Work Effort: A Conceptual and Meta-Analytic
Chad H. Van Iddekinge
University of Iowa
John D. Arnold
University of Missouri
The George Washington University
Jonas W. B. Lang
Ghent University and University of Exeter
Singapore Management University
Work effort has been a key concept in management theories and research for more than a century.
Maintaining and increasing employee effort also isa persistent concern tomanagers. The goal of the
present conceptual and meta-analytic review was to increase clarity and consensus regarding what
effort is and how to measure it. First, we reviewed conceptualizations of effort and provided an inte-
grated deﬁnition that views effort as a direct outcome of motivation that captures (a) what employees
work on, (b) how hard they work, and (c) how long they persist in that work. Second, we identiﬁed
four main ways researchers have operationalized effort and meta-analytically studied the effects of
each operationalization on effort–job performance relationships. For example, measures that
assessed multiple dimensions of effort (ρ=.37) tended to relate more strongly to performance
than measures that focused on only one dimension (e.g., effort intensity) or on effort more generally
(ρ=.18 to .29). Third, we developed and meta-analytically tested a nomological network to gain a
better understanding of effort’s antecedents (e.g., intrinsic motivation, ρ=.46; performance
orientation, ρ=.12) and outcomes (e.g., job performance, ρ=.34; exhaustion, ρ=.04) as well
as constructs that appear to overlap with effort (e.g., work engagement, ρ=.48; grit, ρ=.51).
Finally, on the basis of our conceptual and meta-analytic reviews, we delineated an agenda for
future research on this central, yet often misunderstood, construct.
Acknowledgments: This research was partially supported by the Robert J. Trulaske, Sr. College of Business Large
Grant Program, and Department of Management Excellence Fund.
Corresponding author: Chad Van Iddekinge, Department of Management and Entrepreneurship, Tippie College
of Business, University of Iowa, Iowa City, IA, USA.
Journal of Management
© The Author(s) 2022
Article reuse guidelines:
Keywords: effort; engagement; meta-analysis; motivation; performance
Great increase of work and results can be secured by outside stimulus and by conscious effort.
(Scott, 1911: 9)
Early texts on management and related ﬁelds often were organized around ways to improve
output by increasing various forms of work effort (e.g., Münsterberg, 1913; Scott, 1911; Taylor,
1911). A century later, work effort remains a central construct across management-related the-
ories and domains. For example, effort is a key variable in theories of work motivation (e.g.,
Bandura, 1977; Locke & Latham, 1990; Vroom, 1964), job performance (e.g., Campbell,
1990), and organizational citizenship behavior (OCB; e.g., Borman & Motowidlo, 1993).
Furthermore, strategic management theories refer to the effort CEOs and top management
teams exert for their ﬁrms (e.g., Waldman & Yammarino, 1999), and entrepreneurship scholars
are interested in the effort entrepreneurs devote to their ventures (e.g., Uy, Foo, & Ilies, 2015).
Despite its signiﬁcance, effort is a somewhat “elusive and ill-deﬁned construct”(Macey &
Schneider, 2008: 14). For instance, early researchers found work effort so central that they did
not even formally deﬁne the concept (e.g., Münsterberg, 1913). Today, theories and empirical
research refer to effort in various ways, which has resulted in a “jumble of meanings,
deﬁnitions, and operationalizations”(De Cooman, De Gieter, Pepermans, Jegers, & Van
Acker, 2009: 266). For example, some studies focus on mental effort (e.g., attentional
focus; Kanfer, Ackerman, Murtha, Dugdale, & Nelson, 1994), others on physical effort
(e.g., heart rate; Brehm & Self, 1989), and yet others on effort more generally (e.g., Brown
& Leigh, 1996). Furthermore, some researchers have construed effort as an internal attribute
that can be measured using self-reports (e.g., Brown & Leigh, 1996), whereas others have
conceptualized effort in terms of resource allocation (e.g., Vancouver, More, & Yoder,
2008) and have used objective measures, such as hours spent working (e.g., Katerberg &
Blau, 1983). A lack of consistency and clarity also exists regarding the underlying dimensions
of effort and how to measure them. For example, effort has been deﬁned in terms of direction,
intensity, and persistence (e.g., Campbell, 1990; Kanfer, 1990; Locke, Shaw, Saari, &
Latham, 1981), but most studies do not include separate measures of these dimensions.
In addition to the many ways researchers have deﬁned and operationalized work effort, this
concept also appears as part of other constructs that have surfaced in recent years. For
instance, measures of work engagement (e.g., Rich, LePine, & Crawford, 2010), grit
(Duckworth, Peterson, Matthews, & Kelly, 2007), and work drive (Lounsbury, Gibson, &
Hamrick, 2004) contain items that refer to effort. The popularity of these constructs raises
questions regarding whether and how they differ from effort.
Apart from this conceptual importance, a clear understanding of what effort entails and its
relationship to future performance also is important because managers often make inferences
regarding effort when making judgments. For example, effort is seen as more controllable
than traits such as mental ability (Weiner, Heckhausen, & Meyer, 1972). As such, managers
give more weight to effort when making stafﬁng decisions (e.g., selection, promotions) and
2 Journal of Management
even reward those whose performance is not as high as others if they make efforts to improve
(e.g., Soliman & Buehler, 2018).
Thus, although effort is a central concept, little clarity and consensus exist regarding how
to conceptualize and operationalize the construct. Consequently, we also do not know a lot
about the nature of relations between effort and other constructs. The primary goal of our
review is to synthesize research on work effort, and in doing so, we clarify its meaning, mea-
surement, and relations with other constructs. First, we review conceptualizations of effort
and offer an integrated deﬁnition of work effort that builds upon and subsumes existing
ones. We also identify four main ways researchers have operationalized effort. Second, on
the basis of our review, we propose a nomological network of potential antecedents and out-
comes of effort. Fourth, we conduct a meta-analysis that examines relations between the dif-
ferent operationalizations of effort and job performance as well as provides a preliminary test
of effort’s nomological network. Finally, we discuss implications for theory, practice, and
As researchers have referred to effort in many ways, domains, and contexts, it was impor-
tant to establish some criteria for our review (Aguinis, Ramani, & Alabduljader, 2018). First,
we focus on effort toward work task completion and not effort in educational (e.g., Elliot,
McGregor, & Gable, 1999), athletic (e.g., Hatzigeorgiadis & Biddle, 2007), or other
nonwork settings (e.g., sociology; Bielby & Bielby, 1988). Accordingly, we do not cover pre-
employment forms of effort, such as effort people devote to searching for jobs (e.g., Wanberg,
Zhu, & Van Hooft, 2010) or effort applicants exert during the selection process (e.g., Arvey,
Strickland, Drauden, & Martin, 1990). Second, we focus on effort workers exert and not on
demands jobs place on workers (e.g., Demerouti, Bakker, Nachreiner, & Schaufeli, 2001).
For example, measures of work overload often include items such as “My job requires me
to work very hard”(e.g., Caplan, Cobb, French, Harrison, & Pinneau, 1975). Third, we do
not focus on constructs that focus on lack of effort (e.g., Kidwell, Bennett, & Valentine,
2010), which are sometimes considered aspects of counterproductive work behavior
(CWB; e.g., Bennett & Robinson, 2000).
Conceptualizations of Work Effort
Deﬁnitions of effort. Educational psychologist Dewey (1913: 46) may have been the ﬁrst
to deﬁne effort in its psychological interpretation as “persistency, consecutiveness of activity:
endurance against obstacles and through hindrances.”Ryan (1947: 22) appears to be one of
the ﬁrst organizational researchers to deﬁne effort, suggesting that it reﬂects “the relationship
between actual rate of performance and the capacity of an individual at a given time.”
Additional deﬁnitions of work effort began to emerge in the 1960s and 1970s. Some of
these deﬁnitions focused on energy, such as “the amount of energy an individual expends
in a given situation”(Porter & Lawler, 1968: 21) or the “amount of energy ‘spent’on the
act per unit of time”(Naylor, Pritchard, & Ilgen, 1980: 6). Other deﬁnitions focused on
Van Iddekinge et al. / Work Effort 3
working or trying hard, such as “how hard one works”(Williams & Seiler, 1973: 50) and “the
expenditure of energy or how hard the person tries”(Terborg, 1977: 188). Further, although
most deﬁnitions focused on effort in general, a few distinguished between physical and
mental effort. For example, effort as “job-related physical and mental exertion [that] can
vary from minimum required to maintain work-role to working extremely hard”(Mitchell
& Albright, 1972: 5) and “the amount of physical and mental energy devoted to work”
(Bielby & Bielby, 1988: 1032).
Campbell and Pritchard (1976: 65) were among the ﬁrst to delineate different dimensions
of effort when deﬁning work motivation as “a label of the determinants of (a) the choice to
initiate effort on a certain task, (b) the choice to expend a certain amount of effort, and (c)
the choice to persist in expenditure of effort over a period of time.”Other researchers
deﬁned effort as a resource workers allocate to different activities. For example, according
to Naylor et al. (1980), individuals have an energy reservoir that can be allocated among
various activities, some of which will be productive and others will not. Accordingly, they
suggested that how workers allocate effort between relevant and irrelevant activities is critical
Effort’s relation to motivation and performance. Extensive research has studied effort in
relation to motivation and performance, and there appears to be three perspectives regarding
how these variables are related. First, some researchers have referred to effort as motivation
(i.e., effort =motivation). For example, Bandura and Cervone (1986: 96) suggested that “the
major deﬁning property of motivation is the level of effort mobilized and sustained in a
pursuit,”and Williams and Seiler (1973: 49) suggested that “effort can be viewed as a
measure of work motivation.”Similarly, Walker, Churchill, and Ford (1977: 162) deﬁned
motivation “as the amount of effort the salesman desires to expend on each of the activities
or tasks associated with his job.”
