Please use this identifier to cite or link to this item: https://ah.lib.nccu.edu.tw/handle/140.119/118850
題名: OTT影音平台的使用者研究:以整合科技接受與資訊系統成功模式探討行為意圖與付費意願
A study of OTT TV users’ behavioral intention and willingness to pay on the basis of integration of technology acceptance and information systems success model
作者: 楊雅婷
Yang, Ya-Ting
貢獻者: 陳憶寧
Chen, Yi-Ning
楊雅婷
Yang, Ya-Ting
關鍵詞: 行為意圖
內容類型偏好
OTT影音平台
使用者
付費意願
Behavioral intention
Content genre preferences
OTT video platforms
Users
Willingness to pay
日期: 2018
上傳時間: 24-Jul-2018
摘要: 本研究以科技接受模式與資訊系統成功模式為理論基礎,探究哪些內容品質與系統品質之因素會影響消費者使用OTT影音平台的意圖,針對影音平台使用者進行線上問卷調查,共蒐集到1062個有效樣本。研究結果發現可得性、個人化、觀看品質、內容多元性、搜尋與介面確實間接影響行為意圖,而即時性對行為意圖沒有顯著影響。在行為信念方面,知覺有用性的的影響效果最大,接著是知覺易用性、知覺愉悅性;此外,態度幾乎完全中介知覺有用性與易用性對行為意圖的效果。\n付費意願為本研究的另一個重點,付費意願受到行為意圖與價格價值的直接影響,然而對消費者而言,值不值得付費取決於影音平台提供的內容,因此在研究架構中加入內容類型偏好的構念,研究結果發現內容類型偏好確實能調節行為意圖對付費意願的影響效果,越重視歐美劇、陸劇的使用者其付費意願越高。最後根據研究結果從六個面向提供實務建議予版權平台業者。
This thesis explored what factors of content quality and system quality would influence consumers’ intention to use OTT video platforms on the basis of Integration of Technology Acceptance and Information Systems Success Model. A sample consists of 1,062 respondents was obtained from an online survey of video platform users in Taiwan. The results show that behavioral intention is indirectly influenced by availability, personalization, viewing quality, content variety, and navigation and interface design; however, timeliness has no significant effect on intention. In terms of behavioral belief, perceived usefulness is the most effective predictor, followed by perceived ease of use and perceived enjoyment. Besides, attitude toward usage mediated the effect that perceived usefulness and ease of use have on behavioral intention to use.\nAnother focus of this study is willingness to pay, which is directly influenced by behavioral intention and price value. However, whether a video platform is worth the price depends on what content the platform provides. Thus, the construct of content genre preferences is added to the research framework. The results show that content genre preferences moderate the effect that behavioral intention has on willingness to pay. Users who prefer American and European TV series or TV dramas produced in China are more willing to pay for the content. Implications for OTT video platforms are discussed in six aspects based on the findings.
參考文獻: 中文部分\n甘美玲(2006)。《知覺價格、知覺品質、知覺價值對購買意願之關係研究-以消費者購買數位內容產品為實證》。國立成功大學高階管理碩士在職專班碩士論文。\n李有仁、張書勳、林俊成(2011)。〈影音分享網站使用者意圖之研究〉,《資訊管理學報》,18:53-75。\n李姿億(2017)。《台灣OTT影劇產業分析與使用者使用行為調查》。國立台灣大學管理學院商學研究所碩士論文。\n李春麟、方文昌(2013)。〈科技接受模式再探討:整合資訊科技外部變數之論點〉,《企業管理學報》,97:1-37。\n李淑美、沈婉婷(2015)。〈行動入口網站使用者滿意度量表之建置研究〉,《修平學報》,30:117-146。\n李蔡彥、鄭宇君(2011)。〈資訊科技與新媒體研究之發展〉,《傳播研究與實踐》,1:75-81。\n林心慧、張雲豪(2009)。〈以UTAUT為基礎之消費者電子折價劵使用行為之預測模式:直接與干擾效果〉,《中華管理評論國際學報》,12:1-26。\n徐子媛(2015)。《影音平台在台灣發展挑戰研究─以LINE TV為例》。臺灣藝術大學廣播電視學研究所碩士論文。\n翁晨語、黃惠萍(2016年6月)。〈以延伸整合型科技接受模式和數位生活型態探討LINE TV的使用行為〉,「2016中華傳播學會」,嘉義縣民雄。\n張鴻隆(2014)。《影響使用者OTT影音服務採用行為意圖之探討》。世新大學傳播匯流與創新管理研究所碩士論文。\n曾國峰(2016)。《台灣影視產業困境與轉機:科技創新與新商業模式》。(行政院科技部補助專題研究計畫期末報告,MOST 104-2410-H-004 -112)。臺北:政治大學廣播與電視學系。\n葉志良(2015)。〈我國線上影音內容管制的再塑造:從OTT的發展談起〉,《資訊社會研究》,29:47-92。\n榮泰生(2011)。《AMOS與研究方法》。