1

Journal of Occupational and Organizational Psychology (2015)

© 2015 The British Psychological Society
www.wileyonlinelibrary.com

The relationships of work–family conflict and core

self-evaluations with informal learning in a
managerial context
Michael J. Tews1*, Raymond A. Noe2, Andrew J. Scheurer2 and
John W. Michel3
1
School of Hospitality Management, Pennsylvania State University, University Park,
Pennsylvania, USA
2
Department of Management and Human Resources, Fisher College of Business, The
Ohio State University, Columbus, Ohio, USA
3
Sellinger School of Business & Management, Loyola University Maryland, Baltimore,
Maryland, USA

Existing research on informal learning has been largely descriptive, anecdotal, and
relatively limited regarding its antecedents. This study represents a step forward towards
addressing this gap in the training and development literature by examining the
relationships of work–family conflict and core self-evaluations with informal learning
among managers. The sample included 225 managers companywide from a casual-theme
restaurant chain in the United States. The results demonstrated that time-based work
interference with family conflict (WIF) had a negative relationship with informal learning,
but core self-evaluations had a positive relationship. Further, core self-evaluations
moderated the WIF–informal learning relationship. Counter to the hypothesized
relationship, managers higher in core self-evaluations engaged in less informal learning as
work–family conflict increased. This study broadens the nomological network of informal
learning and highlights that organizations need to limit time demands at work that cause
work–family conflict to facilitate managers’ informal learning.

Practitioner points

Limit work–family conflict so managers have the time to invest in informal learning in addition to their
family and own recovery.

Provide managers with skills in time management and goal setting and provide support necessary to
encourage informal learning.

Promoting informal learning among employees is an issue important to researchers and

HR professionals alike (Bear et al., 2008; Tannenbaum, Beard, McNall, & Salas, 2010).
Watkins and Marsick (1990) characterize informal learning as learning that typically
occurs outside of the classroom, is not highly structured, and is primarily controlled by the
learner. Some examples of informal learning include employees reflecting on how to

*Correspondence should be addressed to Michael J. Tews, School of Hospitality Management, Pennsylvania State University, 121
Mateer Building, University Park, PA 16802, USA (email: mjt17@psu.edu).

DOI:10.1111/joop.12109
2 Michael J. Tews et al.

improve performance, asking questions of mentors and peers, and searching print and
electronic sources to find solutions to work-related problems. Watkins and Marsick
further highlight that such learning can be explicitly ‘encouraged by an organization or it
can take place despite an environment not highly conducive to learning’ (p. 121). Such
learning is critical for survival in today’s dynamic business environment, where
employees are required to adapt to new situations, learn new technologies, and provide
high quality services to internal and external organizational constituents. Moreover,
informal learning is important from the employee’s perspective because today’s careers
require taking personal initiative beyond formal training opportunities (Briscoe, Hall, &
Frautschy DeMuth, 2006; Molloy & Noe, 2010).
Drawing on its practical importance, academic research has begun to more formally
examine the antecedents and outcomes of informal learning. Choi and Jacobs (2011)
found that an individual’s personal learning orientation and participation in formal
training positively influenced informal learning. Furthermore, Ellinger (2005) identified
important contextual factors that facilitated informal learning, including commitment of
management to learning, an internal learning culture, tools and resources, and access to
people to form webs of relationships. Studies focusing on the outcomes of informal
learning have also found that it is positively related to contentment, overall job
satisfaction, and performance (Bear et al., 2008; Rowden & Conine, 2005). Notwith-
standing the importance of these findings, research on informal learning has been
relatively limited, and scholars have argued for additional research that broadens our
understanding in this area (Noe, Clarke, & Klein, 2014).
This study advances informal learning research by examining the relationships of
work–family conflict and core self-evaluations with informal learning among managers.
Because informal learning is largely discretionary, it is important to consider individual
differences and situational factors as antecedents of such learning (Colquitt, LePine, &
Noe, 2000). In this study, we focus on core self-evaluations as an individual difference,
drawing on previous research that has demonstrated that core self-evaluations have a
positive influence on learning motivation (Kim, Oh, Chiaburu, & Brown, 2012). We focus
on work–family conflict, specifically time-based conflict, as a situational antecedent of
informal learning because informal learning may likely be neglected when work and
family demands are too consuming. In fact, Allen, Hurst, Bruck, and Sutton (2000) have
argued that research needs to examine the extent to which work–family conflict is ‘related
to capitalizing on learning and development opportunities’ (p. 289). We also examine
core self-evaluations as a moderator of the work–family conflict–informal learning
relationship. Core self-evaluations may not only have a positive relationship with informal
learning, but may reduce the negative relationship between work–family conflict and
informal learning. Doing so answers calls for research to better understand the interaction
between dispositions and work–family conflict (Allen et al., 2012). In the following
sections, we provide the theoretical background and hypotheses for this research and
then present the methodology, results, and discussion for our study that was conducted
with 225 managers companywide from a casual-theme restaurant chain in the United
States.

