Abstract (Chinese)
本研究旨在了解在使用串流影音電視時，不同的免費增值策略如何影響用戶之人為購買慾望與實際購買行為。同時，我們更基於受測者之使用行為進行群聚分析，將台灣之串流影音用戶分群。免費增值策略已廣泛被應用於網路服務和軟體產業。採用免費增值策略的廠商能在極短的時間內，獲取大量的用戶。然而伴隨免費增值策略而來的低轉換率，免費增值策略因此受到了許多質疑。同時，過去免費增值之相關研究，也未針對不同免費增值策略進行比較。本研究透過田野實驗之方式，使用真實之串流影音平台。將免費增值策略分為內容限制(content limited: many or less free content)及時間限制(time limited)，並隨機將受測者分配到五組實驗中。實驗結束後，除了回答人為購買慾望之相關問題外，受測者可選擇新台幣六十元或付費版之串流影音帳號（新台幣六十元等值）作為他們的獎勵，受測者如果選擇付費帳號，我們則視其購買串流影音帳號。基於人為購買慾望的概念，我們發現不同的免費增值策略會產生不同之人為購買慾望，且時間限制能夠創造更多的人為購買慾望，且越高的人為購買慾望，實際購買之比例越高。同時，透過群聚分析，我們將台灣串流影音用戶分為戲劇狂熱、資訊敏捷及社交活躍用戶，且戲劇狂熱用戶有最高的人為購買慾望。最後，相較於華語內容愛好者來說，外語內容愛好者更有可能在免費增值策略下付費。因此，本研究建議採取免費增值策略之廠商，應將時間限制納入其免費增值策略中，且針對戲劇狂熱用戶和外語內容愛好者之使用行為設計行銷策略。

Nowadays, freemium strategies are widely used in online services and software industry. Those who adapt freemium strategies can have large number of users in such a short time. However, due to their low conversion rate, freemium strategies are always challenged. In the meantime, little do we know about how efficient these freemium strategies are. This study aims to understand how different freemium strategies affect users’ desire and their actual paying behaviors when using streaming TV platforms. Furthermore, we examine Taiwanese streaming TV users’ behaviors and their desire and conduct clustering analysis to segment Taiwanese streaming TV users. Freemium strategies: Content limited (many free content / less free content) and Time limited are selected in our field experiment. Participants were randomly assigned to our scenarios. After the experiment, they are asked about the desire and they can choose $NT 60 or a paid premium streaming TV account (equal to $NT 60) as their reward. The latter represents that participant buy the account. Based on the concept of artificial buying desire, we propose different freemium strategies may result in different desire. Secondly, we then explore the relationship between artificial buying desire and actual paying behaviors. Finally, we segment Taiwanese streaming TV users using lifestyle theory and understand their desire and behaviors. The results show that using different freemium strategies can result in different desire and time limited results in the highest desire. Also, the higher your artificial buying desire is, the higher probability will you buy the premium account. Lastly, there are three types of streaming TV users in Taiwan. They are Drama frenzy users, Agile information users and Actively social users. Drama frenzy users have the highest desire among them. Besides, comparing to Chinese content lovers, foreign content lovers are more likely to pay. The findings suggest that vendors should take time limited as one of their freemium strategies and focus on drama frenzy users and foreign content lovers’ behaviors to design their marketing strategies.

Table of Content
Table of Content I
List of Table II
List of Figure IV
Abstract (Chinese) V
Abstract VI
1 Introduction 1
2 Literature Review 4
2.1 Streaming TV / Over the Top Media (OTT Media) 4
2.2 Freemium 5
3 Theoretical Foundation, Hypotheses and Research Model 11
3.1 Artificial Buying Desire 11
3.2 Lifestyle Theory and Segmentation 13
3.3 Research Model 15
4 Methodology 16
4.1 Pre-study and Interview 16
4.2 Experiment Design 17
4.2.1 Experimental environment and groups 17
4.2.2 Participants 18
4.3 Experiment Tasks and Procedures 19
4.4 Lifestyle Variables 20
5 Data Analysis and Results 23
5.1 Descriptive Statistic 23
5.2 Different Freemium Strategies 24
5.3 Artificial Buying Desire and Actual Buying Behaviors 26
5.4 Moderating Effect of Price Acceptance level 28
5.5 Lifestyle Segmentation and Artificial Buying Desire 30
6 Discussion, Conclusions and Implications 36
Study Limitations and Opportunity for Further Research 37
7 Reference 38
8 Appendix – Survey (for Aiqiyi free plan participants) 42


