Visualizza/Apri

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. However, this easiness comes at the expense of careful thought. Creators simply throw themselves onto the arena and this leads to an initial set of failures. At this point, the creator enters a loop where she’s stuck searching for an audience and promoting the videos she’s making. Eventually, out of luck, one of its videos explodes in popularity. Once that happen, a successful creator tries to replicate that success and keeps on improving the quality of her videos. The overall effect of this second loop is continuous growth on all fronts (views, subscribers and revenues) along with the development of key skills necessary to become a full-time creator, such as video editing, scripting or animation.

Going back to the research questions, for the first one it is possible to affirm that the ecosystem of services is underdeveloped at this stage. Creators main concern is growth and external services (e.g. Reddit or Forums) are used only to gather an audience. For the second question, YouTube’s policies enable creators to easily get into the platform. They are a positive force that lowers entry barriers. Enabling factors are promoted, including free access to a platform and educational content on how to succeed. To allow for the highest number of creators on the platform, YouTube also doesn’t really care about the type of content being posted by the creator at this stage.

Full-Time

During the “Full-Time” moment of the lifecycle, the creator is extremely tied to the platform. Her survival depends on YouTube as her main source of income. Changes on the platform can cause financial struggles. A summary of the creator’s behaviour at this stage is provided in Fig 4.

Figure 4 – “Full-Time” content creator behaviour The main drivers during this phase are generated by the constant feelings of uncertainty and the difficulties on understanding how to behave on the platform. The growth formula used before seems to be broken. In going full-time, people also exposed themselves to financial risk. This combination of feelings pushes creator to find a solution that comes in two main ways: bundling together and expanding their business.

The main effect of this “coming together” is an increased rate of growth for three main reasons. At first sharing information reduce uncertainty and allows creators to follow better strategies. Then, networks protect creators reducing the effects of growth-halting policies. Finally, cross promotions actively drive new viewers to the channel. On the right side of the model instead, creators start to actively look for business alternatives. This process is a careful balancing act between opportunities to monetize people’s attention and the pressure they receive from viewers along with their own moral integrity.

Going back to the research questions, the ecosystem of services is a key component of income stability that allows a content creator to work Full-Time. According to the reaction of its community and own moral code, the creator has access to a variety of potential businesses: sponsorships, merchandise, streaming, donations, websites etc. Policy changes create an uncertain environment that leads creators to look for new avenues. The main objective is to increase chances of survival. Policy changes also have a “community” effect, bringing creators together to share information, collectively increasing their bargaining power and collaborate.

Independence

During the “Independence” phase, the creator has outgrown the platform. His main source of revenue comes from alternative businesses that he built thanks to its popularity. YouTube is still relevant for her, but changes on the platform usually have little impact on her bottom line. A summary of the creator’s behaviour at this stage is provided in Fig 5.

Figure 5 - “Full-Time” content creator behaviour

Going back to the research questions, the ecosystem is what drives most revenues at this level. There are the most disparate opportunities, from writing books up to having your own app. Creators are internet celebrities and thus many of the opportunities are similar to that of other famous people, such as appearing on Television, writing for magazines, fashion advertisement and so on. For the second question regarding policies, to capture the viewers of these “Small Celebrities”, YouTube algorithms and policies actively promotes the biggest content creators. This in turn enlarges their fanbase even more, giving back value to the creator.

Research Conclusions

The results of this thesis contribute to two main streams of literature:

1. “platform leadership”, but explored from the perspective of a complementor (Adner, 2017; Cennamo, 2016; Gawer & Cusumano, 2014; Parker, Van Alstyne, & Choudary, 2016; Santaló, 2015);

2. the effects and impacts of governance with a focus on how it drives the actions of complementors (Bergvall-Kåreborn & Howcroft, 2014; Foerderer et al., 2018).

These two topics are intertwined in the results of this thesis, as one affects the other. Within the three moments highlighted, the control exercised by the platform on its complementors varies, as showed in Fig 6.

Figure 6 - Qualitative representation of "Governance friendliness" as the content creators grows To explain why this data has been observed and give an answer to the third research question, the relationship between governance and the strategies that complementors can use to escape the control of the platform has been explored. Two major strategies have been found: multi-homing and “direct monetization”.

Multi-homing has also been previously found in literature (Armstrong, 2006; Cennamo et al., 2016) and refers to complementors producing content for multiple platforms at the same time. There are two major elements that make this strategy successful: (1) the content requires little to no adaptation, as the same video can be uploaded elsewhere; (2) at the “Independence” phase, content creators have enough power to drive the behaviour of their fanbase. Multi-homing actively harms the growth / size of the network. Therefore, YouTube changes its governance policies to convince big creators to stay in the platform.

“Direct monetization”, as a strategy, means that one of the sides of the platform can directly make transactions with another one. It is the case of creators selling directly merchandise to their viewers or brands contacting directly YouTubers for sponsored videos. This strategy has not been previously described in literature as a valuable path that complementors can follow. In fact, in other contexts this scenario eventually led to the death of the platform itself (Christina Farr, 2015). In this scenario, there is an equilibrium. I argue that there are two elements defining this equilibrium: (1) to make transactions between themselves, the other parties still need YouTube as a technology provider (e.g. video hosting). Therefore, the platform cannot be entirely cut off. (2) the possibility for complementors to make direct transactions reduces YouTube subsidization costs (i.e. all the costs sustained by YouTube to convince content creators to join the platform).

The core logic behind this second proposition can be highlighted through 4 steps:

(1) At first, YouTube incentivizes its creators to make them join the platform. Access to the platform is provided for free and the company invests into knowledge transfer towards the creators. The overall objective is to help them learn about the platform and make sure they can grow. Monetization is not involved yet, and, thus, all the associated policies are not considered, allowing the creators to grow freely on the platform. These governance mechanisms entail some costs for YouTube.

(2) Then, once the creator’s community reaches a critical mass, the creator can start to directly monetize its audience. There are several ways for a creator to take advantage of its community including merchandise, donations and promoted content.

(3) The possibility to directly monetize the other sides of the platform, is a big enough incentive for the creator to stay in the platform regardless of what YouTube does. Content creators start to rely on their own ecosystem to generate value and ancillary revenues. As they grow, the money received from YouTube is increasingly less relevant.

(4) This scenario allows YouTube to stop subsidizing its creators. There is no need for the company to subsidize complementors as the ecosystem managed by a creator is enough to attract her to the platform. Therefore, governance can be 100% used to promote the company’s goals and make more money.

Taken together, these two strategies can explain the observed change of governance “friendliness” by YouTube as shown in Fig 7.

Figure 7 – Match between the observed change of policy over time and the strategies that complementors can adopt.

From the viewpoint of practical contributions, suggestions to both content creators and YouTube are summarized in Fig 8 and Fig 9.

Figure 8 - Recap of the key elements a Platform Owner should pay attention to

Figure 9 - Key suggestions towards content creators to be successful at each stage of their lifecycle

There are three main limitations of this thesis.

The first one is that there is no data coming from key decision makers within YouTube. The interpretation of the “Direct Monetization” strategy requires to also take the perspective of the platform owner to understand why the strategy exists in the first place. Without direct data coming from the company itself, it is not possible to assess to which degree its behaviour is intentional and strategic.

Possible future developments, their relevancy and potential research questions are summarized in Table 1.

Future research path Relevancy Potential Research Questions Integrate the perspective Understand if “Direct • Does “Direct Monetization” of complementors on Monetization” can be used as a truly emerge as a response to “Direct Monetization” strategic lever for platform policies? with that of the platform owners. • Can a platform owner design owner. aggressive policies to increase profits while incentivizing “Direct Monetization” to avoid damaging its network? Compare the platform Understanding in which • Which are the key elements with others presenting a contexts “Direct Monetization” that make “Direct similar structure. can be used and where it Monetization” sustainable in a doesn’t work. platform? • Which characteristics should a platform possess to successfully deploy this strategy? Understand the • Highlight the differences • Which core skills / abilities perspective of between successful and should a complementor complementors that failed unsuccessful develop to successfully multi- on the platform and why. complementors; home? • Identify key success factors • Which are traps undermining of the multi-homing the ability of a complementor strategy. to multi-home? Understand how “Direct • Understand from an • Does “Direct Monetization” Monetization” affects economic perspective what emerge as a viable strategy pricing in platforms. makes “Direct from the pricing and platform Monetization” successful. structure? Future research path Relevancy Potential Research Questions • Investigate the interplay between platform structure and “Direct Monetization” Understand how to Understand which principles • What makes YouTube and correctly classify / model from the literature on platforms similarly structured platforms YouTube and similar can be applied to these type of different from the others? platforms. platforms or not Table 1 - Possible future research paths, their relevancy and potential research questions

Introduction

Within the past 15 years, platforms have increasingly dominated markets. Apple, Amazon, Facebook, Google — today’s most successful businesses are platform based business models. “We live in the age of platforms” (Santaló, 2015). 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. More and more entrepreneurial startups are embracing a platform approach, such as Uber and Snapchat.

Academics started to study these companies addressing many relevant topics such as: which characteristics a product should have to become a platform (Santaló, 2015), what strategies platform owners can use to launch their platform in the market (Bhargava et al., 2013), infrastructure / network requirements (Constantinides et al., 2018), governance (Foerderer et al., 2018) and so on. One of the main themes is innovation (Gawer, 2014) and the interplay between the platform owner and the ecosystem of companies surrounding the platform. Academics from the economics stream of literature, instead, focused on how to create and grow the network surrounding a platform, looking at pricing structure and competition (Armstrong, 2006; A. J. Rochet et al., 2006; Rysman, 2018).

However, despite the growing interest of the scientific community, all these topics have been predominantly researched from the perspective of the platform owner, also called keystone firm (Cennamo et al., 2016). The whole discussion rotates around what the owner should do to be successful, to manage its network and to generate profits (Kim et al., 2017). Little to nothing is known about what the others 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, what 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 (i.e. video makers) 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. A series of guidelines define what the creator can and cannot upload on the platform. 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.

This thesis serves three main purposes:

(1) to investigate what is present in this ecosystem around the creator and how it is created as the creator grows; (2) to understand how policies and control from YouTube affected creators; (3) to synthetize what has been found into proper strategies that creators follow to escape the platform’s control and generate extra profits.

Its results, contribute to two main streams of literature:

(1) “platform leadership”, but explored from the perspective of a complementor (Adner, 2017; Cennamo, 2016; Gawer & Cusumano, 2014; Parker et al., 2016; Santaló, 2015); (2) the effects and impacts of governance with a focus on how it drives the actions of complementors (Bergvall-Kåreborn & Howcroft, 2014; Foerderer et al., 2018).

Overall, clarifying these issues can provide a significant contribution to our current understanding of digital platforms. Given the transformative impact of companies such as YouTube on the world, understanding their inner workings could provide value to:

(1) individuals engaging with the platform; (2) companies trying to build platforms in the digital space; (3) researchers investigating what platforms are and what makes them successful.

To reach these goals, the thesis is structured as follows:

(1) The first chapter focuses on introducing YouTube. It provides a background on the company, going over his history and ultimately tries to assess how important is this company in the world. The chapter discuss elements such as market value, cost structure and profitability. (2) The second chapter is a literature review on the topic of platforms. A formal definition of what platforms are is provided along with a discussion on how different schools focused on different elements. This theoretical background is then used to interpret YouTube’s structure and provide a description of it through the lenses of academic research. The topic of platform governance is also addressed both from a literature perspective and also by reporting the major policies applied by the platform. The chapter ends with a description of potential gaps found within the literature and possible research avenues. (3) The third chapter is about the methodology used for the thesis. At first, research questions are properly formalized. Then the methodology used is highlighted. The chapter explains how the unit of analysis and sample were chosen. Then, it explains how data collection and data analysis were conducted. (4) The fourth chapter presents the results of the thesis. At first a general framework used to explain the results is showed. Emphasis is put on the different stages of growth of the creator and how each of these stages works. The goal of this chapter is to provide a raw description of the lifecycle of a creator, how he creates his own ecosystem and which is the impact of policies. (5) The fifth chapter goes over the interpretation of the results and their discussion. The focus is on making a synthesis of what has been found in the results chapter, creating a link with the scientific literature. Both theoretical and practical contributions are discussed. Then, document reaches its end with a highlight on the major limitations of the thesis and possible future research questions.

1 Background Context and YouTube Case Presentation

YouTube is an American video-sharing website, part of Alphabet Inc. YouTube allows its users to upload, view, and share videos. It offers a wide variety of user-generated content. Videos range from educational and gaming captures, to corporate media content from companies such as BBC, Hulu or Vevo. YouTube earns advertising revenue from Google Adsense, which targets ads from advertisers according to the specific content and viewer. YouTube is not a profitable service from Alphabet, with an estimated operating loss of $1.23B per year (Everysecond.io, 2018).

1.1 YouTube’s History

YouTube was found in 2005 by 3 PayPal employees (Jawed Karim, Steve Chen, and Chad Hurley). The company started after they realized that there was no video sharing website available at the time. The idea received $11.5 million investment from Sequoia Capital and after a Beta testing period the site launched in the U.S. in December of the same year (Engadget, 2016). Google acquired the company less than a year later for approximately $US 1.65 billion. Google had high hopes for YouTube’s future, as Google’s CEO Eric Schmidt commented: “This is the next step in the evolution of the internet” (The Washington Post, 2006).

In 2006, YouTube first addressed its issues with copyright owners, being asked by NBC to remove a clip of its “Saturday Night Live” TV show. YouTube forcefully complied and a year later launched one of its pillars: the “Content Verification Program”, to help content owners like NBC to locate and remove videos that infringes on their copyright (Business Insider, 2013).

After Google’s acquisition, in 2007 the website launched its Partnership program, allowing the most popular content creators to make money out of their videos through Google’s AdSense advertisement (Google, 2007). This was the first time that YouTube allowed everyday people to turn their hobbies into a business.

About a year later, the most successful users were already earning six-figure incomes. Michael Buckley, one of the very first YouTube stars, was among the first people to quit their job and fully dedicating to YouTube as a career with its “What the Buck?” show which eventually helped him to jump to other media such as Television and Radio (New York Times, 2008).

2009 was the next big year for YouTube reaching one billion views per day and starting two important partnerships: one with Channel 4 and another one with Vevo. Channel 4 is a British TV channel, which was among the first to bring official and legal TV content into the platform. Vevo, instead, is a joint venture among the top music labels in the world: Universal Music Group, Sony Music Entertainment and Warner Music Group to legally bring music to the platform (Engadget, 2016; Tech Crunch, 2013).

In 2011 YouTube launched a new livestreaming service, officially entering the broadcast business. It allows the users to stream everything: from concerts to the Olympics (Business Insider, 2013), but also to compete with Twitch, a livestream service dedicated to gaming, thus allowing YouTube’s content creators to stream videogames.

In 2015 YouTube launched its gaming division after failing to acquire Twitch (which was bought by Amazon for almost $1B) with the following rationale: “You can’t ignore how big the hardcore gaming segment is: you’ve got to build an experience for them” (The Guardian, 2015). In 2015, YouTube also launched a YouTube Kids app (in selected countries), a version of YouTube tailored to young children. The success of such application has been massive, with over 70 Billion views and 5 of the top 15 YouTube channels entirely dedicated to young audiences.

In 2017, YouTube followed Facebook as the second largest online platform, with a monthly user base of 1.5 billions (The Verge, 2017). YouTube is also the second most visited website in the world (Alexa, 2018). YouTube has been working on new advertisement regulations, aimed at increasing “brand-safety” and allowing the company to raise ad prices which also increased investors’ enthusiasm in the platform (Business Insider, 2017).

1.2 How much money is in YouTube?

Understanding how much money moves around YouTube is no trivial task. Google publishes all of its financial data in aggregated form under Google’s division in Alphabet’s annual report. However, within the same report, Google’s makes some important remarks about YouTube, for example: “The number of paid clicks through our advertising programs on Google properties increased from 2016 to 2017 due to growth in YouTube engagement ads”, stating the fundamental importance of the platform in growing Google’s business (Alphabet, 2017). Because of this, and the huge popularity of the website, several analysts tried to understand how much money moves around the platform.

1.2.1 Company Value

Victor Anthony, an analyst at Aegis Capital specialized in the technology sector, estimated that if YouTube was an independent company in July 2017, it would be worth around $75B (Yahoo! Finance, 2017). To put things in perspective, at the time of his analysis, Twitter had a market capitalization of $14.5B, roughly 5 times less than YouTube. This estimation is generated by an analysis on how much users engage with the platform and then a comparison with other social media platforms traded on the market stock; as Victor Anthony finally commented: “This site [YouTube] is becoming entrenched in similar ways that Facebook is part of our vernacular and daily usage.” (Yahoo! Finance, 2017).

Other analysts, such as Jack Hough, a Wall Street investments advisor, took a different approach to estimate YouTube’s value: instead of comparing it to other social media platforms, they compared YouTube to other video-streaming services such as Netflix. The rationale is that even though these kinds of companies have a different business model compared to YouTube (typically a subscription based one), they provide a similar service. This makes it possible to compare Netflix and YouTube in terms of cost structure (e.g. how much money they spend on content) and assets (e.g. the infrastructure needed) while also considering the different dimensions of the viewership base. Through this comparison, YouTube is estimated to be worth around $100B dollars, which put in perspective at the time of the analysis is almost double the value of Netflix (Jack Hough, 2015).

Other analysts don’t specify their method of analysis, but still value the company to be worth $70B – $90B (Justin Post, 2015; Merril Lynch, 2016). Given these figures, if it was placed in the S&P 500, it will be among the top 50 companies in terms of market value.

1.2.2 Revenues and Costs

Despite differences in the analyses, the generally accepted idea is that YouTube is a very valuable company, however another question is also important: “Is it profitable?”.

Ken Sena, an analyst at investment bank Evercore ISI, estimated in 2015 YouTube’s yearly revenue to be around $9B, with an annual growth rate of 17%. He expected YouTube to reach $20B of revenue by 2020 – between 15-25% of Alphabet Inc’s revenue at that time. To put things in perspective, Netflix revenues where around $7B in 2015 (Ken Sena, 2016). Sena’s analysis is in line with that of two other analysts, Justin Post from Bank of America and Eric Sheridan from UBS, which estimated the revenues to be around $8.2B - $8.5B (Eric Sheridan, 2015; Justin Post, 2015). In 2017 Instinet, a financial consultancy company, estimated YouTube’s revenues to be around $10.2B, which is aligned with the previous predicted growth rate of 17% (Nomura Instinet, 2017).

An important component of YouTube’s revenues, comes from its partnership with Vevo to provide music on the platform. Compared to other music-streaming services, such as Spotify, YouTube earned almost 8 times more in revenues and accounts for 46% of all time spent listening to on-demand music as reported in Fig 10 (Ifpi, 2017).

However, this “8 times more” figure could change in the future, as the music industry is currently in an open debate with YouTube around the so-called “Value Gap”: the remuneration that YouTube provides to artists is not considered “fair” (Ifpi, 2017; Ken Sena, 2016; Tim Ingham, 2016). The “Value Gap” debate is active with other streaming platforms as well; nonetheless, YouTube pays artists around 5 times less than Spotify, the leading streaming service (Daniel Sanchez, 2018).

Figure 10 - Percentage of time spent by users on different on-demand music platforms (Ifpi, 2017)

Despite high revenues, video-streaming services have huge infrastructure costs. To provide an idea, in 2014 Netflix and YouTube accounted for over 50% of the entire internet traffic in the U.S. (Forbes, 2016). Estimating infrastructure costs for YouTube alone is very difficult, as there are huge economies of scope and scale at play with its parent company Google (e.g. large scale investments, proprietary technologies, internal competences) (Cusumano, 2016).

The other big source of costs comes from acquiring content, as the platform pays its content creators and media companies owning copyrighted content. On average, YouTube pays each year $230M to media companies, while the standard contract between a YouTube’s partner content creator and AdSense splits the revenues generated in 55% to YouTube and 45% to the content creator, meaning that YouTube pays its content creators around $5B each year (Brandwatch, 2018; Google, 2018p; Ken Sena, 2016).

Everysecond.io, a platform specialised in showing real-time data, computed that YouTube enables around 1M views every 25 seconds. It estimates the cost of running the platform for these views in $4000 and the ad revenue generated in $3000 dollars, this totals a net loss of $1000 leading to an estimate operating loss of $1.23B per year.

This goes in line to the general analysts’ consensus that states that the company is not profitable yet (Fortune, 2016; Ken Sena, 2016; Mark Mahaney, 2016). YouTube is still operative due to its strategic relevance in Google’s business. In fact, YouTube provides its data to AdSense and this enables the creation of more accurate user profiles, which in turn allow for higher returns on advertisement for other platforms. This strategic relevance might justify Google’s interest in sustaining YouTube as YouTube’s CEO, Susan Wojcicki, stated in 2016: “There’s no timetable for profitability, […] we are still in investment mode.” (Fortune, 2016). The key financial figures previously discussed are reported in Table 2.

Market Capitalization Estimate $100B (Jack Hough, 2015) $90B (Merril Lynch, 2016) $75B (Yahoo! Finance, 2017) $70B (Justin Post, 2015) Revenues Estimate $10.2B (Nomura Instinet, 2017) $10B (Mark Mahaney, 2016) $9B (Ken Sena, 2016) $8.5B (Eric Sheridan, 2015) $8.2B (Justin Post, 2015) Annual Losses Estimate $1.23B (Everysecond.io, 2018) Table 2 - Key Financial Figures of YouTube

2 Literature Review

2.1 Introduction

The purpose of this section is to provide a theoretical background on the topics of platforms, their key characteristics, governance policies and strategies that actors in the ecosystem can use to increase their benefits. This field of research taps into the streams of “Technology Management”, “Open Innovation”, “Economics” and “Strategic Management” (Facin, De Vasconcelos Gomes, De Mesquita Spinola, & Salerno, 2016; Gawer, 2014; Thomas, Autio, & Gann, 2014).

