Editor’s Note: TAM is an excellent Technology Acceptance Model. Educational institutions have the challenge to migrate to eLearning using this verified model for implementation.
Factors Influencing the Acceptance of e-Learning Courses for Mainstream Faculty in Higher Institutions
Mi-Ryang Kim
Korea
Abstract
E-Learning is a neologism that encapsulates a range of learning modalities in which students process information electronically rather than through face-to-face contact with others
(i.e., teachers and other students) or through traditional paper-based modes of expression and communication. In a school setting, e-learning implies teacher-directed learning activities using computers, including, for example, completing and submitting assignments electronically, participating in group "chats" involving near-simultaneous written dialogue, and obtaining teacher feedback electronically. The potential benefits of e-learning are many, including cost-effectiveness, enhanced responsiveness to changing circumstances, consistency, timely content, accessibility, and more rapid feedback provide customer value. The proponents of e-learning stress the importance of using communities of interest to support and enhance the learning process. They also emphasizes that people learn more effectively when they interact and are involved with people participating in similar endeavors.
Although the use of e-learning in higher education has significantly increased over the past decade, resistance to new uses of technology by professors and lecturers in university and colleges worldwide remains high. The purpose of this study is to identify the determinants of professors' intentions to increase their use of e-learning methods in universities. A survey of a sample of Korean university professors was used to investigate a proposed model of influence, and structural equation modeling was used to analyze the results.
The hypothesized model was largely supported by this analysis, and the overall results indicate that intentions are mainly influenced by two factors - perceived ease of use of e-learning and its perceived usefulness, with perceived ease of use being influenced by the technical support available and, to a lesser extent, both factors being influenced by prior experience in e-learning use. As in other research built from the Technology Acceptance Model (TAM), it can be concluded that Perceived Ease of Use and Perceived Usefulness are critical to the future use of e-learning systems.
Keyword: higher education, information and communication technology, e-learning, distance learning, structural equation model, Technology Acceptance Model (TAM)
Acknowledgement
I am grateful to Professor Henry Jay (Hank) Becker, Dept. of Education, UCI. for his constructive comment and review of this paper.
* This work was supported by the Korea Science and Engineering Foundation(KOSEF) grant funded by the Korea government(MOST) (R01-2006-000-10954-0)(2006)
Introduction
The new information and communication technologies, over the past decade, have had an enormous impact on all levels of education in Korea, as they have elsewhere in the developed world. E-learning in
Despite this growth, professors in many higher education institutions that are well equipped with e-learning technologies are still reluctant to use them on a widespread basis in their teaching.
E-learning technologies have the potential to rescue the isolated and underprivileged students from their loneliness and improve the effectiveness of learning, by providing immediate and individualized interactions with professors, tutors and peer-students. Despite all the positive assertions about the impact that e-learning has on higher education, many faculties remain skeptical about the effectiveness of e-learning and its related technologies. E-learning technologies require professors to adapt to a new way of communicating with their students as well as a new orchestration of learning activities (Dillon and Walsh, 1992; Smith et. al, 2000). Many roles and functions change when faculty incorporate new information and communication technologies into their teaching (Gunawardena, 1992; Baldwin, 1998).
Most of faculties that are reluctant to try e-learning courses prefer the face-to-face contact with their students and want to have a complete control over the flow of teaching materials which they prepared for the courses. In addition, for example, professors are not used to planning interactive teaching strategies in advance of planning their delivery of content. Wolcott (1993) stressed that it is particularly challenging for university faculty to focus on instructional activities, because most faculties are trained to become expert in content areas, not in teaching planning. Another factor hindering the participation in teaching through e-learning may be the limitation of resource allocated for e-learning content development (O'Quinn and Corry, 2003). Many institutions expect their faculties to develop content on their own time with little institutional help or resources. With limited resource allocated for e-learning, fostering e-learning technology acceptance among faculties in higher institutions remains a critical challenge for administrators.
Faculties in higher institutions differ from general end-users of information technology in business settings. For instance, professors are independent and have complete control over what they teach and how they teach. Such characteristics affect faculty adoption of new technologies, and, as a result, the literature on technology adoption processes in private industry may not fully apply to the university setting (Hu et. al, 2003).
This research is concerned with e-learning intentions and plans by faculties in higher education institutions. The goal of the research discussed here is to help us understand the factors influencing the usage and acceptance of e-learning, particularly in Korean higher education institutions. We employ a modified version of the Technology Acceptance Model (TAM), a model which has been widely used and empirically validated to explain why individuals use a particular information technology (IT). In studies using the Technology Acceptance Model, users' perceptions of both the usefulness of IT and its ease-of-use have been found to be key determinants of individual technology adoption (Hsu & Lu, 2004). However, factors motivating the acceptance of e-learning as a particular form of IT may differ from findings based on other IT environments, including traditional off-line IT. This research proposes that additional variables, such as institutional support, uneasiness in preparing e-learning content and e-learning course experiences, enhance our understanding of behaviors of university faculty in accepting e-learning as a teaching strategy. In this study, a structural equation model is employed with survey data from 156 professors in Korean universities to empirically assess the strength of the relationships in the proposed model.
