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Some effects of menu characteristics and userpersonality on performance with menu-driveninterfacesRUDY VAN HOE a , KAREL POUPEYE a , ANDRÉ VANDIERENDONCK a & GEERT DE SOETE aa Department of Experimental Psychology , University of Ghent , Henri Dunantlaan 2, Ghent,B-9000, BelgiumPublished online: 26 Apr 2007.

To cite this article: RUDY VAN HOE , KAREL POUPEYE , ANDRÉ VANDIERENDONCK & GEERT DE SOETE (1990) Some effects ofmenu characteristics and user personality on performance with menu-driven interfaces, Behaviour & Information Technology,9:1, 17-29, DOI: 10.1080/01449299008924222

To link to this article: http://dx.doi.org/10.1080/01449299008924222

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BEHAVIOUR & INFORMATION TECHNOLOGY, 1990, VOL. 9, No.1, 17-29

Some effects of menu cbaracteristics and user personality onperformance witb menu-driven interfaces

RUDY VAN HOE, KAREL POUPEYE,ANDRE VANDIERENDONCK* and GEERT DE SOETE

Department of Experimental Psychology, University of Ghent,Henri Dunantlaan 2, B-9000 Ghent, Belgium .

Abstract. User performance with menu-based systems was studied. Threeexperimentsconcerning the effects of menu structure, menu breadth, menu systemdepth, several support facilities and user personality characteristics on userperformance, measured in terms of search time and accuracy, are reported. Themain findings are that performance is affected by menu structure, the depth of themenusystem,the presenceofescapefacilities, the structure of thealternatives withinthe menu, some methods of informative support and certain personalitycharacteristics. Some expensivesupport facilities are found to have no effect. Theresults are discussed in relation to problems of menu interface design.

1. IntroductiooAlthough menu interfaces are very useful for novices, it is still possible that certain

menu-based interfaces are easier to use than others. The purpose of this paper is todiscuss data obtained from psychological experiments designed to resolve somecontroversies existing in the literature about how a menu system can be bestimplemented. In this paper, the issues of breadth versus depth, of search informationsupports and of on-line help information are studied. In addition, the importance ofsome personality factors is assessed.

2. Experiment 1: depth versus breadth of menu systemsOne possible problem concerns the optimal combination of breadth and depth. The

depth of a menu system is defined as the number of choices one has to make to execute acommand or action. The breadth of a menu is the number of alternatives on which eachchoice is based. For example, if there are 64 possible commands, it is not feasible topresent all 64 alternatives at once. One possibility is to construct a two-level menusystem, with eight choices on the first level and eight on the 'second level. The completesystem consists of nine different menu screens; the breadth is 8 and the depth is 2.Another possibility, at the deep end of the depth continuum, is to construct a six-ievelsystem with two alternatives at each level. Such a system would consist of 63 differentscreens and is probably not very useful, because of the load on the system and the timeneeded to select a command.

This example demonstrates that the time required to perform a complete action willtend to be shorter when there are fewer levels. With an increasing number ofalternatives per level, the time to make a decision will increase. It may be assumed,therefore, that there is an optimal trade-off between breadth and depth.

• Send correspondence to Andre Vandierendonck.

0144-929X/90 S){)()© 1990 Taylor & Francis LId

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18 R. Van Hoe et al.

Other considerations need to be taken into account as well. With practice, the usermemorizes information about the system. In this respect, Shneiderman (1980)suggeststhat as far as novice users are concerned, transfer to long-term memory would befacilitated if the 'units of information' were presented one at a time. This is an argumentfavouring depth to breadth in the implementation of menu-based systems. Simpson(1982)contends that the major advantage of menu selection dialogues is that little or nouser long-term memory is required: the system guides the user. The load on memorycan be minimized by utilizing the dimension of breadth.

The empirical evidence reported in the literature is not conclusive. Miller (1981)compared a number of menu systems, all leading to 64 possible targets but differing inthe number oflevels (1, 2, 3 or 6).On the basis ofthese findings one would recommendmenus of intermediate breadth and depth in menu design. Snowberryet al. (1983) onthe contrary, found evidence supporting Simpson's hypothesis. However, theseexperiments suffer from some methodological shortcomings. The experimental taskwas not very realistic: a set of 64 common English words served as search targets in allconditions, and the experimental factors of breadth and depth were not statisticallyindependent. Therefore, it is difficult to identify the main and interaction effectsof eachfactor.

