臺灣博碩士論文加值系統

隨著智慧型手機的普及，導致行動廣告市場快速成長，也即將帶來龐大商機，因此提昇廣告點擊率即成為每個企業所面臨的問題，而點擊率是衡量廣告成效的一個重要指標，也就是當點擊率愈高時獲利率就相對愈高，因此本研究為了幫助企業提昇其獲利能力，欲找出影響廣告點擊率的因素。

透過收集企業實際投放於行動裝置上的廣告資料，以及使用者行為資料，並進一步使用群集分析和決策樹分析，希望能真正找出影響廣告點擊率的因素，而分析結果也發現，廣告本身以及使用者行為皆會影響廣告點擊率，在廣告本身因素中，不同類型的廣告導致不同的點擊情形，而在使用者行為中，像是使用者觀劇類型偏好、使用者觀劇時段偏好也都和廣告點擊有一定程度的相關，除此之外，本研究亦建立了決策樹預測模型，企業除了可以針對不同廣告類型以及使用者行為投放廣告之外，還能搭配預測模型找出高點擊族群，將預算花在刀口上，用最少的預算達到最有效的獲利。

With the ever-increasing number of smart phones, mobile is growing faster than all other digital advertising formats, as advertisers begin allocating dollars to catch the eyes of a growing class of "mobile-first" users. There is a fertile market for personalized adverting. So, the challenge is how to get your users to click more often on the ads appearing on your mobile property. More users clicking on the ads would primarily mean advertising money for the company.

Hence, we collected datas from a company who has its mobile applications and makes effort to enhane their mobile Ad effectiveness. Then we used the clustering techniques and decision tree model to find out what factors are related to the click through rate (CTR). Finally, we found that both Ad itself and user behavior influence the Ad effectiveness. For Ad itself, different types of Ads such as Ad categories cause different CTR. For user behavior, there are many factors that should be considered also, for instance, their drama preference, watching hours preference and so on would cause different CTR too. In conclusion, knowing the key factors and using the prediction model can help company to enhance their Ad effectiveness.

第一章 緒論 1
第一節 研究背景與動機 2
第二節 研究問題 3
第三節 研究流程 3

第二章 文獻探討 5
第一節 行動廣告 5
壹 行動廣告 5
貳 行動行銷 5
參 行動廣告類型 6
肆 行動應用程式廣告類型 7
第二節行動廣告效果 9
壹 衡量行動廣告成效 / 行動廣告有效性 9
貳 影響行動廣告成效的因素 10

第三章 研究方法 14
第一節 研究架構 14
第二節 資料搜集方法 15
壹 資料來源 15
貳 資料合併與整理方式 18
第三節 研究流程 19
第四節 資料分析方法 20
壹 敘述性統計分析 20
貳 主成分分析 20
參 群集分析 20
肆 屬性選擇 21
陸 決策樹分析 22
第四章 資料分析結果 24
第一節 統計分析結果 25
壹 主成分分析 25
貳 集群分析 26
參 定量分析驗證分析 27
第二節 決策樹分析 42
壹 屬性選取 42
貳 決策樹分析 44

第五章 結論與建議 52
第一節 結論 52
第二節 建議 53

中文部分
石家彥（2014），行動廣告互動性與廣告效果之研究，國立中央大學企業管理研究所
碩士論文。
陳芃婷, & 謝欣蓓. （2013）. 行動寬頻趨勢下之個人化行動廣告設計要素評選. 電子
商務學報, 15（1）, 57-88.
江義平, & 俞帛宏. （2011）. 橫幅廣告點擊效果之影響因素探究. Electronic Commerce
Studies, 9（4）, 433-458.
鄭仁富、林麗真、張淑媛、顏瑄、沈盈吟（2014），資策會FIND: 2014年上半年消費
者行為調查出爐，http://www.iii.org.tw/service/3_1_1_c.aspx?id=1367。
Vpon Inc.（2014）, Vpon 行動廣告 2014年第一季 台灣行動市場數據報告,
http://www.vpon.com/images/datafile/Vpon_2014-Q1_tw.pdf。
林師模, and 陳范欽. "多變量分析-管理上的應用, 雙葉書廊." (2003).

