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論文名稱(中文) 阻擋廣告程式對OTT影音平台之影響-以賽局分析YouTube與閱聽眾為例
論文名稱(英文) The Impacts of Advertisement Avoidance Software on OTT Video Platform: A Game-Based Analysis Between YouTube and Subscribers
校院名稱 成功大學
系所名稱(中) 電信管理研究所
系所名稱(英) Institute of Telecommunications and Management
學年度 104
學期 2
出版年 105
研究生(中文) 張芸華
研究生(英文) Yun-Hua Chang
學號 r96031060
學位類別 碩士
語文別 英文
論文頁數 96頁
口試委員 指導教授-黃光渠
口試委員-蔡東峻
口試委員-黃光雄
中文關鍵字 廣告  阻擋廣告程式  序列賽局 
英文關鍵字 YouTube  OTT  Advertisement  Advertisement Avoidance Software  Sequential game 
學科別分類
中文摘要 網路技術的純熟及行動寬頻技術的發展,讓使用者對於影音的取得不再侷限於電腦或電視中,也能夠透過行動裝置享受OTT (Over The Top)影音服務。OTT影音的快速發展帶動線上廣告的興起,廣告商投入更多資源於這新興平台上,以獲取更多的廣告營收。然而當網頁中充斥著眾多廣告時,對用戶產生許多困擾及不便,阻擋廣告程式的出現大幅解決了使用者的困擾,然而卻衝擊了廣告商及OTT影音平台業者的營收。

本研究採用序列賽局模擬最主要的OTT影音平台-YouTube及用戶的互動情形,將用戶對廣告的容忍程度、使用阻擋廣告程式的成本、YouTube投資偵測的成本加入探討,並討論在YouTube偵測成功率百分之百的情況下,產生的均衡解差異及在YouTube偵測成功率非百分之百的情況下,用戶對偵測成功率的預期所產生的變化,歸納整理各情境下產生的均衡解及偵測成功率範圍並提出相關作法及建議。

研究結果發現在YouTube偵測成功率百分之百下:
1.廣告容忍度低的用戶
(1.)在付費訂閱觀看無廣告影音內容所獲得的價值,勝過略過所有可略過廣告的價值的情況下,會願意直接付費訂閱無廣告服務;或是在使用阻擋廣告程式的成本為零時,使用阻擋廣告程式,若被YouTube偵測出即付費訂閱。
(2.)在付費訂閱觀看無廣告影音內容所獲得的價值大於零,卻低於略過所有可略過廣告的價值的情況下,用戶不會使用阻擋廣告程式,若小於零則用戶會在使用阻擋廣告程式的成本大於或等於零,且YouTube投資偵測的成本大於零的情況下,選擇使用阻擋廣告程式,而YouTube不會投資偵測。
(3.)在付費訂閱觀看無廣告影音內容所獲得的價值,與略過所有可略過廣告的價值相等的情況下,為一特例情形,產生的均衡解為前述(1.)與(2.)兩者均衡解的綜合。
2.廣告容忍度高的用戶不論使用阻檔廣告程式的成本是否為零,不會使用阻擋廣告程式也不會付費訂閱無廣告服務。

在YouTube偵測成功率非百分之百的情況下:
1.廣告容忍度低的用戶在使用阻擋廣告程式的成本不為零的情況下,會在意YouTube的偵測成功率高低,然而當使用阻擋廣告程式的成本為零時,不會考慮YouTube的偵測成功率高低,會選擇使用阻擋廣告程式。對不使用阻擋廣告程式的用戶而言,YouTube的偵測成功率高低對其都不會產生影響。
2.對廣告容忍度高的用戶而言,當使用阻擋廣告程式的成本在合理範圍下,其行為幾乎不會受YouTube偵測成功率高低影響,會選擇使用阻擋廣告程式。

本研究認為YouTube必須投資偵測並設法提高偵測成功率,同時針對不同特性的用戶給與不同策略,首先必須確保在其平台上的廣告具有一定的品質,並透過在線行為定位精準的將廣告投放給有潛在興趣的用戶,以降低廣告帶來的衝擊,同時必須研發不會被阻擋廣告程式阻擋的反制技巧,讓平台內的廣告能不被阻擋廣告程式所阻擋。針對廣告容忍度低者,可藉由提供平台專屬內容以增加用戶對內容的黏著度,吸引廣告容忍度低的用戶願意為了內容而選擇付費訂閱無廣告服務;強化在線行為定位能力,除了記錄用戶的興趣愛好外,也能追蹤用戶的在線行為,透過追蹤記錄投放廣告給廣告容忍度高的用戶其有興趣的廣告,藉此獲取更多的廣告點擊及收入。

