Nursing students behavioral intention to use online courses: A questionnaire survey

International Journal of Nursing Studies 45 (2008) 1299 1309 www.elsevier.com/ijns Nursing students behavioral intention to use online courses: A questionnaire survey Feng-Cheng Tung a,b,, Su-Chao Chang a a Department of Business Administration, National Cheng Kung University, Tainan City, Taiwan, ROC b Diwan College of Management, Taiwan Received 8 August 2006; received in revised form 18 September 2007; accepted 28 September 2007 Abstract Background: The development of network communication science and technology has accorded a special benefit to learning. Online courses have also become the most popular and important learning resource among students. Objectives: Overall, this research aims to explore what are the important factors making students use online Design: The research combines the technology acceptance model and the innovation diffusion theory, and adds four research variables, computer anxiety, computer self-efficacy, perceived financial cost and perceived information quality to propose a new hybrid technology acceptance model to study students behavioral intention to use online Settings: Based on 228 questionnaires collected from nursing students in Taiwan. Methods: The structural equation modeling technique was used to evaluate the causal model and confirmatory factor analysis was performed to examine the reliability and validity of the measurement model. Participants: The survey began with e-mail and telephone interviews in January 2006. The interviewees were 348 students of Taiwan s universities. Because some of the replying subjects have never taken those courses and some did not complete the questionnaires, there were 228 valid questionnaires from students of Taiwan s universities who have taken online The responding rate was 65.52%. Results: This research found that computer anxiety had a negative effect on the behavioral intention to use online courses (g ¼ 0.21, Po0.01). Computer self-efficacy had a positive effect on the behavioral intention to use online courses (g ¼ 0.37, Po0.01). Compatibility had a positive effect on both the behavioral intention to use online courses (g ¼ 0.18, Po0.01) and perceived usefulness (g ¼ 0.3, Po0.01). Perceived usefulness had a positive effect on the behavioral intention to use online courses (b ¼ 0.14, Po0.05). Perceived ease of use had a positive effect on perceived usefulness (g ¼ 0.23, Po0.01), the behavioral intention to use online courses (g ¼ 0.24, Po0.01). Perceived financial cost had a negative effect on the behavioral intention to use online courses (g ¼ 0.16, Po0.01). Perceived information quality had a positive effect on the behavioral intention to use online courses (g ¼ 0.11, Po0.05). The findings of this research help to develop more user friendly online courses for students. Conclusions: (1) Computer anxiety, computer self-efficacy, compatibility, perceived usefulness, perceived ease of use, perceived financial cost, and perceived information quality were the critical factors that impacted on students behavioral intention to use online (2) Computer anxiety and perceived financial cost had a negative effect on the behavioral intention to use online (3) The present study added four new research constructs (computer anxiety, computer self-efficacy, perceived financial cost, and perceived information quality) to the research model so Corresponding author. No. 18, Lane 44, Sector 4, Jinhua Road, West Central District, Tainan City, Taiwan, ROC. Tel.: +886 920 333 756; fax: +886 6 2912983. E-mail address: tungfc66@yahoo.com.tw (F.-C. Tung). 0020-7489/$ - see front matter r 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.ijnurstu.2007.09.011

1300 F.-C. Tung, S.-C. Chang / International Journal of Nursing Studies 45 (2008) 1299 1309 that it would be more complete; this improved the fit of the whole model. (4) Computer self-efficacy, compatibility, perceived usefulness, perceived ease of use, and perceived information quality had a positive effect on the behavioral intention to use online By explaining students behavioral intention from a user s perspective, the findings of this research help to provide insight into the best way to promote new e-learning tools for students. r 2007 Elsevier Ltd. All rights reserved. Keywords: Online course; Computer anxiety; Computer self-efficacy; Perceived information quality; Behavioral intention What is already known about the topic? Online courses are very important e-learning tools for students. Innovation diffusion theory is widely used for adopting relevant information technologies. What this paper adds A hybrid model which combines innovation diffusion theory and the technology acceptance model is proposed for studying students behavioral intention to use online In this paper, it was