EFFECTS OF CALCULATIVE, AFFECTIVE AND MORAL COMMITMENT ON THE
TURNOVER PROCESS:
EVALUATION OF THREE STRUCTURAL EQUATION
MODELS
STEPHEN J. JAROS
JOHN M. JERMIER
JERRY W. KOEHLER
TERRY SINCICH
University of South Florida
College of Business Administration
Tampa, Florida 33620
(813) 974 - 4155
1
Presenter:
Division:
Abstract
This study utilizes structural equation modeling to evaluate the
impact of attitudinal commitment on employee turnover. Three different
forms of attitudinal commitment (affective, continuance, and moral) were
developed and were incorporated into three competing turnover models.
A test of these models using data collected from a sample of employees in
an aerospace firm indicated that the three forms of commitment impact
on turnover indirectly through a latent "withdrawal tendencies" variable.
Implications and suggestions for future research are discussed.
2
The idea that organizational commitment is in some way related to
the employee turnover process has received considerable attention in
recent years.
Progress in documenting organizational commitment as a
correlate of turnover variables is evident from meta-analyses of the
research linking these concepts.
Cotton and Tuttle (1986) identified
organizational commitment as a highly significant (p < .0005), negative
correlate of turnover based on adding Z-values in the 16 samples they
reviewed.
Mathieu and Zajac (1990) combined data in 26 samples and
reported a mean weighted correlation corrected for attenuation between
organizational commitment and turnover of -0.28. Stronger effects were
reported on intention to search for job alternatives (r t = -0.60, 5 samples)
and intention to leave one's job (rt = -0.46, 36 samples), variables believed
to mediate the commitment-turnover relationship.
Despite the abundance of research on the relationship between
commitment and turnover, important problems hamper understanding of
the causal processes involved. Fist, considerable disagreement about the
concept of organizational commitment remains. Confusion persists about
whether commitment is an attitudinal or a behavioral phenomenon (cf.
Mowday,
Porter
&
Steers,
1982;
Mottaz,
1989;
Randall,
Fedor
&
Longnecker, 1990). Among attitudinal theorists, a consensus is emerging
that commitment is multi-dimensional, but existing research does not
completely define the components of commitment nor establish their
antecedents and consequences (Meyer & Allen, 1984; McGee & Ford, 1987;
Penley & Gould, 1988; Meyer et al., 1989; Randall et al., 1990; Allen &
Meyer, 1990).
3
Second, analysis of the commitment-turnover relationship has not
benefitted from the evaluation of alternative causal models or advances in
structural equation modeling that reduce the effects of measurement
errors. Usually, a single model has been specified based on extensions of
Porter and Steers' (1973) framework or modifications of Mobley's (1977)
framework.
Without tests of competing models, especially models with
latent variables, it is difficult to know how much confidence can be placed
in
regression-based
statistical
linkages
between
commitment
and
turnover.
Third, measurement of commitment and turnover constructs has
generated controversy.
Meyer and Allen (1984), Reichers (1985) and
others have argued that the most widely used measures of organizational
commitment confound different types of commitment and overlap with
withdrawal constructs.
Mobley et al. (1979) raised several issues that
recast turnover from an easily measured, "objective" criterion to one
requiring greater attention and (perhaps) experimentation.
The purpose of this study is to expand understanding of the
commitment-turnover relationship through field res1xrch.
The study's
contribution rests on how successfully we address the three problems
outlined above. First, we identified three distinct forms of organizational
commitment:
continuance, affective, and moral.
Second, we specified
three causal models (all with latent variables to reduce measurement
error biasing) to estimate the impacts of the forms of commitment on the
turnover
process.
Third,
we
proposed
4
solutions
for
the
problems
associated with existing measures of organizational commitment, and for
those associated with the measurement of turnover in causal models.
