博碩士論文 102423040 詳細資訊




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姓名 林心誼(Hsin-yi Lin)  查詢紙本館藏   畢業系所 資訊管理學系
論文名稱 社會連結強度、產品知覺風險與沉浸感對消費者行為意圖之影響—以Facebook塗鴉牆推薦訊息為例
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摘要(中) 社群網站逐漸成為消費者獲得口碑訊息的主要管道之一,能藉由瀏覽Facebook Fan Page或塗鴉牆得知其他消費者所分享與推薦的產品資訊與使用體驗。對於企業而言,社群網站不僅是有利於推動行銷活動之平台,目前亦有跨入電子商務市場之趨勢,提供相關商務服務,如Facebook開店應用程式功能。本研究主要探討在社群網站情境下,消費者決策制定過程之影響因素,包括推薦訊息傳遞者與接收者間之連結強度、產品相關知覺風險程度與訊息診斷性程度,對於消費者行為意圖之影響。
研究方法採用實驗法,為結合實務現象,本研究利用Facebook開店應用程式,進一步探討沉浸感對於消費者購買意圖之影響。針對連結強度 (強連結/弱連結)、知覺風險 (高風險/低風險)進行實驗操弄,區分為四種實驗設計組合,隨機分配受測者於實驗組別當中,以情境影片與網路問卷作為媒介。研究結果顯示連結強度對於消費者訊息診斷性具有顯著差異;知覺風險之高低對於連結強度與訊息診斷性間具有干擾效果;訊息診斷性對於消費者行為意圖具有正向影響;以及沉浸感對於訊息診斷性與購買意圖之間具有正向調節效果。
本研究提出之實務建議為,在線上社群環境中,企業應重視強連結對於消費者決策的重要性,以及適合推廣低風險產品。企業可考量自家產品或服務是否適用推廣於社群網站上,進而採用Facebook應用程式功能以增進消費者購買意圖。
摘要(英) Social networking sites (SNSs) have gradually become one of the important sources for consumers to acquire recommendations and product information by browsing Facebook Fan Page or the wall. For companies, the social networking site is a good platform to promote their marketing activities, offer business-related services, such as Facebook shopping applications. Thus, this study investigated the effects of tie strength, perceived risk and perceived diagnosticity on consumers’ behavioral intention. In addition, this research used Facebook application to further explore flow affects on consumers’ purchase intention.
This research has proposed five hypotheses. It conducted a 2 (tie strength: strong tie/weak tie) × 2 (perceived risk: high risk/low risk) experimental design and used a field experiment on Facebook to test these hypotheses. This experiment design was implemented by an online experimental questionnaire and video. All of our respondents were randomly assigned to one of these four scenarios.
The results show that the recommendations provided by strong-tie friends will positively influence perceived diagnosticity. Perceived risk moderates the effect of tie strength on perceived diagnosticity. Both strong-tie and weak-tie groups have higher perceived diagnosticity toward low-risk products than high-risk products. And perceived diagnosticity will positively influence consumers’ behavior intentions. Finally, flow moderates the effect of perceived diagnosticity on purchase intention.
This research proposed some managerial implications based on the result. Social networking sites are good platforms to promote low-risk products. Companies can choose appropriate consumers to recommend products to their strong-tie friends. In addition, companies can consider whether social networking sites can be used to promote their products or service and further adopt the Facebook application for business-related activities.
