Validating Measurements of Perceived Ease Comprehension and Ease of Navigation of an Online Learning Technology: Improving Web Based Learning Tool Adoption and Use

2011 International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies.

International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies

http://www.TuEngr.com, http://go.to/Research

Validating Measurements of Perceived Ease Comprehension and
Ease of Navigation of an Online Learning Technology:
Improving Web Based Learning Tool Adoption and Use
a*
Bangaly KABA

a
Schools of Business, International Relations and Economic Policy (BIREP), International University
of Grand-Bassam, IVORY COAST

ARTICLEINFO A B S T RA C T
Article history: Many universities are realizing that the implementation
Received 21 March 2011
Received in revised form and use of online learning tool become a competitive advantage to
27 May 2011 address the actual learning needs. The purpose of this study is to
Accepted 31 May 2011 determine the factors that influence users’ perceived ease of use of
Available online
01 June 2011 Webct an online learning tool. We administrated a questionnaire
Keywords: to undergraduate students from an university in Quebec, Canada.
Technology; The results tend to corroborate that ease of comprehension and
Acceptance; ease of navigation are the key factors which influence the
Model; perceived ease of use of WebCT. More specifically, the terms used
WebCT (Web course tools);
Measurement; in educational web applications must be as simple and relevant as
E-learning. possible. Jargon and technical terms in the wording of text used for
links should be carefully avoided. This research is extending the
finding of IT adoption studies by specifying what make an online
tool easy to use.

2011 International Transaction Journal of Engineering, Management, &
Applied Sciences & Technologies. Some Rights Reserved.

1. Introduction
Recently, following the example of other organizations, a large number of universities
have been giving primary importance to the use of information and communication
technologies (ICTs), allocating substantial resources to their acquisition. ICTs are used on a
*Corresponding author (Bangaly KABA). Tel/Fax: +225 21 30 34 57 Ext. 111 E-mail addresses:
kbangaly@hotmail.com. 2011. International Transaction Journal of Engineering,
Management, & Applied Sciences & Technologies. Volume 2 No.3. ISSN 2228-9860.
287
eISSN 1906-9642. Online Available at http://TuEngr.com/V02/287-301.pdf

daily basis in universities to build students and employees’ databases, to carry out statistical
analyses, to conduct refined bibliographical research, to send e-mails, to permit multimedia
animation in classrooms, etc. (Bradley et al., 2006; Mbarika et al., 2003a, 2003b). In addition to
these uses, ICTs have become the preferred media for distance learning services (Mbarika,
2004), thus considerably reducing temporal and spatial constraints (geographical disparities).
The current trend is for distance learning to become an option for a great number of instructors
to respond to the new needs of students.

The investments made in order to acquire, implement and use ICT for educational purposes
should be expected to result in positive impacts for the quality of instruction. More specifically,
these investments should materialize in the form of increased productivity, a reduction in
transaction costs, and therefore; in improved performance (Goodhue et al. 2000; Mathieson,
1991).

Many models have allowed researchers to determine and measure the factors involved in
the adoption of a technological innovation (Goodhue et al., 2000; Mathieson, 1991; Taylor and
Todd, 1995). Among these, Davis (1989)’s technology acceptance model (TAM) figures as a
classic in the field of the adoption of technological innovations. TAM is generally referred to as
the most influential and commonly employed theoretical model in information systems
research (Lee et al. 2003). This theory is of particular interest in explaining user behavior with
regard to IT. TAM has been consistently validated by a number of empirical studies (Davis et
al., 1989; Kwon and Chidambaram, 2000; Mathieson, 1991; Taylor and Todd, 1995; Venkatesh
et al., 2003).

However, since most of these studies aim to test the model, opportunities for the
information systems and information technology (IS/IT) community to contribute become
more and more restricted if serious theoretical modifications are not made to the fundamental
model. At least two possible criticisms of TAM can be made. First, TAM is a generalized
theory which does not always seem to take into account particular types of technological
innovations. In fact, the process of acceptance depends upon the nature of the IT (Igbaria, 1994;
Mahler and Rogers 2000; Markus, 1997). Secondly, TAM fails to provide useful explanations
which could help those who design or manufacture IT to increase the level of acceptance of
their products by end users (Venkatesh and Davis, 2000, Benbasat and Barki, 2007).
288 Bangaly KABA

This situation considerably limits the practical application of TAM (Benbasat and Barki,
2007). In light of this finding, we intend in this study to validate new scales of measurement of
the ease of use of WebCT, which is a course management system for online learning.

