:::

詳目顯示

回上一頁
題名:數位學習者滿意度模式建構之研究
作者:李碧香
作者(外文):Li, Pi-Hsiang (Teresa)
校院名稱:國立彰化師範大學
系所名稱:工業教育與技術學系
指導教授:戴文雄教授
學位類別:博士
出版日期:2011
主題關鍵詞:數位學習者滿意度整合模式結構方程模式E-learner satisfactionHybrid modelStructured Equation Modeling
原始連結:連回原系統網址new window
相關次數:
  • 被引用次數被引用次數:期刊(0) 博士論文(0) 專書(0) 專書論文(0)
  • 排除自我引用排除自我引用:0
  • 共同引用共同引用:0
  • 點閱點閱:29
數位學習者滿意度是有效改善數位學習之教學與學習最重要的因素。本論文以科技接受模式及計畫行為理論之因素,建構出評估數位學習者滿意度的整合模式,並以結構方程模式驗證此數位學習者滿意度模式之適配度,進而發展出一個更簡潔精確之整合模式以評估數位學習者滿意度。
本研究以問卷調查法為研究方法,目的在經由檢驗各自變項是否影響中介變項(使用意願),進而影響依變項(學習者滿意度),以建構並檢驗數位學習者滿意度之模式。本研究之研究對象為有數位學習經驗之網路使用者,共獲得239份有效的網路問卷;經文獻回顧相關理論後,以AMOS 18.0進行資料分析,藉由徑路分析之結果檢驗研究假設,並以結構方程模式進行模式之適配度檢驗。
研究結果顯示,認知有用性及認知易用性影響學習者態度;學習者自我效能影響學習者的行為控制和學習者使用數位學習的意願;學習者的態度和自我效能都會影響學習者使用數位學習的意願;而學習者使用數位學習的意願會影響學習者的滿意度。
整體模式之適配度考驗指標結果顯示,p值為.191;卡方與自由度的比值為1.362小於2;RMR值為.037小於.5; GFI和AGFI值分別為.987和.952,大於.90;NFI值為.932大於.90;CN值在.05及.01的考驗下分別為320及406,均大於200,表示模式適配度極佳。
本研究之研究結果可提供學術界與企業界實施數位學習之參考而對後續研究的建議也可讓此模式更完整。
Satisfaction is an important factor in improving teaching and learning effectiveness. The objectives of this research are to specify the important factors of e-learner satisfaction in order to support evaluation-related tasks, to integrate satisfaction model determinants from established technology acceptance models and psychological models cited in research literature and finally to identify critical factors influencing e-learner satisfaction by Structural Equation Modeling (SEM) and develop a simplified but more accurate hybrid evaluation model.
A survey methodology was employed to access whether the independent variables were statistically significant in relation to learner satisfaction. One hundred and eighty-nine valid questionnaires were collected from a public survey website and examined as the pilot test. Two hundred and thirty-nine valid questionnaires were collected from the same website for data analysis. Path analysis was used to test the hypotheses and structural equation modeling by AMOS 18.0 was applied to test the goodness fit of the hypothesized model. The results of the goodness-of-fit indicated that the fit between the hypothesized model and the data (CMIN=13.619 and p= .019) were significantly fit. Additionally, the CMIN/DF for the proposed model was 1.362 which is within the rule of thumb of 2. The standardized regression weights are associated with the path coefficients in the model.
The standardized RMR represents the average value across all standardized residuals and ranges from 0 to 1; in a well-fitting model this value is .05 or less. The value of .037 represents it is a good model fit. The GFI and AGFI values reported in this research are .987 and .952, respectively and it means the model fits the sample data very well. The relative Fit Index represents a derivative of the NFI. The coefficient values ranges for the NFI and CFI are from 0 to 1 and with values close to .95 indicating superior fit. The finding of IFI of .981 is consistent with that of the CFI in reflecting a well-fitting model. Finally, the Tucker-Lewis Index (TLI) consistent with the other indices noted here, yields values ranging from 0 to 1, with values close to .95 being indicative of good fit. The RMSEA value of the model is .39, with the 90% confidence interval ranging from .000 to .086 and the p-value for the test of closeness of fit equal to .594 so the model fits the data well.
The major contributions of this paper are: (1) the investigator developed a novel hybrid e-learner satisfaction model which combined technology factors and psychological factors for evaluating e-learner satisfaction effectively; (2) the study investigates the relationship between the subjective norm of theory of planned behavior and e-learner satisfaction as well as the relationship between e-learners’ intension to use and e-learner satisfaction. The results obtained in this study will provide valuable information for academia and industries in evaluating e-learning satisfaction and to overcome potential obstacles and reduce risks of failure during implementation of e-learning.
Adams, J.S. (1965). Inequity in social exchange. In: L. Berkowitz, Editor, Advances in experimental social psychology. NY: Academic Press. 267-299.

Agarwal, R., Ahuja, M., Carter, P. E., & Gans, M. (1998). Early and late adopters of IT innovations: Extensions to innovation diffusion theory. In Proceedings of the DIGIT conference.