Second, some researchers have considered effort as a component of performance (i.e.,
effort =performance). For instance, Campbell, McCloy, Oppler, and Sager’s (1993: 47)
theory of job performance includes “demonstrating effort”as a dimension of performance,
which they deﬁned as the “consistency of an individual’s effort day by day, the frequency
with which people will expend extra effort when required, and the willingness to keep
working under adverse conditions.”Indeed, Viswesvaran’s (1993) review of job performance
measures identiﬁed effort as one of 11 dimensions most frequently assessed in supervisor
ratings of performance. Further, demonstrating extra effort often is considered a dimension
of contextual performance and OCB. For example, Borman and Motowidlo’s (1993)
model includes a dimension called “persisting with enthusiasm and extra effort”(see also
Coleman & Borman, 2000).
Third, other researchers have conceptualized effort as possibly distinct from motivation
and performance and posited three types of relations among them. First, there is a perspective
of effort as an outcome of motivation (i.e., motivation →effort). Dowling and Sayles (1978:
16), for example, discussed motivation as “an inner desire to make an effort.”Second, some
theories view effort as an antecedent of performance (i.e., effort →performance). For
instance, in Porter and Lawler’s (1968) model of managerial attitudes and performance, per-
ceptions about the value of rewards and probability of the effort–reward linkage are anteced-
ents of effort. Effort, in turn, is an antecedent of performance. Similarly, a key element in the
4 Journal of Management
theory of planned behavior (Ajzen, 1991) is intention to perform a given behavior. Intentions
reﬂect “how hard people are willing to try, of how much of an effort they are planning to
exert, in order to perform the behavior”(Ajzen, 1991: 181). As individuals’intention
increases, so does the likelihood they will engage in the intended behavior. Third, many the-
ories position effort as a mediating construct that links motivation to performance (i.e., moti-
vation →effort →performance). For example, in expectancy theory (Vroom, 1964), effort is
an outcome of workers’expectations and a direct antecedent of performance. In social-
cognitive theory, self-efﬁcacy is “a major determinant of people’s choices of activities,
how hard they strive, and how long they persist in their attempts”(Bandura, Adams, &
Beyer, 1977: 138). Similar ideas have been formulated in the Rubicon model of action
phases (Gollwitzer, 1990, 2012). In this model, effort or volition resides in the postdecisional
phase and captures the intensity individuals invest into action to achieve the goal. In goal-
setting theory (Locke & Latham, 1990), setting goals is thought to induce effort, which
then is an antecedent of performance. Self-regulation theories, such as the risk-taking
model (Atkinson, 1957) and motivational intensity theory (Brehm & Self, 1989) suggest
that individuals demonstrate maximum effort when they believe doing so increases their
chance to be successful on a task.
Integrated deﬁnition of work effort. Thus, effort has been referred to as motivation, as an
in- and extrarole performance dimension, and as an outcome of motivation and an antecedent
of performance. We suggest that effort is related to, but different from, both motivation and
performance. First, work motivation is often deﬁned as an “unobservable force”that initiates
work behavior to determine its direction, intensity, and duration (e.g., Campbell & Pritchard,
1976; Diefendorff & Chandler, 2011; Kanfer, 1990; Pinder, 2008). This deﬁnition suggests
that effort is not motivation but rather is behavior that results from motivation. As the
theory of planned behavior proposes, although workers may desire to act (i.e., be motivated),
external factors may make action impossible or undermine the instrumentality of actions, such
that motivation never results in “trying”(Ajzen, 1991). In this sense, motivation is a necessary
but insufﬁcient condition for effort. For example, sales goals—and incentives for reaching the
goals—may motivate salespeople. The amount of effort they expend to achieve those
goals could then be measured by the number of sales calls made (e.g., Rapp, Bachrach, &
Second, performance “includes only those actions or behaviors that are relevant to the
organization’s goals and that can be scaled (measured) in terms of each individual’s proﬁ-
ciency (that is, level of contribution)”(Campbell et al., 1993: 40). In contrast, effort does
not reﬂect the efﬁciency or effectiveness of behavior. In other words, “how hard a person
works (effort) is different from how well he[sic] works (proﬁciency)”(Williams & Seiler,
1973: 49). Returning to our illustration, a salesperson could make a lot of calls but do so
inefﬁciently (e.g., to the wrong potential customer base) or say the wrong things while
talking to potential customers. Similarly, performance reﬂects behaviors that contribute to
an organizations’goals, whereas a worker’s efforts may focus on activities that do not
contribute to these goals.
Finally, researchers have deﬁned performance as behavior, results, or a combination of
both (Viswesvaran & Ones, 2000). For example, Campbell et al. (1993) and Beck, Beatty,
and Sackett (2014) focused on performance behavior, whereas Bernardin and Beatty
Van Iddekinge et al. / Work Effort 5
(1984) and Minbashian and Luppino (2014) focused on performance results. Effort is related
to performance deﬁned as behavior and deﬁned as results. For instance, salespeople who
make more sales calls (i.e., effort) and whose actions on the calls contribute to the organiza-
tion (i.e., performance as behavior) tend to sell more products (i.e., performance as results).
To conclude, we deﬁne work effort as follows:
How hard workers try to perform their jobs, which includes where they devote their effort
(direction), the amount of their effort (intensity), and how long they persevere in their effort
Thus, effort is a direct outcome of motivation such that motivation reﬂects the psycholog-
ical arousal that inﬂuences choices employees make about what they work on, how hard they
work, and how long they persist in that work. Further, effort is a direct antecedent of perfor-
mance. The key distinction between effort and performance is that, unlike performance, effort
does not reﬂect the efﬁciency or effectiveness of behavior. In one sense, effort is a necessary
but insufﬁcient condition of performance. That is, workers need to exert effort to perform
their roles, but their effort must be efﬁcient and focus on the appropriate behaviors.
Finally, effort inﬂuences performance behavior and results.
Operationalizations of Work Effort
Given that researchers have conceptualized work effort in various ways, effort has conse-
quently been operationalized (i.e., measured) in many ways. On the basis of our review, we
identiﬁed four categories that capture how researchers have operationalized effort.
Effort dimension. As discussed, effort comprises three dimensions: direction, intensity, and
persistence. Most research has focused on intensity and persistence. Effort intensity focuses on
how hard workers try to carry out a chosen behavior (Blau, 1986). Most studies have operation-
alized intensity by asking workers (or their supervisors or peers) to assess the amount of effort
devoted to the job. For example, Brown and Leigh’s (1996) Work Intensity Scale includes
items such as “When I work, I really exert myself to the fullest”and “When there’s a job to
be done, I devote all my energy to getting it done.”Other studies have operationalized intensity
in terms of the amount of effort workers devote to speciﬁc activities. For instance, some have
measured intensity using number of calls employees make to potential customers (e.g., Rapp,
Agnihotri,& Forbes, 2008). In contrast, others have used customer calls to operationalize perfor-
mance (e.g., Grant, 2008). In this case, the number of calls would reﬂect effort, whereas the effec-
tiveness of the calls would reﬂect performance.
Effort persistence focuses on how long workers keep trying until the behavior is accom-
plished. For example, Terborg (1977) coded video recordings to determine the percentage of
time employees were working versus not working. Researchers also have measured persis-
tence based on how many hours employees work. Paterson and O’Driscoll (1990), for
instance, used number of unpaid overtime hours employees worked. Although hours
worked may be a useful proxy for persistence, there also are potential limitations of such mea-
sures. For one, employees could work a lot of hours but not work hard during that time. In
6 Journal of Management
addition, hours worked may confound effort with efﬁciency, such that some employees must
work longer hours because they are inefﬁcient. External factors also could affect work hours,
such as when employees must work overtime to meet a customer order. In addition, type of
position can play a role, such as higher-level managers working more hours than lower-level
managers (e.g., Blau, 1986). Measures of voluntary overtime hours worked (e.g., Grant,
2008) may minimize some of these issues because working overtime is discretionary rather
Measures of effort direction focus on where workers devote their effort or what they
choose to do (Kanfer, 1990). Much less research has focused on effort direction, perhaps
due to uncertainty about how to operationalize this dimension. In Porter and Lawler’s
model (1968), role perceptions reﬂect where workers believe they should direct their effort
(Terborg, 1977). Further, role perceptions are hypothesized to moderate relations between
effort and performance, such that relations should be stronger when role perceptions are accu-
rate and weaker when they are inaccurate. Other researchers have operationalized effort direc-
tion by assessing the frequency with which workers engage in job-relevant activities or
behaviors. Katerberg and Blau (1983), for instance, assessed how frequently realtors
engaged in each of 57 activities. However, such measures may blur the line between effort
and performance. For example, it is unclear whether or how these measures differ from
behavior observation scales (Latham & Wexley, 1977), which require raters to indicate
how frequently they have observed workers perform different aspects of the job. Finally,
some researchers have referred to effort intensity and persistence as “working hard”and
effort direction as “working smart”(Sujan, 1986). The idea is that direction is about choosing
a strategy, whereas intensity and persistence are about working hard to implement that strat-
egy. Measures of effort direction (i.e., working smart) focus on steps workers take to plan and
set goals (e.g., “Each week I make a plan for what I need to do”and “I set personal goals for
each sales call”) as well as adapt to the situation (e.g., “I vary my sales style from situation to
situation”; Sujan, Weitz, & Kumar, 1994).
In addition, rather than assessing one or more of the effort dimensions just described, many
measures assess effort more generally. For example, Bono (2001) used a three-item measure
that included items such as “I put my best effort into this task,”Jamal and Baba (1991) asked
nurses to rate how hard they work compared to other nurses in their unit, and Williams and
Seiler’s (1973) effort measure included a single item that asked employees how hard they
Effort time span. This category focuses on the time span over which effort is assessed and
distinguishes between effort on short-term tasks versus effort employees exert over longer
periods. For example, lab studies often measure how much effort participants devote to a
set of simulated job tasks during a few hours or less. Robinson (2009), for instance, measured
effort with items such as “I put a good deal of effort into learning the material”and “I invested
a lot of energy in order to learn the material.”In contrast, ﬁeld studies typically measure effort
over longer periods. Rapp et al. (2013), for example, measured the number of sales calls
employees made during a 3-month period.
