Directory UMM :Data Elmu:jurnal:I:Information and Management:Vol38.Issue5.Apr2001:

Information & Management 37 (2000) 283±295

The relationship between user participation and system success:
a simultaneous contingency approach
Winston T. Lina,*, Benjamin B.M. Shaoa,b,1
a

Department of Management Science and Systems, School of Management,
State University of New York at Buffalo, Buffalo, NY 14260, USA
b
School of Accountancy and Information Management, College of Business,
Arizona State University, Main Campus, Tempe, AZ 85282, USA
Received 12 April 1999; accepted 23 September 1999

Abstract
The relationship between user participation and information system (IS) success has drawn attention from researchers for
some time. It is assumed that strong participation of future users in the design of IS would lead to successful outcomes in terms
of more IS usage, greater user acceptance, and increased user satisfaction. However, in spite of this, much of the empirical
research so far has been unable to demonstrate its bene®ts. This paper examines the participation±success relationship in a
broader context, where the effects of user participation and two other factors, user attitudes and user involvement, on system
success occur simultaneously. Other contingency variables considered here are: system impact, system complexity, and

development methodology. The theoretical framework and the associated hypotheses are empirically tested by a survey of 32
organizations. Empirical results corroborate the positive link between user participation and user satisfaction and provide
evidence on the interplay between user attitudes and user involvement. # 2000 Elsevier Science B.V. All rights reserved.
Keywords: User participation; System success; User satisfaction; User attitudes; User involvement; System impact; System complexity;
Outsourcing; Contingency; Simultaneity

1. Introduction
User participation has long been regarded as an
important factor to improve the chances of the success
in developing an information system (e.g.,
[6,31,36,68]). User participation refers to `the various
design related behaviors and activities that the target
users or their representatives perform in the systems
development process' [5]. Through participation,
*

Corresponding author. Tel.: ‡1-716-645-3257;
fax: ‡1-716-645-6117.
E-mail addresses: mgtfewtl@acsu.buffalo.edu (W.T. Lin),
ben.shao@asu.edu (B.B.M. Shao)

1
Tel.: ‡1-480-727-6790; fax: ‡1-480-965-8392.

users of an information system (IS) can interact with
system designers in the stages of planning, analysis,
design, testing, and implementation and, hence, aid in
many aspects of the system development process. A
variety of development methodologies, such as codevelopment, participative design (PD) and joint application design (JAD), have been proposed to
operationalize user participation [3,10].
There are a number of bene®ts which can be
expected of such user participative behaviors. Ives
and Olson [37] have pointed out that user participation
in system development can enhance system quality
through a more accurate and complete identi®cation of
user information requirements [60,63], knowledge
and expertise about the organization the system is

0378-7206/00/$ ± see front matter # 2000 Elsevier Science B.V. All rights reserved.
PII: S 0 3 7 8 - 7 2 0 6 ( 9 9 ) 0 0 0 5 5 - 5

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W.T. Lin, B.B.M. Shao / Information & Management 37 (2000) 283±295

intended to support [49], avoidance of unacceptable or
unimportant system features, and a better user understanding about the system. User participation is also
believed to increase user acceptance of the system
with a more realistic expectation about system capabilities [27], an opportunity for users and designers to
resolve con¯icts about design issues [41], users' feelings of ownership toward the system, a decrease in
user resistance to possible changes incurred by the
system, and greater commitment from users [51]. In
consequence, user participation has been extensively
sought and encouraged by practitioners in developing
IS [32].
In the meantime, researchers also have made great
efforts to ®nd the empirical evidence of ef®cacy of
user participation in improving system success. However, the reported results have been mixed and fragmented. In an earlier comprehensive review of 22
empirical studies conducted from 1959 to 1981, Ives
and Olson were able to identify only eight (36%) that
show a positive link between user participation and

some measure of system success, including system
quality [9,24], system usage [68], user behavior/attitudes [1,35], and information satisfaction [22,30,38].
The results from other studies are either inconclusive
or contrary to expectations.
Cavaye [11] has reviewed 19 more recent studies
published from 1982 to 1992, but also found only
seven (37%) of them can substantiate a positive
participation±success
relationship
[2,7,14,16,23,40,42]. Despite the fact that 10 more
years of research have lapsed, the progress seems to be
limited and slow. The ®ndings are still mixed and
fragmented.
Researchers have identi®ed several possible causes
of the ambiguity and contradiction. Poor research
methods and the omission of important contextual
factors are possible causes [53]. Also a simple direct
causal relationship between user participation and
system success may not be suf®cient. DeBrabander
and Edstrom [13] suggested that the context in

