A COMPARISON ANALYSIS ON THE INTENTION TO CONTINUED USE OF A LIFELONG LEARNING WEBSITE

International Journal of Electronic Business Management, Vol. 10, No. 3, pp. 213-223 (2012) 213 A COMPARISON ANALYSIS ON THE INTENTION TO CONTINUED USE OF A LIFELONG LEARNING WEBSITE Hsiu-Li Liao * and Su-Houn Liu Department of Information Management Chung Yuan Christian University Chung Li (32023), Taiwan ABSTRACT Previous research studies, which were performed under different task environments, identified a variety of factors that can affect student satisfaction with e-learning Lifelong e-learning courses can be different from typical e-learning courses because they are designed to provide "lifelong, voluntary, and self-motivated" pursuit of knowledge for either personal or professional reasons. This study uses three popular behavioral models to identify factors that can affect student intent to continue in a lifelong e-learning course. The study integrates elements of the TAM, TPB, and PCI models to create a research model. The study uses the research model to predict student intent to continue in a lifelong e-learning course. The study uses random selection to choose students from members of the SME Online University in Taiwan. The study uses a survey to collect information concerning student intent to continue in a lifelong e-learning course. The study uses a partial least squares structural (PLS) model to analyze survey results. Study results show that two factors (perceived usefulness and compatibility) have significant impacts on student intent to continue in a lifelong e-learning course. Study results also show that the PCI model predicts student intent to continue in a lifelong e-learning course more accurately than the TAM and TPB models. Keywords: Lifelong Learning, E-Learning, Technology Acceptance Model, Theory of Planned Behavior Model, Perceptions of Innovation Characteristics Model * 1. INTRODUCTION The rapid development of lifelong learning courses has made student satisfaction an increasingly critical research issue. Previous research studies, which were performed under different task environments, identified factors that affect student satisfaction with e-learning The factors can be grouped into six categories: course, technology, system design, environment, teacher, and student [4,27]. An exploratory study was used to identify the most important factors [12]. The exploratory study was completed by interviewing 40 students that were enrolled in a lifelong e-learning course. Study results showed that the student, course, and technology factors are the most important factors that affect intent to continue in a lifelong e-learning course. The goal of this study is to create a research model that can be used to predict intent to continue in a lifelong e-learning course. The study integrates elements of the TAM, TPB, and PCI models to create the research model. The Technology Acceptance Model (TAM) has been widely used to predict student satisfaction with e-learning The theory of planned behavior (TPB) model uses the theory of * Corresponding author: hsiuliliao@cycu.edu.tw reasoned action to extend the TAM. The theory of reasoned action is used to consider behaviors for which students have incomplete volitional control [1]. The perceptions of innovation characteristics (PCI) model adds the compatibility construct, which considers student values, beliefs, experiences, and needs [24]. Prior studies have compared performance of the three models. However, prior studies have not integrated the three models. The focus of this study is to answer two specific research questions. First, can the TAM, TPB, and PCI course, technology, and perception factors be used to predict intent to continue in a lifelong e-learning course? Previous studies have not investigated the effects of the factors thoroughly. Second, which model most effectively explains variance in student satisfaction with e-learning Previous studies have not compared the TAM, TPB, and PCI models thoroughly. 2. RESEARCH MODEL Previous studies have not investigated the factors that can affect intent to continue in a lifelong e-learning course thoroughly. Sun et al., [27] reviewed previous studies [3,6,15,30] and showed that the TAM, TPB,

214 International Journal of Electronic Business Management, Vol. 10, No. 3 (2012) and PCI factors that affect student satisfaction with e-learning courses can be grouped into six categories: course, technology, system design, environment, teacher, and student. The focus of this study is to show that the TAM, TPB, and PCI course, system, and student factors can be used to predict intent to continue in a lifelong e-learning course 2.1 Course Factors Sun et al., s [27], Arbaugh [4], Arbaugh and Duray [3], and Liao and Chou s [12] showed that TAM, TPB, and PCI course flexibility, quality, and interaction factors affect student satisfaction and intent to continue in lifelong e-learning The TAM, TPB, and PCI course factors are related to external course situations and managerial controls. They affect internal student feelings and intentions [1,7,24]. The design, flexibility, and quality of course content in lifelong e-learning courses can affect student ability to use course content for learning and, therefore, student satisfaction. Student satisfaction can affect intent to continue in lifelong e-learning The first, second, third, and fourth research hypotheses of this study focus on the relationships between the TAM, TPB, and PCI course factors and student perceptions and intentions: H1: Course factors are positively related to students perceived usefulness in lifelong e-learning H2: Course factors are positively related to students perceived ease of use in lifelong e-learning H3: Course factors are positively related to students perceived compatibility in lifelong e-learning H4: Course factors are positively related to students perceived behavioral control in lifelong e-learning 2.2 System Factors Liao and Chou [12] showed that TAM system functionality and response factors directly affect perceived usefulness and ease of use. Pituch and Lee [22] showed that TAM system functionality, response, and interactivity factors affect student satisfaction and intent to continue in lifelong e-learning The TAM system factors are related to external course situations and management controls. They affect internal student feelings and intentions [1,7,24]. They are critical for the development of lifelong e-learning courses [10,21,25,26]. The fifth, sixth, seventh, and eighth research hypotheses of this study focus on the relationships between the TAM system factors and student perceptions and intentions: H5: System factors are positively related to students perceived usefulness in lifelong e-learning H6: System factors are positively related to students perceived ease of use in lifelong e-learning H7: System factors are positively related to students perceived compatibility in lifelong e-learning H8: System factors are positively related to students perceived behavioral control in lifelong e-learning 2.3 Student Factors The TAM can be used to identify factors that affect perceptions, intentions, and behaviors toward a system. The TAM has been widely used to identify factors that affect student perceptions, intentions, and behaviors toward e-learning Previous studies showed that TAM student ease of use and usefulness factors affect student satisfaction and intent to continue in lifelong e-learning courses [17,18,20,22]. The TAM student ease of use factor is related to the degree to which a student views an e-learning course as easy to use [7]. The TAM student ease of use factor is a predictor of usefulness. The TAM usefulness factor is related to the extent to which a student considers an e-learning course as a better value than other e-learning The TAM student usefulness factor is a predictor of intent to continue in an e-learning course. The ninth, tenth, and eleventh research hypotheses of this study focus on the relationships between the TAM student factors and student perceptions and intentions: H9: Students perceived ease of use is positively related to students perceived usefulness in lifelong e-learning H10: Students perceived usefulness is positively related to students intent to continue in lifelong e-learning H11: Students perceived ease of use is positively related to students intent to continue in lifelong e-learning Rogers [24] showed that PCI innovation factors affect acceptance behavior. The PCI relative advantage factor is similar to the TAM usefulness factor [7]. The PCI complexity factor is similar to the TAM ease of use factor [24]. The PCI compatibility factor is related to the degree to which an innovation is perceived as being consistent with the existing values, needs, and past experiences of potential adopters. The perceived relative advantage, complexity, and compatibility factors affect user intentions and behaviors [2,9,13,16].

H. L. Liao and S. H. Liu: A Comparison Analysis on the Intention to Continued Use of a Lifelong Learning Website 215 The twelfth research hypothesis of this study focuses on the relationships between PCI innovation factors and student perceptions and intentions: H12: compatibility is positively related to students intent to continue in lifelong e-learning The theory of planned behavior (TPB) is an extension of the theory of reasoned action (TRA) that includes behaviors over which system users have incomplete volitional control. System users must decide to perform or not perform behaviors based upon opportunities and resources (time, money, skills, and cooperation) [1]. The opportunities and resources determine the amount of behavioral control that system users have when making decisions. Students depend upon opportunities and resources to complete lifelong e-learning They also depend upon self confidence to complete lifelong e-learning As a result, perceived behavioral control can affect intent to continue in lifelong e-learning [11,14]. The thirteenth research hypotheses of this study focus on the relationship between the TPB behavioral control factor and student perceptions and intentions: H13: behavioral control is positively related to students intent to continue in lifelong e-learning 2.4 The Research Model Figure 1 shows the research model used in this study. The model describes the relationships between course factors, system factors, and student factors (usefulness, ease of use, behavioral control, compatibility, and intent to continue in lifelong e-learning courses). The study uses empirical tests to create the research model. Table 1 presents definitions for each of the factors. Course dimension Course Course Quality Interaction with others System dimension System Functionality System H1 H2 H3 H4 H5 H6 H7 H8 Usefulness H9 Ease of Use Compatibility Behavioral Control H11 H12 Figure 1: The research model H10 H13 Intention to Continued Use Table 1: Research variables and definitions Research Variables Definitions Reference Course flexibility Degree to which students can arrange their work schedules