Journal of Behavioral Decision Making, Vol. 11, 251±261 (1998)
Testing the Incremental Validity of the
Vroom±Jago Versus Vroom±Yetton Models
of Participation in Decision Making
RICHARD H. G. FIELD* and J. P. ANDREWS
University of Alberta, Canada
ABSTRACT
In three samples of manager-reported decisions the Vroom±Jago model's
predictions were supported. Decisions that more closely ®t the recommended
decision method were rated as higher in e€ectiveness. The model was also found
to account for more variance in decision e€ectiveness than the prior Vroom±
Yetton model. It was also found that the Vroom±Jago model's greater precision
in situational assessment and derived prescriptions allow for greater discrimination in choice of decision method across all situations. # 1998 John Wiley &
Sons, Ltd.
Journal of Behavioral Decision Making, 11: 251±261 (1998)
KEY WORDS
Vroom±Yetton; Vroom±Jago; participation; participatory management; leadership
Since Tannenbaum and Schmidt's classic 1958 article titled `How to Choose a Leadership Pattern'
made explicit that di€erent decision methods vary in the amount of participation allowed subordinates,
organizational scholars have considered how subordinate decision participation is related to decision
e€ectiveness. At the forefront of this line of inquiry have been the normative models of Vroom and
Yetton (1973) and Vroom and Jago (1988a, 1995).
The model of Vroom and Yetton took both a situational and a prescriptive stance. In their role as
organizational decision makers, leaders were advised to examine the characteristics of each decision
situation before making a predecision (Wedley and Field, 1984) of what decision method to select for
solving the problem.
In its prescriptive form as a decision tree, the Vroom±Yetton model is familiar to most organizational behavior teachers and researchers. In addition to the initial presentation in Vroom and Yetton's
book, the model has been tested and studied many times and has been widely reproduced in introductory management and organizational behavior textbooks. Many managers have also become aware
of the Vroom±Yetton model through articles in management journals (e.g. Biggs, 1978; Vroom, 1973,
1974, 1976, 1984) and via decision making and leadership workshops (see descriptions in Vroom, 1976;
Vroom and Jago, 1988b).
* Correspondence to: Richard H. G. Field, Faculty of Business Building, University of Alberta, Edmonton, Alberta T6G 2R6,
Canada. E-mail: Richard. Field@ualberta.ca
CCC 0894±3257/98/040251±11$17.50
# 1998 John Wiley & Sons, Ltd.
Accepted 20 April 1998
252
Journal of Behavioral Decision Making
Vol. 11, Iss. No. 4
Exhibit 1. Attribute questions of the Vroom±Yetton and Vroom±Jago models
QR:
CR:
LI:
ST:
CP:
GC:
CO:
SI:
TC:
GD:
MT:
MD:
How important is the technical quality of this decision?
How important is subordinate commitment to the decision?
Do you have sucient information to make a high-quality decision?
Is the problem well structured?
If you were to make the decision by yourself, is it reasonably certain that your subordinates would be
committed to the decision?
Do subordinates share the organizational goals to be attained in solving this problem?
Is con¯ict among subordinates over preferred solutions likely?
Do subordinates have sucient information to make a high quality decision?
Does a critically severe time constraint limit your ability to involve subordinates?
Are the costs involved in bringing together geographically dispersed subordinates prohibitive?
How important is it to you to minimize the time it takes to make the decision?
How important is it to you to maximize the opportunities for subordinate development?
Source: Adapted from Vroom and Jago (1988a, pp. 111±112; 229±230).
Note: Attributes SI, TC, GD, MT and MD were added to the seven attributes of the Vroom±Yetton model.
Exhibit 2. Decision methods
AI
AII
CI
CII
GII
You make the decision yourself using the information available to you at the present time.
You obtain any necessary information from subordinates then make the decision yourself.You may or
may not tell subordinates about the problem you face. The input provided by them is clearly in response
to your request for information. They do not assist in de®ning the problem nor generating or evaluating
alternatives.
You consult with relevant subordinates individually, getting their ideas and suggestions.You then make
the decision. This decision may or may not re¯ect the subordinates' in¯uence.
You consult with relevant subordinates in a group meeting, getting their ideas and suggestions. You then
make the decision. This decision may or may not re¯ect the subordinates' in¯uence.