The remainder of this paper is organized as follows: In Section 2, we give a brief review of the previous studies using TAM and extensions of it applied to a variety of IT fields, and then discuss some possible extensions that might apply particularly to instructional settings in higher education. In Section 3, a research model is presented based on the literature review. Section 4 presents the research methodology employed to test the proposed hypotheses, and Section 5 gives the statistical results from analysis. We conclude the paper with a brief discussion of these results, and implications for researchers.
Theoretical Background
Technology Acceptance Model
TAM has received considerable attention from researchers in the information systems field over the past decade. The theoretical basis of TAM can be found in Fishbein and Ajzen's (1975) theory of reasoned action (TRA). In TRA, an individual's belief influences attitudes, which in turn trigger behavioral intentions and then actual behaviors. TAM proposed by
Davis attempts to explain an individual's actual behaviors or behavioral intentions, based upon the user's perception of the usefulness (PU) and the ease of use (PEU) of a particular IT (Davis, 1989). Both PU and PEU influence the individual's attitude towards that IT use, their intentions to use it, and their actual use. In addition, TAM assumes that Perception of Ease of Use (PEU) also influences a user's Perception of Usefulness (PU) of that IT application.
Many of subsequent studies have extended the original version of TAM to incorporate additional variables with specific contexts. For instance, in studying people's acceptance of the World Wide Web, Moon and Kim (2001) proposed a new construct called "perceived playfulness." Other constructs, such as perceived enjoyment (Teo, Lim and Lai, 1999), perceived critical mass (Luo and Strong, 2000), compatibility in virtual stores (Chen, Gillenson, and Sherrell, 2002), and flow (Hsu and Lu, 2003; Novak, Hoffman, and Yung, 2000) have been added to TAM in e-commerce, multimedia operation and on-line game applications of the theory. And in some studies, the key construct of "attitudes" in the original TAM has been dropped from consideration. Davis, Bagozzi, and Warshaw (1992) found that attitudes do not play a significant role in predicting IT use, and thus several of the subsequent studies did not include the attitude in the model (Igbaria et al., 1997; Venkatesh, 2000; Venkatesh and Davis, 2000).
In most of the TAM related research, considerable efforts have been made to introduce and test a new set of antecedents of perceived usefulness and perceived ease of use. Based on empirical studies by Venkatesh and Davis (1996) and Venkatesh (2000), Venkatesh (2000) lists control, intrinsic motivation, and emotion as responsible for perceived ease of use, where "control" can be interpreted as meaning self-efficacy. According to Bandura (1986), self-efficacy is defined as "people's judgment of their capabilities to organize and execute a course of action required to attain designated types of performances." In other words, self-efficacy is a person's beliefs about his or her ability to accomplish a particular task.
Another important antecedent of PEU is "facilitating conditions." Facilitating conditions refer to "objective factors, 'out there' in the environment, that several judges or observers can agree make an act easy to do" (Triandis, 1980). User judgment of difficulty in using a system will incorporate external dimension of control. External control is expected to exert its influence in the form of resource facilitating conditions. Facilitating conditions serve as situational anchors in the formation of PEU. For instance, Chang, Cheung, and Lai (2000) and Chang and Cheung (2001) studied factors affecting the acceptance of the World Wide Web in workplace settings. In e-learning settings, facilitating conditions might include hardware, software, university policy, etc., that allow faculty to access the expert supports provided by higher institutions.
With respect to the antecedents of perceived usefulness (PU), Venkatesh and Davis (2000) proposed TAM2, in which social influence processes and cognitive instrumental processes are incorporated into the original TAM. The social influence construct emphasizes what the authors refer to as "subjective norms." Subjective norms refer to a person's perception that people who are important to him or her (called "referents") think one should or should not perform the behavior in question (Fishbein and Ajzen, 1975, p. 302). Subjective norms tend to make one incorporate the referent's beliefs into one's own belief structure and thus generate a profound impact on shaping a person's behavior. Theories of conformity in social psychology suggest that group members tend to comply with the group norm, and moreover that these in turn influence the perceptions and behavior of members (Lascu and Zinkhan, 1999). Thus, if one's referents believe a particular innovation is useful, one may come to believe that it is actually useful. In previous studies, subjective norms are included as a direct determinant of perceived usefulness.
Factors affecting e-learning acceptance by faculty
Although the literature on factors affecting university faculty regarding e-learning is limited, there are several interesting studies that discuss motivators and inhibitors for use of e-learning in teaching.
Ertmer (1999) identified two types of barriers, external (related to technical skills needed to operate a computer and use internet) and internal (concerned with intrinsic motivation and fundamental beliefs about current practice). He stressed the importance of clarifying the relationship between these different types of barriers or that delineates effective strategies for addressing different barriers. Betts (1998) looked at the motivation behind the use of distance education and found that faculty were motivated by intrinsic factors (e.g., challenge) and were inhibited by lack of release time and technical support. Rockwell, Schauser, Fritz and Marx (1999) also published similar results.