在眾多免費OTT影音平台環伺下,若YouTube採取全面付費訂閱制將失去優勢,並且若用戶只能選擇付費訂閱的話,廣告商投放廣告的意願將降低,導致YouTube平台內的廣告效應下降。YouTube的營收主要來自廣告,因此若放棄收取廣告費,轉而收取訂閱費的話,恐怕將損失不少。再者,若採取全面付費訂閱制,將降低用戶上傳影音內容至YouTube的意願,若上傳內容減少,將直接影響到觀賞用戶的數量,並且因為觀看YouTube內容不再為免費,用戶必須付費訂閱YouTube才可看到影音內容,可能使得用戶數減少,用戶會選擇將影音內容上傳至其他平台。因此本研究認為YouTube未來「付費訂閱無廣告服務」及「廣告營收」將兩者並存。

對於整個OTT產業,本研究認為各OTT影音平台單靠用戶眾多以維持營運的模式,在阻擋廣告程式的衝擊下已經失去過去獲利豐厚的時代,必須要有其專屬的內容來留住或吸引用戶,使得OTT影音平台業者跟內容供應商將有更緊密的合作,以提供獨家的內容。另外,廣告商也必須製作更能吸引用戶的廣告,並透過OTT影音平台業者精準的投放,能讓用戶看到其有興趣的廣告,為廣告商、平台業者增加收益。
英文摘要 Because of the maturity of Internet and the development of mobile broadband technology, video content for users to access is no longer restricted to computers or televisions. Instead, such content can also be enjoyed via Over The Top (OTT) video services through mobile devices. The rapid growth of OTT video services has driven the development of online advertisements. More and more advertisers are paying attention to this emerging platform in order to acquire more advertising revenue. However, online advertisements cause several problems and inconveniences for OTT viewers when the webpages are replete with advertisements. In response, the appearance of advertisement avoidance software (AAS) has emerged to eliminate the disturbance to users; however, it has affected the revenues of advertisers and OTT video platform operators.

Our research adopts sequential games to simulate the interaction between a major OTT video platform, namely YouTube, and users. In doing so, we separate users’ advertisement tolerance into two groups and take the AAS user-cost and the cost for YouTube to invest in detection into consideration. We discuss the different equilibriums that result when YouTube’s successful detection rate is 100%, as well as the users’ anticipation of the successful detection rate when YouTube’s successful detection rate is not 100%. We then collect each scenario’s equilibrium and the range of the successful detection rates in order to provide relative suggestions for YouTube.

The research results when YouTube’s successful detection rate is 100% are as follows:
1.For low advertisement tolerance users
(1.)In the case where the value of subscribing to watch ad-free videos is greater than the value of skipping all skippable advertisements, users are willing to subscribe directly. In addition, users choose to use AAS when the cost to use AAS is zero and if detected, users will subscribe.
(2.)In the case where the value of subscribing to watch ad-free videos is greater than zero, but is less than the value of skipping all skippable advertisements, users will not use AAS. If the value is lower than zero, users will choose to use AAS in the situation where the cost to use AAS is greater or equal to zero, and the cost for YouTube to detect it is greater than zero.
(3.)The case where the value of subscribing to watch ad-free videos is equal to the value of skipping all skippable advertisements is a special case in our research. The equilibriums are a combination of the above (1.) and (2.).
For high advertisement-tolerance users, regardless of the cost of using AAS being zero or not, users will neither use AAS nor pay to subscribe ad-free service.

The results when YouTube’s successful detection rate is not 100% are described in the following:
1.When the cost to use AAS is not zero, low advertisement-tolerance users will care about what YouTube’s successful detection rate is. In contrast, users will not care about YouTube’s successful detection rate when the cost to use AAS is zero, and will choose to use AAS. YouTube’s successful detection rate will not influence users who don’t use AAS.
2.For high advertisement-tolerance users, when the cost to use AAS is in the rational range that they can accept, their action will seldom be influenced by YouTube’s successful detection rate. High advertisement-tolerance users will choose to use AAS.