List of Table
Table 2.1 Freemium Types …………………………………………………………. 6
Table 3.1 A.I.O. dimensions ………………………………………………………. 15
Table 4.1 A comparison of content and types among platforms …………………. 16
Table 4.2 A rank of free content among platforms ………………………………. 16
Table 4.3 Experimental Groups …………………………………………………... 18
Table 4.4 Demographics of participants …………………………………………. 19
Table 4.5 Lifestyle variables ……………………………………………………… 21
Table 5.1 Purposes to watch streaming TV ………………………………………. 23
Table 5.2 Favorite origin of content ……………………………………………... 23
Table 5.3 Favorite types of content ……………………………………………… 23
Table 5.4 Factors that affect user’s willingness to pay …………………………… 24
Table 5.5 Experimental groups …………………………………………………… 24
Table 5.6 ANOVA test results among three strategies ……………………………. 25
Table 5.7 Post Hoc tests results among three freemium strategies ………………. 25
Table 5.8 Percentage of actual paying behavior among different desire …………. 26
Table 5.9 Chi-square results of desire and actual paying behaviors ……………… 27
Table 5.10 Percentage of actual paying behavior among different strategies………..27
Table 5.11 Chi-square results of freemium strategies and actual paying rate ……. 28
Table 5.12 Percentage of high/ low price acceptance participants ……………….. 29
Table 5.13 Logistic regression results of low price acceptance participants …….. 29
Table 5.14 Logistic regression results of high price acceptance participants ……. 29
Table 5.15 KMO tests of factor analysis ………………………………………… 30
Table 5.16 Factor analysis results ………………………………………………… 31
Table 5.17 Participants in each cluster ……………………………………………… 32
Table 5.18 ANOVA tests of three clusters ………………………………………….. 32
Table 5.19 Factor score of three clusters …………………………………………… 33
Table 5.20 ANOVA tests results among three clusters’ desire ……………………. 34
Table 5.21 Post Hoc tests results among three clusters’ desire …………………… 34
Table 5.22 percentage of content lovers and paying behaviors ……………………35
Table 5.23 Chi-square results of content lovers and paying behaviors ……………35









List of Figure
Figure 3.1 Research Model ………………………………………………………… 15
Figure 5.1 Scree plot of factor analysis ……………………………………………. 30
Figure 5.2 Hierarchical clustering results …………………………………………. 32

Agarwal, R., J. Prasad. (1997), "The role of innovation characteristics and perceived voluntariness in the acceptance of information technologies", Decision Sci. 28 557-582.

Anderson, Chris. (2009). Free: The future of a radical price. Random House.

Brandall, Benjamin. (2016), "Freemium Conversion Rate: Why Spotify Destroys Dropbox by 667%", Retrieved 15th July, 2017, from https://www.process.st/freemium-conversion-rate/

Beltman, M. (2015). Differentiation factors between free and premium among different cloud computing services (Bachelor's thesis, University of Twente).

Dunn, Jeff. (2017). More people now subscribe to Netflix than cable TV in the US. Business Insider. Retrieved 13th July, 2017, from http://www.businessinsider.com/netflix-more-popular-than-cable-us-chart-2017-6

Kopelman, J. (2007). The penny gap. Retrieved 13th July, 2017, from http://redeye.firstround.com/2007/03/the_first_penny.html

Kumar, Vineet. "Making 'Freemium' Work: Many Start-ups Fail to
Recognize the Challenges of This Popular Business Model." Harvard Business Review 92, no. 5 (May 2014): 27–29.

Lammers, H. B. (1991). The effect of free samples on immediate consumer purchase. Journal of Consumer Marketing, 8(2), 31-37.

Lazer, W. (1963). “Life style concept and marketing, proceedings of the American.” Marketing Association, Dec., 130-139.

