簡易檢索 / 詳目顯示

研究生: 黃議正
論文名稱: 以認知負荷、科技接受模式與計畫行為理論取向建構線上學習行為傾向模式之研究
A Study of Constructing an On-line Learning Behavioral Intention Model based on Cognitive Load, Technology Acceptance and Planned Behavior Theory
指導教授: 莊謙本
Chuang, Chien-Pen
學位類別: 博士
Doctor
系所名稱: 工業教育學系
Department of Industrial Education
論文出版年: 2010
畢業學年度: 99
語文別: 中文
論文頁數: 409
中文關鍵詞: 認知負荷論科技接受模式計畫行為理論線上學習結構方程
英文關鍵詞: Cognitive Load Theory (CLT), Technology Acceptance Model (TAM), Theory of Planned Behavior (TPB), On-line Learning, Structural Equation Model (SEM)
論文種類: 學術論文
相關次數: 點閱:96下載:51
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
  • 本研究旨在依據認知負荷論、科技接受模式與計畫行為等理論,建構線上學習科技接受行為傾向的模型,以能較完整的解釋線上學習者的科技應用行為。因此,所探討的範圍包括線上學習者的心理特徵、知覺、態度與行為傾向之間的關係。並將本研究將所建構的實證模型(C-TAM-TPB)與科技接受模式(TAM)、計畫行為理論(TPB)進行比較。
    本研究對象為台灣知識庫數位學堂線上學習系統(TKB e-learning center)的使用者,問卷調查共分兩個階段而以層級隨機抽樣實施。第一階段共抽樣650人,有效樣本為381人,供進行模型信度與效度之驗證;第二階段共抽樣900人,有效樣本為850人,供線上學習科技接受行為傾向模型的再驗,以驗證本研究最終模型的外部性推論效度。研究工具依照理論發展「線上學習科技接受行為傾向問卷」,經過為期八週的問卷調查後,以結構方程(SEM)進行理論模式的驗證,並以單尾單一樣本t檢定、獨立樣本t檢定、單因子變異數分析進行線上學習的心理特徵與差異分析。研究結果發現重點如下:
    一、消費性線上學習的心理特徵感受程度均達同意(滿意)水準之上,並且採用線上學習的實際行為亦達水準之上。
    二、不同人口統計變項和使用線上學習動機因素在消費性線上學習的心理特徵有顯著差異。
    三、消費性線上學習的資訊品質和使用者的電腦自我效能二個因素對認知負荷無顯著性影響,而消費性線上學習的系統品質經由認知負荷影響線上學習行為傾向。
    四、消費性線上學習的易用性知覺和有用性知覺兩個因子均正向顯著性影響使用態度且間接影響線上學習的行為傾向。
    五、消費性線上學習的主觀規範和行為控制知覺兩個因子均正向顯著性影響使用態度且間接影響線上學習的行為傾向。
    六、影響消費性線上學習行為傾向的主要因素依序為態度、行為控制知覺、主觀規範、易用性知覺和有用性知覺。而系統品質、資訊品質和電腦自我效能並非主要影響因素。
    七、本研究C-TAM-TPB模型適配度良好,並且在行為傾向有高度的解釋力。同時本研究C-TAM-TPB提供在消費性線上學習影響因素提供全貌觀點。
    八、本研究C-TAM-TPB模型在消費性線上學習的易用性、有用性、使用態度和行為傾向等解釋力高於TAM模型。而在行為傾向解釋力卻略低TPB模式,因TPB模式未涵概科技變項。

    The purpose of this study is aimed to verify the relationship among learner’s behavior intention of on-line learning and practical usage behavior through theoretical model to establish and examine the conceptual research model named “C-TAM-TPB model” developed by researcher. The conceptual research model consisted of Cognitive Load (CL), Technology Acceptance Model (TAM), and the Theory of Planed Behavior (TPB), yet the conceptual research model attempted to delineate the pattern of on-line learner’s behavior of technolog application. The research scope involved in the relationships among on-line learner’s psychological characteristics, perception, attitude, and behavior intention. Moreover, researcher further compared conceptual research model, Technology Acceptance Model and Theory of Planed Behavior Model.
    A two-step stratified random sampling approach was employed for sample selection. Learners from Taiwan Knowledge Base (TKB) e-Learning Center were random selected as research sample. 381 valid samples from 650 learners were used to verify the reliability and validity of conceptual research model at first step. The conceptual research model had been reverified through 850 valid samples from 900 learners at second step to demonstrate the external validity of C-TAM-TPB model. A survey questionnaire was developed on a theoretical base named “On-line Learning Technology Acceptance Behavior Intention Questionnaire”. Eight weeks period survey was conducted by Taiwan Knowledge Base e-Learning Center branch offices from May to July 2010. Structural equation modeling approach was applied to verify the conceptual research model. In addition, a multi-method was applied to analyze research data that included descriptive statistic, one-tail one sample t-test, independent sample t-test, and one-way ANOVA. Research findings are as:
    1.The perception of consumer on-line learning psychological characteristic was rated upon satisfaction; yet, the practical on-line learning behavior was also reached significant level.
    2.Significant discrepancies appeared on different demographic variables, motivation of on-line learning usage, and consumer on-line learning psychological characteristics.
    3.On one hand, learner’s consumer on-line learning information quality perception and computer self-efficacy will not impact their cognitive load, on the other hand, learner’s consumer on-line learning system quality perception will impact their on-line learning behavior through their cognitive load.
    4.The learner’s ease of use perception and usefulness perception of consumer on-line learning were positive affect their usage attitude directly, and the intention of on-line learning behavior indirectly.
    5.The user’s consumer on-line learning subjective norm and behavior control perception positively affect their usage attitude directly, and the on-line learning behavior indirectly.
    6.The impact factors of learner’s consumer on-line learning behavior intention are attitude, behavior control perception, subject norm, ease of use perception, and usefulness perception. However, the minor impact factors were learner’s system quality perception, information quality perception, and computer self-efficacy.
    7.The C-TAM-TPB model (conceptual research model) is good fit the data with strong variance explanation to behavior intention. Meanwhile, The C-TAM-TPB model create a new perspective regarding to the impact factors of learner’s consumer on-line learning.
    8.The C-TAM-TPB model has stronger variance explanation capability than TAM on learner’s consumer on-line learning perception of ease use, usefulness, usage attitude, and behavior intention, nonetheless, the variance explanation capability slight lower than TPB.
    Avenues for examining and improving the C-TAM-TPB model (conceptual research model) in the context of these findings are discussed at the conclusion of this dissertation.

