Content uploaded by Kenneth P. De Meuse
Author content
All content in this area was uploaded by Kenneth P. De Meuse on Nov 09, 2017
Content may be subject to copyright.
JOURNAL OF MANAGERIAL ISSUES Vol. XXV Number 2 Summer 2013
The Role of Learning Agility in Executive
Career Success: The Results of Two Field Studies
Guangrong Dai
Intellectual Property Research
Korn/Ferry International
Kenneth P. De Meuse
Executive Vice President
Tercon Consulting
King Yii Tang
Intellectual Property Research
Korn/Ferry International
The contemporary business environment is one of constant change due to
extensive globalization, dynamic economic conditions, the increased usage of virtual
interaction and social media, and rapid advancements in technology. This new
environment renders leadership and managerial skills that once created
organizational success insufficient for continued success (Joiner, 2009). There is a
growing recognition that today’s organizational leaders must develop agility as a core
capability, so they can respond effectively to the uncertainty and ambiguity of the
modern marketplace (Yukl and Mahsud, 2010).
Recent employment selection research reflects this trend. Van Iddekinge and
Ployhart (2008) observed that job performance criteria have expanded outside
traditional job-specific task requirements to include adaptive performance. This
expanded performance domain has changed people’s concept with regard to what
individual attributes should be used to predict executive effectiveness. A key
component of contemporary strategic employee selection is to identify talent who has
the potential to adapt in this volatile business environment. As Silzer and Church
JOURNAL OF MANAGERIAL ISSUES
Vol. XXV Number 2 Spring 2013: 108-131
(108)
DAI, DE MEUSE, AND TANG
JOURNAL OF MANAGERIAL ISSUES Vol. XXV Number 2 Summer 2013
asserted, “This is a significant mind shift from short-term selection to long-term
prediction, often over a three to ten year period or more. The prediction process is
not to match an individual to specific known positions and responsibilities, but rather
to predict how much potential an individual has, with additional growth and
development, to be a candidate in the future for a group of possible positions” (2009:
378-379).
Within this context, researchers have begun to identify individual attributes that
are related to long-term potential. To be adaptive, it requires one to learn new ways of
coping with unforeseen problems and opportunities. Learning and skill development
plays an important role in an individual’s long-term effectiveness and career success
(Silzer and Church, 2009; Tannenbaum, 1997). During the past decade, learning
agility has been used increasingly as an assessment for high potential talent (De Meuse
et al., 2010; Lombardo and Eichinger, 2000). Despite its increasing usage in the
practitioner world, there is little scholarly research examining the empirical
relationship of learning agility to leadership effectiveness (DeRue et al., 2012).
Drawing on the findings of the executive leadership research as well as the career
success literature, two field studies were conducted to investigate how learning agility
contributes to executive success.
THEORETICAL OVERVIEW
Learning Agility and Executive Leadership.
Lombardo and Eichinger (2000) initially coined the term learning agility. They
defined it as the ability and willingness to learn from experience and subsequently
apply those lessons to perform successfully in new or first-time situations. The genesis
of this construct can be traced back to a series of executive research studies conducted
during the late-1980s at the Center for Creative Leadership (e.g., Lombardo et al.,
1988; McCall and Lombardo, 1983; McCall et al., 1988). In these studies, corporate
executives were interviewed to describe the key events in their careers that shaped the
development of their leadership skills. Based on a content analysis, the 616 events
described by the interview participants were categorized into different types of career
experiences such as first supervisory jobs, line-to-staff switches, starting from scratch
assignments, turning around businesses, dealing with career setbacks, and handling
business mistakes (McCall et al., 1988). The authors found that successful executives
tended to have more diverse experiences during their careers than unsuccessful (or
derailed) ones. “In the first place, derailed executives had a series of successes, but
usually in similar kinds of situations…By contrast, the arrivers (successful executives)
had more diversity in their successes” (McCall and Lombardo, 1983: 30). Diversity in
experience brings changes in the kind of challenges to be met and the types of
adversity to overcome; consequently, it sets the stage for learning and growth.
To prepare managers for executive-level positions, organizations need to provide
them with a variety of developmental experiences. However, diversity alone is only
part of the story. Simply going through an experience does not guarantee that
learning will occur. What seemed to characterize the successful executives was not their
impressive array of life experience, but the “extraordinary tenacity in extracting
109
LEARNING AGILITY IN EXECUTIVE CAREER SUCCESS
JOURNAL OF MANAGERIAL ISSUES Vol. XXV Number 2 Summer 2013
something worthwhile from their experience and in seeking experiences rich in
opportunity for growth” (McCall et al., 1988: 122). Mistakes and failures accompany
learning from experience. Successful executives handled their mistakes with poise and
grace; they admitted their mistakes and then set about analyzing and fixing them.
Derailed executives, on the other hand, tended to react to failure by going on the
defensive (McCall and Lombardo, 1983). The process of leadership development
requires the ability to learn, not only the developmental experience itself (Van Velsor
and Guthrie, 1998). Thus, from a developmental perspective, the assessment of an
individual’s learning agility would seem to play a vital role to determine whether
someone has the potential for leadership (McCall, 1998).
The importance of learning from leadership experiences has been examined in
other studies as well. For example, Spreitzer et al. (1997) developed a leadership
assessment and observed that dimensions related to learning were significantly related
to leadership competencies and ratings of executive potential. However, Spreitzer et
al.’s (1997) research had a methodological limitation because all the data collected
were from the same source (i.e., the boss). Dragoni et al. (2009) conducted a similar
study, but correlated the self-assessment of learning and developmental experiences
with end-state competencies rated by the bosses. These results reported that managers
with stronger learning orientations were more likely to be in developmental
assignments and achieve higher levels of managerial competencies from the
developmental experiences than those with weaker learning orientations.
Based on the proposition that challenging job experiences may result in the
development of a wide range of skills, abilities, insights, and knowledge, which
increase individuals’ capacities for effective managerial action, another study
investigated how work experience influences one’s promotability (De Pater et al.,
2009). These authors conducted three field studies sampling different types of
employees. In the first study, the authors used formal job analysis to examine
employees’ work content. Experts rated which work activities could be considered as
challenging. In the other two studies, employees were asked to respond to ten items
derived from the JCP (Job Challenge Profile; McCauley et al., 1999) to indicate the
extent to which they had challenging experiences in their current job. The three
studies consistently found that challenging job experiences explained incremental
variance in supervisory and organizational evaluations of an individual’s promotability
over and above current job performance.
New jobs often require different types of knowledge, skills, and capabilities that
employees do not currently possess. Consequently, current job performance may not
be the best predictor of one’s capacity for successfully performing at a higher job level.
Dries et al. (2012) compared supervisory ratings of learning agility and job
performance for a sample of employees who had been identified as high potentials by
their organizations with a carefully matched control group of non-high potential
employees. The study found that high performers were three times more likely to be
identified as high potentials than employees with low performance. On the other
hand, an employee’s likelihood of being identified as a high potential was increased
18-fold if they were high in learning agility. In summary, the above studies all found
that learning agility was a better predictor of individuals being identified as high
potentials than job performance.
