357
F E AT U R E D A R T I C L E
The Effects of Personality,
Affectivity, and Work
Commitment on Motivation to
Improve Work Through Learning
Sharon S. Naquin, Elwood F. Holton III
This study examined the degree to which the dimensions from the Five-Factor
Model of personality, affectivity, and work commitment (including work
ethic, job involvement, affective commitment, and continuance commitment)
influenced motivation to improve work through learning. Data were obtained
from a nonrandom sample of 239 private-sector employees who were
participants of in-house training programs. The hypothesized causal relationships
were tested using structural equation modeling. Findings indicated
that these dispositional effects were significant antecedents of motivation to
improve work through learning. Specifically, 57 percent of the variance in
motivation to improve work through learning was explained by positive
affectivity, work commitment, and extraversion.
According to dispositional theorists, individuals possess relatively stable characteristics
that affect their attitudes and behavior (Davis-Blake and Pfeffer,
1989). However, until recently, most of the research on job attitudes has been
situational—referring, for example, to task characteristics, supervision, pay,
working conditions, organizational structure, workspace characteristics, and
promotional opportunities (compare Berger and Cummings, 1979; Fried
and Ferris, 1987; Hackman and Oldham, 1980; Herzberg, 1966; Locke, 1976;
Loher, Noe, Moeller, and Fitzgerald, 1985; Oldham and Fried, 1987). Little
has been done to study dispositional traits in the context of organizational
HRD. More specifically, there seem to be few empirical studies linking motivation
in learning contexts with personality and other individual characteristics.
Nor is there a model explaining dispositional influences on an employee’s
motivation to improve work through learning. A better understanding of these
HUMAN RESOURCE DEVELOPMENT QUARTERLY, vol. 13, no. 4, Winter 2002
Copyright © 2002 Wiley Periodicals, Inc.
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358 Naquin, Holton
differences and their impact on workplace learning would enable learning
professionals to tailor training interventions more effectively and improve
performance through learning.
Despite the limited number of such studies in HRD, dispositional research
has led to the conclusion that there is a conceptual relationship between disposition
and behavior. This dispositional research model, which is distinctly
different from situational models, undergirds this study. It depicts the basic
relationship between several constructs. Disposition as a variable of interest
includes an individual’s personality, which is made up of traits, affective (mood)
structure, and values. Personality influences attitudes. Attitudes, in turn, affect
motivation, which then leads to behavioral outcomes. In this model, situational
factors do influence attitudes, motivation, and behavior, but they act in
conjunction with dispositional factors.
Thus, the purpose of this study was to develop and test a model of dispositional
effects on motivation. More specifically, the research model incorporated
personality, affectivity, and work commitment as independent constructs
and motivation to improve work through learning as the dependent construct.
Structural equation modeling was used to analyze the research model.
Background of the Study
Evidence supporting the integration of personality, affectivity, work commitment,
and motivation to improve work through learning constructs into a
single research model came from a variety of research studies. This section
briefly outlines some of the background literature.
Motivation to Improve Work Through Learning. Previous research efforts
have focused on two types of motivation: motivation to learn or train and motivation
to transfer (compare Clark, Dobbins, and Ladd, 1993; Noe, 1986; Noe
and Schmidt, 1986; Hicks and Klimoski, 1987; Mathieu and Martineau, 1997;
Mathieu, Tannenbaum, and Salas, 1992; Seyler, Holton, Bates, Burnett, and
Carvalho, 1998; Warr and Bunce, 1995). However, if the desired outcome of
organizational training programs is to improve work outcomes, then using
motivation to learn or train as the dependent variable may be too limited. The
process of improving work through learning also involves an employee’s
willingness to transfer the knowledge acquired to improve work processes. It
is the combined motivational influences that will influence desired training
outcomes. Thus, this study employs a relatively new construct: motivation to
improve work through learning (MTIWL) (Baldwin, Ford, and Naquin, 2000;
Naquin and Holton, 2001). This construct posits that an individual’s MTIWL
is a function of motivation to train and motivation to transfer. Further, it should
more completely capture the motivational influences leading to improved work
outcomes. Thus, the MTIWL is potentially a more powerful motivational
construct because it incorporates both dimensions of motivation critical to
achieving HRD outcomes.
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Personality: The Five-Factor Model. According to the Five-Factor Model
(FFM), there are five broad categories at the top of the personality trait hierarchy:
neuroticism-emotional stability, extraversion, openness to experience,
agreeableness, and conscientiousness (Costa and McCrae, 1992). Considerable
research supports the relationship between personality and job performance
variables, training efficiency, academic performance, and motivation
(compare Barrick and Mount, 1991; Barrick, Stewart, Neubert, and Mount,
1998; Colquitt and Simmering, 1998; Costa and McCrae, 1995; Hogan,
Rybicki, Motowidlo, and Borman, 1998; Salgado, 1997). Researchers have
linked personality dimensions to a number of industrial and organizational
topics, including absenteeism (Mowday, Porter, and Steers, 1982), employee
reliability (Sackett and Harris, 1984), leadership (Ghiselli, 1971), organizational
climate (Schneider, 1985), employee satisfaction (Staw and Ross, 1985), work
motivation (Korman, 1976), and job scope. Kanfer (1990) strongly advocated
the use of this model to advance the current body of motivational research.
Affect: Positive and Negative Affectivity. Affectivity is an emotion-based
trait dimension (Watson, Clark, and Tellegen, 1988) that creates a cognitive
bias through which individuals approach and understand experiences and may
affect how they experience and evaluate jobs (Levin and Stokes, 1989). A
prominent view of affectivity is that there are two independent dimensions of
it: positive (PA) and negative (NA) (Costa and McRae, 1980; Diener and
Emmons, 1984; Watson, Clark, and Tellegen, 1988). PA is the tendency to
experience positive emotional states, NA is the tendency to experience negative
ones ( Judge, Locke, and Durham, 1997; Watson and Clark, 1984).
