From a Commodity to an Asset: Reassessing the Political Economy of the Triple-A Game

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From a Commodity to an Asset:

Reassessing the Political Economy of the Triple-A Game

Alexander Bernevega

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University of Amsterdam Faculty of Humanities

New Media and Digital Culture | MA Thesis Supervisor: dr. Niels van Doorn

Second reader: dr. Alex Gekker Date: 22 June 2020

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Abstract

The market of big-budget console games, also called the Triple-A market, has long been dominated by a monetization approach based on upfront premium-priced purchases of video games. However, this changed with the industry’s appropriation of free-to-play revenue models, which spread coincided with the emergence of a new genre of battle royale games. This thesis is set to evaluate the impact of these models on the field of game design and the console gaming industry in general. In order to answer the posed research question, I analyze three recent battle royale games: Fortnite (2017), Apex Legends (2019), and Call of Duty: Warzone (2020). In my assessment of these games’ user interfaces and gameplay mechanics, I apply insights from media studies, game studies, and behavioral economics to highlight and explain how game designers manipulate players towards valuable actions and extract and utilize their behavioral data. I connect these findings with the broader developments in the Triple-A industry’s approach to game publishing and monetization to build a complete picture of its modern political economy. Through my analysis, I draw parallels between modern game design and the design approaches of the US gambling industry and propose the Triple-A game’s transition from a commodity-based production logic to an asset-based one.

Keywords: Triple-A game, game design, digital interface, hypernudge, datafication, assetization, interface studies, walkthrough.

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1. Introduction

‘I think, ultimately, those microtransactions will be in every game, but the game itself or the access to the game will be free […] Maybe when I'm retired, as this industry progresses, hundreds of millions are playing the games. Zero bought it. Hundreds of millions are playing. We're getting 5 cents, 6 cents ARPU [average revenue per user] a day out of these people. The great majority will never pay us a penny which is perfectly fine with us, but they add to the eco-system and the people who do pay money – the

whales as they are affectionately referred to – to use a Las Vegas term, love it […]’

This is what Peter Moore, the ex-CEO of one of the largest game publishers in the world, Electronic Arts, said about the future of the game industry in an interview with the gaming website Kotaku eight years ago (Totilo, 2012 emphasis added). His words turned out to be an eerily accurate prophecy, in both the console game market’s turn to new monetization strategies and game publishers’ attitude towards players, borrowed from the gambling industry. In 2012, a typical blockbuster console game with large publishing and marketing budgets, which is usually called Triple-A, was distributed through physical disc-based copies sold at retail for a $60 price (Nieborg, 2014). Today, with the industry’s rapid adoption of the free-to-play revenue models, characterized by free access to the core game experience (Nieborg, 2016b, p. 233), both the production and circulation of console games are going through fundamental changes. These transformations in the political economy of the console gaming industry, which Moore predicted, and had direct influence over, are the central object of this study.

This thesis investigates the emerging practices of the modern Triple-A game production and relates them to the broader developments in the console gaming industry, caused by its appropriation of the free-to-play monetization models. The two particular aspects of production that I focus on and use as an entry point for an assessment of the whole console gaming industry are the manipulative design techniques aimed at players and applications of players’ behavioral data present in the modern Triple-A industry. A study of these matters allows to highlight a massive power inequality between the games’ producers and players and show how players’ data becomes central for modern Triple-A game development. By combining this investigation with the analysis of the modern practices of games’ distribution and monetization, I do not only identify these manipulations but also contextualize and explain them through the newly emergent logic of the Triple-A game production. In order to highlight this manipulative side of modern console game design, I will apply insights from interface studies (Ash et al., 2018b), behavioral economics (Yeung, 2017) and literature on user data collection (Sadowski, 2019; Seif El-Nasr et al., 2013a) to the analysis of in-game

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environments. While previous research on manipulation in video games focused primarily on the operation of predatory monetization mechanics (King & Delfabbro, 2018, 2019; Macey & Hamari, 2019), and paid special attention to the gambling mechanic of loot boxes (Brooks & Clark, 2019; Perks, 2019; Xiao & Henderson, 2019), no one has so far considered the role of the in-game interfaces in players’ manipulation. Next, while works documenting the methods of player data extraction and application in game design are in abundance (Medler, 2011; Seif El-Nasr et al., 2013a; Wallner, 2019), a lack of critical engagement with these practices is noticeable in the field of game studies, which I plan to change with my study.

