IRRU Research Seminar, Warwick Business School, 18 November 2015
The Timing of Training Effects: A Learning Curve Perspective
Achim Krausert
Warwick Business School
Author Note
Achim Krausert, Warwick Business School, University of Warwick
Correspondence to: Achim Krausert, Warwick Business School, University of Warwick,
Coventry CV4 7AL.
Phone: 024 7652 4273.
Email: achim.krausert@wbs.ac.uk.
To cite the paper:
Krausert, Achim. 2015. The timing of training effects: A learning curve perspective. In John
Humphreys (Ed.), Proceedings of the Seventy-fifth Annual Meeting of the Academy of
Management. Online ISSN: 2151-6561.
1
IRRU Research Seminar, Warwick Business School, 18 November 2015
Abstract
This conceptual study examines differences in the timing of training effects across types of
training. It proposes a distinction between learning curve shift and learning curve acceleration
effects of training. A multilevel model is developed outlining differences between these effect
types in terms of lag effects as well as the effect duration (attributable to factors such as
synergistic effects among different training activities, “training roll out” across the workforce,
and collective sense-making and role negotiation processes). The model is applicable both
where training is effective by inducing (positive) change and where it is effective by preempting
(negative) change. The study contributes to the identification of future-oriented HR
investments—with implications for corporate governance.
Keywords: Training effects; Time; Learning curve
2
IRRU Research Seminar, Warwick Business School, 18 November 2015
1. Introduction
This paper contributes to recent literature pursuing the idea that, because the capital
market cannot distinguish future-oriented HRM investments from bad management, quarterly
earnings targets may create pressures on managers to limit respective HRM investments
(Krausert, 2014; Liu, van Jaarsveld, Batt, & Frost, 2014). If only managers could credibly
communicate to investors about HRM activities that are linked to future earnings, so the
argument, the future effects would be factored into investor expectations and the stock price,
improving the ability of managers to support HRM investments without incurring a near-term
penalty in their stock-based compensation and job security (Edmans, 2009, 2011).
Consequently, work has been undertaken to develop and establish an HRM signal for the capital
market, indicating to investors whether a firm has made HRM investments linked to future
earnings (Krausert, forthcoming). What is still missing is a comprehensive understanding of
which HRM activities should and which ones should not be considered future-oriented.
The HRM activity that has most commonly been thought to be affected by near-term
performance pressures is employee training (e.g., MacDuffie & Kochan, 1995; Porter, 1992).
The evidence on the timing of training effects is mixed, however, both with respect to lag effects
(e.g. Aguinis & Kraiger, 2009; Ployhart, Van Iddekinge, & MacKenzie, 2011) and with respect
to the effect duration (e.g., Blume, Ford, Baldwin, & Huang, 2010; Sung & Choi, 2014). Theory
pertaining to the timing of training effects is generally limited (see also Kim & Ployhart 2014;
Tharenou, Saks, & Moore 2007). And theory that would explain differences in the timing of
effects across different types of training is entirely absent from the literature.
This conceptual study develops a multilevel model to explain differences in the timing of
training effects. It proposes a classification of training activities based on whether they
3
IRRU Research Seminar, Warwick Business School, 18 November 2015
accelerate or shift the learning curves of the targeted employees. Training effects via learning
curve shift are proposed to be associated with longer lag effects and a longer effect duration than
training effects via learning curve acceleration. The model encompasses a range of mediators
explaining different temporal effects, from synergistic effects among multiple training activities
through “training roll out” across the workforce to collective sense-making and role negotiation
processes. The model is applicable both where training is effective by inducing positive change
(i.e., performance improvements) and where it is effective by preempting unintended (negative)
change. Thus, the study advances the theoretical understanding with regard to both why and
under what circumstances employee training has different temporal effects.
The theoretical basis of the study is learning curve theory, which suggests that
performance improves over time (more specifically: with cumulative task experience), at a
decreasing rate (Wright, 1936). The proposed shape of the learning curve has been evidenced
across a wide range of settings (Lapré & Nembhard, 2010). Given performance continuously
improves with task experience—also without training—learning curve theory provides the
appropriate theoretical basis for the study of temporal effects of training. Hence, this study will
be concerned with training effects on performance (or learning curve) trajectories, as opposed to
effects on performance levels.
2. Learning curve effects of employee training
The central proposition of learning curve theory is that performance improves with
increasing cumulative task experience, at a decreasing rate (Wright, 1936). The proposed shape
of the learning curve has been evidenced across a diverse range of jobs and industries, from
airplane manufacturing to pizza making, and across different levels, from the employee through
the organizational to the industry level (e.g., Argote, Beckman, & Epple, 1990; Dutton &
4
IRRU Research Seminar, Warwick Business School, 18 November 2015
Thomas, 1984). The central construct in learning curve research is the learning rate, defined as
the performance improvement in percent occurring with every doubling of cumulative task
experience. Much of the more recent research has been concerned with differences in learning
rates, relating them for instance to differences in the type of experience accumulated (such as
success versus failure experience) or to learning transfer across tasks, units, and organizations
(which has in turn be related to factors such as the colocation of development and manufacturing
personnel) (Argote, McEvily, & Reagans, 2004; Kim, Krishnan, & Argote, 2012; Lapré &
Nembhard, 2010).
------------------------------------------------------------------------Insert Figure 1 About Here
------------------------------------------------------------------------Less commonly, learning curve research has also been concerned with the impact of
management interventions on the learning curve, including industrial engineering and HRM
activities (resulting in induced as opposed to autonomous learning). The impact of HRM
activities on the learning curve has arguably been insufficiently studied (Argote et al., 2004;
Lapré & Nembhard, 2010). Nevertheless, a handful of studies have also examined how
employee training impacts on the learning curve. Adler and Clark (1991) as well as Nembhard
and Tucker (2011) found training to temporarily disrupt the learning progress before positive
performance effects emerge (after a time lag of two years in Nembhard and Tucker’s study). By
contrast, Hatch and Dyer (2004) reported that training “speeds the rate at which human resources
learn their duties” (p. 1159). Their finding is consistent with an earlier study by Mukherjee,
Lapré, and Wassenhove (1998).
Based on the learning curve literature, it shall then be argued that employee training may
5
IRRU Research Seminar, Warwick Business School, 18 November 2015
have two types of effect on the learning curve. First, it may induce learning curve shift—a more
fundamental change to how employees perform a task, entailing that employees need to apply
and develop a different set of skills. The change is associated with incomplete knowledge
spillover in the sense that at least some of the experience and skills developed previously are no
longer applicable (Benkard, 2000). This may result in a temporary performance reduction, until
equivalent experience is accumulated in relation to the altered skill set (Adler & Clark, 1991).
Second, training may induce learning curve acceleration: The learning rate is increased while
the set of skills needed to master the task is not significantly changed. For example, leadership
training might be effective by altering the predominant leadership style adopted by the managers
of an organization—inducing learning curve shift (Barling, Weber, & Kelloway, 1996). Or it
might be effective by helping recently promoted junior managers to learn the predominant
leadership style of their organization more quickly—inducing learning curve acceleration.
The following sections will develop a model detailing how learning curve shift and
acceleration effects of employee training may be associated with different lag effects and effect
durations. The term employee training shall be understood broadly, as any planned effort aimed
at the enhancement of employee learning with regard to work-related skills, knowledge, and
abilities (Berk & Kaše, 2010; Mathieu & Tesluk, 2010).