PA levels have been shown to be associated with interpersonal relations
and achievement (George and Brief, 1992; Tellegen, 1985), engagement
(McFatter, 1994), learning speed (Masters, Barden, and Ford, 1979), expectations,
estimates of past successes, and self-assessments (Wright and Mischel,
1982). Individuals with higher NA levels tend to have higher levels of anxiety,
focus more on negative aspects of themselves and the world, and dwell on their
mistakes, disappointments, and shortcomings (Levin and Stokes, 1989). Similarly,
individuals scoring high on the neuroticism dimension are vulnerable to
stress, prone to feeling inferior, self-conscious, and uncomfortable around
others (Costa and McCrae, 1991). However, training programs are sometimes
highly interactive, requiring high energy levels. Thus, voluntary participation
in training initiatives and motivation to transfer among individuals who are
highly neurotic or high in NA would be less likely because self-confidence and
energy are required for successful completion of such programs.
The Role of Work Commitment. The suggestion that commitment plays
a key role in training motivation is not new (see Facteau, Dobbins, Russell,
Ladd, and Kudisch, 1995; Noe, 1986; Tannenbaum, Mathieu, Salas, and
Cannon-Bowers, 1991). Morrow (1983) surmised that work commitment is a
function of personal characteristics, including dispositional qualities, and presents
a facet design of work commitment that includes work ethic, career
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360 Naquin, Holton
commitment, organizational commitment (affective and continuance), and job
involvement (Morrow, 1993). Because work commitment is likely to influence
motivation in the workplace, her conceptualization of work commitment foci
provided a starting point for this analysis. Based on research discussed in the
following section, work commitment is hypothesized to mediate the relationship
between some of the predictor variables and motivation to improve work
through learning. Three constructs of Morrow’s (1993) conceptualization—
work ethic, organizational commitment, and job involvement—were
employed. Career commitment was not used, because as Blau (1985) noted,
career commitment is a particularly difficult construct to operationalize in
heterogeneous groups because it means different things to different employee
groups.
Work Ethic. Work ethic, also called the Protestant work ethic (PWE), has
been defined for “an individual as a value or belief concerning the place of
work in one’s life that either (a) serves as a conscious guide to conduct or
(b) is simply implied in manifested attitudes and behavior” (Siegel, 1983,
p. 28). According to Weber’s classic conceptualization of PWE, which
stemmed from Calvinistic and Quaker philosophies of individualism and
asceticism (Macoby, 1983), work is “performed as if it were an end in itself, a
calling” (Weber, 1958, p. 62). Individuals with a strong work ethic are
committed to the values of hard work and embrace the Calvinistic tradition
of frugality, hard work, conservatism, and success (Weber, 1958). However,
the culture today does not necessarily support the same conventions and
values as in earlier days. Work values constantly change and evolve, so the
notion that the work values of 1958 would not be applicable today is
consistent with historical trends. A redefinition of work values has occurred.
Bernstein describes contemporary employees as “inner-directed,” people
“who clearly place their personal wants and aspirations above those of their
employers” (1997, p. 221). Work schedules and business priorities are
secondary to self-fulfillment (Sinetar, 1980). So although the values of
previous generations may have been deeply rooted in nonleisure as the norm,
this is not the case in American society today. In light of the prevailing values,
cultures, and mores, it is possible for an individual to score high on the hard
work scale but low on nonleisure, asceticism, or independence. Compliance
with the norms and values of today’s society could lead an individual to
respond to the PWE instrument in a manner that would contradict the PWE
construct, which requires high scores on all four facets: hard work,
asceticism, independence, and nonleisure. Hard work appears to be the only
component of PWE that is applicable in today’s society (see Naquin and
Holton, 2001).
Organizational Commitment. Recent research efforts have focused on
three types of commitment: affective, continuance, and normative
commitment (Allen and Meyer, 1990; Meyer and Allen, 1984). Allen and
Meyer defined affective commitment as an “emotional attachment to the
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organization such that the strongly committed individual identifies with, is
involved in, and enjoys membership in, the organization” (1990, p. 2).
Continuance commitment is based on “the individual’s recognition of
the costs (or lost side bets) associated with discontinuing the activity”
(Allen and Meyer, 1990, p. 33). Normative commitment, however, is not
included in Morrow’s (1993) work commitment conceptualization, one of
the foundational premises of the present study, and was thus excluded
from it.
Job Involvement. Lodahl and Kejner (1965) defined job involvement as
the degree of daily absorption a worker experiences in work activity.
Job involvement leads individuals to exceed the normal job expectations
(Moorhead and Griffin, 1995) and is a key component in employee
motivation (Lawler, 1986). Brown (1996) confirmed a relationship between
job involvement and work ethic endorsement with growth need strength,
a facet of conscientiousness, concluding that motivation may be both
an antecedent and an outcome of job involvement. Clark (1990) found a
positive relationship between training motivation and job involvement, and
Hensey (1987) found that the effectiveness of training programs suffered
among workers who were less involved with their jobs.
The Research Model. Based on the research briefly discussed, the research
model shown in Figure 1 was developed. It shows the indicator variables (discussed
in the next section), latent variables, and hypothesized structural relationships.
It should be noted that this model does not capture all influences
on MTIWL; situational effects are excluded. Rather, it hypothesizes what are
believed to be key dispositional influences on MTIWL.
Method
This section describes the method used in this study.
Sample. Data were obtained from a nonrandom sample of 247 subjects
from a single private-sector health insurance organization. Listwise deletion for
missing data resulted in a usable sample size of 239. Respondents were participants
of in-house training programs and represented a wide range of years
of work experience and a wide range of job levels. Respondents’ average age
was 35.5 years (overall age range was 19 to 68; SD was 10.516), 28.5 percent,
or 68 of the respondents, were male and 71.5 percent, or 171, were female.
Procedure. Surveys were administered to respondents at the beginning of
in-house training programs. Participants were required to attend these classes,
and questionnaires were presented as part of the training program. Participants
were allowed to withdraw if they had objections to the study, but none
objected.
