Learners-acceptance-of-e-learning-in-South-Korea-Theories-and-results_2009_Computers-Education

Learners’ acceptance of e-learning in South Korea: Theories and resultsa Department of Business Administration, Graduate School of Business Administration, Keimyung University, South Kor

Learners’ acceptance of e-learning in South Korea: Theories and results

a

Department of Business Administration, Graduate School of Business Administration, Keimyung University, South Korea

b

Graduate School of Education, Keimyung University, South Korea

c Department of Information Systems and Decision Sciences, College of Business and Technology, Western Illinois University, Macomb, IL 61455, United States

a r t i c l e i n f o

Article history:

Received 16 February 2009

Received in revised form 18 June 2009

Accepted 21 June 2009

Keywords:

E-learning

Service quality

Playfulness

Technology acceptance

a b s t r a c t

One of the most significant changes in the field of education in this information age is the paradigm shift from teacher-centered to learner-centered education Along with this paradigm shift, understanding of students’ e-learning adoption behavior among various countries is urgently needed South Korea’s dense student population and high educational standards made investment in e-learning very cost-effective However, despite the fact that South Korea is one of the fastest growing countries in e-learning, not much

of the research results have been known to the globalized world By investigating critical factors on e-learning adoption in South Korea, our study attempts to fill a gap in the individual country-level e-learning research

Based on the extensive literature review on flow theory, service quality, and the Technology Accep-tance Model, our study proposes a research model which consists of four independent variables (instruc-tor characteristics, teaching materials, design of learning contents, and playfulness), two belief variables (perceived usefulness and perceived ease of use), and one dependent variable (intention to use e-learn-ing) Results of regression analyses are presented Managerial implications of the findings and future research directions are also discussed

Ó 2009 Elsevier Ltd All rights reserved

1 Introduction

One of the most significant changes in the field of education during the information age is the paradigm shift from teacher-centered to learner-centered education The emergence of electronic learning (e-learning) has further facilitated the wide adoption of learner-centered education and other changes in educational practices E-learning has drawn significant attention from educational institutions, educational software developers, and business organizations due to the potential educational and cost benefits Such benefits are reduced education cost, consistency, timely content, flexible accessibility, and convenience (Cantoni, Cellario, & Porta, 2004; Kelly & Bauer, 2004) Educational values can be also enhanced by customizing content for the learners’ needs (Engelbrecht, 2003)

Many educational institutions are now offering innovative online degree programs, expanding their educational territories without time and space barriers, and complementing their traditional offline class with web-based online educational tools For-profit and non-profit organizations are increasingly replacing traditional offline job training with online training programs They claim that online training saves training costs and enhances learning effectiveness by delivering high-quality training services

The success of e-learning in large part depends on the implementation of an educational model which addresses the learners’ needs and educational objectives Designing good e-learning services is a complicated task and requires a multidisciplinary approach While a number

of studies have investigated success factors and benefits of e-learning, there is still a lack of empirical studies that focus on the relationships among e-learning service factors and learners’ acceptance (Liaw, 2008; Liu, Liao, & Pratt, 2009; Pituch & Lee, 2006; Sánchez-Franco, Martí-nez-López, & Martín-Velicia, 2009)

The development of e-learning in South Korea is strongly related to the rapid growth of its Information and Communications Technol-ogy (ICT) industry (Misko, Choi, Hong, & Lee, 2005) High-quality e-learning services have been rapidly developed due to the nation-wide telecommunication infrastructure and high-speed Internet Korean government has been one of the driving forces behind the rapid growth

of e-learning In 2001, the ‘Law for Developing On-Line Digital Contents Industry’ was enacted to promote digital contents for education and to produce IT professionals

0360-1315/$ - see front matter Ó 2009 Elsevier Ltd All rights reserved.

* Corresponding author Tel.: +1 309 298 1409; fax: +1 309 298 1696.

E-mail address: I-Lee@wiu.edu (I Lee).

Contents lists available atScienceDirect Computers & Education

j o u r n a l h o m e p a g e : w w w e l s e v i e r c o m / l o c a t e / c o m p e d u

South Korea’s highly dense population and high literacy rate of over 97% provides cost-effective conditions for investment in e-learning Due to the great interest of the general public in education, Korea’s enrollment rate in higher education is over 70% The high enrollment rate and dense student population make investments in e-learning cost-effective Realizing the potential benefits of e-learning, companies

in South Korea are increasingly adopting e-learning to train their employees and to improve their productivity

While the diffusion of e-learning in South Korea is rapidly progressing, little of this has been known to the international field of e-learn-ing E-learning has become an important educational method in the internationalization of higher education Increasing number of foreign students are taking online courses from abroad and obtaining online degrees (Hannon & D’Netto, 2007; Huynh, Umesh, & Valacich, 2003) Many higher education institutions in the US are developing degree programs overseas because of academic and business reasons (Bollag,

2006) In South Korea, leading universities such as Korea University and Yonsei University also established and plan to establish branch campuses in the US Therefore, it is increasingly important to promote individual country-level e-learning research in a global society

By investigating e-learning adoption in South Korea from student perspectives, our study attempts to fill a gap in the individual coun-try-level e-learning research

The rest of this study proceeds with a brief review of literature made by previous researchers, a description of the research model and hypotheses for empirical testing, a description of the research methodology, data analyses, a discussion of the results, the implications of the findings for researchers and practitioners, and limitations of the study

2 Literature review

2.1 Definition of e-learning

The term e-learning has been widely used in education since the mid-1990s However, the definition of e-learning has not been clearly agreed on Some researchers view e-learning as the delivery of teaching materials via electronic media, such as Internet, Intranets, Extra-nets, satellite broadcast, audio/video tape, interactive TV, and CD-ROM (Engelbrecht, 2005) Other researchers view e-learning as a web-based learning which utilizes web-web-based communication, collaboration, knowledge transfer, and training to add values to the individuals and the organizations (Kelly & Bauer, 2004) While it is generally accepted by most researchers that e-learning can be delivered by any electronic media other than web-based media, web technologies have made e-learning more widely accepted by academic institutions

as well as business organizations (Alavi & Leidner, 2001; Hiltz & Turoff, 2005) E-learning has become an indispensible part in the compet-itive educational services market Educational service providers offer online lessons, online tests, and educational consulting to meet the diverse demands of the educational customers