Schifter (2000, 2002) studied two other factors for their impact on a faculty member's intention to offer an e-learning course: personal needs (e.g., saving time and monetary rewards) and extrinsic motives (e.g., a requirement of one's department and support of school officials). Their data showed that faculty who had taught online was more likely to name intrinsic motives while those who had not were more likely to name extrinsic motives. Fredericksen, Pickett, Shea, Pelz and Swan (2000) drew similar conclusions, suggesting that faculty motivated to offer e-learning courses rated the experiences more satisfying than those where motivation was a fear of being left behind. To increase the number of participating faculty and cumulative experiences in e-learning courses, rewarding faculty and releasing time for training need to be considered (O'Quinn and Corry, 2003).
Development of the Research Model
Figure 1 illustrates the extended version of TAM examined in this research. Our model uses TAM but excludes attitudes, mainly because of its limited mediation effects on intended behaviors, as discussed in Davis et al. (1989). It also asserts that the intention to develop and offer e-learning courses is a function of its perceived usefulness by faculty, perceived ease-of-use, and the uneasiness that faculty feel in offering courses in a digital form. In the context of e-learning acceptance by faculty, Perceived usefulness can be defined as the extent to which faculty members believe that developing and offering e-learning courses would improve the quality and effectiveness of their courses, whereas perceived ease-of-use refers to the extent to which faculty members believe that developing and offering e-learning courses is relatively easy to accomplish. In terms of the other elements in the model, uneasiness of faculty and prior experience in e-learning courses are hypothesized to be directly related to perceived usefulness of e-learning. We also propose that experiences in e-learning courses and facilitating conditions directly affect perceived ease-of-use.
Figure 1. Proposed Structure model
Since this research model is an extended TAM, the following TAM-related hypotheses are proposed in the context of e-learning acceptance:
Hypothesis 1. Perceived usefulness is positively related to intention to offer an e-learning course.
Hypothesis 2. Perceived ease of use in developing and offering e-learning course is positively related to perceived usefulness of e-learning course.
Hypothesis 3. Perceived ease of use is positively related to intention to offer e-learning course, even beyond its mediation by perceived usefulness.
To develop and offer e-learning courses, faculty need to follow a number of steps: planning and developing curriculum content, developing supporting materials for tutors, integrating multimedia applications, providing feedback to course content, implementing various teaching techniques and strategies, etc. During the course of development, all the educational materials for the course are being digitized and transformed into web-based publication, which enables the open sharing of the teaching materials and strategies with enrolled students and later on with their fellow educators and other students who are interested in topics discussed in course materials. E-learning courses provide users with open access to the syllabi, lecture notes, homework problem solutions, exams, reference lists, even some video clips from lectures. Making faculty's core teaching materials openly available for anyone with access to the Internet may have some negative impact on faculty, who feel uncomfortable with openness, by its nature, of e-learning. In principle, by sharing course materials, along with their teaching know-how and experiences in developing courseware publication process, one can inspire fellow faculties to share their course content, hopefully creating a knowledge web for everyone. But in reality, this principle may not hold (Q'Quinn and Corry, 2003), and faculty reluctant to sharing their course materials and teaching strategies may feel that e-learning is not useful and not worth investing time and efforts, thereby showing no interest in related information technologies. Hence, we propose the following hypotheses:
Hypothesis 4. Uneasiness in sharing their course materials and teaching strategies is negatively related to perceived usefulness of e-learning courses.
Hypothesis 5. Uneasiness in sharing their course materials and teaching strategies is negatively related to intention to develop and offer e-learning courses.
Despite all the positive statements about e-learning, some faculty still remain skeptical about its effectiveness and may be overwhelmed by the technological and pedagogic expertise required to develop e-learning course materials and deliver courses on the internet. Faculty trying to implement e-learning courses faces a variety of challenges when adapting their teaching strategy to internet-based learning system (Rockwell et, al, 2003). For instance, courses need to be designed in such a manner to allow the students to gain access to course materials in a way that makes sense to them (Wolcott, 1993; Carr, 2001). Hence, for faculty to be successful in offering e-learning courses, higher education institutions need to help faculty develop e-learning educational models and instructional techniques for easy implementation.
In TAM, facilitating conditions serve as situational anchors in the formation of perceived ease-of-use [Chang, Cheung, and Lai, 2000; Chang and Cheung, 2001, Karahanna and Straub, 1999]. In the context of our research, facilitating conditions provide or arrange a set of external conditions or social circumstances that make developing and offering e-learning easier for faculty, but whose absence would not prevent it from being achieved. The following list of strategies is illustrative of facilitating conditions in this particular context: an on-line faculty resource and information gateway, a series of workshops and instructional design sessions for e-learning, a comprehensive handbook for course developers, a course template, a faculty help desk and studio for multimedia content development, an instructional design partner to support faculty development and course design, etc. These conditions would facilitate the development and delivery of e-learning courses, and the following hypothesis is proposed:
Hypothesis 6. Facilitating conditions for developing and offering e-learning course is positively related to perceived ease of use.
