information system professionals’ knowledge and application gaps toward web design guidelines

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Computers in

Computers in Human Behavior 24 (2008) 956–968

Human Behavior

www.elsevier.com/locate/comphumbeh

Information system professionals’ knowledgeand application gaps toward Web design guidelines

Yu-Hui Tao *

Department of Information Management, National University of Kaohsiung,

700 Kaohsiung University Road, Nan-Tzu District, Kaohsiung 811, Taiwan, ROC

Available online 5 April 2007

Abstract

Web design guidelines are adopted by many usability evaluation methods as one of the criteria forsuccess, while usability is proven to significantly impact Website performance. Since Web designguidelines cover a broad range of system and interface design solutions, knowledge of them canbe considered as a prominent indicator of Web design skills for information systems (IS) profession-als. This study empirically assessed how much IS professionals know and apply Web design guide-lines via a survey to 500 randomly selected companies from Taiwan’s Fortune 2000 corporations. Asexpected, the knowledge–application gaps of IS professionals were statistically significant in all Webdesign guideline categories. Meanwhile, certain guideline categories were proven to be more difficultto acquire or apply than others. Finally, degree, gender, experience, training hours, and coursestaken were also proven to be determining factors for Web design guideline skills. Implications fordeveloping Web design guideline skills are also discussed.� 2007 Elsevier Ltd. All rights reserved.

Keywords: Guideline; Gap analysis; Usability; Web design

1. Introduction

Conceptually speaking, a guideline provides advice on the solution of a design problemand may suggest possible solution strategies (Newman & Lamming, 1995). Preece et al.(1996) summarized two kinds of guidelines—high-level guiding principles such as ‘‘know

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Y.-H. Tao / Computers in Human Behavior 24 (2008) 956–968 957

the user population’’ and low-level actionable rules such as ‘‘provide a RESET com-mand.’’ In general, guidelines come from psychological theory or practical experience(Preece et al., 1996), and almost all human–computer interaction (HCI)-related textbooksdevote a significant effort in studying design guidelines (Shneiderman & Plaisant, 2004;Pearrow, 2000; Nielsen, 1999; Newman & Lamming, 1995).

Although a Web design guideline may seem to be as trivial as an individual advice to aWeb design problem and solution strategies, its integral importance can be inferred fromits strong tie with usability and Website design, which also influence Web performance.Chevalier and Ivory (2003) suggested Web design guidelines as one of the candidates toimprove Web usability, while many usability evaluation methods actually contain designguidelines (Agrawal & Venkatesh, 2002; Palmer, 2002; Nielsen, 1994). Palmer (2002) fur-ther pointed out that Web usability and Website design significantly influence Web perfor-mance metrics. The link of Web design guideline with usability, Website design, and Webperformance thus becomes evident. The driving force behind this practical importance isthe maturing Internet and World Wide Web (WWW) which together form an increasinglypopular framework for enterprise application development called Web-based InformationSystems (IS) (Satzinger, 2002). Thus, a Web platform has been transformed from its meremarketing presence to support all facets of organizational works (Isakowitz, Bieber, &Vitali, 1998).

The academic effort to ensure Web usability and to emphasize Web design guidelineshas also surfaced in recent years. HCI-related courses have been strongly recommendedto be included in the graduate-level curriculum of IS (Gorgone et al., 2000), in computingcurricula (Chang et al., 2001), and in the master’s level of MIS and e-commerce programs(Chan, Wolfe, & Fang, 2003). However, research-based Web design guidelines are stillconfronted with some challenges in actual applications (Evans, 2000), which is indirectlysupported by Cook and Mings (2005) who pointed out that the gap in usability educationand research exists between the academia and the industry.

As a factor which influences Web performance, Web design guideline can be deemedas a desirable knowledge and skill for e-corporations. Therefore, it is worthwhile toassess the gaps in practice and to address these application and gap issues by examininghow much IS professionals know and apply Web design guidelines. The outcomes willbe helpful in uncovering development opportunities and strategies in Web design guide-lines for IS professionals in both the academia and the industry. To pursue these objec-tives, the remaining sections are organized to present a review of design guidelines, theresearch hypotheses, data collection and analysis, and finally, the implications and lim-itations of this study.