3. The link between the training effect type and lag effects at the employee level
In this section, it will be proposed that the training effect type (learning curve shift versus
acceleration) has a bearing on the lag effect at the employee level (the time lag between the
training activity and the emergence of beneficial effects on employee performance). In the first
instance, the training effect type shall be argued to influence the employee’s relative position on
the learning curve: Following learning curve shift effects of training, employees should
6
IRRU Research Seminar, Warwick Business School, 18 November 2015
generally find themselves at an earlier stage of the learning curve, in comparison with learning
curve acceleration effects (which do not place employees at the start of a new learning curve but
move them down further on a learning curve they have already been progressing down). The
employee’s relative position on the learning curve, in turn, can be argued to influence lag effects
in two ways.
First, it can be argued to relate to the time it takes to integrate the newly developed skill
in the employee’s repertoire of behavioral routines. Integrating a new skill in an employee’s
repertoire requires practice on the job, (a) so that the employee learns to recognize cues that
trigger the application of the skill (Gaudine & Saks, 2004) and (b) to adapt the skill to the
specific job and organizational context (Ackerman & Heggestad, 1997). It can then be argued
that this process of new skill integration takes more time if employees find themselves in an
earlier relative position on the learning curve: The evidence suggests that the early learning
stages are associated with much trial-and-error, mistakes, and false starts, before a usable
repertoire of behavioral routines is developed and before the autonomous learning process
gradually picks up speed (Anderson, 1995; Boisot, 1998; Kozlowski, Gully, Nason, & Smith,
1999). At the same time, there is evidence to suggest that employees incorporate new skills
more easily at more advanced learning stages, where they develop self-regulatory skills
facilitating their reflecting on and making changes to their repertoire of behavioral routines
(Smith, Ford, & Kozlowski, 1997; Volet, 1991). Thus, it shall be proposed that training inducing
learning curve shift rather than acceleration tends to be associated with longer lag effects, firstly,
due to the (initially) slower integration of new skills in the employees’ repertoire of behavioral
routines.
Second, the employee’s relative position on the learning curve may also influence how a
7
IRRU Research Seminar, Warwick Business School, 18 November 2015
training activity relates to synergistic effects among multiple training activities. Strategic HRM
research has demonstrated synergistic effects among complementary HRM practices such as
selective hiring, extensive training, employment security, and involving job designs (Chadwick,
2010). It is plausible that synergistic effects are also associated with sequences of
complementary training activities, that is, foundational and more advanced training activities. If
it can be assumed that complementary training activities tend to relate to different stages of the
learning curve, one can argue that, at an earlier learning stage, the employee is more likely to
engage in foundational training, the effects of which are more likely to (at least partially) depend
on the completion of advanced training at a later stage. Vice versa, at later stages of the learning
curve, training effects should be less likely to depend on further training activities. Or training
may even build on earlier foundational training activities to realize synergistic effects.
Consequently, it can be proposed that training inducing learning curve shift rather than
acceleration tends to be associated with longer lag effects also because the full realization of
effects is more likely to depend on subsequent, complementary training activities.
Apart from the employee’s relative position on the learning curve, the training effect type
may influence the lag effect also due to a factor that shall be referred to as obsolete experience.
Learning curve shift renders experience at least partially obsolete, requiring the accumulation of
experience in relation to a different skill set. This may result in temporary performance
deterioration until a sufficient amount of experience is accumulated in relation to the new skill
set, as evidenced by learning curve research (Adler & Clark, 1991; Benkard, 2000; Vits, Gelders,
& Pintelon, 2007). This factor—obsolete experience—is, by definition, not applicable given
training effects via learning curve acceleration, which do not involve significant change to the
skill set applied and developed.
8
IRRU Research Seminar, Warwick Business School, 18 November 2015
------------------------------------------------------------------------Insert Figure 2 About Here
------------------------------------------------------------------------In summary, the training effect type is proposed to influence the lag effect via three
mediators at the employee level: new skill integration, synergistic effects, and obsolete
experience. This influence shall additionally be proposed to be moderated by task complexity,
which shall be defined in terms of the number of component skills constituting the learning
curve. That is, a learning curve can be seen to consist of a set of component skills (or proximal
learning goals) that need to be developed in relation to the task at hand (e.g., Delaney, Reder,
Staszewski, & Ritter, 1998; Lapré & Nembhard, 2010); a more complex task is associated with a
longer learning curve, consisting of more component skills to be learned in order to master the
task (see also Anderson, 1995; McIver, Lengnick-Hall, Lengnick-Hall, & Ramachandran, 2013).
Hence, the more complex the task (the longer the learning curve), the more likely will it require
multiple, synergistic training activities at different learning stages. And the greater will be the
experience that may potentially become obsolete. Vice versa, given a low level of task
complexity, learning curve shift is less likely to entail long lag effects due to synergistic effects
or obsolete experience. The difference between the timing of training effects via learning curve
shift versus acceleration is then likely to be less pronounced.
------------------------------------------------------------------------Insert Figure 3 About Here
------------------------------------------------------------------------4. The link between the training effect type and lag effects at the organizational level
Organizational learning curves assume the same power-curved shape as employee-level
9
IRRU Research Seminar, Warwick Business School, 18 November 2015
learning curves—that is, organizational performance improves with cumulative (organizationlevel) task experience, at a decreasing rate (Dutton & Thomas, 1984; Lapré & Nembhard, 2010).
Organizational learning is the result of employee-level learning in relation to (a) task
performance and/or (b) collaborative processes (Argote et al., 2004; Ingram, 2002; Kim, 1993).
A similar argument was made in the training effects literature, where employee-level
performance effects of training were argued to impact on organizational performance either
directly or via changes in the processes that define how performance contributions of employees
combine within and across units (Kozlowski, Brown, Weissbein, Cannon-Bowers, & Salas,
2000).
It can then be argued that, at the organizational level, employee training may be effective
through either organizational learning curve shift or organizational learning curve acceleration:
Organizational learning curve shift shall be defined as a change in performance processes and
organizational routines (across employees), involving a step change in the set of skills being
applied and developed at the organizational level. Organizational learning curve shift requires
employee-level learning curve shift across multiple employees (Kozlowski et al., 2000). For
example, leadership training may be effective by altering the predominant leadership style of an
organization, requiring the unlearning and relearning of leadership behaviors across a larger
number of managers (Barling et al., 1996).
By contrast, organizational learning curve acceleration effects of training shall be defined
as positive effects on the organizational learning rate while the set of skills applied and
developed is not significantly altered at the organizational level. Organizational learning curve
acceleration may be associated either with learning curve shift or with learning curve
acceleration at the employee level. For example, a leadership training may be effective by
10
IRRU Research Seminar, Warwick Business School, 18 November 2015
helping a newly promoted junior manager to fundamentally alter her leadership style (inducing
learning curve shift at the employee level) so as to match the preferred leadership style of her
organization and resolve a “bottleneck” caused by her incompatible leadership approach. Or it
may be effective by helping her move down a given learning curve more quickly (inducing
learning curve acceleration at the employee level), improving the effectiveness of organizational
processes and routines in more subtle ways. In either case, the effect at the organizational level
will be learning curve acceleration.
On that basis, it shall be proposed that training effects via organizational learning curve
shift tend to be associated with longer lag effects than training effects via organizational learning
curve acceleration, firstly, because they are more likely to be associated with learning curve shift
at the employee level. That is, according to the above argument, organizational learning curve
shift necessarily requires employee-level learning curve shift. Organizational learning curve
acceleration is likely to be associated with employee-level learning curve shift in some cases and
employee-level learning curve acceleration in other cases. Employee-level learning curve shift
effects of training were associated with generally longer lag effects than employee-level learning
curve acceleration effects in the previous section. Hence, by and large, organizational learning
curve shift should be more likely to be associated with the longer lag effects of employee-level
learning curve shift than organizational learning curve acceleration.
