Critique Journal article and two student responses

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find and critique a journal article related to topics covered in the course. You are to find an online article from an academic journal, provide a pdf copy for review. Points are awarded by the following criteria: 25 points for providing a copy of your specific journal article, 25 points for how well you evaluate the article’s subject matter relevant to the course and CAP guidelines, and 25 points per response to at least two other students’. Note: Only journal articles will be accepted. Be sure you properly cite your journal article – in the text of your response and in the reference list – in accordance to APA writing standards.


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Critique Journal article and two student responses
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See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/315912603

A Meta-Analysis of the Interactive, Additive, and Relative
Effects of Cognitive Ability and Motivation on Performance
Article in Journal of Management · April 2017
DOI: 10.1177/0149206317702220

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Florida State University

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Michigan State University

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JOMXXX10.1177/0149206317702220Journal of ManagementVan Iddekinge et al. / Performance = f(Ability × Motivation)?

Journal of Management
Vol. 44 No. 1, January 2018 249­–279
DOI: https://doi.org/10.1177/0149206317702220
10.1177/0149206317702220
© The Author(s) 2017
Reprints and permissions:
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A Meta-Analysis of the Interactive,
Additive, and Relative Effects of Cognitive
Ability and Motivation on Performance
Chad H. Van Iddekinge
Florida State University

Herman Aguinis
George Washington University

Jeremy D. Mackey
Auburn University

Philip S. DeOrtentiis
Michigan State University

We tested the longstanding belief that performance is a function of the interaction between cognitive ability and motivation. Using raw data or values obtained from primary study authors as
input (k = 40 to 55; N = 8,507 to 11,283), we used meta-analysis to assess the strength and
consistency of the multiplicative effects of ability and motivation on performance. A triangulation
of evidence based on several types of analyses revealed that the effects of ability and motivation
on performance are additive rather than multiplicative. For example, the additive effects of ability and motivation accounted for about 91% of the explained variance in job performance,
whereas the ability-motivation interaction accounted for only about 9% of the explained variance. In addition, when there was an interaction, it did not consistently reflect the predicted form
(i.e., a stronger ability-performance relation when motivation is higher). Other key findings
Acknowledgments: This article was accepted under the editorship of Patrick M. Wright. A previous version of this paper
was presented at the meetings of the Academy of Management, Philadelphia, 2014. We thank Brian Hoffman and two
anonymous reviewers for their useful and constructive feedback, which allowed us to improve our manuscript substantially. We also thank Huy Le for his guidance with the range restriction corrections. In addition, we thank Fred Oswald
for his help with the regression analysis. Finally, we are grateful to the many authors who shared their data, ran analyses
we requested, and answered our questions about their research. This study would not have been possible without their
willingness to help us.
Supplemental material for this article is available with the manuscript on the JOM website.
Corresponding author: Chad H. Van Iddekinge, Department of Management, College of Business, Florida State
University, Tallahassee, FL 32306-1110, USA.
E-mail: cvanidde@business.fsu.edu
249

250   Journal of Management / January 2018

include that ability was relatively more important to training performance and to performance
on work-related tasks in laboratory studies, whereas ability and motivation were similarly important to job performance. In addition, statelike measures of motivation were better predictors of
performance than were traitlike measures. These findings have implications for theories about
predictors of performance, state versus trait motivation, and maximal versus typical performance. They also have implications for talent management practices concerned with human
capital acquisition and the prediction of employee performance.
Keywords:

ability; motivation; performance; interactions; relative importance; meta-analysis

Individual performance is one of the most central and frequently studied constructs in
management and related fields (Campbell & Wiernik, 2015; Cascio & Aguinis, 2008; Dalal,
Bhave, & Fiset, 2014). Conceptual models and considerable empirical evidence suggest that
two key determinants of performance are cognitive ability and motivation. Cognitive ability
is the capacity to mentally process, understand, and learn information (Hunter & Schmidt,
1996). Ability relates to performance primarily through job knowledge, such that high-ability
workers tend to demonstrate higher performance because they are better able to acquire and
use job-relevant knowledge compared to those who possess lower levels of ability (F. L.
Schmidt, Hunter, & Outerbridge, 1986).
Motivation is “an unobservable force that directs, energizes, and sustains behavior”
(Diefendorff & Chandler, 2011: 66; see also Kanfer, Chen, & Pritchard, 2008; Mitchell &
Daniels, 2003). Motivation relates to performance by influencing the direction, intensity, and
persistence of effort (Blau, 1993; Campbell, 1990; Kanfer, 1990). Specifically, motivation is
reflected in the choices workers make about whether to expend effort, the level of effort they
expend, and how much they persist in that level of effort (Campbell, 1990). Furthermore, these
choices can be enduring, such as individuals who generally work with great effort, or situation
specific, such as workers who devote effort toward a specific task or in a particular context.
A longstanding belief exists that ability and motivation interact to affect performance, such
that the relation between ability (motivation) and performance depends on, or is moderated by,
motivation (ability; Maier, 1955; Murphy & Russell, in press; Vroom, 1964). Stated more
formally, Performance = f(Ability × Motivation). This multiplicative model predicts that when
individuals possess little or no motivation, they will demonstrate similarly low levels of performance regardless of their ability level. However, as individuals begin to exert some level of
effort, differences in ability can play a role, and the relation between ability and performance
becomes positive, such that high-ability individuals will outperform low-ability individuals.
Thus, the multiplicative model is noncompensatory in that performance is predicted to be low
whenever ability or motivation is low. This is different from an additive model in which the
effects of ability and motivation on performance are independent and compensatory (Mount,
Barrick, & Strauss, 1999; Sackett, Gruys, & Ellingson, 1998). For instance, in an additive
model, individuals’ level of motivation would not affect the relation between ability and performance. Moreover, individuals who possess a lower level of motivation could compensate
for this deficit to some extent by demonstrating a higher level of ability.

