The effects of communication monitors on user satisfaction

Informorion Processing & Monagemenr Vol. 22, No. 4, pp. 345-351, 1986

Printed in Great Britain. 0306-4573/86 $3.00 + .OO

0 1986 Pergamon Journals Ltd.

THE EFFECTS OF COMMUNICATION MONITORS ON USER SATISFACTION

AVI RUSHINEK Associate Professor, Department of Accounting, University of Miami,

P.O. Box 248031, Coral Gables, FL 33124

and

SARA F. RUSHINEK Associate Professor, Department of Management Science, and Computer Information Systems,

University of Miami, P.O. Box 248237, Coral Gables, FL 33124

fReceived 10 December 1985)

Abstract-This study analyzes the influence of communication monitors (CM) on overall computer user satisfaction as determined by multiple regression. The variables user expectations, manufacturers and vendors are found to be the most significant variables affecting overall satisfaction, whereas the variables number of systems represented and not using communication monitors have the least significant effect in the user satisfaction model. The implications for improvements in CM by updates to a database are discussed

and analyzed.

INTRODUCTION TO COMMUNICATION MONITORS (CM)

RESEARCH AND THEORIES

Data communication is an integral part of the overall computer system. Data communications systems provide for the transmission of data by combining the capabilities of the computer with high-speed electronic communications. Communication monitors (CM) con- stitute an area of data communications that is important to the user. In the literature, it has been shown that a communications monitor can be a manufacturer’s package, an out-

side vendor package or can be a home grown system. Despite the recent growing importance of CM, their contribution to overall satisfac-

tion has not been computed. This study quantifies these contributions and summarizes CM factors of the various computer systems.

Both practitioners and theorists have addressed CM and its various components. Var- ious authors have discussed the use of communications processors, front-end processors and programmable communications controllers [l-3]. In an attempt to help the user avoid the acquisition of inappropriate equipment, the literature has provided approaches and guidelines for its selection [4,5].

Surveys of users of communications processors have indicated that users are satisfied with reliability and throughput but are unhappy with the maintenance, software and technical support that they receive [6,7]. To this date, however, the exact impact of these attrib- utes on overall users’ satisfaction remains in question.

LITERATURE REVIEW AND MAJOR OBJECTIVES

This paper describes the results of a system rating study in which the users were asked to respond to many CM questions. These questions, (independent variables) based on the literature, are considered the primary determinants of overall user satisfaction, the dependent variable. The present study relates user satisfaction to the CM variables with the use of multiple regression analysis.

Several confounding variables may also affect user satisfaction. These variables include expectations, type of system used and its popularity. The effects of consumer expectations

346 A. RUSHINEK and S.F. RUSHINEK

on product satisfaction have been discussed extensively in the consumer behavior literature [8-l I]. Studies have shown that unrealistic expectations lead to the dissatisfaction of consumers. The postulates from the consumer behavior theory have also been extended to the computer industry where expectations influence the attitudes of users. Unrealistic expectations about what computers can do contribute to potential difficulties and dissatisfaction of the users [12-151. The degree of meeting user needs and expectations has definitely an effect on user satisfaction from various systems [16].

One of the more controversial variables attributed to user satisfaction is the type of computer used. Some studies have failed to make a clear distinction among micro, mini, and mainframe computers [ 171. More recent articles have made a distinction among computer types, especially focusing upon mini/microcomputers [ 18-211. They have inferred that users favor microcomputers over mini and mainframe computers, due to their greater per- ceived sense of control, simplicity, affordability, portability, and privacy. Based upon these past studies, the type of computer should affect user satisfaction.

In general, the power and capabilities of computer systems have improved; memory costs have gone down and performance distinctions among systems have blurred [22]. This has made it even more controversial and difficult to differentiate the effects of micros, minis and maxis on user satisfaction.

Setting generally accepted computer standards remains a major controversy in the computer industry. For example, few standards are available in the micro computer market [23]. Under such circumstances, the most popular becomes a de facto standard. Therefore, the number of users, number of computer systems, and their average system life (measures of popularity) are considered important determinants of user satisfaction. The assumption is that the more popular the system, the better the CM is, and therefore, the greater the increase in user satisfaction.

