| 研究生: |
王喬玫 Wang, Ciao-Mei |
|---|---|
| 論文名稱: |
以科技接受模式探討銀髮族對網路學習之行為意圖 A Study of Senior Citizens' Intention of Using Web-based Learning:Based on Technology Acceptance Model |
| 指導教授: |
呂執中
Lyu, Jr-Jung |
| 學位類別: |
碩士 Master |
| 系所名稱: |
管理學院 - 工業與資訊管理學系 Department of Industrial and Information Management |
| 論文出版年: | 2014 |
| 畢業學年度: | 102 |
| 語文別: | 中文 |
| 論文頁數: | 100 |
| 中文關鍵詞: | 科技接受模式 、銀髮族 、網路學習 、行為意圖 |
| 外文關鍵詞: | Technology Acceptance Model(TAM), Web-based Learning, Senior Citizen, Intention Behavior |
| 相關次數: | 點閱:155 下載:0 |
| 分享至: |
| 查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報 |
人口老化的浪潮正一波波襲捲全球,隨著科技的進步,生活水平和醫療技術提升,人類平均壽命不斷延長,致使全球人口發展趨勢逐漸出現高齡化現象。目前台灣地區五十歲以上之銀髮族是世代數位落差之斷層,平板電腦的觸控功及較友善性的人性化介面,有助於消彌銀髮族使用者對於學習網路科技的挫折感。
網路學習為當今學習型態的新趨勢之一,e世代的學習方式,已漸漸從課堂的傳統教學轉換為以網路為基礎的網路學習,且隨著e化教室的普及,學習者接觸網路學習的機會將會越來越頻繁,加上成人教育受到重視,因此冀望透過網路學習型態之發展,讓終生教育得以延伸並落實。
本研究目的在於探討銀髮族對於網路學習之行為意圖和瞭解銀髮族網路學習接受度的關鍵因素,本研究以五十歲以上之銀髮族為研究對象,並以科技接受模式結合認知愉悅性為理論基礎,再以個人因素和社會因素當作外部變數,以影響科技接受模式中使用者意圖、使用者態度、認知易用性和認知有用性程度,探討這些變數和銀髮族認知學習成效之間的關係,以建構出一個延伸式科技接受模式的模型架構。
本研究針對全國樂齡大學學員進行問卷發放,共蒐集260份有效問卷,利用PLS進行結構方程式分析以調查結果。研究結果發現主觀規範和社會協助對於銀髮族網路學習是關鍵因素,整體而言,除了認知吸收對認知易用性、電腦焦慮對認知有用性無顯著正向影響,個人因素和社會因素對於銀髮族對網路學習之行為意圖皆有正向顯著影響,銀髮族若能對網路學習抱持良好的態度,也能促使其學習的意願,此研究結果將可做為樂齡大學網路學習課程設計的參考依據。
In these years, there is a trend that the average age of the society has become older and older. With the development of web-based learning, the purpose of our study is to discuss the key factors of the motivations and the degree of acceptance of web-based learning for the senior citizens. This research surveys the senior citizens over 50 years old and uses Perceived Enjoyment into technology acceptance model as a theory basis, using individual factors and social factors as external variables. This study collected 260 questionnaires from all the students in senior citizen college. The research model is assessed using partial least squares (PLS) analysis. The result finds that social factors play a key role in influencing on web-based learning. Overall, except for cognitive absorption influence on perceived ease of use and computer anxiety influence on perceived usefulness do not have significantly positive impact, individual factors and social factors have significantly positive impact on web-based learning motivation. If the senior citizens possess a good attitude toward web-based learning, it can encourage the willingness for their studying. This result can be used as a reference when designing the web-based learning course.
中文文獻
1. 行政院研究發展考核委員會(2012) 100年個人家戶數位機會調查報告
2. 行政院研究發展考核委員會(2013) 101年個人家戶數位落差調查報告
3. 內政部社會司 (2009)。老人福利法規。
英文文獻
Agarwal, R., & Karahanna, E. (2000). Time flies when you're having fun: Cognitive absorption and beliefs about information technology usage. Mis Quarterly, 24(4), 665-694.
Agarwal, R., & Prasad, J. (1998). The antecedents and consequents of User perceptions in information technology adoption. Decision Support Systems, 22(1), 15-29.
Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(1), 74-94.