Instrumentation. In structural equation modeling, unidimensional latent
constructs are routinely represented by multiple scales, called indicator
variables. Thus, to measure MTIWL, scales measuring both an individual’s
motivation to train and motivation to transfer were necessary. Because it is
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Conscientiousness
Agreeableness
PA
NA
Extraversion
Openness
Neuroticism
NEO-FFI
n
NEO-FFI
c
NEO-FFI
a
PANAS
pa
PANAS
na
NEO-FFI
e
NEO-FFI
o
Job
involvement
Affective
commitment
Continuance
commitment
Hard
work
Work
commitment
MTIWL
Motivation
to train
Performance
outcomes
Training
attitudes
Motivation to
transfer
Figure 1. Dispositional Model of MTIWL with All Indicator Variables
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The Effects of Personality, Affectivity, and Work Commitment 363
desirable to have at least three indicators for latent constructs, four scales were
selected to measure the motivation to improve work through learning construct.
Two seven-item scales from the Strategic Assessment of Readiness
for Training (START) instrument (Weinstein and others, 1994) were selected:
training attitudes and the motivation to train scale. In this study, coefficient
alpha reliabilities were .70 for both scales. The Learning Transfer Systems
Inventory (LTSI) (Holton, Bates, and Ruona, 2000), a sixty-eight-item instrument,
measures factors affecting learning transfer, including motivation. The
motivation to transfer scale (.83) and performance outcomes expectations
( .83) scale were selected. Drawing on expectancy theory, the second scale
was selected to include an outcome component of improving work through
motivation. In this study, coefficient alpha reliabilities were .85 for motivation
to transfer and .78 for performance outcome expectations.
The NEO Five-Factor Inventory (NEO-FFI), a sixty-item measure of
personality (Costa and McCrae, 1992), measured personality dimensions. Raw
scores were converted to t score values using gender-based national norms
(Costa and McCrae, 1991). Internal reliabilities for the NEO-FFI have been
reported as .86, .77, .73, .68, and .81 for neuroticism-emotional stability, extraversion,
openness, agreeableness, and conscientiousness, respectively (Costa
and McCrae, 1991).
The most widely used measure of PA and NA is the twenty-item Positive
and Negative Affectivity Schedule (PANAS) (Watson, Clark, and Tellegen,
1988). Subjects rate PA and NA according to their “general” or “average” feelings,
in order to assess trait affectivity rather than state affectivity. Watson,
Clark, and Tellegen reported internal consistency reliabilities for PA as .87 and
for NA as .88.
Blau and Ryan (1997) revealed a four-dimension construct—hard work,
nonleisure, asceticism, and independence—measured by an eighteen-item secular
work ethic instrument. It was selected because it appeared to contain the
most valid items empirically derived from seven different instruments. As
discussed earlier, only the hard work scale was used in this study. Coefficient
alpha reliability for hard work was .78.
Kanungo (1982) proposed a ten-item job involvement measure with items
derived from Lodahl and Kejner (1965), but it is psychometrically stronger
than the other scales (Blau, 1997) so it was selected for this study. Coefficient
alpha reliability was .71.
Because of its multidimensional structure, the Allen and Meyer (1990)
instrument is increasingly being used to measure organizational commitment.
This instrument consists of three eight-item scales: affective, continuance, and
normative commitment. The affective and continuance commitment scales
from this instrument were selected for use in this study. Coefficient alpha reliabilities
were .84 for affective commitment and .81 for continuance commitment
in this study.
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364 Naquin, Holton
Analysis. Structural equation model analysis was conducted with LISREL
8.3 ( Joreskog and Sorbom, 1996) to test the causal relationships between
variables in the hypothesized model. Input for estimation of the model was
provided by a covariance matrix prepared with PRELIS 2.3. Data analysis
was conducted in two stages in accordance with a procedure suggested by
Anderson and Gerbing (1988) and Hair, Anderson, Tatham, and Black (1998).
In the first stage, the adequacy of the measurement model was examined. The
initial analyses evaluated the loading of individual instrument items on instrument
scales being used as indicator variables. Scale scores were calculated and
used as indicators for the latent constructs. A second analysis evaluated the fit
of the measurement model made up of the scale scores and latent constructs.
Because the NEO and PANAS scales are so well established, these scales
were not included in this stage of analysis. Their established validity allowed
for the treatment of each of these scales as a single indicator for a corresponding
latent construct. As is common practice with single indicators, the error
variance was set to one, minus the reliability of the scale, times the variance of
the scale (Hair, Anderson, Tatham, and Black, 1998). For the NEO-FFI, the
variance and reliability were obtained from the technical manual (Costa and
McRae, 1992), whereas the values used for the PANAS were calculated from
this sample.
The second step of the analysis required assessment of the structural
model describing the relationships among the latent constructs (Anderson and
Gerbing, 1988). Like the evaluation process for the measurement model, structural
model assessment involves examination of multiple fit indices. (For an
explanation of fit indices, see Hair, Anderson, Tatham, and Black, 1998.) In
addition, parameter estimates for each path and the statistical significance were
examined during this stage.
It is increasingly common for researchers to develop and evaluate alternative
models rather than simply examine the absolute fit of the hypothesized
model. Thus, at each step of the analysis the model was carefully examined for
possible modifications as well as overall fit. Each change suggested by weak
factor loadings, nonsignificant paths, or modification indices was carefully
evaluated for alternative theoretical support.
Results
This section presents the study’s results.
Measurement Model Analysis. The first stage of the measurement model
analysis was to examine the loadings of instrument items on each scale except
for the NEO-FFI and the PANAS. For space reasons this step will not be discussed
in detail, but full details are available from the authors.1 Briefly, confirmatory
factor analyses of scales led to deletion of only a few instrument items.
Resulting fit measures were considered adequate. Scale scores were then
calculated using the slightly revised scales.
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The second stage tested the loading of the scales on the designated latent
constructs. In this model, there were two constructs with multiple indicators:
work commitment and MTIWL. The initial fit for the model (see Figure 1) was
acceptable (2
(61) 163.62, GFI .92; AGFI .83; NFI .85; RMSEA .084;
SRMR .064; CFI .89). All paths were significant, so none were eliminated.
Thus, in the final measurement model four indicator variables were retained for
the dependent construct MTIWL and four were retained for work commitment.
Table 1 shows the correlation matrix for all scales.
