Why the availability of telecommuting matters: The effects of telecommuting on engagement via goal pursuit

Abstract

Purpose
In line with conservation of resources theory and signaling theory, the purpose of this paper is to conceptualize and test a multiple mediation model in which telecommuting affects engagement via perceived supervisor goal support and goal progress.

Design/methodology/approach
A three-phase longitudinal study carried out over ten months was used to test the hypotheses.

Findings
Individuals who worked in organizations that offer telecommuting were more engaged than those who worked in organizations that did not offer telecommuting. Furthermore, telecommuting availability was not only directly but also indirectly related to engagement via perceived supervisor goal support and goal progress. Engagement in general decreased over time. However, individuals who attained their personal work goals were able to maintain high levels of engagement.

Research limitations/implications
Giving employees the option to telecommute could increase employee engagement. This study is correlational in nature and relied on self-report data.

Originality/value
This is the first study examining the effects of telecommuting on engagement over a period of ten months. It is also the first study to use perceived supervisor goal support and goal progress as explanatory variables to the teleworking and engagement relationship.

Full text

Career Development International
Why the availability of telecommuting matters: The effects of telecommuting on
engagement via goal pursuit
Aline D. Masuda, Claudia Holtschlag, Jessica M. Nicklin,
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Aline D. Masuda, Claudia Holtschlag, Jessica M. Nicklin, (2017) "Why the availability of
telecommuting matters: The effects of telecommuting on engagement via goal pursuit", Career
Development International, Vol. 22 Issue: 2, pp.200-219, https://doi.org/10.1108/CDI-05-2016-0064
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Why the availability of
telecommuting matters
The effects of telecommuting on engagement
via goal pursuit
Aline D. Masuda
EADA Business School, Barcelona, Spain
Claudia Holtschlag
Centrum Pontificia Universidad Católica de Peru, Lima, Peru, and
Jessica M. Nicklin
University of Hartford, West Hartford, Connecticut, USA
Abstract
Purpose –In line with conservation of resources theory and signaling theory, the purpose of this paper is to
conceptualize and test a multiple mediation model in which telecommuting affects engagement via perceived
supervisor goal support and goal progress.
Design/methodology/approach –A three-phase longitudinal study carried out over ten months was used
to test the hypotheses.
Findings –Individuals who worked in organizations that offer telecommuting were more engaged than
those who worked in organizations that did not offer telecommuting. Furthermore, telecommuting availability
was not only directly but also indirectly related to engagement via perceived supervisor goal support and
goal progress. Engagement in general decreased over time. However, individuals who attained their personal
work goals were able to maintain high levels of engagement.
Research limitations/implications –Giving employees the option to telecommute could increase
employee engagement. This study is correlational in nature and relied on self-report data.
Originality/value –This is the first study examining the effects of telecommuting on engagement over a
period of ten months. It is also the first study to use perceived supervisor goal support and goal progress as
explanatory variables to the teleworking and engagement relationship.
Keywords Motivation ( psychology), Goals, Engagement, Teleworking
Paper type Research paper
As a result of global competitiveness and the recent changes in the workplace, such as
the development of technology that permits workers to do their jobs remotely and the
proliferation of women and dual career couples entering the workplace, companies are
adopting new forms of work arrangements such as telecommuting (Nicklin et al., 2015).
A survey conducted in 2015 reported that 34 percent of business leaders expect that more
than half of their full-time employees will be telecommuting by 2020 (Vanderkam, 2014).
Telecommuting, a type of flexible working arrangement allowing employees to work from
home or a remote location, has important benefits for organizations and employees
(Nieminen et al., 2011). Employees who telecommute are more likely to be satisfied and
committed at work and less likely to experience role-stress and work-family conflict
(Gajendran and Harrison, 2007; Nicklin et al., 2009). Telecommuting has also been shown to
increase productivity, improve employee retention (Harker et al., 2012), and reduce the
corporate real estate expenses of organizations (Ye, 2012). These benefits likely explain why
all companies that made it to the best companies to work for in Working Mothers Magazine
offer telecommuting arrangements (Carlson, 2005).
Despite the positive aspects of telecommuting, many business leaders warn of the
negative consequences of its adoptions for employees’career, including but not limited to
Career Development International
Vol. 22 No. 2, 2017
pp. 200-219
© Emerald Publishing Limited
1362-0436
DOI 10.1108/CDI-05-2016-0064
Received 2 May 2016
Revised 29 November 2016
17 March 2017
Accepted 17 March 2017
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/1362-0436.htm
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social and professional isolation (Crandall and Gao, 2005; Kurland and Bailey, 1999).
Employees may miss out on formal and informal opportunities for socialization, learning,
mentoring, and communication while being away from the main office; consider the
discussions that occur at the proverbial water cooler or banter that happens while walking
to the parking lot at the end of the day. Further, some managers fear a loss of control when
employees are outside of the office (e.g. Kurland and Bailey, 1999). Elsbach and Cable (2012)
reported that employees who do not show up at work may receive lower performance
evaluations, which in turn can influence their chances of promotion and upward career
mobility. As evidence of this, a survey conducted in 2013 by Korn/Ferry International,
showed that 60 percent of employees believe telecommuting can limit career upward
mobility (Baum, 2013).
Thus, there is a lack of agreement among scholars and the business community on
whether telecommuting is a source of premiums or penalties for individuals’career
(Leslie et al., 2012). Telecommuting offers many benefits for individuals and organizations,
but at what cost? Given the demand for telecommuting to meet the challenges of today’s
employees and pervasive adoption of telecommuting by organizations as a recruitment and
retention strategy, but the pervasive concern for telecommuting and career advancement, it
is critical that research examines how telecommuting adoption by organizations relates to
important career outcomes.