Van Iddekinge et al. / Performance = f (Ability × Motivation)?   251

The belief in the veracity of the multiplicative model seems justified given that many
well-established theories predict or assume an interactive relation between ability and motivation. For example, expectancy theory posits that “the effects of ability and motivation on
performance are not additive but interactive” (Vroom, 1964: 203). Another example is goalsetting theory (Locke & Latham, 1990), which predicts that ability and goals (as a motivating
factor) interact to affect performance. Specifically, the effect of ability on performance is
predicted to be stronger when people set difficult goals than when they set easy goals.
Similarly, Lawler and Porter’s (1967) model of managerial attitudes and performance posits
that ability interacts with effort to affect performance. The idea that ability and motivation
have an interactive effect on performance also is evident within theory and models on the
antecedents and determinants of job performance. For example, Campbell’s well-known
theory (e.g., McCloy, Campbell, & Cudeck, 1994) predicts that declarative knowledge and
procedural knowledge and skills (of which ability is an immediate precursor) interact with
motivation to affect performance. Finally, propositions related to the multiplicative model
can be found in theory and research on resource allocation (e.g., Hobfoll, 1989; Kanfer &
Ackerman, 1989) that consider variables such as ability to be resources people can deploy to
achieve a desired outcome.
In short, various theoretical bases exist to support the multiplicative model. Furthermore,
researchers have suggested the idea that Performance = f(Ability × Motivation) is “empirically, logically, and psychologically convincing” (Porter & Lawler, 1968: 33) and have
referred to it as a “well-accepted truism” (Bell & Kozlowski, 2002b: 497). This idea can be
found in textbooks widely used in undergraduate, graduate, and executive courses (e.g.,
Bauer & Erdogan, 2010; Gόmez-Mejίa, Balkin, & Cardy, 2007; Landy & Conte, 2004).
There even is anecdotal evidence that beliefs in the multiplicative model influence the advice
consultants provide organizations (Cerasoli, 2014).
Despite the strong theoretical and logical basis for the multiplicative model, the number
of direct tests of this model is surprisingly small. In addition, of the studies that have been
conducted, some have reported evidence of an ability-motivation interaction on performance
(e.g., Fleishman, 1958; French, 1957; Perry, Hunter, Witt, & Harris, 2010), whereas others
have failed to find evidence of an interaction effect (e.g., Dachler & Mobley, 1973; Gavin,
1970; Terborg, 1977). Furthermore, some studies have found evidence of an interaction, but
its form was not consistent with theory (e.g., Kanfer & Ackerman, 1989; Latham, Seijts, &
Crim, 2008; Wright, Kacmar, McMahan, & Deleeuw, 1995). A related research stream has
assessed whether ability interacts with personality variables to predict performance (e.g.,
Mount et al., 1999; Sackett et al., 1998).
Adding to the lack of clarity regarding the validity of the multiplicative model, the designs
and measures used in many studies make it difficult to draw clear conclusions. For example,
some studies (e.g., Fleishman, 1958) have assessed ability using measures with questionable
construct validity, such as initial performance on an experimental task, self-ratings, or tenure.
Other studies (e.g., Hollenbeck, Brief, Whitener, & Pauli, 1988) have measured motivation
using variables that may not directly capture the underlying construct, such as self-esteem,
integrity, or broad measures of conscientiousness. Other empirical work (e.g., Terborg, 1977)
has included variables (i.e., statistical controls) in addition to ability, motivation, and performance, which complicates the interpretation and comparison of findings across studies
(Bernerth & Aguinis, 2016). Finally, most research has tested the multiplicative hypothesis

252   Journal of Management / January 2018

using significance tests of incremental variance explained. Thus, low or differential levels of
statistical power, which are known problems in research that examines interaction effects
(e.g., Aguinis, Beaty, Boik, & Pierce, 2005; Murphy & Russell, in press), often make it difficult to draw conclusions from tests of the multiplicative model.