Billipp, S. H. (2001). The Psychosocial Impact of Interactive Computer Use Within a Vulnerable Elderly Population: A Report on a Randomized ProspectiveTrial in a Home Health Care Setting. Public Health Nursing, 18(2), 8.
Bone, P. F. (1991). Identifying mature segments. Journal of Consumer Marketing, 8(4), 19-32.
Brown, I. T. J. (2002). Individual and Technological Factors Affecting Perceived Ease of Use of Web-based Learning Technologies in a Developing Country The Electronic Journal of Information Systems in Developing Countries, 9(5), 16.
CheeWei Phang, J. S., Atreyi Kankanhalli, Yan Li, Bernard C. Y. Tan, and Hock-Hai Teo. (2006). Senior Citizens’ Acceptance of Information Systems:
A Study in the Context of e-Government Services. Ieee Transactions On Egineering Management, 53(4), 15.
Chen, M.-L., Su, Z.-Y., Wu, T.-Y., Shieh, T.-Y., & Chiang, C.-H. (2011). Influence of dentistry students’e-learning satisfaction: A Questionnaire Survey. Journal of medical systems, 35(6), 1595-1603.
Chen, Y. W., & Persson, A. (2002). Internet use among young and older adults: Relation to psychological well-being. Educational Gerontology, 28(9), 731-744.
Cheng, Y. M. (2011). Antecedents and consequences of e-learning acceptance. Information Systems Journal, 21(3), 269-299.
Cheung, R., & Vogel, D. (2013). Predicting user acceptance of collaborative technologies: An extension of the technology acceptance model for e-learning. Computers & Education, 63, 160-175.
Chin, W. W. (1998). The partial least squares approach for structural equation modeling. Erlbaum, NJ: Lawrence Erlbum Associates.
Chu, R. J.-c. (2010). How family support and Internet self-efficacy influence the effects of e-learning among higher aged adults – Analyses of gender and age differences. Computers & Education, 55(1), 255-264.
Clark, R. C., & Mayer, R. E. (2011). E-learning and the science of instruction: Proven guidelines for consumers and designers of multimedia learning: Wiley. com.
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User Acceptance Of Computer-Technology - A Comparison Of 2 Theoretical-Models. Management Science, 35(8), 982-1003.
Fishbein, M. A. I. (1975). Belief, attitude, intention, and behavior : an introduction to theory and research. Reading, Mass.: Addison-Wesley Pub. Co.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of marketing research, 18(1), 39-50.
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.
Hair, J. F., Black, B., Babin, B., Anderson, R. E., & Tatham, R. L. (Eds.). (1998). Multivariate Data Analysis (6th ed). New York: Prentice-Hall International, Inc.
Hair, J. F., Black W.C., Babin, B., Anderson, R. E., & Tatham, R. L. (2010). Multivariate Data Analysis. Upper Saddle River, NJ: Pearson Education.
Hair, J. F., Black, W. C., Babin, B., Anderson, R. E., & Tatham, R. L. (Eds.). (2010). Multivariate Data Analysis. Upper Saddle River: Prentice-Hall International, Inc.
Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2010). Multivariate Data Analysis. Upper Saddle River, NJ: Pearson Education.
Hanson, V. L. (2009). Age and web access: the next generation. Paper presented at the Proceedings of the 2009 International Cross-Disciplinary Conference on Web Accessibililty (W4A).
Hassanzadeh, A., Kanaani, F., & Elahi, S. (2012). A model for measuring e-learning systems success in universities. Expert Systems with Applications, 39(12), 10959-10966.
Hsu, M.-H., & Chiu, C.-M. (2004). Internet self-efficacy and electronic service acceptance. Decision Support Systems, 38(3), 369-381.
Hu, P. J., Chau, P. Y. K., Sheng, O. R. L., & Tam, K. Y. (1999). Examining the technology acceptance model using physician acceptance of telemedicine technology. Journal of Management Information Systems, 16(2), 91-112.
Islam, A. K. M. N. (2013). Investigating e-learning system usage outcomes in the university context. Computers & Education, 69, 387-399.
Jarvenpaa, S. L., Shaw, T. R., & Staples, D. S. (2004). Toward contextualized theories of trust: The role of trust in global virtual teams. Information systems research, 15(3), 250-267.
Johnson, R. D., Hornik, S., & Salas, E. (2008). An empirical examination of factors contributing to the creation of successful e-learning environments. International Journal of Human-Computer Studies, 66(5), 356-369.
