
studies may be inherently motivated and thus express relatively
higher levels of Career Adaptability than other teachers. Future
research may address these caveats by deploying measures of
positive and negative affect and by sampling teachers who are not
engaged in postgraduate studies.
The present findings also demonstrate that increased career
adaptability is associated with decreased time-based and
strain-based FIW. Meta-analytic research (e.g., Whiston, Li,
Goodrich Mitts, & Wright, 2017) consistently affirms the
effectiveness of career development interventions for enhancing
positive characteristic adaptations that contribute to career
self-management (e.g., self-efficacy). Research from the career
development field that is focused on teachers’ career
self-management (e.g., MASKED AUTHOR CITATION, 2017; Duffy &
Lent, 2009) and work-family conflict (Cinamon & Rich, 2005a)
may be usefully integrated with models focused on teacher
resilience (Mansfield et al., 2016; Mansfield, Beltman, Price,
& McConney, 2012). Such integration would conceptually and
practically inform the design of bespoke programs for teachers’ to
enhance how well they balance demands and engage in professional
learning for their career.
Conclusion
If teachers, the teaching profession, schools, universities, and
governments are to make headway in addressing the problems of
teacher retention and attrition via better preparing and supporting
teachers’ professional development learning then it is crucial that
the multidimensional qualities of resilience be comprehensively
identified so as to allow for efficient targeting of resources
(Beltman et al., 2011; OECD, 2013). The present research shows that
career adaptability, as positive psychological capital and a
dimension of resilience, has a positive influence on engagement in
professional learning. However, its positive effects are diminished
by family-to-work strain. Therefore, programs of professional
learning for teachers should take a contextualized account of their
engagement in learning, focusing on their career adaptability and
family environment.
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Running head: FAMILY AND PROFESSIONAL LEARNING1
FAMILY AND PROFESSIONAL LEARNING20
This manuscript is the author version of:
McIlveen, P., Perera, H. N., Baguley, M., van Rensburg, H.,
Ganguly, R., Jasman, A., & Veskova, J. (2018). Impact of
teachers’ career adaptability and family on professional learning.
Asia-Pacific Journal of Teacher Education, 1-15. doi:
10.1080/1359866X.2018.1444141
Table 1.
Correlations for the 15 Items of the Measures
Variable
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
1. CFI-1
–
2. CFI-2
.734
–
3. CFI-3
.710
.626
–
4. TFIW-1
–.275
–.205
–.139
–
5. TFIW-2
–.173
–.152
–.148
.741
–
6. TFIW-3
–.268
–.319
–.227
.621
.631
–
7. SFIW-1
–.203
–.236
–.172
.452
.394
.555
–
8. SFIW-2
–.242
–.334
–.184
.519
.425
.566
.885
–
9. SFIW-3
–.338
–.371
–.167
.490
.377
.556
.854
.862
–
10. AMS-1
.114
.080
.061
–.046
–.094
–.025
–.048
–.025
–.062
–
11. AMS-2
.087
.116
.023
–.120
–.138
–.057
–.139
–.165
–.142
.676
–
12. AMS-3
–.019
.156
.004
–.073
–.111
–.020
–.094
–.158
–.085
.470
.630
–
13. AMS-4
.076
.102
.139
–.110
–.139
–.079
–.045
–.105
–.098
.497
.664
.578
–
14. AMS-5
.088
.103
.131
–.160
–.216
–.144
–.126
–.156
–.154
.539
.703
.642
.891
–
15. AMS-6
.123
.155
.147
–.039
–.037
–.057
–.146
–.190
–.202
.339
.626
.698
.547
.623
–
Note. N = 193. M = Mean; SD = Standard Deviation; CFI = Career
Future Inventory; TFIW = time based family interference with work;
SBIW = strain-based family interference with work; AMS = Academic
Major Satisfaction. The number after each acronym indicates the
item within the respective questionnaire measure.
Table 2
Loadings of the 15 Items of the Measures on their Respective
Factors
Latent variable and indicators
λ
λcs
SEa
Career Adaptability
CA-1
1.000 b
.898
.041
CA-2
0.743
.835
.033
CA-3
0.577
.762
.045
TFIW
TFIW-1
1.000 b
.848
.033
TFIW-2
0.807
.790
.032
TFIW-3
0.904
.823
.032
SFIW
SFIW-1
1.000 b
.928
.012
SFIW-2
1.216
.950
.011
SFIW-3
0.915
.916
.013
AMS
AMS-1
1.000 b
.671
.045
AMS-2
2.112
.886
.023
AMS-3
1.189
.732
.040
AMS-4
1.292
.760
.039
AMS-5
1.652
.831
.033
AMS-6
1.087
.701
.041
Note. λ = unstandardized factor loading; λcs = completely
standardized factor loading. a These values are based on
standardized estimates. b These loadings were fixed to 1.00 to
establish the metric of the latent variable. All factor loadings
are significant at p < .001. CA = Career Adaptability; TFIW =
time based family interference with work; SBIW = strain-based
family interference with work; AMS = Academic Major Satisfaction.
The number after each acronym indicates the item within the
respective questionnaire measure.
Figure 1. The retained structural model with standardized path
coefficients. N = 193. * p < .05 ** p < .01, *** p
<.001
Note. TBFIW = time-based family interference with work; SBFIW =
strain-based family interference with work; AMS = academic major
satisfaction.
