簡易檢索 / 詳目顯示

研究生: 吳信宏
Wu, Hsin-Huang
論文名稱: 以基於新型多準則模式之技術接受模型探討隱私悖論於使用者對社群信任之影響
A Hybrid Framework Based TAM for Deriving Influences of Privacy Paradox on User’s Trust on Social Network
指導教授: 黃啟祐
Huang, Chi-Yo
學位類別: 碩士
Master
系所名稱: 工業教育學系
Department of Industrial Education
論文出版年: 2015
畢業學年度: 103
語文別: 英文
論文頁數: 77
中文關鍵詞: 社群網站技術接受模式隱私悖論決策實驗室法基於決策實驗室之網路流程法多準則決策分析偏最小平方法
英文關鍵詞: Privacy Paradox, Decision Making Trial and Evaluation Laboratory Based Network Process
論文種類: 學術論文
相關次數: 點閱:198下載:87
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
  • 社群網站在全球掀起一股熱潮,成為人際聯繫互動之新平台;隨著社群網站功能日增,使得社群網站更容易收集、儲存和發送個人的資訊,隱私也成為使用者與網站經營者關心的議題。經營者為追求利潤最大化,需考量如何充分運用使用者的資訊於行銷和其它網站功能上,同時,使用者也關心於揭露詳細個人資料於社群網站的同時,個人資訊被濫用的程度;由於社群網站經營者亟欲充份利用資訊之企圖與使用者對於資訊揭露之顧慮往往存在差距,因此產生「隱私悖論」相關議題,而隱私悖論於使用者對社群網站之信任與進一步接受該社群網站亦產生直接影響。雖然「隱私悖論」議題對於社群網站之經營管理日益重要,但少有學者探討「隱私悖論」如何影響社群使用者對社群網站之信任與接受,因此,本研究擬探討社群網站中使用者與網站經營者間對使用者的隱私關注程度是否存在顯著差異,並分析此關注程度的差異是否將進一步影響使用者未來持續使用社群的意願。為達成此目的,本研究擬導入技術接受模型 (Technology Acceptance Model),以使用者對社群網站之隱私顧慮、制度信任(含情境常態與結構保證)、計算性信任、與熟悉度做為外在變項,探討「隱私悖論」是否將影響使用者未來持續使用某社群網站;此外,研究發現「認知有用性」與「認知易用性」亦影響新型資訊科技產品之使用意圖,因此,本研究亦導入此二因素作為研究變項。為分析影響使用者信任並進而接受社群網站,持續使用的因素,本研究擬針對社群網站之一般使用者與專家分別以偏最小二乘法 (Partial Least Square,PLS)與基於決策實驗室之網路流程法(Decision Making Trial and Evaluation Laboratory Based Network Process) 建構影響社群網站技術接受因素與使用者行為間之關係架構,並比較專家與一般使用者對於「隱私悖論」於使用者信任社群網站並進一步接受網站、持續使用之重要因素。本研究之結果將可作為社群網站經營者於行銷與服務策略之用。

    Social network sites surged recently all over the world and have become new platforms for intimate communications. As the functionality of social networks was enhanced, users’ own information can be collected, stored, and manipulated much more easily. Privacy concern has thus become the most concerned issue by both users and website operators. The operators intend to maximize the profits and need to consider how users’ confidential information can be fully utilized in marketing and aspects of social network operations. At the same time, users usually concern over the misuse of private information by the website operations at the moment when disclosing individual details on social networking sites. Apparently, a significant gap exists between the website operators’ intention to fully utilize the private information as well as the users’ privacy concerns about disclosing information on the social networking sites. Such cognition gap, or the "privacy paradox", influences users’ trust on a specific social networking site directly and further influence users’ acceptance and continuous usage of the site Albeit such privacy paradox issues have been becoming daily important for the social network operators as well as users, very few scholars tried to uncover how privacy paradox influences users’ privacy concern and the social network sites structural assurance, which will influence the user whether to accept of social network sites or not. In this study, TAM (Technology Acceptance Model) is the theoretical basis, applying users' private disclosure behavior; disclosure risks perception, and the extent of privacy settings in the social networking sites as main variables. Whether it influences users continued interactive in the social networking sites in the future. In addition, in the past study found that perceived usefulness, perceived ease of use and the interaction strength for modern technology services or products influencing use intention, so adding these factors as research variables. It conducted a questionnaire survey targeted at end users and experts, respectively, with Partial Least Square (PLS) and "new multi-criteria model" (Decision Making Trial and Evaluation Laboratory Based Network Process, DNP) as the construction and analysis. To explore which variables influence users in social networks in the future continued interaction intention, and comparing the two methods, end users and experts to that site's privacy concerns for the social networking site, privacy setting mechanism, and technology acceptance at the similarities and differences. Results of this study, the author hopes to provide the site operators in the marketing and service strategies to develop privacy and competitive ref.