110
DAI, DE MEUSE, AND TANG
JOURNAL OF MANAGERIAL ISSUES Vol. XXV Number 2 Summer 2013
Learning Agility and Career Success
Scholars have investigated how several types of personal attributes including
intelligence (Judge et al., 2010), personality (Boudreau et al., 2001; Judge and Hurst,
2007; Sutin et al., 2009; Zhang and Arvey, 2009), political and networking skills (Todd
et al., 2009), and human capital variables such as education and job and organizational
tenure (Judge et al., 1995; Ng et al., 2005) are related to career success. Career success
has been defined in two different ways – objective success and subjective success (Judge
et al., 1995). Objective career success refers to observable career accomplishments that
can be measured by the metrics of pay and position ascendancy. Subjective career
success, in contrast, is defined in terms of psychological outcomes as a result of one’s
career experiences, such as job and career satisfaction.
Learning agility would appear to be related especially to objective career success.
Objective career success often is reflected in upward mobility and increased pay.
According to Turner (1960), there are two systems of upward mobility: (a) contest
mobility and (b) sponsored mobility. The contest-mobility perspective suggests that in
the contest of advancement, victory comes to those individuals who demonstrate the
greatest accomplishments. It operates on the basis of performance and contribution to
the organization. Sponsored-mobility, on the other hand, posits that organizations pay
special attention to those individuals who are considered to have high potential,
provide them sponsoring activities such as training and developmental opportunities
to help them win the competition against others for career advancement. Learning
agility is likely to influence both systems of upward mobility. First, learning agility has
been considered as the primary indicator of high potential (Lombardo and Eichinger,
2000). High learning agile individuals are more likely than others to be identified as
high potentials, receiving more career management sponsorship from organizations
(Dries et al., 2012). Second, learning agile individuals learn from job experiences and
continuously develop new skills. Learning and development enables individuals to
perform their jobs more effectively. Consequently, it leads to greater rewards for
employees in terms of promotion rates and pay increases.
In order to further understand how learning agility influences career success, it is
necessary to consider the temporal nature of careers (cf. Bailyn, 2004). Judge et al.
(2010) demonstrated that career success can be attributed primarily to the following
two components: (a) early career advantages and (b) trajectories of success that unfold
over time. The second component of career success is particularly important in the
modern business world. Obviously, today’s business environment is highly volatile.
Novel situations are bound to arise in the workplace, often requiring applications
beyond an individual’s existing competencies. Early career advantages may remain
stagnant or even diminish over time when individuals fail to learn and develop new
skills and demonstrate adaptability. Building and diversifying one’s skill set and
engaging in continuous learning are essential for career success in today’s economy
(Eby et al., 2003). Hence, it seems reasonable to assume that learning agility would be
an important variable for achieving career success.
111
LEARNING AGILITY IN EXECUTIVE CAREER SUCCESS
JOURNAL OF MANAGERIAL ISSUES Vol. XXV Number 2 Summer 2013
Summary and Integration
The research on executive leadership demonstrates how learning from experience
is related to leadership effectiveness. Similarly, the career success literature highlights
the importance of the continuous growth of skills and knowledge. By integrating those
two lines of research, it becomes apparent that learning agility plays a critical role in
executives’ careers. Both the upward mobility (Ng et al., 2005; Turner, 1960) and the
career trajectory perspectives (Judge et al., 2010) provide the theoretical explanation
for the relationship between learning agility and executive career success. In addition,
the sponsor-mobility mechanism specifies that organizations provide high learning
agile individuals many career facilitating opportunities (e.g., developmental
assignments). The research suggests that high learning agile individuals proactively
seek developmental opportunities (McCall, 1998; McCauley, 2001), and likewise are
more able to capitalize on those opportunities than their low learning agile
counterparts (Dragoni et al., 2009). It seems logical that learning agility contributes to
one’s accumulation of knowledge and skills over time. Consequently, high learning
agility will translate into objective career success such as higher pay, additional
promotions, and overall occupational prestige. As the words of Marshall Goldsmith’s
book suggest, What Got You Here, Won’t Get You There (2007). Without being learning
agile, it seems difficult for individuals to climb the organizational ladder of executive
success.
Further, as the business environment becomes more complex and dynamic,
learning agility becomes even more important for individuals’ long-term success. In
2010, De Meuse et al. published a journal article entitled, “Learning Agility: A
Construct Whose Time Has Come.” They reviewed its historical roots, examined
various approaches to measure it, and identified areas of learning agility that need to
be studied. Recently, the Journal of Industrial and Organizational Psychology published a
series of ten articles investigating the concept of learning agility (e.g., De Meuse et al.,
2012; DeRue et al., 2012). The general conclusion was that more research is required
to establish an empirical linkage between learning agility and leadership effectiveness.
Hezlett and Kuncel (2012) prioritized the learning agility research agenda. The top
priority, according to those authors, is to evaluate the relationship between learning
agility and leaders’ career success. Only a handful of studies have empirically explored
the relationship between learning agility and leadership potential ratings (e.g.,
Bedford, 2011; Dries et al., 2012; Lombardo and Eichinger, 2000; Spreitzer et al.,
1997). The current research advances previous empirical studies by examining the
impact of learning agility on objective career outcomes.
In the two studies to be reported in this article, a self-assessment based on
Lombardo and Eichinger’s (2000) model of learning agility was used. Scholars have
begun to discuss recently the conceptual clarity of learning agility, particularly the
relationship between learning agility and other individual attributes (DeRue et al.,
2012). If the variation of learning agility can be explained largely by other attributes
frequently assessed in the employment setting, the measurement of learning agility
would not be beneficial. However, the extant research suggests that learning agility has
little overlap with cognitive ability and personality (Bedford, 2011; Connolly, 2001).
Further, learning from experience requires one to deal with novelty, adversity,
112
DAI, DE MEUSE, AND TANG
JOURNAL OF MANAGERIAL ISSUES Vol. XXV Number 2 Summer 2013
complexity, difficulty, diversity, uncertainty, accountability, and non-authority – all of
which are inherent in leadership development (McCauley et al., 1994). Because of the
multi-dimensional nature of the characteristics of developmental jobs, learning agility
has been defined as a multi-dimensional concept here (cf. De Meuse et al., 2010).
STUDY 1
Figure I depicts the basic model that guided the research. Study 1 investigated
how learning agility was related to the development of leadership competencies and
career success. The study used compensation and CEO proximity as indicators of
executive career success. Pay is the most often used operationalization of career success
(Judge et al., 2010). CEO proximity is the number of job levels below the CEO
individuals are positioned in their organizations. It also is used frequently as a
measure of career success (Boudreau et al., 2001).
Figure I
Hypothesized Model of Executive Career Success
Initially, the study investigated the relationship between learning agility and
leadership competencies. The leadership literature has reported that leaders learn
leadership skills primarily from job experiences (McCall, 2010). Learning agility is an
early indicator of leadership competence. In fact, the study by Dragoni et al. (2009)
found that managers with strong learning orientations, especially when they perceive
that they have access to on-the-job opportunities, are more likely to be in development
assignments and achieve high levels of competence based on those experiences.
