系統識別號 U0026-1008201510223900
論文名稱(中文) 偏鄉地區同步遠距衛教的持續使用與滿意度之研究
論文名稱(英文) Continuance Intention to Use and Satisfaction of Synchronous Distance Education in Healthcare in the Rural Areas of Taiwan
校院名稱 成功大學
系所名稱(中) 電信管理研究所
系所名稱(英) Institute of Telecommunications and Management
學年度 103
學期 2
出版年 104
研究生(中文) 賴暐昌
研究生(英文) Wei-Chung Lai
學號 R96021112
學位類別 碩士
語文別 英文
論文頁數 55頁
口試委員 指導教授-廖俊雄
中文關鍵字 遠距衛教  預期期望理論  滿意度  持續使用  結構方程模式 
英文關鍵字 Distance education in healthcare (DEH)  Expectation confirmation theory (ECT)  Satisfaction  Continuance to use  Structural equation modeling (SEM) 
中文摘要 目前台灣醫療的困境面臨以下的問題,偏鄉的地區因為資訊不足而缺乏健康照顧的知識,也正因為如此常常服食與購買地下電台或是來路不明的偏方而不是去醫院就醫。遠距衛教的發展主要是幫助住在偏鄉地區的人們可以透過直接與間接的方式與醫院或是衛生組織能讓他們能便利並簡單地得到關於健康方面的知識並可以減少因地理方面的問題的成本。民眾透過遠距衛教,利用電腦視訊影像同步收聽本院醫師、護理師和其他醫事專業人員的專題演講並可提問像是網路查詢用藥、健康養生與衛教等相關知識,或請求協助掛號與查詢醫院交通資訊,藉由網路克服偏遠地區距離與資源缺乏的障礙,提升民眾健康照護服務的可近性。此篇研究目的主要是找出偏鄉地區使用者有哪些因素會想使用遠距衛教。本次的研究架構主要是以預期期望理論為主,加上學習內容、導師特性、教學器材、主觀規範、使用動機去做結合進而討論偏鄉地區使用者使用遠距衛教的滿意度與後續使用意願。訪問對像是以偏鄉地區的中老年人真正有上過衛距衛教的民眾為主。所收集到的資料先進行敘述性統計以及驗證性因素分析之後,接著運用結構方程模式來檢視實證分析結果是否與研究假設相符。
英文摘要 A predicament Taiwan’s health care system currently faces is that people in rural areas lack of health knowledge and are reluctant to seek for helps from formal medical resources but instead take folk prescriptions and unidentified drugs. Distance education in healthcare (DEH) is designed to help users via synchronously and asynchronously interacting with healthcare professionals to effectively and efficiently learn about correct knowledge of medical care such as health consultation, health care, and heath regimen. The aim of this study is to measure the level of the satisfaction of and continuance to use DEH for the residents who have taken at least one synchronous DEH course in the rural areas of Taiwan and to understand their motives. Expectation confirmation theory are used as the theoretical framework and five additional factors of learning content, instructor characteristics, teaching materials, motivation, and subjective norm are incorporated in the model. The casual relationships among the constructs in the research model will be examined. Confirmatory factor analysis are conducted to examine the discrepancy between hypotheses and empirical data and to test whether proposed theoretical model fits empirical data. Subsequently, structural equation modeling (SEM) is applied to test the causal model and understand the relationship among constructs.
The results of the study are summarized as follow. Satisfaction positively influences continuance intention to use, confirmation and perceived usefulness positively influence satisfaction, and confirmation positively influences perceived usefulness. In addition, satisfaction is positively affected by motivation but not subjective norm, and perceived usefulness is positively affected by learning content and teaching materials but not instructor characteristics. In particular, the comparison of standardized path coefficients reveals that motivation has the strongest impact on satisfaction, followed by confirmation and perceived usefulness. Further, the ANOVA results revel there are significant and positive relationships among demographic characteristics (i.e., education, monthly disposable income, and the times to take DEH), satisfaction, and continuance intention to use. That is, the respondents with higher of education, monthly disposable income, and the times to take DEH tend to have higher level of satisfaction and continuance intention. In the end, managerial suggestions are provided for hospitals and DOCs in order to increase satisfaction and continuance intention to use in DEH.