Effort level of analysis. This category refers to whether effort is measured between
different workers at a given point in time (i.e., between-person level) or within the
same worker over time (i.e., within-person level). Most research has used between-person
Van Iddekinge et al. / Work Effort 7
designs by measuring effort across workers in the same or in different organizations. A
few studies have focused on within-person changes in effort on a task over time. For
example, Yeo and Neal (2004) measured participants’effort across multiple trials of an
air trafﬁc control simulation. Findings suggested that the relationship between effort
and performance on short-term tasks often is curvilinear, such that the relation is positive
at the start and then weakens, or even becomes negative, once participants learn how to
perform the task. In other words, as workers gain experience performing a task or a
role, they can maintain high levels of performance with less effort (see also Kanfer &
Effort subjectivity. This category distinguishes between subjective perceptions of effort
and more objective measures. Some researchers have construed effort as an internal attribute
that workers themselves are in the best position to assess. For instance, Brown and Peterson
(1994) asked salespeople to rate their agreement with items such as “Work hard to make as
many sales calls as I can each day”and “Work harder to reach my sales goals.”Examples of
potentially more objective measures include the percentage of time employees are working
based on video recordings (e.g., Blau, 1993) and the amount of time participants spend study-
ing training materials (e.g., Fisher & Ford, 1998).
Nomological Network of Work Effort
Figure 1 presents a nomological network of potential antecedents, overlapping constructs,
and outcomes of effort based on our review. The network includes constructs (a) for which
there was a theoretical or conceptual basis for a relationship with effort and (b) that were
included in primary studies identiﬁed for the meta-analysis we describe in the next section.
Nomological Network of Work Effort
8 Journal of Management
Antecedents of Effort
We organize the proposed antecedents into two general categories. Person-focused con-
structs assess individual differences that may inﬂuence or covary with the effort workers
exert, and job- and organization-focused constructs assess aspects of workers’job or organi-
zation that may inﬂuence the extent to which individuals exert effort.
Person-focused antecedents. Conscientiousness is thought to be the personality factor
most relevant to work effort (Schmidt & Hunter, 1992). People who are highly conscientious
tend to be achievement oriented, be hardworking, and have high expectations of themselves
(Barrick, Mount, & Strauss, 1993). Further, conscientious individuals tend to set goals and
exert effort to achieve those goals (Barrick et al., 1993). Thus, deﬁnitions of conscientious-
ness refer to effort-related concepts, such as hard work. However, conscientiousness is
broader than effort because it includes additional dimensions, such as order, dependability,
and self-control (Costa & McCrae, 1992; Roberts, Chernyshenko, Stark, & Goldberg, 2005).
Achievement striving (which also is referred to as achievement motivation and need for
achievement) is the dimension of conscientiousness that seems the most relevant to effort.
People who are high on this characteristic “strive to do their best at being competent at any
job they do and often do more than they planned”(Mount & Barrick, 2002: 54). They are “hard-
working and persistent with high aspiration levels.”In fact, achievement-striving measures some-
times include items that refer to effort. For example, the Personal Characteristics Inventory
(Mount & Barrick, 2002) Achievement Striving scale includes items such as “Others have
described me as an extremely hard-worker”and “I like to do the best I can, even if it requires
a lot of extra effort.”Similarly, the NEO Personality Inventory Achievement Striving scale
(Costa & McCrae, 1992) includes items such as “work hard”and “put little time and effort
into my work”(reverse scored). At the same time, such measures also include items that may
be somewhat outside the effort domain, such as “I strive for excellence in everything I do”
and “I approach most jobs with great enthusiasm.”
Self-efﬁcacy refers to individuals’conﬁdence in their ability to perform in a speciﬁc
domain (Bandura, 1997). General self-efﬁcacy reﬂects individuals’perception of their
ability to perform across various situations, whereas speciﬁc self-efﬁcacy reﬂects individuals’
beliefs about their ability to perform a particular task (Chen, Gully, & Eden, 2004). As dis-
cussed, social-cognitive theory (Bandura, 1977) proposes that workers who possess higher
task-speciﬁc self-efﬁcacy will devote greater effort to performing their jobs.
Goal orientation focuses on what motivates workers. Individuals with a mastery or learning
goal orientation (LGO) are motivated to increase their competence by acquiring new knowledge
or skills, whereas individuals with a performance goal orientation (PGO) are motivated to dem-
onstrate competence compared with others (Dweck, 1986). Workers who are high on either or
both dimensions should be more likely to demonstrate effort to achieve those goals than
workers who are lower on these dimensions. However, LGO tends to covary with other motiva-
tional constructs, whereas PGO does not (e.g., Payne, Youngcourt, & Beaubien, 2007). Thus, we
anticipate that only LGO will correlate positively with effort.
Intrinsic motivation refers to the desire to expend effort based on interest in and enjoy-
ment of the work itself, whereas extrinsic motivation reﬂects the desire to obtain external
outcomes, such as rewards or recognition (Deci & Ryan, 2000). Thus, workers who
Van Iddekinge et al. / Work Effort 9
possess higher motivation, whether it be intrinsic or extrinsic, should exert more effort.
However, similar to LGO and PGO, intrinsic motivation tends to relate more strongly to
other motivation-related constructs than does extrinsic motivation (e.g., Dysvik &
Kuvaas, 2013). As such, we expect that intrinsic motivation may also be more strongly
related to effort than extrinsic motivation.
Job- and organization-focused antecedents. Person–environment ﬁtis thought to relate to
performance through its effects on job attitudes, such as job satisfaction and organizational com-
mitment (Arthur, Bell, Villado, & Doverspike, 2006). Person–job ﬁt focuses on the match
between workers’knowledge, skills, and abilities and the characteristics required for successful
job performance (i.e., abilities–demands ﬁt) as well as the match between what workers want
or need in a job (e.g., ﬂexibility) and what the job supplies regarding those needs (i.e., needs–sup-
plies ﬁt; Cable & DeRue, 2002). Person–organization ﬁt focuses on the match between workers
and the organization overall, such as the ﬁt between what workers value and the values of the
organization. Overall, the better the ﬁt between workers and their job and organization, the
more satisﬁed and committed they will be, and the better they will perform. We suggest that
ﬁt may affect effort in a similar way, such that workers with better ﬁt will exert more effort to
perform their jobs and contribute to the organization.
According to the job characteristics model (Hackman & Oldham, 1976), certain job char-
acteristics have a positive effect on workers by making jobs more challenging, meaningful,
and autonomous. Speciﬁcally, this model proposes that jobs are more meaningful when
workers have the chance to use different skills in performing a job (skill variety), when
they can see the ﬁnished product of their work (task identity), when their job is important
to the organization or society (task signiﬁcance), when they have freedom to decide how
to accomplish their work (autonomy), and when they receive input about how they are
doing (feedback). These characteristics, in turn, are thought to increase performance by moti-
vating employees to invest time and energy to complete assigned tasks (Grant, 2008). For
instance, when jobs can positively impact other people, workers are more likely to exert
effort to successfully perform the job.
Workers or their supervisors often set goals, which are thought to help direct workers’
effort toward goal attainment (Diefendorff & Chandler, 2011; Katerberg & Blau, 1983).
According to goal-setting theory (Locke & Latham, 1990), workers who set speciﬁc goals
should devote greater effort to their work than workers who set more general goals or no
goals at all. For example, goals may help workers persist despite difﬁculty and setbacks.
Relatedly, goal commitment concerns how committed workers are to achieve their goals.
Workers who possess higher levels of goal commitment should exert greater effort than
those who are less committed.
Job involvement represents “the perceived importance of work in one’s life or the degree of
psychological identiﬁcation with work”(Kanungo, 1981: 7). Workers who possess high
levels of job involvement identify with their job and think about it even when outside of
work (Kanungo, 1982). For example, Lodahl and Kejner’s (1965) widely used measure
includes items such as “The most important things that happen to me involve my work”
and “I live, eat, and breathe my job.”The more people identify with their work, the
greater the amount of time, energy, and effort they are likely to commit to work activities
(Brown & Leigh, 1996).
10 Journal of Management
Job satisfaction also has been linked to work effort. Some research suggests that job sat-
isfaction is an antecedent of effort (e.g., Riketta, 2008) and mediates the relation between sat-
isfaction and performance (e.g., Mulki, Jaramillo, & Locander, 2009). That is, workers who
are more satisﬁed with their jobs devote more effort, which, in turn, leads to higher perfor-
mance. Other research suggests that effort is an antecedent of satisfaction (Brown &
Peterson, 1994). For example, effort may lead to intrinsic and extrinsic rewards that increase
workers’satisfaction. Regardless of the causal direction, we expect satisfaction and effort will
be positively related.
Finally, organizational commitment reﬂects workers’commitment to their current
employer and includes three dimensions (Meyer & Allen, 1991). Affective commitment
refers to workers’emotional attachment to, identiﬁcation with, and involvement in the orga-
nization. Workers with a strong affective commitment may exert greater effort because they
are attached to the organization and want it to be successful. Continuance commitment
focuses on the potential costs of leaving an organization. Employees with a strong continu-
ance commitment remain with the organization because they must (e.g., due to lack of alter-
natives) and, thus, may exert more effort to avoid losing their job. Finally, normative
commitment reﬂects a feeling of obligation to stay with an organization. Workers with a
high level of normative commitment feel that they ought to remain with the organization,
such as to reciprocate goodwill to the organization for providing them a job. Thus,
workers who possess strong normative commitment may exert effort because they believe
they owe the organization their best effort.
Constructs That Overlap With Effort
There has been much debate regarding the meaning and measurement of work engagement
(e.g., Byrne, Peters, & Weston, 2016; Macey & Schneider, 2008; Newman & Harrison,
2008). Kahn (1990: 700) deﬁned engagement as “the simultaneous employment and expres-
sion of a person’s‘preferred self’in task behaviors that promote connections to work and to
others, personal presence (physical, cognitive, and emotional), and active, full performances.”