In this paper, we test the effects of telecommuting availability on engagement, defined as
a“positive, fulfilling, and work-related state of mind that is characterized by vigor,
dedication, and absorption”(Schaufeli and Bakker, 2004, p. 295). We also examine two
important career-related mediators: perceived supervisor goal support, defined as
employees’perception that their supervisors support their personal work goals
(Maier and Brunstein, 2001; Pomaki et al., 2004) and work goals progress, defined as
progress made on career-related objectives, projects, and plans that reflect individuals’most
salient thoughts, emotions, and behaviors in the workplace (Maier and Brunstein, 2001).
We chose to study the effects of telecommuting availability on engagement because nearly
80 percent of HR leaders consider employee engagement an important issue
(e.g. Bersin, 2014), and research has shown that engagement is related to important
career outcomes. Specifically, engagement is related to higher performance, occupational
success, career satisfaction, lower absenteeism, and turnover intentions (Soane et al., 2013;
Christian et al., 2011; Rich et al., 2010; Rooy et al., 2011). Thus far, however, very few studies
have examined how telecommuting relates to work engagement (for an exception,
see Sardeshmukh et al., 2012, who examined telecommuting frequency of usage).
Because engagement is linked to important career-related outcomes, a closer look into the
telecommuting-engagement relationship could shed light on whether and how
telecommuting availability influences employees’careers.
We chose to examine personal work goals as mediators in response to previous research
calls for studying more mediators of telecommuting with outcome variables relationships
(Gajendran and Harrison, 2007). Further, work goals provide a “powerful framework for
studying fundamental questions in the field of motivation”(Sheldon and Elliot, 1999, p. 493).
Additionally, despite theoretical support for work goals as mediators of the telecommuting
availability-engagement relationship (Demerouti et al., 2001), to our knowledge no previous
empirical studies have tested this proposition.
We use signaling theory (Casper and Harris, 2008; Grover and Crooker, 1995) and
conservation of resources (COR) theory (Hobfoll, 1989) to develop our hypotheses.
Specifically, we argue that telecommuting availability is a secondary resource that improves
engagement by promoting primary resource gains (e.g. perceptions of supervisor support
for work goals) to employees as they pursue their work goals (Bakker and Demerouti, 2007;
Demerouti et al., 2001). This is because companies that allow employees to telecommute
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provide a “signal”that they care about their employees’welfare (Casper and Harris, 2008;
Grover and Crooker, 1995). Specifically, employees interpret the availability of
telecommuting as a type of support provided by supervisors to accomplish their goals.
Perceptions of goal support will lead to goal progress and consequently higher engagement.
By testing these hypotheses, we hope to contribute to our understanding on how
telecommuting adoption by organizations may benefit employees via career goal progress
and improvements in engagement.
Theoretical background
Based on COR theory (Hobfoll, 1989), engagement is a function of individuals’intrinsic
energetic resources, and thus depends on the resources that people lose or gain over time
(Gorgievski and Hobfoll, 2008). Hobfoll (2002) suggested that people seek to “obtain, retain,
and protect resources and that stress occurs when resources are threatened with loss or lost
when individuals fail to gain resources after substantive resource investment”(p. 312).
COR theory posits that resource losses have disproportionate effects, in terms of
their degree and speed, and thus have stronger motivational power than resource gains
(Hobfoll, 2011). It further states that changes in engagement over a longer period of time
depend on people’s ability to offset the negative effects of resource losses. In this sense,
individuals can compensate for resource losses (i.e. time, money, sleep) by using other
resources (i.e. telecommuting and social support), which means that those who are endowed
with more resources are less vulnerable to resource losses, and are therefore more capable of
achieving sustained levels of engagement (Gorgievski and Hobfoll, 2008).
COR theory identifies different types of resources defined as energies, personal features,
situations, or objects valued by most people and that facilitates the attainment of other
objects, conditions, and personal features. This theory postulates that resources can be:
direct/primary –because of their inherent quality –or indirect/secondary –because they
may enable people to obtain primary resources or to prevent from losing primary resources.
Indirect resources can influence direct resources that can either be reinvested to create more
resources or used to prevent resource losses. Flexibility in the form of telecommuting has
been identified as a type of resource (see Golden, 2006; Greenhaus and Powell, 2006). In fact,
previous researchers have used COR theory to explain how telecommuting could serve as a
resource to protect against resource loss (Golden, 2006).
While COR theory can be used to explain why certain resources can improve work
engagement by offsetting resource losses or promoting resource gains, signaling theory
(Spence, 1973) has been used to explain why work-life policies such as telecommuting predicts
attitudes via improvements in perceptions of support (Casper and Buffardi, 2004). According to
this theory, some observable actions of the organization can senda signal to employees that the
company cares for their welfare. When employees perceive that they are being cared for by
their organization they reciprocate by increasing commitment and developing positive
attitudes toward the organization (Blau, 1964). Hence the signals sent by the organization
promote greater psychological commitment and lower tendency to quit (Rhoades and
Eisenberger, 2002). Signaling theory is often used to explain how telecommuting availability
relates with employee attitudes in organizations (Allen et al., 2013; Casper and Harris, 2008;
Grover and Crooker, 1995; Masuda et al., 2012; McNall et al., 2009).
Based on signaling theory (Spence, 1973), we argue that telecommuting availability is an
observable action of the organization, which symbolizes that the organization cares for the
employees’well-being. This is because, as previously explained, employees will interpret
the option to telecommute as a resource that aids employees’work, and will tend to
reciprocate by developing positive attitudes. Allen et al. (2013) explain that “employees
appreciate having flexibility available as a resource and respond with more favorable job
attitudes toward the organization”( p. 322), and showed that the availability of benefits,
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which included telecommuting, predicted job satisfaction, turnover intentions, and
work-family conflict. Similarly, Casper and Buffardi (2004) found in a sample of job hunters
that the availability of work-life benefits (such as telecommuting) leads to job pursuit
intentions because participants anticipated having support from their organizations.
In the present study, we integrate COR theory and signaling theory to explain how
telecommuting influences goal variables and consequently engagement over time.