Consequently, in this study, it was hypothesized that:
Hypothesis 1: Learning agility will be positively related to overall leadership
competence.
Learning
Agility
Overall
Leadership
Competence
Total
Compensation
CEO
Proximity
113
LEARNING AGILITY IN EXECUTIVE CAREER SUCCESS
JOURNAL OF MANAGERIAL ISSUES Vol. XXV Number 2 Summer 2013
The trajectory perspective of career success implies a positive relationship
between the growth of skills and career success. Since learning agile individuals are
likely to be in developmental job assignments, and challenging job experiences
influence the evaluation of leadership potential (De Pater et al., 2009), a positive
relationship between learning agility and career success was expected.
Hypothesis 2a: Learning agility will be positively related to CEO proximity.
Hypothesis 2b: Learning agility will be positively related to total compensation.
Human Capital Theory (Becker, 1964) posits that efforts to develop knowledge,
skills, and abilities increase an individual’s value to the organization, and that this
value will be rewarded in the form of high compensation and upward mobility.
Research on employability underscores the importance of competency development
(De Vos et al., 2011). Hence, the study expected a positive relationship between the
development of overall leadership competence and career success.
Hypothesis 3a: Leadership competence will be positively related to CEO proximity.
Hypothesis 3b: Leadership competence will be positively related to total
compensation.
Finally, the proposed model describes the mediating role of overall leadership
competence between learning agility and career success (see Figure I). Highly learning
agile individuals are more likely to take on developmental opportunities than less agile
ones. The impact of learning agility on career success is contingent on the skills
individuals develop and the value of those skills to their organizations. Ultimately,
organizations reward employees for their performance contributions (Ng and
Feldman, 2010). Thus, it was hypothesized:
Hypothesis 4a: Leadership competence will mediate the relationship between
learning agility and CEO proximity.
Hypothesis 4b: Leadership competence will mediate the relationship between
learning agility and total compensation.
In Figure I, there is a dotted line from CEO proximity to total compensation,
suggesting that organizational ascendency results in increased earning. This causal
relationship has been tested in previous research (Zhang and Arvey, 2009). Consistent
with typical career success research, the two variables were considered separately as the
career success outcomes.
Study 1 Method
6DPSOH
In total, 114 managers from a large, multinational consumer products company
headquartered in North America participated in a leadership development program
in 2011. Complete assessment data were collected from 101 managers. A majority of
114
DAI, DE MEUSE, AND TANG
JOURNAL OF MANAGERIAL ISSUES Vol. XXV Number 2 Summer 2013
those managers were male (62%). The age distribution was as follows: (a) 31-35 years
old (4.3%), (b) 36-40 years old (30.9%), (c) 41-50 years old (58.5%), and (d) 51-60 years
old (6.3%). Their education level breakdown was 38.3% undergraduate degrees, 57.4%
master’s degrees, and 4.3% doctoral degrees.
0HDVXUHV
Learning agility. A self-assessment instrument was used to measure learning agility
(De Meuse et al., 2011). The development of this instrument was based on Lombardo
and Eichinger’s (2000) theory of learning agility. The authors proposed a
multidimensional model, reflecting the complexity of developmental job experiences.
According to McCauley et al. (1994), most developmental jobs are novel, ambiguous,
challenging, and adverse. To benefit from diverse job experiences, managers need to
possess several attributes. Learning agility represents the amalgamation of those
attributes. Lombardo and Eichinger (2000) defined the following four factors of
learning agility: (a) mental agility, (b) people agility, (c) change agility, and (d) results
agility. The self-assessment instrument extends the four-factor model to include
another factor – self-awareness, which was initially embedded in people agility.
Individuals who are self-aware are reflective, understand their strengths and
weaknesses, and gain insights from missteps. Mentally agile individuals have broad
interests, are highly curious and comfortable with complexity and ambiguity.
Individuals who are people agile are open to diverse and different opinions from
others, and adjust their approach to situations based on the needs and preferences of
others. Change agile individuals are not satisfied with status quo, willingly take risks,
and continually experiment and implement new ideas. Finally, individuals who are
results agile drive for excellence, are energetic, resourceful, and resilient.
The construct validity of this self-assessment has been demonstrated by
correlating it with different methodologies of assessing learning agility (such as multi-
source ratings and structured interview evaluations, see DeMeuse et al., 2011). Each
factor scale has eight items. Overall learning agility is the composite of the five factors,
plus an additional 13 global items. Participating managers and executives were asked
to rate themselves on a five-point scale, ranging from 1 (strongly disagree), 2 (disagree), 3
(neutral), 4 (agree), and 5 (strongly agree). According to De Meuse et al. (2011), the
overall scale has an internal consistency reliability of 0.88 and a test-retest reliability of
0.90. The internal consistency reliabilities of the factor scales ranged from 0.74 to
0.78. The internal consistency coefficient alpha of the overall scale was 0.85 in this
study. The results of Shapiro-Wilk test suggested a normal distribution of learning
agility (p > 0.05).
Leadership competence. The company that participated in the study had a
competency model consisting of 40 items assessing different facets of leadership
behavior (e.g., “Dealing with Ambiguity,” “Business Acumen,” “Creativity,”
“Innovation Management,” “Strategic Agility,” “Managing Vision and Purpose,”
“Conflict Management,” “Managing Diversity,” “Motivating Others,” “Sizing up
People,” and “Building Effective Teams”). To avoid common method bias, the self-
assessment of learning agility was used to predict boss ratings of leader competence.
Participants were rated by their bosses on a five-point Likert scale to measure how
115
LEARNING AGILITY IN EXECUTIVE CAREER SUCCESS
JOURNAL OF MANAGERIAL ISSUES Vol. XXV Number 2 Summer 2013
skillful the target manager was on each item (1 = a serious issue, 2 = a weakness, 3 =
skilled/ok, 4 = talented, and 5 = a towering strength). The overall mean of these 40 items
was used as an index of overall leadership competence. Internal consistency of this
assessment was 0.93 in this study. The results of Shapiro-Wilk test suggested a normal
distribution of boss ratings of leadership competence (p > 0.05).
Career success. The study measured career success using annual compensation and
CEO proximity. Managers were asked to report their income ranges for annual salary
and bonus. The scales were in $25,000 increments. The lowest income was coded as
“1.” The next lowest income was coded as “2.” This approach continued until all the
reported income ranges were coded. Total compensation was the sum of annual salary
and bonus. CEO proximity was measured by asking managers to report the number of
job levels below the CEO they were positioned in their current organization. The
distribution of managerial reporting relationships was as follows: (a) 1% C-level
executives or those reporting directly to the CEO, (b) 11% vice presidents reporting to
C-level executives, (c) 33% directors reporting to vice presidents, (d) 50% middle
managers reporting to directors, and (e) 5% supervisors or first-line managers
reporting to middle managers. Both career success variables in this study were
positively skewed. The skewness could be reduced through data transformation such as
taking the natural log of the score. However, previous research cautioned against the
conventional use of such a transformation on both statistical and theoretical grounds.
