線上串流音樂服務之使用者抗拒意圖研究

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：9

、訪客IP：18.119.253.93

姓名

江炯圻(Chiung-chi Chiang) 查詢紙本館藏

畢業系所

企業管理學系

論文名稱

線上串流音樂服務之使用者抗拒意圖研究
(Investigating User Resistance to music streaming service)

相關論文

★ 二氧化鈦技術生命週期之研究	★ 整體後勤業參與同步工程於產品開發績效關係之研究—以中科院為例
★ 筆記型電腦之IFA/PIFA天線技術生命週期分析	★ 國籍航空公司經營績效分析－以資料包絡分析方法分析
★ 從專利分析看3D IC技術與市場發展	★ 影響企業導入電子發票系統成效之因素探討
★ 影響企業導入數位學習成功因素之探討-以個案公司為例	★ 產品生命週期管理系統導入成功要素之探討--以S科技公司為例--
★ 組織創新能力影響因素研究	★ 製造業閒置資產轉售平台製造業閒置資產轉售平台-以廣達電腦股份有限公司為例
★ 供應商先行者優勢探討-以宸鴻科技為例	★ 團隊領導者創新特質與開放式創新專案關係之研究
★ 從商業生態系統談樞紐者策略-以Apple 與Nokia 為例	★ 個人電腦的競爭與發展策略-以台灣電子產業為例
★ 應用兩階段資料包絡分析法評估高級職業學校之經營績效	★ ERP導入的促進因素:使用者觀點

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

至系統瀏覽論文 ( 永不開放)

摘要(中)

過去十餘年來，由於網際網路興起，音樂產業深受盜版所害。消費者紛紛使用P2P(peer to peer)、網站下載等方式來交換音樂，都導致實體唱片銷售量一落千丈，各界都在苦思是否有拯救音樂產業的辦法。但同樣地，由於網路速度越來越快，一種新型態的商業模式出現：線上串流音樂服務，這是一種租用（rent）音樂的概念，訂戶繳納一固定月費（monthly fee）後即可不限制次數以串流形式聆聽資料庫中的任何音樂檔案，故創作者可授權給線上串流音樂業者撥放其作品並收取權利金，業者再提供聆聽音樂服務給消費者，消費者再藉由支付月費或收聽廣告使業者獲利，三者互惠互利，過去業者和創作者皆無法獲得收入之慘境應不復見。但台灣在2012年盜版音樂比率仍高達85%，顯見使用線上串流音樂服務之人數仍不夠多，使用者仍傾向於過去聆聽盜版音樂的選擇，冒著違法風險、變相傷害創作者和唱片公司，而不選擇各方面明顯較優質的線上串流音樂服務，此狀況與現狀偏差理論描述之狀況相似：「使用者固守過去選擇，就算有更好的選擇也不改變」。本研究透過網路問卷調查，共回收308份問卷，有效問卷為292份 (回收率占94%)。本研究以結構方程式進行資料分析，研究結果顯示，測量模型信、效度良好，而在驗證假說檢定方面則發現：(1)既有慣性、轉換成本、認知利益、社會規範、損失績效成本對抗拒態度有顯著影響。(2)不確定性成本對抗拒態度不具有顯著影響。(3)調節效果方面，性別對於損失績效成本、不確定性成本對抗拒態度具有調節效果。

摘要(英)