Consistent with previous research that identifies telecommuting as a job resource that
helps to prevent resource depletion (Golden, 2006; Greenhaus and Powell, 2006), we expect
that telecommuting availability will be perceived as a secondary resource that influences
engagement by creating other primary resources such as perceptions of supervisor goal
support and goal progress. Below we develop our research hypotheses that guide this study.
Development of hypotheses
Telecommuting and perceptions of supervisor goal support
Based on signaling theory (Casper and Harris, 2008; Spence, 1973) and COR theory,
we expect that employees who work in organizations offering telecommuting will perceive
support from their supervisors to attain their personal work goals. Consistent with previous
research showing that telecommuting is a resource (Greenhaus and Powell, 2006; Golden,
2006), we argue that telecommuting availability is a secondary resource that promotes
primary resource gains in the form of perceptions of supervisor support to attainting career
goals. Specifically, companies that give the employees the opportunity to telecommute
provide a signal that they care for their well-being because telecommuting is a desirable
policy introduced by organizations to help employees balance work-family demands
(Gajendran and Harrison, 2007; Nicklin et al., 2009). This argument is consistent with
empirical research showing that workers often cite flexibility as desirable to work
(Stone and Lovejoy, 2004). Specifically, 87 percent of federal US employees would like to
telecommute (Global Workplace Analytics) and 79 percent of US employees state
they would like to telecommute at least part of their time (WorldatWork Teleworking
Trendlines, 2009). Hence, the introduction of this policy is likely be perceived as an
action that symbolizes the organization cares and supports employees’well-being
(Bakker et al., 2005, 2007).
Second, telecommuting availability may relate to perceptions of support from
supervisors to attain their goals in particular because research has shown that
supervisors are agents of organizations and play an important role in the implementation
of such practices (Beham et al., 2014; Lautsch et al., 2009). Specifically, supervisors not only
represent the decisions that organizations make regarding adoption of HR practices, but
they also serve as “gate-keepers”who decide who should use such practices (Lautsch and
Kossek, 2011). As Beham et al. (2014) stated “managers play an important role in the
implementation of telework in organizations since they frequently have final approval over
employees’requests for telework arrangements”( p. 1). In fact, managers often perceive
allowing employees to telecommute as an “idiosyncratic deal,”which is a special condition
given to certain employees (Peters et al., 2010). In this sense, because telecommuting is a
desirable practice that helps employees to manage their work better by reducing work
interruptions (Bailey and Kurland, 2002), improving employees’time management
(Apgar, 1998), and giving employees more autonomy to design their work environment
(Elsbach, 2003; Standen, 2000); and since supervisor represents the organization´s decision
to adopt this policy (Beham et al., 2014), the adoption of such practice can be perceived as an
act of support from the supervisor to help employees attain their work goals. For example,
employees may perceive that this time can be used to care for more personal career projects
such as taking a language or an online university course. This argument is consistent with
empirical studies that have shown that work-life policy availability relates with positive
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outcomes via improvements in perceptions of organizational support (Allen, 2001;
Casper and Buffardi, 2004). Based on these considerations, we predict:
H1. Telecommuting availability (T1) has a direct and positive relationship with
supervisor perceptions of goal support (T2).
Goal support and goal progress
As previously stated, COR theory states that some secondary resources can breed more
resources or can offset the loss of primary resources. This is consistent with Bakker and
Demerouti’s (2007) explanations that job resources are found at different levels of analysis;
not only are they found at the organizational level (e.g. HR policies), they are also present at
the interpersonal (e.g. supervisor support) and job (e.g. autonomy, task variety) levels.
We argue that perceived supervisor goal support is an interpersonal type of job resource
that helps individuals make progress towards their personal work goals. This is consistent
with previous empirical finding that show a positive relationship between goal support and
goal achievement. Lent et al. (2005) found that students who report receiving more support
for attaining their goals are more likely to make progress toward those goals. Duffy and
Lent (2009) arrived at similar findings when testing this relationship among a sample of
teachers. Hence, we hypothesize the following:
H2. Perceived supervisor goal support (T2) has a direct and positive relationship with
goal progress (T3).
Goal progress and engagement
Although goal progress is regarded as having a mediating role in linking resources to
engagement (Bakker and Demerouti, 2007), previous research has not yet analyzed how
goal-directed behavior relates to engagement. However, studies have shown that goal
progress is critical for understanding work attitudes (Maier and Brunstein, 2001). Further,
according to motivational theories (Carver and Scheier, 1981; Locke and Latham, 1990),
goals influence individuals’effort, persistence, and affect, which are important aspects of
engagement (cf. Bakker, 2009). Specifically, according to control theory (Carver and Scheier,
1981), positive affect is a function of perceived goal progress. Accordingly, people may feel
elated and positive when they reach their goals. For example, Williams and Alliger (1994)
found that individuals who made progress toward their goals were more likely to report
enjoying their task and having a positive mood. Additionally, Amabile and Kramer (2011)
demonstrated that employees were more motivated when they experience daily progress
toward their goals. Based on the aforementioned results, we expect to find a direct
relationship between goal attainment and engagement, and thus hypothesize the following:
H3. Goal progress (T3) has a direct and positive relationship with work engagement (T3)
at the end of the work year.
The indirect effects of telecommuting on engagement
Following the rationale described in the previous sections, we argue that there is an indirect
relationship between telecommuting availability and the level of employee engagement at the
end of the work year, and that this relationship is mediated by perceived supervisor goal
support and goal progress. This is based on COR theory, which states that secondary resources
(i.e. telecommuting availability) promotes more proximal resource gains, namely perceived
supervisor goal support. This is in turn related to goal research, which posits that the pursuit of
personal work goals is a mediator of more distal job policies, like telecommuting availability and
work outcomes (Little, 2007). As such, telecommuting availability will predict perceived
supervisor goal support because telecommuting is a working condition that provides employees
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with the support they need to accomplish their work goals. Consequently, perceived supervisor
goal support will facilitate goal progress, which will directly relate to engagement (Bakker and
Demerouti, 2008). Furthermore, consistent with COR theory, telecommuting availability is a
resource generated at one level (the organizational), which impacts engagement through the
generation of resources at the supervisor level (e.g. goal support). In other words, the generation
of resources at one level breeds goal engagement, partially through the generation of additional
resources at another more proximal level (see Figure 1):
H4. Telecommuting availability (T1) indirectly relates to engagement at the end of the
work year (T3) via perceived supervisor goal support (T2) and goal progress (T3).