This study based on technology acceptance model (TAM) is initiated to validate the
measurement of the factor that influence users’ perceived ease of use of WebcT in order to
enhance our understanding of online learning tools use. TAM stated that easier is to use a
system or a technology high is the probability of its adoption and use. Unfortunately, the model
does not indicate what make practically a technology easy to use. Our main research question
is: what are the practical factors or measures which could be considered as alternative of users’
perceived ease of use? We consider that perceived ease of comprehension and perceived ease of
navigation as good alternatives which could serve as measurements of the ease of use of
WebCT even other online learning tools. Before outlining the conceptual framework of this
study, we consider it is useful to present the characteristics and the attractions of WebCT which
may be unfamiliar to the general public.

2. Overview of WebCT
Among internet and Web-based applications for online courseware, WebCT emerges as a
leader (Clark, 2002). This application was designed by the information systems department of
the University of British Columbia about a decade ago. Since then, the functionality of WebCT
has constantly improved, and it is now used by more than 2,200 institutions in more than 70
countries (WebCT, 2005). WebCT is a powerful tool for the creation of a distance learning
environment. It provides a complete set of tools for the delivery of an online course (Palloff
and Pratt, 2001 ; Mioduser et al. 2000). Once instructors and students become familiar with the
software, it can be used for e-learning. WebCT offers the possibility of synchronous and
asynchronous communication, sending e-mails, file sharing, student evaluations, access to
course materials, and access to outside resources dedicated to learning.

3. Theoretical Framework
Chris et al. (2004) emphasize the importance of the online knowledge management tool’s
user interface as a critical factor for its adoption and for online learning. Indeed, as a link

289

between the user and the system, the user interface allows a reduction of effort by making the
navigation among the different components of the system easier.

The success of an online application also relies upon the terminology used. The
terminology of a system refers to all words, phrases, and abbreviations it uses (Lindgaard,
1994). For example, a frequent problem with online courseware systems has to do with the
technical jargon used. This jargon includes technical or professional vocabularies with which
general users are often unfamiliar. In such cases, great effort must be made by end users in order
to utilize the system to its full potential. A clear and comprehensible terminology can thus
reduce the effort necessary to master the system and to make users more productive.
Consequently, it may be concluded that clarity of terminology is a good measurement of
perceived ease of use.

Davis et al. (1989) states that a technology or a system designed in such a way as to allow
its potential user to expend little time or energy (avoiding the constant need to refer to the user’s
manual or to contact the provider for help, etc.) will encounter few obstacles to its adoption.
These authors predict that the more a technology is perceived to be easy to use, the greater the
likelihood of its adoption. According to Davis et al. (1989), ease of use corresponds to the
degree to which a person believes that using a new IT will be easy. It is measured by the
following three indicators using Likert scales: the technology is easy to master, the technology
is user-friendly, the technology, in general, is easy to use. These measurements are for general
purposes and do not always appear to take into account the specific characteristics of a given
type of technology. This lack of specificity is susceptible to make the task of IS designers more
arduous when it comes time to determine the specific aspects of the system which could
influence users’ perceptions.

The previous shortcoming has led Moore and Benbasat (1991) to argue that one of the
problems facing the theories related to the adoption of technological innovations is the lack of
valid, trustworthy instruments to measure users’ perceptions in the context of adoption of these
innovations. Our intent in the current study is to identify and validate measurements of the
perception of ease of use which takes into account the features of a specific technological
innovation, which is WebCT.