You share the problem with your subordinates in a group meeting. Together you generate and evaluate
alternatives and attempt to reach consensus on a solution. Your role is to facilitate and coordinate the
meeting, making sure that critical issues are discussed. You can provide information but must ensure that
your views are not given greater weight because of your position. You are willing to accept and implement
any solution that has the support of the group.
Source: Adapted from Vroom and Jago (1978, p. 152).
One key component of the Vroom±Yetton model is that managers are advised to examine seven
factors in the decision situation before deciding how much participation to allow subordinates in the
making of that particular decision (see Exhibit 1). The other key component is that ®ve decision
methods are anticipated, ranging from autocratic to consultative to group decision. These methods are
described in Exhibit 2. By answering each of the ®rst seven questions in Exhibit 1, a manager follows a
path on the Vroom±Yetton decision tree. At the end of each branch is the feasible set of one or more
recommended decision methods for that situation. Decisions that are made using methods that fall
within the feasible set prescribed by the model are predicted to result in higher decision quality,
subordinate acceptance, and overall e€ectiveness than decisions that are made with methods that are
not within the feasible set.
Considerable research has investigated the validity of the Vroom±Yetton model. Field studies
(Margerison and Glube, 1979; Paul and Ebadi, 1989; Thomas, 1990), laboratory studies (Field, 1982)
and managerial self-reports (Vroom and Jago, 1978; Tjosvold, Wedley and Field, 1986; Field and
House, 1990; Brown and Finstuen, 1993) have generally supported the model. In reviewing this literature Vroom and Jago (1988a) estimate that the rate of success of manager behavior conforming to the
# 1998 John Wiley & Sons, Ltd.
Journal of Behavioral Decision Making, Vol. 11, 251±261 (1998)
R. H. G. Field and J. P. Andrews
Models of Participation in Decision Making
253
Vroom±Yetton model was 62% whereas it was only 37% when the decision behavior failed to conform
to the model.
On the basis of this empirical examination of the Vroom±Yetton model, Vroom and Jago (1988a)
presented a revised form of the model. These revisions extend the model by allowing a more detailed
analysis of the decision situation. The breadth of the situation analysis is increased as twelve situational
characteristics are considered rather than seven (see Exhibit 1). The precision of the evaluation of the
situation is also increased as ten of these characteristics are ranked on a 5-point scale rather than the
yes/no question used in the Vroom±Yetton model. Finally, the Vroom±Jago model is more detailed in
its prescriptions. Rather than the decision rules used by the Vroom±Yetton model to produce a feasible
set, situational attribute measures are combined in the Vroom±Jago model by a series of structural
equations to derive an Overall E€ectiveness score for each decision method in the given situation.
Overall E€ectiveness (Oe€ ) is de®ned as equal to Decision Quality (Dqual) plus Decision Commitment
(Dcomm) less any penalty due to the time taken to make the decision(Dtp), less the cost of making the
decision (Cost), plus any developmental bene®ts to subordinates from engaging in the process of
making the decision (Devpt), i.e.:
Oeff ˆ Dqual ‡ Dcomm ÿ Dtp ÿ Cost ‡ Devpt
1†
To use the Vroom±Jago model a manager would analyze the situation by responding to the 12 items
listed in Exhibit 1. These responses would then be input into the equations and the overall e€ectiveness
score would be calculated for each method in that situation. In this way the ®ve methods may be ranked
from most e€ective to least e€ective. The model posits that the higher the Oe€ ranking, the more
e€ective the decision process will be. Therefore the manager is advised to use the method with the
highest score.
Available research testing the revised model is scarce and the evidence mixed. In support of the
model, Field, Read and Louviere (1990) found that the ®ve situation attributes added by Vroom and
Jago all had a signi®cant e€ect on decision makers' choices of decision method. In addition, Pasewark
and Strawser (1994) collected data from 41 audit managers about the decision style used to establish
budgeted audit hours, the decision situation attributes, and decision e€ectiveness de®ned as the
di€erence in actual audit hours from planned audit hours. Results were supportive of the Vroom±Jago
model. Decisions that were made with methods consistent with the recommendations of the model
were more e€ective.