Data transformation changes the meaning of the distribution of the variable, replacing
a linear relationship with a nonlinear one. It implies a diminishing return relationship
between the predictors and career success outcomes, which is not in line with the
theoretical expectation. Therefore, similar to Judge and Hurst (2007), the current
study did not transform the two career success variables.
Control variables. The study controlled for age, education level, and gender,
because previous research has shown they may influence career success (cf. Judge et al.,
1995; Ng et al., 2005). Gender was coded 0 = male, 1 = female. For education, high
school was coded as 1, college was coded as 2, master’s degree was coded as 3, and
doctoral degree was coded as 4. Participants were asked to indicate their age category
in five-year increments, ranging from younger than 25 years old (coded as 1), 25 to 30
years old (coded as 2), through older than 60 years old (coded as 9).
116
DAI, DE MEUSE, AND TANG
JOURNAL OF MANAGERIAL ISSUES Vol. XXV Number 2 Summer 2013
Table 1
Zero-Order Correlations among the Variables: Study 1
Variables Mean Std. 1 2 3 4 5 6 7 8 9 10 11
1. Age 4.67 0.66
2. Education 2.66 0.56 -0.24*
3. Gender 0.38 0.49 -0.01 0.02
4. Mental Agility 3.75 0.49 0.10 0.20† -0.10
5. People Agility 3.71 0.41 0.12 0.06 0.08 0.25*
6. Change Agility 3.54 0.49 -0.01 -0.01 -0.06 0.42** 0.22*
7. Results Agility 3.93 0.48 0.05 -0.12 -0.04 0.25* 0.23* 0.21*
8. Self-Awareness 3.97 0.34 0.07 -0.09 0.06 0.24* 0.28** 0.18† 0.50**
9. Overall Learning
Agility
3.77 0.27 0.11 0.01 -0.06 0.65** 0.56** 0.58** 0.66** 0.56**
10. Leadership
Competence
3.83 0.35 0.12 -0.15 -0.06 0.14 0.06 0.13 0.27** 0.19† 0.29**
11. CEO Proximity 3.52 0.79 0.11 -0.15 -0.09 0.16 0.08 0.07 0.17† 0.12 0.25* 0.25*
12. Compensation 10.27 4.78 0.14 -0.01 -0.28** 0.30** 0.21* 0.34** 0.24* 0.16 0.38** 0.18† 0.22*
Note. †
p
< 0.10
;
*
p
< 0.05
;
**
p
< 0.01.
117
LEARNING AGILITY IN EXECUTIVE CAREER SUCCESS
JOURNAL OF MANAGERIAL ISSUES Vol. XXV Number 2 Summer 2013
Table 2
Hierarchical Regression Analyses of Overall Learning Agility: Study 1
Independent Variables Leadership Competence CEO Proximity Compensation
Step 1 Step 2 Step 1 Step 2 Step 1 Step 2
Age 0.09 0.06 0.08 0.05 0.14 0.10
Education -0.13 -0.14 -0.13 -0.14 0.03 0.02
Gender -0.06 -0.04 -0.09 -0.08 -0.28* -0.26*
Overall Learning Agility 0.28** 0.24* 0.35**
R2 0.03 0.11* 0.04 0.09† 0.10* 0.21**
ǻR2 0.08** 0.06* 0.11**
Note. Numbers in cells in this table and subsequent regression tables are standardized coefficients.
†p < 0.10; *p < 0.05; **p < 0.01.
118
DAI, DE MEUSE, AND TANG
JOURNAL OF MANAGERIAL ISSUES Vol. XXV Number 2 Summer 2013
Study 1 Results
The zero-order correlations among all the variables are presented in Table 1. As
can be seen, learning agility was significantly related to boss ratings of leadership
competence (r = 0.29, p < 0.01). Learning agility also was significantly related to CEO
proximity (r = 0.25, p < 0.05) and total compensation (r = 0.38, p < 0.01). In
addition, leadership competence was significantly related to CEO proximity (r = 0.25,
p < 0.05) and marginally related to total compensation (r = 0.18, p < 0.10). The two
career success variables were significantly correlated (r = 0.22, p < 0.05), suggesting
that higher level managers generally earned more than lower level managers.
Hierarchical regression analyses were conducted to test the hypotheses. In these
analyses, age, education, and gender were entered into the regression equation first to
control their impact. Table 2 provides the results of the hierarchical regression
analyses to examine the effect of learning agility on overall leadership competence and
career success. The results depict that learning agility was significantly related to
leadership competence (ȕ = 0.28, p < 0.01), thus supporting Hypothesis 1. Learning
agility also was a significant predictor of CEO proximity (ȕ = 0.24, p < 0.05) and
compensation (ȕ = 0.35, p < 0.01). Thus, Hypothesis 2a and Hypothesis 2b likewise
were supported.
Table 3 presents the results of hierarchical regression analyses investigating the
relationship between overall leadership competence and career success, controlling for
age, education, and gender. The findings show that overall leadership competence was
a significant predictor of CEO proximity (ȕ = 0.22, p < 0.05), supporting Hypothesis
3a. In contrast, leadership competence did not predict compensation over age,
education, and gender (ȕ = 0.16, ns). Therefore, Hypothesis 3b was not supported.
Finally, the process articulated by Baron and Kenny (1986) was followed to test
the mediating role of leadership competence in the relationship between learning
agility and career success outcomes. This process specifies three conditions to
demonstrate the mediating role of a variable. The first condition is the significant
relationship between the independent variable and the dependent variable, in this case
learning agility and career outcomes. The second condition is the significant
relationship between the independent variable (i.e., learning agility) and the
mediating variable (i.e., leadership competence). Finally, when the mediating variable
was entered in the full regression model, the effect of the independent variable (i.e.,
learning agility) on the dependent variable (i.e., career success outcomes) disappears,
while the effect of the mediating variable (i.e. leadership competence) was still
significant. Both condition 1 and condition 2 have already been supported (See Table
2). Table 4 presents the results of the full regression model. For CEO proximity,
learning agility was a significant predictor in step 1 (ȕ = 0.24, p < 0.05). When
leadership competence was entered into the regression, the standardized regression
coefficient of learning agility was slightly reduced and marginally significant (ȕ = 0.20,
p < 0.10). However, leadership competence was not a significant predictor of CEO
proximity (ȕ = 0.17, ns). Hence, Hypothesis 4a was not supported. Learning agility
provided incremental validity in predicting CEO proximity over leadership
competence. For total compensation, learning agility was a significant predictor in step
1 (ȕ = 0.35, p < 0.01). When overall leadership competence was entered into the
119
LEARNING AGILITY IN EXECUTIVE CAREER SUCCESS
JOURNAL OF MANAGERIAL ISSUES Vol. XXV Number 2 Summer 2013
regression, learning agility remained a significant predictor (ȕ = 0.33, p < 0.01), but
leadership competence was not a significant predictor of total compensation (ȕ = 0.06,
ns). Thus, Hypothesis 4b was not supported.