THEORETICAL AND METHODOLOGICAL ISSUES IN
COMMITMENT-TURNOVER RESEARCH
Concepts of Organizational Commitment
Becker's (1960) Side-Bet Theory of Commitment has been highly
influential in shaping research in this area (e.g., Ritzer & Trice, 1969;
Sheldon, 1971; Hrebiniak & Alutto, 1972; Aranya & Jacobson, 1975;
Stevens, Beyer & Trice, 1978; Farrell & Rusbelt, 1981; Meyer & Allen,
1984; Mottaz, 1989). This work developed the idea that as an employee
makes certain investments or "side-bets" in an organization (e.g., tenure
toward pensions, work friendships, organization-specific skills, political
deals, job efforts), these "sunk costs" diminish the attractiveness of
external
employment
commitment
as
a
alternatives.
psychological
state
Thus,
in
we
which
define
the
continuance
employee
feels
compelled to commit to the organization because the monetary, social,
psychological and other costs associated with leaving are binding.
Two counterpoints to Becker's (1960) view of commitment have
been developed.
The most widely discussed form of psychological
attachment to an employing organization is affective commitment.
The
roots of this view lie in the work of Kanter (1968) who defined
commitment as "the willingness of social actors to give energy and loyalty
to the organization" (p. 499), and as "the attachment of an individual's
fund of affectivity to the group" (p. 507). In a similar vein, Lee (1971),
Buchanan (1974a, 1974b) and Porter, Steers, Mowday and Boulian
5
(1974) directed attention to a sense of belonging and the experience of
loyalty.
simply
Unlike continuance commitment where the attachment may
reflect
a
cold
calculation
of
costs
and
benefits,
affective
commitment refers to the possibility of the formation of an emotional
bond.
The other counterpoint to Becker's (1960) view of commitment,
moral commitment, is based on internalization of norms and identification
with organizational authority (Etzioni, 1975, pp. 10-11).
The theme of
incorporation of an organization's goals and values into one's identity has
been
central
to
organizational
commitment
research,
but
most
researchers developing this theme have not linked their work to a concept
of moral commitment (e.g., Brown, 1969; Hall, Schneider & Nygren, 1970;
Buchanan, 1974a, 1974b; Porter et al., 1974; Steers, 1977; Morris &
Sherman, 1981; Angle & Perry, 1983; Bateman & Strasser, 1984).
Usually, in fact, studies of organizational commitment as identification
are construed as work on affective or attitudinal commitment (see
Mowday et al., 1982; Reichers, 1985; Mathieu & Zajac, 1990).
In more recent work, attempts have been made to emphasize moral
commitment
and
clarify
its
meaning.
Ferris
and
Aranya
(1983)
reconceptualized the Porter et al. (1974) approach in terms of moral
involvement.
Penley and Gould (1988), Allen and Meyer (1990), and
Randall et al. (1990) differentiated moral commitment from other forms
and empirically examined it. Based on this, we define moral commitment
as the degree to which an individual is psychologically attached to an
employing organization through internalization of its goals, values and
6
missions.
This form of commitment differs from affective commitment
because it reflects a higher sense of duty, obligation or "calling" to work in
the organization, but not necessarily emotional attachment.
It differs
from continuance commitment because it does not necessarily fluctuate
with personal calculations of inducements or "sunk costs".
Models of the Commitment-Turnover Relationship
Porter et al. (1974) theorized that individuals highly committed to
an organization would stay with it to assist in the realization of its goals.
While this study hypothesized a direct linkage between commitment and
turnover, Porter and Steers (1973) previously argued for the possibility
that
the
turnover
decision
is
the
culmination
of
a
psychological
withdrawal process characterized by a sequence of intervening variables
that mediate the commitment-turnover relationship.
Similarly, Mobley
(1977) presented a ten-stage withdrawal process model linking job
satisfaction and turnover.
refined:
After testing, the model was trimmed and
(1) Job Satisfaction (2) Thinking of Quitting (3) Search
Intentions
(4)
Intent
Hollingsworth, 1978).
to
Leave
(5)
Turnover
(Mobley,
Horner
&
While Mobley and his colleagues focussed on job
satisfaction as the precursor to the withdrawal process, other researchers
have found that attitudinal commitment plays a key role in the turnover
process (e.g., Porter, Crampton & Smith, 1976; Koch & Steers, 1978;
Michaels & Spector, 1982; Mowday, Koberg & McArthur, 1984; Williams &
Hazer, 1986; Farkas & Tetrick, 1989).