Structural Model Analysis. The fit for the initial structural model (see
Figure 2) was not as strong as desired (2
(68) 169.88, GFI .91; AGFI .85;
NFI .84; RMSEA .079; SRMR .072; CFI .89). Several paths were
also nonsignificant (t 1.96), including from openness to motivation
to improve work through learning (t.80); from neuroticism to WCATT
(t .19); from NA to MTIWL (t 1.07); and from extraversion to MTIWL
(t 1.38). It was decided that three of the nonsignificant paths should
be eliminated. The path from extraversion to MTIWL was retained due to theoretical
support and previous research findings. Analysis of this model indicated
slightly improved fit (2
(47) 108.06, GFI .93, AGFI .88, NFI .86,
RMSEA .074, SRMR .072, CFI .91).
Figure 2 shows the final path model with standardized path coefficients.
Parameter estimates indicated that all but one path was statistically significant.
The t values ranged from 1.20 to 4.85. Although the path from extraversion to
MTIWL was not statistically significant (t 1.20), it was retained because
of research findings indicating its importance (Barrick and Mount, 1991).
In addition, deletion of this path did not result in a significant improvement in
the model fit. The remaining five paths were statistically significant (t 1.96).
Table 2 summarizes the effect size statistics and t values. Conscientiousness
and agreeableness explained 53 percent of the variance in work commitment.
Work commitment, extraversion, and PA explained 57 percent of the variance
in MTIWL. The standardized coefficients for the total effects on MTIWL show
that PA had the strongest influence ( .44), whereas conscientiousness
had the second strongest influence ( .21) even though it occurred indirectly
through work commitment. Extraversion ( .10) and agreeableness
(.10) had smaller effects, with agreeableness also operating through work
commitment. Conscientiousness had the strongest influence on work commitment
(.54), twice that of agreeableness (.27).
Discussion
MTIWL is a new construct devised to assess individuals’ motivation to train
and their motivation to transfer knowledge or skills acquired through training
initiatives to work settings. This is the first known use of this construct. Confirmatory
factor analysis showed that the four scales selected loaded on this
latent construct. The squared multiple correlations for all scales were good
The Effects of Personality, Affectivity, and Work Commitment 365
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Table 1. Correlation Matrix
Independent Variables 1 2 3 4 5 6 7 8 9 10 11
1 Neuroticism —
2 Extraversion .16*
3 Openness .05 .48**
4 Agreeableness .25** .40** .34**
5 Conscientiousness .24** .33** .32** .42**
6 Job involvement .05 .20** .30** .27** .37**
7 Affective commitment .25** .26** .15* .28** .43** .55**
8 Continuance
commitment .14* .29* .29** .13 .13 .14* .06
9 Negative affectivity .48** .16* .01 .31** .18** .02 .19** .16*
10 Positive affectivity .40** .47** .35** .37** .46** .19** .31** .19** .37**
11 Hard work .04 .23** .23** .25** .36** .22** .27** .01 .04 .34**
12 Motivation to transfer .14* .37** .31** .27** .30** .24** .33** .14* .18** .47** .42**
13 Performance outcome
expectations .17** .32** .19** .30** .30** .34** .44** .23** .19** .46** .40** .54**
14 Attitudes toward
training .21** .24** .20** .24** .31** .24** .29** .15* .17** .43** .26 .59** .30**
15 Motivation to train .16* .27** .20** .20 .26** .05 .18** .12 .24 .43** .25** .48** .40** .51**
Note: *correlation is significant at the .05 level; **correlation is significant at the .01 level.
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Conscientiousness
Agreeableness
PA
Extraversion
NEO-FFI
c
NEO-FFI
a
PANAS
pa
NEO-FFI
e
Job
involvement
Affective
commitment
Continuance
commitment
Hard
work
Work
commitment
MTIWL
Motivation
to train
Performance
outcomes
Training
attitudes
Motivation to
transfer
.59 (6.90)
.54 (4.47)
.27 (2.44)
.44 (4.85)
.10 (1.20)
.69 (9.24)
.67 (9.02)
.38 (3.98)
.69 (7.41) .21 (2.76) .48 (5.94)
.63 (8.57)
.77 (10.04)
Figure 2. Final Model with Standardized Path Coefficients and t Values
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368 Naquin, Holton
(motivation to transfer .60; motivation to train .48; performance outcome
expectations .45; attitudes toward training .40). Each of the separate
scales selected had evidence of initial content construct validity from previous
studies.
Four of the dispositional traits assessed in this study were found to be
antecedents of MTIWL, two directly and two indirectly through work commitment.
Extraversion and PA directly and positively influenced MTIWL,
whereas the effects of conscientiousness and agreeableness were mediated by
work commitment, which positively influenced the dependent construct. More
specifically, 57 percent of the variance in MTIWL was explained by PA, work
commitment, and extraversion, whereas 53 percent of the variance in the
mediator construct, work commitment, was explained by conscientiousness
and agreeableness. This indicates that these dispositional effects are, in fact,
important considerations in predicting MTIWL.
The significance of the path from PA to MTIWL, the strongest path found
in this study, supports previous research. George and Brief (1992) found that
achievement motivation, which is closely related to motivation, is associated
with PA. They later proposed a theory in which PA influences proximal and
distal motivation (George and Brief, 1996). The engagement component of PA
(McFatter, 1994) also seems to be directly associated with motivation.
Table 2. t Values and Regression Equations for Final Model
t Values
Path t Value
Conscientiousness to work commitment 4.47
Agreeableness to work commitment 2.41
Extraversion to MTIWL 1.20
PA to MTIWL 4.85
Work commitment to MTIWL 4.02
Regression Equations
WC 0.54*NEOCONSC 0.27*NEOAGREE, Errorvar. 0.47, R2 0.53
(0.12) (0.11)
4.47 2.44
MTIWL 0.38*WC 0.10*NEOEXTRA 0.44*PA, Errorvar. 0.43, R2 0.57
(0.09) (0.08) (0.09)
4.02 1.20 4.85
Effect Size Coefficients for Exogenous Variables and MTIWL (Standardized Total Effects)
PA .44
Conscientiousness .21
Extraversion .10
Agreeableness .10
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Individuals scoring high on the PA scale would be more likely to become
engaged in the training program, thereby increasing the likelihood of their success
in the program and subsequent transfer of training.