論文目次 Table of Contents
Table of Contents I
List of Tables II
List of Figures III
Introduction 1
1.1 Background and Motivation 1
1.2 Research objective 4
Literature Review 6
2.1 Expectancy-Confirmation Theory 6
2.2 Satisfaction and Continuance Intention 8
2.3 Subjective Norm and Motivation 10
2.4 Perceived Usefulness 11
2.5 Confirmation 12
2.6 Learning Content, Instructor Characteristics, and Teaching Materials 13
Research Model and Design 16
3.1 Research Model 16
3.2 Measurement Development 16
3.3 Data Collection and Sampling 20
Empirical Results 21
4.1 Descriptive Statistics Analysis 21
4.1.1 Respondent Profile 21
4.1.2 Analysis of Variance Analysis on Satisfaction and Continuance Intention 23
4.1.3 Mean and standard deviation of the items 26
4.2 Confirmatory Factor Analysis 29
4.3 Structural Equation Modeling 33
Conclusion and Discussion 37
5.1 Summary of the Results 37
5.2 Managerial Implication 38
5.3 Limitation and Future Research 39
References 40
Appendix A: Items in Questionnaire 50
Appendix B: Items in Chinese Questionnaire 52
參考文獻 Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211.
Algina, J., Oshima, T. C., & Lin, W. Y. (1994). Type I error rates for Welch’s test and James second-order test under nonnormality and inequality of variance when there are two groups. Journal of Educational and Behavioral Statistics, 19(3), 275-291.
Amabile, T. M. (1993). Motivational synergy: Toward new conceptualizations of intrinsic and extrinsic motivation in the workplace. Human Resource Management Review, 3(3), 185-201.
Amoroso, D. L., & Cheney, P. H. (1997). Testing a causal model of end-user application effectiveness. Journal of Management Information Systems, 8(1), 63-89.
Anderson, E. W., & Sullivan, M. W. (1993). The antecedents and consequences of customer satisfaction for firms. Marketing Science, 12(2), 125-143.
Arbaugh, J. B. (2000). Virtual classroom characteristics and student satisfaction with internet-based MBA courses. Journal of Management Education, 24(1), 32-54.
Arbaugh, J. B., & Benbunan-Fich, R. (2007). The importance of participant interaction in online environments. Decision Support Systems, 43(3), 853-865.
Ayanso, A., Herath, T. C., & O'Brien, N. (2015). Understanding continuance intentions of physicians with electronic medical records (EMR): An expectancy-confirmation perspective. Decision Support Systems, 77(3), 112-122.
Barker, V. (2009). Older adolescents' motivations for social network site use: The influence of gender, group identity, and collective self-esteem. CyberPsychology & Behavior, 12(2), 209-213.
Baker-Eveleth, L., & Stone, R. W. (2015). Usability, expectation, confirmation, and continuance intentions to use electronic textbooks. Behavior & Information Technology, 2(5), 1-13.
Barki, H., & Hartwick, J. (1994). Measuring user participation, user involvement, and user attitude. MIS Quarterly, 18(1), 59-82.
Bearden, W. O., & Teel, J. E. (1983). Selected determinants of consumer satisfaction and complaint reports. Journal of Marketing Research, 20(1), 21-28.
Benabou, R., & Tirole, J. (2003). Intrinsic and extrinsic motivation. The Review of Economic Studies, 70(3), 489-520.
Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS Quarterly, 25(3), 351-370.
Bollen, K. A. (1989). Structural Equations with Latent Variables. New York, NY: John Wiley & Sons.
Bolton, R. N., & Drew, J. H. (1991). A multistage model of customers' assessments of service quality and value. Journal of Consumer Research, 17(4), 375-384.
Bolton, R. N., & Lemon, K. N. (1999). A dynamic model of customers' usage of services: Usage as an antecedent and consequence of satisfaction. Journal of Marketing Research, 36(2), 171-186.
Brophy, J. (2000). Teaching. Educational practices series--1. Retrieved from http:// www.ibe.unesco.org/publications/EducationalPracticesSeriesPdf/prac01e.pdf
Brown, M. B., & Forsythe, A. B. (1974). Robust tests for the equality of variances. Journal of the American Statistical Association, 69(346), 364-367.