According to Parker (2016), companies are increasingly focused on managing value that is created outside the company. Their focus is on orchestrating the output that ecosystems around them produce. Some of the most valued companies, such as Alibaba, Amazon, Facebook, and Google are platform businesses. They conquered the market in short time and experienced exponential growth (Constantinides et al., 2018). At the same time, more traditional companies are considering how they can adopt platform strategies to improve performance. For instance, General Electric has made significant investments in platforms for the industrial Internet of things (Constantinides et al., 2018). Within literature, scholars refer to all kinds of physical or intangible products (e.g. software products) as platforms, that among others include credit cards, ATMs, shopping malls, personal computers, mobile phones, operating systems, videogame consoles (Facin et al., 2016; Gawer & Cusumano, 2014).

Overall, the concept of “platform”, while being extremely important to understand many modern-day companies, it is still relatively new with first studies starting around the 2000s (West, 2003). Within the following sections, relevant themes will be explored:

(1) At first, several definitions of the concept of platforms are presented. Two main streams of research are considered, one with a focus on innovation and the other one with a focus on platforms’ economics. The purpose of this section is to limit the boundaries (and characteristics) of which platforms are considered in this thesis. Then, YouTube is described as a platform using the theoretical background provided by the discussion.

(2) Then, the theme of governance will be addressed. Platforms face challenges in managing their ecosystem. These issues will be explored so that findings in the literature can later on be compared to how YouTube uses and takes advantage of its complementors. The two main mechanisms of governance used by YouTube, policies and algorithms, are presented. From there the impact on creators is assessed with a discussion on major scandals that involved the platform and forced changes.

(3) Subsequently, the theme of how benefits and profits are distributed in a platform is discussed. Both the perspective of the platform owner and the agent in the ecosystem will be taken. However the perspective of the platform owner will just be briefly touched upon as an introduction, even if it is heavily researched compared to the other perspective (Constantinides et al., 2018). The whole purpose of this part is to understand what is known in the literature about the dynamics between a platform owner and complementors when it comes to profits.

(4) Finally, research gaps within the literature will be highlighted along with possible research avenues.

The literature on platforms touches also upon other key issues, such as innovation (Gawer, 2014), which characteristics a product should have to become a platform (Santaló, 2015), what strategies platform owners can use to launch their platform in the market (Bhargava et al., 2013) and infrastructure / network requirements (Constantinides et al., 2018). Although these are important topics, they are less relevant to interpret and analyze the case study.

2.2 Definitions and key characteristics of platforms

The concept of platforms has been addressed by many scholars throughout the past 15 years. Several definitions can be found in literature, according to the different streams of research. Gawer and Cusumano in their 2014 literature review define two macro-types of platforms: internal platforms and external platforms.

Internal platforms, also called company-specific, are defined as “a set of assets organized in a common structure from which a company can efficiently develop and produce a stream of derivative products” (Cusumano, 2010; Gawer, 2014). As an example, the Office suite (e.g. Excel and Word) is built around shared components, such as text-processing, file-management or graphics modules. External platforms, or industry-wide, are defined as: “products, services, or technologies that act as a foundation upon which external innovators, organized as an innovative business ecosystem, can develop their own complementary products, technologies, or services” (Facin et al., 2016; Gawer, 2014). Given “YouTube” as the case study presented in this thesis, the focus will be on external platforms.

There are two theoretical perspectives that describe external platforms (Gawer, 2014): (1) Economics, which sees platforms as double-sided markets. The focus of this research stream is on price and competition. This point of view will be addressed later in the chapter as it explains several criticalities of platforms. (2) Engineering design, which sees platforms as technological architectures and is focused around innovation.

2.2.1 Innovation Perspective

Within this second perspective, the “Industrial Organization” stream of research defines them as “interfaces that mediate transactions between two or more parties” (McIntyre & Srinivasan, 2017). They can be embodied in products, services or technologies. An example of this would be eBay and its network of sellers and buyers. The “Technology Management” stream extends this notion: “platforms are building blocks that serve as the foundation on which other firms can build related product or services” (Gawer, 2014; McIntyre & Srinivasan, 2017). An example of this would be Windows, that allows developer to build products upon a common framework. In wider terms, the word “ecosystem” has often been used to define a community of interacting firms (Eisenmann, Parker, & Van Alstyne, 2006; Huang, Tafti, & Mithas, 2018; Iansiti & Levien, 2004). Within the context platforms, the ecosystem refers to the “Keystone Firm” and the network of complementors & complements that enhance platform value (Facin et al., 2016; Gawer, 2014; McIntyre & Srinivasan, 2017).

The “Keystone Frim” is the one developing the platform and thus orchestrating the ecosystem to generate value (Gawer, 2014). The Complementors are the independent providers of complementary products. The Complements are the product/services themselves (Gawer, 2014; McIntyre & Srinivasan, 2017). An example would be a videogame developed for a console, such as a PS4. Complements are key to the success of a platform, since they extend the functionalities of the core system to deliver greater value to users (Cennamo, 2016). Given this importance, other scholars defined a platform as: “a system that brings system adopters together with firms that provide complements” (Foerderer et al., 2018). An example would be the AppStore, which brings together smartphone users and developers, while also allowing the development of complements.

Some scholars highlighted specifically “Digital Platforms” as a “set of digital resources, including content, that enable value-creating interactions between external producers and consumers”. This definition captures pretty well YouTube as its video-hosting capabilities allow content creators and viewers to have “value-creating” interactions (Constantinides et al., 2018).

On top of the difference in scope, external platforms are also characterized by the presence of network effects (Cusumano, 2010). The more users adopt the platform, the more the platform becomes valuable to other users and complementors. As the network grows, so do the incentives to join it (Gawer, 2014). An example would be social media, as more value is generated for a user each time one of his friends join the platform.

In general, the availability of complementary goods positively influences the adoption decisions of consumers (McIntyre & Srinivasan, 2017). People are attracted to YouTube because of the availability of videos. At the same time, complementors’ decision to invest in the platform depends on its network size. In fact, complementors will find themselves better off developing for the dominant platform, given its large installed base of users (McIntyre & Srinivasan, 2017). As an example, content creators are attracted to YouTube because of the amount of people watching videos on the platform.

Due to these effects, Cusumano in 2010 expressed the necessity to build a “positive feedback loop” between the complements and the platform as a core element of a platform strategy. To be successful as an industry-wide platform however, it must possess two characteristics (Gawer & Cusumano, 2014): (1) The platform must perform a function that is essential for the complementors. As an example, a videogame console provides the hardware for the software, or YouTube provides hosting capabilities to its creators. (2) The platform must solve a business problem for many complementors / users. As an example, it would be difficult for an eBay seller to be noticed by a potential customer without the platform and payments would be based upon trust.

In Digital Advertising Platforms, such as YouTube, platforms often compete on the advertiser side, in terms of technological superiority (i.e., good targeting) while providing users the service free of charge. Recent research also highlights the trade-off between advertising effectiveness and user privacy considerations. This tension has an impact on the willingness to join the network of the different sides, although its effects and causes require more studying (Gal-Or, Gal-Or, & Penmetsa, 2018).

2.2.2 Economics Perspective

From an economics’ viewpoint, platforms are seen as multisided markets (Gawer, 2014). A two-sided market possesses two characteristics: (1) at least two sets of agents interact through an intermediary / platform. (2) the decisions of each set of agents affects the outcomes of the other set of agents, typically through an externality (Rysman, 2018). The necessity of an intermediary is often due to transaction costs: “frictions that make it costly for one side of the market to pass through a redistribution of charges to the other side” (J.-C. Rochet, Tirole, King, Rochet, & Tirole, 2013). From this perspective, Gawer (2014) argues that not all two-sided markets can be considered as industry platforms, since the vast majority merely facilitates transactions between two groups of actors rather than actually stimulating reuse or thriving innovation.

This definition is aligned with the previously explained concepts of ecosystem and complementors. However the focus of this research stream is to create economic models that describe the behaviour of agents interacting with the platform (Armstrong, 2006; A. J. Rochet et al., 2006). To understand agents’ behaviors within platforms, network effects are systematized into two categories: direct and indirect. Direct network effects arise when the benefit of network participation to a user depends on the number of other network users with whom they can interact (McIntyre & Srinivasan, 2017). An example would be emails, where benefits grow for each user as there are more people to send emails to. Indirect network effect, or cross-group externalities (Armstrong, 2006), generate benefits for one group based upon the dimension of another group (Armstrong, 2006; Gawer & Cusumano, 2014; A. J. Rochet et al., 2006). In YouTube’s case, advertisers value increases as there are more creators and vice versa.

Cross-network effects affect the desirability and adoption rate of the platform (Bhargava et al., 2013; McIntyre & Srinivasan, 2017). Therefore, the ability to attract a group of users also depends on the platform’s capability to attract customers from the other group (Armstrong, 2006). This concept is similar to the one of “positive feedback loop” previously presented in the strategic stream of research. This issue gives rise to the so-called “chicken and egg problem”: to have one side, the other one is also necessary. “Where do we start?” (Bhargava et al., 2013). The economic stream of research elaborates on this “positive feedback loop” by focusing on two main topics: price and competition.

Starting with price, compared to a traditional product, pricing for platforms must account also for the other side’s growth and willingness to pay (Eisenmann et al., 2006; Rysman, 2018). Therefore, the price-cost mark-up must consider not only the marginal cost and the elasticity of demand, but also the mark-up charged to the other side (J.-C. Rochet et al., 2013; Rysman, 2018). This inclusion can create anomalies where prices are below marginal cost or even negative prices can be set for one of the groups (Rysman, 2018). As an example, videogame consoles are sold at a price below marginal cost to incentivize diffusion or credit cards have fidelity programs that give benefits back to users (i.e. negative price). This is possible as long as it attracts a large number of participants on the other side who are relatively price inelastic (and hence have a higher mark-up) (Rysman, 2018). In light of this, the general idea is that to attract one side to the platform, it is necessary to subsidize another one (Eisenmann et al., 2006; Rysman, 2018). To decide which side to subsidize, a series of factors have been considered in literature:

(1) Sensitivity to price (Eisenmann et al., 2006); the idea is to subsidize the group more sensitive to price and charge the group that benefits the most from the presence of the subsidized group.

(2) Identify “marquee” users (Eisenmann et al., 2006); a marquee user is someone who is influential on the market. The idea is to subsidize only key users instead of the whole side if their participation can bring in more people. An example would be the launch of Apple Pay, where Apple signed different agreements with major retailers. Once the system was diffused enough, others started to adopt it as their customers requested it (Kovach, 2014).

(3) Sensitivity to quality (Eisenmann et al., 2006); if one side strongly demands quality, then it is necessary to charge the suppliers of quality. It is counterintuitive but charging the suppliers of quality filters out from the market low-effort products. An example would be the home- console videogames market (adapted from Steven, 2001; Thies, Wessel, & Benlian, 2018; Tiwana, 2015a). On the one hand, producing a video game has a high fixed cost due to the development of technology. On the other hand, customers demand quality but do not necessarily have the ability to recognize it. Due to high fixed costs, developers are incentivized to produce as many games as possible. Since customers can’t recognize quality properly, they would still buy those games. Eventually, this situation led to a market crash in 1983 (also known as the “Atari Shock”) where consumers distrust in the products, due to multiple “bad purchases”, led revenues down by 97% across the industry. At the time Nintendo also entered the market with their Nintendo Entertainment System (NES). To avoid the issues of the past they deployed a strict policy that limited the number of games that each publisher could develop each year for the platform. Developers were forced to increase the quality of games to recoup technology development costs. This strategy, called “Input Control” eventually led the market out of the crisis.

(4) Relative size of cross-group externalities (Armstrong, 2006); the idea is to subsidize the group with the strongest positive network effects on the other sides;

(5) Coordination Bias (Hałaburda & Yehezkel, 2016); the idea is to subsidize either buyers or sellers according to who will have more difficulty in finding the other group for the transaction.

This necessity to subsidize one of the sizes to reach a critical mass, leads to the so-called growth vs profitability dilemma: do we sacrifice profits for higher market share? (Bhargava et al., 2013). This issue arises because there’s uncertainty on whether a platform can secure the participation of the other side (Bhargava et al., 2013; Cennamo & Santaló, 2013). Firms developing platforms have low direct control over the number of third parties joining the platforms. They can only attract them through indirect mechanisms, such as pricing, which entails costs (Bhargava et al., 2013). Bhargava and colleagues (2013) tried to solve this dilemma by proposing a multiple-version platform launch. A premium one, with higher margins, and a basic one, to gain market share and, thus, generate relevant network effects. An example of this strategy would be Twitch, a video-streaming platform that has a premium subscription to remove ads and for enhanced functionalities.

Talking about the competition factor, due to network effects and presence of a “Positive Feedback Loop”, the market often takes the form of “Winner Takes it All” (WTA) (Cennamo & Santaló, 2013). Since each side derives value from the size of the other one, a new user would find more benefit in joining the bigger network. Overtime only a single platform will remain in the market as the users concentrate on it (Cennamo & Santaló, 2013). In this scenario, the dominant strategy is to race to win the market (Cennamo & Santaló, 2013). Even with superior technology / performance it is difficult to catch up to platforms with early advantage due to the strong network effects (Constantinides et al., 2018). This market condition of a WTA also means that sometimes random events can lead a platform to reach a critical mass before the others (Schilling, 2014). The critical mass is the minimum number of users needed to have significant network effects (Schilling, 2014). Once the platform reaches critical mass, switching costs become very high for users, thus creating a lock-in effect and high entry barriers (Gawer & Cusumano, 2014; Schilling, 2014). Signaling is also a key factor as convincing customers that the platform will grow can push them to join it (Hałaburda & Yehezkel, 2016). In that, expectations play a key role in promoting the growth of the platform (Hałaburda & Yehezkel, 2016).

The strategic stream of literature offers a solution to this problem: differentiation (Cennamo & Santaló, 2013). Platforms can decide to compete straight head-to-head in the same market or try to focus on a smaller niche with less intense competition (Cennamo & Santaló, 2013). Choosing a smaller niche in a platform context may lead to smaller network effects, making the strategy more difficult to execute (Cennamo & Santaló, 2013). Cennamo & Santalò (2013) argue that differentiation in the context of platforms can be successful only if the platform is highly distinctive compared to its rivals. In this case however, the market doesn’t take the form of WTA and companies with less market overlap can coexist in the market. An example would be the Nintendo Wii, which offered a value proposition very different from other consoles to a different target of customers (Cennamo & Santaló, 2013). Sometimes, this strategy might also not be viable if the demand for differentiated features is limited (McIntyre & Srinivasan, 2017).

An alternative strategy is to offer “killer apps” that are available exclusively on the platform. A “killer app” is a feature / complementary product that in itself has enough value to convince customers to join the platform (Santaló, 2015). An example are video game exclusives in the console market. The presence of a “killer app” can entail customers to join the platform regardless of network effects. This phenomenon can generate enough critical mass to then convince the rest of the market (Santaló, 2015). This strategy however can have a strong downside, which is the “crowding out” effect of complementors. Since exceptional complements are available on the platform (the “killer app”), complementors face increased competition. There’s a heated debate in literature on whether platforms themselves should produce high quality complements to generate traction for their platform or if they should avoid joining the market space of their complementors (Förderer & Kude, 2016; Karhu, Gustafsson, & Lyytinen, 2018; Tiwana, 2015). Santalò (2015) argues that the two approaches, offering a lot of content but also developing “killer apps”, should not be combined. Cennamo (2016) argues instead that developing complements in the early stages of the platform shows commitment and encourages complementors to participate in the platform while also providing value to early users.

2.2.3 YouTube as a Platform

Building upon the previously mentioned literature, YouTube can be mapped as a platform with three key sides: (1) Content Creators, (2) Viewers, (3) Brands and Companies.

Content creators provide the videos to the platform. This side of the platform is heavily subsidized by the company as they receive payments for their videos. Payments are based upon the performance of the video and its ability to attract viewers. Brands and companies are the ones that put in the money for the advertisement, thus payments are also connected to how “advertiser-friendly” the video is. In this sense, pricing on this side of the platform is aligned with what has been proposed by the economic stream of literature (Eisenmann et al., 2006; Rysman, 2018): the willingness to pay (or “willingness to view”) of the other sides is directly accounted into the subsidies to the content creators side.

In the early stages of the platform, YouTube also heavily invested into content creation through its YouTube Original Channel Initiative (Marc Hustvedt, 2011). The company poured over 100$M dollars into the platform to launch original content. This strategy is similar to the one proposed by Cennamo (2016) as YouTube entered its complementors market to both signal commitment and provide value to early viewers. However, they adopted a co-creation approach with the content creators, as the company did not directly provide videos, but rather engaged in funding and producing the content of the complementors. In this sense, YouTube’s strategy surpassed the dilemma on wether joining the complementors market leads to a “crowding out” effect or not (Foerderer et al., 2018).

This program was eventually discontinued due to heavy costs (Marc Hustvedt, 2011). However, in recent years YouTube continued to adopt a similar strategy with its YouTube Red Original series (YouTube, 2017). YouTube Red is a premium subscription that viewers can pay to avoid advertisment on the platform and get access to “premium” videos created in collaboration between the company and big content creators. First of all, this strategy retraces Bhargava’s (2013) approach to offer both a premium and basic version of the platform to solve the profit versus growth dilemma. In fact, as presented in the introduction, the company is still sustaining losses to foster its growth and still considers itself in “investment mode” (Fortune, 2016). Second, the company is still adopting a co-creation approach, but focused on already successful complementors, following Eisenmann et al. (2006) of identifying and subsidizing “marquee” agents. Third, this subscription also diverts money from the viewers side of the platform directly to content creators. The fact that viewers themselves start to pay for content is aligned with the “killer app” strategy proposed by Santaló (2015). Exceptional content (the original videos created by big creators in collaboration with YouTube) exclusive to the platform generates enough value for customers to pay for it.

On the viewers side, YouTube also subsidize them by providing access to the platform for free. The value provided to the ecosystem by them comes in the form of their data and attention. On the side of viewers and creators, YouTube act as a platform by serving the right video to the right viewer, thus facilitating a “transaction” (McIntyre & Srinivasan, 2017). Viewers are the necessary component for brands and companies to pour money into the platform. This money then sustains content creators which generate the content that brings in the viewers. This is the basic “Positive Feedback Loop” of the platform as mentioned by Cusumano (2010). The “chicken and egg” problem has been solved by subsidizing creators and viewers which then bring in advertising companies. Aligned with Gal-or’s findings (2018) it is still unclear whether privacy concerns affect the decision of viewers to join the platform.

On the brands side, YouTube provides a match between: the right viewer, the right video and the right time. To advertise on the platform, brands compete in an online auction to put their advertisement in front of a specific video (CNBC, 2017). A synthesis on what each side gets and provides from/to the platform is reported in Fig 11.

Figure 11 - Synthesis of the three sides of YouTube and the exchanges on the platform.

Once the three sides of the platform are on board, network effects can also be analyzed (Armstrong, 2006; A. J. Rochet et al., 2006). In terms of direct network effects, only viewers experience an advantage. In fact, the more viewers watch a piece of content, the greater the discussion on it that is generated in the form additional value interactions (e.g. more comments under a video). The other two sides instead experience an increased level of competition. The more creators within the same niche of videos, the more difficult it is to emerge, or extra costs must be sustained to further differentiate from other YouTubers. Brands instead will face increased competition to advertise on the same piece of content / market target, leading to higher prices and costs.

Indirect network effects can be analyzed by taking the sides in couple. Between the content creators and the viewers there are positive cross network effects. More creators mean more content variety for viewers. On the other hand, more viewers mean increased profits for creators and chances to find an unexplored niche. Between content creators and advertisers there are also positive indirect network effects. More content creators means less competition on prices for brands and better market segmentation. More brands mean more money into the market and increased profits for content creators. Between viewers and brands network effects are mixed. More viewers mean an increased value of advertising on YouTube and better chances of targeting a specific niche. However, more companies means more advertisement for viewers, which detracts from their watching experience. A recap of all the effects is presented in Fig 12.

Figure 12 - Direct and Indirect Network Effects among the three YouTube sides

2.3 Governance

Governance is one of the key aspects of making a platform successful. There are many definitions of governance, each one highlighting a different aspect. Tiwana broadly defines it as “who decides what” (Tiwana, 2015). A stronger definition links governance to the “fundamental decisions” that platform owners must take towards the ecosystem of complementors (Foerderer et al., 2018). Gawer defines governance as the set of decisions regarding the ownership of the platform, entrance into complementary markets and community building activities (Gawer, 2014). Constandinides et al. (2018) see platform governance as the right balance between rules to control the ecosystem and incentives to join it.

Song et al. (2018) distinguish between at least 3 different types of governance with different effects. The first is pricing itself. As already discussed it has a very strong impact on who joins the platform and it is therefore considered as a governance mechanism (A. J. Rochet et al., 2006). The second one refers to the policies used to manage the interaction between different sides. Finally, the third one encompasses the platform own self-development as new features have an impact on users and complementors (Song, Xue, Rai, & Zhang, 2018). These definitions build upon each other, from more generic ones to more specific ones. For the purposes of this thesis, there are two main aspects that are relevant to highlight:

(1) The balance between control and openness

(2) How knowledge is shaped and transferred across the platform owner and the complementors.

There is a big trade-off between control and openness. Control is needed to align the ecosystem to the platform’s owner objectives (Lee, Ba, Li, & Stallaert, 2018; Tiwana, 2015). Through control mechanisms the platform owner can reward and punish behaviour, while also establish good practices on the platform (Evans & Schmalensee, 2007). Openness, instead, is necessary to avoid constraining the generativity / innovativeness of complementors (Constantinides et al., 2018). One of the key challenges comes from the multitude of actors often involved in the ecosystem of platform, that makes it difficult to agree upon a single governance policy (Constantinides et al., 2018). Platform owners priorities are therefore to protect their own interests as well as secure the position of producers and consumers that can contribute to value-creation on the platform (Constantinides et al., 2018).

