Author Bianca Haun, BSc

Submission Department of Work, Organizational and Media Psychology

Thesis Supervisor Univ.-Prof. Dr. Bernad Batinic

Assistant Thesis Supervisor Mag.a Fabiola Gattringer

September 2016

LET’S (WATCH ME) PLAY

Which factors relate to Let’s Play addiction?

Master’s Thesis to confer the academic degree of

Master of Science in the Master’s Program

Web Science – Social Web.

JOHANNES KEPLER UNIVERSITY LINZ Altenberger Str. 69 4040 Linz, Austria www.jku.at DVR 0093696

SWORN DECLARATION

I hereby declare under oath that the submitted Master’s Thesis has been written solely by me without any third-party assistance, information other than provided sources or aids have not been used and those used have been fully documented. Sources for literal, paraphrased and cited quotes have been accurately credited.

The submitted document here present is identical to the electronically submitted text document.

Linz, September 1, 2016

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Abstract

With the growing popularity of Let’s Plays, the question arises if this phenomenon carries a similar addictive potential like playing video games or watching TV, as Let’s Plays pose a link between those two pastime activities. Video games, as a traditionally active medium, are transformed into a passive medium through the addition of spectatorship and live streams, whereas the passivity of watching a video or someone else play gets the added possibility of interaction and communication. In order to measure Let’s Play addiction, a Let’s Play Addiction Scale was developed by modifying Lemmens’, Valkenburg’s, and Peter’s (2009) Video Game Addiction Scale. The scale was then used to measure not only Let’s Play addiction but also TV addiction and video game addiction. Through the use of one basic scale, the different addiction potentials can be easily compared. An online survey among 369 participants who watched Let’s Plays in the last three months was conducted. Findings include the participants’ categorization into risk groups, viewers’ motivations for watching Let’s Plays and a viewers’ personality traits breakdown. While more time spent watching Let’s Plays correlates with a higher Let’s Play addiction score, Let’s Play usage alone is not necessarily a predictable indicator for Let’s Play addiction. Let’s Plays were found to pose a higher addiction potential than TV but a lower addiction potential than video games. The results are interpreted and discussed, and future research direction is suggested.

Keywords: Let’s Play, Twitch, YouTube, Addiction, Gaming, TV, Games, Addiction Patterns, User Generated Content, Behavioural Addictions, Video Game Streaming

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Table of Contents

Abstract ...... 3 1. Introduction ...... 7 2. Let’s Plays ...... 8 2.1. History and Development of TV ...... 8 2.2. History and Development of Video Games ...... 9 2.3. History and Development of Let’s Plays ...... 10 2.4. Defining Let’s Plays ...... 12 2.5. Previous Literature about Let’s Plays ...... 13 2.6. Different Let’s Play Experiences ...... 14 2.6.1. Specific Types of Let’s Plays ...... 15 2.6.1.1. eSports ...... 15 2.6.1.2. Speedruns ...... 16 2.6.2. Let’s Plays on Different Platforms ...... 16 2.6.2.1. Twitch ...... 16 2.6.2.2. YouTube ...... 17 2.7. Let’s Play Game Genres ...... 17 3. Theoretical Approach ...... 18 3.1. Addiction ...... 18 3.1.1. TV Addiction ...... 19 3.1.2. Internet Addiction ...... 20 3.1.3. Video Game Addiction ...... 21 3.2. Personality ...... 24 4. Research Model and Hypotheses ...... 25 4.1. Research Question and Model ...... 25 4.2. Hypotheses ...... 26 5. Method ...... 28 5.1. Participants and Procedure ...... 28 5.2. Sample and Demographic Information ...... 29 5.3. Questionnaire and Measurement Scales ...... 30 5.4. Statistical Analysis ...... 36 6. Findings ...... 36 6.1. Hypotheses Testing ...... 36 6.2. Further Findings ...... 42 6.2.1. Correlations ...... 42 6.2.2. Let’s Play Addiction Scale ...... 43

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6.2.3. Categorization into Risk Groups ...... 44 6.2.4. Differences between Risk Groups ...... 45 6.2.5. Moderating Effects on LP addiction ...... 47 6.2.6. Motivations ...... 49 6.2.7. Game Genres ...... 53 6.2.8. Let’s Play Platforms ...... 54 6.2.9. Let’s Play Watching Patterns ...... 55 6.2.10. Let’s Play Producers ...... 58 7. Discussion and Conclusion ...... 60 7.1. Addiction ...... 60 7.1.1. Measuring Addiction ...... 60 7.1.2. Addiction Correlations ...... 61 7.1.3. Let’s Play Usage Pattern ...... 62 7.1.4. Predictors for Let’s Play Addiction ...... 63 7.1.5. Watching Patterns ...... 64 7.2. Motivations ...... 64 7.3. Game Genres ...... 65 7.4. Let’s Play Producer vs. Non-Producer Comparison ...... 65 7.5. An Average Let’s Play Streamer Profile ...... 66 8. Limitations and Suggestions for Future Research ...... 66 8.1. Limitations of the Study ...... 66 8.1.1. Sample Size Issues ...... 67 8.1.2. Vague Items ...... 67 8.1.3. Survey Design ...... 68 8.2. Suggestions for Future Research ...... 68 8.2.1. Length of the Survey ...... 68 8.2.2. Let’s Play Addiction Scale ...... 69 8.2.3. Further Research Questions in the Field of Let’s Plays ...... 69 9. Bibliography ...... 71 Appendix ...... 76

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Table of Figures

Figure 1: Let’s Play Trend ...... 11 Figure 2: Research Model of the Thesis ...... 26 Figure 3: Interaction Effect of Openness ...... 42 Figure 4: Interaction Effect of Escape ...... 48 Figure 5: Interaction Effect of Recreation ...... 49 Figure 6: Let’s Play Platforms ...... 54 Figure 7: Twitch Subscription ...... 55 Figure 8: Let’s Play Watching Pattern ...... 56 Figure 9: Let's Play Watching Pattern - Friends ...... 57 Figure 10: Let’s Play Watching Pattern – Active/Passive ...... 57

Table of Tables

Table 1: Sample Demographics ...... 29 Table 2: Motivations Overview ...... 33 Table 3: Game Genres Overview ...... 34 Table 4: Hypotheses Testing Overview ...... 36 Table 5: Correlation Matrix ...... 38 Table 6: Regression Model for Let's Play Addiction Predictors ...... 40 Table 7: Let's Play Addiction Scale ...... 43 Table 8: Categorization Into Risk Groups ...... 44 Table 9: Differences between Risk Groups ...... 46 Table 10: Motivations Factor Analysis ...... 50 Table 11: Motivations Results ...... 52 Table 12: Distribution of Game Genres by Risk Groups ...... 53 Table 13: Differences between Let's Play Producers and Non-Producers ...... 59

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1. Introduction

Playing video games as a leisure activity has changed a lot since the early beginnings. The video game industry has been in a constant development since then and got more popular than ever. According to the Entertainment Software Association (2015) consumers in the US alone spent $22.41 billion dollars on games, hardware and accessories in 2014. Over the last decades, video games have been faced with a battle for legitimacy, eventually gaining a large following in pop culture. Like in the early stage of video games, new developments and trends inside the sphere of video games have yet to prove their own legitimacy and usefulness - one of these trends being Let’s Plays. With the upcoming of the Web 2.0, various platforms, that allow users to create their own content, like YouTube, Flickr, Wordpress, and Wikipedia became popular. Internet users did not only have the possibility, but rather were encouraged to create their own content. People started to write, make and create food blogs, political blogs, public photo collections, make-up reviews or short videos on vine, and among that user-generated content, people also started recording themselves playing video games. In short, most Let’s Plays can be described as a video or a series of videos in which gamers record themselves playing a video game while commenting on the game and their actions in the game. Viewers watching Let’s Plays (LPs) see the gameplay (usually taking up most of the screen) and a video of the Let’s Player (typically in one of the corners). Originally, the development of Let’s Plays started with screenshot-based playthroughs, which are still produced today, however video-based LPs are the majority. A more detailed explanation of Let’s Plays and various definitions can be found in the second chapter. While it was not unusual to watch other people play video games in the 90s, where a computer in every household was not something that common, it is now often interpreted as boring to watch someone play. With this thesis, I hope to shed some light on the trend of Let’s Plays in general, the viewers’ motivation and personality. However, the main focus lies on the relation between Let’s Plays and the addiction potential compared to addiction potentials for other media - in this case television (TV) and video games - as LPs pose a link between those two. In order to measure the survey respondents’ Let’s Play addiction potential and to break them down into risk groups, a Let’s Play Addiction Scale was developed. With the personal interest in video games and researching video gaming culture and gender swapping in Massively Multiplayer Online Role-Playing Games (MMORPGs) throughout the years of studying, the idea of making the phenomenon of Let’s Play videos the subject of this master thesis arose. As Let’s Play videos are a relatively new trend and not many researches have set foot in this field, it provides the perfect base for a research topic, that fit the web science background of my universitary field of study. This master thesis connects theoretical background with the assessed empirical data. Chapter 2 provides general information about Let’s Plays, starting from the history and

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development of TV (2.1) and video games (2.2) (in which Let’s Plays are deeply rooted) then going to the history and development of Let’s Plays (2.3), including an approach to define Let’s Plays (2.4), a glance about existing literature in the field (2.5), different Let’s Play experiences (2.6) including specific types like eSports and speedruns as well as different platforms, and the different Let’s Play game genres (2.7). Chapter 3 includes a theoretical approach to the construct of addiction (3.1), including TV addiction, Internet addiction and video game addiction as well as personality. This theoretical chapter should provide a basic understanding about the methods and frameworks used in this research field. Chapter 4 provides information about the research question and model (4.1) and poses the hypotheses (4.2) for the thesis. Chapter 5 explains the method that is used for the survey and data. Information about the participants and procedure can be found in 5.1, sample and demographic information can be found in 5.2, questionnaire info and measurement scales in 5.3 and statistical analysis information is explained in 5.4. Chapter 6 lists the findings including hypotheses testing (6.1) and further findings (6.2). These findings are described and analyzed further in chapter 7, the conclusion. Finally, chapter 8 provides the limitations of the study and poses suggestions for future research in this field of study.

2. Let’s Plays

Let’s Plays have their roots stemming from TV and video games. Therefore, the following introduction into the history and development of TV and video games should provide insights of how the Let’s Play phenomenon is interlinked with these two activities. Later in the chapter, existing definitions of the Let’s Play term are discussed and the key aspects of the activity are emphasized. With a constructed definition in mind, a glance into previous literature allows further insights. Let’s Plays in relation to TV and video games are discussed, as well as the different types of video game live streams, Let’s Play platforms and game genres.

2.1. History and Development of TV

First experimental forms of television have emerged in the late 1920s, but it was not until after World War II that television got popular and it was commonplace for people to have a TV set at home. Since then watching television has been a major pastime activity for many people. According to Abramson (2003), one of the main motivations to watch TV is the relaxation aspect. Watching television is a passive activity that technically does not need any viewer's input except turning the TV on once. Television serves various social purposes like providing subjects for conversations or supporting feelings of togetherness, as watching TV is often used as a group activity with family, friends or even strangers at a sports bar, for example. Greenberg (1974) identifies eight clusters of reasons why british children are watching television: to pass time, to forget, as a mean of diversion, to learn about things, to learn about

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themselves, for arousal, for relaxation, for companionship and as a habit, Rubin (1983) uses the categories relaxation, companionship, habit, pass time, entertainment, social interaction, information, arousal and escape to describe viewers’ motivations. Different forms of communication through TV sets have been developed to bring an active social component to television watching. These various approaches can be grouped together by the term Social TV, which is a “label for Interactive TV (iTV) systems that support the sociable aspects of TV viewing” (Harboe, Massey & Metcalf, 2007, p. 116). While most of the early forms of Social TV concentrated on sending text messages, which appeared on the television screen, many television viewers have nowadays created their own Social TV by using a second screen (smartphones, tablets, notebooks, ...) to communicate with others. In various shows, people are asked to participate, ask questions, to vote or share their opinion via social media channels, the network’s own social site, SMS or calling in. Especially Twitter is often used for these kinds of social interactions, due to the easy integration of hashtags in shows, which makes it in return easy to find people’s opinions to a specific show or even a scene. Online streaming platforms like Netflix, Hulu, Amazon Prime or HBO go have made it even easier for people to access TV series and movies on demand, as well as integrated shared reviews, ratings and comments. Not only is the selection of available series and movies on streaming platforms much broader than on traditional television sets with fixed channels, but it also brings watching TV to different devices including smartphones, tablets, PCs or notebooks.

2.2. History and Development of Video Games

Kent (2001) describes the development of video games from earlier arcade games. Video games, in their form we know them nowadays, have emerged from slot machines, which have been on the market since the 1950s. Later on, the first game on an oscilloscope was developed by William Higinbotham in 1958 and the first interactive computer game called Spacewar was created by MIT student Steve Russel in 1961. Atari’s PONG was among the first popular games, next to Pac-Man, Space Invaders, and Donkey Kong. However, the successful journey of video games came to an abrupt pause in the years from 1983 to 1985. That time is also known as the north-american video game crisis. Triggered by the release of various video game consoles and games, which could not hold up to industry standards, sales of video games and consoles plummeted. The video game industry was able to recover from this shock, by releasing quality consoles and games. Console systems remain to be popular, however with the growing demand for PCs in homes and the increased power those offered, games found their way onto PCs. Yee (2007) finds out that the motivations for online gaming can be grouped in three main categories: Achievement, Social and Immersion. Achievement includes the advancement to others, gaining knowledge about the mechanics to optimize the character performance and competing with others. The social component includes socializing with others, building long-term

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relationships and being part of a team. The immersion category includes gaining insider knowledge about the game, using the character to role-play and customizing the appearance of the character. Sherry, Lucas, Greenberg, and Lachlan (2006) study the video game uses and gratifications and categorize them in five basic constructs: competition, challenge, diversion, fantasy, social interaction and arousal.

2.3. History and Development of Let’s Plays

Let’s Plays are strongly related to TV and video games in regards of how they are used and why they are used. Elements of television watching, as well as elements of video gaming can both be found in the phenomenon of watching Let’s Plays. Video games are, in their traditional form, an active medium, however in the case of watching someone else playing video games, the engagement aspect has gone missing. Let’s Plays add an element of passivity to video games, whereas when compared to watching TV, Let’s Plays add an active element - the communication aspect - to the traditional passive activity. YouTube (and similar video portals) can be argued to be a new form of television. Watching YouTube videos can be as passive as watching traditional TV (with the use of playlists) but also allows social aspects like commenting on the videos, communicating with others, sharing videos on social media channels or even creating reaction videos. The videos available on these platforms are user-generated and also include Let’s Plays. Depending on the viewer’s preferences, Let’s Plays can be consumed just like traditional television. Similarities between watching Let’s Plays and traditional TV are the primarily passive component and the relaxation and entertainment motivations for the viewers. Differences can be found in how the video material is produced. TV series and movies have producers, writers and a whole staff, whereas most videos on YouTube, especially Let’s Plays, are often produced by one single person - the content creator. User-created content can be monetized through ads, affiliate links or sponsored opportunities like creating whole videos for a company or working together with a business to promote a certain product. Whereas TV shows and movies have a production budget as well as the possibility to make money after completing the film with fees and licenses. Sometimes there are also in-show monetization strategies like product placements, telephone or SMS votings. Let’s Plays and video games have in common that they are both highly immersive. The story of the game (providing there is one) is still is the same, the only component missing with Let’s Plays is that the viewer does not control the gameplay directly. Video games are an active activity whereas watching Let’s Plays is a passive activity with interactive elements. Some viewers enjoy the lacking interactivity by using LPs passively and streaming them at the same time they are doing other tasks like homework or sports, while others use them more actively and chat with the Let’s Player or other viewers in the chat section. The degree of activeness and

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passiveness of LPs can vary because the viewer does have interaction possibilities like chatting, sending smileys or donating small amounts of money. As with playing video games, there is no limitation when it comes to genres. It is possible to watch Let’s Plays of older classics like Monopoly and chess, role-playing games like World of Warcraft and Dark Souls, First-Person-Shooter like Counter-Strike and Doom or any other genre. However, mainly games played on the PC are streamed or recorded, as it is more challenging to capture the game played on a console, although there is a workaround and a rise of applications allowing screen capturing on consoles. According to a blog post by Slowbeef (2013), the first mentions of the term Let’s Play in the gaming context appeared on the Something Awful Forums1. In 2006, the forum member Michael “SlowBeef” Sawyer started forming what now is known as Let’s Play videos by uploading screenshots with captions as comments of the game Oregon Trail. In 2007, he uploaded videos with actual in-game content and audio commentary of the game The Immortal. Michael Sawyer himself claims to have adopted the format of Vlaphor’s I Have No Mouth and I Must Scream series, which can be found in the Let’s Play Archive. The Let’s Play development was a process that was only made possibly by people starting to express themselves about the subject, sharing their screenshots, videos and views and trying to create something that was valuable and interesting to watch. Without tying the invention of Let’s Plays to a singular person, it was rather a group of motivated people, mainly found in the Something Awful Forums. The creation of Let’s Play videos got rather popular, eventually leading to the creation of its own subforum on the Something Awful Forum in 2007. The trend spread to other sites on the Internet like the video portal YouTube, and even got its own streaming service Twitch.

Figure 1 Let’s Play Trend

Figure 1 shows the development of the Google Trends (2016) search interest for the term “Let’s Play” through the Google search engine from 2005 to april 2016. Since 2010, the search interest for Let’s Plays began to increase tremendously. As of 2014, the search requests for Let’s Play

1 https://forums.somethingawful.com/

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have decreased again, but this does not necessarily mean that Let’s Plays are less popular. A reason for this decline could be the increased viewers’ knowledge about where to find Let’s Plays and them going straight to YouTube or Twitch to watch or stream Let’s Plays there, instead of looking it up on Google.

2.4. Defining Let’s Plays

Let’s Play videos are a relatively new trend in the video game sphere, therefore there is no official definition of the Let’s Play term. However, there are a few various first approaches in a nutshell: • “A Let’s Play is a YouTube video showing a screen captured video of a gaming session wherein the player provides commentary over what is happening.” (White, 2013) • “LPs show a video game being played while the player talks about what they're doing in commentary with video, screenshots or both. Rarely some sections are done "off screen" or sped up, but in most cases the playthrough is a complete run of the game done in informative or humourous style so as to keep your attention.” (The Let’s Play Archive, 2015) • “Let's Play (Sometimes called Learn to play): One or more people that record themselves playing video games through screenshots or captured video (Mostly the latter). [...] Usually Let's Play videos consist of jokes (Good, bad, and/or corny), frustration, and bewilderment by the ones playing. Some also explain gameplay, easter eggs, and general trivia pertaining to the game being played.” (Reddit, 2015)

The approaches from the Let’s Play Archive and the Let’s Play subforum on Reddit have in common that they include Let’s Plays in the form of screenshots, which has been the original type of Let’s Plays, whereas White’s approach only mentions Let’s Plays in the form of a YouTube video. The player’s commentary is typical for Let’s Plays, she or he often tries to entertain the viewers with interesting or funny comments, playing particularily well or lacking exactly that skill. The player allows the viewers to attain insider knowledge about the game or simply the pleasure of seeing other people’s emotions and reactions to the game. The key aspects that arise through studying these three approaches of Let’s Play definitions are: 1) A video game is shown by capturing a video or screenshots, 2) a player who plays the game and comments on the gameplay. Without referring to a particular definition, Let’s Plays can be described as a series of videos in which the streamer records the gameplay of a video game while commenting on the game and/or their actions. The streamer often seeks to entertain the viewers in order to gain

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more followers/subscribers, more views, a broader audience and therefore gains the possibility to generate more income. The viewer sees the video game being played, which is captured using a screen capturing software, the streamer who records themselves while playing and who comments on the gameplay, and - in the case of a streaming platform - a chat box that gives the viewers the possibility to communicate with the streamer and other viewers. The smaller video of the streamer itself is often found in a corner of the gameplay video. This allows the viewer to see the reactions of the streamer and helps to create authenticity and allows further levels of entertainment. The Let’s Play community is broad and diverse and allows an easy entrance for anyone who is interested in live streaming his or her own gameplay. As diverse as the players, so are the reasons for the viewers to watch Let’s Plays (a full list of the main motivations for watching Let’s Plays can be found in chapter 6.2 - further findings). The entertainment aspect is an important one when it comes to Let’s Plays. Streamers comment on their game playing and often provide further information and inside knowledge about the game or tell jokes and make amusing reactions to add to the experience.

