Encyclopedia of Information Communication Technology

Encyclopedia of Information Communication Technology Antonio Cartelli University of Cassino, Italy Marco Palma University of Cassino, Italy

Volume II In-Z

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Section: Process ICT

Web-Based Course Management Systems (WCMS) Acceptance with College Students in Estonia Princely Ifinedo Cape Breton University, Canada

IntroductIon Increasingly, higher education institutions worldwide are adopting information and communication technologies (ICTs) to enhance pedagogy (Ifinedo, 2006; Lee, Cho, Gay, Davidson, & Ingraffea, 2003; Leidner & Jarvenpaa, 1993). Web-based course management systems (WCMS), such as WebCT and Learning Space, are among the notable ICTs diffusing in higher learning environments globally (Ifinedo, 2006; Tavangarian, Leypold, Nölting, Röser, & Voigt, 2004). WCMS are sometimes referred to as course management systems (CMSs). An instructor using a CMS can place course materials online, communicate with students, track their progress, and conduct online tests, quizzes, and so forth. Sometimes, CMSs are confused with another group of learning technology known as learning management systems (LMSs). Carliner (2005) provides a clear distinction between the two technologies; he notes that CMSs are used in the management of asynchronous educational environments (AEEs) whereas LMSs are basically registrars that perform various enrollment and registration tasks electronically. Examples of LMSs include Saba, NetDimensions EKP, and SumTotal. Both technologies are essential for an effective virtual learning environment (VLE) (Carliner, 2005; Tavangarian et al., 2004). We focus solely on WCMSs in this article in the bid to not generalize the two technologies. The objective of this article is to present the results of a study that investigates the acceptance of WCMS among college students in Estonia. The country is an emerging economy in the Baltic region of Europe. Estonia has made remarkable progress with respect to the use of ICT products in enhancing education at all levels (The Tiger Leap Foundation, 1997). Recently, Estonia joined forces with a pan-European e-learning project called the UNIVe (Estonian eUniversity, 2004; Ifinedo, 2005). Among other goals, the project aims at “increasing the availability of quality education for

students and other people willing to learn …and, educating lecturers of universities to compile and practice quality and efficient e-courses” (Ifinedo, 2006). In brief, the UNIVe project aspires to improve VLEs for the participating countries. WebCT is among the VLE tools being used by college students in Estonia. In this respect, this study will increase our understanding regarding the acceptance of such technologies in the region. The research is important for three reasons: (1) first, to provide empirical information about the acceptance of WCMS among Estonian college students, (2) to complement a recent study in Estonia in which the experiences of college teachers on WCMS was investigated, and (3) to answer calls being made for ICT studies to be extended to the other regions of the world, including Eastern Europe (see Ifinedo, 2006). Furthermore, this study draws from the technology acceptance model (TAM); for a theory to be considered valid, its veracity across a wide range of contexts needs to be established. Importantly, the findings of the study will be beneficial to administrators, instructors, and other entities involved in various e-learning projects in Estonia and comparable countries in the region.

lIterature revIew Among the most widely used theoretical frameworks for assessing the adoption or acceptance of technologies in the literature is the TAM, which was developed by Davis (1989). The model is comprised of three constructs (Figure 1). In brief, the TAM proposes that users’ acceptance of new information systems (ISs) can be predicted by the users’ perceptions of the ease of use and usefulness of the IS (Davis, 1989). The perceived ease of use construct in the TAM describes “the degree to which a person believes that using a particular system would be free of effort” (Davis, 1989, p. 320). The second construct is the perceived

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WCMS Acceptance with College Students in Estonia

usefulness which describes the user’s perceptions of the expected benefits derived from using a particular IS system (Davis, 1989). Usage is the dependent variable in the TAM, and it is “theorized to be influenced by perceived usefulness and perceived ease of use” (p. 320). In general, IS researchers have confirmed the relationships in the TAM (see Straub, Limayem, & Karahanna, 1995). Researchers (e.g., Brown, 2002; Lee et al., 2003; Limayem, Cheung, & Chan, 2003; Pan, Siva, & Brophy, 2003) have studied the adoption and acceptance of WCMS in college environments. Brown (2002) studied the acceptance of WebCT among college students in a developing country, The Republic of South Africa (RSA). He found that perceived ease of use is strongly related to usage and perceived usefulness. Limayem et al. (2003) found support among the constructs used to investigate the adoption (and continuance intention) of WCMS among students in Hong Kong, a developed economy. Lee et al. (2003) reported strong relationships between perceived ease of use and perceived usefulness as did Brown (2002). However, other researchers have reported equivocal results regarding the suitability and relevance of the TAM for WCMS in higher learning contexts. For example, Pan et al. (2003) concluded that the TAM may in fact not be applicable to higher educational settings following the lack of support among the relationships for the constructs in their study. In the same vein, other findings in the IS literature examining the relationships in the TAM framework have indicated mixed results as well (Gefen & Straub, 2000). Nevertheless, the TAM remains the most widely used framework for studying technology adoption and acceptance by IS researchers.