Over the last decades, the music industry has harmed by free MP3 download on in website. Consumers not only use the download site P2P (peer to peer) but also other ways to exchange music. And those have been impacting on the sales of physical album. People are still thinking about how to deal with this difficulty. Because the speed of Internet is getting faster and faster, there is a new kind of business model appearing: Online streaming music service, which is a concept of rent music. Subscribers can just pay the fixed monthly fee, and then they can unlimitedly enjoy the music of database. The original author can authorize vendors to offer online streaming music through charging them the property right fee. And the vendors can earn profit from monthly fee of membership and let advertiser publish commercial on website. This way can benefit all three parts, and it also makes industry revive.
However, the proportion of using pirated music in 2012 is still around 85%, it means that the people using the online streaming music is still not enough. MP3 users still intend to listen to pirated music, taking the risk to break the law. And it indeed damages original authors and record companies. They aren’t willing to choose higher quality, online streaming music service, to enjoy music. This kind of issue is similar to the theoretical description of the status quo bias: "Users stick at the previous options, even if there is a better one."
In this study, the total number of questionnaire is 308 that were collected on Internet. 292 of them are valid (response rate 94%). In this study, we used structural equation to analyze data, and the results showed that reliability and validity of the measurement model are good. In terms of the test of verifying the hypothesis are that: First, inertia, switching costs, cognitive benefits, social norms and the cost for the loss of performance have a significant impact on the attitude of resistance. Second, cost of uncertainty does not have a significant impact on the attitude of resistance. Third, the moderator of gender between the cost of the loss of performance and cost uncertainty is obvious.

關鍵字(中)

★ 現狀偏差
★ 線上串流音樂服務
★ 轉換成本
★ 認知利益
★ 使用者抗拒

關鍵字(英)

論文目次

目錄
摘要 iii
Abstract iv
誌謝 v
目錄 vi
圖目錄 vii
表目錄 viii
第一章緒論 1
1-1 研究背景與動機 1
1-2 研究目的 4
1-3 研究流程 5
第二章文獻探討 6
2-1 使用者抗拒 6
2-2 現狀偏差理論 8
2-3 習慣與慣性 10
2-4 轉換成本 11
2-5 變數定義整理 13
第三章研究方法 15
3-1 研究對象與資料收集 15
3-2 資料分析方法 15
3-3 變數定義與衡量 19
3-4 問卷設計與內容 21
3-5 問項統計資料 29
3-6 探索性因素分析 32
3-7 研究架構 36
3-8 研究假說 38
第四章研究結果 41
4-1 樣本基本資料分析 41
4-2 信度分析 43
4-3 效度分析 44
4-4 結構方程模式與調節效果之測量分析 50
第五章結論與建議 55
5-1 研究結論 55
5-2 研究貢獻 58
5-3 研究限制與未來研究建議 60
參考文獻 61

圖目錄

圖 1 研究流程 5
圖 2研究架構圖 37
圖 3驗證性因素分析路徑圖 47
圖 4架構路徑分析圖 51

表目錄
表 1文獻整理變數 13
表 2 各變數操作型定義以及文獻來源 19
表 3相對利益之問卷設計 21
表 4不確定性成本之問卷設計 22
表 5損失規避之問卷設計 22
表 6沉沒成本之問卷設計 23
表 7社會規範之問卷設計 23
表 8控制下的努力之問卷設計 24
表 9慣性之問卷設計 24
表 10既有系統習慣之問卷設計 25
表 11情感成本之問卷設計 26
表 12安裝成本之問卷設計 26
表 13學習成本之問卷設計 27
表 14損失績效成本之問卷設計 27
表 15抗拒態度之問卷設計 28
表 16抗拒態度之問卷設計 28
表 17測量問項平均統計表 29
表 18 KMO值檢定結果標準 32
表 19 KMO與Bartlett檢定 32
表 20現狀偏差因素之探索性因素分析成分矩陣 32
表 21現狀偏差變數之因素分析問項整理表 34
表 22 主觀規範之前置因素整體變數一覽表 35
表 23基本資料統計分析 42
表 24各構面之信度分析表 43
表 25收斂效度分析 48
表 26區別效度分析 49
表 27整體模型適配度 50
表 28各構面之關係檢定 52
表 29調節變數之卡方差異檢定表 53
表 30研究假說檢定 54

參考文獻

參考文獻
中文資料
[1] 文化部影視及流行音樂產業局. (2013). 台灣流行音樂產業發展現況概述. 文化部 Retrieved 04/24, 2014, from http://www.bamid.gov.tw/BAMID/Code/NewListContent93.aspx?id=dabf5ada-0004-4c19-b32c-237ab2c8dea1.