which the system is being developed has to be
considered. Hence, the contingency approach has
been widely utilized to address this issue (e.g.,
[17,43,48,50,54,64,65,69]). This is helpful in identifying factors that may alter the consequences of a
process. However, most of these models are sequential, with assumed direct causal relationships.

Furthermore, it is suggested that several confounding constructs have to be carefully de®ned to reduce
the possibility of confusing results (e.g., [6,44]).
Hwang and Thorn [34] meta-analyzed 25 studies
and found `both user involvement and user participation are bene®cial, but the magnitude of these bene®ts
much depend on how involvement and its effects are
de®ned'.
The purpose of this paper is to explore the relationship between user participation and system success
in a broader context where all major effects on
system success are simultaneously and jointly determined. Due to the complexity of this scenario, we
believe it is bene®cial and appropriate to study the
relationship by means of a simultaneity and contingency approach.

2. Theoretical model and hypotheses
The terms user participation and user involvement

in virtually all MIS research were used interchangeably in the past. To align research with the relevant
concepts in such disciplines as psychology, marketing,
and organizational behavior and, hence, to take advantage of their experience, Barki and Hartwick [6] felt is
was necessary to differentiate between these two
constructs, with user participation referring to `the
behaviors and activities that the target users or their
representatives perform in the systems development
process,' and user involvement to `a subjective psychological state of the individual,' depending on the
importance and personal relevance that users attach to
a particular system or to IS in general. Fig. 1 presents
our theoretical model.
2.1. Participation±success relationship
User participation in the IS development process
has been regarded as a special case of participative
decision making (PDM), which refers to group decision making. Participation from users can be classi®ed
into type and extent. The type may be consultative,
representative, or consensus. The extent increases in
degree from consultative to consensus [57]. Because
the degree of participation is a more general concept
than the type, it is used in this study to measure user

participation.

W.T. Lin, B.B.M. Shao / Information & Management 37 (2000) 283±295

285

Fig. 1. A simultaneous contingency model for user participation.

System outcome, though dif®cult to be measured in
economic terms, has a number of surrogates [6].
Garrity and Sanders [26] have suggested considering
multiple perspectives. System use is frequently used to
measure system success when system use is discretionary or voluntary [15]. However, user satisfaction is
the most widely used measure for system success in
the literature. In this study, user satisfaction is used as
the surrogate for system success.
Signi®cant empirical results from PDM suggest a
positive relationship between the degree of participation in decision making and decision acceptance (e.g.,
[33,47,66]). This would suggest that there is a positive
relationship between the degree of user participation

and system success. Accordingly, it is hypothesized that:
Hypothesis 1. User satisfaction (X1) of using the
information system increases with higher degree of
user participation (X2) in the development process.
2.2. Simultaneous relationship: user participation,
attitudes, and involvement
There is a difference between user involvement and
user participation. User involvement is a subjective
psychological state re¯ecting the importance and

personal relevance that a user attaches to a given system.
At the same time, user involvement is different from user
attitude, which has been de®ned as an affective or
evaluative judgment toward some object or behavior,
measured by a procedure that speci®es the individual on
a bipolar scale (e.g., good or bad) [21]. Therefore, in IS
development, user attitude re¯ects a psychological state
that shows the user's feelings about a new IS.
Thus, user participation is conceptualized as a
behavioral construct (the degree of participative behaviors of users during the development process), while

user involvement and user attitudes are psychological
constructs. The relationships among user participation, user attitudes, and user involvement appear to be
interrelated and complex. This leads to the hypotheses:
Hypothesis 2A. User participation (X2) increases
with more favorable user attitudes (X3) toward the
proposed system.
Hypothesis 2B. User participation (X2) increases
with more user involvement (X4).
Many early researchers claimed that users who are
actively participants in the system development