Wright and Mischel’s (1982) findings—relating to heightened expectations,
greater estimates of past successes, and more favorable self-assessments among
individuals with high PA—supports a direct link between PA and MTIWL. Individuals
with high PA, like highly extraverted individuals, may be more optimistic
about training and have a stronger belief in their ability to complete it
successfully. They may also have more confidence in their ability to improve
work situations as a result of the knowledge and skills they acquire through
training. Their optimism and positive self-assessments may also make them feel
more empowered to effect change in their job performance.
Conscientiousness was the second strongest predictor, mediated by
work commitment. Findings of this study on conscientiousness are consistent
with previous research that suggests a relationship between this personality
dimension and work commitment. Conscientiousness, according to Costa
and McCrae (1991), comprises facets such as competence, order, dutifulness,
achievement striving, self-discipline, and deliberation. These descriptors
are similar to those of the work ethic component of work commitment, which
includes an orientation toward hard work and achievement, dependability,
and persistence (Weber, 1958). Conscientiousness has also been found
to be associated with volitional variables such as hard work, perseverance,
and achievement orientation (Costa and McCrae, 1988a, 1988b; Digman and
Takemoto-Chock, 1981; Peabody and Goldberg, 1989), which are aspects of
work commitment.
These findings also support conscientiousness studies that are more closely
related to training-learning and motivation. For instance, studies in educational
settings have reported correlations between conscientiousness and educational
achievement (Digman and Takemoto-Chock, 1981; Smith, 1967) and
vocational achievement (Takemoto, 1979) in the .50 to .60 range (Barrick
and Mount, 1991). Barrick and Mount (1991) found conscientiousness to be
a significant predictor of training proficiency (r .23) across all occupational
groups studied. In addition, Mathieu and Martineau (1997) and Mathieu,
Martineau, and Tannenbaum (1993) found that achievement motivation
(a facet of conscientiousness) positively influenced training motivation. Finally,
Colquitt and Simmering (1998) found a positive correlation between motivation
to learn and conscientiousness.
The next strongest predictor in the model was agreeableness. However, the
relationship between agreeableness and work commitment found in this study
provides new information on this personality dimension because no other
studies were located that directly tested this relationship. An important characteristic
of individuals scoring high in agreeableness is willingness to assist
others (Costa and McCrae, 1991). When applied to employment situations,
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370 Naquin, Holton
the parallel to willingness to assist others may be that these individuals are willing
to assist the organization by improving work; thus, they would be more
committed to work.
Extraversion had the same effect size as agreeableness, even though the
path from extraversion to motivation was nonsignificant. Because prior theory
and research supported its influence on training outcomes, it was retained in
the model. Further research is needed to determine if it is, in fact, a significant
predictor. Logically, it would seem to be, because extraverts, somewhat like
individuals with high PA, tend to be optimistic, energetic, enthusiastic, and
actively seek both interpersonal relations and achievement (George and Brief,
1992). Each of these characteristics is an important component of motivation
and motivation to improve work through learning. These individuals may perceive
training events as enjoyable and may feel that successful completion of
the training program is likely and that they can effect change or improve their
work with the information and skills they acquire.
Somewhat unexpectedly, openness to experience was not a significant predictor
of the dependent variable, nor did it significantly influence work commitment.
In some ways, this contradicts previous research. For instance,
Barrick and Mount (1991) found openness correlated with training proficiency
(r .25). Others (Driskell, Hogan, Salas, and Hoskin, 1994; Gough, 1987;
McCrae, Costa, and Piedmont, 1992; Salgado, 1997) have also found a positive
relationship between openness to experience and learning. One explanation
may lie in the fact that the MTIWL construct included a transfer and
performance outcome component, unlike the more frequently assessed motivation
to learn construct. One facet of the openness to experience personality
dimension is intellectual curiosity, which often translates to “an active pursuit
of intellectual interests for their own sake” (Costa and McRae, 1991, p. 17).
Because the dependent construct was more work-focused than just learning
for the sake of learning, it may have created an outcome orientation element
within the construct rather than just a learning orientation.
Others have explored a related dispositional variable, goal orientation,
which refers to whether individuals view training situations as learning opportunities
(mastery orientation) or opportunities to achieve their goals (goal orientation)
(Colquitt and Simmering, 1998; Mathieu and Martineau, 1997). Had
the dependent variable been more learning-oriented (that is, learning for the
sake of learning) as opposed to being outcome-oriented (geared toward
the application of the training knowledge and skills attained), openness to
experience might have remained in the model.
The finding that neuroticism and NA were not related to MTIWL is somewhat
surprising. Although there was limited research directly linking these
constructs to training, the characteristics of individuals scoring high on
these scales would seem to suggest they would influence motivation. The
fact that they did not further supports George and Brief’s (1992, 1996) contention
that PA would be the strongest predictor.
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Implications
The findings suggest that motivation is significantly affected by dispositional
factors. Organizations whose performance depends on their employees’ willingness
to learn continually and use their learning to make changes in the
workplace must be concerned with the dispositional profile of those employees.
Therefore, organizations must be prepared to respond to the motivation
of current and potential employees. These findings suggest that each individual
has a dispositionally affected motivational profile for improving work
through learning based on four factors: PA, conscientiousness, extraversion,
and agreeableness. Leadership research indicates that the trait approach facilitates
the selection of leaders. It is applicable here as well. Viewed from a selection
perspective, organizations can determine the desired employee profile to
meet their needs. From a humanistic perspective, employers must consider
how to work with individuals who are not predisposed to be motivated to
improve work through learning. Careful consideration must be given to what
motivates employees who do not fit the profile found to be significant in this
study. The large contribution of these dispositional characteristics in predicting
motivation to improve work through learning suggests that HRD professionals
should attend more closely to the motivational levels of employees who
score low on these dispositional dimensions and develop and implement interventions
to heighten their pretraining motivation. Knowledge of the dispositional
profiles of employees, coupled with an awareness of the optimum
motivational profile, should enable employers to accomplish this task more
easily.