Rich et al. (2010: 619) suggested that engagement reﬂects “a multi-dimensional motivational
concept reﬂecting the simultaneous investment of an individual’s physical, cognitive, and
emotional energy in active, full work performance.”On the basis of these deﬁnitions, engage-
ment seems to be a broad and somewhat ambiguous concept that includes elements of effort
(e.g., the “investment of energy”to one’s performance) but also other constructs (e.g., emo-
tional engagement seems similar to job involvement).
Measures of work engagement also appear to overlap with effort. The Job Engagement Scale
(JES; Rich et al., 2010) and the Utrecht Work Engagement Scale (UWES; Schaufeli, Salanova,
González-Romá, & Bakker, 2002) are two of the most widely used engagement measures. The
JES includes three subscales. The Physical Engagement subscale focuses directly on effort and
comprises items such as “I exert my full effort to my job”and “I try my hardest to perform well on
my job.”The Cognitive Engagement subscale captures mental effort using items such as “At
work, I pay a lot of attention to my job”and “At work, I concentrate on my job.”In contrast,
the Emotional Engagement subscale focuses on more attitudinal elements, such as interest,
pride, and excitement about one’s job. Example items include “Iaminterestedinmyjob,”“I
am proud of my job,”and “I am enthusiastic in my job.”
Van Iddekinge et al. / Work Effort 11
The UWES also includes three subscales. The Vigor subscale appears to overlap with
effort the most by assessing energy (e.g., “At my work, I am bursting with energy”) and per-
sistence (e.g., “I can continue working for very long periods at a time”). In contrast, the
Dedication subscale focuses on feelings about one’s job, such as “I am proud of the work
that I do,”and the Absorption subscale assesses the extent to which workers get absorbed
in their work, such as “I get carried away when I’m working”(see Byrne et al.  for
a detailed empirical comparison between these two engagement measures). Thus, two of
the most widely used engagement measures capture aspects of effort.
Grit is the “perseverance and passion for long-term goals”(Duckworth et al., 2007: 1087)
and comprises two dimensions. The consistency-of-interests dimension focuses on whether
people maintain or change their interests and goals over time. Although this dimension
does not seem to overlap with effort, the perseverance-of-effort dimension does in both
name and content. Indeed, the scale for this dimension includes items such as “I am a hard
worker”and “Iﬁnish whatever I begin”as well as related but somewhat different concepts,
including overcoming setbacks and diligence.
Outcomes of Effort
As discussed, workers who try hard to perform their jobs will tend to demonstrate better job
performance in terms of both behaviors and results. We anticipate that effort will relate positively
to task performance, as well as to contextual performance (e.g., OCB), particularly to measures
that focus on or include items about the discretionary effort workers devote to helping their
coworkers or the organization. In contrast, we expect effort will be negatively related to
CWB. Speciﬁcally, individuals who work harder may be less likely to demonstrate counterpro-
ductive behaviors, particularly those conceptually similar to reduced effort, such as lateness and
loaﬁng on the job. In addition, if effort is indeed more proximal to job performance than its pro-
posed antecedents (as the model in Figure 1 proposes), then effort should be more strongly related
to performance than its antecedents. To explore this possibility, we also compare relations
between effort and performance to relations between the antecedents and performance.
We also explore the possibility that effort could be related to outcomes in addition to per-
formance. First, exerting high levels of effort could lead to emotional exhaustion, which is a
key dimension of work-related well-being (Wright & Cropanzano, 1998). Second, effort may
also be relevant to withdrawal from the organization. For example, progressive withdrawal
theory suggests that workers decrease effort as they begin to withdraw from an organization
(Koslowsky, Sagie, Krausz, & Singer, 1997). In addition, workers who decide to leave may
reduce their effort due to the lack of long-term consequences. Thus, we also explore whether
effort is associated with turnover intentions or actual turnover.
Literature search. We began by searching the following electronic databases for poten-
tially relevant published and unpublished studies: Google Scholar, PsycINFO, and Web of
Science. We also searched the metaBUS database (Bosco, Field, Larsen, Chang, &
12 Journal of Management
Uggerslev, 2020). We searched for studies that included the terms “work effort”or “hard
work.”We also searched more broadly for studies that included effort and the other constructs
we referred to in the previous sections, including burnout, commitment, citizenship behavior,
counterproductive behavior, efﬁcacy, effectiveness, efﬁciency, goals, motivation, success,
performance, productivity, satisfaction, sales, supervisor ratings, and turnover. Next, we
searched for studies that included speciﬁc measures we identiﬁed as assessing effort, includ-
ing measures from Brockner, Grover, Reed, and Dewitt (1992); Brown and Leigh (1996);
Brown and Peterson (1994); De Cooman et al. (2009); Kanfer et al. (1994); Kuvaas and
Dysvik (2009); Rich et al. (2010); and Sujan et al. (1994). Finally, we reviewed the
References sections of the studies we obtained to identify additional sources. In total, we
reviewed over 3,500 studies for potential inclusion in the meta-analysis.
Inclusion criteria. We used seven criteria to determine whether to include primary studies
in the meta-analysis. First, we included studies that measured effort employees devote to their
job, participants devote to simulated job tasks in laboratory settings,
or entrepreneurs devote
to new ventures. Second, we included studies that measured effort direction, intensity, persis-
tence, or effort more generally.
For number of hours worked (as a measure of effort persis-
tence), we included only primary studies for which the entire sample (a) held the same or a
highly similar job (i.e., to help ensure similar work schedules/demands) and (b) was full- or
part-time and not some combination of the two (i.e., to avoid potential extraneous variance
due to differing work schedules). Third, we included studies that measured effort and at
least one of the constructs in the nomological network. Fourth, for studies that measured
effort and a performance-related outcome, we included outcome measures that reﬂected
task performance, contextual performance, CWB, or overall performance using supervisor,
peer, or customer ratings or results-based measures (e.g., sales). Although we excluded self-
ratings of job performance, consistent with previous meta-analyses (e.g., Gonzalez-Mulé,
Mount, & Oh, 2014), we included self-ratings of CWB, which most primary studies
measure using self-reports (e.g., because CWBs such as theft often go unobserved). We
also included self-ratings of exhaustion and turnover intentions because most primary
studies measured these more intrapersonal constructs using self-reports. Fifth, we included
studies that measured effort and performance using different sources.
Sixth, we included
studies that measured effort and the other constructs at the individual level (see Torka,
Mazei, & Hüffmeier  for a review of effort in team contexts). Seventh, we included
studies based on independent samples. A total of 141 studies, including 171 independent
samples, met all the criteria. Studies comprised 119 journal articles, 20 dissertations and
theses, one book, and one conference paper (see online supplement for references for all
the primary studies).
Coding of primary studies. The ﬁrst two authors independently coded 20 primary studies
and compared their results. Both coders have expertise in the substantive domain and have
conducted several meta-analyses. We coded sample size, reliability estimates, and correla-
tions between effort and other variables. We also coded several aspects of the effort mea-
sure(s) in each study, including its standing on the categories in Table 1. In addition, we
coded the type (e.g., task performance vs. performance results) and source (e.g., supervisor
ratings vs. records) of the outcome measures. The percentage of intercoder agreement
ranged from 90% (for effort dimensions assessed) to 100% (e.g., for effort–outcome
Van Iddekinge et al. / Work Effort 13
correlations), with an overall agreement of 96.3% across nearly 300 individual codes. After
resolving any disagreements via discussion, the ﬁrst author then coded the remaining studies.
Meta-analytic approach. We used Schmidt and Hunter’s (2015) psychometric meta-
analysis using a Microsoft Excel program developed by Huy Le. First, we computed the
mean sample size–weighted observed correlations (r) between effort and the proposed ante-
cedents and outcomes in Figure 1. A few studies reported means and standard deviations,
which we converted to rs. For studies that included multiple effort and/or outcome measures,
we computed composite rs for inclusion in the overall meta-analyses (Schmidt & Le, 2004).