The computer system buyers should look at advantages and/or disadvantages in cost, storage, reliability, etc. Another way to approach this problem is simply going by the popularity and the size of the vending firms (well-established and larger firms vs. newer

and smaller firms) [24]. In other words, one may expect the most popular system (especially within a certain profession or application) to be indicative of the quality of this system. Therefore, the present study includes several measures of system popularity, number of systems, number of users, and system life in the model.

Sources of information on computers are very valuable to computer buyers and sellers. This information is available from textbooks and periodicals providing guidance on computer selection [24,25]. Choosing the appropriate system from the bewildering array of machines, vendors, and configurations can be a frustrating and expensive experience for both buyers and sellers [26].

SURVEY METHODOLOGY AND DATA COLLECTION

This survey was based on results received from questionnaires mailed to a very care- fully controlled nth sampling from specific subsets of computer users lists. A total of 15,218 questionnaires were sent to computer users. The specific subsets were identified and quali- fied by a panel of experts. In an effort to improve the response rate and thereby increase the statistical validity, the users were contacted twice; a first request was followed weeks later by a second request. The response rate was 32070, representing 4,597 users, who responded to 4,870 questionnaires (some users evaluated more than 1 computer model and filled out more than 1 questionnaire).

Judges invalidated 379 responses, including 178 users who rated two different computers at the same time; another 43 users rated more than 2 different systems simulta- neously. Datapro (1984) batched the remaining 4,448 valid returns by vendor, model, users, and computer types [mainframes or plug compatible mainframe computers (maxis), minicomputers and small business computers (minis), and desk-top personal and microcomputers (micros)] as follows:

Effects of communication monitors 347

Subjects Maxis Minis Micros Total

Users . . . . . . . . . 1,919 + 2,192 + 337 = 4,448

Computers. . . . . . 67 + 93 + 19 = 179

Vendors . . . . . . . . 10 + 28 + 17 = 55

Each questionnaire allowed the user to rate one system. The recipient was encouraged to reproduce the form if he/she wished to rate more than one system. For each system the responses were averaged and recorded, Labels were used as initial validation vehicles and for identification and elimination of duplicate returns. Recipients were asked to summa- rize their experiences with computer systems currently being used and to answer questions about their systems.

METHODS AND PROCEDURES

A total of 179 computer systems were surveyed. The present authors coded and stored the responses to the questions (variables) on the computer (see variable legend). The data were tested for validity and consistency. For example, the percentage values were checked for the range between 0 and 100. Nonresponse bias was evaluated with an F-test and found to be insignificant.

Forward step-wise multiple regression analysis was used in this study to determine the relationship between the overall satisfaction (dependent) variable and the CM (independent) confounding variables. Some of the assumptions for multiple regression are: (1) The rela- tionships among the variables are linear and additive, (2) The variables have a multivariate normal distribution, equal variance, no multicollinearity, and no autocorrelation [27,28].

These assumptions have been met by the present sample.

HYPOTHESES

Regression analysis is applied to the present sample in order to generalize to the entire

population. The procedure is used for both:

1. Estimating the population parameters from the sample regression statistics, ranking them and

2. Testing the statistical hypotheses about the population parameters.

Three hypotheses are tested:

1. The “overall” test for goodness of fit of the regression equation, 2. The test for each regression coefficient, and 3. The test for subsets of the regression coefficients.

RESULTS AND DISCUSSION

The dependent variable (overall satisfaction) is regressed over the criterion (independent) variables. This is done by a stepwise procedure in a manner which provides consid- erable control over the inclusion of independent variables in the regression equation [29-301.

Table 1 presents the statistics used for the overall test for goodness of fit for step No. 10 of the regression model. This step was selected because each variable added to the model increased the multiple R of the model, while having an overall F value statistically significant at the .Ol level. This table shows the multiple R, R squared, the standard error and an analysis of variance (ANOVA) for the regression model.