Brown, S. A., & Venkatesh, V. (2005). Model of adoption of technology in households: A baseline model test and extension incorporating household life cycle. MIS Quarterly, 29(3), 399-426.
Brown, S. A., Venkatesh, V., & Goyal, S. (2014). Expectation confirmation in information systems research: A test of six competing models. MIS Quarterly, 38(3), 729-756.
Burnkrant, R. E., & Cousineau, A. (1975). Informational and normative social influence in buyer behavior. Journal of Consumer Research, 2(3), 206-215.
Butler, D. L., & Winne, P. H. (1995). Feedback and self-regulated learning: A theoretical synthesis. Review of Educational Research, 65(3), 245-281.
Centers for Disease Control and Prevention (CDC). (2012). National vital statistic reports. Retrieved from http://www.cdc.gov/nchs/data/nvsr/nv sr61/nvsr61_06.pdf
Cheng, Y. M. (2014). Extending the expectation-confirmation model with quality and flow to explore nurses’ continued blended e-learning intention. Information Technology & People, 27(3), 230-258.
Cheung, R., & Vogel, D. (2013). Predicting user acceptance of collaborative technologies: An extension of the technology acceptance model for e-learning. Computers & Education, 63(2), 160-175.
Chiu, C. M., Hsu, M. H., Sun, S. Y., Lin, T. C., & Sun, P. C. (2005). Usability, quality, value and e-learning continuance decisions. Computers & Education, 45(4), 399-416.
Chou, H. K., Lin, I. C., Woung, L. C., & Tsai, M. T. (2012). Engagement in e-learning opportunities: An empirical study on patient education using expectation confirmation theory. Journal of Medical Systems, 36(3), 1697-1706.
Chow, W. S., & Shi, S. (2014). Investigating students’ satisfaction and continuance intention toward e-learning: An extension of the expectation–confirmation model. Procedia-Social and Behavioral Sciences, 141(25), 1145-1149.
Churchill Jr, G. A., & Surprenant, C. (1982). An investigation into the determinants of customer satisfaction. Journal of Marketing Research, 19(4), 491-504.
Compeau, D. R., & Higgins, C. A. (1995). Computer self-efficacy: Development of a measure and initial test. MIS Quarterly, 19(2),189-211.
Dabolkar, P. A., Shepherd, C. D., & Thorpe, D. I. (2000). A comprehensive framework for service quality: An investigation of critical conceptual and measurement issues through a longitudinal study. Journal of Retailing, 76(2), 139-173.
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.
Delone, W. H., & McLean, E. R. (2003). The DeLone and McLean model of information systems success: A ten-year update. Journal of Management Information Systems, 19(4), 9-30.
Chang, S. L. (2006). The systematic design of instruction. Educational Technology Research and Development, 54(4), 417-420.
Doong, H.S., Wang, H.C., & Chen, P.H. (2007). An empirical study of online reputation system continuance. Retrieved from http:// xplore.org/xpls/abs_all.jsp?ar number=5068907
Eom, S. B., Wen, H. J., & Ashill, N. (2006). The determinants of students' perceived learning outcomes and satisfaction in university online education: An empirical investigation. Decision Sciences Journal of Innovative Education, 4(2), 215-235.
Fan, X., Thompson, B., & Wang, L. (1999). Effects of sample size, estimation methods, and model specification on structural equation modeling fit indexes. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 56-83.
Food and Drug Administration (FDA). (2013). Drug abuse survey. Retrieved form http://www.fda.gov.tw/TC/site.aspx?sid=3670
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50.
Gagné, M., & Deci, E. L. (2005). Self‐determination theory and work motivation. Journal of Organizational Behavior, 26(4), 331-362.
Gefen, D., Straub, D. W., & Boudreau, M. C. (2000). Structural equation modeling and regression: Guidelines for research practice. Communications of the Association for Information Systems, 4(1), 1-77.
Geissbuhler, A., Bagayoko, C. O., & Ly, O. (2007). The RAFT network: 5 years of distance continuing medical education and tele-consultations over the Internet in French-speaking Africa. International Journal of Medical Informatics, 76(5), 351-356.
Graham, J. E. (2015). Transfusion e‐learning for junior doctors: The educational role of “learn blood transfusion”. Transfusion Medicine, 25(3), 144-150.