Alavi, M., Yoo, Y., & Vogel, E. R. (1997). Using information technology to add value to management education. Academy of Management Journal, 40(6), 1310-1333.

Allen, T. D., Russell, J. E., A., Pottet, M. L., & Dobbins, G. H. (1999). Learning and development factors related to perceptions of job content and hierarchical plateauing. Journal of Organizational Behavior, 20(12), 1113-1137.

Allen, M. W. (2003). Michael Allen’s Guide to e-Learning: Building interactive, fun, and effective learning programs for any company. Hoboken, NJ: John Wiley & Sons, Inc.

Alreck, P. L., & Sette, R. B. (2004). The survey research handbook (3rd ed.). New york: McGraw-Hill.

Ajzen, I., & Fishbein, M. (1977). Attitude-behavior relations: a theoretical analysis and review of empirical research. Psychological Bulletin, 84, 888-918.

Ajzen, I. (1985). From intentions to actions: A theory of planned behavior. In J. Kuhl & J. Beckman (Eds.). Action-control: From cognition to behavior (pp. 11-39). Heidelberg: Springer.

Ajzen I., & Madden T.J. (1986). Prediction of goal-directed behavior: Attitudes, intentions and perceived behavioral control. Journal of Experimental Social Psychology, 22, 453-474.

Ajzen I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211.
Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: a review and recommended two-step approach. Psychological Bulletin, 103, 411-423.

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. (2002). Managing the one-line classroom: a study of technological and behavioral characteristics of web-based MBA courses. Journal of High Technology management Research, 13, 203-223.

Arbaugh J.B., & Duray, R. (2002). Technological and structural characteristics, student learning and satisfaction with web-based courses – An exploratory study of two on-line MBA programs. Management learning, 33(3), 331-347.

Aronen, R., & Dieressen, G. (2001). Improvement equipment reliability through e-learning. Hydrocarbon Processing, 47-57.

Astin, A. W. (1993). What matters in college: Four critical years revisited. San Francisco: Jossey-Bass.

Atkinson, M., & Kydd, C. (1997). Individual characteristics associated with World Wide Web use: an empirical study of playfulness and motivation. DATA BASE for Advances in Information Systems, 28(2), 53-62.

Bagozzi, R.P., Davis, F.D., & Warshaw, P.R. (1992). Development and test of a theory of technological learning and usage. Human Relations, 45, 659-686.new window

Bagozzi, R., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(1), 74-94.

Bailey, J.E., & Pearson, S.W. (1983). Development of a tool for measuring and analyzing computer user satisfaction. Management Science, 29(5), 530-545.

Bandura, A. (1986). Social Foundations of Thought and Action: A Social Cognitive Theory. Englewood Cliffs, New Jersey: Prentice Hall, Inc.

Bandura, A. (1997). Self-efficacy: the exercise of control. New York: W.H. Freeman and company.
Belton, V., & Gear, A.E. (1983). On a shortcoming of Saaty's method of analytical hierarchies. Omega, 11, 227-230.

Bhattacherjee, A. (2001). Understanding information systems continuous: an expectation confirmation model. MIS Quarterly, 25(3), 270-351.

Bies, R. J., & Moag, J. S. (1986). Interactional justice: communication criteria of fairness. In R. J. Lewicki, B. H. Sheppard, & M. H. bazerman (Eds.), Research on negotiation in organizations. 43-55. Greenwich, CT: JAI press.

Billings, D. M., Connors, H. R., &. Skiba, D. J. (2001). Benchmarking best practices in Web-based nursing courses. Advances in Nursing Science
23 (3), 41-52.

Bollen, K. A. (1989a). Structural equations with latent variables. New York, NY: Wiley-Interscience.

Bollen, K. A. (1989b). A new incremental fit index for general structural models. Sociological Review, 46, 232-239.

Bolton, R.N. & Drew, J.H. (1991). A multistage model of customers’ assessments of service quality and value. Journal of Consumer Research 17, 375-384.

Borbely, E. (1994). Challenges and opportunities in extending the classroom and the campus via digital compressed Video. In: R. Mason and P. Bacsich, Editors, ISDN: Applications in education and training, Institution of Electrical Engineers. London, pp. 65-82.

Bosnjak M., Obermeier D., & Tuten T.L.(2006). Predicting and explaining the propensity to bid in online auctions: A comparison of two action-theoretical models. Journal of Consumer Behaviour 5(2), 102-116.

Bouhnik, D. & Marcus, T. (2006). Interaction in distance-learning courses. Journal of the American Society Information Science and Technology, 57(3), 299-305.

Bouhnik, D. & Marcus, T. (2006). Interaction in distance-learning courses. Journal of the American Society Information Science and Technology, 57(3), 299-305.
Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In K. A. Bollen & J.S. Long (Eds.). Testing structural equation models (pp. 136-162). Newbury Park, CA: Sage.

Bryson, N. & Joseph, A. (1999). Generating consensus priority point vectors a logarithmic goal programming approach. Computers & Operations Research 26 (6), 637-643.