Method
This study employed a longitudinal design with three measurement phases. Participants
were alumni from a business school in Spain. We chose September as the starting point for
our study because, in general, business employees in Spain take August off for their
summer holiday. As such, we conceptualize the work year as lasting from September to July.
The first phase of our study took place in September 2011, the second phase in February
2012, and the last phase in June 2012.
To encourage participation, we offered participants individualized reports on the study’s
findings. In the first phase, we contacted 1,050 alumni. In all, 353 responded to our online
questionnaire, which makes a 35 percent initial response rate. This response rate is higher than
response rates of studies with similar samples of alumni of business students (see Greenhaus
et al., 2012). In all, 11 participants were currently unemployed and, for this reason, were excluded
from subsequent statistical analyses. At the end of the survey, we asked participants if they
were interested in participating in a second phase of this study. Our invitation to participate in
the three measurement phases was accepted by 294 participants. In February and June,
we conducted follow-up measurements, which were filled out by 63 and 71 percent of the
original 294 participants, respectively. After excluding individuals for whom data for one of the
independent variables were missing, we were able to match the data across all time points for
139 of the participants. In order to test for selective drop-outs, we compared participants who
dropped out after the first measurement with those who participated in all three measurement
phases. No significant differences in any of the study variables were found between these
groups, suggesting that we were unlikely to haveproblemsassociatedwithnonrandomsample
attrition. Most participants were men, 79 percent. Of the participants, 28 percent were younger
than 35 years old, 50.4 percent were between 36 and 45 years old, and 21.6 percent were over
46 years old. Most participants held managerial positions, 78 percent.
Measures
The survey items that were not originally in Spanish were translated from English into Spanish,
using Brislin’s (1980) back-translation procedure. At Time 1, we measured telecommuting
availability and asked participants about their personal work goals for the following year.
At Time 2, we asked participants about the level of goal support they received from their
Telecommuting
availability (T1)
Supervisor goal
support (T2)
Goal progress
(T3)
Engagement at the end of
the working year (T3)
H1 H2 H3
Notes: Telecommuting availability is hypothesized to exert an effect on engagement through
perceived supervisor goal support and goal progress. Not shown in this model is H4, which
represent the postulated indirect effects of telecommuting on engagement at the end of the work
year (H4)
Figure 1.
Hypothesized serial
mediation model
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supervisors, and at Time 3, we measured the extent to which individuals achieved their personal
work goals. Engagement was assessed at all three time points.
Telecommuting availability[1]. We measured telecommuting availability by asking
“Is telecommuting available to you at work?”We dummy coded as 1 ¼yes it is and 0 ¼it
is not (Masuda et al., 2012). This question measures whether the employee has the ability
to work from home.
Personal work goals. In the first measurement phase, participants filled out a revised form
of Little’s (1983) personal project analysis inventory. Specifically, we asked each of the
respondents to think about the changes they would like to see happen in their personal work
situation in the coming year, and to write down their most important personal work goal for
the next 12 months. Personal work goals were defined as “objectives, projects, and plans
related to your job, career and occupation”(Maier and Brunstein, 2001, p. 1036).
Perceived supervisor goal support. In the second phase, we presented each of the
respondents with the goals they recorded at Time 1. Then, we asked them to report their
level of goal-related support using two items: “My superior will support me in case I have
trouble attaining this goal”and “My superior supports this goal”(α¼0.95) (Maier and
Brunstein, 2001; Pomaki et al., 2004).
Goal progress. In the third phase, we again reminded respondents of the personal work
goals that recorded at Time 1 and asked them to rate their progress toward achieving those
goals using two items: for example, “I accomplished what I set out to do with this goal”
(α¼0.84) ( Judge et al., 2005; Sheldon and Elliot, 1999). The goal support and goal attainment
items were rated on a scale from 1 (strongly disagree) to 7 (strongly agree).
Work engagement. We used the Spanish nine-item version of the Utrecht work engagement
scale to measure engagement (Schaufeli et al., 2006). On a scale ranging from 0 (never) to 6
(always), participants rated items such as “At my work, I feel bursting with energy”
(T1: α¼0.92; T2: α¼0.93; T3: α¼0.94). Using confirmatory factor analyses with Mplus, we
also tested whether the repeated measures of engagement were invariant across the three
phases. To maintain a favorable indicator to sample size ratio, we measured engagement with
three-item parcels (represented by absorption, dedication, and vigor; see also Schaufeli et al.,
2006). The good fit of the model (i.e. root mean square error of approximation (RMSEA) values
below 0.06, standardized root mean square residual (SRMR) values below 0.08, and
comparative fit index (CFI) values close to 0.95) and non-significant χ
2
tests indicate that the
measurement of engagement demonstrates longitudinal invariance with regard to every
equivalence property (Lance et al., 2000) (see Table I). The conventional levels of measurement
invariance are met by our model (Pitts et al., 1996) and we can conclude that the meaning of the
construct is unlikely to have changed over time.
Control variables. During the first phase, we measured for the following demographic- and
work-related variables: age (1 ⩽25 years, 2 ¼between 26 and 35 years, 3 ¼between 36 and 45
years, 4 ¼between 46 and 55 years, and 5 ⩾55 years), g ender ( 0 ¼male and 1 ¼female),
weekly working hours, managerial position (0 ¼not a manager and 1 ¼manager), job tenure
(1⩽1year,2¼between1and5years,3¼between 5 and 10 years, 4 ⩾10 years), and work
experience. We also controlled for goal progress measured at Time 2 and goal self-efficacy
(Pomaki et al., 2004), as self-efficacy beliefs affect engagement (see Xanthopoulou et al., 2007).