Theoretical background and study hypotheses

Informal learning is a broad construct, yet three primary characteristics serve to define
informal learning. One, informal learning occurs outside of a formal classroom context.
Informal learning may be embedded in the natural work environment or may span beyond
 Informal learning 3

the immediate workplace (Watkins & Marsick, 1990). Two, informal learning is largely
under an individual’s discretion and control unlike formal classroom training (Berg &
Chyung, 2008; Watkins & Marsick, 1990). Informal learning can occur both incidentally as
an individual performs their daily work activities and purposefully when the learner
determines the need to learn and chooses what, when, and how to learn (Marsick &
Watkins, 2001). Three, informal learning encompasses a variety of cognitive activities and
behaviours (Doornbos, Simons, & Denessen, 2008; Lohman, 2005; Lohman & Woolf,
2001). These activities and behaviours include learning from oneself through self-
reflection; learning from others such as peers, supervisors, and mentors; and learning
from non-interpersonal sources, such as reading print or online material (Noe, Tews, &
Marand, 2013).
Informal learning is especially important to understand because it reflects the majority
of learning that occurs in organizations (Bear et al., 2008). In one respect, the prevalence
and importance of informal learning can be attributed to the reality that it reflects how
individuals naturally learn on the job. Moreover, given the need to streamline HR
processes and limited budgets for formal training, informal learning represents a
substitute for, and extension of, formal training (Marsick & Watkins, 1999). Formal
training is certainly valuable for individuals to learn the foundation of new knowledge and
skills. However, new knowledge and skills are not typically fully developed in a formal
classroom context, requiring individuals to further develop their skills on the job (Baldwin
& Ford, 1988; Kozlowski & Salas, 1997; Tews & Tracey, 2008). Informal learning enables
individuals to practice their skills in a natural context and provides for potentially more
meaningful learning experiences (Rossett & Nguyen, 2012; Tannenbaum et al., 2010).
Because it occurs outside of the formal classroom, informal learning represents natural
learning that can occur on an as-needed basis. Individuals can engage in learning when
they encounter an immediate need or believe a situation provides an opportunity to learn.
There are no prescribed guidelines as to when to engage in informal learning. As informal
learning has potential benefits, yet is primarily based on individual motivation and choice,
research is necessary to investigate which situational characteristics and individual
differences can facilitate or impede the informal learning process.

The work–family conflict – Informal learning relationship

Nearly 30 years ago, Greenhaus and Beutell (1985) defined work–family conflict as ‘a form
of inter-role conflict in which the role pressures from the work and family domains are
mutually incompatible in some respect. That is, participation in the work (family) role is
made more difficult by virtue of participation in the family (work) role’ (p. 77). The
negative consequences of such conflict have been demonstrated by research validating
that work–family conflict leads to decreased career and life satisfaction, decreased well-
being, and greater levels of stress, depression, and anxiety (Allen, 2012; Greenhaus, Allen,
& Spector, 2006). Work–family conflict is typically considered to involve three types:
Time-based, strain-based, and behaviour-based conflict. This study will focus on time-
based conflict, which occurs when the time devoted to one role makes it difficult to
participate or comply with expectations of another role (Bartolome & Evans, 1979;
Greenhaus & Beutell, 1985). We consider both time-based conflict resulting from both
work interference with family (WIF) and family interference with work (FIW).
We chose to focus on time-based conflict instead of strain- or behaviour-based conflict
for two primary reasons. First, our focus in this research is on the extent to which
individuals purposefully engage in informal learning behaviours. According to Edwards
4 Michael J. Tews et al.