Kannan, V. R., & Tan, K. C. (2005). Just in time, total quality management, and supply chain management: Understanding their linkages and impact on business performance. Omega, 33(2), 153-162.
Kelley, C. L., & Charness, N. (1995). Issues in training older adults to use computers. Behaviour & Information Technology, 14(2), 107-120.
Kim, T. G., Lee, J. H., & Law, R. (2008). An empirical examination of the acceptance behaviour of hotel front office systems: An extended technology acceptance model. Tourism Management, 29(3), 500-513.
Lam, J., & Lee, M. (2005, 03-06 Jan. 2005). Bridging the Digital Divide - The Roles of Internet Self-Efficacy towards Learning Computer and the Internet among Elderly in Hong Kong, China. Paper presented at the System Sciences, 2005. HICSS '05. Proceedings of the 38th Annual Hawaii International Conference on.
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, M. K. O., Cheung, C. M. K., & Chen, Z. (2005). Acceptance of Internet-based learning medium: the role of extrinsic and intrinsic motivation. Information & Management, 42(8), 1095-1104.
Lee, W., Xiong, L., & Hu, C. (2012). The effect of Facebook users’ arousal and valence on intention to go to the festival: Applying an extension of the technology acceptance model. International Journal of Hospitality Management, 31(3), 819-827.
Lee, Y.-C. (2006). An empirical investigation into factors influencing the adoption of an e-learning system. Online Information Review, 30(5), 517-541.
Li. (2011). Online social network acceptance: a social perspective. Internet Research, 21(5), 562-580.
Li, D., Browne, G. J., & Wetherbe, J. C. (2006). Why do internet users stick with a specific web site? A relationship perspective. International Journal of Electronic Commerce, 10(4), 105-141.
Liaw, S.-S. (2002). Understanding user perceptions of World-wide web environments. Journal of Computer Assisted Learning, 18(2), 12.
Mei-Liang Chen, T.-E. L., Kuang-Jung Chen and Chumei E. Liu. (2011). A TAM-based study on senior citizens’ digital learning
and user behavioral intention toward use of broadband
network technology services provided via television. African Journal of Business Management, 5(16), 12.
Mizrachi, D., & Shoham, S. (2004). Computer attitudes and library anxiety among undergraduates: a study of Israeli B.Ed students. International Information & Library Review, 36(1), 29-38.
Moon, J.-W., & Kim, Y.-G. (2001). Extending the TAM for a World-Wide-Web context. Information & Management, 38(4), 217-230.
Neuman, W. L. (2012). Social research methods : Quantitative and qualitative approaches. New Delhi: Pearson.
Ong, C.-S., Lai, J.-Y., & Wang, Y.-S. (2004). Factors affecting engineers’ acceptance of asynchronous e-learning systems in high-tech companies. Information & Management, 41(6), 795-804.
Park, Y., Son, H., & Kim, C. (2012). Investigating the determinants of construction professionals' acceptance of web-based training: An extension of the technology acceptance model. Automation in Construction, 22, 377-386.
Pituch, K. A., & Lee, Y.-k. (2006). The influence of system characteristics on e-learning use. Computers & Education, 47(2), 222-244.
Premkumar, G., & Bhattacherjee, A. (2008). Explaining information technology usage: A test of competing models. Omega, 36(1), 64-75.
Purdie, N., & Boulton-Lewis, G. (2003). The learning needs of older adults. Educational Gerontology, 29(2), 129-149.
Qureshi, I., & Compeau, D. (2009). Assessing between-group differences in information systems research: A comparison of covariance-and component-based SEM. MIS quarterly, 33(1), 197.
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.
Rosenberg, M. J. (2001). E-learning: Strategies for delivering knowledge in the digital age (Vol. 9): McGraw-Hill New York.
Saadé, R., & Bahli, B. (2005). The impact of cognitive absorption on perceived usefulness and perceived ease of use in on-line learning: an extension of the technology acceptance model. Information & Management, 42(2), 317-327.
Schepers, J., & Wetzels, M. (2007). A meta-analysis of the technology acceptance model: Investigating subjective norm and moderation effects. Information & Management, 44(1), 90-103.
Stoel, L., & Lee, K. H. (2003). Modeling the effect of experience on student acceptance of Web-based courseware. Internet Research-Electronic Networking Applications and Policy, 13(5), 364-374.