There are, of course, ethical issues for organizations to consider as they
become aware of the dispositional profile of both employees and candidates
for employment. For instance, the accuracy of the information is at risk if selfratings
alone are relied on. Decisions made on the basis of inaccurate information
could potentially negatively affect the employee or the organization
or both. And disclosure of such information, either inside or outside of the
organization—whether the information is accurate or not—brings up
the issue of the employee’s right to privacy. Similarly, an organization’s ability
to control employee behavior through such knowledge could also present an
ethical challenge. Despite these risks and ethical dilemmas, the ability to
enhance employee and organizational effectiveness through interventions
developed with this knowledge warrants consideration of employee dispositional
profiles.
Dispositional characteristics have not been emphasized in previous HRD
studies, which have tended to rely more heavily on situational variables. (As
Figure 1 indicates, situational factors do influence attitudes, motivation, and
behavior, but they act in conjunction with dispositional factors.) However,
these findings highlight the need for HRD researchers to include dispositional
and individual difference factors in more research efforts. Because the effects
The Effects of Personality, Affectivity, and Work Commitment 371
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372 Naquin, Holton
of dispositions were so powerful, models of training in the workplace should
control for them. Failure to include dispositional effects in such models
may result in an overestimation of situational effects and could lead organizational
researchers down the wrong path in their attempts to enhance training
outcomes.
Further research should aim at expanding or refining the dependent construct
as well. Because this is the first known study to examine MTIWL, it
should be tested on other sample populations, both in single organizations and
across several organizations. Other scales should also be investigated to see if
they should be considered as possible indicators of this construct. Researchers
should also examine the convergent and divergent validity of the construct
with other variables in its nomological net, or use only attitudes toward training
and motivation to train scales to determine if the same results are observed.
Finally, researchers should examine its criterion validity by examining the relationship
between this construct and performance.
Several possible limitations of this research should be mentioned. First,
because the study is based on self-report data and the surveys were administered
at the beginning of in-house training programs, there is the possibility
of common method variance, although examination of the correlation
matrix suggests this is not a significant problem. For example, correlations
between the predictor variables and motivation to train ranged from –.24 to
.48, and with motivation to transfer they ranged from –.18 to .47. Second,
data for this study came from a nonrandom sample, thereby limiting the generalizability
of the findings. Respondents worked for a single company and
there was an overrepresentation of females (that is, only 28.5 percent of
respondents were male). Third, a nonexperimental cross-sectional research
design was used in this study. Caution is necessary when using even the most
sophisticated statistical techniques available for making causal inferences.
Nevertheless, the theoretical underpinnings of the model development and
testing provide credibility for the study’s results. Fourth, even though respondents
were assured of anonymity and confidentiality, an element of social
desirability could be present in the data. Attempts were made, however, to
ameliorate these effects by providing a script of instructions and an assurance
of confidentiality. Fifth, research in this area is clearly in its exploratory
phases, so the theoretical foundations and causal models need further work.
In particular, the model modifications made in this study will clearly need
cross-validation. Despite these limitations, this research makes valuable
contributions in applying existing theory and research methods to a
new construct domain.
Note
1. Please direct any correspondence to the authors at naquin1@bellsouth.net, (225) 578-
2456, or fax (225) 578-5755.
hrd13402.qxp 11/1/02 11:21 AM Page 372
References
Allen, N. J., & Meyer, J. P. (1990). The measurement and antecedents of affective, continuance
and normative commitment to the organization. Journal of Occupational Psychology, 63,
1–18.
Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review
and recommended two-step approach. Psychological Bulletin, 103, 411–423.
Baldwin, T. T., Ford, J. K., & Naquin, S. S. (2000). Framing training before it begins: Enhancing
the motivation to improve work through learning. In E. F. Holton, T. T. Baldwin, & S. S.
Naquin (Eds.), Managing and changing learning transfer systems in organizations [Monograph].
Advances in Developing Human Resources, 80, 23–35.
Barrick, M. R., & Mount, M. K. (1991). The Big Five personality dimensions and job performance:
A meta-analysis. Personnel Psychology, 44, 1–26.
Barrick, M. R., Stewart, G. L., Neubert, M. J., & Mount, G. L. (1998). Relating member ability
and personality to work-team processes and team effectiveness. Journal of Applied Psychology,
83, 377–391.
Berger, C., & Cummings, L. (1979). Organizational structure, attitudes and behaviors. In B. M.
Staw & L. L. Cummings (Eds.), Research in organizational behavior (pp. 169–208). Greenwich,
CT: JAI Press.
Bernstein, P. (1997). American work values. Albany: State University of New York Press.
Blau, G. J. (1985). A multiple study investigation of the dimensionality of job involvement.
Journal of Vocational Behavior, 27, 19–26.
Blau, G. J. (1997). On measuring work ethic: A neglected work commitment fact. Journal of
Vocational Behavior, 51, 435–448.
Blau, G. J., & Ryan, J. (1997). On measuring work ethic: A neglected work commitment facet.
Journal of Vocational Behavior, 51, 435–448.
Brown, S. (1996). A meta-analysis and review of organizational research on job involvement.
Psychological Bulletin, 120, 235–255.
Clark, C. (1990). Social processes in work groups: A model of the effect of involvement, credibility, and
goal linkage on training success. Unpublished doctoral dissertation research, University of
Tennessee, Knoxville.
Clark, C. S., Dobbins, G. H., & Ladd, R. T. (1993). Exploratory field study of training motivation:
Influence of involvement, credibility, and transfer climate. Group and Organization
Management, 18, 292–307.
Colquitt, J. A., & Simmering, M. J. (1998). Conscientiousness, goal orientation, and motivation
to learn during the learning process: A longitudinal study. Journal of Applied Psychology, 83 (4),
654–665.
Costa, P. T., & McCrae, R. R. (1980). Influence of extraversion and neuroticism on subjective
well-being: Happy and unhappy people. Journal of Personality and Social Psychology, 38,
668–678.
Costa, P. T., & McCrae, R. R. (1988a). Personality in adulthood: A six-year longitudinal study of
self-reports and spouse ratings on the NEO personality inventory. Journal of Personality and
Social Psychology, 54, 853–863.