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W.T. Lin, B.B.M. Shao / Information & Management 37 (2000) 283±295

process are likely to establish the feeling that the
system is good. Participants become acquainted with
the new system. Their uncertainty and fear is alleviated through the participative process.
Information systems which are thought to be both
important and personally relevant to users are also

likely to result in positive affective or evaluative feelings
of the users. Similar supports for this argument can be
drawn from relevant research in other disciplines. Persons who are highly involved with an issue have been
found to possess more favorable attitudes about the
issue (e.g., [67]). In marketing, customers who are
highly involved with a product have been found to
develop more positive feelings toward this product
(e.g., [25,62]). Organizational behavior research
shows that employees who are highly involved with
their jobs have been found to appreciate their jobs
(e.g., [39]). It is, therefore, hypothesized that:
Hypothesis 3A. User attitudes (X3) become more
favorable through more user participation (X2).
Hypothesis 3B. User attitudes (X3) become more
favorable with more user involvement (X4).
A number of psychological theories are able to
elucidate the positive in¯uence of user participation
on user involvement. Cognitive dissonance theory [20]
and attribution theory [8] suggest that a person's
beliefs will be aligned with his or her behaviors.

Robey and Farrow [63] have claimed that participative
users may change system attributes to meet their needs
and desires, and help develop a system deemed as
important and personally relevant.
The correlation between user involvement and user
attitudes is bi-directional. Persons with extreme
attitudes toward an issue tend to develop belief that
the issue is both important and personally relevant.
Millman and Hartwick [55] have argued that users
with positive attitudes toward the new systems tend to
become more involved. As a result, two hypotheses
are made:
Hypothesis 4A. User involvement (X4) increases
with more user participation (X2).
Hypothesis 4B. User involvement (X4) increases
with more favorable user attitudes (X3).

2.3. Contingency factors
The contingency factors which have been studied in
previous research include system complexity (e.g.,
[52]), stage development, resource constraints (e.g.,
[18]), communication, user attitudes, degree of user
involvement, management styles, top management
support, system impact, etc. It would be unwieldy
and imprudent to incorporate all of these factors into
one model. This study considers three other important
contingency factors: system impact (X5), system complexity (X6), and development methodology (X7).
System impact refers to the potential changes in the
organization brought about by the system. A new IS
may incur organizational changes, such as jobs, interpersonal relationships, and organizational structure.
Positions in the organization may be eliminated or
created, and the job descriptions of people may be
altered to incorporate different responsibilities.
Based on organizational change theory, it is known
that people are used to the status quo and any disruption of the organizational balance would evoke human
resistance. It is, therefore, hypothesized that:
Hypothesis 5. System impact (X5) has a negative
influence on user attitudes (X3).
System complexity arises in the developer's environment and refers to the ambiguity and uncertainty
that surround the practice of development. A complex
IS is considered dif®cult to develop due to the large
number of interdependent parts and the lack of a
model structure. It is hypothesized that
Hypothesis 6. Higher degree of system complexity
(X6) results in more user participation (X2).
Development methodologies range from traditional
system development life cycle, prototyping, off-theshelf software, and end-user computing, to outsourcing. Among these development methodologies, outsourcing has drawn much attention recently and is now
in vogue in the IS domain [56].
When outsourcing shifts the burden of developing a
system to outside parties, it also appears to reduce user
responsibility for developing and validating the complex evolving IS functions as well as the need to learn
how to apply new technologies. Such a mitigation in

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W.T. Lin, B.B.M. Shao / Information & Management 37 (2000) 283±295

the responsibility as a consequence of outsourcing
may mislead users to the belief that the outsourced IS
is less important and less personally relevant. Moreover, one major disadvantage of outsourcing is the loss
of control [46]. Therefore, it is hypothesized that
Hypothesis 7. Outsourcing (X7) leads to less user
involvement (X4).
3. Estimation models
3.1. The structural form
The simultaneous contingency model can be
expressed as a simultaneous-equations system of the
following form:
X1i ˆ b10 ‡ b12 X2i ‡ v1i ;

i ˆ 1; 2; . . . ; n

X2i ˆ b20 ‡ b23 X3i ‡ b24 X4i ‡ b26 X6i ‡ v2i ;
i ˆ 1; 2; . . . ; n

(3.1)
(3.2)

