The evolution of the Internet diminishes learning’s limits on time and location and attracts more and more students to learning websites or online learning environments to pursue their chosen studies. The purpose of this study is to explore Taiwanese college students’ needs for English E-learning websites. Accordingly, this paper uses the unified theory of acceptance and use of technology to investigate and explain Taiwanese college students’ acceptance of English E-learning websites. After analysis, the results demonstrate that performance expectations, effort expectancy, and social influence have positive effects on behavior intentions and facilitating conditions; behavioral intentions also have positive effects on use behavior. Overall, if students believe that English E-learning websites can help them increase their performance and that they are easy to use, there is an increase in their intention to use them. This suggests that web designers should improve knowledge management functions and improve user interfaces to be easier to operate.
- unified theory of acceptance and use of technology (UTAUT)
- technology acceptance model (TAM)
- model combining the technology acceptance model and theory of planned behavior (C-TAM-TPB)
- motivational model (MM)
- English E-learning behavior
With the advent of the Internet, students not only can go to school to listen to live lectures but are also able to develop skills through Internet platforms. In other words, learning via the Internet can enhance studying efficiency. Everyone has access to knowledge more than ever before through the Internet. In addition, E-learning is accessible from any location. The Internet, computers, satellite broadcasting, audio and videotapes, interactive television and CDs are all examples of multimedia. Moreover, we can learn about a wide range of topics through the Internet and are not limited by physical constraints, such as the need to cram schools and classrooms with students, and so on.
Tsai (2009) has developed courseware for semiconductor technology that overcomes problems encountered in developing English Special Programs (ESP) in Taiwan. In the design of the courseware, five skills for learning English (listening, speaking, reading, writing, and translation) have been considered and a 3D multimedia technique has been used to promote learning interest, student engagement, and efficiency. Students report they have benefited from the courseware implementation. They report that the multimedia-assisted environment promotes learning effectiveness (Sihar, Hj Ab Aziz, & Sulaiman, 2011).
Taiwan has an extremely competitive infrastructure for information and communication technology. West (2005) ranked it 1st in e-government, while Waseda University ranked it 7th (Waseda University of e-Government, 2006). In terms of promoting digital business and Information and Communication Technology (ICT) services, the Economist Intelligence Unit (2009) ranked Taiwan 16th (Tao, 2008).
The Internet and computers are becoming a part of daily life for Taiwanese college students. E-learning supplies high-speed access to knowledge and information. According to a study by the Bank of Taiwan, 67.4% of people are willing to use E-learning, rather than going to school or reading books, to complete learning activities; the convenience of the Internet is most attractive to them. However, only 40.4% of people have had E-learning experience. Thus, we can conclude the Internet is very common in Taiwan and has many benefits. We still have many obstacles to overcome in increasing the rate of E-learning usage. Therefore, the goal of this study is to understand the decisions affecting choices of English E-learning websites. The research results may be utilized in future English E-learning website service development suggestions, and to improve usability and adaptability. Although this research is based on Taiwanese college students, the results should be relevant to other language learners.
We attempted to explore Taiwanese college students’ intentions to use English E-learning websites. The unified theory of acceptance and use of technology (UTAUT) model was used to assess the technological and value issues and thus obtain an understanding of Taiwanese college students’ decisions to use English E-learning website services.
Based on these facts, this study aims to focus on three objectives:
To identify the influence of the UTAUT factors on the adoption of English E-learning websites.
To understand college students’ needs for English E-learning websites in Taiwan.
To understand the influence of online English E-learning on students’ behavior in Taiwan.
E-learning is changing the way education is implemented and perceived. Schools can take advantage of this technology to make learning faster, cheaper, and more effective. These types of improvements are especially appealing to corporations. Corporate executives have begun to recognize that high-quality training creates long-term competitive advantages. They increasingly realize that effective education’s strategic benefits can outweigh its costs. (Hambrect and Co, 2001)
With the rapid growth of e-learning, a technological revolution is currently taking place in institutions of higher learning (Sihar et al., 2011). E-learning is a learner-centered educational system that enables learners to learn whenever, wherever, and whatever they wish, according to their learning objectives (Rosenberg, 2001).
The organizational structure of learning should be consistent with knowledge management practices in schools. In addition to social interaction among teachers, it is necessary to facilitate resource management (e.g., time and space sharing) that contributes to teaching and learning because it provides an environment where knowledge management practices take place. For example, schools need to consider what types of IT resources are important to develop physical and online environments for sharing and whether teachers are able to use them effectively (Leung, 2010).
As research observes, however, the use of technology has positive performance effects when learning foreign languages and also improves students’ motivation.