Active learning is an instructional method that engages students in the learning process by requiring students to do meaningful learning activities (Bonwell & Eison, 1991) Active learning is often contrasted to the traditional lecture where students passively receive informa-tion from the instructor The online learner must be active in the process, cognitively complex and motivated for quality e-learning (Alley & Jansak, 2001; Clark, 2002) E-learning provides many opportunities for media-based, student-centered, and interactive learning environ-ments that support active learning (Huffaker & Calvert, 2003; Zhang, Zhao, Zhou, & Nunamaker, 2004)

Based on the definitions used in the existing studies, for this research e-learning is defined as based learning which utilizes web-based communication, collaboration, multimedia, knowledge transfer, and training to support learners’ active learning without the time and space barriers

Even though the potential benefits of e-learning may be significant, there are a number limitations and challenges to e-learning prac-tices E-learning generally requires a high upfront cost, new pedagogical skills, and learners’ self-discipline and motivation (Cantoni et al.,

2004) Security issues such as cyber attacks and hacking to e-learning systems are of concern to the learners and service providers (Ramim

& Levy, 2006) In administering online tests, authenticating test-takers is one of the major challenges due to the inability to directly mon-itor the exam takers To enhance the assessment of learning performance, some educational service providers or higher education institu-tions offer a mixture of online tests and offline tests (Gunasekaran, McNeil, & Shaul, 2002)

A number of studies indicated that the degrees of learner satisfaction with e-learning have been widely used to evaluate the effective-ness of e-learning (Zhang et al., 2004;Eom, Wen, & Ashill, 2006; Levy, 2007) The early studies show that technology, technical competency, motivation, instructor characteristics, and student characteristics are factors that affect the effectiveness of e-learning (Dillon & Gunawar-dena, 1995; Leidner & Jarvenpaa, 1993; Soong, Chan, Chua, & Loh, 2001; Volery & Lord, 2000) Recent studies focused on a wider variety of factors that affect the students’ acceptance of e-learning Pedagogical design and students/facilitator interaction are shown to affect stu-dent’s acceptance of e-learning (Martínez, del Bosch, Herrero, & Nuño, 2007) Roca, Chiu, and Martinez (2006)applied the Technology Acceptance Model (TAM) and found that users’ continuance intention is determined by satisfaction, which in turn is jointly determined

by perceived usefulness, information quality, confirmation, service quality, system quality, perceived ease of use and cognitive absorption More recently,Levy (2008)investigated issues related to learners’ perceived value by uncovering the critical value factors (CVFs) of online learning activities His study identified five reliable CVFs that contribute to learners’ perceived value: (a) collaborative, social, and passive learning activities; (b) formal communication activities; (c) formal learning activities; (d) logistic activities; and (e) printing activities While the majority of studies focused on the learners’ acceptance of e-learning, instructors’ acceptance of e-learning is also of great con-cern for educational institutions Many educational institutions have provided special training and incentives to the instructors who are willing to incorporate e-learning to their curriculum A number of studies have investigated instructors’ perception on e-learning and suc-cess factors (Hu, Clark, & Ma, 2003; Kollias, Mamalougos, Vamvakoussi, Lakkala, & Vosniadou, 2005; Liaw, Huang, & Chen, 2007; Myers, Bennett, Brown, & Henderson, 2004) In the following, we review in detail the Technology Acceptance Model (TAM), service quality, and flow theory in an e-learning context upon which our research model is based

2.2 E-learning Technology Acceptance Model (TAM)

TAM was introduced by Davis (1986) to explain computer-usage behavior Since then, TAM has been the most frequently cited and influential model for understanding the acceptance of information technology and has received extensive empirical support

(e.g.,Venkatesh, Morris, Davis, & Davis, 2003) The theoretical basis of TAM wasFishbein and Ajzen’s (1975)Theory of Reasoned Action (TRA) TRA is a widely-studied model from social psychology which is concerned with the determinants of consciously intended behaviors According to TRA, a person’s performance of a specified behavior is determined by his or her behavioral intention (BI) to perform the behav-ior, and BI is jointly determined by the person’s attitude (A) and subjective norm (SN) concerning the behavior in question

TAM proposes external variables as the basis for tracing the impact of external factors on two main internal beliefs, perceived usefulness (PU) and perceived ease of use (PEU) According toDavis (1989), perceived ease of use is the degree to which a person believes that using a particular system would be free of effort and perceived usefulness is the degree to which a person believes that using a particular system would enhance his or her job performance These two beliefs both influence users’ attitude toward using information systems (IS) Despite the potential of e-learning as a tool to enhance education and training performance, its value will not be realized if users do not accept it as a learning tool Since e-learning utilizes information technology, TAM has been extensively utilized and extended for research

in an e-learning context The two TAM constructs (perceived usefulness and ease of use) were applied to assess university students’ accep-tance of course websites as an effective learning tool (Selim, 2003) Results revealed that perceived usefulness and ease of use of course website proved to be key determinants of the acceptance and usage of course website as an effective and efficient learning technology

To understand an engineer’s acceptance of e-learning,Ong, Lai, and Wang (2004)proposed a construct, perceived credibility, which mea-sures the degree to which a person believes that a particular system would be free of privacy and security threats Their empirical study supports that perceived credibility has a positive effect on engineers’ intention to use e-learning, suggesting learners must be assured that they are free of privacy and security threats