2.5. Previous Literature about Let’s Plays

The trend of Let’s Plays is a relatively new one and not many researchers have studied this field of research yet. However, there are three scientific articles exploring the sphere of Let’s Play videos and video game live streams. For many people watching Let’s Play videos are a genuine alternative to playing themselves. A first step to getting to know the motivations of Let’s Play viewers makes Fjællingsdal (2014) by conducting qualitative interviews. He finds that people describe the following aspects positive about the LP experience: Getting to know other people with similar interests, having a platform to discuss this hobby, discovering new games, being part of a virtual community and receiving valuable information that they wouldn’t have access to otherwise. While some participants watch Let’s Plays mainly because of their entertaining commentary, others prefer to hear about the technological or artistic aspects of the games. Further in his study, Fjællingsdal uses a thematic analysis to determine the key themes of the motivational aspects for viewing and producing Let’s Play videos. Through interviewing nine participants online, the researcher finds five major themes of motivations: To socialize online, to entertain themselves, to gain technological competence, to generate an income and to create interpersonal relations with the audience. Let’s Play viewers, who do not produce Let’s Plays themselves, are mainly motivated by the entertainment aspect and the additional knowledge they gain through watching the videos, whereas the gamers / streamers themselves create these videos to establish social networks / communities and further income opportunities. Although the study only covers a small sample size and is therefore lacking data saturation, it is

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one of the first studies in the field of Let’s Plays and is offering a valuable insight into the motivations of Let’s Play viewers and creators. Hamilton, Garretson, and Kerne (2014) describe in their article how communities on streaming platforms are formed and what motivates members. Focusing on the social aspect of the video game streams, the researchers describe most of the streams as participatory communities, which are characterized by their openness as well as their encouragement for participation, mainly on a social level. Through interviewing streamers and viewers alike, they find that most of the viewers start watching Let’s Plays to improve in the game and to gain further knowledge. Some viewers fulfill their emotional need to communicate with others through these new kind of online communities. Many streamers especially seek to include their viewers into the gameplay and to build a strong community. With the growing popularity of Let’s Plays, some streaming communities are arguably getting too big to guarantee interaction. Chat rooms tend to be filled with too many people, therefore some viewers comments are likely to be overlooked in the chat section, making it hard for every member of the community to stand their ground and to be included equally. In this case, the researchers propose to divide these large communities into smaller subdivisions to allow the community to thrive again. Kaytoue, Silva, Cerf, Meira, and Raissi (2012) specialise in the field of eSport live streaming. They make it possible to predict the number of viewers of a streaming session and try to explain audience peaks. The researchers use the streaming platform Twitch to gather the needed data. Twitch promotes streamers with a large viewer base on their frontpage, therefore leading more new viewers directly to these already popular streamers. Popular live streams are expected to highly correlate with future popularity. Examining the top games with the broadest audiences, they find that video games played at a professional level and which are often used in tournaments are more likely to be included on the list. Also many of the top games included are among the top 10 video game sales on Amazon. Tournaments and new game releases usually result in viewership peaks.

2.6. Different Let’s Play Experiences

Let’s Plays is the general term used for various types of gameplay videos. They can be in fact live streamed or pre-recorded. As long as they fit in the Let’s Play definition principles, they fall under the Let’s Play term. In this very broad sphere of Let’s Plays, two specific types are worth mentioning: eSports and speedruns. In terms of where Let’s Plays are being watched, two major platforms dominate the web: YouTube and Twitch. These two platforms could not have been more different in the beginning. While YouTube was primarily known for for watching pre-recorded videos of all kinds including Let’s Plays, Twitch focused on live-streams of only Let’s Plays and eSport tournaments. YouTube has now added an additional focus on video game live streaming, which reduces the

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differences to Twitch, yet they are still different enough to take a closer look at the distinguisihing marks.

2.6.1. Specific Types of Let’s Plays

Let’s Plays are often used as the general term for video game live streams or pre-recorded videos of game sessions. Different kinds of Let’s Plays have emerged. While some people play in highly competitive matches (eSports), others try to finish the whole game as quickly as possible (speedruns). Both genres still count to the group of Let’s Plays. However, the games, the reasons for watching as well as playing, and the audiences may differ substantially. Let’s Plays are not necessarily competitive like it is in eSports and not focused on time like in speedruns. They allow the streamer to get immersed in a game and to explore it. This is very similar to how usually gamers, who do not stream their gameplay, would get to know a game. Therefore it can be a special motivation for the viewers to see the Let’s Player’s reaction to a game they either played themselves before or they haven’t played at all. Let’s Plays are not focused on completing a game without any distractions, but rather allow the player to play the game how he wants it to, often taking unusual routes if those add to the overall experience. The general term of Let’s Plays is often used for all kinds of video game live streams or recordings, also including speedruns or eSports. This means that Let’s Plays are not primarily competitive. A Let’s Player does not have to be skilled in a game to be successful, but rather can be extremely popular by lacking exactly this skill. 2.6.1.1. eSports eSports describe the phenomenon of highly competitively played video games. eSport tournaments are usually broadcasted live and uploaded for people to watch it any time after. In some cases, a moderator takes over the game commentary instead of the gamers themselves. According to De Guzman (2015), stadiums are filled with up to 12,000 fans regularly for various events to watch eSports players compete for the win. For the League of Legends 2014 World Finals, the Sang-am World Cup Stadium in Seoul was filled with 45,000 fans. As Major League Gaming (2015) states on its website, they are the global leader in eSports. They also include MLG.tv, an online broadcast network for professional competitive gaming, with over 180 of the best players, teams and leagues streaming on their very own platform. Games played in eSports usually fall into a few popular game genres: Fighting games (like Street Fighter or Super Smash Bros.), first-person shooters (like Counter-Strike, Doom, Quake, Call of Duty,...), real-time strategy (StarCraft or Warcraft), sports games (FIFA), multiplayer online battle arenas or also called MOBA (Dota, League of Legends, Heroes of the Storm). These games and game genres have proven to be ideal for competitive gamers because they allow either individual or team-based positions.

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2.6.1.2. Speedruns Another form of Let’s Plays is known as speedruns. In speedrun sessions gamers attempt to complete a game as quickly as possible. While it was formerly the skill of practicing a game and getting better at it, it also includes using any bugs that might help skipping game contents. The concept of speedruns has been around long before live-streaming sessions have evolved, but seeing someone else trying to perform a new record in real time instead of a pre-recorded video adds an extra challenge that has helped speedruns to quickly gain popularity through the live- streaming services. According to Smith, Obrist, and Wright (2013), speedruns can often be seen as a community effort because people are sharing their tips on how to skip game contents and on how to complete games as quickly as possible and therefore allowing others to break existing records.

2.6.2. Let’s Plays on Different Platforms

There are many platforms, on which Let’s Plays can be streamed (or uploaded and watched afterwards). The most popular ones being Twitch and YouTube, which will be covered in this chapter, but there are a couple of smaller ones that offer alternatives and are sometimes even created by the Let’s Players themselves. Twitch and YouTube alone differ substantially in their way of how Let’s Plays are streamed or watched. While Twitch focuses on live-streams, YouTube is best known for pre-recorded Let’s Plays. The possibility of interaction amongst the viewers or the opportunity to communicate with the streamer add to the fact that viewers are able to support the streamer via donations, giving the streamer one of many potential sources of income. 2.6.2.1. Twitch According to the Twitch (2015a) website, it was founded in 2011 as a spin-off of Justin.tv, which was a general video portal. Twitch is now known as the most popular social video platform and community for gamers. More than 100 million viewers watch Let’s Plays through Twitch each month, which distributes to around 1.7 million broadcasters. Twitch offers more than just game streamings from broadcasters, it also includes twitch channels for game publishers and developers, as well as channels for editorial staff, streaming of events like press conferences or panels, eSport tournaments as well as charity events. On Twitch, every streamer or broadcaster has their own channel that people can subscribe to, where they can comment and message the gamer privately. While streams are being broadcasted, the viewers have the possibility to interact with the gamer via an integrated chat section. The gamer can then react to the comments made. Therefore viewers can have an impact on the gamer’s decisions and help out if the gamer is stuck in the gameplay somehow. Income can be generated on Twitch through advertisements, channel subscriptions, donations, merchandise, product placements, sponsored sessions (paid by businesses to play a

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particular game) or game affiliates (commission per game sold). Twitch (2015b) offers more information on the regulations on how to make money on their website. To use all the monetization channels, a streamer has to be a Twitch partner, which is linked to specific requirements like having over 500 average viewers per stream or a regular broadcast schedule of at least 3 times a week. 2.6.2.2. YouTube Burgess, Green, Jenkins, and Hartley (2009) state that YouTube was founded by Chad Hurley, Steve Chen and Jawed Karim and went online in 2005. At that time, there were a few competing services aiming to provide a platform to allow easy video uploading, sharing and streaming. YouTube however was able to prevail due to the removal of technical barriers and the implementation of an easy-to-use interface. YouTube is a general video platform, which is also used by Let’s Players to upload their videos. Each user has their own channel that shows all videos uploaded to the platform. YouTube also supports the feature to create playlists, which allows the user to categorize the videos into different themes, games or game genres. The viewers have the possibility to leave comments on the videos, providing that the broadcaster hasn’t disabled the comment section on his channel, but this has no influence on the gameplay itself as these comments are made when the video is already recorded, edited and published. As of 2015, YouTube introduced a new section called YouTube Gaming, which makes it easier for users to find and subscribe to Let’s Plays, and for streamers to do live streams (YouTube, 2015). Through this new focus on gaming, YouTube reduces the differences to Twitch. A search on the general YouTube platform for the term “Let’s Play” brings up over 35 million results in June 2016 and there are even more channels focusing solely on Let’s Plays in the gaming section of YouTube. Monetizing Let’s Play videos through YouTube is possible with the partner program YouTube offers. Income can be generated by including advertisements in the videos either prior, in the middle or after the video or during the video with overlay-ads, as well as game affiliates, sponsored posts or product placements.

2.7. Let’s Play Game Genres

There are Let’s Plays for all different video game genres. In this study, the game genre is one of possible influencing variables of Let’s Play usage and addiction. Therefore, previous literature on game genres was studied to create a list of game genres suitable for the field of Let’s Play. Lucas and Sherry (2004) focus on gender differences in video game usage and observe preferred game genres. Through previous research and consulting video game magazines, gaming websites, game departments and retail stores, the researchers identify thirteen game genres: strategy, puzzle, fantasy/role playing, action/adventure, sports, simulation, racing/speed, shooter, fighter, arcade, card/dice, quiz/trivia and classic board games. The researchers also

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provide descriptions and examples for each game genre. The findings show that female players prefer traditional games like card/dice, classic board games, quiz/trivia, puzzle and arcade, while men prefer physical enactment games including game genres like fighter, shooter, sports and racing/speed. Holtz and Appel (2011) observe the relations between video games, communicational Internet use, online gaming and problematic behavior. The researchers use an adapted list of game genres from Lucas and Sherry (2004). The game genre shooter is re-named to first- person shooter and puzzle, card/dice and quiz/trivia are remodeled to parlor games and activity games, resulting in 11 game genres: simulation, arcade/jump’n’run, action/adventure, first- person shooter, sports, fantasy/role playing, racing, strategy, parlor games, activity games and fighter. A comparison of prefered game genres between adolescents aged 10-12 and adolescents between 13-14 do not reveal significant differences, except for the first-person shooters. Among the younger group, playing first-person shooters is much more uncommon (6%) than in the older group (22%). Let’s Play videos are not limited to games with a story, rather there are Let’s Plays for every available video game genre. However some game genres are more popular for Let’s Play videos than others. Looking at the frontpage of Twitch, some of the most popular game genres on Twitch are horror games because viewers enjoy seeing gamers’ jumpscare reactions, first- person shooters because they offer insider knowledge on recommended strategic positions and real-time strategy games because some people enjoy seeing a game played by a professional and skilled player.

3. Theoretical Approach

Scientific literature on the Let’s Play phenomenon has been, at the time of gathering resources for this master thesis, scarce. Therefore an interdisciplinary approach has been taken to acquire relevant literature and to establish a basic understanding about how addiction can be measured. In this study, various forms of addictions – TV addiction, video game addiction and Let’s Play addiction – are key variables to find out how Let’s Plays relate to TV and video games. A literature review of the concept of addiction and personality will provide a basic understanding, which is important to work with these terms later on and set them in perspective with media usage.

3.1. Addiction

Whereas the literature for Let’s Plays is scarce due to it’s relatively newness, the basic construct of addiction as well as media addiction always have been popular research topics. Addiction in its traditional form is linked to substance abuse like addiction to drugs, alcohol or smoking. Leung (2007) argues that the term of addiction should be broadened to include a greater range

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of behaviors. Grant, Potenza, Weinstein, and Gorelick (2010) describe, that not only substance abuse but also behavioral addictions may lead to influenced behavior. Behavioral addictions include pathological gambling, compulsive buying, Internet addiction, video/computer game addiction, sexual addiction and excessive tanning. Short-term rewards diminish the control over the behavior despite the knowledge of eventual negative consequences. The researchers found that behavioral addictions are similar to substance addictions in the fields of “...natural history, phenomenology, tolerance, comorbidity, overlapping genetic contribution, neurobiological mechanisms, and response to treatment…”. (Grant, Potenza, Weinstein, & Gorelick, 2010, p.1) Griffiths’ (1996) concept of technological addictions includes various activities like online gaming, TV watching and online shopping. A human-machine interaction is the key aspect of these kinds of addictions. Sussman (2012) identifies 16 categories of addictions, such as drugs, food-related, exercise-related, shopping, hoarding, working, technology/communications-related. In the last category fall the addictions prominent in this study: TV addiction, video game addiction and Let’s Play addiction. LaRose, Lin, and Ethian (2003) also see media addiction as a type of behavioural addiction, without an external chemical substance involved. Addicted media consumers feel the need to consume media although it potentially causes negative consequences and the continued use appears out of control and irrational. This habit, as a result of cognitive processes or willful acts, is a vital component of the definition of addiction. The researchers refer to social psychology, in which habit and conscious decision-making are separate processes. LaRose and colleagues analyze the symptoms of media addition as indications of a deficiency in self- regulation. Although the term of media addiction is often criticized in the literature, the researchers are convinced that it best describes the phenomenon of media dependence, without interfering with other articles using the word dependence for the relationship between media sources and consumers. The authors rather propose the redefinition of Internet addiction to deficient self-regulation, which contributes to the habit formation. Although deficient self- regulation has led to the formation of bad media consumption habits, it did not necessarily lead to addictive patterns.

3.1.1. TV Addiction

Sussman and Moran (2013) state that TV addiction is part of the media addictions and classified as a behavioral addiction. Through the Internet, access to TV shows and movies is now easier than ever. Over the last years, traditional TV watching has been extended by streaming services like Netflix, Hulu and Amazon Prime. Frey, Benesch, and Stutzer (2007) describe watching TV as a major human activity. More leisure time resulted in more time spent in front of the TV. The researchers find that people watching a lot of TV are less satisfied with their lives, less satisfied with their financial situation,

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feel less safe, trust others less and feel that they are involved in less social activities than others. People with imperfect foresight and diminished control over their own behaviour are found to be watching more TV than others. The viewing time alone is not an indicator whether someone is addicted to watching television or not. Horvath (2013) summarizes the similarities of TV addiction, substance abuse and dependence disorders. Excessive television watching can jeopardize one’s ability to function in roles at work or home or reduce the likelihood to participate in community activities or sports. TV watching may need increased time to achieve the same desired emotional effect or there may be urges to continue watching even when one tries to stop, which would be signs of withdrawal. It may be hard for heavy TV viewers to stop watching even when they are aware of their dependence, which could lead to important, social or recreational activities to be given up or reduced. Comparing the amount of TV watching by self-identified TV addicts in McIlwraith’s (1998) study with the amount of TV watching by average viewers as of a report of the U.S. Bureau of Labor Statistics (2014), both groups spend about three hours per day in front of a TV. With new ways of watching movies and series including subscription-based videos on demand and more possibilities like mobile viewing, watching TV is even easier to access, a broader selection is offered and it is made more intriguing. Govaert (2014) observes the effects of binge watching on the body. Watching TV allows to quickly acquire positive impulses, therefore releasing dopamine from the brain which gives the viewer a positive feeling. This positive feeling is often linked to various addictions, including substance-related addictions. The easy access to shows and movies, the low cost, not having to leave the house, the improved quality of content, the large selection of movies and shows and the omission of ads contribute to the popularity of TV watching. Through a questionnaire among 197 respondents, the researcher finds that 82.7% of the participants are considered binge- watchers to his definition, which means watching two or more TV shows in one sitting. Sussman and Moran (2013) claim that television dependence is a socially tolerated problem that can result in various potential negative consequences. Legal consequences are unlikely, however heavy TV watching can result in more or less serious problems such as creating political or social biases, increased aggression, attention and cognitive deficits and sleep difficulties.

3.1.2. Internet Addiction

Internet addiction, or often called Internet addiction disorder, problematic Internet use or compulsive Internet use, refers to the addictive pattern or behaviour towards the Internet in general, without relating to particular applications or activities. As Kim and Haridakis (2009) mention in their article, the term Internet addiction has been controversial. The American Psychiatric Association has considered a proposal to include

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problematic Internet use as an addiction, but argues that further investigations would be necessary before including it in the DSM-5 (Diagnostic and Statistical Manual of Mental Disorders). DSM is the standard classification of mental disorders, which is used internationally by health professionals and also for diagnostic and research purposes. While some researchers promote the idea of categorizing problematic Internet use as an addiction (Griffiths, 1999; Young, 1998), others argue that the focus should be on more precise sources than the Internet alone (Walther, 1999). Using the DSM criteria for substance abuse as a basis, Goldberg (1996) concludes four main criteria for measuring Internet addiction: 1) tolerance, 2) withdrawal, 3) craving and 4) negative life outcomes. Griffiths (1998) then adds three additional criteria: 1) salience, 2) mood modification and 3) relapse. The broad term of Internet addiction includes several activities that could be used in a problematic way, for example spending too much time on social media, watching videos for hours straight, playing social media games or other online games and neglecting school/work, uncontrolled online shopping or excessive use of online pornography. Spending a lot of time online or with any of the aforementioned activities does not necessarily mean it’s an addiction, but rather a necessity. The Internet is an essential part of modern life. Many activities that were formerly happening entirely offline like working, talking to colleagues, friends or relatives, watching TV or reading the news are now often done to a large part online. Therefore, it would be careless to assess a variable called “Internet addiction”, which simply accumulates all Internet-related activities. However existing scales or checklists (Goldberg, 1996; Griffiths, 1998; Young, 1998; Scherer, 1997; Brenner, 1997) to measure Internet addiction are a great starting point for creating specific modified scales for measuring Internet-related behavioral addictions such as video game addiction, online gaming addiction, online shopping addictions, online pornography addiction, online gambling addiction or social media addiction. This short excerpt should provide an explanation why Internet addiction is not one of the variables assessed in this survey, as it would seem likely that a thesis about Let’s Plays would include the measurement of Internet addiction as a kind of supra-addiction over various Internet- related behavioral addictions or dependencies. Internet addiction is a too unspecific term and does not provide a useful characteristic of the study’s participants. This study rather focuses on specific Internet-related behavioral addictions, such as video game addiction and Let’s Play addiction.