Secondly, organizational learning curve shift involves change to performance processes
and organizational routines across employees. This requires change in shared assumptions,
beliefs, and expectations in the organization that define and govern how the contributions of
employees are combined within and across the units of the organization. Such shared
assumptions, beliefs, and expectations have been studied previously as shared mental models
11
IRRU Research Seminar, Warwick Business School, 18 November 2015
(e.g., Kim, 1993; Lorinkova, Pearsall, & Sims, 2013). Change to shared mental models was
shown to involve role negotiation (Brueller & Carmeli, 2011; Garfield & Dennis, 2013;
Kozlowski et al., 1999). Role negotiation can be time consuming due to the need to coordinate
across long cause-effect chains, interpersonal and intergroup conflicts, and resistance to change
(Lorinkova et al., 2013; Marks, Mathieu & Zaccaro, 2001). Role negotiation does not occur at a
single point in time but at different times in the collective sense-making process, which requires
time itself and is inseparably interwoven with the role negotiation process (Brueller & Carmeli,
2011; Chen & Klimoski, 2007). In other words, it may take time for employees in various parts
of the organization to think through and discuss the ramifications of different ways of
implementing and coping with the change. And this process of sense-making is informed by
and, simultaneously, feeds into the role negotiation process at multiple points in time. Hence,
training effects via organizational learning curve shift can be proposed to be associated with
longer lag effects, secondly, due to collective sense-making and role negotiation processes—in
comparison with organizational learning curve acceleration effects (which improve performance
within the confines of existing shared mental models).
Third, training effects via organizational learning curve shift may be more lagging also
because the training may have to be “rolled out” across a larger number of employees for effects
to emerge—while effects via organizational learning curve acceleration should not hinge on
training roll out. A study in the human capital resource literature has yielded that the effects of
raised human capital levels (skill levels) on organizational performance depended on human
capital being raised across a wider part of the workforce (a “stock” of human capital being built
up) (Ployhart et al., 2011; see also Dierickx & Cool, 1989). Applying this finding in the context
of the present study, it means employee training may have to be “rolled out” across a larger
12
IRRU Research Seminar, Warwick Business School, 18 November 2015
number of employees for performance effects to emerge. Rolling out training may take time in
particular where the training takes longer and relates to firm-specific skills, such as management
trainee and apprenticeship programs (typically taking one to three years to complete). Firmspecific skills demand the involvement of experienced employees in conducting the training (as
opposed to the training being conducted by a training provider in the market) (McIver et al.,
2013). Given experienced employees with the skills needed to conduct the training are available
in limited numbers within an organization, respective training programs must be rolled out
cohort by cohort, year after year.
Thus, “training roll out” constitutes a factor that may potentially cause lag effects. This
factor shall be proposed to be more likely applicable where training is effective by inducing
organizational learning curve shift rather than acceleration. Organizational learning curve shift
involves change in performance processes and organizational routines as well as the shared
mental models that govern them. This implies that training must change the assumptions and
behaviors of more than one employee—according to Ployhart et al. (2011) of a majority of
employees in the unit—for effects to emerge at the organizational level (see also Kozlowski et
al., 2000). By contrast, organizational learning curve acceleration effects do not involve change
in performance processes, organizational routines, and the underlying shared mental models.
Hence, performance improvements at the individual level translate directly into performance
effects at the organizational level (assuming the training is effective) (Kozlowski et al., 2000).
An example of a training effective via organizational learning curve shift might be a
management trainee or apprenticeship program when it is first introduced in an organization.
Applying the arguments by Ployhart et al. (2011), effects on organizational performance should,
at that point, depend on the completion of the program by a larger number of employees over
13
IRRU Research Seminar, Warwick Business School, 18 November 2015
time (for the “human capital stock” to be built up, for behavior change to emerge on a noticeable
scale, and for the organizational learning curve to be shifted). By contrast, training that brings
up to speed an underperforming employee (resolving a “bottleneck” in the performance
processes and organizational routines of the firm) would be an example of a training effective via
organizational learning curve acceleration, the effects of which should not depend on the training
being rolled out (Kozlowski et al., 2000).
------------------------------------------------------------------------Insert Figure 4 About Here
------------------------------------------------------------------------In summary, the training effect type at the organizational level is proposed to relate to
organizational-level lag effects via the employee-level training effect type, collective sensemaking and role negotiation processes, and training roll out. Its impact shall additionally be
proposed to be moderated by task complexity (represented by the length of the learning curve,
that is, the number of component skills to be learned in order to master the task). A more
complex skill is likely associated with more extensive training (covering more component skills),
which is likely to result in a longer training roll out process, according to the above arguments.
In a similar vein, changing shared mental models in relation to more complex tasks is likely to be
associated with a longer collective sense-making and role negotiation process (relating to a
greater number of component tasks and corresponding work processes). For example, Nembhard
and Tucker (2011) reported on a training program inducing a complex change affecting 93
different work processes across different hospital units. This type of training will likely involve
both longer sense-making and negotiation processes and longer training roll out than, for
instance, a training introducing a new office supplies ordering process (inducing organizational
14
IRRU Research Seminar, Warwick Business School, 18 November 2015
learning curve shift on a smaller scale).
------------------------------------------------------------------------Insert Figure 5 About Here
------------------------------------------------------------------------5. The link between the training effect type and the training effect duration at the employee
level
The extent to which employee training constitutes an investment in future performance
depends, apart from lag effects, also on the effect duration, that is, the extent to which positive
effects are recurring rather than temporary. In this respect, the learning curve logic implies that
effects via learning curve acceleration should generally be temporary while effects via learning
curve shift may potentially be lasting. Learning curve acceleration gives employees
metaphorically speaking a “head start” in moving down the learning curve, compared to
employees in a scenario without training. This advantage will gradually be reduced as the
employees approach the learning curve plateau, at which most employees will have developed
most component skills through experience and informal learning (Dutton & Thomas, 1984). By
contrast, learning curve shift raises the learning curve plateau to a level not reached in a scenario
without training. Hence, one can propose a direct, unmediated relationship between the training
effect type and the effect duration at the employee level.
------------------------------------------------------------------------Insert Figure 6 About Here
------------------------------------------------------------------------Additionally, this relationship shall be proposed to be moderated by job tenure, the halflife of knowledge, and task complexity. Job tenure (or employee turnover) is commonly
15
IRRU Research Seminar, Warwick Business School, 18 November 2015
mentioned as a factor constraining the training effect duration (e.g., Boudreau, 1983; Lapré &
Nembhard, 2010). The “half-life of knowledge” can be defined as the amount of time over
which the trained skill is applicable on the job. It relates (inversely) to change in technology and
in products or services marketed, advancements in professional expertise, and temporary needs
for skills. The consequence of a limited half-life of knowledge is what the learning curve
literature refers to as organizational forgetting: The relationship between cumulative task
experience and performance is weaker to the extent task experience dates back far (Benkard,
2000; Kleiner, Nickelsburg and Pilarski, 2012; Thompson, 2007). Thus, if the job tenure or the
half-life of knowledge tends to be short, training effects are more likely to be temporary, not only
given learning curve acceleration but also given learning curve shift effects. Finally, task
complexity is defined in terms of the number of component skills constituting the learning curve
(see also Anderson, 1995; McIver et al., 2013). Effects via learning curve acceleration (giving
employees a head start in moving down the learning curve) should last longer given a longer
learning curve (or, more specifically, a longer learning curve segment from the point at which
the training is provided to the learning curve plateau). The difference in the effect duration
between learning curve shift and acceleration effects should then, generally, be inversely related
to the length of the learning curve (from the point at which the training is provided to its
plateau).