Golonka, E. M., Bowles, A. R., Frank, V. M., Richardson, D. L., & Freynik, S. (2014). Technologies for foreign language learning: A review of technology types and their effectiveness. Computer Assisted Language Learning, 27(1), 70-105.
Hair, J. F., Jr, Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate Data Analysis (7th ed.). Upper Saddle River, NJ: Prentice Hall.
Hayashi, A., Chen, C., Ryan, T., & Wu, J. (2004). The role of social presence and moderating role of computer self-efficacy in predicting the continuance usage of e-learning systems. Journal of Information Systems Education, 15(2), 139-154.
Henard, D. H., & Szymanski, D. M. (2001). Why some new products are more successful than others. Journal of Marketing Research, 38(3), 362-375.
Hong, J. C., Hwang, M. Y., Hsu, C. H., Tai, K. H., & Kuo, Y. C. (2015). Belief in dangerous virtual communities as a predictor of continuance intention mediated by general and online social anxiety: The Facebook perspective. Computers in Human Behavior, 48(3), 663-670.
Hooper, D., Coughlan, J., & Mullen, M. (2008). Structural equation modelling: Guidelines for determining model fit. Electronic Journal of Business Research Methods, 6(1), 53-60.
Hsu, M. H., & Chiu, C. M. (2004). Predicting electronic service continuance with a decomposed theory of planned behavior. Behavior & Information Technology, 23(5), 359-373.
Hsu, C. L., & Lin, J. C. C. (2008). Acceptance of blog usage: The roles of technology acceptance, social influence and knowledge sharing motivation. Information & Management, 45(1), 65-74.
Hsu, C. L., Wang, C. F., & Lin, J. C. C. (2011). Investigating customer adoption behaviors in mobile financial services. International Journal of Mobile Communications, 9(5), 477-494.
Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1-55.
Hung, S. Y., Yu, W. J., Liou, K. L., & Hsu, S. C. (2009). Exploring e-learning effectiveness based on activity theory: An example of asynchronous distance learning. MIS REVIEW: An International Journal, 15(1), 63-87.
Islam, A. K. M. (2012). The role of perceived system quality as educators’ motivation to continue e-learning system use. AIS Transactions on Human-Computer Interaction, 4(1), 25-43.
Jöreskog, K. G., & Sörbom, D. (1984). LISREL VI: Analysis of Linear Structural Relationships: By the Method of Maximum Likelihood. Chicago, IL: National Educational Resources.
Kanuka, H., & Nocente, N. (2003). Exploring the effects of personality type on perceived satisfaction with web-based learning in continuing professional development. Distance Education, 24(2), 227-244.
Keller, J., & Suzuki, K. (2004). Learner motivation and e-learning design: A multi nationally validated process. Journal of Educational Media, 29(3), 229-239.
Kim, B. (2010). An empirical investigation of mobile data service continuance: Incorporating the theory of planned behavior into the expectation–confirmation model. Expert Systems with Applications, 37(10), 7033-7039.
Knebel, E. (2001). The use and effect of distance education in healthcare: What do we know. Operations Research Issue Paper, 2(2), 1-24.
Lee, M. C. (2010). Explaining and predicting users’ continuance intention toward e-learning: An extension of the expectation–confirmation model. Computers & Education, 54(2), 506-516.
Lee, Y., & Kwon, O. (2011). Intimacy, familiarity and continuance intention: An extended expectation–confirmation model in web-based services. Electronic Commerce Research and Applications, 10(3), 342-357.
Lee, B. C., Yoon, J. O., & Lee, I. (2009). Learners’ acceptance of e-learning in South Korea: Theories and results. Computers & Education, 53(4), 1320-1329.
Li, H., & Liu, Y. (2014). Understanding post-adoption behaviors of e-service users in the context of online travel services. Information & Management,51(8), 1043-1052.
Liaw, S. S. (2008). Investigating students’ perceived satisfaction, behavioral intention, and effectiveness of e-learning: A case study of the blackboard system. Computers & Education, 51(2), 864-873.
Limayem, M., Hirt, S. G., & Cheung, C. M. (2007). How habit limits the predictive power of intention: The case of information systems continuance. MIS Quarterly, 31(4), 705-737.
Lim, C. K. (2001). Computer self‐efficacy, academic self‐concept, and other predictors of satisfaction and future participation of adult distance learners. American Journal of Distance Education, 15(2), 41-51.