Table 3
Hierarchical Regression Analyses of Leadership Competence: Study 1
Independent Variables CEO Proximit
y
Compensation
Step 1 Step 2 Step 1 Step 2
Age 0.08 0.06 0.14 0.13
Education -0.13 -0.10 0.03 0.05
Gender -0.09 -0.08 -0.28* -0.27*
Overall Leadership
Competence 0.22* 0.16
R2 0.03 0.08 0.10* 0.12*
ǻR2 0.05* 0.02
Note. *p < 0.05
Table 4
Regression Analyses Testing Mediating Role of
Leadership Competence: Study 1
Independent Variables
CEO Proximit
y
Compensation
Step 1 Step 2 Step 1 Step 2
Age 0.05 0.04 0.10 0.10
Education -0.14 -0.12 0.02 0.03
Gender -0.08 -0.07 -0.26* -0.26*
Learning Agilit
y
0.24* 0.20† 0.35** 0.33**
Leadership
Competence 0.17 0.06
R2 0.09† 0.12† 0.21** 0.22**
ǻR2 0.03 0.01
Note. †p < 0.10; *p < 0.05; **p < 0.01.
120
DAI, DE MEUSE, AND TANG
JOURNAL OF MANAGERIAL ISSUES Vol. XXV Number 2 Summer 2013
STUDY 2
Study 1 investigated the extent to which between-individual differences in learning
agility were related to career success. However, it should be noted that the cross-
sectional nature of the design does not enable one to describe the progress of career
advancement within-individuals. Research conducted by Judge et al. (2010) revealed
that certain individual attributes predict career achievement as well as growth
trajectory. For example, individuals with higher general mental ability usually advance
faster than those individuals who are relatively lower on general mental ability (Dreher
and Bretz, 1991; Judge et al., 1999). This differential growth trajectory is explained by
the contest-mobility mechanisms identified earlier in the paper (Ng et al., 2005).
Individuals who are learning agile likely will actively seek (McCall, 1998; McCauley,
2001) and capitalize on developmental opportunities (Dragoni et al., 2009). Eby et al.
(2003) demonstrated that learning enhances career marketability. Consequently, one
would expect that high learning agile individuals possess steeper career growth
trajectories than less learning agile individuals.
Study 2 adopted a retrospective design to investigate how learning agility is
related to the progress of career advancement. The study collected two career growth
variables – promotion rates and salary increases – during a ten-year period (between
2002 and 2011). It is believed that these two variables reflect an individual’s long-term
performance (i.e., individuals who consistently demonstrate superior performance will
be rewarded by organizations in the form of more frequent promotions and pay
increases). Therefore, it was hypothesized:
Hypothesis 5a: Learning agility will be positively related to the promotion rates
between 2002 and 2011.
Hypothesis 5b: Learning agility will be positively related to salary increases
between 2002 and 2011.
Study 2 Method
6DPSOH
The data were collected from 83 district sales managers at a global pharmaceutical
company based in North America. Of the 83 participants, 73% were male. The mean
age was 43.06 with a standard deviation of 7.27. With regard to education level, 55.4%
had a college degree (coded as 1), 42.2% had a master’s degree (coded as 2), and 2.4%
had a doctoral degree (coded as 3).
0HDVXUHV
Learning agility. The same self-assessment instrument described in Study 1 was
used to assess learning agility. All learning agility data were collected in 2011. The
internal consistency coefficient alpha of the overall learning agility scale was 0.84 in
this study. The Shapiro-Wilk test suggested a normal distribution of learning agility (p
> 0.05).
121
LEARNING AGILITY IN EXECUTIVE CAREER SUCCESS
JOURNAL OF MANAGERIAL ISSUES Vol. XXV Number 2 Summer 2013
Career growth. The study collected the following two career growth variables: (a)
promotion rate and (b) average annual salary increase between 2002 and 2011.
Promotion rate represents the total number of promotions the employee received at
the company during this ten-year period. Since not all of the participants had been
employed at the company for the entire ten-year period, the study adjusted the
numbers based on the participants’ years of service (the ratio of number of promotions
to the tenure times 10). The number of promotions for employees examined during
the period ranged from one to five, with a mean of 1.93 and a standard deviation of
1.01. The average annual salary increase of the sample ranged between 2.33% and
13.87%, with a mean of 7.35% and a standard deviation of 2.71%. Both promotion rate
and average salary increase were positively skewed. Consistent with Judge and Hurst
(2007), the current study did not transform the two career success variables.
Control variables. The study again controlled for education level and gender. For
the coding of gender, 0 = male, 1 = female. Age, however, was not controlled in Study
2 because all of the participants occupied the same position (i.e., district sales
manager) at the time of the assessment. Study 2 took a retrospective approach to
examine how learning agility was related to career growth. The same position imposed
a ceiling on career growth. Consequently, age demonstrated highly negative
correlations with the two career growth variables (see the next section). This ceiling
effect of age reduced the ability to detect the significant impact that learning agility
might have had on career growth.
Study 2 Results
Zero-order correlations among the variables are presented in Table 5. The results
indicated that learning agility was significantly related to promotion rate (r = 0.44, p
< 0.01) and average salary increase (r = 0.35, p < 0.01). As expected, the results also
revealed that age had negative and very high correlations with both promotion rate (r
= -0.66, p < 0.01) and average salary increase (r = -0.73, p < 0.01).
Hierarchical regression analyses were conducted to test the hypotheses (see Table
6). The results show that learning agility was a significant predictor of promotion rate
over gender and education (ȕ = 0.49, p < 0.01), supporting Hypothesis 5a. In
addition, learning agility was a significant predictor of the average salary increase over
gender and education (ȕ = 0.24, p < 0.05). Thus, Hypothesis 5b also was supported.
122
DAI, DE MEUSE, AND TANG
JOURNAL OF MANAGERIAL ISSUES Vol. XXV Number 2 Summer 2013
Table 5
Zero-Order Correlations among the Variables: Study 2
Mean Std. 1 2 3 4 5 6 7 8 9 10
1. Gender 0.27 0.44
2. Age 43.06 7.27 -0.19†
3. Education 1.47 0.55 -0.28* -0.04
4. Mental Agility 3.77 0.46 0.15 -0.12 -0.17
5. People Agility 3.99 0.47 0.36** -0.30** -0.47** 0.36**
6. Change Agility 3.43 0.52 0.16 -0.05 -0.24* 0.36** 0.24*
7. Results Agility 4.09 0.48 0.31** -0.25* -0.11 0.31* 0.43** 0.22*
8. Self-Awareness 4.17 0.41 0.29** -0.27* -0.19† 0.46** 0.62** 0.21† 0.53**
9. Overall Learning
Agility
3.94 0.39 0.36** -0.32* -0.43** 0.59** 0.73** 0.47** 0.75** 0.70**
10. Promotion Rate 1.91 1.04 0.23* -0.66** -0.02 0.26* 0.33** 0.20† 0.38** 0.29** 0.44**
11. Average Salary
Growth
0.07 0.03 0.41** -0.73** -0.18 0.09 0.33** 0.22* 0.33** 0.13 0.35** 0.74**
Note. †p < 0.10; *p < 0.05; **p < 0.01.