Second, we computed rs corrected for measurement error in all the measures to estimate
Descriptive Statistics for Reliability Estimates for Study Variables
Construct kMedian Mean
Subjective measures 156 .87 .85
Objective measures 4 .80 .76
Conscientiousness 16 .79 .80
Self-efﬁcacy 24 .84 .85
Mastery 9 .81 .81
Performance 9 .80 .79
Intrinsic motivation 14 .89 .83
Person–job ﬁt 6 .88 .86
Person–organization ﬁt 6 .91 .87
Job characteristics 14 .82 .83
Goal setting 8 .82 .81
Goal commitment 4 .86 .84
Job involvement 12 .83 .78
Job satisfaction 55 .86 .85
Organizational commitment 32 .86 .84
Customer ratings 3 .98 .95
Peer ratings 4 .86 .86
Self-ratings (CWB only) 7 .92 .91
Supervisor ratings 28 .90 .88
Performance results 18 .81 .78
Exhaustion 8 .88 .87
Turnover intentions 18 .91 .86
Note:k=number of correlations from independent samples. All reliability estimates are based on internal consistency
reliabilities (i.e., Cronbach’s alpha). The only exceptions were two reliabilities for results-based outcomes, one of
which was an intercoder reliability and the other was a test-retest reliability. CWB =counterproductive work
14 Journal of Management
construct-level relations. For studies that reported reliability estimates, we used those values
in the meta-analyses. Almost all of these were internal consistency reliability estimates
(alpha). For studies that did not report reliability estimates, we used the median estimate
based on other studies in the data set as seen in Table 1. Internal consistency reliability
tends to overestimate the reliability of ratings-based criteria (and, in turn, underestimate cor-
rected correlations) because intrarater errors typically are smaller than interrater errors
(LeBreton & Senter, 2008). Unfortunately, none of the primary studies reported estimates
of interrater reliability for supervisor, coworker, or customer ratings of performance. We
therefore report two corrections for rs involving performance ratings: one using internal con-
sistency estimates from the primary studies and one using an interrater reliability of .60 from
Meta-Analytic Correlations Between Work Effort and Its Proposed Antecedents and
ρ95% CI SD
% VE 80% CV
Conscientiousness 19 4,005 .30 .35 [.25, .46] .21 10.3 [.08, .63]
Achievement striving 3 847 .06 .07 [–.01, .14] .00 100.0 [.07, .07]
Self-efﬁcacy 25 5,303 .34 .40 [.34, .47] .15 18.4 [.21, .60]
Mastery orientation 10 10,161 .35 .43 [.35, .50] .12 7.2 [.27, .58]
Excluding outliers 8 1,642 .20 .24 [.16, .32] .08 51.2 [.14, .35]
Performance orientation 10 10,161 .10 .12 [.07, .16] .06 25.7 [.04, .20]
Intrinsic motivation 14 2,986 .38 .46 [.39, .54] .12 25.2 [.31, .62]
Job and organization focused
Person–job ﬁt 4 1,032 .40 .46 [.40, .52] .02 85.3 [.43, .49]
Person–organization ﬁt 6 1,212 .28 .31 [.22, .41] .09 39.2 [.19, .43]
Autonomy 8 1,724 .25 .28 [.17, .40] .15 19.8 [.10, .47]
Excluding outlier 7 1,429 .20 .23 [.16, .29] .05 74.3 [.17, .28]
Skill variety 3 744 .43 .49 [.41, .56] .03 84.4 [.45, .52]
Task signiﬁcance 4 925 .35 .41 [.33, .50] .05 65.2 [.35, .48]
Goal setting 12 2,267 .27 .32 [.25, .39] .09 47.7 [.21, .43]
Goal commitment 5 892 .31 .36 [.17, .54] .20 13.1 [.10, .61]
Excluding outlier 4 655 .41 .47 [.38, .57] .06 61.7 [.40, .55]
Job involvement 14 4,728 .32 .39 [.31, .46] .13 17.0 [.22, .56]
Job satisfaction 58 18,927 .30 .35 [.30, .40] .19 8.6 [.10, .59]
Organizational commitment 29 16,068 .28 .33 [.25, .40] .21 4.7 [.06, .59]
Work engagement 9 1,679 .42 .48 [.37, .59] .15 18.1 [.29, .67]
Grit 3 1,277 .42 .51 [.29, .73] .19 6.1 [.27, .75]
Note:k=number of correlations from independent samples; r=sample size–weighted mean observed correlation; ˆ
=mean correlation corrected for measurement error in both variables; 95% CI =lower and upper bounds of the 95%
conﬁdence interval for ˆ
=standard deviation of ˆ
ρvalues; % VE =percentage of variance in ˆ
ρaccounted for by
sampling error and measurement error; 80% CV =lower and upper bounds of the 80% credibility value for ˆ
Van Iddekinge et al. / Work Effort 15
prior meta-analyses (e.g., Rothstein, 1990; Van Iddekinge, Arnold, Frieder, & Roth, 2019).
The latter estimate reﬂects the reliability of a single rater, which was appropriate given that
all the primary studies used only one rater to evaluate each worker.
We report results for analyses based on three or more primary studies. This included all the
proposed relationships in the nomological network summarized in Figure 1 except for rela-
tions between effort and actual turnover. In addition, to identify potential inﬂuential
studies, we used a modiﬁed version of the sample adjusted meta-analytic deviancy statistic
(Beal, Corey, & Dunlap, 2002) available in Meta-Analysis Mark XIII (Piers Steel, personal
communication, March 21, 2022). If exclusion of a study changed the original mean corrected
rby 20% or more, we report the results with and without the inﬂuential study (and we discuss
results without the inﬂuential study) based on best practices in outlier management (e.g.,
DeSimone, Brannick, O’Boyle, & Ryu, 2021; Steel, Beugelsdijk, & Aguinis, 2021).
Relations between effort and its proposed antecedents and correlates. Table 2 displays
correlations between effort and its proposed antecedents as well as the constructs that
Meta-Analytic Correlations Between Work Effort and Outcomes
ρ295% CI SD
% VE 80% CV
Job performance 62 12,219 .27 .31 .34 [.25, .36] .21 11.6 [.04, .58]
Performance behavior 34 6,872 .25 .27 .32 [.21, .34] .18 14.3 [.04, .51]
Task performance 10 1,917 .22 .24 .30 [.16, .33] .12 28.6 [.09, .40]
Contextual performance 8 1,433 .19 .21 .26 [.12, .30] .10 38.1 [.08, .34]
CWB 8 1,837 –.29 –.32 —[–.14, –.49] .25 6.7 [–.00, –.63]
No outlier 7 1,510 –.21 –.23 —[–.10, –.35] .15 17.8 [–.03, –.42]
Overall performance 18 3,558 .24 .28 .34 [.19, .36] .17 16.2 [.05, .50]
Performance results 32 5,926 .29 .36 —[.28, .44] .22 12.2 [.08, .63]
Turnover intentions 23 11,934 –.14 –.16 —[–.10, –.22] .13 12.2 [.01, –.33]
Exhaustion 17 6,705 .06 .07 —[–.01, .14] .13 15.5 [–.10, .24]
No outlier 16 6,134 .04 .04 —[–.02, .11] .11 21.0 [–.10, .19]
Note: CWB =counterproductive work behavior. For the performance behavior outcome, we changed the sign of
correlations between effort and CWB (i.e., from negative to positive) to be consistent with the direction of studies that
measured task, contextual, or overall job performance. The overall performance outcome includes (a) measures that
assess performance across various job dimensions and (b) measures that ask about employees’performance in general
and do not refer to speciﬁc dimensions. k=number of correlations from independent samples; r=sample size–
weighted mean observed correlation; ˆ
ρ1=mean correlation corrected for measurement error in both variables (using
internal consistency reliability estimates for job performance ratings); ˆ
ρ2=mean correlation corrected for
measurement error in both variables (using an interrater reliability of .60 for job performance ratings); 95% CI =
lower and upper bounds of the 95% conﬁdence interval for ˆ
=standard deviation of ˆ
ρ1values; % VE =
percentage of variance in ˆ
ρ1accounted for by sampling error and measurement error; 80% CV=lower and upper
bounds of the 80% credibility value for ˆ
ρ1. Analyses with “—” for ˆ
ρ2did not change due to no supervisor or peer
ratings criteria within those analyses.
16 Journal of Management
appear to overlap with effort. Among the person-focused antecedents, intrinsic motivation
and self-efﬁcacy were most strongly related to effort ( ˆ
ρ=.46 and .40, respectively). In addi-
tion, results were consistent with our expectation that mastery goal orientation (excluding two
outliers) would relate more strongly with effort (ˆ
ρ=.24) than performance goal orientation
ρ=.12). One surprising ﬁnding was the very small relation between achievement striving
and effort (ˆ
ρ=0.7), although this result was based on only three studies. Regarding job-
and organizational-focused constructs, except for autonomy, all corrected rs were at least
.31. Skill variety and goal commitment (excluding one outlier) were the strongest antecedent
ρ=.49 and .47). Interestingly, the median corrected rbetween person-focused
antecedents and effort (.35) was nearly identical to the median-corrected rbetween the job
and organization-focused antecedents and effort (.36).
Regarding the constructs that overlap with effort, the mean corrected rbetween effort and
engagement was .48, and the mean corrected rbetween effort and grit was .51. Also, in addi-
tion to the overall grit scale, one of the primary authors (Jordan, Hochwarter, Ferris, & Ejaz,
2018) provided us separate correlations for each of the two grit subscales. As expected, effort
was more strongly related to perseverance of effort than to consistency of interests (ˆ
Relations between effort and outcomes. Table 3 displays correlations between effort and
the proposed outcomes. For job performance, the table provides rs corrected for measurement
error in performance ratings using internal consistency reliabilities from the primary studies
ρ1) and corrected rs using an interrater reliability estimate of .60 for ratings-based measures
of performance (ˆ
ρ2). Although we focus on the internal consistency–corrected rs (and asso-
ciated statistics, such as SD
), these rs likely are conservative. The interrater reliability–cor-
rected rs may be more useful for comparison with rs for other predictors of performance (e.g.,
Schmidt & Hunter, 1998).
The overall corrected rbetween effort and job performance was .31, and there was con-
siderable variability around this mean estimate (SD
=.21). Effort was a better predictor of
performance results (ˆ
ρ1=.36) than of performance behavior (ˆ
ρ1=.27). However, because
all lab studies used results-based measures, this comparison partially reﬂects a difference
between lab and ﬁeld studies. For a more direct comparison between performance behavior
and results, we estimated the mean corrected ramong ﬁeld studies that used results-based
measures (k=14, N=2,539). The resulting corrected rof .25 was more in line with the cor-
rected rfor performance behavior. As expected, among measures of performance behavior,
effort was positively related to task and contextual performance ( ˆ
ρ1=.24 and .21, respec-
tively) and was negatively related to CWB (ˆ
ρ1=-.23, excluding one outlier). Effort was a
somewhat better predictor of performance behavior measures that focused on overall perfor-
ρ1=.28). Finally, effort was negatively related to turnover intentions ( ˆ
was largely unrelated to exhaustion (ˆ
ρ=.04, excluding one outlier).