The governance mechanisms deployed encompasses three elements: (1) how decision rights are divided between the platform owner and the ecosystem; (2) what types of formal and informal control mechanisms are used by the platform owner; (3) and incentive structures (Constantinides et al., 2018). Specifically, among formal and informal control mechanisms the transfer of knowledge between the owner and the ecosystem plays a vital role in defining what can or can’t be done on / through the platform (Foerderer et al., 2018). As an example, in the software development field, software developments kits, libraries or documentation act both as a governance mechanism defining what can be done on the platform and as a way to transfer knowledge. Correctly identifying what knowledge needs to be shared with the ecosystem versus what needs to be maintained internally is key to the success of the platform (Foerderer et al., 2018). The goal of both platform owners and complementors is to integrate knowledge to maximize their profits and foster innovation (Foerderer et al., 2018). Howcroft and colleagues argue that one of the reasons platforms fail is due to an insufficient provision of knowledge and an excessive control over the platform (Bergvall-Kåreborn & Howcroft, 2014). To overcome knowledge boundaries, platform owners often provide various resources such as information portals, helpdesks, workshops or MOOCs (Foerderer et al., 2018; Huber, Kude, & Dibbern, 2017). Knowledge management is a new stream of research within platform literature and as Foerderer and colleagues pointed out, most of the research has been focused on mechanisms that are not applicable to one-to-many situations, such as those found in digital platforms. There are three streams related to platform literature that explored knowledge management in the context of platforms (Foerderer et al., 2018).

The first stream (Foerderer et al., 2018), relates to platform governance from an organizational perspective. From this perspective, governance outcomes are the result of negotiations between the keystone firm and the ecosystem. As an example, when developing a new videogame console, the keystone firm heavily interacts with complementors to decide how the system should be and upon which common frameworks should be built.

The second stream considers knowledge management as a part of platform governance from an engineering perspective. Since platforms focus on the systematic reuse of building blocks, there’s emphasis on architectural aspects. Standardization and modularity affect governance costs. These studies also suggest that there is a strong difference in knowledge between the platform owner and its complementors (Foerderer et al., 2018).

The third stream focuses on a strategic dimension and sees investments in knowledge boundaries as a form of cooperation with complementors to avoid “crowding out” effects and spur innovation (Foerderer et al., 2018).

Taken together, these three different perspective assess that governance through knowledge is the result of negotiation, affects platform architecture / design and has a strategic relevance (Foerderer et al., 2018). Foerderer and colleagues also identified three different types of knowledge boundaries resources according to two parameters:

(1) Scope: what gap needs to be covered; (2) Scale: how many complementors are going to use the resource;

And they found three different types of resources: broadcasting, brokering and bridging. Explanations are reported in Table 3.

BROADCASTING BROKERING BRIDGING DESCRIPTION Standardized resources Mediation mechanisms Individualized that can be easily that help complementors knowledge provided to accessed by find the resources they complementors directly complementors without need. from the platform owner. having to interact with Often in an informal way. the platform owner. SCOPE General Knowledge Meta-Knowledge: Gives Specific Issues direction on where to find information SCALE Entire ecosystem Subset of Complementors Individual Complementor EXAMPLES • Policies online • Helpdesks • One-to-one • MOOCs • Email contacts assistance • Guidelines • Workshops

Table 3 -Types of Knowledge Resources and relative proprieties (adapted from Foerderer et al., 2018).

A platform owner can deploy multiple mechanisms at the same time. Generally there’s a trade-off between the right scope and the scalability of knowledge resources to the whole ecosystem (Foerderer et al., 2018; Huber et al., 2017). However, companies have an incentive to provide more specific resources to those complementors who can contribute more to the platform (Huang et al., 2018). The biggest issues are usually generated when the boundaries between the platform and ecosystem changes as it requires complementors to adapt to new rules (Foerderer et al., 2018; Song et al., 2018). As Song and colleagues (2018) found, too many updates to policies and the platform “inevitably disrupt the smooth interactions across sides”.

Closely following standards reduces governance costs, but also limits the ability to respond to local needs (i.e. of a few / single complementor) which may constrain innovation (Huber et al., 2017). Occasionally adapt to local needs instead might foster innovation but becomes inefficient when managing a large network (Huber et al., 2017).

Knowledge resources also interact with another common governance mechanisms which is “input control” (Thies et al., 2018). In fact, complementors may lack the necessary knowledge about the process of being accepted into a platform (Ye & Kankanhalli, 2018). Input control, also known as gatekeeping, refers to the degree to which platform owners use predefined criteria for judging what is allowed on the platform and thus ensure higher quality (Thies et al., 2018). As in previous mechanisms control, there is a balance between deciding what favors and benefits the platform along with ensuring innovativeness on the platform by allowing a wider variety of content (Huang et al., 2018; Thies et al., 2018; Ye & Kankanhalli, 2018). In fact, strict input control can discourage complementors to innovate as it increases the risk of not being accepted into the platform (Thies et al., 2018). Knowledge management is relevant to properly communicate to complementors what can or can’t be accepted into the platform (Thies et al., 2018). The platform owner acts as a curator of the ecosystem, creating a number of benefits as well as challenges to complementors (Thies et al., 2018). The keystone firm though sustains upfront costs to enable input control, as it is necessary to screen complements and enforce rules (Thies et al., 2018; Tiwana, 2015).

2.3.1 YouTube’s control over its platform: policies and algorithms

YouTube success depends on “user-generated content” made by its community of creators (Alexa, 2018). Over the years, the company invested in its “suppliers” through many programs. Such examples are “YouTube Spaces”: incubators designed to foster the creativity of YouTube’s best creators (Google, 2018s; Michael Schrage, 2013). Activities like this are considered to be a form of knowledge bridging as previously discussed, providing specific help to individual complementors (Foerderer et al., 2018).

Yet, the majority of creators claim a lack of interaction with the company. 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). YouTube exercises a total control over the definition of policies. In particular community guidelines, clarifying which content is allowed on the platform; or algorithms, that define which videos are promoted and shown to viewers. YouTube therefore heavily employees “Input Control” as its main policy to manage the platform (Thies et al., 2018), however creating distrust within its network of complementors.

The net result of this behaviour is a community of content creators that often feels powerless against changes undermining not only their income, but also the possibility to grow and sustain themselves over the long term (Michael Sawyer, 2017). In the following sections, the two main levers used by YouTube to manage its platform, policies and algorithms, will be discussed. The focus will be on the most significant changes that happened and the impacts on content creators.

2.3.2 Policies

All videos uploaded to YouTube must be compliant with their “Community Guidelines”. To be suitable for advertisement, the video must be compliant with “Advertiser-friendly Content Guidelines”. However, these guidelines are often applied inconsistently. There are continuous changes to what is considered acceptable, often acting only as a consequence of public outcry (Louise Matsakis, 2018). In an article published in “Wired Magazine” (2018) called: “YouTube doesn’t know where its own line is”, the author reports the actions of the company against the channel Atomwaffen Division. The channel was founded by a Neo-Nazi group that used YouTube to promote their messages. In an interview with “The Daily Beast” (Kelly Weill, 2018) on the 26th of February 2018, the company expressed its intention to allow the channel on the platform while applying some restrictions, such as no-featuring on recommended videos. YouTube stated: “[…] this approach strikes a good balance between allowing free expression and limiting affected videos' ability to be widely promoted on YouTube.”

However, on the same day, the magazine “Motherboard” brought those statements to the attention of the Anti-Defamation League, a Jewish ONG against discrimination, stirring up the public opinion on the topic (Emanuel Maiberg, 2018). As a consequence, only two days later on the 28th of February, YouTube forcibly deleted the Atomwaffen Division channel regardless of its previous statements. On top of that, the consequences of that decision were explicit “Ad Hominem”. In fact, it is still possible to find on the platform many other channels promoting Neo-Nazi groups (Louise Matsakis, 2018).

This example shows how YouTube is still trying to figure out how to manage and monitor its platform. In Wired’s words: “it's a positive that public outrage led to the removal of a hate group's videos. But public pressure can come in all forms and point at any target—it can't be what the platform relies on to make hard decisions” (Louise Matsakis, 2018). It also shows, in accordance to the organizational stream of research how policies are often the result of negotiation and the same policy might not be applied consistently (Foerderer et al., 2018).

A synthesis of the content of the “Community Guidelines” that creators must follow is presented in appendix A. An infringement to these rules, may lead to a community guideline strike. A strike poses restrictions on the uploader channel. Each account is allowed up to two strikes within a three-month period as the third one will cause the termination of the account (Google, 2018e).

The “Advertiser friendly Content Guidelines” instead, do not issue strikes to content creators, but remove the possibility to advertise a video. The whole channel could get demonetized, with a suspension from YouTube’s partnership program. According to YouTube’s best practices (Google, 2018b), what is considered advertiser-friendly, does not differ from the “Community Guidelines”. However, “YouTube also reserves the right, at its discretion, to not show ads on videos and watch pages”; thus giving the company the possibility to demonetize any video at any moment without explanations needed.

YouTube put effort into clarifying what is allowed on the platform, trying to reduce uncertainty for complementors as a key factor suggested by Song and colleagues (2018). An entire online training program on the topic is available on "YouTube Academy" (YouTube Academy, 2018), thus deploying a broadcasting type of knowledge management strategy.

Regardless, each guideline leaves itself open to interpretation. What is considered acceptable or not is still fuzzy, de facto leaving content creators to wonder what they can upload? The creator behind the YouTube channel NativLang, a channel providing short documentaries on the history of languages with half a million subscribers, turned to its Patreons to mitigate upload risks: “Most videos take over 100 hours for audio + art assets + animation + edits, on top of the research […] it’s hard to invest this amount of time knowing that my videos could be removed or demonetized […]” (Joshua Rudder, 2018).

On top of this, an overwhelming amount of content creators claim a complete lack of transparency by YouTube whenever one of their videos receive a strike or gets demonetized (boogie2988, 2016; Joe Vargas, 2013, 2016; Philip DeFranco, 2017; Reddit, 2016). Whenever a strike is issued, there is no explicit indication to which part of the video has been flagged for inappropriate content. Creators are greeted with a message pointing at the same guidelines previously mentioned with generic explanations as to “why” their video was inappropriate but without information on their specific case, which often leaves them wondering what went wrong.

The vast majority of strikes are the result of automated robots according to Google’s Transparency Report (Google, 2018q). From January 2018 to March 2018, almost 10 million videos have been removed from the platform, of which 72% through automated flagging. Automation is therefore the preferred strategy deployed by YouTube to reduce the upfront costs of Input Control as showed by Tiwana in 2015.

However, the previously cited creators often accuse the automated system to be unreliable and prone to errors. This results on the one hand in strikes issued to content creators that do not infringe community guidelines. On the other hand results in problematic content being still allowed on the platform.

Content creators can appeal to a demonetized video to solve the first problem, requesting a moderator to manually review it. But this process often takes up to 48 hours, which is problematic since the first 48 hours are those who generate the most views and money for a creator.

By looking instead at content that is not caught by the automated review system, one of the prime examples is the so-called “ElsaGate”. The term refers to the controversy surrounding apparently child-friendly videos which often contain themes inappropriate for kids as shown in Fig 13.

Figure 13 - Thumbnails of "Elsagate" content (Wikipedia, 2018) The videos often feature characters from popular media aimed at children presenting content such as violence, sexual situations, fetishes, drugs, and dangerous activities; which obviously violates many of the previously mentioned guidelines. These videos are extremely popular on YouTube, as an example the YouTube channel: “Webs & Tiaras” features over 30 videos with more than 1 million views, with the most popular being titled: “Spiderman & Frozen Elsa TROLLEY CRASH! w/ Maleficent Joker Disney Princess Anna Toys! Superhero IRL” and reaching over 24 million views (YouTube, 2018e).

In its TED talk titled: “The nightmare videos of children's’ YouTube”, the British author James Bridle (2018) points out how the titles of these videos are often: “a mash of non-sensical language” as the intended audience for the video itself are small children who can’t read. Everything else is designed to play with YouTube’s algorithm, and exploit its recommended system to pump out millions of views (and ad-revenue) into these videos by reaching kids; then attract them with sensational thumbnails.

Going on in its talk, James Bridle attributes this phenomenon to the extreme automation used in managing the platform, which also morally excuse YouTube from actively preventing these videos on the platform as he ironically states: “Ehy, it’s not us! [It’s not our fault] It’s the technology!”. These videos flourished on the platform, with first reports of their existence emerging in 2014 (Yoo Se-Bin, 2018). In 2017, after increasingly published articles on many newspapers such as “The Verge” or “BBC” (Anisa Subedar, 2017; Ben Popper, 2017; Rachel Deal, 2017), YouTube started to address the issue by mass deleting related channels and applying stricter policy guidelines.

However, many of them are still available on the platform and the community responded by creating the subreddit /r/ElsaGate, with almost 50.000 subscribers, that monitors the platform for inappropriate content and mass report videos, highlighting the inefficacies of the YouTube review system (“Reddit,” 2018).

The increased strictness of guidelines by YouTube was also the result of a major scandal involving one of YouTube’s biggest content creators, Logan Paul, with over 18 million subscribers, defined as YouTube’s “Golden Boy” (Philip DeFranco, 2017). He and his team went and filmed a video at the Aokigahara forest, near Mt. Fuji in Japan. The forest is also called “The Suicide” forest, as it is a place where people often go to end their lives. The resulting video was titled: “We found a dead body in the Japanese Suicide Forest...” featuring in the thumbnail a blurred body hanging from a tree which was fully shown in the video itself. The video received almost 7 million views upon its release, became one of the top trending videos in the world and was actively promoted and recommended to people by YouTube’s automated system. Many content creators publicly called out Logan Paul for his disrespect in showing a dead body (Akana, 2018; Casey Neistat, 2018; Pansino, 2018) stirring up a controversy that led to the removal of the video. YouTube commented on the issue of releasing a press release (Andrew Griffin, 2018):

“Our hearts go out to the family of the person featured in the video. YouTube prohibits violent or gory content posted in a shocking, sensational or disrespectful manner. If a video is graphic, it can only remain on the site when supported by appropriate educational or documentary information and in some cases, it will be age-gated. We partner with safety groups such as the National Suicide Prevention Lifeline to provide educational resources that are incorporated in our YouTube Safety Center."

YouTube’s response was criticized for being extremely general, vague, and not addressing some of the major concerns about how content is promoted on the platform (Philip DeFranco, 2017). As a consequence to advertising being shown on controversial content, including the previously mentioned “ElsaGate” and the appearance of advertisement on extremist channels, a long list of major brands (such as Amazon, Verizon, Walmart, Cisco, Facebook, LinkedIn, Under Armour, Pepsi, and many more) decided to retire their advertisement campaigns from the platform leading to big losses for YouTube (CNBC, 2017; Paul P. Murphy, 2018; The Guardian, 2018a).

To restore brands’ faith in the platform, YouTube significantly increased in both 2017 and 2018 the investments to improve and enforce their community guidelines, with a commitment to hire 10.000 manual reviewers by the end of 2018 and heavily improve their machine learning technology (Susan Wojcicki, 2017). This increased strictness by YouTube in monitoring what is allowed on the platform often meant that what was deemed ok “yesterday”, it would lead to demonetization “today” without a clear explanation of the causes.

This effect along with a decrease in the advertisement, led to the so-called “Adpocalypse” event which meant a reduction in ad revenues for all the channels on the platform. Philip DeFranco, a content creator with 6 million subscribers focused on news, saw an 80% decline in revenues which led him to the foundation of a crowdfunding network to support his channel (Sam Gutelle, 2017). Since then, his Adsense revenue has leveled out to a 30% decrease instead of the original 80%. But many other channels, such as “Military Arms Channel”, with 800.000 thousand subscriber doing gun reviews, lost almost 99% of its Ad Revenue overnight. Since then, the channels have moved to sustain himself through Patreon with more than 6.000 donators (Geoff Weiss, 2018).

In an article issued on its own “Creators Blog”, the company explained the necessity for these new policies to enable a safer environment for advertisers, while yet prioritizing: “the protection of our creator’s ecosystem and ensuring stable revenues” (YouTube, 2018g). Despite this, content creators are still suffering major swings in Ad revenue on a month-to-month basis and demonetization issues are still extremely unclear as YouTube refuses to provide detailed explanations on what was deemed appropriate in every video, leaving the community in a state of constant speculation about what they can and cannot do (Lizzie Plaugic, 2018). On top of that, updates in guidelines and any change in policies are governed unilaterally by YouTube without ever involving content creators into the discussion nor allowing the wider public community to decide how the platform should be managed or it should evolve in the future. YouTube maintains total control over its platform and takes every decision regarding policies on its own (Julia Alexander, 2018).

2.3.3 Algorithms

Algorithms are the other significant lever that YouTube has to manage its platform and control revenues. Their main purpose is to suggest the right video to the right viewer by finding what they are most likely to watch and enjoy. YouTube made explicit that their main goal is to make sure viewers come back to the platform regularly (YouTube, 2018d). Algorithms are applied in search results, suggested videos, suggested channels, trending videos, viewer’s custom homepage and notifications priority. They determine whether a content creator can succeed on the platform by constantly promoting its videos or, vice versa, limit their growth and revenue potential by burying their videos in search results and often not even promoting to their subscribers (YouTube Recommendations, 2017).

Algorithms are so relevant that the web is scattered with articles trying to speculate on how they work and what creators should do to “optimize” their videos (HootSuite, 2018; Octoly, 2018; ReplayScience, 2017). The main information that has been officially disclosed by YouTube is reported in Appendix B.

This information was first released in 2017 during the official launch of the YouTube Academy platform (Google, 2018i), but before then, official information was scarcely available. The introduction of YouTube Academy was positively received by many creators. Yet, others also argued that the content and tips provided were extremely vague and not enough to properly understand what is going on (The Guardian, 2018b). In YouTube’s defense, popular science communicator Tom Scott explained in his video: “Why The YouTube Algorithm Will Always Be A Mystery” (Tom Scott, 2018), that by knowing the inner workings of the algorithm many people would be able to “play the game” by exploiting its features to promote any kind of content regardless of its quality. But most importantly, in 2016 Google itself published a paper titled: “Deep Neural Networks for YouTube Recommendations” (Covington, Adams, & Sargin, 2016) describing the whole architecture of the system providing recommendations.

Tom Scott argues that a deep neural network is extremely difficult to analyze to understand its inner workings. YouTube has the power to manipulate parameters and signals that feed the system, but the actual way in which the algorithm decides to recommend a certain video over another one is unknown even to YouTube itself.

Despite this, the main concern of creators is changes to the algorithm instead of its actual working mechanisms. Changes can be significantly disruptive due to the lack of communication from YouTube. This disruption effect is aligned with that reported by Song and colleagues 2018. Content creators find difficult to understand if videos are underperforming because viewers are not interested or because something changed in the way videos are promoted (boogie2988, 2018b).

The channel: “The Game Theorists”, with over 10 million subscribers, published one of their most popular videos on the topic detailing the impact of some of these changes (The Game Theorists, 2016). Prior to 2015, the so-called “channel stats”, such as how many views a video had or how many likes it had, were considered to be the most important factors in making a video discoverable and promoted.

In 2015 YouTube started to focus on retention time as the main driver, specifically looking at: “Watch Minutes” (i.e. for how many minutes people watched your videos). “Watch minutes” were also introduced as an official statistic in the analytics page. This change actively promoted longer videos to an extreme extent: a 20-minute video that has been watched for only 20% of its time will be more promoted than a 1-minute video that has been watched entirely. To stay competitive, many YouTubers focused on shorter clips would have needed tens of millions of views to generate the same amount of “Watch Minutes”.

In 2016 another change has been made and creators speculated that “Daily Active Viewers” was the new main driver. The daily content was highly promoted on the platform, “Watch Minutes” were still important, but making sure that people were coming back to the channel was the key. This change had a severe impact on channels with less frequent update schedules, which often created higher quality videos.

Instead, people uploading daily videos increased in popularity while creators whose content did not fit the daily format had a hard time. This change also meant that the lifespan of a video was much shorter. Because of that, the backlog of videos of any content creator saw a decrease in viewership numbers and subsequent ad revenue.

To stay relevant, creators started to put out more and more content, which for many led to a creative burnout. Boogie2988, a creator with 4.5 million subscribers, defined himself as a: “slave to the algorithm” given the necessity to constantly upload content (boogie2988, 2018b). Even a few days without posting content would punish future videos by making them less promoted and suggested.

For creators, it was difficult to keep up with the rhythm, but this change was perfect for mainstream media who had the manpower to churn out multiple videos a day. In fact, during this time, many channels such as “The Late Show with Stephen Colbert” started to grow exponentially as shown in Fig 14. (Socialblade, 2018).

Figure 14 - Total views and subscribers per month for "The Late Show with Stephen Colbert” show. Although growth in subscribers did not change over the years, viewership skyrocketed towards the end of 2016, due to changes in the suggestion system (Socialblade, 2018)

Although this reconstruction of events is a strong speculation, since no information has been officially released, many creators reported similar experiences and these changes in metrics are a generally accepted staple within the community (BBC, 2018; PewDiePie, 2016; The New York Times, 2017). Throughout the years, YouTube continued to make changes and adjustments to the algorithm which often favored some creators and significantly harmed others. Overall, the community of creators is constantly chasing YouTube and it’s very difficult to predict what could be the next change and how to react to it (Scott Mahoney, 2018).

2.4 Benefits distribution and strategies to gain power

The keystone firm is at the core of an innovation ecosystem and can benefit the most from it (Gawer & Cusumano, 2014; Parker et al., 2016). Despite this, they are highly dependent on innovations and investments made by other firms and the rest of the ecosystem (Foerderer et al., 2018). However, the keystone firm can deploy a set of strategies to influence the direction of innovation in complementary products and services (Adner, 2017). By doing so, the keystone firm becomes a platform leader: “an organization that successfully establish their product, service, or technology as an industry platform and rise to a position where they can influence the trajectory of the overall technological and business system of which the platform is a core element.” (Gawer & Cusumano, 2014).