2. Web design guidelines

Web design guidelines are not about programming techniques, but are rather relatedto system and user interface design. Among the explanatory theory, empirical law, anddynamic model used by sociology for human reasoning, the empirical law has a betterprediction power than the explanatory theory, although it cannot precisely predictperformance as the dynamic model (Newman & Lamming, 1995). Therefore, whenlacking in conceptual design methods or facing unfamiliar design problems, Webdesigners usually turn to available guiding principles or design rules (Preece et al.,1996).

958 Y.-H. Tao / Computers in Human Behavior 24 (2008) 956–968

The biggest issue in applying guidelines is the selection and implementation of designguidelines. Traditional design guidelines are recorded throughout the years in literature(Shneiderman & Plaisant, 2004). The great volume of guidelines also brings forward anew challenge in applying the most adequate number of design guidelines for satisfactorymarginal usability effects while minimizing resource waste. These issues make the selectionand application of design guidelines more of an art rather than a science.

The variety of design guidelines can be seen in the following literature. The popularbook by Shneiderman and Plaisant (2004) on user interface contains a good collectionof design guidelines from early terminal-based interface principles to modern WWW inter-face guidelines. Shum and McKnight (1997) also introduced the usability of WWW. TheWWW influence can be seen from Ramsay and DeBord (1999) suggestion of a group offactors, which is based on seven commonly seen design guidelines, affecting user-friendlyinterfaces, and Jones (1996) suggestion of seven high-level WWW guidelines for a businessto start involving the Internet. Bayers (1991) suggested nine instructional design principlesfor computer-based training (CBT), which has been a popular research and applicationarea in recent years (Mengel & Adams, 1996; Robin & McNeil, 1997). These instructionaldesign guidelines are also suitable for designing the user interface in the Web-based envi-ronment, such as online help or tutorials. Moreno and Mayer (1999) inferred manyinstructional design guidelines in multimedia simulation environment, a popular applica-tion domain filled with works by prominent authors such as Kazman and Kominek (1997)and Najjar (1998).

Guidelines can also be seen in HCI/usability-related models and systems perspectives.Rook and Donnel (1993) reviewed and validated that the mental model can lift the perfor-mance of man–machine interactions. Sundstrom (1993) proposed design guidelines fromthe angle of model-based user support with the hope that the association between thismodel and the operational type can be considered when choosing a model. Finally, Ham-alainen, Holsapple, Suh, and Whinston (1991) proposed man–machine guidelines forgroup support systems.

Nielsen (1993) originally classified design principles into five factors, including inter-face, response time, mapping and metaphors, interface style, and multimedia and audio-visual. Newman and Lamming (1995) categorized design guidelines slightly differentlyinto general guidelines, interactive screen layout, interaction style, user interface compo-nent, and formative content. To cope with the Web environment, Nielsen (1999) lateradded navigation, credibility, and content. Nevertheless, the tremendous number and vari-ety of design guidelines makes it difficult for any of the available taxonomy schemes to becomprehensive or representative. Therefore, the creation of a satisfactory taxonomy whichencompasses the wide variety of guidelines such as those in traditional HCI, WWW HCI,instructional design, multimedia environment, and business Website development as pre-sented above remains a research issue.

3. Research hypotheses

As stated above, Web usability or design guidelines were rarely the focus of learningmaterials in school curricula until recently (Gorgone et al., 2000; Chang et al., 2001; Chanet al., 2003). Moreover, the current format of research-based Web design guidelines mayeven inhibit their smooth adoption for application (Evans, 2000). Therefore, the firstresearch hypothesis is as follows:


[H1]: There exists a significant gap between the knowledge and application levels ofWeb design guidelines by IS professionals.

The rationale behind H1 may be attributed to the theory–practice gap, which is com-monly used in determining ‘‘the distance of theoretical knowledge from the actual doingof practice’’ in Nursing press (Corlett, Palfreyman, Staines, & Marr, 2003). Althoughthe IS community lacks such a formal terminology, the gaps of different IS aspects havebeen studied. For example, Hornik, Chen, Klein, and Jiang (2003) investigated the com-munication skills of IS providers using gap analysis from three stakeholder perspectives.For the importance and proficiency of communication skills, Chen, Miller, Jiang, andKlein (2005) studied the perception differences between IS staff and IS users. Related toour subject, Yen, Chen, Lee, and Koh (2003) explored the perception gap of IS knowledgeand skills between the academia and the industry.