Costa, P. T., & McCrae, R. R. (1988b). From catalog to classification: Murray’s needs and the Five-
Factor Model. Journal of Personality and Social Psychology, 49, 1266–1282.
Costa, P. T., & McCrae, R. R. (1991). The NEO Personality Inventory: Using the Five-Factor
Model in counseling. Journal of Counseling and Development, 69, 367–372.
Costa, P. T., & McCrae, R. R. (1992). Revised NEO Personality Inventory and the Five-Factor Inventory
professional manual. Odessa, FL: Psychological Assessment Resources.
Costa, P. T., & McCrae, R. R. (1995). Domains and facets: Hierarchical personality assessment
using the revised NEO Personality Inventory. Journal of Personality Assessment, 68, 86–94.
Davis-Blake, A., & Pfeffer, J. (1989). Just a mirage: The search for dispositional effects in organizational
research. Academy of Management Review, 14, 385–400.
The Effects of Personality, Affectivity, and Work Commitment 373
hrd13402.qxp 11/1/02 11:21 AM Page 373
374 Naquin, Holton
Diener, E., & Emmons, R. A. (1984). The independence of positive and negative affect. Journal
of Personality and Social Psychology, 43, 1105–1117.
Digman, J. M., & Takemoto-Chock, N. K. (1981). Factors in the natural language of personality:
Re-analysis and comparison of six major studies. Multivariate Behavioral Research, 16, 149–170.
Driskell, J. E., Hogan, J., Salas, E., & Hoskin, B. (1994). Cognitive and personality predictors of
training performance. Military Psychology, 6, 31–46.
Facteau, J. D., Dobbins, G. H., Russell, J.E.A., Ladd, R. T., & Kudisch, J. D. (1995). The influence
of general perceptions of the training environment on pre-training motivation and
perceived training transfer. Journal of Management, 21, 1–25.
Fried, Y., & Ferris, G. R. (1987). The validity of the job characteristics model: A review and metaanalysis.
Personnel Psychology, 40, 287–322.
George, J. M., & Brief, A. P. (1992). Feeling good, doing good: A conceptual analysis of the mood
at work-organizational spontaneity relationship. Psychological Bulletin, 112, 310–329.
George, J. M., & Brief, A. P. (1996). Motivational agendas in the workplace: The effects of feelings
on focus of attention and work motivation. In B. M. Staw & L. L. Cummings (Eds.),
Research in Organizational Behavior (Vol. 18). Greenwich, CT: JAI Press.
Ghiselli, E. E. (1971). Explorations in managerial talent. Pacific Palisades, CA: Goodyear
Publishing.
Gough, H. G. (1987). California Psychological Inventory administrator’s guide. Palo Alto, CA:
Consulting Psychologists Press.
Hackman, J. R., & Oldham, G. R. (1980). Work redesign. Reading, MA: Addison-Wesley.
Hair, J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (1998). Multivariate data analysis
(5th ed.). Englewood Cliffs, NJ: Prentice Hall.
Hensey, M. (1987). Commitment as an aspect of leadership. Organization Development Journal, 5,
53–55.
Herzberg, F. (1966). Work and the nature of man. Cleveland: World.
Hicks, W. D., & Klimoski, F. J. (1987). Entry into training programs and its effects on training
outcomes: A field experiment. Academy of Management Journal, 30, 542–552.
Hogan, R., Hogan, J., & Busch, C. (1984). How to measure service orientation. Journal of Applied
Psychology, 69, 157–163.
Hogan, J., Rybicki, S. L., Motowidlo, S. J., & Borman, W. C. (1998). Relations between contextual
performance, personality, and occupational advancement. Human Performance, 11 (2/3),
189–207.
Holton, E. F. III, Bates, R. A., & Ruona, W. A. (2000). Development and validation of a generalized
learning transfer climate questionnaire. Human Resource Development Quarterly, 11,
333–360.
Joreskog, K. G., & Sorbom, D. (1996). LISREL 8: A guide to the program and applications.
Chicago: SPSS, Inc.
Judge, T. A., Locke, E. A., & Durham, C. (1997). The dispositional causes of job satisfaction:
A core evaluation approach. Research in Organizational Behavior, 19, 151–188.
Kanfer, R. (1990). Motivation theory and industrial and organizational psychology. In M.
Dunnette and L. Hough (Eds.), Handbook of industrial and organizational psychology (2nd ed.,
vol. 1). Palo Alto, CA: Consulting Psychologists Press.
Kanungo, R. N. (1982). Measurement of job and work involvement. Journal of Applied Psychology,
67, 341–349.
Korman, A. K. (1976). Hypothesis of work behavior revisited and an extension. Academy of
Management Review, 1 (1), 50–53.
Lawler, E. E. III. (1986). High-involvement management: Participative strategies for improving organizational
performance. San Francisco: Jossey-Bass.
Levin, I., & Stokes, J. P. (1989). Dispositional approach to job satisfaction: Role of negative affectivity.
Journal of Applied Psychology, 74, 752–758.
Locke, E. A. (1976). The nature and cause of job satisfaction. In M. D. Dunnett (Ed.), Handbook
of industrial and organizational psychology (pp. 1297–1349). Skokie, IL: Rand McNally.
hrd13402.qxp 11/1/02 11:21 AM Page 374
Lodahl, T., & Kejner, M. (1965). The definition and measurement of job involvement. Journal of
Applied Psychology, 49, 1, 24–33.
Loher, B. T., Noe, R. A., Moeller, N. L., & Fitzgerald, M. P. (1985). A meta-analysis of the relation
of job characteristics to job satisfaction. Journal of Applied Psychology, 70, 280–289.
Macoby, M. (1983). The managerial work ethic in America. In J. Barbash, R. J. Lampan, S. A.
Levitan, & G. Tyler (Eds.), The work ethic: A critical analysis. Madison, WI: Industrial Relations
Research Association.
Masters, J. C., Barden, R. C., & Ford, M. E. (1979). Affective states, expressive behavior, and
learning in children. Journal of Personality and Social Psychology, 37, 380–390.
Mathieu, J. E., & Martineau, J. W. (1997). Individual and situational influences on training motivation.
