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研究生:徐永穎
研究生(外文):Yung-Ying Hsu
論文名稱:線上社群之成員事後採用行為研究-以Mobile01為例
論文名稱(外文):Understanding the Information Adoption Behavior in Online Communities:An Empirical Study of Mobile01
指導教授:陳建文陳建文引用關係
學位類別:碩士
校院名稱:逢甲大學
系所名稱:企業管理所
學門:商業及管理學門
學類:企業管理學類
論文種類:學術論文
論文出版年:2010
畢業學年度:98
語文別:中文
論文頁數:106
中文關鍵詞:持續拜訪意圖資訊採用可依賴度有用性線上社群
外文關鍵詞:Intention to revisitInformation AdoptionOnline communityUsefulnessCredibility
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由於資訊技術的進步與電子商務的蓬勃發展,網路已成為消費者分享產品經驗與意見交流的平台,而線上社群更是消費者最常瀏覽商品資訊與評論的地方,因此,了解影響消費者持續上網與購買意圖的因素,是線上社群經營者關切的議題。消費者認為線上社群上資訊的有用性,將是其決定採用的關鍵;此外,線上社群上資訊除了能幫助消費者完成購買決策外,還能吸引他們持續拜訪線上社群。另外,可依賴度也被視為網友決定採納網路資訊的另外一項參考依據,而先前信念確認與推薦一致性則是影響可依賴度的前置因素。因此本研究以資訊採用模型為基礎,加入先前信念確認與推薦一致性兩項前置因素;再者,加入可依賴度及持續拜訪意圖,一併探討消費者在線上社群上資訊採用後的行為。
本研究以使用過Mobile01的使用者為研究對象,探討影響消費者願意採用線上社群評論的因素,及資訊採用和持續拜訪的意圖。研究結果顯示,論點品質、來源可信度皆會正向影響資訊有用性和可依賴度,因此Mobile01的經營者應注重評論者所發表出評論的品質及其專業程度、可信任度,使用者才會認同評論的有用性及可依賴度;而先前信念確認與推薦一致性同樣也會正向影響可依賴度,所以線上社群經營者需持續提升此四項因素,以強化使用者採用線上社群的推薦。此外,可依賴度會正向影響資訊有用性,因此Mobile01的經營者需強化線上社群上評論的可依賴度,讓使用者認為Mobile01上評論是有用的,進而促進其資訊採用及持續拜訪線上社群的意願。
Due to the gradual proficient of Information Technology and e-commerce, Interent has become an interface for consumer sharing and exchanges their experiences. Online community is the most importance place for consumers which they usually used to search product information and reviews. Therefore, understanding the motivation and reasons that affect on consumer’s continually in using internet and having purchasing intention online is important subject for operators of an online community. In addition, information usefulness of online community would be the critical factor to adopt. However, online communities are not only providing information for decision making, but also attracting them to revisit. Moreover, the credibility is also the important reference when consumers decide to adopt online information. While the confirmation of prior belief and recommendation consistency are antecedents which affect the credibility. This study is based on the Information Adoption Model. Considering confirmation of prior belief and recommendation consistency of information are as two antecedents. Furthermore, joining the credibility and intention to revisit, this study explores consumers’ post-behavior intention of adopting online community information.
The testing subjects of this study are users of Mobile01. This study explores the
factors that consumer willing to adopt online community’s review. Also, the information adoption and intention to revisit are investigated. The finding of this study shows the quality of review and the credibility of resource affects usefulness of the information and credibility in a positive way. The operator of Mobile01 is suggested to pay attention to the reviewers’ comment, their expertise, and their trustworthiness, so users will agree with the usefulness and the credibility of comment. The confirmation of prior belief and recommendation consistency can also have a positive effect on the creditability of reviews. Therefore, online community operators should continue to promote these four factors compels users in adopting recommendations’ of online community. In addition, the credibility of reviews influences usefulness of the information. The operator of Mobile01 should also improve credibility of comments so users will believe comments on Mobile01 are useful. Moreover, it will promote users information adoption and intention to revisit.