O'Quinn and Corry (2003) suggest that one means of enabling faculty to overcome their reluctance to participate in distance education would be to provide them with opportunities where they can integrate elements of distance education in their offline courses, a combination that is often called a blended course. By using a combination of e-learning and a classroom setting, faculty would be able to experience the taste of multimedia content, on-line learner-to-learner interaction, and web-based feedback to their content. Familiarity with course delivery mechanism and interaction with students allows the faculty to concentrate efforts on subject matter, and thus improving the self-efficacy and effectiveness of courses utilizing information technologies. It has also been suggested that development and delivery time and effort in web-based distance courses may partially depend on the accumulation of instructor experience and the level of institutional support (Visser, 2000).
In the original statement of TAM, it was suggested that intention to use information technology might be better explained if perceptions of ease of use varies as a function of user experience level (Adams et al., 1992). Later, "revised" TAM presentations specifically addressed how the interrelationships among variables vary as a function of time and experience (Taylor and Todd, 1995; Morris and Turner, 2001). In this research, we suggest that knowledge and skills gained from past e-learning experiences will help faculty shape intention through heightened level of ease of use as well as perceived usefulness.
Hypothesis 7. Experiences in developing and offering e-learning course are positively related to perceived usefulness.
Hypothesis 8. Experiences in developing and offering e-learning course are positively related to perceived ease of use.
Research Method
Data Collection
The data used to test the research model were obtained mainly from universities offering e-learning courses for credits in Seoul, Korea. More than 500 faculty members received cover letters that provided an overview of the study and a copy of the survey. Of more than 500 surveys, 35% responded, including 89 men and 67 women. The respondents averaged 40.8 years in age and had an average of 9.7 years of teaching; the male-to-female ratio was approximately All respondents had completed doctoral degrees. Table 1 summarizes the profile of respondents.
Table 1
Profile of respondents
| frequency | Percentage | frequency | Percentage | ||||
the number of | none | 81 | 45.5 | teaching experiences | < 5 years | 51 | 28.8 | |
1 | 30 | 16.9 | 6-10 years | 49 | 27.7 | |||
2 | 22 | 12.4 | 11-15 years | 34 | 19.2 | |||
3 | 11 | 6.2 | 16-20 years | 26 | 14.7 | |||
4 | 11 | 6.2 | > 20 years | 17 | 9.6 | |||
more than 4 | 23 | 12.9 | ||||||
※ n=178 | ||||||||
Measurement
To ensure the content validity of the scales, the questionnaires were developed from the literature; the list of the items is presented in the Appendix. The scales to measure perceived usefulness, perceived ease of use, and behavioral intention to use were adapted from prior studies (Davis, 1989; Venkatesh & Davis, 1996; Hu et al., 2003; long; Ong & Lai, 2006). The scale for facilitating conditions was adapted from Cheung et al. (2000) and Thompson et al. (1999). To develop a scale for measuring uneasiness of faculty, we utilized measures introduced in a study by Q'Quinn and Corry (2003).
The respondents indicated their agreement or disagreement with the survey items using a five-point Likert-type scale. In addition, to measure experience with e-learning, we asked respondents to provide the number of e-learning courses they have developed and offered for credit. Consistent with prior research on TAM, we measured demographic variables such as gender, education, position, and years of teaching experience. Table 2 presents a list of the items used in this study.
Data Analysis and Results
Measurement Model
A confirmatory factor analysis using LISREL 8.30 was conducted to test the measurement model. The fit of the overall measurement model was estimated by various indices provided by LISREL (see Table 2).
Table 2
Fit indices for measurement model
Construct | questions | factor loading | t- value | composite reliability | average variance extracted | |
perceived usefulness | PU1 PU2 PU3 | Developing and offering an e-learning course improves the quality of education. Developing and offering an e-learning course makes it easier to do my job. I find developing and offering an e-learning course to be useful in my job. | 1.04 0.89 0.78 | 13.85 12.21 11.38 | 0.84 | 0.73 |
perceived ease of use | PEU1 PEU2 PEU3 | The process of developing and delivering an e-learning course is clear and understandable. Learning to develop and offer an e-learning course is easy for me. I find it easy to develop and deliver e-learning content for courses. | 0.63 1.01 0.94 | 7.70 10.96 9.88 | 0.75 | 0.61 |
facilitating conditions | FAC1 | Technical and clerical support is available for assistance whenever I have a problem with the process of developing or delivering an e-learning course. Specialized instruction and guidelines concerning the development and delivery of e-learning courses are available to me. | 0.88 0.69 | 10.22 8.24 | 0.75 | 0.60 |
uneasiness | UNEASY1 UNEASY2 | I feel uncomfortable since teaching materials and know-how of my own may be open to everybody. I feel like I am losing control over the teaching and learning process | 0.90 0.63 | 7.55 6.87 | 0.79 | 0.66 |
experiences | EXP | How many e-learning courses have you developed and offered in the past? | 1.00 | - | - | - |
intention to develop and offer | INTUSE1 | To the extent possible, I would develop and offer e-learning courses. I intend to increase my use of the e-learning system in the future. | 1.00 1.11 | 13.09 13.54 | 0.84 | 0.72 |
χ2=42.47, p<0.065, TLI=0.97, CFI=0.98, Normedχ2=1.41, GFI=0.96, RMSEA=0.045 | ||||||
The ratio of χ2 to degrees-of-freedom (df) was used, and a value of 1.424 was obtained, which does not exceed 3 (Carmines and McIver, 1981). Also note the normed χ2 were 1.41, well below the recommended level of 2. The goodness-of-fit (GFI), non-normed fit index (NNFI), and comparative fit index (CFI) are other indices of fit. These indices typically range from 0 to 1, with values greater than 0.9 representing reasonable model fit. For the measurement model, values of 0.96, 0.97, and 0.98 for GFI, NNFI, and CFI, respectively, observed, providing a good fit to the data. Root mean square error of approximation (RMSEA) describes the discrepancy between the proposed model and the population covariance matrix. RMSEA was 0.045, which is within the recommended cutoff values of 0.08 (RMSEA) for good fit (Byrne, 1998).