123
LEARNING AGILITY IN EXECUTIVE CAREER SUCCESS
JOURNAL OF MANAGERIAL ISSUES Vol. XXV Number 2 Summer 2013
Table 6
Hierarchical Regression Analyses in Predicting Career Growth:
Study 2
Independent
Variables
Promotion Rate Average Salar
y
Growth
Step 1 Step 2 Step 1 Step 2
Gender 0.24* 0.12 0.39** 0.33**
Education 0.05 0.22 -0.07 0.02
Learning Agilit
y
0.49** 0.24*
R2 0.05 0.23** 0.17** 0.22**
ǻR2 0.18** 0.05*
Note. *p < 0.05; ** p < 0.01
DISCUSSION
Empirical evidence on the linkage between learning agility and leadership and
career success is limited in the literature. The purpose of the two field studies was to
shed some light on it. Study 1 found that learning agility was significantly and
positively related to ratings of leadership competence. This finding is consistent with
the results of Spreitzer et al. (1997) and Dragoni et al. (2009). The study also showed
that learning agility was significantly related to career success outcomes such as CEO
proximity and total compensation, even after controlling for age, education, and
gender. Overall, these findings suggest that managers and executives who are learning
agile are more likely to learn from their experiences, increase their value to their
organization, and (in turn) are rewarded by upward mobility and higher
compensation. Further, Study 2 revealed that individuals who were more learning
agile ascended the corporate ladder and enhanced their income faster than their
relatively lower learning agile counterparts. Thus, the career advantages of high
learning agility appear to actually increase over time.
The research also found that evaluations of overall leadership competence were
significantly related to CEO proximity. As one would suspect, higher performance
contributes to greater career success (as manifested in upward mobility). Leadership
competence did not predict compensation over age, education, and gender in the
study. However, since leadership competence was related to CEO proximity, and CEO
proximity subsequently was related to compensation, leadership competence appears
to have an indirect effect on compensation. In sum, these findings support Human
Capital Theory which posits that the effort taken to develop knowledge, skills, and
abilities enhances one’s career success (Ng and Feldman, 2010).
124
DAI, DE MEUSE, AND TANG
JOURNAL OF MANAGERIAL ISSUES Vol. XXV Number 2 Summer 2013
Study 1 investigated whether the relationship between learning agility and career
success was mediated by leadership competence. However, the mediating role of
leadership competence was not supported in this study, in that leadership competence
did not predict career success over learning agility. In contrast, learning agility did
predict career success over leadership competence. There are several explanations for
this observation. Obviously, high potential talent is extremely valued in today’s labor
market. Companies offer very competitive compensation packages to attract and retain
talent. Since individuals who are learning agile tend to be successful in leadership
positions, they may receive – perhaps, implicitly more than explicitly – higher
compensation and developmental opportunities than lower agile employees. Further,
it probably is much easier for them to make career moves across organizations toward
higher pay since such high learning agile individuals have many employment
opportunities. Likewise, high learning agile individuals may be very skilled at
negotiating compensation packages with hiring organizations. High learning agile
individuals also may be very adept at managing their careers, networking and
obtaining career support from within their organizations.
Talent management professionals have learned that leadership talent develops
and grows on the job (McCall, 2010; McCall et al., 1988). Consequently, the findings of
these field studies should encourage a more deliberate attempt to utilize learning agile
leaders. The heads of talent management should continue to promote high learning
agile employees, offering stretch opportunities to develop their leadership skills. In
other words, highly learning agile individuals should enjoy the benefits of obtaining
what is called “organizational sponsored-mobility” in addition to “contest-mobility”
(Judge et al., 2010). The sponsored-mobility perspective suggests that organizations
should focus special attention on those individuals who are deemed to have high
potential and will engage in actions to increase their chances for upward career
mobility (Ng et al., 2007). Highly learning agile individuals are likely to be sponsored
by their organizations, because their capability to learn and grow from developmental
experience is perceived as an indicator of leadership potential (Lombardo and
Eichinger, 2000; Silzer and Church, 2009).
Previous research has observed a complex relationship between gender and career
success (Ng et al., 2005). Nevertheless, the scholarly literature suggests that male
managers may have a slight career advantage over female managers. The gender-
salary relationship appears to have lessened over time, but the gender-promotion
relationship may not have. Results from the two studies reported in this paper are
consistent with Ng et al.’s (2005) findings. Across individuals, males were employed at
slightly higher position levels and received relatively higher compensations than
females (in Study 1). However, when the career progress was examined within
individuals, females were promoted faster and received more salary increases than
males (in Study 2). This steeper growth trajectory for females reduces the gender
difference over time (especially for compensation), due to the relatively higher effect
size for salary increases than promotion rates observed in this study. Additional
research needs to be conducted to determine whether this finding generalizes to other
samples in other organizations.
125
LEARNING AGILITY IN EXECUTIVE CAREER SUCCESS
JOURNAL OF MANAGERIAL ISSUES Vol. XXV Number 2 Summer 2013
Implications for Talent Management and Leadership Development
The need for identifying and developing high potential employees has become
increasingly important for organizations. Leadership shortage is a global
phenomenon, not only existing in developed countries but in emerging markets as
well (cf. Ready et al., 2008). Too often, the assessment and identification of high
potential talent is based on poorly defined models (Silzer and Church, 2009). The
findings from the studies presented here clearly demonstrate that learning agility is
important. It significantly predicted the development of leadership competence and
executive career success. A psychometrically sound measure of learning agility could
play a vital role in helping organizations enhance their leadership development and
succession management pipeline.
Highly learning agile leaders are more successful in this turbulent business
environment. They are nimble and adaptable. They possess an ability to learn from
experiences and develop new skills (see Dragoni et al., 2009; Dries et al., 2012). Talent
management professionals should understand the importance of learning agility when
developing leadership competencies. Further, they should provide challenging
developmental assignments and training that provide highly learning agile managers
the opportunity to learn new skills, new behaviors, and new viewpoints. According to
McCauley et al. (1994), features of highly developmental assignments fall into the
following five areas: (a) tackling unfamiliar responsibilities, (b) creating and managing
change, (c) handling high levels of responsibility, (d) managing lateral interactions
with others, and (e) managing adversity. Directors of talent management should create
development plans for managers to systematically increase their exposure to such
developmental experiences through challenging projects, international assignments,
and job rotations.
A dilemma facing organizations today is the need to attract, engage, and retain
high potential talent, while simultaneously reducing an organization’s hierarchical
structure in order to keep it lean and flexible. A flattened hierarchy usually denotes
fewer career advancement opportunities. Organizations risk losing talented employees
to competitors when highly learning agile individuals do not perceive their careers as
being on track. It is particularly a talent management problem when more and more
individuals are embracing the concept of boundaryless careers (Arthur and Rousseau,
1996). The current research found that learning agility has higher correlations with
compensation and salary growth than with position level and promotion rate.