Definition of E-learning
E-learning is also called computer-assisted instruction, Web-based learning, distributed learning, online learning, or Internet-based learning. There are two E-learning modes. The first is computer-assisted instruction, which uses computers to aid in the delivery of standalone multimedia packages for teaching and learning. The second mode is distance learning, which uses information technologies to deliver instruction to remote learners from a central site.
A traditional approach is a face-to-face approach, which is similar to Osborn’s definition. An electronic approach may incorporate teleconferencing, chat rooms, or discussion boards. Instant messaging is a most common communication channel on the web, through such famous services like Microsoft MSN, Yahoo Messenger and Skype (Lin, 2009).
E-learning is becoming a major component in academia today. There is a need for formalized guidelines in E-Learning that instruct the designer (course instructor) on how to design, maintain, and manage a course. There are a wide variety of E-learning systems available on the market. Content available web learning is variable: some of it is excellent, but much is mediocre. The needs of content developers, educators, and students cannot be addressed through many available E-learning services; there are gaps that need to be addressed (Jayanthi, Srivatsa, & Ramesh, 2007).
Benefit of E-learning
Communication technologies such as the Internet are creating abundant opportunities to facilitate learning (Wang, 2008). One drawback may be that learners must be more responsible for themselves in E-learning environments. However, this also provides more opportunities for learners to choose their own directions and set their own pace. Systems can also provide materials that are fine-tuned to users’ needs.
As .NET framework-specific distributed technology, .NET remoting is not designed to provide interoperability or crossing trust boundaries to third-party clients. On the other hand, .NET remoting provides faster communication speed over internal networks. (Amirian & Alesheikh, 2008).
Chen and Tsai (2011) conducted a study regarding Virtual Classroom development, providing several strategies for building up prospective e-classroom districts or schools.
In December 2009, another study evaluated three E-learning systems in Iran that have been used in well-known universities: the Iran University of Science and Technology (IUST), the AmirKabir University of Technology (AUT), and the Virtual University of Shiraz (SVU), all of which are located in Tehran. All of these universities provided high-quality E-learning systems for students and have collected some information regarding the systems’ performance through interviews with students and staff (Etaati, Sadi-Nezhad, & Makue, 2011).
Empirical studies have applied media psychology to examine esthetic-emotion items, treated as adjectives associated with the two motivational models (MMs) developed by Keller, Malone, and Lepper, which are suited for formal and informal visual environments, respectively. Exploratory factor analysis (EFA) has been performed on aesthetic-emotion items in two studies to develop a scale to measure learners’ motivation (Riaz, Rambli, Salleh, & Mushtaq, 2011).
Furthermore, the expanding multimedia capabilities of new technologies provide vast opportunities to engage and motivate learners.
Table 1 shows the comparisons between traditional classroom learning and E-learning.
A wide body of research focuses on identifying factors affecting people’s intentions to use new technologies and how these intentions predict actual usage (Davis, Bagozzi, & Warshaw, 1989). The following sections summarize some of the major theories.
Innovation diffusion theory (IDT)
IDT seeks to explain the process by which users adapt technological advances (Rogers, 1995; Figure 1). The theory’s core constructs and definitions are shown in Table 2. Since the 1960s, it has been applied to the study of topics as diverse as agricultural tools and organizational innovation (Tornatzky & Klein, 1982). The five factors from this model along with two additional factors introduced by Moore and Benbasat (1991) were adapted to information system innovations (Table 3; Figure 2).
Theory of reasoned action (TRA)
The TRA is a fundamental model that was created by social psychologists to study conscious intentional behavior (Fishbein, & Ajzen, 1975; Figure 3). It has been incredibly influential and applied to a wide variety of behavior (Sheppard, Hartwick, & Warshaw, 1988). Davis et al. (1989) used it to study acceptance of new technologies and obtained results that were consistent with previous studies of other behavior. The core constructs and definitions are shown in Table 4.
Theory of planned behavior (TPB)
TPB expanded TRA with the concept of “perceived behavioral control” (Table 5). Ajzen (1991) reviewed studies that used TPB successfully for a wide range of intentions and behaviors (Figure 4). It has been effective in predicting acceptance and use of many different technologies (Harrison, Mykytyn, & Riemenschneider, 1997).
Technology Acceptance Model (TAM) and Extended TAM (TAM2)
TAM was designed to predict information technology acceptance and usage related to labor (Figure 5). Unlike TRA, the final conception of TAM does not include the attitude construct; this is to better explain intention parsimoniously. TAM has been widely applied to a diverse set of technologies and users (Table 6).