------------------------------------------------------------------------Insert Figure 7 About Here
------------------------------------------------------------------------6. The link between the training effect type and the training effect duration at the
organizational level
16
IRRU Research Seminar, Warwick Business School, 18 November 2015
This section is concerned with the link between the type of training effect on the
organizational learning curve (shift versus acceleration) and the effect duration at the
organizational level. The first mediator of this link shall be proposed to be the employee-level
training effect type, analogous to the earlier argument: Organizational learning curve shift is
necessarily associated with employee-level learning curve shift. Organizational learning curve
acceleration may be associated with either employee-level learning curve shift or employee-level
learning curve acceleration. According to the previous section, employee-level learning curve
shift tends to be associated with a longer effect duration (in comparison with employee-level
learning curve acceleration). Hence, one must expect that training effects via organizational
learning curve shift tend to be associated with the longer effect duration of employee-level
learning curve shift, as a general trend and in comparison with effects via organizational learning
curve acceleration.
A second potential mediator of the link between the training effect type and the effect
duration at the organizational level is the reversibility of the change effected by the training.
This factor can be related to a phenomenon referred to as learning decay in the literature. That
is, it may occur that employees use trained skills on the job for a limited period of time and then
revert to behaviors adopted prior to the training (Blume et al., 2010; Taylor, Russ-Eft, and Chan,
2005). Learning decay can be interpreted from the perspective of this study as follows: Given
employee-level learning curve shift effects, learning decay implies that employees revert to the
learning curve they were progressing down prior to the training. By contrast, given employeelevel learning curve acceleration effects, learning decay implies that employees cease to apply a
new component skill while remaining on the same learning curve. That is, following the
training, their performance initially improves at a more rapid pace. Then performance either
17
IRRU Research Seminar, Warwick Business School, 18 November 2015
regresses to the pretraining level or—assuming continuous experience-based learning—it
develops at a slower pace. In relation to learning curve shift effects at the organizational level,
learning decay implies that multiple employees revert to pretraining learning curves as well as
the undoing of change to shared mental models. Finally, given learning curve acceleration
effects at the organizational level, learning decay may imply any of the following: a reversal of
employee-level learning curve shift, a reversal of employee-level learning curve acceleration,
and fluctuation among trained employees.
On that basis, it shall then be proposed that a reversal of organizational learning curve
shift effects is less likely than a reversal of organizational learning curve acceleration effects.
Organizational learning curve shift but not acceleration effects involve change to organizational
routines and the shared mental models that govern them. The reversal of change to shared
mental models is less likely than the reversal of change within the confines of existing shared
mental models for three reasons (Gersick, 1991). First, change to shared mental models—once
accomplished—involves change in cognition, which has a stabilizing effect on new shared
mental models and the corresponding processes and routines. Second, changing shared mental
models involves employee investments in terms of effort and discomfort (for instance related to
sense-making and role negotiation processes). Consequently, the employees’ motivation to
revert to prior shared mental models may be low. Third, different shared mental models are
associated with different networks of obligations among employees, locking shared mental
models into a stable equilibrium. These arguments by Gersick (1991) are supported by evidence
in so far as major organizational change was shown to be typically followed by longer periods of
stability (Langley, Smallman, Tsoukas, & Van de Ven, 2013; Lorinkova et al., 2013; Summers,
Humphrey, & Ferris, 2012). On that basis, it can be proposed that training effects are likely to be
18
IRRU Research Seminar, Warwick Business School, 18 November 2015
longer given organizational learning curve shift (rather than acceleration) effects because the
effected change is less likely to be reversed, that is, learning decay should be less likely.
------------------------------------------------------------------------Insert Figure 8 About Here
------------------------------------------------------------------------The link between the training effect type and the reversibility of change (and
consequently learning decay) shall furthermore be proposed to be moderated by task complexity.
Learning curve shift in relation to a more complex task (involving a larger number of component
skills) should involve more extensive change of employee assumptions and cognition, more
extensive employee investments in terms of effort and discomfort (sense-making and role
negotiation processes in relation to a greater number of component processes), and more
extensive change to obligation networks—rendering reversal to prior organizational learning
curves more costly and less likely. Vice versa, given low levels of task complexity, the reversal
of organizational learning curve shift should be less costly and more likely, entailing less
pronounced differences in the effect duration between organizational learning curve shift and
acceleration effects.
------------------------------------------------------------------------Insert Figure 9 About Here
------------------------------------------------------------------------7. Training that is effective by preempting unintended change
Apart from performance improvement effects, training may also be effective by
sustaining the “human capital resource” of the organization (its aggregate skills) and, thus,
preempting organizational performance deterioration against the backdrop of employee turnover
19
IRRU Research Seminar, Warwick Business School, 18 November 2015
(Dierickx & Cool, 1989; Ployhart et al., 2011). To interpret this type of training effect from the
learning curve theoretical perspective of this study, one needs to consider what happens if an
organization suspends the respective training activity. If an organization suspends a regular
training activity, such as a management trainee or apprenticeship program, it may, as a first
possibility, incur unintended organizational learning curve shift. That is, given employee
turnover, vacancies may be staffed with insufficiently trained and developed employees and such
employees may start to question, negotiate about, and ultimately change their unit’s work
processes, organizational routines, and shared mental models. The research evidence supports
both (a) that the joining of a unit or work group by new employees may trigger such negotiations
and consequent change (e.g., Garfield & Dennis, 2013; Summers et al., 2012) and (b) that the
outcome of this change may be less effective work processes and organizational routines,
especially if the new employees are insufficiently trained and prepared for the specific context of
their job and organization (Boisot, 1998; Boisot, MacMillan, & Han 2007). Alternatively, as a
second possibility, the staffing of vacancies with less trained, less skilled employees might result
in what shall be referred to as unintended organizational learning curve deceleration rather than
shift. That is, new job incumbents may progress down the same learning curve as their
predecessors while they have mastered fewer skill components, performing the same processes
and routines at a lower level of effectiveness. Whether the staffing of vacancies with
insufficiently trained employees results in unintended organizational learning curve shift or
deceleration may depend on factors such as the amount of job discretion (e.g., Taylorist versus
enriched or team-based forms of work organization), the amount of turnover (a larger number of
new employees might be more likely to effect unintended organizational learning curve shift—
see also Ployhart et al., 2011), the amount of time for which the training is postponed, and the
20
IRRU Research Seminar, Warwick Business School, 18 November 2015
strength of professional norms governing work processes and routines where professional work
is affected (Argote & Ophir, 2002; Freidson, 2001).
It can then be argued that the earlier developed model of the timing of training effects is
applicable, too, where training is effective by sustaining rather than improving organizational
performance. That is, whether training is effective by preempting unintended organizational
learning curve shift or by preempting organizational learning curve deceleration should have a
bearing on lag effects via two of the earlier proposed mediators. Lag effects may conceivably be
influenced by collective sense-making and role negotiation processes—given training preempts
unintended organizational learning curve shift: Following a reduction of respective training
activities, shared mental models are unlikely to change instantaneously as positions are staffed
with less skilled employees. Experienced employees may uphold the existing organizational
routines for some time. Any change to shared mental models will likely involve processes of
questioning, negotiating, and collective sense-making before new routines become established
(Lorinkova et al., 2013; Summers et al., 2012). By contrast, where the reduction of training
results in organizational learning curve deceleration, the effect does not depend on change to
shared mental models or the sense-making and role negotiation processes associated with it.
Apart from collective sense-making and role negotiation, unintended organizational
learning curve shift may also depend on lower levels of skill and experience being “rolled out”
across a greater number of employees. That is, employee turnover tends to be a gradual process,
reducing aggregate skill levels (the “human capital resource”) one employee at a time.
(Unintended) organizational learning curve shift, however, requires change in assumptions,
beliefs, and expectations across a larger number of employees (Ployhart et al., 2011)—skill
levels need to be reduced across multiple positions. By contrast, less effective performance of
21
IRRU Research Seminar, Warwick Business School, 18 November 2015
existing processes and routines may in principle result from skill reductions in a single job (e.g.,
reduced sales in a sales job or emergence of a “bottleneck” due to inadequate performance
behaviors of an employee) (Kozlowski et al., 2000).