中文摘要 I
英文摘要 II
目 錄 IV
圖目錄 VI
表目錄 VII
第一章 緒論 1
1.1研究背景 1
1.2研究動機 5
1.3 研究目的 10
1.4 研究流程 11
第二章 文獻回顧 12
2.1 線上社群 12
2.1.1 線上社群的定義 12
2.1.2線上社群的目的 14
2.1.3 線上社群之採用後行為 15
2.1.4台灣線上社群發展狀況 15
2.1.5 小結 16
2.2 資訊採用模型 17
2.2.1 資訊採用模型的緣起與內涵 17
2.2.2 資訊採用模型相關研究 19
2.2.3 雙程序理論 20
2.3持續拜訪意圖 24
2.4可依賴度 26
第三章 研究方法 28
3.1研究架構與假說 28
3.1.1研究架構 28
3.1.2 研究假說 30
3.2 研究變數 34
3.2.1 論點品質 34
3.2.2 來源可信度 34
3.2.3 先前信念確認 35
3.2.4 推薦一致性 36
3.2.5 可依賴度 36
3.2.6 資訊有用性 37
3.2.7 資訊採用 38
3.2.8 持續拜訪意圖 38
3.3 研究設計 40
3.3.1 研究對象 40
3.3.2 資料收集方法 40
3.4 資料分析方法 41
3.4.1 敘述性統計分析 41
3.4.2信度與效度分析 41
3.4.3 部分最小平方法 42
3.4.4 PLS執行步驟 43
第四章 研究結果分析 51
4.1 樣本結構分析 51
4.1.1樣本個人特徵 51
4.1.2樣本使用特徵 53
4.2 敘述性統計資料分析 56
4.3信效度分析 59
4.3.1信度分析 59
4.3.2 效度分析 61
4.4研究假設與驗證 64
4.5 效果分析 68
第五章 結論與建議 71
5.1 結論 71
5.2 研究貢獻 73
5.2.1 學術貢獻 73
5.2.2實務上的貢獻 73
5.3 未來研究建議 76
5.4 研究限制 77
參考文獻 78
中文部分
[1] 吳明隆,2008,結構方程模式Amos的操作與應用之,台北市:五南圖書出版股份有限公司。
[2] 詹超于,2008,台灣網友行為與B2C消費發展趨勢,資策MIC。
[3] 張恩博、盧諭緯,2008,數位時代,2008年台灣Web總排名,第166期,60~75頁。
[4] 何宛芳,2010,數位時代,第190期,2010台灣人氣網站報告,43~54頁。
[5] 江逸之,2009,天下雜誌,第429期,揪團力量大,107~11頁。
[6] 黃淑芬,2009,台灣地區筆記型電腦搜集資訊管道與關注度之分析,資策會MIC。
[7] 劉楚慧,2010,2009年台灣網友線上購物行為暨消費發展勢,資策會MIC。

英文部分
[1] Agarwal, R. (2000), “Individual Acceptance of Information Technology,” In R. W. Zmud (Ed.), Faming the Domains of it Management: Projecting the future through the past, Cincinnati, OH: Pinnafles Education Resources, pp. 85-104.
[2] Ahn, T., Ryub, S., Han, I. (2004), “The Impact of the Online and Offline Features on the User Acceptance of Internet Shopping Malls,” Electronic Commerce Research and Applications, 3(4), pp. 405-420.
[3] Alloy, L.B., & Naomi, T. (1984), “Assessment of Covariation by Humans and Animals: The Joint Influence of Prior Expectations and Current Situational Information,” Psychological Review, 91(1), pp. 112-149.
[4] Anderson, J.C., & Gerbing, D.W. (1988), “Structural Equation Modeling in Practice: A Review and Recommended Two-Step Approach,” Psychological Bulletin, 103(3), pp. 411-423.
[5] Armstrong, A. & Hagel, J. III (1997), “Net Gain: Expanding Markets through Virtual Communities,” Simon & Schuster Inc.
[6] Arndt, J. (1967), “Role of Product-Related Conversations in the Diffusion of a New Product,” Journalof Marketing Research, 4(3), pp. 291-295.
[7] Bagozzi, R. P., & Dholakia, U. (2002), “Intentional Social Action in Virtual Communities,” Journal of Interactive Marketing, 16(2), pp. 2-21.
[8] Bergquist, M., & Liungberg, J. (2001), “The Power of Gifts: Organizing Social
relationships in open source communities,” Information Systems Journal, 11(4), pp. 305-320.