Table 3
Discriminant validity for the measurement model
Construct | average variance extracted (AVE) | shared variance | |||||
experiences | perceived usefulness | perceived ease of use | facilitating conditions | uneasiness | intention to develop and offer | ||
experiences | - | 3.23 (0.35)* 9.35* | |||||
perceived usefulness | 0.73 | 0.45 (0.14) 3.13 | 1.00 | ||||
perceived ease of use | 0.61 | 0.47 (0.15) 3.25 | 0.37 (0.08) 4.53 | 1.00 | |||
facilitating conditions | 0.60 | 0.46 (0.15) 3.02 | 0.33 (0.09) 3.88 | 0.55 (0.08) 7.02 | 1.00 | ||
uneasiness | 0.66 | 0.10 (0.15) 0.66 | -0.17 (0.09) -1.98 | -0.12 (0.09) -1.39 | -0.31 (0.09) -3.44 | 1.00 | |
intention to develop and offer | 0.72 | 0.69 (0.14) 4.88 | 0.83 (0.04) 19.91 | 0.58 (0.04) 8.14 | 0.45 (0.08) 5.59 | -0.23 (0.09) -2.56 | 1.00 |
*) standard error, **) t-value | |||||||
Reliability and convergent validity of the constructs were estimated by Cronbach's alpha, factor loading, and average variance extracted (see Table 2). Cronbach's alphas for all constructs were above the 0.70 threshold for explanatory research. The average extracted variances were all above 0.50, recommended level (Hair et al., 1998), which meant that more than one-half of the variances observed in the items were accounted for by their hypothesized constructs. All of the factor loadings of the items were greater than 0.50, with most of them above 0.70, well above the recommended level for significance (Hair et al., 1998). To examine discriminant validity, shared variances between constructs were compared with the average variance extracted of the individual constructs (Fornell and Lacker, 1981). The results showed that the shared variance between constructs were lower than the average variance extracted of the individual constructs, confirming discriminant validity (see Table 3). Consequently, the observed reliability and validity suggested adequacy of the measurements used in this research.
For the rest of paths from the external variables to the TAM constructs, the results were mixed. Two hypotheses concerning the effects of external variables (experiences and facilitating conditions) on perceived ease of use were supported (H6 and H8). Faculty with more experiences with e-learning courses and more favorable perception toward facilitating conditions found the e-learning easier to implement. Experiences with e-learning course also had a positive effect on perceived usefulness (H7). Contrary to our expectation, uneasiness did not have significant effects on perceived usefulness as well as perceived ease of us as hypothesized. Therefore, hypotheses H4 and H5 were not supported.
Figure 2. Results of structure model
In Figure 2, significant paths are depicted by bold lines and insignificant paths by dash lines.
The explanatory power of the structured model was also shown in Table 4. R2 values show that perceived ease of use and perceived usefulness account for 80% of variance in behavior intention. Experiences and facilitating conditions account for 34% of variance in perceived ease of use, whereas experiences and uneasiness account for 20% of variance in perceived usefulness.
Table 4
Structural equation model analysis of the research model
hypothesis | path | direct effects | indirect effects | |||
coefficient | t-value | coefficient | t-value | |||
H1 | perceived usefulness → intention | 0.70 | 8.75* | |||
H2 | perceived ease of use → intention | 0.34 | 4.58* | |||
H3 | perceived ease of use → perceived | 0.24 | 2.11** | |||
H4 | uneasiness → perceived usefulness | -0.12 | -1.31 | |||
H5 | uneasiness → intention | -0.05 | -0.76 | |||
H6 | facilitating conditions → perceived | 0.52 | 4.65* | |||
H7 | experiences → perceived usefulness | 0.11 | 2.39* | |||
H8 | experiences → perceived ease of | 0.08 | 1.90** | |||
uneasiness → intention | -0.08 | -1.30 | ||||
facilitating conditions → intention | 0.26 | 4.02* | ||||
experiences → intention | 0.12 | 3.12* | ||||
perceived ease of use → intention | 0.17 | 2.10** | ||||
| R2(perceived usefulness ) | 0.20 | | |||
| R2(perceived ease of use) | 0.34 | | |||
| R2(intention) | 0.80 | | |||
Fit indices: χ2=48.47, p<0.051, TLI=0.97, CFI=0.98, Normedχ2=1.468, GFI=0.95, RMSEA=0.05
Discussion and Conclusions
Discussion
This research proposed an extended version of technology acceptance model in the context of an e-learning implementation. Prior research on e-learning acceptance by faculty in higher education institutions focused mainly on identifying the list of motivational factors, and providing the relative importance of these factors (Betts, 1998; Ertmer, 1999; Schifter 2002; Fredericksen et al., 2000, Rockwell et al. 2000). This study examined the causal relationships among determinants of e-learning acceptance. Moreover, the sample of faculty has more diversity in their background than the subjects used in most prior studies, mainly because the subjects in this research were selected from several institutions. This might increase the generalizability of the results from this study.