Davis, Misra, and Auken (2002) defined knowledge as the conceptual and theory-basedaspect of a domain, and skill as the ability that can be refined through practice. Knowledgehas been stated to be achieved by learning, which helps in the formation of creative solu-tions to new problems (Kaplan, 1964). This concept is similar to acquiring the basic sim-ulation skill in which ‘‘at least 720 h of formal class instruction plus another 1440 hours ofoutside study’’ (Shannon, 1985) are required. In the case of IS professionals, Web designguidelines can be a category of knowledge taught in classes or learned from work, whichstill demands a lot of practice to make them ready-to-use skills. Since gaining knowledgeon Web design guidelines may be an ad-hoc opportunity for designers in terms of improv-ing their personal experiences in education and career development, two hypotheses areformed accordingly:

[H2:] Certain design guidelines are more difficult to acquire or apply than others.[H3:] There exist important determining factors on the acquisition as well as the appli-cation of Web design guidelines.

The expectation for H2 is that by observing the relationship between gap size andknowledge–application levels, challenging design guidelines as development opportunitiesfor school curricula design and corporate training and development can be uncovered. Tojustify H3, in the study of communication skills of IS staff and users by Chen et al. (2005),gender, age, education, and work experience were used to profile the subjects. Similarly, itis necessary to identify the characteristics of IS professionals which influence the know-eldge and application levels of Web design guidelines.

4. Methodology

To address the three research hypotheses, a survey questionnaire was used to collectdata from IS professionals. This section describes the sample selection, questionnairedesign, and analysis methods used in this study.

4.1. Sample design

To better assess the average knowledge and application levels of Web design guidelinesfor IS professionals, the software industry was excluded from our sample. Meanwhile, to


make sure that an IS department exists in a sample company which will receive the surveyquestionnaire, only large enterprises were targeted. Thus, the target population is ISemployees in the top 2000 enterprises as ranked by Taiwan’s CommonWealth magazine(www.cw.com.tw). Among these companies, 1176 are in the manufacturing industry,640 are in the service industry, and 183 are in financial industry. A sample of 500 compa-nies to which the survey questionnaires will be distributed was randomly drawn from thelist. In the survey cover letter addressed to the head of the companies’ respective IS depart-ment, it was requested that the questionnaires be forwarded for filling out to their internalstaff with Web design responsibilities. Among the 500 questionnaires, 290 were sent tomanufacturing companies, 140 to service companies, and 70 to financial companies.

4.2. Questionnaire design

The questionnaire items contain Web design guidelines as well as a sample profile. Sincethere is no de facto standard of taxonomy for Web design guidelines, a senior domainexpert was asked to select 30 representative design guidelines according to the categoriesof Newman and Lamming (1995) and Bayers (1991). The questionnaire format is similarto the service quality measurement SERVQUL of Parasuraman, Zeitham, and Berry(1985) in which each design guideline question item requires two answers, one for knowl-edge level and another for application level.

The factors gender, age, education, and work experience used for profiling IS profes-sionals by Chen et al. (2005) were adopted by this study with some modifications. Com-pany type was added to the user profile since the top 2000 enterprises were engaged indifferent lines of business. Education was expanded to include degree, major, and relatedclasses taken. Likewise, experience was expanded to include Web-related position held,years in software development, years in Web development, number of project developmentparticipated in, years in project development, number of non-Web development projectsparticipated in, and hours of Web-related training courses. The questionnaire was pre-tested to three IS graduate students who had industrial experiences in software develop-ment and had taken an HCI course focusing on design guidelines. For the knowledgeand application levels, the questionnaire adopted a Likert-type scale, with 1 as the lowest1 and 5 as the highest.

4.3. Data analysis

Briefly speaking, the data analysis methods used in this study include descriptive statis-tics for the sample analysis, exploratory factor analysis for reducing the 30 guidelines intoa smaller number of factors, ranking of averages for the knowledge–application gaps,paired-sample T-test for the gap analysis, and ANOVA and cross-tabulation for identify-ing the determining factors of the profile items on the design guideline factors.