Snowberry et al. (1983) used two versions of the broadest menu: one in whichcategorical relationships between words were maintained by presenting all members ofa category in the same area of the display, and one in which the location of the wordswas randomly determined. With the randomized version, Miller's results werereplicated. In the categorized version, search time and accuracy improved as depthdecreased and breadth increased. Although an interpretation in terms of breadth anddepth seems plausible, it should be noticed that the organization of the alternativeswithin the menu seems to interact with breadth and depth.

Because of these shortcomings, an experimental test of the two alternativehypotheses concerning the optimal depth-breadth trade-off is required, taking intoaccount the possibility that an optimal breadth-depth trade-off depends on thestructure of the menu. For these experiments, a small-scale simulation of a databasesystem was implemented. The simulated database contained information about booksin a library. The subject's task consisted of retrieving the number codes of some books.The database contained for each book the author name, the publication date, the title,the name and location of the publisher etc. Thus, the entries of the menu systemsconsisted of discipline names, author names, dates etc.

Two data models frequently used in database design - the hierarchical and therelational data model- were used to implement a realistic 'manipulation of the menustructure. In the hierarchical menu system, individual items were grouped intocategories, and the categories were organized into tree structures. In the relationalmodel, data items were organized into relations. The linkage between relations wasaccomplished by their common attributes. In our operationalization of a relationalmenu model, the individual items consisted of a linkage between two categories.

2.1. MethodNinety-six first-year students enrolled in the Faculty of Psychological and

Educational Sciences at the University of Ghent participated in the experiment forcourse requirements. They were randomly assigned to the cells of a 2 (menu structure:hierarchical vs.relational) x 4 (depth: 2, 3,4 or 5) x 3 (counterbalancing of presentationorder) factorial design with four replications.

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Menu-driven interfaces 19

Table 1. Structure and number of frames in the menu systems as a function of depth andbreadth variations.

Breadth type

Depth Increasing Peaked Decreasing

2 4-16 (5) 8-8 (9) 16-4(17)3 2--4-8 (II) 4-8-2 (37) 8-4-2 (41)4 2-2-4-4 (23) 2-4-4--2 (43) 4-4-2-2 (53)5 2-2-2-2-4 (31) 2-2-4-2-2 (55) 4-2-2-2-2 (61)

In addition to the factors of structure and depth, the breadth of the menus wasvaried as a within-subjects factor with three levels: increasing (the deeper menu levelsare also the broadest, 4-16, 2-4-8, 2-2--4-4 and 2-2-2-2--4), peaked (the intermediatelevels are the broadest, 8-8, 4-8-2, 2--4-4-2 and 2-2-4-2-2), decreasing (the higherlevelsare the broadest, 16-4, 8-4-2,4--4-2-2 and 4-2-2-2-2). The order of presentationof the three breadth levels across the tasks was counterbalanced to control for learningand fatigue effects.

Table I displays all the different structures that have been used, as well as the totalnumber of different frames each menu system consists of. The same structures havebeen used in the hierarchical and relational implementation of the system. Theadvantage of this design is that the 24 different menu systems used were of comparablecomplexity: all systems interfaced the same database with 64 records. The menusystems differed from each other in the clustering of these items into categories and inthe successive decisions that had to be made. For example, a 4-8-2 hierarchical menusystem consisted of a top level with four discipline names (attitude and motivation,learning and thinking, intelligence and personality), an intermediate level with eightauthor names and a bottom level with two publication dates.

The clustering ofthe records reflecting a relational database organization was lessstraightforward. In the 4-8-2 case, for example, at the first level, choices consisted of acombination of author name (first or last half of alphabet) and publication date (beforeor after 1960). At the intermediate level the choice was further narrowed down to aspecific author name and date, and at the bottom level a decision between two topicsremained.

Except for the top level, each menu contained an alternative to return to theprevious menu. In this way, it was possible to correct errors. The absence of an escape­to-the-top facility was an intentional feature of the design. This has the advantages that(a) the number of steps needed correctly to locate a specific book directly reflects thenumber of erroneous decisions on the path through the menu system, and (b) it allowsan exploration of the type of ,errors that are made.

Performance was measured as the time needed to make one choice (decision time),the total time needed to reach the correct end-point (search time) and the number ofunnecessary steps performed to retrieve the desired information (accuracy).