Present Study
We conducted the present study to provide a comprehensive test of a longstanding hypothesis regarding how two of the most central and widely studied individual differences in management and related fields—ability and motivation—relate to performance. To do so, we
engaged in a multistage data collection process that began by identifying published and
unpublished studies that included measures of ability, motivation, and performance. Next, we
requested raw data from the original authors, which we used to calculate the multiplicative
effects of ability and motivation on performance for each study. We then used meta-analysis
to assess the level and consistency of support for the multiplicative model across the primary
studies. We also used meta-analysis to assess the relative importance of ability versus motivation for explaining variance in performance. Taken together, this methodology enabled us
to test the multiplicative hypothesis in a way that overcomes many of the challenges and
limitations of previous research.
Our study makes several contributions. First, the findings contribute to theory by testing
a hypothesis that can be found in several highly influential theories. Although previous
research has tested the Ability × Motivation hypothesis, the findings have been inconsistent
and have failed to provide clear conclusions regarding the level of support for this model. By
focusing on studies whose designs and measures reflect the constructs of interest, collecting
previously unreported data obtained directly from authors, and cumulating results across a
large number of studies, the present meta-analysis provides a direct test of the interactive
effects of ability and motivation on performance.
Second, we extend existing research by investigating a number of potential boundary
conditions of the multiplicative model. For example, it has been suggested that support for
this model may be stronger in lab settings than in field settings and stronger for more complex jobs or tasks than for less complex ones (e.g., Sackett et al., 1998; Terborg, 1977). We
test both of these possibilities. Researchers also have noted that motivation can be enduring
(i.e., a trait) or situation specific (i.e., a state; e.g., Chen, Gully, Whiteman, & Kilcullen,
2000; Kanfer & Heggestad, 1997). We examine whether the trait versus state motivation
affects support for the multiplicative model. In addition, we explore several other factors that
could affect support for the multiplicative model, including publication status (published vs.
unpublished studies), type of organization (civilian vs. military), study sample size, performance dimension (task vs. contextual performance), and the manner with which performance
is operationalized (objective vs. subjective measures). An examination of these factors
enabled us to explore situations when the multiplicative effects may be stronger or weaker,
as well as to provide information to guide future research and make context-specific recommendations for practice.
Third, the present study also improves our understanding of the relative importance of ability and motivation. Although many primary and meta-analytic studies have examined how
ability relates to performance or how motivation relates to performance, surprisingly few

Van Iddekinge et al. / Performance = f (Ability × Motivation)?   253

studies have directly compared the importance of these two predictors, particularly based on
data from the same set of primary studies. Our results shed light on whether ability or motivation is relatively more important to performance in general, as well as in different contexts
(e.g., during training vs. on the job). Furthermore, to our knowledge, we provide the first
meta-analytic test of the trait versus state motivation distinction as it relates to the prediction
of performance. Our findings regarding this distinction contribute to the literature on the prediction of performance, as well as to the vast body of work on motivation, by highlighting
which operationalization of motivation is the best predictor of performance and when.
Finally, the present findings inform how organizations should use data on ability and
motivation to facilitate staffing decisions. For example, if ability and motivation combine
multiplicatively, this suggests that applicants may need to possess a high level of both variables to perform well on the job. This, in turn, could reduce the pool of potentially acceptable
applicants. Conversely, if ability and motivation combine additively rather than multiplicatively to influence performance, it may be possible to select job applicants who possess a
high level of one variable but a more moderate level of the other. The results also have implications for other human resources practices that attempt to affect, or are influenced by, ability
and motivation, including training and incentive practices.

Hypotheses and Research Questions
Tests of the Multiplicative Model
As we mentioned, propositions concerning the multiplicative model can be found in several theories and models of job performance. The idea that ability and motivation interact to
influence performance also has logical appeal. At the same time, empirical evidence for the
Performance = f(Ability × Motivation) hypothesis is inconclusive and often difficult to interpret. As such, it was difficult to hypothesize what we expected to find. We did anticipate that
any support we might find for the multiplicative model would be modest. For one, the likely
strong main effects of ability and motivation may make it difficult for the interaction between
the two variables to explain a large amount of additional variance in performance (Murphy
& Russell, in press). Furthermore, the incremental contribution of interaction effects beyond
first-order (i.e., “main”) effects tends to be quite small (Aguinis et al., 2005).
Thus, the first goal of our study was to assess the level and consistency of support for the
multiplicative model. The novel methodological approach we used enabled us to test the
multiplicative hypothesis in a more valid and comprehensive manner than past research.
First, we focused on studies that avoided the design and measurement limitations noted
above (e.g., use of proxies to measure ability and/or motivation). Second, we obtained raw
data or analysis output from the original authors. This was important because it helped to
ensure all the data were treated in the same way and analyzed using a consistent approach.
Third, in contrast to previous research that has tended to focus on the statistical significance
of ability-motivation interactions, we focused on effect sizes. Specifically, we examined support for the multiplicative model by calculating the amount of change in the multiple correlation coefficient (R) between the additive and multiplicative models, as well as by assessing
the relative importance of ability, motivation, and the ability-motivation interaction for
explaining variance in performance.