Thus, my thesis aims to fill this gap and draw the connection between the Triple-A industry emerging application of free-to-play monetization models, datafication, and manipulative game design. “[T]he logic of videogame interfaces is spreading to other areas and forms of life” (Ash, 2015, pp. 4–5), so the interface analysis of video games as digital artifacts can illuminate new aspects of the power inequality between designers and users, prevalent in the broader entertainment industry.

The video games market represents one of the fastest-growing segments of the global cultural industries (Kerr, 2017), with its total estimated value reaching $152 billion in the last year (Wijman, 2019). The console gaming industry attracts large audiences and generates enormous revenues, which means that studying aspects of Triple-A game development can reveal trends common for global cultural production. Despite that, the political-economic context of their production remains an underdeveloped area of study, as recently scholars’ attention is largely drawn to other sectors of the video game industry, such as mobile games (Evans, 2016; Koeder & Tanaka, 2017; Leaver & Willson, 2016) and indie, or independently published small-scale games (Whitson, 2019; Whitson et al., 2018). Other studies, which are wholly or partially dedicated to the Triple-A industry, are quite old (Dyer-Witheford & De Peuter, 2009; Zackariasson & Wilson, 2012), which means that they cannot possibly reflect the current state of the rapidly changing environment of video game production.

One prominent theorist of this subject is a new media scholar David Nieborg (2014, 2015, 2016b, 2016a), who analyzed the production, circulation, and monetization of both Triple-A and mobile games and contrasted their approaches, as well as developed the conceptualization of the game as a cultural commodity. However, his theorization is losing its relevance, as the free-to-play revenue models have now entered the Triple-A market, making the latter comparable, rather than contrastable, to the mobile gaming one.

Aphra Kerr (2017), a media and communication researcher, provides a more recent and extensive overview of the global gaming industry and pays special attention to its networked, globalized and restructured nature; however, her account is rendered incomplete by the emergence of free-to-play Triple-A games, which, as I demonstrate with my analysis, disrupted the console segment

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3 of the gaming industry. The developments investigated in this thesis are very recent, as they started gaining traction by the second half of 2017. This means that they could not be covered by Kerr in her thorough work. Therefore, these circumstances call for new research of the relevant monetization and publishing strategies utilized in the modern Triple-A industry.

Thus, by synthesizing the conceptual frameworks of game, interface and new media studies, I aim to contribute to these fields by providing a new perspective on the massive power inequality between interface designers and their users, that will incorporate insights from the critique of behavioral economics theory of the nudge (Thaler & Sunstein, 2008; Yeung, 2012, 2017). In order to achieve that, I pose the following question for my research:

In what ways do the free-to-play monetization models steer the design of the

eighth-generation Triple-A game towards manipulating the players, and how do they reshape the modern game industry?

In order to answer this question and fulfill the purpose of this thesis, I utilize the walkthrough method, developed by Light et al. (2018), to analyze three popular Triple-A titles of the recently emerged battle royale game genre: Fortnite, Apex Legends and Call Of Duty: Warzone. Initially conceived for the analysis of mobile apps, this method can be repurposed to study the intentions of console game developers. Qualitative game studies performed through direct engagement with the games are often associated with problems in creating clear and reproducible methodologies (Aarseth, 2007; Consalvo & Dutton, 2006), so, by following Light et al.’s (2018) guidelines I can circumvent these struggles by structuring and formalizing my analytical play. The walkthrough method is combined with the thematic analysis (Guest et al., 2012) of articles from specialized websites and Privacy Policies of the selected games’ publishers. This two-step methodology allows me to both investigate the behavioral manipulations embedded in modern game design, and build a complete up-to-date picture of the modern Triple-A industry political economy.

The remaining part of this thesis is divided into five chapters. In Chapter 2 I synthesize literature from behavioral economics, its critique, media studies, and game studies to create a lens that I use for the analysis. In Chapter 3 I provide a detailed justification and explanation of the chosen methodology and games’ sample. In Chapter 4 I present and discuss the main empirical findings of my research. In Chapter 5 I unpack the consequences of the revealed developments on the gaming market and overall digital entertainment industry, and suggest a new theoretical lens for video game studies. Finally, I conclude the thesis by highlighting the contributions of my study, discussing the limitations of my work, and proposing new directions for future research.