Another study investigated the effect of system characteristics on e-learning system use (Pituch & Lee, 2006) After examining a variety

of general information systems characteristics, they selected three system characteristics: system functionality, interactivity, and response time System functionality refers to the ability of e-learning systems to provide flexible access to instructional materials via various types of media such as video, audio, and text Interactivity refers to the ability of e-learning systems to facilitate the interactions among students and between faculty and students Tools commonly used in interactive e-learning are e-mail, bulletin boards, and chat room Response time

is the degree to which e-learning systems’ response to learners’ inquiries is fast, consistent, and reasonable All these three characteristics are shown to affect the usefulness and intention to use e-learning systems

Self-determination theory (SDT) was applied to examine the effects of motivational factors affecting TAM constructs in e-learning in a work setting (Roca & Gagné, 2008) They introduced three motivational factors (perceived autonomy support, perceived competence, and perceived relatedness) based on SDT Perceived autonomy support refers to the e-learning support for the learners’ desire to self-organize their actions Perceived competence refers to the belief that one can successfully perform a distinct set of actions required to utilize effec-tively e-learning Perceived relatedness refers to the belief that one feels connected and supported by important people, such as instructors

or other learners The perceived autonomy support, competence, and relatedness were shown to influence perceived usefulness, playful-ness, and ease of use

Other factors such as learner computer anxiety, instructor attitude toward e-learning, e-learning course flexibility, e-learning course quality, and diversity in assessments also seem to affect learners’ satisfaction (Sun, Tsai, Finger, Chen, & Yeh, 2008) Perceived usefulness and self-efficacy were shown to influence behavioral intention to use e-learning (Liaw et al., 2007)

2.3 E-learning service quality

While TAM was developed to understand computer-usage behavior,Parasuraman, Zeithaml, and Berry (1985), Parasuraman, Zeithaml, and Berry (1988)developed SERVQUAL, a conceptual model of service quality from their work in the area of retail marketing SERVQUAL is based on the assumption that satisfaction is found in situations where perceptions of service quality meet or exceed consumer expecta-tions.Parasuraman, Zeithaml, and Berry (1988)developed the original 22-item five-dimension SERVQUAL based on extensive focus group research (Parasuraman, Zeithaml, & Berry, 1985) The five-dimensions underlying the 22-items include:

 Tangibles: Physical facilities, equipment and appearance of personnel

 Reliability: Ability to perform the promised service dependably and accurately

 Responsiveness: Willingness to help customers and provide prompt service

 Assurance (including competence, courtesy, credibility and security): Knowledge and courtesy of employees and their ability to inspire trust and confidence

 Empathy (including access, communication, understanding the customer): Caring, individualized attention the firm provides its customers Recognizing that SERVQUAL is not sufficient for measuring e-business service quality,Kaynama and Black (2000)developed an e-service quality measure comprised of seven dimensions: content, access, navigation, design, response, background, and personalization.Aladwani and Palvia (2002)reported on the development of an instrument that captures key characteristics of web site quality from the user’s per-spective The 25-item instrument measures four dimensions of web quality: specific content, content quality, appearance and technical adequacy The instrument exhibits psychometric properties and provides an aggregate measure of web quality Given the importance of

IS support,DeLone and McLean (2003)recommended that service quality be added as an important dimension of IS success, especially

in the e-commerce environment where customer service is crucial

Service quality measurement tools have also been developed in the e-learning context In e-learning, the commitment and ability of the instructors are important factors that affect the confidence and trust level of the learners (Dillon & Gunawardena, 1995; Webster & Hack-ley, 1997) The quality of e-learning teaching materials affects the satisfaction of the learners (Sun et al., 2008) The more confidence and trust the learners have in the quality of teaching materials used for their learning, the more satisfied they are with e-learning environ-ments If teaching materials do not meet learners’ expectations, learners tend to be easily distracted and feel uncomfortable and thus over-burdened with e-learning High-quality teaching materials motivate learners to continue e-learning by generating more value Therefore, the development of learner-centered teaching materials is critical to the success of e-learning Personalization is important in web-based learning Personalization of web-based learning requires collection of personal data to profile learner preferences, interests, and browsing behaviors in providing personalized services (Chen, Lee, & Chen, 2005)

2.4 Flow theory and e-learning

Flow theory emphasizes the role of a specific context rather than individual differences in explaining human motivated behaviors Csiks-zentimihalyi (1975)pioneered Flow Theory, and defined ‘flow’ as ‘‘the holistic sensation that people feel when they act with total involve-ment” (p 36) Different researchers developed different measurement tools for flow which reflect the unique aspects of analysis For example,Novak, Hoffman, and Yung (2000)measured pleasure which people can experience when they are immersed in certain activities While a number of researchers suggested methodologies and measurement items to measure flow, there has not been a universal mea-surement tool Playfulness is a concept that is used most widely to measure flow Playfulness is a complex variable which includes indi-vidual’s pleasure, psychological stimulation, and interests (Csikszentmihalyi, 1990).Moon and Kim (2001)view playfulness as a situational characteristic of the interaction between an individual and the situation Three dimensions of perceived playfulness proposed byMoon and Kim (2001)are the extent to which the individual: (1) perceives that his or her attention is focused on the interaction with the web-based system; (b) is curious during the interaction; and (3) finds the interaction intrinsically enjoyable or interesting Since an online consumer is both a buyer and a computer user, one’s experience level (or self-efficacy) and one’s degree of product involvement should influence one’s degree of playfulness in a particular online purchasing context (Koufaris, 2002) Other studies also show that computer experience affect playfulness (Hackbarth, Grover, & Yi, 2003; Webster & Martocchio, 1992)