關鍵字(中) ★ 連結強度
★ 知覺風險
★ 訊息診斷性
★ 沉浸感
★ 購買意圖
★ 推薦意圖
關鍵字(英) ★ Tie strength
★ Perceived risk
★ Perceived diagnosticity
★ Flow
★ Purchase intention
★ Recommendation intention
論文目次 中文摘要 i
Abstract ii
致謝辭 iii
目錄 iv
圖目錄 vi
表目錄 vii
第一章、緒論 1
1-1 研究背景與動機 1
1-2 研究目的 5
1-3 研究流程 6
第二章、文獻探討 7
2-1 連結強度 7
2-2 知覺風險 11
2-3 訊息診斷性 15
2-4 沉浸感 17
2-5 行為意圖 22
第三章、研究方法 25
3-1 研究架構 25
3-2 研究假說 25
3-3 研究設計 29
3-4 變數定義與衡量 35
第四章、資料分析與結果 40
4-1 前測分析 40
4-2 正式實驗樣本之分析 48
4-3 信度與效度分析 58
4-4 操弄檢定 61
4-5 假說檢定 62
4-6 延伸探討 72
第五章、結論與建議 74
5-1 研究結論 74
5-2 管理意涵 77
5-3 研究限制 79
5-4 研究建議 80
參考文獻 81
一、網路資料 81
二、英文文獻 81
附錄一 研究問卷 (情境一) 87
附錄二 研究問卷 (情境二) 92
附錄三 研究問卷 (情境三) 97
附錄四 研究問卷 (情境四) 102
參考文獻 一、網路資料
[1] 張嘉伶、熊方瑜 (2014)。2014網路人氣賣家總榜單。數位時代,130-135。
[2] 資策會產業情報研究所 (2013)。2013資通訊服務產業年鑑-數位媒體篇,9-18。
[3] 資策會產業情報研究所 (2014)。社群發展專題研究精選。擷取日期:2015年5月16日。取自:http://mic.iii.org.tw/aisp/book/bookdetail.asp?bptype=&bsqno=711
[4] 資策會產業情報研究所 (2015)。2015上半年Facebook廣告於消費行為影響力分析。擷取日期:2015年5月16日。取自: http://mic.iii.org.tw/aisp/reports/reportdetail.asp?docid=CDOC20150319002&doctype=RC&smode=1
[5] 路透社 (2008)。Facebook登陸中國市場推出簡體中文版。擷取日期:2015年5月16日。取自:http://cn.reuters.com/article/2008/06/20/idCNChina-1472720080620
[6] 熊方瑜 (2014)。Facebook測試「購買」按鈕,站內輕鬆完成購物。數位時代。擷取日期:2015年5月16日。取自:http://www.bnext.com.tw/article/view/id/33081
[7] 模範市場研究顧問 (2014)。Facebook台灣消費者線上行為調查。擷取日期:2015年5月16日。取自:http://share.inside.com.tw/posts/5249
[8] 羅之盈 (2012)。樂天市場挖深社群,推Facebook開店應用程式。數位時代。擷取日期:2015年5月16日。取自:http://www.bnext.com.tw/article/view/id/23759
二、英文文獻
[1] Ajzen, I. (1985). From intentions to actions: A theory of planned behavior. Springer Berlin Heidelberg, 11-39.
[2] Anderson, E. W. (1998). Customer satisfaction and word of mouth. Journal of Service Research, 1(1), 5-17.
[3] Arndt, J. (1967a). Role of product-related conversations in the diffusion of a new product. Journal of Marketing Research, 4(3), 291-295.
[4] Arndt, J. (1967b). Word of mouth advertising: A review of the literature. Advertising Research Foundation.
[5] Bansal, H. S., & Voyer, P. A. (2000). Word-of-mouth processes within a services purchase decision context. Journal of Service Research, 3(2), 166-177.
[6] Baron, R. M., & Kenny, D. A. (1986). The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51(6), 1173.
[7] Bauer, R. A. (1960). Consumer behavior as risk taking. Proceedings of the 43rd Conference of the American Marketing Association, 389-398.
[8] Beatty, S. E., & Smith, S. M. (1987). External Search Effort: An Investigation across Several Product Categories. Journal of Consumer Research, 14(1), 83-95.
[9] Bell, H., & Tang, N. K. (1998). The effectiveness of commercial Internet Web sites: a user′s perspective. Internet Research, 8(3), 219-228.
[10] Bettman, J. R. (1973). Perceived risk and its components: a model and empirical test. Journal of Marketing Research, 10(2), 184-190.
[11] Braun-Latour, K. A., & Zaltman, G. (2006). Memory change: An intimate measure of persuasion. Journal of Advertising Research, 46(1), 57-72.
[12] Brown, J. J., & Reingen, P. H. (1987). Social ties and word-of-mouth referral behavior. Journal of Consumer Research, 14(3), 350-362.
[13] Cases, A.-S. (2002). Perceived risk and risk-reduction strategies in Internet shopping. The International Review of Retail, Distribution and Consumer Research, 12(4), 375-394.
[14] Chen, Q., & Well, W. D. (1999). Attitude toward the site. Journal of Advertising Research, 39(5), 28-38.
