跳到主要內容

臺灣博碩士論文加值系統

(216.73.216.110) 您好!臺灣時間:2026/05/05 20:16
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果 :::

詳目顯示

: 
twitterline
研究生:王孟威
研究生(外文):WANG,MENG-WEI
論文名稱:影響腦機介面接受採用之因素
論文名稱(外文):Factors Influencing the Adoption of Brain Computer Interfaces
指導教授:王育民王育民引用關係
口試委員:施穎偉王怡舜林心慧
口試日期:2017-07-14
學位類別:碩士
校院名稱:國立暨南國際大學
系所名稱:資訊管理學系
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2017
畢業學年度:105
語文別:中文
論文頁數:94
中文關鍵詞:腦機介面腦電波圖科技接受模式任務科技適配度分解式計畫行為理論
外文關鍵詞:Brain-Computer InterfaceElectroencephalogramTechnology Acceptance ModeTask-Technology fit ModelDecomposed Theory of Planned Behavior
相關次數:
  • 被引用被引用:0
  • 點閱點閱:293
  • 評分評分:
  • 下載下載:3
  • 收藏至我的研究室書目清單書目收藏:0
近幾年來,腦機介面(Brain-Computer Interface,BCI)的發展與應用逐漸受到重視與討論,腦電波圖(Electroencephalogram,EEG)產品的問世,腦機介面開始應用在日常生活中,相信不久將來會成為主要趨勢。
本研究運用科技接受模式、任務科技適配度、分解式計畫行為理論來探討使用者接受或採用腦機介面之因素,並讓使用者操作腦機介面體驗腦波遊戲,充分熟悉後,接著發放問卷調查,希望藉此了解使用者的習慣與行為模式,最後根據實驗結果做出結論與建議,供往後的研究參考。

Recently, the development of Brain-Computer Interface (BCI) is getting more and more notice and discussion. As the introduction of Electroencephalogram (EEG) products, BCI starts to be taken advantage in our daily life. We can see it will be the mainstream in the near future.
This research utilizes Technology Acceptance Mode, Task-Technology fit Model, Decomposed Theory of Planned Behavior to find out the factors that influence users accept or use BCI. And let them operate Brain-Computer Interface to experience how it works by themselves. After fully understood, we have a survey for them. We would like to figure out users' habit and behavior mode. Last, this study makes the conclusion and suggestion according to the results of the experiment, This will be the reference for further research.

目次
致謝辭 i
摘要 ii
Abstract iii
目次 iv
表目次 vii
圖目次 ix
第一章 緒論 1
1.1 研究背景 1
1.2 研究目的 2
1.3 研究流程 3
第二章 文獻探討 4
2.1 腦波介面 4
2.2 科技接受模式 6
2.3 任務科技適配度 8
2.4 計畫行為理論 9
2.5 分解式計畫行為理論 11
2.6 有趣性 12
2.7 風險 12
第三章 研究方法 13
3.1 研究架構 13
3.2 研究假說 16
3.3 研究變數定義與問卷設計 20
3.4 資料收集與分析方法 38
3.4.1 系統硬體 38
3.4.2 實驗流程 39
3.4.3 資料收集 44
3.4.4 實驗照片 45
3.4.5 分析方法 47
第四章 資料分析 48
4.1 樣本資料分析 48
4.2 信效度分析 51
4.2.1信度分析 51
4.2.2 效度分析 55
4.2.3 收斂效度(Convergent Validity) 55
4.2.4 區別效度(Discriminant Validity) 59
4.3 研究模型驗證 72
第五章 結論 81
5.1 討論 81
5.2 研究貢獻 84
5.3 研究限制與建議 84
參考文獻 85
一、中文文獻 85
二、英文文獻 86
附錄 90
附錄一、調查問卷 90


參考文獻
一、中文文獻
1.王育民(2015),網路創業自我效能量表之建構,working paper。
2.林威志( 2005)。音樂刺激下腦波信號分析。台北醫學大學醫學資訊研究所 碩士論文,未出版,台北。
3.林韋成(2007)。消費者使用感應式信用卡意願之研究。暨南大學資訊管理學系學位論文,頁1-74。
4.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, 第十二卷,第四期,頁 211-234。
5.國際腦波學會 http://www.oset.org/Home.html
6.張紹勳(2001)。研究方法。台中市:滄海。
7.張遠哲(2015)。科技接受模式以腦波遊戲為例。暨南大學資訊管理學系學位論文,頁1-48。
8.陳順宇(2005)。多變量分析。台北: 華泰書局。
9.蔡繡容(2001),創業家之認知與行為意向之研究:計畫行為理論與社會認知理 論之應用。高雄第一科技大學金融營運研究所碩士論文。