[2] 吳政傑. (2012). 雲端系統採用研究: 現狀偏好觀點. 中央大學資訊管理學系學位論文.

[3] 周文賢. (2004). 多變量統計分析, 台北: 智勝文化事業有限公司.

[4] 林東清, 孫培真, & 徐景智. (2000). 影響資訊系統使用者抗拒行為之原因: 以計劃行為理論為基礎之整合研究. 資訊管理研究, 2(2), 1-26.

[5] 財團法人台灣唱片出版事業基金會. (2013). 台灣唱片業發展現況. 財團法人台灣唱片出版事業基金會.

[6] 陳正昌. (2005). 多變量分析方法: 統計軟體應用: 五南圖書出版股份有限公司.

[7] 硬塞的網路趨勢觀察, I. (2014). Spotify 付費使用者即將突破 1000 萬大關. Retrieved 04/28, 2014, from http://share.inside.com.tw/posts/4625

[8] 鄭緯筌. (2013). 線上音樂商機真無窮？土洋大戰開打，串流音樂Spotify的600萬大軍壓境. Retrieved 04/24, 2014, from http://www.cw.com.tw/blog/blogTopic.action?id=279&nid=3786

英文資料

[1] Ajzen, I. (1985). From intentions to actions: A theory of planned behavior: Springer.
[2] Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(1), 74-94.
[3] Bargh, J. A. (1989). Conditional automaticity: Varieties of automatic influence in social perception and cognition. Unintended thought, 3, 51-69.
[4] Barnes, W., Gartland, M., & Stack, M. (2004). Old habits die hard: path dependency and behavioral lock-in. Journal of Economic Issues, 371-377.
[5] Bawa, K. (1990). Modeling inertia and variety seeking tendencies in brand choice behavior. Marketing Science, 9(3), 263-278.
[6] Bentler, P. M., & Bonett, D. G. (1980). Significance tests and goodness of fit in the analysis of covariance structures. Psychological bulletin, 88(3), 588.
[7] Bhattacherjee, A., & Hikmet, N. (2007). Physicians′ resistance toward healthcare information technology: a theoretical model and empirical test. European Journal of Information Systems, 16(6), 725-737.
[8] Bozzo, C. (2002). Understanding inertia in an industrial context. Journal of Customer Behaviour, 1(3), 335-355.
[9] Browne, M. W., Cudeck, R., Bollen, K. A., & Long, J. S. (1993). Alternative ways of assessing model fit. Sage Focus Editions, 154, 136-136.
[10] Burmeister, K., & Schade, C. (2007). Are entrepreneurs′ decisions more biased? An experimental investigation of the susceptibility to status quo bias. Journal of Business Venturing, 22(3), 340-362.
[11] Burnham, T. A., Frels, J. K., & Mahajan, V. (2003). Consumer switching costs: a typology, antecedents, and consequences. Journal of the Academy of Marketing Science, 31(2), 109-126.
[12] Carlsson, F., & Löfgren, Å. (2006). Airline choice, switching costs and frequent flyer programmes. Applied Economics, 38(13), 1469-1475.
[13] Cenfetelli, R. T. (2004). Inhibitors and enablers as dual factor concepts in technology usage. Journal of the Association for Information Systems, 5(11), 3.
[14] Darley, W. K., & Smith, R. E. (1995). Gender differences in information processing strategies: An empirical test of the selectivity model in advertising response. Journal of Advertising, 24(1), 41-56.
[15] Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 319-340.
[16] DeSanctis, G., & Courtney, J. F. (1983). Toward friendly user MIS implementation. Communications of the ACM, 26(10), 732-738.
[17] Doll, W. J., Xia, W., Hall, M., & Torkzadeh, G. (1994). A Confirmatory Factor Analysis of the End-User Computing Satisfaction Instrument. MIS Quarterly, 18(4), 453-461.
[18] Durndell, A., & Haag, Z. (2002). Computer self efficacy, computer anxiety, attitudes towards the Internet and reported experience with the Internet, by gender, in an East European sample. Computers in human behavior, 18(5), 521-535.
[19] Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and behavior: An introduction to theory and research.
[20] Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research (JMR), 18(1).
[21] Guilford, J. P. (1965). Fundamental statistics in psychology and education.