A few e-learning studies address contribution of playfulness to instructors’ and learners’ acceptance of e-learning service Integrating a motivational perspective into the Technology Acceptance Model,Lee, Cheung, and Chen (2005)captured both extrinsic (perceived useful-ness and ease of use) and intrinsic (perceived enjoyment) motivators for explaining students’ intention to use e-learning services The results showed that both perceived usefulness and perceived enjoyment significantly and directly impacted their intention to use e-learn-ing services On the other hand, perceived ease of use did not have a significant effect on student attitude or intention to use e-learne-learn-ing services Our literature review reveals that further research is still needed to understand playfulness and the adoption of e-learning The next section discusses our research model and hypotheses

3 Research model and hypotheses

3.1 Research model

Based on the literature review, we believe that comprehensive research is needed to assess the intention to use e-learning by the current and future learners The proposed model consists of four independent variables, two belief variables, and one dependent variable The four independent variables are playfulness and three service quality constructs – instructor characteristics, teaching materials, and design of learning contents Instructor characteristics are defined as the extent to which instructors are caring, helpful, and accommodating to stu-dents Teaching materials are defined as the extent to which teaching materials are suitable for e-leaning Design of learning contents is defined as the extent to which learning contents are designed and developed to fit students’ needs Two belief variables are perceived use-fulness and perceived ease of use Perceived useuse-fulness is the degree to which a person believes that a particular e-learning service would enhance his/her learning performance Perceived ease of use is the degree to which a person believes that using a particular e-learning service would be free of effort The dependent variable is the intention to use e-learning.Fig 1shows our conceptual research model 3.2 Hypotheses

Instructor’s attitude and ability affect learners’ attitude toward e-learning, and instructor’s teaching style affects learners’ enthusiasm, participation, and attitude toward e-learning (Dillon & Gunawardena, 1995; Webster & Hackley, 1997) An empirical study on student atti-tude towards using e-learning reveals that instructor characteristics are the most critical factor in e-learning success, followed by IT infra-structure and university support (Selim, 2007) A recent study suggests that e-learning course quality affect learners’ perceived satisfaction (Sun et al., 2008) Thus, this study hypothesizes the followings:

3.2.1 Hypothesis 1

Instructor characteristics positively affect learners’ perceived usefulness in the e-leaning context

3.2.2 Hypothesis 2

Teaching materials positively affect learners’ perceived usefulness in the e-leaning context

Lederer, Maupin, Sena, and Zhuang (2000)demonstrated that ease of understanding and ease of finding various web contents predict ease of use Learners will be more inclined to feel that using the e-learning services is easy if e-learning services are provided with plentiful contents designed to meet their needs In the e-learning context, learner-centered services which provide learners with learning contents accurately and consistently will facilitate perceived ease of e-learning use These lead to the following hypothesis

3.2.3 Hypothesis 3

Design of learning contents positively affects their perceived ease of use in the e-leaning context

The fundamental constructs of TAM are perceived usefulness and perceived ease of use Researchers indicate that perceived ease of use affects usage directly and indirectly through perceived usefulness (Venkatesh & Davis, 2000) In the e-learning context, research indicates that ease of use positively affects the system use and perceived usefulness (Pituch & Lee, 2006) Thus, this study hypothesized the following

3.2.4 Hypothesis 4

Learners’ perceived ease of use positively affects their perceived usefulness

Previous research suggests that the success of e-learning depend on continued usage (Chiu, Hsu, Sun, Lin, & Sun, 2005) Studies indicated perceived usefulness contribute to the learners’ behavioral intention to use the e-learning system (Liaw, 2008) Perceived ease of use is shown to affect behavioral intention (Ong et al., 2004) However, contrary to previous studies, perceived ease of use was the sole determi-nant of intention to use, while perceived usefulness did not have significant effect on intention to use (Yuen & Ma, 2008) Thus, this study hypothesized the followings

3.2.5 Hypothesis 5

Learners’ perceived usefulness positively affects their intention to use e-learning

3.2.6 Hypothesis 6

Learners’ perceived ease of use positively affects their intention to use e-learning services

Venkatesh and Brown (2001)indicate that hedonic outcomes such as pleasure, enjoyment, playfulness, happiness are intrinsic motiva-tors of system adoption Intrinsic motivation is considered to be a reward Playfulness is a factor that reflects the user’s intrinsic belief in WWW acceptance (Moon & Kim, 2001) Another study also shows that perceived playfulness contributed significantly to the users’ intent

to use a web site (Lin, Wu, & Tsai, 2005) Thus, this study hypothesized the following

3.2.7 Hypothesis 7

E-learning’s playfulness positively affects their intention to use e-learning

4 Research methodology

4.1 Instrument construction

A questionnaire instrument was developed for this study Individual scale items are listed inAppendix A These scale items were devel-oped based on the existing literature discussed in the previous sections Our research model consists of seven variables: instructor char-acteristics, teaching materials, design of learning contents, playfulness, perceived usefulness, perceived ease of use, and intention to use e-learning

We developed multi-item Likert scales which have been widely used in the questionnaire-based perception studies All variables are subjectively measured using the five-point Likert Scale, with 5 being ‘‘Strongly Agree” and 1 being ‘‘Strongly Disagree.”Table 1shows the above-mentioned operational definition of each variable

4.2 Data collection

The survey was conducted in a comprehensive university in South Korea during October of 2007 250 undergraduate students who had attended at least one e-learning class participated in this study through an anonymous survey instrument All of the survey participants

Table 1

Variables and operational definitions.

