研究生: |
鄭靖潔 Ching-Chieh Cheng |
---|---|
論文名稱: |
手機使用者隱私顧慮影響因素之探討—以個人化廣告為例 Drivers and Inhibitors of Mobile Users’ Privacy Concerns—Personalized Advertising |
指導教授: |
欒斌
Pin Luarn |
口試委員: |
陳正綱
Cheng-Kang Chen 林鴻文 Hong-Wen Lin |
學位類別: |
碩士 Master |
系所名稱: |
管理學院 - 企業管理系 Department of Business Administration |
論文出版年: | 2022 |
畢業學年度: | 110 |
語文別: | 中文 |
論文頁數: | 48 |
中文關鍵詞: | 手機隱私顧慮 、個人化廣告 、廣告識別碼 、多維發展理論 、結構方程模型 |
外文關鍵詞: | Mobile privacy concerns, Personalized advertising, Advertising ID, Multidimensional development theory, Structural equation modeling |
相關次數: | 點閱:345 下載:0 |
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 |
全球智慧型手機的用戶數截至 2021 年已超過 60 億,智慧型手機已是每個人日常生活中隨身攜帶的物品,生活中的大小事幾乎都會使用到手機。隨著手機能處理的事情越多,使用者亦有越多資訊存取在手機中,因此隱私顧慮的議題也伴隨而來。商家因意識到現今社會每個人幾乎手機不離身,花費在手機的時間大幅增加,因此將廣告由傳統的看板、電視廣告等等轉移到手機上。
每隻智慧型手機都配有一組廣告識別碼,iOS系統的稱為IDFA,Android系統的稱為AAID,而在手機瀏覽器上的則是第三方Cookie,其可讓 App 開發商和行銷人員出於廣告目的追蹤使用者活動,讓商家投放個人化廣告。然而個人化廣告容易讓使用者感到隱私被侵犯。國際市場與歐美政府對於用戶隱私權日益重視,因此科技大廠紛紛祭出隱私權政策,iOS與Android系統在 2021 年都發行了新的系統版本,其可讓使用者自行決定是否要接受個人化廣告的服務,Google也將於 2023 年底停用第三方Cookie。
面對科技大廠政策的改變,本研究引用多維發展理論探討手機使用者隱私顧慮的影響因素,研究方法透過發放線上問卷做調查,並使用結構方程模型做資料分析。研究結果證實,熟悉政府立法、過往隱私被侵犯的經驗、風險規避及資訊敏感性皆會影響手機隱私顧慮。透過了解這些因素,可提供App開發商作為參考,發展良好的配套措施提高手機使用者願意接受個人化廣告的意願。
The number of smartphone users worldwide has exceeded 6 billion by 2021. Everyone brings a smartphone every day, it has become a necessary in daily life. Mobile can deal with lots of things in our daily life. As more things can deal with on mobile, much personal information is accessed on mobile devices. Therefore, there are bring on the issues of privacy concerns. Nowadays, everyone has a mobile and spends lots of time using mobile. Since businesses realized the phenomenon, they changed their advertising way. They used billboards and TV commercials in the past. However, they have already focused on mobile advertising.
Each mobile comes with an advertising ID. It is called IDFA for iOS devices, AAID for Android devices, and third-party Cookie on the mobile browser. Advertising ID allows App developers and marketers to track activity for advertising purposes. It may be used by App developers and businesses to deliver personalized advertising. However, mobile users often feel their privacy has been invaded when they receive personalized advertising. The international market and the governments of European and American are paying more attention to user privacy. As a result, technology corporations announced new privacy policies. iOS and Android released new system versions in 2021, which allow users to decide whether to accept personalized advertising services. Google also announced to stop the use of third-party Cookie by the end of 2023.
Facing the change in the policies of technology corporations, the study applied the multidimensional development theory to explore the drivers and inhibitors that affect mobile privacy concerns. The study method was conducted by distributing online surveys. After receiving surveys, the study used structural equation modeling to analyze data. The study results show that familiarity with government legislation, previous privacy invasion experience, risk avoidance, and information sensitivity will affect mobile privacy concerns. App developers can refer to these factors and come up with some measurements that will increase the willingness of mobile phone users to accept personalized advertising services.
Anand, B. N., & Shachar, R. (2009). Targeted advertising as a signal. QME, 7(3), 237-266.
Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological bulletin, 103(3), 411.
Bélanger, F., & Crossler, R. E. (2011). Privacy in the digital age: a review of information privacy research in information systems. MIS quarterly, 1017-1041.
Baek, T. H., & Morimoto, M. (2012). Stay away from me. Journal of advertising, 41(1), 59-76.
Bleier, A., & Eisenbeiss, M. (2015). The importance of trust for personalized online advertising. Journal of Retailing, 91(3), 390-409.
Campbell, A. J. (1997). Relationship marketing in consumer markets: A comparison of managerial and consumer attitudes about information privacy. Journal of Direct Marketing, 11(3), 44-57.
Caudill, E. M., & Murphy, P. E. (2000). Consumer online privacy: Legal and ethical issues. Journal of public policy & marketing, 19(1), 7-19.