Further, out of 139 people, 113 did not change their jobs in the nine-months period of our study.
For this reason, we conducted robustness checks controlling for people who had changed their
jobs in the previous nine months.
Confirmatory factor analysis
Before testing our hypotheses, we examined the fit of our postulated measurement model within
a structural equation modeling framework, by using Mplus 6.0. To maintain a favorable
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indicator to sample size ratio, we measured engagement with three-item parcels (represented by
the sub-dimensions of absorption, dedication, and vigor; see also Schaufeli et al., 2006) and
modeled goal support with two item parcels. Because goal attainment was measured with only
two items and goal efficacy with only three, we used each item as a separate indicator for the
constructs (Hall et al., 1999). We evaluated the model fit based on the χ
2
value, the CFI, the
RMSEA, and the SRMR. According to Hu and Bentler (1999), RMSEA values below 0.06, SRMR
values below 0.08, and CFI values close to 0.95 are indicators of good model fit. We first
conducted a confirmatory factor analysis, specifying each of our six latent constructs:
goal efficacy (Time 1), goal support (Time 2), goal attainment (Time 3), and engagement
(Times 1, 2, 3). In order to account for non-independence, we allowed the measurement errors
for items that were repeatedly measured across time to correlate (Pitts et al., 1996).
The hypothesized measurement model fits the data well ( χ
2
(80) ¼95.72, p¼0.11, CFI ¼0.99,
TLI ¼0.99, RMSEA ¼0.04), with a 95 percent confidence interval of [0.00, 0.06].
Discriminant validity
Following the suggestions of Kelloway (1996), we compared the fit of our hypothesized
measurement model to the fits of alternative models, in which two distinct factors were set
to load on one factor. The significant χ
2
comparison tests indicate that the fit of the
hypothesized measurement model is superior to the alternative models, suggesting that the
factors are empirically distinct from each other (see Table I).
Data analysis
To explain how telecommuting availability affects engagement at the between- individual
(level 2) and within-individual (level 1) level, we adopted a multilevel framework
using individual growth modeling. A multilevel model of change tests two sub-models:
the level 1 model, which examines how participants’engagement changes over time,
χ
2
df CFI TLI RMSEA [CI] χ
2
–difference test
Hypothesized measurement model 95.72 80 0.99 0.99 0.04 [0.00; 0.06]
Discriminant validity
Alternative model 1 (combining goal efficacy
and goal support) 204.88 85 0.94 0.91 0.10 [0.08; 0.12] Δχ
2
(5) ¼109.16**
Alternative model 2 (combining goal efficacy
and goal attainment) 224.53 88 0.93 0.90 0.11 [0.09; 0.12] Δχ
2
(5) ¼128.81**
Alternative model 3 (combining goal support
and attainment) 220.14 85 0.93 0.90 0.11 [0.09; 0.12] Δχ
2
(5) ¼124.42**
Alternative model 4 (combining goal efficacy
and engagement at T1) 172.61 85 0.96 0.94 0.09 [0.07; 0.11] Δχ
2
(5) ¼76.89**
Alternative model 5 (combining goal support
and engagement at T2) 391.85 85 0.84 0.77 0.16 [0.15, 0.18] Δχ
2
(5) ¼296.13**
Alternative model 6 (combining goal attainment
and engagement at T3) 188.58 85 0.95 0.93 0.09 [0.08; 0.11] Δχ
2
(5) ¼92.86**
Measurement invariance
Configural invariance 15.33 15 1.00 0.99 0.01 [0.00; 0.08]
Full metric invariance 18.98 19 1.00 1.00 0.00 [0.00; 0.07] Δχ
2
(4) ¼3.65
Full scalar invariance 27.39 25 0.99 0.99 0.03 [0.00; 0.08] Δχ
2
(6) ¼8.41
Factor variance invariance 27.43 27 1.00 1.00 0.01 [0.00; 0.07] Δχ
2
(2) ¼0.04
Error variance invariance 35.35 33 0.99 0.99 0.02 [0.00; 0.07] Δχ
2
(6) ¼7.92
Notes: CFI, comparative fit index; TLI, Tucker-Lewis index; RMSEA, root mean square error of
approximation; CI, confidence interval. *po0.05; **po0.01
Table I.
Model fit statistics for
measurement model
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and the level 2 model, which describes how these changes differ across individuals
(Singer and Willet, 2003).
We chose “months since the onset of the working year”as the metric for capturing how
engagement evolves over time. We centered the time variable so that the intercept refers to
the value of engagement in the last measurement phase. Thus, the two main parameters
describing individuals’true change trajectories –intercept and slope –refer to their final
engagement at the end of the work year and to their rate of change over the work year,
respectively (Singer and Willet, 2003). The multilevel regression models were estimated in
Mplus, with full maximum likelihood.
Taking the multilevel nature of our data into consideration, we tested the postulated
multiple mediation effects by using the general multilevel structural equation modeling
(MSEM) approach (Preacher et al., 2010, 2011). The MSEM framework is suited for testing
multilevel mediation hypotheses because it reduces the bias for indirect effects and does not
conflate the within- and between-individuals components. Following Preacher et al.’sMplus
codes for testing multilevel mediation within a MSEM approach, we fit an upper-level
mediation model to our data and calculated the indirect effects of telecommuting on
engagement at the end of the work year, as well as the effects of telecommuting availability on
change in engagement. We conducted all analyses both with and without the covariates. As the
results were substantially the same, we report below only the results without covariates
(Becker, 2005; Spector and Brannick, 2011)[2].
Results
The correlation and descriptive statistics for all of the study variables are presented in
Table II. Tables III and IV present the results of the individual growth modeling analyses.
Intra- and inter-individual variation in engagement
In line with Singer and Willet’s (2003) taxonomy of multilevel models, we first examined the
extent to which engagement scores varied across time and among individuals. Specifically,
we fitted an unconditional means model, which describes the variation in engagement at the
intra- and inter-individual levels. The results show a significant variance at both the
within-individual and between-individual levels, indicating that engagement varies
over time and participants’engagement scores differ from each other. The intra-class
correlation of 0.80 indicates that 20 percent of the variance in engagement can be attributed
to intra-individual differences.
