RESEARCH CONTRlBVllONS

Management of Computing An Empirical Study of the Gordon B. Davis Editor Impact of User Involvement

on System Usage and Information Satisfaction

JACK J. BAROUDI, MARGRETHE H. OLSON, and BLAKE IVES

ABSTRACT: “User involvement” in information system development is generally considered an important mechanism for improving system quality and ensuring successful system implementation. The common assumption that user involvement leads to s:ystem usage and/or information satisfaction is examined in a survey of 200 production managers. Alternative models exploring the causal ordering of the three variables are developed and tested via path analysis. The results demonstrate that user involvement in the development of information systems will enhance both system usage and the user‘s satisfaction with the system. Further, the study provides evidence that the user‘s satisfaction with the system will lead to greater system usage.

1. INTRODUCTION The process of involving users in the design of manage- men! information systems (MIS] is time-consuming and costly and can politicize the issues surrounding the de- velopment of MIS Ill]. Yet a persistent theme in the MIS literature is the need for user involvement in the analysis and design of computer-based systems. Re-

- 01986 ACM OOOl-0782/86/0300-0232 750

searchers’ reasons for emphasizing user involvement range from the “moral” obligation to involve users in the system design process [29] to the exploration of user involvement as an important antecedent variable in MIS research [31]. A recent review of the literature on user involvement [15] identified over 30 empirical studies where it was a key variable. In general the studies relate user involvement to system quality, sys- tem usage, user attitudes. and user information satisfac- tion (i.e., the users’ satisfaction with the information system and its outputs). The results of these empirical studies. however, are far from conclusive. For example. while eight of thirteen studies of user involvement and information satisfaction found significant relationships [9, 10, 12, 14. 17. 28. 31, 371, only four of nine studies examining system usage and user involvement reported significant findings [I. 19. 23. 371.

The failure to find convergence in the empirical work on user involvement may be a function of serious flaws in the studies identified. First. the operational definition of user involvement is imprecise. Ives and Olson [15] report a number of different mechanisms for involving users; for example, providing users with feed- back from questionnaires [?I]. assigning users to the de-

232 Communications of the ACM March 1986 Volume 29 Number 3

Research Contributions

sign team 1211, and employing formal user liaisons [24]. Additionally. terms such as “user involvement.” “evolu- tionary design, ” “participative design.” and “user influ- ence” are frequently employed to identify the same construct (151. Conceptually, user influence differs from the other terms: influence refers to substantive involvement while participative and evolutionary de- sign may refer to symbolic involvement. If the result of user involvement affects the outcome of the design process, the users may be said to have influenced the process: symbolic involvement, on the other hand, may be ignored by the system designers [21].

Second, the measurement of user involvement and its related constructs is psychometrically weak. The re- liability and validity of the scales employed in the stud- ies are generally not considered. To illustrate, the con- struct of user information satisfaction is measured with only one item in three of the studies [3. 26, 271 and the measurement issue is ignored altogether in two studies [33. 351.

Another problem is that most studies focus on a sin- gle system or, at best, a single organization, restricting the ability to generalize or replicate results, and limit- ing the contribution to our understanding of user in- volvement and information system implementation.

Finally, a major flaw in most of the user involvement studies is their failure to ground their hypotheses in existing theory or to develop new theory to explain the phenomena observed.

The study reported here addresses several of the above problems. Considerable care was taken in the selection and development of instruments. Over 200 individuals from different organizations were included in the study to assure generalizability of results, and the study models are grounded in behavioral theory. These models are described below.

2. THEORETICAL MODELS OF USER INVOLVEMENT User involvement research is typically based on the assumptions that user involvement in the design of an information system leads to increased system usage, more favorable perceptions of system quality, or greater user information satisfaction. Generally, these con- structs are assumed to be indirect indicators of im- proved decision-making performance, which is the ulti- mate, but usually unmeasurable, goal of system imple- mentation [l].