TAM2 enlarged TAM by including “subjective norm” as an additional predictor of intention in the case of mandatory settings (Venkatesh & Davis, 2000; Figure 6). It is modified from TAM and includes more variables (Table 7).
Combined TAM and TPB (C-TAM-TPB)
Social cognitive theory (SCT)
Model of Personal Computing (PC) utilization (MPCU)
Derived largely from a theory of human behavior, this model presents a competing perspective to those proposed by TRA and TPB (Table 10). Thompson, Higgins, and Howell (1991) adapted and refined a model for intermediate system contexts and used the model to predict personal computer utilization. However, the nature of the model makes it particularly suitable for predicting individual acceptance and use of a range of information technologies.
A significant body of research in psychology has sustained general motivation theory as an explanation for behavior. Several studies have examined motivational theory and adapted it to specific contexts (Table 11).
Each of the constructs mentioned in IDT, TRA, TAM, TPB, C-TAM-TPB, MPCU, MM, and SCT pertained to one of UTAUT’s main constructs and measurement items (Table 12).
The purpose of formulating UTAUT was to integrate the fragmented theory and research on individual acceptance of information technology into a unified theoretical model (Venkatesh et al., 2003). To do so, the eight specific models of the determinants of intention and usage of information technology were compared and conceptual and empirical similarities across these models were used to formulate UTAUT (Venkatesh et al., 2003; Table 13).
To conclude, UTAUT advanced individual acceptance research by unifying the theoretical perspectives common in the literature and incorporating four moderators to account for dynamic influences, including gender, age, voluntariness, and experience (Venkatesh et al., 2003). It seems reasonable to assume that UTAUT could be used to study the acceptance and use of English learning websites. We therefore introduced subjective task value to UTAUT in addressing our research question.
In this study, we use UTAUT to study acceptance and use of English E-learning websites by Taiwanese college students. According to UTAUT, four factors influence use of English E-learning websites: performance expectancy, effort expectancy, social influence, and facilitating conditions.
We did not consider the moderating effect of gender, age, experience, and voluntariness in this study. Because our participants are all college students, the gender, age, experience, and voluntariness are similar. Therefore, we have made some alterations to our research model (Figure 9).
The UTAUT model integrates the eight theoretical models noted above and is composed of the core determinants of usage intention (Venkatesh et al., 2003). Of the four core determinants, performance expectancy, effort expectancy, and social influence significantly predict intention. The UTAUT model is well suited to the context of this study. Based to these observations, we developed the hypotheses of this study.
Hypothesis 1: Performance expectancy positively affects users’ intentions to use English E-learning websites.
Hypothesis 2: Effect expectancy positively affects users’ intentions to use English E-learning websites.
Hypothesis 3: Social influence positively affects users’ intentions to use English E-learning websites.
Hypothesis 4: Facilitating conditions of English E-learning websites positively affects users’ use behaviors of actually using English E-learning websites.
Hypothesis 5: Users’ behavioral intentions to use English E-learning websites positively affect the users’ use behavior of actually using English E-learning websites.
The data were gathered from college students in Taiwan. The questionnaire of this study was modified from the question items of Venkatesh et al. (2003). Because the questions from the Chinese questionnaire were translated from English, the questionnaire was first pretested on four Taiwanese college students and was then slightly modified according to their feedback before being scanned by two foreign language professors. The initial tests demonstrated high reliability. The questionnaire was placed on the MY3Q questionnaire website (http://www.my3q.com) and sent to a random sample of Taiwanese college students.
The participants of this study are college students in Taiwan. We collected data from 176 respondents from more than 10 Taiwanese colleges. The main purpose was to collect data regarding Taiwanese college students’ English E-learning websites use intentions.
A survey questionnaire was used to collect data regarding use of English E-learning websites among college students in Taiwan. In addition to demographic information, this paper-based questionnaire collected data from individual users of English E-learning websites based on a number of constructs in the research model. Earlier research by Venkatesh et al. (2003) had validated measures for each of the constructs; we decided to include those validated items in our questionnaire. We used a Likert-type 5-point scale: 1 = strongly disagree and 5 = strongly agree. A list of validated items for each construct is provided in Table 14.
We used the Statistics Package for Social Science (SPSS) system to analyze the data using reliability analysis, correlation analysis, and regression analysis.
Reliability analysis is a measure to define the degree to which measurements are free from error and therefore yield consistent results.
Correlation analysis is a measure of the degree to which a change in the independent variable will result in a change in the dependent variable.
Regression analysis includes any techniques for modeling and analyzing several variables, with a focus on the relationship between a dependent variable and one or more independent variables.