X3i ˆ b30 ‡ b32 X2i ‡ b34 X4i ‡ b35 X5i ‡ v3i ;
i ˆ 1; 2; . . . ; n

(3.3)

X4i ˆ b40 ‡ b42 X2i ‡ b43 X3i ‡ b47 X7i ‡ v4i ;
i ˆ 1; 2; . . . ; n:

(3.4)

Three contingency variables X5, X6 and X7 (system
impact, system complexity, and outsourcing) are the
exogenous (independent) variables which are determined outside of the model. Variables X1, X2, X3 and
X4 (system success, user participation, user attitudes,
and user involvement) are the endogenous (jointly
dependent) variables that are interdependent and
should be determined jointly. Since there are four
endogenous variables, there are exactly four equations
in the simultaneous-equations system.
The hypotheses established from the relationships
in the simultaneous contingency model as well as the
associated coef®cients are summarized in Table 1, in
which the dependent variable is highlighted in boldface and an independent variable is expressed in
normal font.
A model is identi®ed if it is in a unique statistical
form, enabling unique estimates of its parameters to be
subsequently made from sample data. An equation is
under-identi®ed if its statistical form is not unique;
otherwise, it is exactly- or over-identi®ed. It is neces-

Table 1
Hypotheses tested and associated coefficients
Relationship
System success (X1)
User participation (X2)
User participation (X2)
User attitudes (X3)
User involvement (X4)
User attitudes (X3)
User participation (X2)
User involvement (X4)
User involvement (X4)
User participation (X2)
User attitudes (X3)
System impact (X5)
User attitudes (X3)
System complexity (X6)
User participation (X2)
Outsourcing (X7)
User involvement (X4)

Hypothesis Coefficient Direction
1

b12

‡

2A
2B

b23
b24

‡
‡

3A
3B

b32
b34

‡
‡

4A
4B

b42
b43

‡
‡

5

b35

ÿ

6

b26

‡

7

b47

ÿ

sary that the order condition for identi®cation be
satis®ed for each equation in the system. The order
condition stipulates that the number of variables (exogenous) excluded from an equation should be greater
than (over-identi®cation) or equal to (exact identi®cation) the number of endogenous variables in the
equation less one [28,29].
The application of the order condition to the simultaneous Eqs. (3.1)±(3.4) reveals that Eq. (3.1) is overidenti®ed, and Eqs. (3.2)±(3.4) are exactly-identi®ed.
None of the equations is under-identi®ed and, consequently, there is no problem of identi®cation.
3.2. Estimation issues
The ordinary least squares (OLS) estimation
method is not appropriate because the estimators of
the structural coef®cients are biased and inconsistent
due to the so-called simultaneity bias or simultaneousequation bias [45]. Instead, the methods of two-stage
least squares (2SLS) and three-stage least squares
(3SLS) should be used.
The major difference between 2SLS and 3SLS lies
in the assumptions underlying the random errors v1, v2,
v3 and v4 of Eqs. (3.1)±(3.4). If the v's of these
6 0, for j, k 2
equations are correlated (i.e., E…vj vk † ˆ
{1, 2, 3, 4} and j 6ˆ k), then 3SLS is more appropriate
than 2SLS because it produces more ef®cient

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W.T. Lin, B.B.M. Shao / Information & Management 37 (2000) 283±295

estimates. Such correlations among the random errors
could be present if other possible contingency variables are unintentionally omitted from the simultaneous contingency model, leaving the in¯uence of
these omitted variables to be absorbed by the random
errors of the equations and rendering the random
errors correlated.
3.3. The reduced form

BYi ‡ GXi ˆ Vi

(3.5)

where
Yi ˆ …X1i ; X2i ; X3i ; X4i †0 is the 4  1 column vector of the endogenous or jointly dependent variables,
Xi ˆ …1; X5i ; X6i ; X7i †0 is the 4  1 column vector of
the exogenous variables and Vi ˆ …v1i ; v2i ; v3i ; v4i †0 is
the 4  1 column vector of the random disturbances,
2
3
0
0
1 ÿb12
60
1
ÿb23 ÿb24 7
7
Bˆ6
4 0 ÿb32
1
ÿb34 5
0 ÿb42 ÿb43
1
is the 4  4 matrix of coefficients of the simultaneously dependent variables, and
2
3
0
0
0
ÿb10
6 ÿb20
0
ÿb26
0 7
7
Gˆ6
4 ÿb30 ÿb35
0
0 5
0
0
ÿb47
ÿb40
is the 4  4 matrix of coefficients of the exogenous
variables.
Secondly, premultiplying the structural form (3.5)
through by Bÿ1, the inverse of B, and rearranging
yields the reduced form in matrix terms:
Yi ˆ PXi ‡ Wi ;