3.1.3. Video Game Addiction

In the literature problematic video gaming has often been linked to the amount of time spent playing, but it is not agreed upon how many hours of playing are too much. Messias, Castro, Saini, Usman, and Peeples (2011) classify more than 5 hours of play per day as excessive video

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gaming, whereas Huang (2006) defines 10 hours per week as dependent video gaming. Griffiths (2010) uses case studies to show that the time spent in games is not necessarily a reliable indicator for a problematic use of games. When using the time as a criterion for addictive gaming, it is important to take the context into account. Different kinds and game genres of video games have a varying addiction potential. When it comes to MMORPGs (Massively Multiplayer Online Role-Playing Games) for example, Ng and Wiemer-Hastings (2005) find that the social aspects mostly draw users into MMORPGs. Gamers choose to spend some of their time in the games rather than using other forms of socializing such as emails or chat rooms. However, the researchers emphasize that even with high usage times, the gamers are not necessarily addicted as they do not show the behaviors of addicts. As of 2013, Internet Gaming Disorder has been included into the Diagnostic and Statistical Manual of Mental Disorders (DSM-5), Section III as a condition warranting more clinical research. The Internet Gaming Disorder has not been classified as a disorder yet, but the American Psychiatric Association (2013) wants to encourage more research in this field to find out whether it should be included as one in the future edition of the manual. The APA proposes that five or more of the following criteria must be met in a 12-month period: 1) Preoccupation with Internet games, 2) withdrawal symptoms, 3) tolerance, 4) unsuccessful attempts to control the participation, 5) loss of interests in previous hobbies, 6) continued excessive use, 7) deceived family members, therapists or others, 8) using Internet games to escape or relieve a negative mood, and 9) jeopardizing or losing a significant relationship, job, educational or career opportunity because of Internet games. The APA further specifies that the Internet Gaming Disorder can be mild, moderate or severe depending on the level of influence on normal activities. The Internet Gaming Disorder could be seen as rather similar to the gambling disorder as it is the only non-substance-related disorder already included in the DSM. Although there are quite a few research articles on gaming disorder/addiction from Asian countries, where the dependence seems to be a huge problem especially for male adolescents between the age of 12 to 20, more research papers are needed from Europe and America. The proposed consequences by American Psychiatric Association (2013) of Internet Gaming Disorders are severe and can include school failure, job loss, neglected family responsibilities, failed marriages and relationships. King, Haagsma, Delfabbro, Gradisar, and Griffith (2013) analyze and compare 18 different instruments measuring pathological video-gaming. They state that the results in articles in this field of study are broadly inconsistent. The core criteria of video game addiction are proposed as the following: withdrawal symptoms, loss of control and being in conflict with others and/or with school or work commitments. The researchers see the strength of the existing

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measures in their short length and ease of scoring, their excellent internal consistency and validity and their valuable data that can be used to develop standardized norms for the adolescent population. King and Delfabbro (2014) further discover through reviewing quantitative and treatment studies that there are four cognitive factors underlying the Internet Gaming Disorder: 1) beliefs about game reward value and tangibility, 2) maladaptive and inflexible rules about gaming behaviour, 3) over-reliance on gaming to meet self-esteem needs and 4) gaming as a method of gaining social acceptance. The beliefs about game reward value and tangibility also include the overvaluation of game items, virtual currencies or rewards, avatar attachment or even obsession with the character. Compared to the proposed criteria for Internet Gaming Disorder in the DSM- 5, these beliefs are mostly relevant to the preoccupation with Internet games and the loss of interests in previous hobbies. The maladaptive and inflexible rules about gaming behaviour describe how the participants justified their continued engagement in Internet games despite their possibly negative consequences. It includes a sunk cost bias, a behaviour completion, a procrastination on Internet gaming activities and maladaptive rules and decision-making during the plays. These cognitions are most relevant to three Internet Gaming Disorder criteria: Tolerance, unsuccessful attempts to control the participation in Internet games and the continued excessive use of Internet games despite the knowledge of psychological problems. The over-reliance on gaming to meet self-esteem needs includes a gaming self-esteem, expectancy beliefs, beliefs about control, vulnerability and achievement. The researchers argue that these characteristics are most relevant to two criteria of the Internet gaming disorder: Withdrawal symptoms and to use Internet games to escape or relieve a negative mood. As the last category of Internet Gaming Disorder cognition the researchers include: gaming as a method of gaining social acceptance. It includes the social relatedness to other gamers, the need to be ranked higher than others in a competitive online environment to fulfill social needs, the belief that a game protects against experiencing failure in off-screen life and to a sense of belonging, which is prevailing in an online-community. The researchers find the criterion of having “jeopardized or lost a significant relationship, job, or educational or career opportunity because of participation in Internet games” (American Psychiatric Association, 2013, p.795) the most relevant to this category of cognition. Lemmens, Valkenburg, and Peter (2009) develop and validate the Game Addiction Scale based on criteria for pathological gambling as they can be found in the DSM-5. The 21-item version as well as the shortened version with 7 items - are valuable instruments to measure game addiction among adolescents. Both scales show a good concurrent validity across the samples. The initial research tested the Game Addiction Scale only among the Dutch gamers, but it has been used in several more studies (Brunborg, Mentzoni, & Frøyland, 2014; Hussain, Griffiths, & Baguley, 2012; Van Rooij, Schoenmakers, Vermulst, Van Den Eijnden, & Van De

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Mheen, 2011; MacGregor, 2014). The shortened version is used for measuring all three kinds of addictions in this study, with each of the seven items measuring one of the seven underlying criteria proposed in the DSM-5 for pathological gambling addiction (Salience, tolerance, mood modification, withdrawal, relapse, conflict and problems).

3.2. Personality

Personality in relation to video games is an often assessed construct in research articles. Previous studies indicated correlations between anxiety, depression, shyness, loneliness and aggression with technological addictions. Chak and Leung (2004) study the predictors shyness and locus of control for Internet addiction and Internet use. For measuring Internet addiction, Young’s (1998) 8-item questionnaire based on the criteria for pathological gambling according to the DSM-IV is used. Shyness is described as “the fear to meet people and the discomfort in other’s presence” (p. 561). For measuring shyness, the researchers use the 13-item Cheek and Buss Shyness Scale (Cheek & Buss, 1981). The Internet offers an alternative way to approach other people and to communicate without the need for face-to-face situations. Through faceless cyberspaces, alternative personas and informal chat rooms, emotional, psychological and social needs can be met. The findings show that high Internet addiction tendencies correlate with a high shyness score as well as less faith in her/his control over her/his own life. The Big Five Inventory (BFI) model by John, Naumann, and Soto (2008) is arguably the most popular model to measure personality. Each of the five factors is extremely broad and summarizes a larger number of specific personality characteristics. The model includes extraversion, which can be defined as a more outgoing, energetic and talkative personality facet. It includes personality traits like sociability, assertiveness, activity and positive emotionality. The second factor is agreeableness, which implies a prosocial and communal orientation towards other human beings and includes traits like altruism, tender-mindedness, trust and modesty. The third factor is conscientiousness and includes traits like carefulness, self-discipline and reliability. People with a higher conscientiousness are more likely to arrive early or on time, study hard to get good grades in class or double-check papers for typing or spelling errors. Neuroticism, as the fourth factor, includes personality traits like feelings of anxiousness, nervousness, sadness or being tense. This factor contrasts emotional stability with negative emotionality. People with a high score of neuroticism are more likely to have a hard time taking things easy and not getting upset when someone is angry with them. The fifth factor is openness and describes the depth and originality of one’s mental and experimental life. People with a higher score in openness are more likely to learn something simply for the pleasure of learning or enjoy watching informational and educational documentaries.

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The Big Five Inventory model has been proven in various studies to be valuable to measure the personality dimensions of extraversion, agreeableness, conscientiousness, neuroticism and openness in various video game related articles. Markey and Markey (2010) find that a three-trait spherical model of neuroticism, agreeableness and conscientiousness moderate the effects of violent video games and therefore can predict aggression and hostility after playing these kinds of games. The researchers state that individuals scoring higher on the neuroticism scale and lower on the agreeableness and conscientiousness scale are to be most adversely affected by violent video games. Bessière, Seay, and Kiesler (2007) use the Big Five Inventory model to not only measure the personality characteristics of their participants, but also of their ideal selves as well as their virtual characters in massively multiplayer online role player games. The researchers discover that on average, the participants view their characters as more conscientious, extraverted and less neurotic than themselves.

4. Research Model and Hypotheses

4.1. Research Question and Model

In a nutshell, Let’s Plays can be described as videos in which a gamer records himself and the gameplay. The Let’s Player tries to entertain the viewers by making interesting or funny comments, playing extraordinarily well or in a funny way or by reacting to the game otherwise. Let’s Plays have taken video games, which are an active activity in their natural form and have transformed them into a passive activity. Watching someone else play video games might seem boring to some, however, viewers are attracted to it for various reasons, making Let’s Plays rather similar to traditional television e.g. a football match or a volleyball tournament. Let’s Plays, like sporting events, are a game of skill, viewers watch it because some people are extraordinarily well in it or at least they make it interesting enough to watch. Like watching TV, Let’s Plays do have an addiction potential as well. Addictive Let’s Play use can be observed as a behavioral, technological and non- substance abuse addiction or disorder, similar to video game addiction or TV addiction. With intent, the relatively broad and open term of Let’s Plays is used in this study. This includes pre- recorded and live-streamed LPs, whether they are watched on YouTube, Twitch or other similar platforms as well as specific types of Let’s Plays like speedruns, eSports or typical uncompetitive LPs not stressed on time, focusing on entertainment or providing insider knowledge. This study investigates LP watching motivations and personality as possible predictors for LP viewing behavior and LP addiction. The study also aims to examine to what extent the viewers can be sorted into risk groups and explains their usage patterns of watching Let’s Plays and previous addictive behaviors in regards to video games and television.

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Q1: Which factors relate to Let’s Play addiction?

Figure 2 Research Model of the Thesis

The research model, as seen in figure 2, provides an overview of the main variables used in this study. Participants’ usage of watching TV and playing video games is assessed in a first step. For each activity (TV and video games), respondents are asked to fill out a modified addiction test. That way, the usage and addiction can be set in relation. The usage and addiction potential is also assessed for Let’s Plays. By using the same addiction scale, only using slight modifications, the different addictions can be compared. Additionally, it is studied if social motivation or personality traits do have a moderating effect on the relationship between Let’s Play usage and Let’s Play addiction. Personality traits assessed include the Big Five Inventory (openness, conscientiousness, extraversion, agreeableness and neuroticism) as well as shyness. More information about how the factors are measured and assessed in the survey can be found in chapter 5 - Method, the results can be found in chapter 6 - Findings.

4.2. Hypotheses

With the research model in mind, eight hypotheses were formed. H1a and H1b test for the correlation between TV/video game addiction and Let’s Play addiction. H2 checks the correlation between LP usage and LP addiction. H3a and H3b test for the correlation between TV/video game usage and Let’s Play usage. H4 evaluates if social motivation has a moderating effect on the relationship between Let’s Play usage and Let’s Play addiction. H5a and H5b checks for a correlation between personality traits and Let’s Play addiction, and a possible moderating effect of personality traits on the relationship between LP usage and LP addiction. Due to the similarity of watching TV and watching Let’s Plays, it is argued that people having a higher addiction potential for watching TV also do have a higher chance of getting addicted to Let’s Plays and vice versa. Both activities are passive, however they do have a great

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potential for immersion. Co-existing links between addiction to TV and addiction to Let’s Plays are studied and examined for a possible cross addiction. H1a Addictive behaviours of watching TV correlate with a higher Let’s Play addiction score. Let’s Plays are practically video games played by another person, therefore both activities evolve around video games and are in the same area of interest. Therefore, people addicted to video games are more likely to show a greater addiction to Let’s Plays as well. A possible cross addiction of video games and Let’s Plays is suspected. H1b Addictive behaviours of playing video games correlate with a higher Let’s Play addiction score. Griffiths (2010) proposes with the backing of case studies that the time spent on video games is not a reliable indicator for addiction alone and therefore the context of playing should be taken into account. However more time spent on watching Let’s Plays could indicate or result in a higher dependence on Let’s Play videos. H2 A higher Let’s Play watching usage correlates with a higher Let’s Play addiction score. Some people are more likely to spend more time on entertainment in general than others. Therefore a possible link between time spent on TV and time spent on Let’s Plays is suspected. H3a A higher TV watching usage correlates with a higher Let’s Play usage pattern. It is suspected that people, who spend more time playing video games, will also spend more time watching Let’s Plays because their field of interest is very similar. Watching others play video games can either result in more video game playing, when Let’s Plays are used as a source of game information or as a motivator, or less, when Let’s Plays are used as an alternative to playing video games. For the hypotheses, it is suspected that more time spent playing video games correlates with more time spent watching Let’s Plays. H3b A higher video game usage correlates with a higher Let’s Play usage pattern. Human beings, in general, have a need to belong and seek social interactions (Baumeister & Leary, 1995). With the upcoming of the Internet, a lot of these interactions now can be found online via social media, mail, chat or video games. Especially online role-playing games, which prominently feature the ability to communicate and play with other individuals, are often used for the purpose of social motivation. Cole and Griffiths (2007) describe MMO (Massively Multiplayer Online) games as extremely social games and argue that positive social interactions are a necessary success criterion for these games. Ng and Wiemer-Hastings (2005) claim that “it is the social aspects inherent in MMORPGs that draws in the “hard-core” players who show patterns of addiction.” (p.112-113). Video games and Let’s Plays are strongly interlinked, therefore it is argued that social motivation also has an effect on the relationship between the weekly hours spent on watching Let’s Plays and the Let’s Play addiction.

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H4 Social motivation moderates the relationship between the Let’s Play usage and Let’s Play addiction. Personality traits like openness, conscientiousness, extraversion, agreeableness, neuroticism and shyness might be directly linked to Let’s Play dependence. While Rozin and Stoess (1993) doubt that there is an addictive personality, which indicates a general tendency to become addicted, singular personality traits might account for a higher probability of getting addicted to Let’s Plays. H5a Personality traits correlate with Let's Play addiction. In regards to online gaming addiction, Kim, Namkoong, Ku, and Kim (2008) find narcissistic traits not only to be positively correlating with online game addiction score, but also a significant predictor for online game addiction. If personality traits correlate with Let’s Play addiction, it is possible that they are also playing a significant moderating factor in the relationship between the weekly hours spent watching Let’s Plays and the viewers’ Let’s Play addiction score. The hypothesis tests for different personality traits having a moderating effect on the relationship between usage and addiction. The predicted direction is that those being less extraverted, less agreeable, less conscientious, less open, more neurotic and shyer would be more likely to be addicted to Let’s Plays when spending a lot of time watching Let’s Plays. As an example, when a person ranks high on the shyness variable, a high LP usage is more likely to result in a higher LP addiction score. Compared to more extroverted people, whose high Let’s Play usage does not - or to a lesser extent - result in a higher addiction score. H5b Personality traits moderate the relationship between Let's Play usage and Let's Play addiction.

5. Method

5.1. Participants and Procedure

As Let’s Plays are available on the Internet and most of it is consumed online, the viewers, who are also the target group for the survey, are therefore best reached online. Using an online survey for data collection is therefore the obvious and best choice. The online survey was published in English in order to not produce any language barriers or limitations. The online survey was created with the open-source survey tool Limesurvey. The link to the survey, together with a short explanation and a motivational text to participate, was posted on the following platforms and social media channels: reddit/r/LetsPlay, reddit/r/SampleSize, Facebook, reddit/r/gamegrumps, Twitter, reddit/r/mindcrack, Let-Plays.de (in the order of referrals). As an incentive, the participants had the chance to win one of the following prizes: Fallout 4 (a preorder purchase for a popular game), 3 Steam Wallets in the value of 10$ and 3 Humble Bundle Packages (game packages consisting of 5-12 games).

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Through the survey, insights about Let’s Plays and Let’s Play viewers were retrieved. Only participants who have watched Let’s Plays at least once during the last 3 months were eligible to complete the survey. This requirement was stated in the description text when promoting the survey, in the introductory text on the first page of the survey, and participants were asked if they had watched Let’s Plays in the last 3 months. Non-eligible respondents were redirected to the end of the survey. The usage of other media like TV or video games were retrieved but the usage was not a requirement for participating in the survey. 601 people followed the link to the online questionnaire, but only 388 completed it. Partially filled-out surveys were excluded. 15 respondents had to be excluded because the control question had not been checked correctly (“to ensure the data quality please check this item with “agree a little”. Thank you!”), another 2 were removed due to non-variance, a conspicuously short response time and inconsistent responses. During the analysis, 2 additional entries had to be eliminated due to being outliers in the statistical data. The cleaned data set for the analysis consisted of n = 369 responses.

5.2. Sample and Demographic Information

As seen in table 1, among the 369 valid respondents were 302 (82.1%) male participants and 61 (16.5%) female participants. The age ranged from 13 to 42 (M = 22.20, SD = 4.72), 14.1% were under 18 years, 48.0% were between 18 and 23 years, 33.0% between 24 and 30 years, 4.9% over 30 years old. Table 1 Sample Demographics

Demographics N (%) or mean ± SD Age 22.20 ± 4.72 Sex Male 302 (82.1%) Female 61 (16.5%) Other 5 (1.4%) LP producers 193 (52.3%) Weekly hours spent with TV 18.96 ± 23.73 Video Games 31.40 ± 30.62 Let's Plays 18.62 ± 18.69 Average length of LP watching in minutes 61.49 ± 54.80 TV addiction score 1.48 ± 0.50 VG addicion score 2.32 ± 0.76 LP addiction score 1.64 ± 0.61 Shyness 3.04 ± 0.87 Extraversion 2.65 ± 0.87 Agreeableness 3.63 ± 0.62 Conscientiousness 3.16 ± 0.62

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Neuroticism 3.03 ± 0.87 Openness 3.65 ± 0.58

Notes: Non Deviant N = 369 Deviant N: sex = 368; weekly TV usage = 327; weekly VG usage = 367; length of LP session = 368; TV + VG addiction = 367; shyness = 357

While most of the participants were from the United States (55.3%), there were also respondents from the United Kingdom of Great Britain (8.2%), Canada (6.8%), Austria (5.7%), Australia (3.5%), Germany (2.2%) and 28 other nations including Puerto Rico, Singapore, Saudi Arabia, India and Bahrain. 47.7% of the respondents never created and posted a Let’s Play video themselves, whereas 19.5% at least rarely or sometimes and 33.8% often or very often published LPs themselves. The respondents’ average weekly hours spent watching TV were 18.96 (SD = 23.73), playing video games were 31.40 (SD = 30.62) and watching Let’s Plays were 18.62 (SD = 18.69). The average length of a Let’s Play watching session was 61.49 minutes (SD = 54.80). The average TV addiction score was 1.48 (SD = 0.50), video game addiction score was 2.32 (SD = 0.76) and Let’s Play addiction score was 1.64 (SD = 0.61). In terms of personality, on average the shyness scale scored 3.04 (SD = 0.87), extraversion scored 2.65 (SD = 0.87), agreeableness scored 3.63 (SD = 0.62), conscientiousness scored 3.16 (SD = 0.62), neuroticism scored 3.03 (SD = 0.87) and openness scored 3.65 (SD = 0.58).