As expected, our findings supported the appropriateness of using TAM to understand the intention of faculty to develop and implement e-learning courses. Both perceived usefulness and perceived ease of use on behavior intention appeared to be significant determinants, with perceived usefulness exerting a stronger influence than perceived ease of use, similar to the results from a majority of previous research comparing the relative explanatory power of perceived usefulness and perceived ease of use (Davis, 1993; Venkatesh and Davis, 1996).
Consistent with our hypothesis, faculty with more experience with e-learning will find it more useful and easier to offer e-learning. Faculty in higher institutions, in general, enjoy complete autonomy in choosing instructional methods, and thus feel independent in decision-making about using e-learning. But as they gain additional knowledge and experiences in e-learning, perceived level of usefulness and ease of use might improve and indirectly exert a strong influence on the intention to use. In the early stages of adopting the e-learning technology, perceived ease of use can be a major determinant of technology use for mainstream faculty. Once they gain experience through comprehensive support and robust technology, perceived level of usefulness and ease of use would improve, enabling them to have high levels of success in e-learning courses. However, when faculty have accumulated more experience with developing and offering e-learning courses, the significance of perceived ease of use may decrease while the significance of perceived usefulness may increase.
Facilitating conditions, another determinant of perceived ease of use, have been found to have a significant positive impact on ease of use, supporting the claims in prior research (Rockwell et al., 1999 and 2000; Fredericksen et al., 2000). This validates the importance of facilitating conditions in understanding user acceptance of e-learning technologies. Faculty who have higher levels of trust in supportiveness of institution, and having a higher level of self-efficacy, are more likely to find the e-learning technology easy to use. Various supporting strategies, such as on-line faculty resources, instructional design sessions for new faculty, a faculty help desk and an instructional design partner to support faculty development and course design, need to be developed to improve facilitating conditions. Contrary to our expectation, uneasiness did not have significant effects on perceived usefulness or on intention to offer e-learning courses, although the sign of the paths from uneasiness to both constructs was negative. By offering e-learning courses, faculty inevitably open their own lecture notes, exams, reference lists, and video clips from lectures to the public, sharing their teaching know-how and substantive knowledge with fellow faculty, most of whom are totally unknown to them. These findings suggest that e-learning is another way of creating a community of knowledge that will benefit not only the students but also the faculty teaching throughout all higher institutions.
Conclusions and Future Research
Information and communication technology is dramatically affecting the way faculty teaches in higher education institutions. The introduction of e-learning technology designed to help students facilitate learning processes is removing distance constraints and changing interpersonal communication dynamics. As new information technologies infiltrate classrooms, research on user acceptance of e-learning systems has started to receive much attention from professionals as well as academic researchers.
In summary, this study successfully uses TAM to examine the decision-making processes for faculty. General findings of this study were similar to findings of prior research to the extent that while both perceived usefulness and perceived ease of use exerted a direct influence on behavior intention, perceived ease of use also had an indirect effect on behavior intention through perceived usefulness. This research also revealed that experiences are important determinants of perceived ease of use and usefulness, with facilitating conditions significant in determining perceived ease of use. In addition, it has been found that feelings of uneasiness have no significant impact on the perceived level of usefulness or behavior intention.
The findings of this study have several implications for e-learning administrators in higher institutions. First, to motivate the participation of mainstream faculty, it is important for them to perceive that developing and offering e-learning courses can improve the quality and the effectiveness of teaching and learning, enhancing their performance and productivity. Providing reluctant faculty with pedagogical principles, including principles of developing and packaging e-learning content, through frequent workshops and faculty development sessions, can be helpful to achieve this goal, also improving their perceived level of ease of use. Second, experience is a salient factor affecting both perceived usefulness and perceived ease of use. Administrators may need to develop comprehensive supporting systems so that inexperienced faculty can take advantage of opportunities for conducting e-learning courses without any hesitation. Flexible support systems and faculty development sessions gradually increase the level of familiarity with e-learning technologies, helping faculty to more easily develop positive beliefs about the usefulness and ease of use of relatively new information and communication technology.
Results should be treated with caution for several reasons. First, the findings presented here were obtained from a single study that targeted mainstream faculty in research-oriented higher institutions in Korea. Caution needs to be taken when generalizing our findings to distance-learning institutions or teaching-oriented institutions. Second, responses to this study were voluntary and thus subject to self-selection biases. Faculty who were interested in, or were currently offering e-learning courses may have been more likely to respond to the survey. Third, this research examined only a limited set of determinants of behavior intention. Additional research is needed to evaluate more fully the e-learning acceptance model. Longitudinal evidence may also enhance understanding of causal relationships among factors.