(3.6)

where
p10
6 p20
P ˆ ÿB G ˆ 6
4 p30
p40
ÿ1

X1i ˆ p10 ‡ p11 X5i ‡ p12 X6i ‡ p13 X7i ‡ w1i

(3.8)

X2i ˆ p20 ‡ p21 X5i ‡ p22 X6i ‡ p23 X7i ‡ w2i

(3.9)

X3i ˆ p30 ‡ p31 X5i ‡ p32 X6i ‡ p33 X7i ‡ w3i (3.10)

The reduced form of the structural form can readily
be obtained through an algebraic operation. First, the
structural-form equations are written in matrix formulation,

2

and Wi ˆ Bÿ1 Vi is the 4  4 matrix of the reducedform random disturbances.
Alternatively, we can write out the reduced-form
system (3.6) algebraically, analogous to the structuralform Eqs. (3.1)±(3.4):

p11
p21
p31
p41

p12
p22
p32
p42

3

p13
p23 7
7
p33 5
p43

(3.7)

is the 4  4 matrix of the reduced-form coefficients,

X4i ˆ p40 ‡ p41 X5i ‡ p42 X6i ‡ p43 X7i ‡ w4i (3.11)
Each of the reduced-form equations shows the
dependence of an endogenous variable on all of the
exogenous variables and a random disturbance. Thus,
it enables us to ®nd the total effect (sum of the
coef®cient estimates) of the exogenous contingency
variables on an endogenous variable.

4. Research method
4.1. Data collection
We conducted a survey of 154 organizations randomly selected throughout the US to gather the data.
The survey was directed to organizations that were
under or had just completed the implementation of a
new IS. Most of the organizations surveyed belong to
the manufacture and service industries, and some were
non-pro®t entities. The response rate was 54%.
Excluding those returned blank or with incomplete
information requested, the valid responses from 32 of
the surveyed organizations were used to estimate the
simultaneous contingency model and test the hypotheses presented. From the viewpoint of statistics, the
sample size is considered large enough (n  25).
Users and system designers were surveyed separately
to elicit data on different measures.
4.2. Measurement of variables
Several previous and useful studies were used as
guidelines to design the survey questionnaire and to
measure the relevant variables. They are listed in
Table 2.
The survey involves 53 questions. Most of the
questions contain ®ve to six bipolar adjective pairs
in a question. For each pair, the respondents were

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W.T. Lin, B.B.M. Shao / Information & Management 37 (2000) 283±295
Table 2
Instruments to measure variables
Variable

Instrument

User satisfaction (X1)
User participation (X2)
User attitudes (X3)
User involvement (X4)
System impact (X5)
System complexity (X6)
Outsourcing (X7)

[4,36]
[53,61]
[19]
A Likert-type scale from 1 to 7
[52]
[52]
Dummy variable
(1: outsourced; 0: inhouse)

requested to check or circle the numbered box that is
considered most suitable in the seven interval scale
(from 1 to 7). The weights used to quantify the scaling
of the seven intervals (boxes) went from ÿ3 through
‡3. During computation, these were assigned weights
1, 0.85, 0.70, 0.55, 0.40, 0.25, and 0.10. The score was
then computed using
Si ˆ

n
X

Rij Wij

jˆ1

where Rij ˆ the reaction to factor j by individual i, and
Wij ˆ the importance of factor j to individual i [4].
4.3. Data analysis
The Cronback alpha reliability coef®cient [12] was
computed to assess the reliability of the responses to