According to these tests the authors conclude that the sample R square of 0.52 indicates that 52% of the variation in overall satisfaction is explained by these independent variables. Standard error or estimate at the final step is 8.2. This means that, on the average,


Table I. Multiple regression. Overall significance test for goodness of fit and analysis of variance (ANOVA)

=====:=================I===I=======:=========================================

Analysis of Sum of Mean Multiple R.... .52 Variance DF squares square

R Square... . . . .27 Regression.. . . . 10 4199.17 419.92

Adjusted R square........ .23 Residual....... 168 11285.44 67.17

Standard error.........8.20 Critical F..... 2.32 < F Value.... 6.25’

* R-Square is significant at the .Ol level

predicted overall satisfaction will deviate from the actual scores by 8.2 units on the overall satisfaction scale.

The relative importance of each of the predictor or independent variables on the predicted or dependent variable is described in Table 2. This relative importance is described by the BETA, the change in satisfaction, due to one standard deviation change in the predictor (criterion) variable value.

According to Table 2, the following model describes the overall satisfaction as a func- tion of the criterion predictor variables, in descending order of their BETA values (relative importance):

Overall Satisfaction = .416 * (User Expectations) + .396 * (Manufacturer) + .253 * (Vendor) + 218 x (Home Grown) - .145 * (Mainframe) - .13 1 * (System Life) + .123 *(No. Users) - .072 * (No. Systems) + .062 * (Not Using CM) + .054 * (Mini)

The ranking of the independent variables affecting the overall satisfaction reveals some interesting results. First, it appears that the variables User Expectations, CM obtained from the manufacturer of the system, CM obtained from an outside vendor, and Home Grown CM (ranked I, 2, 3 and 4) have the strongest effects (the largest BETA values) on the dependent variable. Contrary to the literature, the factor indicating popularity (Number of users) is of lesser importance.

Based on the positive or negative values of the BETA coefficients, the effects of the independent variables upon the dependent variable, the overall satisfaction, can be classi- fied as either having expanding (positive) or contracting (negative) effects. The variables Manufacturers of Communication Monitors, User Expectations, Vendors of Communi- cation Monitors, Mini Computers, Home Grown Communication Monitors, Number of Users and Not Using Communication Monitors have expanding effects on the overall satisfaction, while System Life, Number of Systems, and Mainframes have a contracting effect. The negative coefficient of the system life variable may be explained by the rapid techno- logical changes that occur. The longer the system life, then the greater the technical obsolescence. This obsolescence may account for the decrease in user satisfaction. Another negative coefficient, mainframe, may be explained by the increasing popularity and capabilities of minis and micros.

When multiple regression is used as a stepwise procedure for adding independent variables in the equation, the variables are included according to their partial correlations. Vari- ables not included in the regression are given in Table 3. As can be seen, the variable Micro was not included since it did not contribute to the multiple R and the overall F-value of the regression model. The variable Micro has a coefficient very close to 0; therefore, it appears in Table 3. Due to the mutually exclusive nature of computer types, microcom-


Table 2. Variables in the equation. Significance test for specific coefficient in the regression equation

====================I=======_========I=======================================

l * ** Std .error Variable B BETA Rank of B F

1.

2.

3.

4.

5.

6.

7.

8.

9.

10.

User Expectations ....

Manufacturers of CM . .

Vendors of CM ........

Home Grown CM ........

Mainframe ............

System Life ..........

No. Users ............

No. Systems ..........

Not Using CM .........

Mini .................

(Constant) ...........

.243+0

.920-l

.840-l

.868-l

-2.828+0

- * 200+0

2.614+0

-1.363+0

.204-l

1.003+0

49.31296

.416 1

.396 2

.253 3

.218 4

-. 145 5

-.I31 6

.123 7

.072 8

.062 9

.054 10

.023

.029

,032

2.726

.103

9.820

8.754

.032

2.215

36.958*

16.261*

8.662*

7.382*

1.077

3.782*

.07 1

.024

.404

.205

l

l *

l **

This F value is significant at the .05 level.

Ranked in descending order of contribution to the explained variance (R-square change - Table 4)

Ranked according to BETA, which indicates change in satisfaction due to one standard deviation change in the respective variable.

puters did not contribute any further information to the regression model since mainframes and mini-computers were already included.

Table 4 shows the significance test for specific coefficients of the model. The coefficients in Table 4 show the R-square (RSQ) contribution to the model due to the addition of each variable. The RSQ change determines the inclusion sequence.