[9] Bhattacherjee, A (2001), “Understanding Information Sysytem Expectation-Cofirmation Model,” MIS Quarterly, 25(3), pp. 351-370.
[10] Bhattacherjee, A., & Sanford, C. (2006), “Influence Processes for Information Technology Acceptance: An Elaboration Likelihood Model,” MIS Quarterly, 30(4), pp. 805-825.
[11] Bickart, B., & Schindler, R. M. (2001), “Internet Forums as Influential Sources of Consumer Information,” Journal of Interactive Marketing, 15(3), pp.31-40.
[12] Bontis, N. (1998), “Intellectual Capital: An Exploratory Study That Develops Measures and Models,” Management Decision, 63, pp. 63-76.
[13] Boughzala I., Kaouane F. (2005), “Vers un Cadre Méthodologique Pour la Conception des Communautés Professionnelles Virtuelles,” 10ème Colloque de l''AIM, pp. 21-23, Toulouse, France.
[14] Bulkeley, W. M. (2005), “Marketers Scan Blogs for Brand Insights,” The Wall Street Journal, pp. B1.
[15] Bunker, A. M. (1994), “Credibility and Argument Strength: Persuasive Effects When Processing Ability Is Impaired,” East Lansing: Michigan State University Press.
[16] Cacioppo, J. T., Petty, R. E., & Morris, K. J. (1983), “Effects of Need for Cognition on Message Evaluation, Recall, and Persuasion,” Journal of Personality and Social Psychology, 45(4), pp. 805-818.
[17] Chaiken, S. (1980), "Heuristic Versus Systematic Information Processing and the Use of Source Versus Message Cues in Persuasion," Journal of Personality and Social Psychology, 39(5), pp. 752-766.
[18] Carmines, Edward G. & Richard A. Zeller (1979), “Reliability and Validity Assessment,” Beverly Hills, CA: Sage Publications.
[19] Chaiken, S., & Eagly, A. H. (1976), “Communication Modality as a Determinant of Message Persuasiveness and Message”.
[20] Chaiken, S. & Maheswaran, D. (1991), “Heuristic Processing Can Bias SystematicProcessing: Effects of Source Credibility, Argument Ambiguity,and Task Importanceon Attitude Judgment.” Journal of Personality and Social Psychology, 34, pp. 605-614.
[21] Chang, M. K., & Chring, W. (2001), “Determinants of the Intention to Use Internet/WWW at Work: A Confirmatory Sudy,” Information & Management, 39(1), pp. 1-14.
[22] Chen, Y. L. (2007), “The Factors Influencing Members’ Continuance Intentions in Professional Virtual Communities - A Longitudinal Study,” Journal of Information Science, 33(4), pp. 451-467.
[23] Cheung M. Y. (2006), “Do People Believe Electronic Word- of –Mouth? A Study on Factors Affecting Readers’Rerceived Credibility of Online Consumer Reviews”, pp. 1-184.
[24] Cheung, C. M. K., & Lee, M. K. O. (2009a), “Understanding the Sustainability of a Virtual Community: Model Development and Empirical Test,” Journal of Information Science, 35(3), pp. 279-298.
[25] Cheung, C. M. K., Lee, M. K. O., & Rabjohn, N. (2008), “The Impact of Electronic Word of Mouth: The Adoption of Online Opinions in Online Consumer Communities,” Internet Research, 18(3), pp. 229-247.
[26] Cheung, M. Y., Luo, C., Sia, C. L., & Chen, H. (2009b), “Credibility of Electronic Word-of-Mouth: Informational and Normative Determinants of On-line Consumer Recommendations,” International Journal of Electronic Commerce, 13(4), pp. 9-38.
[27] Chin, W.W. (1998), “The Partial Least Squares Approach to Structural Equation Modeling,” In: Marcoulides, G.A. (Ed.). Modern Business Research Methods. Mahwah, NJ: Lawrence Erlabaum Associates, pp. 295-336.
[28] Chin, W. W., & Newsted, P. R.(1999), “Structural Equation Modeling Analysis with Small Samples Using Partial Least Squares,” In Statistical Strategies for Small Sample Research, R. H. Hoyle (ed.), Sage Publications, Thousand Oaks, CA, pp. 307-341.