5.3. Questionnaire and Measurement Scales

The questionnaire (see Appendix for the complete survey) was designed in seven parts: 1) Demographics, 2) General filter questions regarding the participants’ media usage pattern (TV, video gaming and Let’s Plays), 3) TV usage pattern, 4) Video Game usage pattern 5) Let’s Play usage pattern, 6) Motivations to watch Let’s Plays, 7) Personality Traits (including a shyness scale and the Big Five Inventory). Demographic Variables. The first questions assessed the respondents’ age, gender and country in which they are currently living in. Usage Patterns. The participants’ general media usage for TV, video gaming and Let’s Plays helped filtering out any questions and scales that were not applicable for a respondent. Only respondents who checked that they had e.g. watched TV in the last six months were asked the follow-up questions to their TV watching behaviour.

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In reference to Lee, Ko, Song, Kwon, Lee, and Nam (2007), the daily Internet use time, daily TV use time, daily video gaming time and daily Let’s Play viewing time on weekdays and weekends are part of the usage patterns. The daily time spent watching TV, playing video games and watching Let’s Plays on weekdays and weekends was assessed (“How many hours do you spend daily watching TV on weekdays?”). In the process of analyzing the data, a variable of weekly hours spent on each media channel was created by multiplying the daily weekday hours by 5 and adding the daily weekend hours (by 2). Video Game Addiction. In preparation for the survey creation, different addiction scales were studied and compared among each other and to the DSM addiction criteria. The game addiction scale by Lemmens, Valkenburg, and Peter (2009) provided the most suitable base scale, as this scale covers all addiction dimensions mentioned in the DSM, is short and yields a good reliability. Therefore, the shortened game addiction scale consisting of 7 items was used. Each item measured one of the seven underlying criteria proposed in the DSM-5 for pathological gambling addiction: salience (“How often during the last six months did you think about playing a game all day long?”), tolerance, mood modification, relapse, withdrawal, conflict and problems. Participants rated all items on a 5-point scale: 1=never, 2=rarely, 3=sometimes, 4=often, 5=very often. The Cronbach’s α was .81. TV Addiction. In order to identify the extent of TV addiction, the seven items of the Game Addiction Scale by Lemmens, Valkenburg, and Peter (2009) were modified. For the response options a 5-point scale was used with 1=never, 2=rarely, 3=sometimes, 4=often, 5=very often. The scale provided a Cronbach’s α of .75. Let’s Play Addiction. In order to identify the extent of Let’s Play addiction, the 7 items of the Game Addiction Scale by Lemmens, Valkenburg, and Peter (2009) were modified. A 5-point scale with the response options 1=never, 2=rarely, 3=sometimes, 4=often, 5=very often was used. The Cronbach’s α was .80 and showed a good reliability. The scale’s reliability can be improved by adding the additional modified item “How often during the last six months did you find yourself watching Let’s Plays longer than you intended?” from the Internet Addiction Test (Young, 1998) or by adding the two proposed and modified items by King, Haagsma, Delfabbro, Gradisar, and Griffiths (2013) “Would you consider your Let’s Play behavior as problematic?” and “Would someone in your life (friends, relatives, colleagues,...) consider your Let’s Play behavior as problematic?”. However for measuring the addiction scores in the survey, the basic 7-item scale was used. More information on how the Let’s Play Addiction Scale’s reliability could be improved can be found in chapter 6.2.2. Let’s Play Addiction Scale. According to Young (1998), an addiction item is met when it applies to a person during a period of six months. To comply with this criterion, all of the addiction scales used in this survey used the statement “How often during the last six months…?” in the instruction. Although the

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time spent watching Let’s Plays should not be used as a basis for classifying individuals as addicted or not, addicted viewers were expected to spend more time on watching LPs than others. Therefore, a strong correlation between time spent on LPs and the LP addiction scale was considered as evidence of the scale’s concurrent validity. Two ways of measuring Let’s Play addiction have been used: The mean of the modified 7-item Game Addiction Scale and the classification of the participants into risk groups according to the monothetic and polythetic format of meeting addiction criteria. A higher mean score indicated a higher level of Let’s Play dependence. Furthermore, the Let’s Play addiction in a monothetic and polythetic format (Lemmens, Valkenburg & Peter, 2009) was assessed. According to the polythetic format, an item is considered met when a respondent answers at least a 3 (“sometimes”) on a 5-point scale on at least four of the seven items. In the monothetic format, all seven criteria for Let’s Play addiction have to be answered with at least 3 (“sometimes”) in order to be identified as a Let’s Play addict. Let’s Play Motivations. Understanding the key motivational elements for video gaming and television watching helped constructing a scale of items to retrieve the viewers’ motivations for watching Let’s Play videos. The various approaches mentioned in previous literature (chapter 2.5) were adapted to create a list of motivational items as seen in table 2. In order to find out more about the viewers’ motivations for watching Let’s Plays, the list was complemented by Let’s Play-related items. The items were grouped into 12 underlying categories: emotional reflection, entertainment, escape, habitual pass time, knowledge, money, motivation to play, relaxation, review, social offline interaction, social online interaction and involvement. Each of the motivation categories consisted of two items, except motivation to play, which only consisted of one item (“to motivate myself to play a game again“). One of the items (“to entertain myself”) of the entertainment category, as well as both items of the escape and habitual pass time categories were adapted from Rubin (1983) to fit into the field of Let’s Plays. The items of the category social offline interaction were adapted from Sherry, Lucas, Greenberg, and Lachlan (2006). The rest of the categories (emotional reflection, knowledge, money, motivation to play, review, social online interaction as well as involvement) were created for this Let’s Play motivation scale. After obtaining the data from the survey, the motivational items were aggregated into seven categories using a factor analysis: recreation (entertainment & relaxation), pastime activity, game information, money, escape, social online interaction and social offline interaction. The factor analysis helped combining the initial items and finding the main categories. Response options were 1=strongly disagree to 5=strongly agree. For each category the mean of the two items were calculated. For the analyses including the motivations, the eight aggregated factors and their categories’ means were used.

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Table 2 Motivations Overview

Item Category Adapted from Aggregated Factors to see how others play the same Emotional Game Information games I own Reflection because I enjoy experiencing the Emotional Recreation emotional reactions of others in Reflection certain games to entertain myself Entertainment Rubin (1983) Recreation because the Let's Player is funny Entertainment Recreation so I can forget about school / work Escape Rubin (1983) Escape so I can get away from the rest of the Escape Rubin (1983) Escape family or others to pass the time, particularly when I'm Habitual Pass Rubin (1983) Pastime Activity bored Time when I have nothing better to do Habitual Pass Rubin (1983) Pastime Activity Time to gain more knowledge about a Knowledge Game Information game that I own to help me out when I'm stuck at a Knowledge Game Information game to save some money, because I don't Money Money have to buy the game then because I can't afford all the games I Money Money want to watch to motivate myself to play a game Motivation to Game Information again Play because they relax me Relaxation Rubin (1983) Recreation because they are a pleasant rest Relaxation Rubin (1983) Recreation to see new games I might be Review Game Information interested in buying because I want to see someone Review Game Information else's opinion on a game because my friends and I use Let's Social Offline Sherry, Lucas, Social Offline Plays as a reason to get together. Interaction Greenberg, & Interaction Lachlan (2006) because often, a group of friends and Social Offline Sherry, Lucas, Social Offline I will spend time watching Let's Plays. Interaction Greenberg, & Interaction Lachlan (2006) to chat with others Social Online Social Online Interaction Interaction to chat with the Let's Player Social Online Social Online Interaction Interaction to influence the game play Involvement Social Online Interaction to get recognition by the Let’s Player Involvement Social Online Interaction

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Game Genre. The participants were asked to disclose their two favorite games they prefer to watch on Let’s Plays and to assign those games to one of the 14 game genre categories. The game genre options were based on previous research (Holtz & Appel, 2011) and updated with modern game examples and complemented with recent developments. All games and game genres given by the participants were verified by the researcher whether they fit into the correct genre. The top ten featured games on Twitch as of 9th April 2015 (League of Legends, Hearthstone: Heroes of Warcraft, Dota 2, Counter-Strike: Global Offensive, StarCraft II: Heart of the Swarm, Heroes of the Storm, RuneScape, Tom Clancy’s Rainbow Siege, Diablo III: Reaper of Souls, Minecraft) were included into the list, while creating new game genres when they wouldn’t fit into existing ones. The game genres simulation, arcade/jump’n’run, action/adventure, first-person shooter, sports, fantasy/role playing, racing, parlor games and activity games were used from Holtz and Appel’s (2011) list of game genres and partly updated with new descriptions and example games. The game genre strategy was divided into turn-based strategy and real-time strategy. The fighter genre was renamed to beat’em ups, which is the common description in the gaming community. Two game genres were added to the list: sandbox, which includes games that happen in an open world and allowing the gamer to explore freely without too many limitations, and horror, which included games that attempt to frighten or jumpscare the gamers. A complete list of all the game genres used in the survey can be seen in table 3 – Game Genres Overview. A set of 14 key game genres were identified: simulation, arcade/jump’n’run, action/adventure, first-person shooter, sports, fantasy/role playing, racing, parlor games, activity games, turn- based strategy, real-time strategy, beat’em ups, sandbox and horror.

Table 3 Game Genres Overview

Adapted Genre Description Examples from Simulation Games involving a simulation SimCity, The Sims, Holtz & of (close to) real-life activities Rollercoaster Tycoon Appel (2011) Arcade/ Rather simple games requiring Pinball, Super Mario, Limbo, Holtz & Jump’n’Run dexterity and speed Tetris Appel (2011) Action/ Rather complex games Resident Evil, Tomb Raider, Holtz & Adventure involving ‘action elements’ like Grand Theft Auto Appel shooting and fighting in which (2011) you go on an adventure First-Person Games in which you shoot Counterstrike, Battlefield, Call Holtz & Shooter other characters (involving a of Duty, FarCry, Tom Clancy’s Appel first-person perspective) Rainbow Siege (2011) Sports Games based on athletic FIFA Soccer, Pro Evolution Holtz & teams and events Soccer, NBA 2K Series Appel

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(2011) Fantasy/Role Games that let you assume a World of Warcraft, Final Holtz & Playing character role in a typical Fantasy, Gothic, RuneScape, Appel ‘fantasy’ environment Diablo, Reign of Kings (2011) Racing Games that focus on driving Gran Turismo, Need for Speed, Holtz & fast in vehicles Mario Kart Appel (2011) Parlor Games Video game versions of ‘old- Chess, Checkers, Poker, Holtz & time favorites’ Monopoly Appel (2011) Activity Games involving a real-life Sing Star, Guitar Hero, Dance Holtz & Games activity or which are meant to Dance Revolution Appel improve reallife abilities (2011) Beat ‘Em Ups Games that focus on martial Mortal Combat, Tekken, Street arts Fighter Sandbox Games in an open world with Minecraft, Don’t Starve, Rust, nonlinear activities and a focus Life is Feudal on free exploration Turn-based Games that use strategic Civilization, Hearthstone, Strategy planning skills and are turn- Shadowrun based. Real-time Real-time strategy games are League of Legends, Dota 2, Strategy a subgenre of strategy games Starcraft, Command & (RTS) that do not progress in turns Conquer, Starcraft II, Heroes of the Storm Horror Games that attempt to frighten Alan Wake, Five Nights at or jumpscare the gamer Freddy’s, SOMA, Silent Hill

Shyness. The revised 13-item Cheek and Buss Shyness Scale (Cheek & Buss, 1981) was used to measure shyness among the respondents. The participants were asked to rank their agreement with the 13 items using a five-point Likert scale: 1=very uncharacteristic or untrue, strongly disagree, 2=uncharacteristic, 3=neutral, 4=characteristic and 5=very characteristic or true, strongly agree. Four items (3, 6, 9 and 12) were reverse-scored and the mean of the scale was calculated. A higher mean score indicated a higher level of shyness. The Cronbach’s α, which measures the scale’s reliability, was .85. Personality factors. For measuring the personality traits of the participants, the 44-item version of the Big Five Inventory (John, Naumann & Soto, 2008) was used. The BFI consists of five subscales measuring the Big Five personality factors (Extraversion, Agreeableness, Conscientiousness, Neuroticism, and Openness), with 8-10 items per subscale. Survey participants rated each item (e.g. “I see myself as someone who is relaxed, handles stress well”) on a 5-point scale ranging from 1=disagree strongly to 5=agree strongly. Negatively-keyed items were reverse-scored and the means of each subscale were calculated. The Cronbach’s α was .87 for Extraversion, .76 for Agreeableness, .77 for Conscientiousness, .86 for Neuroticism and .76 for Openness.

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5.4. Statistical Analysis

All statistical analyses were performed using SPSS for Mac. For H1 - H3b and H5b correlation analyses were performed. For aggregating the motivations for watching Let’s Plays a principal- components analysis (PCA) with oblique (Oblimin) rotation was conducted. T-Tests were performed for differences between the Let’s Play risk groups as well as Let’s Play producers and non-producers. Following the procedures described in Aiken’s and West’s article (1991), a stepwise multiple regression analysis was conducted to explore significant moderators between Let’s Play usage and Let’s Play addiction, independent variables were centralized prior to creating interaction terms.

6. Findings

The findings include the tested hypotheses, as well as further findings. As the field of Let’s Plays has not been studied in detail yet, the results in further findings try to cover a wide range of untouched relationships and correlations.

6.1. Hypotheses Testing

Table 4 provides an overview of the hypotheses tested and their outcome. Hypotheses H1a to H3b and H5a test for correlations between variables in the research model. Table 5 reveals the correlation matrix and shows the bivariate results for the 21 main variables in this study.

Table 4 Hypotheses Testing Overview

Hypotheses Result H1a Addictive behaviours of watching TV correlate with a supported higher Let’s Play addiction score. H1b Addictive behaviours of playing video games supported correlate with a higher Let’s Play addiction score. H2 A higher Let's Play watching usage correlates with a supported higher Let’s Play addiction score. H3a A higher TV watching usage correlates with a higher supported Let’s Play usage pattern. H3b A higher video game usage correlates with a higher supported Let’s Play usage pattern. H4 Social motivation moderates the relationship between not the Let’s Play usage and Let’s Play addiction. supported H5a Personality traits correlate with Let's Play addiction. partially supported H5b Personality traits moderate the relationship between partially Let's Play usage and Let's Play addiction. supported

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H1a Addictive behaviours of watching TV correlate with a higher Let’s Play addiction score. As expected, bivariate results show that the TV addiction score was significantly related to the LP addiction score (r = .41, p < .01). Thus, H1a is confirmed. H1b Addictive behaviours of playing video games correlate with a higher Let’s Play addiction score. A higher video game addiction score correlates significantly with a higher Let’s Play addiction score (r = .47, p < .01). Thus, hypothesis H1b is also supported. H2 A higher Let’s Play watching usage correlates with a higher Let’s Play addiction score. Bivariate results show a correlation of .36, p < .01, therefore the hypothesis is confirmed. H3a A higher TV watching usage correlates with a higher Let’s Play usage pattern. Bivariate results show a correlation of .30, p < .01 and therefore the hypothesis is supported. H3b A higher video game usage correlates with a higher Let’s Play usage pattern. Bivariate results show a correlation of .33, p < .01. Thus, H3b is confirmed.

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Table 5 Correlation Matrix 21. (.77) 20. (.70) .21*** 19. (.79) .23*** .25*** 18. (.75) .26*** .05 .15** 17. (.77) .13* .24*** .31*** .30*** 16. (.78) .14** .25*** .13** .06 .10 15. (.85) .48*** .20*** .32*** .17** .09 .04 14. (.76) .08 .15** .03 .14** -.01 -.10 -.06 13. (.86) -.09 -.05 .05 .14** .03 .08 .34*** .10 12. (.77) -.32*** .17** .04 -.01 -.02 .01 -.01 -.21*** -.08 11. (.76) .26*** -.19*** .18*** .13* .02 .12* .04 -.03 -.02 .02 10. (.87) .13* .19*** -.42*** .29*** .14** .12* -.06 .05 -.08 -.21*** -.08 9. (.85) -.73*** -.13* -.31*** .62*** -.22*** -.03 .00 .13* .01 .09 .32*** .12* 8. (.80) .26*** -.12* .04 -.16** .23*** -.04 .21*** .15** .44*** .09 .29*** .56*** .15** 7. (.81) .47*** .21*** .00 -.06 -.26*** .15** -.05 .14** .09 .08 .20*** .07 .38*** .02 6. (.75) .43** .41** .17** .01 -.03 -.24*** .20*** -.08 .09 .08 .12* .17** .19*** .29*** .11* 5. .05 .04 .36*** .07 -.07 .03 .07 .05 .01 .13* .13* .29*** .09 .16** .13* .17** 4. .33*** .05 .23*** .02 -.11* .08 -.05 .08 -.08 .03 .02 .07 -.04 .07 .00 -.03 .02 3. .39*** .30*** .25*** -.01 .07 -.07 .05 .04 .14* -.04 .07 .08 .10 .06 .10 .07 -.02 .06 2. .17** .06 -.01 -.06 -.13* -.13 -.12* .09 .05 .17** -.04 .11* .02 .04 -.02 .07 -.11* -.26*** -.15** 1. -.11* .01 .14** -.03 -.10* .14** -.05 -.12* .10 -.01 .05 -.29*** .02 .03 .04 -.05 .00 -.10 -.03 .01 SD - 4.72 23.73 30.62 18.69 .50 .76 .61 .87 .87 .62 .62 .87 .58 .82 .96 .72 .78 1.27 1.08 .94 M - 22.20 18.96 31.40 18.62 1.48 2.32 1.64 3.04 2.65 3.63 3.16 3.03 3.65 1.75 1.71 4.02 3.34 2.63 2.14 3.77 Variable Sex Age Weekly TV Usage (hours) Weekly VG Usage (hours) Weekly LP Usage (hours) TV Addiction Score VG Addiction Score LP Addiction Score Shyness BFI Extraversion BFI Agreeableness BFI Conscientiousness BFI Neuroticism BFI Openness Social Online Interaction Social Offline Interaction Recreation Game Information Money Escape Pastime Activity 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. N = 369, Cronbach's alphas are in parentheses * p<.05; **p<.01; ***p<.001 Items 9 - 14 are personality traits; Items 15 - 21 are motivations for watching LPs

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To test hypotheses H4 and H5b a multiple linear regression was used. All independent variables were centered at their means before creating interaction terms. The control variables age and gender were entered in the first step of the regression analysis, LP usage, motivations and personality traits were entered in the second step and interaction terms were entered in the third step. A significant regression equation was found (F(29, 326) = 12.53, p < .000), with an R2 of .53. Model 1, consisting of the two control variables age and gender, accounts for 2.40% of the variance, while model 2, consisting of the control variables, LP usage, motivations and personality traits, accounts for 48.3% of the variance. Model 3, which additionally includes the interaction terms, provides the best explained variance with 52.7%. With the control variables entered in the first model and LP usage, motivations and personality traits entered at the second step, the R2 change is .46 (F change = 21.49, p < .01). The R2 change for the third model is .04 (F change = 2.33, p < .01). Results of the regression analysis can be seen in table 6.