and Rothbard (2000), the shift of time between work and family domains results from
intentional allocation decisions, which we believe influence the degree to which
individuals engage in informal learning. The influence of strain- and behaviour-based
conflict on role performance in another domain is likely unintentional. For example,
family demands may lead to fatigue and tension that interferes with work performance
without the individual making a conscious choice to not engage in informal learning.
Second, research has found that time above and beyond what it takes to complete core job
responsibilities, rather than strain or incompatible role demands, is an important
antecedent of informal learning (Ellinger, 2005; Lohman, 2005; Sambrook & Stewart,
2000; Tannenbaum et al., 2010; Volpe, 1999). Because informal learning is volitional and
not a core job responsibility per se, individuals need autonomy and flexibility in their
schedule to engage in informal learning as opposed to what they perceive as pressing job
demands or activities more closely aligned with their personal interests. Research has
demonstrated that work–family conflict has a negative relationship with employees’
perceptions of the flexibility they have in their work schedule and their control and timing
of work (Anderson, Coffey, & Byerly, 2002). In turn, these perceptions likely impede
informal learning.
Conservation of resources theory provides a framework for understanding why time-
based conflict can inhibit informal learning. Conservation of resources theory focuses on
the balance between one’s resources and the demands that require those resources
(Hobfoll, 1989). This theory posits that ‘resource loss is disproportionately more salient
than resource gain’ and ‘people must invest resources in order to protect against resource
loss, recover from losses, and gain resources’ (Hobfoll, 2011, p. 117). With respect to
work–family interactions, Hobfoll contends that these different domains are ‘jealous
demanders of individuals’ resources’ where a ‘battle for resources’ is common (p. 118).
Grandey and Cropanzano (1999) assert that as more conflict is experienced in one
domain, fewer resources are available to fulfil one’s role in another. Tompson and Werner
(1997) argue that if an individual has limited time at work or anticipates interruptions form
another role, an employee will focus on required tasks as opposed to those that are more
discretionary. In one of the few studies investigating work–family conflict in a learning
context, Rego and Cunha (2009) examined the moderating role of work–family
conciliation, the degree to which an organization creates conditions for employees to
reconcile their work and family lives, on the relationship between opportunities for
learning and development and well-being. Their results demonstrated that when work–
family conciliation is low, opportunities for learning and development do not lead to
greater well-being.
Fundamentally, time-based work–family conflict may be especially detrimental for
informal learning because it exhausts the supply of resources necessary to motivate
individuals to engage in discretionary activities such as informal learning. Due to depletion
of resources, FIW makes it difficult for individuals to meet the responsibilities of their
work role which inhibits job performance (Wallace & Young, 2008). As a result, FIW
makes it unlikely that individuals have the time necessary for discretionary learning
behaviours that are part of informal learning. In the other direction, WIF should have a
negative relationship with informal learning because work demands are impeding on
one’s personal time. Similar to the case for FIW, individuals would either not have the
resources necessary to engage in learning or will choose to use any personal resources
remaining after meeting work responsibilities to reduce conflict by fulfilling personal and
family obligations. To the extent that work must be completed at home, such as
completing paperwork or responding to email, an individual’s capability to engage in any
 Informal learning 5

informal learning outside of work would likely be further constrained. In addition,

because an individual is likely experiencing work overload with greater WIF, WIF would
have a negative relationship with informal learning occurring on the job because an
individual would not have the time required time to engage in such learning. Based on
these arguments, we hypothesize:
Hypothesis 1: Time-based FIW will be negatively related to informal learning.
Hypothesis 2: Time-based WIF will be negatively related to informal learning.

The core self-evaluations–informal learning relationship

Core self-evaluations is a higher-order individual differences construct comprised of four
broad traits representing a global self-evaluation about oneself and self-worth (Judge,
Locke, & Durham, 1997; Judge, Locke, Durham, & Kluger, 1998). Specifically, self-esteem,
generalized self-efficacy, emotional stability, and locus of control comprise the core self-
evaluations construct. Self-esteem reflects the overall appraisal of one’s self-worth
(Rosenberg, Schooler, Schoenbach, & Rosenberg, 1995). Generalized self-efficacy refers
to one’s beliefs that he or she can succeed in any domain, irrespective of the specific task at
hand (Stajkovic & Luthans, 1998). Emotional stability refers to the extent to which
individuals are calm, relaxed, and free from worry and anxiety (Costa & McCrae, 1992).
Locus of control relates to whether individuals believe that events are under their own
personal control or under the control of others and the situation (Rotter, 1966). When
individuals have more favourable perceptions of themselves, they are more likely to be
successful and happy in all domains of their lives (Judge, Bono, Erez, & Locke, 2005; Judge
& Hurst, 2008). Chang, Ferris, Johnson, Rosen, and Tan (2012) suggest that this is so
because core self-evaluations relate to a motivational orientation that allows individuals to
better identify and pursue beneficial opportunities. Individuals with high core self-
evaluations are less likely to feel that time demands are overwhelming or constraining.
From a learning perspective, core self-evaluations have been shown to have a significant
positive relationship with learning motivation and performance in formal learning
contexts (Kim et al., 2012; Stanhope, Pond, & Surface, 2013).
Core self-evaluations are likely related to informal learning for several reasons. First,
individuals with high core self-evaluations are more likely to perceive informal learning
as valuable (Beier & Kanfer, 2010) and reflect on their actions (Lohman, 2005).
Individuals with high core self-evaluations are also more apt to seek feedback and
undertake assignments that require using new skills or adapting current ones
(Tannenbaum et al., 2010). Furthermore, they may be more willing to act on external
jolts and acquire new skills to adapt to such challenges (Feeney, Gardiner, Johnston,
Jones, & McEvoy, 2005; Ng, Sorensen, & Eby, 2006). Such individuals likely have less
anxiety and doubts about learning and have higher expectations about the benefits they
can gain from such endeavours (Feldman & Ng, 2011). They are better able to divert
attention away from performing their core job responsibilities and engage in meta-
cognitive processes necessary for informal learning (Gully & Chen, 2010; Kanfer &
Ackerman, 1989). Finally, individuals high in core self-evaluations are likely to engage in
informal learning because they feel in control of and more responsible for managing
their work situations.
Overall, individuals higher in core self-evaluations value learning and career success,
possess greater emotional resources, and believe they have greater control over their
success in the workplace. Accordingly, we expect that individuals with higher core
6 Michael J. Tews et al.

self-evaluations will engage in more informal learning because they have a greater capacity
and motivation to do so. They likely understand that informal learning can help them
improve their performance, which will reinforce their positive self-concept, and possess
the resources to engage in learning in turbulent work environments with competing
demands. Thus, we hypothesize:
Hypothesis 3: Core self-evaluations will be positively related to informal learning.