The concept of ease of use is generally used in the literature on user acceptance of

290 Bangaly KABA

technology and on user behavior. As previously mentioned, Davis et al. (1989) identify ease of
use as one of the important determinants of the use of ICTs. Davis (1989) suggests that the
perceived ease of use can in fact determine the perceived usefulness. Mathieson (1991) and
Szajna (1996) report that ease of use accounts in large part for variations in perceived
usefulness. Therefore, in light of the aforementioned contributions, we can assert that a better
comprehension of the measurements of ease of use of WebCT constitutes a worthwhile domain
to investigate, because it could have a beneficial effect on the other determinants of ICT
success.

Inspired by the study of Lederer et al. (2000), we propose in the current research, the ease
of comprehension and the ease of navigation as alternative measurements of WebCT’s ease of
use. However, unlike Lederer et al. (2000), we consider that these two variables are rather
measurements of perceived ease of use, not the antecedents.

After having pinpointed the various theoretical contributions that are relevant to our
analysis, the next section focuses on the methodology adopted in this research.

4. Methodology

4.1 Questionnaire Development
The data for this study was collected through a questionnaire survey that was divided into
different sections. Each section was devoted to each variable of the research model: Task
characteristics, group characteristics, facilitating conditions, social influence, and the intention
of the users. A seven-point Likert scale, where 1 indicates “strongly disagree” and 7 “strongly
agree” (see questionnaire in appendix) was used to measure the latent variables used in the
study, with the exception of socio-demographic factors. These latent variables included:
perceived ease of use, perceived ease of comprehension, perceived ease of navigation,
perceived competency, computer anxiety, technical support and user help, and experience using
the internet. Variables measurements were inspired by Lederer et al. (2000) and Davis et al.
(1989), and adapted to the context of this study. Each variable’s was measured using multiple
items. Aside from demographic factors, the present analysis is only concerned with two
variables, “perceived ease of use” and “ease of navigation”. In the following section we
present the results obtained by our analysis.
291

A pre-test of the questionnaire was performed in order to assure its content validity before
its final distribution to the respondents. First, we designed a preliminary version of the
questionnaire. This version was given to researchers in the field of IT and information systems
(IS), and to experts in the industry familiar with the African context. Each individual provided
some comments on the formulation, the syntax, and the number of items included in the
questionnaire. Taking into account the various comments, we made minor changes to the
questionnaire. The various comments also permitted us to eliminate biases which could exist in
the questionnaire. .

4.2 Data Collection

Data in this study were collected using a questionnaire survey. Orlikowski and Baroudi
(1991) maintain that the questionnaire survey is the method of data collection mostly used in IT
research. This method is often indicated for gathering data, describing and explaining people’s
perceptions, attitudes, or behaviors. Questionnaires have the advantage of being structured and
assuring standardization in the formulation of questions and in their sequence. We administered
a survey to undergraduate students at a French-speaking university in Canada that use WebCT
in their course of studies. It should be noted that in this university, WebCT served as an
instructional supportive tool.

In order to be assured of a high response rate, we administered the survey by direct contact.
This mode of communication is very demanding in terms of investment, both in the time it takes
and in the amount of travel required. However, it seems to be the richest data collection
technique (Emory, 1980). With the instructors’ assistance, we solicited students’ direct
participation in their classrooms. The questionnaires were filled out on a voluntary basis before
the beginning of courses. We obtained 172 usable responses out of 230 questionnaires
administered, yielding a 75% response rate.

4.3 Data Analysis

The statistical analysis for this study employed the SPSS statistical software. The
assessment of the collected data’s descriptive statistics, construct validity and the testing of the
indicators’ reliabilities were conducted in SPSS. The factor analysis of principal component
was mainly applied to validate the measurement of easy of comprehension and easy of
navigation.
292 Bangaly KABA

5. Results
Details of the socio-demographic variables chosen for this study are given in Table 1.

Table 1: Socio-demographic profiles.