[22] Guiltinan, J. P. (1989). A classification of switching costs with implications for relationship marketing. Paper presented at the AMA Winter Educators’ Conference: Marketing Theory and Practice.
[23] Hair, J. F., Anderson, R. E., Tatham, R. L., & William, C. (1998). Black (1998), Multivariate data analysis: Upper Saddle River, NJ: Prentice Hall.
[24] Hall, J., Shaw, M., Lascheit, J., & Robertson, N. (2000). Gender differences in a modified perceived value construct for intangible products. ANZMAC 2000 Visionary Marketing for the 21st Century: Facing the Challenge.
[25] Hirschheim, R., & Newman, M. (1988). Information systems and user resistance: theory and practice. The Computer Journal, 31(5), 398-408.
[26] Hu, L. t., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1-55.
[27] Inder, B., & O′Brien, T. (2003). The endowment effect and the role of uncertainty. Bulletin of Economic Research, 55(3), 289-301.
[28] Jones, M. A., Mothersbaugh, D. L., & Beatty, S. E. (2002). Why customers stay: measuring the underlying dimensions of services switching costs and managing their differential strategic outcomes. Journal of Business Research, 55(6), 441-450.
[29] Kahneman, D., Knetsch, J. L., & Thaler, R. H. (1991). Anomalies: The endowment effect, loss aversion, and status quo bias. The journal of economic perspectives, 193-206.
[30] Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica: Journal of the Econometric Society, 263-291.
[31] Kaiser, H. F. (1974). An index of factorial simplicity. Psychometrika, 39(1), 31-36.
[32] Kenny, D. A. (2011). Measuring model fit. Retrieved November, 29, 2011.
[33] Kim, D.-Y., Lehto, X. Y., & Morrison, A. M. (2007). Gender differences in online travel information search: Implications for marketing communications on the internet. Tourism management, 28(2), 423-433.
[34] Kim, H.-W., & Kankanhalli, A. (2009). Investigating user resistance to information systems implementation: A status quo bias perspective. MIS Quarterly, 33(3), 567-582.
[35] Kim, H. W., & Gupta, S. (2012). Investigating Customer Resistance to Change in Transaction Relationship with an Internet Vendor. Psychology & Marketing, 29(4), 257-269.
[36] Lee, Y.-C. (2006). An empirical investigation into factors influencing the adoption of an e-learning system. Online Information Review, 30(5), 517-541.
[37] Lewis, W., Agarwal, R., & Sambamurthy, V. (2003). Sources of influence on beliefs about information technology use: an empirical study of knowledge workers. MIS Quarterly, 657-678.
[38] Limayenm, M., Hirt, S. G., & Cheung, C. M. (2003). Habit in the context of IS continuance: theory extension and scale development.
[39] MacMullen, I. (2011). On Status Quo Bias in Civic Education. The Journal of Politics, 73(03), 872-886.
[40] Marakas, G. M., & Hornik, S. (1996). Passive resistance misuse: overt support and covert recalcitrance in IS implementation. European Journal of Information Systems, 5(3), 208-219.
[41] Markus, M. L. (1983). Power, politics, and MIS implementation. Communications of the ACM, 26(6), 430-444.
[42] Meyers-Levy, J. (1988). The influence of sex roles on judgment. Journal of consumer research.
[43] Meyers-Levy, J., & Sternthal, B. (1991). Gender differences in the use of message cues and judgments. Journal of Marketing Research, 84-96.
[44] Moore, G. C., & Benbasat, I. (1991). Development of an instrument to measure the perceptions of adopting an information technology innovation. Information systems research, 2(3), 192-222.
[45] Moskowitz, G. (2014). Which Music Service Makes Artists the Most Money? time.