7
However, no prior studies were uncovered that assessed the impacts
of continuance, affective and moral commitment on the turnover process.
Three structural equation models were specified to estimate the unique
effects of all three forms of commitment (See Figure 1).
Model M1: Traditional Theory. This is a modification of the Mobley
et
al.
(1978)
turnover
model.
Affective,
continuance
and
moral
commitment replace job satisfaction as the direct precursors of the
withdrawal process. Another change is that the commitment constructs
are specified as latent variables and estimated with confirmatory factor
analysis.
A third change involves the use of a "days stayed" variable
instead of a dichotomous turnover variable. This shifts the meaning of
turnover slightly, but conforms with statistical assumptions that should
be met in structural equation modeling and has other advantages to be
discussed.
It is hypothesized that each type of commitment directly
affects thinking of quitting, which leads to intent to leave the present
employer, which affects the number of days stayed with the firm. In this
model, commitment affects turnover indirectly (Figure 1).
Model M2:
Constrained Latent Correlation.
This model represents
the withdrawal variables as indicators of an unmeasured latent factor-withdrawal tendency. It replaces the sequential stages of withdrawal with
a more cyclical and interactive view of the ways employees consider
leaving
the
firm.
This
model
more
closely
resembles
cognitive
psychologists' descriptions of human mental processes that emphasize
vague, general orientations and lack of distinction-making in everyday life
(e.g., Feldman, 1981; Langer & Piper, 1987).
8
Thus, it specifies that a
change in one's level of commitment will impact the formation of an
overall tendency to withdraw from or stay with the organization.
The
withdrawal tendency underpins more specific withdrawal concepts which
derive their interrelationships from this source. Contrary to traditional
theory, turnover is unrelated to the three measured withdrawal variables.
Instead,
it
depends
most
directly
on
the
unmeasured
withdrawal
tendency variable (Figure 1).
Model M3: Saturated Latent Correlation. This model is identical to
M2 except that it includes direct paths between each type of commitment
and days stayed (Figure 1).
It is based on research findings suggesting
that attitudinal variables contribute significantly to the prediction of
turnover beyond that attributable to behavioral intentions (Arnold &
Feldman, 1982; Steel & Ovalle, 1984; Miller, Powell & Seltzer, 1990). It
allows for the possibility that the forms of commitment impact the
turnover decision both directly and indirectly.
Measurement Issues
Disagreement over the nature of organizational commitment has
resulted in the development of measuring instruments that may not be
valid. For example, Reichers (1985) pointed out that the scale used most
frequently
to
Commitment
measure
attitudinal
Questionnaire--OCQ"
commitment,
(Porter
extensively with intent to quit items.
et
the
al.,
"Organizational
1974),
Tetrick and
overlaps
Farkas (1988)
discovered that the negatively worded items of the OCQ lacked stability.
And, since the Porter et al. (1974) concept of commitment is focused on
intent
to
quit,
willingness
to
exert
9
effort,
and
moral
beliefs
and
cognitions, there are reasons to doubt the wisdom of exclusive reliance on
the OCQ to measure attitudinal commitment.
In research where the
objective is to assess the impact of specific forms of commitment on the
turnover process, the OCQ has limited value.
Thus, we built on work by
Meyer and Allen (1984), Allen and Meyer (1990), Randall et al. (1990)
and others in estimating the effects of more specific measures of the
forms of commitment.
Dichotomous
measurement
problems in causal modeling.
of
the
turnover
variable
presents
According to Bollen (1989), conducting
LISREL analyses with one or more categorical endogenous variables can
lead to biased parameter estimates and invalid statistical tests. To avoid
this, we measured turnover using a ratio variable that was scaled as the
number of days the employee remained with the organization after the
questionnaire
was
administered.
In
addition
to
conforming
with
statistical assumptions underlying maximum likelihood estimation in
LISREL, the days stayed variable allows a more precise estimate of the
impacts of commitment and withdrawal variables. It allows prediction of
how long employees stayed before leaving, if they did leave.