Absolute Percentage
Variables Characteristics
Frequencies
Gender Male 57 33.1%
Female 115 66.9%
Age 16 - 21 years 67 39%
22 - 27 years 87 50.6%
28 - 33 years 10 5.8%
34 - 39 years 6 3.6%
40 or older 2 1.2%
Years of Less than 1 year 1 0.6%
experience using 1 year 1 0.6%
the internet 1 to 2 years 7 4.1%
2 to 3 years 15 8.7%
3 to 4 years 27 15.7%
4 to 5 years 33 19.2%
5 or more years 88 51.2%
Different uses of Information seeking 6.32 1.04
the internet Downloading 5.10 1.84
Sending email 6.61 0.93
Chat 3.29 2.16
Forum 2.92 1.87

The socio-demographic variables examined in this study are concerned with gender, age,
years of experience using the internet, and the uses made of the internet. Only a third of the 172
respondents were men. The predominance of women in university programs is a reality which
cannot be ignored. The respondents were relatively young, since 154 of the respondents
(89.6%) are less than 30 years old. According to Paré (2002), the new generation of students has
an unprecedented level of mastery of ICTs (computers and the Internet). It is interesting, but not
surprising in the North American context, that the vast majority of respondents seem to be
familiar with the use of the internet. Indeed, 70.4% of respondents possess more than four
years’ experience using the internet, which could favor their acceptance of WebCT which is a
web-based application.

However, the respondents show a very weak score in terms of their use of the online chat
and of discussion forum. This low score is a bad sign of WebCT usage as these functionalities
are nonetheless among the essential components of the application, since they permit both
synchronous and asynchronous communication among learners as well as with the instructor.
293

5.1 Validation of the scales of measurement used
Variables measurements were validated through convergent and discriminant validity
testing. A principal components factorial analysis (PCA) was performed on each variables
measurement items in order to verify both types of validity. Additionally, the reliability of
each variable measurement was established by calculating Cronbach’s Alpha coefficient. The
tests of convergent and discriminant validity and of reliability are three measures necessary for
the validation of a scale of measurement. In the following section, the results of these three tests
are presented.

5.1.1. Test of convergent validity
An analysis of the correlations among the items measuring each variable was first carried

out, followed by a principal components analysis (PCA) with Varimax rotation when more than

one factorial axis was found.

The use of this method must satisfy three criteria. The first one is the criterion relative to

the eigenvalue which aids in identifying the number of components (factors) to retain. In this

study, we refer to Kaiser (1958)’s rule according to which only the axes whose eigenvalue is

higher than 1 are retained. The second criteria is related to the factorial contributions (loadings)

which aims at identifying relevant items or indicators that better explain a factor. According to

this criterion, only items with factorial contributions greater than 0.3 are accepted (Blau et al.,

1993). The last criterion deals with the communalities of items and it indicates the proportion of

explained variance in the combination of each factor. This criterion allows the assessment of

the level of representation of each item in the principal components. In this study, an item

whose communality was inferior to 0.4 was dropped from the analysis, in compliance with the

suggestions of Evrard et al. (2003).

5.1.1. Measurement of Perceived Ease of Comprehension
Table 2 includes items measuring the ease of comprehension. Results in Table 3 show
that the correlations among the items of measuring the ease of comprehension are positive and

294 Bangaly KABA

significant, which might be a manifestation of the uniqueness of this measurement. The PCA
yields a factor which explains 68.36% of the total variance, with important positive factorial
contributions (loadings) and a good quality of representation for each item (>0.4) (see Table 4).
Based on the above results, we can state that the unidimensionality of this measurement has
been proven.

Table 2: Presentation of items measuring the ease of comprehension.
Variable Codification Items description
Ease of 3.1 WebCT uses relevant
comprehension terms
3.2 WebCT uses simple
terms
3.3 WebCT includes
links that give
detailed information
3.4 WebCT has a
pleasant design
3.5 WebCT posts pages
that are easy to read

Table 3: Correlations matrix of ease of comprehension.
Items 3.1 3.2 3.3 3.4 3.5
3.1 1
3.2 0.753** 1
3.3 0.646** 0.555** 1
3.4 0.486** 0.490** 0.573** 1
3.5 0.587** 0.589** 0.608** 0.750** 1
*** p< 0.01; ** p<0.05; *p<0.1 ns: not significant

Table 4: Factorial solution of ease of comprehension.
Variables (Ease of Quality of
Items comprehension) representation
3.1 0.842 0.709
3.2 0.820 0.673
3.3 0.818 0.668
3.4 0.795 0.633
3.5 0.857 0.734
Eigenvalue 3.418
Explained variation 68.356

5.1.1 Measurement of perceived ease of navigation
Table 5 shows items measuring the ease of navigation. The correlations among the items

of the perceived ease of navigation variable are all positive and significant (Table 6) and

295

demonstrate the uniqueness of measurement of this variable. The PCA results in Table 7 show a

unique factor explaining 79.65% of the variance. All the items have a very good quality of

representation (>0.4).