Instructor characteristics The extent to which instructors are caring, helpful, and accommodating to students

Design of learning contents The extent to which learning contents are designed for the consistent and accurate delivery

Perceived usefulness The extent to which students believe that e-learning will enhance learning outcomes Perceived ease of use The extent to which students believe that e-learning will be easy to use

Intention to use e-learning The extent to which students intend to participate in e-learning

majored in one of the five disciplines (accounting, business management, management information systems, taxation, and tourism) offered

by the College of Business Administration The courses selected for the study combined both e-learning and traditional face-to-face learn-ing methods The traditional face-to-face learnlearn-ing methods include required attendance, regular textbook, and presence of instructor dur-ing the scheduled class time and office hours Both synchronous and asynchronous web-based technologies were used for the e-learndur-ing support The asynchronous e-learning support includes online lecture notes, online quizzes, online announcements, online assignments, electronic student–student and student–instructor communication, audio and video streaming, and threaded discussions The synchronous e-learning support includes chat and video conferencing 98% of the survey participants are in the age range of 17–30 years 18% of the survey participants are freshmen, 36% sophomores, 31% juniors, and 15% seniors Of the 250 distributed questionnaires, 22 were not com-pleted validly, and 14 were not returned, resulting in 214 valid responses (a response rate of 85.6%).Table 2summarizes the demographic profile of the survey participants who returned the valid responses

5 Data analysis

5.1 Model validation

SPSS version 12.0 was used to analyze the collected data Given the theory-driven approach to scale development, scale validation was done using exploratory factor analysis and confirmatory factor analysis The factor analysis utilized the principal component extraction method and Varimax rotation It required that factor loadings exceed 0.40 One item (IC5) from Instructor Characteristics, one item (TM3) from Teaching Materials, and two items (LC2, LC3) from Design of Learning Contents were deleted due to a low factor loading While four items were removed from the three factors in the independent variables, no items were deleted from the two belief variables and the dependent variable The high reliability of these variables can be attributed to the fact that numerous previous studies validated the factor items

Table 3summarizes factor loadings, Cronbach’s alpha, Eigenvalues, and variances explained of all indicator variables The results indi-cated the presence of seven factors with Eigenvalues greater than one This questionnaire used the Cronbach’sacoefficient to test the inter-nal consistency among items of the same construct

According toCuieford (1965), a Cronbach’savalue that is greater than 0.7 indicates high reliability and a Cronbach’savalue that is less than 0.35 represents unacceptable reliability A Cronbach’savalue between 0.35 and 0.7 has fair but acceptable reliability Researchers suggest Cronbach alpha of 0.70 for confirmatory research and 0.60 for exploratory research as acceptable (Fornell & Larcker, 1981; Hair, Anderson, Tatham, & Black, 1998) Thus, all constructs can be considered reliable The reliability values of the constructs are in the range

of 0.634–0.903 suggesting acceptable reliability

Cumulative variance explained for all the variables are measured to be acceptable: for the independent variables, 60.34%; for the belief variables, 81.65%; and for the dependent variable, 65.56% The factor loading values of all indicator variables are over 0.494, far exceeding 0.30, which, as a rule of thumb, is considered the minimum loading for interpretability (Tabachnick & Fidell, 1996)

Table 2

Demographic profile and descriptive statistics of surveyed students.

Gender

Age

Year in college

Table 3

Factor analysis and reliability.

PL = Playfulness; IC = Instructor characteristics; LC = Design of learning contents; TM = Teaching materials; PU = Perceived usefulness; PE: Perceived ease of use;

5.2 Hypotheses testing

Although structural equation modeling (SEM) has advantages over traditional statistical techniques such as regression, it is recom-mended that for a model with two to four factors, an investigator plan on collecting at least 100 cases, with 200 being better (Loehlin,

1992) Another rule of thumb, based onStevens (1996), is to have at least 15 cases per measured variable or indicator Due to the smaller sample size than recommended for SEM, a regression model is used for testing the hypotheses.Table 4summarizes the test results All predictors are significant in explaining the relationships Instructor characteristics (b = 0.433, p < 0.001), teaching materials (b = 0.235, p < 0.001), and perceived ease of use (b = 0.389, p < 0.001) are positively related to perceived usefulness as hypothesized Thus, hypotheses 1 and 2 are supported Design of learning contents (b = 0.287, p < 0.001) is positively related to the perceived ease of use, thus confirming hypothesis 3 Perceived ease of use (b = 0.389, p < 0.001) is positively related to perceived usefulness as hypothesized Thus, hypothesis 4 is supported Perceived usefulness (b = 0.679, p < 0.001) is shown to have positive effect on intention to use e-learning, thus confirming hypothesis 5 Perceived ease of use (b = 0.117, p < 0.05) is related to intention to use e-learning at a significance level of 0.05, offering support for hypothesis 6 However, hypothesis 6 is statistically the weakest among the seven hypotheses Finally, playfulness is shown to have positive effect on intention to use e-learning (b = 0.586, p < 0.001), thus confirming hypothesis 7

6 Discussions

In this empirical study, we analyzed learners’ acceptance of e-learning services from student perspectives in South Korea First, we ana-lyzed the relationships between the three service quality constructs (instructor characteristics, teaching materials, and design of learning contents) and the two belief constructs (perceived usefulness and perceived ease of use) Second, we analyzed the relationships between the belief constructs (perceived usefulness and perceived ease of use) and intention to use e-learning Third, we analyzed flow construct (playfulness) and intention to use e-learning

Instructor characteristics and teaching materials are positively related to perceived usefulness Design of learning contents is positively related to the perceived ease of use These results indicate that as the service quality of e-learning improves, the learners tend to be more positive towards e-learning Compared to traditional offline education, the growth opportunities of e-learning abound As web technologies advance, e-learning providers can enhance e-learning services without additional costs by taking advantage of the declining cost of tech-nologies, thus resulting in greater adoption by learners

Among the variables under study, perceived usefulness is the greatest predictor of intention to use e-learning The result shows that the easier to use the students feel e-learning is, the more useful they feel e-learning is Perceived usefulness in turn has a positive effect on the intention to use e-learning For learners to continue to use e-learning, e-learning should be designed and developed to deliver value to them The usefulness can be enhanced by providing enhanced e-learning services without increasing the complexity of the e-learning process