2,2. ProcedureThe experimenter welcomed the subjects and explained the experimental task.

Subjects were then seated at a terminal of an Altos ACS-68000 computer running underthe Unix™ operating system. They had a booklet that contained the tasks they had toperform. For each task, the computer waited until the subject was ready to proceed.

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20 R. Van Hoe et al.

The top-level menu was presented, and the subject could go on to make the choicesneeded to locate the information requested. On arrival at the end-point, the subject wasgiven the code number of the book, which he or she wrote down on a list. In each of thethree menu systems, the subject had to retrieve three books that were present in thedatabase.

2.3. ResultsDecision time was averaged across screens and tasks and was subjected to an

ANOVA mixed model with breadth as within-subjects factor. The main effects ofstructure (F(l, 72)= 16'18, p<O·ool) and depth were significant (F(3, 72)= 15'09,p < 0'001). Table 2 shows that decision time was higher in the relational data modelthan in the hierarchical data models (respective means: 8·85 and 7'22).The average timeper screen decreased with the depth of the menu system: 9'89, 8'64, 7·32 and 6·29 for therespective depth conditions. These data reflect to a large extent the time needed to readone screen and to make a decision. It is evident that these times are monotonic with thebreadth of the screen.

The time needed to perform one complete search is a better measure of efficiency,Taking the number oflevels ofthe menu system into account, the average time neededto locate a reference increased with the number oflevels: 19'78,25-92,29-28 and 31·45 sfor 2-, 3-, 4- and 5-level systems respectively (see table 3).

Significant interaction effects were obtained of structure x depth (F(3, 72)= 5-88,p<O-OI), structure x depth x breadth type (F(6,142)=3-18, p<O'Ol) and counter­balancing x breadth type (F(4, 142)=83'96, p<O·ool). The last one indicates a strongimpact of order, which is probably due to learning effects, since time tended to decreasewith each new task.

Further analysis of the structure x depth interaction revealed an effect of structureon the intermediate levels of depth and not on the extreme levels.The interaction of thequadratic trend on depth with structure was highly significant: F(I, 72)= 13-55,p<O-OOl.

As tables 2 and 3 show, the interaction among structure, depth and breadth limitsthe generalizability of any conclusions about the effectsof structure and depth. Breadthseems to have different effects as a function of structure, depth and their combination.

Average time per screen seems to be linearly related to the number of alternativespresented per screen, the structure coded as -lor + I, and their bilinear interaction. Amultiple regression analysis yielded a significant multiple correlation coefficient of 0-82(F(3, 20)= \3'80, p <0,001), which is quite high given the simplification involved in thisanalysis,

Apart from the effectsoftask, no significant effects were obtained in a mixed-modelANOVA performed on the accuracy of performance. This is because the incidence oferrors is very low: 0-22 on average.

2.4. ConclusionWhile subjects performed equally accurately with the hierarchical and the

relational menu system, the search times were shorter in the hierarchical conditionsthan in the relational conditions. If this effect is independent from the implementationin the present study, it may be concluded that for novice users the traditionalhierarchical menu structure should be preferred.

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Menu-driven interfaces 21

Table 2. Average searchtime per screenin secondsas a functionof breadth spread, depth anddata model.

Breadth

Increasing Peaked Decreasing

Hierarchical2 ](}46 7·77 15·543 6·30 6·80 6·474 6·29 5·87 6·785 5·99 5'92 6·25

Relational2 8'70 ](}45 1(}233 11·90 9·66 8·624 8·46 8·49 8·0]5 5·90 7·15 6·52

Table 3. Average search time per task as a function of breadth, depth and menu structure.

Breadth

Increasing Peaked Decreasing

Hierarchical2 2(}92 15·54 23-443 18·90 2(}4O 19·414 25·16 23·48 27-125 29·95 29·60 31·25

Relational2 17·40 2(}90 2(}463 35·70 28·29 25·864 33-84 33-96 32·045 29·50 35·75 31·60

Considering the total time needed to reach a correct end-point, it appears thatshallow menu systems should be preferred. With the hierarchical data model, systemsof two or three levels were about equally effective for a search in the relational datamodel.