the research framework and hypotheses Figure 1 illustrates the TAM as well as a research model used for the study. The arrows in Figure 1 indicate the directions of the hypotheses (H1–H3) that are discussed in-depth below. Perceived ease of use and perceived usefulness have been noted as important predictors of information systems (IS) usage (Adams, Nelson, & Todd, 1992; Davis, 1989; Igbaria, Zinatelli, Cragg, & Cavaye, 1997; Straub et al., 1995). Studies have shown that perceived usefulness and perceived ease of use are good predic840

tors of usage (e.g., Adams et al., 1992, Igbaria et al., 1997; Straub et al., 1995). As previously noted, others have raised doubts as to the veracity of the framework or model for VLE tools in higher educational settings (Pan et al., 2003). Gefen and Straub (2000) suggested that perhaps some aspects of the TAM may be relevant for IS acceptance in the developed West. They listed 42 studies using the TAM and noted that 25 of these studies did not show perceived ease of use to be a significant predictor; the others showed mixed results and only nine studies seemed to uphold the view that perceived ease of use is a predictor of usage. At a general level, Gefen and Straub suggested that the perceived usefulness construct tends to support the TAM consistently. Similarly, other researchers (e.g., Anandarajan, Igbaria, & Anakwe, 2002; Brown, 2002) using the TAM to research IS acceptance in developing countries underscored the pertinence of relevance of regional contextual influences. For example, Anandarajan et al. (2002) and Brown (2002)—researching WebCT acceptance among students—found that perceived usefulness is not a significant predictor of usage, which is contrary to the view in Gefen and Straub. This information indirectly strengthens the observations in Gefen and Straub highlighting the role of contextual considerations. In this regard, results for the acceptance of WCMS in a developing country like RSA indicated that a strong relationship exists between perceived ease of use and usage. Despite its size, Estonia leads Eastern Europe with regard to the adoption and use of ICT products for socio-economic development. With regard to the Internet diffusion per capita, Estonia is among the world’s lead-

Figure 1. The technology acceptance model Perceived Usefulness

H1

Perceived Ease of Use

Usage

H2

H3


ers (WEF, 2004). Estonian leadership in ICT products’ use and diffusion has benefited various Web-based and e-learning initiatives in the country (Estonian eUniversity, 2004; The Tiger Leap Foundation, 1997). Given these favorable conditions, it would be reasonable to conjecture that use of, and experience with, Web-based technologies, including WCMS, among students in the country will be positive. Following the foregoing discussion, we propose a set of hypotheses: H1: Perceived ease of use of WCMS among Estonian college students will have a positive effect on perceived usefulness of WebCT. H2: Perceived usefulness of WCMS among Estonian college students will have a positive effect on usage of WebCT. H3: Perceived ease of use of WCMS among Estonian college students will have a positive effect on usage of WebCT.

research methodology Method and Research Constructs This exploratory study used a convenient sample of 72 students. The participants came from four tertiary institutions in Estonia, that is, The Estonian Business School, Tallinn University of Technology, Tartu University, and Estonian IT College. The study used judgmental sampling (Neuman, 1997), an approach that permits the researchers to self-select research elements with experience or expertise in the research theme. The author self-administered a two-page questionnaire to students with WebCT experience. The students were classified into two groups. Thirty students in the information technology (IT) and engineering disciplines were labeled “IT savvy” and those from the social sciences and the arts as “non-IT savvy.” (See Table 1 for their mean scores for the TAM constructs.) Importantly, a one-way ANOVA test between the two groups of students indicates little or no statistical differences between the two groups. (Recall the purpose of the study is to elicit views of students that have used WebCT; thus, we did not consider the views of “nonadopting” students.)