Finally, playfulness positively affects the intention to use e-learning One of the recent trends in educational services is to improve the educational outcomes by incorporating amusement For example, edutainment typically seeks to instruct its participants by embedding entertainment into lessons Incorporation of playfulness into teaching materials presents the greatest challenge to instructors who do not have sufficient computer skills Educational institutions need to provide adequate resources to instructors and need to train them

to use a variety of educational tools innovatively A variety of entertainment tools are easily available in the online game industry Periodic survey and assessment of new entertainment tools for educational use seem worth conducting

Most of our findings support recent studies in the TAM domain conducted in various countries As indicated by our findings, perceived ease of use was found to be a significant antecedent of perceived usefulness (Imamoglu, 2007; Ong et al., 2004) Perceived usefulness pos-itively affects the intention to use e-learning (Liaw, 2008; Roca & Gagné, 2008; Sánchez-Franco et al., 2009) Design of learning contents was found to affect perceived ease of use (Pituch & Lee, 2006) Teaching materials affect the e-learning effectiveness (Littlejohn, Falconer, & Mcgill, 2008; Zhang et al., 2004) E-learning’s playfulness positively affects learners’ intention to use e-learning (Roca et al., 2006) How-ever, it is noted that the enjoyment of e-learning does not affect the intention to use e-learning among Mediterranean educators, while the enjoyment of e-learning affects the intention to use e-learning among Nordic educators (Sánchez-Franco et al., 2009) Nordic educators live

in individualistic and weak uncertainty avoidance societies and Mediterranean educators live in collectivistic and high uncertainty avoid-ance societies Results of this study indicate that there may be a relationship between learners’ culture and intentions to use e-learning While learner characteristics such as learner computer anxiety and self-efficacy have been investigated in other studies, our study fo-cuses on e-learning service quality constructs to make our model parsimonious We believe that the selected constructs are considered to

be critical for the successful development of e-learning Recently,Pituch and Lee (2006)found there is no significant relationship between self-efficacy and usefulness and intention to use e-learning Future research is needed to fully understand the relationships between stu-dent characteristics and service quality constructs that improve or undermine learners’ intention to use e-learning

Table 4

Test results.



p < 0.05.

 p < 0.001.

7 Conclusions

Because of the time and space barriers, learners in the traditional offline education are required to receive education at a certain time and location On the contrary, the Internet-based e-learning is less restricted in terms of time and space In addition, e-learning is known to save educational costs and facilitate dissemination of knowledge in a timely fashion As e-learning is increasingly adopted by educational institutions and corporations, learning success factors need to be evaluated and taken into consideration in the development of the e-learning systems to deliver the most effective services

South Korea’s dense student population and high educational standards make investments in e-learning very cost-effective Despite the fact that South Korea is one of the fastest growing countries in e-learning, e-learning literature from South Korean perspectives are rela-tively small In the globalized educational environment, understanding and investigating the country specific e-learning phenomena are of great importance By investigating critical factors on e-learning adoption in South Korea, our study attempts to fill a gap in the individual country-level e-learning research

Our survey results confirm the seven hypotheses Our findings indicate that instructor characteristics and teaching materials are the predictors of the perceived usefulness of e-learning, and perceived usefulness and playfulness are the predictors of the intention to use e-learning While statistically significant, perceived ease of use was shown to have the weakest effect on the intention to use e-learning among the three predictors All these results are very consistent with the previous studies conducted in other countries, proving the uni-versal nature of the learners’ perceptions and behavior towards e-learning

As is typical in many empirical studies, this study is not without limitations First, while we limited e-learning service quality to three factors (instructor characteristics, teaching materials, and design of learning contents), additional service factors such as systems quality, security, and responsiveness exist These additional service factors may influence the belief and dependent variables Therefore, future re-search needs to include such factors to build a comprehensive model while maintaining the conciseness of the model Second, this study focused on the higher education institutions and did not reflect on the perceptions of employees on the e-learning in business settings Future research needs to address the perceptions of students and corporate employees and analyze perception differences between them Lastly, as the world gets more globalized, understanding cross-cultural issues in e-learning will draw more attention from researchers, edu-cation institutions, and business organizations While our study is limited to e-learning in South Korea, cross-cultural e-learning studies may shed valuable new insights into this ever-growing area

Appendix A

Instrument: All items were measured on a five-point Likert scale

Instructor characteristics IC1 The instructor provides high-quality instruction

IC2 The instructor provides information on learning progress IC3 The instructor delivers instructions clearly

IC4 The instructor’s measurement of student performance is fair IC5a The instructor motivates me to use e-learning

Teaching materials TM1 E-learning provides me with sufficient teaching materials

TM2 E-learning provides me with teaching materials that fit with the learning objectives TM3a E-learning provides me with teaching materials that are easy to use

Design of learning contents LC1 The level of difficulty of the learning contents is appropriate

LC2a The content of assignments is easy to understand LC3a The amount of learning contents is appropriate LC4 The delivery schedule of learning contents is flexible LC5 E-learning provides individualized learning management LC6 E-learning provides a variety of learning methods Playfulness P1 I feel e-learning helps me improve my creativity

P2 I feel e-learning helps me improve my imagination by obtaining information P3 I feel I can have a variety of experiences without any interference

P4 I feel e-learning is fun regardless of usage purposes Perceived usefulness PU1 E-learning improves my learning outcomes

PU2 E-learning is very useful to me PU3 E-learning helps me accomplish my learning effectively Perceived ease of use PE1 E-learning study methods are easy to understand

PE2 E-learning is easy to use Intention to use e-learning IU1 I prefer e-learning to traditional learning

IU2 I am willing to participate in other e-learning opportunities IU3 I think e-learning should be implemented in other classes IU4 I will recommend e-learning classes to other students

a Deleted due to a low factor loading.