The key to achieve this position of “platform leadership” is to align the whole ecosystem to the objectives of the platform owner (Adner, 2017). Neglecting the ecosystem and what they must do to make sure that the platform is successful in the market is considered to be one of the traps to avoid in developing a platform (Santaló, 2015). As Iansiti & Levien (2004) pointed out, standalone strategies don’t work when the company’s success “depends on the collective health of organization that influence the creation and delivery of your product. Their suggestion is to immediately identify agents in the ecosystem which are mostly intertwined to the platform success and understand which are the most critical dependencies (Iansiti & Levien, 2004). Adner, in his book “The Wide Lens” (Adner, 2012) proposes a tool called “The Value Blueprint” to help companies assess their ecosystem and find the key dependencies that need to be solved.

The topic of “platform leadership” has been extensively studied in literature (Adner, 2017; Cennamo, 2016; Gawer & Cusumano, 2014; Parker et al., 2016; Santaló, 2015). More relevant to this thesis, instead, is the perspective of complementors and what they can do to reap more benefits for themselves and escape the control of the platform leader. This topic has seen little development in literature (Constantinides et al., 2018), and two major strategies have been proposed: (1) multi- homing and (2) forking.

Multi-homing refers to the decision of an agent to operate on multiple platforms (Armstrong, 2006; Cennamo et al., 2016). This strategy gives immediate benefits to complementors as it gives them the opportunity to expand their market, while also lowering the risk of being dependant from a single platform (Constantinides et al., 2018). However multi-homing also entails some specialization costs (Cennamo et al., 2016; Gawer & Cusumano, 2014). Different platforms might have different technological specifications, which requires complementors to adapt their products in order to multi- home (Cennamo et al., 2016). Cennamo and colleagues (2016) found in the context of videogames that titles released on a platform different from the focal one have lower-quality performance due to the technical difficulties of adapting their game (Cennamo et al., 2016). Platform leaders can prevent multi-homing by increasing the specificity required to join their platform and abandoning compatibility. However, this strategy can backfire as it affects the growth rate of the network by increasing barriers to entry (Kim et al., 2017). Platform leaders can still mitigate the effects of multi- homing on one side if they can secure participation to the platform of a single-homing side (Armstrong, 2006). In this scenario, the platform can act as a monopoly over access to the single- homing side and thus increase prices for the multi-homing one, cutting into their benefits (Armstrong, 2006; Cennamo et al., 2016). However, platforms still need to compete to gain control of the single- homing agents, often subsidizing them and thus transferring the profits generated from the multi- homing side to the single-homing one (Armstrong, 2006). Because of these effects, it is unclear in the literature the impact of multi-homing on both the platform and the complementors (Cennamo et al., 2016; J.-C. Rochet et al., 2013). Multi-homing can also affect the strategic positioning of platforms. In fact, if the costs to adapt a product are particularly low, agents can easily multi-home and thus reduce the likelihood of a single platform to become dominant in the market (Cennamo et al., 2016). The availibility of the same complements on other platforms also reduces their differentiation and increases competition (Cennamo et al., 2016).

Forking, instead, refers to the act of: “exploiting the platform’s shared resources, core and complements to create a competing platform business” (Karhu et al., 2018). It can be considered as a strategic move made by a complementor to enter the market of the platform leader (Karhu et al., 2018). An example of this are the variations of Android often made by smartphone manufacturers to provide custom features to their users (Karhu et al., 2018). What makes this strategy successful, and thus avoids the “Winner Takes it All” scenario, is that the new platform is entirely built upon the one that gets forked. Because of this, it mantains compatibility with all of the other complements (e.g. other apps available in Google’s Play Store), thus leading complementors to multi-home as its easy for them to do so and can take advantage of the same user base (Karhu et al., 2018). Platform forking is considered a hostile competitive strategy because the forker contributes little to nothing to the original platform. By contrast they can take advantage of the shared resources, user base and complements without contributing to their development (Karhu et al., 2018).

2.5 Gaps and Future Avenues

Research on these topics is still fairly new and several scholars highlighted key issues that still need to be addressed by research (Constantinides et al., 2018; Facin et al., 2016; Gawer, 2014). Constantinides et al. (2018) highlight five key issues: (1) New organizational structures enabled by platform and products (mirroring hypothesis); (2) How traditional products can be turned into platforms (platformization); (3) Blockchain as a new type of infrastructure; (4) Online labor platforms and the shift from permanent employment to need-based outsourcing; (5) Finally, the last issue is the nature of competitive strategy on digital platforms. Within this last stream, the researchers highlight its relevance as a necessity to understand the competitive dynamics of the digital era. Specifically, they mention platform owners as well as “complementors seeking competitive ways to advance their business” (Constantinides et al., 2018). They provide three possible research questions that still need to be explored in literature:

(1) “What is the strategic interplay between platform owners and ecosystem actors over time?”

(2) “What are the competitive moves that platform owners can use to design boundaries to surrounding ecosystem actors?”

(3) “How can third-party developers advance their businesses across multiple platforms?”

Another element that has not been discussed in the literature (at least the one presented in this thesis) are the differences between platforms with two sides and platforms with more than one side. Frameworks, strategies and models are presented considering two sides of the market. Since this thesis presents the case study of a platform with three sides, some emergent interactions between the groups or types of governance that the company can adopt do not have a solid theoretical background and might need further exploration.

Some examples of possible research questions would be: “How sides should be subsidized? What factors determine a prioritization of one side over another one?”. As an example, within the three sides of YouTube, which one should be the first to get on board?, “Which are cross-side impacts of governance?, How input control on one side affects the ecosystem if the other two-sides perceive value in a different way?”. As an example, YouTube’s restrictions on which video get into the platform have contrasting effects on its sides: brands don’t want their products on controversial videos while the followers of that content creator are interested in controversial content. How should the company decide in this dilemma? Which factors should it consider?

Network effects in the context of 2+ sides are also not explored. As previously highlighted, in the case of YouTube certain effects might be negative towards a group instead of being positive (e.g. advertisers on viewers). The general assumption taken by the multi-sided market perspective in economics is that network effects are positive and thus lead to the “Winner Takes it All” market condition (Cennamo & Santaló, 2013; Gawer, 2014; Kim et al., 2017). In this context, a perspective on what a platform should in terms of strategy and behaviour of agents involved is missing. Is the early mover advantage still relevant? Is “Winner Takes it All” the only market condition? Can differentiation be exploited by competitive platforms to capture the side receiving negative effects from other agents?

The perspective of the complementors is also heavily under represented in the literature discussed. Apart from the few strategies reported that complementors can adopt to gain benefits, the rest is focused on the platform owner and what he should do to maintain leadership, grow the platform and control it.

A very strong theme is also the ability of the platform to spur innovations and technological complements. There’s emphasis on the necessity to align the ecosystem to the key characteristics of the innovation to make it successful. However, in digital platforms where one of the sides generating value is individuals instead of firms (e.g. Uber, Facebook, YouTube, Airbnb) the notion of complementors’ technological innovation is not readily applicable. There is a huge disproportion between the keystone firm and the individuals in terms of resources and power, leading to autocratic behaviour. Threats to the ecosystem and the platform owner start to appear only when these indivduals come together to form syndicates or network. Some examples are Uber’s drivers protests against low wages and working rights (Telegraph, 2015) that might undermine the company’s success. Some potential research questions in this area might be: “How can individual come together to gain power? What factors determine the success of their actions?”, “What platform owners can do to prevent individuals from gaining momentum? Which governance policies are effective in managing a platform that connects with individuals?”.

3 Methodology

This thesis focuses on three main goals:

• Understanding what other sources of revenues complementors can build; • Understanding the impact of policies defined by the platform owner on complementors’ behavior; • Making a synthesis of these two elements to understand which strategies complementors can adopt to escape the platform’s control and make extra profits.

To investigate these elements and develop an overarching theory, interpretative research has been applied through multiple case studies. Building theory from case studies is a research strategy that aims at creating “theoretical constructs” from case-based, empirical evidence (Eisenhardt, 1989) Eisenhardt (2007) stresses the necessity for researchers to justify the need for theory building, rather than providing an extension of existing theory. As explained within the literature review, the three foci of the thesis are underrepresented in academic research. The most adopted perspective is that of the platform owner (Adner, 2017; Cennamo, 2016; Gawer & Cusumano, 2014; Parker et al., 2016; Santaló, 2015), with a strong focus on “platform leadership” and what he can do to make its platform successful and profitable. Only two strategies for complementors, forking and multi-homing, emerged from the review. However, even in this scenario multi-homing is often addressed from the perspective of a platform owner, and what it should do to prevent it. Forking, instead, is limited to narrow scenarios where complementors have access to the code of the digital platform and can adapt it to build their own platform. In many other scenarios, including YouTube, the notion of forking cannot be readily applied. Platform governance and knowledge transfer are topics more developed within the literature. However, as Foerderer et al. (2018) pointed out, most of the research has been focused on mechanisms that are not applicable to one-to-many situations, such as those found in digital platforms. YouTube, Instagram, and Facebook are digital colossi that built value by connecting billions of people together. The way governance is used by these companies and its strategic relevance on such a massive scale is missing in current literature.

Clarifying these issues can provide a significant contribution to our current understanding of digital platforms. Given the transformative impact of companies such as YouTube on the world understanding their inner workings could provide value to:

• Individuals engaging with the platform; • Companies trying to build platforms in the digital space; • Researchers investigating what platforms are and what makes them successful.

Within the following chapter, the framework adopted to conduct the research is presented. At first, research questions are formalized. Then, a general overview of the methodology followed to answer them is presented. Sample selection is discussed in detail, as it represented one of the biggest challenges of developing meaningful case studies.

3.1 Research Questions

The three focuses of the thesis are translated into three research questions:

1. How the relationship between a content creator and the ecosystem of services surrounding it changes as he grows on the platform? Interesting sub-research questions from this viewpoint are the following ones: a. Which services exist? b. How do they compete? c. When do they appear? d. How does the content creator find them? e. Which services are mostly used?

2. How are content creators affected by YouTube’s policy changes? How does this vary if the content creator’s community sized vary?

3. Which strategies can content creators use to escape YouTube control? Which is the relationship between those strategies and governance?

Following Eisenhardt’s (2007) suggestions regarding the theory building approach, research questions are broadly defined to allow for flexibility. The first two questions are descriptive in nature. Answering them is necessary to find out which are all the elements at play. A strong accent is put on the differences between “big” and “small” complementors. In fact, a priori it can be assumed that there are differences in both the ecosystem and governance as creators grow. This reasoning is in line with the secondary sources on YouTube presented in the literature review and with the work of Foerderer (2018) and his classification of different governance mechanisms according to size. Finally, the third question tries to synthetize the elements previously found into a strategy that complementors can adopt.

3.2 Unit of Analysis

Starting from the research questions, content creators are used as the main unit of analysis. However, given the importance of finding meaningful case studies (Yin, 2003), three criteria have been applied to find creators with valuable insights:

1. No corporations; many big channels on YouTube are run by corporations (e.g. VEVO, Buzzfeed) that use YouTube as a secondary source of revenue for their business. Given the purposes of the thesis, the ideal target for the sample is people. The interest is in understanding their journey and relationship with the platform.

2. Big enough creators; although there is no hard number defining what is “big enough”, effort has been put to find creators with sizeable communities and viewership. More details are provided in the next section.

3. Only English-speaking creators; this constraint was necessary given the necessity to communicate and interact with the other party.

3.3 Cases Selection

To find the sample of content creators, theoretical sampling was used. According to Eisenhardt (2007), a theoretical sample is one where cases are selected because of their likelihood of providing a theoretical insight. Selected cases are chosen because they are unusually revelatory, extreme examples and reveal precious insights. Eisenhardt stresses the necessity to carefully select cases to be successful at theory building. Multiple cases are also favored, as theory building from multiple sources is more robust.

Finding the right cases in YouTube’s scenario was particularly challenging for two main reasons:

(1) There is no official aggregate data. YouTube itself does not publish statistics about which are the biggest creators on the platform, what they are doing, their growth rate and so on. Because of this, it is difficult to understand “how big” a creator is. It is therefore very difficult to correctly identify “big” and “small” creators to assess what happens at different sizes.

(2) There is a lot of variety on the platform. Content creators differ in many ways for example content niche (e.g. gaming, comedy, video essays) or video type (e.g. videoblog, animation, music video). These differences are extremely relevant as they affect the type of ecosystem a creator can build around himself/herself. As an example, a creator publishing its own music and songs, can sell this music to his/her viewers or organize live concerts. These possibilities do not exist in other video genres. It is therefore necessary to find a group of creators who consistently make similar things to reduce the amount of variety in the data and have consistency. The main challenge comes from the fact the YouTube does not provide data about channel categories and classification.

In the present section I put forth a sample selection strategy whose aim is to choose a relevant and comparable set of channels. The basic idea is to apply a funnel and progressively reduce the list of channels considered at each step. Eventually, an ideal set of creators was identified. The strategy is divided into 4 steps.

3.3.1 Finding Data

Although YouTube does not provide aggregate data, it does offer APIs to check and monitor daily statistics about channels. Social Blade is a platform specialized in building aggregate data from these APIs. To gather this data, I developed a web scraper in Python using the library Beautifulsoup as HTML parser. The code is provided in Appendix D. The scraper can provide data for the first 5,000 channels measured on total number of views. 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.

This third element of “categorization” provides a first approach to classify content creators. Whenever a creator uploads a video, she can manually choose to which category it pertains. Overall, there are 16 categories, as reported in Fig 15 and Fig 16. Socialblade’s data classify a creator in a certain category by looking at its most 10 recent videos and picking the most common category. For the purposes of this thesis, from the original 5,000 list, only the first 25 channels per category were considered, cutting the number of channels considered to 400.

Music 252,225,808,771 Entertainment 147,224,475,461 Gaming 133,490,566,284 Education 91,587,226,797 Comedy 89,550,540,066 Film 80,506,734,718 People & Blogs 71,830,165,263 Sports 52,923,732,296 Show 51,011,872,048 News & Politics 44,737,575,402 How to & Style 34,478,135,182 Science & Tech 27,031,584,956 Auto & Vehicles 12,667,110,397 Pets & Animals 12,471,970,700 Nonprofit & Activism 8,688,189,507 Travel 8,010,125,306

Figure 15 - Total Number of Views per Video Category for the first 100 channels on YouTube (Social Blade, 2018)

Music 522,272,861 Entertainment 393,231,160 Comedy 390,804,408 Gaming 354,984,032 How to & Style 233,201,519 People & Blogs 224,098,925 Film 169,724,189 Education 162,322,091 Sports 151,789,489 Science & Tech 144,672,805 News & Politics 68,475,221 Show 66,938,665 Auto & Vehicles 59,888,457 Pets & Animals 43,139,460 Nonprofit & Activism 38,596,450 Travel 30,976,211

Figure 16 - Total Number of Subscribers per Video Category for the first 100 channels on YouTube (Social Blade, 2018)

3.3.2 Screening Data

Once data has been collected, it was necessary to remove all the channels / video categories that might not be suited to provide valuable insights. The three criteria that defined what is considered a “unit of analysis” where applied in practice.

To apply the first criteria of finding people rather than corporations, every category containing more than 25% of mainstream channels within itself was removed from the platform. This categorization was done manually going over the first 100 channels by views. As shown in Fig 17, the following categories were discarded: Music, News & Politics, Sports, Shows, Nonprofit & Activism and Film.

Music 96% News & Politics 64% Sports 32% Show 32% Nonprofit & Activism 28% Film 28% Entertainment 24% Science & Tech 16% People & Blogs 12% Auto & Vehicles 12% Pets & Animals 8% Comedy 4% Gaming 4% Education 4% Travel 4% How to & Style 0%

Figure 17 - Percentage of Mainstream Channels per Video Category

To apply the second criteria of finding “Big enough” creators, the last 3 categories in both subscriptions and views were discarded due to their much lower numbers compared to the other categories, thus removing: Travel, Pets&Animals and Auto&Vehicles.

Finally the third criteria of English-only speaking channels was applied by manually checking each channel remained in the list.

By applying all three criteria, from the starting 400 channels only 103 remained.

3.3.3 In-depth Video Category Analysis

As previously mentioned, one of the key challenges is to find a set of channels which are sufficiently similar in the content they produce / video style to be able to have consistent stories during the interviews. The classification of channels provided so-far is not good enough for this objective as there is still a lot of heterogeneity within a single category. For example, categories such as “Comedy” contain many different types of videos within themselves: sketches, reactions (i.e. people filming themselves react to different things) or music parodies. This is an issue for YouTube itself, as they partnered with Kaggle in 2017 to offer a $100.000 prize to the developers of the best algorithm capable of classifying videos (Tech Crunch, 2017). To make the categories more homogeneous, each channel was analyzed according to the “topic” of its video and reorganized into a new form of categorization. Overall, 12 different typologies were found as reported in Table 4.

TOTAL NUMBER TYPOLOGY DEFINITION VIEWS OF [BILLIONS] CHANNELS

Videos talking about gaming or showing VIDEOGAMES 90.51 13 videogame footage

NURSERY Songs for children 72.96 12 RHYMES

Videos dedicated to showing the LIFESTYLE 42.70 16 lifestyle/everyday life of the content creator

SKETCHES Comic videos with acting involved 33.88 10

REVIEWS Videos dedicated to reviewing products 27.34 8

Videos focused on capturing the reaction and REACTIONS opinion of people to a variety of things (e.g. 21.15 9 foreign food, trending videos) Videos dedicated at showing people how to HOW TO perform certain activities, DIY crafting or 21.10 13 testing/comparing techniques to perform activities

Videos trying to answer curiosities, teach INFORMATIVE 15.47 18 complex content or explain concepts

VIEWERS Videos dedicated to compilations of user- 4.52 1 CONTENT submitted content Videos dedicated at showing the performance of SPORT 4.03 1 an athlete

CARTOON Animated cartoons for young audiences 3.05 1 MUSIC Parodies of famous songs 2.85 1 (PARODY) Table 4 - New categorization of channels

Since one of the original purposes of this sample selection was to find a meaningful set of channels to compare, all of the new categories with 10 channels and below were removed, leaving 67 channels left in the sample.

3.3.4 Final list

To reach a final list of people to interview, I tried to contact each content creator among those 67. However, the response rate was extremely low (1.5%) and only 1 creator agreed to have a Skype interview among the people within the list. To find other people to interview, I leveraged two techniques: (1) When looking at a channel, YouTube provides a list of recommended creators similar to the channel you are viewing. By starting with a creator in the list, I tried to contact all the people that still met the same video typology definition and were recommended by YouTube. (2) Snowballing sampling; after each interview I asked for the contacts of other creators who were possibly willing to share their story and fit all the criteria. Over 350 creators were contacted this way and this process eventually led to 14 interviews. The list is presented in Table 5.

Content Creator Typology Total Views Total Subscribers Bijuu Mike Videogames 405.533.663 1.651.915 Casually Explained Informative 150.804.764 1.914.068 General Sam Videogames 122.831.401 563.273 Guru Larry Videogames 57.01.103 333.902 minutephysics Informative 383.814.819 4.500.673 NativLang Informative 33.994.654 499.129 Practical Engineering Informative 33.187.705 737.569 Ramsey Videogames 20.732.085 253.341 Slazo Videogames 73.362.569 633.465 Super Eyepatch Wolf Informative 37.770.456 532.934 The Closer Look Informative 13.368.549 313.074 Tale Foundry Informative 4.647.668 147.077 The Russian Badger Videogames 372.520.270 1.803.132 The Armchair Informative 12.583.441 190.671 Historian Table 5 - Content creators interviewed and their main statistics

Within the list, two categories of videos remained, informative and videogames, as a consequence of the availability of creators to do interviews. The final list also provides a wide range of community sizes, thus providing insights for creators of different dimensions.

3.4 Data Collection

Data was collected by interviewing the 14 previously listed content creators and by using secondary sources such as: emails between creators and YouTube, videos and statements on social media platforms.

The interviews were conducted using a semi-structured protocol. Semi-structured interviews are used whenever the interviewee possess a complex stock of knowledge about the topic studied (Flick, 2009). The idea behind semi-structured interviews is that people develop their own subjective theory to explain things. A subjective theory includes assumptions that are immediate and that interviewees can spontaneously express through open questions. To complement this perspective, people also have implicit assumptions in their own subjective theory. To articulate these assumptions, interviewees are supported by more punctual questions. The overall objective of this methodology is to reconstruct which is the interviewee’s own subjective theory. The full interview protocol is presented in Appendix C. The protocol was developed by conducting extensive research on the platform first to identify possible relevant themes. Following Flick’s (2009) suggestion, questions were developed with a focus on “narrative” to highlight the interviewee development over time and support theory building. Then, from each theme a set of open questions has been developed. Each question was complemented by probing questions based on the preliminary research. Interviews were conducted over Skype or Discord, with the average interview being 40 minutes long. During the interview, each open question was asked first and then some probing questions were asked according to time available and missing elements in the interviewee’s narration. According to Flick (2009), this strategy to conduct interviews has the benefit of reducing biases introduced by the researcher, as it lets people “speak first” through open questions. More closed ones are only used afterwards. To further avoid biases, each interview was recorded and then transcribed using a combination of manual work and automated subtitles generation.

3.5 Data Analysis

To interpret the empirical data from interviews, grounded theory coding was applied through Nvivo. Coding is the process of breaking down, conceptualize and put back in a new way empirical data (Flick, 2009). Starting from data, coding leads to the development of theories from a process of abstraction. Concepts are attached to codes and empirical material. At first, they are as close as possible to the material and later on are more abstract. The data was analyzed through axial coding. 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 between the remaining codes was created. This structure is presented in the “Results” chapter of this thesis in-line with a commentary and explanation of the results. 4 Results

The framework used to answer the research questions is divided into three main parts:

1) Identification of relevant moments in the lifecycle of a creator;

The research questions address the perspective of time and growth (“… as the content creator’s viewer community grows.”). To maintain this perspective the model is split into three views, with different characteristics and elements, to understand differences as size changes.