Brown and Finstuen (1993) provide evidence that is less supportive of the model. Using self-report
data from 36 active duty military ocers they collected information on 132 decision episodes. Each
ocer was asked to describe the decision method and situation for two e€ective and two ine€ective
decisions. They then determined if the decision method used was rated by the model as the best of the
®ve possible methods. Fifteen of the 31 decisions in which the highest rated decision method was
actually used were rated as successful by the ocer. Of the remaining 101 decisions made with nonoptimal methods, 52 were rated as successful. The di€erence between these two proportions was not
signi®cant (w2…1† ˆ 0.09, ns). This result fails to support the Vroom±Jago model. However, this is not an
entirely appropriate test of the model, which provides, for a given situation, a ranking from best to
worst of the ®ve decision methods. The better the rank, the better should be the predicted outcome of
the decision. Brown and Finstuen's test compared decisions ranked ®rst to decisions ranked second
through ®fth Ð a comparison not anticipated by the model. Further, because each ocer contributed
multiple decisions, each data point was not independent of the others. This violation of the chi-square
test's assumptions can serve to weaken the robustness of the results (Lewis and Burke, 1949).
Exploring further, Brown and Finstuen noted that for many decision situations there were one or
more other decision processes with Oe€ scores very close to that of the maximum for that situation.
# 1998 John Wiley & Sons, Ltd.
Journal of Behavioral Decision Making, Vol. 11, 251±261 (1998)
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Journal of Behavioral Decision Making
Vol. 11, Iss. No. 4
They decided to create alternative decision sets for each situation by counting decision processes close
to that of the best process as equally recommended by the model. The rule employed was that any
decision method with an Oe€ score within 5% of the maximum score for that decision situation would
be included in the alternative set.
Retesting, Brown and Finstuen found that 34 of 56 decisions (61%) made with processes in the
alternative set were successful, whereas 33 of 76 decisions (43%) made with processes outside the
alternative set were unsuccessful. The test of these proportions was signi®cant (w2…1† ˆ 3.86, p 5 0.05).
Brown and Finstuen concluded that `the performance of the Vroom±Jago model is very comparable to
that of the Vroom±Yetton feasible set model' (p. 215) when decisions within 5% of the recommendation are included in the recommended decision set.
Overall, the Vroom±Jago model is more complex than its predecessor. It is an expert system for
managerial decision making that codi®es theory about subordinate participation in decision making
into a set of equations (see Vroom and Jago, 1988a, Appendix B). The more complex description of the
decision situation may increase the face validity of the Vroom±Jago model as managers see the model
as more closely resembling the complexity they face. However, in doing so it loses the intuitive feel that
is a major bene®t of the Vroom±Yetton model. In addition, the model has only mixed empirical
support.
Evidence to date on the Vroom±Jago model indicates that the value of its complexity is not yet
proven. Therefore, the purpose of this research is threefold. First, we wish to determine if the model can
be supported empirically. Second, we aim to determine whether the additional diagnosticity of the
Vroom±Jago model over the Vroom±Yetton model warrants its additional complexity. Third, we seek
to determine why the model may outperform the Vroom±Yetton model; that is, whether any increase
in the power of the Vroom±Jago model is due to discrimination in ranking alternatives within the
feasible set, or whether it is independent of feasible set classi®cation.
METHOD
Sample 1 Ð Telephone interviews
Following previous research we sought data from managers about real organizational decisions.
Managers selected at random from organizations in the local white pages telephone directory were
asked to `think of a problem that you faced and a decision that you and/or your subordinates made
which potentially a€ected two or more of your subordinates, and that you know the outcome'. Since it
has been reported that managers are likely to describe an e€ective decision (Field and House, 1990), in
order to increase the variance of the dependent variable of decision e€ectiveness half the managers
interviewed were asked to describe decisions that turned out to be ine€ective, that `didn't work out as
well as you had hoped'.
The manager was asked ®rst to choose which of the ®ve Vroom±Yetton±Jago decision methods was
closest to that used to make the decision. The ®ve decision methods were named and described to the
manager. Second, the manager was asked to rate the decision situation on the twelve Vroom±Jago
situation attribute questions. Each of the twelve Vroom±Jago questions was asked and the scale used
for the answer was given. Managers who had diculty with a question were provided a more detailed
description of the attribute. The questions, answer scales and attribute descriptions were taken from
Appendix A of Vroom and Jago (1988a). Third, managers rated the overall e€ectiveness of the decision
on a 7-point scale where 1 ˆ very low, 2 ˆ low, 3 ˆ somewhat low, 4 ˆ average, 5 ˆ somewhat high,
6 ˆ high, and 7 ˆ very high. Finally, several demographic questions were asked. The interview schedule
was pretested with 11 managers. These data were not used in later analyses.