Table 5: Presentation Items measuring the ease of navigation
Variable Codification Items description
Ease of 4.1 WebCT allows me to easily return
Navigation to previously-viewed pages
4.2 I can always tell where I am when
navigating WebCT
4.3 WebCT is an easy site to navigate

Table 6: Correlations matrix of ease of navigation
Items 4.1 4.2 4.3
4.1 1
4.2 0.715** 1
4.3 0.647** 0.721** 1
*** p< 0,01; ** p<0,05; *p<0,1 ns: not significant

Table 7: Factorial solution of ease of navigation
Variables (Perceived ease of navigation) Quality of representation
Items
4.1 0.881 0.776
4..2 0.912 0.832
4.3 0.884 0.781
Eigenvalue 2.389
Explained variation 79.649%

5.1.2 Discriminant Validity
The objective of this test is to verify the independence of the variables. Like for the test of

convergent validity, a principal components analysis was carried out on the items measuring

each variable. Three items were dropped from the analyses because each of them had a loading

greater than 0.3 on the two selected factors. These items are: Item 3.3 (WebCT includes links

that give detailed information) and item 3.4 (WebCT has a pleasant and agreeable design) for

the variable ease of comprehension; and item 4.3 (WebCT is an easy site to navigate) for ease of

navigation. According to the results discussed above and shown in table 8, we can assume the

independence of the two variables of the research.

296 Bangaly KABA

The reliability test will conclude this validation of scales. The results appear in the

following table:

Table 8: Results of the test of discriminant validity
Variables Ease of Ease of
Items comprehension navigation
3.1 0.887
3.2 0.872
3.5 0.809
4.1 0.924
4.2 0.884
Eigenvalue 2.894 1.139
Explained variation 57.888% 22.783%

5.1.3 Analysis of the Reliability of the Measurement
In order to ascertain the degree to which the measurement instrument (the questionnaire)

used in this study evaluates the perceptions of respondents in a consistent manner, we

performed a reliability analysis by calculating Cronbach’s Alpha coefficient. The results for the

two constructs of the study appear in the Table 9.

Table 9: Results of the reliability test.
Variables of the study
Variables Items Cronbach’s Alpha
Wording
Ease of comprehension 3.1.; 3.2; 3.5 0.8367
Ease of navigation 4.1; 4.2 0.8315

Throughout these results, we notice that the value of Cronbach’s Alpha for all the variables

is superior to 0.7, which shows the reliability of the adopted measurement instrument (Evrard et

al., 2003 ; Teo et al., 1999).

6. Conclusion, Limits, and Directions for Future Research
The goal of the present study was to determine and validate measurements of the

perception of ease of use which takes into account the features of an online tool, which is
297

WebCT. According to the analysis carried out in this work, perceived ease of comprehension

and perceived ease of navigation emerge as good alternatives which could serve as

measurements of the ease of use of WebCT, and indeed of other online interaction and learning

tools.

The implications of these results, for the designers of Web-based educational applications

in general and for those of WebCT in particular, are to continue to work toward making their

product as user-friendly as possible. More specifically, the terms used in educational web

applications must be as simple and relevant as possible. Jargon and technical terms in the

wording of text used for links should be carefully avoided. These recommendations are equally

valid for the academic content on WebCT. The results of this study can also be of benefit to

those individuals responsible for selecting online applications, in that they would know in

advance the relevant factors to take into account in order to increase the likelihood of success of

the chosen technologies.