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Table 6 Regression Model for Let’s Play Addiction Predictors

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H4 Social motivation moderates the relationship between the Let’s Play usage and Let’s Play addiction. A multiple regression model was tested to investigate if the relationship between Let’s Play usage and Let’s Play addiction depends on the motivation for watching Let’s Plays, in this case, social motivation. Social Motivation, as a mean variable of social online interaction and social offline interaction, results in a non-significant relationship with Let’s Play addiction (β = .04, p =.30), the same is true for the interaction effect of social motivation (β = -.05, p =.35). Neither the interaction effect for social online interaction (β = -.05, p = .50), nor the interaction effect for social offline interaction (β = -.01, p = .84) results in a significant moderating effect. Thus, social motivation does not moderate the relationship between LP usage and LP addiction. While none of the socially-related motivations result in a significantly predicting effect on the relationship between Let’s Play usage and Let’s Play addiction, two other motivational categories – escape and recreation - are found to have a significant moderating effect. Detailed findings about those two categories with a moderating effect can be found in the further findings of this chapter. H5a Personality traits correlate with Let's Play addiction. Personality traits include the Big Five (Extraversion, Agreeableness, Conscientiousness, Openness and Neuroticism) and shyness. Bivariate results in table 5 show that extraversion correlates negatively with the LP addiction Score (r = -.12, p < .01). Agreeableness and Let’s Play addiction show a non- significant correlation (r = .04, p = .46). Conscientiousness correlates with a lower LP addiction score (r = -.16, p <. 01). Neuroticism correlates with a higher LP addiction score (r = .23, p < .01). The correlation of openness and LP addiction score is small and insignificant (r = -.04, p = .40). Bivariate results show a significant positive correlation between shyness and the LP addiction score (r = .26, p <. 01). Significantly correlating personality traits are extraversion (negatively), conscientiousness (negatively), neuroticism (positively) and shyness (positively), therefore the hypothesis receives partial support. H5b Personality traits moderate the relationship between Let's Play usage and Let's Play addiction. The assumption was that those being less extraverted, less agreeable, less conscientious, less open, more neurotic and shyer would be more likely to be addicted to Let’s Plays when spending a lot of time watching Let’s Plays. When conducting a multiple regression analysis with Let’s Play addiction as the dependent variable and age, gender, motivations, personality traits and interaction terms entered stepwise as the independent variables, only one of the personality traits shows a significant moderating effect: openness (β = -.10, p < .05). Figure 3 shows the interaction effect for the moderator variable openness. Participants with a higher openness score are more likely to reach a higher LP addiction score when the weekly hours spent on Let’s Plays are low, whereas participants with a lower openness score are more likely to score a lower LP addiction score with a low LP usage. This relationship shifts when looking at high LP usage, participants with a low openness score are more likely to reach a

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higher LP addiction score, whereas participants with a higher openness score are more likely reaching a lower LP addiction score.

Figure 3 Interaction Effect of Openness

6.2. Further Findings

6.2.1. Correlations

Table 5 shows the correlations between the assessed variables. Items 9 to 14 are personality traits, whereas items 15 to 21 are motivations for watching Let’s Plays. Results of internal consistency analyses for each scale are shown in parentheses. The media (TV, video games and Let’s Play) usages, measured as the weekly hours spent with each of the medium, all show significant correlations to each other. Weekly hours spent on watching Let’s Plays correlate significantly with weekly TV usage (r = .30, p <.001), and weekly video game usage (r = .33, p <.001). This indicates that the selection of media usages for this study is relevant and share same characteristics. The same correlation scenario can be found when looking at the addiction scores of TV, video games and Let’s Plays. All of the addiction scores correlate to each other. The Let’s Play addiction score correlates significantly with TV addiction score (r = .41, p <.01) and with the video game addiction score (r = .47, p < .001). These correlations indicate that TV, video games and Let’s Plays are not only related when it comes to the usage, but also when it comes to the addiction potential. Most of the personality traits correlate with each other, except the relationship between openness and neuroticisim, which shows no significant correlation. While none of the personality traits correlate significantly with LP usage, shyness (r = .26, p < .001), extraversion (r = -.12, p <

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.05), conscientiousness (r = -.16, p < .05) and neuroticism (r = .23, p < .001) correlate significantly with the Let’s Play addiction score. These correlations indicate a base relationship between some of the personality traits and the addiction potential, making it even more crucial to test for predicting effects in form of a regression analysis. All of the motivations for watching Let’s Plays except game information correlate significantly with Let’s Play usage at least to a small effect. The same can be said when looking at the relationship between the motivations and the Let’s Play addiction score. The Let’s Play addiction score correlates significantly with social online interaction (r = .21, p < .001), social offline interaction (r = .15, p < .01), recreation (r = .44, p < .001), money (r = .29, p < .001), escape (r = .56, p < .001) and pastime activity (r = .15, p < .01). The motivation to escape everyday life by forgetting about school/work or to get mentially away from family members or others has the strongest correlation with Let’s Play addiction. Strongly immersive video games, especially MMORPGs are often used to help escape the repetitive routine of life.

6.2.2. Let’s Play Addiction Scale

The Let’s Play Addiction Scale used in this study, as seen in table 7, is a modified game addiction scale (Lemmens, Valkenburg, Peter, 2009) consisting of 7 items. The scale shows a good reliability with a Cronbach’s α of .80. For reasons of better comparability with video game addiction (measured with original game addiction scale) and TV addiction (measured with the modified game addiction scale) all addiction measurement instruments are based on the same scale. The mean of the modified Game Addiction Scale has been used to compare the results to the other addiction scales for TV and video games. Table 7 Let’s Play Addiction Scale

Let's Play Addiction Scale Adapted from Cronbach's α ... did you think about watching LPs all day long? Lemmens, Valkenburg, Peter (2009) ... did you spend increasing amounts of time watching Lemmens, LPs? Valkenburg, Peter (2009) ... did you watch LPs to forget about real life? Lemmens, Valkenburg, Peter (2009) ... have others unsuccessfully tried to reduce your LP Lemmens, watching? Valkenburg, Peter (2009) ... have you felt bad when you were unable to watch Lemmens, LPs? Valkenburg, Peter (2009) ... did you have fights with others (e.g. family, friends) Lemmens, over your time spent on watching LPs? Valkenburg, Peter .80 .83 .84

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(2009) ... have you neglected other important activities (e.g. Lemmens, school, work, sports) to watch LPs? Valkenburg, Peter (2009) ... did you find yourself watching Let's Plays longer Young, 1998 than you intended? Would you consider your Let's Play behavior as King, Haagsma, problematic? Delfabbro, Gradisar & Griffiths (2013) Would someone in your life (friends, relatives, King, Haagsma, colleagues,...) consider your Let's Play behavior as Delfabbro, Gradisar problematic? & Griffiths (2013)

Note: The first 8 items started in the survey with "How often during the last six months"

Adding an additional item measuring the length of sessions from the Internet Addiction Test (Young, 1998) in a modified form “How often during the last six months did you find yourself watching Let’s Plays longer than you intended?” into the Let’s Play addiction tests, results in a better reliability - a Cronbach’s α of .83. The two proposed items by King, Haagsma, Delfabbro, Gradisar, and Griffiths (2013) were modified to fit into the Let’s Play Addiction Scale - “Would you consider your Let’s Play behavior as problematic?” and “Would someone in your life (friends, relatives, colleagues, ...) consider your Let’s Play behavior as problematic?”. The correlation between these two items is .51 (p < 0.01) and each of the items correlates significantly with the 7-item Let’s Play Addiction Scale (r = .45, p < 0.01 and r = .41, p < 0.01). Adding these two items to the LP Addiction Scale results in a higher reliability - a Cronbach’s α of .82. In total, the reliability of the Let’s Play Addiction Scale can be raised to .84 when adding the above mentioned item from the Internet Addiction Test and both items proposed by King et al. (2013), resulting in a scale of 10 items.

6.2.3. Categorization into Risk Groups

Table 8 Risk Group Categorization

Risk Groups Television Video Games Let's Plays Low Risk 350 (94.9) 219 (59.3) 293 (79.4) Potential 19 (5.1) 135 (36.6) 73 (19.8) Risk High Risk - 15 (4.1) 3 (0.8) N 369 (100) 369 (100) 369 (100)

Notes: Numbers in brackets are percent

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Table 8 shows an overview of the participants’ categorization into risk groups for their television, video games and Let’s Play watching behavior. Lemmens’, Valkenburg’s, and Peter’s (2009) differentiation between monothetic and polythetic addiction criteria were used to create three different risk groups: Low risk group (does not meet the polythetic criteria), potential risk group (includes participants who were categorized as addicts following the polythetic format, therefore have answered at least four of the seven items of the addiction scale with at least “sometimes”) and high risk group (meet the monothetic criteria and therefore have answered all addiction scale items with at least “sometimes”). Participants show the least risk to get addicted to television. 94.9% fall into the low-risk group, 5.1% in the potential risk group and none in the high-risk group. Whereas regarding video games, 59.3% of the participants are categorized into the low-risk group, 36.6% in the potential risk group and 4.1% in the high-risk group. As for Let’s Plays, 79.4% of the participants are categorized in the low-risk group, 19.8% in the potential risk group and 0.8% in the high-risk group. Due to the small number of participants who could be categorized as addicts according to the monothetic format, the findings only differentiate between two different risk groups: Low risk group (do not meet polythetic criteria) and potential risk group (include participants who were categorized as addicts following the polythetic format and therefore also include the participants who would have met the monothetic criteria as well).

6.2.4. Differences between Risk Groups

T-Test Analysis - an overview of the results can be seen in table 9 - shows significant differences between participants in the low-risk group and participants in the potential risk group for various variables. The average weekly hours spent on watching Let’s Plays differ between low-risk users (M = 15.80, SD = 16.19, n = 293) and potential risk users (M = 29.50, SD = 23.31, n = 76) at the .05 level of significance (t = -4.83, df = 94.55, p < .05).

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Table 9 Differences between Risk Groups .72 .69 .07 .66 .09 .12 .06 .13 Sig. .00* .00* .00* .00* .03* .00* .00* .00* .00* .00* .00* T -.36 -.40 -.43 -.53 1.81 2.20 1.70 -4.83 -4.96 -8.01 -4.01 -4.20 -3.20 -1.57 -8.36 -5.45 -8.61 -1.54 -21.71 df 325 365 365 367 367 367 367 367 355 367 367 367 367 367 94.55 92.02 101.16 233.25 101.29 N 68 75 76 75 75 76 76 76 76 76 76 75 76 76 76 76 76 76 76 SD .64 .73 .45 .94 .63 .58 .83 .62 .87 .94 .39 .81 .91 1.03 1.29 1.14 32.07 38.51 23.31 Potential Risk Group M 1.79 2.90 2.59 2.49 3.66 3.02 3.38 3.55 3.40 2.02 1.86 4.44 3.38 3.32 3.11 3.92 19.88 32.66 29.50 N 259 292 293 292 292 293 293 293 293 293 293 282 293 293 293 293 293 293 293 SD .43 .70 .36 .85 .61 .62 .86 .57 .85 .78 .94 .75 .77 .91 .94 1.21 21.08 28.31 16.19 Low-Risk Group M 1.41 2.17 1.39 2.70 3.62 3.20 2.94 3.67 2.94 1.69 1.67 3.92 3.32 2.46 1.89 3.74 18.72 31.08 15.80

Weekly TV Usage (hours) Weekly VG Usage (hours) Weekly LP Usage (hours) TV Addiction Score VG Addiction Score LP Addiction Score BFI Extraversion Score BFI Agreeableness Score BFI Conscientiousness Score BFI Neuroticism Score BFI Openness Score Shyness Score Social Online Interaction Social Offline Interaction Recreation (Entertainment & Relaxation) Game Information Money Escape Pastime Activity Notes: *p<.05; TV = Television, VG = Video Games, LP = Let's Plays

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All three addiction scores for television, video games and Let’s Plays differ between the two risk groups. A higher mean TV addiction score is found in the group of potential risk users (M = 1.79, SD = 0.64, n = 75), compared to low-risk users (M = 1.41, SD = 0.43, n = 292) at the .05 level of significance (t = -4.96, df = 92.02, p < .05). A higher mean addiction score for video games is also found in the potential risk group (M = 2.90, SD = 0.73, n = 75), compared to the low-risk group (M = 2.17, SD = 0.70, n = 292) at the .05 level of significance (t = -8.01, df = 365, p < .05). A higher LP addiction score, measured with a modified Video Game Addiction Scale, is found in the potential risk group (M = 2.59, SD = 0.45, n = 76), compared to the low-risk group (M = 1.39, SD = 0.36, n = 293) at the .05 level of significance (t = -21.71, df = 101.16, p < .05). Only three of the six assessed personality traits show significant differences between low-risk users and potential risk users: Conscientiousness, neuroticism and shyness. The mean conscientiousness score differs between low-risk users (M = 3.20, SD = 0.62, n = 293) and potential risk users (M = 3.02, SD = 0.58, n = 76) at the .05 level of significance (t = 2.20, df = 367, p < .05). On average, potential risk users tend to be less conscientious than low-risk users. A higher neuroticism score is found in potential risk users (M = 3.38, SD = 0.83, n = 76) than in low-risk users (M = 2.94, SD = 0.86, n = 293) at the .05 level of significance (t = -4.01, df = 367, p < .05). The mean shyness score differs between low-risk users (M = 2.94, SD = 0.85, n = 282) and potential risk users (M = 3.40, SD = 0.87, n = 75) at the .05 level of significance (t = -4.20, df = 355, p < .05). On average, potential risk users tend to be shyer than low-risk users. Three of the seven motivational categories show no significant differences between the two risk groups: Social offline interaction, game information and pastime activity. The mean for social online interaction as a motivation to watch LPs differs between low-risk users (M = 1.69, SD = 0.78, n = 293) and potential risk users (M = 2.02, SD = 0.94, n = 76) at the .05 level of significance (t = -3.20, df = 367, p < .05). On average, the motivation of social online interaction is more prevalent in low-risk users than in potential risk users. The motivation of recreation including entertainment and relaxation differs between low-risk users (M = 3.92, SD = 0.75, n = 293) and potential risk users (M = 4.44, SD = 0.39, n = 76) at the .05 level of significance (t = - 8.36, df = 233.25, p < .05). On average, the motivational aspect of recreation is more crucial for potential risk users. The mean of the motivation to save money differs between low-risk users (M = 2.46, SD = 1.21, n = 293) and potential risk users (M = 3.32, SD = 1.29, n = 76) at the .05 level of significance (t = -5.45, df = 367, p < .05). On average, a higher mean of escape as a motivation is found in potential risk users (M = 3.11, SD = 1.14, n = 76) than in low-risk users (M = 1.89, SD = 0.91, n = 293) at the .05 level of significance (t = -8.61, df = 101.29, p < .05).

6.2.5. Moderating Effects on LP addiction

To predict Let’s Play addiction based on motivations and personality traits, a multiple linear regression was used, which can be seen in table 6. LP usage (β = .21, p < .01), the motivations

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recreation (β = .32, p < .01), money (β = .12, p < .01) and escape (β = .42, p <.01), as well as the interaction terms for the motivation recreation (β = .15, p < .05) and escape (β = .14, p < .01) and the interaction term for the personality trait openness (β = -.10, p < .05) are significant predictors for Let’s Play addiction. For the motivational categories of recreation and escape, significant interaction effects for the relationship between LP usage and LP addiction are found. A high motivation for escape leads in both cases of low LP usage and high LP usage to a higher LP addiction score compared to participants with a smaller motivation to escape real-life, family or friends. The difference in predicting Let’s Play addiction between the two groups of low and high escape motivation is greater when the LP usage is higher. The same is true for the motivational category of recreation. Participants with a higher motivation for recreation are more likely to convert a high LP usage to LP addiction, the interaction effect is smaller for participants with low LP usage, yet still applicable. Figure 4 plots the simple slopes for the motivational category of escape, whereas figure 5 plots the simple slopes for recreation.

Figure 4 Interaction Effect of Escape

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Figure 5 Interaction Effect of Recreation

6.2.6. Motivations

A principal-components analysis (PCA) with oblique (Oblimin) rotation of 23 items from the Let’s Play survey was conducted on data gathered from 369 participants. An examination of the Kaiser-Meyer Olkin measure of sampling adequacy suggested that the sample was factorable (KMO = .770), above the recommended value of .6, and Bartlett’s test of sphericity was significant (χ2 (253) = 3036.77, p < .001). Finally, the communalities were all above .3, further confirming that each item shared some common variance with other items. Given these overall indicators, the factor analysis was conducted with all 23 items. The initial eigenvalues show that the first factor explained 21.21% of the variance, the second factor 12.90%, the third factor 9.10%, the fourth 6.53%, the fifth 5.76%, the sixth 5.43% and the seventh explain 4.61% of the variance. Starting from the eighth component, eigenvalues are below 1.0. The factor analysis resulted in a possible aggregation of the 23 items (with 12 underlying categories) into 7 motivational factors: game information, recreation (entertainment & relaxation), escape, pastime activity, money, social offline interaction and social online interaction. The 7 factors explain 65.55% of the total variance. The factor loading matrix is presented in table 10.