Providing competitive pay is one way to attract and retain high potentials. However,
this approach eventually will raise human capital costs. Organizations need to identify
alternative talent management solutions. For example, companies may wish to adopt a
broadbanding career structure (Gilbert and Abosch, 1996). Broadbanding decreases
the emphasis on vertical job status and hierarchy, but stresses lateral and diagonal job
movements within a company.
Finally, although learning agility has been assessed primarily for the purpose of
identifying individuals with leadership potential (Dries et al., 2012; Lombardo and
Eichinger, 2000), learning agility has broad practical implications. In essence, any
stretch job assignment that contains the elements specified by McCauley et al. (1994)
requires individuals to step out of their comfort zone and develop new skills. Examples
126
DAI, DE MEUSE, AND TANG
JOURNAL OF MANAGERIAL ISSUES Vol. XXV Number 2 Summer 2013
of stretch job assignments include transferring to different job functions, managing
challenging projects, taking international assignments, coordinating multi-functional
teams, championing innovative ideas, and so on. Learning agility assessment can help
organizations enhance the appropriate deployment of talent. Nevertheless,
organizations need to recognize that job specialization in today’s business world is as
equally important as talent mobility. While talent mobility (moving across job functions
and hierarchical levels) entails learning agility, specialization requires a different set of
individual attributes. Although some employees may not possess high learning agility,
they contribute to the organization’s success in other ways. Dries and Pepermans
(2008) articulated how organizations can segment the workforce and establish different
career structures for different types of talent. Professionals of talent management must
remember that all employees – whether high learning agile or not – desire career
success. How it is defined and how it is achieved differs vastly across individuals.
LIMITATIONS AND FUTURE RESEARCH
As it is with all research, there are some limitations with the current field studies.
First, solely objective career outcomes were employed. The literature reports that
different aspects of career success only are moderately related (Ng et al., 2005). Future
researchers may want to explore the impact of learning agility on other types of career
success measures such as career satisfaction. One characteristic that is associated with
being highly learning agile is proactivity (McCauley, 2001). Individuals who are
learning agile take initiative and are self-directed in their efforts to satisfy their
learning and development needs. Proactivity is positively related to subjective career
success (Fuller and Marler, 2009). Consequently, learning agility also should be
theoretically related to subjective career success.
Second, each of the two studies reported in this article examined the career
success of participants within the same organization. How learning agility influences
individuals’ inter-organizational career mobility should be investigated. Future
researchers should collect data from participants across a variety of different
organizations. A third limitation is the retrospective outcome measures used in Study
2. Learning agility was related to career progress, which occurred during the past. This
longitudinal approach is counter to the intended use of the assessment, which is to
predict future success. The findings of this study provided strong, initial evidence for
the relationship between learning agility and long-term performance. The predictive
validity will be strengthened by correlating learning agility with future performance
and future career outcomes. Fourth, the data in Study 1 were collected from a
convenience sample – managers and executives who participated in a leadership
development program. The fact that they were selected for the program might suggest
they represent a specific talent pool, raising the question about the generalizability of
the findings. On the other hand, one can argue that the effect of learning agility might
have been underestimated due to the range restriction.
Finally, although the two field studies controlled some social demographic
variables, there are other individual differences potentially related to career success
(e.g., personality, intelligence). In all the regression analyses, the sizes of R2 were
relatively small, suggesting that a substantial amount of variance in career success has
127
LEARNING AGILITY IN EXECUTIVE CAREER SUCCESS
JOURNAL OF MANAGERIAL ISSUES Vol. XXV Number 2 Summer 2013
not been explained. By incorporating multiple individual difference variables in the
same study, future researchers can test the unique contribution of learning agility in
executive career success.
In summary, learning agility is crucial for leaders as they attempt to adapt to the
constantly changing, complex business environment organizations face today. The
findings of this field research suggest that managers and executives who have been the
most successful are highly learning agile. They learn the right lessons from experience
by developing new competencies, which in turn leads to career success. In addition,
highly learning agile individuals enjoy steeper career growth than their low learning
agile counterparts. It is recommended that companies select high learning agile talent
for strategic roles and provide them opportunities to develop critical leadership skills.
By doing so, organizations will keep those employees challenged, engaged, and
retained. Their development depends on it, as well as the future leadership and
prosperity of their organization.
References
Arthur, M. B. and D. M. Rousseau. 1996. The Boundaryless Career: A New Employment
Principle for a New Organizational Era. New York, NY: Oxford University Press. pp.
237-255.
Bailyn, L. 2004. “Time in Careers – Careers in Time.” Human Relations 57: 1507-1521.
Baron, R. M. and D. A. Kenny. 1986. “The Moderator-Mediator Variable Distinction
in Social Psychological Research: Conceptual, Strategic, and Statistical
Considerations.” Journal of Personality and Social Psychology 51(6): 1173-1182.
Becker, G. 1964. Human Capital: A Theoretical and Empirical Analysis with Special
Reference to Education. New York, NY: Columbia University Press.
Bedford, C. L. 2011. “The Role of Learning Agility in Workplace Performance and
Career Advancement.” Unpublished doctoral dissertation. University of
Minnesota.
Boudreau, J. W., W. R. Boswell, and T. A. Judge. 2001. “Effects of Personality on
Executive Career Success in the United States and Europe.” Journal of Vocational
Behavior 58: 53-81.
Connolly, J. J. 2001. “Assessing the Construct Validity of a Measure of Learning
Agility.” Unpublished doctoral dissertation. Florida International University.
De Meuse, K. P., G. Dai, R. W. Eichinger, R. C. Page, L. P. Clark, and S. Zewdie. 2011.
The Development and Validation of a Self-assessment of Learning Agility [Technical
Report]. Minneapolis, MN: Korn/Ferry International.
————, ————, and G. S. Hallenbeck. 2010. “Learning Agility: A Construct Whose
Time has Come.” Consulting Psychology Journal: Practice and Research 62: 119-130.
————, ————, V. V. Swisher, R. W. Eichinger, and M. M. Lombard. 2012.
“Leadership Development: Exploring, Clarifying, and Expanding Our
Understanding of Learning Agility.” Industrial and Organizational Psychology:
Perspectives on Science and Practice 5: 280-286.
128
DAI, DE MEUSE, AND TANG
JOURNAL OF MANAGERIAL ISSUES Vol. XXV Number 2 Summer 2013
De Pater, I. E., A. E. Van Vianen, M. N. Bechtoldt, and U. C. Klehe. 2009.
“Employees’ Challenging Job Experiences and Supervisors’ Evaluations of
Promotability.” Personnel Psychology 62: 297-325.
DeRue, D. S., S. J. Ashford, and C. G. Myers. 2012. “Learning Agility: In Search of
Conceptual Clarity and Theoretical Grounding.” Industrial and Organizational
Psychology: Perspectives on Science and Practice 5: 258-279.
De Vos, A., S. De Hauw, and B. I. J. M. Van der Heijden. 2011. “Competency
Development and Career Success: The Mediating Role of Employability.” Journal
of Vocational Behavior 79: 438-447.
Dragoni, L., P. E. Tesluk, and I. Oh. 2009. “Understanding Managerial Development:
Integrating Developmental Assignments, Learning Orientation, and Access to
Developmental Opportunities in Predicting Managerial Competencies.” Academy
of Management Journal 52: 731-743.