Chin, W. W. (1998a). Commentary: Issues and opinion on structural equation modeling. In (pp. vii-xvi): JSTOR.
Chin, W. W. (1998b). The partial least squares approach to structural equation modeling. Modern methods for business research, 295(2), 295-336.
Culnan, M. J. (1995). Consumer awareness of name removal procedures: Implications for direct marketing. Journal of Direct Marketing, 9(2), 10-19.
Fornell, C., & Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement error: Algebra and statistics. In: Sage Publications Sage CA: Los Angeles, CA.
Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing theory and Practice, 19(2), 139-152.
Hardt, M., & Nath, S. (2012). Privacy-aware personalization for mobile advertising. Proceedings of the 2012 ACM conference on Computer and communications security,
Hoffman, D. L., & Novak, T. P. (1996). Marketing in hypermedia computer-mediated environments: Conceptual foundations. Journal of marketing, 60(3), 50-68.
Hoffman, D. L., Novak, T. P., & Peralta, M. (1999). Building consumer trust online. Communications of the ACM, 42(4), 80-85.
Hong, W., Chan, F. K., & Thong, J. Y. (2019). Drivers and inhibitors of internet privacy concern: a multidimensional development theory perspective. Journal of Business Ethics, 1-26.
Kireyev, P., Pauwels, K., & Gupta, S. (2016). Do display ads influence search? Attribution and dynamics in online advertising. International Journal of Research in Marketing, 33(3), 475-490.
Lambrecht, A., & Tucker, C. (2013). When does retargeting work? Information specificity in online advertising. Journal of Marketing research, 50(5), 561-576.
Laufer, R. S., & Wolfe, M. (1977). Privacy as a concept and a social issue: A multidimensional developmental theory. Journal of social Issues, 33(3), 22-42.
Lee, K.-C., Jalali, A., & Dasdan, A. (2013). Real time bid optimization with smooth budget delivery in online advertising. Proceedings of the seventh international workshop on data mining for online advertising,
Malhotra, N. K., Kim, S. S., & Agarwal, J. (2004). Internet users' information privacy concerns (IUIPC): The construct, the scale, and a causal model. Information systems research, 15(4), 336-355.
Nunally, J., & Bernstein, L. (1994). Psychometric Theory. New York: MacGrow-Hill Higher. In: INC.
Palos-Sanchez, P., Saura, J. R., & Martin-Velicia, F. (2019). A study of the effects of programmatic advertising on users' concerns about privacy overtime. Journal of Business Research, 96, 61-72.
Ringle, C. M., Sarstedt, M., & Straub, D. W. (2012). Editor's comments: a critical look at the use of PLS-SEM in" MIS Quarterly". MIS quarterly, iii-xiv.
Sheehan, K. B. (2002). Toward a typology of Internet users and online privacy concerns. The information society, 18(1), 21-32.
Sheehan, K. B., & Hoy, M. G. (2000). Dimensions of privacy concern among online consumers. Journal of public policy & marketing, 19(1), 62-73.
Smith, H. J. (1994). Managing privacy: Information technology and corporate America. UNC Press Books.
Smith, H. J., Dinev, T., & Xu, H. (2011). Information privacy research: an interdisciplinary review. MIS quarterly, 989-1015.
Smith, H. J., Milberg, S. J., & Burke, S. J. (1996). Information privacy: Measuring individuals' concerns about organizational practices. MIS quarterly, 167-196.
Stewart, K. A., & Segars, A. H. (2002). An empirical examination of the concern for information privacy instrument. Information systems research, 13(1), 36-49.
Stone, E. F., Gueutal, H. G., Gardner, D. G., & McClure, S. (1983). A field experiment comparing information-privacy values, beliefs, and attitudes across several types of organizations. Journal of Applied Psychology, 68(3), 459-468. https://doi.org/10.1037/0021-9010.68.3.459
Tam, K. Y., & Ho, S. Y. (2005). Web personalization as a persuasion strategy: An elaboration likelihood model perspective. Information systems research, 16(3), 271-291.
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.
Valentino-Devries, J. (2010). Unique phone ID numbers explained. The Wall Street Journal.
Ward, S., Bridges, K., & Chitty, B. (2005). Do incentives matter? An examination of on‐line privacy concerns and willingness to provide personal and financial information. Journal of Marketing Communications, 11(1), 21-40.
Wetzels, M., Odekerken-Schröder, G., & Van Oppen, C. (2009). Using PLS path modeling for assessing hierarchical construct models: Guidelines and empirical illustration. MIS quarterly, 177-195.
Xu, H., Dinev, T., Smith, H. J., & Hart, P. (2008). Examining the formation of individual's privacy concerns: Toward an integrative view.
Xu, H., Dinev, T., Smith, J., & Hart, P. (2011). Information privacy concerns: Linking individual perceptions with institutional privacy assurances. Journal of the Association for Information Systems, 12(12), 1.
Xu, H., Gupta, S., Rosson, M. B., & Carroll, J. M. (2012). Measuring mobile users' concerns for information privacy.
許昕慈. (2017). 巨量資料隱私風險特性和資訊隱私顧慮對使用者掩飾行為之影響. 中山大學資訊管理學系研究所學位論文, 1-89.