METHOD
Sample, Context and Procedures
The study is based on data obtained from a sample of personnel
employed by an aerospace firm located in a major metropolitan area in
the southeastern U.S. The firm was a wholly owned American subsidiary
of a multinational corporation (revenues = $700 million per year; 20,000
employees) headquartered in London, England.
10
Approximately four weeks prior to collection of questionnaire data,
intensive, semi-structured interviews were conducted with 62 employees
(about 21 percent of the organization's members).
Interviews were
conducted at the workplace during normal working hours and required
from 30 minutes to three hours to complete.
Prior to the interviews, a
letter from the firm's general manager was distributed to all employees.
It guaranteed them total confidentiality and encouraged them to be
candid and sincere in their responses. Interviews were supplemented by
several partial days of on-site observation to develop an understanding of
the firm's products, physical layout, task interdependencies, etc., and to
built rapport with employees.
After completing these activities, a questionnaire was designed and
administered to 92 percent of the organization's employees (270 of 293)
during normal working hours. Completion time was between 75 and 100
minutes, including time taken to explain the study and the survey.
Respondents were assured of total confidentiality.
Five respondents
missed questionnaire items and were deleted from the multivariate
analyses.
Sixty-eight percent of the sample were male.
The average age of
respondents was about 35 years (one-third were 30 years old or younger,
one-fourth were older than 45 years).
Eighty-one percent completed
schooling beyond high school, 26 percent held college diplomas, and an
additional 15 percent had begun graduate study or earned a graduate
degree.
Forty-two percent had been with the firm two years or less,
another 44 percent between 2 years and 10, and 14 percent had more
11
than
10
years
tenure.
percent
had
served
in
the
military.
Approximately 22 percent of the sample were engineers, 22 percent were
technicians,
17
percent
were
hourly
workers
(mostly
assigned
to
manufacturing), 23 percent were clerical and support staff, and 16
percent were administrative, managerial or supervisory.
Measures
The continuance form of organizational commitment was measured
with three items developed by Meyer and Allen (1984).
The items were
chosen to capture the idea of sunk costs and investments while not
confounding
the
concept
with
options
and
alternatives
available.
Responses were made on seven-point scales ("strongly disagree-strongly
agree").
One of the items was reflected.
In previous research (Meyer &
Allen, 1984; Allen & Meyer, 1990), the continuance commitment scale
was unrelated to affective and moral commitment measures.
Becker's
(1960) notion of side-bets can be assessed more appropriately with this
measure.
The affective form of organizational commitment was measured with
14 bipolar adjective items written for this study.
scale,
respondents
were
asked
to
report
the
Using a
feelings
seven-point
they
usually
experience when thinking of their employing organization. Sample items
include:
sadness-happiness; pleasure-pain; anger-peace; and loyalty-
disloyalty. Five of the items were reflected.
The moral form of organizational commitment was measured with 4
items developed by Gould and Penley (1982) and Werbel and Gould
(1984).
The items reflect a sense of duty and dedication to the
12
organization and its mission. Responses were made on seven-point scales
("strongly disagree-strongly agree"). Allen and Meyer's (1990) normative
commitment scale was not available when the study was conducted, but
the items appear to overlap considerably with concepts of staying and
leaving.
In testing commitment-withdrawal models, it was preferable to
avoid this degree of overlap.
Thinking of quitting the organization and intention to search for a
new job, two aspects of the tendency to leave concept, were measured with
single items written by Mobley et al. (1978).
made
on
five-point
scales
(never-constantly;
Responses to both were
very
unlikely-certain).
Bluedorn's (1982) two items were used to measure intent to leave the
organization. The simplicity and straight-forward character of withdrawal
tendency variables has led researchers to rely on one and two item
measures.
Contrary to popular belief, this does not produce inherently
unreliable and invalid estimates.
Ordinarily, measurement of turnover is uncomplicated:
employees
sign questionnaires and personnel records are checked months or years
later to determine employment status. If the employee left, the turnover
criterion is coded 1; if the employee did not leave, the turnover criterion
is coded 0. Due to the prevailing climate in the organization at the time of
the study, it was decided that the questionnaire data would be more useful
if no names were requested.