[29] Chu, K.-M. (2009), “A Study of Members’ Helping Behaviors in Online Community,” Internet Research, 19(3), pp. 279-292.
[30] Cool, K.; Dierickx, & D Jemison, (1989), “Business Strategy, Market Structure and Risk-Return Relationships: A Structural Approach,” Strategic Management Journal, 10(6), pp. 507-522.
[31] Cool, K. & Schendel, D., (1988), “Performance Differences among Strategic Group Members”, Strategic Management Journal, 9(3), pp. 207-233.
[32] Crocker, J. (1981), “Judgment of Covariation by Social Perceivers,” Psychological Bulletin,” 90, pp. 272–292.
[33] Davy, C. (2006), “Recipients: The key to Information Transfer,” Knowledge Management Research and Practice, 4(1), pp. 17-25.
[34] Deutsch, M., & Gerrard, H. B. (1995), “A Study of Normative and Informational Social Influence upon Individual Judgment,” Journal of Abnormal and Social Psychology, 53(3), pp. 629-636.
[35] Dholakia, R. R., & Sternthal, B. (1977), “Highly Credible Sources: Persuasive Facilitators or Persuasive Liabilities?,” The Journal of Consumer Research, 3(3), pp. 223-232.
[36]Efron, B. (1979), “Bootstrap Methods: Another Look at the Jackknife,” Annals of Statistics, 7, pp. 1-26.
[37] Engel, J.E., Blackwell, R.D. & Kegerreis, R.J. (1969), “How Information is Used to Adopt an Innovation,” Journal of Advertising Research, 9(4), pp. 3-8.
[38] Eysenbach, G. (2000), “Towards Ethical Guidelines for e-Health: JMIR Theme Issue on eHealth Ethics,” Journal of Medical Internet Research 2(1), p. e7, available at: http://www.jmir.org/2000/1/e7/index.htm.
[39] F.D.Davis (1989), “Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology,” MIS Quarterly, 13(3), pp. 319-340.
[40]Faison, E. W. J. (1969), “Effectiveness of One-Sided and Two-Sided Mass Communication in Advertising,” Public Opinion Quarterly, 25, pp. 468-469.
[41] Flanagin, A. J., & Metzger, M. J. (2001), “Internet Use in the Contemporary Media Environment,” Human Communication Research, 27(1), pp. 153-181.
[42] Fornell, C., & Larcker, F. (1981), “Evaluating Structural Equation Models with Unobservable Variables and Measurement Error,” Journal of Marketing Research, 18(1), pp. 39-50.
[43] Fornell, C. R. & Bookstein, F.L., (1982), “Two Structural Equation Model: LISREL and PLS Applied to Consumer Exit-Voice Theory,” Journal of Marketing Research, (19), pp.440-452.
[44] Fornell, C., Lorange, P., & Roos, J., (1990), "The Cooperative Venture Formation Process: A Latent Variable Structural Modeling Approach," Management Science, 36(10), pp. 1246-1255.
[45] Fogg. B.J., (2003), “Persuasive Technology: Using Computers to Change What We Think and Do,” San Francisco,” CA: Morgan Kaufmann Publishers. 18(1), pp. 39-50.
[46] Fogg, B.J.; Marshall, J.; Laraki, O.; Osipovich, A.; Varma, C.; Fang, N.;Paul, J.; Rangnekar, A.; Shon, J.; Swani, P.; & Treinen, M.(2001), “What Makes Websites Credible? A Report on a Large Quantitative Study,” In:Proceedings of the Conference on Human Factors in Computing Systems, ACM Press, New York, pp. 61–68.
[47] Grewal, D., Gotlieb, J., & Marmorstein, H. (1994), “The Moderating Effects of Message Framing and Source Credibility on the Price-Perceived Risk Relationship,” Journal of Consumer Research, 21(1), pp. 145-153.
[48] Hall, H., & Graham, D. (2004), “Creation and Recreation: Motivating Collaboration to Generate Knowledge Capital in Online Communities,” International Journal of Information Management Science, 24(4), pp. 235-246.
[49] Hempel, J. & P. Lehman (2005), “The MySpace Generation;They Live Online.;They Buy Online;They Play Online;Their Power is Growing,” BusinessWeek, 12(12), pp. 86.