Byrne, B. M. (2010). Structural equation modeling with AMOS:Basic concepts, applications, and programming (2nd ed.). New York: Routledge/Taylor & Francis.

Chambers, T., & Lee, D. (2004). Web-based training in corporations: Organizational considerations. International Journal of Instructional Media, 31(4), 345-354.

Capper, J. (2001). E-learning growth and promise for the developing world. TechKnowLogia, May/June. Retrieved from http://www.techknowlogia.org. July 6, 2008.

Carmines, E. G. & Zeller, R.A. (1991). Reliability and validity assessment. Newbury Park: Sage Publications.

Chau, P. Y. K., & Hu, P. J.-W. (2001). Information technology acceptance by individual professionnals: a model comparison approach. Decision sciences, 32(4), 699-719.

Chen, Yu-Li. (2008). Modeling the determinants of internet use. Computer & Education, 51, 545-558.

Chen, W. L. C., & Bagakas, J. G. (2003). Understanding the dimensions of self-exploration in web-based learning environments. Journal of Research on Technology in Education, 34(3), 364-373.

Cheng, C. H. (1999). Evaluating weapon systems using ranking fuzzy numbers. Fuzzy Sets and systems, 107(1), 25-35.

Chin, W. W., & Todd, P. A. (1995). On the use, usefulness, and ease of structural equation modeling in MIS research. MIS Quarterly, 19, 237-246.

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, 399-416.

Chiu, C. M., Sun, S. Y., Sun, P. C., & Ju, T. L. (2007). An empirical analysis of the antecedents of web-based learning continuance. Computers and Education, 49, 1224-1245.

Choo, E.U., & Wedley, W.C. (2004). A common framework for deriving preference values from pair-wise comparison matrices. Computers & Operations Research, 31 (6), 893-908.

Clark, R. C. & Mayer, R. E. (2008). e-Learning and the Science of Instruction: Proven Guidelines for Consumers and Designers of Multimedia Learning (2nd ed.). CA: John Wiley & Sons, Inc.

Cohen, J., & Cohen, P. (1983). Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences (2nd ed.). Hillsdale, NJ: Lawence of Erlbaum.

Compeau, D. R., & Higgins, C. A. (1995a). Application of social cognitive theory to training for computer skills. Information systems Research, 6(2), 118-143.

Compeau, D. R., & Higgins, C. A. (1995b). Computer self-efficacy: Development of a measure and initial test. MIS Quarterly, 19(2), 189-211.new window

Compeau, D. R., & Higgins, C. A., & Huff, S. (1999). Social cognitive theory and individual reactions to computing technology: a longitudinal study. MIS Quarterly, 23(2), 145-158.

Crawford, G., & Williams, C. (1985). A note on the analysis of subjective judgment matrices. Journal of Mathematical Psychology, 29. 387-405.

Curran, P. J., West, S. G., & Finch, J. F. (1997). The robustness of test statistic to nonnormality and specification error in confirmatory factor analysis. Psychological methods, 1, 16-29.new window

Davis, F. D. (1986). A technological acceptance model for empirically testing new end-user information systems: theory and results. Ph. D. dissertation, MIT Sloan School of Management, Cambridge, MA.

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 318-340.

Davis, F. D. (1993). User acceptance of information technology: System characteristics, user perceptions and behavioral impacts. International Journal of Man-Machines, 38, 475-487.

Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982-1003.

DeLon, W., & Mclean, E. (1992). Extrinsic and intrinsic motivation to use computer technology: A comparison of two theoretical models. Management Science, 35(8), 982-1003.

DeLone, W.H., & McLean, E.R. (2002). Information Systems Success Revisited in Proceedings of the 35th Hawaii International Conference on System Sciences (HICSS 02). Big Island, Hawaii, 238-249.

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.

DeVellis, R. F. (1991). Scale development: theory and application. Newbury Park: Sage.

Dillon, C. L., & Gunawardena, C. N. (1995). A framework for the evaluation of telecommunications-based distance education. In Selected Papers from the 17th Congress of the International Council for Distance Education, 2, 348-351. Milton Keynes: Open University.

Dillon, C. L., Hengst, H. R. & Zoller, D. (1991). Instructional strategies and student involvement in distance education: a study of the Oklahoma televised instruction system. Journal of Distance Education, 6(1), 28-41.

Doll, W. J., & Torkzadeh, G. (1988). The measurement of end-user computing satisfaction. MIS Quarterly, 12(2), 259-274.

Doll, W. J., & Torkzadeh, G. (1991). The measurement of end-user computing satisfaction: theoretical and methodological issues. MIS Quarterly, 15(1), 5-10.

Doll, W.J., Raghunathan, T.S., Lim J.U. & Gupta, Y.P. (1995). A confirmatory factor analysis of the user information satisfaction instrument. Information Systems Research, 6(2), 177-189.

Dutton J. & Perry J. (2002). How do online students differ from lecture students? Journal of Management Information Systems, 18(4), 169-190.

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, 6, 56-83.

Fishbein M. & Ajzen I. (1973). Attribution of responsibility: A theoretical note. Journal of Experimental Social Psychology, 9, 148-153.