for the course. [4,27] Course quality Degree to which students are satisfied with the design and quality of the course [4,27] Course interaction Degree to which students consider student-to-student interaction easier than in a traditional course. [4,27] System functionality Degree to which students can control their learning activities. [22] System response Degree to which students consider the response time of the course acceptable. [22] Usefulness Degree to which students consider the course to be superior to other [7,22] Ease of use Degree to which consider the course to be relatively easy to understand and use. [7,22] Behavioral Degree to which students have the opportunities, resources, knowledge, and control abilities needed to complete the course. [1,11] Compatibility Degree to which students consider the course to be compatible with their values, beliefs, experiences, and needs. [13,14] Intent to continue Degree to which students intend to continue in the course. [1,11] 3. RESEARCH METHODOLOGY 3.1 Experiment Design The study was completed with students from the SME Online University The SME Online University was established to provide lifelong learning for SME employees. The SME Online University website URL is http://www.smelearning.org.tw. The website is the first e-learning website which was developed for small and medium enterprises (SME) in Asia. The SME Online University is one of the largest e-learning universities in the world. The website offers more than 900 online courses in five major categories. Over 360,000 SME employees worldwide have enrolled in the SME Online University. To complete the study, questionnaires were sent to 500 randomly selected students. Only 166 surveys were returned. The survey response rate was 33.2%. The respondents were both female and male; 79 were female (48%) and 87 were male (52%). The respondents were 20 50 years old. Most of the

216 International Journal of Electronic Business Management, Vol. 10, No. 3 (2012) respondents had less than one year of e-learning experience. Table 2 presents subject demographics. Table 2: Subject demographics (n=166) Measure and items Frequency Percentage Gender Male 87 52% Female 79 48% Age 20-30 61 37% 31-40 66 40% 41-50 39 23% E-learning experience < 1 year 98 59% 1~2 year 30 18% 2~3 year 18 11% >3 year 20 12% 3.2 Survey Design The questionnaire included items from previous studies and items that were developed for this study. The questionnaire included items related to course flexibility, course quality, and interaction with others from Arbaugh [4] and Sun et al., [27] The questionnaire included items related to system functionality and system response from Pituch and Lee [22]. The questionnaire included items related to compatibility from Rogers [24]. The questionnaire included items related to behavioral control from Lee [11]. The questionnaire also included items related to usefulness, ease of use, and intent to continue in the lifelong e-learning course, which were developed for this study, based upon the findings in Davis et al., [7]. The survey used seven-point Likert scales for each questionnaire item. The seven-point Likert scales ranged from strongly disagree to strongly agree. The questionnaire also included items which were used to collect demographic data. 4. ANALYSIS AND RESULTS The partial least squares (PLS) structural modeling approach was used to analyze the survey results. The PLS approach can be used to create complex models [5] by maximizing explained variance for the model constructs [8]. The TAM, TPB, and PCI elements of the survey results were analyzed separately to determine the effects of each of the three different types of elements. Three models were constructed. 4.1 Model Constructs The model constructs were tested for reliability, convergent validity, and discriminant validity [23,29]. Table 3 presents numbers of items, means, standard deviations, reliabilities, and average variance extracted (AVE) measures for all the constructs. All of the reliability alpha measures were greater than 0.7. The results show that all of the constructs had a reasonable level of internal consistency [7]. All of the convergent validity AVE measures were greater than 0.5. The results show that all of the constructs had an acceptable level of convergent validity [5]. Table 3: Construct means, standard deviations, reliabilities, and AVE Construct Number of Items Mean Standard Deviation Cronbach Alpha AVE 1. Course Flexibility (CF) 3 5.831 1.248 0.771 0.688 2. Course Quality (CQ) 3 5.211 1.259 0.839 0.756 3. Interaction with Others (PIO) 2 3.807 1.517 0.860 0.877 4. System Functionality (SF) 3 5.725 1.158 0.868 0.791 5. System Response (SR) 3 4.996 1.328 0.930 0.878 6. Usefulness (PU) 4 5.700 1.128 0.943 0.848 7. Ease of Use (PE) 4 5.772 1.097 0.948 0.817 8. Behavioral Control (PBC) 3 5.757 1.133 0.949 0.880 9. Compatibility (CO) 3 5.554 1.142 0.928 0.872 10. Intention of Continued Use (ICU) 3 5.916 1.125 0.862 0.796 Table 4 shows intercorrelation and square root of AVE values for each of the constructs. The intercorrelation values ranged from 0.140 to 0.793. The square root of AVE values were all greater than the intercorrelation values. The results show that all of the constructs had an acceptable level of discriminant validity [29]. Table 5 shows factor loading and cross-loading values for each of the constructs. The factor loading values were greater than the cross-loading values. The results show that all of the constructs had an acceptable level of discriminant validity.