Second, we estimated an unconditional growth model that tests time as an intra-individual
predictor of engagement. The linear component of growth was significantly negative, such that
engagement scores were estimated to go down by 0.02 per month, p¼0.03. Next, a model in
which the time component was allowed to vary was compared to the model that only included
the random intercept for engagement. The more complex model did not result in an improved
model fit (Δ−2×log ¼0.50, Δdf ¼1, ns), suggesting that individuals might not have different
ratesofchangeinengagementovertime(seeTableIV).
Hypothesis testing
Engagement at the end of the work year.H1 proposed that telecommuting availability is
associated with perceived supervisor goal support. Consistent with H1, our results indicated
that telecommuting availability is a positive predictor of perceived supervisor goal support,
B¼0.80, p¼0.02. Perceived supervisor goal support, in turn, positively relates to goal
progress (B¼0.36, po0.001), which supports H2. Additionally, as is consistent with H3,
goal progress is positively associated with the level of engagement at the end of the work
year (i.e. final status of engagement), B¼0.27, po0.001. H4 proposed that telecommuting
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Mean SD 1 2 3 4 5 6 7 8 9 10 11 12 13 14
1 Job tenure 2.79 2.23 1.00
2 Work experience 17.21 8.22 0.25** 1.00
3 Managerial position 0.77 0.42 0.08 0.26** 1.00
4 Age 2.95 0.79 0.18* 0.85** 0.29** 1.00
5 Gender 0.21 0.41 −0.10 −0.29** −0.15 −0.28** 1.00
6 Working hours 43.52 11.38 0.04 0.05 0.09 −0.01 −0.13 1.00
7 Goal efficacy (T1) 5.86 0.88 −0.13 −0.03 0.03 −0.02 −0.08 0.05 1.00
8 Goal progress (T2) 4.49 1.50 0.04 0.05 0.09 0.06 −0.05 0.02 0.33** 1.00
9 Telecommuting (T1) 0.35 0.48 −0.07 0.16 0.10 0.18* −0.11 −0.09 0.12 0.14 1.00
10 Goal support (T2) 4.08 1.96 0.11 0.05 0.07 0.06 −0.06 0.09 0.26** 0.53** 0.19** 1.00
11 Goal attainment (T3) 4.51 1.73 −0.05 0.01 0.08 0.00 0.10 0.01 0.36** 0.65** 0.05 0.41** 1.00
12 Job engagement (T1) 4.33 1.10 0.02 0.11 0.11 0.17* 0.03 0.02 0.41** 0.35** 0.28** 0.42** 0.36** 1.00
13 Job engagement (T2) 4.22 1.12 0.09 0.15 0.10 0.18* −0.03 −0.01 0.36** 0.43** 0.28** 0.40** 0.41** 0.82** 1.00
14 Job engagement (T3) 4.16 1.12 0.01 0.17* 0.17* 0.27** −0.03 −0.02 0.39** 0.45** 0.32** 0.35** 0.51** 0.79** 0.82** 1.00
Notes: Pairwise correlations resulted in a range from n¼129 to n¼139. *po0.05; **po0.01
Table II.
Correlations and
descriptive statistics
for study variables
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availability is indirectly associated with engagement at the end of the work year through
perceived supervisor goal support and goal progress. The multilevel mediation results
indicated that telecommuting availability is not only directly (B¼0.54, po0.01), but also
indirectly related to engagement at the end of the work year. In particular, our findings
showed that telecommuting leads to engagement via perceived supervisor goal support and
goal progress, B¼0.08, p¼0.05. Hence, H4 is supported (see Table IV).
Post hoc analyses. Previous research has shown that people’s engagement level is not the
same every day, but rather fluctuates depending on the available resources. In this respect,
studies have shown that people are more engaged on days when they feel recovered in the
morning (Sonnentag, 2003; Sonnentag et al., 2012) and in weeks during which they have
more job resources at their disposal (Bakker and Bal, 2010). Changes in engagement over a
longer period of time depend on people’s ability to offset the negative effects of resource loss.
As such, we conducted further analyses to test if telecommuting or goal progress explains
inter-individual differences in engagement change over time. Specifically, we tested
cross-level interactions and found that goal progress significantly moderated the
relationship between time and engagement (B¼0.01, po0.01). To illustrate the
interactive effect of time and goal progress, we estimated the region of significance
for the effect of time and graphed the cross-level interaction for individuals with high
and low levels of goal attainment (mean ±1 SD) (see Figure 2). For individuals with low
levels of goal progress, we found that engagement significantly decreases over time
Unconditional means model Unconditional growth model Final model
Coeff. SE Coeff. SE Coeff. SE
Intercept (final status) 4.24** 0.09 4.17** 0.09 3.98** 0.09
Time (change) −0.02* 0.01 −0.02* 0.01
Telecommuting 0.54** 0.15
Goal support 0.12** 0.04
Goal attainment 0.27** 0.05
Time ×goal attainment 0.01** 0.004
Variance components
Level 1 Within-person 0.24** 0.02 0.22** 0.03 0.21** 0.02
Level 2 In final status 0.98** 0.13 0.97** 0.13 0.61** 0.09
In rate of change 0.001 0.001 0.001 0.001
R
2
(within) 9% 14%
R
2
(between) 1% 38%
Log-Likelihood −464.068 −461.06 −427.874
Notes: *po0.05; **po0.01
Table III.
Results of the
multilevel models
of change for
engagement
Effect SE 95% CI
Step 1: telecommuting to goal support 0.80* 0.34 [0.13, 1.47]
Step 2: goal support to goal attainment 0.36** 0.07 [0.22, 0.49]
Step 3 (a): goal attainment to final status of engagement 0.27** 0.05 [0.17, 0.36]
Step 3 (b): goal attainment to change in engagement 0.01** 0.004 [0.004, 0.02]
Final status of engagement: Indirect effect via goal support and
goal attainment 0.08* 0.04 [0.002, 0.15]
Change in engagement: Indirect effect via goal support and
goal attainment 0.003*** 0.002 [0.001, 0.007]
Notes: CI, confidence interval. *po0.05; **po0.01; ***po0.10
Table IV.