2) Description of four main themes for each moment of the lifecycle: Motivation, Opportunity, Community and Policies;

The first two research questions are descriptive in nature, as one of the main objectives of this thesis is to understand what’s in the ecosystem of a content creator. The “Motivation” theme explores what are the drivers behind a content creator actions and choices. The “Opportunity” theme describes chances for business and growth. The “Community” theme address the relationship between the content creator and its viewership. The “Policies” theme tackles the influence of YouTube policies on the life of a content creator. The four themes together provide a synthetic perspective of a specific moment in the lifecycle of a creator.

3) Exploration of connections between different themes.

In a specific moment, the four themes are connected together to provide a holistic picture of how that moment works from the point of view of a content creator. The overall objective is to understand their behaviour and how they strategize in that moment.

Figure 18 - General Framework: Synthetic View

A recap of the general framework is provided in Fig 18.

Within the next sections, the main results found from the interviews will be presented according to the three steps of the framework. At first, a proper definition of the three moments is presented. For each moment, the 4 main themes are described using the words of the interviewees. Then, to summarize what has been found in each moment, a model is proposed highlighting the main dynamics between the themes.

4.1 Framework: The three moments

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 19.

Figure 19 - The three moments of a content creator lifecycle

The main parameter that distinguish the three phases is: “how much the creator is tied to YouTube and depends from it”.

At the “Starting Point”, a content creator is considered an amateur. His main occupation in life is generally something else: a job or pursuing a university degree for example. To him YouTube is mostly a hobby, something that he does on the side. According to his lifestyle, he might invest more or less time on making videos. However, it is never a priority over the rest of his life (e.g. spending time with friends and family).

During the “Full-Time” moment of the lifecycle, the creator is extremely tied to the platform. His survival depends on YouTube as his main source of income. Changes on the platform can cause financial struggles. Most of his time is dedicated to the platform, often at the expense of other hobbies or activities. Being a YouTube creator is formally considered their job by themselves and they actively pursue this career.

During the “Independence” phase, the creator has outgrown the platform. His main source of revenue comes from alternative businesses that he built thanks to its popularity. This behaviour, closely mimics the multi-homing strategy found in literature as he moved to other platforms to generate business (Cennamo et al., 2016; Kim et al., 2017). YouTube is still relevant for him, but changes on the platform usually have little impact on his bottom line. Creators at this level often behave like small celebrities or have built media companies to run the channel. Consequently, most of their time is dedicated to enjoying the lifestyle or managing the company, rather than editing videos or doing “low level” activities. They see themselves as something bigger than the platform and their fanbase will follow them even outside of YouTube.

4.2 “Starting Point”

A synthetic view of the “Starting Point” is provided in Fig 20. The main themes are presented with a hierarchical structure directly from the codes used to interpret the interviews.

Figure 20 - Synthetic view of all the codes and themes in the "Starting Point" moment

4.2.1 “Starting Point”: Motivation

The main idea under the “Motivation” theme is that content creators “Just Start” on the platform.

“I bought a computer, like a laptop. It was only like $300 and I just started making videos and that's how I got into it really”(Bijuu Mike)

This “I just started” mentality, is enabled by two set of factors: external and internal. External factors are related to the availability of cheap equipment, as well as the fact that they often don’t have other constraints (e.g. no job).

“I didn’t really have recording equipment, just some stuff useful for my previous job. It worked out really well. I didn't intend for it.”(Ramsey)

“I was also living at home, so for me it was a no-brainer.”(Ramsey)

Internal motivation, instead, connects to both positive and negative feelings. Negative feelings stem from a sense of dissatisfaction with their current life. Content creators who started on the platform often don’t know what they would like to do with their life.

“Out of school I did not know what I wanted to do. I always had an interest in filmmaking and editing and stuff like that, but I didn't really know where I wanted that to take me.”(Slazo)

“I worked in a Walmart […] and I hated it.”(Bijuu Mike)

Positive feelings stem from the idea of escaping their current life, either in a small way just doing it as hobby or as a dream to be a full-time YouTuber.

“I don't know if it was originally the idea that I would do a business, but at least sort of a hobby and income on the side.” (NativLang) “Like I'd fantasize about it every day, because that's how bad I wanted to do this. It really was a dream of mine, suit to go full-time, but you know, I wasn't making a lot of money.” (Bijuu Mike)

Other creators got their inspiration by consuming content on the platform, getting inspired by YouTubers.

“I never really had the intention to start YouTube directly or anything like that, but I started from watching people's content.”(Ramsey)

Overall, the combination of these feelings along with an easy access to the necessary technology led creators to just start making videos and throw themselves on the platform without any particular plan or direction in mind.

4.2.2 “Starting Point”: Opportunity

The main theme under the opportunity topic is that content creators who are successful in this moment experience growth in their Ad Revenue. However, that’s the result of a process that starts from an initial failure. The first videos are generally random and often “Wrong” in the sense that they don’t really have an audience. Analytics are also not part of the equation and doesn’t affect content creation.

“I was struggling because, more than anything, I was producing the wrong content. Stuff that people wasn't interested in.”(Gury Larry)

“Well, at first I was posting a lot of random videos”. (Bijuu Mike)

“For me I try not to worry too much about the metrics and the analytics.”(Super Eyepatch Wolf)

This attitude leads to failure. However, content creators still try to promote their content. Usually reddit or niche forums are the preferred platforms for exposure. There’s usually a good component of luck in getting views, to the point where one creator wasn’t even sure if he would be able to replicate his success.

“I drew things in MS paint and that one [video] got right to the front page of Reddit, […] got me up to maybe 20,000 subscribers.” (Casually Explained)

“If you took away all my videos and you just erased everyone's memory of me and had me to start from zero subscribers, I can't tell you if I'd be able to do it again. There's that amount of luck in it.” (Super Eyepatch Wolf)

This combination of luck and self-promotion, though, leads to the explosion of one video.

“Occasionally, when they're [videos] popular enough on Reddit, they blow up even on YouTube. So I'd say it's these occasional really big hitting videos that got me most of the traffic”. (Casually Explained)

This success leads people to replicate it while also improving the quality of the videos to higher standards.

“So when I got a thousand views, I was like: “Woah, wow, like I can't believe it”. I started seeing so many comments and I was like so happy to see that people enjoyed my video, so I basically just kept doing more of you know, “Yandere Simulator” [The video game he was playing].”(Bijuu Mike)

“I focused increasingly more on those interesting stories and questions that seemed to fit for that audience. […] They haven't all been as successful, but that set up a formula for me that I've continued to follow, which is now the flagship series on the channel.”(NativLang)

“I basically made a whole bunch of very mediocre content, until I started adding little elements that made it more highly edited, a little bit more polished and then I continued to do that. I banged my head against a wall until my content became really good and differentiated from everybody else […] I think that's what has led to the success so far.”(The Russian Badger)

This process is what over time leads to Ad Revenue growth. However, there are also some traps that should be avoided along the journey. Specifically, many Multi Channel Networks try to recruit smaller creators with empty promises. A Multi-Channel Network is an organization that offers cross promotion possibilities and sponsorship deals to channels under their network. In exchange, they take a fee on the Ad Revenue generated. At this level though, the business model of these networks is to recruit as many content creators as possible, often tens of thousands, and make money off of them without really promoting growth.

“Some of these companies are sketchy, they just include as much content creators as possible and get a cut of their Ad Revenue. They'll take you on, and they'll sort of say that there are opportunities and sponsorships, which there is some elements of truth, but they take a percentage of your income, right? The crappier networks don’t even have a one-to-one relationship. There’s a manager for a bunch of people and if you have any problem you need to go through emails and it gets complicated. They just steal money from you and lock your channel into long term contracts.”(Ramsey)

The lock-in effect can be extremely detrimental if you, as a content creator, do happen to grow.

“Some networks take even 40% of your revenues which is really bad.” (Ramsey)

“There's a lot of predatory activities going on in this industry. Some creators literally had to start an entire new channel just to get out of bad networks that would keep their channels hostage.” (The Russian Badger)

4.2.3 “Starting Point”: YouTube Policy

Policies at this level promote an easy access to the platform. First of all, the platform is free of charge, and some creators even started on YouTube just because of its hosting capabilities.

“YouTube had an easy place for me to store videos and then embed them into a web page. So that's all I was doing initially on YouTube: I was just using it as a back-end to upload videos and then I would link them or embed them in the pages I was building. I noticed though that the best engagement and the best community was growing not on the the website I was creating, but actually on the YouTube platform itself. So I just naturally diverted my attention to focus towards YouTube.”(NativLang)

Initially, there’s also no restriction on the type of content that can be uploaded on the platform.

“I don't curse, but sometimes a game that I will play will have curse words in it. That stuff was okay, like you know, they were cool with it, like you never got a complaint or anything like that.”(Bijuu Mike)

YouTube also tries to help creators grow through their YouTube Academy program or YouTube Spaces.

“YouTube provides both classes and instruction as well as actual creator creative spaces, including recording studios to creators who live in major urban markets. They have these YouTube spaces in Los Angeles, New York, Tokyo a few other cities”. (NativLang)

4.2.4 “Starting Point”: Community

In this moment, the community doesn’t really exist. Content creators are in the process of searching for their audience and that usually happens by either focusing on a niche or emulating what other big YouTubers are doing.

“So I started, and I realized that my style of content wasn’t there. I don't see anybody doing what I do.”

“I was influenced by what other creators were doing at the time, I also I'm a YouTube consumer like most of us you know. I watch primarily educational or education light videos, […] and tried to understand how they were succeeding”.

4.2.5 “Starting Point”: Connections

The model expressing the connections among different themes in this moment is presented in Fig 21.

Figure 21 – “Starting Point” Growth Model

At the beginning, the “Just Started” mentality along with the “Easy Access” policies provided by YouTube allows the content creator to enter the platform quite easily. However, this easiness comes at the expense of careful thought. Creators simply throw themselves onto the arena and this leads to their initial set of failures. At this point, the creator enters a loop where he’s stuck searching for an audience and promoting the videos he’s making. Eventually, out of luck, one of their videos explodes in popularity.

“I then posted it [my video] to the Rust subreddit, and I just got lucky in a sense. I was in the right place at the right time. There wasn't much content into that little niche and I had created something that was somewhat engaging. It was a really accessible video and it started getting attention.”(Ramsey)

Once that happen, a successful creator tries to replicate that success and keeps on improving the quality of his videos.

“There was a change in me as I was interacting with the audience and noticed that the things that worked and the things I was most engaged in, weren't so much tutorials or lessons or sharing facts, but sharing more personal experiences. […] I focused more on those stories and questions that seemed to fit that audience.”(NativLang)

“I wanted, I needed to be a better storyteller. […] I had to learn the hard way, I had to learn by experience and that's where I sort of fall towards where I am today.”(Casually Explained)

The overall effect of this second loop is continuous growth on all fronts (views, subscribers and revenues) along with the development of key skills necessary to become a full-time creator, such as video editing, scripting or animation.

“As you go, you grow and you develop your style, and you develop your quality.” (Ramsey)

“Worst case scenario you will learn, get experience and understand what to do next. If you're interested in filmmaking, you'll develop your skill and ability. I've had no formal training and all my editing is just self-taught.”(The Russian Badger)

The model is distinctively separated into two parts, a first exploration loop and a second exploitation loop, using the terminology introduced by Gupta and colleagues in their review on the exploration- exploitation topic (Gupta, Smith, Shalley, & Smith, 2006). In the first loop, the creator is trying different things, going in many directions at the same time to explore possible avenues. According to Usher (2000): “Exploration refers to learning gained through processes of concerted variation, planned experimentation, and play”. The object of learning in this scenario is the match between video content and the audience, which is found through variation and “play”. Returns from exploration activities are generally uncertain, risky and less predictable in time (Brusoni, 2017). These characteristics match pretty well the reported feeling of succeeding because of “luck”. It was extremely difficult to perceive the returns of their activities given the explorative nature of the loop, thus a video exploding was often considered a matter of luck. The second loop instead, focus on exploitation, given Usher’s definition: “Exploitation refers to learning gained via local search, experiential refinement, and selection and reuse of existing routines.”. Once they have found a type of video that works, an exploitation process starts to further refine its quality and rack up “easy” views. Returns from exploitation are more reliable and linked to the specific time and actions through which they happen (Brusoni, 2017). These characteristics match the idea that skills development and growth happen as a consequence of this loop, since those benefits are closely connected to each video produced in a “successful” format. Growth is explicitly the main objective pursued by a creator at this stage: “I just basically want to focus on the content, focus on the growth”.

Going back to the research questions, it is possible to answer synthetically given what is happening in this moment. For the first question: “How is the relationship between a content creator and the ecosystem of services?” - The ecosystem of services is underdeveloped. Creators main concern is growth and external services (e.g. Reddit or Forums) are used only to gather an audience. There are also traps in this ecosystem presented by Multi Channel Networks. They can shut down the growth of a channel, locking them into unfavorable long-term contracts. Creators should avoid them at all costs and focus on building their audience first.

For the second question: “How are content creators affected by YouTube’s policy changes?” - YouTube’s policies enable creators to easily get into the platform. They are a positive force that lowers entry barriers. Enabling factors are promoted, including free access to a platform and educational content on how to succeed. To allow for the highest number of creators on the platform, YouTube also doesn’t really care about the type of content being posted by the creator at this stage. Overall, the hypothesis that unfavorable policy changes lead creators to build the ecosystem is rejected at this stage, or at the very least it can’t be observed for two main reasons: there isn’t yet a fully developed ecosystem to speak of, and policies are actually favorable towards creators.

4.3 “Full-Time”

The next moment in the lifecycle of a successful content creator is going “Full-Time”. At this stage, the creator depends on the platform for its survival and YouTube represents the primary source of income. A holistic view of this moment is presented in Fig 22.

Figure 22 - Synthetic view of all the codes and themes in the "Full-Time" moment

4.3.1 “Full-Time”: Motivation

At this stage, there are three main elements that dictate the actions of a content creator: a business attitude, maintaining moral integrity and feelings of uncertainty. Since creators have moved to a full-time job and consider YouTube their career, the approach to the platform is much more business oriented. Analytics start to play a bigger role and Trends dictate the topics of new videos.

“[Analytics] informs a lot of the decision-making. [It is important to] compare different metrics to each other and figure out different ways to find which metrics I care about, figure out how to optimize for those metrics.” (minutephysics)

“Every day I check to see what new games are coming out, to try to stay you know, relevant, to try to basically catch the next wave of what could be popular.” (Bijuu Mike)

Content is often adapted to be aligned with YouTube’s policies and some creators also start to structure their business a little by hiring some collaborators:

“Most of the time I censor swear words and things like that.” (Ramsey)

“I have a couple of people that I pay to edit my videos.” (Super Eyepatch Wolf)

“I have accountants to make sure that everything is kept track of as far as the bookkeeping, and paying taxes, and basically doing all of the business side of it.” (The Russian Badger)

Increasing the quality of content remains one of the key aspects, defined by a creator as “the backbone” of its activities. Overall, compared to the previous moment were creators just tried their best often cluelessly, in this moment there’s the presence of an explicit strategy:

“I could see a a strategy, you know, of how to do things and what I was doing differently from others.”(Ramsey)

Morality is another big driver of actions. At this stage creators often perceive contrast between what they are supposed to do to earn income, what their creativity would tell them to do and what the audience actually wants. Earning income at this stage is fundamental to stay afloat and survive and creators often need to choose between what pays and what feels right.

One of the interviewees, making videogame videos, received offers of sponsorship from gambling companies. Certain videogames allow the trading of digital goods in a real market with real money. Often the traded goods are so called “loot boxes”, which are digital lotteries who could grant high value items and thus a lot of money in this market. There are websites allowing this type of trades and a creator commented on his moral dilemma:

“At that time I had a lot of the similar emails of gambling stuff. […] They're offering a good amount, like these companies were offering more than I would get for a single video in his lifetime. [However] as creators we have responsibilities to a young audience. There are a lot of kids that don't have parents that really care and monitor what they're doing. So for me morally, that's the main thing with those types of websites. I denounce gambling websites”. (Ramsey)

Other creators, instead, are much more interested in the earning potential and care less about their moral values:

“I pulled a sponsorship for 4,500$. I was like: “Holy shit!”, that paid all my bills and then plus some just for shilling out a crappy game. As the great Dave Chappelle once said: “I will do advertisements for Pepsi or Coke, all I know is that Pepsi paid me recently, so it tastes better”. Yeah when you're talking 4,500$, I'll endorse anything men.”(General Sam)

For other creators, the compromise is more of an artistic dilemma:

“I see these videos as a bit of an artistic endeavor and for me [they are] these sort of timeless intriguing tales. It's hard for me to actively imagine, unless it's the right perfect fit, getting sponsors embedded in the content. So, because of that, I've avoided most brand deals.”(NativLang)

The pressure to keep earning income, often leads creators to produce a lot more content compared to the previous “Starting Point”, leading to a creative burnout.

“I got to a point where I didn't see the way my content was going. I didn't have any more footage that I could find something juicy in”. “I had to take a break and that improved the quality of the videos a lot more, but also on the other hand I feel more pressure [because I haven’t uploaded].”(The Closer Look)

For some creators, the struggle to stay afloat was so harsh that they significantly sacrificed other aspects of their life:

“I was just working all day and it was affecting my personal life. That's the other thing that people don’t say about youtubers. If they're struggling financially, they'll be literally working as much as they can just to put food on their table. But if it comes at the price of your family and friends, and I was putting people off, I can't manage that.”(The Tale Foundry)

Finally, as the last component, feelings of uncertainty drive a lot of their decisions. What bugs most creators is that it’s very difficult to interpret the platform:

“I've never felt like I've been in big trouble. But it does cause me a lot of stress sometimes, especially if you see your channel doing worse even if you're doing kind of the same things.” (Bijuu Mike)

At this stage, most creators feel extremely confined in YouTube and perceive the platform as a monopoly: “If you want an answer as to why YouTube doesn't treat people very well, it's because it has a monopoly on video sharing. There is no competitor to YouTube and I don't think there ever will be.”(The Closer Look)

Some of them are even scared at the idea of what could happen to their channel:

“You shouldn’t always have to fight to keep your channel alive, but that's what it kind of always feels like for me.” (Bijuu Mike)

On top of that, the income provided by AdSense is unstable:

“It's basically pure chance how much money you will get each month. So there's no stable income ever.”(Guru Larry)

Overall, creators at this stage treat YouTube as a career, thus taking decisions like business mans. However, since running a channel is such a personal thing and they are self-employed, feelings of stress, uncertainty, risk and moral hazards dictate a lot of their decision-making process.

4.3.2 “Full-Time”: Opportunity

In terms of opportunities, the overarching theme is that creators are actively looking for new businesses. First of all, going full-time allows for growth simply because they invest more time and effort into their channel:

“I got so much more growth doing full-time you know, videos every single day, it was totally a game-changer.”(Bijuu Mike)

“I decided to take the time off school and go into it, put extra time in and that really helped out a lot.” (Casually Explained)

This full-time growth, often allows creators to compensate losses from demonetization or other policy issues, because they are growing at rate higher than the decline of revenues:

“I mean just the the amount of money you get per view is not what it was before. Thankfully I'm growing faster than the amount is declining per view.” (General Sam)

Because of this effect, sustained growth is still considered an important business element, although in this phase creators try stabilize their income through other sources. There are two main avenues to do so: exploit connections with other YouTubers and find alternative ways to monetize people’s attention. Cross promotions are considered effective:

“You get a good 10-15% of the audience coming over, so the more videos you do with other people the quicker you build that way.”(Guru Larry)

Networks are also a good way to take advantage of collective power. Usually they deal with sponsorships as middle man, negotiating deals for the whole group. They also provide assistance on more technical stuff like creating thumbnails, managing contacts and emails or taxes. There’s a pretty personal connection between a single YouTuber and a manager in a premium network. In exchange they get between 5% or 10% of the Ad Revenues. For most YouTubers it saves time and allows them to take advantage of services that they couldn’t afford to have on their own. For example, a collaborator managing the contacts would be too expensive to hire for some creators, but together they can have a manager that deals with that.

“I've just been recently in contact with a few different networks who I was hoping would be able to help me with like some small more menial delegation type things... like you know email contacts and business contact and you know little responsibilities that are just kind of like clogging up my day.”(Super Eyepatch Wolf)

When it comes to monetizing people’s attention, the key idea is that there’s value in people watching your content regardless of the Ad Revenue.