    謝誌 I 摘要 III 目錄 VII 表目錄 XII 圖目錄 XVII 第一章 緒論 1 第一節 研究背景與動機 1 第二節 研究目的 5 第三節 研究問題 6 第四節 研究範圍與限制 9 壹、研究範圍 9 貳、研究限制 10 第五節 名詞釋義 11 壹、系統品質知覺(perceived system quality, PSQ) 11 貳、資訊品質知覺(perceived information quality, PIQ) 11 参、電腦自我效能(computer self-efficiency, CSE) 12 肆、認知負荷(cognitive load, CL) 12 伍、有用性知覺(perceived usefulness, PU) 13 陸、易用性知覺(perceived ease of use, PEOU) 13 柒、態度(attitude, ATT) 14 捌、主觀規範(subjective norms, SN) 14 玖、行為控制知覺(perceived behavior control, PBC) 15 拾、行為傾向(behavior intention, BI) 15 拾壹、實際行為(actual behavior, B) 16 第二章 文獻探討 17 第一節 計畫行為理論之探討 17 壹、計畫行為的立論基礎 18 貳、計畫行為理論的源起 20 参、計畫行為理論構面的探討 22 一、信念(belief) 23 二、態度(attitude, ATT) 24 三、主觀規範(subjective norms, SN) 25 四、行為控制知覺 (perceived behavior control, PBC) 26 五、行為傾向(BI) 27 六、實際行為(actual behavior;B) 28 第二節 科技接受模式之探討 29 壹、科技接受模式的緣起 29 貳、科技接受模式構面的探討 32 一、易用性知覺(perceived ease of use, PEOU) 32 二、有用性知覺(perceived usefulness, PU) 32 參、科技接受模式理論維度的擴展 33 一、第二代科技接受模式(TAM2) 33 二、擴張式TAM模型(Extending TAM, E-TAM)) 34 三、整合型科技模式(UTAUT) 36 肆、科技接受模式之外生變數探討 40 伍、科技接受模式相關研究 46 陸、本節小結 47 第三節 認知負荷理論之探討 49 壹、認知負荷論的立論基礎 49 一、認知工具取向(instrument approach) 50 二、認知類比取向(analogy approach) 51 貳、認知負荷理論與應用 59 一、認知負荷論起源 59 二、認知負荷定義 59 三、認知負荷特性 61 參、認知負荷理論架構 63 一、網路多媒體教學的認知負荷架構 63 二、後設認知負荷架構 64 肆、認知負荷來源類型 65 一、Pass和Van Merrienboer認知負荷因果評估架構 65 二、Marcus認知負荷來源要素架構 66 三、Sweller認知負荷來源要素架構 67 伍、認知負荷論在資訊科技上的應用 71 一、資訊系統成功模式(information successful model, ISM ) 71 二、網站資訊品質(information quality) 77 第三章 研究設計 89 第一節 研究架構 89 第二節 研究方法 91 壹、調查研究法(survey method) 91 貳、訪談法(interview method) 91 第三節 研究母群與樣本 92 壹、第一階段抽樣樣本(測試樣本) 94 貳、第二階段抽樣樣本(再驗樣本) 100 第四節 研究工具 105 壹、「線上學習科技接受行為傾向」問卷試題發展 105 貳、「線上學習科技接受行為傾向」問卷信度與效度分析 115 參、「線上學習科技接受行為傾向」測量模式之結論 147 第五節 研究步驟 151 第六節 研究統計 154 壹、描述性統計 154 貳、相關性分析 154 参、結構方程模式(Structural Equation Modeling, SEM) 154 第四章 線上學習科技接受行為傾向模型建構與驗證 163 第一節 模型建構與研究假設 163 第二節 線上學習科技接受行為傾向模型驗證 169 壹、模型適配度檢定 170 貳、模型路徑檢定 175 参、模型整體效果檢定 178 肆、線上學習科技接受行為傾向模型綜合討論 189 第三節 線上學習行為傾向再驗檢定與總體檢定 192 第四節 本章小結 194 第五章 資料統計分析 201 第一節 線上學習科技接受行為傾向心理特徵分析 201 第二節 學習者在線上學習科技接受行為傾向差異分析 225 第三節 線上學習科技接受行為傾向模式比較分析 307 壹、TAM與TPB結構模型適配檢定 307 貳、C-TAM-TPB、TAM與TPB模型路徑比較分析 320 参、C-TAM-TPB、TAM與TPB模型解釋量比較 324 肆、本節小結 327 第六章 結論與建議 329 第一節 結論 329 第二節 建議 335 壹、在管理實務方面 335 貳、在研究對象方面 337 参、在研究方法方面 338 肆、在研究脈絡方面 338 伍、在施測方式的改進 339 參考文獻 341 附錄1 線上學習科技接受行為傾向原始問卷 379 附錄2 線上學習科技接受行為傾向專家意見表 385 附錄3 「線上學習科技接受行為傾向」問卷 395 附錄4 台灣知識庫數位學堂簡介 403

    一、中文部分
    朱文禎、陳哲賢 (2007)。探討虛擬社群之知識分享行為:以線上遊戲為例。電子商務研究,5(1),55-80。
    朱斌妤、黃仟文、翁少白(2008)。以科技接受模式探討即時交通資訊系統之使用意願。電子商務學報,10(1),173-200。
    余泰魁、吳桂森、李能慧(2005)。我國技職體系學生MP3使用行為模式之研究。資訊管理學報(TSSCI),12(3),189-222。
    吳亞馨、朱素玥、方文昌(2008)。網路購物信任與科技接受模式之實證研究。資訊管理學報,15(1),123-152。
    吳明隆(2007)。結構方程模式AMOS的操作與應用。台北:五南。
    吳為聖、張惠博、郭重吉(2007)。影響國中自然科教師接受資訊科技融入教學之個人因素研究。科學教育學刊,15(5),543-563。
    吳美美(2004)。數位學習現況與未來發展。圖書館與資訊科學,30(2),92-106。
    吳錦波、林佳蓉(2008)。使用者接受商業智慧系統之研究。Electronic Commerce Studies ,6(3),353-376。
    宋曜廷(2000)。先前知識文章結構和多媒體呈現對文章學習的影響。國立臺灣師範大學教育心理與輔導研究所博士論文。
    李能慧、古東源、吳桂森、余泰魁(2004)。金門觀光客行為傾向模式之建構。管理學報,21(1),131-151。
    周家慧(2006)。以DeLone & McLean 模式探討入口網站成功之影響因素。資訊管理展望,8(1),109-131。
    林文寶、楊淑斐(2005)。影響線上學習市場使用意向模式建構之研究 - 模糊類神經網路方法之應用。中山管理評論,13(2),721-748。
    林政坤、曹文瑜、劉宜菁、楊惠貞(2007)。影響技專院校學生創新採用相關因素之研究-以即時通為例。勤益學報,25(2)。
    林益民、余泰魁(2003)。線上學習行為傾向模式建構與實證。資訊管理學報,10(1),205-232。
    林益民、邱郁文、施東河(2005)。線上學習使用傾向對立模式之比較實證。臺大管理論叢,16(1),41-66。
    邱郁文、林益民、施東河(2007)。系統特性、任務特性與電腦自我效能對個人線上學習行為傾向影響。電子商務學報,9(2),235-266。
    邱皓政(2002)。量化研究與統計分析。台北:五南。
    邱皓政(2004):結構方程模式-LISREL 的理論、技術與應用。台北市:雙葉書廊有限公司
    姚開屏、陳坤虎(1998)。如何編製一份問卷以「健康相同生活品質」為例。職能治療學會雜誌,16,1-23。
    洪新原、梁定澎、張嘉銘(2005)。科技接受模式之彙總研究。資訊管理學報,12(4),211-234。
    國科會(2008)。2008數位學習白皮書。臺北市:工業局,國科會,數位典藏學習型科技計畫辦公室。
    張紹勳(2001)。多變量統計分析。台北:松崗。
    張鴻昌(2004)。員工對企業內部網路接受度之研究-以中鋼為例。國立中山大學企業管理學系碩士論文,未出版,高雄市。
    莊謙本、黃議正、張德正、許碧珊(2007)。認知負荷理論對技職教育適性教學理念之啟示。2007年技職教育永續發展學術研討會,2007/06/06,台北科技大學。
    莊謙本、黃議正、張德正、許碧珊(2007)。認知負荷量表之設計-高職電子學為例。認知負荷:理論與實務國際研討會,2007/10/31, 佛光大學。
    許文楷、黃秀慧、陳榮方(2006)。企業員工對新導入資訊科技之學習態度研究-以ERP系統之使用者為例。教育心理學報,38(1),19-36。
    許孟琪、蔡明昌(2009)。國小教師教育信念及其生命態度關係之探討。教育心理學報(TSSCI)。
    許麗玲、徐村和、吳憲政(2009)。影響部落格使用意向的前置因素。電子商務期刊,11(1),1-28。
    郭英峰、游景文(2007)。消費者採用行動加值服務行為意向之研究—以年輕族群為例。資訊管理學報,14(3),125-135。
    陳文廣(2003)。影響使用者接受知識社群分享知識因素之研究。輔仁大學資訊管理研究所碩士論文,未出版,台北縣。
    陳建文、馮朝進、吳姿樺(2009)。影響國中小教師線上學習滿意度因素之研究:以彰化縣K12數位學校爲例。臺中教育大學學報,23(2),29-50。
    陳禹辰、尚榮安、何照義、謝素娟(2007)。公用服務事業員工的e化科技接受意圖:以TAM與TTF探討。電子商務學報,10(1),305-327。
    陳密桃(2003)。認知負荷理論及其對教學的啟示。國立高雄師範大學教育系教育學刊,21,29-51。