5. Analyses and results

5.1. Sample analysis

We received 45 valid responses from the first mailing, and 44 from the second mail-ing, which added up to an overall valid return rate of 17.8%. To assess if responses from


the two mailings demonstrated any significant differences, a T-test was conducted toeach data item as shown in Table 1. The P-values were greater than 0.01 for all itemsexcept ‘‘Major.’’ This is due to the percentage of the IS-related major responses whichwas much higher in the second mailing than in the first mailing (61.4% vs. 26.7%).Because many IS professionals came from non IS-related majors such as Engineeringand Science in Taiwan, the difference in academic majors from the two mailings maynot necessarily generate any significant impact. However, this will still be stated as a lim-itation of the study.

5.2. Reliability and validity analyses

The reliability of the measurement was tested using Cronbach’s a. The values were0.9160 for knowledge level and 0.9104 for application level, which demonstrate a verygood reliability according to the suggested minimum value of 0.7 by Nunnally (1978).Content validity was also ensured, since the design guidelines were selected from a poolof academic literature and were based on psychological theory or practical experienceaccording to Preece et al. (1996). The 30 guidelines were first reduced to a small numberof factors using exploratory factor analysis. In order to compare the knowledge level andthe application level using the same basis, only the scores from knowledge level were usedin factor analysis.

The Kaiser–Meyer–Olkin (KMO) test for sampling adequacy and Bartlett’s test forsphericity were used to examine the suitability of selected adoption variables for factoranalysis (Bryman, 1989). The KMO test resulted in a 0.813 value that is greater thanthe suggested minimum value of 0.5 for adequacy, and Bartlett’s test also demonstrateda very good sphericity (v2 = 1402.1231, d.f. = 435, p < 0.000). Both results indicated thatthe 30 variables were suitable for the following factor analysis.

In order to preserve the convergent validity of the selected factors, only thoseextracted factors with eigenvalues bigger than one in principle component factor analysiswere selected. The proamax rotation with Kaiser Normalization presented the best out-come, with the variables more evenly distributed in selected factors. Eight factorsemerged, which account for 68.247% of the accumulated variances. To achieve the so-called ‘‘practically’’ significant factor loadings (Hair, Anderson, Tatham, & Black,

Table 1Differences between two mailings

Profile item P-value

Gender 0.938Company type 0.135Major 0.000a

Degree 0.160Years in Web related position 0.998Number of the Web development projects 0.959Years in software development 0.199Number of non-Web software development projects 0.298Hours of Web related training courses 0.303

a P-value < 0.01.


1992), only those variables with factor loadings greater than 0.5 were selected. Discrim-inant validity was thus demonstrated.

For easy reference, the eight factors were named as close as possible to the categorynames listed in the review of ‘‘Web Design Guidelines.’’ Table 2 lists the variables thatmake up the factors named page content (F1), general principle (F2), instructional design(F3), interface usability (F4), multimedia presentation (F5), screen layout (F6), interactionstyle (F7), and interaction usability (F8).

Table 2Guideline variables versus factors

Variable name Factor name

V19. Font size is determined by the appropriateness for users to navigateV21. Page topic is clear for that pageV22. Text title is provided to each page F1: Page contentV23. Paragraph is concise and focusedV24. Precise item name is used, such as ‘‘next’’ and ‘‘previous’’ page

V1. The website is designed with users’ perspective in mindV2. Users will have a chance to participate in the testing processV3. Operations are made simple to reduce user’s load during navigationV4. The content is presented in languages familiar to the user. F2: General principleV5. The consistence of the complete system is maintained as much as possible

V28. Present stimuli and vivid learning contentV29. Provide appropriate user feedback for understanding learning

performance.F3: Instructional design

V30. Provide practice for users to review or self-assess the learning content

V6. All hyperlinks are marked with obvious, pre-specified colorsV11. Provide Website guidance or user helpV12. Provide alternate text to describe important functions F4: Interface usabilityV13. Locate the command line near the bottom of the screen

V16. Less than five colors are usedV17 Less than five levels of hyperlinks in depth are used F5: Multimedia

presentationV18. Only meaningful graphics/images are shownV25. Figures or examples are provided only when necessary

V14. Retain main window when popping up a new window for extrainformation

V15. Classify functions into logical groupings F6: Screen layoutV20. Avoid complicated frames

V7. Lengthy content is reorganized into a hierarchical structure for multiplehyperlinks