Nevertheless, this research has its limits. For a better assessment of the face validity or the

content validity of the measurement used, it would have been helpful to recruit experts to

examine them. Increasing the survey sample size would also have been quite useful to ensure

that the study’s findings could be generalized. In the future, this study could be extended to

include other departments or universities where the level of ICT use is heterogeneous in order

to evaluate and understand possible differences in results. Further, the extension of the research

to other countries where the level of students’ access to e-learning tools is limited or at least is

still at an embryonic stage would constitute a relevant basis for comparison of the external

validity of the measurement instrument validated by this study. In such a study, it would be

beneficial to proceed with a confirmatory factor analysis.

298 Bangaly KABA

7. Acknowledgment
A very special thank you is due to Associate Professor Dr. Boonsap Witchayangkoon for
insightful comments, helping clarify and improve the manuscript.

8. References
Blau, G., A. Paul and N. St. John. (1993). “On developing a general index of work
commitment,” Journal of vocational behavior, Vol. 42, no. 3, pp. 298-314.

Benbasat, I. and Barki, H. 2007. Quo vadis, TAM? Journal for the Association of Information
Systems, Vol. 8, no.4, pp. 211-218

Bradley, R., Sankar, C., Clayton, H., Raju, PK, and Mbarika, V. (2006). “An Empirical
Investigation of the Impact of GPA on Perceived Improvement of Higher-Order
Cognitive Skills,” Decision Sciences Journal of Innovative Education.

Chris, Evans A, Nicola J. G. B., Kavita S. B, Darren K. G. (2004). “Virtual learning in the
biological sciences: pitfalls of simply ‘‘putting notes on the web”,” Computers &
education, Vol. 43, pp. 49–61.

Clark, J. (2002). “A product review of WebCT,” Internet and Higher education, Vol. 5, p.
79–82

Davis, F. D. (1989). “Perceived usefulness, perceived ease of use, and user acceptance of
information technology,” MIS quarterly, Vol.13, no. 3, pp.318-343.

Davis, F. D., R. Bagozzi P. and P. R. Warshaw. (1989). “User acceptance of computer
technology: a comparaison of two theoretical models,” Management science, Vol. 35,
no. 8, pp. 982-1003.

Emory, C. W. (1980). Business research methods. Revised edition, Homewood Irwin Inc.

Evrard, Y., B. Pras and E. Roux. (2003). Market: études et recherches en marketing. 3rd edition,
Paris, Dunod.

Goodhue, D. L., B. D., Klein and S. T., March. (2000). “User evaluation of IS as surrogates for
objective performance,” Information & management, Vol. 38, pp. 87-101.

Igbaria, M. (1994). “An examination of the factors contributing to microcomputer technology
acceptance,” Accounting, management and information technology, Vol. 4, no.4, pp.
205-224.

Kaiser, H.F (1958). “The varimax criterion for analytic rotation in factor analysis,”
Psychometrika, Vol. 23, p. 187-200.

http://www.itcortex.com/Stat_Failure_Cause.htm (visited 20 December 2003)

299

Lindgaard, G. (1994). Usability testing and system evaluation: A guide for designing useful
computer systems, Chapman & Hall, London.

Lederer, D.; Maupin, D.; Sena, M. and Zhuang, Y. (2000). “The technology acceptance model
and world wide web,” Decision support systems, Vol. 29, no. 3, p. 269-282.

Lee, Y., Kozar, K.A., Larsen, K.R.T. (2003). The Technology Acceptance model: Past, Present,
and future. CAIS, Vol. 12, pp. 752-780.

Legris, P. , J., Ingham and P. Collerette. (2003). “Why do people use information technology?
A Critical review of the technology acceptance model,” Information & management,
Vol. 40, pp. 191-204.

Mahler, A. and Rogers M. Everett. (2000). “The diffusion of interactive communication
innovations and the critical mass : the adoption of telecommunication services by
German banks,”.Telecommunication policy, Vol. 23, pp. 719-740

Markus, M. L. (1987). “Toward a critical mass theory of interactive media: universal access,
interdependence and diffusion,” Communication research, Vol. 14, pp. 491-511.

Mathieson, K. (1991). “Predicting user intentions: comparing the Technology Acceptance
Model with the Theory of Planned Behavior,” Information systems research, Vol. 2, no.
3, pp.173-191.