Fishbein, M. & Ajzen, I. (1975). Belief, Attitude, Intention and Behavior: An Introduction to Theory and Research. CA: Addison-Wesley.

Fitzgerald, L.M. & Kiel G.C. (2001). Applying a consumer acceptance of technology model to examine adoption of online purchasing. Proceedings of the Australian and New Zealand marketing academy conference, ANZMAC, Aukland.

Folger, R., & Konovsky, M. A. (1989). Effects of procedural and distributive justice on reactions to pay raise decisions. Academy of management journal, 32(1), 115-130.

Fornell, C. R., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of marketing research, 18, 39-50.

Fraenkel, J. R., & Wallen, N. E. (1990). How to design and evaluate research in education. New York:McGraw-Hill.

Gall, M. D., Borg, W. R., & Gall, J. P. (1996). Educational research: An introduction. White plains, NY: Longman.

Gattiker, U. E., & Hlavka, A. (1992). Computer attitudes and learning performance: issues for management education and training. Journal of Organizational Behavior, 13(1), 89-101.

Gay, L. R. (1992). Educational research competencies for Analysis and Application. New York: Macmillan.

Gefen, D. & Straub, D.W. (1997). Gender differences in the perception and use of E-mail: an extension to the Technology Acceptance Model. MIS Quarterly, 21(4), 389-400.

Gefen, D., Straub, D. W., & Boudreau, M. (2000). Structural equation modeling and regression: Guidelines for research practice. Communications of the Association for Information systems, 4(7), 1-79.

Gelderman, M. (1998). The measure of end-user computing satisfaction. MIS Quarterly, 12(2), 259-274.

George, J. F. (2004). The theory of planned behavior and Internet purchasing. Internet Research, 14(3), 198 - 212

Giese, J. L., & Gote, J. A. (2000). Defining consumer satisfaction. Academy of marketing Science Review, 1, 1-24. Retrieved October 17, 2005, from http://www.amsreview.org/article.htm

Glock, C. Y. (Ed.). (1967). Survey research in the social sciences. New York: Russell Sage Foundation.

Gong M., Xu Y., & Yu Y. (2004). An Enhanced Technology Acceptance Model for Web-based Learning. Journal of Information Systems Education 15(4), 365-374.

Hair, J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (1995). Multivariate data analysis with readings, 4th ed., Englewood Cliffs, NJ: Prentice-Hall.

Hair, J., Black, B. Babin, B., Anderson, R., & Tatham, R. (2006). Multivariate Data Analysis (6th edition). Upper Saddle River, NJ: Prentice-Hall.

Hall, M. L., & Nania, S. (1997). Training design and evaluation: An example from a satellite based distance learning program. Public Administration Quarterly, 21(3), 370-385.

Hall, B., & LeCavalier J. (2000). e-Learning Across the Enterprise: The benchmarking study of best practices. Retrieved July 6, 2008 from: http://www.brandon-hall.com/elacenbenstu.html

Harker, P., & Vargas, L. (1987). The theory of ratio scale estimation: Saaty’s analytic hierarchy process. Management Science, 33(11), 1383-1403.
Hart, C. W., Heskett, J. L., & Sasser, W.E., Jr. (1990). The profitable art of service recovery. Harvard Business Review, 68(4), 148-156.

Hinkle, D., Wiersma, W., & Jurs, S. (2003). Applied statistics for the behavioral sciences (5th Ed.). Boston: Houghton Mifflin company.

Ho, Robert. (2006). Handbook of univariate and multivariate data analysis and interpretation with SPSS. Boca Raton, FL: Chapman & Hall/CRC.

Hoelter, J. W. (1983). The Analysis of CovarianceStructures: Goodness-of-fit Indices. Sociological Methods and Research, 11, 325-344.

Homans, G. (1961). Social Behaviour: Its Elementary Forms. London: Routledge and Kegan Paul.

Homer, P. M. & L. R. Kahle (1988). A Structural Equation Test of the Value-Attitude-Behavior Hierarchy. Journal of Personality and Social Psychology, 54, 638-646.

Hoyle, R. H. (1995). The structural equation modeling approach: basic concepts and fundamental issues. In R. H. Hoyle (Ed.), Structural equation modeling: concepts, issues, and applications (pp. 1-14). Thousand oaks, CA: Sage Publications.

Hong, K. S. (2002). Relationships between students’ and instructional variables with satisfaction and learning from a Web-based course. Internet and Higher Education, 5, 267-281.

Hsieh, P. Y. (2004). Web-based training design for human resources topics: A case study. TechTrends, 48(2), 60-68.

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.

Hu, L-T., & Bentler, P. M. (1995). Evaluating model fit. In R. H. Hoyle (Ed.), Structural equation modeling: Concepts, issues, and applications (pp. 76-99). Thoudsand Oaks, CA: Sage.

Hu, L-T., & Bentler, P. M. (1998). Fit indices in covariance structure modeling: Sensitivity to underparameterized model misspecification. Psycological Methodes, 3, 424-453.

Hu, L-T., & Bentler, P.M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6, 1-55.