The moderating role of core self-evaluations in the work–family conflict – Informal learning
relationship
The final issue to be examined in this research is the extent to which core self-evaluations
moderate the relationship between work–family conflict and informal learning. Accord-
ing to Hobfoll (1989), resources are things that individuals value, with an emphasis on
objects, states, and conditions. In their review of the conservation of resources literature,
Halbesleben, Neveu, Paustian-Underdahl, and Westman (2014) propose an alternative
definition of resources to include anything perceived by individuals to help attain their
goals. They argue that because conservation of resources is a motivational theory, this
broader definition is necessary for enhancing our understanding of the properties of
resources and their dynamics. Congruent with this motivational perspective, research has
demonstrated that individual differences can be considered resources because they affect
how individuals react to resource processes of investment, gain, and loss (Grandey &
Cropanzano, 1999). For example, Halbesleben, Harvey, and Bolino (2009) found that the
relationship between organization citizenship behaviours and WIF was weaker for
employees with high levels of conscientiousness. This finding suggests that conscien-
tiousness serves as a resource by allowing highly conscientious individuals to anticipate
potential problems between roles and take actions to address them.
In the present context, we argue that the negative relationship between the
dimensions of work–family conflict and informal learning should be attenuated for those
higher in core self-evaluations. We specifically focus on core self-evaluation as a resource
because it is a personality characteristic that shapes individuals’ beliefs about their control
over the work environment and how they perceive and cope with multiple demands
(Chang et al., 2012). Kammeyer-Mueller, Judge, and Scott (2009) found that individuals
with high core self-evaluations perceive fewer work stressors and less strain than
individual with low core self-evaluations. Those higher in core self-evaluations may be
better able to cope with the challenges associated with work–family conflict, including
resource losses, and thereby engage in more informal learning. In support of this
proposition, Rantanen, Pulkkinen, and Kinnunen (2005) found that women lower in
emotional stability experienced greater emotional distress when facing work–family
conflict. Grandey and Cropanzano (1999) demonstrated that those higher in self-esteem
can cope better with resource loss. We argue that individuals higher in core self-
evaluations have greater emotional resources to combat the challenges associated with
work–family conflict and are thus able to devote the time and energy towards their work
responsibilities, including the discretionary effort required for informal learning.
Hypothesis 4: Core self-evaluations will attenuate the negative relationship between
time-based FIW and informal learning.
Hypothesis 5: Core self-evaluations will attenuate the negative relationship between
time-based WIF and informal learning.
 Informal learning 7

Method
Sample and procedure
The sample for this study includes 225 managers from an organization that owns and
operates approximately 100 casual-theme restaurants throughout the United States. At
time of hire, the managers received formal training in basic management skills at the
organization’s regional training facility. However, after managers are assigned to their
restaurants, most learning occurs informally because there are limited opportunities for
formal training. Work–family conflict is also likely a challenge for these managers.
Managers in the hospitality industry, and in restaurants in particular, often work more than
eight hours on a daily basis, more than 50 hr per week, and are scheduled to work during
the week and on weekends (Bureau of Labor Statistics, 2013). Given the importance of
informal learning, examining its relationship to work–conflict, core self-evaluations, and
their interaction is particularly warranted in this employment context.
Data were obtained using online surveys administered at two points. At time 1, data
were obtained on core self-evaluations from 265 managers, representing approximately
92% of the managers in the organization. At time 2, approximately 6 months later, 225 of
the 265 managers provided an assessment of their work–family conflict and their
participation in informal learning. This final sample was 73% male, 86% Caucasian, on
average 40 years of age, and on average employed 7 years with the company at the
beginning of the study. There were no significant differences between the final sample
(n = 225) and those who provided wave one data only (n = 40) in demographic
characteristics and core self-evaluations, suggesting that sample attrition was not
problematic.

Measures
Work–family conflict
Carlson, Kacmar, and Williams’s (2000) scale was used to measure the two dimensions of
time-based work–family conflict. Three items each were used to measure FIW and WIF.
Sample items for FIW included: The time I spend on family responsibilities often
interfere with my work responsibilities and The time I spend with my family often
causes me not to spend time in activities at work that could be helpful to my career.
Sample items for WIF included: My work keeps me from my family activities more than I
would like and I have to miss family activities due to the amount of time I must spend on
work responsibilities. The respondents indicated the extent to which they agreed with
each statement with a 5-point scale ranging from 1 = strongly disagree to 5 = strongly
agree. The internal consistency reliability estimates for FIW and WIF were .93 and .89,
respectively.