V9. Streaming technology is used for downloading multimedia data F7: Interaction styleV10. Feedback message is provided to users in dialogue window or multimedia

format

V8. File downloading is limited to file size less than 32 KB or 6 sV26. Provide necessary means, such as animation, for attracting user’s attention

when neededF8: Interaction usability

V27. Inform the user learning objectives


5.3. Statistical analysis

5.3.1. Q1: There exists a significant gap between the knowledge and application levels

of Web design guidelines by IS professionals

The means and standard deviations of the knowledge and application levels for theeight design guideline categories (factors) are listed in Table 3. The knowledge levels arehigh with averages either above or close to 4, while the application levels are mostly under4 and just a little over 3. In other words, the subjects were quite knowledgeable, but theirapplication skills were just a little above adequate on these eight categories of designguidelines.

The gap between the knowledge and application levels was examined at the factor levelon Web design guideline categories. Although the mean differences between knowledgeand application levels are not as big as we expected (<1.0), the mean standard errorsare all very small (between .04 and .09). As seen in Table 4, all eight categories show sig-nificant gaps at a = 0.001 by paired-sample T-tests. This means that Q1 is supported,which also implies that the subjects could not apply their knowledge thoroughly inpractice.

5.3.2. Q2: Certain design guidelines are more difficult to acquire or apply than others

To further explore these gaps, the average gaps of the eight factors were ranked. Table 5lists the rank in a descending order of F3, F8, F2, F7, F4, F6, F1, and F5. Page content(F1) has the highest average score in both knowledge and application levels, but the gap isamong the smallest. Interestingly, instructional design (F3) has the largest gap with theknowledge level and application level among the lowest, which indicates that the IS staffgenerally lack a good grip of instructional design guidelines. It may be because instruc-tional design guidelines were seldom directly encountered in classes or Web developmentprojects. On the contrary, multimedia presentation (F5) has the smallest gap with both lev-els among the lowest. In other words, multimedia presentation appeared to be not wellperceived or applied in practice, although it is a frequently encountered issue in Webdevelopment.

Since all the gaps in Table 5 are significant, we attempted to classify them into threeclasses which are as follows: (1) difficult to acquire and learn, which includes F3, F5,F5; (2) easy to acquire and learn, which includes only F1; and (3) different levels of

Table 3Average scores of design guideline categories

Factor N = 89 Knowledge Application

Meana Std. deviation Meana Std. deviation

F1 4.425 .519 4.130 .610F2 4.389 .495 3.762 .554F3 3.933 .742 3.213 .962F4 4.236 .584 3.688 .728F5 3.916 .663 3.694 .666F6 4.259 .623 3.925 .579F7 3.974 .669 3.363 .669F8 4.120 .580 3.490 .652

a Averages of the Likert scale values from the highest 5 to the lowest 1.

Table 4Knowledge–application gaps at category level

Category Mean difference Mean standard error P-value

F1 .2944 .05377 .000a

F2 .6270 .05444 .000a

F3 .7197 .08909 .000a

F4 .5478 .06636 .000a

F5 .2219 .04884 .000a

F6 .3338 .05194 .000a

F7 .6108 .06716 .000a

F8 .6296 .07082 .000a

a a = 0.001.

Table 5Ranking of the gaps

Rank Factor Knowledge Application Average gap

1 F3 3.933 3.213 0.7202 F8 4.120 3.490 0.6303 F2 4.389 3.762 0.6274 F7 3.974 3.363 0.6115 F4 4.236 3.688 0.5486 F6 4.259 3.925 0.3347 F1 4.425 4.130 0.2958 F5 3.916 3.694 0.222


difficulty to acquire and to learn, which includes F8, F2, F4, and F6. Accordingly, H2 ispartially supported.

5.3.3. Q3: There exist important determining factors on the acquisition as well as theapplication of Web design guidelines

To assess what can be accounted for these gaps, ANOVA was used to inspect whetheror not the sample profile items cause significant differences on the knowledge and applica-tion levels. To easily analyze the ANOVA test results, Table 6 was rearranged in such away that for each profile item, the significantly impacted categories with a certain signif-icance level of 0.001, 0.01, or 0.05 are listed on its right-hand side. Each impacted categoryis further distinguished between the knowledge and application level. Based on theANOVA results and cross-tabulations, four major observations were arrived at whichwere as follows:

1. Most profile items have a certain degree of significant impacts on one to three Webdesign guideline categories at either the knowledge or application level.