References

Aladwani, A M., & Palvia, P C (2002) Developing and validating an instrument for measuring user-perceived web quality Information and Management, 39(6), 457–476 Alavi, M., & Leidner, D (2001) Research commentary: Technology mediated learning-a call for greater depth and breadth of research Information Systems Research, 12(1),

Alley, L R., & Jansak, K E (2001) The ten keys to quality assurance and assessment in online learning Journal of Interactive Instruction Development, 13(3), 3–18 Bollag, B (2006) America’s hot new export: Higher education The Chronicle of Higher Education < http://chronicle.com/weekly/v52/i24/24a04401.htm > (2/17/2006) Bonwell, C.C., & Eison, J.A (1991) Active learning: Creating excitement in the classroom ASHEERIC Higher Education Report No 1 Washington, DC: George Washington University.

Cantoni, V., Cellario, M., & Porta, M (2004) Perspectives and challenges in elearning: Towards natural interaction paradigms Journal of Visual Languages and Computing, 15, 333–345.

Chen, C M., Lee, H M., & Chen, Y H (2005) Personalized e-learning system using item response theory Computers and Education, 44(3), 237–255.

Chiu, C M., Hsu, M H., Sun, S Y., Lin, T C., & Sun, P C (2005) Usability, quality, value and e-learning continuance decisions Computers and Education, 45(4), 399–416 Clark, D (2002) Psychological myths in e-learning Medical Teacher, 24(6), 598–604.

Csikszentimihalyi, M (1975) Beyond boredom and anxiety San Francisco, CA: Jossey-Bass.

Csikszentmihalyi, M (1990) Flow: The psychology of optimal experience New York: Harper and Row.

Cuieford, J P (1965) Fundamental statistics in psychology and education (4th ed.) New York: McGraw Hill.

Davis, F.D (1986) A technology acceptance model for empirically testing new end-user information system: Theory and results Doctoral Dissertation, Sloan School of Management, Massachusetts Institute of Technology.

Davis, F D (1989) Perceived usefulness, perceived ease of use, and user acceptance of information technology MIS Quarterly, 13(3), 319–340.

DeLone, W H., & McLean, E R T (2003) The DeLone and McLean model of information systems success: A ten-year update Journal of Management Information Systems, 19(4), 9–30.

Dillon, C L., & Gunawardena, C N (1995) A framework for the evaluation of telecommunications-based distance education In D Sewart (Ed.), Selected papers from the 17th world congress of the International Council for Distance Education Milton Keynes: UK Open University.

Eom, S B., Wen, H J., & Ashill, N (2006) The determinants of students’ perceived learning outcomes and satisfaction in university online education: An empirical investigation Decision Sciences Journal of Innovative Education, 4(2), 215–235.

Engelbrecht, E (2003) A look at e-learning models: investigating their value for developing an e-learning strategy Progressio, 25(2), 38–47.

Engelbrecht, E (2005) Adapting to changing expectations: Postgraduate students’ experience of an e-learning tax program Computers and Education, 45(2), 217–229 Fishbein, M., & Ajzen, I (1975) Belief, attitude, intentions and behavior: An introduction to theory and research Boston: Addison-Wesley.

Fornell, C., & Larcker, D (1981) Evaluating structural equation models with unobservable variables and measurement error Journal of Marketing Research, 18(3), 39–50 Gunasekaran, A., McNeil, R D., & Shaul, D (2002) E-learning: Research and applications Industrial and Commercial Training, 34(2), 44–54.

Hackbarth, G., Grover, V., & Yi, M Y (2003) Computer playfulness and anxiety: positive and negative mediators of the system experience effect on perceived ease of use Information and Management, 40(3), 221–232.

Hair, J F., Anderson, R E., Tatham, R L., & Black, W C (1998) Multivariate data analysis with readings (5th ed.) Englewood Cliffs, NJ: Prentice-Hall.

Hannon, J., & D’Netto, B (2007) Cultural diversity online: Student engagement with learning technologies International Journal of Educational Management, 21(5), 418–432 Hiltz, S R., & Turoff, M (2005) Education goes digital: The evolution of online learning and the revolution in higher education Communication of ACM, 48(10), 59–64.

Hu, P J., Clark, T., & Ma, W (2003) Examining technology acceptance by school teachers: A longitudinal study Information and Management, 41(2), 227–241.

Huffaker, D A., & Calvert, S l (2003) The new science of learning: Active learning, metacognition, and transfer of knowledge in e-learning applications Journal of Educational Computing Research, 29(3), 325–334.

Huynh, M Q., Umesh, U M., & Valacich, J S (2003) E-Learning as an emerging entrepreneurial enterprise in universities and firms Communications of the Association for Information Systems, 12(3), 48–68.

Imamoglu, S Z (2007) An empirical analysis concerning the user acceptance of e-learning Journal of American Academy of Business, 11(1), 132–137.

Kaynama, S A., & Black, C I (2000) A proposal to assess the service quality of online travel agencies Journal of Professional Services Marketing, 21(1), 63–68.

Kelly, T., & Bauer, D (2004) Managing Intellectual capital via e-learning at Cisco In C Holsapple (Ed.), Handbook on knowledge management 2: Knowledge directions (pp 511–532) Berlin, Germany: Springer.

Kollias, V., Mamalougos, N., Vamvakoussi, X., Lakkala, M., & Vosniadou, S (2005) Teachers’ attitudes to and beliefs about web-based collaborative learning environments in the context of an international implementation Computers and Education, 45(3), 295–315.

Koufaris, M (2002) Applying the Technology Acceptance Model and flow theory to online consumer behavior Information Systems Research, 13(2), 205–223.

Lederer, A L., Maupin, D J., Sena, M P., & Zhuang, Y (2000) The technology acceptance model and the World Wide Web Decision Support Systems, 29(3), 269–282 Lee, M K O., Cheung, C M K., & Chen, Z (2005) Acceptance of Internet-based learning medium: The role of extrinsic and intrinsic motivation Information and Management, 42(8), 1095–1104.

Leidner, D E., & Jarvenpaa, S L (1993) The information age confronts education: Case studies on electronic classrooms Information Systems Research, 4(1), 24–54 Levy, Y (2007) Comparing dropouts and persistence in e-learning courses Computers and Education, 48(2), 185–204.