“So I have 450.000 subs and that has to be worth something. Just having their attention, them wanting to click on something, has to be worth. YouTube pays me whatever advertisement stuff that doesn't matter, but if you can use the people in order to generate different streams of income then… I mean, hell!”(General Sam)

The overall objective is to rely less and less from YouTube, leading to independence. There are many alternatives that creators can adopt to build their ecosystem. Merchandise is a big one, including T-shirt, mugs, magnets and so on. The most used company is Teespring (Teespring, 2018). They have a very streamlined process by which a creator simply provides them with a design and then everything is handled by them. A viewer who wants to buy a T-shirt from a creator, would go on Teespring and the website would manage the payment, manufacturing and shipping of the product. Similar companies started to arise, such as Bonfire, but their business model is the same. The relationship works pretty well because creators can focus on their core business, making videos, without the need to manage other stuff. They can promote the merchandise through their videos and often the designs themselves are provided by the community itself through fan art or recurring memes. Other creators instead, rely on drop shipping for merchandise. In this practice, the content creator build a front end store (e.g. a website) where viewers can buy the merchandise. The store is connected to either its bank account or PayPal that issues a production order to a third party company upon payment from the front end store. The merchandise will then be manufactured and sent to the viewer. This approach is more complex and less diffused as the creators needs to intervene in many steps of the process to build the business. The main advantage is that it provides higher cuts compared to Teespring. Patreon is another widely spread service that allow viewers to directly donate money to creators they support. They have a low commission fee, 5%, and it is the company’s mission to minimize transaction fees to give as much as possible back to creators (Patreon, 2018). The use of Patreon is a highly divisive topic among creators. Some believe that one should not build a business upon others generosity:

“I have an aversion to Patreon because I think that it's like internet panhandling if you ask me. Now that might rub some people the wrong way, but if you need a Patreon account to run your business, I don't feel like your business has matured enough yet or you are simply not meant to do it, you're not good enough at it.”(The Russian Badger)

For others, Patreon goes beyond being a simple source of revenue, but becomes a way to better engage with their own community:

“I find it really rewarding, I love Patreon. […] You can donate money if you want or not, but I can at least share [my work] with the audience in a more intimate setting. That's how I started out on Patreon, not as a way necessarily to earn a bunch of money off the videos, but just as a way to sort of connect more with community.” (NativLang)

In any case, for those who decide to use Patreon it can become the primary source of income, surpassing even Ad Sense. To achieve that, it is important to connect with the audience and offer rewards to Patreons that allow to get and experience more content. Common examples are: daily posts, weekly skype sessions, pols to ask for which content to produce, shout outs during the videos or early access to videos. Sponsorships tend to be the most lucrative form of alternative business. A sponsorship can either be a shout out to a company/product during a video or an entire video dedicated to showcase product. A single sponsorship on a video can provide enough income to sustain the business for an entire month. Most often creators are the ones contacted by brands for branded deals, with a general consensus that you start receiving emails around the 100.000 subscribers mark. However other creators are more proactive in this:

“I was sending out 50+ emails a week to several different companies. The vast majority of my sponsored videos are because I took the initiative.” (The Armchair Historian)

Usually the creator, or its manager in a network, negotiate a deal. The length of the promotional message, along with specific targets on audience reached, either views or comments, are the key drivers for compensation. Companies often require to see the video script beforehand or the video itself before publication. If approved it can go online. Despite this seemingly complicated process, many creators described it as “pretty straightforward”. Streaming is a valid business alternative, very popular in the gaming video genre, but limited to it. Creators not pertaining to this genre can still stream from time to time, often with Patreons just to hangout or show their behind the scenes editing process. Despite this though, the real money with streaming is made by gamers. The biggest platform to stream on is currently Twitch, which was bought by Amazon in 2014 for 970M$ in cash (Business Insider, 2014). There are two main ways to earn money through streaming: subscriptions and cash donations. A subscription is a monthly recurring payment of 5$. It allows a subscriber to have more interactions with the streamer to the event. For example special emoticons are available, visibility in chat is provided and there’s no time- limit between messages. Twitch also has an advertisement system where videos are put in front of a stream just like AdSense on YouTube. Being a subscriber removes entirely advertisement. Of this 5$, usually half of them go to the creator and the other half to Twitch. Subscribers are considered a pretty stable source of income with “aficionados” renewing the subscription for years at the time. Donations, instead, are live cash donations made by viewers to a streamer. It is often possible to attach messages to the donation that will be read live by the streamer. A donation can also trigger something during the stream, such as music or in-game actions. They are a higher form of interactivity and often people who like to donate either express appreciation for the creator or promote “memes and jokes”. Bigger creators tend to have minimum donation limits, such as 20$, to avoid excessive spam during the stream. How much can be made through streaming is heavily affected by a streamer personality and its interaction with the community. Some bigger creators can rack up thousands of dollars in donations per single stream (Etika, 2017). YouTube also launched its own streaming platform, however most creators still wanted to move to another platform:

“So the whole point of streaming to begin with, was to diversify my revenue streams outside of YouTube, because what if YouTube kicks me out one day?”(General Sam)

“I didn't want all my eggs in the same basket, so I started streaming on Twitch.”(The Russian Badger)

There are also some less spread possibilities such as the use of personal website or affiliate links. Affiliate links are usually links to products put into the description of a video. Whenever someone buys through that link, the creators gets a percentage off the purchase. Among the genre of creators interviewed it is not particularly relevant, although it is more important in other genres such as “Tech Reviews”. This reasoning principle applies to many other categories, for examples YouTuber producing music often directly sell their compositions. To have a complete perspective on all possible services available on the platform, a larger study across all genres would be needed. However, sponsorships seem to be the backbone of income generated at this stage for many creators.

4.3.3 “Full-Time”: YouTube Policy

The main theme around YouTube’s policies is that content creators don’t really know how to behave on the platform and often discuss together to understand what should be done.

“We often discuss things like: “Hey this keyword is not good, don't put this keyword in your video” you know, things like that. We just basically have to gather information that we just see and share it with each other.”(Bijuu Mike)

This speculative environment is the result of two main issues: it is difficult to speak with YouTube and policy issues directly affect the bottom line of creators. One of the interviewees reported:

“I would like to speak to an actual human about these problems, because what it feels like, you know, is that they just don't care.” (Slazo)

The general perspective from a content creator’s point of view is that YouTube behaves “as a machine”. They are very methodical, they apply their algorithms, and most of the decisions are taken by machines before being checked by humans if something goes wrong.

“YouTube is so mechanical and analytical in the way that they just process information. They find things that check little boxes and red flags and then they just wipe out a channel if it hits enough of those flags.” (General Sam)

Even contact through emails doesn’t feel much human:

“I've sent multiple emails to them about issues, but it's always a really robotic kind of response.” (Bijuu Mike)

As creators grow on the platform, they get access to preferential lines of communication and often a YouTube representative is appointed as a contact. However, most creators don’t take advantage of this opportunity as it doesn’t provide “value”.

“There are preferred customers, like if you have 100.000 subscribers you are allowed to reply to their emails. So they do have it, but I never use it because it's just useless. They'll just send you a generic email, sort of a FAQ a lot of the time.” (Guru Larry)

“I had a representative from YouTube who was in contact with me and would regularly set up meetings and I could ask, you know, any questions. Typically I would get links, just like you can see externally, to policy guidelines or pages that you can find if you just start digging through some of the forums or guidelines and answers would often go along with that. It wasn’t that useful.”(General Sam)

There is also a sense of perceived frustration since not only it is difficult to talk about issues, but it’s also complicated to make suggestions to the company itself. Policies hurt the bottom line by both limiting growth and revenues. It all starts from videos not being considered suitable for advertisement. The biggest problems are that:

1) There’s no transparency in how changes are applied to the platform;

“YouTube purposely don't tell anyone what the rules are, because they are paranoid that people might take advantage of it and game the system. So nobody knows which are the rules, which is incredibly infuriating! You're in a perpetual state of paranoia, “Will this video get monetized?” and you really don't know it until it's gone up, so that's the worst thing.”(Guru Lary)

“They didn't say that this video has been flagged for whatever reason. We just weren't being told what was going on. I have a couple videos that didn't do that well and then when the demonization came out they were immediately demonetized, confirming what was going on.”(The Closer Look)

“Lately it feels like YouTube has been on hard mode. I can do the latest freshest content that I know you guys want to see and it barely gets suggested if not at all. It really messes with my brain. You never know if it's you or the algorithm.”(General Sam)

2) Changes happen very frequently and requires constant adaptations efforts;

“Two months ago, my channel was doing really good, it was amazing. But then out of nowhere it just seems like everything came to a halt and videos just stopped getting recommended, I think it's due to the algorithm changes that we don't know about.”(Bijuu Mike)

3) Whenever a video is demonetized, the creator is greeted with a very generic explanation of what happened. Details are very vague and there’s no pin-point explanation of what is wrong with the video. This creates an environment where creators don’t even understand how to correct their mistakes.

“I try to pride myself in being a kid-friendly channel for the most part and I don't curse and anything like that, but still I have some problems with getting my video demonetized for absolutely no reason and YouTube doesn't say anything.”(Bijuu Mike)

“So even if your video got taken down and you've got a guideline strike, they'll say you the strike category, but they won't say specifically what was wrong with the video.”(General Sam)

These three issues trickle down onto other problems. Some creators take a very aggressive stance against YouTube:

“It's not even our fault because they don’t tell us anything.”(Guru Larry)

Others start to perceive their entire niche (in terms of content) at risk on the platform:

“Tomorrow they could update their policies and my niche on the the channel could be down the drain.”(General Sam)

Growth can also be severely halted, since getting demonetized not only cuts advertisement possibilities, but also removes the video from the recommendation system of YouTube. That is considered to be the main engine for views on the platform.

“I was so upset because it's not just about having an age requirement on you video [or monetization], but it actually pulls it from the recommended system and this is where the problem with YouTube begins. It stops you from growing.” (Ramsey)

Another main concern is that a lot of these issues seem to stem from technological issues, where algorithms incorrectly detects videos as “non-suitable”, even if they are not. YouTubers can ask for a manual verification, but it takes up to 48hrs. Given the extreme importance of the first few hours for a video, since this is when they rack up most of their views, it means that even if the mistake gets corrected, most of the revenue is effectively lost.

“I have to fill out a manual review, and by the time it's finally accepted I'll only make a couple hundred dollars on the video.” (The Armchair Historian)

There’s some speculation that even if your video gets accepted, it still remains pulled from the suggestion algorithms. Some other creators instead, in very ambiguous niches that are controversial, prefer to not request a manual review. If the video gest manually denied, it will not be shown to others.

“If they manually deny your video, then not only your making money, you are also punished by the recommendation system. You don’t get views anymore.”(Ramsey)

These creators perceive their position as very difficult, because when they started, since YouTube didn’t really care about their content, it allowed anything on the platform. They often decided to go full-time before policy changes that put at risk their niche and now they are struggling for survival on the platform. Overall, policies in this moment of the lifecycle tend to be very negative towards the creator and confine them to behave in way that are aligned with YouTube’s overarching strategy.

4.3.4 “Full-Time”: Community

The strongest element coming from the community is a sense of “Pressure”. Many interviewees spoke about their relationship with the community with a generally positive attitude. They often have a sense of fulfillment at the idea that people is watching their content and the relationship is perceived as satisfying. Especially among those that interact with Patreon. However, when speaking about sustainability of their lifestyle and business, creators recognize that viewers have a strong voice. There is a request to be up to certain standards of quality. Often viewers perceive a drop in quality when and if the creator engage with sponsorships, especially with full promotional videos.

“They [the viewers] are used to a certain type of content and when they click on a video they expect something. When I published my first sponsorship there was some strong backlash. The video did not feel the same to them and so they complained. A lot.” (Guru Larry)

Usually the more viewers are loyal (e.g. “I have been there since the beginning”) the stronger the complaints.

“There's a thin line between how many adverts you wanna put in before it becomes annoying for people.” (General Sam)

People who also have Patreons, often feel guilty towards them if the quality of a certain video is not high enough or if they don’t provide enough value.

“I felt kind of guilty if I didn't make enough videos or I'd feel guilty if the video wasn't very good and people would be paying for it.”(NativLang)

Some creators even rejected this business opportunity specifically because of this feelings:

“Right now I really don’t need Patreon, I’d rather not have the pressure to keep up with expectations.”(Casually Explained)

Overall, keeping the community satisfied is a primary objective of a creator that directly feeds into growth. To achieve satisfaction, they need to compromise what they would like to do with what the viewers asks of them.

4.3.5 “Full-Time”: Connections

The model expressing the connections among different themes in this moment is presented in Fig 23.

Figure 23 – “Full-Time” Growth Model

The main drivers during this phase are generated by the constant feelings of uncertainty and the difficulties on understanding how to behave on the platform. To them, it feels like they don’t understand the platform anymore:

“You just end up questioning everything about yourself, especially when your channel does bad and you know, it could just be YouTube not serving your video for no reason.”(Bijuu Mike)

The growth formula used before seems to be broken. In going full-time, people also exposed themselves to financial risk:

“I just closed an offer on a house with my wife. My mortgage is for 30 years, and I'm 27, so it's longer than my actual life has been so far. So I kind of panicked [when the Adpocalipse happened] and I kept going.”(General Sam)

This combination of feelings pushes creator to find a solution that comes in two main ways: bundling together and expanding their business. Creators start to bundle together to share the information they possess on the platform and discuss common strategies:

“I have some discord chats with some other creators and they're always talking about how the algorithm works, and you know, what they should put in their title, how they could have changed the thumbnail, what words work better and work worse and and like they get a lot of views for certain things.” (Super Eyepatch Wolf)

The overall objective is to reduce the uncertainty on the platform. However, once they have come together, they also start to take advantage of these connections. The feelings of uncertainty are a catalyst that leads to other avenues: building networks and cross promotions. Networks are a way to increase their collective power. A network representative can often represent the creator with YouTube and protect them from abuses:

“The way that works is I give them 10% of my ad revenue and they safeguard my videos from any little Content ID robots. So the Content ID robots can't find me because Screen Wave [the network] have contacts at YouTube and it basically said "this guy's okay".” (Super Eyepatch Wolf)

YouTube is also a company that often reacts to public outcries and creators that get demonetized or punished can leverage their viewers and their friends’ network to pressure YouTube. An example is that of Noble (Noble, 2018), a YouTuber whose account was terminated based on a false spam accusation. To defend himself, he shared his story on YouTube and Twitter, asking for people to spread it. Other YouTubers joined his campaign asking their communities to express disappointment. This reaction forced YouTube to publicly reply to him, asking to appeal again against the decision, which was ultimately overturned (YouTube, 2018h). This is a direct example through which Networks often act as syndicates of creators, forcing the hand of YouTube through their total viewers. Cross promotions instead rely on leveraging each other’s audience. There are two main types of cross promotions. In the first one, a creator appears as a guest on the video of another creator. Its role in the video is usually minor and appears for just a couple of seconds-minutes. Usually the guest is simply thanked in the description of the video. In the second type, creators make a video together. The video is usually split into two parts, one uploaded by a creator and the other one uploaded by the other creator. These types of videos usually include shout-outs to the other channel and explicitly invite the viewer to go on the other channel to watch the rest of the video. The overall goal is to increase each one viewership by leveraging on the other creator’s base. This is generally considered to be non-competitive, as the amount of time a single video takes to be watched is far less than the average amount of time a viewer spends on the platform. Therefore, it is more likely that a viewer will start watching both creators rather than move to one viewership base to the other one. The main effect of this “coming together” is an increased rate of growth for three main reasons. At first sharing information reduce uncertainty and allows creators to follow better strategies. Then, networks protect creators reducing the effects of growth-halting policies. Finally, cross promotions actively drive new viewers to the channel. On the right side of the model instead, always starting from the feelings of uncertainty and perceived risk, creators start to actively look for business alternatives. This process is a careful balancing act between opportunities to monetize people’s attention and the pressure they receive from viewers along with their own moral integrity.

“It feels like an uphill battle, to just try to stay relevant and to make sure people are happy.”(The Russian Badger)

There is no common recipe on how to do so, but it depends on the sensibility of the creator and its relationship with his own community. One of the interviewees shared his thoughts:

“Have you ever watched any of my videos? Imagine: "that's why you should watch Yu-Yu Hakushou... brought to you by eddies razors!!" It would be such a weird fit for the channel, because I think that a part of the appeal of my channel is that people develop a bit of a personal connection with me in those videos and I feel like a sponsorship would take away from that.” (Super Eyepatch Wolf)

Pressure from viewers can also be much more direct, with people deciding to unsubscribe from the channel. “I have done a sponsorship for a TV streaming service from Sky called NowTV. People don't like that sort of sponsored stuff, I've lost subscribers immediately as I put up that video. I was a little bit apprehensious about that. People just want free content, they don’t want to watch an advert. They don't want to give you any money and they want things on demand just constantly. So it's a little bit upsetting when they just turn on you like that, just because you're just trying to make money.” (Guru Larry)

“People don't like it and they just go: oh you're a sold out, I don't wanna watch your videos anymore, so it's a very thin line.” (Guru Larry)

Overall, as one of the interviewees commented: “There's a thin line between how many adverts you wanna put in before it becomes annoying for people.”. The main objective is to achieve income stability and to protect themselves from potential changes on the platform that may hurt them or their niche.

Going back to the research questions, it is possible to answer synthetically given what is happening in this moment. For the first question: “How is the relationship between a content creator and the ecosystem of services?” - The ecosystem of services is a key component of income stability that allows a content creator to work Full-Time. According to the reaction of its community and own moral code, the creator has access to a variety of potential businesses: sponsorship, merchandise, streaming, donations, websites etc…

For the second question: “How are content creators affected by YouTube’s policy changes?” - Policy changes create an uncertain environment that leads creator to look for new avenues. The main objective is to increase chances of survival. Policy changes also have a “community” effect, bringing creators together to share information, collectively increase bargaining power and collaborate. Overall, the hypothesis that unfavorable policy changes lead creators to build the ecosystem is accepted at this stage.

4.4 “Independence”

At this point, creators have achieved a financial independence from the platform. Their viewers follow them around even on other platforms and YouTube represents only one of the things they are doing. A synthetic view of the “Independence” moment is provided in Fig 24. The main themes are presented with a hierarchical structure directly from the codes used to interpret the interviews.

Figure 24 - Synthetic view of all the codes and themes in the "Independence" moment

4.4.1 “Independence”: Motivation

At this stage, creators start to enjoy their lifestyle and what they are doing. The pressure from the previous moment is largely relieved, which is in contrast with the sacrifices they previously did to stay afloat.

“To me, being a YouTuber is one of the best jobs on planet because of the lifestyle, you can get a lot of money by doing things that you love without too much pressure as a normal job.” (Casually Explained)

“Let's say that you take a youtuber that makes $300,000 a year and you put them in a corporate position that lands them $300,000. Things like investment banking or consulting. The lifestyle is so much more of a grin and you're constantly stressed. You're pulling your hair out and it's just “pressure pressure pressure” all the time. I feel like so many people that have become full time youtubers don't realize how great they have it because they've never worked in a professional environment before.” (The Russian Badger)

They also have outgrown the platform, they are something much bigger than YouTube. Because of that, it can feel as a new chapter in their life, with new possibilities open to them. One of the main topics, in terms of what drives decision making, is still diversification.

“I always try to advocate creators to diversify where their income is coming from and where their videos are being watched. Try to think about growing on Instagram, growing on Twitter, growing a Facebook page, just because in the event that something disastrous does happen on YouTube you have these other avenues that you can lean on as well” (Super Eyepatch Wolf)

“The biggest thing for online creators in general is the diversification of where your content is going. […] Look even if there's a licensing deal, for TV or over-the-top media like Netflix or Hulu.”(Bijuu Mike)

Usually, most of their activities are also much more focused. Previously they were testing new business possibilities and trying different opportunities. At this stage, they start to integrate in a more coherent way what they are doing. As an example, streaming it’s not a separate activity, but it’s part of a bigger process where the footage produced is then re-used to make videos.

“When it comes to revenue, maintain a focus on your YouTube videos and on creating things that you not only enjoy creating, but that are high-quality in nature. Then add on these other things [merchandise / streaming] that basically contribute or are already a part of the process.” (The Russian Badger)

There’s also no particular brand allegiance to YouTube. It is still very important to them, especially to engage with the audience, but there is no loyalty to the platform itself.

“I don't have any particular brand allegiance to YouTube”. “In my head I don’t see YouTube, I see a Platform.” “Be ready and prepared to jump ship if anything bad happens and always be diversified in your online video portfolio”. (The Russian Badger)

Finally, at this stage creators also go back to an “exploration” phase, trying to innovate their content a bit more.

“I think on YouTube you have to innovate at least to some degree. Otherwise you'll begin to get stale and then your audience will move on. So I'll tend to always have a flow videos that I know works, but I'll also every now and then branch into a new thing and see if it works or not.” (Slazo)

4.4.2 “Independence”: Opportunity

The key element at this stage is that the YouTuber starts to be a small celebrity. They usually drive big numbers in terms of viewers and subscribers, which combined with their policy of diversification, allow them to take advantage of their audience in many ways. One big example is the crowdfunding of projects.

“Philip DeFranco is trying to make [its own newspaper] and hire a whole bunch of people so that he can bring the right news message to all of his viewers. […] He’s really trying to do something novel and crowdfunding was necessary to make new content, hire new people, editors and scripters and all of that” (The Russian Badger)

They keep expanding on many social media platforms and the core set of skill they developed can allow them to take on other opportunities.

“I could easily still sustain myself purely off of sponsored videos alone, but at the same time I'm very aware that social media is always going to be a delicate career to continue with. I have full confidence that I can do it forever or until I'm well into my 40s and 50s, because I think I'm so good at making content that is differentiated from other people, entertaining and that people want to watch.” (Casually Explained)

Patreon is generally abandoned at this stage. Creators feel like it’s not necessary to survive and they rather not feel the pressure that comes with having viewers that pay for content through donations. This also goes along with the idea that the scale and amount of money they receive is more than enough to absorb fluctuations in AdSense revenue.

“[AdSense fluctuations] have never really been all that annoying, because they have never really been a difference between it being a career versus not being a career. So to give you an example, it's annoying to make 35 grand a month versus 60 grand a month on ad revenue. But there's no difference to me as far as it being a career. But of course, if you're a smaller creator, let's say instead of the difference between 35 grand and 60 grand, it's the difference between 3500 and 6000, that could be the difference between you making it a full-time job and you not.”(The Russian Badger)

At this stage a lot of variety can be found, and should be analyzed case by case. Some creators write books, others have their own app or videogame. Other creators start to make content for more traditional media, like television or even a Netflix series. There is a lot of diversity in opportunities and depends a lot on the genre of videos that started the creator’s career along with its relationship with the viewers.

4.4.3 “Independence”: YouTube Policy

YouTube policies at this stage significant shift from the previous moment and are generally positive. One of the interviewees specifically mentioned a “Creators Preferred List”, that bigger brands can directly choose from for their advertisement.