In J. K. Ford, S.W.J. Kozlowski, K. Kraiger, E. Salas, & M. S. Teachout (Eds.), Improving
training effectiveness in work organizations (pp. 193–221). Hillsdale, NJ: Erlbaum.
Mathieu, J. E., Martineau, J. W., & Tannenbaum, S. I. (1993). Individual and situational influences
on the development of self-efficacy: Implications for training effectiveness. Personnel Psychology,
46, 125–147.
Mathieu, J. E., Tannenbaum, S. I., & Salas, E. (1992). Influences of individual and situational
characteristics on training effectiveness measures. Academy of Management Journal, 35,
828–847.
McCrae, R. R., Costa, P. T., & Piedmont, R. L. (1992). Folk concepts, natural language, and
psychological constructs: The California Psychological Inventory and the Five-Factor Model.
Journal of Personality, 61, 1–26.
McFatter, R. M. (1994). Interactions in predicting mood from extraversion and neuroticism.
Journal of Personality and Social Psychology, 66, 570–578.
Meyer, J. P., & Allen, N. J. (1984). Testing the “side-bet theory” of organizational commitment:
Some methodological considerations. Journal of Applied Psychology, 69, 372–378.
Moorhead, G., & Griffin, R. (1995). Organizational behavior: Managing people and organizations.
Boston: Houghton Mifflin.
Morrow, P. C. (1983). Concept redundancy in organizational research: The case of work
commitment. Academy of Management Review, 8, 486–500.
Morrow, P. C. (1993). The theory and measurement of work commitment. Greenwich, CT: JAI Press.
Mowday, R. T., Porter, L. W., & Steers, R. M. (1982). Employee-organizational commitment: The
psychology of commitment, absenteeism, and turnover. New York: Academic Press.
Naquin, S. S., & Holton, E. F. (2001). Motivation to improve work through learning in human
resource development. In O. Aliage (Ed.), 2001 Academy of Human Resource Development Proceedings
and Annual Conference (pp. 1040–1047). Baton Rouge, LA: Academy of Human
Resource Development.
Noe, R. A. (1986). Trainee attributes and attitudes: Neglected influences on training effectiveness.
Academy of Management Review, 11, 736–749.
Noe, R. A., & Schmidt, N. (1986). The influence of trainee attitudes on training effectiveness:
Test of a model. Personnel Psychology, 39, 497–523.
Oldham, G. R., & Fried, Y. (1987). Employee reactions to workplace characteristics. Journal of
Applied Psychology, 72, 75–80.
Peabody, D., & Goldberg, L. R. (1989). Some determinants of factor structures from personalitytrait
descriptors. Journal of Personality and Social Psychology, 57, 552–567.
Sackett, P. R., & Harris, M. M. (1984). Honesty testing for personnel selection: A review and
critique. Personnel Psychology, 37 (2), 221–246.
Salgado, J. F. (1997). The Five-Factor Model of personality and job performance in the European
community. Journal of Applied Psychology, 82 (1), 30–43.
Schneider, W. E. (1985). Viewpoint: The paradigm shift in human resources. Personnel Journal,
64, 11, 17.
Seyler, D. L., Holton, E. F. III, Bates, R. A., Burnett, M. F., & Carvalho, M. A. (1998). Factors
affecting motivation to use training. International Journal of Training and Development, 2,
2–16.
The Effects of Personality, Affectivity, and Work Commitment 375
hrd13402.qxp 11/1/02 11:21 AM Page 375
376 Naquin, Holton
Siegel, I. H. (1983). Work ethic and productivity. In J. Barbash et al. (Eds.), The work ethic: A critical
analysis. Madison: University of Wisconsin, Industrial Relations Research Association.
Sinetar, M. (1980). Management in a new age: An exploration of changing work values. Personnel
Journal, 59, 740–755.
Smith, G. M. (1967). Usefulness of peer ratings of personality in educational research. Educational
and Psychological Measurement, 27, 967–984.
Staw, B. M., & Ross, J. (1985). Stability in the midst of change: A dispositional approach to job
attitudes. Journal of Applied Psychology, 70, 469–480.
Takemoto, N. K. (1979). The prediction of occupational choice from childhood and adolescent
antecedents. Unpublished master’s thesis, University of Hawaii, Honolulu.
Tannenbaum, S. I., Mathieu, J. E., Salas, E., & Cannon-Bowers, J. A. (1991). Meeting trainees’
expectations: The influence of training fulfillment on the development of commitment, selfefficacy,
and motivation. Journal of Applied Psychology, 76, 759–769.
Tellegen, A. (1985). Structures of mood and personality and their relevance to assessing anxiety,
with an emphasis on self-report. In A. H. Tuma & J. Mason (Eds.), Anxiety and the anxiety
disorders (pp. 681–706). Hillsdale, NJ: Erlbaum.
Warr, P., & Bunce, D. (1995). Trainee characteristics and the outcomes of open learning. Personnel
Psychology, 48, 347–375.
Watson, D., & Clark, L. A. (1984). Negative affectivity: the disposition to experience negative
emotional states. Psychological Bulletin, 96, 465–490.
Watson, D., Clark, L. A., & Tellegen, A. (1988). Development and validation of brief measures
of positive and negative affect: The PANAS scales. Journal of Personality and Social Psychology,
54, 1063–1070.
Weber, M. (1958). The Protestant work ethic and the spirit of capitalism. New York: Scribner’s.
Webster’s Third New International Dictionary of the English Language Unabridged. (1986). Springfield,
MA: Merriam-Webster.
Weinstein, C. E., Palmer, D. R., Hanson, G. R., Dierking, D. R., McCann, E., Soper, M., & Nath, I.
(1994, March). Design and development of an assessment of readiness for training: The START.
Paper presented at the annual conference of the Academy of Human Resource Development,
San Antonio, TX.
Wright, J., & Mischel, W. (1982). Influence of affect on cognitive social learning person variables.
Journal of Personality and Social Psychology, 43, 901–914.
Sharon S. Naquin is executive director of the Public Management Program and assistant
professor of human resource development at the School of Human Resource Education &
Workforce Development, Louisiana State University.
Elwood F. Holton III is professor of human resource development at the School of
Human Resource Education & Workforce Development, Louisiana State University.
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