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Table 10 Motivations Factor Analysis

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Four items load onto factor 1. These four items all relate to communication either with other Let’s Play viewers or the Let’s Play streamer. Items also include the chance to influence the gameplay by talking to the streamer, giving him tips or asking him to go in a distinct direction, to pick something up, etc. and to get recognition by the Let’s Player by helping him or supporting him either emotionally or financially. This factor loads onto the communicational online aspect of Let’s Plays. This factor is labelled “Social Online Interaction”. Five items load onto the second factor relating to the recreational motivational aspect to watch Let’s Plays, which includes entertainment and relaxation purposes. This factor consists of various items, such as watching Let’s Plays because the Let’s Player is funny or because they provide a pleasant rest. This factor is labelled, “Recreation”. The six items that load onto the third factor relate to the motivation that Let’s Plays provide useful game information, either for games that the viewers already own or for games they might be interested in buying. This factor is labelled, “Game Information”. The two items that load onto factor 4 identify the motivation to forget about school or work by watching Let’s Plays or to get away from the family or others. This factor is labelled, “Escape”. Two items load onto factor 5 and relate to the motivation of watching Let’s Plays because viewers want to pass some time when they are bored or when they have nothing better to do. This is labelled, “Pastime Activity”. The two items for factor 6 relate to the motivation to watch Let’s Plays to save some money instead of buying and playing the games themselves. This factor was labelled, “Money”. Two items that load for factor 7 identify the motivation to watch Let’s Plays together offline. This includes watching LPs with friends as a reason to get together. This factor is labelled, “Social Offline Interaction”. Table 11 shows the motivations, ranked by their highest means. The 10 strongest single motivations for the respondents to watch Let’s Plays are “to entertain myself” (M = 4.45, SD = 0.70), “because the Let’s Player is funny” (M = 4.34, SD = 0.94), “to see new games I might be interested in buying” (M = 3.98, SD = 1.01), “to pass the time, particularly when I’m bored” (M = 3.94, SD = 0.99), “because I enjoy experiencing the emotional reflections of others in certain games” (M = 3.85, SD = 1.17), “because they are a pleasant rest” (M = 3.80, SD = 1.06), “because they relax me” (M = 3.67, SD = 1.06), “when I have nothing better to do” (M = 3.61, SD = 1.10), “to see how others played the same games I own” (M = 3.60, SD = 1.18), “because I want to know someone else’s opinion on a game” (M = 3.59, SD = 1.05). Further motivations to watch Let’s Plays mentioned by the respondents are to get inspirations for their own LP videos, to use them as a background noise while working, doing homework or sleeping, to alleviate loneliness, to see a game that one wouldn’t want to play themselves and to connect and support befriended Let’s Players.

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As for the new categories, recreation comes through as the strongest motivator (M = 4.02, SD = 0.72), followed by pastime activity (M = 3.77, SD = 0.94), game information (M = 3.34, SD = 0.78), money (M = 2.63, SD = 1.27), escape (M = 2.14, SD = 1.08), social online interaction (M = 1.75, SD = 0.82) and social offline interaction (M = 1.71, SD = 0.96). Table 11 Motivations Results

I watch Let's Play videos... M SD Recreation (Entertainment & Relaxation) (.77) 4.02 0.72 to entertain myself 4.45 0.70 because the Let's Player is funny 4.34 0.94 because I enjoy experiencing the emotional reactions of others in 3.85 1.17 certain games because they are a pleasant rest 3.80 1.06 because they relax me 3.67 1.06 Pastime Activity (.77) 3.77 0.94 to pass the time, particularly when I'm bored 3.94 0.99 when I have nothing better to do 3.61 1.10 Game Information (.75) 3.34 0.78 to see new games I might be interested in buying 3.98 1.01 to see how others play the same games I own 3.60 1.18 because I want to see someone else's opinion on a game 3.59 1.05 to gain more knowledge about a game that I own 3.36 1.23 to help me out when I'm stuck at a game 2.84 1.31 to motivate myself to play a game again 2.65 1.23 Money (.79) 2.63 1.27 because I can't afford all the games I want to watch 2.68 1.40 to save some money, because I don't have to buy the game then 2.59 1.39 Escape (.70) 2.14 1.08 so I can forget about school / work 2.37 1.26 so I can get away from the rest of the family or others 1.91 1.19 Social Online Interaction (.85) 1.75 0.82 to chat with the Let's Player 1.93 1.10 to chat with others 1.86 1.06 to influence the game play 1.74 0.95 to get recognition by the Let’s Player 1.49 0.84 Social Offline Interaction (.78) 1.71 0.96 because often, a group of friends and I will spend time watching Let's 1.78 1.12 Plays because my friends and I use Let's Plays as a reason to get together 1.63 1.00

Notes: N=369, Cronbach's α are in brackets 1=strongly disagree, 2=disagree, 3=neither agree nor disagree, 4=agree, 5=strongly agree

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6.2.7. Game Genres

Survey respondents were asked about their favorite games and their favorite game genres to watch Let’s Plays about. Game Genres with the most mentions are action/adventure (32.0%), fantasy/role playing (14.8%), sandbox (13.9%), first-person shooter (8.4%), arcade/jump’n’run (6.1%), simulation (5.3%), real-time strategy (5.0%), turn-based strategy (4.7%), sports (2.2%), horror (1.4%), parlor games (.8%), beat’em’ups (.8%), activity games (.6%), and racing (0.3%). In the game genre of action/adventure the games Until Dawn, GTA V, Life is Strange and Binding of Isaac are the games mentioned most often. In the genre of fantasy/role playing, the games Dark Souls, Skyrim, Bloodborne and Pokemon are the most popular among the Let’s Play viewers. As for sandbox games, Minecraft is the most popular game amongst the respondents, followed by Garry’s Mod, Super Mario Maker and Fallout. The preferred game genres compared by two groups - low-risk user and potential risk user - can be seen in table 12. Action/adventure is, in both risk groups, the most preferred game genre. For potential risk users, sandbox games (20.3%), first-person shooter (10.8%), fantasy/role-playing (9.5%) and arcade/jump’n’run (8.1%) account for the most popular game genres. Whereas for low-risk users fantasy/role-playing (16.1%), sandbox (12.3%), first-person shooters (7.7%) and real-time strategy games (6.3%) are the preferred game genres. Table 12 Distribution of Game Genres by Risk Groups

Low-risk user Potential risk Total (%) (%) user (%) Most preferred Action/Adventure 90 (31.6) 25 (33.8) 115 (32.0) game genre Fantasy/Role-Playing 46 (16.1) 7 (9.5) 53 (14.8) Sandbox 35 (12.3) 15 (20.3) 50 (13.9) First-Person Shooter 22 (7.7) 8 (10.8) 30 (8.4) Arcade/Jump'n'run 16 (5.6) 6 (8.1) 22 (6.1) Simulation 14 (4.9) 5 (6.8) 19 (5.3) Real-time Strategy 18 (6.3) 0 (0.0) 18 (5.0) (RTS) Turn-based Strategy 13 (4.6) 4 (5.4) 17 (4.7) Sports 6 (2.1) 2 (2.7) 8 (2.2) Horror 5 (1.8) 0 (0.0) 5 (1.4) Parlor Games 3 (1.1) 0 (0.0) 3 (0.8) Beat ‘Em Ups 3 (1.1) 0 (0.0) 3 (0.8) Activity Games 2 (0.7) 0 (0.0) 2 (0.6) Racing 1 (0.4) 0 (0.0) 1 (0.3) N/A 11 (3.9) 2 (2.7) 13 (3.6) Total 285 (100) 74 (100) 359 (100)

Notes: N/A includes responses where participants had no particular preferred game genre

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6.2.8. Let’s Play Platforms

As seen in figure 6, respondents were asked on which platform or website they are watching Let’s Plays. The 369 respondents were asked to check all the platforms they used, so this resulted in 568 mentions. 365 (98.90%) respondents checked YouTube, 179 (48.50%) checked Twitch, 14 (3.90%) checked the Let’s Play Archive and 10 (2.90%) were watching on various other platforms. Other platforms include mentions like hitbox.tv, letsplayindiegames.com, roosterteeth.com, reddit (only picture-based Let’s Plays) as well as the personal sites of Let’s Players or groups of Let’s Players (mostly with embedded videos from YouTube) like achievementhunter.com or teamfourstar.com.

Figure 6 Let’s Play Platforms

Twitch offers a premium service, which allows ad-free browsing on the website, a new emoticon set, badges, new colors for the chat and more storage for LP videos. 6.4% of the respondents were paying for a Twitch subscription at the time of the survey, while 93.6% of the respondents were not subscribed to Twitch’s premium service. Figure 7 shows how much people are spending on Let’s Plays per month. In general, 82.6% of the survey participants claim not to be spending any money on Let’s Plays, while 12.2% responded spending $1 to $20 monthly on Let’s Plays, 2.2% spent $21 to $40, 0.8% of the respondents spent $41 to $60 and 2.2% spent more than $60 on Let’s Plays per month, while the highest amount spent on Let’s Plays is 200$ per month. All claims to be spending more than $30 for Let’s Plays came from respondents who also said that they would at least sometimes create Let’s Play videos themselves. Therefore, in those cases, it is not clear if the money spent on Let’s Plays is actually is used to watch Let’s Plays on various platforms, for

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subscription services, donations or if the money is spent to create Let’s Plays e.g. spent on camera equipment.

Figure 7 Money Spent on Let’s Plays

6.2.9. Let’s Play Watching Patterns

The term of Let’s Play Watching Pattern is used in this study for a series of items including if Let’s Plays are watched alone or together with friends, and in case they are watched with friends if the friends are in the same room or not, if Let’s Plays are watched actively or passively and if Let’s Plays are a topic to be discussed in school/work/etc. A first overview provided crosstabulation analysis, to test for significant differences between risk groups T-Tests were later on performed. Regarding with whom the respondents watch Let’s Plays, 82,9% of the respondents watch Let’s Plays primarily (over 80% of the time) alone, 3.8% watch them primarily with friends and 13.2% watch them sometimes with friends and sometimes alone, as seen in figure 8.

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Figure 8 Let’s Play Watching Pattern

Comparing users in the low-risk group with potential risk users, the risk groups show a similar watching pattern. Most of them watch Let’s Plays primarily alone (84% of low-risk users, 78.9% of potential risk users), 3.8% of the low-risk users and 3.9% of the potential risk users watch primarily with friends, 12.3% of the low-risk users and 17.1% of the potential risk users watch Let’s Plays sometimes with friends and sometimes alone. Furthermore, the survey respondents were asked if they were watching Let’s Plays with friends in the same room or not in the same room, of which the results can be seen in figure 9. An additional T-Test analysis actually show a significant difference between low-risk users and potential risk users for the following statistics; t(51)=-2.68, p = .01. 20.8% (27.5% of the low-risk users, 0% of the potential risk users) are primarily watching Let’s Plays with friends in the same room, 75.5% (67.5% of low-risk users, 100% of potential risk users) opted for sometimes with friends in the same room, sometimes not in the same room. 3.8% of the participants (5% of low- risk user and 0% of potential risk users) are watching Let’s Plays primarily with friends not in the same room.

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Figure 9 Let’s Play Watching Pattern - Friends

A T-Test analysis shows a significant difference, t(367)=3.261, p < .01, between the low-risk user (M = 2.17, SD = 0.87) and the potential risk user (M = 1.80, SD = 0.86) when they were asked whether they are watching Let’s Plays more actively or more passively. Active LP use means that an individual watches Let’s Plays pretty much without any distractions, while passive LP watching happens while doing other tasks such as doing homework or working out. A correlation analysis shows a weak negative, but significant correlation (r = -.159, p < 0.01) between the risk group and active/passive watching behavior. In the sample, the participants were divided between active viewers, mixed viewers and passive viewers, as well as low-risk

Figure 10 Let’s Play Watching Pattern – Active/Passive

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users and potential risk users. As seen in figure 10, 47.4% of the low-risk watch Let’s Plays primarily passively, whereas among the potential risk user group, only 28.9% watch them while doing other tasks. Participants who watch Let’s Plays mixed, sometimes active, sometimes passive, account to 21.9% of the low-risk users and 22.4% of the potential risk users. Active Let’s Play watching patterns are seen in 30.7% of the low-risk users and 48.7% of the potential risk users. According to respondents’ answers, Let’s Plays show the least potential to be discussed offline e.g. in school, lunch breaks, at work or when meeting with friends (M = 2.27, SD = 1.00) compared to talking about TV series or shows (M = 2.97, SD = 0.95) or video games (M = 3.65, SD = 0.97). Comparing the Let’s Play watching patterns between the low-risk group and the potential risk group, a T-Test showed a significant difference (t(103.57)=-4.32, p = .00). Potential risk users are more likely to talk about Let’s Plays with their friends, co-workers and family (M = 2.75, SD = 1.12) than low-risk users (M = 2.15, SD = 0.93).

6.2.10. Let’s Play Producers

A comparison between the group of Let’s Play producers (who claim to produce at least sometimes Let’s Plays) and non-producers show a significant differentiation between almost all personality traits. Producers score higher on the extraversion scale (M = 2.82, SD = 0.80, t(367) = -4.01, p < .001), higher on the agreeableness scale (M = 3.70, SD = 0.60, t(367) = -2.22, p < .05), lower on the neuroticism scale (M = 2.94, SD = 0.84, t(367) = 2.16, p < .05), higher on the openness scale (M = 3.72, SD = 0.56, t(367) = -2.39, p < .05), as well as lower on the shyness scale (M = 2.93, SD = 0.86, t(355) = 2.43, p < .05). Conscientiousness is the only personality trait that does not result in significant differences between producers and non-producers. Producers are also found to spend more hours (M = 20.50, SD = 20.29, t(367) = -2.03, p < .05) watching Let’s Plays than non-producers (M = 16.58, SD = 16.58, t(367) = -2.03, p < .05). The results of the T-Test analysis can be seen in table 13.

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Table 13 Differences between Let’s Play Producers and Non-Producers

Sig. .471 .547 .000* .027* .031* .018* .016* .043* T -.72 -.60 2.16 2.43 -4.01 -2.22 -2.39 -2.03 df 367 367 367 367 367 355 367 367 N 176 176 176 176 176 168 176 176 SD .91 .63 .64 .90 .60 .88 .64 16.58 Non-Producer M 2.47 3.55 3.14 3.13 3.57 3.15 1.62 16.56 N 193 193 193 193 193 189 193 193 SD .80 .60 .60 .84 .56 .86 .59 20.29 Producer M 2.82 3.70 3.19 2.94 3.72 2.93 1.65 20.50 BFI Extraversion Scale Score BFI Agreeableness Scale Score BFI Conscientiousness Scale Score BFI Neuroticism Scale Score BFI Openness Scale Score Shyness Scale Score Weekly LP hours LP Addiction Score *p<.05

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7. Discussion and Conclusion

The phenomenon of Let’s Plays has quickly gained traction in the web focused on user- generated content. Low barriers to get started with streaming Let’s Plays and the presence of Let’s Play streamer’s success stories, many gamers started to their own Let’s Play channels – and even more watch them. This study primarily focuses on exploring the Let’s Play viewers’ motivations and personality traits and and how these variables can predict Let’s Play addiction and the relationship between LP use/addiction and other related media use. In addition, the study provides further results including a risk group categorization, a close look at game genres and different Let’s Play platforms, how Let’s Play watching patterns differentiate as well as comparisons between Let’s Play producers and non-producers.

7.1. Addiction

Addiction is the key construct in this thesis. While traditional forms of addiction are linked to substance abuse like alcohol, drugs or smoking, the addictions in this study are categorized as behavioral addictions (Grant, Potenza, Weinstein & Gorelick, 2010), technological addictions (Griffiths, 1996) and media addictions (LaRose, Lin & Ethian, 2003). TV addiction, Video Game addiction and Let’s Play addiction are all measured with the same basic addiction scale modified from the Game Addiction Scale by Lemmens, Valkenburg, and Peter (2009). Internet addiction has not been included in the list of assessed addictions in this study as it is merely a conglomerate of various addictive potentials on the Internet such as online shopping, social media, or online pornography. However, studying the literature about Internet addiction has helped choosing a suitable addiction scale using the DSM criteria for pathological gambling as a basis: salience, tolerance, mood modification, withdrawal, relapse, conflict, and problems.

7.1.1. Measuring Addiction

The addiction potentials for TV, video games and Let’s Plays were measured using the shortened 7-item Game Addiction Scale by Lemmens, Valkenburg, and Peter (2009). The items have been modified to fit for TV and Let’s Plays. By using the same measurement scale, it was possible to compare the risk of potential addictions for the aforementioned entertainment activities. In this survey, out of 369 participants, 94.9% fall in the group of low risk TV addiction and only 5.1% show a potential risk for TV addiction. In regards to video games, 59.3% of the participants fall into the low risk group video game addiction group, 36.6% show signs of potential risk and 4.1% are categorized as high risk for video game addiction. When it comes to Let’s Plays, 79.4% of the participants fall into the low risk Let’s Play addiction group, 19.8% show signs of potential risk and 0.8% are categorized into the high risk group for Let’s Play addiction. Due to the small sample of high risk candidates, the potential risk group and high risk

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group were combined when compared to the low risk group. In this survey sample, television shows by far the least potential for addiction, whereas the largest addiction potential is identified for video games. Television, as a passive activity, is often watched while doing other tasks like sports or doing homework, therefore being less immersed in the shows and movies is connected with a weaker addiction potential. Let’s Plays can be found - in regards to the addiction potential - between TV and video games. This result has been expected, because theoretically, Let’s Plays are a link between TV and video games. Let’s Plays could be seen as interactive television or passive watching of video games, therefore the addiction potential ranking was expected by the researcher. Depending on the method used measuring game addiction, Lemmens, Valkenburg, and Peter (2009) report 2% of their sample being addicted to video games when the monothetic format is applied and 9% of their sample being addicted when the polythetic format is used. Addiction, according to the monothetic format, means that all seven items of the addiction scale had to be checked with at least “sometimes”, whereas addiction under the polythetic format means that a minimum of four items of the addiction scale had to be answered with at least “sometimes”. In this survey, 4.1% of the sample are addicted to video games when the monothetic format is applied and 36.6% show signs of addiction when the polythetic format is used. In other studies, reported numbers of video game addicts, measured with the polythetic format, include 16% (Griffiths, 1997), 20% (Griffiths & Hunt, 1998), and 39% (Charlton & Danforth, 2007). A monothetic format applied to Charlton’s and Danforth’s (2007) sample results in 1.8% video game addicts. The percentage of video game addicts in this survey compared to previous studies is high, especially when the monothetic format is applied. Taking into account, that all of the participants have had experiences with Let’s Plays in the last six months as a requirement to fill out the survey in the first place, and therefore are more likely to have a stronger involvement in video games compared to other players, these percentages seem plausible.

7.1.2. Addiction Correlations

The findings of the online survey partly support the hypotheses posed. Hypothesis 1a, that addictive behaviours of watching TV correlate with a higher Let’s Play addiction score, and Hypothesis 1b, that addictive behaviours of playing video games correlate with a higher Let’s Play addiction score, were tested with a correlation analysis and were both supported. Hypothesis 2 tested for a higher Let’s Play watching usage correlating with a higher Let’s Play addiction score. This hypothesis was supported, as the results can be seen in the correlation matrix table 5. Hypothesis 5a, that personality traits correlate with Let’s Play addiction, were partly supported, as only four of the six personality traits show a significant correlation: shyness, extraversion, conscientiousness and neuroticism.

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The correlation analysis results indicate that weekly hours spent watching LPs, TV addiction score, video game addiction score, the personality traits shyness and neuroticism as well as the motivations social online interaction, social offline interaction, recreation, money, escape and pastime activity were significantly positively correlate with Let’s Play addiction, whereas extraversion and conscientiousness are significantly negatively correlated with Let’s Play addiction. More time spent watching Let’s Plays indicates a higher Let’s Play addiction potential, however, the hours spent in front of Let’s Plays alone are not enough to differentiate between problematic and unproblematic Let’s Play usage. Connections between the assessed addictions of Let’s Play, TV and video games are found. They all correlate positively, meaning that participants with higher TV and video game addiction scores are more likely to have a higher Let’s Play addiction score. Higher values of the personality traits shyness and neuroticism are linked to a higher Let’s Play addiction score, while higher values of extraversion and neuroticism have the opposite relationship. The correlation matrix (table 5) shows that all viewers’ motivations except game information show a link to Let’s Play addiction. If the motivation is stronger, the addiction score is higher. An explanation why game information does not act like the other motivations in terms of Let’s Play addiction potential, could be that when Let’s Plays are watched for the reason of gaining game information, oftentimes Let’s Plays are watched only for a short period of time until the seeked information, help or walkthrough section is found, these viewers are therefore less immersed in Let’s Plays than people watching it for the purpose of entertainment.