Hu, P., Chau, P., Sheng, O., & Tam, K. (1999). Examining the technology acceptance model using physician acceptance of telemedicine technology. Journal of Management Information Systems, 16(2), 91-112.

Huitt, William G. (1998). Internal and External Validity. Available from http://www.valdosta.peachnet.edu/~whuitt/psy702/intro/valdgn.html

Industrial Report (2006). Training, 38(12), 20-32. Accessed July 8, 2008, from www.Trainingmag.com.

Isaacs, E.A., Morris, T., Rodriguez, T.K. & Tang, J.C. (1995). A comparison of face-to-face and distributed presentations. In: R.R. Katz, R. Mack, L. Marks, M.B. Rosson and J. Nelson (Eds.) Proceedings of the association for computing machinery (ACM) special interest group on computers and human interaction (CHI) 95 conference (pp. 354–361) ACM Press, New York.

Jaccard, J. & Wan, C. K. (1996). LISREL approaches to interaction effects in multiple regression. Thousand Oaks, CA: Sage Publications.

James, L. R., Mulaik, S. A., & Brett, J. M. (1982). Causal analysis: Assumptions, models, and data. Beverly Hills, CA: Sage.

Jiang, M., & Ting, E. (1998). Course design, instruction, and students’ online behaviors: A study of instructional variables and student perceptions of online learning. In Paper presented at the annual meeting of the American Educational Research Association, San Diego, CA.

Joo, Y. J., Bong, M., & Choi, H. J. (2000). Self-efficacy for self-regulated learning, academic self-efficacy, and Internet self-efficacy in web-based instruction. Educational Technology research and Development, 48(2), 5-17.

Jöreskog, Karl G. (1970). A General Method for Analysis of Covariance Structures. Biometrika, 57, 239-251.

Jöreskog, K. G., & Sörbom, D. (1993). LISEREL 8: Structural equation modeling with the SIMPLIS command language. Hillside, NJ: Erlbaum.

Kaiser, H. F. (1974). An index of factorial simplicity. Psychomertrika, 39, 31-35

Katz, Y. J. (2000). The comparative suitability of three ICT distance learning methodologies for college level instruction. Educational Media international, 37(1), 25-30.

Katz, Y. J. (2002). Attitudes affecting college students’ preference for distance learning. Journal of Computer Assisted Learning, 18, 2-9.

Kanuka, H. & Nocente, N. (2003). Exploring the effects of personality type on perceived satisfaction with web-based learning in countinuing professional development. Distance Education, 24 (2), 227-245.

Klobas, J.E. & Clyde, L.A. (2000). Adults learning to use internet: A longitudinal study of attitudes and other factors associated wit intended internet use. Library and Information Science Research 22(1), 5-34.

Khan, B. (Ed.) (2001). Web-based training. Englewood Cliffs, N.J.: Educational Technology Publications.

Kelloway, E. K. (1998). Using LISREL for structural equation modeling-A researcher’s guide. Thousand oaks, CA: Sage Publication.

Kline, R. B. (1998). Principles and practice of structural equation modeling. New York: Guilford Press.

Kline, R. B. (1998 b). Software programs for structural equation modeling: AMOS, EQS, and LISREL. Journal of Psychoeducational Assessment (16), 343-364.

Kozak, M. & Kang, M.S. (2006). Note on modern path analysis in application to crop science. Communications in Biometry and Crop Science, 1 (1), 32-34.

Kydd, C.T. & Ferry, D.L. (1994). Case study: Managerial use of video conferencing. Information & Management, 27, 369-375.

Kumar, N., Scheer, L. K. & Steenkamp, J. E. (1995). The effects of supplier fairness on vulnerable resellers. Journal of Marketing research, 32(1), 54-65.

Lachem, C., Mitchell, J. & R. Atkinson (1994). ISDN-based video conferencing in Australian tertiary education. In: R. Mason and P. Bacsich (Eds.) ISDN: Applications in education and training, pp. 99-113. Institution of Electrical Engineers. London.

Lau, A., Yen, J., & Chau, P.Y.K. (2001). Adoption of on-line trading in the Hong Kong financial market. Journal of Electronic Commerce Research, 2 (2), 58-65.

Lee, A. H. I., Kang, H.Y. & Wang, W. P. (2006). Analysis of priority mix planning for semiconductor fabrication under uncertainty. International Journal of Advanced manufacturing technology, 28(3-4), 351-361.

Lee, K.C., Kang, I. & Kim, J.S.(2007). Exploring the user interface of negotiation support systems from the user acceptance perspective. Computers in Human Behavior, 23, 220-239.

Leidner, D. E., & Fuller, M. (1997). Improving student learning of conceptual information: GSS supported collaborative learning vs. individual constructive learning. Decision Support Systems, 20(2), 149-163.

Lee, J. S., Cho, G., Davidson B. G., & Ingraffea, A. (2003). Technology acceptance and social networking in distance learning. Educational Technology & Society, 6(2), 50-61.

