臺灣博碩士論文加值系統

在知識經濟時代，世界各國政府無不致力於推動公務人員的終身學習以提升政府的人力素質，而推動公部門的數位學習更是其中一個重點項目。因此，瞭解公務人員對於數位學習的使用意向與行為之影響因素有其必要性。本研究以整合型科技接受使用理論(UTAUT)為基礎，採用理論中的「績效預期」、「易用預期」、「社會影響」及「協助情況」四個構面，再另外納入了影響組織學習的「時間情況」構面來探討公務人員數位學習使用意向與行為的影響因子。此外，四個調節變項(性別、年齡、經驗及自願性)對於各因子影響路徑的調節效果亦在本研究中進行驗證。
本研究發展了一份結構化問卷並針對台灣公務人員進行隨機抽樣調查，計回收有效問卷600份。研究使用了變異數分析(ANOVA)、結構方程模式(SEM)及調節作用廻歸分析等方法進行資料分析並驗證研究假設的正確性，最後提出一個公務人員數位學習使用意向與行為之影響因素的概念模型。
研究結果顯示「績效預期」、「易用預期」、「社會影響」及「協助情況」對於公務人員數位學習的使用意向有直接正向的影響，而其中以「績效預期」的影響性最大；結果亦顯示「行為意向」、「時間情況」及「協助情況」對於公務人員數位學習的實際使用行為有直接正向的影響，而其中又以「行為意向」及「時間情況」為最重要的影響因子。在調節變項方面，結果指出「績效預期」、「易用預期」、「社會影響」及「時間情況」對於數位學習使用意向的影響效果在非自願使用數位學習者較強；而「協助情況」和「時間情況」對數位學習實際使用行為的影響效果則在數位學習經驗較少者較強。特別的是，「社會影響」對於數位學習使用意向的影響效果在男性公務員較強，這和先前相關的研究有所不同。

In the age of knowledge economy, governments around the world have been actively promoting lifelong learning for public servants to enhance the quality of government manpower, and e-learning is an important policy within that. Therefore, understanding the factors that affect public servants’ acceptance and behavior toward e-learning is very important. In this study, we investigated possible affecting factors based on the Unified Theory of Acceptance and Use of Technology (UTAUT). Four core determinants of intention and usage in UTAUT (i.e., performance expectancy, effort expectancy, social influence and facilitating conditions) and time condition of organization learning climate were examined. Besides, time condition of organization learning climate was also considered to investigate the affecting factors.
A structured online questionnaire was developed to collect data from randomly chosen public servants in Taiwan. There were 600 effective questionnaires. In order to test the adaptability of the hypothetical model, we used ANOVA, structural equation modelling (SEM) and moderated regression analysis approach to analyze the data. Finally, this study proposed a conceptual model for understanding the factors of public servants’ intention and behavior toward e-learning.
The results show that performance expectancy, effort expectancy, social influence and facilitating conditions have a direct positive impact on public servants’ intention toward e-learning. Among these constructs, performance expectancy has the largest influence. The results also show that behavioral intention, time condition and facilitating conditions have a direct positive influence on public servants’ e-learning usage. Among these constructs, behavioral intention and time condition are the most important two factors. In the moderator analysis, the results indicate that the influences of performance expectancy, effort expectancy, social influence and time condition on e-learning intention are stronger for involuntary users. The influence of facilitating conditions and time condition on e-learning usage are stronger with limited experience users. Specially, the influence of social influence on public servants’ e-learning intention is stronger for male that is different from previous research.