Core self-evaluations
Core self-evaluations were measured using Judge, Erez, Bono, and Thoresen’s (2003) 12-
item measure. Sample items included: Overall, I am satisfied with myself (self-esteem), I
complete tasks successfully (generalized self-efficacy), Sometimes I feel depressed
(emotional stability, reverse-scored), and I determine what will happen in my life (locus
of control). The respondents indicated the extent to which they agreed with each
statement with a 5-point scale ranging from 1 = strongly disagree to 5 = strongly agree.
8 Michael J. Tews et al.

The internal consistency reliability estimate for the scale was .87. A confirmatory factor
analysis (CFA) was performed to further validate the one-dimensional core-evaluations
measure. Although the model possessed a statistically significant chi-square statistic,
v2(51, n = 225) = 123.25, p < .01, the individual fit indices provided adequate support
for the 1-factor model (Hu & Bentler, 1999). The Comparative Fit Index (CFI) was .93, the
Tucker–Lewis Index (TLI) was .90, the root-mean-square error of approximation (RMSEA)
was .07 (90% confidence interval ranging from .06 to .09), and the standardized root-mean-
square residual (SRMR) was .05. It is noteworthy that the fit of the core self-evaluation
scale for this study was consistent with the validation evidence provided by Judge et al.
(2003).

Informal learning
Informal learning was measured using the 9-item measure developed by Noe et al. (2013).
Sample items included: Experimenting with new ways of performing my work (learning
from oneself), interacting with a mentor to learn new knowledge and skills (learning
from others), and reading professional magazines and vendor publications to learn
new knowledge and skills (learning from non-interpersonal sources). The study
participants were asked to consider the past 3 months and indicate how often they
engaged in the informal learning behaviours during a typical work week. A 5-point
response scale was provided (1 = never, 5 = all the time). The internal consistency
reliability estimate for the scale was .90.

Control variables
Manager support for learning, age, ethnicity, gender, and organizational tenure were
included as control variables in the analyses. These variables were included as controls
because prior research shows that they influence motivation to learn, decisions to engage
in learning-related behaviours such as participation in development activities, or work–
family conflict (Byron, 2005; Colquitt et al., 2000; Hurtz & Williams, 2009). Managerial
support for learning was measured with Tracey and Tews’s (2005) 5-item scale. Sample
items included: My managers expect continuing excellence and competence and My
managers encourage independent and innovative thinking. The participants in this
study, who were managers themselves, responded to each of the items with respect to
their direct supervisors with a 5-point scale ranging from 1 = strongly disagree to
5 = strongly agree. The internal consistency reliability estimate for the scale was .79.

Discriminant validity
Additional CFAs were conducted to assess the discriminant validity of the FIW, WIF, core
self-evaluations, informal learning, and manager support for learning constructs. First,
a 5-factor model in which the items loaded on their respective constructs was assessed
using Mplus 7 with the sample covariance matrix as input and a maximum likelihood
solution (Muth
en & Muth
en, 2012). Although the model possessed a statistically
significant chi-square statistic, v2(449, n = 225) = 778.55, p < .01, the individual fit
indices provided adequate support for the 5-factor model (Hu & Bentler, 1999). The CFI
was .92, the TLI was .91, the RMSEA was .06 (90% confidence interval ranging from .05 to
.06), and the SRMR was .06. In addition, the chi-square/degrees of freedom ratio were less
than 2.0, which suggest adequate model fit (Byrne, 1989). To further assess discriminant
 Informal learning 9

validity, we tested two 4-factor models. In the first 4-factor model, we loaded all of the WIF
conflict and FIW conflict items onto one construct. This model did not fit the data well,
v2(453, n = 225) = 1354.28, p < .01, CFI = .77, TLI = .74, RMSEA = .09, and the
SRMR = .09, and was a worse fit, v2Differenceð4Þ = 575.73, p < .01, than the proposed 5-
factor model. In the second 4-factor model, we loaded all of the informal learning and
manager support for informal learning items onto one construct. This model also did not
fit the data well, v2(453, n = 225) = 1048.48, p < .01, CFI = .85, TLI = .83,
RMSEA = .08, and the SRMR = .10, and was a worse fit, v2Differenceð4Þ = 269.93, p < .001,
than the 5-factor model. In all, the results from the CFAs demonstrate that our measures
demonstrate acceptable discriminant validity among the five constructs (Hu & Bentler,
1999).

Analytic strategy
To serve as the basis for testing the study hypotheses, informal learning was regressed on
the independent variables in four steps. Informal learning was regressed on the control
variables in step 1, with the inclusion of FIW, WIF, and core self-evaluations in step 2. Then
in steps 3a and 3b, the FIW 9 core self-evaluations and WIF 9 core self-evaluations
interaction terms were alternately added to the model individually. Finally, all variables
were included in the model in step 4. Prior to creating the interaction terms, the FIW, WIF,
and core self-evaluations variables were centred to limit the potential for multicollinearity
that might otherwise bias the results (Aiken & West, 1991).