H. L. Liao and S. H. Liu: A Comparison Analysis on the Intention to Continued Use of a Lifelong Learning Website 217 Table 4: Intercorrelations and square root of AVE values CF CQ PIO SF SR PU PE PBC CO INT CF 0.829 CQ 0.613 ** 0.869 PIO 0.147 0.213 ** 0.936 SF 0.661 ** 0.759 ** 0.148 0.889 SR 0.524 ** 0.519 ** 0.302 ** 0.559 ** 0.937 PU 0.628 ** 0.737 ** 0.254 ** 0.772 ** 0.637 ** 0.921 PE 0.647 ** 0.640 ** 0.167 ** 0.692 ** 0.665 ** 0.735 ** 0.904 PBC 0.615 ** 0.550 ** 0.140 0.622 ** 0.569 ** 0.662 ** 0.823 ** 0.938 CO 0.645 ** 0.735 ** 0.208 ** 0.752 ** 0.529 ** 0.793 ** 0.673 ** 0.696 ** 0.934 INT 0.659 ** 0.691 ** 0.249 ** 0.708 ** 0.469 ** 0.722 ** 0.637 ** 0.636 ** 0.748 ** 0.892 ** Intercorrelation are significant at the 0.01 level Diagonal bolded elements are square root of AVE values 4.2 The Structural Model The PLS analysis results were used to create a structural model. The PLS analysis results included the structural model path coefficients and the R 2 values. The path coefficients represent the relationships between the dependent and independent constructs. The R 2 values represent the variance explained by the independent constructs. Figure. 2 and Table 6 show the structural model standardized path coefficients and the significance of each structural model standardized path coefficient. Course quality (β=0.238, p<0.01), system functionality (β=0.293, p<0.01), and system response (β=0.137, p<0.05) are positively related to perceived usefulness. The three constructs account for 72.4% of the variance in perceived usefulness. System functionality has the strongest impact on perceived usefulness. Course flexibility (β=0.189, p<0.05), course quality (β=0.174, p<0.05), system functionality (β=0.277, p<0.01), and system response (β=0.327, p<0.01) are positively related to perceived ease of use. The four constructs account for 66.6% of the variance in perceived ease of use. System response has the strongest impact on perceived ease of use. Course flexibility (β=0.182, p<0.05), course quality (β=0.312, p<0.01), and system functionality (β=0.354, p<0.01) are positively related to compatibility. The three constructs account for 65.5% of the variance in compatibility. System functionality has the strongest impact on compatibility. Course flexibility (β=0.303, p<0.01), system functionality (β=0.256, p<0.05), and system response (β=0.262, p<0.01) are positively related to perceived behavioral control. The three constructs account for 53.6% of the variance in perceived behavioral control. Course flexibility has the strongest impact on perceived behavioral control. usefulness (β=0.245, p<0.1) and compatibility (β=0.401, p<0.01) are positively related to intent to continue in a lifelong e-learning course. Compatibility has the strongest impact on intent to continue in a lifelong e-learning course. ease of use is also positively related to perceived usefulness (β=0.234, p<0.05). ease of use (β=0.081, p>0.1) and perceived behavioral control (β=0.151, p>0.1) do not significant impact on intent to continue in a lifelong e-learning course. The results are not consistent with results from some previous research studies [11,14]. However, the results are consistent with results from Moss [19] research study. Table 5: Factor loadings and cross-loadings CF CQ PIO SF SR PU PE PBC CO INT CF 0.8249 0.5043 0.1943 0.5186 0.5216 0.5349 0.6340 0.6135 0.5864 0.5584 0.8745 0.5298 0.1314 0.5966 0.4279 0.5748 0.4910 0.4800 0.5345 0.5558 0.8029 0.4989 0.0240 0.5416 0.3525 0.4894 0.4941 0.4809 