Thus, two of the earlier proposed organizational-level mediators of lag effects should be
applicable not only where training improves but also where it sustains organizational
performance. The earlier proposed employee-level mediators of lag effects, however, can be
argued to not be applicable where training is effective by sustaining instead of improving
organizational performance. The reduction of skills at the employee (job) level occurs
instantaneously with the replacement of a fully trained employee by an insufficiently trained
employee—both in the context of training being effective by preempting unintended
organizational learning curve shift and in the context of training being effective by preempting
organizational learning curve deceleration (although the effect may be buffered at the
organizational level if the organization has previously built up a pool of skilled employees
exceeding their immediate demand). Nevertheless, due to collective sense-making and role
negotiation processes as well as the time it may take to “roll out” lower levels of skill, lag effects
are likely to be longer to the extent training is effective by preempting unintended organizational
learning curve shift rather than organizational learning curve deceleration.
Finally, training effects via sustained (as opposed to improved) organizational
performance trajectories are also subject to the earlier arguments pertaining to differences in the
training effect duration. If the postponement of a regular training activity results in employees
progressing down the same organizational learning curve more slowly (organizational learning
curve deceleration), they may be able to “catch up” as they accumulate task experience, learn
informally, and receive training at a later point in time (in comparison to a scenario in which
22
IRRU Research Seminar, Warwick Business School, 18 November 2015
training is provided throughout). However, if the failure to train results in a shift to a less
effective learning curve, performance will plateau at a lower level (in comparison to the
continuous training scenario). Moreover, once unintended organizational learning curve shift
has occurred, the altered shared mental models associated with it should render a reversal of the
shift costly (Gersick, 1991). In summary, the final proposition of this paper shall be that training
that preempts unintended organizational learning curve shift tends to be associated both with
longer lag effects and with longer effect durations than training that preempts organizational
learning curve deceleration.
8. Discussion
This paper contributes to recent literature concerned with relationships between various
types of HR investment and future firm performance (Edmans, 2012; Krausert, 2014; Nyberg,
Pieper, & Trevor, forthcoming; Ployhart et al., 2011). It has been argued that future-oriented HR
investments should be of interest to investors in the capital market as signals of future earnings
growth (Hendry, Woodward, Harvey-Cook, & Gaved, 1999; Edmans, 2011; Krausert,
forthcoming). If investors do attend to respective HR investments, so the argument, their
performance effects will be factored into investor expectations and, consequently, the stock price
(Edmans, 2009, 2011; Cohn, Khurana, & Reeves, 2005). This should, in turn, improve the
ability of managers to support HR investments without incurring a near-term penalty in their
stock-based compensation and job security (Gentry & Shen, 2013). Hence, work has been
undertaken to develop an HRM signal for the capital market, which is intended to signal to
investors whether a firm has made HR investments predictive of future earnings (Krausert,
forthcoming). What is still lacking is a comprehensive understanding of which HRM activities
should and which ones should not be considered future oriented. A type of HRM activity that is
23
IRRU Research Seminar, Warwick Business School, 18 November 2015
often thought to be future-oriented is employee training (e.g., MacDuffie & Kochan, 1995;
Porter, 1992). However, the evidence is both limited and mixed, some studies suggesting
lagging effects (Bartel, 1994; d’Arcimoles, 1997; Nembhard & Tucker, 2011; Ployhart et al.,
2011), others suggesting immediate effects of employee training (Aguinis & Kraiger, 2009;
Taylor et al., 2005). Similarly, the available evidence concerning the training effect duration
partly suggests temporary effects (Blume et al., 2010; Holzer, Block, Cheatham, & Knott, 1993;
Murray & Raffaele, 1997), partly lasting effects (Aguinis & Kraiger, 2009; Taylor et al., 2005;
Sung & Choi, 2014).
The first contribution of this paper is a theoretical model providing grounds to argue that
the timing of training effects may systematically differ depending on whether training induces
organizational learning curve shift or organizational learning curve acceleration. That is,
organizational learning curve shift effects of training were argued to be associated with both
generally longer lag effects and a generally longer effect duration. By contrast, training inducing
organizational learning curve acceleration was argued to have a more immediate as well as a
more temporary effect. It can then be argued further that training inducing organizational
learning curve shift should be particularly relevant for the work undertaken in relation to the
HRM signal for the capital market, especially where the training relates to relatively complex
tasks and where both the job tenure and the “half-life of knowledge” tend to be reasonably long.
Training impacting via organizational learning curve acceleration should by and large have a
more immediate effect and, hence, be of less concern from a corporate governance point of view
(the effect is more likely to be reflected in near-term earnings, which are linked to managerial
incentives in immediate ways). It was furthermore argued that training can be effective not only
by inducing positive change effects but also by preempting unintended change. If training is
24
IRRU Research Seminar, Warwick Business School, 18 November 2015
effective by sustaining rather than improving organizational performance trajectories, it should
be associated with both a longer lag effect and a longer effect duration to the extent it preempts
undesirable organizational learning curve shift rather than deceleration. Respective training
activities should then also be taken into account for the HRM signal for the capital market.
As a second contribution, the study proposed a multilevel model offering a
comprehensive explanation of how and under what circumstances various factors influence the
timing of training effects. The model encompasses mediators and moderators of the link
between the training effect type on the one hand and lag effects and the effect duration on the
other—both at the employee and organizational levels. This is the first model to offer a
comprehensive explanation of why the timing of training effects may differ across training
activities. To be sure, the prior literature does encompass a number of factors explaining why
effects may be lagging or why effects may be temporary as opposed to lasting. However, it has
not been suggested that (or how) these factors might apply differentially across types of training.
Finally, as a third contribution, this study has covered a wider range of influences on lag
effects and the effect duration than the prior literature. Potential causes of lag effects in the prior
literature include, in essence (often implied rather than explicit), the integration of new skills in
an employee’s repertoire of behaviors (in the training transfer literature: e.g., Blume et al., 2010;
Gaudine & Saks, 2004; Sitzmann & Weinhardt, forthcoming). They include the accumulation of
a stock of human capital (in the human capital resource literature: e.g., Dierickx & Cool, 1989;
Ployhart et al., 2011; Ployhart, Weekley, and Ramsey 2009)—broadly corresponding to the
training roll out construct in the current study (while the current study provides a more specific
explanation of why the accumulation of human capital stock may require time). Lag effects
related to, in essence, collective sense-making and role negotiation processes were mentioned by
25
IRRU Research Seminar, Warwick Business School, 18 November 2015
Kozlowski et al. (2000) (in the training effects literature). Finally, obsolete experience was
covered as a potential cause of lag effects in the learning curve literature (Adler & Clark, 1991;
Nembhard & Tucker, 2011). Beyond these factors, this study has proposed synergistic effects
among multiple training activities as an additional potential cause of lag effects. The relative
position on the learning curve was proposed as an antecedent of time to new skill integration and
of synergistic effects among multiple training activities. Task complexity was proposed as a
moderator. With respect to influences on the effect duration, the training effects literature has
predominantly been concerned with learning decay (Blume et al., 2010; Salas & CannonBowers, 2001; Taylor et al., 2005) and employee turnover (Boudreau, 1983; Schmidt, Hunter, &
Pearlman 1982). Beyond that, this study proposed a direct effect of the training effect type on
the effect duration at the employee level and the reversibility of change as a mediator at the
organizational level (as well as tenure, the half-life of knowledge, and task complexity as
moderators). Hence, the study provided not only a model integrating various factors to explain
how temporal effects of training may differ across types of training but it also provided a more
comprehensive perspective on potential influences on the timing of training effects in
comparison with the prior literature.