2.1 The Traditional Model Several models of user involvement and its impact on user information satisfaction and system usage have been implied by or explicitly tested in previous re- search [19, 25, 28, 32, 371. The dominant or “tradi- tional” model (see Figure 1 on p. 234) hypothesizes that user involvement leads to increases in both user infor- mation satisfaction and system usage. Presumably,

involvement will lead users to develop a better under- standing of the system, and it will be better tailored to their specific needs. Therefore they will be more inclined to use the system and be more satisfied with it than if they had not been involved in its design.

Six studies have investigated the link between user involvement and system usage. Three [26, 28, 341 found no relationship and three [19, 20, 251 found mixed support.

The evidence is also mixed regarding the relationship between user involvement and user information satis- faction Two studies showed no significant relationship [26. 351. Several studies reported a significant positive relationship [lo. 12, 14, 28, 371. Others found mixed evidence [7, 311.

The expectation that user involvement will increase system usage and user information satisfaction is con- sistent with the theories of participative decision mak- ing (PDM) [22] and planned organizational change [38]. Ives and Olson [15] argue that user involvement can be viewed as a special case of PDM; involvement may lead to improved system quality as well as increased user acceptance, reflected in increased use of and satisfac- tion with the system. Involvement is seen as a neces- sary condition for decreasing resistance and increasing acceptance of planned change [15].

In summary, the empirical evidence is mixed regard- ing the relationships between user involvement, system usage, and user information satisfaction. However, the empirical support which has been found for these rela- tionships is consistent with theories of PDM and organi- zational change.

2.2 System Usage and Information Satisfaction Although the literature is explicit regarding the causal relationship between user involvement, system usage, and user information satisfaction, it is silent regarding the causal relationship between system usage and user information satisfaction. Building on the traditional model of user involvement and its outcomes, two rival models of the relationship between system usage and user information satisfaction emerge (see Figure 2).

In both models, use of the system is assumed to be voluntary.

Model I (Figure 2, p. 234) hypothesizes that user involvement will lead to both system usage and user information satisfaction but as system usage in- creases it leads to increased user information satisfac- tion This model is based on the belief that system use leads users to be more familiar with the system and to discover new uses for it which will, in turn, lead to enhanced user satisfaction with the system.

Model II (Figure 2) proposes that user involvement will also lead to both system usage and user informa- tion satisfaction but that the more satisfied the user is with the system the more he or she will be inclined to use it. This model assumes that as use demonstrates that a system meets a user’s needs, satisfaction with the

March 1986 Voluwle 29 Nulnber 3 Communications of the ACM 233

Research Contributions

User

User ,/2’

involvement \

vb System usage

FIGURE 1. Traditional Model

User ,A

involvement \

v

-‘\ Syst,i, usage

Model I

User

+

HA

/ L%EYZ

User involvement \

1

+

wh System usage

Model II

FIGURE 2. Alternative Models

system should increase, which should further lead to greater use of that system. Conversely, if system use does not meet the user’s needs, satisfaction will not increase and further use will be avoided.

User information satisfaction is an attitude toward the information system while system usage is a behav- ior. Theory regarding the relationships between atti- tudes and behaviors provides support for both models. Fishbein and Ajzen’s [8] model of attitudes and behav- iors suggests that attitudes toward an object (in this case an information system) will influence intentions and ultimately influence behavior with respect to that object (the use of the system or its outputs). This frame- work can be interpreted as supporting Model II (Figure z), that user information satisfaction (an attitude) will lead to system usage (a behavior).

Dissonance theory 181. on the other hand, suggests that behaviors can lead to attitudes. When dissonance with a presently held attitude is created by the per- formance of a contradictory behavior, the individual may change the belief or attitude to remove or reduce the dissonance [8]. For example, individuals who con- tinue to use a system with which they are dissatisfied will experience dissonance. To justify continuing use of the system (assuming usage is voluntary), the individu- als may reevaluate the system more positively to re- duce the dissonance. Dissonance theory thus supports Model I (Figure 2). which suggests that system usage (a behavior) leads to user information satisfaction (an atti- tude).

3. THE STUDY AND RESEARCH METHODOLOGY Data regarding user involvement, system usage, and user information satisfaction were gathered from pro- duction managers in over 200 large manufacturing firms, located in the continental United States. Data were gathered via mail survey.