Mbarika, V., Sankar, C.S., Raju, P.K. (2003a). “Role of Multimedia Instructional Materials on
Multicriteria Technology and Engineering Decisions,” Decision Sciences Journal of
Innovative Education, Vol. 1, #2.

Mbarika, V., Sankar, C.S., Raju, P.K. (2003b). ”Identification of Factors that Lead to Perceived
Learning Improvements for Female Students,” IEEE Transactions on Education, Vol.
46, # 1, pp. 26-36.

Mbarika, V. (2004). “TeleEducation in Sub-Saharan Africa: A Breakout Approach to
Sub-Saharan Africa’s Education Dilemma,” IEEE Technology and Society,Vol. 22, #4,
pp. 20-26.

Mioduser, D.; Nachmis, R.; Lahave, O.; Oren, A. (2000). “Web-based learning environments:
current pedagogical and technological state,” Journal of research on computing in
education, Vol. 33, no. 1, pp. 55-76.

Moore, G. C. and Benbasat, I. (1991). “Development of an instrument to measure the
perception of adopting an information technology innovation,” Information systems
research, Vol.2 no.3, pp. 37-46.

Mustonen-Ollila, E. and K. Lyytien. (2003). “Why organizations adopt information system
process innovations: a longitudinal study using diffusion of innovation theory,”
Information systems journal, Vol. 13, p. 275-297.

Orlikowski, W.J. and J. J. Baroudi, (1991). “Studying information technology in organizations:

300 Bangaly KABA

research approaches and assumptions,” Information systems research, Vol. 2, no. 2, pp.
1-28.

Palloff, R., and Lamaster, K. (2001). “Lessons from the cyberspace classroom,” The realities of
online teaching, Sans Francisco: Jossey-Bass.

Paré, G. (2002). “La génération Internet : un nouveau profile d’employé,” Gestion, Vol. 27, no.
2, pp.47-57.

Szajna, B. (1996). “Empirical evaluation of the revised technology acceptance model,”
Management science, Vol. 42, no. 1, pp. 85-92.

Taylor, S and Todd, P.A. (1995). “Understanding information technology usage: a test of
competing models,” Information systems research, Vol. 6, no. 2, p. 91-108.

Teo, T.; Lim, V. and Lai, R. (1999). “Intrinsic and extrinsic motivation in Internet usage,”
Omega, International Journal of Management Science, Vol. 27, no. 1, pp. 25-37.

Venkatesh, V. and F. D. Davis (2000). “A theoretical extension of the technology acceptance
model: four longitudinal field studies,” Management science, Vol. 46, no. 2, p. 186-204.

WebCT (2005). http://WebCT.com/entrypage, visited September 2007

Dr. Bangaly Kaba earned his PhD degree in Information Systems from a joint PhD program administered by
the four largest universities in Montreal (UQAM, HEC, Concordia University and McGill University). He is a
visiting professor at International university of Grand-Bassam. His research interests include the adoption and
implementation of information and communication technologies (ICT), especially mobile technologies, the
impact of ICT on organizations, cultural issues in ICT adoption and use, tele-education, multimedia learning
case study, quantitative methods, and management of international projects.

Peer Review: This article has been internationally peer-reviewed and accepted for publication
according to the guidelines given at the journal’s website.

301

Validating Measurements of Perceived Ease Comprehension and Ease of Navigation of an Online Learning Technology: Improving Web Based Learning Tool Adoption and Use

Empfohlen

Empfohlen

Weitere ähnliche Inhalte

Was ist angesagt?

Was ist angesagt? (17)

Ähnlich wie Validating Measurements of Perceived Ease Comprehension and Ease of Navigation of an Online Learning Technology: Improving Web Based Learning Tool Adoption and Use

Ähnlich wie Validating Measurements of Perceived Ease Comprehension and Ease of Navigation of an Online Learning Technology: Improving Web Based Learning Tool Adoption and Use (20)

Mehr von drboon

Mehr von drboon (20)

Kürzlich hochgeladen

Kürzlich hochgeladen (20)

Validating Measurements of Perceived Ease Comprehension and Ease of Navigation of an Online Learning Technology: Improving Web Based Learning Tool Adoption and Use