Lin, K. M. (2011). E-learning continuance intention: Moderating effects of user e-learning experience. Computers & Education, 56(2), 515-526.
Lin, C. S., Wu, S., & Tsai, R. J. (2005). Integrating perceived playfulness into expectation-confirmation model for web portal context. Information & Management, 42(5), 683-693.
Lin, T. C., & Chen, C. J. (2012). Validating the satisfaction and continuance intention of e-learning systems: Combining TAM and IS success models. International Journal of Distance Education Technologies, 10(1), 44-54.
Lin, T. C., Wu, S., Hsu, J. S. C., & Chou, Y. C. (2012). The integration of value-based adoption and expectation–confirmation models: An example of IPTV continuance intention. Decision Support Systems, 54(1), 63-75.
Mallat, N., Rossi, M., Tuunainen, V. K., & Öörni, A. (2009). The impact of use context on mobile services acceptance: The case of mobile ticketing. Information & Management, 46(3), 190-195.
McLawhon, R., & Cutright, M. (2012). Instructor learning styles as indicators of online faculty satisfaction. Journal of Educational Technology & Society, 15(2), 341-353.
Melchiorre, M. G., Chiatti, C., Lamura, G., Torres Gonzales, F., Stankunas, M., Lindert, J., Soares, J.F.J. (2013). Social support, socio-economic status, health and abuse among older people in seven European countries. PLOS ONE, 8(1), 1-10.
Ministry of Economic (MOE). (2008). Healthcare industry analysis and investment opportunitie. Retrieved From http://www.pharmaceuticalsinsight.com/industrrend-analysis-investment-opportunities-within-microinsurance-market-sept-2015
Ministry of Health and Welfare (MOHW). (2014). Registered medical personnel in hospitals, clinics and other medical care institutions. Retrieved from http://www.mohw.gov.tw/EN/Ministry/Statistic.aspx?f_list_no=474&fod_list_no=5172
Ministry of the Interior (MOI). (2014). Statistical yearbook of interior 2013. Retrieved from http://sowf.moi.gov.tw/stat/year/list.htm
Martens, R., Gulikers, J., & Bastiaens, T. (2004). The impact of intrinsic motivation on e‐learning in authentic computer tasks. Journal of Computer Assisted Learning, 20(5), 368-376.
Masrom, M. (2007). Technology acceptance model and e-learning. Technology, 24(21), 1- 10.
National Council on Aging (NCOA). (2014). Aging on the world stage. Retrieved form http://www.ncoa.org/?referrer=https://www.google.com.tw/
National Health Interview Survey (NHIS). (2009). 2009 National health interview survey. Retrieved form http://nhis.nhri.org.tw/files/2009NHIS_report_2.pdf
Nunnally, J. C. (1978). Psychometric Theory (2nd ed.). New York, NY: McGraw-Hill
Nysveen, H., Pedersen, P., & Thorbjørnsen, H. (2005). Intentions to use mobile services: Antecedents and cross-service comparisons. Journal of the Academy of Marketing Science, 33(3), 330-346.
Ogbu, J. E. (2015). Influences of inadequate instructional materials and facilities in teaching and learning of electrical/electronic technology education courses. International Journal of Vocational and Technical Education, 7(3), 20-27.
Ogwa, C.E. (2002). Effective Teaching Methods. Enugu, Nigeria: Cheston Ltd.
Organization for Economic Co-operation and Development (OECD). (2014). Compare your country-health profile. Retrieved from http://www.compareyourcountr y.org/health?&lg=en
Oliver, R. L. (1980). A cognitive model of the antecedents and consequences of satisfaction decisions. Journal of Marketing Research, 17(4), 460-469.
Oliver, R. L. (1993). Cognitive, affective, and attribute bases of the satisfaction response. Journal of Consumer Research, 20(3), 418-430.
Ozkan, S., & Koseler, R. (2009). Multi-dimensional students’ evaluation of e-learning systems in the higher education context: An empirical investigation. Computers & Education, 53(4), 1285-1296.
Patterson, P. G., Johnson, L. W., & Spreng, R. A. (1996). Modeling the determinants of customer satisfaction for business-to-business professional services. Journal of the Academy of Marketing Science, 25(1), 4-17.