    目錄 致謝………………………………………………………………………I 摘要……………………………………………………………………II Abstract………………………………………………………………..III 目錄....................................................V List of Table............................................................................................ VII List of Figure…………………………………………………………….IX Chapter 1 Introduction …………………………………………………...1 1.1 Research Background…………………………………………………….…1 1.2 Research Motivation………………………………………………………..2 1.3 Research Objectives. ……………………………………………………….4 1.4 Research Procedure…………………………………………………………4 1.5 Research Contribution and Limitation……………………………………..5 1.6 Research Framework……………………………………………………….6 Chapter 2 Literature Review…………………………………………….7 2.1 Social Networking Sites Definition…………………………………………7 2.2 Privacy Concerns……………………………………………………………8 2.3 Factors Influencing Users’ Trust of Social Network Sites………………..12 2.4 Technology Acceptance Model (TAM)……………………………………15 2.5 Research Hypothesis……………………………………………………....17 Chapter 3 Research Methodology……………………………………...19 3.1 Modified Delphi Method…………………………………………………..20 3.2 Decision Making Trial and Evaluation Laboratory (DEMATEL)…………22 3.3 DEMATEL based Network Process (DNP)……………………………….26 VI 3.4 Partial Least Squares (PLS)……………………………………………….30 Chapter 4 Empirical Study…………………………………………….35 4.1 Industry Background and Research Problem Description……………….35 4.2 Deriving Factors for Social Networking Site by the Modified Delphi…36 4.3 The Casual Relationships and Weight Derivations by the DNP………..38 4.4 Basic Data Analysis and Questionnaire Response Rate (Mass Users)…42 4.5 Description Statistics Analysis (Mass Users)…………………………...44 4.6 Statistics and Analysis Confirmatory Factor Analysis and Reliability Analysis (Mass Users)…………………………………………………......45 4.7 Verification of Hypotheses and Discussion of Empirical Results (Mass Users).........................................................................................................49 Chapter 5 Discussion…………………………………………………51 5.1 Managerial Implication………………………………………………….51 5.2 Advanced in Research Methods…………………………………………58 Chapter 6 Conclusions………………………………………………..61 6.1 Conclusions………………………………………………………………61 6.2 Research Limitations and Directions for Future Researches……………62 References…………………………………………………………….63 Appendix A: Expert List…………………………………………....68 Appendix B 1: Questionnaire………………………………………..69 Appendix B 2: Questionnaire………………………………………..73

    Abdi, H. (2010). Partial least squares regression and projection on latent structure regression (PLS Regression). Wiley Interdisciplinary Reviews: Computational Statistics, 2(1), 97-106.
    Al Sawah, M., Concerto, C., Chusid, E., Bikson, M., Knotkova, H., & Battaglia, F. (2015). Optimization of transcranial direct current stimulation (tDCS) parameters for lower-extremity pain perception. Brain Stimulation, 2(8), 405.
    Berghman, L., Matthyssens, P., & Vandenbempt, K. (2012). Value innovation, deliberate learning mechanisms and information from supply chain partners. Industrial marketing management, 41(1), 27-39.
    Bland, J. M., & Altman, D. G. (1997). Statistics notes: Cronbach's alpha. Bmj, 314(7080), 572.
    Bryman, A. (2012). Social research methods: Oxford university press.
    Chen, J.-K., & Chen, I.-S. (2010). Using a novel conjunctive MCDM approach based on DEMATEL, fuzzy ANP, and TOPSIS as an innovation support system for Taiwanese higher education. Expert Systems with Applications, 37(3), 1981-1990.
    Chenju, Z., Qi, H., & Qinghua, Z. (2014). Privacy disclosure in Chinese social networking sites: An investigation of Renren users.
    Chiang, Y.-H. (2013). Using a combined AHP and PLS path modelling on blog site evaluation in Taiwan. Computers in Human Behavior, 29(4), 1325-1333.