0.4847 0.5711 CQ 0.5504 0.8519 0.1300 0.6043 0.3647 0.6003 0.5114 0.4522 0.6175 0.5633 0.5119 0.8895 0.1938 0.6686 0.3794 0.6754 0.5434 0.4591 0.6851 0.6300 0.5460 0.8819 0.2366 0.7203 0.6167 0.6735 0.7062 0.5583 0.6241 0.5977 PIO 0.1672 0.1981 0.9310 0.1510 0.2673 0.2128 0.1758 0.1129 0.1904 0.1814 0.1132 0.2094 0.9534 0.1277 0.3073 0.2546 0.2342 0.1306 0.1985 0.2339 SF 0.5677 0.5784 0.0609 0.8437 0.4471 0.6173 0.6101 0.5601 0.6314 0.5668 0.5834 0.7187 0.1401 0.9202 0.4867 0.7130 0.6271 0.5465 0.6437 0.6609 0.6196 0.7415 0.1849 0.9186 0.5629 0.7374 0.6920 0.5997 0.7387 0.6710 SR 0.5055 0.5095 0.3498 0.5605 0.9357 0.6130 0.6311 0.5492 0.5108 0.4252

218 International Journal of Electronic Business Management, Vol. 10, No. 3 (2012) CF CQ PIO SF SR PU PE PBC CO INT 0.4938 0.4716 0.2853 0.4991 0.9456 0.6078 0.6467 0.5475 0.5133 0.4182 0.4906 0.5055 0.2299 0.5242 0.9464 0.5790 0.6391 0.5377 0.4849 0.4326 PU 0.5926 0.6994 0.2496 0.7337 0.6224 0.9307 0.7044 0.6356 0.7202 0.6636 0.5309 0.6587 0.2566 0.7099 0.5847 0.9026 0.7139 0.6084 0.7227 0.6475 0.6215 0.6842 0.2206 0.7011 0.5827 0.9225 0.6750 0.6558 0.7397 0.6839 0.5923 0.7091 0.2268 0.7317 0.5864 0.9504 0.7281 0.6065 0.7720 0.6961 PE 0.6061 0.5650 0.1219 0.6426 0.5764 0.6797 0.8933 0.7825 0.6244 0.6068 0.5762 0.5843 0.1166 0.6163 0.5825 0.6529 0.8669 0.7358 0.5993 0.5821 0.6296 0.6765 0.2563 0.7084 0.6733 0.7384 0.9802 0.7042 0.6913 0.6455 0.6191 0.5865 0.1335 0.6255 0.6497 0.6683 0.8922 0.7835 0.6165 0.5783 PBC 0.5313 0.5089 0.1700 0.5456 0.5309 0.5949 0.7571 0.8703 0.6276 0.5960 0.6155 0.5046 0.0890 0.6115 0.5086 0.6286 0.7407 0.9484 0.6708 0.6519 0.6355 0.5786 0.1428 0.6386 0.5980 0.6862 0.7660 0.9913 0.7188 0.6579 CO 0.6103 0.7043 0.2306 0.7186 0.5157 0.7718 0.6780 0.6671 0.9285 0.6831 0.6020 0.6884 0.1774 0.7081 0.5248 0.7555 0.6628 0.6895 0.9503 0.7340 0.6002 0.6682 0.1750 0.6840 0.4428 0.7100 0.6231 0.6384 0.9230 0.7165 INT 0.6621 0.6259 0.1826 0.6684 0.3956 0.6944 0.6250 0.6700 0.7370 0.9681 0.5899 0.6095 0.3311 0.6172 0.4276 0.6570 0.5924 0.5804 0.6461 0.8713 0.5472 0.6448 0.1765 0.6394 0.4478 0.6311 0.5919 0.5015 0.6559 0.8489 Course Flexibility 0.303*** 0.189** 0.238*** Usefulness R 2 =0.724 Course Quality 0.234** 0.245* Interaction with Others 0.293*** System Functionality System Response 0.137** 0.354*** 0.262*** 0.174** 0.277*** 0.312*** 0.256** 0.327*** 0.182** Ease of Use R 2 =0.666 Compatibility R 2 =0.655 Behavioral Control R 2 =0.536 0.081 0.401*** 0.151 Intent to Continue R 2 =0.639 *** p<0.01,** p<0.05,* p<0.1 Significant path Non-Significant path Figure 2: A structural model for lifelong e-learning courses 4.3 The TAM, TPB, and PCI Models The results were used to create separate TAM, TPB, and PCI models. Figure 3 shows the three models. The TAM model does not include perceived behavioral control and perceived compatibility constructs. The TAM model explains 56.5% of the variance in intent to continue in a lifelong e-learning course (R 2 =56.5%). In the TAM model, perceived usefulness and perceived ease of use are positively related to intent to continue in a lifelong e-learning course. The TPB model includes all of the TAM model constructs and the TPB perceived behavioral control construct. The TPB model explains 59.0% of the variance in intent to continue in a lifelong e-learning course (R 2 =59.0%). The TPB model shows that perceived ease of use does not have a significant effect on intent to continue in a lifelong e-learning course. usefulness has a greater effect than perceived behavioral control.