台北:五南。\n劉幼琍(主編)(2017)。《OTT TV的創新服務、經營模式與政策法規》。台北:五南。\n劉敦瑞(2015)。《跨越高牆尋找花園:當前台灣OTT(Over-The-Top)影音平台經營策略從有線電視談起》。世新大學傳播研究所博士論文。\n蘇慧慈(2016)。《複合商業模式創新:以OTT營運商影音內容服務為例》。東吳大學企業管理學研究所碩士論文。\n\n英文部分\nAhn, T., Ryu, 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), 405-420.\nAjzen, I. & Fishbein, M. (1980). Understanding attitudes and predicting social behavior. Englewood Cliffs, N J: Prentice-Hall.\nAjzen, I. & Fishbein, M. (2005). The Influence of Attitudes on Behavior. The handbook of attitudes. 173. 173-221.\nAjzen, I. (1991). The Theory of Planned Behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211.\nBaccarne, B., Evens, T., & Schuurman, D. (2013). The television struggle: an assessment of over-the-top television evolutions in a cable dominant market. Communications & Strategies, 92(4), 43-61.\nBagozzi, R. P. & Yi, Y. (1988). On the evaluation of structural equation models.\nJournal of the Academy of Marketing Science, 16(1), 74-94.\nBailey, J. E. & Pearson, S. W. (1983). Development of a tool for measuring and analyzing computer user satisfaction. Management Science, 29(5), 530-545.\nBenbasat, I., & Barki, H. (2007). Quo Vadis, TAM? Journal of the Association for Information System, 8(4), 211-218.\nCha, J. (2013a). Does genre type influence choice of video platform? A study of college student use of internet and television for specific video genres. Telematics and Informatics, 30(2), 189-200.\nCha, J. (2013b). Predictors of television and online video platform use: A coexistence model of old and new video platforms. Telematics and Informatics, 30(4), 296-310.\nCha, J. (2014). Usage of video sharing websites: Drivers and barriers. Telematics and Informatics, 31(1), 16-26.\nDavis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340.\nDavis, 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.\nDelone, W. H., & Mclean, E. R. (1992). Information Systems Success: The Quest for the Dependent Variable. Information Systems Research, 3(1), 60-95.\nDeLone, 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.\nDodds, W. B., Monroe, K. B., & Grewal, D. (1991). Effect of price, brand and store information on buyers’ product evaluations. Journal of Marketing Research, 28(3), 307-319.\nFornell, C., & Larcker, D. F. (1981). Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. Journal of Marketing Research, 18(1), 39-50.\nFriedlander, J. (2017, March 2). The Future of OTT: Giving Every Consumer a Personalized Viewing Experience. Verizon Digital insight. Retrieved from https://www.verizondigitalmedia.com/blog/2017/03/the-future-of-ott/\nGall-Ely, M. L. (2009). Definition, Measurement and Determinants of the Consumers Willingness to Pay: A Critical Synthesis and Avenues for Further Research. Recherche et Applications en Marketing (English Edition), 24(2), 91-112.\nGimpel, G. (2015). The Future of Video Platforms: Key Questions Shaping the TV and Video Industry. International Journal on Media Management, 17(1), 25-46.\nHartwick, J. & Barki, H. (1994). Explaining the role of user participation in information system use. Management Science, 40(4), 440-465.