Experiment I is related to an experiment reported by Norman and Chin (1988).They held depth constant at four levels, and varied breadth and the target type(explicitly named vs. targets appropriate for a scenario situation). Their explicitconditions with decreasing, increasing and convex menu structures are similar to ourhierarchical conditions with depth 4. However, because of the difference in thematerials used, an explicit comparison is difficult. At least, the two studies do not seemto contradict each other. However, because the present results indicate that depthinteracts with both breadth type and structure, it would be interesting to replicate theirstudy with depth as an independent variable.

Neither Shneiderman's (1980) nor Simpson's (1982) hypothesis is strengthened bythese results. The effects of structure, depth and breadth are far more complicated thanany such simple hypothesis can explain. The findings obtained in the multiple

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22 R. Van Hoe et al.

regression analysis strongly suggest that decision time per screen is an increasingfunction of the number of alternatives contained in the screen, and of the structure (andits combination with breadth). This suggests that decision time highly depends on theamount of information present in the screen, and on a practically constant amount oftime needed to make up one's mind, which appears to be quite large (about 5·6 s), Everynew level in the menu structure, therefore, adds 5 s to the time needed to reach the end ofthe path, although it may be assumed that these times will be much shorter inexperienced users.

The present experiment suggests that menu systems should preferentially beshallow hierarchical structures, with quite broad menus. Any generalization of thisconclusion should be handled carefully, because performance also appears to bedependent on other factors not considered in this experiment.

3. Experiment 2: searcb information supportsMenu selection systems are often used because of the ease with which novice users

are believed to be able to learn and use them. Nevertheless, novice users sometimeshave difficulties in interacting with a menu system. This can be remedied with the helpof a support often called as searching aid, i.e. a display of information that enhances asearch through the menu hierarchy. A study was performed to compare some of thesetools. Four searching aids were developed: a level aid (indication of the current level inthe structure), a choice-trace aid (indication of previously selected options), a previousmenu aid (display of the entire preceding menu screen) and a menu structure aid(representation of the menu structure). A control group received no searching aid.During the experiment, these searching aids were continuously present in a smallwindow in the lower right-hand corner of the terminal display.

Based on similar studies by Tombaugh and McEwen (1981), it was predicted that:

(a) subjects in the searching aid conditions would perform significantly better thanthose in the control group;

(b) a choice-trace aid should enhance performance in comparison with a previous 'page aid; and

(c) a menu structure aid should increase performance in comparison with a levelaid. .

The study was also designed to find out whether performance would be better if thesubjects could escape to the top of the menu tree with a single keystroke. It washypothesized that this escape facility would significantly improve performance.

3.1. Method

3.1.1. Materials and procedureThe materials and the procedure were the same as in the previous experiment,

except for the changes in the menu interface to implement the different searching aids.All experiments were run with a four-level hierarchical menu system which was alsoused in the first experiment. To enhance the use of the searching aids and the ESCAPEkey, eight of the 24 books were not present in the database. In these cases, subjects couldleave the menu system by hitting the Z-key. The first two tasks were considered astraining trials and were not included in the data analysis.

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Menu-driven interfaces 23

3.1.2. Subjects and designEighty first-year students enrolled in the Faculty of Psychological and Educational

Sciences at the University of Ghent participated in the experiment for courserequirements. They were randomly assigned to the cells of a 5 (searching aid) x 2(escape facilities) design with eight replications. They did not participate inexperiment I.

3.2. ResultsANOVAs were performed separately for the two types of task: those leading to a

correct end-point in the menu tree (book present) and those which could not be solved(book not present in the database).

The only significant effect in the ANOVA performed on the decision time for thereferences not present in the database was a main effect of the factor escape facilities:F(I, 70)=3-99, p<0·05. Subjects in the ESCAPE key conditions needed significantlyless time to decide (mean 6·79s) than those without such a facility (mean 7·65s).

3.2. ConclusionContrary to our predictions, no effectsof searching aid methods were observed. An

additional ANOVA of the two first tasks yielded no main and interaction effects.Therefore, a possible explanation for the absence of searching aid effects is that menusystems are already very easy to learn, so that novice users do not need additionalsearching aids. Hence, one cannot expect significant performance differences as afunction of these tools. This reasoning was confirmed by data from a post-experimentalquestionnaire. Subjects declared that they did not use the window information in theirsearch activities. However, this interpretation is only ad hoc, and more research isneeded to clarify the issue.

At least, there is an indication that the presence of an escape-to-the-top facilityenhances decision performance.