[15] Cheng, F.-F., Liu, T.-Y., & Wu, C.-S. (2013). Perceived Risks and Risk Reduction Strategies in Online Group-Buying. Paper presented at the Diversity, Technology, and Innovation for Operational Competitiveness: Proceedings of the 2013 International Conference on Technology Innovation and Industrial Management.
[16] Clarke, S. G., & Haworth, J. T. (1994). ‘Flow’experience in the daily lives of sixth‐form college students. British Journal of Psychology, 85(4), 511-523.
[17] Cox, D. F., & Rich, S. U. (1964). Perceived risk and consumer decision-making: The case of telephone shopping. Journal of Marketing Research, 1(4), 32-39.
[18] Coyle, J. R., & Thorson, E. (2001). The effects of progressive levels of interactivity and vividness in web marketing sites. Journal of Advertising, 30(3), 65-77.
[19] Csikszentmihalyi. (1975). Beyond boredom and anxiety. The Jossey-Bass Behavioral Science Series.
[20] Csikszentmihalyi. (1977). Beyond Boredom and Anxiety. San Francisco: Jossey-Bass.
[21] Csikszentmihalyi, M., & Csikszentmihalyi, I. S. (1992). Optimal experience: Psychological studies of flow in consciousness. Cambridge University Press.
[22] Cunningham, S. M. (1967). The major dimensions of perceived risk. Risk Taking and Information Handling in ConsumerBehavior, 82-111.
[23] De Bruyn, A., & Lilien, G. L. (2008). A multi-stage model of word-of-mouth influence through viral marketing. International Journal of Research in Marketing, 25(3), 151-163.
[24] Dowling, G. R., & Staelin, R. (1994). A model of perceived risk and intended risk-handling activity. Journal of Consumer Research, 21(1), 119-134.
[25] Duhan, D. F., Johnson, S. D., Wilcox, J. B., & Harrell, G. D. (1997). Influences on consumer use of word-of-mouth recommendation sources. Journal of the Academy of Marketing Science, 25(4), 283-295.
[26] Eroglu, S. A., Machleit, K. A., & Davis, L. M. (2003). Empirical testing of a model of online store atmospherics and shopper responses. Psychology & Marketing, 20(2), 139-150.
[27] Filieri, R. (2015). What makes online reviews helpful? A diagnosticity-adoption framework to explain informational and normative influences in e-WOM. Journal of Business Research, 68(6), 1261-1270.
[28] Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and behavior: An introduction to theory and research. Addison-Wesley Pub (Sd).
[29] Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50.
[30] Frenzen, J., & Nakamoto, K. (1993). Structure, cooperation, and the flow of market information. Journal of Consumer Research, 20(3), 360-375.
[31] Gao, L., & Bai, X. (2014). Online consumer behaviour and its relationship to website atmospheric induced flow: Insights into online travel agencies in China. Journal of Retailing and Consumer Services, 21(4), 653-665.
[32] Gilbert, E., & Karahalios, K. (2009). Predicting tie strength with social media. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 211-220.
[33] Granovetter, M. S. (1973). The strength of weak ties. American Journal of Sociology, 78(6), 1360-1380.
[34] Granovetter, M. S. (1983). The strength of weak ties: A network theory revisited. Sociological Theory, 1(1), 201-233.
[35] Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivariate data analysis. Pearson Prentice Hall Upper Saddle River, NJ, 6.
[36] Hausman, A. V., & Siekpe, J. S. (2009). The effect of web interface features on consumer online purchase intentions. Journal of Business Research, 62(1), 5-13.
[37] Hennig-Thurau, T., Walsh, G., & Walsh, G. (2003). Electronic word-of-mouth: motives for and consequences of reading customer articulations on the internet. International Journal of Electronic Commerce, 8(2), 51-74.
[38] Hennig‐Thurau, T., Gwinner, K. P., Walsh, G., & Gremler, D. 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), 38-52.
[39] Hoffman, D. L., & Novak, T. P. (1996). Marketing in hypermedia computer-mediated environments: conceptual foundations. Journal of Marketing, 60(July), 50-68.
[40] Hong, I. B., & Cha, H. S. (2013). The mediating role of consumer trust in an online merchant in predicting purchase intention. International Journal of Information Management, 33(6), 927-939.
[41] Jacoby, J., & Kaplan, L. B. (1972). The components of perceived risk. Proceedings of the 43rd Conference of the American Marketing Association, 389-398.