    陳銘薰、許國賓(2007)。非同步電子化學習情境對學習知覺與使用態度傾向之影響。人力資源管理學報,7(3),25-44。
    陳羅傑、陳凱凌(2008)。以科技接受模型探討矯正機構遠距接見系統接受度之研究。績效與策略研究,5(2),17-32。
    粟四維、莊友豪(2009)。Wiki使用者與使用行為之研究。電子商務學報,11(1),185-212。
    黃 騰、蔡今中、陳國棟 (2007)。台灣數位教育之現況與展望。2007全國計算機會議研討會,2007/12,中華民國電腦學會。
    黃巧琪(2004)。認知負合理論及其在教學上的啟示。教育資料與研究,61,77-83。
    黃克文(1996)。認知負荷與個人特質及學習成就之關聯。國立台北師範學院國民教育研究所碩士論文。
    黃芳銘(2004)。結構方程模式:理論與應用(修訂版)。台北市:五南圖書公司。
    黃柏勳(2004)。課程與教學的研究新取向:認知負荷論。中等教育,55(6),128-138。
    楊惠合(2004)。以科技接受模型探討數位學習滿意度之研究。大葉大學資訊管理學研究所碩士論文。
    經濟部工業局(2008)。2008台灣數位內容產業年鑑。
    經濟部工業局(2009)。2009台灣數位內容產業年鑑。
    葉美春、阮明淑(2007)。使用者採用知識管理系統之影響因素研究—理論模型的比較取向。圖書資訊學刊,5(1/2),69-90。
    蔡今中(2010)。數位學習研究議題發展趨勢。2010/08/15,取自http:// www.elcl-teldap.org
    二、英文部分
    Adams, D. A., Nelson, R. R., & Todd, P. A. (1992). Perceived usefulness, ease of use, and usage of information technology: a replication. MIS Quarterly, 16(2), 227-247.
    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. (1997a). Facilitating COBOL Programmers Transition To The C Language, San Francisco, California, United States: ACM, 117-126.
    Agarwal, R., & Prasad, J. (1997b). The Role Of Innovation Characteristics And Perceived Voluntariness In The Acceptance Of Information Technologies, Decision Sciences, 28(3),.557-582.
    Agarwal, R., & Prasad, J. (1997c). Targeting COBOL Programmers for C Training: The Role Of Job Insecurity And Organizational Tenure, Journal of Systems and Software, 37(1), 5-17.
    Agarwal, R., & Prasad, J. (1998a). A conceptual and opereasoned definition of personal innovativeness in the domain of information technology, Information Systems Research, 9(2), .204-215.
    Agarwal, R., & Prasad, J. (1998b). The antecedents and consequents of user perceptions in information technology adoption, Decision Support Systems, 22(1),.15-29.
    Agarwal, R., & Prasad, J. (1999). Are Individual Differences Germane To The Acceptance Of New Information Technologies? , Decision Sciences, 30(2), 361-391.
    Agarwal, R., & Venkatesh, V. (2002). Assessing a Firm's Web Presence: A Heuristic Evaluation Procedure for the Measurement of Usability.Information Systems Research, 13(2), 168-186.
    AI-Khaldi, M. A., & Wallace, R. S. O. (1999). The Influence of Attitudes on Personal Computer Utilization among Knowledge Workers: The Case of Saudi Arabia. Information and Management, 36(4), 185-204.
    Ajjan, H., & Hartshorne, R. (2008). Investigating faculty decisions to adopt Web 2.0 technologies: Theory and empirical tests. The Internet and Higher Education, 11(2),.71-80.
    Ajzen, I. (1985). From Intention to actions: A theory of planned behavior. In J. Kuhl and J. Beckman(Eds.), Actions-control : From cognition to behavior, Heidelberg, 11-39.
    Ajzen, I. (1989). Attitude, Personality, and Behavior, Milton Keynes: Open University Press.
    Ajzen, I. (1991). The Theory of Planned Behavior, Organizational Behavior and Human Decision Processes, 50, 179-211.
    Ajzen, I. (2002). Constructing a TPB Questionnaire: Conceptual and Methodological-Considerations. Retrieved Feb 26, 2010 from http://www-unix.oit.umass.edu/~aizen/pdf/tpb.measurement.pdf.
    Ajzen, I., & Fishbein, M. (1980). Understanding Attitudes and Predicting Social Behavior, Engelwood Cliffs, N J: Prentice-Hall.
    Ajzen, I., Timko, C. & White, J. B. (1982), Self-monitoring and the attitude-behavior relation, Journal of Personality and Social Psychology, 42(3), 426-435.
    Alexander, J. E., & Tate, M. A., (1999) Web wisdom: How to evaluate and create information quality on the web. Mahwah, NJ: Erlbaum.
    Anderson, J. C., & Gerbing, D.W. (1988). Stuctural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin,103(3),411-423.
    ARMITAGE, C. J., & Conner, M. (2001). Efficacy of the theory of planned behaviour: A meta-analytic review. British Journal of Social Psychology, 40, 471-499.
    Artino, A. R., Jr. (2008). Cognitive load theory and the role of learner experience: An abbreviated review for educational practitioners. AACE Journal, 16(4),.425-439.
    Atkinson, M. A., & Kydd, C.(1997). Individual characteristics associated with World Wide Web use: an empirical study of playfulness and motivation. The DATA BASE for Advances in Information Systems, .28 (2), 53-62.
    Baddeley, A. (1992). Working Memory, Science,.255, 556-559.
    Bagozzi, P. R. (2007) The Legacy of the Technology Acceptance Model and a Proposal for A Paradigm Shift, Journal of the Association for Information Systems,.8(4),.244-254.