4, Experiment 3: on-line help informationOn-line help information is often provided to make it easier to learn an interface.

What kind of help information is important as an aid in using a menu selection system?It can be assumed that successful use of a menu system depends on the user's properunderstanding of the category-names developed by the system designers. Landaueret al. (1983)found that users and designers use the same name for editor operations onlyin 10 to 15%ofthe cases. Jorgenson et al.(1983)found that even designers rarely use thesame identifier for database functions. It can be hypothesized that information aboutthe meaning of the category names and partitions would result in a betterunderstanding of the menu items and hence result in a better user performance.

A study by Parton et al. (1985) revealed that a learning method based on a globaltree diagram of the menu system was superior to other methods. Besides categoryinformation, it seems that structural information is also of importance in theinteraction with menu systems.

An experiment was designed to evaluate such help information. Four help functionswere developed: standard information (general information on the functioning andprinciples behind menu systems),category information (definitions of the menu systemitems), structural information (a representation of the menu structure) and acombination of category and structural information.

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24 R. Van Hoe et aI.

In addition, the organization of the menu screens was varied because it is animportant factor in the ease of menu system usage (Liebelt et al. 1983).An alphabeticalorganization was compared with a randomized order of the menu alternatives. It wasexpected that an alphabetical display would result in a better performance.

4.1. Method

4.1.1. Materials and procedure. The same procedure and materials were used as in the previous experiments.

Subjects could call a help function by using the? key. The help information consisted oftwo or three screen pages. The next page could be called with the + key; and theprevious page with the - key. Return to the menu system was achieved with the. key.The usage of these keys was reminded on the bottom line of the screen.

The first two tasks were considered as practice trials and were not used in the dataanalysis.

4.1.2. Subjects and designFifty-six first-year students enrolled in the Faculty of Psychological and

Educational Sciences at the University of Ghent participated in the experiment forcourse requirements. They were randomly assigned to the cells of a 2 (alphabetical vs.randomized) x 4 (help information types) design with seven replications. They did notparticipate in either experiment 1 or 2.

The levels of the help information factor were category information, structuralinformation, a combination of both and standard information.

Table 4. Decision time'and accuracydata as a function of help information type and screenorganization in experiment 3.

Decision time (s) Category Standard Structural Combination

All tasksAlphabetical 6·19 8·35 4·63 8·48Randomized 6·08 9·23 6'15 7-02

Target in databaseAlphabetical 4·82 6·59 3-96 6-09Randomized 4·90 5·90 5'0,7 5·81

Target not in databaseAlphabetical 9·12 12·11 6'06 13·59Randomized 8·59 16·35 8·47 9-61

Accuracy (number ofunnecessary steps)All tasks

Alphabetical 4·77 2-82 4·29 3-43Randomized 4·05 1·56 2-14 4-46

Target in databaseAlphabetical 1·67 1·71 2·17 1-82Randomized 1·85 (}99 1·09 2-23

Target not in databaseAlphabetical 11·40 5·20 8·83 6·89Randomized 8·79 2·79 4-40 9·24

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Menu-driven interfaces 25

4.2. Results4.2.1. Decision time

A 4 x 2 ANOVA on the average decision time over all tasks yielded a significanteffect of help information type: F(3,48)= 7-17, p < 0-001. Table 4 shows that categoryinformation (6'13 s) and structural information (5'39 s) result in faster decision timesthan standard (8'79s) and combined (7'75s) information types: F(I,48)=14'77,p<O·OO1. The main effect of display organization fell short of significance:F(I,48)=3·97,0·1O>p>0·05.

A 4 x 2 ANOVA on the average decision time over tasks with targets present in thedatabase yielded a main effect of help information type: F(3, 48)=4'15, p<0-05. Again,the most important difference is between structural (4'51 s) and categorical (4'86 s)information on the one hand, and the other information types on the other (6'25sand5·95s for standard combined information, respectively): F(I,48)= 10'37, p<O·01.

Finally, a 4 x 2 ANOVA on the average decision time over tasks for which the targetwas not present in the database revealed also a main effect of help information:F(3,48)=5'25, p<O·01. Again, category information (mean 8'86s) and structuralinformation (mean 7·26s)were superior to standard information (mean 14·23s)and thecombination of category and structure information (mean 11·60s). The main effect ofdisplay organization was also significant: F(I, 48)= 7'43, P< 0,01,with an advantage forthe alphabetical (10'22s) over the randomized displays (10-76s).