“If you're a big enough creator, you can get on YouTube's like preferred creator list. That means that when an advertiser wants to deliver ads, they'll go to YouTube and say: "Okay well, who should we put ads on?" and they're like: "Well here's some preferred creators who we know are good" and you get more revenue from that. Your CPM [Cost Per Mile – How much the advertiser pay, and thus the creator get, per 1000 views] goes up a lot. The bigger your channel, the more you get paid.”(Casually Explained)

“YouTube Trending” is another policy introduced by YouTube to heavily promote popular videos. It is algorithm based and potentially anyone could have a video trending. However, the bigger you are, the more likely you are to get this promotion. This in turn will allow you to reach an even bigger audience that will make your next video more likely to get onto YouTube trending again. It is a feedback-loop that heavily favors big creators.

“Once you get to, say maybe 500 to 800 thousand subscribers, you have enough reach where if you make a good video, it's really likely that it'll get on YouTube Trending. With YouTube Trending it's such a wide audience that, you know, all of a sudden you are getting a whole bunch more subscribers.”(Casually Explained)

Also, changes in the algorithm tend to be perceived favorable. It is difficult to assess causality and why this happens, but a reasonable hypothesis is that creators that got really big today are the more aligned with YouTube’s overall strategy. Thus, if the strategy doesn’t change, any change to the algorithm will favor what they are doing as it also directly benefits YouTube. For example, creators that described their content as family-friendly, generally perceived a positive feedback from algorithm changes.

“Well I think from that front [algorithm changes], I think fortunately for the most part I haven't really noticed too many changes. But I feel like the changes would likely benefit my channel because it seems like they're trying to do things that encourage very advertiser friendly videos like mine's.” (The Russian Badger)

Overall, if you’re big enough, YouTube caters to you and your audience.

4.4.4 “Independence”: Community

The community at this stage for a content creator feels like a “fanbase”. Within the interviews there’s little content about this theme at this stage. However, creators still made some comments that apply to this definition. Some main elements of this definition are: viewers that will watch anything; viewers that produce and share fan art (i.e. drawings made by fans depicting them); sometimes the fanbase itself identifies with a “nickname”, for example PewDiePie fanbase is called “The Bro Army” and there’s an entire terminology on specific language they use within the community (PewDiePie, 2018). The viewers also engage with creators on other platforms, for example Discord, which is a big chat- room that allows creators to have their own server and to moderate communities.

“I know there's a a dedicated fan base that will watch whatever I upload.”(The Russian Badger) “[Other] Youtubers get fan art, and drawings from their community.”(Ramsey)

Overall, the community is actively engaged in what the creator does. They follow him around, generate content about him and identify themselves with his attitudes, terminology and whatever else is in fashion at the moment.

4.4.5 “Independence”: Connections

The model expressing the connections among different themes in this moment is presented in Fig 25.

Figure 25 - “Full-Time” Growth Model

The entering point of this loop is that the creator became a “Small Celebrity”. If the creator followed the growth model of the previous moment of his lifecycle, eventually he would get big enough to start getting the attention of other media, along with a very sizeable community. The direct consequence of this status is that he “Drives Big Numbers”. Creators of this size are followed by millions of people and this size is relevant for many businesses, including YouTube itself. Because of that, YouTube doesn’t want his bigger creators to potentially move to other platforms. They try to keep them within their platform. Since the creator doesn’t depend anymore from YouTube for its revenue, YouTube encourages creators to stay on their platform with favorable policies. The general feeling of these policies is that: “The bigger you are, the bigger you will get on YouTube”. YouTube tries to promote the growth and exposure of this creators as much as possible. This in turn allows the creator to get even bigger. Their audience, their fanbase, keeps getting bigger and bigger. This allows the creator to also grow as a celebrity, since their power in the first place comes from the people following them. This loop is generally favorable to both creators and YouTubers as win-win situation. Creators can keep their momentum going and to grow their audience. YouTube, on the other hand, ties to its platform the creators that most people want to watch and thus the creators that can be advertised for more money. There is more competition from brands to put an Ads on one of these preferred creators and therefore more money involved for YouTube. YouTube even launched a spin off company called “Famebit” (Famebit, 2018) that focus on influencer marketing. Brands can find YouTube creators they like on this platform to create custom marketing campaigns, including sponsored videos, but also content on other platforms such as Twitter, Facebook or Instagram. Famebit itself describe their creators as celebrities:

“Creators are more than a new generation of celebrity - they are writers, producers and distributors of content to a large and already engaged audience.”

There’s a very strong relationship between these celebrities and YouTube. Compared to the previous moments in the lifecycle, it is possible to affirm that when they reach “Independence”, they also gain for the first time a form of power over YouTube, without the necessity to bundle up into Networks.

Going back to the research questions, it is possible to answer synthetically given what is happening in this moment. For the first question: “How is the relationship between a content creator and the ecosystem of services?” - The ecosystem is what drives most revenues at this level. There are the most disparate opportunities, from writing books up to having your own app. Creators are internet celebrities and thus many of the opportunities are similar to that of other famous people, such as appearing on Television, writing for magazines, fashion advertisement and so on.

For the second question: “How are content creators affected by YouTube’s policy changes?” - To capture these “Small Celebrities” viewers, YouTube algorithms and policies actively promotes the biggest content creators. This in turn enlarges their fanbase even more, giving back value to the creator. YouTube caters as much as possible to these celebrities and builds a stream of its revenues directly on top of them. Overall, the hypothesis that unfavorable policy changes lead creators to build the ecosystem is rejected. Creators have power thanks to their size and celebrity status. Changes to policies, generally don’t hurt them because fluctuations in revenue do not affect directly their financial stability. The power granted by their viewers allows them to freely move onto other platforms. To avoid that, YouTube specifically deploys policies to keep them within their loop and platform. To a certain extent, the philosophy of the relationship could be even reversed compared to the previous stages: YouTube isn’t providing them a “job”, they are driving “clients” to YouTube.

5 Results Interpretation & Discussion

Within this chapter the conclusions that can be derived from the results of this thesis are discussed. At first, the theoretical contributions are highlighted. The focus is on linking the results of the thesis with the background of scientific literature previously discussed. Then, practical contributions are presented in the form of suggestions towards both content creators and YouTube. Finally, some limitations of the research are highlighted along with possible new avenues to explore.

5.1 Theoretical Contribution

The results of this thesis contribute to two main streams of literature:

(1) “platform leadership”, but explored from the perspective of a complementor (Adner, 2017; Cennamo, 2016; Gawer & Cusumano, 2014; Parker et al., 2016; Santaló, 2015);

(2) the effects and impacts of governance with a focus on how it drives the actions of complementors (Bergvall-Kåreborn & Howcroft, 2014; Foerderer et al., 2018).

These two topics are intertwined in the results of this thesis, as one affects the other. From the interviews with content creators, three main moments of their lifecycle have been identified: the “Starting point”, “Full-Time Job” and “Independence”. Within these three moments, the control exercised by the platform on its complementors varies, as showed in Fig 26.

Figure 26 - Qualitative representation of "Governance friendliness" as the content creators grows

To explain why this data has been observed, the relationship between governance and the strategies that complementors can use to escape the control of the platform has been explored.

Previous literature highlighted two main strategies that complementors can adopt to escape from the overarching control of platform owners: multi-homing and forking. If the empirical evidence collected through the cases confirm multi-homing as one key strategy for complementors (Armstrong, 2006; Cennamo et al., 2016) we have found also a new strategy that has not been previously highlighted into the literature: “Direct Monetization”.

Within this chapter, at first multi-homing will be discussed in its relationship with the observed governance mechanisms. Then, “Direct Monetization” will be explored, providing an explanation of what it is and how it works.

5.1.1 Multi-Homing

One of the key elements of the “Independence” phase is that creators heavily differentiate their income sources by producing content not only for YouTube but also for other platforms. There are two major elements that make this strategy successful: (1) the content requires little to no adaptation; (2) they have enough power to drive the behaviour of their fanbase.

Within the literature, one of the problems that complementors find when using multi-homing is that their product needs to be adapted for others platforms (Cennamo et al., 2016; Gawer & Cusumano, 2014). As an example, an app developed for iOS needs to be written in a different coding language for Android. In the context of our case study, however, the same video and content produced for YouTube can be promoted and uploaded elsewhere. As an example, many creators directly upload their videos in their Facebook pages. There’s no need to make further investments to adapt the complement.

The second element is that creators in the “Independence” phase reached a celebrity status. Their community of viewers behave as a fanbase and takes actions if pushed by the celebrity they follow. This means that if a creator starts pushing its content to another platform, its viewers will move to that platform to watch it. Creators at this stage possess the power to move people outside the platform. Because of this, multi-homing is a strong threat to YouTube platform owner.

Multi-homing actively harms the growth / size of the network. Therefore, YouTube changes its governance policies to convince big creators to stay in the platform. As an example, recently YouTube developed a new section of the website called “Trending” which actively promotes the content of big creators. Sometimes, it can also happen that the creators themselves are the ones forcing the hand of YouTube to make changes.

As reported from the interviews, creators start organizing themselves into networks. Negative ruling against one of the members leads to the activation of the whole network. For example, a creator who received punishment from YouTube can tweet about this issue to its audience. This tweet is also picked up by other creators who share the message with their own audiences. Eventually, if they can make a big enough fuzz, YouTube is forced to reverse the ruling, as shown in the example of Noble in the results chapter. This mechanism is aligned with the organizational perspective stream of governance found into the literature, as it shows how often policies are the result of negotiation (Foerderer et al., 2018).

This strategy can therefore explain the third part of the graph on policies as shown in Fig 27.

Figure 27 - The possibility for complementors to multi-home explains why governance friendliness increases Since a prerequisite for Multi-homing is the ability to drive the behaviour of the audience outside the platform, it is necessary to be big enough and to have built the fanbase. Multi-homing is a strategy that is available only to “Big Enough” creators to reduce the control of YouTube.

Within the interviews, it emerged that creators from the “Full-Time Job” phase and onwards are actively trying to mitigate YouTube’s control. It is within this phase that they start thinking about putting their content onto other platforms to reduce the risk of being in YouTube’s hands. This is generally the phase where they start small experiments to see if the content sticks on the new platform or not.

Not every creator is successful in this strategy and the mechanism describing how to apply multi- homing correctly is unclear in the data collected. Given this, there are possible explanations as to why failure happens and what a creator should do to multi-home successfully.

A first reason for failure could be the choice of a not “Big Enough” niche. A video niche within YouTube might be big enough to sustain a creator during its “Full-Time Job” phase, however there might be not enough people interested in that content. This means that the maximum growth achievable, even if everything has been done correctly, does not lead to a “celebrity” status.

A second reason for failure could be imitation. Usually, imitation among creators of similar size has positive effects. As reported in the interviews, creators often look at each others to understand trends, what the audience is interested in and so on. This works pretty well for creators within the same moment of their lifecycle. However, smaller creators trying to imitate what “celebrities” have done to reach that stage seem to have a hard time. One of the reasons is that those who are celebrities today, grew to that level 2-5 years ago. YouTube changed a lot during the years thanks to its explosive growth. The policies and the way the platform used to be are very different from what it is right now. Therefore, copying what worked in the past might not work today. Another reason could simply be “market saturation” and general competition. By imitating “celebrities” from a content perspective, smaller creators are competing head-to-head with those who are supposed to be the best in their genre. This might obviously lead to failure.

A third reason could be “not playing by the rules”. Growth is fundamental to achieve the “Independence” phase. YouTube exercises a lot of control within the middle phase, and to not comply means to stop growing. Creators making videos not aligned with YouTube policies are not promoted on the platform, their content is less shown to viewers and, overall, the platform impedes their growth. Many creators grew into specific niches that are controversial and YouTube does not like in terms of “Advertiser Friendliness”. A good example would be black humor. There’s demand for this type of content, so it’s not a matter of picking a small niche. However, this controversial content is not aligned with the platform’s goals and therefore it is extremely difficult to grow into this niche. A creator might get stuck into this situation and the only way if he wants to start multi-home is to pivot towards a different type of content.

A fourth, and final reason, as to why creators don’t multi-home is that they simply have not realized yet that they can. To understand if a creator can multi-home, they need to test their ability to influence the actions of their audience. Some examples would be asking the viewers to sign a petition, buy a book or follow on other social media. In itself if these tests are successful, it does not mean that a creator can start uploading his/her content somewhere else right away. However, these moments provide valuable feedback to creators on how to approach multi-homing. There’s no “hard number” telling them: “if you are this big, you can multi-home”. They need to test their audience and understand their behaviour.

5.1.2 “Direct Monetization”

Direct monetization, as a strategy, means that one of the sides of the platform can directly make transactions with another one. It is the case of creators selling directly merchandise to their viewers or brands contacting directly YouTubers for sponsored videos. In these deals, YouTube is not directly involved and does not make a profit out of them. Complementors are effectively cutting the middle- man to enable the transaction.

This strategy has not been previously described in literature as a valuable path that complementors can follow. In fact, in other contexts this scenario eventually led to the death of the platform itself. An example is the case of Homejoy (Christina Farr, 2015). Homejoy was a cleaning service platform that connected homeowners with people willing to clean their houses. It raised 40$M dollars from investors including Google Ventures. Ultimately it was killed by unsustainable customer acquisition costs coupled with poor retention. Homejoy offered deeply discounted first time cleanings deals on sites like Groupon, even though its own internal data showed most of these people never used the service again. Why? Because repeated interaction did not need the platform. After the first service, homeowners were directly in contact with cleaners, and they would simply schedule the next service without the platform.

YouTube, however, is not dying. The platform experienced tremendous growth in the past years. Its network did not shrink as a consequence of “Direct Monetization”. In this scenario, there is an equilibrium. While multi-homing was a hostile strategy towards YouTube, I argue that “Direct Monetization” provides benefits to both the ecosystem and the platform owner. To explore these benefits and understand the overall mechanisms going on in this strategy, both the perspectives of the platform owner and the content creator are considered.

I argue that there are two characteristics that enable this equilibrium and generate advantages for all parties involved. First of all, repeated transactions between the complementors still require YouTube. In Homejoy’s case, the platform was not needed for the cleaner to perform its actions. In YouTube’s case, even sponsored deals between creators and brands require the platform. In fact, YouTube provides hosting for the sponsored video itself and propagates it to the audience of the creator. Even if YouTube is not directly involved into the transaction, its technology is still necessary to enable the deal between its complementors. From this perspective, YouTube is at a net loss. Not only it’s not a part of the transaction, but it also sustains costs to enable it. The complementors act as parasites by taking advantage of the resources provided by the platform without giving back to it, in a similar way to the forking strategy found in literature (Karhu et al., 2018). If that is the case, however, why YouTube does not try to prevent this type of interactions?

I argue, as a second key element for equilibrium, that the possibility for one side to directly monetize the other reduces YouTube’s subsidization costs. There are four key logical steps to follow to understand why this happens:

(5) One of the strongest elements found literature, is the idea of subsidizing one side to achieve critical mass and attract the other one (Armstrong, 2006; Bhargava et al., 2013; Gawer & Cusumano, 2014; McIntyre & Srinivasan, 2017; A. J. Rochet et al., 2006). At first, YouTube incentivizes its creators to make them join the platform. Access to the platform is provided for free and the company invests into knowledge transfer towards the creators. The overall objective is to help them learn about the platform and make sure they can grow. Monetization is not involved yet, and, thus, all the associated policies are not considered, allowing the creators to grow freely on the platform. These governance mechanisms entail some costs for YouTube.

(6) Then, once the creator’s community reaches a critical mass, the creator can start to directly monetize its audience. Within the interviews a “hard number” emerged as most people agreed that around the 100k subscribers, companies start to be interested into sponsored deals and promotions. There are several ways for a creator to take advantage of its community as reported during the results, including merchandise, donations and promoted content.

(7) The possibility to directly monetize the other sides of the platform, is a big enough incentive for the creator to stay in the platform regardless of what YouTube does. This emerged quite well during the interviews. As a wrap-up to the interview, creators were asked: “What if your channel got demonetized? What if YouTube decided you’re not fit for the platform, would you still continue to make videos?”. All the creators interviewed within the “Full-Time Job” phase replied with a sound yes. There will be increased challenges, as part of their revenues will be cut, however even in this extreme scenario, they would be able to sustain themselves without YouTube’s support. Content creators start to rely on their own ecosystem to generate value and ancillary revenues. As they grow, the money received from YouTube is increasingly less relevant.

(8) This scenario allows YouTube to stop subsidizing its creators. There is no need for the company to subsidize complementors as the ecosystem managed by a creator is enough to attract him to the platform. Therefore, governance can be 100% used to promote the company’s goals. Videos are heavily scanned to make sure that they are “advertiser-friendly” and respect all the guidelines. YouTube’s behaviour is extremely autocratic as most decisions are final, non-transparent and heavily automated. Changes to policies can happen at any time and in any way, without ever asking for the opinion of the content creators. The company promotes its interests and goals and, arguably, these policies allow the company to make more money.

These four steps are recapped in Fig 28.

Figure 28 - The core logic that allows YouTube to reduce its subsidization costs

By taking the two elements described together an equilibrium can be reached. On the one hand, complementors will keep using the platform for their deals thanks to its technology, which enables the transaction. On the other hand, the platform owner permits complementors to adopt this strategy, as it allows to reduce subsidization. The keystone firm can enact better control over the platform, strongly aligning the complementors’ goals to that of the company. Complementors will still be attracted to the platform, despite the unfavorable governance, by the possibility of making direct business with one of the sides. Therefore, there is no need for the keystone firm to subsidize their presence. This mechanism, thus, provides an explanation to the first two parts of the “Governance Friendliness” graph as shown in Fig 29.

Figure 29 - "Direct Monetization" explains the observed governance

This strategy provides benefits to both complementors and platform owner. As a complementor, the main benefit reported from this strategy is income stability. Content creators rely less on YouTube for their survival and rather engage with their viewers or brands. Eventually, as they keep growing, they can escape YouTube’s excessive control, threaten to multi-home and gain back power. As the platform owner instead, the profit versus growth dilemma can be solved (Bhargava et al., 2013; Cennamo & Santaló, 2013). Uncertainty on whether platform owners can secure complementors participation, or not, leads to heavy subsidization that cuts into the company’s profit. It is difficult to understand the right level of subsidization necessary to foster growth. In this scenario, however, YouTube can experience growth at marginally zero cost.

As an example, let’s consider the case of a video uploaded on the platform that goes against the “Advertiser friendly Content Guidelines”. The video does not directly generate revenue for YouTube, nor it does for content creators as it is not advertiser friendly. Without the previously described strategy, content creators do not have an incentive to provide this type of content. They will invest effort to create the video without returns. As the guidelines get updated over time, due to pressures from advertisers as described in the paragraph on YouTube’s policies, some content niches might disappear. This would lead to the exit of those creators from the platform along with some of their viewers. By exercising control and aligning the complementors’ goals to the goals of the platform, YouTube would effectively harm its growth and the size of its network. However, if creators can directly monetize one of the sides of the platform, they would still have an incentive to create videos in that niche. Therefore, YouTube would be able to exercise control and increase its profits while not harming its growth. Subsidization is not needed and, thus, YouTube can experience explosive growth at reduced costs.

This mechanism also provides a stabilization effect for the platform. In fact, sudden changes in governance are often the effect of public outcry which forces YouTube’s hands as previously reported. As found in literature (Foerderer et al., 2018; Song et al., 2018) and in the interviews, changes often require the complementors to suddenly adapt. This adaptation disrupts their workflow, can lead to losses and eventually their exit from the platform. By allowing the sides to make direct transactions between them, the financial impact of a change is reduced, and its effects are mitigated. In turn, this reduces the chances of a complementor exiting the platform. From the perspective of the platform owner, this mechanism provides stability to the network from changes. Pressure from outside stakeholders has limited effects on growth. It also has limited effects on the revenues of the platform, as YouTube can enforce its new rules at low cost as previously explained.

This third strategy however poses some potential threats to the platform owner. Specifically, by heavily promoting the growth of its complementors, YouTube also increases their ability to multi- home. The bigger creators get, the higher their capability to influence their audience. Eventually, in the “Independence” phase, they reach a point where they can influence their audience to follow them on other platforms. At the same time, the growth of its complementors is necessary to grow the platform and, thus, it’s an objective that must be pursued. YouTube faces a dilemma where on the one hand creators gaining power over their audiences benefits the platform as YouTube can apply its governance more easily; on the other hand, it increases their ability to escape control.

It could be argued against this potential threat that it might be ok for YouTube to let big creators go. The platform generates the most revenues thanks to its size and the multitude of creators that can keep under control. Potentially, the story of some creators becoming celebrities might inspire other people to join the platform. It’s an effect that feeds back into the system leading to more growth and more people stuck in the “Full-Time Job” phase.

At the same time, creators face increased challenges to stay afloat and achieve financial stability. Although on a macro-level this third strategy ultimately provides benefits to the complementors side, from the perspective of a single content creator they must overcome the disadvantages of tighter governance to build their own business. As shown in the interviews, content creators must actively look for solutions to the unfavorable policies (e.g. demonetization). This aspect entails some costs and increased uncertainty that might lead less capable complementors to exit the platform. Even the set of skills is different. In the “Starting point” phase the focus is on developing video-making skills and produce high quality videos. Later on, on top of these important skills it is also necessary to develop a business attituded, as showed by the interviews. Running a business requires a different skill set from making videos, and not every creator is capable of doing so. Overall, it becomes more difficult to grow on YouTube and be successful.

5.2 Practical Contribution

To effectively pursue “Direct Monetization”, and limit its negative effects, both the complementors and the platform owner can adopt a series of actions. These actions can change according to the different phases in which content creators find themselves.

During the “Starting phase”, creators generate a lot of content, for free, trying to find a niche that is interested. At this stage they don’t have an audience yet and their power over the platform owner is non-existent. They don’t even possess a strategy and their behaviour is amatorial. To be successful in this stage, it is necessary to: quickly find an audience and stick to a “proven formula”. To find an audience it is important to keep exploring different genres and types of videos. Being quick is essential to start monetizing early and reduce exploration costs. Once an audience has been found, it is fundamental to stick to it. The creator should focus on the key elements that generated an initial success and try to replicate them. This would allow the creator to further build its skills and eventually grow a considerable audience.