The study opens up a number of avenues for further research. For example, one could
examine a wider range of specific training activities of organizations and determine to what
extent they are effective by inducing learning curve shift versus acceleration. One could
examine to what extent learning curve shift and acceleration at the employee level relate to
learning curve shift and acceleration at the organizational level. One could examine to what
extent different training activities are effective by inducing intended change versus preempting
unintended change. One could study the proposed mediators and moderators, relating them to
26
IRRU Research Seminar, Warwick Business School, 18 November 2015
different training activities, training effect types, lag effects, and the training effect duration.
Based on a better understanding of the relative importance of various influences on lag effects
and the effect duration, one might explore measures to minimize lag effects and maximize the
effect duration.
From a corporate governance point of view, one could study influences of the training
effect type, the various mediating and moderating factors, and the lag effect and effect duration
on decisions to invest in training. One could study such influences in the presence of near-term
performance pressures (for instance related to quarterly earnings targets) and in their absence.
One might relate firm expenditures on different types of training activity to long-term returns on
investment in the capital market. Bassi and McMurrer (2005) obtained abnormal long-term
returns on investment for a portfolio of firms selected on the basis of their training expenditures.
Following the present study, one might select portfolios of firms based on their expenditures
across different types of training activity in order to examine differences in the earnings
predictability of these types. The study then provides an input for the development of an HRM
signal for the capital market (Krausert, forthcoming). In that context, further work could, for
instance, seek to provide a comprehensive listing of specific training activities with near- versus
longer-term effects, specifying expected lag effects and effect durations. One could examine
how those collating the HRM signal might obtain reliable information about respective training
activities and how the coverage of respective training activities by the HRM signal affects
training investment decisions (see also Hoque, 2003; Hoque & Bacon, 2008).
Finally, future research could examine some factors that had to remain outside the scope
of this study. First, the scope of the study was confined to the employee and organizational
levels. Future research could examine additional factors that may influence the timing of
27
IRRU Research Seminar, Warwick Business School, 18 November 2015
training effects at the financial level. Such factors were previously covered by a literature
conducting economic utility analyses of training. This literature was not concerned with the
timing of training effects as such or differences across types of training activity. However, it did
include the training effect duration as a factor influencing the economic utility of training as well
as a number of antecedents of the training effect duration at the financial level, such as rising
wages in the wake of training, tax-related factors, the cost of capital, and rates of return on
alternative investment opportunities (e.g., Birati & Tziner, 1999; Boudreau, 1983). Future
research should take into account such factors and their interaction with the factors covered by
the current study. Related to that is the question whether returns on training investment are
appropriated by the firm (through higher profits) or by employees (through higher wages), where
research in economics and industrial relations suggests it tends to be the former (e.g., Ballot,
Fakhfakh, & Taymaz, 2006; Bartel, 2000). Representing yet another factor above the
organizational level, future research could also study the timing of training effects contingent on
whether performance improvement effects lead to increased productivity (that is, ultimately,
reduced costs at the financial level) or improved product/service attributes (ultimately improved
revenues).
Second, another subject outside the scope of this study was training effects via employee
attitudes. While this study was focused on training effects via employee learning, there is
evidence to suggest that training may also have effects via organizational commitment, employee
motivation, morale, effort, employee turnover, and absenteeism (Galunic & Anderson, 2000;
Tharenou et al., 2007). In a related vein, training activities might influence the employer
attractiveness of a firm. It is likely that the timing of training effects via employee attitudes and
employer attractiveness differs from the timing of effects via learning. Yet another factor not
28
IRRU Research Seminar, Warwick Business School, 18 November 2015
directly related to learning is time gaps between the training and the application of developed
skills on the job. For example, leadership development programs for nonmanagerial employees
might have little bearing on current job performance—effects might start to emerge only as and
if the employees are promoted into managerial positions. Related to that, another factor may be
organizations building up pools of employees with certain skills beyond their immediate
demand. This may potentially create a buffer mitigating negative effects of the postponement of
training activities for some time. Thus, future empirical research should examine a wider range
of influences on temporal effects of training, including the factors that were derived through the
learning curve theoretical perspective of this study and other factors.
Apart from providing a basis for further research, this study provides immediate practical
guidance where empirical research may (currently) not be able to provide needed answers.
Specifically, as discussed above, it should guide efforts to develop and establish an HRM signal
for the capital market. Related to that, a better understanding of the timing of effects of various
training activities may also provide a basis for more effective direct communication between
managers and the investment community (Krausert, forthcoming; zu Knyphausen-Aufseß,
Mirow, & Schweizer, 2011). That is, the managers’ ability to credibly communicate about
effects of intangibles investments on future earnings may enable investors to better distinguish
current earnings that are low due to bad management from earnings that are low because the firm
has made necessary HR-related investments. Ultimately, this may enable managers to support
investments in employees without incurring a near-term penalty in their stock-based
compensation and job security (Gentry & Shen, 2013).
Based on the model developed with this study, the communication between managers and
investors should cover training activities aimed at changing existing ways of working in an
29
IRRU Research Seminar, Warwick Business School, 18 November 2015
organization, be it by changing the predominant leadership style of an organization (Barling et
al., 1996), cross-unit interaction patterns (Nembhard & Tucker, 2011), or levels of employee
involvement (Birdi et al., 2008). Another type of training activity their communication may
cover is training programs aimed at building up skills across the workforce (or a segment of the
workforce), such as management trainee and apprenticeship programs. Such programs may be
associated with lag effects in particular when they are first implemented, requiring the rolling out
of higher levels of skill across the workforce, collective sense-making and role negotiation
processes associated with emerging better ways of working, as well as learning processes at the
employee level: Respective training programs typically encompass a sequence of synergistic
training activities and they require a larger number of skill components to be integrated in the
employees’ repertoires of behavioral routines (see also McIver et al., 2013). Once they are
established in the organization, such training programs should serve to sustain rather than
improve organizational performance trajectories. Nevertheless, they may potentially still be
considered future-oriented HR investments since postponing respective training activities too
far—coupled with employee turnover—might result in unintended change to the ways of
working in the organization, which might be difficult to reverse once new routines have taken
hold. Hence, firm communications with the capital market might encompass such training
program not only when they are first implemented but also when they are sustained by a firm for
instance during an economic downturn (especially if they are suspended by other firms at the
same time). Regardless of changed ways of working at the organizational level, training
activities might also need to be accounted for as future-oriented if they are associated with
substantial lag effects at the employee level, caused by synergistic effects among multiple
training activities, new skill integration, and obsolete experience given employee-level learning
30
IRRU Research Seminar, Warwick Business School, 18 November 2015
curve shift.
31
IRRU Research Seminar, Warwick Business School, 18 November 2015
References
Ackerman, P. L., & Heggestad, E. D. (1997). Intelligence, personality, and interests: Evidence
for overlapping traits. Psychological Bulletin, 121, 219-245.
Adler, P. S., & Clark, K. B. (1991). Behind the learning curve: A sketch of the learning process.
Management Science, 37, 267-281.
Aguinis, H., & Kraiger, K. (2009). Benefits of training and development for individuals and
teams, organizations, and society. Annual Review of Psychology, 60, 451-474.
Anderson, J. R. (1995). Learning and memory: An integrated approach. New York: Wiley.
Argote, L., Beckman, S. L., & Epple, D. (1990). The persistence and transfer of learning in
industrial settings. Management Science, 36, 140-154.
Argote, L., McEvily, B., & Reagans, R. (2004). Managing knowledge in organizations: An
integrative framework and review of emerging themes. Management Science, 49, 571582.