All the 176 respondents of the questionnaire were Taiwanese college students. Table 15 represents the demographics of the respondents.
The results showed that more males than females participated in the study. According to these descriptive statistics, most of the respondents were seniors. Sixty-seven percent were not language majors (Figure 10).
The data indicate that the measures are robust in terms of their internal consistency reliability as indexed by composite reliability. The reliability of the collected data in this study was assessed by the Statistical Package for Social Science (SPSS). The composite reliabilities ranged from 0.76 to 0.95, which exceed the recommended threshold value of 0.70. Reliability results are given in Table 16.
Convergent validity and discriminant validity are assessed by Pearson correlation analysis. Guidelines suggest that factor loadings be greater than 0.50 (Hair, Anderson, Tatham, & Black, 1998) or, under a stricter criterion, greater than 0.70 (Fornell, 1982). All of the factor results of items in this research model are higher than 0.50; most of them are above 0.70. Every item is loaded significantly (p < .01 in all cases) on its constructs. Therefore, all constructs in the model have adequate reliability and convergent validity. Correlation results are shown in Table 17.
We use regression analysis to investigate the influence of performance expectancy, effort expectancy and social influence on intention to use. The results show that performance expectancy, effort expectancy, and social influence significantly affect intention to use. The results are presented in Table 18.
We again use regression analysis to study the influence of intention to use on user behavior. The results show that facilitating conditions and intention to use significantly affect use behavior. The results are presented in Table 19.
Confirmation of Hypotheses
The influence of students’ performance expectancy for using English E-learning websites on intention to use English E-learning websites
The results showed that Performance Expectancy positively affects users’ intentions to use English E-learning websites (β = .346, p < .001). Therefore, H1 is supported. This means that when students expect an English E-learning website to increase their performance, they increase their intentions to use it.
The influence of students’ Effect Expectancy for using English E-learning websites on intention to use English E-learning websites
The results showed that Effect Expectancy positively affects users’ intentions to use English E-learning websites (β = .154, p < .05). Therefore, H2 is supported. This means that when students expect an English E-learning website to be easy to use, they increase their intentions to use it.
The influence of students’ Social Influence to use English E-learning websites on intention to use English E-learning websites
The results showed that Performance Expectancy positively affects users’ intentions to use English E-learning websites (β = .282, p < .001). Therefore, H3 is supported. This means that when students’ teachers, peers or someone important to them suggests that they use English E-learning websites, they increase their intentions to use them.
The influence of students’ Facilitating Conditions for using English E-learning websites on use behavior
The results showed that Facilitating Conditions positively affect users’ use behavior of actually using English E-learning websites (β = .066, p < .05). Therefore, H4 is supported. This means that when students receive more facilitating conditions to use English E-learning website, they use the websites more frequently.
The influence of students’ Intention to Use English E-learning websites on use behavior
The results showed that Intention to Use positively affects users’ use behavior of actually using English E-learning websites (β = .098, p < .01). Therefore, H5 is supported. This means that when students have more intent to use an English E-learning website, they use the website more frequently (Table 20).
The results support the UTAUT model’s use to study the acceptance of English E-learning websites. The UTAUT model shows that students’ use behavior of English E-learning websites depends on performance expectancy, effort expectancy, and social influence. Therefore, we suggest that web designers improve knowledge management functions and make user interfaces easier to operate. Furthermore, students should be notified that the websites can be supported by facilitating conditions.
Limitations and suggestions
Because this study only examines the acceptance of English E-learning websites among Taiwanese college students, the results may not be generalized to other E-learning systems and countries. Therefore, we suggest that a future researcher validate the model and findings in other E-learning systems or other countries.
Declaration of Conflicting Interests The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding The author(s) received no financial support for the research and/or authorship of this article.
- © The Author(s) 2013
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Paul Juinn Bing Tan, attended Oklahoma City University, Oklahoma, USA to study Psychology in 1986. He then went on to earn his masters of education in teaching English as Second Language and Counseling at Oklahoma, USA in 1991. In 1994, he decided to further his education after Mikhail Gorbachev visited in Taiwan, so he attended Moscow State Pedagogical University. In 1997, he received his doctorate degree in the faculty of Pedagogic. Paul Juinn Bing Tan joined NPU (National Penghu University) in August 2000. Prior to attending Moscow State Pedagogical University, he was an Associate Professor at the Institute of Education, Moscow State Pedagogical University (Russia) where he taught English, Chinese and Psychology. Paul Juinn Bing Tan currently serves as an Assistant Professor at the department of Applied Foreign Languages of National Penghu University in Taiwan.