Population Reference Bureau. (2014). 2014 World population data sheet. Retrieved from http://www.prb.org/pdf14/2014-world-population-data-sheet_eng.pdf
Porac, J. F., & Meindl, J. (1982). Undermining over justification: Inducing intrinsic and extrinsic task representations. Organizational Behavior and Human Performance, 29(2), 208-226.
Piccoli, G., Ahmad, R., & Ives, B. (2001). Web-based virtual learning environments: A research framework and a preliminary assessment of effectiveness in basic IT skills training. MIS Quarterly, 25(4), 401-426.
Rauniar, R., Rawski, G., Yang, J., & Johnson, B. (2014). Technology acceptance model (TAM) and social media usage: An empirical study on Facebook. Journal of Enterprise Information Management, 27(1), 6-30.
Research, Development and Evaluation Commission, Executive Yuan (RDEC) (2012). Personal and households digital opportunity survey report 2012. Retrieved from http:// www.rdec.gov.tw/public/Attachment/312113493071.pdf
Roca, J. C., Chiu, C. M., & Martínez, F. J. (2006). Understanding e-learning continuance intention: An extension of the technology acceptance model. International Journal of Human-Computer Studies, 64(8), 683-696.
Ryan, R. M., & Deci, E. L. (2000). Intrinsic and extrinsic motivations: Classic definitions and new directions. Contemporary Educational Psychology, 25(1), 54-67.
Saadé, R., & Kira, D. (2009). Computer anxiety in e-learning: The effect of computer self-efficacy. Journal of Information Technology Education: Research, 8(1), 177-191.
Saga, V. L., & Zmud, R. W. (1993). The nature and determinants of IT acceptance, routinization, and infusion. Retrieved from http://dl.acm.org/citation.cf m?id=686655
Santhanamery, T., & Ramayah, T. (2010). Explaining the e-government usage using expectation confirmation model: The case of electronic tax filing in Malaysia. Government E-Strategic Planning and Management, 15(1), 287-303.
Sawada, I., A. Sugiyama, A. Ishikawa, T. Ohyanagi, K. Saeki, H. Izumi, S. Kawase, & K. Matsukura. (2000). Upgrading rural Japanese nurses’ respiratory rehabilitation skills through videoconferencing. Journal of Telemedicine and Telecare, 6(2), 69-71.
Scott Jr, W. E., Farh, J. L., & Podsakoff, P. M. (1988). The effects of “intrinsic” and “extrinsic” reinforcement contingencies on task behavior. Organizational Behavior and Human Decision Processes, 41(3), 405-425.
Seyal, A. H., Rahman, M. N. A., & Rahim, M. M. (2002). Determinants of academic use of the Internet: A structural equation model. Behavior & Information Technology, 21(1), 71-86.
Shapiro, E. G. (1990). Effect of instructor and class characteristics on students' class evaluations. Research in Higher Education, 31(2), 135-148.
Sharma, S. (1996). Applied Multivariate Techniques. New York, NY: John Wiley & Sons Inc.
Shee, D., & Wang, Y. H. (2008). Multi-criteria evaluation of the web-based e-learning system: A methodology based on learner satisfaction and its applications. Computers & Education, 50(3), 894–905.
Shin, D. H., Shin, Y. J., Choo, H., & Beom, K. (2011). Smartphones as smart pedagogical tools: Implications for smartphones as u-learning devices. Computers in Human Behavior, 27(6), 2207-2214.
Soon, K. H., Sook, K. I., Jung, C. W., & Im, K. M. (1999). The effects of internet-based distance learning in nursing. Computers in Nursing, 18(1), 19-25.
Sørebø, Ø., Halvari, H., Gulli, V. F., & Kristiansen, R. (2009). The role of self-determination theory in explaining teachers’ motivation to continue to use e-learning technology. Computers & Education, 53(4), 1177-1187.
Spreng, R. A., MacKenzie, S. B., & Olshavsky, R. W. (1996). A reexamination of the consumer satisfaction. The Journal of Marketing, 60(3), 15-32.
Stone, R. W., & Baker-Eveleth, L. (2013). Students’ expectation, confirmation, and continuance intention to use electronic textbooks. Computers in Human Behavior, 29(3), 984-990.
