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POLITECNICO DI MILANO School of Industrial & Information Engineering Master of Science in Management Engineering Digital Platforms and Complementors: An Empirical Analysis based on YouTube Content Creators SUPERVISOR: Professor Luca Gastaldi CO-SUPERVISORS: Professor Stefano Brusoni Professor Paolo Neirotti Dr. Jose Arrieta MASTER GRADUATION THESIS: Andrea Belli 878261 Academic Year 2017-2018 Table of Contents Executive Summary 5 Research Context 5 Research Process 6 Research Results 8 Research Conclusions 11 Introduction 18 1 Background Context and YouTube Case Presentation 21 1.1 YouTube’s History 21 1.2 How much money is in YouTube? 22 1.2.1 Company Value 23 1.2.2 Revenues and Costs 23 2 Literature Review 27 2.1 Introduction 27 2.2 Definitions and key characteristics of platforms 28 2.2.1 Innovation Perspective 29 2.2.2 Economics Perspective 31 2.2.3 YouTube as a Platform 34 2.3 Governance 37 2.3.1 YouTube’s control over its platform: policies and algorithms 41 2.3.2 Policies 42 2.3.3 Algorithms 47 2.4 Benefits distribution and strategies to gain power 50 2.5 Gaps and Future Avenues 52 3 Methodology 55 3.1 Research Questions 56 3.2 Unit of Analysis 57 3.3 Cases Selection 57 3.3.1 Finding Data 58 3.3.2 Screening Data 59 3.3.3 In-depth Video Category Analysis 60 3.3.4 Final list 62 3.4 Data Collection 63 3.5 Data Analysis 64 4 Results 65 4.1 Framework: The three moments 66 4.2 “Starting Point” 67 4.2.1 “Starting Point”: Motivation 68 4.2.2 “Starting Point”: Opportunity 69 4.2.3 “Starting Point”: YouTube Policy 71 4.2.4 “Starting Point”: Community 72 4.2.5 “Starting Point”: Connections 72 4.3 “Full-Time” 75 4.3.1 “Full-Time”: Motivation 76 4.3.2 “Full-Time”: Opportunity 79 4.3.3 “Full-Time”: YouTube Policy 84 4.3.4 “Full-Time”: Community 88 4.3.5 “Full-Time”: Connections 89 4.4 “Independence” 92 4.4.1 “Independence”: Motivation 93 4.4.2 “Independence”: Opportunity 95 4.4.3 “Independence”: YouTube Policy 96 4.4.4 “Independence”: Community 97 4.4.5 “Independence”: Connections 98 5 Results Interpretation & Discussion 100 5.1 Theoretical Contribution 100 5.1.1 Multi-Homing 101 5.1.2 “Direct Monetization” 104 5.2 Practical Contribution 110 5.3 Limitations and Future research 113 6 Bibliography 118 7 Appendix 131 Appendix A – YouTube’s Community Guidelines 131 Appendix B – YouTube’s “Get Discovered” 134 Appendix C – Interview Protocol 136 Appendix D – Socialblade scraping code 138 Executive Summary Research Context Within the past 15 years, platforms have increasingly dominated markets. Today’s most successful firms – Apple, Amazon, Facebook, Google —have platform-based business models. At their core, platforms bring together two or more group of users and facilitate transactions among them: eBay links buyers and sellers; Playstation links gamers with developers; Airbnb connects home owners with travelers. Academics started to study these companies addressing many relevant topics such as: which characteristics a product should have to develop a platform on it (Santaló, 2015), which strategies platform owners can use to launch their platform (Bhargava, Kim, & Sun, 2013), infrastructure / network requirements (Constantinides, Henfridsson, & Parker, 2018), governance (Foerderer, Kude, Schuetz, & Heinzl, 2018) and many others. One of the main themes is innovation (Gawer, 2014) and the interplay between platform owner and the ecosystem of actors surrounding the platform. Academics from the economics stream of literature, instead, focused on how creating and growing the network surrounding a platform, looking at pricing structure and competition (Armstrong, 2006; A. J. Rochet, Tirole, & Rochet, 2006; Rysman, 2018). However, despite the growing interest of the scientific community, all these topics have been predominantly deepened from the perspective of platform owner, also called keystone firm (Cennamo, Ozalp, & Kretschmer, 2016). The whole discussion rotates around what the owner should do to be successful, to manage its network and to generate profits (Kim, Lee, & Park, 2017). Little is known about what the others actors around the platform, often called complementors (Förderer & Kude, 2016), can do to reap benefits for themselves. There is a strong interplay between the governance policies adopted by the keystone firm, which freedom is left to the rest of the ecosystem and ultimately which strategies they can adopt to escape the platform owner’s control. To explore this research avenue, this thesis looks at the YouTube platform owned by Google. At the heart of the ecosystem surrounding the company, there are content creators who produce the videos that can be watched on the platform. YouTube puts advertisement in front of these videos and the revenues generated are split with the content creator. Creators, viewers and advertising companies are all part of the ecosystem surrounding the platform, which can be summarized in its main interactions as in Fig 1. Figure 1 - Synthesis of the three sides of YouTube