Results
Table 1 presents the study variable means, standard deviations, and correlations. The
regression results are shown in Table 2. Overall, the regression model explained 21% of
the variance in informal learning (F = 5.84, p < .01). The control variables entered as a
block were significantly related to informal learning, explaining 12% of the variance
(F = 5.85, p > .01). In step 2, FIW, WIF, and core self-evaluations explained an additional
7% of variance in informal learning beyond the control variables (DF = 6.80, p < .01). The
FIW 9 core self-evaluations interaction term was not significant (see step 3a), but the
WIF 9 core self-evaluations interaction (see step 3b) explained a statistically significant
amount of additional variance in informal learning (DR2 = .02, DF = 5.34, p < .05). In
step 4, inclusion of the FIW 9 core self-evaluations interaction term did not account for
additional variance beyond step 3b, while inclusion of the WIF 9 core self-evaluations
interaction term did account for additional variance beyond step 3a (DR2 = .01,
DF = 4.58, p < .05).
Some of the research hypotheses were supported, while others were not. Hypothesis
1, which proposed that time-based FIW would be negatively related to informal learning,
was not supported as indicated by the non-significant regression coefficients in step 2,
step 3a, step 3b, and step 4. Hypothesis 2, which proposed that time-based WIF would be
negatively related to informal learning, was supported. The regression coefficients for
WIF were significant in all steps in which it was included, ranging from .20 in step 2
(p < .01) to .17 in steps 3b and 4 (p < .05). Hypothesis 3, which proposed that core self-
evaluations would be positively related to informal learning, was supported. The
regression coefficients for core self-evaluations were significant in all steps in which it was
included ranging from .23 in step 2 to .28 in step 4 (p < .01). Hypothesis 4, which
 10
Michael J. Tews et al.

Table 1. Descriptive statistics and correlations among study variables

Variable M SD 1 2 3 4 5 6 7 8 9

1 Age 40.05 9.27 –

2 Ethnicity 0.86 0.35 .06 –
3 Gender 0.73 0.44 .31** .01 –
4 Tenure 6.95 5.36 .23** .05 .05 –
5 Manager Support 4.02 0.57 .08 .09 .02 .06 –
6 FIW 1.86 0.92 .10 .07 .05 .12* .14* –
7 WIF 3.55 0.99 .22** .07 .12* .04 .27** .03 –
8 Core Self-Evaluations 4.29 0.46 .00 .12* .08 .03 .62** .21** .30** –
9 Informal Learning 3.63 0.73 .03 .13* .03 .01 .33** .00 .29** .36** –

Note. n = 225. Ethnicity: Other = 0 and Caucasian = 1. Gender: Female = 0 and Male = 1.
*p < .05; **p < .01.
Table 2. Regression results for informal learning

Step 1 Step 2 Step 3a Step 3b Step 4

Predictors b t b t b t b t b t

Age .01 0.12 .06 0.81 .06 0.83 .07 1.04 .07 1.03
Ethnicity .10 1.64 .07 1.12 .07 1.18 .07 1.10 .07 1.13
Gender .02 0.37 .02 0.29 .01 0.21 .02 0.27 .01 0.23
Tenure .01 0.19 .01 0.17 .01 0.18 .02 0.35 .02 0.35
Manager Support for Learning .32** 4.96 .13 1.67 .12 1.52 .10 1.32 .10 1.26
FIW .08 1.26 .07 1.15 .11 1.69 .10 1.59
WIF .20** 2.96 .19** 2.76 .17* 2.58 .17* 2.49
Core Self-Evaluations .23** 2.81 .25** 2.96 .27** 3.29 .28** 3.31
FIW 9 Core Self-Evaluations .06 0.97 .03 0.46
WIF 9 Core Self-Evaluations .15* 2.31 .14* 2.14
R2 .12 .19 .20 .21 .21
F 5.85** 6.50** 5.88** 6.49** 5.84**
DR2 .07 .01 .02 .01/.00a
DF 6.80** 0.94 5.34* 4.58*/0.21

Note. n = 225. Ethnicity: Other = 0 and Caucasian = 1. Gender: Female = 0 and Male = 1. Standardized regression coefficients are reported.
a
First and second entries reflect changes from steps 3a and 3b, respectively.
*p < .05; **p < .01.
Informal learning
11
12 Michael J. Tews et al.

Informal learning

2 Low CSE
High CSE

1
Low WIF High WIF

Figure 1. Interaction between work interference with family (WIF) and core self-evaluations (CSE) on
informal learning.

proposed core self-evaluations would attenuate the negative relationship between

time-based FIW and informal learning, was not supported as indicated by the non-
significant regression coefficients for the FIW 9 core self-evaluations interaction term in
steps 3a and 4. Finally, Hypothesis 5, which proposed core self-evaluations would
attenuate the negative relationship between time-based WIF and informal learning, was
not supported. While the WIF 9 core self-evaluations interaction term was significant, it
was negative in both step 3b and step 4.
Figure 1 graphically depicts the WIF–informal learning relationship for individuals
with core self-evaluations one standard deviation above and one standard deviation below
the mean (Cohen, Cohen, West, & Aiken, 2003). Inspection of the graphs indicates that
WIF has a greater negative relationship with informal learning for those higher in core self-
evaluations. Further, a simple slope analysis indicated that increases in WIF resulted in less
informal learning for individuals with high core self-evaluations (b = .26, t = 1.84,
p < .05). However, the WIF–informal learning relationship was not significant for
individuals with low core self-evaluations (b = .14, t = .78, p > .05).