中文摘要 I
英文摘要 III
目 錄 V
表 目 錄 VII
圖 目 錄 IX
第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 2
1.3 研究範圍與對象 3
1.4 研究流程 3
第二章 文獻探討 5
2.1 國內公務人員數位學習推動現況 5
2.2 資訊科技接受相關模型 7
2.2.1 理性行動理論(TRA) 7
2.2.2 創新擴散理論(IDT) 8
2.2.3 計畫行為理論(TPB) 9
2.2.4 社會認知理論(SCT) 10
2.2.5 科技接受模式(TAM) 11
2.2.6 個人電腦使用模式(MPCU) 13
2.2.7 動機模式(MM) 14
2.2.8 結合科技接受模式與計畫行為理論(C-TAM-TPB) 15
2.3 整合型科技接受使用理論(UTAUT) 16
第三章 研究方法 19
3.1 研究架構 19
3.2 研究假設推導 20
3.3 研究變項定義與衡量 24
3.4 問卷設計 28
3.5 問卷前測 29
3.6 調查方法 33
3.7 資料分析方法概述 34
第四章 資料分析與討論 37
4.1 敘述性統計分析 37
4.1.1 樣本結構 37
4.1.2 敘述性統計 38
4.2 信度效度分析 43
4.2.1 信度分析 43
4.2.2 驗證性因素分析 43
4.3 T檢定與單因子變異數分析 46
4.4 SEM路徑分析 55
4.4.1 原始模型路徑分析 55
4.4.2 調整後模型路徑分析 58
4.5 調節變項的影響 60
第五章 結論與建議 68
5.1 結論與建議 68
5.2 研究貢獻 74
5.3 研究限制 75
5.4 後續研究建議 76
參考文獻 77
附錄 線上問卷 82

1.邱皓政(2006)，量化研究與統計分析(第三版)，台北：五南。
2.吳三靈(2006)，「推動公務人員數位學習之現況與展望」，研習論壇月刊，71期，第99-113頁。
3.吳明隆(2006)，SPSS統計應用學習實務--問卷分析與應用統計(第三版)，台北：知城。
4.吳明隆、涂金堂(2006)，SPSS與統計應用分析修訂版，台北：五南。
5.吳明隆(2007)，結構方程模式AMOS的操作與應用，台北：五南。
6.周均育(2008)，「地方行政研習e學中心之數位學習成效」，人事月刊，270期，第36-41頁。
7.范祥偉(2008)，「運用數位學習推動錦囊構築公部門數位學習新視界」，人事月刊，277期，第33-37頁。
8.經濟部工業局(2006)，「重點產業數位學習導入現況調查分析報告」，2008年10月16日取自http://www.epark.org.tw/achievements/term/107/20080421/54。
9.趙美聲(2005)，「國內公務人員數位學習需求調查及具體推動策略與步驟」，行政院人事行政局委託研究案。
10.Ajzen, I. (1985). From Intentions to Actions: A Theory of Planned Behavior. In J. Kuhl & J. Beckman(Eds.), Action Control: From Cognition to Behavior, Heidelberg: Springer.
11.Ajzen, I. and Madden, T. J. (1986). Prediction of Goal-directed Behavior: Attitudes, Intentions, and Perceived Behavioral Control. Journal of Experimental Social Psychology, 22, 453-474.
12.Ajzen, I. (1989). Attitude Structure and Behavior. In A. R. Pratkanis, S. J. Breckler and A. G. Greenwald(Eds.), Attitude Structure and Function(pp.241-274). Lawrence Erlbaum Associates, Hillsdale, NJ.
13.Ajzen, I. (1991). The Theory of Planned Behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211.
14.Bandura, A. (1986). Social Fundations of Thought and Action: A Social Cognitive Theory. Englewood, Cliffs NJ: Prentice-Hall.
15.Bentler, P. M. (1992). EQS structural equations program manual, Los Angeles: BMDP.
16.Bollen, K. A. (1989). Structural equations with latent variables, New York: Wiley.
17.Bryman, A., Cramer, D. (1997). Quantitative Data Analysis with SPSS for Windows, London: Routledge.
18.Carter, V. (1996). Do media influence learning. Revisiting the debate in the context of distance education. Open Learning, 11(1), 31-40.
19.Chang, M.K. (1998). Prediciting Unethical Behavior: A Comparison of the Theory of Reasoned Action and the Theory of Planned Behavior. Journal of Business Ethics, 17(16), 1825-1834.
20.Compearu, D. R. and Higgins, C. A. (1995). Computer Self-Efficacy: Development of a Measure and Initial Test. MIS Quarterly, 19(2), 189-211.
21.Cronbach, L.J. (1987). Statistical tests for moderator variables: Flaws in analyses recently proposed. Psychological Bulletin, 102, 414-417.
22.Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13(3), 319-339.
23.Davis, F. D., Bagozzi, R. P. and Warshaw, P. R. (1989). User Acceptance of Computer Technology: A Comparison of Two Theoretical Models. Management Science, 35(8), 982-1002.
24.Davis, F. D., Bagozzi, R. P. and Warshaw, P. R. (1992). Extrinsic and Intrinsic Motivation to Use Computers in the Workplace. Journal of Applied Social Psychology, 22(14), 1111-1132.