found that computer anxiety, computer self-efficacy, compatibility, perceived usefulness, perceived ease of use, perceived financial cost, and perceived information quality were critical factors for determining students behavioral intention to use online 1. Introduction Through the Internet and other information technologies, new media can bring highly dynamic communication. With a flexible timetable and the time saved by not having to commute, online learning has quickly become an important tool for students (Rosenlund and Damark- Bembenek, 1999). Online courses are becoming more popular as people come to appreciate the advantages of online learning. As discussed in the literature, the advantages of using computer-assisted tools include their convenience providing access to information, their flexibility adapting to the educational needs of students, and their cost-effectiveness, opening educational opportunities to large numbers of students (Sery-Ble et al., 2001; Herrin, 2001; Soon et al., 2000). Recent studies have found that online courses are quite helpful for students (Halter et al., 2006; Ostrow and DiMaria- Ghalili, 2005). Halter et al. (2006) studied five female doctoral students in nursing, and found that online courses were perceived to save money and improve learning efficiency. Ostrow and DiMaria-Ghalili (2005) found that lessons learned from one school s long history with distance education can be analyzed from the theoretical perspectives of both adult learners and virtual students. Nursing academia is searching for ways to widen nurses educational opportunities and to facilitate on demand education that suits individual nursing student s needs (Bentley et al., 2003). Lin and Hsieh (2001) reviewed the research evidence on learner control in a Web-based teaching environment and analyzed the conditions under which learning is most effectively facilitated. In Taiwan in 2005, according to the statistics of the Ministry of Education, 57 universities (40% of all universities in Taiwan) offered online courses, for a total number of online credit courses exceeding 500. This indicates that Taiwan has developed a sound base of online Instead of pure distance learning, existing online courses in Taiwan mainly offer flexible learning opportunities that require some attendance at teaching sessions; 38 universities in Taiwan offer such Currently, all of the universities in Taiwan provide students with computer labs and instructions on how to take online courses; the universities websites help students understand how their online courses operate and help improve their students learning efficiency. In Taiwan, online courses are very important e-learning tools for nursing students. This study determined the factors that influenced nursing students to use online The innovation diffusion theory (IDT) has been widely applied in relevant information technology (IT) and information systems (IS) research (Karahanna et al., 1999). The technology acceptance model (TAM) (Davis et al., 1989) has received significant attention in the IT/IS acceptance literature. The present study combined the TAM and the IDT, along with four research variables (computer anxiety, computer self-efficacy, perceived financial cost, and perceived information quality); thus, a new hybrid TAM was developed for studying nursing students behavioral intention to use online 2. Theoretical background 2.1. Technology acceptance model The TAM (Davis et al., 1989) was adapted from the theory of reasoned action (Ajzen and Fishbein, 1980).

F.-C. Tung, S.-C. Chang / International Journal of Nursing Studies 45 (2008) 1299 1309 1301 In TAM, Davis et al. originally suggested that two beliefs perceived usefulness and perceived ease of use are instrumental in explaining variance in users intentions. Perceived usefulness is the degree to which a person believes that using a particular system will enhance his or her job performance. Perceived ease of use is the degree to which a person believes that using a particular system will involve minimal effort. These determinants are easy for system developers to understand, and can be considered specifically during system requirement analyses and other system development stages. These factors are common in technology-usage settings and can be applied widely to solve acceptance problems (Taylor and Todd, 1995). The objective of TAM is to provide an explanation for the determinants of computer acceptance, an explanation that, in general, is capable of explaining user behavior across a broad range of end-user computing technologies and user populations (Davis et al., 1989). Venkatesh and Davis (2000) proposed an extension, called TAM2, which includes social influence processes (subjective norms, voluntarism, and image) and cognitive instrumental processes (job relevance, output quality and result demonstrability, in addition to the already-integrated perceived ease of use). 