Relations between effort categories and performance. Table 4 reports meta-analytic rs
between effort and job performance (including both behavior- and results-based measures)
separated by the effort categories we identiﬁed. We focused on performance as the
outcome for these analyses and did not incorporate relations between effort and the other out-
comes (i.e., turnover intentions and exhaustion) given conceptual and empirical differences
Van Iddekinge et al. / Work Effort 17
across the outcomes (e.g., effort was more strongly related to performance than to the other
Regarding effort dimension, we found that direction, intensity, and persistence of effort
were similarly related to job performance ( ˆ
ρ1=.24, .25, and .29, respectively). In addition,
measures that assessed multiple effort dimensions (e.g., intensity and persistence) were
most strongly related to performance (ˆ
ρ1=.37), whereas measures that were more general
and did not specify a dimension of effort demonstrated the weakest relations to performance
For effort time span, many studies did not report or measure the amount of time over which
effort was assessed. For example, most self-report measures ask workers how much effort
they exert on their jobs and do not specify a particular time frame (e.g., “within the past
month”). Of the studies that did provide a time frame the effort measure covered, we separated
Meta-Analytic Correlations Between Work Effort and Job Performance by Effort
Effort category kN
ρ295% CI SD
% VE 80% CV
Direction 5 512 .18 .24 .26 [.09, .39] .11 56.2 [.10, .38]
Intensity 31 5,570 .22 .25 .28 [.17, .32] .21 13.3 [–.02, .51]
Persistence 18 3,729 .25 .29 .30 [.18, .40] .22 10.8 [.00, .57]
Multiple dimensions 22 4,055 .33 .37 .42 [.28, .45] .19 12.4 [.12, .61]
General 5 1,221 .16 .18 .22 [.10, .27] .07 53.6 [.10, .27]
Very short (1–4 hr) 16 3,102 .36 .43 —[.33, .54] .21 11.8 [.17, .70]
Short (1 week or less) 3 310 .48 .58 —[.46, .70] .05 75.2 [.51, .65]
Intermediate (1–3 months) 6 935 .39 .45 .49 [.35, .55] .10 40.8 [.33, .57]
No outlier 5 555 .30 .36 .37 [.28, .45] .00 100.0 [.36, .36]
Long (3 months or longer) 4 732 .26 .30 .31 [.05, .55] .24 9.9 [–.01, .61]
No outlier 3 608 .16 .19 .20 [.08, .31] .07 58.4 [.11, .28]
Level of analysis
Between persons 58 11,877 .27 .31 .35 [.26, .37] .20 12.0 [.05, .57]
Within persons 4 342 –.06 –.07 —[–.28, .14] .17 36.3 [–.29, .15]
No outlier 3 243 .06 .08 —[.06, .09] .00 100.0 [.08, .08]
Subjective 45 8,455 .24 .28 .31 [.22, .33] .17 17.5 [.06, .50]
Objective 24 4,710 .27 .31 .33 [.21, .42] .25 9.0 [–.00, .63]
Note: For effort categories that included primary studies that measured counterproductive work behavior (CWB), we
changed the sign of correlations between effort and CWB (i.e., from negative to positive) to be consistent with the
direction of studies that measured task, contextual, or overall job performance. k=number of correlations from
independent samples; r=sample size–weighted mean observed correlation; ˆ
ρ1=mean correlation corrected for
measurement error in both variables (using internal consistency reliability estimates for job performance ratings); ˆ
=mean correlation corrected for measurement error in both variables (using an interrater reliability of .60 for job
performance ratings); 95% CI =lower and upper bounds of the 95% conﬁdence interval for ˆ
deviation of ˆ
ρ1values; % VE =percentage of variance in ˆ
ρ1accounted for by sampling error and measurement error;
80% CV =lower and upper bounds of the 80% credibility value for ˆ
ρ1. Analyses with “—” for ˆ
ρ2did not change due
to no ratings-based criteria within those analyses.
18 Journal of Management
them into four categories that reﬂected very short (1–4 hr), short (1 week or less), intermediate
(1–3 months), and long (more than 3 months) time frames. Results suggested that effort–per-
formance relations generally decreased as the time frame increased. For example, corrected rs
were .43 and .58 for effort exerted during a few hours and over 1 week, respectively, com-
pared with .19 for effort exerted over 3 months or longer.
With respect to effort level of analysis, almost all studies used between-person designs (k=
58); only a few used within-person designs (k=4). The mean corrected rwas much stronger
for between-person studies (.31) than for within-person studies (.08). Finally, for effort sub-
jectivity, corrected rs were slightly stronger for more objective measures (.31) than for more
subjective measures (.28).
Relative validity of effort and its proposed antecedents. As discussed, if effort is indeed
more proximal to job performance than its proposed antecedents, then effort should be
more strongly related to performance than its antecedents. To examine this possibility,
Table 5 presents relations between the antecedents of effort and job performance (including
behavior- and results-based measures). In addition, the last column of the table reports cor-
rected rs between effort and performance based on the same sets of primary studies on
which each antecedent–performance relationship is based. This approach controls for
Meta-Analytic Correlations Between Proposed Antecedents and Job Performance
Antecedent kN rˆ
ρ295% CI SD
% VE 80% CV ˆ
Conscientiousness 7 1,514 .04 .05 .04 [–.02, .12] .06 61.5 [–.03, .12] .41
Mastery orientation 7 1,520 .03 .04 —[–.04, .12] .06 64.4 [–.04, .12] .34
Performance orientation 7 1,520 –.02 –.03 —[–.14, .08] .12 32.4 [–.19, .13] .34
Intrinsic motivation 4 767 .15 .17 .19 [.09, .25] .00 100.0 [.17, .17] .31
Self-efﬁcacy 11 2,219 .34 .41 .41 [.29, .52] .18 15.4 [.18, .64] .44
Job and organization focused
Person–organization ﬁt 3 645 .23 .29 —[.14, .45] .11 34.4 [.15, .44] .46
Goal setting 8 1,015 .38 .46 .47 [.34, .58] .14 28.6 [.28, .64] .45
Goal commitment 4 781 .17 .20 .20 [.10, .29] .05 74.4 [.14, .26] .45
Job involvement 4 594 .19 .23 .28 [.17, .29] .00 100.0 [.23, .23] .29
Job satisfaction 13 2,958 .22 .24 .26 [.18, .30] .08 45.0 [.14, .34] .28
5 924 .14 .16 .18 [.10, .22] .00 100.0 [.16, .16] .14
Note:k=number of correlations from independent samples;
r=sample size–weighted mean observed correlation; ˆ
=mean correlation corrected for measurement error in both variables (using internal consistency reliability estimates
for job performance ratings); ˆ
ρ2=mean correlation corrected for measurement error in both variables (using .60 as an
estimate of interrater reliability for performance ratings); 95% CI =lower and upper bounds of the 95% conﬁdence
interval for ˆ
=standard deviation of ˆ
ρ1values; % VE =percentage of variance in ˆ
ρ1accounted for by sampling
error and measurement error; 80% CV =lower and upper bounds of the 80% credibility value for ˆ
correlation between effort and outcomes (corrected for measurement error in both variables using internal consistency
reliability estimates for job performance ratings) based on the same primary studies as the antecedent–outcome rs.
Van Iddekinge et al. / Work Effort 19
sample-speciﬁc differences in these comparisons. For both sets of relationships, we focus on
rs corrected for intrarater reliability in performance (i.e., ˆ
ρ1for the antecedents and ˆ
effort). For example, the corrected rof .05 between conscientiousness and performance and
the corrected rof .41 between effort and performance are based on data from the same seven
Among the person-focused constructs, only self-efﬁcacy emerged as a strong predictor of
ρ1=.41). Further, effort was more strongly related to performance than all the
person-focused constructs, including self-efﬁcacy (ˆ
ρ1effort =.31 to .44). Regarding the job-
and organizational-focused antecedents, goal setting was by far the strongest predictor of per-
ρ1=.46). Among the remaining antecedents, corrected rs with performance ranged
from .16 for organizational commitment to .29 for person–organization ﬁt. Relations between
effort and performance were more varied across the primary studies that also measured job/
organizational-focused antecedents. Although effort was a stronger predictor than person–
organization ﬁt, goal commitment, and job involvement, it was a slightly weaker predictor
of performance than goal setting and organizational commitment.
Implications for Theory
Although work effort has been a key concept in management theories for more than a
century (e.g., Porter & Lawler, 1968; Ryan, 1947; Taylor, 1911), our review suggests
effort has been a somewhat overlooked construct empirically. One reason effort has not
received more empirical attention may be uncertainty regarding what effort is and where it
ﬁts into theoretical models. For instance, our review revealed that effort has been conceptu-
alized as motivation, an outcome of motivation, an antecedent of performance, and a compo-
nent of performance. Without a clear deﬁnition of effort, it is difﬁcult to understand its role in
various theories. Our review addressed this key issue by providing an integrated deﬁnition of
effort that clariﬁes what it is and how it is similar to, but different from, motivation and per-
formance. Speciﬁcally, motivation is an “unobservable force”that causes workers to exert
effort to accomplish their work. Effort is about what workers do and how hard they try to
do those things. In contrast, performance is about how efﬁciently and effectively workers
do those things. Thus, effort is necessary, but insufﬁcient, for performance in that workers
need to exert effort to perform their roles, but their effort must be efﬁcient and focus on
the appropriate behaviors.
An implication of the distinction between effort and performance is the need to reconsider
theories and models that include effort as a dimension of performance. Viswesvaran (1993),
for instance, found that many performance measures include effort-related dimensions. Some
measures of extrarole behavior also refer to effort. For example, Van Scotter and Motowidlo’s
(1996: 526) conceptualization of contextual performance includes a dimension called job
dedication, which they deﬁned as “self-disciplined behaviors such as following rules,
working hard, and taking the initiative to solve a problem at work.”Because effort is an ante-
cedent of performance, these conceptualizations (and measures) need to be reﬁned.