As data were gathered on a variety of different sys- tems, emphasis was placed on users’ general involve- ment in the design of information systems, their overall use of information systems which support their busi- ness function, and their overall information satisfac- tion. The measurement of general involvement, usage, and satisfaction allows data to be gathered across both systems and organizations and thus enhances the gen- erality of the results.

3.1 Development of Instruments A measure of information satisfaction developed and partially validated by Bailey and Pearson [2, 301 was chosen for the study. Data generated in this study were also used to further validate and modify this instru- ment [IS]. The instrument measures 39 aspects of infor- mation satisfaction using a semantic differential tech- nique. A sample item is shown below.

Timeliness of output information

timely:-:-: : : : : :untimely

reasonable: : : : : : : : unreasonable __----- consistent: : : : : : : : inconsistent -------

punctual: : . : : : : .tardy --.-----’

A measure of system usage was developed specifi- cally for the sample of production managers. A set of activities typically performed or supervised by produc- tion managers was identified from production manage- ment textbooks and handbooks. The list of activities was reviewed by two experts in production manage-

234 Communications of the ACM March 1986 Volume 29 Number 3

ment who suggested minor modifications. A set of ques- tions was then formulated regarding a manager’s past use of information systems to support the identified activities. A sample item is shown below.

Plant (Facilities) Location

How much do you or your subordinates utilize computer support for this activity?

Does not

apply

Not at all Minimally

Extensively

Moderately

As no satisfactory measure of user involvement ex- isted, the authors developed a multiple-item instru- ment to measure user involvement across a number of specific activities and stages in the system development life cycle. The procedures that were followed to de- velop the instrument are described below.

Through a review of the prescriptive literature, the authors identified 35 different activities comprising user involvement. Fifty people working in the field of information systems were then asked to provide a list of “mechanisms for ensuring user involvement in the sys- tem development process.” Half of the experts were professors of information systems in schools of business administration and half were information systems man- agers. The questionnaire was open-ended, to allow the answers to reflect the respondents’ own concept of user involvement rather than the biases of the researchers.

A composite list of activities comprising’user involve- ment was developed from the literature search and the 35 responses (75 percent). Forty-seven distinct activities were identified.

The activities were then classified by the stage in the system development life cycle during which they were performed. The stages, as defined by Davis [6]. are: system definition, system design, and system imple- mentation. There were 27 activities that were appropri- ate for one or more stages and were therefore listed under each appropriate one. The remaining 20 mecha- nisms were listed separately.

Finally, information systems practitioners were asked to rate the significance of the derived activities. This step was intended to eliminate activities representing symbolic involvement, leaving the items which repre- sented true “user influence” over the system develop- ment process. The respondents were asked to what de- gree each activity effectively increased user involve- ment. They were also requested to rate (on a four-point scale from “almost never” to “almost always”) how much that activity was carried out in their own organi- zations.

Based on this feedback. 30 items rated as highly ef- fective mechanisms for increasing user involvement

Research Cmfributiom

TABLE I. Description of Variables Measured

Variable Standard Cronbach’s

Mean deviation alpha

User information satisfaction 27.54 35.34 .97

System usage 14.59 4.50 .74

Prior user involvement 34.75 12.23 .90

were retained for the final instrument. Each item was formulated into a question, addressing whether the ac- tivity had been performed (by users) in the past. A sample question is given below (a complete copy of the questionnaire is available from the authors).

Have you (or your subordinates) developed the cost justification for a new information system?

3.2 Sample Selection and Procedures Participants were selected from a commercially-ob- tained mailing list of production managers of manufac- turing organizations located in the United States. Two separate mailings were made during a two-month pe- riod. A total of 800 questionnaires were sent in the first mailing. To control for common method variance and time effects, the order of questionnaires was varied. In the first mailing, half of the sample received the user involvement questionnaire and half received the infor- mation satisfaction questionnaire. Half of each group also received the system usage questionnaire. Only those subjects who responded to the first mailing were sent the second.