4.2.2. AccuracyA 4 x 2 ANOVA on the accuracy measure averaged over all tasks yielded a

significant main effectofdisplay organization F(1,48)= 6'90, P< 0-05,with alphabeticaldisplays resulting in slightly more unnecessary steps than randomized displays. Theinteraction of item organization and help information type was also significant:F(3,48) =3-37, p<0·05.

The ANOVAs on the average accuracy data for tasks with targets in the databaseand for tasks without target in the database yielded entirely similar results.

4.3. ConclusionThe findings of experiment 3 indicate that decision time is positively affected by

specific types of on-line help information. It appears that decision time is shorter withcategory information and with structural information than with standard informationand than with a combination of structural and category information. Seemingly, themismatch between the system designer's and the user's interpretation of the categoryname is the major problem in using menu-based information systems. This problemcan be handled by on-line information clarifying the meaning ofthe categories used orclarifying the structure of the menu system. Category information can also be given byproviding examples (Dumais and Landauer 1984).

It is not clear why the combined information condition resulted in poorerperformance than either the categorical or the structural information conditions. Onepossible explanation is that the combination of the two types of information resulted inan information overload. Without further tests, this suggestion remains highlyspeculative, however.

In addition, the experiment yields mixed evidence about the usefulness of displayorganization. On the one hand, decision time appears to be speeded up in the presenceof an alphabetical organization, but at the same time the number of errors increases.Unless this is a case of speed-accuracy trade-off, the finding needs explaining, for it is at

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variance with expectations arising from the research literature on memory (e.g.Baddeley 1976), which shows that categorized lists are easier to learn and to remeinberthan ones which are not categorized. In a series or experiments by Vandierendoncket at.(1988), menu organization seems to be an important factor in the search strategiesused by the subjects. In this study, search patterns and eye movement patterns werestudied as a number or factors, one or which was menu organization, which wasoperationalized as randomized versus categorized menu frames. These experimentsalso demonstrated that sheer repetition of a randomized menu leads to changes in thesearch strategy: with repetition the search strategy comes more and more to resemblethe strategy used with categorized menus.

5. Personality factorsPersonality factors are relatively stable psychological characteristics of the user.

Since one cannot expect to change the personality of the user, it is important to adaptthe interface in such a way that certain types or novices will not refrain from using acomputer system. In the experimental data reported, the effects of several personalityfactors werestudied. For most of the subjects who participated in the three experimentsreported in this article, scores were available on a number of personality tests. In thissection, we present a short report or the findings based on the data of experiment 2.

Scores were available on the Eysenck Personality Inventory (Eysenck 1970), theRotter (1954)locus ofcontrol test, Bern's (1974)sex role type test and the Witkin test forfield dependence (Witkin et at. 1977). The correlation or these tests with the data wererather modest, ranging from -0·133 to 0·233 for the time data and from -0-140 to0·004 for the accuracy data.

In the accuracy data, none of the correlations was significant. Only the introversionscale ofthe EPI (Eysenck 1970)correlated significantly with the average decision time(r=0·233, p<0·05). An analysis of the time data based on the multivariate generallinear model, with the factors or the experiment as independent variables (dummycoded) and the personality scales as covariates, showed that only the neuroticism scale(EPI) was systematically related to the time data: F(l, 54)= 4·72, p <0·05, with personsscoring high on neuroticism needing more time than the others. This weakerperformance or persons high on neuroticism was due to their bad performance at thebeginning ofthe experiment. Persons high on neuroticism are sometimes characterizedas labile.

The effects of personality factors on performance when using a menu interlace arerestricted to the initial interactions. In particular, the factors of introversion andneuroticism seem to be important. As the effect is short lived, the interlace need not beadapted to the user's personality. However, it may be expected, on the basis of theseresults and what is known about these personality types, that introverts will need morehuman and social support in moments of trouble when using computer systems thanextroverts. Nevertheless, it should be pointed out that the relationships are ratherweak, and it would certainly be recommendable that more data are collected on thisissue before trying to generalize the present findings.

6. DiscussionThe experiments reported in this article were intended to study some user

characteristics or menu-based interlaces by means of objective behaviouralmeasurements taken in standardized laboratory conditions but in an otherwiserealistic task environment. It is wellknown and generally accepted that menu interlaces

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Menu-driven interfaces 27

are very useful to optimize human--eomputer interaction, provided that the users arenovices and that the task environment is not too complex.