Our review also revealed a lack of consensus regarding how effort should be operation-
alized. Indeed, researchers have attempted to measure effort in various ways, many of
20 Journal of Management
which do not focus on effort, or they focus on effort but also on other constructs (i.e., they
are contaminated; Messick, 1995). Even measures that focus solely on effort typically do
not measure all dimensions of effort (i.e., they are deﬁcient). Theories involving effort
cannot be tested if there is not consensus regarding how to measure the construct. Our
review provides a framework for understanding the ways effort can be operationalized,
and our meta-analysis suggested that some measures may be more effective than
others. For example, relations between effort and outcomes are stronger for (a) measures
that focus on persistence or multiple effort dimensions (ˆ
ρ1=.37), (b) measures that assess
effort over shorter time spans (e.g., ˆ
ρ1=.43 for a few hours), (c) studies that operation-
alize effort as a between-persons construct (ˆ
ρ1=.31), and (d) measures that use objective
indicators of effort (ˆ
Further, the nomological network we developed clariﬁes what constructs are anteced-
ents and outcomes of effort as well as how these constructs are distinct from—or in some
instances overlap with—effort. Meta-analytic correlations of the network relationships
revealed that intrinsic motivation (ˆ
ρ1=.46) and self-efﬁcacy (ˆ
ρ1=.40) are the strongest
person-focused antecedents of effort. This suggests that workers who are more intrinsi-
cally motivated and possess greater conﬁdence regarding their ability to perform tend
to exert greater effort. Among job- and organization-focused antecedents, the job charac-
teristics of skill variety and task signiﬁcance, along with goal commitment and person–
job ﬁt, are most strongly related to effort (ˆ
ρ1=.41 to .49). The fact that the strongest rela-
tions between the antecedents and effort were less than .50 provides empirical support for
the idea that motivation and effort are related but distinct constructs. Perhaps the strongest
evidence of this distinction is the rof .46 between intrinsic motivation and effort, given
that intrinsic motivation may be the most direct measure of motivation among the ante-
cedents we examined.
Our review also provided a critical examination of constructs that appear similar to effort.
Our results reveal that deﬁnitions of grit refer to effort. In addition, several widely used mea-
sures of work engagement focus on effort. This conceptual and operational overlap with effort
raises questions about the uniqueness of these other constructs (Newman, Harrison,
Carpenter, & Rariden, 2016).
Further, the present results reveal that effort is a moderately strong predictor of job perfor-
mance. For example, the overall corrected rbetween effort and performance was .31 (see
Table 3). The corrected rof .37 between measures that assess multiple effort dimensions
and performance also is notable (see Table 4). Thus, both theoretically and empirically,
effort is related to, but distinct from, job performance.
Finally, we found evidence to
suggest that effort is more strongly related to performance than most of its proposed anteced-
ents. Only self-efﬁcacy, goal setting, job satisfaction, and organizational commitment were
similarly related to performance compared with effort.
Implications for Future Research and Organizational Practices
First, we hope our review will inform decisions about how to measure effort in differ-
ent situations and for different purposes. Speciﬁcally, if the goal is to maximize predic-
tion, the somewhat stronger validity evidence for objective over subjective measures of
effort suggests using more objective measures. For example, measures such as number
Van Iddekinge et al. / Work Effort 21
of sales calls made during a certain period reﬂect effort intensity. Measures such as hours
worked reﬂect persistence and may be particularly appropriate when comparing workers
in the same roles. Time on task also may be a useful measure of persistence, particularly in
laboratory settings. However, researchers need to take care that these measures capture
effort that is under workers’control and not inﬂuenced by external factors, such as
limited availability to schedule work hours. Additionally, because lower intensity of
effort could result in longer work hours, researchers need to consider multiple dimensions
of effort. Indeed, results suggested measures that assess multiple effort dimensions relate
more strongly to performance than measures that focus on only one dimension or on effort
If objective measurement is not possible, more subjective measures of effort can be used.
For example, Brown and Leigh’s (1996) Work Intensity scale appears to be a good measure of
effort intensity. For measuring persistence, we suggest measures such as Sujan et al. (1994)
and Fang, Palmatier, and Evans (2004, which is based on Sujan et al., 1994) that assess
working long hours and persisting despite obstacles. However, even this scale could be
expanded to capture effort persistence more fully (e.g., additional items to assess overcoming
obstacles). In addition, most or all subjective measures ask how much effort workers typically
exert. Given that effort is more predictive of outcomes over shorter time spans, we recom-
mend including a time frame, such as effort during the past week. Referring to shorter
time spans may also help raters recall the effort level. Finally, if researchers wish to assess
effort on tasks or projects, we suggest measures that capture on-task effort (e.g., Robinson,
2009). Overall, there does not appear to be one “perfect”measure of work effort. Thus,
when possible, researchers should incorporate multiple measures and consider their advan-
tages and disadvantages carefully.
The present results also have implications for human resource practices involving
employee selection, compensation and rewards, performance management, and job
design. First, if it is infeasible to measure effort directly, organizations might measure
antecedents that are most strongly related to effort, for instance, selecting job applicants
who possess a high level of intrinsic motivationtoperformaparticularjobortakingsteps
to increase workers’self-efﬁcacy through training or practice. Second, the fact that goals
and goal commitment were relatively strong antecedents of effort provides further evi-
dence for the importance of having employees set goals. Accordingly, practices about
compensation and rewards, and the role goals play within those practices, should consider
their impact on work effort. Third, skill variety and task signiﬁcance are the two job char-
acteristics most strongly related to effort. This underscores the importance of designing
jobs that allow employees to develop and use different skills as well as considering
ways to increase the (perceived) impact jobs have on other aspects of the organization
and/or society in general. Similarly, we found that person–job ﬁt is more strongly
relatedtoeffortthanperson–organization ﬁt (see Table 2). This ﬁnding reinforces the
importance of assessing person–job ﬁt during the stafﬁng process as well as ﬁnding
ways to improve ﬁt post-hire (e.g., through job crafting). Finally, not only is greater
effort related to positive outcomes, such as better job performance, but decreased effort
is related to higher CWB and intentions to leave. This suggests that performance manage-
ment systems should include checks for monitoring effort because decreases in effort may
be a sign of bad things to come.
22 Journal of Management
Agenda for Future Research
Measurement of effort. Our meta-analytic review highlights some critical needs for future
research. First, we found that many effort measures are contaminated or deﬁcient. Thus, one
avenue for future research would be to conduct a systematic review of the content (substan-
tive) validity of effort measures to identify sources of contamination and deﬁciency within
these measures. Further, we identiﬁed eight primary studies from our data set (N=1,283)
that reported correlations between two distinct measures of effort. The mean corrected corre-
lation between effort measures was only .25, which suggests that alternative measures are
only moderately correlated. Therefore, more work is needed to validate effort measures cur-
rently in use, including attempting to sort out the similarities and differences among the
The meta-analysis revealed a lack of data on several aspects of effort. Among the dimen-
sions of effort, relatively few studies have attempted to measure effort direction. As Brown
and Leigh (1996: 361) noted, although “decisions employees make regarding allocation of
effort across tasks constitutes an additional important dimension of effort, it also entails con-
siderable complexity (e.g., related to knowledge structures and cognition, or working smart).”
One approach to assess effort direction may be to adopt resource allocation methods (e.g.,
Vancouver et al., 2008), for example, asking workers to indicate the percentage of their
total effort devoted to various in- and extrarole activities and then scoring responses accord-
ing to the relative importance of the activities to the job. However, effort direction may blur
the line between effort and performance because effort direction focuses on whether workers
direct their efforts to the most important tasks. If so, effort direction may capture effectiveness
of behavior more so than the intensity and persistence dimensions.
Similarly, very few studies have examined the consistency with which workers exert effort
over time (for an exception, see experimental studies by Yeo and Neal, 2004, 2008). For
example, do some workers exert consistently high levels of effort, whereas others exert
high effort only when necessary, such as in response to a deadline or an incentive?
Relatedly, future research could examine a possible distinction between typical and
maximal effort that may parallel the typical-maximal distinction for job performance (Beus
& Whitman, 2012).
In addition, although we identiﬁed several factors on which effort measures can vary,
future research might consider additional factors. For example, we noted that some measures
are labeled and/or focus on mental effort, whereas other measures focus on physical effort.
For example, Rich et al.’s (2010) Cognitive Engagement scale includes items such as “At
work, my mind is focused on my job”and “At work, I concentrate on my job.”Other mea-
sures focus on attentional effort, such as whether people’s thoughts focus on the task or on
off-task things. For instance, Kanfer et al.’s (1994) On-Task Thoughts scale includes items
like “I focused my total attention on learning a speciﬁc rule,”whereas their Off-Task
Thoughts scale includes items like “I took ‘mental breaks’during the task.”In contrast, we
did not ﬁnd any measures of physical effort that met our inclusion criteria. Some measures
include “physical”in their name, such as the Rich et al. (2010) and May, Gilson, and
Harter (2004) Physical Engagement scales. However, the items often are not speciﬁc to phys-
ical effort (e.g., “I work with intensity on my job,”“I exert my full effort to my job”). Future
research might explore ways to measure physical effort within the work context, such as
Van Iddekinge et al. / Work Effort 23
adapting physiological measures used in research on health and physical ability. For example,
Halper and Vancouver (2016) measured the level and persistence of handgrip force partici-
pants applied. Similarly, Feltz et al. (2016) measured effort using cycle ergometer power
output in a study designed to improve astronauts’exercise in space.
Furthermore, we focused on effort workers devote to job tasks. However, effort also can be
interpersonal, such as effort workers devote to developing and maintaining relationships with
coworkers. For instance, emotional labor measures sometimes refer to effort workers devote
to the emotional display rules organizations ask them to show (e.g., smiling toward custom-
ers; Beal, Trougakos, Weiss, & Green, 2006). Similarly, measures of deep acting (e.g.,
Diefendorff, Croyle, & Gosserand, 2005) sometimes refer to effort employees devote to expe-
riencing the emotions of others (e.g., “I work hard to feel the emotions that I need to show to
customers”). Thus, research is needed to examine the possibility of interpersonal effort,
including whether it is indeed distinct from concepts such as helping-focused OCB and emo-
Finally, results suggest that effort is a stronger predictor of outcomes than many more com-
monly measured constructs. However, all the primary studies measured effort in a postem-
ployment context. Thus, research is needed to examine how to best measure effort in a
selection context. Given the transparent nature of most self-report effort measures, response
distortion is likely to be an issue when administering such measures to job applicants. If so,
researchers could explore measuring effort using methods that may be less susceptible to dis-
tortion, such as situational judgment tests and assessment center exercises.