[50] Hennig-Thurau, T., & Walsh, G. (2004), “Electronic Word of Mouth: Motives for and Consequences of Reading Customer Articulations on the Internet,” International Journal of Electronic Commerce, 8(2), pp. 51–74.
[51] Hennig-Thurau, T., Gwinner, K., Walsh, G., & Gremler, D. (2004), “Electronic Word-of-Mouth via Consumer-Opinion Platforms: What Motivates Consumers to Articulate themselves on the Internet?,” Journal of Interactive Marketing, 18(1), pp. 38–52.
[52] Holvand, C.L. (1959), “Reconciling Conflicting Results Derived from Experimental and Survey Results Studies of Attitude Change,” American Psychologist, 14, pp. 8-17.
[53] Hong, T. (2006), “The Influence of Structural and Message Features on Web Site Credibility,” Journal of the American Society for Information Science and Technology, 57(1), pp. 114-127.
[54] Hsu, M. H., Chiu, C. M., & Ju, T. L. (2004), “Determinants of Continued Use of the WWW: An Integration of Two Theoretical Models,” Industrial Management & Data Systems, 104(9), pp. 766–775.
[55] Hulland, J. (1999), “Use of Partial Least Squares (PLS) in Strategic Management Research: A Review of Four Recent Studies,” Strategic Management Journal, 22(2), pp. 195-204.
[56] Jasperson, J. S., Carter, P. E., & Zmud, R. W. (2005), “A Comprehensive Conceptualization of Post-Adoptive Behaviors Associated with IT Enabled Work Systems,” MIS Quarterly, 29(3), pp. 525–557.
[57] Jin, X.-L., Cheung, C. M. K., Lee, M. K. O., & Chen, H.-P. (2009), “How to Keep Members Using the Information in a Computer-Supported Social Network,” Computers in Human Behavior, 25, pp. 1172-1181.
[58] Johansson, Johny K. & Yip, George S., (1994), "Exploiting Globalization Potential: U.S.and Japanese Strategies, " Strategic Management Journal, 15(8), pp. 579-601.
[59] Kardaras, D., Karakostas, B., & Papathanassiou, E. (2003), “The Potential of Virtual Communities in the Insurance Industry in the UK and Greece,” International Journal of Information Management, 23(1), pp. 41-53.
[60] Kaplan, M. F., & Miller, C. E. (1987), “Group Decision Making and Normative versus Informational Influence: Effects of Type of Issue and Assigned Decision Rule,” Journal of Personality and Social Psychology, 53(2), pp. 306-313.
[61] Karahanna, E., Straub, D. W., & Chervany, N. L. (1999), “IT Adoption across Time,” MIS Quarterly,” 23(2), pp. 183-213.
[62] Katz, E. & Lazarfeld, P.F. (1955), “Personal Influence,” Free Press, Glencoe, IL.
[63] Kelley, H.H & Michela, J.L. (1980), “Attribution Theory and Research,” Annual Review Psychology, 31, pp. 457-501.
[64] Khoshoie, T. (2006), “Stickiness in Virtual Community,” Master Thesis of Lulea University of Technology.
[65] Kim, S., & Malhotra, N. K. (2005), “A Longitudinal Model of Continued is Use: An Integrative View of Four Mechanisms Underlying Post-Adoption Phenomena,” Management Science, 51(5), pp. 741-755.
[66] Kim, S. S., & Son, J.-Y. (2009), “Out of Dedication or Constranint? A Empirical Test in the Context of Oline Services the Context of Online Services,” MIS Quarterly, 33(1), pp. 49-70.
[67] Kozinets, R. V. (1998), “On Netnography: Initial Reflections on Consumer Research Investigations of Cyberculture,” Advances in Consumer Research, 25, pp. 366-371.
[68] Ko, D. G., Kirsch, L. J., & King, W. R. (2005), “Antecedents of Knowledge Transfer From Consultants to Clients in Enterprise System Implementations,” MIS Quarterly, 29(1), pp. 59-85.
[69]Kwon, T.H., & Zmud, R.W. (1987), “Unifying the Fragmented Models of Information Systems Implementation,” In J. R. Boland, & R. Hirsheim (Eds.), Critical Issues in Information Systems Research, New York: Wiley, pp. 227-251.