Discussion
It is important for individuals to engage in informal learning in today’s dynamic and
competitive business environment. At the same time, the pressures inherent in today’s
workplace constrain the extent to which individuals have the opportunity to engage in
informal learning. Thus, informal learning may not occur when it is arguably most needed.
Through this research, we have broadened the nomological network of informal learning
and highlighted what efforts may be employed to help ensure individuals incorporate
informal learning in their professional development. Our results demonstrated that work–
family conflict does have a negative relationship with managers’ participation in informal
learning. Specifically, WIF, but not FIW, was found to have a negative relationship with
informal learning. This finding suggests that conflict resulting from time allocation
between work and family might constrain one’s ability to engage in informal learning. As
emphasized by conservation of resources theory, individuals have limited resources and
 Informal learning 13

will be less apt to engage in informal learning when the time allocated for work inhibits
participating in family activities. Because informal learning reflects extra-role behaviour,
individuals will focus on core task demands when confronted with the need to spend
excessive time at work. Conversely, individuals need to experience less time pressure at
work if they are to engage in informal learning.
By demonstrating that core-evaluations are positively related to informal learning, this
research served to further validate the importance and generalizability of core self-
evaluations in a learning context. Those higher in core self-evaluations likely seek out
challenges, value success on the job, and have the capability to ‘do it all’, including taking
the time to learn even when work is challenging. While Judge and colleagues have found
that core self-evaluations have a positive relationship with employee job satisfaction,
performance, and life satisfaction (Judge & Bono, 2001; Judge et al., 2005), this study
suggests that those higher in core self-evaluations are more apt to incorporate informal
learning into their regular routines. One of the mediating processes through which core
self-evaluations might influence performance is through informal learning to enhance
one’s skills, and future research should explicitly test this proposition.
Core self-evaluations moderated the WIF–informal learning relationship, but not in the
expected direction. Individuals low in core self-evaluations engaged in less informal
learning regardless of the level of WIF. Contrary to the hypothesis, WIF had a greater
negative relationship with informal learning for individuals with higher core self-
evaluations. This finding suggests that regardless of their level of work–family conflict,
individuals with low core self-evaluations likely engage in less informal learning.
However, work–family conflict had a negative relationship with informal learning for
individuals high in core self-evaluations. One plausible explanation for this result is that
individuals higher in core self-evaluations may be more likely to attend to their work–
family conflict because they feel they are more capable of doing so and value goal
attainment in work and family domains. Through ‘doing it all,’ these individuals likely
focus the majority of their energy meeting demands that directly contribute to their self-
worth, leaving some but less time for discretionary informal learning. It should be noted
that although WIF reduced informal learning for those higher in core self-evaluations, such
individuals still engaged in more informal learning than those with lower core self-
evaluations. To provide a clearer picture of the moderating role of core self-evaluations,
future research should examine how core self-evaluations influence individuals’
allocation of time to work, family, and informal learning when facing work–family
conflict. For example, time-based conflict reduces people’s available resources leaving
them too depleted to engage in learning activities. As a result, depletion or exhaustion may
potentially mediate the relationship of work–family conflict on informal learning.
However, high core self-evaluations may help individuals better cope with depleted
resources. Testing a conceptual model including second stage moderation (i.e., a core self-
evaluations by depletion/exhaustion interaction predicting informal learning) may be
more appropriate than the first stage moderation used in this study.
Some of the relationships demonstrated by the bivariate correlations between the
managers’ demographic characteristics and the focal study variables are also noteworthy.
For example, older individuals experienced less work–family conflict, namely WIF. Older
individuals thus appear better able to allocate time necessary for meeting their managerial
responsibilities. Contrary to the results of other studies suggesting that age has a negative
relationship to formal learning (Warr & Bunce, 1995), age was unrelated to informal
learning. Interestingly, Caucasians, who represented the majority of the sample, were
lower in core self-evaluations and engaged in less informal learning. Thus, the minority
14 Michael J. Tews et al.