Indirect effects of
telecommuting
availability on
engagement
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(B¼−0.04, po0.001), whereas for individuals with high levels of goal progress,
engagement does not significantly change over the work year, B¼0.01, p¼0.56.
Our analysis of the region of significance indicates that time has a significantly
negative effect on engagement only if individuals have average or below-average levels of
goal attainment.
We also tested if telecommuting indirectly related to the change in levels of engagement
over time through goal support and goal attainment. However, we found that the indirect
effect of telecommuting on change in engagement was non-significant, B¼0.003, p¼0.09
(see Table IV).
Discussion
Our findings contribute to the telecommuting and engagement literature in several ways.
First, we help minimize the debate as to whether telecommuting is beneficial or prejudicial to
employees’careers (see Elsbach and Cable, 2012). Contrary to the perceptions of some
managers that individuals who work from home are less motivated or effective, our research
shows that employees who had the opportunity to work from home were more engaged at
the end of the work year. This finding contributes to and helps minimize the debate as to
whether telecommuting has benefits for employees and organizations. In this study, the
availability of telecommuting predicted engagement; thus, we argue that telecommuting
availability is indeed beneficial.
This study also advances telecommuting theory by identifying how telecommuting
relates to engagement. The research shows that telecommuting not only directly relates to
engagement at the end of the work year, but also positively correlates with career-related
variables such as perceptions of supervisor goal support. Specifically, individuals who work
in organizations that offer this practice experience greater perceived supervisor goal
support. This is in line with signaling theory stating that certain observable actions send a
signal that the employee is cared for by their organizations, thus enhancing perceptions
of perceived supervisor goal support. We also find that individuals who experience high
perceived supervisor goal support are likely to attain such goals. In turn, goal
progress explains both engagement at the end of the work year and changes in engagement
over time. This is consistent with the COR model’s assumption that secondary resources
breed more primary resources, which influences engagement. Although the indirect effect of
telecommuting on engagement is rather small, these findings suggest that it would be useful
0
4.5
4.0
3.5
3.0
246810
Time
Engagement
Low goal attainment
High goal attainment
Figure 2.
Cross-level interaction
of time and goal
attainment on
engagement
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to further explore other goal variables as mediators of telecommuting’s relationship with
other outcome variables. For example, the effects of telecommuting on work-family
conflict could be indirect rather than direct, which may explain why some authors have not
found significant direct effects (e.g. Lapierre and Allen, 2006). We suggest that it is
insightful to examine both the boundary conditions of telecommuting on outcome
variables and the indirect effects of telecommuting. It is possible that the indirect effect of
telecommuting is small in this case because we do not include more proximal
mediators such as perceived autonomy or work-family conflict. We encourage other
studies to explore how telecommuting could indirectly influence other resources and
consequently improve engagement.
We also reported exploratory findings that could provide some insight on how and why
engagement shifts over a year. Although previous studies have shown that engagement
fluctuates during the day (Bakker, 2014) and our design did not allow us to test fluctuations
of engagement throughout the year, we report that systematic changes in engagement
occurs and that roughly 50 percent of employees are not able to maintain their level of
engagement over time. This is in line with previous studies of engagement. As Macey and
Schneider (2008, p. 25) stated, “[…] there are limits on the pool of energy and resources
available to employees”that make it difficult to sustain their levels of engagement.
Our findings also complement other studies that have shown the positive effects of
vacations decrease over a period of four weeks (Kühnel and Sonnentag, 2011).
Together, these studies indicate that high levels of engagement are not only hard to
directly sustain after coming back from vacations, but also that engagement is sensitive to
resource loss over longer time. In this sense, our repeated-measurement design over ten
months adds to the current knowledge of how engagement changes over time. Specifically,
it adds to studies that have analyzed daily (e.g. Sonnentag et al., 2012) and weekly
fluctuations (Bakker and Bal, 2010), and those that have examined increments in
engagement over one (e.g. Schaufeli et al., 2009), two (Mauno et al., 2007), or three years
(Hakanen et al., 2008).
Finally, the findings of this study indicate that personal work goals are an insightful
construct for better understanding engagement. Specifically, we find that goal attainment
prevents a decline in engagement over time. In this respect, the findings of this study expand
on previous research on personal goals that has linked goal content (Hyvönen et al., 2009), and
the facilitation of work and family goals (Wiese and Salmela-Aro, 2008), to an individual’s
overall level of engagement. Our finding also shows that goal support indirectly predicts
engagement over time.
We also highlight the importance of support as a job resource to predict engagement
over time; this may shed light on possible reasons why previous research did not find a link
between social support and engagement over time (see Dikkers et al., 2010). It is possible that
the link between social support and engagement is rather indirect than direct. Our study
provides support for this explanation and encourages the future examination of the indirect
effects of resources and demands on engagement using goal variables.
Limitations and directions for future research
This study has limitations that should be acknowledged. First, it relies on self-reported data,
which raises issues of potential common-method bias. Given the nature of our study
variables, it would have been difficult to obtain accurate information about people’s
evaluation of their personal goals with any method other than self-report. Further, since we
did not measure all constructs at every time we could not perform cross-lagged structural
equation modeling. Future research could extend our findings to assigned work goals and
obtain supervisor evaluations of goal attainment. Ideally, future work should include
repeated measures at every point in time. Although we could not completely eliminate the
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possibility of common-method bias in this study, we aimed to minimize it by temporally
separating the measurements of our independent, mediator, and dependent variables
(Podsakoff et al., 2012).