[70] Laroche, M., Kim, C., Zhou, L., (1996), “Brand Familiarity and Confidence as Determinant of Purchase Intention: An Empirical Test in a Multiple Brand Context,” Journal of Business Research, 37, pp.115–120.
[71]Liang, T.P, & Lai, H.J. (2002), “Effect If Store Design on Consumer Purchases: van Empirical Study of On-line Bookstores,” Information & Management, 39, pp. 431-444.
[72] Lee, Y. H., & Mason, C. (1999), “Responses to Information Incongruency in Advertising: The Role of Expectancy, Relevancy, and Humor,” Journal of Consumer Research, 25(9), pp. 156-169.
[73] Lee, M. K. O., Cheung, C. M. K., Lim, K. H., & Sia, C. L. (2006), “Understanding Customer Knowledge Sharing in Web-Based Discussion Boards: An Exploratory Study,” Internet Research, 16(3), pp. 289-303.
[74] Limayem, M., Hirt, S. G., & Cheung, C. M. K. (2007), “How Habit Limits the Predictive Power of Intention: The Case of Information Systems Continuance,” MIS Quarterly, 31(4), pp. 705-737.
[75] Lin, H.-F. (2007), “The Role of Online and Offline Features in Sustaining Virtual Communities: An Empirical Study,” Internet Research, 47(2), pp. 119-138.
[76] Lin, C., & Lu, H., (2000), “Towards an Understanding of the Behavioral Intention to Use a Web Site,” International Journal of Information Management, 20, pp. 197-208.
[77] Liu, Z. (2004), “Perceptions of Credibility of Scholarly Information on the Web,” Information Processing and Management, 40, pp. 1027-1038.
[78] Lu, H.P., & Lin, J. C.-C. (2002), “Predicting Customer Behavior in the Market-Space: A Study of Rayport and Sviokla’s Framework,” Information & Management, 40, pp. 1-10.
[79]Matei, S. (2004), “The Impact of State-Level Social Capital on the Emergence of Virtual Communities,” Journal of Broadcasting and Electronic Media, 48(1), pp. 23-40.
[80] Mayo, J., & Leshner, G. (2000), “Assessing the Credibility of Computer Assisted
Reporting,” Newspaper Research Journal, 21(4), pp. 68-82.
[81] Mcknight, D.H., Choudhury, V., Kacmar, C., (2001), “Developing and Validating Trust Measures for E-Commerce: An Integrative Typology,” Information Systems Research, 13 (3), pp. 334–359.
[82] McKnight, D. H., Choudhury, V., & Kacmar, C. (2002), “The Impact of Initial Consumer Trust on Intentions to Transact with a Web Site: A Trust Building Model,” Journal of Strategic Information Systems, 11(3/4), pp. 297-232.
[83]McKnight, D.H., & Kacmar, C. (2006), “Factors of Information Credibility for an Internet Advice Site,” In R.H. Sprague Jr. (ed.), Proceedings of the 39th Hawaii International Conference on System Sciences. Los Alamitos, CA: IEEE Computer Society Press, 2006,available at :
www2.computer.org/plugins/dl/pdf/proceedings/hicss/2006/2507/06/250760113b.pdf?template=1&loginState1&userData=anonymous-IP%253A%253A127.0.0.1.
[84] McKnight, D. H., & Charles, J. K. (2007), “Factors and Effects of Information Credibility,” Proceedings of the Ninth International Conference on Electronic Commerce. Minneapolis, MN, USA, ACM.
[85] Nabi, R. L., & Hendriks, A. (2003), “The Persuasive Effect of Host and Audience Reaction Shots in Television Talk Shows,” Journal of Communication, 53(3), pp. 527-543.
[86] Nie, N. H. (2001), “Sociability, Interpersonal Relations, and the Internet: Reconciling Conflicting Findings,” American Behavioral Scientist, 45, pp. 420- 435.
[87] Nunnally, J.C. (1978), “Psychometric Theory,” New York: McGraw-Hill.
[88] O’Dea, F. (2000), “Creating ‘Sticky’ Customers in the E-Economy.Accountancy Ireland,” June, p. 18.