    Chin, W. W. (1998). The partial least squares approach for structural equation modeling.
    Chiu, W.-Y., Tzeng, G.-H., & Li, H.-L. (2010). Improving the e-store business model for satisfying customers’ needs using a hybrid MCDM combined DANP with grey relational model Advances in Intelligent Decision Technologies (pp. 113-121): Springer.
    Chiu, Y.-T. H., Lee, W.-I., & Chen, T.-H. (2014). Environmentally responsible behavior in ecotourism: Antecedents and implications. Tourism Management, 40, 321-329.
    Cohen, J. (1988). Statistical power analysis for the behavioral sciencies: Routledge.
    Dalkey, N. C. (1972). STUDIES IN THE QUALITY OF LIFE; DELPHI AND DECISION-MAKING.
    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.
    Falatoonitoosi, E., Leman, Z., Sorooshian, S., & Salimi, M. (2013). Decision-making trial and evaluation laboratory. Research Journal of Applied Sciences, Engineering and Technology, 5(13), 3476-3480.
    Featherman, M. S., Miyazaki, A. D., & Sprott, D. E. (2010). Reducing online privacy risk to facilitate e-service adoption: the influence of perceived ease of use and corporate credibility. Journal of Services Marketing, 24(3), 219-229.
    Fornell, C., & Cha, J. (1994). Partial least squares. Advanced methods of marketing research, 407, 52-78.
    Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of marketing research, 39-50.
    Gabus, A., & Fontela, E. (1972). World problems, an invitation to further thought within the framework of DEMATEL. Battelle Geneva Research Center, Geneva, Switzerland.
    Gefen, D., Gefen, G., & Carmel, E. (2015). How project description length and expected duration affect bidding and project success in crowdsourcing software development. Journal of Systems and Software.
    Geisser, S. (1975). The predictive sample reuse method with applications. Journal of the American Statistical Association, 70(350), 320-328.
    Hair, J. F. (2009). Multivariate data analysis.
    Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. The Journal of Marketing Theory and Practice, 19(2), 139-152.
    Heiko, A. (2012). Consensus measurement in Delphi studies: review and implications for future quality assurance. Technological Forecasting and Social Change, 79(8), 1525-1536.
    Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2009). The use of partial least squares path modeling in international marketing. Advances in international marketing, 20, 277-319.
    Holahan, P., Mooney, A. C., Mayer, R. C., & Finnerty Paul, L. (2014). Virtuality and Media Synchronicity: Their Effects on Conflict in Virtual Teams. Howe School Research Paper(2014-27).
    Hsu, C.-L., & Lu, H.-P. (2007). Consumer behavior in online game communities: A motivational factor perspective. Computers in Human Behavior, 23(3), 1642-1659.
    Huang, C.-Y., Shyu, J. Z., & Tzeng, G.-H. (2007). Reconfiguring the innovation policy portfolios for Taiwan's SIP Mall industry. Technovation, 27(12), 744-765.
    Huber, F., Herrmann, A., Meyer, F., Vogel, J., & Vollhardt, K. (2007). Kausalmodellierung mit Partial Least Squares: Eine anwendungsorientierte Einführung: Springer DE.
    Land Jr, W. H., Ford, W., Park, J.-W., Mathur, R., Hotchkiss, N., Heine, J., . . . Yeatman, T. (2011). Partial Least Squares (PLS) Applied to Medical Bioinformatics. Procedia Computer Science, 6, 273-278.
    Li, E. Y., Yen, H. R., Liu, C.-C., & Chang, L. F. (2013). From Structural Assurances to Trusting Beliefs: Validating Persuasion Principles in the Context of Online Shopping. Paper presented at the PACIS.
    Liu, Y., Gummadi, K. P., Krishnamurthy, B., & Mislove, A. (2011). Analyzing facebook privacy settings: user expectations vs. reality. Paper presented at the Proceedings of the 2011 ACM SIGCOMM conference on Internet measurement conference.
    Lu, M.-T., Lin, S.-W., & Tzeng, G.-H. (2013). Improving RFID adoption in Taiwan's healthcare industry based on a DEMATEL technique with a hybrid MCDM model. Decision Support Systems, 56, 259-269.
    Martínez, A. B., De Andrés, J., & García, J. (2014). Determinants of the Web accessibility of European banks. Information Processing & Management, 50(1), 69-86.