\nHino, H. (2015). TV Today, Mobile TV Tomorrow? Extrapolating Lessons from Israeli Consumers’ Adoption of Innovative TV Viewing Technology. International Journal on Media Management, 17(2), 69-92.\nHair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivariate data analysis (6th ed.). New Jersey: Prentice-Hall.\nHsiao, K. L. (2011). Why internet users are willing to pay for social networking services. Online Information Review, 35(5), 770-788.\nIves, B., Olson, M. H., & Baroudi, J. J. (1983). The measurement of user information satisfaction. Communications of the ACM, 26(10), 785-793.\nKim, T., Lee, J., & Law, R. (2008). An empirical examination of the acceptance behaviour of hotel front office systems: An extended technology acceptance model. Tourism Management, 29(3), 500-513.\nKwon, T. H. & Zmud, R. W. (1987). Unifying the fragmented models of information systems implementation. In Boland, R. J. & Hirschheim, R. A. (Eds.), Critical Issues in Information Systems Research. (pp. 227-251.). New York: John Wiley & Sons\nLee, G. & McGuiggan, R. (2009). Preferences for TV content genre: what Sydney viewers want. Paper presented at the 2009 conference of Australia & New Zealand Marketing Academy, Melbourne, Australia.\nLimayem, M., Hirt, S. G., & Cheung, C. M. K. (2007). How Habit Limits the Predictive Power of Intentions: The Case of IS Continuance. MIS Quarterly, 31(4), 705-737.\nLopes, A. and Galletta, D. (2006). Consumer perceptions and willingness to pay for intrinsically motivated online content. Journal of Management Information Systems, 23(2), 205-234.\nMelone, N. (1990). A theoretical assessment of the user-satisfaction construct in information systems research. Management Science, 36(1), 76-91.\nSeddon, P. (1997). A respecification and extension of the DeLone and McLean model of IS success. Information Systems Research, 8(3), 240-253.\nSzajna, B. (1996). Empirical evaluation of the revised technology acceptance model. Management Science, 42(1), 85-92.\nTaylor, S. & Todd, P. A. (1995). Understanding information technology usage: A test of competing models. Information Systems Research, 6(2), 145-176\ntechnology: A comparison of two theoretical models. Management Science, 35(8), 982-1003.\nVenkatesh, V. & Davis, F. D. (1996). A Model of the Antecedents of Perceived Ease of Use: Development and Test. Decision Sciences, 27(3), 451-481.\nVenkatesh, V., Morris, M. G., Davis, Davis G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478.\nVenkatesh, V., Thong, J. Y., & 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.\nWang, C. Y., Chang, H. C., Chou, S. C. T., & Chen, F. F. (2013). Acceptance and Willingness to Pay for Mobile TV Apps. PACIS 2013 Proceedings, 260.\nWebster, I.G. (1985). Program audience duplication: A study of television inheritance effects. Journal of Broadcasting & Electronic Media, 29, 121-133.\nWebster, I.G., Wakshlag, J. (1983). A theory of television program choice. Communication Research, 10, 430-446.\nWixom, B. H., & Todd, P. A. (2005). A Theoretical Integration of User Satisfaction and Technology Acceptance. Information Systems Research, 16(1), 85-102.\nZeithaml, V. A. (1988). Consumer Perceptions of Price, Quality, and Value: A Means-End Model and Synthesis of Evidence. Journal of Marketing, 52, 2-22.