2. Certain positions and classes taken have concrete influences on some categories.3. Work experience-related items show significant impacts on some factors. However,

‘‘years in Web development’’ does not show the same impact, which may be due tothe short history of Web development in Taiwan.

Table 6ANOVA analysis of the profile

Profile item Significant on categories of

Gender F1 knowledgeb,F1 applicationa, F3 knowledgea

Company type –Degree F3 knowledgea

Major –Years in software development F2 applicationa, F6 knowledgea

Years in Web development –Number of project development F3 applicationa,

F5 applicationa, F8 applicationb

Years in project development F5 knowledgea

Number of non-Web software development projects F5 knowledgea, F8 knowledgea

Hours of Web related training classes F7 applicationa

Position Software R&D F1 applicationa,F5 applicationa-

Web page designer F6 applicationb

Project manager F4 knowledgea

Programmer –Classes taken Human factors F3 applicationc, F8 knowledgea

Cognitive engineering F1 knowledgea

Interactive system design, humancomputer interaction, usabilityengineering

–

a a = 0.05.b a = 0.01.c a = 0.001.


4. Gender exerts greater influence on the knowledge and application of design guidelinesthan degree, and major and company type have no significant impact on it at all. Basedon the brief analyses above, H3 is concluded to be partially supported.

6. Implications and limitations

As an insightful study to further explore the concerns of Evans (2000) and Cook andMings (2005) regarding the application and gap in education and research between theacademia and the industry, three implications for skill development opportunities onWeb design guidelines are summarized as follows:

6.1. The knowledge–practice gap theory is affirmed in Web design guideline skills

Since the average application scores are all above 3 and most knowledge levels areabove 4, this implies that Taiwan’s IS professionals in Fortune 2000 corporations areequipped with good knowledge and have adequate skills on Web design guidelines. How-ever, the significant gaps between the knowledge and application levels indicate that theknowledge–practice gap (Corlett et al., 2003) does exist in Web design guideline skills.How to improve the application level of Web design guidelines in order to address thisgap is thus a more important issue than improving the knowledge level.


6.2. Customized strategies for developing Web design guideline skills are necessary

In addition to the knowledge–practice gap, this study also partially affirmed that certainWeb design guidelines are more difficult to acquire or learn than others. In other words,merely providing HCI-related courses as suggested by Gorgone et al. (2000), Chang et al.(2001) and Chan et al. (2003) may not be adequate for developing appropriate and bal-anced Web design guideline skills. As such, the school curriculum or job training designought to have a customized strategy for developing Web design guideline skills for stu-dents and IS employees.

6.3. Identified determining factors may be useful in curriculum design

Generally speaking, degree, gender, experience, training hours, and classes taken havebeen demonstrated to partially affect the knowledge or application levels at various Webdesign guideline categories in this study.

Although HCI-related courses did demonstrate some influence on Web design guidelineskills, only a small percentage of IS professionals in Taiwan had such formal classroomexperiences. Curriculum redesign effort may thus take a few years before its significanteffects are seen, particularly after HCI-related courses are commonly offered in schoolsfor IS-related majors. As an alternative, since all HCI-related textbooks are focusedmostly on design guidelines, the weights for each Web design guideline category can beadjusted based on the results of this study. For example, the instructional design guidelinecategory, which involves the largest gap but is seldom covered in HCI-related courses,does require more attention in course content arrangement. However, it should be notedthat the knowledge–practice gap may have existed already before IS professionals enteredthe job market, since experience still plays a big part in addressing this kind of gap asimplied by Shannon (1985).

Despite our careful efforts, the conclusions and implications should be considered inlight of some limitations. First, the analyses were conducted on a classification of Webdesign guidelines based only on 30 sampled guidelines. The author thus believes that amore comprehensive study to develop a standard taxonomy covering all major Web designguidelines is needed. Second, there was a significant difference on respondents’ ‘‘major’’between the two mailings during data collection. This may limit the generalizability ofthe study’s results to the population. Third, contrary to our stereotypical thinking,‘‘major’’ and some classical courses such as Human–Computer Interaction showed no sig-nificant influences on any Web design guideline categories. This may deserve furtherinvestigation.

Acknowledgement

This research is partially funded by the National Science Council, Taiwan, ROC underGrant number NSC 89-2416-H-214-036.

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