Legris, P., Ingham, J. & Collerette, P. (2003). Why do people use information technology? A critical review of the technology acceptance model. Information & Management, 40(3), 191-204.

Lewis, C. (2002). Driving factors for e-Learning: an organizational perspective. Perspectives, 6(2), 50-54.

Liao, S., Shao, Y. ,Wang, P., H. & Chen, A. (1999). The adoption of virtual banking: An empirical study, International Journal of Information Management, 19, 63-74.

Liao, C., Chen, J.L. & Yen, D.C. (2007). Theory of planning behavior (TPB) and customer satisfaction in the continued use of e-service: An integrated model. Computers in Human Behavior, 23 (6), 2804-2822.
Liaw, S.S. & Huang, H.M. (2007). Developing a Collaborative e-learning System Based on Users’ Perceptions. Lecture Notes in Computer Science, 4402, 751-759.

Likert, R. (1932). The method of constructing an attitude scale. Archives of psychology, 140, 44-53.

Lin, H. H., & Wang, Y. S. (2006). An examination of the determinants of customer loyalty in mobile commerce contexts. Information and management, 43(3), 271-282.

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.

Lind, E. A., & Tyler, T. R. (1988). The social psychology of procedural justice. New York: Plenum Press.

Lootsma, F.A. (1999). Multi-criteria decision analysis via ratio and difference judgement. Kluwer Academic, Dordrecht.

Lu, J., Liu, C., Yu, C. S. &Yao, J. E. (2001). Exploring factors associated with wireless internet via mobile technology acceptance in mainland China. Communications of the International Information Management Association 3(1), 101–119.

Luarn, P. & Lin, H. H.(2005). Toward an understanding of the behavioral intention to use mobile banking. Computers in Human Behavior 21, 873–891.

MacCallum, R. C., & Austin, J. T. (2000). Applications of structural equation modeling in psychological research. Annual Review of Psychological Methods, 1, 130-149.

MacCallum, R. C., & Browne, M. W., & Sugawara, H. M. (1996). Power analysis and determination of sample size for covariance structure models. Multivariate Behavioral Research, 29, 1-32.

Mathieson, K. (1991). Predicting user intentions: Comparing the Technology Acceptances Model with the Theory of Planned Behavior. Information Systems Research, 2(3), 173-191.
Matross, R. (1999). Designing questionnaires and surveys: A professional development course on questionnaire research. Minneapolis: University of Minnesota.

Maxham, J. G., & Netemeyer, R. G. (2002). Modeling Customer Perceptions of Complaint Handling over Time: The Effects of Perceived Justice on Satisfaction and Intent. Journal of Retailing, 78(4), 239-252.

McGorry, S. Y. (2003). Measuring quality in online learning programs. The Internet and Higher Education, 6, 159-177.

McHaney, R., Hightower, R. & White, D. (1999). EUCS test–retest reliability in representational model decision support systems. Information and Management 36(2), 109–119.

McHaney, R., Hightower, R. & Pearson, J. (2002). A validation of the end-user computing satisfaction instrument in Taiwan. Information and Management 39(6), 503–511.

Meade, L. M., & Presley, A. (2002). R & D project selection using the analytic network process. IEEE Transactions on Engineering Management, 49(1), 59-66.

Melone, N. P. (1990). A theoretical assessment of the user-satisfaction construct in information systems research. Management Science, 36(1), 76-91.

Mertler, C., & Vanatta, R. (2002). Advanced and multivariate statistical methods. Los Angeles: Pyrczak Publishing.

Moore, M.G. (1989). Three types of interaction. The American Journal of Distance Education 3(2), 1–6.

Moorman, R. H. (1991). Relationship between organizational justice and organizational citizenship behaviors: do fairness perceptions influence employee citizenship? Journal of Applied Psychology, 76(6), 845-855.

Mulaik, S. A., James, L. R., Van Altine, J., Bennett, N., Lind, S., & Stilwell, C. D. (1989). Evaluation of goodness-of-fit indices for structural equation models. Psychological Bulletin, 105, 430-445.

Murtaza, M. B. (2003). Fuzzy-AHP application to country risk assessment. American Business Review, 21(2), 109-116.

Muthuen, B., & Kaplan, D. (1985). A comparison of methodologies for the factor analysis of nonnormal Likert variables. British Journal of Mathematical and Statistical Psychology, 38, 171-189.

Nahl, D. (1993). Communication dynamics of a live interactive television system for distance education. Journal of Education for Library and Information Science, 34(3), 200–217.

Ngai, E. W. T., Poon, J. K. L., & Chan, Y. H. C. (2007). Empirical examination of the adoption of WebCT using TAM. Computers & Education, 48, 250-267.

Neal, W. D. (1999). Satisfaction is nice, but value drives loyalty. Marketing Research, 11, 21–23.

Nunnally, Y. J. (1978). Psychometric theory. New York: McGraw Hill.

Oliver, R. L. (1980). A cognitive model for the antecedents and consequences of satisfaction. Journal of Marketing Research, 17, 460-469.

Palloff, R.M., & Pratt, K. (1999). Building learning communities in cyberspace: Effective strategies for the online classroom. San Francisco, CA: Jossey-Bass.