The questionnaire included validated measures from the relevant literature. It contained multiple indicators for each of the constructs (see Appendix). Three and four items were used to measure perceived usefulness (PUS) and perceived ease of use (PEOU), respectively. The seven items were taken from Davis (1989) and Brown (2002). The usage (USG) scale consists of two items from Igbaria (1990). The measurements were operationalized using a Likert-type scale ranging from 1 (strongly disagree) to 7 (strongly agree) except for WebCT usage measures which used five and six options. SPSS 10.0 and PLS Graph 3.0 were used for data analysis. The students’ demographic profile and the diversity in WebCT use are shown in Table 2.

data analysis The PLS (partial least squares) procedure is used to assess the casual model. The approach is suitable for studies with small-sized samples (Chin, 1998) such as this one. PLS recognizes two components of a casual model: the measurement model and the structural model. The measurement model consists of relationships among the factors of interest (i.e., the observed variables) and the measures underlying each construct; it demonstrates the construct validity of the research instrument (i.e., how well the instrument measures what it purports to measure). The two main dimensions are the convergent validity and discriminant validity. First, convergent validity (composite reliability) assesses the extent to which items on a scale are theoretically related. Chin (1998) recommends items with loadings of greater than 0.70. Second, discriminant validity checks the extent to which items measure a construct. The square root of the average variance extracted (AVE) for each construct is used to check this measure. Fornell and Larcker (1981) recommend values higher than 0.50. In the structural model, this measure gives information as to how well the theoretical model predicts the hypothesized paths or relationships. PLS software provides the squared multiple correlations (R2) for each endogenous construct in the model and the path coefficients. The R2 indicates the percentage of a construct’s variance in the model while the path coefficients indicate the strengths of relationships between constructs (Chin, 1998).

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Table 1. The breakdown of the respondents Measures and constructs

IT savvy students (mean)

Non-IT savvy students (mean)

All students (mean)

Perceived usefulness (PUS) Perceived ease of use (PEOU) Usage (time spent) (USG1) Usage (frequency) (USG2)

4.09 5.96 3.33 3.90

3.28 4.65 3.40 4.29

3.62 5.12 3.37 4.13

Number

Percent

Table 2. Demographic profile of respondents

Gender

Male Female

32 40

44.4 55.6

Age

Less than 25 years 26-39 years

63 9

87.5 12.5

Education (level)

Year 1 Year 2 Year 3 Year 4

13 22 13 24

18.1 30.6 18.1 33.3

Study program (Department)

Business / Economics Information Technology Mechanical Engineering Philosophy Electrical Engineering

36 16 9 6 5

50 22.2 12.5 8.3 6.9

1-2 yrs 2-3 3-4 4-5 more than 5 yrs

1 5 9 23 34

1.4 6.9 12.5 31.9 42.7

Mean

Standard deviation

5.13 5.00 5.61 5.53 3.32

1.79 1.50 1.57 1.99 1.52

Years of experience with the Internet

Diversity of WebCT use

Web browsing downloading e-mail chat room discussion lists

Each task was anchored on a Likert scale ranging from 1 (Never use it) to 7 (Use it a great extent).

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Table 3. Psychometric properties of constructs

Construct

W

Item

Loading

t-value

PEOU1

0.8037

11.2561

PEOU2

0.8640

18.9045

PEOU3

0.9008

42.8258

PEOU4

0.8519

22.8740

Perceived usefulness (AVE = 0.815)

PUS1

0.9004

30.0409

PUS2

0.9352

57.0995

PUS3

0.8722

24.2192

Usage (AVE = 0.914)

USG1

0.9513

52.2206

USG2

0.9611

82.7979

AVE

PUS

PEOU

PUS

0.815

0.903

PEOU

0.732

0.650

0.856

USG

0.914

0.503

0.714

Perceived ease of use (AVE = 0.732)

Composite reliability

0.916

0.930 0.955

Table 4. Correlations of latent constructs

Assessing the Measurement Model Table 3 shows the results of the measurement model. The composite reliability values were consistently above 0.9, which exceeds the recommended values by Chin (1998). The items loadings meet Chin’s (1998) guideline of between 0.60 and 0.70. Each of the construct’s AVEs exceeds the 0.5 guideline as suggested by Fornell and Larcker (1981). Table 4 shows the AVEs, intercorrelations among the constructs, and the square root of AVE (in bold text). No correlations were equal to or greater than the squared root of AVE in the leading diagonal. This suggests that our measures are distinct and unidimensional. In brief, the convergent and discriminant validity in this study is psychometrically adequate.