Two hundred subjects completed both sets of ques- tionnaires. Somewhat low response rates were expected as the mailing list was over a year old, the survey was mailed unsolicited without prior knowledge on the part of the respondents, and they were requested to com- plete several relatively lengthy forms on two separate occasions.

For each subject, a total score was derived by sum- ming all questions for current information satisfaction, prior user involvement, and current system usage. The means, standard deviations, and reliabilities (Cron- bath’s alpha) for each of these variables are presented in Table I.

4. RESULTS To test the relationships hypothesized by the tradi- tional model, that user involvement is positively associ- ated with both system usage and user information satis- faction, zero-order correlations were calculated. The

March 1986 Volume 29 Number 3 Communications of the ACM 235

Resenrch Contributions

TABLE II. Correlation Matrix

User System Prior user information usage involvement satisfaction -

System usage -

Prior user involvement .28” -

User information satisfaction

.28” .18’* -

correlations are presented in ‘Table II. The results pro- vide support for the traditional model, as user involve- ment correlated positively and significantly with both system usage and user information satisfaction.

A significant relationship was also found between user information satisfaction (and system usage. To de- termine the causal ordering of this relationship the ri- val models were tested using path analysis [4, 181. Path analysis is a robust data analytic technique that allows the testing of causal models using cross-sectional data. Although path analysis can determine if the relation- ships observed in the data are consistent with the model. causality can only be inferred as the data are cross-sectional rather than longitudinal.

Model I (Figure 2) was tested first. Path coefficients were calculated and tested for statistical significance at the .05 (two-tailed) level. Of the three paths, the path between user involvement and user information satis- faction was found not to be significant at the .O5 level. This path was removed from the model by employing theory trimming [13]. The path coefficients for the trimmed model were calculated and tested and all were found to be significant at the .05 level. The trimmed model and its corresponding beta coefficients are shown in Figure 3.

The trimmed model suggests that user involvement leads directly only to system usage and impacts user information satisfaction indirectly via system usage. To test how well the trimmed model describes the ob- served relationships in the data, the correlation matrix was rebuilt. This was done by calculating and summing the direct, indirect, and common cause effects between each pair of variables. A detailed description of each of these effects can be found in Stumpf and Hartman [36]. The reconstructed correlation matrix is presented in Table III.

The difference between the original and recon- structed correlation between user involvement and user information satisfaction is .ll. Billings and Wroten [4] argue that the difference between the original and reconstructed correlation should be no more than .05 if the model accurately captures the relationships in the data. Therefore, Model I (Figure 2) is rejected based on this analysis.

Model II was then tested using the same procedure. Based on the preliminary analysis all of the paths were found to be significant at the .05 level: accordingly none of the paths were trimmed. The path model and path coefficients are presented in Figure 4.

This path model suggests that user involvement leads directly to user information satisfaction and has both direct and indirect effects on system usage, operating indirectly via user information satisfaction. The results of rebuilding the correlation matrix are presented in Table IV.

The differences between the original and recon- structed correlations are all within the acceptable .05

level [4]; therefore a final test of the fit of Model II was performed. To determine if any variables had been omitted that would make the observed relationships spurious, the residuals were calculated and correlated according to recommendations outlined in Billings and Wroten [4]. None of the correlations were significant or large, thus providing further evidence to support Model II.

The analysis suggests that Model II accurately de- scribes the relationships in the data while Model I does not. The analysis strongly supports the traditional hy- pothesis that user involvement leads directly to both user information satisfaction and system usage, while

User information satisfaction

1‘ Zvement \ ,stj~~sage

FIGURE 3. Model I Path Coefficients

User information

Evement < :~~~e

FIGURE 4. Model II Path Coefficients

236 Communications of’ the ACM March 1986 Volume 29 Number 3

Research Contributions

TABLE III. Reconstructed Correlation Matrix-Model I

User involvement

Original Indirect Direct %r conelation R-mputed :effect effect effect Difference

+ System usage .28 .28 .oo .28 .oo .oo

System usage + User information

satisfaction .28

User involvement + User information

satisfaction .18

.oo .28 .oo

.07 .oo .oo

TABLE IV. Reconstructed Correlation Matrix-Model II

Original cmlatbn Recomputed

common Indirect D&t CWSC!