Levy, Y (2008) An empirical development of critical value factors (CVF) of online learning activities: An application of activity theory and cognitive value theory Computers and Education, 51(4), 1664–1675.

Liaw, S S (2008) Investigating students’ perceived satisfaction, behavioral intention, and effectiveness of e-learning: A case study of the Blackboard system Computers and Education, 51(2), 864–873.

Liaw, S S., Huang, H M., & Chen, G D (2007) Surveying instructor and learner attitudes toward e-learning Computers and Education, 49(4), 1066–1080.

Lin, C S., Wu, S., & Tsai, R J (2005) Integrating perceived playfulness into expectation-confirmation model for web portal context Information and Management, 42(5), 683–693.

Liu, S.-H., Liao, H.-L., & Pratt, J A (2009) Impact of media richness and flow on e-learning technology acceptance Computers and Education, 52(3), 599–607.

Littlejohn, A., Falconer, I., & Mcgill, L (2008) Characterising effective eLearning resources Computers and Education, 50(3), 757–771.

Loehlin, J C (1992) Latent variable models Hillsdale, NJ: Lawrence Erlbaum Publishers.

Martínez, R.-A., del Bosch, M M., Herrero, H P., & Nuño, A S (2007) Psychopedagogical components and processes in e-learning Lessons from an unsuccessful on-line course Computers in Human Behavior, 23(1), 146–161.

Misko, J., Choi, J., Hong, S.Y., & Lee, I.S (2005) E-learning in Australia and Korea: Learning from practice Korea Research Institute for Vocational Education & Training and National Centre for Vocational Education Research < http://www.ncver.edu.au/research/core/cp0306.pdf >.

Moon, J., & Kim, Y (2001) Extending the TAM for a World-Wide-Web context Information and Management, 38(4), 217–230.

Myers, C., Bennett, D., Brown, G., & Henderson, T (2004) Emerging online learning environments and student learning: An analysis of faculty perceptions Educational Technology and Society, 7(1), 71–86.

Novak, T P., Hoffman, D L., & Yung, Y F (2000) Measuring the flow construct in on-line environments: A structural modeling approach Marketing Science, 19(1), 22–42 Ong, C S., Lai, J Y., & Wang, Y S (2004) Factors affecting engineers’ acceptance of asynchronous e-learning systems in high-tech companies Information and Management, 41(6), 795–804.

Parasuraman, A., Zeithaml, V A., & Berry, L L (1985) A conceptual model of service quality and its implications for future research Journal of Marketing, 49(4), 41–50 Parasuraman, A., Zeithaml, V A., & Berry, L L (1988) SERVQUAL: A multiple-item scale for measuring consumer perceptions of service quality Journal of Retailing, 64(1), 12–40.

Pituch, K., & Lee, Y (2006) The influence of system characteristics on e-learning use Computers and Education, 47(2), 222–244.

Ramim, M., & Levy, Y (2006) Securing e-learning systems: A case of insider cyber attacks and novice IT management in a small university Journal of Cases on Information Technology, 8(4), 24–34.

Roca, J C., & Gagné, M (2008) Understanding e-learning continuance intention in the workplace A self-determination theory perspective Computers in Human Behavior, 24(4), 1585–1604.

Roca, J C., Chiu, C.-M., & Martinez, F J (2006) Understanding e-learning continuance intention: An extension of the technology acceptance model International Journal of Human–Computer Studies, 64(8), 683–696.

Sánchez-Franco, M J., Martínez-López, F J., & Martín-Velicia, F A (2009) Exploring the impact of individualism and uncertainty avoidance in Web-based electronic learning:

An empirical analysis in European higher education Computers and Education, 52(3), 588–598.

Selim, H M (2003) An empirical investigation of student acceptance of course websites Computers and Education, 40(4), 343–360.

Selim, H M (2007) E-learning critical success factors: An exploratory investigation of student perceptions International Journal of Technology Marketing, 2(2), 157–182 Soong, B M H., Chan, H C., Chua, B C., & Loh, K F (2001) Critical success factors for on-line course resources Computers and Education, 36(2), 101–120.

Stevens, J (1996) Applied multivariate statistics for the social sciences Mahwah, NJ: Lawrence Erlbaum Publishers.

Sun, P C., Tsai, R J., Finger, G., Chen, Y Y., & Yeh, D (2008) What drives a successful e-Learning? An empirical investigation of the critical factors influencing learner

Tabachnick, B G., & Fidell, L S (1996) Using multivariate statistics (2nd ed.) New York, NY: HarperCollins College Publishers.

Venkatesh, V., & Davis, F D (2000) A theoretical extension of the Technology Acceptance Model: Four longitudinal field studies Management Science, 46(2), 186–204 Venkatesh, V., & Brown, S A (2001) A longitudinal investigation of personal computers in homes: Adoption determinants and emerging challenges MIS Quarterly, 25(1), 71–102.

Venkatesh, V., Morris, M G., Davis, G B., & Davis, F D (2003) User acceptance of information technology: Toward a unified view MIS Quarterly, 27(3), 425–478 Volery, T., & Lord, D (2000) Critical success factors in online education The International Journal of Educational Management, 14(5), 216–223.

Webster, J., & Hackley, P (1997) Teaching effectiveness in technology-mediated distance learning Academy of Management Journal, 40(6), 1282–1309.

Webster, J., & Martocchio, J J (1992) Microcomputer playfulness: Development of a measure with workplace implications MIS Quarterly, 16(2), 201–226.

Yuen, A., & Ma, W (2008) Exploring teacher acceptance of e-learning technology Asia-Pacific Journal of Teacher Education, 36(3), 229–243.

Zhang, D., Zhao, J L., Zhou, L., & Nunamaker, J F Jr., (2004) Can e-learning replace classroom learning? Communications of the ACM, 47(5), 75–79.