Dreher, G. F. and R. D. Bretz, Jr. 1991. “Cognitive Ability and Career Attainment:
Moderating Effects of Early Career Success.” Journal of Applied Psychology 76: 392-
397.
Dries, N. and R. Pepermans. 2008. “Real High-Potential Careers: An Empirical Study
into the Perspectives of Organizations and High Potentials.” Personnel Review
37(1): 85-108.
————, T. Vantilborgh, and R. Pepermans. 2012. “The Role of Learning Agility and
Career Variety in the Identification and Development of High Potential
Employees.” Personnel Review 41: 340-358.
Eby, L. T., M. Butts, and A. Lockwood. 2003. “Predictors of Success in the Era of the
Boundaryless Career.” Journal of Organizational Behavior 24: 689-708.
Fuller, B., Jr. and L. E. Marler. 2009. “Change Driven by Nature: A Meta-Analytic
Review of the Proactive Personality Literature.” Journal of Vocational Behavior
75(3): 329-345.
Gilbert, D. and K. S. Abosch. 1996. Improving Organizational Effectiveness Through
Broadbanding. Scottsdale, AZ: American Compensation Association.
Goldsmith, M. 2007. What Got You Here Won’t Get You There: How Successful People
Become Even More Successful. New York, NY: Hyperion.
Hezlett, S. A. and N. R. Kuncel. 2012. “Prioritizing the Learning Agility Research
Agenda.” Industrial and Organizational Psychology: Perspectives on Science and Practice 5:
296-301.
Joiner, B. 2009. “Creating a Culture of Agile Leaders: A Developmental Approach.”
People & Strategy 32(4): 28-35.
Judge, T. A., D. M. Cable, J. W. Boudreau, and R. D. Bretz. 1995. “An Empirical
Investigation of the Predictors of Executive Career Success.” Personnel Psychology
48: 485-519.
————, C. A. Higgins, C. J. Thoresen, and M. R. Barrick. 1999. “The Big Five
Personality Traits, General Mental Ability, and Career Success across the Life
Span.” Personnel Psychology 52: 621-652.
———— and C. Hurst. 2007. “Capitalizing on One’s Advantages: Role of Core Self-
Evaluations.” Journal of Applied Psychology 92: 1212-1227.
129
LEARNING AGILITY IN EXECUTIVE CAREER SUCCESS
JOURNAL OF MANAGERIAL ISSUES Vol. XXV Number 2 Summer 2013
————, R. L. Klinger, and L. S. Simon. 2010. “Time is on My Side: Time, General
Mental Ability, Human Capital, and Extrinsic Career Success.” Journal of Applied
Psychology 95: 92-107.
Lombardo, M. M. and R. W. Eichinger. 2000. “High Potentials as High Learners.”
Human Resource Management 39: 321-330.
————, M. N. Ruderman, and C. D. McCauley. 1988. “Explanations of Success and
Derailment in Upper-level Management Positions.” Journal of Business and
Psychology 2(3): 199-216.
McCall, M. W., Jr. 2010. “Recasting Leadership Development.” Industrial and
Organizational Psychology 3: 3-19.
————. 1998. High Flyers: Developing the Next Generation of Leaders. Boston, MA:
Harvard Business School Press.
———— and M. M. Lombardo. 1983. “What Makes a Top Executive?” Psychology
Today 17(2): 26-31.
————, ————, and A. M. Morrison. 1988. The Lessons of Experience: How Successful
Executives Develop on the Job. New York, NY: The Free Press.
McCauley, C. D. 2001. “Leadership Training and Development.” In The Nature of
Organizational Leadership. Eds. S. J. Zaccaro and R. J. Klimoski. San Francisco, CA:
Jossey-Bass. pp. 347-383.
————, P. J. Ohlott, and M. N. Ruderman. 1999. Job Challenge Profile. New York,
NY: Jossey-Bass.
————, M. N. Ruderman, P. J. Ohlott, and J. E. Morrow. 1994. “Assessing the
Developmental Components of Managerial Jobs.” Journal of Applied Psychology 79:
544-560.
Ng, T. W. H., L. T. Eby, K. L. Sorensen, and D. C. Feldman. 2005. “Predictors of
Objective and Subjective Career Success: A Meta-analysis.” Personnel Psychology 58:
367-408.
———— and D. C. Feldman. 2010. “Human Capital and Objective Indicators of
Career Success: The Mediating Effects of Cognitive Ability and
Conscientiousness.” Journal of Occupational and Organizational Psychology 83: 207-
235.
————, K. L. Sorensen, L. T. Eby, and D. C. Feldman. 2007. “Determinants of Job
Mobility: A Theoretical Integration and Extension.” Journal of Occupational and
Organizational Psychology 80: 363-386.
Ready, D. A., L. A. Hill, and J. A. Conger. 2008. “Winning the Race for Talent in
Emerging Markets.” Harvard Business Review 86(11): 63-70.
Silzer, R. and A. H. Church. 2009. “The Pearls and Perils of Identifying Potential.”
Industrial and Organizational Psychology: Perspectives on Science and Practice 2: 377-412.
Spreitzer, G. M., M. W. McCall, and J. D. Mahoney. 1997. “Early Identification of
International Executive Potential.” Journal of Applied Psychology 82: 6-29.
Sutin, A. R., P. T. Costa, R. Miech, and W. W. Eaton. 2009. “Personality and Career
Success: Concurrent and Longitudinal Relations.” European Journal of Personality
23: 71-84.
Tannenbaum, S. I. 1997. “Enhancing Continuous Learning: Diagnostic Findings from
Multiple Companies.” Human Resource Management 36: 437-452.
130
DAI, DE MEUSE, AND TANG
JOURNAL OF MANAGERIAL ISSUES Vol. XXV Number 2 Summer 2013
Todd, S. Y., K. K. Harris, R. B. Harris, and A. R. Wheeler. 2009. “Career Success
Implications of Political Skill.” Journal of Social Psychology 149: 179-204.
Turner, R. H. 1960. “Sponsored and Contest Mobility and the School System.”
American Sociological Review 25: 855-867.
Van Iddekinge, C. H. and R. Ployhart. 2008. “Developments in the Criterion-related
Validation of Selection Procedures: A Critical Review and Recommendations for
Practice.” Personnel Psychology 61:871-925.
Van Velsor, E. and V. Guthrie. 1998. “Enhancing the Ability to Learn from
Experience.” In The Center for Creative Leadership Handbook of Leadership
Development. Eds. C. D. McCauley, R. S. Moxley, and E. Van Velsor. San Francisco,
CA: Jossey-Bass. pp. 242-261.
Yukl, G. and R. Mahsud. 2010. “Why Flexible and Adaptive Leadership is Essential.”
Consulting Psychology Journal: Practice and Research 62: 81-93.
Zhang, Z. and R. D. Arvey. 2009. “Effects of Personality on Individual Earnings:
Leadership Role Occupancy as a Mediator.” Journal of Business Psychology 24: 271-
280.
131