USG

0.956

path’s coefficients and the size of the R2 values. The values are generated by PLS Graph 3.0. The test of significance of all the paths was done using the bootstrap resampling procedure with 200 resamples. It can be seen that perceived ease of use has a strong effect on perceived usefulness with a path coefficient of 0.375 and accounts for 66% in the variation of perceived usefulness. The perceived ease of use construct has a significant effect on usage of WebCT (path coefficient = 0.676). Inconsistent with our hypothesis (H2), perceived usefulness did not have a significant effect on WebCT usage (i.e., the path coefficient is 0.072). Together, perceived ease of use and perceived usefulness explained 21% of the variance in the usage construct.

Assessing the Structural Model

dIscussIon and conclusIon

As noted above, the structural model is concerned with the explanatory power of variables. Figure 2 shows the

This study investigates the acceptance of WCMS by college students in Estonia, which is an emerging 843


Figure 2. The results of PLS Graph 3.0 analysis R2 = 0.66

R2 = 0.21 0.072

Perceived usefulness

Usage

0.375

0.676

Perceived ease of use

economy in Eastern Europe. The study found support for two of the paths (i.e., hypotheses) in the TAM. To our knowledge, this study is among the first to investigate this theme in the Baltic region. The study found support for relationships between perceived ease of use and usage and perceived usefulness, but the data did not support the relationship between perceived usefulness and usage. The empirical evidence from this study can make both practical and theoretical contributions. Practically, the findings suggest that college students and other entities using WCMS and related technologies use them more when the use of such tools is perceived to be less difficult to use. It is not sufficient to expect that the perceived usefulness of WCMS and related VLE tools will lead to an increase in their use (and success). This finding is useful for e-learning and Web-based learning projects administrators in Estonia and similar countries that may be looking for empiric information that could enable them to maximize their returns on investment in VLE and AEE technologies. Theoretically, the results in this study add to the debate regarding the results obtainable in the TAM studies in general (Gefen & Straub, 2000) and for WCMS acceptance in higher education settings in particular (Pan et al., 2003). As was discussed, Gefen and Straub (2000) suggested that some aspects of the TAM may be more important for IS acceptance in richer nations. Anandarajan et al. (2002) and Brown (2002) provided evidence for developing countries. In this study, we enriched insight with evidence from an emerging economy country. The data analysis and conclusions lend support to the 844

observations with regard to the results in the TAM studies in the developing and emerging countries; that is, perceived usefulness and usage are less of a predictor in the TAM for IS acceptance in these contexts. With this contribution, the body of knowledge in the IS field is enriched and new insights could emerge. For example, researchers could also investigate why such differences exist. There are limitations in this study. The research is exploratory; as such, a convenient sample size of 72 may be limiting. The selected subjects may not be representative of all college students in Estonia (or the region). The measurement of WebCT usage was selfreported; and this might limit insight. This study is a cross-sectional study; a longitudinal study may be enlightening. In order to improve upon the generalizability of this study’s findings, future studies could increase the sample size as well make an effort to incorporate other relevant variables such as age, peer pressure, and facilitating conditions into the research model.

references Adams, D.A., Nelson, R.R., & Todd, P.A. (1992). Perceived usefulness, ease of use and usage of information technology: A replication. MIS Quarterly, 16(2), 227-247. Anandarajan, M., Igbaria, M., & Anakwe, U. (2002). IT acceptance in a less-developed country: A motivational factor perspective. International Journal of Information Management, 22(1), 47-65.