H. L. Liao and S. H. Liu: A Comparison Analysis on the Intention to Continued Use of a Lifelong Learning Website 219 The PCI model includes the TAM model and the PCI perceived compatibility construct. The PCI model explains 63.2% of the variance in intent to continue in a lifelong e-learning course (R 2 =63.2%). The PCI model shows that perceived ease of use does not have a significant affect on intent to continue in a lifelong e-learning course. compatibility has a greater effect than perceived usefulness/ The results shows that with either the perceived behavioral control construct or the perceived compatibility construct in the model, the perceived ease of use construct does not have a significant affect on intent to continue in a lifelong e-learning course. The R 2 value for the PCI model is greater than the R 2 value for the TAM model and the TPB model. The results show that the PCI model is the best model to use for predicting intent to continue in a lifelong e-learning course. 5. CONCLUSIONS This study integrated the constructs of the TAM, PCI, and TPB models to create a single research model. The study used the research model to determine the model constructs that affect students intent to continue in lifelong e-learning The study used the research model to show that students perceptions and system factors can be used to explain and predict students intent to continue in lifelong e-learning Lifelong learning courses are different than other courses because they are designed for "lifelong, voluntary, and self-motivated" pursuit of knowledge for either personal or professional reasons [28], Study results show that the learning behaviors of students in lifelong e-learning courses are also unique: Study results show that course quality, system functionality, and system response have significant effects on perceived usefulness. System functionality has the strongest impact on perceived usefulness. Study results show that perceived usefulness and compatibility have significant effects on students intent to continue in lifelong learning Study results indicate that presenting lifelong e-learning course material in an organized and readable manner and creating a system that has good functionality and fast response time increases perceived usefulness and intent to continue in lifelong e-learning Table 6: Structural model path coefficients Hypotheses β T-Statistic Support H1a Course flexibility usefulness 0.068 1.159 No H1b Course quality usefulness 0.238 3.416 Yes H1c Interaction with others usefulness 0.052 1.315 No H2a Course flexibility ease of use 0.189 2.310 Yes H2b Course quality ease of use 0.174 2.447 Yes H2c Interaction with others ease of use 0.014 0.359 No H3a Course flexibility Compatibility 0.182 2.392 Yes H3b Course quality Compatibility 0.312 3.966 Yes H3c Interaction with others Compatibility 0.042 1.258 No H4a Course flexibility behavioral control 0.303 3.297 Yes H4b Course quality behavioral control 0.057 0.770 No H4c Interaction with others behavioral control -0.044-1.008 No H5a System functionality usefulness 0.293 3.303 Yes H5b System response usefulness 0.137 2.102 Yes H6a System functionality ease of use 0.277 2.957 Yes H6b System response ease of use 0.327 4.227 Yes H7a System functionality Compatibility 0.354 4.896 Yes H7b System response Compatibility 0.063 1.015 No H8a System functionality behavioral control 0.256 2.241 Yes H8b System response behavioral control 0.262 2.981 Yes H9 ease of use usefulness 0.234 1.999 Yes H10 usefulness Intention of continued use 0.245 1.824 Yes H11 ease of use Intention of continued use 0.081 1.000 No H12 Compatibility Intention of continued use 0.401 3.542 Yes H13 behavioral control Intention of continued use 0.151 1.462 No