2.2. Innovation diffusion theory An innovation is an idea, practice, or object that is perceived as new by an individual or another unit of adoption (Rogers, 1995). Diffusion, on the other hand, is the process by which an innovation is communicated through certain channels over time among the members of a social system (Rogers, 1995). IDT includes five significant innovation characteristics: relative advantage, compatibility, complexity, trial ability and observability. It has been applied widely in disciplines like education, sociology, communication and marketing (Rogers, 1995). Relative advantage means that innovations yield advantages over traditional methods. Compatibility is the degree to which an innovation is perceived to be consistent with the potential users existing values, previous experiences, and needs. Greater compatibility generally results in a faster rate of system adoption. Complexity represents the level of difficulty required to understand a given innovation, and its ease of use. Trial-ability refers to the degree to which innovations can be tested. And observability refers to the degree to which the results of innovations can be observed by others (Agarwal and Prasad, 1998). IDT involves the formation of a favorable or unfavorable attitude toward an innovation; however, it does not provide further evidence on how the attitude evolves into a decision to accept or reject that innovation. TAM, on the other hand, provides theoretical linkages between beliefs, attitudes, intentions, and actions. TAM has been criticized for ignoring social influences on technology acceptance. Previous studies have found that the relative advantage construct in IDT is similar to the perceived usefulness proposed in the TAM, and that the complexity construct in IDT is similar to TAM s perceived ease of use (Moore and Benbasat, 1991). But, in TAM research, compatibility has not been studied. While evaluating this, one must examine an innovation s compatibility with existing values and beliefs, previously introduced ideas, and potential adopters needs. Greater compatibility results in a faster rate of adoption. In order to increase the credibility and effectiveness of our study, we expanded TAM to include compatibility as an additional research construct. Since previous research has revealed no apparent correlations between trial-ability, observability and IT adoption, we excluded these research constructs (Agarwal and Prasad, 1998). Chen et al. (2002) combined the original TAM with the compatibility construct of IDT to evaluate and explain consumer behavior in the virtual store context. Wu and Wang (2005) integrated IDT into TAM to investigate what determines user mobile commerce (MC) acceptance. They found that compatibility has a direct effect upon perceived usefulness and one s behavioral intention to use. Thus, in the present study, we combined IDT and TAM, and added a compatibility component. 2.3. Computer anxiety and computer self-efficacy Computer anxiety has been defined as a fear of computers while using a computer, or fearing the possibility of using a computer (Chua et al., 1999). Computer anxiety refers to fears about the implications of computer-based technology use, such as the loss of important data or the fear of making other possible mistakes. As such, it is the product of a combination of certain psychological variables, including neuroticism and locus of control (Marakas et al., 2000). Researchers have found that computer anxiety has a negative relationship with total hours of Internet use (Joiner et al., 2005). The higher the anxiety about computer use that the subjects have, the less likely they are to use the information system. Computer self-efficacy refers to individuals judgment of their own capability to use a computer. It is not concerned with individuals past performance, but rather with an assessment of their future potential performance. Moreover, it does not refer to simple component subskills, like formatting diskettes or entering formulas into a spreadsheet. Rather, it incorporates judgments about the ability to apply those skills to broader tasks, such as preparing written reports or analyzing financial data. Computer selfefficacy is defined as an individual s perceptions of his or her ability to use computers in the accomplishment of a task rather than reflecting simple component skills.