Parasuraman, A., & Grewal, D. (2000). The Impact of technology on the quality–value–loyalty chain: a research agenda. Journal of the Academy of Marketing Science, 28(1), 168–174.

Patterson, P. G., & Spreng, R. A. (1997). Modeling the relationship between perceived value, satisfaction and repurchase intentions in a business-to-business, services context: an empirical examination. International Journal of Service Industry Management 8 (5), 414–434.

Pedersen, P. E., & Nysveen, H. (2002). Using the theory of planned behavior to explain teenagers adoption of text messaging services. Working Paper, Agder University College.

Pedhazur, E. J. (1997). Multiple regression in behavioral research: Explanation and prediction. Fort Worth, TX: Harcourt brace College Publishers.

Piccoli, G., Ahmad, R., & Ives, B. (2001). Web-based virtual learning environments: a research framework and a preliminary assessment of effectiveness in basic IT skill training. MIS Quarterly, 25(4), 401-426.

Pinsonneault, A., & Kraemer, K. (1991). Survey research methodology in management information systems: An assessment. Working paper presented at the Graduate School of management, Irvine, CA.

Raab, R. T., Ellis W. W., & Abdon, B. R. (2002). Multisectoral partnerships in e-learning. A potential force for improved human capital development in the Asia Pacific. Internet and Higher Education, 4. 217–229.

Raykov, T., & Penev, S. (1998). Nested Covariance Structure Models: Noncentrality and Power of Restriction Test. Structural Equation Modeling, 5, 229-246.

Raymond, L. (1987). Validating and applying user satisfaction as a measure of MIS success in small organization. Information and management, 12(40), 173-179.

Reichheld, F., & Schefter, P. (2000). E-loyalty: your secret weapon on the Web. Harvard Business Review. 78(4), 105-113.

Rokeach, M. (1968). Beliefs, attitudes, and values: A theory of organization and change. San Francisco, CA: Jossey-Bass.

Rosenberg, M. J. (2001). E-learning: Strategies for delivering knowledge in the digital age. New York: McGraw-Hill.

Satty, T. L. (1980). Multicriteria Decision Making: The Analytic Hierarchy Process. New York: McGraw-Hill.

Saaty, T. L., & Vargas, L. G. (1984). Comparison of eigenvalues, logarithmic least squares and least squares methods in estimating ratios. Mathematical Modeling, 5, 309-324.

Schifter D. B., & Ajzen I.(1985). Intention, perceived control, and weight loss: An application of the theory of planned behaviour. Journal of Personality and Social Psychology, 49(3), 842–851.

Schofield J. W. (1974). Effect of norms, public disclosure and need for approval on volunteering behavior consistent with attitudes. Journal of Personality and Social Psychology, 31, 1126–1133.

Segars, A. H., & Grover, V. (1993). Re-examining perceived ease of use and usefulness: a confirmatory factor analysis. MIS Quarterly, 17, 517-525.

Seiders, K., & Berry, L. L. (1998). Service fairness: what it is and why it matters. The Academy of Management Executive, 12(2), 8-21.

Shee, D. Y., Tzeng, G. H., & Tang, T. I. (2003). AHP, fuzzy measure and fuzzy integral approaches for the appraisal of information service provides in Taiwan. Journal of Global information Technology management, 6(1), 8-30.

Shee, D. Y., & Wang, Y. S. (2008). Multi-criteria evaluation of the web-based e-learning system: a methodology based on learner satisfaction and its implementation. Computer & Education, 50, 894-905.

Sherry, A.C., Fulford, C.P., & Zhang, S. (1998). Assessing distance learners' satisfaction with instruction: a quantitative and a qualitative measure. American Journal of Distance Education, 12(3), 4–28.

Shih, Hung-Pin. (2008). Using a cognition-motivation-control view to assess the adoption intention for Web-based learning. Computer & Education, 50, 327 – 337.

Shim, S., & Drake, M.F. (1990). Consumer intention to utilize electronic shopping. The Fishbein behavioral intention model. Journal of Direct Marketing, 4(3), 22–33.

Shotsberger, P. G. (2000).The human touch: Synchronous communication in web-based learning. Educational Technology, 40(1). 53–56.

Sirdeshmukh, D., Singh, J., & Sabol, B. (2002). Consumer trust, value, and loyalty in relational exchange. Journal of Marketing, 66, 15–37.

Smith, A. K., Bolton, R. N., & Wagner, J. (1999). A model of customer satisfaction with service encounters involving failure and recovery. Journal of Marketing Research, 36(3), 356-372.

SPSS (1998). Statistical Package for the Social science. Chicago.

Steiger, J. H. (1998). A note on multiple sample extensions of the RMSEA fit index. Structural Equation Modeling, 5, 411-419.

Steiger, J. H., & Lind, J. C. (1980, May). Statistically-based tests for the number of common factors. Paper presented at the annual Spring meeting of the Psychometric Society, Iowa city, IA.
Stokes, S.P. (2001). Satisfaction of college students with the digital learning environment. Do Learners’ temperaments make a difference? Internet and High Education, 4, 31-44.