220 International Journal of Electronic Business Management, Vol. 10, No. 3 (2012) PCI Model TPB Model Usefulness 0.234** Ease of Use 0.248** 0.163 Intention to Continue Use R 2 =0.632 Usefulness 0.233** Ease of Use 0.097 0.471*** Intention to Continue Use R 2 =0.590 Compatibility 0.448*** Behavioral Control 0.272*** TAM Model Usefulness 0.233* Ease of Use 0.530*** 0.267** Intention to Continue Use R 2 =0.565 Significant path Non-Significant path *** p<0.01,** p<0.05,* p<0.1 Figure 3: The TAM, TPB, and PCI models Course flexibility, course quality, and system functionality have significant effects on perceived compatibility. System functionality has the strongest effect on perceived compatibility. Online courses are generally more flexible than traditional Online course material must generally be well-organized and readable. In addition, e-learning systems must be created with high levels of functionality. As a result, students may feel that e-learning courses have higher levels of compatibility than traditional Higher levels of perceived compatibility increase students intent to continue in lifelong e-learning Study results show that interaction with other students does not have a significant effect on perceived usefulness, perceived ease of use, compatibility, or perceived behavioral control in lifelong e-learning A possible reason for the result is that students take lifelong e-learning courses to achieve specific learning objectives (for example, to acquire knowledge to enhance their job performance). As a result, they are not concerned about the social aspects of taking Study results were also used to create separate TAM, TPB, and PCI models. Study results show that the PCI model explains the variance in students intent to continue in lifelong e-learning courses better than the TAM and TPB models. The results show that the PCI model is more reliable for predicting intent to continue in lifelong e-learning courses than the TAM and TPB models. The study results are preliminary. However, they provide some evidence that the research models can be used to predict students perceptions and intentions in lifelong e-learning As a result, e-learning managers can use study results to improve lifelong e-learning courses and increase students intent to continue in lifelong e-learning REFERENCES 1. Ajzen, I., 1991, The Theory of Planned Behavior, Organizational Behavior and Human Decision Processes, Vol. 50, pp. 179-211. 2. Alam, S. S. and Khafibi, A., 2007, Factors affecting e-commerce adoption in the electronic manufacturing companies in Malaysia, International Journal of Commerce and Management, Vol. 17, No. 1, pp. 125-139. 3. Arbaugh, J. B. and Duray, R., 2002, Technological and structural characteristics, student learning and satisfaction with web-based courses - An exploratory study of two on-line

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222 International Journal of Electronic Business Management, Vol. 10, No. 3 (2012) 29. Wixom, B. H. and Todd, A P., 2005, A theoretical integration of user satisfaction and technology acceptance, Information Systems Research, Vol. 16, No. 1, pp. 85-102. 30. Wu, J. P., Tsai, R. J., Chen, C. C. and Wu, Y. C., 2006, An integrative model to predict the continuance use of electronic learning systems: hints for teaching, International Journal on E-Learning, Vol. 5, No. 2, pp. 287-302. ABOUT THE AUTHORS Hsiu-Li Liao is an Associate Professor in the Department of Information Management at CYCU. She has published refereed papers in Computers and Education, Social Behavior and Personality, Lecture Notes in Computer Science, Issues in Information Systems, and several Chinese management journals. She has also served as a reviewer for eight international journals. Su-Houn Liu is a Professor of the Department of Information Management at CYCU. His recent publications can be found in Computers and Education, Social Behavior and Personality, International Jounal of Technolgy Management, Issues in Information Systems, and severak Chinese management journals. (Received May 2012, revised August 2012, accepted September 2012)

H. L. Liao and S. H. Liu: A Comparison Analysis on the Intention to Continued Use of a Lifelong Learning Website 223 終身學習網站繼續使用意願之比較研究廖秀莉 * 劉士豪中原大學資訊管理學系桃園縣中壢市中北路 200 號摘要先前研究在不同的任務環境下學者提出影響使用者數位學習滿意度的各種因素, 數位學習不同於傳統的學習, 由於它是無論在個人或專業方面, 終身自願與自我激勵地追求知識, 因此本研究從三個著名的行為模型觀點來探索終身數位學習本研究之研究模型整合科技接受模型計畫行為理論以及創新特質認知理論以預測終身學習者繼續使用數位學習網站的意願, 本研究的研究對象是對於台灣的中小企業網路大學校會員進行隨機抽樣, 使用淨最小平方法分析結構模型研究發現提出有用認知與相容性顯著影響終身學習者繼續使用數位學習網站的意願, 分析結果顯示創新特質認知模型相較於科技接受模型與計畫行為理論模型對於預測與解釋終身數位學習意願具有較顯著的解釋力關鍵詞 : 終生學習, 數位學習, 科技接受模型, 計畫行為理論, 創新特質認知 (* 聯絡人 :hsiuliliao@cycu.edu.tw)