254   Journal of Management / January 2018

In addition, prior studies that have found evidence of an ability-motivation interaction
have not always interpreted the nature of the interaction. To address this omission, we calculated simple slopes for the ability-performance relationship across different levels of motivation. Fourth, we then used meta-analysis to assess the mean and variability of the multiplicative
effect across studies, as well as the consistency of the magnitude and direction of differences
between the simple slopes. This methodology avoids common problems in testing interaction
effects, including low statistical power (i.e., we focused on effect sizes based on dozens of
studies and thousands of observations) and low reliability of the product term (which we corrected for in our analyses). Finally, cumulating effects across primary studies also allowed us
to investigate potential boundary conditions of the multiplicative model, as well as factors
that may moderate the relative importance of ability and motivation to performance. We
describe these boundary conditions next.

Boundary Conditions of the Multiplicative and Relative Effects of Ability and
Motivation
Conceptualization of motivation. Work motivation is a broad construct that has been
defined and measured in many ways. We reviewed existing definitions of work motivation
and found that most of them share two common elements. First, they refer to “unobservable forces” that energize behavior. The forces that energize behavior are innumerable and
originate both within and outside workers. For example, Diefendorff and Chandler noted
that “motivation for a given activity at a particular point in time may be shaped by an infinite
number of factors, including biological processes, needs, values, group norms, personality,
emotions, job characteristics, cultural context, and many others” (2011: 66). Moreover, the
factors that motivate workers are personal, and different workers have different needs and
think different features of the work environment are important (Mitchell & Daniels, 2003).
Second, most definitions refer to the idea that work motivation directly affects the direction, intensity, and duration or persistence of effort. Motivation is reflected in the choices
workers make about whether to expend effort, the level of effort they expend, and how much
they persist in that level of effort (Campbell, 1990). Furthermore, these choices can be enduring, such as employees who generally exhibit high levels of effort, or situation specific, such
as employees who devote effort toward a specific task. Following previous definitions, we
define work motivation as an unobservable force that initiates work-related behavior and
determines its direction, intensity, and duration.
Several theories and areas of research distinguish between traits and states (Steyer,
Schmitt, & Eid, 1999). For example, researchers have identified differences between trait
and state affect (e.g., D. Watson, Clark, & Tellegen, 1988), anger (e.g., Gibson & Callister,
2010), anxiety (Speilberger, Sydeman, Owen, & Marsh, 1999), and self-efficacy (Bandura,
1997). Similarly, motivation can be enduring (i.e., a trait) or situation specific (i.e., a state;
e.g., Chen et al., 2000; Kanfer & Heggestad, 1997). Trait motivation reflects a relatively
stable tendency to exert effort and demonstrate persistence on work tasks. Measures such as
achievement motivation, achievement striving, and work drive are thought to capture trait
motivation (Chen, Gully, & Eden, 2004; Kanfer & Heggestad, 1997; Perry et al., 2010). In
contrast, state motivation reflects workers’ level of motivation at a specific moment in time.
Measures of state motivation typically assess the amount of time, effort, or attention devoted

Van Iddekinge et al. / Performance = f (Ability × Motivation)?   255

to a task (Chen et al., 2004). Goal-related measures also are thought to capture state motivation because goals help direct workers’ effort toward specific tasks (Katerberg & Blau, 1983).
Results of previous research suggest that the way motivation is conceptualized may affect
support for the multiplicative model. For example, Hirschfeld, Lawson, and Mossholder
(2004) found that the ability-motivation interaction was stronger when the motivation measure was more task specific (i.e., academic motivation) than when it was more general (i.e.,
achievement motivation). Similarly, Perry et al. (2010) found greater support for the multiplicative model with a measure that focused more directly on motivation (i.e., achievement
striving) than for measures that assessed less relevant constructs (e.g., other facets of conscientiousness). However, we are not aware of a theoretical basis to hypothesize that support for
the multiplicative model will be stronger or weaker for any specific conceptualization of
motivation. Thus, we pose the following research question:
Research Question 1: Does the way motivation is conceptualized (i.e., trait vs. state) affect the
strength of the multiplicative effect of ability and motivatio...

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