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.
Agarwal, R., & Karahanna, E. (2000). Time flies when you're having fun: Cognitive absorption and beliefs about information technology usage. MIS Quarterly, 24(4), 665-694.
Agarwal, R., & Prasad, J. (1999). Are individual differences germane to the acceptance of new information technologies? Decision Sciences, 30(2), 361-391.
Agarwal, R., & Prasad, J. (1998). The antecedents and consequents of user perceptions in information technology acceptance. Decision Support Systems, 22(1), 15-29.
Ajzen, I., & Fishbein, M. (1975). Belief, attitude, intention and behavior: An introduction to theory and research. MA: Addison-Wesley.
Ajzen, I., & Fishbein, M. (1980). Understanding the attitudes and predicting social behavior. Englewood Cliffs, NJ: Prentice-Hall.
Anderson, J. C., & Gerbing, D. W. (1984). The effect of sampling error on convergence, improper solution, and goodness-of-fit indices for maximum likelihood confirmatory factor analysis. Psychometrika, 49, 155-173.
Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103, 411-423.
Arvan, L., & Musumeci, D. (2000). Instructor attitudes within the SCALE efficiency projects. Journal of ALN, 4(3), 196-215. http://www.aln.org/publications/jaln/v4n3/pdf/v4n3_arvan.pdf.
Betts, K. S. (1998). An institutional overview: Factors influencing faculty participation in distance education in postsecondary education in the United States: An institutional study. Online Journal of Distance Learning Administration, 1(3). http://www.westga.edu/ ~distance/betts13.html.
Byrne, B. M. (1998). Structural equation modeling with LISREL, PRELIS, and SIMPLIS: Basic concepts, applications, and programming. Mahwah, NJ: Lawrence Erlbaum.
Carmines, E. G., & McIver, J. P. (1981). Analyzing models with unobserved variables: Analysis of covariance structures. In G. W. Bohrnstedt, & E. F. Borgatta (Eds.), Social measurement: Current issues (pp. 65-110). Newbury Park, CA: Sage.
Carswell, L., Thomas, P. Petre, M., Price, B., & Richards, M. (2000). Distance education via the Internet: The student experience. British Journal of Educational Technology, 31(1), 29-46.
Cheung, W., Chang, M. K., & Lai, V. S. (2000). Prediction of Internet and World Wide Web usage at work: A test of an extended triandis model. Decision Support Systems, 30 (1), 83-100.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340.
Davis, F. D. (1993). User acceptance of information technology: System characteristics, user perceptions and behavior impacts. International Journal of Man-Machine Studies, 38(3), 475-487.
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982-1003.
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1992). Extrinsic and intrinsic motivation to use computers in the workplace. Journal of Applied Social Science, 35(8), 1111-1132.
Devaraj, S., Fan, M., & Kohli, R. (2002). Antecedent of B2C channel satisfaction and preference: Validating e-Commerce metrics. Information Systems Research, 13(3), 316-333.
DiBiase, D. (2000). Is distance teaching more work or less work? American Journal of Distance Education, 14(3), 6-20.
Ethmer, P. A. (1999). Addressing first- and second-order barriers to changes: Strategies for technology integration. Educational Technology Research and Development, 47(4),
47-61.
Fishbein, M. (1963). An investigation of the relationships between beliefs about an object and the attitude toward that object. Human Relations, 16(3), 233-240.
Fornell, C., & Lacker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(February), 39-50.
Fornell, C., & Yi, Y. (1992). Assumption of the two-step approach to latent variable modeling. Sociological Methods and Research, 20, 291-320.
Fredericksen, E., Pickett, A., Shea, P., Pelz, W., & Swan, K. (2000). Factors influencing faculty satisfaction with asynchronous teaching and learning in the SUNY learning network. JALN, 4(3).
http://www.aln.org/alnweb/journal/Vol4_issue3/fs/Fredericksen/fs-fredericksen.htm.
Gefen, D., & Straub, D. W. (1997). Gender differences in the perception and use of e-mail: An extension to the technology acceptance model. MIS Quarterly, 21(4), 389-400.
Hagner, P. R., & Schneebeck, C. A. (2001). Engaging the faculty. In C. A. Barone, & P. R. Hagner (Eds.), Technology-mediated teaching and learning(pp. 1-12).
San Francisco: Jossey-Bass.
Hair, J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (1998). Multivariate Data Analysis( 9th Ed.). NJ: Prentice Hall.
Hayduk, L. A. (1987). LISREL issues, debates and strategies, Baltimore: Johns Hopkins University Press.
Hsu, C., & Lu, H. (2004). Why do people play on-line games? An extended TAM with social influences and flow experience. Information & Management, 41(7), 853-868.
Hu, P. J., Chau, P. Y., Liu Sheng, O. R., & Tam, K. Y. (1999) Examining the technology acceptance model using physician acceptance of telemedicine technology. Journal of Management Information Systems, 16(2), 91 - 112.