[46] Nel, J., & Raleting, T. (2012). Gender differences in low-income non-users′ attitude towards Wireless Internet Gateway cellphone banking. South African Journal of Business Management, 43(3), 51-63.
[47] Ouellette, J. A., & Wood, W. (1998). Habit and intention in everyday life: the multiple processes by which past behavior predicts future behavior. Psychological bulletin, 124(1), 54.
[48] Perera, N., & Kim, H.-W. (2002). The effect of switching costs on resistance to change in the use of software. Department of Information Systems, National University of Singapore.
[49] Polites, G. L. (2009). The duality of habit in information technology acceptance. University of Georgia.
[50] Polites, G. L., & Karahanna, E. (2012). Shackled to the Status Quo: The Inhibiting Effects of Incumbent System Habit, Switching Costs, and Inertia on New System Acceptance. MIS Quarterly, 36(1), 21-42.
[51] Ram, S., & Sheth, J. N. (1989). Consumer resistance to innovations: the marketing problem and its solutions. Journal of Consumer Marketing, 6(2), 5-14.
[52] Rankin, B. (2009). DRM-Free Music. Retrieved 0506, 2014, from http://askbobrankin.com/drmfree_music.html
[53] Rumelt, R. P. (1995). Inertia and transformation: Springer.
[54] Samuelson, W., & Zeckhauser, R. (1988). Status quo bias in decision making. Journal of risk and uncertainty, 1(1), 7-59.
[55] Segars, A. H., & Grover, V. (1993). Re-examining perceived ease of use and usefulness: a confirmatory factor analysis. MIS Quarterly, 517-525.
[56] Sheth, J. N., & Stellner, W. H. (1979). Psychology of innovation resistance: the less developed concept (LDC) in diffusion research: College of Commerce and Business Administration, University of Illinois at Urbana-Champaign.
[57] Snedecor, G. W., & Cochran, W. G. (1989). Statistic methods. Eighths Edition, Iowa.
[58] Suri, G., Sheppes, G., Schwartz, C., & Gross, J. J. (2013). Patient Inertia and the Status Quo Bias When an Inferior Option Is Preferred. Psychological science, 24(9), 1763-1769.
[59] Szmigin, I., & Foxall, G. (1998). Three forms of innovation resistance: the case of retail payment methods. Technovation, 18(6), 459-468.
[60] Taylor, S., & Todd, P. A. (1995). Understanding information technology usage: A test of competing models. Information systems research, 6(2), 144-176.
[61] Van Raaij, E. M., & Schepers, J. J. (2008). The acceptance and use of a virtual learning environment in China. Computers & Education, 50(3), 838-852.
[62] Van Slyke, C., Comunale, C. L., & Belanger, F. (2002). Gender differences in perceptions of web-based shopping. Communications of the ACM, 45(8), 82-86.
[63] Venkatesh, V. (2000). Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information systems research, 11(4), 342-365.
[64] Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3).
[65] Verplanken, B., & Aarts, H. (1999). Habit, attitude, and planned behaviour: is habit an empty construct or an interesting case of goal-directed automaticity? European review of social psychology, 10(1), 101-134.
[66] Verplanken, B., & Orbell, S. (2003). Reflections on Past Behavior: A Self‐Report Index of Habit Strength1. Journal of Applied Social Psychology, 33(6), 1313-1330.
[67] Wieringa, J. E., & Verhoef, P. C. (2007). Understanding customer switching behavior in a liberalizing service market an exploratory study. Journal of Service Research, 10(2), 174-186.
[68] Wunsch-Vincent, S., & Vickery, G. (2005). Working Party on the Information Economy: Digital Broadband Content, Music: Organisation for Economic Co-operation and Development.
[69] Zaltman, G., & Wallendorf, M. (1979). Consumer behavior, basic findings and management implications: Wiley.

指導教授

洪秀婉、沈建文

審核日期

2014-7-17

推文