Instead, respondents were asked to put a
code on the front page of their survey that would be memorable to them
without revealing their identity (e.g., mother's maiden name).
When an
employee left the firm, the Employee Relations Director asked for the
13
survey code during the exit interview.
employee
could
not
In the few cases where the
remember the code, it was possible to match
demographic data from the survey with personnel records to identify the
respondent. Turnover data were collected 99 weeks after collection of the
survey data.
firm.
Forty-eight of the original 270 respondents had left the
Since the exact date of separation was known, a "days stayed"
variable
was
coded.
Hypothetically,
this
could
range
from
one
(representing a respondent who separated the day after the survey was
administered) to 688 (representing a respondent who did not separate).
Analyses
Means, standard deviations, bivariate correlations, and internal
consistency estimates were calculated. Principal axes factor analysis with
both orthogonal and oblique rotation was then used to determine the
dimensionality of the organizational commitment construct.
Identification of Models. Before attempting to estimate the models, it
must be determined if unique values exist for the unknown model
parameters.
This is commonly known as the "identification" step in
structural equation modeling. A model is identified if all unknown model
parameters are identified, i.e., if all parameters can be written as
algebraic functions of the elements of the sample covariance matrix of
observed variables. Identified (or overidentified) models have sufficient
information
available
in
the
sample
estimate the model parameters.
covariance
matrix
to
uniquely
An underidentified model has at least
one parameter which is not identified;
14
there is insufficient information
available in the sample covariance matrix to estimate the parameters of
this type of model.
Upon
scaling
the
latent
commitment
variables
(this
amounts
to
constraining the path coefficient for one of the observed x's to 1 for each
latent variable), we can apply the recursive rule (see Bollen, 1989) to
establish that Models M1, M2, and M3 are identified.
Evaluation of Models.
The three identified structural models were fit
and analyzed using the LISREL 7 software package (Joreskog & Sorbom,
1988).
The sample covariance matrix, based on complete data (n=265
cases) for the 25 observed variables (21 commitment variables and 4
turnover variables), was used as input to LISREL in order to obtain
maximum likelihood estimates of the path coefficients.
Measures of Overall Model Fit. Table 2 lists several overall fit measures
for the models. Four goodness of fit measures are provided automatically
by LISREL: (1) the chi-square statistic; (2) the root mean square residual
(RMSR);
(3)
the goodness-of-fit index (GFI); and, (4) the adjusted
goodness-of-fit index (AGFI).
Each of these statistics measures how well
the reproduced covariance matrix, based on the specified constraints,
estimates
the
observed
(sample)
covariance
matrix.
Several
other
goodness-of-fit indices designed to allow a comparison of fit of two nested
models were also calculated (Table 2):
(5) Bentler and Bonett's (1980)
normed-fit index (NFI), which measures the improvement in fit for the
model in question relative to some baseline, or more restrictive, model;
(6) the parsimonious normed-fit index (PNFI), suggested by James et al.
(1982),
which
adjusts
the
NFI
for
15
degrees
of
freedom;
(7)
the
parsimonious goodness-of-fit index (PGFI), proposed by Mulaik et al.
(1989), which adjusts the GFI for degrees of freedom; and, (8) the
relative normed-fit index (RNFI), an index designed to assess the relative
fit of the structural relationships among the latent variables proposed by
the model, independent of the measurement portion of the model
(Hertzog, 1990; Mulaik et al., 1989; and Lerner et al., 1988).
RESULTS
Factor Analysis
The results of an SPSSx principal axes factor analysis of the 21 measures
of commitment are summarized in Table 1. The SPSS x program extracted
three factors, which account for 56% of the total variance present in the
21 measures. The factor loadings for both an orthogonal (VARIMAX) and
oblique (OBLIMIN) rotation of the three factors are shown in Table 1.