Our research results suggest that YouTube should invest in detection, find a way to improve its successful detection rate, and provide users with different strategies depending on their characteristics. First, YouTube should ensure advertisements’ quality on their platform, and use online behavioral targeting to give users precisely the advertisements they may be interested in to reduce the impact from advertisements. In addition, YouTube should develop a technique to counter AAS to ensure their advertisements are not blocked. For low advertisement-tolerance users, YouTube could attempt to enhance user stickiness by providing exclusive content and enticing them to subscribe to YouTube Red because of the content. Additionally, YouTube is recommended to strengthen their online targeting technique, because it can not only record users’ habits but also trace users’ online activities. By tracing records to post advertisements according to high advertisement-tolerance users’ habits could enhance the click through rate and advertising revenue.

Because there are many free OTT video platforms competing in this industry, if YouTube adopts the strategy of whole subscription, it may lose its advantage of being the biggest free video platform. Besides, if users have no choice but to subscribe, it may lower the willingness for advertisers to post advertisements on YouTube, which would lead to lower advertising effects. Thus far, the majority of YouTube’s revenue is from advertising fees; as such, if it gives up advertising fees and turns to acquiring subscription fees, it would face substantial loss. Moreover, it would also reduce the willingness of users to upload videos. If the number of videos was to reduce, it would directly influence the number of users. Moreover, if YouTube were not free, and users needed to subscribe to watch content, the amount of users would decrease and likely choose to upload their videos to other platforms. Consequently, our research findings indicate that the strategy of “pay to watch ad-free videos” and “advertising fees” for YouTube will coexist.

For the OTT video industry, our research found that each OTT video platform depends on an enormous number of users to operate their business, and due to AAS, has lost a large amount of revenue. Hence, each OTT video platform must have exclusive content to keep or attract users, which means that OTT video platform operators should maintain close cooperation with content providers in order to provide exclusive content. Moreover, advertisers also need to produce more attractive advertisements to attract users and through the OTT video platform operator’s precisely posted advertisements, users can watch ads they are interested in. In this manner, revenue for both advertisers and platform operators could be gained.
論文目次 Abstract I
摘要 V
誌謝 VIII
List of Figures XI
List of Tables XII
Chapter 1 Introduction 1
1.1 Research background and motivation 1
1.2 Research purposes 4
1.3 Research process 4
Chapter 2 Literature Review 6
2.1 Advertising 6
2.2 Advertising disturbance 7
2.3 Advertisement avoidance software 8
2.3.1 White-list mechanism 10
2.3.2 Technologies to block advertisements 11
2.4 YouTube 14
2.4.1 YouTube’s advertising formats 16
2.4.2 Strategies against AAS 17
2.5 Game theory 18
2.5.1 Sequential game 19
2.6 Related research using game theory 20
Chapter 3 Research Method 22
3.1 Research design 22
3.2 Decision behavior analysis of YouTube and users 23
3.3 Use of symbols to estimate the payoffs of YouTube and users 24
3.3.1 Low advertisement-tolerance users 26
3.3.2 High advertisement-tolerance users 29
3.4 Scenario classification 31
Chapter 4 YouTube’s successful detection rate is 100% 32
4.1 In the case of CB=0 and CD=0 33
4.1.1 Low advertisement-tolerance users 33
4.1.2 High advertisement-tolerance users 39
4.2 In the case of CB=0 and CD>0 41
4.2.1 Low advertisement-tolerance users 41
4.2.2 High advertisement-tolerance users 47
4.3 In the case of CB>0 and CD=0 49
4.3.1 Low advertisement-tolerance users 49
4.3.2 High advertisement-tolerance users 55
4.4 In the case of CB>0 and CD>0 57
4.4.1 Low advertisement-tolerance users 57
4.4.2 High advertisement-tolerance users 63
4.5 Summary 65
Chapter 5 YouTube’s successful detection rate is not 100% 66
5.1 In the case of CB>0 66
5.1.1 Low advertisement-tolerance users 66
5.1.2 High advertisement-tolerance users 72
5.2 In the case of CB=0 75
5.2.1 Low advertisement-tolerance users 75
5.2.2 High advertisement-tolerance users 79
5.3 Summary 83
Chapter 6 Conclusions and suggestions 85
6.1 Conclusions 85
6.2 Suggestions and research inferences 87
6.2.1 Suggestions 87
6.2.2 Research inferences 89
6.3 Limitations and future works 91
Reference 93
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