Effort antecedents and covariates. Several of the relationships in the nomological
network we tested were based on data from small numbers of primary studies. In addition,
there may be additional constructs within effort’s network for which we did not ﬁnd any
data. For example, work drive is “a disposition to work long hours, take on extra responsibil-
ities at work, display a high level of energy at work, and to see oneself as being a hard worker
compared to other people”(Lounsbury et al., 2004: 429). Several elements of this deﬁnition
appear to overlap with effort, including “energy at work”and “being a hard worker.”
Lounsbury et al.’s (2004) measure of work drive also taps elements of effort, such as “I
tend to work more hours every week than most people I know”and “I would say that I
have more work drive and energy than most people.”Similarly, individuals who possess a
proactive personality “show initiative, take action, and persevere until they bring about mean-
ingful change”(Crant, 1995: 532), and personal initiative is “a behaviour syndrome resulting
in an individual’s taking an active and self-starting approach to work and going beyond what
is formally required in a given job”(Frese, Fay, Hilburger, Leng, & Tag, 1997: 140).
Proactive personality and personal initiative appear similar to one another but somewhat dif-
ferent from effort. For example, these constructs focus on initiating behaviors, whereas effort
focuses more on the intensity and persistence of behaviors.
Another related construct, work ethic, originates from the concept of Protestant work
ethic and “reﬂects a constellation of attitudes and beliefs pertaining to work behavior”
(Miller, Woehr, & Hudspeth, 2002: 455). Miller et al. (2002) identiﬁed seven dimensions
of work ethic: centrality of work, self-reliance, hard work, leisure, morality/ethics, delay of
gratiﬁcation, and wasted time. Thus, work ethic appears to be much broader than work
effort. The two dimensions that may be most relevant to effort are hard work and wasted
24 Journal of Management
time. However, hard work focuses on beliefs about the value of working hard (e.g., “Hard
work makes one a better person”) rather than on how hard one actually works, and wasted
time focuses more on planning one’stime(e.g.,“I try to plan out my workday so as not to
Finally, several recent research streams have focused on the concept of work passion
(Pollack, Ho, O’Boyle, & Kirkman, 2020). General passion represents positive feelings
toward work (e.g., Baum & Locke, 2004) and is measured with items such as “I love my
work”and “I love to work hard.”The dualistic model deﬁnes passion “as a strong inclination
toward an activity that people like, that they ﬁnd important, and in which they invest time and
energy”(Vallerand et al., 2003: 757) and encompasses two dimensions. Harmonious passion
is measured by items such as “My work is in harmony with the other activities in my life,”
whereas obsessive passion is measured by items such as “I have an obsessive feeling for
my work.”It is not yet clear whether passion is distinct from constructs such as engagement,
job satisfaction, and positive affect, all of which demonstrated substantial correlations with
certain aspects of work passion in Pollack et al.’s (2020) meta-analysis.
Outcomes of effort. Research has focused primarily on effort as a predictor of job perfor-
mance, but effort may also be related to other work-related outcomes. For example, although
theoretical models of training often refer to effort (e.g., Kraiger, Ford, & Salas, 1993) or
related constructs, such as motivation to learn (e.g., Colquitt, LePine, & Noe, 2000), we
did not come across any ﬁeld studies that measured effort during training. Although effort
appears to be a promising construct for understanding the performance of entrepreneurs
(e.g., Uy et al., 2015), research relating effort to entrepreneurial success is very limited.
We focused on linear relations between effort and outcomes and found that effort is related
to better performance and, to a lesser extent, negatively related to CWB and turnover inten-
tions. Future research could explore whether high levels of effort could be “too much of a
good thing”(Pierce & Aguinis, 2013). For example, perhaps a moderate amount of effort
is optimal for increasing positive outcomes without having negative consequences, such as
withdrawal. Relatedly, we did not incorporate research linking ﬁt between effort and
rewards to strain and poor health (e.g., Lang, Van Hoeck, & Runge, in press). In addition,
we came across only a few studies that examined effort at levels higher than the individual,
such as team- or unit-level effort (e.g., Morgeson, Johnson, Campion, Medsker, & Mumford,
2006). Future research could examine the nature of collective effort as well as whether and
how this form of effort relates to higher-level outcomes, such as unit and ﬁrm performance.
Future research also is needed to better understand how decision makers perceive effort
versus ability. For example, social psychology research suggests that judges tend to give
higher evaluations to those who are naturally talented than to those who are less talented
but exert high effort (e.g., Tsay & Banaji, 2011). Other research has found that decision
makers sometimes provide higher evaluations to those who exert effort to improve than to
those whose performance is consistently high from the outset (Alessandri, Cortina, Sheng,
& Borgogni, 2020; Soliman & Buehler, 2018). Future research is needed to examine how
these tendencies and potential biases for talent versus effort manifest in decisions involving
selection and performance appraisals.
Additional moderators of effort–outcomes relations. Our review identiﬁed situations in
which relations between effort and outcomes are stronger or weaker. However, we need
Van Iddekinge et al. / Work Effort 25
more research that examines conditions under which effort has the greatest impact on out-
comes. For example, Blau (1993) found that performance was highest when both effort
direction (i.e., frequency of performing job-relevant behaviors) and effort intensity
(i.e., time spent working) were high. Another avenue would be to see if effort is relatively
more important in certain types of jobs. Perhaps effort is relatively more important than
constructs such as intelligence in less complex jobs. Similarly, Kanfer and Ackerman
(2004) proposed that effort may be a substitute for cognitive abilities, such that
workers who possess lower cognitive capacity may still achieve high levels of perfor-
mance by exerting substantial effort (see also Van Iddekinge, Aguinis, Mackey, &
DeOrtentiis, 2018). Kanfer and Ackerman also distinguished between jobs and tasks
that require high levels of expertise and those that require high levels of ﬂuid processing
capacity. Expertise-intense jobs and tasks depend on effort only during skill acquisition.
As expertise accumulates, workers can execute the tasks with less effort. In contrast, tasks
that require ﬂuid capacity depend on effort.
Finally, future research could explore cross-cultural differences in the amount or effects of
effort. For instance, in Japan, a culture of working long hours has led to the deterioration of
workers’health in a phenomenon known as karoshi (which means “work to death”; Kanai,
2009). Further, Hofstede’s (2011) model of cultural values includes a dimension (i.e., long-
term vs. short-term orientation) that reﬂects whether people focus their effort on the future or
on the past and present. Future research also could examine subgroup differences in effort,
which could be important if organizations try to assess effort when making selection or pro-
Organizational researchers have written about work effort for more than a century, and
managers are continually looking for ways to maintain and increase effort from their employ-
ees. However, it seems as though the ﬁeld has assumed we know what effort is and, for
example, often “lumps”it in with related but distinct constructs, such as motivation and per-
formance. This is in stark contrast to the considerable research attention devoted to constructs
that theoretically are more distal to outcomes such as job performance (e.g., personality,
person–environment ﬁt, goal setting). “New”and exciting concepts, such as work engage-
ment, grit, and work passion, have also received much more attention than effort. Our
review provides some clarity about what effort is, what it is not, and how to measure it. In
addition, we hope some of our ideas will spur future research, which will be challenging
given some of the complexities we identiﬁed regarding how and when to measure effort.
Nonetheless, we hope the ﬁeld is willing to put in the “effort”to help researchers and man-
agers better understand, measure, and affect this long-standing, yet still very relevant, ante-
cedent of work behavior.
Herman Aguinis https://orcid.org/0000-0002-3485-9484
Jonas W. B. Lang https://orcid.org/0000-0003-1115-3443
26 Journal of Management
1. For lab studies that included a manipulation(s), we used results for the control condition whenever possible. If
control group–speciﬁc results were not reported, we used results across groups. We compared results from lab
studies that included a manipulation with those that did not and found no substantive differences.
2. The ﬁrst two authors independently reviewed every effort measure. We included measures in which at least 80%
of the items appeared to assess effort and the primary studies reported all effort items or the items were available
from an alternative source.
3. Seven studies used supervisors to rate both employee effort and performance (e.g., Dysvik & Kuvaas, 2011).
Initial analyses revealed that these same-source designs yielded unusually strong correlations between effort
and performance (mean observed and corrected rs=.63 and .70, respectively). Accordingly, we excluded
these correlations to maintain consistency both within the current meta-analysis and with other meta-analyses.
Speciﬁcally, none of the proposed antecedents were supervisor rated, and we similarly excluded correlations
based on self-ratings of both effort and performance. In addition, predictors of performance in previous meta-
analyses typically are not based on supervisor ratings, so this decision also facilitates comparisons with those
4. The codes and values for each primary study are available from the ﬁrst author upon request.
5. We also attempted to incorporate antecedent–performance estimates from prior meta-analyses as an additional
point of comparison. However, prior meta-analyses often used different inclusion criteria, such as the inclusion
of self-report measures of job performance (e.g., Cerasoli, Nicklin, & Ford’s  meta-analysis of intrinsic
motivation) or experiments that may have included other manipulations and/or tasks irrelevant to work perfor-
mance (e.g., Mento, Steel, & Karren’s  meta-analysis of goal setting). The prior meta-analyses also incor-
porated different corrections for statistical artifacts. Overall, we found very few prior estimates that would be
comparable to the estimates we report.
6. As all the primary studies in our data set measured effort after employees had been selected into the organization,
there was no direct range restriction on effort. However, there could be indirect range restriction due to selection
on variables that correlate with effort (e.g., measures related to some of the antecedents in Table 2). As such,
these effort–outcome correlations may represent conservative estimates of the true relationships (Le, Oh,
Schmidt, & Wooldridge, 2016). This also underscores that caution should be exerted when comparing these cor-
relations with correlations of selection constructs and procedures that are used in actual selection settings.
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