Focusing only on factor loadings that exceed .4 in absolute value, a clear
pattern emerges:
commitment
one factor is a linear combination of the 14 affective
variables (X4 -
X17),
while
the
remaining two factors
represent continuance commitment (X1 - X3) and moral commitment (X18 X21), respectively. This "clean" structure identified in the factor analysis
provides
strong
statistical
support
for
the
notion
of
continuance,
affective, and moral commitment as latent variables in the structural
equation models proposed in this study.
LISREL results. The chi-square tests in Table 2 reveal that all models must
be rejected (p < .001) as the "perfect" fit according to the chi-square test.
However, the three models provide, at a minimum, an adequate fit to the
16
data based on the other goodness-of-fit measures listed in Table 2. First,
consider the Traditional Theory Model, M1, shown in the first row of Table
2. The fit statistics point towards a reasonable fit to the data: GFI, AGFI,
NFI0, and PNFI0 all exceed .8 (with NFI0 approximately equal to .9); PGFI is
approximately .7; and both NFIb and PNFIb are near .6. The two models
which propose a single latent withdrawal variable as the underlying cause
of THINKQ (Y1), SEARCH (Y2), and INTLV (Y3), are M2 and M3. These two
models are nested (i.e., the free parameters of M2 are a subset of the free
parameters of M3).
Consequently, we can compare them inferentially
using a likelihood ratio (chi-square) test.
The null hypothesis takes the
form
Ho: γ4 = γ5 = γ6 = 0
Note that the parameters specified in Ho are the path coefficients for the
three additional paths hypothesized by M3. The test statistic is X2 = (X2)2 (X2)3 = 4, with df = df2 -
df3 = 3.
For this small value of X2, there is
insufficient evidence to reject Ho (p > .10); hence, the three additional
paths proposed by M3 do not improve the fit of the structural model. In
the interest of parsimony, M2 is deemed superior to M3.
How does M2 compare to the traditional model, M1? For each of the
fit indices reported in Table 2, the index for M 2 always exceeds those for
M1. The differences range from small (NFI0 values of .932, and .898 for
M2, and M1, respectively), to moderate (PNFIb values of .712, and .586), to
large (RNFI values of .997, and .602).
It is important to note that the
largest differences occur with RNFI. This implies that the goodness-of-fit
attained
by
M1
is
somewhat
inflated
17
due
to
the
large number of
parameters estimated in the measurement portion of the model.
When
the fit index is calculated independent of the measurement portion of the
model, M2 is clearly the superior of the two models.
Detailed Analysis of Model M2
Although we must reject the hypothesis that Model M 2 is the
"perfect" fit (X2 significant at p < .001), the goodness-of-fit measures in
Table 2 suggest strongly that M2 fits the data adequately, i.e., adequately
reproduces the observed covariance matrix. In addition, an examination
of the parameter estimates and normalized residuals indicated that the
model was not misspecified. The overall coefficient of determination for
the measurement portion of the models reported at the bottom of Table 3,
is .990.
This high overall R2 summarizes the joint effect of the latent
variables on the observed variables and indicates that the observed X's
are excellent indicators of the latent commitment variables. The R 2 values
for the structural equations relating the latent variables C 1-C3 to W, and W
to y4, are .663 and .078, respectively. Thus, the three latent commitment
variables account for over 66% of the variance in the tendency for a
worker to withdraw from the organization, while the latent withdrawal
tendency variable accounts for about 8% of the variance in days stayed.
The overall R2 for the structural portion of the model, which measures the
joint
effect
of
the
latent
variables
Ci
and
W
on
days
stayed
is
approximately 66%.
The overall fit measures, normalized residuals, individual parameter
estimates and corresponding t statistics, and overall coefficients of
18
determination all support Model M2 as a reasonable representation of the
causal structure underlying the withdrawal tendencies of workers.
Based on the asymptotic standard errors (shown in Table 4),
approximate 95% confidence intervals for the effects of the latent
variables on days stayed were computed. These intervals, given in the last
column of Table 4, are interpreted as follows:
C1: A 1-point increase on the latent continuance commitment scale leads
indirectly to an increase in the length of stay with the organization
between 7.18 and 23.12 days;
C2:
A 1-point increase on the latent affective commitment scale leads
indirectly to an increase in length of stay between 11.52 and 37.67 days;
C3:
A 1-point increase on the latent moral commitment scale leads
indirectly to a change in length of stay ranging from -5.47 to 10.98 days;
W:
A 1-point increase on the latent withdrawal tendency scale leads
directly to a decrease in the length of stay between 34.02 and 90.99 days.