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2. Theoretical Framework: Behavioral Manipulation, Interfacial Power and

Datafication in the Modern Triple-A Game

This chapter provides the theoretical framework of my research. In the first section, I unpack the notion of the nudge (Thaler & Sunstein, 2008) as a main conceptual tool for my analysis of interface power, discuss its main underlying principles, and compare it to the concept of affordances (Norman, 1988), widely adopted in the field of media studies. In the second section, I proceed to contextualize my inquiry in the economic conceptualization of platform capitalism (Srnicek & De Sutter, 2017), characterized by a datafication logic (Sadowski, 2019) and the spread of platform business models. In it, I also discuss how platform capitalism relates to the two key elements of my argument – user interface design (Ash et al., 2018b) and nudging (Yeung, 2017). Finally, in the last section, I closely inspect the influence of platform capitalism principles on the modern game industry, frame the Triple-A game (Nieborg, 2014) as the object of my analysis, and discuss the monetization methods that I will investigate in my research.

2.1 Nudges and affordances as design approaches

In this thesis, I aim to assess how video games’ gameplay mechanics and user interfaces can steer players towards behavior that generates value for the stakeholders present in the modern game industry. In order to do that, I utilize Richard Thaler and Cass Sunstein’s (2008) nudge as a lens to spot and highlight the manipulative techniques embedded in modern big-budget game design. Thus, an introduction to the key concepts of nudge theory, as well as its underlying intellectual foundation, is needed for my further analysis. In this section, I introduce nudge and contrast it to another influential approach for both practical design and its theoretical analysis – the concept of affordances – and explain their value for my analysis.

Nudge theory, just as the general branch of behavioral economics, originates from the collaborative work of two psychologists, Amos Tversky and Daniel Kahneman, who first met on the grounds of Hebrew University in the 1970s. In their experiments on decision-making in uncertainty, Tversky and Kahneman found (1974, p. 1124) that, when assessing probabilities and predicting values, individuals employ mental shortcuts that simplify these complex tasks, called heuristics. These rules of thumb are associated with human intuition and they precede and often replace rational thinking (Kahneman, 2003). Heuristics are instant and automatic and they make people prone to

biases – common decision-making mistakes.

Tversky and Kahneman’s most significant finding is that heuristics’ application, thus also the mistakes, is systematic and, therefore, predictable. This insight formed the basis of prospect theory – a new economic decision-making model that improved the previously adopted expected utility theory.

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5 While this acclaimed theory considered humans as rational economic actors that calculate the probabilities of risky choices and always choose the most valuable ones, Kahneman and Tversky (1979) revised it by taking into account the irrationalities of human actual choice behavior. Their attempts to formalize the decision-making process and predict such irrationalities have provided the groundwork for Richard Thaler and Cass Sunstein’s (2008) nudge theory.

A nudge is any feature of the environment that alters the choice behavior of a decision-maker in a subtle yet predictable way without delimiting the number of options or restricting one’s ability to choose in other ways (Thaler & Sunstein, 2008, p. 6). This altering of behavior has to be predictable for the nudge implementor and avoidable by the decision-maker. Nudges appeal to the imperfections of the human thought process, as they take advantage of the heuristics and biases employed in decision-making (Thaler & Sunstein, 2008, pp. 17–40). As Sunstein (2015, p. 15) later added, nudges can be used not only to reinforce the workings of heuristics but also to reduce them.

Nudges are manipulative by nature. They utilize the heuristics of human intuition to boost or curtail the biases with the ultimate goal of systematically steering subjects towards desired decision-making behavior. To justify the usage of these manipulative techniques, Thaler and Sunstein (2008, pp. 4–6) argue that nudges should only be used to improve people’s lives and promote their movement of libertarian paternalism, introduced in an older article (Sunstein & Thaler, 2003). By this oxymoron, they present two essential principles of their behavioral approach to policy-making: the libertarian insistence on universal freedom of choice and the paternalistic claim for legitimizing behavioral interventions in choice environments where the choosers are left better off because of them.

Thaler and Sunstein defend the nudge by associating it only with benign use cases of guiding people to longer, healthier and wealthier lives. However, while libertarian paternalism can arguably be applied to public policy since the latter’s ultimate goal is to do best for the country and its citizens, it could be harder to practice the same principles in the private sector. Private companies, with the exclusion of non-profit non-governmental organizations, operate with the ultimate goal of maximizing their profits, so the beneficial nudges – the nudges that steer customers towards their long-term interests (Beggs, 2016, p. 127), – are only implemented by corporations if they will bring more profit in the future. Customer satisfaction and retention are essential for the success of many businesses; however, it would be naïve to assume that companies would only use nudges for these purposes.