曾憲雄、蔡秀滿、蘇東興、曾秋蓉與王慶堯(2005)。資料探勘。台北：旗標出版社。

英文部分
Thakur, R. （2013）. Customer adoption of mobile payment services by professionals
across two cities in India: An empirical study using modified technology acceptance
model. Business Perspectives and Research, 1, 17.
Shih, Y. Y., & Chen, C. Y. （2013）. The study of behavioral intention for mobile
commerce: via integrated model of TAM and TTF. Quality & Quantity, 47（2）,
1009-1020.
Hérault, S. （2013）. Investigating innovations in information systems: how to evaluate the
m-advertising effectiveness?.
Rogers, E. M. （2010）. Diffusion of innovations. Simon and Schuster.
Park, T., Shenoy, R., & Salvendy, G. （2008）. Effective advertising on mobile phones: a
literature review and presentation of results from 53 case studies.Behaviour & Information Technology, 27（5）, 355-373.
Liu, C. L. E., Sinkovics, R. R., Pezderka, N., & Haghirian, P. （2012）. Determinants of
consumer perceptions toward mobile advertising—A comparison between Japan
and Austria. Journal of Interactive Marketing, 26（1）, 21-32.
Danaher, P. J., & Mullarkey, G. W. （2003）. Factors affecting online advertising recall: A
study of students. Journal of Advertising Research, 43（03）, 252-267.
Singh, S. N., & Cole, C. A. （1993）. The effects of length, content, and repetition on television commercial effectiveness. Journal of Marketing Research.
Hristova, N., & O''Hare, G. M. （2004, January）. Ad-me: wireless advertising adapted to the
user location, device and emotions. In System Sciences, 2004. Proceedings of the 37th
Annual Hawaii International Conference on （pp. 10-pp）. IEEE.
Elliott, M. T., & Speck, P. S. （1998）. Consumer perceptions of advertising clutter and its
impact across various media. Journal of Advertising Research, 38, 29-42.
Cho, C. H. （1999）. How advertising works on the WWW: Modified elaboration likelihood
model. Journal of Current Issues & Research in Advertising, 21（1）, 34-50.
Chandon, J. L., Chtourou, M. S., & Fortin, D. R. （2003）. Effects of configuration and
exposure levels on responses to web advertisements. Journal of Advertising Research,
43（02）, 217-229.
MacKenzie, S. B., & Lutz, R. J. （1989）. An empirical examination of the structural
antecedents of attitude toward the ad in an advertising pretesting context. The Journal of
Marketing, 48-65.
Bauer, R. A., & Greyser, S. A. （1968）. Advertising in America, the consumer view.
Haghirian, P., & Madlberger, M. （2005）. Consumer attitude toward advertising via mobile
devices-An empirical investigation among Austrian users.
Sussman, S. W., & Siegal, W. S. （2003）. Informational influence in organizations: an
integrated approach to knowledge adoption. Information Systems Research, 14（1）,
47-65.
Te’eni-Harari, T. （2013）. Clarifying the Relationship between Involvement Variables and
Advertising Effectiveness among Young People. Journal of Consumer Policy, 1-21.
Agarwal, A., Hosanagar, K., & Smith, M. D. （2011）. Location, location, location: An
analysis of profitability of position in online advertising markets. Journal of Marketing
Research, 48（6）, 1057-1073.
Gao, Q., Rau, P. L. P., & Salvendy, G. （2009）. Perception of interactivity: Affects of four
key variables in mobile advertising. International Journal of Human-Computer
Interaction, 25（6）, 479-505.

Lin, H., Zhou, X., & Chen, Z. （2014）. Impact of the Content Characteristic of Short
Message Service Advertising on Consumer Attitudes. Social Behavior and Personality:
an international journal, 42（9）, 1409-1419.
Martín-Santana, J. D., & Beerli-Palacio, A. （2012）. The effectiveness of web ads: rectangle
vs contextual banners. Online Information Review, 36（3）, 420-441.
Dhar, S., & Varshney, U. （2011）. Challenges and business models for mobile
location-based services and advertising. Communications of the ACM, 54（5）, 121-128.
Rao, B., & Minakakis, L. （2003）. Evolution of mobile location-based services.
Communications of the ACM, 46（12）, 61-65.
Zhang, D. （2003）. Delivery of personalized and adaptive content to mobile devices: a
framework and enabling technology. Communications of the Association for
Information Systems, 12（1）, 13.
Varshney, U., & Vetter, R. （2001, January）. A framework for the emerging mobile
commerce applications. In System Sciences, 2001. Proceedings of the 34th Annual
Hawaii International Conference on （pp. 10-pp）. IEEE.
Belch George, E., & Belch Michael, A. （2001）. Advertising and promotion. An Integrated
Marketing Communications Perspective. New York: MacGraw Hill Higher Education.
Mobile Marketing Association. （2011）. Mobile Advertising Guidelines （version 5.0）.

eMarketer （2014）, Driven by Facebook and Google, Mobile Ad Market Soars 105% in 2013.

SONG, Yan-yan, and Ying LU. "Decision tree methods: applications for classification and
prediction." Shanghai Archives of Psychiatry 27.2 （2015）: 130.

Chen, Guanling, and David Kotz. A survey of context-aware mobile computing research.
Vol.1. No. 2.1. Technical Report TR2000-381, Dept. of Computer Science, Dartmouth
College, 2000.

Gao, Qin, Pei-Luen Patrick Rau, and Gavriel Salvendy. "Measuring perceived interactivity of
mobile advertisements." Behaviour & Information Technology29.1 (2010): 35-44.

Gao, Qin, Pei-Luen Patrick Rau, and Gavriel Salvendy. "Perception of interactivity: Affects
of four key variables in mobile advertising." International Journal of Human-Computer Interaction 25.6 (2009): 479-505.

Lin, Hongyan, Xing Zhou, and Zhankui Chen. "Impact of the Content Characteristic of Short
Message Service Advertising on Consumer Attitudes."Social Behavior and Personality:
an international journal 42.9 (2014): 1409-1419.
Jolliffe, Ian. Principal component analysis. John Wiley & Sons, Ltd, 2002.