Lee, C., Kumar, V., & Gupta, S. (2013). Designing freemium: a model of consumer usage, upgrade, and referral dynamics. MimeoLeung, Michael (2013), Two-step estimation of network formation models with incomplete information.

Niculescu, M. F., & Wu, D. J. (2011). When should software firms commercialize new products via freemium business models. Under Review.

Pujol, N. (2010). Pujol, Nicolas, Freemium: Attributes of an Emerging Business Model (December 1, 2010). Available at SSRN:
http://dx.doi.org/10.2139/ssrn.1718663

Plummer, J. T. (1974). The concept and application of life style segmentation. the Journal of Marketing, 33-37.

Scott, C. (1976). The Effects of Trial and Incentives on Repeat Purchase Behavior. Journal of Marketing Research, 13(3), 263-269. doi:10.2307/3150736

Shampanier, K., Mazar, N., & Ariely, D. (2007). Zero as a special price: The true value of free products. Marketing science, 26(6), 742-757.

Shapiro, C., & Varian, H. R. (1998). Versioning: the smart way to. Harvard Business Review, 107(6), 107.

Semenzin, D., Meulendijks, E., Seele, W., Wagner, C., & Brinkkemper, S. (2012). Differentiation in Freemium: Where Does the Line Lies ?. Software Business, 291-296.

T. M. Wagner, A. Benlian and T. Hess, "The Advertising Effect of Free -- Do Free Basic Versions Promote Premium Versions within the Freemium Business Model of Music Services ?, " 2013 46th Hawaii International Conference on System Sciences, Wailea, HI, USA, 2013, pp. 2928-2937.doi: 10.1109/HICSS.2013.21

Wagner, T. M., Benlian, A., & Hess, T. (2014). Converting freemium customers from free to premium—the role of the perceived premium fit in the case of music as a service. Electronic Markets, 24(4), 259-268.

Wally, L. (2011). 6 ways freemium can kill your startup. Retrieved 13th July, 2017, From https://www.chargify.com/blog/6-ways-freemium-can-kill-your-startup/

Wang, H., & Chin, A. (2011). Social influence on being a pay user in freemium-based social networks. In Advanced Information Networking and Applications (AINA), 2011 IEEE International Conference on (pp. 526-533). IEEE.

Walker, R., Raghunathan, R., & Ramanathan, S. (2003). Unnecessary Purchases: Creating Artificial Buying Pressure Through the Use of Coupons. ACR European Advances.

Wilson, F. (2006) Freemium. Retrieved 15th July , 2017, from
https://en.wikipedia.org/wiki/Freemium

葉乃綺（2004）。滿額現金回饋促銷型態對非計劃性購買行為之影響（博士
論文）。取自台灣學術文數據庫系統。（系統編號U00260812200911080059）

王俊懿 (2013)。誰願意付費?在免費增值策略下，如何使雲端軟體的免費用
戶成為付費用戶(碩士論文)。

蔡偉智 (2015)。從免費到付費，費增值模式下SaaS用戶的使用者行為研究(碩士論文)。

蘇一 (2017)。Netflix 美國付費使用者首次超過有線電視！串流媒體獲得　　　階段性勝利。2017年7月13日，取自 https://meet.bnext.com.tw/articles/view/40915。

顏里謙 (2016)。內容決勝負！台灣OTT百家爭鳴。2017年7月13日，取自https://www.bnext.com.tw/article/41346/taiwan-ott。

黃有容 (2017)。Netflix高管：信不信由你，電影產業的未來在線上訂閱。
2017年7月13日，取自https://cnews.com.tw/netflix高管：不信由你電影產業的未來在線上訂閱/。

李威龍、黎意文（2009）。試用品之品牌知名度及試用量與品牌認知、品牌
態度及購買意願的關係－以資生堂、盛香堂潔面乳為例。行銷科學學報，5(1)，79-97。

王東昇 (2001)。台北地區電影城消費行為之研究(碩士論文)。

彭佳琪 (1999)。電影閱聽人之生活型態分析(碩士論文)。

溫子豪(2016)。15%民眾放棄傳統電視　83%願付費看串流影音。2017年7 月13日，取自http://www.cardu.com.tw/news/detail.php?nt_pk=27&ns_pk=31215