    Bagozzi, R. P., & Yi, Y. (1988). On the Evaluation for Structural Equation Models. Journal of the Academy of Marketing Science, 16, 74-94.
    Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models, Journal of the Academy of Marketing Science,16(1),74-94.
    Bagozzi, R. P., Yi, Y., & Phillips, L. W. (1991). Assessing construct validity in organizational research. Administrative Science Quarterly, 36(3), 421-458.
    Bailey, J. E., & Pearson S.W. (1983). Development of a tool for measuring and analyzing computer user satisfaction, Management Science, 29(5), 530-545.
    Bajaj, A. & Nidumolu, S. R. (1998). A feedback model to understand information system usage, Information and Management, 33(4), .213-224.
    Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84, 191-215.
    Bandura, A. (1986). Self-Efficacy Mechanism in Human Agency, American Psychologist,. 37(2),. 122-147
    Bandura, A., Adams, N. E., & Beyer, J. (1977). Cognitive processes mediating behavioral change. Journal of Personality and Social Psychology, 35, 125-139.
    Bartlett, F. C. (1932). Remembering: A study in experimental and social psychology. Cambridge, UK: Cambridge University Press.
    Beck, L., & Ajzen, I. (1991). Predicting Dishonest Actions using the Theory of Planned Behavior. Journal of Research in Personality, 25, 285-301.
    Bem, D. J. (1970). Beliefs, Attitudes, and Human affairs. Belmont, CA: Brooks Cole.
    Bentler, P. M. (1988). Theory and Imlementation of EQS: A Structural Equations Program, Los Angeles, California: BMDP Statistical Software.
    Bentler, P. M., & Bonett, D. G. (1980). Significance tests and goodness-of-fit in the analysis of covariance structures. Psychological Bulletin, 88, 588-600.
    Bernadette Szajna (1996). Empirical Evaluation of the Revised Technology Acceptance Model. Management Science,.42(1), 85-92.
    Bhattacherjee, A. (2001a). An empirical analysis of the antecedents of electronic commerce service continuance, Decision support systems,. 32(2), 201-214.
    Bhattacherjee, A. (2001b). Understanding information systems continuance: An expectation- confirmation model, MIS Quarterly, 25(3), 351-370.
    Bock, G. W., Zmud, R. W., Kim, Y. G., & Lee, J. N (2005). Behavioral intention formation in knowledge sharing: Examining the role of extrinsic motivators, social-psychological force, and organizational climate. MIS Quarterly,.29(1),.87-112
    Briggs, R. O., Nunamaker, J. F., & Tobey, D. (2001).The Technology Transition Model:A Key to Self-Sustaining and Growing Communities of GSS Users. Proceedings of the 34th Hawaii international Conference on System Science, .1-9
    Browne, M. W. & Cudeck, R. (1993). .Alternative Ways of Assessing Model" Testing Structural Equation Models, Bollen, K. A. and Long J. S.(ed.), Newbury Park, California: Sage, 136-162.
    Bruner, G. C. & Kumar, A. (2005). Explaining consumer acceptance of handheld Internet devices, Journal of Business Research, 58, 553-558.
    Byrne, B. M.(1998). Structural Equation Modeling with LISREL, PRELIS, and SMPLIS, Mahwah, New Jersey: Lawrence Erlbaaum Associates.
    Byrne, M. B.(2001). Structural Equation Modeling with AMOS: Basic Concepts, Applications, and Programming, Lawrence Erlbaum Associates, Mahwah, NJ.,
    Carmines, E., & Mclver, j. ( 1981). Analyzing modles with unobserved variables: Analysis of covariance structures. In G. Bohmstedt, & E. Borgatta(Eds.), Social measurement: Current Issues. Beverly Hills ,Calif:Sage
    Carswell, A. D. & Venkatesh, V. (2002). Learner outcomes in an asynchronous distance education environment. International journal of human-computer studies, 56,.475-494.
    Chang, M. K. (1998). Predicting Unethical Behavior: A Comparison of the Theory of Reasoned Action and the Theory of Planned Behavior, Journal of Business Ethics, 17(16), 1828-1834.
    Chase, W. G., & Simon, H. A. (1973). Perception in chess. Cognitive Psychology, 4,.55-81.
    Chau, P. Y. K. (1996). An empirical investigation on factors affection the acceptance of CASE by systems developers, Information and Management, 30(6), 269-280.
    Chau, P. Y. K., & Hu, P. J. H. (2001). Information technology acceptance by individual professionals: A model comparison approach, Decision Sciences, 32(4), 699-719.
    Chau, P. Y. K., & Hu, P. J. H. (2002a). Examining a model of information technology acceptance by individual professionals: An exploratory study, Journal of Management Information Systems, 18(4), 191-229.
    Chau, P. Y. K., & Hu, P. J. H. (2002b). Investigating healthcare professionals’ decisions to accept telemedicine technology: An empirical test of competing theories, Information and Management, 39(4), 297-311.
    Chen, L. D., Gillenson, M. L. & Sherrell, D. L. (2002). Enticing online consumers: an extended technology acceptance perspective, Information and Management, 39, 705-719.
    Chin, W. W. (1998). The partial least squares approach to structural equation modeling In G. A. Marcoulides (Eds.), Modern Methods for Business Research(Lawrence Erlbaum Associates, Mahwah, NJ.), 295-336.
    Chin, W. W., & Todd, P. A. (1995). On the use, usefulness, and ease of use of structural equation modeling in MIS research: A note of caution, MIS Quarterly, 19(2), 237-246.
    Chiou, J. S.(1998). behavioral control on consumers’ purchase intentions: the moderating effects of product knowledge and attention to social comparison information, Proc. Natl. Sci. Counc, 9(2), 298-308.
    Chiou, J. S.(1998).The effects of attitude, subjective norm, and perceived behavioral control on consumers’purchase intentions: the moderating effects of product knowledge and attention to social comparison information, Proc. Natl. Sci. Counc., 9(2) , 298-308.
    Cho, Vincent , Cheng, T. C. Edwin , & Lai, W. M. Jennifer (2009), The role of perceived user-interface design in continued usage intention of self-paced e-learning tools, Computers & Education, 53(2), 216-227.
    Chuang, C. P., Lu, C. T., Wang, B. J., Lin, L.H., & Huang, Y. J.(2010). Applying Cognitive-Load-Theory for Designing Adaptive e-Learning System and its Learning Performance in Foundmental Electronics Egineering, 2010/9/29-30, 29th IEEE International Conference on Engineering Education, Kuala Lumpur, Malaysia.
    Chuttur, M. Y. (2009). Overview of the Technology Acceptance Model:Origins,Developments and Future Directions ,Indiana University, USA . Sprouts: Working Papers on Information Systems, 9(37). http://sprouts.aisnet.org/9-37
    Compeau, D. R., & Higgins, C. A. (1995a). Application of social cognitive theory to training for computer skill. 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
    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.
    Compeau, D., & Higgins, C.(1995). Computer self-efficacy: development of a measure and initial test. MIS Quarterly, 12(2), 189-211.
    Cowan, N. (2001). The magical number 4 in short-term memory: a reconsideration of mental storage capacity, Behavioral and Brain Sciences, 24(1).
    Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of Information technology, MIS Quarterly, 13(3),. 319-340.
    Davis, F. D. (1993). User Acceptance of information technology: System characteristics,user perceptions and behavioral impacts. Int. J. Man-Machine Studies, 38, 475-487.
    Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical model, Management Science, 35(8), 982-1003
    Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1992). Extrinsic and intrinsic motivation to use computers in the workplace. Journal of Applied Social Psychology, .22, 1111-1132.
    Davis. (1986). A Technology Acceptance Model for Empirically Testing New End-User Information System: Theory and Results, Doctoral Dissertation, MIT Sloan School of Management
    De Groot, A. (1965). Thought and choice in chess. The Hague, Netherlands: Mount. (Original work published 1946).
    Dedeke, A. (2000). A conceptual framework for developing quality measures for information systems. Proceedings of 5th International Conference on Information Quality 126–128.
    DeLone, W. H. & McLean, E. R. (1992). Information systems success: The quest for the dependent variable, Information Systems Research, 3(1), 60-95.
    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.
    Devaraj, S., Fan, M., & Kohli, R. (2002). Antecedents of B2C channel satisfaction and preference: Validation e-commerce metrics, Information Systems Research, .13(3), 316-333.
    Dishawa, M. T. & Strong, D. M. (1999). Extending the technology acceptance model with task-technology fit constructs. Information and Management, 36, 9-21.
    Doll, W. J., & Torkzadeh, G. (1988). The measurement of end-user computing satisfaction, MIS Quarterly. 12(2), 259-274.
    Ducoffe, R. H. (1996). Advertising value and advertising on the Web. Journal of Advertising Research, 23(5), 21-35.
    Egan, D. E., & Schwartz, B. J. (1979). Chunking in recall of symbolic drawings. Memory and Cognition, 149-158.
    Engel, J. F., Blackwell, R. D. & Miniard P. W. (1995). Consumer Behavior,10th ed., Fort Worth : Dryden Press.
    Eppler, M., & Muenzenmayer, P. (2002) Measuring information quality in the web context: A survey of state-of-the-art instruments and an application methodology. Proceedings of 7th International Conference on Information Quality, 187–196.
    Ericsson, K. A., & Kintsch, W. (1995) Long-term working memory. Psychological Review, 102, 211-245.
    Fenech, T. (1998). Using perceived ease of use and perceived usefulness to predict acceptance of the worldwide web. Computer Networks and ISDN Systems,.30(1-7), 629-630.
    Ferrell, O. C. & Larry, G. Gresham. (1985). A Contingency Framework for Understanding Ethical Decision Making in Marketing. Journal of Marketing 49 (Summer): 87-96.
    Fishbein, M., & Ajzen, I. (1975). Beliefs, Attitude, Intentions and Behavior: An Introduction to Theory and Research, Addition-Wesley, Boston, MA.
    Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable and measurement error. Journal of Marketing Research, 18, 39-50.
    Gefen, D., & Keil, M. (1998). The Impact of Developer Responsiveness on Perceptions of Usefulness and Ease of Use: An Extension of the Technology Acceptance Model, Data Base, 29(2),.35-49.
    Gefen, D., & Straub, D. W. (1997). Gender Difference 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. (2000). The Relative Importance of Perceived Ease of Use in IS Adoption: A Study of E-Commerce Adoption. Journal of the Association for Information Systems, 1(8),.1-28.
    Gefen, D., & Straub, D. W. (2003). Managing User Trust in B2C E-Serices, E-Service Journal,2(2),7-23.
    Gefen, D., Karahanna, E. & D. W. Straub. (2003). Trust and TAM in Online Shopping: An Integrated Model, MIS Quarterly, 27(1), 52-90.
    Gefen, D., Karahanna, E., & Straub, D. W.(2003b). Tust and TAM in Online Shopping: An Integrated Model, MIS Quarterly,27(1),51-90.
    Gerjects, P., & Scheiter, K. (2003). Goal configurations and processing strategies as moderators between instructional design.
    Goodhue, D. L., & Thompson, R. L. (1995). Task-technology fit and individual performance, MIS Quarterly, 19(2), 213-236.
    Hairs, Jr. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (1998). Multivariate data analysis. 5th ed. New York: Macmillan.
    Hannafin, M. J., & Rieber, L. P. (1989). Psychological functions of instructional design for emerging computer-based instructional technologies. Part II. Educational Technology Research and Development, 37(2), 102-114.
    Harrison, D. A., Mykytyn P. P., & Riemenschneider, C. K. (June 1997). Executive Decisions About Adoption of Information Technology in Small Business: Theory and Empirical Tests, Information Systems Research, 8(2), 171-195.
    Hart, S. G., & Staveland, L. E. (1988). Development of NASA-TLX (Task Load Index): Results of experimental and theoretical research. In P. A. Hancock, & N. Meshkati (Eds.), Human mental workload, 139-183. Amsterdam: North Holland.
    Hasan, B. (2006). Delineating the Effects of Multilevel Computer Self‐Efficacy on Determinants of IS Acceptance. Information & Management, 43(5), 565‐571.
    Heijden, H. (2003). Factors Influencing the Usage of Websites: The Case of a Generic Portal in the Netherlands. Information and Management, 40, 541-549.
    Henderson, R., & Divett, M. J. (2003). Perceived usefulness, ease of use andelectronic supermarket use. International Journal of Human-Computer Studies, 59, 383-395.
    Hendrickson, A. R., Massey, P. D., & Cronan, T. P. (1993). On the test-retest reliability of perceived usefulness and perceived ease of use scales, MIS Quarterly, 17(2), 227-230.
    Henry, A. (1995). Consumer Behavior and Marketing Action, Ohio, USA, South-western college publishing.
    Hoelter, J. W. (1983). The analysis of covariance structures: Goodness-of-fit indices. Sociological Methods and Research, 11, 325-344.
    Hong, K. .K. & Kim, Y. G.(2002). The Critical Success Factors for ERP Implementation: an Organizational Fit Perspective, Information and Management, 40(1), 25-40.
    Hong, W., Thong, J. Y. L., Wong, W. M., & Tam, K. Y. (2002). Determinants of user acceptance of digital libraries: An empirical examination of individual characteristics and system characteristics, Journal of Management Information Systems, 18(3), 97-124.
    Hong, W., Thong, J. Y. L., Wong, W. M., & Tam, K.Y. (2001). Determinants of User Acceptance of Digital Libraries: An Empirical Examination of Individual Differences and System Characteristics, Journal of Management Information Systems, (3), 97-124.
    Hong, W., Thong, J. Y. L., Wong, W.M., & Tam, K.Y. (2002). Determinants of user acceptance of digital libraries: an empirical examination of individual differences and system characteristics, Journal of Management Information Systems (JMIS), 18(3,) 97-124.
    Hsieh, N. T., Huang, Y. J., & Chuang, C. P. (2010). The Study of Design and Performance Analysis of Adaptive Learning and Teaching System for Junior Vocational Education Students in Taiwan: A Cognitive-load Perspective, 2010/11/27-28, 3th IEEE International Conference on Education Technology and Training, Wuhan, China.
    Hsu, C. L., Lu, H. P. (2004). Why do people play on-line games? An extended TAM with social influences and flow experience, Information & Management, 41(7), 853-868.
    Hu, L. T., & Bentler, P. (1995). Evaluating model fit. In R. H. Hoyle (Ed.), Structural Equation Modeling. Concepts, Issues, and Applications 76-99 London: Sage.
    Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance. STRUCTURAL eQUATION mODELING, 6(1),1-55.
    Igbaria, M., & Tan, M. (1997). The Consequences Of Information Technology Acceptance On Subsequent Individual Performance, Information and Management, 32(3), 113-121.
    Igbaria, M., Parasuraman, S., & Baroudi, J. J. (1996). A motivational model of microcomputer usage. Journal of Management Information Systems, 13(1), 127-143.
    Igbaria, M., Zinatelli, N., Cragg, A., & Cavaye, A.(1997). Personal computing acceptance factors in small firms: A structural equation model. MIS Quarterly, 21(3), 279-305.
    Igbaria, M., Zinatelli, N., Cragg, P., and Cavaye, A. L. M. (1997), Personal Computing Acceptance Factors in Small Firms: A Structural Equation Model, MIS Quarterly, 21(3), 279-305.
    Jackson, C. M., Chow, S. & Leitch, R. A. (1997), Toward An Understanding Of The Behavioral Intention To Use An Information System, Decision Sciences, 28(2), 357-389.
    Jeffries, R., Turner, A., Polson, P., & Atwood, M. (1981). Processes involved in designing software. Cognitive Skills and their acquisition, 255-283. Hillsdale, NJ: Erlbaum.
    Jiang, J. J., Klein, G., & Crampton, S. M.(2000). "A Note on SERVQUAL Reliability and Validity in Information System Service Quality Measurement," Decision Sciences,31(3),725-744.
    Johnson, R. A., & Hignite, M. A. (2000). Applying the technology acceptance model to the WWW. Academy of Information and Management Sciences Journal, 3(2), 130-142.
    Joreskog, K. G., & Sorborn, D.(1989). LISREL VII User’s Guide, Scientific Software, Inc., Mooresville.
    Jyoti, C., & Kumar, D. Y. (2004), Analysing the factors of broadband adoption in the household. Proceedings of the 13th European Conference on Information Systems.
    Kahn, B. K., Strong, D. M. & Wang, R. Y. (2002) Information quality benchmarks: Product and service performance. Communications of the ACM, 45 (4), 84–192.
    Kalyuga, S. & Sweller, J. (2004). Measuring Knowledge to Optimize Cognitive Load factors During Instruction. Journal of Education Psychology, 96(3), 558-568
    Kankanhalli, A., Tan, B. C. Y., & Wei, K. (2005a), Understanding Seeking From Electronic Knowledge Repositories: An Empirical Study, Journal of the American Society for Information Science and Technology, 56(11), 1156-1166.
    Kankanhalli, A., Tan, B. C. Y., & Wei, K. (2005b), Contributing Knowledge To Electronic Knowledge Repositories: An Empirical Investigation, MIS Quarterly, 29(1), 113-143.
    Karahanna, E. & Straub D.W. (1999). The psychological origins of perceived usefulness and ease-of-use. Information and Management, 35(4) , 237-250.
    Katerattanakul, P. & Siau, K. (1999) Measuring information quality of web sites: Development of an instrument. Proceedings of the 20th international conference on Information Systems. Charlotte, North Carolina, United States; 279–285.
    Kay, R. H., & Knaack, L. (2007a). A systematic evaluation of learning objects for secondary school students.Journal of Educational Technology Systems, 35(4), 411-448
    Kay, R. H., & Knaack, L.(2007). Evaluating the learning in learning objects ,University of Ontario Institute of Technology, Canada.Open Learning, 22, No. 1,February 2007, 1-28
    Kim, H., Kim J., Lee, Y., & Lee., I. (2003). Post-adoption behavior of mobile internet users: An empirical validation with a structural equation model. Working Paper, retrieved June 6, 2010 from: http://hci.yonsei.ac.kr/publications.htm.
    Klein B. D. (2002). When do users detect information quality problems on the World Wide Web? American Conference in Information Systems, 1101.
    Klein, B. D. (2001). User perceptions of data quality: Internet and traditional text sources. The Journal of Computer Information System, 41 (4), 9–18.
    Kline, R. B.(1998). Principles and practice of structural equation modeling.New York: Guilford Press.
    Klopping, I. M., & McKinney, E. (2004). Extending the technology acceptance model and the task-technology fit model to consumer e-commerce. Information Technology, Learning, and Performance Journal, 22(1), 35-47.
    Knight, S.-a., & Burn, J. (2005) Developing a framework for assessing information quality on the world wide web. Information Science Journal, 8. Retrieved Oct. 10, 2009, from http://inform.nu/Articles/Vol8/v8p159-172Knig.pdf
    Kuo, Y. F., & Yen, S. N. (2009). Towards an understanding of the behavioral intention to use 3G mobile value-added services. Computers in Human Behavior, 25, 103-110.
    Lee, J. S., Cho, H., Gay, G., Davidson, B. & Ingraffea, A. (2003). Technology acceptance and social networking in distance learning. Journal of Educational Technology and Society, 6(2), 50-61.
    Lee, Y., Kozar, K. A ., & Larsen, K. R.T. (2003). The Technology Acceptance Model: Past. Presen Systems, 12, 752-780.
    Legris, P., Ingham, J. & Collerette, P. (2003). Why do people use information technology? a critical review of the technology acceptance model. Information and Management, 40( 3), 191-204.
    Leung, H. K. N. (2001). Quality metrics for intranet applications. Information and Management, 38 (3), 137-152.
    Lin, C. S., & Wu S.(2002). Exploring the Impact of Online Service Quality on Portal Site Usage. Proceedings of the 35th Annual Hawaii International Conference on System Sciences.
    Lin, Chuan-Chuan, & Lu, Hsipeng,(2000), Towards an understanding of the behavioural intention touse a web site, International Journalof Information Management, 20, 197-208.
    Lin, H. F. (2007). The role of online and offline features in sustaining virtual communities:an empirical study. Internet Research, 17(2),. 119-138.
    Lin, J. & Lu, H. (2000), Toward an understanding of the behavioural intention to use a website, International Journal of Information Management, 20, 197-208.
    Loehlin, J. C. (1992). Latent variable model:An introduction to factor, path, and structural analysis(2nd). Hillsdale, NJ:Lawrence Erlbaum.
    Luarn, P. & Lin, H. H. (2005), Toward an Understanding of the Behavioral Intention to Use Mobile Banking. Computers in Human Behavior, 21(6), 873-891.
    Lucas Jr, H. C., & Spitler, V. K. (1999), Technology Use And Performance: A Field Study Of Broker Workstations, Decision Sciences, 30(2), 291-311.
    Luis L. M. & Franz W. K. (2004). A Model of Business School Students’ Acceptance of a Web-Based Course Management System. Academy of Management Learning and Education.
    Lynn, M. N. (1986). Determination and quantification of content validity. Nursing Research, 35(6), 382-385
    Mallach, E. (1988). Climbing Castle od Data, Computer Word, 23
    Mallach, E.,(1988). Climbing Castle od Data , Computer Word, 23, October.
    Marcus, N., Cooper, M. & Sweller, J. (1996). Understanding Instructions. Journal of Educational Psychology, 88(1), 49-63.
    Mardia, K. V.(1985). Mardia`s test of multinormality. In S. Kotz and N. L. Marketing Review Johnson(Eds. In chief), Encyclopedia of statistical sciences, 5, 217-221.
    Mason, R. O. (1978). Measuring Information Output: A Communication Systems Approach. Information and Management, 1(5), 219-234.