Brown, I. (2002). Individual and technological factors affecting perceived ease of use of Web-based learning technologies in a developing country. Electronic Journal of Information Systems in Developing Countries, 9(5), 1-15. Carliner, S. (2005). Course management systems versus learning management systems. Retrieved March 6, 2008, from http://www.learningcircuits.org/2005/ nov2005/carliner.htm Chin, W. (1998). Issues and opinion on structural equation modeling. MIS Quarterly, 22(1), vii-xvi. Davis, F.D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-339. Estonian eUniversity. (2004). The UNIVe Project. Retrieved March 6, 2008, from http://www.e-uni. ee/Minerva/ Fornell, C., & Larcker, D.F. (1981). Evaluating structural equations models with unobservable variables and measurement error. Journal of Marketing Research, 18, 39-50. Gefen, D., & Straub, D. (2000). The relative importance of perceived ease of use in the IS adoption: A study of e-commerce adoption. Journal of AIS, 1(8), 1-28. Ifinedo, P. (2005, September 12-15). E-learning technology adoption factors in an Eastern European country: An exploratory study. In Proceedings of the 9th EastEuropean Conference on Advances in Databases and Information Systems (ADBIS’2005), Tallinn, Estonia (pp. 249-262). Ifinedo, P. (2006). Acceptance and continuance intention of Web-based learning technologies (WLT) use among university students in a Baltic country. Electronic Journal of Information Systems in Developing Countries (EJISDC), 23(6), 1-20. Igbaria, M. (1990). End-user computing effectiveness: A structural equation model. Omega, 18(6), 637-652. Igbaria, M., Zinatelli, N., Cragg, P., & Cavaye, A.L.M. (1997). Personal computing acceptance factors in small firms: A structural equation model. MIS Quarterly, 21(3), 279-305. Lee, J., Cho, H., Gay, G., Davidson, B., & Ingraffea, A. (2003). Technology acceptance and social network-

ing in distance learning. Educational Technology & Society, 6(2), 50-61. Leidner, D.E., & Jarvenpaa, S.L (1993). The information age confronts education: Case studies on electronic classrooms. Information Systems Research, 4(1), 24-54. Limayem, M., Cheung, C.M.K., & Chan, G.W.W. (2003). Explaining information systems adoption and post-adoption: Towards an integrative model. In Proceedings of the 24th. International Conference on Information Systems. Neuman, W.L. (1997). Social research method. London: Allyn and Bacon. Pan, C., Siva, S., & Brophy, J. (2003). Students’ attitude in a Web-enhanced hybrid course: A structural equation modeling inquiry. Journal of Educational Media and Library Sciences, 41(2), 181-194. Straub, D., Limayem, M., & Karahanna, E. (1995). Measuring system usage: Implications for IS theory testing. Management Science, 41(8), 1328-1342. Tavangarian, D., Leypold, M.E., Nölting, K., Röser, K.M., & Voigt, D. (2004). Is e-learning the solution for individual learning? Electronic Journal of E-Learning, 2(2), 273-280. Tiger Leap Foundation, The. (1997). Retrieved March 6, 2008, from http://www.tiigrihype.ee/eng/sihtasutus/ tootajad.html World Economic Forum (WEF). (2004). The networked readiness index rankings 2003. Retrieved March 6, 2008, from http://www.weforum.org/pdf/Gcr/GITR_ 2003_2004/Framework_Chapter.pdf

key terms Asynchronous Educational Environment (AEE): This is a learning–teaching environment in which there is no timing requirement. Students can access course material any time from anywhere. Emerging Economy: This term has differing interpretations; however, for the purposes of this study, we used it to refer an economy-type whose socio-economic indicators (e.g., poverty and technological levels) are better than those of developing countries (e.g., Nigeria and Burma), but lower in comparison to richer coun845

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tries (e.g., Italy and Canada). Examples of emerging economy countries include Estonia and Hungary. Learning Management Systems (LMS): These are tools that primarily act as electronic registrars and allow the monitoring of various enrollment and related tasks in a virtual learning environment. Examples of LMS include Saba, NetDimensions EKP, and SumTotal. Perceived Ease of Use: This refers to the degree to which an individual believes using a particular information system would be free of effort. Perceived Usefulness: This refers to an individual’s perceptions of the expected benefits from using a particular IS system

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Technology Acceptance Model (TAM): This is a theoretical framework designed by Davis (1989) that proposes a relationship between users’ acceptance of a new IS and the users’ perceptions of the ease of use and usefulness of the IS. Usage: This is the dependent variable in the TAM, and it gives an indication of the use of the information system. Virtual Learning Environment (VLE): An environment that facilitates the management of courses between the instructor(s) and students. It is used to be used to support flexible and distance learning. Web-Based Course Management Systems (WCMS): These are tools that permit the management of asynchronous learning environments. Examples include WebCT and Blackboard.