The concept of the nudge as a design feature that cues those who encounter them towards desired behavior can be compared to Donald Norman’s (1988) interpretation of affordances. Norman (1988, pp. 9–13) extends James Gibson’s (1979, p. 127) explanation of animals’ perception of the surrounding environment to propose a principle, according to which objects’ design can encourage

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one line of actions and discourage others. According to Norman (1988, p. 9), “the term affordance refers to the perceived and actual properties of the thing, primarily those fundamental properties that determine just how the thing could possibly be used”. Despite his later clarifications in separating

affordances, as the possible and perceived ways to interact with an object, and signifiers, as the actual

properties of the object that communicate affordances to the user (Norman, 2013, pp. 13–14), it was the initial interpretation that used the concept of affordance interchangeably for both of these purposes, which was widely adopted in many disciplines, including design and human-computer interaction (HCI) (Bucher & Helmond, 2018, p. 238).

In fact, even Thaler and Sunstein (2008, pp. 81–83) themselves admit that their theory was influenced by Norman’s ideas, as by nudge they sought to incorporate human factors into the design of merchandise, healthcare and finance products, and other areas. Similar to affordances, particularly in Gibson’s (1979, p. 127) original understanding, who used it for describing natural environments, nudges can exist without the will of object’s creator. Thaler and Sunstein (2008, p. 9) warn designers to take such unintentional nudges into account and minimize them.

Affordances, of course, provide much more opportunities as an analytical tool because of their flexible nature. While nudging approach assumes the inevitability of the power exerted by the environment over the subject, basing on the predictability of the latter’s behavior, the approach of affordances frames the interaction between the subject and the object as two-way. In a sense, affordances comprise nudges, especially in Davis and Chouinard’s (2016) interpretation, who render affordances as a mechanism that allows objects to request, demand, allow, encourage, discourage,

and refuse actions from its user. On that scale, nudges fall mostly into encouraging and, sometimes,

discouraging affordances.

The very example of a plate size that motivates different eating habits, which Davis and Chouinard (2016, p. 243) provide to demonstrate the encouraging function of the affordances, could also serve as a perfect example of a nudge, as it steers the person to eat smaller meals by simply making it more effortless than eating larger ones. Contrariwise, an opt-out system, characterized by the need to explicitly show your desire to discontinue a subscription or membership, discussed by Thaler and Sunstein (2008, pp. 177–179) as an example of a nudge, could also be used to explain the discouraging function of Davis and Chouinard’s affordances, as it requires more effort from a person to change their behavior. In essence, nudges boil down to these two functions, while affordances provide a scope to analyze many other types of relations between an object and its users (Bucher & Helmond, 2018; for an overview see Davis & Chouinard, 2016). Therefore, affordances comprise an indispensable instrument for any assessment of the interaction between users and digital artifacts, and will also be employed in my analysis of video game interfaces for descriptive purposes.

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7 However, the notion of the nudge is still essential for my analysis of modern video games due to its manipulative (Wilkinson, 2013; Yeung, 2012), and hidden nature (Bovens, 2009; Hansen & Jespersen, 2013), for which the behavioral theory is harshly criticized.

Firstly, Wilkinson (2013, pp. 344–347) combines different conceptualizations of manipulation to frame it as “intentionally and successfully influencing someone using methods that pervert choice”, and I will adopt this understanding of manipulation in my further analysis. Wilkinson’s main argument against nudge is that, in cases when a nudge satisfies all of these criteria and lacks subject’s explicit consent, it violates the subject’s autonomy (Wilkinson, 2013, pp. 347, 354). According to Karen Yeung (2012, p. 135), the very fact of autonomy infringement, associated with nudging, diminishes its liberty-preserving nature defended by Thaler and Sunstein (2008, pp. 4– 6). These comments thus render nudge as unethical or at least anti-libertarian even in cases, when it is used to leave the subjects “better off” (Thaler & Sunstein, 2008, p. 5).