    Mayer, R. E., Moreno, R., Boire, M., & Vagge, S. (1999) Maximizing Constructivist Learning From Multimedia Communications by Minimzing Cognitive Load. Journal of Educational Psychology, 90(2), 312-320.
    McDonald, R. P., & Ho, M. R. (2002). Principles and practice in reporting structural equation analysis. Psychological Methods, 7, 64-82.
    McGill, T., Hobbs, V., & Klobas, J. (2003), User-developed applications and information systems success: A test of DeLone and McLean model, Information Resource Management Journal, 16(1), 24-45.
    Mckinney, V., Yoon K. & Zahedi, F. M. (2002), Themeasurement of web-customer satisfaction: an expectation and disconfirmation approach.Information System Research 13(3), 296-315.
    Meuter, M.L., Ostrom, A.L., Roundtree, R. & Bitner, M.J. (2000),“Self-Service Technologies: Understanding Customer Satisfaction with Technology-Based Service Encounters.” Journal of Marketing, 64, July, 50-64.
    Miller, J. (1956) The magic number seven, plus or minus two: some limit on our capacity to process information. Psychological Review, 63, 81-87.
    Moon, J. W. & Kim, Y. G. (2001), Extending the TAM for a World-Wide-Web context, Information & Management, 38 (4), 217–230.
    Moon, J. W. & Kim, Y. G. (2001), Extending the TAM for a World-Wide-Web context. Information and Management, 38(4), 217-230.
    Moore, G. C. & Benbasat, I. (1991). Development of an Instrument to Measure the Perceptions of Adopting an Information Technology Innovation. Information Systems Research, 2(3), 192-222.
    Moreno, R. (2005). Instructional technology: Promise and pitfalls. In L. M. Pyllikzillig, M. Bodvarsson, & R. Bruning (Eds.), Technology-based education: Brining researchers and practitioners together 1-19.Greenwich, CT: Information Age.
    Moreno, R. (2006). When worked examples don’t work: Is cognitive load theory at an impasse? Learning and Instruction, 16, 170-181.
    Mulaik, S. A., James, L. R., Altine, J. V., Bennett, N., Lind, S., & Stilwell, C. D. (1989). Evaluation of goodness-of-fit indices for structural equation models. Psychological Bulletin, 105, 430-445.
    Myers, B. L., Kappelman, L. A., & Prybutok, V. R.(1997), A comprehensive model for assessing the quality and productivity of the information systems function: Toward a theory for information systems assessment, Information Resources Management Journal, 10(1), 6-25.
    Naumann, F. & Rolker, C. (2000) Assessment methods for information quality criteria. Proceedings of 5th International Conference on Information Quality, 148–162.
    Neale, L.(2008). Antecedents of Web Site Loyalty: Results from Four Countries.Proceedings of the Annual Conference of the Academy of Marketing Science, Vancouver, 1-19
    O’Donnell, R. D. & Eggemeier, F. T. (1986). Workload assessment methodology, in K.R. Boff, L. Kaufman and J.P.Thomas (eds), Handbook of Perception and Human Performance, II, Cognitive Processes and Performance (New York: Wiley Interscience), 42-1 ~ 42-49.
    Oh, S., Ahn, J., & Kim, B. (2003). Adoption of broadband Internet in Korea: the role of experience in building attitudes. Journal of Information Technology, 18(4), 267-280.
    Ong, C. S. & Lai, J. Y. (2006). Gender differences in perceptions and relationships among dominants of e-learning acceptance. Computers in Human Behavior, 22(5), 816-829.
    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.
    Pass, F. G. W. & Van Merrienboer, J. J. G. (1994). Variability of worked examples and transfer of geometrical problem solving: A cognitive load approach. Journal of Educational Psyhology, 86, 122-133.
    Pass, F. G. W. (1992) Training strategies for attaining transfer of problem – solving skill in statistics: A cognitive load approach Journal of Educational Psychology, 87(2), 319-334.
    Pass, F. Renkle, A. & Sweller, J. (2003). Cognitive load theory and instructional design: Recent developments. Educational Psychologist, 38(1) , 1-4.
    Peterson & Peterson (1959). Duration of STM.
    Polya, J. (1957). How to solve it (2nd ed.) Garden City, NY:Doubleday Books.
    Rai, A., Lang, S. & Welker, R. (2002), Assessing the validity of is success models: An empirical test and theoretical analysis, Information Systems Research, 13(1), 50-69.
    Riemenschneider, C. K., Harrison,D. A. & Mykytyn, Jr P. P. (2003). Understanding IT adoption decisions in small business: integrating current theories, Information & Management, 40, 269-285.
    Rigdon, E.(2005). SEM FAQ. Retrieved July 10, 2010 from http://www.gsu.edu/~mkteer/sem.html.
    Rokeach, M. (1968). Belief, Attitudes, and Values. San Francisco: Jossey-Bass Inc., Publishers.
    Rose, G., Khoo, H., & Straub, D. W. (1999). Current technological impediments to business-to-consumer electronic commerce. Communications of the AIS, 1(16), 1-73.
    Saadé, R. & Otrakji, C. (2007). First impressions last a lifetime: Effect of disorientation and cognitive load. Computers in human behavior, 23(1), 525-535.
    Seddon, P. B., & Kiew, M. Y. (1994). A partial test and development of the DeLone and McLean model of IS success. Proceedings of the International Conference on Information Systems, 99–110.
    Seddon, P. B., Staples,S., Patnayakuni, R., & Bowtell, M. (1999) Dimensions of Information Systems Success.
    Segars, A. H. & Grover, V. (1993), Re-examining perceived ease of use and usefulness: A confirmatory factor analysis, MIS Quarterly, 17(4), 517-525.
    Shanks, G. & Corbitt, B. (1999) Understanding data quality: Social and cultural aspects. Proceedings of the 10th Australasian Conference on Information Systems, 785.
    Shannon C. E. & Weaver, W. (1949) The Mathematical Theory of Communication, The University of Illinois Press, Urbana, Illinois.
    Shaw, N. C., DeLone, W. H. & Niederman, F. (2002), Source of dissatisfaction in end-user support: An empirical study, The Database for Advances in Information Systems, 33(2), 41-56.
    Sheppard, B. H., Hartwick, J., & Warshaw, P.R. (1988). The theory of reasoned action: A meta-analysis of past research with recommendations for modifications and future research. Journal of Consumer Research, 15, 325-343.
    Sheppard, B. H., Hartwick, J., & Warshaw, P.R. (1988). The theory of reasoned action: A meta-analysis of past research with recommendations for modifications and future research. Journal of Consumer Research, 15, 325-343.
    Shin, N.(2003). Transactional Presence as a Critical Predictor of Success in Distance Learning.Distance Education, 24, NO. 1, 2003, 69-86
    Smith, T. J. (2008). Senior citizens and e-commerce websites: The role of perceived usefulness, perceived ease of use, and web site usability. Informing Science: the International Journal of an Emerging Transdiscipline, 11, .59-83.
    Straub, D., Keil, M. & Brenner, W. (1997) Testing the Technology Acceptance Model Across Cultures: A Three Country Study, Information and Management, 33(1), 1-11.
    Straub, D.W., Keil, M., & Brennan, W. (1997). Testing the Technology Acceptance Model Across Cultures: A Three Country Study. Information & Management, 33, 1-11.