Second, this study is correlational in nature. That is, goal progress could lead to
engagement just as much as engagement leads to goal progress. It is also possible that
supervisors or companies allow only engaged employees to telecommute. Although field
experiments are required to better understand the nature of these relationships, the
longitudinal approach of our study provides some insight into their direction, and shows
that telecommuting, measured at the beginning of the work year, predicts the level of
engagement at the end of the work year. Future studies could expand on this study by
examining how telecommuting affects the pursuit of consecutive short-term goals.
This research focus would allow researchers to assess the mediating variables of goal
support and goal attainment multiple times, making it possible to conduct cross-lagged
SEM and rule out the reverse or reciprocal causation of the study variables. In fact, one
study found a positive relationship between the daily use of new work methods –including
telecommuting, flextime, and electronic communication –with daily levels of engagement
(ten Brummelhuis et al., 2012). Although ten Brummelhuis et al. (2012) did not specifically
look at the unique effect of telecommuting, and instead looked at the composite score of
several work methods, their study could provide some indication that the daily use of
telecommuting could relate to daily levels of engagement by helping employees accomplish
their daily goals.
Third, the present study examined the effects that telecommuting had on an individual
level of engagement. Although telecommuting was shown to have positive effects on
individual levels of engagement via perceived supervisor goal support and goal progress,
we cannot confirm that the same results would be found at the team level. That is, the use of
telecommuting could lead to lower levels of engagement among co-workers by impeding the
group’s goal attainment. As Van Dyne et al. (2007) proposed, practices that reduce face time
could lead to negative effects at the group level, such as lower group motivation and loss of
coordination. Hence, we encourage future studies to examine telecommuting’s effects on
engagement at the group level, using goals as mediators.
Fourth, this study was conducted with Spanish employees. As research has shown that
culture may influence the relationship of telecommuting with important outcome variables
(Masuda et al., 2012), our findings’generalizability to samples from different cultures
remains to be tested. For example, while some cultures may perceive telecommuting as a
resource and interpret the adoption of telecommuting as signal that the company cares for
their employees, others may perceive it as an impediment for personal goal progress or a
signal that the company is not committed or caring for their well-being. Furthermore, while
research does indeed suggest that telecommuting availability is interpreted positively, like
many other positive resources (money, socialization, optimism), too much of something is
not always a good thing. As such, telecommuting should be an available resource and not an
imposed policy so that employees have no choice or autonomy to choose where to work.
Hence, we encourage the exploration of possible moderators of telecommuting adoption and
perceptions of support. For example, the concept of collectivism may be an important
moderator of this relationship. We also recommend future research explore what is
considered “too much”telecommuting.
Fifth, we also encourage the examination of antecedents of telecommuting availability as
a more distal predictor of goal support, goal progress and engagement. That is, it is possible
that telecommuting adoption is only a proxy to other forms of support implemented in the
organization. For example, telecommuting is also part of a bundle of HRM practices
(Peters et al., 2014), which allows employees more autonomy and could predict perceptions
of support and engagement. In this study, we only measured telecommuting availability
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with one item and we did not include all possible working arrangements that the employee
could have used in order to test the unique effect of telecommuting on engagement above
and beyond other work arrangements. Hence, we encourage the exploration of antecedents
of telecommuting availability and the role of telecommuting as a mediator of these other
forms of resources with engagement relationships while controlling to other forms of work
arrangements and using a more comprehensive measure of telecommuting availability.
Finally, because we have used a smaller sample of business professionals, the study may
have limited generalizability to other occupations. It is possible that results could be
contaminated by sample effects. However, we chose this sample because, in general, business
professionals are more likely to be given the opportunity to telecommute than other employees,
such as health care professionals, for whom telecommuting may be more difficult. Nonetheless,
we encourage the replication of our findings in different types of jobs and cultures.
Conclusions and practical implications
Overall, we found that telecommuting availability had positive effects on end of the year
engagement. We also found support that telecommuting indirectly influences engagement via
goal support and goal progress, although this effect was rather small. Our findings have
implications for practitioners who are searching for best practices for improving work
engagement. First, they highlight the importance of adopting practices that signal to employees
that the company cares for their welfare. This practice leads to higher goal progress because it
improves perceptions of goal support. Additionally, in line with research on the goal progress
principle (Amabile and Kramer, 2011), which says that employees are more engaged on days
when they progress toward their goals, our study shows that employees are also more engaged
when they attain their annual work goals. As such, we encourage practitioners to explore
working conditions that facilitate the attainment of long-term goals. For example, coaching and
mentoring could lead to engagement by supporting goal attainment. Other practices –such as
compressed workweeks, part-time work, and job sharing –could have similar benefits.
Second, our study suggests that it is beneficial for managers to be aware of the
possibility that engagement may decrease over a long period of time (e.g. one year). As such,
it might be useful to implement practices that help employees replenish their resources, in
order to maintain sustainable levels of engagement. In this respect, it is important that
managers become aware of vacations’value in helping employees maintain their level of
engagement (Kühnel and Sonnentag, 2011). In companies where employees are not currently
able to choose the timing of vacations, giving them this opportunity could help sustain
employee engagement over time.
Third, our study contributes to the debate on whether telecommuting has benefits for
employees. Specifically, it shows that employees who are giving the opportunity to
telecommute are likely to perceive higher support to attain work goals, higher goal progress
and also higher engagement at the end of the work year. In summary, despite some potential
negative effects of telecommuting on work outcomes (Allen and Shockley, 2009;
Elsbach and Cable, 2012; Gajendran and Harrison, 2007), when it comes to employee
engagement, “showing your face at work”does not necessarily matter.
Notes
1. We also measured telecommuting frequency by asking individuals to indicate how many days per
week they worked from home on average (ten Brummelhuis and van der Lippe, 2010; Virick et al.,
2010). We tested all hypotheses using this variable and results are similar. We chose to study
telecommuting availability because the link between availability and support makes more
theoretical sense based on signaling theory (see Allen and Shockley, 2009)
2. The results including covariates are available from the first author upon request.
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Corresponding author
Aline D. Masuda can be contacted at: amasuda@eada.edu
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