Stoel, L. & Lee, K. H. (2003). Modeling the effect of experience on student acceptance of Web-based courseware. Internet Research 13(5), 364–374.

Strother, J. B. (2002). An assessment of the effectiveness of e-learning in corporate training programs. International Review of Research in Open and Distance Learning, available at: http://www.irrodl.org/index.php/irrodl/article/viewArticle/83

Sun, P. C., Tsai, R. J., Finger, G., & Chen, Y. Y. (2008). What drives a successful e-learning? An empirical investigation of the critical factors influencing learner satisfaction. Computer & Education, 50, 1183-1202.

Tabachnick, B. G., & Fidell, L. S. (2007). Using multivariate statistics (5th ed.). Needham Heights, MA: Allyn and Bacon.

Tate, R. (1998). An introduction to modeling outcomes in the behavioral and social sciences (2nd ed.). Edina, MN: Burgess.

Taylor, S., & Todd, P. A. (1995). Understanding information technology usage: a test of competing models. Information Systems Research, 6(2), 144 -176.new window

Tax, S. S., Brown, S. W., & Chandrashekaran, M. (1998). Customer evaluations of service complaint experience: implications for relationship marketing. Journal of marketing, 62(2), 60-76.

Thach, E. C., & Murphy, K. L. (1995). Competencies for distance education professionals. Educational Technology Research and Development, 43(1) 57–79.

Thibaut, J., & Walker, L. (1975). Procedural justice: a psychological analysis. Hillsdale, NJ: Lawrence Erlbaum.

Thurmond, V. A., Wambach, K., & Connors, H. R. (2002). Evaluation of student satisfaction: determine the impact of a web-based environment by controlling for student characteristics. The American journal of Distance education, 16(3), 169-189.

Trentin, G. (1997). Telematics and on-line teacher training: the Polairs project. Journal of Computer Assisted learning, 13, 261-270.

Tucker, L.R., & Lewis, C. (1973). A reliability coefficient for maximum likelihood factor analysis. Psychometrika, 38, 1-10.

Urdan, T. A., & Weggen, C. C. (2000). Transfer of information technology to the Arab World: A test of cultural influence modeling. Journal of global Information Management, 9(4), 6-28.new window

Venkatesh, V., & Davis, F. D. (1996). A Model of the Antecedents of perceived Ease of Use: Development and Test. Decision Science, 27(3), 451-481.

Venkatesh, V., Morris, M.G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: toward a unified view, MIS Quarterly 27(3), 425-478.

Vogel, D. R., Davison, R. M., & Shroff, R. H. (2001). Socio-cultural learning: a perspective on GSS-enable global education. Communications of the AIS, 7, 1-39.

Wang J. H., Ran, M., W., Yang, S. J. H. & Liao, J, Chiu, D. K. W. (2011). Design of a performance-oriented workplace e-learning system using ontology. Expert system with Applications 38 (4), 3372-3382.

Wang, L.L., Fan, X., & Wilson, V.L. (1996). Effects of nonnormal data on parameter estimates for a model with latent and manifest variables: an empirical study. Structural Equation Modeling, 3(3), 228-247.

Wang, Y. S. (2003). Assessment of learner satisfaction with asynchronous electronic learning systems. Information & Management, 41(1), 75-86.

Wang, Y.S., Wang, H.-Y., & Shee, D.Y. (2007). Measuring E-Learning
Systems Success in an Organizational Context: Scale Development and
Validation. Computers in Human Behavior, 23(4), 1792-1808.

Warner L. G., & DeFleur, M. L. (1969). Attitude as an interaction concept: Social constraint and social distance as intervening variables between attitudes and action. American Sociological Review, 34, 153–169.

Webster, J. & Hackley, P. (1997). Teaching effectiveness in technology-mediated distance learning. Academy of Management Journal 40(6), 1282–1309.

Wikipedia (2008). Retrieved from http://en.wikipedia.org/wiki/Elearning

Wu, M. L & Tu, Z. T. (2006). SPSS & the Application and Analysis of Statistics (2nd. ed.). Taiwan: Wunan.

Wu, J. P., Tsai, R. J., Chen, C. C., & Wu, Y. C. (2006). An integrative model to predict the continuance use of electronic learning systems; hints for teaching. International journal on E-Learning, 5(2), 287-302.

Yilmaz, C., Sezen, B., & Kabadayi, E. T. (2004). Supplier fairness as a mediating factor in the supplier performance- reseller satisfaction relationship. Journal of Business research, 57(8), 854-863.

Zahedi, F. (1986). The analytic hierarchy process-a survey of the method and its applications. Interfaces, 16(4), 96-108.

Zeithaml, V.A. (1988). Consumer perceptions of price, quality and value: a means-end model and synthesis of evidence. Journal of Marketing, 52(3), 2–22.

Zimmerman, E. (2001). Better training is just a click away. Workforce, 80, 36.

 
 
 
 
第一頁 上一頁 下一頁 最後一頁 top
QR Code
QRCODE