Notice that the 95% confidence interval for C 3 includes 0, implying that
the indirect effect of moral commitment on DAYS is nonsignificant. This
result was expected since γ3, the path coefficient relating C3 to W, is not
significantly different from 0. Thus, while the effects of continuance and
affective commitment on "days stayed" are statistically indistinguishable
from each other, both have a greater impact than moral commitment.
DISCUSSION
The purpose of this study was to expand understanding of the
commitment-turnover relationship by clarifying the dimensionality of the
19
commitment
construct,
evaluating
alternative
causal
models,
and
addressing problems associated with the measurement of commitment
and turnover variables. Each of these areas will be discussed in turn.
First,
the
conceptualize
commitment:
results
and
of
the
measure
study
three
indicate
distinct
that
forms
we
of
are
able
to
organizational
affective, moral, and continuance commitment.
This
provides further support for the idea that attitudinal commitment is
multi-dimensional in nature.
In addition, it supports the contention
(Allen & Meyer, 1990; Randall et al., 1990) that affective and moral
commitment are indeed distinct and separate concepts. Thus, further
research could focus on testing for the separate impact of each form of
commitment on other potential correlates, such as job performance,
absenteeism, and job satisfaction.
Second, the use of causal models enabled us to compare two
alternative theories:
Mobley et al.'s (1978) model; and latent variable
models based on findings from cognitive psychology. Although the Mobley
et al. model provided an adequate fit to the data and was otherwise found
to be useful, the clear superiority of the latent withdrawal tendencies
model is noteworthy. It suggests that researchers working in this area can
benefit from studying findings in the field of cognitive psychology. These
results show that individuals' typically exhibit vague, general orientations
and tendencies toward a particular behavior (such as turnover) - as
opposed to the distinct, sequential withdrawal process proposed by
Mobley, et al. (cf. Feldman, 1981; Langer & Piper, 1987).
20
Third,
the
use
of
alternative
measures
of
commitment
and
withdrawal process variables that do not confound the different types of
commitment or overlap each other resulted in simple structure estimates
of commitment constructs and more realistic estimates of the effects of
commitment on the turnover process.
In addition, the use of a "days
stayed" variable, rather than a simple dichotomous turnover variable,
permitted
the
actual
prediction
of
an
employee's
tenure
with
the
organization based on his or her level of commitment.
Finally, we must note two important limitations of this study. First
since
researchers
have
questioned
the
usefulness
of
intervening
withdrawal variables proposed by Mobley et al., such as thinking of
quitting (Bluedorn, 1982) and search intentions (Price & Mueller, 1981),
causal models using structural equation methods could be used to
compare models that trim these stages from the withdrawal process. In
fact, we did test a total of nine competing models, including ones that trim
Thinking of Quitting, Intent to Leave, and Search Intention.
The results
indicated that all three of these intervening variables were valuable trimming them did not improve the usefulness of the Mobley, et al. model.
However, because of space constraints, we were unable to fully report the
results of these additional tests.
Second, our data shows that 48 out of
275 sampled employees (18%) had left the firm after 99 weeks - a rate of
turnover lower than that typically experienced in many industries,
particularly
service
industries.
This
generalizability of our findings.
21
could
somewhat
limit
the
Conclusion
In summary, this study makes important contributions to the study
of the commitment-turnover relationship.
The ability to measure three
separate forms of commitment highlights the need for future research
that would further refine the concept of organizational commitment.
Also, because our findings show that the types of commitment differed in
their relative impact on the turnover decision suggests that future
research could explore the reasons behind these differences.
The
identification of the latent Withdrawal Tendencies variable within the
commitment-turnover process indicates that future research should focus
on the usefulness of latent variables, comparing these to more precise,
sequentially ordered cognitive constructs.