Once creators have moved to the “Full-time Job” phase, they have enough power to start monetizing the other sides of the platform, but not enough to seriously threaten YouTube of multi-homing. This phase has two main objectives: build ancillary revenues and foster growth. Creators should be proactive in looking for new business opportunities, even if their income is not currently at risk, because new changes to either the policies or the algorithm could happen anytime. It is important to be aware of the dangers of these changes, and start building a “security net”. Over time, the revenues coming from YouTube should be increasingly less relevant, while the revenues generated by exploiting the ecosystem should take priority. However, to keep growing, creators must “play by the rules” (i.e. follow policies). YouTube actively promotes, through its algorithm and policies, only videos that benefit them and are aligned with their goals. In this sense, creators should still care about YouTube control, not because it’s a source of revenue, but rather because it’s the main engine of growth. By disregarding the control imposed by YouTube, their videos would be heavily penalized. It is also important to maintain a strong relationship with the audience. Pushing too much sponsored videos could be negatively perceived by viewers. A balance between what users wants and what pays on the platform should be maintained all the times. Creative and artistic principles should also be factored in. For example, promoting a product through a sponsored video that “doesn't fit” the style of the creator might generate backlash from the community.

If creators keep growing by investing into their content and adapting it to the needs of the platform, eventually they would reach the “Independence” phase. In this phase they have outgrown the platform and can drive the behaviour of their audience. The most important element of this phase is to recognize when “Independence” is achieved. Going “Full-Time” is marked by a very precise moment: the creator chooses to sustain himself exclusively from making videos on YouTube. The “Independence phase” instead can be difficult to recognize and often creators become fully aware of the situation only after an event that shows them the power they possess over the audience. It is important to actively try to engage the viewers onto other platforms, such as Facebook, Twitter or Instagram. If these tries are successful, multi-homing should be fully embraced. The fanbase can also be used to force YouTube’s hand in changing policies. Negative policies might be reversed by generating enough ferment within the community. Through this mechanism, big creators can change policies through negotiation. A recap of the key suggestions towards content creators is provided in Fig 30.

Figure 30 - Key suggestions towards content creators to be successful at each stage of their lifecycle

From the perspective of YouTube, there are three key elements they should pay attention to. The first one is the moment in which content creators go full-time. To take fully advantage of the possibility to push control without affecting the growth of its platform, YouTube must manage correctly the subsidies given to creators. In fact, the amount of money a creator makes when he decides to go full- time should be linked to a big enough community to help him start build its own ecosystem. Too much money and he would be able to go full-time with a smaller community. This scenario would be a menace to both YouTube and the content creator. The creator would be extremely tied to YouTube and sudden changes would lead to its exit from the platform. YouTube on the other hand loses the ability to push for control with low subsidies. Not enough money, on the other hand, might lead the creator to not being able to sustain initial growth as it entails costs for him.

The second element is taking advantage of the ecosystem created by the complementors. The platform owner should be aware of the dynamics between the different sides of the platform. In this way he can pushes for high control at low costs and without stopping growth. Not realizing this perspective removes the strongest benefits for the platform owner, while leaving its complementors to act as parasites on the platform. To do so, it is important to monitor the ecosystem and map the interactions between the different sides.

The third element, finally, is to limit the threats of multi-homing. The platform owner objective should be to keep his complementors within the perimeter of its platform. Growth is a goal of both the complementors and the keystone firm. Given the structure of the platform, growth gives power to the complementors and inevitably can lead to multi-homing. To limit this effect, the platform owner should work on maintaining a technological superiority over its competitors. Despite the transaction between the different sides of the platform, the technology provided by YouTube it’s still at the core of the activities of its ecosystem. As long as it remains effective, it would be hard for the creators, and their viewers, to move onto other platforms entirely. A recap of the three elements that a platform owner should pay attention to is provided in Fig 31.

Figure 31 - Recap of the key elements a Platform Owner should pay attention to

5.3 Limitations and Future research

There are three main limitations of the proposed study and future paths that stem from them.

The first limitation is that in this study there is no data coming from key decision makers within YouTube. The interpretation of the “Direct Monetization” strategy requires to also take the perspective of the platform owner to understand why the strategy exists in the first place. Without direct data coming from the company itself, it is not possible to assess to which degree their behaviour is intentional and strategic. In describing the “Direct Monetization” strategy, I assumed a pretty strong causality effect: direct transactions lead to lower subsidization costs through higher control. One of the original hypotheses behind the study was, instead, that tougher control led creators to build their own ecosystem and make direct transactions. It is unclear in which way causality goes: did creators started first to make transactions, and then YouTube took advantage of the situation realizing it could enforce lower policies? Or did YouTube started to push for control and the ecosystem reacted by developing this strategy? By only taking the viewpoint of creators, the second question seems to be the right one as reported into the interviews. To have a full picture it is necessary to collect data from key decision makers within the company itself. This issue is relevant to understand how to potentially replicate this strategy in other platforms. In one scenario, creators make the first move and the company strategically reacts. In the other scenario, the company makes a strategic decision first and the ecosystem adapts to it.

The second limitation is that no comparisons have been made with other platforms. This is relevant to fully understand why “Direct Monetization” is a valid alternative in this context, while it’s not in other cases. A full set of factors defining in which platforms “Direct Monetization” is applicable or not is missing. I argue that this new strategy exists in this platform because of its own peculiar configuration and design. In fact, starting from the creators’ perspective, and assuming that their goal is to be sponsored directly by companies, their behaviour is similar to those of platform owners. They are in-between viewers and brands. They must subsidize one of their sides to also attract the other one. To their viewers, they offer free content that they like and engage with. This way, creators build their own niche and offer brands the possibility to monetize people who are specifically interested in certain topics. Whenever a creator’s community reaches a certain critical mass, they can start to make sponsorships. From the perspective of YouTube then, the platform is not simply modelled as a keystone firm with three sides (content creators, viewers and brands). But also as a platform that must manage a series of networks within itself: those between the creators and the viewers.

I argue that this type of platform structure allows creators to directly monetize other sides. As a future avenue for research, it could be possible to look at other platforms structured in a similar way, such as Instagram and Twitch, to assess if a similar strategy is in place and why it works. Potentially, platforms with a more traditional structure might behave similarly to Homejoy if “Direct Monetization” was to happen. This issue is relevant because it would help identify in which platforms “Direct Monetization” can be used as a strategic lever to benefit the ecosystem and where, instead, it represents a threat that could kill the whole platform.

A third limitation is that only successful content creators have been interviewed. The lack of failure stories provides a limited picture on which are the critical success factors to properly apply multi- homing. Failure stories could provide insights on what successful content creators are correctly doing. It can help identify the difference between a good approach to diversification from a bad one. As previously mentioned, I proposed four hypotheses as to why a multi-homing strategy could fail. (1) The chosen niche is not big enough; (2) Imitation reproduces behaviour that worked in the past, but not necessarily today; (3) Creators do not follow governance rules; (4) Difficult to understand when multi-homing becomes a viable strategy. Clarifying this issue would provide an insight on how to “win digital platforms” as a complementor. It is also pretty unclear at the moment why creators of a certain size can drive the behaviour of their audience toward a new platform. The bigger the creator is, the higher the chances its multi-homing strategy being successful. Why is that? Which mechanism connects the size of the audience to the ability to multi-home? Understanding this issue is crucial as this unexplained mechanism is the source of power of complementors in this context.

Other potential streams of research don’t stem directly from the limitations of this study, but rather are a way to integrate what has been found with the literature.

Pricing in multi-sided markets is one of the biggest areas of research (Armstrong, 2006; Eisenmann et al., 2006; Hałaburda & Yehezkel, 2016; Rysman, 2018). Within the literature review, several key elements that define the price on platforms have been highlighted. The new proposed strategy of “Direct Monetization” adds a new component to how prices are structured in platforms in the form of direct transactions between the sides. By also associating costs with more favorable policies and extra profits with higher control, it might be possible to reproduce the observed policy curve. A mathematical model describing the behaviour of all involved agents in this scenario would provide insights on what makes this strategy sustainable from an economic perspective and to which degree subsidization is still needed to make the platform function. This research avenue could also provide insights on wether the platform structure is a key element of stability or not as previously argued. In fact, by factoring in the structure of the platform, it could be possible to observe if a platform either reaches stability as in YouTube’s case or if it spirals out of control as in Homejoy’s case.

Also related to structure, it would also be useful to properly classify what YouTube is and other similar platforms are. An interpretation of YouTube as a platform with three sides is provided in the literature review chapter. However, the perspective of YouTube being a platform managing networks (i.e. the content creator – viewer relationship) has not been addressed in literature. It could be described as a “Network of Networks” or as a “Platform for Networks”. These types of organizations are increasingly becoming popular in the digital age. There is no clear definition of what they are and if standard principles applying to platforms are also valid for them. For example, certain digital platforms within the “sharing economy” paradigm might present similar challenges in how they manage the relationship with individuals working for them. For Uber, multi-homing might still be a threat as its riders also work for Lyft or other car sharing companies at the same time. Yet, the multi- homing strategy in that scenario is wildly different from that observed in YouTube. The growth and popularity of certain riders are probably not critical factors. What makes YouTube and similarly structured platforms different from the others? Which are the intrinsic characteristics that define them? In literature a detailed hierarchy and structure of different types of platforms is still missing, despite many trials (Eisenmann et al., 2006; Facin et al., 2016; Thomas et al., 2014).

Following the practice of Constantinides and colleagues (2018), future research avenues are reported in Table 6, highlighting what is missing, why is it relevant and some potential questions.

Future research path Relevancy Potential Research Questions Integrate the perspective Understand if “Direct • Does “Direct Monetization” of complementors on Monetization” can be used as a truly emerge as a response to “Direct Monetization” strategic lever for platform policies? with that of the platform owners. • Can a platform owner design owner. aggressive policies to increase profits while incentivizing “Direct Monetization” to avoid damaging its network? Compare the platform Understanding in which • Which are the key elements with others presenting a contexts “Direct Monetization” that make “Direct similar structure. can be used and where it Monetization” sustainable in a doesn’t work. platform? • Which characteristics should a platform possess to successfully deploy this strategy? Future research path Relevancy Potential Research Questions Understand the • Highlight the differences • Which core skills / abilities perspective of between successful and should a complementor complementors that failed unsuccessful develop to successfully multi- on the platform and why. complementors; home? • Identify key success factors • Which are traps undermining of the multi-homing the ability of a complementor strategy. to multi-home? Understand how “Direct • Understand from an • Does “Direct Monetization” Monetization” affects economic perspective what emerge as a viable strategy pricing in platforms. makes “Direct from the pricing and platform Monetization” successful. structure? • Investigate the interplay between platform structure and “Direct Monetization” Understand how to Understand which principles • What makes YouTube and correctly classify / model from the literature on platforms similarly structured platforms YouTube and similar can be applied to these type of different from the others? platforms. platforms or not Table 6 - Possible future research paths, their relevancy and potential research questions

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7 Appendix

Appendix A – YouTube’s Community Guidelines

Within the following paragraphs, the previously mentioned guidelines will be presented in their main parts, highlighting passages that make their boundaries fuzzy. The “Community Guidelines” are defined as “common-sense rules that will help you steer clear of trouble” (Google, 2018d). They cover 12 main areas:

1) Nudity or Sexual Content

YouTube does not allow pornography or sexually explicit content on the platform. However, a video that contains nudity or other sexual content may be allowed if the primary purpose is educational, documentary, scientific or artistic (EDSA) (Google, 2018j).

2) Harmful or Dangerous Content

YouTube does not allow videos that encourage others to do something that might hurt them unless the video pertains to the EDSA category. Within the guidelines, YouTube also states that: “While it might not seem fair to say you can’t show something because of what viewers might do in response, we draw the line at content that intends to incite violence” (Google, 2018k).

3) Hateful Content

YouTube does not allow content that promotes violence against individuals or groups based on discriminating factors, such as gender identity or religion. YouTube also states that “our products are platforms for free expressions” and that “this can be a delicate balancing act” (Google, 2018h).

4) Violent or Graphic Content

YouTube does not allow violent or gory content that’s intended to be shocking, sensational or gratuitous. Graphic content is still allowed in a news or documentary context, under the condition that enough information is provided “to help people understand what’s going on in the video” (Google, 2018r).

5) Harassment and Cyberbullying

YouTube does not allow abusive videos or comments on its platform. YouTube’s policies encourage people to report an harassment if it “crosses the line into a malicious attack”, but they also state that “in other cases, users may be mildly annoying […] and should be ignored” (Google, 2018g).

6) Spam, Misleading metadata, and scams

YouTube does not allow misleading descriptions, tags, titles or thumbnails in order to increase views. YouTube also encourages thumbnails that are the best representative of the actual content of the video and selecting a sexually provocative thumbnail might result in the removal of the video. YouTube might also remove videos that are deemed deceptive with a potential strike for the uploader (Google, 2018o).

7) Threats

Predatory behavior, including stalking, harassment, and intimidation might result in a permanent ban from the platform. The guidelines on this issue simply state the commitment of YouTube to deal with these situations, but without any further specification (Google, 2018m).

8) Copyright

YouTube only allows the upload of videos that are either made by the content creator or allowed for authorized use. Copyright is considered to be one of the most problematic issued for the platform by YouTube and tools are offered to copyright owners to fight against potential infringements (Google, 2018f).

9) Privacy

YouTube offers tools to request the removal of any video containing the personal information of someone or of a video that was uploaded without that person consent (Google, 2018n).

10) Impersonation

YouTube commits to remove channels that are established to impersonate another channel or individual. Channels pretending to represent a business are not considered under these guidelines, but YouTube still offers reporting forms to address this other issue (Google, 2018l).

11) Child Safety

YouTube commits to safeguarding the emotional and physical well-being of minors, but rely primarily on YouTube community members to report content that they find inappropriate. They advise content creators to keep minors physically safe, do not cause emotional harm and respect a minor’s privacy, but the company acts against abuses only after community reports (Google, 2018c).

12) Additional Policies

Additional policies are smaller policies that cover minor topics such as inactive accounts. Under this cap falls the policy on vulgar language which simply states that: “use of excessive profanity in your video or associated metadata may lead to restrictions” (Google, 2018a).

Appendix B – YouTube’s “Get Discovered”

Within its “YouTube Academy” program, the company offers a series of lessons and training on many topics, including a course called: “Get Discovered” that goes into the details of their recommendation system (YouTube, 2018d). The course is divided into 5 lessons:

1) “Search and discovery on YouTube”

Within this lesson, YouTube states some of the key elements that are considered by their algorithm: title, thumbnail, description, how other viewers “seem to enjoy the video”, likes & dislikes, and a number of comments. For each unique viewer, they also consider what they watched in the past and for how much time. YouTube states that: “Instead of worrying about what the algorithm ‘likes’, it’s better to focus on what your audience likes […] if you do that and people watch, the algorithm will follow”. To find the answers on what the audience likes, YouTube suggest to engage with the channel’s analytics (YouTube, 2018d).

2) “Make effective thumbnails and titles”

Thumbnails and titles are considered by YouTube as the “billboard” of the video. It’s one of the first pieces of metadata viewed and if someone clicks on the video, the algorithm will register that viewers like the video. However, if the video doesn’t deliver on its promise, viewers often immediately leave and the algorithm will punish the discoverability of the video. Overall, YouTube values more content actually being watched rather than simply clicked on (YouTube, 2018c).

3) “ Write smart descriptions”

Descriptions are the core keywords used by YouTube’s search engine for discoverability. On top of that, advertisers can choose to put their advertisement directly on specific videos containing certain keywords or to avoid certain others. YouTube provides an analytics suite to creators to understand which keywords they used are most effective in reaching users, but no information is provided regarding advertisers (YouTube, 2018f).

4) “Let cards and end screens do the work”

Cards are pieces of interactive media, such as polls or other videos, that can be inserted into videos. They are often featured at the end of videos to encourage viewers to watch other content. YouTube positively favors clicks on cards as they show engagement with the audience. Creators can also take advantage of them for other purposes such as promoting merchandising or their brand, however, promotion to external websites might violate community guidelines (Google, 2018o; YouTube, 2018b).

5) “Keep your channel fresh with uploads and playlists”

Frequent updates and content are promoted by the algorithm. YouTube also suggests creators have a regular publishing schedule as a way to: “draw viewers back to your channel”. YouTube, however, also advises for flexibility to quickly respond to trends or timely topics. YouTube also encourages content creators who don’t post regularly to keep engaging with their community through comments. Overall, activity on the platform is promoted by the algorithm (YouTube, 2018a).

Appendix C – Interview Protocol

Structure: 1 - 8 are the main open questions to address first in the interview, a – s are the probing questions to use afterwards.

1. What did you do before entering YouTube? a. How did this influence your decision to enter the platform?

2. How did you decide to enter YouTube? b. How was the process? c. Tools? Courses? Word of mouth? Mentors?

3. When did you decide to focus on YouTube as a full time job? a. What were the main aspects you based the decision on? And why? b. How did you make the decision? c. How many subscribers/views did you have at the time? d. How volatile was your income from YouTube? e. How volatile was your income from other sources?

4. How did your channel grow on YouTube? f. Were there special milestones or events that changed your YouTube trajectory? g. How was growth affected by the interaction with the community? h. Did growth happen steadily or through big jumps after certain videos? i. Which type of investments did you make to foster growth? (e.g. hired a collaborator, bought new equipment etc…) j. What strategies, if any, did you apply to increase growth?

5. How do YouTube’s policy changes affect your income? k. Has the effect changed as your channel grew? l. Did you change your videos’ content to adapt to these changes? m. Did you start to look for alternative platforms to post your content? n. Did you start to rely more on other sources of income (e.g. sponsored videos) as a result of changes?

6. As the channel grew, which new sources of income and opportunities for revenue opened up or did you develop? o. Which services have you used so far? i. Patreon? ii. Selling merchandise? iii. In-video advertisement (e.g. Audible)? iv. Sponsored content videos? v. Affiliation links? vi. Joining a Network of content creators? p. Is YouTube your main source of revenue at the moment?

7. Did you look actively for other sources of income (e.g. deals with companies) or did these sources contact you? q. Do you have an agent/manager who secures deals? r. How do you manage relationships with external companies? s. Have you ever thought of founding your own company?

8. What if your channel got entirely monetized, would you still continue to do YouTube as a full-time job?

Appendix D – Socialblade scraping code

#The following code scrapes data about the top 5000 YouTube Channels from Socialblade #The output is in .csv and can be imported directly into excel

import requests import csv import urllib.request from datetime import datetime from bs4 import BeautifulSoup page = requests.get("https://socialblade.com/youtube/top/5000/mostviewed") soup = BeautifulSoup(page.content, 'html.parser') data_even = soup.find_all('div', attrs= {'style': 'width: 860px; background: #f8f8f8;; padding: 0px 20px; color:#444; font-size: 10pt; border-bottom: 1px solid #eee; line-height: 30px;'}) data_odd = soup.find_all('div', attrs= {'style': 'width: 860px; background: #fafafa; padding: 0px 20px; color:#444; font-size: 10pt; border-bottom: 1px solid #eee; line-height: 30px;'}) top_10_even = soup.find_all('div', attrs= {'style': 'width: 860px; background: #fafafa; padding: 10px 20px; color:#444; font-size: 10pt; border-bottom: 1px solid #eee; line-height: 40px;'}) top_10_odd = soup.find_all('div', attrs= {'style': 'width: 860px; background: #f8f8f8;; padding: 10px 20px; color:#444; font-size: 10pt; border-bottom: 1px solid #eee; line-height: 40px;'}) csv_file = open('Socialblade-data.csv', 'a') rank = [] username =[] subscribers = [] views = [] for item in top_10_odd: try: rank = item.find('div', style="float: left; width: 50px; color:#888;").get_text() except: pass try: username = item.find('a').get_text() except: pass try: subs_views = item.find_all('div', style="float: left; width: 150px;") subscribers = subs_views[0].get_text() subscribers = subscribers.strip('\n') subscribers = subscribers.rstrip() views = subs_views[1].get_text() views = views.strip('\n') views = views.rstrip() except: pass writer = csv.writer(csv_file) writer.writerow([rank,username,subscribers,views]) for item in data_odd: try: rank = item.find('div', style="float: left; width: 50px; color:#888;").get_text() except: pass try: username = item.find('a').get_text() except: pass try: subs_views = item.find_all('div', style="float: left; width: 150px;") subscribers = subs_views[0].get_text() subscribers = subscribers.strip('\n') subscribers = subscribers.rstrip() views = subs_views[1].get_text() views = views.strip('\n') views = views.rstrip() except: pass writer = csv.writer(csv_file) writer.writerow([rank,username,subscribers,views]) for item in data_even: try: rank = item.find('div', style="float: left; width: 50px; color:#888;").get_text() except: pass try: username = item.find('a').get_text() except: pass try: subs_views = item.find_all('div', style="float: left; width: 150px;") subscribers = subs_views[0].get_text() subscribers = subscribers.strip('\n') subscribers = subscribers.rstrip() views = subs_views[1].get_text() views = views.strip('\n') views = views.rstrip() except: pass writer = csv.writer(csv_file) writer.writerow([rank,username,subscribers,views]) for item in top_10_even: try: rank = item.find('div', style="float: left; width: 50px; color:#888;").get_text() except: pass try: username = item.find('a').get_text() except: pass try: subs_views = item.find_all('div', style="float: left; width: 150px;") subscribers = subs_views[0].get_text() subscribers = subscribers.strip('\n') subscribers = subscribers.rstrip() views = subs_views[1].get_text() views = views.strip('\n') views = views.rstrip() except: pass writer = csv.writer(csv_file) writer.writerow([rank,username,subscribers,views])