\n\n網路資料\n〈國內外線上影音平台大車拼!本土myVideo與KKTV表現不凡 資策會FIND:消費者一周平均看7.3小時線上影音內容 、1/4收看超過12小時〉(2017年7月4日)。取自財團法人資訊工業策進會網頁https://www.iii.org.tw/Press/NewsDtl.aspx?nsp_sqno=1975&fm_sqno=14\nBerton、Becky(2017年9月26日)。〈電視已死,電視永生?花開燦爛的本土OTT,離結果有多久?|OTT 產業系列報導(一)〉,《娛樂重擊》。取自http://punchline.asia/archives/46457\nlinli(2017年4月19日)。〈Netflix新一季再增500萬訂閱帳號,總使用戶即將超過1億〉,《科技新報》。取自http://technews.tw/2017/04/19/netflix-standing-on-the-threshold-of-100-million-subscribers/\n元智大學大數據與數位匯流創新中心政策法規研究團隊(2016年5月)。《OTT產業政策白皮書》。取自https://goo.gl/y0rhZP\n吳柏羲(2016年9月30日)。〈2016下半年線上影音內容收看載具與平台分析〉。取自「資策會MIC AISP情報顧問服務資料庫」http://mic.iii.org.tw/aisp/ReportS.aspx?id=CDOC20160930002\n吳柏羲(2017年7月26日)。〈娛樂優先,螢幕分眾化-2017上半年影視內容觀看與線上平台使用分析〉。取自「資策會MIC AISP情報顧問服務資料庫」https://mic.iii.org.tw/aisp/ReportS.aspx?id=CDOC20170724005\n吳柏羲(2017年9月26日)。〈搶占返家小確幸時間-2017上半年追劇行為與影劇串流平台偏好分析〉。取自「資策會MIC AISP情報顧問服務資料庫」https://mic.iii.org.tw/aisp/ReportS.aspx?id=CDOC20170924001\n吳柏羲(2017年9月29日)。〈需求不同,付我所樂-2017上半年數位影視平台付費與互動意願分析〉。取自「資策會MIC AISP情報顧問服務資料庫」https://mic.iii.org.tw/aisp/ReportS.aspx?id=CDOC20170924002\n秦偉翔(2015年8月25日)。〈線上影音平台發展趨勢與商機〉。取自「資策會MIC AISP情報顧問服務資料庫」http://mic.iii.org.tw.autorpa.lib.nccu.edu.tw/AISP/ReportS.aspx?id=PPT1040828-1\n商台玉(2016年1月8日)。〈從NETFLIX優缺點看台灣影音平台的衝擊〉,《娛樂重擊》。取自http://punchline.asia/archives/19692\n商台玉(2016年5月26日)。〈台灣娛樂產業史新一頁 一篇搞懂境外境內 OTT〉,《娛樂重擊》。取自http://punchline.asia/archives/26226\n畢畢(2016年5月19日)。〈專訪民視「四季線上影視4gTV」總經理王宗弘/不投資做內容只是死路一條〉,《娛樂重擊》。取自http://punchline.asia/archives/25885\n莊書怡(2016年9月26日)。〈眼球之爭手機大獲全勝 台灣民眾平均每天滑手機205分鐘 是看電視時間的2倍!〉,《FIND市場情報》。取自https://www.find.org.tw/market_info.aspx?k=2&n_ID=8926\n創市際市場研究顧問(2016年9月24日)。〈台灣網友的影音網站使用調查:創市際調查報告〉,《火箭科技評論》。取自https://rocket.cafe/talks/79419\n黃晶琳(2016年10月22日)。〈愛爾達要衝高OTT用戶〉,《經濟日報》。取自https://money.udn.com/money/story/5710/2039902\n黃慧雯(2017年9月28日)。〈台灣人每週看YouTube近15小時 趨勢無法擋〉,《中時電子報》。取自http://www.chinatimes.com/realtimenews/20170928003580-260412\n愛立信(2017年10月)。《2017年電視與媒體消費趨勢報告》。取自https://www.ericsson.com/en/trends-and-insights/consumerlab/consumer-insights/reports/tv-and-media-2017\n愛立信(2017年2月2日)。〈台灣一半的觀影時間是透過行動裝置進行〉,《動腦新聞》。取自http://www.brain.com.tw/news/articlecontent?ID=44360\n楊安琪(2017年9月19日)。〈福斯串流影音服務「FOX+」搶進 OTT 市場,首波與中華電信合作上線〉,《科技新報》。取自https://technews.tw/2017/09/19/fox-networks-group-launched-its-streaming-service-fox-plus-app-in-taiwan/\n廖佩玲(2017年3月23日)。〈【愛奇藝求落地】《鬼怪》加上《藍色海洋》 點擊為何大輸《太陽的後裔》?〉,《鏡週刊》。取自https://www.mirrormedia.mg/story/20170320ent015/\n劉孋瑩(2016年5月24日)。〈專訪三立行動媒體部副總林慧珍/Vidol專攻偶像劇 打造粉絲經濟〉,《娛樂重擊》。取自http://punchline.asia/archives/26117\n顏理謙(2017年1月26日)。〈台灣影音消費趨勢調查:消費者最在意畫質和字幕、行動裝置和固定裝置黃金交叉〉,《數位時代》。取自https://www.bnext.com.tw/article/42956/ericsson-consumerlab-published-media-consumer-behavior-report\n譚偉晟(2016年1月29日)。〈2015年線上影音調查!Yahoo分析:女性熱衷韓劇、長輩愛看中國影集〉,《自由時報》。取自http://3c.ltn.com.tw/news/22752?fref=gc&dti=188995454544784\n蘇元和(2017年8月31日)。〈進軍OTT 中嘉聯手CATCHPLAY搶攻數位匯流影音商機〉,《匯流新聞網》。取自https://cnews.com.tw/119170831-01/
描述: 碩士
國立政治大學
傳播學院傳播碩士學位學程
103464023
資料來源: http://thesis.lib.nccu.edu.tw/record/#G1034640231
資料類型: thesis
Appears in Collections:學位論文

Files in This Item:
File SizeFormat
023101.pdf1.41 MBAdobe PDF2View/Open
Show full item record

Google ScholarTM

Check

Altmetric

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.