[42] Jamieson, L. F., & Bass, F. M. (1989). Adjusting stated intention measures to predict trial purchase of new products: A comparison of models and methods. Journal of Marketing Research, 26(3), 336-345.
[43] Jiang, Z., & Benbasat, I. (2004). Virtual product experience: Effects of visual and functional control of products on perceived diagnosticity and flow in electronic shopping. Journal of Management Information Systems, 21(3), 111-147.
[44] Jiang, Z., & Benbasat, I. (2007). Research Note-Investigating the Influence of the Functional Mechanisms of Online Product Presentations. Information Systems Research, 18(4), 454-470.
[45] Jiuan Tan, S. (1999). Strategies for reducing consumers′ risk aversion in Internet shopping. Journal of Consumer Marketing, 16(2), 163-180.
[46] Kang, Y.-S., & Kim, Y. J. (2006). Do visitors′ interest level and perceived quantity of web page content matter in shaping the attitude toward a web site? Decision Support Systems, 42(2), 1187-1202.
[47] Kelley, T. L. (1939). The selection of upper and lower groups for the validation of test items. Journal of Educational Psychology, 30(1), 17.
[48] Kempf, D. S., & Smith, R. E. (1998). Consumer processing of product trial and the influence of prior advertising: A structural modeling approach. Journal of Marketing Research, 35(3), 325-338.
[49] Kim, J., & Gupta, P. (2012). Emotional expressions in online user reviews: How they influence consumers′ product evaluations. Journal of Business Research, 65(7), 985-992.
[50] Kim, S., & Park, H. (2013). Effects of various characteristics of social commerce (s-commerce) on consumers’ trust and trust performance. International Journal of Information Management, 33(2), 318-332.
[51] Kim, Y. J., & Kim, H. Y. (2010). The effect of justice and trust on eWOM in social media marketing: focused on power blog and meta blog. The Journal of Internet Electronic Commerce Research, 10(3), 131-155.
[52] Kirmani, A., & Rao, A. R. (2000). No pain, no gain: A critical review of the literature on signaling unobservable product quality. Journal of Marketing, 64(2), 66-79.
[53] Korzaan, M. L. (2003). Going with the flow: Predicting online purchase intentions. Journal of Computer Information Systems, 43(4), 25.
[54] Koufaris, M. (2002). Applying the technology acceptance model and flow theory to online consumer behavior. Information Systems Research, 13(2), 205-223.
[55] Levin, D. Z., & Cross, R. (2004). The strength of weak ties you can trust: The mediating role of trust in effective knowledge transfer. Management Science, 50(11), 1477-1490.
[56] Marsden, P. V., & Campbell, K. E. (1984). Measuring tie strength. Social forces, 63(2), 482-501.
[57] Mazaheri, E., Richard, M. O., Laroche, M., & Ueltschy, L. C. (2014). The influence of culture, emotions, intangibility, and atmospheric cues on online behavior. Journal of Business Research, 67(3), 253-259.
[58] Mesch, G., & Talmud, I. (2006). The quality of online and offline relationships: The role of multiplexity and duration of social relationships. The Information Society, 22(3), 137-148.
[59] Mollen, A., & Wilson, H. (2010). Engagement, telepresence and interactivity in online consumer experience: Reconciling scholastic and managerial perspectives. Journal of Business Research, 63(9), 919-925.
[60] Mudambi, S. M., & Schuff, D. (2010). What makes a helpful online review? A study of customer reviews on Amazon.com. MIS Quarterly, 34(1), 185-200.
[61] Novak, T. P., & Hoffman, D. L. (1997). Measuring the flow experience among web users. Interval Research Corporation, 31.
[62] Novak, T. P., Hoffman, D. L., & Yung, Y.-F. (2000). Measuring the customer experience in online environments: A structural modeling approach. Marketing Science, 19(1), 22-42.
[63] Nunnally Jum, C., & Bernstein Ira, H. (1978). Psychometric theory. New York: McGraw-Hill.
[64] Ong, F. S., Khong, K. W., Faziharudean, T. M., & Dai, X. (2012). Path analysis of atmospherics and convenience on flow: the mediation effects of brand affect and brand trust. The International Review of Retail, Distribution and Consumer Research, 22(3), 277-291.
[65] Pöyry, E., Parvinen, P., & Malmivaara, T. (2013). Can we get from liking to buying? Behavioral differences in hedonic and utilitarian Facebook usage. Electronic Commerce Research and Applications, 12(4), 224-235.