Finally, it seems clear that
commitment-turnover research should utilize causal modeling techniques,
such as LISREL, which provide more sophisticated measures of overall
model fit, and permit the comparison of competing turnover models. Each
of these suggestions would further our understanding of the relationship
between organizational commitment and employee turnover.
22
REFERENCES
Allen, N. & Meyer, J. 1990. The measurement and antecedents of affective,
continuance, and normative commitment. Journal of Occupational
Psychology,
63: 1 - 18.
Becker, H. 1960. Notes on the concept of commitment. American Journal
of Sociology, 66: 32 - 40.
Buchanan,
B.
1974a.
Building
organizational
commitment:
The
socialization of managers in work organizations. Administrative Science
Quarterly, 19: 533 - 546.
Farkas, A. & Tetrick, L. 1989. A three - wave longitudinal analysis of the
causal
ordering
decisions. Journal of
of
satisfaction
and
commitment
on
turnover
Applied Psychology, 74: 855 - 868.
Feldman, J. 1981. Beyond attribution theory: Cognitive processes in
performance appraisal. Journal of Applied Psychology, 66: 127 - 148.
Mathieu, J. & Zajac, D. 1990. A review and meta - analysis of the
antecedents,
correlates,
and
consequences
of
organizational
commitment. Psychological Bulletin, 108: 171 - 194.
Mobley, W. 1977. Intermediate linkages in the relationship between job
satisfaction and employee turnover. Journal of Applied Psychology, 62:
237 - 240.
Mobley, W., Horner, S. & Hollingsworth, A. 1978. An evaluation of the
precursors
of
hospital
employee
Psychology, 63: 408 - 414.
23
turnover.
Journal
of
Applied
Mottaz, C. 1989. An analysis of the relationship between attitudinal
commitment and behavioral commitment. Sociological Quarterly, 30:
143 - 158.
Porter, L., Steers, R., Mowday, R. & Boulian, P. 1974. Organizational
commitment,
job
satisfaction,
and
turnover
technicians. Journal of Applied Psychology,
among
psychiatric
59: 603 - 609.
Reichers, A. 1985. A review and re - conceptualization of organizational
commitment. Academy of Management Review, 10: 465 - 476.
24
TABLE 2
Lisrel Results
Model Description
df
X2
RMSR
GFI
AGFI
PGFI
NFI0
PNFI0
NFIb
PNFIb
RNFI
M1
Traditional 270 680
6.0
.841 .809
.699 .898
.808
.606
.586
.602
M2
Constr. L.V. 269 542
4.2
.862 .833
.713 .932
.836
.738
.712
.997
M3
Full L.V.
.863 .832
.706 .932
Null
266 538
300 4272
11.1
279 1311
11.1
889
.791 .754
11.1
3.0
.198 .131
.674 .620
.183 .000
.579
.672 .846
.000
.779
.408
.826
.740
.000
.705
M base
.000
Mcfa
M null
Baseline
No LV Cor.
276
.403
Note: All chi-square tests significant at p < .001.
TABLE 3
Analysis of Path Coefficients of Model M2
Causal Relation
Parameter ML Estimate Standard Error t-value
C1 ---> W
γ1
-.242
.032
-7.657
C2 ---> W
γ2
-.393
.054
-7.351 .663
C3 ---> W
γ3
-.044
.065
-.675
W ---> y4
β1
-62.506
14.243
-4.388
.078
Notes: c = constrained constant
Overall R2 for measurement portion of model = .990
Overall R2 for structural portion of model = .663
R2
TABLE 4
Effects of Latent Variables on DAYS (y4):
Model M2
Latent Variable
Total Effects Estimate Standard Error Approx. 95% CI
Continuance Commitment (C1)
γ1β1
20
15.155
3.985
(7.18, 23.12)
Affective Commitment (C2)
Moral Commitment
Withdrawal Tendency
(C3)
(W)
24.595
γ2β1
γ3β1
β1
2.754
-62.506
21
6.536
4.111
14.243
(11.52, 37.67)
(-5.47, 10.98)
(-90.99, -34.02)