[89] O’Keefe, D. J. (2002), “Persuasion:Theory & Research,” Thousans Oaks, CA:Sage Publications.
[90] Park, D.-H., Han, I., & Lee, J. (2007), “The Effects of On-Line Consumer Reviews on Consumer Purchasing Intention: The Moderating Role of Involvemen,” International Journal of Electronic Commerce, 11(4), pp. 125-148.
[91] Park, J. K., Chung, H. E., & Yoo, W. S. (2009), “Is the Internet a Primary Source for Consumer Information Search?: Group Comparison for Channel Choices,” Journal of Retailing and Consumer Services, 16, pp. 92-99.
[92] Pentina, I., Prybutok, V. R., & Zhang, X. (2008), “The Role of Virtual Community as Shopping Reference Group,” Journal of Electronic Commerce Research, 9(2), pp. 114-136.
[93] Peterson, R.A., & William R.W. (1987), “Perceived Risk and Price-Reliance Schema and Price-Perceived-Quality Mediators,” In J. Jacoby and J. Olson (eds.), Perceived Quality. Lexington, MA: Lexington Books, pp. 247–268.
[94] Petty, R., & Cacioppo, J.T. (1986), “Elaboration Likelihood Model,” In L.Berkowitz (ed.), Advances in Experimental Social Psychology. San Diego: Academic Press, pp. 123–205.
[95] Pitta, D. A., & Fowler, D. (2005), “Internet Community Forums: An Untapped Resource for Consumer Marketers,” Journal of Consumer Marketing, 22(5), pp. 265-274.
[96] Porter, E. C. (2004), “A Typology of Virtual Communities: A Multi-Disciplinary Foundation for Future Research,” Journal of Computer Mediated Communication, 10(1).
[97]Ratchford, B. T., Talukdar, D., & Lee, M.-S. (2001), “A Model of Consumer Choice of the Internet as an Information Source,” International Journal of Electronic Commerce, 5(3), pp. 7-22.
[98] Rheingold, H., (1993), “The Virtual Community?,” In Proceedings of the Annual ACM SIGCHI Conference on Human Factor in Computing System, pp. 360-367.
[99] Richins, M.L. (1983), “Negative Word-of-Mouth by Dissatisfied Consumers: a Pilot Study,” Journal of Marketing, 47(1), pp. 68-78.
[100]Ridings, C., Gefen, D., & Arinze, B. (2006), “Psychological Barriers: Lurker and Poster Motivation and Behavior in Online Community,” Communications of the Association for Information Systems, 18, pp. 329-354.
[101]Ridings, C. M., Gefen, D., & Arinze, B. (2002), “Some Antecedents and Effects of Trust in Virtual Communities,” Journal of Strategic Information Systems, 11(3), pp. 271-295.
[102]Rieh, S. Y. (2002), “Judgment of Information Quality and Cognitive Authority in the Web,” Journal of the American Society for Information Science and Technology, 53(2), pp. 145-161.
[103]Rosen, S. (2001), “Sticky Web Site is Key to Success,” Communication World, 18(3), pp. 36-37.
[104] Saeed, K. A., & Abdinnour-Helm, S. (2008), “Examining the Effects of Information System Characteristics and Perceived Usefulness on Post Adoption Usage of Information Systems,” Information & Management, 45(9), pp.376-386.
[105] Senecal, S. & Nantel, J. (2004), “The Influence of Online Product Recommendations on Consumers’ Online Choices,” Journal of Retailing, 80, pp. 159-169.
[106] Sengupta Jaideep, & Gita Johar V. (2002), “Effect of Inconsist Attribute Information on the Predictive Value of Product Attitudes: Toward a Resolution of Opposing Perspectives,” Journal of Consumer Research, 29, pp. 39-56.
[107] Shang, R.-A., Chen, Y.-C., & Liao, H.-J. (2006), “The Value of Participation in Virtual Consumer Communities on Brand Loyalty,” Internet Research, 16(4), pp. 398-418.
[108] Souza, d., & Preece, J. (2004), “A Framework for Analyzing and Understanding Online Communities,” Interacting with Computers, 16(5), pp. 579-610.
[109] Subramani, M.R. & Rajagopalan, B. (2003), “Knowledge-Sharing and Influence in Online Social Networks via Viral Marketing,” Communications of the ACM, 46(12), pp. 300-7.