Argote, L, & Ophir, R. (2002). Intraorganizational learning. In J. A. C. Baum (Ed.), Companion
to organizations (pp. 181-207). Malden, MA: Blackwell.
Ballot, G., Fakhfakh, F., & Taymaz, E. (2006). Who benefits from training and R&D, the firm or
the workers? British Journal of Industrial Relations, 44, 473-495.
Barling, J., Weber, T., & Kelloway, E. K. (1996). Effects of transformational leadership training
on attitudinal and financial outcomes: A field experiment. Journal of Applied
Psychology, 81, 827-832.
Bartel, A. P. (1994). Productivity gains from the implementation of employee training programs.
Industrial Relations, 33, 411-425.
Bartel, A. P. (2000). Measuring the employer’s return on investments in training: Evidence from
the literature. Industrial Relations, 39, 502-524.
Bassi, L., & McMurrer, D. (2005). Developing measurement systems for managing in the
knowledge era. Organizational Dynamics, 34, 185-196.
Benkard, C. L. (2000). Learning and forgetting: The dynamics of aircraft production. American
Economic Review, 90, 1034-1054
Berk, A., & Kaše, R. (2010). Establishing the value of flexibility created by training: Applying
real options methodology to a single HR practice. Organization Science, 21, 765-780.
Birati, A., & Tziner, A. (1999). Economic utility of training programs. Journal of Business and
Psychology, 14, 155-164.
Birdi, K., Clegg, C., Patterson, M., Robinson, A., Stride, C. B., Wall, T. D., & Wood, S. J.
(2008). The impact of human resource and operational management practices on
company productivity: A longitudinal study. Personnel Psychology, 61, 467–501.
Blume, B. D., Ford, J. K., Baldwin, T. T., & Huang, J. L. (2010). Transfer of training: A metaanalytic review. Journal of Management, 36, 1065-1105.
Boisot, M. H. (1998). Knowledge assets: Securing competitive advantage in the information
economy. New York: Oxford University Press.
Boisot, M. H., MacMillan, I. C., & Han, K. S. (2007). Explorations in information space:
Knowledge, actors, and firms. New York: Oxford University Press.
Boudreau, J. W. (1983). Effects of employee flows on utility analysis of human resource
productivity improvement programs. Journal of Applied Psychology, 68, 396-406.
Brueller, D., & Carmeli, A. (2011). Linking capacities of high-quality relationships to team
learning and performance in service organizations. Human Resource Management, 50,
455-477.
32
IRRU Research Seminar, Warwick Business School, 18 November 2015
Chadwick, C. (2010). Theoretic insights on the nature of performance synergies in human
resource systems: Toward greater precision. Human Resource Management Review, 20,
85–101.
Chen, G., & Klimoski, R. J. (2007). Training and development of human resources at work: Is
the state of our science strong? Human Resource Management Review, 17: 180-190.
Cohn, J. M., Khurana, R., & Reeves, L. (2005). Growing talent as if your business depended on
it. Harvard Business Review, 83(10), 62-70.
d’Arcimoles, C. (1997). Human resource policies and company performance: A quantitative
approach using longitudinal data. Organization Studies, 18, 857-874.
Delaney, P. F., Reder, L. M., Staszewski, J. J., & Ritter, F. E. (1998). The strategy-specific
nature of improvement: The power law applies by strategy within task. Psychological
Science, 9, 1-7.
Dierickx, I., & Cool, K. (1989). Asset stock accumulation and sustainability of competitive
advantage. Management Science, 35, 1504-1511.
Dutton, J. M., & Thomas, A. (1984). Treating progress functions as a managerial opportunity.
Academy of Management Review, 9, 235-247.
Edmans, A. (2009). Blockholder trading, market efficiency, and managerial myopia. Journal of
Finance, 64, 2481–2513.
Edmans, A. (2011). Does the stock market fully value intangibles? Employee satisfaction and
equity prices. Journal of Financial Economics, 101, 621–640.
Edmans, A. (2012). The link between job satisfaction and firm value, with implications for
corporate social responsibility. Academy of Management Perspectives, 26, 1-19.
Freidson, E. (2001). Professionalism: The third logic. London: Polity.
Galunic, D. C., & Anderson, E. (2000). From security to mobility: Generalized investments in
human capital and agent commitment. Organization Science, 11, 1-20.
Garfield, M. J., & Dennis, A. R. (2013). Toward an integrated model of group development:
Disruption of routines by technology-induced change. Journal of Management
Information Systems, 29, 43-86.
Gaudine, A. P., & Saks, A. M. (2004). A longitudinal quasi-experiment on the effects of
posttraining transfer interventions. Human Resource Development Quarterly, 15, 57-76.
Gentry, R. J., & Shen, W. (2013). The impacts of performance relative to analyst forecasts and
analyst coverage on firm R&D intensity. Strategic Management Journal, 34, 121-130.
Gersick, C. J. G. (1991). Revolutionary change theories: A multilevel exploration of the
punctuated equilibrium paradigm. Academy of Management Review, 16, 10-36.
Hatch, N. W., & Dyer, J. H. (2004). Human capital and learning as a source of sustainable
competitive advantage. Strategic Management Journal, 25, 1155-1178.
Hendry, C., Woodward, S., Harvey-Cook, J., & Gaved, M. (1999). Investors’ views of people
management. Corporate Governance, 7, 324-337.
Holzer, H. J., Block, R. N., Cheatham, M., & Knott, J. H. (1993). Are training subsidies for firms
effective? The Michigan experience. Industrial and Labor Relations Review, 46, 625636.
Hoque, K. (2003). All in all, it’s just another plaque on the wall. Journal of Management Studies,
40, 543-571.
Hoque, K., & Bacon, N. (2008). Investors in People and training in the British SME sector.
Human Relations, 61, 451-482.
Ingram, P. (2002). Interorganizational learning. In J. A. C. Baum (Ed.), Companion to
33
IRRU Research Seminar, Warwick Business School, 18 November 2015
organizations (pp. 642-663). Malden, MA: Blackwell.
Kim, D. H. (1993). The link between individual and organizational learning. Sloan Management
Review, 35, 37-50.
Kim, Y., Krishnan, R., & Argote, L. (2012). The learning curve of IT knowledge workers in a
computing call center. Information Systems Research, 23, 887-902.
Kim, Y., & Ployhart, R. E. (2014). The effects of staffing and training on firm productivity and
profit growth before, during, and after the great recession. Journal of Applied
Psychology, 99, 361-389.
Kleiner, M. M., Nickelsburg, J., & Pilarski, A. M. (2012). Organizational and individual learning
and forgetting. Industrial and Labor Relations Review, 65, 68-81.
Kozlowski, S. W. J., Brown, K. G., Weissbein, D. A., Cannon-Bowers, J. A., & Salas, E. (2000).
A multilevel approach to training effectiveness: Enhancing horizontal and vertical
transfer. In K. J. Klein & S. W. J. Kozlowski (Eds.), Multilevel theory, research, and
methods in organizations (pp. 157-210). San Francisco: Jossey-Bass.
Kozlowski, S. W. J., Gully, S. M., Nason, E. R., & Smith, E. M. (1999). Developing adaptive
teams: A theory of compilation and performance across levels and time. In D. R. Ilgen &
E. D. Pulakos (Eds.), The changing nature of performance (pp. 240-292). San Francisco:
Jossey-Bass Publishers.
Krausert, A. (2014). HRM systems for knowledge workers: Differences among top managers,
middle managers, and professional employees. Human Resource Management, 53, 67-87.
Krausert, A. (forthcoming). HRM signals for the capital market. Human Resource Management.
Published online before print (Early View).
Langley, A., Smallman, C., Tsoukas, H., & Van de Ven, A. H. (2013). Process studies of change
in organization and management: Unveiling temporality, activity, and flow. Academy of
Management Journal, 56, 1-13.