7.1.3. Let’s Play Usage Pattern

The Let’s Play usage pattern measures the weekly hours spent watching Let’s Plays. An excessive time spent watching Let’s Plays does not necessarily mean than a user is addicted to Let’s Plays. In the regression analysis, time turned out to be a predictor for Let’s Play addiction, but it definitely does not provide any prove for addiction on its own. This is in accordance to Griffiths (2010), who claims that other factors than the time spent have to be taken into context when studying video game addiction. Hypothesis 3a, that a higher TV watching usage correlates with a higher Let’s Play usage pattern, and hypotheses 3b, that a higher video game usage correlates with a higher Let’s Play usage pattern, both were supported with a correlation analysis. The findings of this survey show a significant medium correlation (.36, p < .01) between the weekly hours spent on Let’s Plays and the Let’s Play addiction score. The correlation between weekly hours spent and the LP addiction score is the highest when compared with the weekly hours spent watching TV and TV addiction score (.25, p < .01) as well as weekly hours spent playing video games and video game addiction score (.23, p < .01). Other factors such as viewers’ personality traits or viewing motivations are taken into account.

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7.1.4. Predictors for Let’s Play Addiction

In order to test to what extent usage, motives and personality traits can predict Let’s Play addiction, a multiple regression model was formulated and tested. Possible moderating effects on the relationship between Let’s Play usage and Let’s Play addiction were assessed and put into the regression model. Hypothesis 5b, that personality traits moderate the relationship between Let’s Play usage and Let’s Play addiction, was tested with the regression analysis and partly supported. Among all the personality traits, the interaction effect of openness is the only personality trait in the model significantly moderating the relationship, while the interaction terms of escape and recreation are the only motivations for watching Let’s Plays that have a significant moderating effect on the relationship. The variables Let’s Play usage, recreation, money and escape show significant predicting effects even without the use of interaction terms. Escapism shows the strongest predicting effect on Let’s Play addiction, followed by the motivation recreation, Let’s Play usage and the motivation to save money. Yee (2007), who studies the predictors for problematic usage of online games, also argues escapism to be the strongest predictor, followed by hours played per week, and the advancement component consisting of progress, power, accumulation and status. According to the results of the regression analysis, a person’s shyness or motivation for social interaction does not intensify the risk of getting addicted to Let’s Plays, like it is often found for video games (Liu & Peng, 2009) or the Internet in general (Chak & Leung, 2004). However, a positive correlation (.26, p < .001) between shyness and Let’s Play addiction is found. The majority of the respondents (82.90%) answered to watch Let’s Plays primarily alone. Even among those watching Let’s Plays primarily with friends (3.80%), they are not necessarily watching with friends in the same room (20.80% of those who watch Let’s Plays primarily with friends), but most often mixed (75.50%) which can be in the same room with friends or with friends not in the same room, for example through the Let’s Plays chat section. This indicates that Let’s Plays are not primarily used for social interaction or out of a social motivation, unlike it is often the case for MMORPGs. Strongly immersive activities like playing video games, and especially MMORPGs, often have escapism as a strong predictor for addiction. Getting immersed in role-playing games and acting out a different persona, oftentimes a much stronger one, or at least very different to their real-life identity, allows people to forget about problems with school, work, friends or family. The virtual world offers a welcome alternative to the complex real world for many players. While watching Let’s Plays is not quite as immersive as playing video games themselves, due to the element of passivity and missing control of the game, they still offer a gaze into another world, played and steered by the Let’s Player.

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7.1.5. Watching Patterns

The respondents’ watching patterns in terms of if they’re watching Let’s Plays alone or with friends – in the same room or not – and if they are watching Let’s Plays actively or passively (fully focused on Let’s Plays or while doing other tasks) are taken into account. Besides the identified predictors for LP addiction, significant differences between low-risk users and potential risk users in terms of watching patterns are found. A correlation analysis shows a weak negative, but significant correlation between the risk group and active/passive watching behavior, which is supported by a T-Test analysis showing a significant difference between the low-risk users and potential risk users. There is a tendency for a higher addiction potential among users watching LPs actively without any distraction, opposing to users who watch LP while doing other tasks or activities such as doing homework or sports. This is where immersion could play a relevant part, as users who watch LPs passively obviously are not as immersed in the videos as others, because their attention is limited. No significant differences are found between low-risk users and potential risk users in terms of if the Let’s Plays are watched primarily with friends, alone or mixed, but they do show a similar preference. Most of the Let’s Play viewers watch LPs alone. Among those, who watch Let’s Plays with friends, a differentiation has been made between watching with friends in the same room or not. And in this case, a T-Test shows a significant difference between low-risk users and potential risk users. Low-risk users prefer to watch LPs with friends in the same room or not in the same room, while potential risk users mix it up more often.

7.2. Motivations

The initial 23 motivation items with 12 underlying constructs were used to learn more about the viewers’ motivations for watching Let’s Plays. The items were later on aggregated into 7 motivational factors with the use of a principal-components analysis: game information, recreation (entertainment & relaxation), escape, pastime activity, money, social offline interaction and social online interaction. The strongest motivational categories in the survey conducted for the master thesis are recreation (entertainment & relaxation), pastime activity, game information and money. Weaker, yet still motivational categories include escape, social online interaction and social offline interaction. Hypotheses 4, that social motivation moderates the relationship between the Let’s Play usage and Let’s Play addiction, was tested with a multiple regression analysis. However, it does not yield a significant moderating effect. Although only a minority of viewers is motivated by the wish to escape from the real world and real-life problems, the motive does have a significant moderating effect on the relationship between Let’s Play usage and Let’s Play addiction, meaning that more hours spent on watching Let’s Plays can result in a higher Let’s Play addiction score when the escape motive is prevalent. The viewers’ motivations to watch Let’s Plays assessed in the online survey are in line with previous research findings.

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Fjællingsdal (2014) discovers in his thematic analysis entertainment and additional knowledge to be the main viewers’ motivations, whereas Hamilton, Garretson, and Kerne (2014) find through interviewing streamers and viewers that the wish to gain further knowledge, to improve in a game and the emotional need to communicate are the strongest motivations.

7.3. Game Genres

The most popular game genres mentioned by the survey respondents are action/adventure, fantasy/role playing, sandbox, first-person shooter and arcade/jump’n’run. The game genres include some of the most popular games titles on amazon (as proposed by Kaytoue, Silva, Cerf, Meira, and Raissi (2012)), as well as older games with a large community such as World of Warcraft, League of Legends and Hearthstone. Gender differences in prefered game genres are only partly found. In contrast to findings by Lucas and Sherry (2004), the top three game genres mentioned do not differ in gender: action/adventure, fantasy/role playing and sandbox. However comparing the most preferred games genres between the low risk group and potential risk group, the low risk group mentioned the most often action/adventure, fantasy/role-playing, sandbox and first-person shooter, while the potential risk group prefers action/adventure, sandbox, first-person shooter and fantasy/role-playing. There’s no difference in the top four mentions, but a difference in their rankings.

7.4. Let’s Play Producer vs. Non-Producer Comparison

While most of the group comparisons in this survey are made between low-risk and potential risk users, the personality traits between Let’s Play producers and non-producers are also set side by side in a T-Test analysis. Participants, who produce Let’s Plays themselves, show a higher extraversion, higher agreeableness, higher openness, lower neuroticism and a lower shyness compared to non-producers. Conscientiousness does not result in significant differences between those two groups. The results are not too surprising. Let’s Players display themselves publicly, depending on the followership are often viewed by hundreds or thousands of people at a time, therefore it seems likely that Let’s Players are – on average – more extraverted and less shy than non- producers. Let’s Players also have been found to spend more time watching Let’s Players than non-producers. This may be due to the fact that producing Let’s Plays takes a lot of time, especially when the videos have to be edited and cut. The community of Let’s Plays is one where Let’s Players often watch colleagues for the purposes of entertainment, to support each other or to compare themselves. The lines between production and consumption, not just in the Let’s Play scene but in the realm of user-generated content in general, are getting blurrier. Prosumption, which contains both production and

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consumption rather than focusing on only one, regains prevalence with the boom of platforms and services like YouTube, Wikipedia, Yelp and social networks (Ritzer & Jurgenson, 2010). Often, digital content producers are found to spend more time watching and using other people’s content than non-producers. Also, it can be observed that producers are more likely to engage in other people’s content such as commenting, liking or sharing. Content creators are more aware of the significance of sharing other people’s content and how it helps to reach a larger audience. In this study, there are no significant differences between producers and non-producers when it comes to the LP addiction score, therefore it is argued that the higher ratio of Let’s Play producer in this sample does not bias the LP addiction findings.

7.5. An Average Let’s Play Streamer Profile

According to this study’s sample, the average Let’s Play viewer is a 22-year-old male from the United States, who likes to produce Let’s Plays himself. He spends 19 hours per week watching TV, 31 hours per week playing video games and 18 hours per week watching Let’s Plays. He watches Let’s Plays for the reasons of recreation, as a pastime activity and to gain game information he wouldn’t get anywhere else. When he starts watching Let’s Plays, a session usually takes an hour. He prefers Let’s Plays from the action/adventure game genre and watches them mainly on YouTube, therefore he does not spend any money on his activity. He watches them primarily alone while doing other tasks like doing homework, chores or sport. He scores low on all addiction scales, while video games show a slightly higher potential for addiction.

8. Limitations and Suggestions for Future Research

This chapter contains a list of limitations of the study as well as implications for future research in the field of Let’s Plays. Future research in this field is highly encouraged, as Let’s Plays are an interesting phenomenon. Not only teens are spending more and more time watching other people play video games for various reasons.

8.1. Limitations of the Study

The results of this study reveal numerous important findings to expand our understanding of the Let’s Play phenomenon and its addictive potential. However, certain limitations should be considered. Limitations of the study were found in regards of the sample size, the survey items and the design of the survey itself. The limitations are listed and discussed in this chapter.

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8.1.1. Sample Size Issues

The survey includes answers by respondents from 34 different countries. Most of the participants are situated in the US. However, especially in China and Korea, eSports and Let’s Plays have been very popular, therefore the survey is subject to a strong western bias. Paying attention to culture specific differences to gain more knowledge about different Let’s Play practices is recommended for future studies. The online survey conducted for this master thesis does not give an adequate idea of the ratio of Let’s Play producers to people only watching Let’s Plays, as the survey has been promoted on various platforms, some of which are not exclusively but also used for LP producers to promote their channels or to communicate with other LPers. Therefore, it is assumed that the percentage of people who produce and stream Let’s Plays themselves is higher in this sample than it is in the population. In this study, the occupation status and income have not been retrieved, however in respect to the motivations (especially the motivational category of money), there might be some interesting differences between people with more and less money to spend. As this survey focuses on finding out more about people watching Let’s Plays, participants who had not watched Let’s Plays in the last 3 months, were excluded from the survey. Thus, the study gives no information about how large the percentage of people watching Let’s Plays in the society is or how Let’s Play viewers’ personality traits are in any way different to non-LP-viewers. The small sample of high-risk LP users (4) among the respondents makes it impossible to provide any generalizations for this group. High-risk users have been interpreted as those, who have checked all of the 7 LP addiction criteria with at least “sometimes” (3). This group is also known as addicts as of the monothetic format, according to Lemmens, Valkenburg, and Peter (2009). For the data analyzation, high-risk users were combined with the potential risk user group. The data including the high-risk users provides an interesting insight, but it is recommended to focus on a larger sample size if one wants to conclude more about high-risk LP users.

8.1.2. Vague Items

Considering this study to be among the first ones in the field of Let’s Plays, it aims to provide various general analyzations about the Let’s Play viewers’ usage, behaviour, motivations and personality traits and their addiction potential, also compared to other media dependencies. Due to the general approach of the study, some of the items asked in the survey could have benefited from a more detailed and precise question. For example, when the participants were asked about how much money they are usually spending on Let’s Plays per month, some of the respondents, who were also creating Let’s Plays themselves, included the expenses to produce

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the videos such as camera equipment, or server and domain fees. At least a few of the respondents gave that information in the field for additional comments at the end of the survey. Therefore, this item’s answers do not necessarily demonstrate the real money spent on Let’s Plays, like it was intended by the researcher. Let’s Plays as a relatively loose and overall term was used in the study. Some participants were not sure whether their definition of Let’s Plays was in line with the researcher’s definition. Especially if Let’s Plays also included playthroughs without a commentary, eSport tournaments or videos streamed on Twitch. A short description of the Let’s Play term on the first introductory page of the survey could have resolved participants’ concerns. In the survey itself, there was no differentiation made between the various types of Let’s Plays, as it aimed to provide a first, non-restrictive overview of the LP phenomenon.

8.1.3. Survey Design

The survey consists of 150 items and is rather long. The median response time was 14 minutes with a standard deviation of 38 minutes. Parts of the survey had to be skipped for people who did not watch TV or who did not play video games in the last 3 months. Yet, the survey is still longer than usual online survey. In order to motivate people to fill out the online survey despite it’s length, the questionnaire was linked with a giveaway (7 game-related prizes). Because of that, it was especially important to include a careless response item to filter out the participants who were just randomly clicking through the survey. Although a careless response items was implemented in the last third of the survey, it is still possible that some participants were less attentive during the end of the survey. The problem with online surveys in general is, that the researcher can never be sure that respondents answer the questions truthfully or only take part in the survey once. In this survey, participants were only able to fill out the survey once per IP address. That way, the risk of multiple filled out surveys by one participant has been greatly reduced.

8.2. Suggestions for Future Research

Despite several limitations, the study shows that certain motivations and personality traits are associated with Let’s Play addiction score. This chapter contains suggestions for future research in terms of length of the survey and the Let’s Play Addiction Scale as well as provides more ideas for possible research questions.

8.2.1. Length of the Survey

The questionnaire was rather long (150 Items) and although there were some items to answer faster such as the BFI items, some of the participants still perceived the survey as time- consuming and too long for an online survey. Therefore, I suggest that future researchers design their questionnaire shorter and focus on a specific subject in the Let’s Play field, especially if

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they do not wish to combine the survey with a giveaway. A giveaway motivates more people to participate, however using one or two careless response items in the survey is a valid method to filter out those data sets that falsify the results and do not provide valid findings.

8.2.2. Let’s Play Addiction Scale

The two proposed items by King (2013) were implemented in this survey, however they were not used in the data analyzation due to better comparability with TV addiction and video game addiction with the original items of the Game Addiction Scale. The items asked the participants if 1) the participant thinks that his or her Let’s Play watching behaviour is problematic and 2) if someone in the participant's life would believe that his or her Let’s Play watching behaviour is problematic. The items show a significant and high correlation (.52) with the Let’s Play Addiction Scale. It is proposed for future research in the field of Let’s Plays to also include these two items as they provide further insights to a scale’s validity and reliability. Furthermore, the additional item “How often during the last six months did you find yourself watching Let’s Plays longer than you intended?” modified from the Internet Addiction Test (Young, 1998) helps raise the reliability of the Let’s Play Addiction Scale to .85. If Let’s Play addiction will not be compared with other addictions measured by the Game Addiction Scale, it is recommended to measure LP Addiction with the 10-item Game Addiction Scale as extended and modified in this paper.

8.2.3. Further Research Questions in the Field of Let’s Plays

Let’s Play videos are an interesting research field with a lot of questions still unanswered. The phenomenon of Let’s Plays could be studied in each of the five disciplines of Web Sciences: As for the business aspect, the following questions could lead to interesting findings: Do Let’s Play videos have a promotional value for video game sales? Does the sale of video games, that are popular for Let’s Play, increase? Or do Let’s Plays actually reduce the sales as Let’s Play viewers prefer to watch the Let’s Plays instead of buying and playing the games themselves? Although the master thesis was written in the aspect of the social web, there are further questions worth studying: Why do some people prefer watching someone else playing a game instead of playing it themselves? Is it possible to create Let’s Play viewer typologies? What motivates Let’s Play streamers to produce LP videos? Let’s Plays in the light of the technical side of engineering could be studied by proposing the following questions: What requirements does a server like Twitch has to meet to allow a smoothly running streaming of hundreds of channels simultaneously? What requirements do the Let’s Play streamers have to meet to be able to produce flawlessly videos and what equipment do they need? And how exactly does Let’s Play streaming work? In the aspect of the law the following questions arise: Are Let’s Play streamers allowed to record and upload video game material and make money off of this? Is the music in the games

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more protected than the gameplay itself and the graphics? What rights do video game producers have when they do not want Let’s Plays made with their games? Even in the aspect of art Let’s Plays allow interesting research questions: What differentiates a popular streamer from the typical Let’s Play producer? How are Let’s Play channels designed and which role do the design guidelines play? Let’s Plays are worth studying in all kinds of web-related disciplines. Until now, research on the Let’s Play phenomenon has been scarce. The researcher hopes to provide with this master thesis a starting point for further and more detailed studies on the phenomenon of Let’s Plays.