SUMMARY, CONCLUSIONS AND IMPLICATIONS

In summary, the multiple regression has been used to study the dependence of overall satisfaction with computer systems as it relates to several CM independent variables. The overall significance tests of the goodness of fit of the model have been conducted. The multiple correlation coefficient was found to be 0.52, and the null hypothesis stating that the correlation equals 0 has been rejected.

The variables were rank ordered according to the BETA values. User Expectations,

Table 3. Variables not in the regression equation ==------:==:===========l====_=c===============================================

Variable BETA In Partial Tolerance F

Micro* l ** l ** l ** l **

* Statlstlcs which cannot be computed are printed as l **


Table 4. Multiple regression summary table: Significance test for subsets of the model =====:1==11===1==1==3==========2-============================================

Variable hltiple R RSQf Simpl.

R Square Change R B BETA

User Expectations ... .361 .131 .131 .361 .243+0

System Life ......... .403 .162 .032 -.164 -.200+0

Manufacturers of CM. .427 .I82 .020 .I23 .920-l

Mainframe ........... .460 .211 .030 -.061 -2.828+0

Vendors of CM ....... .486 .236 .025 .072 .840-l

Home Crown CM ....... -516 .266 .030 .006 .860-l

No. Users ........... .518 .266 .003 .I00 2.614+0

Not Using CM ........ .520 .270 .002 -.054 .204-l

Mini ................ .521 .271 .OOl .061 1.004+0

No. Systems ......... .521 .271 .ooo .I03 - 1.363+0

(Constant) ............................................. 49.313+0

.416

-.131

.396

-.145

.253

.218

.123

.062

.054

-.072

l Primary key for forward step-wise inclusion of criteria variables

Manufacturers of CM, and Vendors of CM were ranked the highest, while Minis, Not Using CM, and Number of Systems ranked lower. The variables, CM obtained from the manufacturer of the user’s system, CM obtained from an outside vendor, and home grown CM (ranked 1, 2, and 3) all had a positive effect on user satisfaction. Manufacturers of CM had the largest positive effect of these three variables. The higher positive effect that this variable had on user satisfaction could be a direct result of the user’s familiarity with his computer system as opposed to, for example, CM obtained from an outside vendor with whom the user has had little contact. In addition, the user may have more confidence in CM provided by the manufacturer of his system than home grown CM or CM obtained from an outside vendor.

The implications of the present study are many. The overall satisfaction of computer systems can be measured by answering certain questions. The resulting information can be very useful to computer users, buyers, and sellers. Buyers can compare different computers (cross-sectional) and thus can calculate the overall satisfaction they would derive by buy- ing a given system. This way buyers can cognitively maximize their satisfaction.

Those providing CM to the user (both outside vendors and manufacturer of the user’s system) can maximize the level of sales by controlling and promoting the CM variables which increase the overall satisfaction of their products. Thus, they would like to incor- porate some features to improve the overall satisfaction. This could lead to new and better CM by specific feedback from the users. For example, vendors may provide their users with a toll-free, hot-line number for communications monitors.

This data could be used as a marketing tool for products if the systems have the features (variables) which were ranked higher. The high-standing in user satisfaction studies can be advertised. These kinds of advertisements could give an advantage over competi- tion. Those involved in the home grown CM can attempt to add or enhance the features which were ranked higher in order to make the CM more attractive to the user.

REFERENCES

1. Bergman, M. “Communications Processors and Data Network Efficiency.” Office, 96; 1983. 2. Stiefel, M. L. “Front-end Processors.” Mini Systems, 58-62; 1977.