# 1998 John Wiley & Sons, Ltd.
Journal of Behavioral Decision Making, Vol. 11, 251±261 (1998)
R. H. G. Field and J. P. Andrews
Models of Participation in Decision Making
255
Exhibit 3. Decision and demographic descriptive data
Demographics:
Years as manager
Hierarchical level of managera
Number of subordinates supervised
Percent female managers
Decisions reported:
Number of subordinates a€ected
Decision methodb
Overall e€ectiveness
a
Sample 1
Telephone
interviews
Sample 2
Mail
questionnaires
Sample 3
Large
organizations
13.48
6.17
15.57
23
12.69
5.90
11.99
21
12.71
4.71
17.85
18
5.00
2.96
5.15
7.61
3.86
5.13
8.72
4.15
5.76
Reported on a 7-point scale where 1 is the lowest and 7 is the highest.
Decision method is coded as AI ˆ 1, AII ˆ 2, CI ˆ 3, CII ˆ 4, GII ˆ 5.
b
In this ®rst sample the rate of agreement to participate was 40%. Interviews were conducted over the
telephone at the time of the ®rst contact or with a later call-back and 52 telephone interviews were
completed. Descriptive and demographic data are shown in Exhibit 3. The average number of years as
a manager was 13.48, average hierarchical level was 6.17, and the mean number of subordinates supervised was 15.57. It was found that the 12 female managers reported a signi®cantly lower hierarchical
level than the 40 male managers, a mean of 5.42 versus 6.40 (p 5 0.01).
Sample 2 Ð Mail questionnaires
After completing 52 telephone interviews, it was decided to create a questionnaire similar to the script
for the telephone interviews, and mail it to managers.
The questionnaire asked managers to `think of the most recent decision that you and/or your
subordinates made which potentially a€ected two or more of your subordinates, and that you know the
outcome was ine€ective/(e€ective)'. Half of all the questionnaires mailed asked for an ine€ective and
half asked for an e€ective decision.
A total of 326 managers agreed to be sent a questionnaire. Of these, 108 usable returns were received,
a response rate of 33%. It was found that the response rate for the report of an e€ective decision was
higher, at 40% (65 of 163), than for questionnaires asking for an ine€ective decision report, at 26%
(43 of 163). Managers may have found it more dicult to recall ine€ective decisions or were less willing
to invest e€ort in the description of a failure. It was also found that when an e€ective decision was
asked for, it was so rated (5 or higher on the measure of overall e€ectiveness) in 64 of 65 decisions
reported. A di€erent result was found when an ine€ective decision was requested. Of 43 reported, only
20 decisions were evaluated by the manager as less than or equal to average e€ectiveness.
As a manipulation check of the instructions to report a successful or an unsuccessful decision, selfreported e€ectiveness was 5.67 in the `e€ective' condition and 4.30 in the `ine€ective' condition, a
statistically signi®cant di€erence (t60 ˆ 5.41, p 5 0.01). The manipulation therefore served to increase
variance on the dependent variable of decision e€ectiveness.
In order to test for possible bias caused by managers rating decision e€ectiveness in a self-serving
manner, the data in Sample 2 were analyzed using initial instructions to recall an e€ective or ine€ective
decision as the rating of decision e€ectiveness. It was found that managers asked to recall an e€ective
decision were more likely to report one in which the decision method used fell within the feasible set,
and those asked to recall an ine€ective decision were more likely to report using a method not within
the feasible set (w2…1† ˆ 4.73, p 5 0.05). As in Brown and Finstuen (1993), the data in Sample 2 analyzed
# 1998 John Wiley & Sons, Ltd.
Journal of Behavioral Decision Making, Vol. 11, 251±261 (1998)
256
Journal of Behavioral Decision Making
Vol. 11, Iss. No. 4
in this way did not support the Vroom±Jago model (w2…1† ˆ 2.11, p ˆ ns), unless decision methods that
scored within 5% of the maximum Oe€ were included. In this case subjects asked to recall an e€ective
decision were more likely to report using the maximum (or near maximum) Oe€ method, and those
asked to report an ine€ective decision were more likely to report using a non-maximum Oe€ method
(w2 ˆ 2.77, p 5 0.10). These results are very similar to the data collected for previous tests of the
models. The conclusion is that managers' ratings of decision e€ectiveness are not biased.
Of the 108 decisions, 79% were reported by males and 21% by females (Exhibit 3). The average
number of years as a manager was 12.69, average hierarchical level was 5.90, and the mean number of
subordinates supervised was 12. Female managers reported signi®cantly fewer years as a manager than
did male managers (10 versus 13, p 5 0.05).