    Mcknight, D. H., Carter, M., Thatcher, J. B., & Clay, P. F. (2011). Trust in a specific technology: An investigation of its components and measures. ACM Transactions on Management Information Systems (TMIS), 2(2), 12.
    Moon, J., Park, J., Jung, G. H., & Choe, Y. C. (2010). The impact of IT use on migration intentions in rural communities. Technological Forecasting and Social Change, 77(8), 1401-1411.
    Nam, C., Song, C., Lee, E., & Park, C. I. (2006). Consumers' privacy concerns and willingness to provide marketing-related personal information online. Advances in Consumer Research, 33, 212.
    Nosko, A., Wood, E., Kenney, M., Archer, K., De Pasquale, D., Molema, S., & Zivcakova, L. (2012). Examining priming and gender as a means to reduce risk in a social networking context: Can stories change disclosure and privacy setting use when personal profiles are constructed? Computers in Human Behavior, 28(6), 2067-2074.
    Nunkoo, R., & Ramkissoon, H. (2012). Power, trust, social exchange and community support. Annals of Tourism Research, 39(2), 997-1023.
    Park, N., Kee, K. F., & Valenzuela, S. (2009). Being immersed in social networking environment: Facebook groups, uses and gratifications, and social outcomes. CyberPsychology & Behavior, 12(6), 729-733.
    Platenius, M. C., Arifulina, S., Petrlic, R., & Schäfer, W. (2014). Matching of incomplete service specifications exemplified by privacy policy matching Advances in Service-Oriented and Cloud Computing (pp. 6-17): Springer.
    Preibusch, S. (2013). Guide to measuring privacy concern: Review of survey and observational instruments. International Journal of Human-Computer Studies, 71(12), 1133-1143.
    Ringle, C. M. (2004). Gütemaße für den Partial-least-squares-Ansatz zur Bestimmung von Kausalmodellen: Univ. Hamburg, Inst. für Industriebetriebslehre und Organisation, Arbeitsbereich Industrielles Management.
    Sherchan, W., Nepal, S., & Paris, C. (2013). A survey of trust in social networks. ACM Computing Surveys (CSUR), 45(4), 47.
    Snape, D., Kirkham, J., Preston, J., Popay, J., Britten, N., Collins, M., . . . Wyatt, K. (2014). Exploring areas of consensus and conflict around values underpinning public involvement in health and social care research: a modified Delphi study. BMJ open, 4(1), e004217.
    Staddon, J., Acquisti, A., & LeFevre, K. (2013). Self-reported social network behavior: Accuracy predictors and implications for the privacy paradox. Paper presented at the Social Computing (SocialCom), 2013 International Conference on.
    Stone, M. (1974). Cross-validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society. Series B (Methodological), 111-147.
    Taddei, S., & Contena, B. (2013). Privacy, trust and control: Which relationships with online self-disclosure? Computers in Human Behavior, 29(3), 821-826.
    Tzeng, G.-H., Chiang, C.-H., & Li, C.-W. (2007). Evaluating intertwined effects in e-learning programs: A novel hybrid MCDM model based on factor analysis and DEMATEL. Expert Systems with Applications, 32(4), 1028-1044.
    Tzeng, G.-H., & Huang, C.-Y. (2012). Combined DEMATEL technique with hybrid MCDM methods for creating the aspired intelligent global manufacturing & logistics systems. Annals of Operations Research, 197(1), 159-190.
    Urbach, N., & Ahlemann, F. (2010). Structural equation modeling in information systems research using partial least squares. Journal of Information Technology Theory and Application, 11(2), 5-40.
    Van Lange, P. A. (2015). Generalized Trust Four Lessons From Genetics and Culture. Current Directions in Psychological Science, 24(1), 71-76.
    Van Slyke, C., Shim, J., Johnson, R., & Jiang, J. J. (2006). Concern for information privacy and online consumer purchasing. Journal of the Association for Information Systems, 7(1), 16.
    Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management science, 46(2), 186-204.
    Venkatesh, V., Thong, J., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS quarterly, 36(1), 157-178.
    Zhou, T., & Li, H. (2014). Understanding mobile SNS continuance usage in China from the perspectives of social influence and privacy concern. Computers in Human Behavior, 37, 283-289.

    下載圖示
    QR CODE