all instruments. The alpha coef®cients for system
success, user participation, user attitudes, user involvement, system impact, and system complexity are
0.91, 0.86, 0.83, 0.89, 0.78, and 0.72, respectively.
These alpha reliability coef®cients are favorably comparable to those reported in the literature and exceed
the commonly accepted level of 0.70 used in social
sciences studies. The descriptive statistics of the variables and the corresponding correlation matrix of the
variables are shown in Tables 3 and 4, respectively.
4.4. Estimation results
Three estimation methods, OLS, 2SLS, and 3SLS,
are applied to estimate Eqs. (3.1)±(3.4) of the simultaneous contingency model. The system estimates by
3SLS are more ef®cient than the 2SLS estimates if the
random errors v's of structural Eqs. (3.1)±(3.4) are
correlated. The estimation results by OLS, 2SLS and
3SLS are summarized in Table 5, which shows that the
OLS estimation yields one estimate (b35, which is
underlined) with the sign contrary to the expected
direction, while the 2SLS and 3SLS estimates correspond to all the theoretical expectations. This abnormal sign may be attributed to the simultaneousequation bias resulting from the inappropriateness
of the OLS applied to estimate Eqs. (3.1)±(3.4) of
the simultaneous-equations system. Therefore, we
conclude that, for the simultaneous contingency

Table 3
Descriptive statistics of the variables studied
Variable

X1

X2

X3

X4

X5

X6

X7

Mean
Std. dev.

0.986
0.459

1.276
0.824

1.521
0.759

1.661
1.093

1.057
0.633

ÿ0.024
0.427

0.607
0.497

Table 4
Correlation matrix of the variables under consideration

X1
X2
X3
X4
X5
X6
X7

X1

X2

X3

X4

X5

X6

X7

1
0.368
0.596
0.634
0.655
0.347
0.129

0.368
1
0.181
0.009
0.090
0.355
0.211

0.596
0.181
1
0.470
0.555
0.010
0.324

0.634
0.009
0.470
1
0.753
0.095
0.148

0.655
0.090
0.555
0.753
1
0.208
0.132

0.347
0.355
0.010
0.095
0.208
1
0.067

0.129
0.211
0.324
0.148
0.132
0.067
1

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W.T. Lin, B.B.M. Shao / Information & Management 37 (2000) 283±295

Table 5
Estimates of the structural-form coefficients of the model
Hypothesis
Coefficient
Sign
OLS
2SLS
3SLS

1
b12
‡
0.334
1.088*
1.269**

2A
b23
‡
0.331
0.340
0.354

2B
b24
‡
0.007
0.042
0.186

6
b26
‡
0.593*
0.608*
0.407*

3A
b32
‡
0.255
1.061
0.871

3B
b34
‡
0.033
0.995
0.181

5
b35
ÿ
0:346
ÿ0.998
ÿ0.024

4A
b42
‡
0.025
1.953
0.922

4B
b43
‡
0.531
1.005
0.986*

7
b47
ÿ
ÿ0.061
ÿ0.212
ÿ0.082

* significant at the 0.05 level; and ** significant at the 0.01 level.

model developed above, 2SLS and 3SLS are more
appropriate than OLS to obtain the estimates of the
structural coef®cients in Eqs. (3.1)±(3.4).
In addition, one can observe that the 3SLS estimates
are more ef®cient than their 2SLS counterparts. This
then suggests that some other contingency variables
were unintentionally omitted from the simultaneous
contingency model, as it is theoretically and practically impossible to consider all contingency variables.
The reduced form (3.6) in matrix formulation or
Eqs. (3.8)±(3.11) can be estimated by using the relationship (3.7) between the structural coef®cients (B
and G) and the reduced-form coef®cients (P). The
estimates of P are given in Table 6 and re¯ect the total
effect of one exogenous contingency variable on each
endogenous variable.

5. Discussion

Table 6
Estimates of the reduced form
P

p10
p11
p12
p13
p20
p21
p22
p23
p30
p31
p32
p33
p40
p41
p42
p43

From the hypothesized directions, it is expected
that the total effects of system impact on system
success, user participation, attitudes, and involvement
(i.e., p11, p21, p31 and p41, respectively) must be all
negative. All the 3SLS estimates match, and all
the 2SLS estimates violate, the expectations. This
serves as a further evidence that 3SLS is more appropriate than 2SLS in estimating the simultaneous
contingency model. Similarly, the total effects of
system complexity and outsourcing on the endogenous variables must be positive and negative, respectively. Again, the 3SLS estimates correspond to,
but the 2SLS estimates fail to meet, the anticipated
directions. Thus, we may conclude that the 3SLS
method is indeed a better choice than the 2SLS
method.