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Kaytoue, M., Silva, A., Cerf, L., Meira, W., & Raïssi, C. (2012). Watch me playing, I am a professional: a first study on video game live streaming. Proceedings of the 21st International Conference Companion on World Wide Web, (June 2009), 1181–1188. Kent, S. L. (2001). The Ultimate History of Video Games: From Pong to Pokemon - The Story Behind the Craze that Touched Our Lives and Changed the World. Prima Communications, Inc., Rocklin, CA, USA. Kim, E. J ., Namkoong, K., Ku, T. & Kim, S. J. (2008). The relationship between online game addiction and aggression, self-control and narcissistic personality traits. European Psychiatry, 23, 212-218. Kim, J., & Haridakis, P. M. (2009). The role of Internet user characteristics and motives in explaining three dimensions of Internet addiction. Journal of Computer-Mediated Communication, 14, 988-1015. King, D. L., & Delfabbro, P. H. (2014). The cognitive psychology of Internet gaming disorder. Clinical Psychology Review, 34, 298-308. King, D. L., Haagsma, M. C., Delfabbro, P. H., Gradisar, M., & Griffiths, M. D. (2013). Toward a consensus definition of pathological video-gaming: A systematic review of psychometric assessment tools. Clinical Psychology Review, 33, 331-342. LaRose, R., Lin, C., & Eastin, M.S. (2003). Internet addiction, habits and deficient self-regulation. Media Psychology, 5, 225–253. Lee, M. S., Ko, Y. H., Song, H. S., Kwon, K. H., Lee, H. S., & Nam, M. (2007). Characteristics of Internet use in Relation to Game Genre in Korean adolescents. Cyberpsychology & Behavior, 10(2), 278-285. Lemmens, J. S., Valkenburg, P. M., & Peter, J. (2009). Development and Validation of a Game Addiction Scale for Adolescent. Media Psychology, 12:1, 77-95. Leung, L. (2008). Linking Psychological Attributes to Addiction and Improper Use of the Mobile Phone among Adolescents in Hong Kong. Journal of Children & Media, 2(2), 93-113. Liu, M., & Peng, W. (2009). Cognitive and psychological predictors of the negative outcomes associated with playing MMOGs (massively multiplayer online games). Computers in Human Behavior, 25(6), 1306-1311. Lucas, K., & Sherry, J. L. (2004). Sex Differences in Video Game Play: A Communication-Based Explanation. Communication Research, 31(5), 499-523. MacGregor, S. A. (2014). Extension and Validation of an Adult Gaming Addiction Scale. Antioch University - New England. Major League Gaming. (2015). Major League Gaming - About. Retrieved from http://www.majorleaguegaming.com/mlg/about

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Markey, P.M. & Markey, C.N. (2010). Vulnerability to violent video games: A review and integration of personality research. Review of General Psychology, 14(2), 82–91. McIlwraith, R., Jacobvitz, R. S., Kubey, R. & Alexander, A. (1991). Television addiction: Theories and data behind the ubiquitous metaphor. American Behavioral Scientist, 35, 104–121. Messias, E., Castro, J., Saini, A., Usman, M., & Peeples, D. (2011). Sadness, suicide, and their association with video game and Internet overuse among teens: Results from the Youth Risk Behavior Survey 2007 and 2009. Suicide and Threatening Behavior, 41, 307–315. Ng, B. D., & Wiemer-Hastings, P. (2005). Addiction to the Internet and online gaming. CyberPsychology and Behavior, 8, 110–115. Pawlikowski, M., Altstötter-Gleich, C., & Brand, M. (2013). Validation and psychometric properties of a short version of Young’s Internet Addiction Test. Computers in Human Behavior, 29, 1212-1223. Reddit. (2015). Reddit Let’s Play. Retrieved from http://www.reddit.com/r/letsplay Ritzer, G., & Jurgenson, N. (2010). Production, consumption, prosumption: The nature of capitalism in the age of the digital ‘prosumer’. Journal of Consumer Culture, 10(1), 13-36. Rozin, P., & Stoess, C. (1993). Is there a general tendency to become addicted? Addictive Behaviors, 18, 81–87. Rubin, A. M. (1983). Television uses and gratifications: The interactions of viewing patterns and motivations. Journal of Broadcasting, 27, 37-51. Scherer, K. (1997). College life online: Healthy and unhealthy Internet use. J. College Stud. Dev., 38(6), 655–665. Sherry, J. L., Lucas, K., Greenberg, B. S., & Lachlan, K. (2006). Video game uses and gratifications as predictors of use and game preference. In P. Vorderer & J. Bryant (Eds.), Playing computer games: Motives, responses, and consequences (pp. 213-224). Mahwah, NJ: Lawrence Erlbaum. Slowbeef. (2013, January 30th). Did I start Let’s Play? Slowblr. Retrieved from http://slowbeef.tumblr.com/post/41879526522/did-i-start-lets-play Smith, T., Obrist, M., & Wright, P. (2013). Live-streaming changes the (video) game. Proceedings of the 11th european conference on Interactive TV and video, EuroITV ’13. ACM, New York, NY, USA, 131-138. Sussman, S. (2012). Steve Sussman on Rudolf H. Moo’s “Iatro- genic effects of psychosocial interventions: Treatment, life context and personal risk factors” – A clarification. Substance Use & Misuse 47, 1601–1602. Sussman, S., & Moran, M. B. (2013). Hidden addiction: Television. Journal of Behavioral Addictions, 2(3), 125–132. The Let’s Play Archive. (2015). Frequently Asked Questions. Retrieved from http://lparchive.org/faq

September 8, 2016 Bianca Haun 74/75

Twitch. (2015a). Twitch - Social Video for Gamers. Retrieved from http://www.twitch.tv/p/about Twitch. (2015b). Twitch Partner Program. Retrieved from http://www.twitch.tv/p/partners U.S. Bureau of Labor Statistics (June 18, 2014). American Time Use Survey – 2013 Results. Retrieved from http://www.bls.gov/news.release/pdf/atus.pdf Van Rooij, A. J., Schoenmakers, T. M., Vermulst, A. a., Van Den Eijnden, R. J. J. M., & Van De Mheen, D. (2011). Online video game addiction: Identification of addicted adolescent gamers. Addiction, 106(1), 205–212. Walther, J. B. (1999). Communication Addiction Disorder: Concern over Media, Behavior and Effects. Retrieved from http://psychcentral.com/archives/walther_cad.pdf White, P. (2013). Fan fiction more creative than most people think. Retrieved from http://www.kstatecollegian.com/2013/04/18/fan-fiction-more-creative-than-most-people- think/ Yee, N. (2007). Motivations of Play in Online Games. Cyber Psychology and Behavior, 9(6), 772-775. Young, K.S. (1998). Internet addiction: The emergence of a new clinical disorder. CyberPsychology & Behavior 1(3), 237-244. YouTube. (2015). A YouTube built for gamers. Retrieved from https://youtube.googleblog.com/2015/06/a-youtube-built-for-gamers.html

September 8, 2016 Bianca Haun 75/75 Appendix Which country are you currently living in?

Please choose only one of the following:

Afghanistan Albania Algeria Andorra Angola Antigua and Barbuda Argentina Armenia Australia Austria Azerbaijan Bahamas Bahrain Bangladesh Barbados Belarus Belgium Belize Benin Bhutan Bolivia (Plurinational State of) Bosnia and Herzegovina Botswana Brazil Brunei Darussalam Bulgaria Burkina Faso Burundi Cabo Verde Cambodia Cameroon Canada Central African Republic Chad Chile China Colombia Comoros Congo Cook Islands Costa Rica Côte d’Ivoire Croatia Cuba Cyprus Czech Republic Democratic People’s Republic of Korea Congo Denmark Djibouti Dominica Dominican Republic Ecuador Egypt El Salvador Equatorial Guinea Eritrea Estonia Ethiopia Fiji Finland France Gabon Gambia Georgia Germany Ghana Greece Grenada Guatemala Guinea Guinea-Bissau Guyana Haiti Honduras Hungary Iceland India Indonesia Iran (Islamic Republic of) Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Kiribati Kuwait Kyrgyzstan Lao People’s Democratic Republic Latvia Lebanon Lesotho Liberia Libya Lithuania Luxembourg Madagascar Malawi Malaysia Maldives Mali Malta Marshall Islands Mauritania Mauritius Mexico Micronesia (Federated States of) Monaco Mongolia Montenegro Morocco Mozambique Myanmar Namibia Nauru Nepal Netherlands New Zealand Nicaragua Niger Nigeria Niue Norway Oman Pakistan Palau Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Puerto Rico Qatar Republic of Korea Republic of Moldova Romania Russian Federation Rwanda Saint Kitts and Nevis Saint Lucia Saint Vincent and the Grenadines Samoa San Marino Sao Tome and Principe Saudi Arabia Senegal Serbia Seychelles Sierra Leone Singapore Slovakia Slovenia Solomon Islands Somalia South Africa South Sudan Spain Sri Lanka Sudan Suriname Swaziland Sweden Switzerland Syrian Arab Republic Tajikistan Thailand The former Yugoslav Republic of Macedonia Timor-Leste Togo Tokelau Tonga Trinidad and Tobago Tunisia Turkey Turkmenistan Tuvalu Uganda Ukraine United Arab Emirates the United Kingdom of Great Britain and Northern Ireland United Republic of Tanzania United States of America Uruguay Uzbekistan Vanuatu Venezuela (Bolivarian Republic of) Viet Nam Yemen Zambia Zimbabwe Media Usage

Have you watched TV (including streaming services such as netflix, hulu, hbo go,..) in the last 3 months? *

Please choose only one of the following:

Yes No

Have you played video games (PC, XBOX, PS, Nintendo 3DS, PS Vita, Online Games, Social Games,...) in the last 3 months? *

Please choose only one of the following:

Yes No

Have you watched Let's Plays (on YouTube, Twitch, Let's Play Archive,...) in the last 3 months? *

Please choose only one of the following:

Yes No TV Use

TV

How much time do you spend on weekdays watching TV (including streaming services such as netflix, hulu, hbo go,..)? *

Only answer this question if the following conditions are met: Answer was 'Yes' at question '4 [G2Q00001]' (Have you watched TV (including streaming services such as netflix, hulu, hbo go,..) in the last 3 months? )

Please write your answer(s) here:

hour(s) and

minute(s) per day

How much time do you spend on weekends watching TV (including streaming services such as netflix, hulu, hbo go,..)? *

Only answer this question if the following conditions are met: Answer was 'Yes' at question '4 [G2Q00001]' (Have you watched TV (including streaming services such as netflix, hulu, hbo go,..) in the last 3 months? )

Please write your answer(s) here:

hour(s) and

minute(s) per day

How often do you talk about TV series or movies with others when you're not watching e.g. in school lunch breaks, at the workplace or when you meet with friends?

Only answer this question if the following conditions are met: Answer was 'Yes' at question '4 [G2Q00001]' (Have you watched TV (including streaming services such as netflix, hulu, hbo go,..) in the last 3 months? )

Please choose the appropriate response for each item: never rarely sometimes often very often

Please rate how actively (as the main activity) or passively (while doing other tasks) you tend to watch TV.

Only answer this question if the following conditions are met: Answer was 'Yes' at question '4 [G2Q00001]' (Have you watched TV (including streaming services such as netflix, hulu, hbo go,..) in the last 3 months? )

Please choose the appropriate response for each item: I watch TV in addition to other activities like doing homework I watch TV as a main activity or sports

TV Scale2

TV

How often during the last six months... *

Only answer this question if the following conditions are met: ((G2Q00002.NAOK == "Y"))

Please choose the appropriate response for each item: never rarely sometimes often very often ...did you think about watching TV all day long? ...did you spend increasing amounts of time watching TV? ...did you watch TV to forget about real life? ...have others unsuccessfully tried to reduce your time spent watching TV? ...have you felt bad when you were unable to watch TV? ...did you have fights with others (e.g., family, friends) over your time spent watching TV? ...have you neglected other important activities (e.g., school, work, sports) to watch TV? Video Game Use

Video games

How much time do you spend on weekdays playing video games (PC, XBOX, PS, Nintendo 3DS, PS Vita, online games, social games,...)? *

Only answer this question if the following conditions are met: Answer was 'Yes' at question '5 [G2Q00002]' (Have you played video games (PC, XBOX, PS, Nintendo 3DS, PS Vita, Online Games, Social Games,...) in the last 3 months? )

Please write your answer(s) here:

hour(s) and

minute(s) per day

How much time do you spend on weekends playing video games (PC, XBOX, PS, Nintendo 3DS, PS Vita, online games, social games,...)? *

Only answer this question if the following conditions are met: Answer was 'Yes' at question '5 [G2Q00002]' (Have you played video games (PC, XBOX, PS, Nintendo 3DS, PS Vita, Online Games, Social Games,...) in the last 3 months? )

Please write your answer(s) here:

hour(s) and

minute(s) per day

How often do you talk about video games with others when you're not playing e.g. in school lunch breaks, at the workplace or when you meet with friends?

Only answer this question if the following conditions are met: Answer was 'Yes' at question '5 [G2Q00002]' (Have you played video games (PC, XBOX, PS, Nintendo 3DS, PS Vita, Online Games, Social Games,...) in the last 3 months? )

Please choose the appropriate response for each item: never rarely sometimes often very often

Video Game Scale 2

Video games

How often during the last six months... *

Only answer this question if the following conditions are met: Answer was 'Yes' at question '5 [G2Q00002]' (Have you played video games (PC, XBOX, PS, Nintendo 3DS, PS Vita, Online Games, Social Games,...) in the last 3 months? )

Please choose the appropriate response for each item: never rarely sometimes often very often ...did you think about playing a game all day long? ...did you spend increasing amounts of time on games? ...did you play games to forget about real life? ...have others unsuccessfully tried to reduce your game use? ...have you felt bad when you were unable to play? ...did you have fights with others (e.g., family, friends) over your time spent on games? ...have you neglected other important activities (e.g., school, work, sports) to play games? LetsPlayUsage

Let's Plays

How much time do you spend on weekdays watching Let’s Plays (on YouTube, Twitch, Let's Play Archive,...)? *

Please write your answer(s) here:

hour(s) and

minute(s) per day

How much time do you spend on weekends watching LPs (on YouTube, Twitch, Let's Play Archive,...)? *

Please write your answer(s) here:

hour(s) and

minute(s) per day

When you watch LPs, for how long do you usually watch at one sitting?

Your answer must be between 0 and 1440 Only an integer value may be entered in this field.

Please write your answer here:

minutes

On which platform or website do you watch LPs?

Please choose all that apply:

YouTube Twitch Let's Play Archive

Other: LP Usage 2

Let's Plays

To what percentage do you spend watching LPs alone or with friends?

Please write your answer(s) here:

alone

with friends in the same room

with friends not in the same room (via chat, skype,...)

To what percentages do you communicate with others while watching LPs?

Please write your answer(s) here:

I don't communicate with others

online with other viewers

offline with other viewers

online with the Let's Player(s)

How often do you talk about LPs with others when you're not watching e.g. in school lunch breaks, at the workplace or when you meet with friends?

Please choose the appropriate response for each item: never rarely sometimes often very often

Please rate how actively (as the main activity) or passively (while doing other tasks) you tend to watch LPs.

Please choose the appropriate response for each item: I watch LP in addition to other activities like doing homework I watch LP as a main activity or sports

Do you make and post LP videos yourself?

Please choose the appropriate response for each item: never rarely sometimes often very often

Let's Play Usage 2

Let's Plays

How much money (including donations) do you spend on Let's Plays per month (in USD)?

Only an integer value may be entered in this field.

Please write your answer here:

$

Are you currently paying for a subscription on Twitch?

Please choose only one of the following:

yes no

For how long have you been paying for Twitch subscriptions?

Only answer this question if the following conditions are met: Answer was 'yes' at question '26 [G9Q00002]' (Are you currently paying for a subscription on Twitch?)

Your answer must be at most 100 Only an integer value may be entered in this field.

Please write your answer here:

months

Skip the question if you don't know

What are your favorite and second favorite game to watch in LPs at the moment?

Please write your answer(s) here:

Favorite game

Second favorite game

Please specify the game genre(s) of your favorite game(s) mentioned above!

Please choose the appropriate response for each item: Favorite game I don't know

/ I'm First- Turn- Beat not Person Fantasy/Role based Parlor Activity ‘Em sure Simulation Arcade/Jump’n’Run Action/Adventure Shooter Sports Playing Racing Strategy Games Games Ups Game genre Let's Play Scale

Let's Plays

Thank you so much for taking your time to fill out this survey. When it comes to LPs there isn't much research yet so it's especially important for the survey's findings that you answer the following questions. There are only 5 pages of questions left and at the end you'll have the possibility to enter the giveaway.

How often during the last six months... *

Please choose the appropriate response for each item: never rarely sometimes often very often ...did you think about watching LPs all day long? ...did you spend increasing amounts of time watching LPs? ...did you watch LPs to forget about real life? ...have others unsuccessfully tried to reduce your time spent watching LPs? ...have you felt bad when you were unable to watch LPs? ...did you have fights with others (e.g., family, friends) over your time spent on watching LPs? ...have you neglected other important activities (e.g., school, work, sports) to watch LPs? ...did you find yourself watching Let’s Plays longer than you intended?

Please select the appropriate responses regarding your Let's Play usage. *

Please choose the appropriate response for each item: very

not at all problematic Would you consider your Let's Play behavior as problematic? Would someone in your life (friends, relatives, colleagues,...) consider your Let's Play behavior as problematic? Motivation

Motivation for watching LPs

Why do you watch LPs? I watch Let's Play videos... *

Please choose the appropriate response for each item: neither agree

strongly nor strongly disagree disagree disagree agree agree ...to see how others play the same games I own ...because I enjoy experiencing the emotional reactions of others in certain games ...to entertain myself ...because the Let's Player is funny ...so I can forget about school / work ...so I can get away from the rest of my family or others ...to pass the time, particularly when I'm bored ...when I have nothing better to do ...to gain more knowledge about a game that I own ...to help me out when I'm stuck in a game ...to save some money, because I don't have to buy the game Motivation2

Motivation for watching LPs

Why do you watch LPs? I watch Let's Play videos... *

Please choose the appropriate response for each item: neither agree

strongly nor strongly disagree disagree disagree agree agree ...because I can't afford all the games I want to play ...to motivate myself to play a game again ...because they relax me ...because they are a way to relax ...to see new games I might be interested in buying ...because I want to know someone else's opinion on a game ...because my friends and I use Let's Plays as a reason to get together ...because often a group of friends and I spend time watching Let's Plays ...to chat with others ...to chat with the Let's Player ...to influence the game play of the LPs ...to get recognition from the Let’s Player

If you watch LPs because of another reason not mentioned above, please enter it here.

Please write your answer here:

Personality

Please read each item carefully and decide to what extent it is characteristic of your feelings and behavior.

Please choose the appropriate response for each item: very uncharacteristic very or untrue, characteristic or strongly true, strongly disagree uncharacteristic neutral characteristic agree I feel tense when I'm with people I don't know well. I am socially somewhat awkward. I do not find it difficult to ask other people for information. I am often uncomfortable at parties and other social functions. When in a group of people, I have trouble thinking of the right things to talk about. It does not take me long to overcome my shyness in new situations. It is hard for me to act natural when I am meeting new people. I feel nervous when speaking to someone in authority. I have no doubts about my social competence. I have trouble looking someone right in the eye. I feel inhibited in social situations. I do not find it hard to talk to strangers. I am more shy with members of the opposite sex. BFI

Here are a number of characteristics that may or may not apply to you.

I see myself as someone who...

*

Please choose the appropriate response for each item: neither agree nor

disagree strongly disagree a little disagree agree a little agree strongly ...is talkative ...tends to find fault with others ...does a thorough job ...is depressed, blue ...is original, comes up with new ideas ...is reserved ...is helpful and unselfish with others ...can be somewhat careless ...is relaxed, handles stress well ...is curious about many different things ...is full of energy ...starts quarrels with others ...is a reliable worker ...can be tense ...is ingenious, a deep thinker ...generates a lot of enthusiasm ...has a forgiving nature ...tends to be disorganized ...worries a lot ...has an active imagination ...tends to be quiet ...is generally trusting ...to ensure data quality please check this item with "agree a little". Thank you! BFI2

Here are a number of characteristics that may or may not apply to you.

I see myself as someone who...

*

Please choose the appropriate response for each item: neither agree nor

disagree strongly disagree a little disagree agree a little agree strongly ...tends to be lazy ...is emotionally stable, not easily upset ...is inventive ...has an assertive personality ...can be cold and aloof ...perseveres until the task is finished ...can be moody ...values artistic, aesthetic experiences ...is sometimes shy, inhibited ...is considerate and kind to almost everyone ...does things efficiently ...remains calm in tense situations ...prefers work that is routine ...is outgoing, sociable ...is sometimes rude to others ...makes plans and follows through with them ...gets nervous easily ...likes to reflect, play with ideas ...has few artistic interests ...likes to cooperate with others ...is easily distracted ...is sophisticated in art, music, or literature addcomments

Thank you so much for filling out the survey so far and helping me with my research! If you have any additional comments or questions, please feel free to write them here.

Please write your answer here:

Thank you so much for completing the survey!

If you want to enter the giveaway for 1x Fallout 4 Preorder, 3x 10$ Steam Wallets, 3x game bundles (Humble Bundle) please click HERE, otherwise you can now close the window.

If you have any questions or feedback regarding the survey, please feel free to send me an e-mail!

Bianca Haun ([email protected])

Johannes Kepler University Linz, Austria

10-13-2015 – 01:26

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