and the exchanges on the platform. YouTube exercises a strict control over content creators. Two main governance mechanisms are at play: policies and algorithms. Policies directly affect what is allowed to be present on the platform and what can be monetized within it. Algorithms, instead, define how videos are promoted through the platform and showed to viewers. When it comes down to deciding how the platform should be managed or evolved in the future, creators cannot express their voices (boogie2988, 2018a; Julia Alexander, 2018; The Completionist, 2018). Creators often feel powerless against changes undermining not only their income, but also the possibility to grow and sustain themselves over the long term (Michael Sawyer, 2017). To make money, compliance is mandatory. However, over the years, many creators tried to create alternative sources of revenues: from selling merchandise to their audience, to accepting donations or even create videos sponsored directly by companies. Each creator created around himself a network of opportunities to leverage their community of viewers and ability to engage people. Research Process This thesis focuses on three research questions: 1. How the relationship between a content creator and the ecosystem of services surrounding it changes as she grows on the platform? 2. How are content creators affected by YouTube’s policy changes? 3. Which strategies can content creators use to escape YouTube control? Which is the relationship between those strategies and governance? To investigate these elements and develop an overarching theory, interpretative research has been applied through multiple case studies. As unit of analysis, content creators with three specific characteristics have been considered: (1) No YouTube channels run by big corporations; (2) “Big enough” creators who could provide valuable theoretical insights; (3) English-only speaking channels. Theoretical sampling has been applied to find interesting stories with a strategy divided into four steps: 1. Finding data: YouTube does not provide aggregate data nor a way to classify the variety of different content creators on the platform. It does provide an API that other websites, such as Socialblade, can use to monitor daily statistics. To gather this data, I developed a web scraper in Python using the library Beautifulsoup as HTML parser. This data contains three key elements: (1) the number of views ever received by a channel; (2) its current number of subscribers; (3) a categorization of the channel. 400 channels have been considered. 2. Screening Data: The three criteria defining what is a unit of analysis have been applied in practice, cutting down the number of channels considered to 103. 3. In-depth Video Category Analysis: To reduce the amount of variety within the different channels an analysis of the content produced by them has been made. Overall, 12 different typologies of channels have been found. Every typology with less than 10 channels within itself has been discarded, leaving 67 channels left in the sample. 4. Final List: To reach a final list of creators, I tried to contact each creator among those 67. However, the response rate was extremely low (1.5%) and only 1 creator agreed to participate. To find others, I leveraged two techniques: suggestions from YouTube matching the before mentioned criteria and snowballing sampling. At the end of this process, 14 cases were gathered. Data collection was done through a combination of semi-structured interviews and secondary sources. Interviews were conducted over Skype or Discord, with the average interview being 40 minutes long. Data analysis was accomplished through coding in Nvivo. Initial codes were created a priori from the main themes identified through preliminary research and study of secondary material. Then, each interview was scanned for passages pertaining to certain codes and to generate new ones. Interviews were then complemented through secondary sources to generate a unified case perspective. Through an iterative process, going back and forth between the different cases, codes were either merged together or deleted. To develop a cross-case analysis, a hierarchical structure among the remaining codes was created. Research Results From the interviews, three specific moments in the lifecycle of a creator emerged: the “Starting Point”, going “Full-Time” and achieving “Independence” as shown in Fig 2. Each moment has been explored and described to give an answer to the first two research questions. Figure 2 - The three moments of a content creator lifecycle Starting Point At the “Starting Point”, a content creator is considered an amateur. To her, YouTube is mostly a hobby, something that she does “on the side”. A summary of the creator’s behaviour at this stage is given in Fig 3. Figure 3 – “Starting Point” content creator behaviour At the beginning, a “Just Started” mentality along with some “Easy Access” policies provided by YouTube, allow creators to enter the platform quite easily.