1302 F.-C. Tung, S.-C. Chang / International Journal of Nursing Studies 45 (2008) 1299 1309 Computer self-efficacy is a belief in one s capability to use the computer (Compeau and Higgins, 1995). The more experience one acquires online, the more important are concerns of control over personal information; this implies that computer self-efficacy has a positive effect on the behavioral intention to use IT systems (Vijayasarathy, 2004). 2.4. Perceived financial cost Perceived financial cost is defined as the extent to which a person believes that using online courses will cost money. Indeed, economic motivations and outcomes are most often the focus of IS acceptance studies. Perceived financial resources were also found to be a significant antecedent of the behavioral intention to use an IS (Mathieson et al., 2001). Luarn and Lin (2005) found that perceived financial cost had a negative effect on the behavioral intention to use mobile banking. 2.5. Perceived information quality Variables used for measuring information quality include accuracy, timeliness, relevance, flexible information presentation, customized information presentation, price information, product/service comparability, product/service differentiation, and complete product/ service description (Liu and Arnett, 2000). Most online customers want to serve themselves and will serve themselves by locating information as long as it is relatively easy to find. Since the information resources of the World Wide Web are so vast and rich, it is important that desired information can be easily accessed. Although search costs are dramatically reduced on the Web, improvements are still needed to reduce irrelevant information, improve information organization, and offer better information processing aids (Wolfinbarger and Gilly, 2001). Kuo et al. (2005) found perceived information quality has a positive effect on the behavioral intention to use an e-service. 3. Research model and hypotheses 3.1. Research model The research model used for this research is depicted in Fig. 1. We have modified it, in accordance with prior research on IDT (Moore and Benbasat, 1991) and TAM (Taylor and Todd, 1995; Venkatesh and Davis, 1996, 2000; Gefen, 2004). 3.2. Hypotheses The present study integrated the TAM and the IDT, and added four research variables (computer anxiety, computer self-efficacy, perceived financial cost, and perceived information quality) to propose a new hybrid TAM for studying nursing students behavioral intention to use online Nine hypotheses are shown in Table 1. 4. Research methodology 4.1. Sampling method The Departments of Nursing of five universities that offered online courses were approached, and we presented our questionnaire. With the approval of the university authorities, the e-mail addresses and phone numbers of 835 students were obtained for an e-mail survey and telephone interview in January 2006. However, due to an e-mail virus issue, a total of only 348 questionnaires were distributed; 286 questionnaires were e-mailed and 62 students were interviewed by telephone. Due to some incompletely answered questionnaires, a total of 228 valid questionnaires were obtained from nursing students attending Taiwan s universities who had taken online The response rate was 65.52%. 4.2. Instrument To ensure content validity of the scales used, the items selected must represent the concept around which the generalizations are to be made. Items selected for the constructs were adapted from prior studies in order to ensure content validity. All the 25 items used a sevenpoint Lickert scale with 1 representing exceptional disagreement and 7 representing exceptional agreement. The questionnaire included eight parts. Part 1 of the questionnaire dealt with the computer anxiety construct and was adapted from Compeau and Higgins (1995); it contained four items. Part 2 of the questionnaire dealt with the computer self-efficacy construct and was adapted from Vijayasarathy (2004); it contained two items. Part 3 of the questionnaire dealt with the compatibility construct of IDT and was adapted from Wu and Wang (2005); it contained three items. Part 4 of the questionnaire dealt with the perceived usefulness construct of TAM and was adapted from Venkatesh and Davis (2000); it contained four items. Part 5 of the questionnaire dealt with the perceived ease of use construct of TAM adapted from Venkatesh and Davis (2000); it contained four items. Part 6 of the questionnaire dealt with the perceived financial cost construct and was adapted from Luarn and Lin (2005); it contained two items. Part 7 of the questionnaire dealt with the perceived information quality construct and was adapted from Kuo et al. (2005); it contained four items. Part 8 of the questionnaire dealt with the behavioral intention to use construct and was adapted from Venkatesh and Davis (2000); it contained two items. All items are listed in Table 2.