3. Farmer, D. “Programmable Communications Controllers.” Computer Communications, 215-20; 1981. 4. Frisch, I., Kaczmarek, R.; Occhiogrosso, B. “7 Steps to Picking the Best Communications Processor.” Data

Communications, 25-37; 1976. 5. Seaman, J. “Putting Data Communications Hardware to Work.” Computer Decisions, 114-l 16; 1982. 6. Schultz, B. “Communications Processors Rated High.” Computerworld, 29-30; 1979. 7. “Communications Processors: Tech Support Found Lagging.” Data Communications, 76-77; 1984. 8. Resnick, A. J.; Harmon, R. R. “Consumer Complaints and Managerial Response.” Journal of Marketing,

47(l): 86-97; 1983. 9. Juster, T. F. “An Expectational View of Consumer Spending Prospects.” Journal of Economic Psychology,

l(2): 87-103; 1981. 10. Taylor, J. L.; Durand, R. M. “Effect of Expectation and Disconfirmation on Postexposure Product Evalu-

ations.” Psychological Reports, 45(3): 803-810; 1979. 11. Olshavsky, R. W.; Jaffe, B. L. “Responsiveness of Consumer Expectations and Intentions to Economic Fore-

casts: An Experimental Approach.” Review of Economics and Statistics, 63(2): 98-102; 1981. 12. Good, R. E.; Jenkins, K. M. “Managing with MicroComputers.” Business, 33(l): 37-43; 1983. 13. Scannel, T. “Singer Rated Tops in Mainframe Survey/Users Find Software Support Problem Areas.” Com-

puterworld, 16(23): 56-63; 1982. 14. Stair, R. M. “Using In-House Computer: Some Basic Tips.” Association Management, 33(10): 143-144; 1981. 15. Faerber, L. G.; Ratliff, R. L. “People Problems Behind MIS Failures.” FinancialExecutive, 84(4): 18-24; 1980. 16. Grilz, A. F. “Designing a Successful User Computer Dialogue.” Computerworld, 15(5): 13-16; 1981. 17. Gates, P. 0. “How Can Accountants and Auditors Help Prevent Computer Fraud?” The Government’s

Accountant Journal, 27: 10-15; 1978. 18. Claret, J. “Microtechnology: Friend or Foe. 7” Management Accounting, 18-19; 1982. 19. Bates, A. “Choosing a Micro? Here’s a Cautionary Tale.” Accountancy, 98-101; 1982. 20. Farmer, D. F. “Comparing the 4341 and M80/42.” Computerworld, 9-20; 1981. 21. McGrath, M. E. “How to Cope with the Microcomputer Revolution.” Practical Accountant, 46-49; 1982. 22. Sample, R. L, “Minis-Moving Beyond the Small Business User.” Administrative Management, 58-64; 198 I. 23. Canion, R. “Few Standards Available in Micro Market.” Computerworld, 17(13): 9-14; 1983. 24. Cheney, P. H. “Selecting, Acquiring and Coping with Your First Computer.” Journal of Small Business Man-

agement, 43-50; 1979. 25. Turney, P. E.; Laitala, P. H. “A Strategy for Computer Selection by Small Companies.” Managerial Plan-

ning, 24-29; 1976. 26. Barcus, S. W.; Boer, G. B. “How a Small Company Evaluates Acquisition of a Mini-Computer.” Manage-

ment Accounting, 13-23; 1981. 27. Kerlinger, F. N.; Pedhazer, E. Multiple Regression in Behavioral Research, New York: Holt, Rinehart and

Winston; 1973. 28. Overall, J. E.; Klett, C. Applied Multivariate Analysis, New York: McGraw-Hill; 1973. 29. Theil, H. Principles of Econometrics, New York: John Wiley; 1971. 30. Gordon, R. A. “Issues in Multiple Regression.” American Journal of Sociology, 73: 592-616; 1968.

APPENDIX A

Variable legend

I. Number of computer users sharing the system. ................. (No. Users) 2. Number of computer systems at your site. ................... (No. Systems) 3. Average life of computer systems in months. ................. (System Life) 4. Microcomputer based systems. ................................... (Micro) 5. Minicomputer based systems. ..................................... (Mini) 6. Mainframe computer based systems. ........................ (Mainframes) 7. Systems meeting user expectations. ..................... (User Expectations) 8. Communication monitors obtained from outside vendors. .. (Vendors of CM) 9. Communication monitors obtained from manufacturer

of user’s system. ................................. (Manufacturers of CM) 10. Home grown communication monitors. ................ (Home Grown CM) 11. Not using communication monitors ....................... (Not Using CM)

Documents

The effects of communication monitors on user satisfaction