Sample 3 Ð Large organizations
At this point in the research, the decision was made to approach two large organizations to ask for
permission to distribute the decision-making questionnaire to managers within the organization.
A total of 122 questionnaires were delivered to the two companies, and 34 completed returns were
received, a response rate of 28%.
Of the decisions reported, 82% were by males, 18% by females (Exhibit 3). Average number of years
as a manager was 13, average hierarchical level was 4.71, and mean number of subordinates was 18.
The six female managers had signi®cantly fewer years experience as a manager (7 versus 14 for males,
p 5 0.05).
RESULTS
To allow comparison of the Vroom±Jago model with the Vroom±Yetton model, decision attributes
were used to determine the feasible set of decision methods for the particular decision. Because
managers rated the seven Vroom±Yetton situation attributes on the 5-point scales used by Vroom±
Jago, a manager's answers had to be recoded into the Yes/No format of the Vroom±Yetton model.
Answers of `1' or `2' were recoded as `No', `4' or `5' were recoded as `Yes'. Midpoint responses of `3',
however, require a judgment. Midpoint scale responses were coded as follows: Quality Requirement ˆ
Yes; Commitment Requirement ˆ Yes; Leader Information ˆ No; Problem Structure ˆ No; Commitment Probability ˆ No; Goal Congruence ˆ Yes; Subordinate Con¯ict ˆ Yes (Jago, 1993). Then the
actual method used was compared to this feasible set and coded as inside or outside (0 ˆ Out, 1 ˆ In).
The Vroom±Jago model was tested by entering manager rated decision attributes into the expert
system software (as done by Pasewark and Strawser, 1994) called Managing Participation in Organizations, designed by Vroom and Jago. For each decision, the decision methods were ranked as follows:
best ˆ 5, second best ˆ 4, third best ˆ 3, fourth best ˆ 2, ®fth best ˆ 1. The prediction of the Vroom±
Jago model is that the better the ranking, the more e€ective the decision should be.
It is possible to pool the data from the three samples if they are similar enough to be considered as
coming from the same population. It would be expected that the ®rst two would be similar, given their
method of selection. Managers in Sample 3 were selected from two large organizations in the same city
and therefore might share attributes less commonly represented in the wider pool of managers in
general.
The managers in Sample 3 did not di€er from those of Samples 1 and 2 on years as a manager,
number of subordinates supervised, percent of female respondents, and number of subordinates
a€ected by the decision (Exhibit 3). Sample 3 managers were of lower hierarchical level than the
managers of Samples 1 and 2. This likely re¯ects the fact that they worked in a large organization with
# 1998 John Wiley & Sons, Ltd.
Journal of Behavioral Decision Making, Vol. 11, 251±261 (1998)
R. H. G. Field and J. P. Andrews
Models of Participation in Decision Making
257
Exhibit 4. Results of samples 1, 2, and 3
Sample number
Sample 1
Sample 2
Sample 3
Samples 1 and 2 pooled
Samples 1, 2 and 3 pooled
Variance explained
n
Ine€ective/
e€ective
decision ratioa
52
108
34
160
194
14/38
28/80
3/31
42/118
45/149
rVroom±Yettonb
rhoVroom±Jagob
0.05
0.28c
ÿ0.06
0.21c
0.19c
3.6%
0.34c
0.18d
0.32d
0.22c
0.25c
6.25%
Signi®cance of
the di€erence
ns
ns
ns
ns
c
a
For the purpose of this comparison, e€ective decisions coded as 5, 6, or 7 on a 7-point scale.
Pearson correlations and Spearman rank order correlations for each study are not materially di€erent.
c
p 5 0.01.
dp 5 0.05.
b
many levels and their more middle management status in the organization. In Samples 1 and 2,
managers were more likely near the top of smaller organizations. Sample 3 managers also reported
decisions that were on average signi®cantly higher in overall e€ectiveness. This most likely re¯ects the
fact that of the 34 questionnaires returned, only 10% were about ine€ective decisions. The managers of
Sample 3 are similar in some ways to the managers of Samples 1 and 2, and di€erent in others. Overall,
managers in the three samples are similar enough that the data may be studied as a whole.