25.Davis, F. D., Venkatesh, V. (1996). A critical assessment of potential measurement biases in the technology acceptance model: three experiments. International Journal of Human-Computer Studies, 45, 19-45.
26.Fishbein, M., Ajzen, I. (1975). Belief, Attitude, Intentions and Behavior: An Introduction to Theory and Research. Addison-Wesley, Reading, Ma.
27.Fornell, C., and Larcker, D. F. (1981). Evaluating Structural Equations with Unobservable Variables and Measurement Error. Journal of Marketing Research, 18, 39-50.
28.Harrison, D. A., Mykytyn, P. P., Riemenschneider, C. K. (1997). Executive 108 Decisions about Adoption of Information Technology in Small Business: Theory and Empirical Tests. Information Systems Research, 8(2), 171-195.
29.Hartwick, J. and Barki, H. (1994). Explaining the Role of User Participation in Information System Use. Management Science, 40(4), 400-465.
30.Hsu, M.H. & Kuo, F.Y. (2002). An Investigation of Volitional Control in Information Ethics. Behaviour and Information Technology, 1-10.
31.Kleinbaum, D.G., Kupper, L.L. and Muller K.E. (1988). Applied Regression Analysis and Other Multivariable Methods(2nd ed.), Boston: PWS-KENT.
32.Mikkelsen, A., Gronhaug, K. (1999). Measuring organizational learning climate: A cross-national replication and instrument validation study among public sector employees. Review of Public Personnel Administration, 19 (4), 31-44.
33.Miller, J. B. (1976). Toward a New Psychology of Women, Beacon Press, Boston.
34.Moore, G. C. and Benbasat, I. (1991). Development of an Instrument to Measure the Perceptions of Adopting an Information Technology Innovation. Information Systems Research, 2(3), 192-222.
35.Morris, M. G. and Venkatesh, V. (2000). Age Differencesin Technology Adoption Decisions: Implications for a Changing Workforce. Personnel Psychology, 53(2), 375-403.
36.Nunnally, J. (1978). Psychometric Theory, 2nd Editon. New York: McGraw-Hill.
37.Parker, A. (1999). A study of variables that predict dropout from distance education. International Journal of Educational Technology, 1(2), 1-12.
38.Parker, A. (2003). Identifying predictors of academic persistence in distance education. USDLA Journal, 17(1), 55-62.
39.Rogers, E. M. (1983). Diffusion of Innovations 3rd. New York Free Press.
40.Ryu, S. Ho, S.H., & Han, I. (2003). Knowledge Sharing Behavior of Physicians in Hospitals. Expert Systems with Applications, 25, 113-122.
41.Taylor, S. and Todd, P. A. (1995a). Assessing IT Usage:The Role of Prior Experience. MIS Quarterly, 19(2), 561-570.
42.Taylor, S. and Todd, P. A. (1995b). Understanding Information Technology Usage: a Test of Competing Models. Information Systems Research, 6(2), 144-176.
43.Triandis, H. C. (1977). Interpersonal Behavior. Monterey, CA: Brooke/Cole.
44.The Economist Intelligence Unit (2004). The 2003 e-learning readiness rankings. The Economist Intelligence Unit Limited and IBM Corporation.
45.Thompson, R. L., Higgins, C. A. and Howell, J. M. (1991). Personal Computing: Toward a Conceptual Model of Utilization. MIS Quarterly, 15(1), 124-143.
46.Thompson, R. L., Higgins, C. A. and Howell, J. M. (1994). Influence of Experience on Personal Computer Utilization: Testing a Conceptual Model. Journal of Management Information Systems, 11(1), 167-187.
47.Vallerand, R. J. (1997). Toward a Hierarchical Model of Intrinsic and Extrinsic Motivation. Advances in Experimental Social Psychology, 29, 271-360.
48.Venkatesh, V. and Davis, F. D. (2000). A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies. Management Science, 45(2), 186-204.
49.Venkatesh, V. and Morris, M. G. (2000). Why Don’t MenEver Stop to Ask For Directions? Gender, Social Influence, and Their Role in Technology Acceptance and Usage Behavior. MIS Quarterly, 24(1), 115-139.
50.Venkatesh, V., Morris, M.G., Davis, G.B., and Davis, F.D. (2003). User acceptance of information technology: toward a unified view. MIS Quarterly, 27(3), 425-478.
51.Xenos, M. (2004). Prediction and assessment of student behavior in open and distance education in computers using Bayesian networks. Computers & Education, 43(4), 345-359.