Lapré, M. A., & Nembhard, I. M. (2010). Inside the organizational learning curve:
Understanding the organizational learning process. Foundations and Trends in
Technology, Information and Operations Management, 4, 1-103.
Liu, X., van Jaarsveld, D. D., Batt, R., & Frost, A. C. (2014). The influence of capital structure
on strategic human capital: Evidence from U.S. and Canadian firms. Journal of
Management, 40, 422-448.
Lorinkova, N. M., Pearsall, M. J., & Sims, H. P., Jr. (2013). Examining the differential
longitudinal performance of directive versus empowering leadership in teams. Academy
of Management Journal, 56, 573-596.
MacDuffie, J. P., & Kochan, T. A. (1995). Do U.S. firms invest less in human resources?
Training in the World Auto Industry. Industrial Relations, 34, 147-168.
Marks, M. A., Mathieu, J. E., & Zaccaro, S. J. (2001). A temporally based framework and
taxonomy of team processes. Academy of Management Review, 26, 356-376.
Mathieu, J. E., & Tesluk, P. E. (2010). A multilevel perspective on training and development
effectiveness. In S. W. J. Kozlowski & E. Salas (Eds.), Learning, training, and
development in organizations, Volume 1 (pp. 405-442). New York: Routledge.
McIver, D., Lengnick-Hall, C. A., Lengnick-Hall, M. L., & Ramachandran, I. (2013).
Understanding work and knowledge management from a knowledge-in-practice
perspective. Academy of Management Review, 38, 597-620.
Mukherjee, A., Lapré, M., & Wassenhove, L. (1998). Knowledge driven quality improvement.
Management Science, 44, S35-S49.
34
IRRU Research Seminar, Warwick Business School, 18 November 2015
Murray, B., & Raffaele, G. C. (1997). Single-site, results-level evaluation of quality awareness
training. Human Resource Development Quarterly, 8, 229-245.
Nembhard, I. M., & Tucker, A. L. (2011). Deliberate learning to improve performance in
dynamic service settings: Evidence from hospital intensive care units. Organization
Science, 22, 907-922.
Nyberg, A. J., Pieper, J. R., & Trevor, C. O. (forthcoming). Pay-for-performance’s effect on
future employee performance: Integrating psychological and economic principles toward
a contingency perspective. Journal of Management. Published online before print.
Ployhart, R. E., Van Iddekinge, C. H., & MacKenzie, W. I. (2011). Acquiring and developing
human capital in service contexts: The interconnectedness of human capital resources.
Academy of Management Journal, 54, 353-368.
Ployhart, R. E., Weekley, J. A., & Ramsey, J. (2009). The consequences of human resource
stocks and flows: A longitudinal examination of unit service orientation and unit
effectiveness. Academy of Management Journal, 52, 996-1015.
Porter, M. (1992). Capital disadvantage: America’s failing capital investment system. Harvard
Business Review, 70(5): 65-82.
Salas, E., & Cannon-Bowers, J. A. (2001). The science of training: A decade of progress. Annual
Review of Psychology, 52, 471-99.
Schmidt, F. L., Hunter, J. E., & Pearlman, K. (1982). Assessing the economic impact of
personnel programs on workforce productivity. Personnel Psychology, 35, 333-347.
Sitzmann, T., & Weinhardt, J. M. (forthcoming). Training engagement theory: A multilevel
perspective on the effectiveness of work-related training. Journal of Management.
Published online before print.
Smith, E. M., Ford, J. K., & Kozlowski, S. W. J. (1997). Building adaptive expertise:
Implications for training design strategies. In M. A. Quinones & A. Ehrenstein (Eds.),
Training for a rapidly changing workplace: Applications of psychological research (pp.
89-118). Washington, DC: American Psychological Association.
Summers, J. K., Humphrey, S. E., & Ferris, G. R. (2012). Team member change, flux in
coordination, and performance: Effects of strategic core roles, information transfer, and
cognitive ability. Academy of Management Journal, 55, 314-338.
Sung, S. Y., & Choi, J. N. (2014). Do organizations spend wisely on employees? Effects of
training and development investments on learning and innovation in organizations.
Journal of Organizational Behavior, 35, 393-412.
Taylor, P. J., Russ-Eft, D. F., & Chan, D. (2005). A meta-analytic review of behavior modeling
training. Journal of Applied Psychology, 90, 692-709.
Tharenou, P., Saks, A. M., & Moore, C. (2007). A review and critique of research on training
and organizational-level outcomes. Human Resource Management Review, 17, 251-273.
Thompson, P. (2007). How much did the liberty shipbuilders forget? Management Science, 53,
908-918.
Vits, J., Gelders, L., & Pintelon, L. (2007). Manufacturing process changes: Myopic and longterm planning. International Journal of Production Research, 45, 1-27.
Volet, S. E. (1991). Modeling and coaching of relevant metacognitive strategies for enhancing
university students’ learning. Learning and Instruction, 1, 319-336.
Wright, T. P. (1936). Factors affecting the cost of airplanes. Journal of Aeronautical Sciences, 3,
122-128.
zu Knyphausen-Aufseß, D., Mirow, M., & Schweizer, L. (2011). The role of financial analysts in
35
IRRU Research Seminar, Warwick Business School, 18 November 2015
the strategy formation process of business firms. Industrial and Corporate Change, 20,
1153-1187.
36
IRRU Research Seminar, Warwick Business School, 18 November 2015
Performance
Figure 1: Learning curve
Cumulative task experience
37
IRRU Research Seminar, Warwick Business School, 18 November 2015
Figure 2: Mediators of the link between the training effect type and lag effects at the
employee level
New skill
integration
Training effect
type (learning
curve shift vs.
acceleration)
Synergistic
training
activities
Lag effect
(employee level)
Obsolete
experience
38
IRRU Research Seminar, Warwick Business School, 18 November 2015
Figure 3: Moderator of the link between the training effect type and the lag effect at the
employee level
Task complexity
Training effect
type (learning
curve shift vs.
acceleration)
Lag effect
(employee level)
39
IRRU Research Seminar, Warwick Business School, 18 November 2015
Figure 4: Mediators of the link between the training effect type and the lag effect at the
organizational level
Collective sense
making and role
negotiation
Training effect
type (learning
curve shift vs.
acceleration)
“Training roll
out”
Lag effect
(organizational
level)
Lag effect
(employee level)
40
IRRU Research Seminar, Warwick Business School, 18 November 2015
Figure 5: Moderator of the link between the training effect type and the lag effect at the
organizational level
Task complexity
Training effect
type (learning
curve shift vs.
acceleration)
Lag effect
(organizational
level)
41
IRRU Research Seminar, Warwick Business School, 18 November 2015
Figure 6: Direct effect of the training effect type on the effect duration at the employee level
Training effect
type (learning
curve shift vs.
acceleration)
Effect duration
(employee level)
42
IRRU Research Seminar, Warwick Business School, 18 November 2015
Figure 7: Moderators of the link between the training effect type and the effect duration at
the employee level
Task complexity
Training effect
type (learning
curve shift vs.
acceleration)
Job tenure
Effect duration
(employee level)
“Half-life of
knowledge”
43
IRRU Research Seminar, Warwick Business School, 18 November 2015
Figure 8: Mediators of the link between the training effect type and the effect duration at
the organizational level
Reversibility of
change
Training effect
type (learning
curve shift vs.
acceleration)
Effect duration
(organizational
level)
Effect duration
(employee level)
44
IRRU Research Seminar, Warwick Business School, 18 November 2015
Figure 9: Moderator of the link between the training effect type and the effect duration at
the organizational level
Task complexity
Training effect
type (learning
curve shift vs.
acceleration)
Effect duration
(organizational
level)
45