[110] Suh, B., & Han, I. (2003), “The Impact of Customer Trust and Perception of Security Control on the Acceptance of Electronic Commerce,” Journal of Electronic Commerce, 7, pp. 13 -161.
[111] Sundar, S. S., Knobloch-Westerwick, S., & Hastall, M. R. (2007), “News Cues: Information Scent and Cognitive Heuristics,” Journal of the American Society for Information Technology, 58(3), pp. 366-378.
[112] Sussman, S. W., & Siegal, W. S. (2003), “Informational Influence in Organizations: An Integrated Approach to Knowledge Adoption,” Information Systems Researc, 14(1), pp. 47-65.
[113] Teng, W., Lu, H. & Yu, H. (2009), “Exploring the Mass Adoption of Third-generation (3G) Mobile Phones in Taiwan,” Telecommunications Policy, 33, pp.628-641.
[114] Tseng, S., & Fogg, B.J. (1999), “Credibility and Computing Technology,” Communications of the ACM, 42(5), pp. 39–44.
[115] Tiwana, A., & Bush, A. A. (2005), “Continuance in Expertise-Sharing Networks: A Social Perspective,” IEEE Transactions on Engineering Management, 52(1), pp. 85–101.
[116] Trafimow, D., & Davis, J. (1993), “The Effects of Anticipated Informational and Normative Influence on Perceptions of Hypothetical Opinion Change,” Basic and Applied Social. Psychology, 14, pp. 487-496.
[117] Vandenbosch, B., & Higgins, C. (1996), “Information Acquisition and Mental Models: An Investigation into the Relationship between Behavior,” Information Systems Research, 7(2), pp. 198-214.
[118] Vara, V. (2004), “Researchers Mine Web for Focus Groups," The Wall Street Journal Online, November 17.
[119] Venkatesh,V.,Morris,M.G.,&Ackerman,P.L. (2000), “A longitudinal Feld in Investigation of Gender Difference in Individual Technology Adoption Decision Making processes,” Organizational and Human Decision Processes, 83, pp. 33-60.
[120]Venkatesh, V., & Brown, S.A. (2001), “A longitudinal Investigation of Personal Computers in Homes: Adoption Determinants and Emerging Challenges,” MIS Quarterly, 25(1), pp71-102.
[121] Vijayasarathy, L. R. (2004), “Predicting Consumer Intentions to Use On-Line Shopping: The Case for an Augmented Technology Acceptance Model,” Information & Management, 41(6), pp. 747-762.
[122] Wang, Y., Yu, Q., & Fesenmaier, D. R. (2002), “Defining the Virtual Tourist Community: Implications for Tourism marketing,” Tourism Management, 23(4), pp. 407-417.
[123] Wathen, C. N., & Burkell, J. (2002), “Believe It or Not: Factors Influencing Credibility on the Web,” Journal of the American Society for Information Science and Technology, 53(2), pp. 134-144.
[124] Wind, Y., & Mahajan, V. (2002), “Convergence Marketing,” Journal of Interactive Marketing, 16(2), pp. 64-79.
[125] Wixom, B. H. & Watson, H. J. (2001), “An Empirical Investigation of The Factors Affecting Data Warehousing Success,” MIS Quarterly, 25(1), pp.17-41.
[126] Wold, H. (1982), “Systems under Indirect Observation Using PLS"In Fornell, C. A Second Generation of Mutivariate Analysis, New York: Praeger, pp. 325-347.
[127] Xu, Y., Tan, C. Y., & Yang, L. (2006), “Who Will You Ask? An Empirical Study of Interpersonal Task Information Seeking,” Journal of the American Society for Information Science and Technology, 57(12), pp. 1666-1677.
[128] Zeithaml, V. A. (1988), “Consumer Perceptions of Price, Quality, and Value: A Means-End Model and Synthesis of Evidence Means-End Model and Synthesis of Evidence,” Journal of Marketing Research, 55, pp. 2-22.
[129] Zhang, W., and Watts, S. (2003), “Knowledge Adoption in Online Communities of Practice,” In S.T. March, A. Massey, and J.I. DeGross (eds.), 24th International Conference on Information Systems. Atlanta: AIS, pp. 96–109.
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