Tan, M., & Teo, T. S. H. (2000). Factors Influencing the Adoption of Internet Banking. Journal of the Association for Information Systems, 1(5), 45.
Tanaka, J. S. (1987). How big is big enough? Sample size and goodness of fit in structural equation models with Latent Variables. Child Development, 58(1), 134-146.
Tayie, S. (2005). Research method and writing research proposals Cairo: Center for Advancement of Postgraduate Studies and Research in Engineering Sciences, Faculty of Engineering-Cairo University (CAPSCU)
Teo, T., & Noyes, J. (2011). An assessment of the influence of perceived enjoyment and attitude on the intention to use technology among pre-service teachers: A structural equation modeling approach. Computers & Education, 57(2), 1645-1653.
Teo, T. S. H., Lim, V. K. G., & Lai, R. Y. C. (1999). Intrinsic and extrinsic motivation in Internet usage. Omega-International Journal of Management Science, 27(1), 25-37.
Thatcher, J. B., Loughry, M. L., Lim, J., & McKnight, D. H. (2007). Internet anxiety: An empirical study of the effects of personality, beliefs, and social support. Information & Management, 44(4), 353-363.
Trentin, G. (2004). E-learning and the third age. Journal of Computer Assisted Learning, 20(1), 21-30.
Tsai, M.-J., & Tsai, C.-C. (2003). Information searching strategies in web-based science learning: the role of internet self-efficacy. Innovations in Education and Teaching International, 40(1), 43-50.
van der Heijden, H. (2004). User acceptance of hedonic information systems. Mis Quarterly, 28(4), 695-704.
Van Raaij, E. M., & Schepers, J. J. (2008). The acceptance and use of a virtual learning environment in China. Computers & Education, 50(3), 838-852.
Wagner, N., Hassanein, K., & Head, M. (2010). Computer use by older adults: A multi-disciplinary review. Computers in Human Behavior, 26(5), 870-882.
Wan, Z. Y., Wang, Y. L., & Haggerty, N. (2008). Why people benefit from e-learning differently: The effects of psychological processes on e-learning outcomes. Information & Management, 45(8), 513-521.
Wang, H., Chung, J. E., Park, N., McLaughlin, M. L., & Fulk, J. (2011). Understanding Online Community Participation: A Technology Acceptance Perspective. Communication Research, 39(6), 781-801.
Wang, H., Chung, J. E., Park, N., McLaughlin, M. L., & Fulk, J. (2012). Understanding Online Community Participation: A Technology Acceptance Perspective. Communication Research, 39(6), 781-801.
Weggen, C. C. (2000). Corporate E-learning: Exploring A New Frontier. WRHAMBRECHT+ CO Equity Research.
Wu, J., & Liu, D. (2007a). The effects of trust and enjoyment on intention to play online games. Journal of electronic commerce research, 8(2), 128-140.
Wu, J., & Liu, D. (2007b). THE EFFECTS OF TRUST AND ENJOYMENT ON INTENTION TO PLAY ONLINE GAMES. Journal of electronic commerce research, 8(2).
Wu, Y.-T., & Tsai, C.-C. (2006). University Students' Internet Attitudes and Internet Self-Efficacy: AStudy at Three Universities in Taiwan. CyberPsychology & Behavior, 9(4), 10.
Yang, K., & Jolly, L. D. (2008). Age cohort analysis in adoption of mobile data services: gen Xers versus baby boomers. Journal of Consumer Marketing, 25(5), 272-280.
Yeung, P., & Jordan, E. (2007). The continued usage of business e-learning courses in Hong Kong corporations. Education and Information Technologies, 12(3), 175-188.
Yi, M. Y., & Hwang, Y. (2003). Predicting the use of web-based information systems: self-efficacy, enjoyment, learning goal orientation, and the technology acceptance model. International Journal of Human-Computer Studies, 59(4), 431-449.
Yoo, S. J., Han, S.-h., & Huang, W. (2012). The roles of intrinsic motivators and extrinsic motivators in promoting e-learning in the workplace: A case from South Korea. Computers in Human Behavior, 28(3), 942-950.
參考網站
1. 內政部統計處 http://www.moi.gov.tw/stat/index.aspx
2. 台灣網路資訊中心http://www.twnic.net.tw/index4.php
校內:2024-12-31公開