二、英文文獻
1.Ahmed, E. (2014). Analysis of motivational factors influencing acceptance of technologically-enhanced personal, academic and professional development portfolios (Doctoral dissertation, University of Huddersfield).
2.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.
3.Ajzen, I. , & Fishbein, M. (1980). Understanding attitudes and predicting social behavior.
4.Ajzen, I. and B. L. Driver (1991) , Prediction of leisure participation from behavior, Normative and Control Beliefs: An Application of the Theory of Planned Behavior, Leisure Sciences, Vol. 13, pp. 185-204.
5.Ajzen, I. The Theory of Planned Behavior, Organizational Behavior and Human Decision Processes (50), 1991, pp.179-211.
6.Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: a review and recommended two-step approach, Psychological Bulletin, Vol.103. 411-423.
7.Anupama, H. S., Cauvery, N. K., & Lingaraju, G. M. (2012). Brain computer interface and its types-A study. International Journal of Advances in Engineering & Technology, 3(2), pp. 739-745.
8.Bandura, A. (1977). Self-efficacy: toward a unifying theory of behavioral change. Psychological review, 84(2) , 191.
9.Barnett, L. A. , 1990. ‘Playfulness: definition, design, and measurement’. Play and Culture, 3: 319-336.
10.Bauer, R. A. (1960). Consumer behavior as risk taking. In Hancock, R. (Ed.) , Dynamic Marketing for a Changing World: Proceedings of 43rd Conference, American Marketing Association (pp. 389-398). Chicago, IL, USA.
11.Bhattacherjee, A. (2000). Acceptance of e-commerce services: the case of electronic brokerages. Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on, 30(4) , 411-420.
12.Birbaumer N. and Ghanayim, N. and Hinterberger, T. and Iversen, I., Kotchoubey, B. and Kübler, A. and Perelmouter, J. and Taub, E. and Flor, H.:1999, A Spelling Device for the paralyzed, In Nature vol. 398: 297-298.
13.Burnkrant, R. E. and Page, T. J., The structure and antecedents of the normative and attitudinal components of Fishbein’s Theory of Reasoned Action, Journal of experimental social psychology, 1988, Vol. 24, pp.66-87.
14.Chin, W.W. (1998b), “Issues and opinion on structural equation model,”
15.Cronbach, L.J. (1951). Coefficient alpha and the internal structure of tests. Psyphometrika, 16, 297-334.
16.Csikszentmihalyi, M., 1975. Beyond Boredom and Anxiety, Jossey-Bass, San Francisco.
17.Curran, J.M., Meuter, M.L. , 2005. Self-Service Technology Adoption: Comparing Three Technologies. Journal of Services Marketing 19 (2) , 103-113.
18.Davis, F. D. (1989) , “Perceived usefulness, perceived ease of use, and user acceptance of information technology” , MIS Quarterly, 13(3) , 319-337.
19.Federici S., & Scherer M. (2012). Assistive Technology Assessment Handbook. USA: CRC Press.
20.Fishbein, M. , & Ajzen, I. (1975). Belief, attitude, intention and behavior: An introduction to theory and research.
21.Fornell, C. R. & Larcker, F. F. (1981) , “Structural Equation Models with Unobservable Variables and Measurement Error,” Journal of Marketing Research, 18, pp.39-51.
22.Gaski, J. F. and Nevin, J. R., 1985. The Differential Effects of Exercised and Unexercised Power Sources in a Marketing Channel. Journal of Marketing Research, 2(2), 130-142.
23.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.
24.Gelderen, M. , Kautonen, T. , & Fink, M. (2015). Robustness of the theory of planned behavior in predicting entrepreneurial intentions and actions.Entrepreneurship Theory and Practice 39 (3) , 655-674.
25.Gollwitzer, P . M . (1999). Implementation intentions: strong effects of simple plans. American Psychologist, 54(7) , pp. 493-503.
26.Goodhue DL, Thompson RL: Task-technology fit and individual performance. MIS Quarterly 1995; 19(2):213.
27.Ifinedo, P. (2013). Information systems security policy compliance: An empirical study of the effects of socialisation, influence, and cognition. issues,36(42) , 45-52.
28.Lee, M. C. (2009). Predicting and explaining the adoption of online trading: An empirical study in Taiwan. Decision Support Systems, 47(2) , 133-142.
29.Lin, H.F. , Predicting consumer intentions to shop online: An empirical test of competing theories, Electronic Commerce Research and Applications ,6(2007) ,433-442.
MIS Quarterly, 22(1), pp. 7-16.
30.Moon, J.W. & Kim, Y.G. , 2001. ‘Extending the TAM for a World-Wide-Web context’. Information & Management, 38(4) : 217-230.
31.Moore, G. C. & Benbasat, I. , 1991. Development of an Instrument to Measure the Perceptions of Adopting an Information Technology Innovation, Information Systems Research, vol. 2 no. 3, 192-222.
32.Nunnally, J. C. (1978) , “Psychometric theory” , New York, Mcgraw-Hil
33.O’cass, A. , & Fenech, T. (2003). Web retailing adoption: exploring the nature of internet users Web retailing behaviour. Journal of Retailing and Consumer services, 10(2) , 81-94.
34.Priyanka A. Abhang & Bharti W. Gawali. (2015). Correlation of EEG Images and Speech Signals for Emotion Analysis. British Journal of Applied Science & Technology, ISSN: 2231-0843,Vol.: 10, Issue.: 5
35.Sarah E. Brewster, Mark A. Elliott, Steve W. Kelly. (2015). Evidence that implementation intentions reduce drivers’ speeding behavior: Testing a new intervention to change driver behavior. Accident Analysis & Prevention, Volume 74, Pages 229-242.
36.Sun, Y. , Wang, N. , Guo, X. , & Peng, Z. (2013). Understanding the acceptance of mobile health services: a comparison and integration of alternative models.Journal of Electronic Commerce Research, 14(2) , 183-200.
37.Taylor, S. , & Todd, P. (1995). Decomposition and crossover effects in the theory of planned behavior: A study of consumer adoption intentions. International journal of research in marketing, 12(2) , 137-155.
38.Van der Heijden, H. (2003). Factors influencing the usage of websites: The case of a generic portal in The Netherlands. Information and Management, 40(6), 541-549.
39.Vidal J. J. (1973). Toward direct brain-computer communication. Annual Review of Biophysics and Bioengineering, 2. pp.157-180.
40.Wolpaw, Jonathan R, Birbaumer, Niels, McFarland, Dennis J, Pfurtscheller, Gert, & Vaughan, Theresa M. (2002). Brain-computer interfaces for communication and control. Clinical neurophysiology, 113(6) , 767-791.

QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top