managers in our sample had more favourable perceptions of themselves and engaged in
more learning. Finally, in line with the results of Byron’s (2005) meta-analytic review of the
antecedents of work–family conflict, female managers experienced greater WIF than their
male counterparts. Given these findings, further research should more fully examine
demographic characteristics in conjunction with work–family conflict and core self-
evaluations to obtain a more nuanced understanding of informal learning. For example,
one opportunity for future research would be to examine interactions with demographic
characteristics, core self-evaluations, and work–family conflict as they relate to informal
learning.
The findings from this study suggest several practical strategies for facilitating
managers’ informal learning. Managers who value their career and recognize the value of
informal learning should still have enough time for family and reducing the stressors
associated with their work. Bosses should reduce unnecessary or unrealistic time
demands to help managers better balance work and family and facilitate informal learning.
If reducing time demands at work is not possible, then managers need to be trained how to
best manage their time, including scheduling time for informal learning. Training in self-
management skills needed to reduce stress and anxiety related to work–family conflict
may be especially helpful (Robbins, Oh, Le, & Button, 2009). Encouraging managers to
schedule, set goals, and engage in informal learning may ultimately give them more time
for family activities because it helps them learn how to reduce time demands at work by
working ‘smarter not harder.’ Their bosses should also support and acknowledge
achievement of goals related to informal learning. Such strategies may be valuable for all
managers but especially those who have low core self-evaluations.
The findings from this study should be interpreted in the context of its limitations. This
research focused on managers in a restaurant setting, who were predominately middle-
aged white males. Wicker and August (1995) advocate applying ‘constant methods’ to
different groups to effectively assess the generalizability of results. In particular, research
would be worthwhile that focuses more on females who are likely to experience greater
levels of work–family conflict (Grandey & Cropanzano, 1999). A second limitation is that
the work–family conflict data and the informal learning data were obtained at the same
point in time. Future research should strive to collect work–family conflict data prior to
informal learning data and use a longitudinal design to substantiate a cause-and-effect
relationship. Third, this study only examined time-based work–family conflict, and as
such, future research should also focus on strain- and behaviour-based conflict. If we had
included all three forms of work–family conflict in this study, it is possible that we may
have found a different pattern of results. A final limitation is that we did not assess job
demands, home demands, or family characteristics. Control over one’s time, schedule,
and flexibility, and home demands are likely determinants of work–family conflict and
informal learning (Anderson et al., 2002). Byron’s (2005) meta-analysis demonstrated that
the age of one’s youngest child and number of children had significant relationships with
both WIF and FIW. Future research should measure job demands, family characteristics,
flexibility of working hours, or flexibility of the work itself (Clark, 2001) to determine how
they influence WIF and FIW, which in turn influence informal learning.
Another opportunity for future research is to more fully examine informal learning that
occurs both within and outside of the workplace. The relationship between WIF and FIW
and informal learning are likely more complex and nuanced than as examined in this
study. WIF might more strongly influence informal learning that occurs at home, and FIW
might more strongly influence informal learning at work. Furthermore, informal learning
at work might potentially be an antecedent of WIF if it took away from time that otherwise
 Informal learning 15

would be devoted to one’s family. Informal learning is not constrained by time and place,
and as such, future research should examine the antecedents and outcomes of informal
learning that occurs in different spheres of one’s life. A useful approach for further
examining informal learning would be using an experience sampling methodology.
Through experience sampling, respondents can report on their experiences at multiple
intervals throughout a day. Such research could focus on how often, why, and in what
form individuals engage in informal learning at home and at work relative to other
performance activities. Furthermore, such an approach could determine whether
individuals engage in massed or distributed practice with respect to informal learning.
Formal training research generally supports the notion that skill acquisition is maximized
when individuals distribute a given amount of practice over time (Baldwin & Ford, 1988).
With informal learning, however, massed practice may be better when individuals devote
an extended period of time to learning where they can focus without distractions, rather
than taking a piecemeal approach.
Research is needed to further examine how work–family issues influence informal
learning and other types of learning endeavours. Intuitively, we recognize that work–
family issues likely influence choices to participate in learning activities and the extent to
which individuals have the effort to learn. However, few studies have examined the
interface between work–family and learning. Work–family conflict may determine
whether employees choose to participate in voluntary, yet important, learning oppor-
tunities such as development programs or mentoring. Further, work–family conflict may
influence motivation to complete on-line training content which individuals may be
expected to complete at any time throughout a day. Research in this area is especially
important because learning contexts are becoming more user- rather than instructor-
driven (Kraiger, 2008). Another potentially promising individual difference is a person’s
preference for segmenting their work and family lives versus integrating them (Ashforth,
Kreiner, & Fugate, 2000). An important question to investigate is whether integration, in
which work and family domains are overlapping, rather than segmentation, increases
work–family conflict and reduces informal learning.
Molloy and Noe (2010) argued that ‘learning for a living’ is critical both for employees
and the organizational itself. Informal learning represents a key means through which
individuals keep their knowledge and skill sets current and remain competitive. Our
results suggest that greater work–life balance may be fundamental for promoting informal
learning, along with emphasizing the need to provide individuals with personal support
and skills necessary to facilitate informal learning. Previous research on informal learning
has been largely descriptive and anecdotal, and research on the antecedents of informal
learning has been relatively limited to date. The present study represents a step forward
towards understanding how work–family conflict and one key individual difference, core
self-evaluations, influence informal learning.

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Received 10 January 2014; revised version received 25 January 2015