Hu, P. J., Chau, P. Y., Clark, T. H., & Ma, W. W. (2003). Examining technology acceptance by school teachers: A longitudinal Study. Information Management, 41, 227-241.
Igbaria, M., Zinatelli, N., Cragg, P., & and Cavaye, A. L. M. (1997). Personal computing acceptance factors in small firms: A structural equation model. MIS Quarterly, 21(3), 279-305.
Jackson, C. M., Chow, S., & and Leitch, R. A. (1997). Towards an understanding of the behavioral intention to use an information system. Decision Sciences, 28(2), 357-389.
Jöreskog, K. G., & Sörbom, D. (1989). LISREL 7: A guide to the program and applications. Chicago: SPSS Publications.
Karahanna, E., & Straub, D. W. (1999). The psychological origins of perceived usefulness and ease of use. Information and Management, 35, 237-250.
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.
McKenzie, B. K. (2000), Needs, Concerns and Practices of Online Instructors. Online Journal of Distant Education Administration, 3(3), Fall, State University of West Georgia, Distance Education Center.
http://www.westga.edu/~distance/ojdla/fall33/ mckenzie33. html.
McLendon, E., & Cronk, P. (1999). Rethinking academic management practices: A case of meeting new challenges in online delivery. Online Journal of Distance Learning Administration. 2(1). http:www.westga.edu/ ~distance/ mclendon21.html.
Morris, M.G., & Turner, J. M. (2001). Assessing users' quality of experiences with the World Wide Web: An exploratory examination of temporal changes in technology acceptance. Int. J. Human-Computer Studies, 54, 877-901.
National Center for Education Statistics (2002). Distance education instruction by postsecondary faculty and staff.http://nces.ed.gov/pubs2002/2002155.pdf.
Nunnally, J. C. (1978). Psychometric theory (2nd Ed.). New York: McGraw-Hill.
Ong, C-S., & Lai, J-Y. (2006). Gender difference in perceptions and relationships among dominants of e-Learning acceptance, Computer in Human Behavior, 22(5), 816-829.
Rockwell, S. K., Schauer, J., Fritz, S. M., & Marx, D. B. (1999). Incentives and Obstacles Influencing Higher Education Faculty and Administrators to Teach via Distance. Online Journal of Distance Learning Administration, 2(4), http://www.westga.edu/~distance/ rockwell24.html.
Rockwell, S. K., Schauer, J., Fritz, S. M., & Marx, D. B. (2000). Faculty education, assistance and support needed to deliver education via Distance. Online Journal of Distance Learning Administration, 3(2), http://www.westga.edu/~distance/rockwell324.html.
Rogers, E. M. (1983, 1995), Diffusion of Innovation, New York, The Free Press.
Romiszowski, A., & Mason, R. (1996). Computer-mediated communication. In D.H. Jonassen (Ed.), Handbook of research for educational communications and technology(pp. 438-456). NY: Prentice Hall International.
Schifter, C. (2002). Perception differences about participating in distance education. Online Journal of Distance Learning Administration, 5(1).
http://www.westga.edu/~distance/ojdla/spring51/schifter51.html.
Schrum, L., & Berenfeld, B. (1997). Teaching and learning in the information age: A guide to educational telecommunications. Needham Heights, MA: A Viacom Company.
Smith, S., Tyler, J. M., & Benscote, A. (2000). Internet supported teaching: Advice from the trenches. ED at a Distance, 13(1).
http://www.usdla.org/html/journal/JAN00_Issue/ Internet.htm
Taylor, S., & Todd, P. (1995). Assessing IT usage: The role of prior experience. MIS Quarterly, 19, 561-569.
Thompson, R. L., Higgin, C. A., & Howell, J. M. (1991). Personal computing: Toward a conceptual model of utilization, MIS Quarterly, 15(1). 125-143.
Triandis, H. C. (1980). Values, attitudes, and interpersonal behavior. In: H. Howe, & M. M. Page (Eds.), Nebraska symposium on motivation(pp. 195-259). University of Nebraska Press.
Taylor, S., & Todd, P. A. (1995). Understanding information technology usage: A test of competing models. Information Systems Research, 6(2), 144-174.
Venkatesh, V., & Davis, F. D. (1996). A model of the antecedents of perceived ease of use: Development and test. Decision Sciences, 27(3), 451-481.
Venkatesh, V. (1999). Creation of favorable user perceptions: Exploring the role of intrinsic motivation. MIS Quarterly, 23(2), 239-260.
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186-204.
Visser, J. A. (2000). Faculty work in developing and teaching Web-based distance courses: A case study of time and effort. American Journal of Distance Education, 14(3), 21-32.
Whittington, C. D., & Sclater, N. (1998). Building and testing a virtual university. Computers and Education, 30(1/2), 41-47.
Willis, B., & Dickinson, J. (1997). Distance education and the World Wide Web. In B. H. Khan(Ed.), Web-based instruction(pp. 81-84). NJ: Educational Technology Publications.
About the Author
Mi-Ryang Kim (Ph. D.) is Associate Professor in the Department of Computer Education, College of Education, SungKyunKwan University in Seoul, Korea
e-mail: mrkim@skku.ac.kr