The experiments studied performance measured in terms of accuracy and decisiontime, when using a small-scale simulated database management program containing a64-item library. The findings can be summarized as follows:

(a) Menu selection time was shorter in a menu system based on a hierarchical datamodel than in a system based on a relational data model, although menuselection accuracy did not differ between the two conditions. This does notimply that the hierarchical model is better than the relational model in everytype of interface. Menu systems are in fact hierarchically organized, so thatthere is a higher degree of compatibility between the menu organization andthe data organization in the case of the hierarchical model.

(b) Average time per screen is an increasing function of the number of alternativespresented on the screen. Total time needed to locate an item in the database,however, is shorter the shallower the menu structure. Two- and three-levelstructures are clearly superior to deeper structures. One of the reasons is thatthere is a minimal amount of time needed to reach a decision. The finding islargely consistent with Simpson's (1982) hypothesis, but because of aninteraction among menu structure and menu depth the interpretation must bemade with care.

(c) There is an interaction of data structure, menu depth and breadth, that is notsimple to explain. However, the explanation in terms of the number ofalternatives per menu comes quite close.The present data suggest that decisiontime per screen is a linearfunction of the amount of information (the number ofalternatives) in it. More detailed analyses in line with those performed by Card(1984)are called for, and have actually been performed (see Vandierendoncket al. 1988).

(d) Any time a user makes a wrong decision in a menu tree, he or she has to performtwo extra decisions (one to undo the effectof the wrong decision, i.e. go backone screen, and one to replace the wrong choice with the correct one).Therefore, it is expected that average selection time and the number ofunnecessary steps would be smaller when the user has the opportunity to jumpback to the top of the menu system at once. This facility is especially useful toundo the effectof an early error. The findings ofour second experiment confirmthis expectation.

(e) Many modem applications programs have a facility to request on-lineinformation. Even in a menu-driven system such information appears to beuseful, especially to convey information about the menu categorizationemployed rather than to present information about the menu structure(cf. experiment 3).

(f) An alphabetical organization - and for that matter, probably, any systematicorganization - ofthe alternatives within a menu leads to better performance, asevidenced by the degree of help requested in both versions of otherwise similarmenus (experiment 3).

(g) Some personality factors, such as introversion and neuroticism (Eysenck 1970),seem to affect user performance to some extent. It appears that this is atransient effect limited to the early stages of learning and probably to othermoments of great difficulty.

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28 R. Van Hoe et aI.

(h) Some support facilities proposed in the literature seem to have little or no effecton user performance. Supporting the menu system with searching aids seems tobe totally superfluous. This is important because the systematicimplementation of menu systems supported with these kinds offacilities wouldlead to considerable implementation and operation costs.

It is evident that with these experiments the last word on menu interfaces has notbeen said yet. For practical purposes, it can be concluded that hierarchically organizedalphabetical (or functional) menus in a tree preferring breadth to depth provided with anescape to the top and a category information support should lead to good userperformance. Several problems remain to be studied, however.

One of the problems relates the way in which menu screens are scanned. Our datashow that decision time depends on the amount of information in the screen. HoweverCard (1984) has shown that subjects use an unsystematic non-exhaustive searchstrategy. Preliminary analyses done in our laboratory have confirmed this strategy. Animportant matter remaining, therefore, is to find an explanation of these findings. Whydo subjects not use a linear search? Is scanning affected by experience and byorganizational factors? Are there any other factors?

Another interesting problem for systems designers concerns the possibility ofintegrating menu and command interfaces in a flexible way, so that novices could startwith the menu interface, while experienced users could profit from the power and theflexibility of the computer system by using commands ifappropriate. This is not only aproblem ofdesign. Studies of how the user perceives such a transition, and of how sucha transition may be made flexible, are problems for cognitive psychology and cognitiveergonomics.

AcknowledgmentThe research presented in this paper was sponsored by 'Diensten voor

Programmatie van het Wetenschapsbeleid' within the framework of the Belgiansupport of the FAST research programme, Project no. 38. The authors take fullscientific responsibility.

Geert De Soete is 'Bevoegdverkaard Navorser' of the Belgian 'NationaaI Fondsvoor Wetenschappelijk Onderzoek' at the University of Ghent.

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