5.1. Effect of user participation on system success

Method
2SLS

3SLS

0.527
0.361
0.000
0.076
0.859
0.332
0.000
0.070
0.578
0.798
ÿ1.591
0.192
ÿ0.018
1.451
ÿ1.599
0.118

1.399
ÿ0.133
3.453
ÿ0.212
1.608
ÿ0.105
2.721
ÿ0.167
2.086
ÿ0.162
3.437
ÿ0.229
2.423
ÿ0.256
5.898
ÿ0.462

One of the main hypotheses of this study is empirically to corroborate the positive in¯uence of user
participation on the successful outcome of system
implementation. Based on the results, Hypothesis 1
is con®rmed.
It should be pointed out that this ®nding does not
come from a hypothesized simple direct causal equation of user participation and system success, estimated by the classical OLS. Instead, it is implied
by the simultaneous contingency model estimated
by the more rigid methodologies of estimation. The
simultaneous contingency model appears to be a better
and more appropriate alternative to examining the
relationship between user participation and system
success.

W.T. Lin, B.B.M. Shao / Information & Management 37 (2000) 283±295

5.2. Simultaneous relationship: user participation,
attitudes, and involvement
The positive effects of user attitudes and involvement on user participation are consistent with our
theoretical expectations. But both are statistically
insigni®cant (and hence Hypotheses 2A and 2B are
not supported by the data).
The in¯uences of user participation and user involvement upon user attitudes are consistent with the
hypothesized directions but the estimates are not signi®cant, and Hypotheses 3A and 3B are not con®rmed.
There is a relatively large and positive association
of user participation with user involvement but is
again not statistically signi®cant and Hypothesis 4A
is not con®rmed. User attitudes have been found to
exert a signi®cant and positive effect on involvement
(b43 ˆ 0.986 from 3SLS which is signi®cant at the 5%
level) and, hence, Hypothesis 4B is supported by the
3SLS estimate.

291

is not signi®cant. Hence, Hypothesis 5 is not supported.
The association of system complexity with user
participation is positive and signi®cant at the 5% level
(b26 ˆ 0.608 from 2SLS and 0.407 from 3SLS) and,
hence, Hypothesis 6 is supported. IS managers ask
users to participate more in systems which are
technically complex, in order to facilitate the development process and increase the likelihood of system
success.
Outsourcing has a negative effect on user involvement but the result is not signi®cant. Therefore,
Hypothesis 7 is not supported. This may suggest that
outsourcing is rarely simple. Which aspects of outsourcing are signi®cant to users is an interesting
research question.
Fig. 2 presents the simultaneous contingency model
along with the hypotheses tested and the structural
coef®cient estimates obtained by 3SLS.

5.3. Effects of contingency factors

5.4. Total effects of exogenous variables on the
jointly dependent variables

System impact as a contingency factor has been
found to have a negative effect on user attitudes but it

Based on the estimates of the structural form and the
reduced form, we are able to compute the total effects

Fig. 2. Hypotheses and coefficient estimates by 3SLS of the proposed model.

292

W.T. Lin, B.B.M. Shao / Information & Management 37 (2000) 283±295

Table 7
Total effects
Jointly dependent variable

System success (X1)
User participation (X2)
User attitudes (X3)
User involvement (X4)