F.-C. Tung, S.-C. Chang / International Journal of Nursing Studies 45 (2008) 1299 1309 1303 Computer anxiety H1 Computer self-efficacy H2 Compatibility H3 Perceived usefulness (PU) H4 H5 Behavioral intention to use online courses Perceived ease of use (PEOU) H6 H7 H8 Perceived financial cost (PFC) H9 Perceived information quality Fig. 1. Research model. 4.3. Sample demographics 7.02% of the subjects were male, and 92.98% were female; 166 (72.8%) students had already obtained their nursing certificate, while the remaining 62 (27.2%) had not yet obtained their nursing certificate. With respect to age, the single largest group consisted of 18 year olds (34.21%), followed by 19 year olds (28.51%) and then 20 year olds (22.81%); 9.21% of the subjects were older than 20 years of age and 5.26% were younger than 18 years. Overall, 44.30% had used online courses for 1 2 years, 28.51% had used them for up to 1 year, and 27.19% had used them for more than 2 years. Of the courses that the students had taken, 76.31% were major required courses, 64.03% were information courses, and 54.38% were life skill Most of the courses that were taken had been established by the students own universities. 4.4. Data analysis The statistical analysis software packages used were LISREL 8.3 and SPSS 13.0. Structural equation modeling (SEM) was used for the data analysis to study

1304 F.-C. Tung, S.-C. Chang / International Journal of Nursing Studies 45 (2008) 1299 1309 Table 1 Summary of hypotheses of the research model Construct Items Hypotheses Part 1. Computer anxiety Part 2. Computer selfefficacy Part 3. Compatibility Part 4. Perceived usefulness Part 5. Perceived ease of use Part 6. Perceived financial cost Part 7. Perceived information quality Part 8. Behavioral intention CA1, CA2, CA3, CA4 CSE1, CSE2 COM1, COM2, COM3 PU1, PU2, PU3, PU4 PEOU1, PEOU2, PEOU3, PEOU4 PFC1, PFC2 PIQ1, PIQ2, PIQ3, PIQ4 BI1, BI2 H1. Computer anxiety will have a negative effect on the behavioral intention to use online H2. Computer selfefficacy will have a positive effect on the behavioral intention to use online H3. Compatibility will have a positive effect on behavioral intention to use online H4. Compatibility will have a positive effect on perceived usefulness. H5. Perceived usefulness will have a positive effect on the behavioral intention to use online H6. Perceived ease of use will have a positive effect on the perceived usefulness of online H7. Perceived ease of use will have a positive effect on the behavioral intention to use online H8. Perceived financial cost will have a negative effect on the behavioral intention to use online H9. Perceived information quality will have a positive effect on the behavioral intention to use online CA, computer anxiety; CSE, computer self-efficacy; COM, compatibility; PU, perceived usefulness; PEOU, perceived ease of use; PFC, perceived financial cost; PIQ, perceived information quality; BI, behavioral intention. the causalities among all parameters constructed in each model. The estimation of the parameters used the maximum likelihood estimation (MLE); the sample size cannot be too small when this estimation is used. The data obtained were tested for reliability and validity using confirmatory factor analysis (CFA). In order to measure the reliability, convergent validity, and discriminant validity of the theoretical constructs, the three most frequently used indices (individual item reliability, composite reliability (CR), and average variance extracted (AVE)) suggested by Bagozzi and Yi (1988) were used to rate the evaluation model. 5. Results 5.1. Analysis of the research model Individual item reliability assesses the factor loading of a potential variable stemming from its corresponding measurement item. Fornell and Larcker (1981) suggested that the CR value should be greater than 0.6, and that the AVE value should be greater than 0.5. As shown in Table 3, the average value of the variables used in the research model is in accordance with the suggested values of the three indices; this means that these research variables have good convergent validity, their total AVE is larger than their correlation value, and that these research variables have discriminant validity. Table 2 displays the completely standardized factor loadings and individual item reliability of the items related to the constructs that they were designed to measure. The ratio of w 2 to the degrees-of-freedom for the measurement model was calculated to be 1.47. The normalized fit index was 0.94, which is greater than the 0.90 benchmark suggested by Bentler (1989). The comparative fit index was 0.98, which is greater than the 0.90 benchmark suggested by Bentler (1989). The incremental fit index was 0.98, which is greater than the 0.90 benchmark suggested by Bentler (1989). The root mean square error of approximation was less than 0.05 (0.046). The goodness-of-fit statistics are summarized in Table 4. There is a good overall fit of the measurement model to the data (w 2 ¼ 369.51, d.f. ¼ 251, P-value ¼ 0.00, 90% confidence interval). The significant structural relationships among the research variables and the standardized path coefficients are presented in Fig. 2. The data show that computer anxiety had a negative effect on the behavioral intention to use online courses (m ¼ 0.21, Po0.01), and that computer self-efficacy had a positive effect on the behavioral intention to use online courses (g ¼ 0.37, Po0.01). Therefore, hypotheses H1 and H2 were supported. The data show that compatibility had a positive effect on both the behavioral intention to use online courses (g ¼ 0.18, Po0.01) and perceived usefulness (g ¼ 0.3, Po0.01). Therefore, hypotheses H3 and H4 were supported. Perceived usefulness had a positive effect on the behavioral intention to use online courses