effect effect effect Difl erence

User involvement + System usage .28 .28 .04 .?A .oo .oo

System usage + User information

satisfaction .28

User involvement + User information

satisfaction .18

.oo .24 .04

.oo .18 .oo

further supporting the hypothesis that user information There are, however, a number of significant issues satisfaction leads to system usage. remaining. Some limitations of the research are:

5. CONCLUSIONS The study presented in this article takes an important step forward in the examination of user involvement and its impacts on user information satisfaction and system usage. Even though the data were collected in a cross-sectional survey, attention was paid to the causal ordering of the variables.

Care was also taken to address many of the methodo- logical problems existing in prior research. Generaliza- bility across organizations and systems was enhanced by including over 200 users in the same function from many different organizations. User involvement and system usage were measured with rigorously-developed instruments, while information satisfaction was meas- ured by an instrument with demonstrated reliability and validity [2, 301. Finally, common method variance was reduced by using separate questionnaires for each of the major variables and randomly ordering their presentation. In total, the careful construction of this study permits the tentative conclusion that user in- volvement in system development leads to increased user information satisfaction and increased system usage. Additionally, this study provides some of the first empirical evidence that user information satisfac- tion may lead to system usage.

1. The study was limited to a certain level (i.e., mid- dle-level management) and type (i.e., production man- ager) of user. The research should be replicated for other levels and types of user.

2. Perceptual rather than objective measures of sys- tem usage were utilized.

3. The measure of user involvement, although ad- dressing activities generally regarded as substantive, did not clearly differentiate the degree of actual user influence in the design process.

4. The concept of user involvement was dealt with in total. A logical extension of this study is to focus on specific types of user involvement to determine which types, under what conditiops, have the greatest effect on system usage and information satisfaction.

5. Although path models can provide evidence for the causal ordering of variables, longitudinal studies are needed to more rigorously test the causal hy- potheses implied by this analysis.

6. An alternative explanation for the reported results is that some other variable influences user involve- ment, information satisfaction, and system usage. For

March 1986 Volume 29 Number 3 Communications of the ACM 237

Research Contributions

example, informal.ion systems management may insist on user involvement while simultaneously producing high quality systems. Experimental research designs are required to eliminate these environmental factors when examining the variables of interest.

Information systems practitioners may consider this study as reasonably strong ju:stification for actively in- volving users in system development activities. Until further research is conducted, however, practitioners will need to continue to rely on experience, intuition, and the prescriptive literature for guidance in deter- mining the appropriate type and degree of user involve- ment required.

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CR Categories and Subject Descriptors: D.2.6 [Software Engineer- ing]: Metrics-performance measures; D.2.9 [Software Engineering]: Management; H.1.2 [Models and Principles]: User/Machine Systems- human factors: K.6.1 [Management of Computing and Information Sys- tems]: Project and People Management-systems development: K.6.3 [Management of Computing and Information Systems]: Software Man- agement-software deuelopmenf

General Terms: Experimentation. Human Factors, Management. Measurement

Additional Key Words and Phrases: information satisfaction, user involvement, system usage

Received l/85; revised 5/85; accepted 7/85

Authors’ Present Addresses: Jack J. Baroudi and Margrethe H. Olson, Department of Computer Applications and Information Systems, Cradu- ate School of Business Administration, New York University. 100 Trinity Place, New York, NY 10006. Blake Ives, Program in Computers and Information Science, Dartmouth College, Nathan Smith Building. Hano- ver. NH 03755.

Permission to copy without fee all or part of this material is granted provided that the copies are not made or distributed for direct commer- cial advantage, the ACM copyright notice and the title of the publication and its date appear. and notice is given that copying is by permission of the Association for Computing Machinery. To copy otherwise, or to republish. requires a fee and/or specific permission.

23% Communications of the ACM March 1986 Volume 29 Number 3