Structural form

Reduced form

2SLS

3SLS

2SLS

3SLS

1.088
0.990
1.058
2.746

1.269
0.947
1.028
1.826

0.437
0.402
ÿ0.601
ÿ0.030

3.108
2.449
3.046
5.180

of exogenous variables on the endogenous variables,
as presented in Table 7.
The structural total effect on a jointly dependent
(endogenous) variable includes the effect of other
jointly dependent variable(s) and the contingency
variable(s) appearing in the same equation, while
the reduced total effect on a jointly dependent variable
is the sum of the effects of all contingency variables
serving as the exogenous variables in the simultaneous
contingency model. The difference between 2SLS
and 3SLS is comparatively larger in the reduced
form than in the structural form; this is because the
reduced form shows the compound effects of all
the exogenous contingency variables on each endogenous variable, while the structural form basically
re¯ects the sum of the direct effects of relevant
variables in the equation corresponding to this endogenous variable.
For system success, the structural total effect (1.088
from 2SLS and 1.269 from 3SLS) is the same as the
direct effect of user participation since user participation is the only dependent variable in the system
success equation.
5.5. Managerial implications
Several managerial implications can be drawn for
IS practitioners. First and foremost, this study con®rms the positive contribution of user participation to
successful system outcomes. Therefore, IS managers
should encourage (or stipulate) active participative
behaviors from users during the system development
process.
Such a need for user participation is especially
important when the systems to be developed are
technically complex. User participation may be promoted as a social process of interaction between users

and designers through which both parties can learn
about each other's expectations and requirements, and
hence resolve their con¯icts [58,59].
Managers should also pay attention to the psychological states of users toward the systems. The
feelings of the users for or against the systems are
interrelated with the importance and personal
relevance users perceive about the systems. User
participation should have more ef®cacy if such
behaviors originate from the underlying favorable
attitudes and spontaneous involvement, instead of
from manager's forcible orders. Therefore, management may want to foster an atmosphere that helps
users perceive the importance of the system and
enhances their favorable attitudes toward the system,
in order to facilitate user participation in the development process.

6. Conclusions
In the design and development process of a new
IS, the effect of user participation on system outcome
is supposedly positive but should not be taken
for granted. Such an effect must be scrutinized by
considering the contextual environment. It is necessary to include the relevant contingency factors that
may affect user participation and system success
both directly and indirectly. However, it is equally
important to notice that the scenario may not be as
sequential. A research model based on the contingency approach and the simultaneity analysis was
proposed and empirically tested to investigate the
proposition that the relationships among the contingent factors are determined simultaneously, rather
than sequentially.
The estimation results af®rm the positive link
between user participation and system success, and
suggest that user participation, user attitudes, and user
involvement form a circular relationship. It implies
that getting users involved in the development process
may improve their attitudes toward the system and
enhance the importance and relevance users perceive
about the system. Furthermore, the empirical results
show that system complexity has a signi®cant effect
on user participation.
We conclude by indicating that the simultaneity or
interdependence methodology is in sharp contrast with

W.T. Lin, B.B.M. Shao / Information & Management 37 (2000) 283±295

the sequential causality methodology and that the
former may be superior in applied MIS research. This
research may answer the call of methodological rigor
in IS empirical studies. We hope that more answers to
this call are forthcoming.

[14]

[15]

Acknowledgements
The authors are indebted to two anonymous referees, the Editor, Professor Edgar H. Sibley, of this
journal, Larry Sanders, and Bonnie Glassberg for their
constructive and valuable suggestions and comments.
The usual disclaimer applies.

[16]

[17]

[18]

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W.T. Lin, B.B.M. Shao / Information & Management 37 (2000) 283±295
Winston T. Lin is Professor in the
School of Management at the State
University of New York at Buffalo. He
received his B.A. from National Taiwan
University and Ph.D. from Northwestern
University, Evanston, Illinois. His current research interests are in the areas of
forecasting, information systems, and
multinational finance. He has published
53 articles in refereed proceedings and
leading academic and professional journals including Journal of the
Association for Information Systems, Communications of the
ACM, Information & Management, Journal of Business &
Economic Statistics, The Financial Review, Journal of Financial
and Quantitative Analysis, International Journal of Forecasting,
Journal of Forecasting, etc. He has presented his papers at 48
national and international conferences. He has been awarded
numerous significant research grants and awards.

295

Benjamin B.M. Shao is Assistant Professor at the School of Accountancy and
Information Management, College of
Business, Arizona State University,
Tempe, AZ. He received a B.S. in
Computer Science and an M.S. in
Information Management from National
Chiao Tung University, Hsinchu, Taiwan. He is currently completing his
Ph.D. in Management Information Systems from the State University of New York at Buffalo. His major
research interests include economics of information systems,
information system security, participative system design, and
distributed and parallel problem solving. His articles have been
or will be published in Computer Journal, Computers & Security,
Journal of the Association for Information Systems, and Information & Management. He has also presented his research work at the
AIS and INFORMS conferences.