F.-C. Tung, S.-C. Chang / International Journal of Nursing Studies 45 (2008) 1299 1309 1305 Table 2 Standardized factor loadings and individual item reliability Item Measure Factor loading R 2 40.5 CA1 I feel apprehensive about using online 0.84 0.71 CA2 It scares me to think that I could cause online courses to destroy a large 0.84 0.71 amount of information by hitting the wrong key. CA3 I hesitate to use online courses for fear of making mistakes that I cannot 0.83 0.69 correct. CA4 Online courses are somewhat intimidating to me. 0.90 0.81 CSE1 I expect to become proficient in using nursing 0.95 0.90 CSE2 I would feel confident that I can use online 0.82 0.67 COM1 Using online courses is compatible with most of my learning. 0.89 0.79 COM2 Using online courses is appropriate for my life style. 0.89 0.79 COM3 Using online courses is appropriate for my learning. 0.85 0.72 PU1 Online courses can improve my learning efficiency. 0.87 0.76 PU2 Online courses can enhance my learning performance. 0.79 0.62 PU3 Online courses increase my learning output. 0.82 0.67 PU4 I find online courses are useful for my learning. 0.77 0.59 PEOU1 It is easy to operate online courses and get it to do what I want it to do. 0.75 0.56 PEOU2 I find that online courses are very easy to use. 0.84 0.71 PEOU3 I find that the human interface of the online courses is clear and easy to 0.83 0.69 understand. PEOU4 I find that interacting with online courses doesn t demand much care or 0.72 0.52 attention. PFC1 I think the equipment required to use online courses is expensive. 0.96 0.92 PFC2 I think it costs a lot to use online 0.83 0.69 PIQ1 Information of online courses is current and timely. 0.97 0.94 PIQ2 Information of online courses is accurate and relevant. 0.86 0.74 PIQ3 Hyperlinks are valid. 0.97 0.94 PIQ4 Information of online courses is rich in detail. 0.96 0.92 BI1 If I get to use online courses I intend to use the online nursing 0.88 0.77 BI2 If I get to use online courses, I expect I that will use online 0.84 0.71 CA, computer anxiety; CSE, computer self-efficacy; COM, compatibility; PU, perceived usefulness; PEOU, perceived ease of use; PFC, perceived financial cost; PIQ, perceived information quality; BI, behavioral intention. Table 3 Construct reliability, convergent validity and discriminate validity Construct CR Factor correlations AVE CA CSE COM PU PEOU PFC PIQ BI CA 0.91 0.72 CSE 0.88 0.79 0.21 COM 0.91 0.77 0.27 0.32 PU 0.89 0.66 0.15 0.17 0.35 PEOU 0.87 0.62 0.28 0.34 0.23 0.30 PFC 0.89 0.81 0.07 0.07 0.01 0.03 0.12 PIQ 0.96 0.86 0.17 0.28 0.18 0.13 0.31 0.05 BI 0.86 0.75 0.43 0.62 0.48 0.38 0.53 0.13 0.37 CA, computer anxiety; CSE, computer self-efficacy; COM, compatibility; PU, perceived usefulness; PEOU, perceived ease of use; PFC, perceived financial cost; PIQ, perceived information quality; BI, behavioral intention. (b ¼ 0.14, Po0.05). This means that hypothesis H5 was supported. Perceived ease of use had a positive effect on perceived usefulness (g ¼ 0.23, Po0.01) and the behavioral intention to use online courses (g ¼ 0.24, Po0.01). Thus, hypotheses H6 and H7 were supported. Perceived financial cost had a negative effect on the

1306 F.-C. Tung, S.-C. Chang / International Journal of Nursing Studies 45 (2008) 1299 1309 Table 4 Goodness-of-fit measures of the research model Fit indices w 2 d.f. w 2 :d.f. NFI CFI IFI RMSEA Recommended value N/A N/A %3.0 ^0.9 ^0.9 ^0.9 %0.05 The research model 369.51 251 1.47 0.94 0.98 0.98 0.046 Original TAM 85.52 32 2.67 0.93 0.96 0.96 0.086 Computer anxiety -0.21 H1 Computer self-efficacy 0.37 H2 Compatibility 0.18 H3 0.30 H4 Perceived usefulness (PU) (R 2 =0.17) 0.14 H5 Behavioral intention to use online courses (R 2 =0.65) 0.23 H6 0.24 H7 Perceived ease of use (PEOU) -0.16 H8 Perceived financial cost (PFC) H9 Perceived information quality 0.11 *:p<0.05 **:p<0.01 Fig. 2. Results of the testing of the hypotheses. behavioral intention to use online courses (g ¼ 0.16, Po0.01). This means that hypothesis H8 was supported. Finally, the data imply that perceived information quality had a positive effect on the behavioral intention to use online courses (g ¼ 0.11, Po0.05). Thus, hypothesis H9 was supported.