    Strub, P. J. & Priest, T. B. (1976). Two Patterns of establishing trust: The marijuana user. Sociological Focus, 9, 399-411.
    Stumpf, S. A., Brief, A. P., & Hartman, K (1987). Self-efficacy expectations and coping with career-related events. Journal of Vocational Behavior, 31, 91-108.
    Sundar, S. S., & Nass, C. (2001). Conceptualizing sources in online news. Journal of Communication, 51 (1), 52-72.
    Sweller, J. & Cooper, G. (1985) The use of worked examples as a substitute for problem solving in learning algebra. Cognition and Instruction, 2, 59-89.
    Sweller, J. (1988) Cognitive load during problem solving: Effects on learning Cognitive Science , 12, 257-285.
    Sweller, J. (1989) Cognitive technology : some procedures for facilitating learning and problem solving in mathematics and science. Journal of Educational Psychology , 81, 457-466.
    Sweller, J. (1990) On the limit evidence for the strategies. Journal for Research in Mathematics Education, 21(5), 411-415.
    Sweller, J. (2003) Evolution of human cognitive architecture. The psychology of learning and motivation, 43, 215-266.
    Sweller, J. (2004) Instructional design consequence of an analogy between evolution by natural selection and human cognitive architecture. Instructional Science, 32, 9-31.
    Sweller, J. (2006). The worled example effect and human cognition. Learning and Instruction, 16, 165-169.
    Sweller, J., Kirschner, P. A. & Clark, R. E. (2007). Why Minimally Guided Teaching Techniques Do Not Work: A Reply to Commentaries. Educational psychologist, 42(2), 115-121
    Szajna, B. (1996).Empirical Evaluation of the Revised Technology Acceptance Model, Management Science, 42(1), 85-92.
    Tabachnick, B. G. & Fidell, L. S. (2007). Using Multivariate Statistics , 5th ed. Boston: Allyn and Bacon.
    Tan, G. W. & Wei, K. K. (2006), An Empirical Study of Web Browsing Behavior: Towards An Effective Website Design, Electronic Commerce Research and Applications, 5(4), 261-271.
    Taylor, S. & Todd, P. A. (1995), “Assessing IT usage: the role of prior experience”, MIS Quarterly, 19(4), 561-570.
    Taylor, S. & Todd, P. A.(1995), Understanding Information Technology Usage: a Test of Competing Models, Information Systems Research, 6(2), 144-176.
    Taylor, S., & Todd, P. A. (1995). Decomposition and Cross Effects in the Theory of Planned Behavior: A Study of Consumer Adoption Intentions. Inter Journal of Research in Marketing, 12(2), 137-155.
    Teo T (2009).The Impact of Subjective Norm and Facilitating Conditions on Pre-Service Teachers' Attitude toward Computer Use: A Structural Equation Modeling of an Extended Technology Acceptance Model. Journal of Educational Computing Research, 40(1), 80-109.
    Teo T.(2010). Examining the influences of subjective norm and facilitating conditions on the intention to use technology among pre-service teachers: A structural equation modeling of an extended technology acceptance model. Asia Pacific Education Review, 11, 253-262.
    Teo, T. S. H. (2001). Demographic and motivation variables associated with Internet usage activities. Internet Research: Electronic Networking Applications and Policy, 11(2), 125-137
    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.
    Teo, Thompson S. H., Vivien K. G. L. & Raye Y. C. L. (1999). Intrinsic and Extrinsic Motivation in Internet Usage. The International Journal of Management Science, 27, 25-37.
    Thompson, R. L., Higgins, C. A. & Howell, J. M. (1991). Personal computing: toward a conceptual model of utilization. MIS Quarterly, 15(1), 124-143.

    Thong, J. Y. L., Hong, W., & Tam, K. Y. (2004). What leads to user acceptance of digital libraries? Communications of the ACM, 47(11), 79-83.
    Thong, J.Y.L., Hong, S.-J., & Tam, K.Y. (2006), The effects of post-adoption beliefs on the expectation-confirmation model for information technology continuance, Internation Journal of Human-Computer Studies, 64(9), 799-810.
    Treiblmaier, H., Neale, L., Chong, S., Haghirian, P., & Oelsiechutag, E. (2008) "Antecedents of Web Site Loyalty: Results from Four Countries", Annual Conference of the Academy of Marketing Science, 243
    Valcke, M. (2002). Cognitive load: Updating the memory? Learning and Instruction, 12 , 147-154.
    Vallerand, R .D., Deshaies, P., Cuerrier, J., Pelletier, J. G., & Mongeau, C. (1992), Ajzen and Fishbein’s Theory of Reasoned Action as Applied to Moral Behavior: A Confirmatory Analysis. Journal of Personality and Social Psychology, 62(1), 98-109.
    Venkatesh, V. (2000), Determinants of Perceived Ease of Use: Integrating Control, Intrinsic Motivation, and Emotion into the Technology Acceptance Model, Information Systems Research, 11(4), 342-365.
    Venkatesh, V., & Davis, F. D. (2000). A Theoretical Extension of the Technology Acceptance Modes:Four Longitudinal Field Studies. Management Science. 46(2), 186-204.
    Venkatesh, V., & Davis, F.(1996). The model of the antecedents of perceived ease of use:Development and test. Decision Sciences, 27(3), 451-481.
    Venkatesh, V., & Davis, F.(1996).The model of the antecedents of perceived ease of use: Development and test. Decision Sciences, 27 (3) ,451-481.
    Venkatesh, V., Morris, M. G., & Ackerman , P. L. (2000). A Longitudinal Field Investigation of Gender Differences in Individual Technology Adoption Decision-Making Processes. Organizational Behavior and Human Decision Processes, 83(1), 33–60.
    Venkatesh, V., Morris, M. G., Davis, G. B. & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27, 425-478.
    Wang, R. Y. & Strong, D. M.(1996), Beyond accuracy: What data quality means to data consumers, Journal of Management Information Systems, 12(4), 5-33.
    Willkinson, J. W. (1993), Accounting Information Systems: Essential Concepts and Applications, Second Edition.
    Wu, J. H., & Wang, S. C. (2005). What drives mobile commerce? An empirical evaluation of the revised technology acceptance model. Information & Management, 42(5), 719–729.
    Yu ,Jieun ,Imsook Ha,Munkee Choi & Jaejeung Rho. (2005). Extending the TAM for a t-commerce, Information & Management, 42(7), 965-976.
    Yusliza Mohd.Yusoff, Zikri Muhammad, Mohd.Salehuddin Mohd.Zahari, Ermy Syaifuddin Pasah, & Emmaliana Robert (2009). Individual Differences, Perceived Ease of Use, and Perceived Usefulness in the E-Library Usage. Computer and Information Science, 2 (1), 76-83.
    Zack, M. H. (1993). Interactivity and Communication Mode Choice in Ongoing Management Groups. Information System Research, 4(3), 207-239.
    Zeist, R. H. J. & Hendriks, P. R. H. (1996). Specifying software quality with the extended ISO model. Software Quality Management IV – Improving Quality, BCS, 145-160.
    Zhu, X. & Gauch, S. (2000). Incorporating quality metrics in centralized/distributed information retrieval on the World Wide Web. Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval, Athens, Greece. 288–295

    下載圖示
    QR CODE