Straub, E. T. (2009). Understanding technology adoption: Theory and future directions for informal learning. Review of Educational Research, 79(2), 625-649.
Sun, Y., Liu, L., Peng, X., Dong, Y., & Barnes, S. J. (2014). Understanding Chinese users’ continuance intention toward online social networks: An integrative theoretical model. Electronic Markets, 24(1), 57-66.
Sun, P. C., Tsai, R. J., Finger, G., Chen, Y. Y., & Yeh, D. (2008). What drives a successful e-learning? An empirical investigation of the critical factors influencing learner satisfaction. Computers & Education, 50(4), 1183-1202.
Swan, J. E., & Oliver, R. L. (1989). Post purchase communications by consumers. Journal of Retailing, 65(4), 516-533.
Swan, J. E., & Trawick, I. F. (1981). Disconfirmation of expectations and satisfaction with a retail service. Retrieved from http://psycnet.apa.org/psycinfo/1984-10999-001
Symonds, R. (1986). Oxford and empire: The last lost cause? Retrieved from http://www. palgrave-journals.com/cpt/journal/v6/n4/abs/9300307a.html
Tarhini, A., Hone, K., & Liu, X. (2014). The effects of individual differences on e-learning users’ behavior in developing countries: A structural equation model. Computers in Human Behavior, 41(2), 153-163.
Tang, J. T. E., Tang, T. I., & Chiang, C. H. (2014). Blog learning: Effects of users' usefulness and efficiency towards continuance intention. Behavior & Information Technology, 33(1), 36-50.
Taylor, S., & Todd, P. (1995). Assessing IT usage: The role of prior experience. MIS Quarterly, 19(4), 561-570.
The World Bank. (2014). Annual report 2014. Retrieved from http://databank.worldban k. org/Data/Views/VariableSelection/SelectVariables.aspx?source=Health%20Nutrition%20and%20Population%20Statistics
Thong, J. Y., Hong, S. J., & Tam, K. Y. (2006). The effects of post-adoption beliefs on the expectation confirmation model for information technology continuance. International Journal of Human-Computer Studies, 64(9), 799-810.
Tse, D. K., & Wilton, P. C. (1988). Models of consumer satisfaction formation: An extension. Journal of Marketing Research, 25(2), 204-212.
Urdan, T. A., & Weggen, C. C. (2000). Corporate e-learning: Exploring new frontier. Retrieved from http://cumincad.scix.net/cgi-bin/works/Show&_id=caadria2010_000&sort=DEFAULT&search=series:caadria/Show?2c7d
United States Renal Data System. (2013). 2013 Annual data report. Retrieved form: http://www.usrds.org/adr.aspx
Valenta, A., Therriault, D., Dieter, M., & Mrtek, R. (2001). Identifying student attitudes and learning styles in distance education. Journal of Asynchronous Learning Networks, 5(2), 111-127.
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186-204.
Venkatesh, V., & Speier, C. (1999). Computer technology training in the workplace: A longitudinal investigation of the effect of mood. Organizational Behavior and Human Decision Processes, 79(1), 1-28.
Volery, T., & Lord, D. (2000). Critical success factors in online education. International Journal of Educational Management, 14(5), 216-223.
Whitten, P., Ford, D. J., Davis, N., Speicher, R., & Collins, B. (1998). Comparison of face‐to‐face versus interactive video continuing medical education delivery modalities. Journal of Continuing Education in the Health Professions, 18(2), 93-99.
World Health Organization (WHO). (2014). World health statistics 2014. Retrieved from http://apps.who.int/iris/bitstream/10665/112738/1/9789240692671_eng.pdf?ua=
Xu, D., Huang, W. W., Wang, H., & Heales, J. (2014). Enhancing e-learning effectiveness using an intelligent agent-supported personalized virtual learning environment: An empirical investigation. Information & Management, 51(4), 430-440.
Zhang, T., & Shen, R. (2011). Intelligent and collaborative Q&A mechanism based on learning communities. Advances in Information Technology and Education, 20(1), 362-368.
  • 同意授權校內瀏覽/列印電子全文服務,於2020-08-13起公開。
  • 同意授權校外瀏覽/列印電子全文服務,於2020-08-13起公開。

  • 如您有疑問,請聯絡圖書館