Pooling the data from the three samples, there are a total of 194 decisions. Decision method AI was
used 14 times, AII 25 times, CI 44 times, CII 39 times, and GII 72 times. Results for the study are
shown in Exhibit 4. Overall, there is support for the Vroom±Yetton model. Decisions made with
methods in the recommended feasible set were more e€ective (r ˆ 0.19, p 5 0.01). This ®nding was
expected, based on the cumulative research evidence that upholds the model's validity. The data is also
supportive of the validity of the Vroom±Jago model. Decisions made with methods ranked as better by
the model were more e€ective (rho ˆ 0.25, p 5 0.01). Further, it was found that the Vroom±Jago
model added signi®cant variance to that explained by the Vroom±Yetton model. In a hierarchical
regression analysis the multiple R of decision e€ectiveness explained by Vroom±Yetton feasible set
agreement was 0.19 (F1,189 ˆ 7.17, p 5 0.01). The multiple R when the Vroom±Jago ranking was added
was 0.27 (F2,188 ˆ 7.55, p 5 0.01).
Examining the mean decision e€ectiveness scores displayed in Exhibit 5 provides additional support
for both models. On average, managers reported signi®cantly higher decision e€ectiveness when the
method used fell within the Vroom±Yetton feasible set than when it did not. Similarly, the mean
Exhibit 5. Mean decision e€ectiveness scores
Vroom±Jago
Decision set1
Max Oe€ used
Sets 1 and 2
Set 1
Set 2
a
5.73
5.82b
5.64c
Vroom±Yetton
Max Oe€ not used
a
5.04
4.76b
5.21c
Inside feasible set
d
5.35
5.45e
na
Outside feasible set
4.67d
4.67e
na
a,b,d
Signi®cantly di€erent at p 5 0.01.
Signi®cantly di€erent at p 5 0.05.
cSigni®cantly di€erent at p ˆ 0.10.
1Decision set 1 contains those decisions in which the Vroom±Yetton feasible set did not include all ®ve possible
methods (n ˆ 115). Decision set 2 contains those decisions in which the Vroom±Yetton feasible set included all ®ve possible
methods (n ˆ 79).
e
# 1998 John Wiley & Sons, Ltd.
Journal of Behavioral Decision Making, Vol. 11, 251±261 (1998)
258
Journal of Behavioral Decision Making
Vol. 11, Iss. No. 4
decision e€ectiveness rating when the method with maximum Oe€ score is used is signi®cantly higher
than when the method used is not the maximum Oe€ score.
Given that the Vroom±Jago model explains signi®cantly more variance in decision e€ectiveness than
the Vroom±Yetton model it is of interest to question the source of that increased prescriptive power.
Speci®cally, is the gain due to better discrimination within the feasible set or is it independent of
feasible set classi®cation?
Of the 194 decision descriptions collected for this research, the method with the highest Oe€ score fell
within the feasible set as determined by the Vroom±Yetton model 168 times. The gain in decision
e€ectiveness associated with the Oe€ score of the method used was not signi®cantly moderated by the
feasible set classi®cation (F4,190 ˆ 0.391, p ˆ ns). However, it is worth noting that in 115 of the decision
situations the Vroom±Yetton feasible set included all ®ve decision methods. Clearly, in 59% of the
decisions that we sampled the Vroom±Yetton model did not discriminate among decision methods.
For the 79 decision situations in which the feasible set did not include all methods, both membership
in the Vroom±Yetton feasible set and Vroom±Jago e€ectiveness rank were signi®cantly correlated with
rated decision e€ectiveness (r ˆ 0.27, p 5 0.05 and rho ˆ 0.33, p 5 0.01 respectively). But for the 115
decisions in which the Vroom±Yetton feasible set included all ®ve decision methods the Spearman
rank-order correlation between the Vroom±Jago method ranking and decision e€ectiveness was
rho ˆ 0.23, signi®cant at p 5 0.05. This suggests that decision e€ectiveness is rated higher when the Oe€
score is higher and that the Vroom±Jago model provides greater diagnostic power than the Vroom±
Yetton model. Further, the data of Exhibit 5 indicates that the mean decision e€ectiveness score is
greater when managers use the maximum Oe€ method than when they do not, even for those decisions
in which the Vroom±Yetton model does not discriminate between the ®ve possible methods (see
Exhibit 5, Set 2 data, p ˆ 0.10).
DISCUSSION
This test of the Vroom±Jago model shows that for three separate samples of manager-reported
decisions the Vroom±Jago model's ranking of the decision method used correlated signi®cantly with
rated overall e€ectiveness Ð the better the ranking, the higher was the decision's overall e€ectiveness.