[66] Park, D.-H., & Kim, S. (2009). The effects of consumer knowledge on message processing of electronic word-of-mouth via online consumer reviews. Electronic Commerce Research and Applications, 7(4), 399-410.
[67] Pavlou, P. A., Liang, H., & Xue, Y. (2006). Understanding and mitigating uncertainty in online environments: a principal-agent perspective. MIS Quarterly, 31(1), 105-136.
[68] Peter, J. P., & Tarpey Sr, L. X. (1975). A comparative analysis of three consumer decision strategies. Journal of Consumer Research, 2(1), 29-37.
[69] Petróczi, A., Nepusz, T., & Bazsó, F. (2007). Measuring tie-strength in virtual social networks. Connections, 27(2), 39-52.
[70] Phelps, J. E., Lewis, R., Mobilio, L., Perry, D., & Raman, N. (2004). Viral marketing or electronic word-of-mouth advertising: Examining consumer responses and motivations to pass along email. Journal of Advertising Research, 44(04), 333-348.
[71] Pollet, T. V., Roberts, S. G., & Dunbar, R. I. (2011). Use of social network sites and instant messaging does not lead to increased offline social network size, or to emotionally closer relationships with offline network members. Cyberpsychology, Behavior, and Social Networking, 14(4), 253-258.
[72] Richard, M.-O. (2005). Modeling the impact of internet atmospherics on surfer behavior. Journal of Business Research, 58(12), 1632-1642.
[73] Richard, M.-O., Chebat, J.-C., Yang, Z., & Putrevu, S. (2010). A proposed model of online consumer behavior: Assessing the role of gender. Journal of Business Research, 63(9), 926-934.
[74] Richardson, P. S., Jain, A. K., & Dick, A. (1996). Household store brand proneness: a framework. Journal of Retailing, 72(2), 159-185.
[75] Schiffman, L. G., & Kanuk, L. L. (2000). Consumer Behavior. Prentice Hall.
[76] Singh, J., & Pandya, S. (1991). Exploring the effects of consumers′ dissatisfaction level on complaint behaviours. European Journal of Marketing, 25(9), 7-21.
[77] Skadberg, Y. X., & Kimmel, J. R. (2004). Visitors’ flow experience while browsing a Web site: its measurement, contributing factors and consequences. Computers in Human Behavior, 20(3), 403-422.
[78] Song, J. H., & Zinkhan, G. M. (2003). Features of web site design, perceptions of web site quality, and patronage behavior. ACME 2003 Proceedings, 106-114.
[79] Sun, T., Youn, S., Wu, G., & Kuntaraporn, M. (2006). Online word‐of‐mouth (or mouse): An exploration of its antecedents and consequences. Journal of Computer‐Mediated Communication, 11(4), 1104-1127.
[80] Van Noort, G., Voorveld, H. A., & van Reijmersdal, E. A. (2012). Interactivity in brand web sites: cognitive, affective, and behavioral responses explained by consumers′ online flow experience. Journal of Interactive Marketing, 26(4), 223-234.
[81] Wang, J. C., & Chang, C. H. (2013). How online social ties and product-related risks influence purchase intentions: A Facebook experiment. Electronic Commerce Research and Applications, 12(5), 337-346.
[82] Ward, M. R., & Lee, M. J. (2000). Internet shopping, consumer search and product branding. Journal of Product & Brand Management, 9(1), 6-20.
[83] Wathieu, L., & Bertini, M. (2007). Price as a stimulus to think: The case for willful overpricing. Marketing Science, 26(1), 118-129.
[84] Webster, J., Trevino, L. K., & Ryan, L. (1994). The dimensionality and correlates of flow in human-computer interactions. Computers in Human Behavior, 9(4), 411-426.
[85] Wen, C., Tan, B. C., & Chang, K. T.-T. (2009). Advertising effectiveness on social network sites: an investigation of tie strength, endorser expertise and product type on consumer purchase intention. ICIS 2009 Proceedings, 151.
[86] Zeithaml, V. A., Berry, L. L., & Parasuraman, A. (1996). The behavioral consequences of service quality. Journal of Marketing, 60(2), 31-46.
[87] Zhou, T. (2013). An empirical examination of continuance intention of mobile payment services. Decision Support Systems, 54(2), 1085-1091.
指導教授 李小梅(Shau-mei Li) 審核日期 2015-6-30
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