F.-C. Tung, S.-C. Chang / International Journal of Nursing Studies 45 (2008) 1299 1309 1307 5.2. Analysis of the proposed model using the original TAM To understand whether or not the research model we proposed really contributed to a better model, this study made the original model (see Fig. 3) with the variables of perceived usefulness and perceived ease of use, to compare their fit with the data. Both the research model and the original TAM explained a significant proportion of the variance in users behavioral intention to use online The original TAM explained 40% of the variance, and the research model we proposed accounted for 65% of the variance. The original TAM explained 8% of perceived usefulness compared to 17% in our research model. In addition, based on the goodness-of-fit statistics, this study concluded that this research model is better than the original TAM in most of the aspects (see Table 4). This difference indicated that the research model with five research variables, compatibility, computer anxiety, computer self-efficacy, perceived financial cost and perceived information quality, is more explicative than the original TAM without them to predict users behavioral intention to use. 6. Discussion All of the nine hypotheses we postulated were supported by the data generated in this study. For example, we identified a negative relationship between computer anxiety and behavioral intention. Consequently, if we want to increase students intention to use online courses, we first must reduce their computer anxiety. Higher levels of computer anxiety have an unfavorable influence upon the use of online This finding is consistent with what Marakas et al. (2000) proposed, based upon their own results. A positive relationship also exists between computer self-efficacy and behavioral intention. In other words, the more confident students are in their own ability to use computers, the more likely they are to use online courses, consistent with what Vijayasarathy (2004) has proposed. Consequently, students who often use the computer as an auxiliary learning tool, do not have computer anxiety, or often work with high-tech products are more likely to use online Given that Hypotheses 3 and 4 both were supported, using integrated IDT and the TAM to explore students behavioral intention to use online courses seems valid. The positive association between compatibility and intention to use suggests that the more compatible students lifestyles and learning styles are with IT, the greater their intention to use online courses will be, consistent with findings by Wu and Wang (2005). Students who think that online courses are useful tend to be more willing to use online courses, but the courses themselves must be perceived to be user-friendly. In fact, to increase students willingness to use online courses, the operation processes of online courses should be as straightforward as possible. More complicated operation processes are a significant barrier to the promotion of online courses, as previously suggested by Venkatesh and Davis (1996, 2000). The study also shows that higher perceived financial cost also decreases the behavioral intention to use online This result is consistent with the finding of Luarn and Lin (2005). As most of the students are not financially rich, more expensive online courses actually attract fewer students. In order to promote online courses, the cost has to be carefully considered. The study also shows that higher perceived information quality also increases the behavioral intention to use online This suggests that the content of online courses has to be up to date and comprehensive in order to attract the students. If the information provided in online courses is not frequently updated or not comprehensive enough, it will significantly reduce the students behavioral intention to use online As a final point, we have found that SEM is a useful tool to study the causal relationships between the various parameters involved in the choice to use online Perceived usefulness (PU) (R 2 =0.08) 0.33 Perceived ease of use (PEOU) 0.35 0.45 Behavioral intention to use online courses (R 2 =0.40) *:p<0.05 **:p<0.01 Fig. 3. Results of the testing of the original TAM.

1308 F.-C. Tung, S.-C. Chang / International Journal of Nursing Studies 45 (2008) 1299 1309 7. Conclusions The present study combined the TAM and the IDT, along with four research variables (computer anxiety, computer self-efficacy, perceived financial cost, and perceived information quality). Thus, a new hybrid TAM was created to study nursing students behavioral intention to use online courses in Taiwan. The major conclusions are: 1. Computer anxiety, computer self-efficacy, compatibility, perceived usefulness, perceived ease of use, perceived financial cost, and perceived information quality were the critical factors that impacted on students behavioral intention to use online 2. Computer anxiety and perceived financial cost had a negative effect on the behavioral intention to use online 3. The present study added four new research constructs (computer anxiety, computer self-efficacy, perceived financial cost, and perceived information quality) to the research model so that it would be more complete; this improved the fit of the whole model. 4. Computer self-efficacy, compatibility, perceived usefulness, perceived ease of use, and perceived information quality had a positive effect on the behavioral intention to use online 8. Limitations The subjects in this study were nursing students in Taiwan. However, there are very few male nursing students in Taiwan; in fact, only 7.02% of the subjects were male. Future studies should examine the effect of gender on students behavioral intention to use online Moreover, due to the ever-changing technology, the questionnaire items that were designed to measure information quality may need to be updated. 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