Therefore, unlike the result of Brown and Finstuen (1993), this study found evidence for the prescriptive
validity of the Vroom±Jago model. We speculate that this is due to using a more appropriate test of the
model. The Vroom±Jago model proposes that the higher the Oe€ score, the more e€ective the decision.
Slight di€erences in Oe€ scores will be re¯ected in slight di€erences in rated e€ectiveness. Hence, it is
possible to report highly e€ective decisions when not using the recommended method. Therefore,
classifying methods as optimum/not optimum and e€ective/not e€ective does not provide an
appropriate test of the model's prescriptions.
The amount of variance in decision e€ectiveness accounted for by the Vroom±Jago model was a
relatively low 6.25% (Exhibit 4). This can be cause for concern when it is considered that this model is
meant to be a prescriptive tool for managers to help them make better decisions. If only 6.25% of
decision e€ectiveness is explained, then perhaps the model is not powerful enough to warrant its use.
There are two arguments that counter this objection.
The ®rst is that even small amounts of variance explained can prove important (Prentice and Miller,
1992). The Vroom±Jago model can be used as an expert system to provide advice to a decision maker
about how to go about making a particular decision. In this practical use as a programmed decision
tool, a small improvement in decision success could translate into large bene®ts for an organization
when decisions are of such importance that any small improvement in success rate is crucial. Or, when
the model is used routinely, small incremental improvements can add up to large bene®ts over a
# 1998 John Wiley & Sons, Ltd.
Journal of Behavioral Decision Making, Vol. 11, 251±261 (1998)
R. H. G. Field and J. P. Andrews
Models of Participation in Decision Making
259
number of decisions. Abelson (1985) has shown that baseball batting skill accounts for less than 1% of
the variance in performance in any one at-bat, yet is an important attribute since players bat over
600 times in one season. Similarly, the e€ect of the Vroom±Jago model might account for a low
proportion of decision e€ectiveness on any one decision, but when manager decisions are accumulated
over a year or a career, a small increment in performance is likely to be of great practical importance.
The second argument is that the models of Vroom±Yetton±Jago are useful as training tools and as
diagnostic devices, without necessarily being used as decision aids. Workshops conducted to explain
the theory behind the models can help managers to become more aware of situational contingencies
and the possibility of using decision processes that vary in the amount of subordinate participation in
di€erent situations. With this perspective on the models, it is less important how well they actually
predict decision e€ectiveness.
The results reported herein also provide some insight into the source of the increase in variance
explained by the Vroom±Jago model over the Vroom±Yetton model. While both models' prescriptions
were signi®cantly related to reported e€ectiveness overall, in 59% of the scenarios reported the Vroom±
Yetton model did not discriminate amongst methods. However, for those decisions the Vroom±Jago
model's prescriptions were signi®cantly related to reported e€ectiveness, albeit with less variance
explained than for the remaining 41% of decisions. This, combined with the fact that the highest
ranked method as determined by Vroom±Jago fell within the Vroom±Yetton feasible set in 87% of the
decisions reported, suggests that the Vroom±Jago model provides guidance to managers in selecting
from within the feasible sets. Further, both models were more e€ective when prescribing action in cases
in which the feasible set did not include all methods. This suggests that neither model is equally
e€ective across all situations, but that there exists a subset of contexts in which they have greater
prescriptive ecacy.
There are several issues that must be explored that arise from the nature of this test. First, all data
collected was retrospective self-reports by managers. The models are prescriptive in nature, asking the
decision maker to consider the situation as it is currently faced, and then to choose an amount of
participation by subordinates to use when making the decision. In these tests the decisions had already
been made and managers were looking back in time to report how they thought the decision situation
existed. It is possible that bias is introduced into these tests of the model if managers, in their
retrospections, can not or do not accurately recall the state of the decision situation attributes. A
decision maker could, for example, determine that subordinates would accept his autocratic decision,
and then be autocratic when solving the problem. If troubles occurred when implementing the decision
and subordinates did not accept the decision made, the manager may recall the situation as one where
subordinates would not accept his autocratic decision. The passing of time may lead to errors in recall.
Further, there may be a recall bias such that managers selectively recalled information that would
support the model. However, the model is complex both in the number of situational attributes
considered and in the equations that are used to create the model's prescriptions. Even with a good deal
of familiarity with the model it is not clear how to respond to the situation attribute questions in order
to manipulate Oe€ scores for decision methods.
Second, similar to previously published studies of both Vroom±Yetton and Vroom±Jago models,
there is the possibility in these tests of single-source bias, in that decision makers reported both the
attributes of the decision situation and the e€ectiveness of the decision. Independent and dependent
variables supplied by the same individual can raise questions about possible percept-percept in¯ation
(Crampton and Wagner, 1994). If this were the case then the correlation between the model's prediction and decision e€ectiveness might be too high. In their review of the Vroom±Yetton model, Vroom
and Jago (1988a) noted that the e€ectiveness of behavior conforming to the model was consistent
across studies using ®eld data, laboratory data and self-report data. This suggests that, in this type of
study, single source self-report data does not unduly bias the results.
# 1998 John Wiley & Sons, Ltd.
Journal of Behavioral Decision Making, Vol. 11, 251±261 (1998)
260
Journal of Behavioral Decision Making
Vol. 11, Iss. No. 4
Third, one must consider the high level of e€ectiveness of the decisions reported. Earlier self-report
tests of the Vroom±Yetton model (e.g. Vroom and Jago, 1978; Brown and Finstuen, 1993) have
typically asked a decision maker to categorize decisions as e€ective or ine€ective, and to report at least
one from each category. This procedure serves to provide required variance on the dependent variable
of decision e€ectiveness. In the tests reported here, managers were asked to report only one decision
and to rate its e€ectiveness on a 7-point scale. Decisions reported tended to be much more of the
e€ective than ine€ective kind. Average overall e€ectiveness ranged from 5.15 and 5.13 in the ®rst two
samples to 5.76 in Sample 3 (Exhibit 3). Recall bias could be in play here, as managers recall more
easily those decisions that were well made and worked out. In addition, we believe that managers are
less likely to return the questionnaire if they have to report an ine€ective decision. Also likely is a time
e€ect. A decision might be ine€ective through several cycles of being reworked and retried before
®nally being implemented successfully. This decision can be seen to have been ine€ective several times
before ®nally being e€ective. Looking back allows the manager to recall it as ®nally e€ective. In sum,
these biases serve to reduce the variance of reported decision e€ectiveness, making it more dicult to
®nd an e€ect of the models. Our test is therefore a conservative one. If the Vroom±Yetton±Jago
models were used in real time by managers, their recommendations might more precisely predict when
decisions are ine€ective.
Fourth, in this study managers were not trained in the interpretation of the model's decision situation
questions, which may have reduced their understanding of the model's questions and thereby reduced
the validity of this test. However, training managers in the models could lead to results supporting them
because of a shared understanding of what problem situations are likely to be e€ectively solved by which
decision methods. Previous results using manager self-reports and various levels of manager training
(e.g. Vroom and Jago, 1978; Tjosvold, Wedley and Field, 1986; Brown and Finstuen, 1993) supported
the validity of the Vroom±Yetton model, as did this study. Therefore, level of training does not appear
to determine the results found.
Finally, managers were asked which of the ®ve methods were used to make the decision. This misses
the point that a decision may involve multiple decision methods and procedures, but was required to
test the model as currently prescribed. Future tests could more fully examine the di€erent decision
methods and combinations of methods used by managers as they go about ®nding solutions to
problems.
In conclusion, using methods similar to those used earlier to test the Vroom±Yetton model,
statistically signi®cant support was found for the Vroom±Jago model in three manager samples.
While the Vroom±Jago model is more complex, it accounts for more variance in decision e€ectiveness.
It was also found that the greater precision in the situational assessment and prescriptions derived by
the Vroom±Jago model allows for greater discrimination in choice of decision method across all
situations.
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Authors' biographies :
Richard H. G. Field is a Professor of Organizational Analysis in the Faculty of Business at the University of
Alberta. His PhD is in Organizational Behaviour from the University of Toronto. His interests are in leadership
and decision making.
J. P. Andrews is a PhD candidate in the Organizational Analysis Department at the University of Alberta's Faculty
of Business. His interests are in managerial decision making, policy formulation and leadership processes.
Authors' address :
Richard H. G. Field and Julian Andrews, Faculty of Business Building, University of Alberta, Edmonton, Alberta
T6G 2R6 Canada.
# 1998 John Wiley & Sons, Ltd.
Journal of Behavioral Decision Making, Vol. 11, 251±261 (1998)