Secondly, despite Hansen and Jespersen’s (2013) attempt to separate the nudge from its manipulative nature by providing explicit, transparent nudges, I agree with Bovens (2009, p. 217) when he says that “these techniques work best in the dark”, meaning that nudges are only effective when their subjects are unaware of them. It is exactly the lack of transparency that makes nudge unconsented and therefore problematic. Thus, these two qualities of nudging highlighted by the critics – its manipulative character and lack of transparency, – will allow me to assess the power that contemporary video games’ designers exert over players.

The central claim of Nudge is that these manipulative and hidden techniques can be utilized in choice architecture, or the organization of the contexts of decision-making (Thaler & Sunstein, 2008, p. 3), to improve people’s lives. However, it is important to ask: according to whom? Thaler and Sunstein, take a liberal paternalistic stance and assert that it is choice architects’ job to judge what is best for subjects of the nudge and to steer them towards corresponding choice behavior. In my thesis, I attempt to show that, in the case of video games’ developers, their “judged by themselves” (Thaler & Sunstein, 2008, p. 5) may not always be in the long-term interests of ones nudged.

2.2 User interface design and nudging in the context of platform capitalism

Thaler and Sunstein’s book was first published in 2008, a year when US economy was disrupted by the biggest financial crisis since the Great Depression (Thaler & Sunstein, 2008, p. 269), caused by the burst of the housing bubble. According to Nick Srnicek (2017, pp. 25–33), the new monetary policies, low interest rate environment, and growth in corporate savings and tax evasion that followed as a response to this crisis have played a major role in the establishment of the new digital economy – platform capitalism. In this section, I situate my inquiry in the contemporary conditions of platform

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capitalism and discuss the influence of its logic on the development of digital user interface (UI) design and nudging. I further sharpen my understanding of the nudge by drawing from Yeung’s (2017) critical interpretation of hypernudge and contrasting it to the digital nudge introduced by Thaler and Sunstein’s theory supporters, Weinmann et al. (2016).

2.2.1 Characterizing platform capitalism

Srnicek indicates two tendencies that characterize the platform capitalism of the 21st_{century. The}

first one is the centralization of high-income economies around the extraction and utilization of data as a “new raw material” (Srnicek & De Sutter, 2017, p. 39). While it is important to note that data are never raw, as without an interpretation they provide no insight, and therefore, no value (Gitelman, 2013, p. 7; van Dijck, 2014, pp. 201–202), the increasing importance of data for today’s economy is indeed noted by many scholars (Fourcade & Healy, 2016; Kenney & Zysman, 2016; Langley & Leyshon, 2017; Sadowski, 2019). It is also practically demonstrated by the performance of companies like Amazon, Facebook, Alphabet, and AT&T, which make most of their profit through either accumulating and selling data or providing the infrastructure for their accumulation.

Rendered as a new form of capital, data are collected and stored by companies to create value, which comes in many forms. For example, they are used for profiling and targeting people, optimizing existing systems, improving management, generating models, creating new products and bringing new value to existing assets (Sadowski, 2019, pp. 5–6). Even organizations that do not know how to utilize collected data right away are driven by the data imperative (Fourcade & Healy, 2016, pp. 14– 16) to extract and store as much data as possible due to the potential for their future monetization. In order to amass the new form of capital companies change their products and services and tune their business processes to achieve the utmost datafication, or the conversion of human behavior into quantifiable data (Cukier & Mayer-Schoenberger, 2013).

The second characterizing tendency of platform capitalism is the rise and massive expansion of the platform business model itself (Srnicek & De Sutter, 2017, pp. 42–45). Datafication and data imperative did not only bring changes to the processes of traditional organizations but also produced a new business model of platforms, which Srnicek (2017, p. 43) defines as “digital infrastructures that enable two or more groups to interact”. While this definition does not consider the vast differences between platforms and infrastructures (Plantin et al., 2018), it conveys the main principle of this business model – an intermediary position between different types of users similar to that of a multi-sided market.

The position of mere facilitation, or a flat open neutral space provider, is often promoted by platforms’ owners as a key component of their public image (Gillespie, 2010). However, platforms are not at all neutral, as Langley and Leyshon argue that platforms do not simply channel data

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9 circulations that unfold on flat spaces, but “actively induce, produce and programme [these] circulations” (Langley & Leyshon, 2017, p. 19). Bratton’s (2016, pp. 42 emphasis in original, 47, 49) more abstract account of a platform as “a standards-based technical-economic system that

simultaneously distributes interfaces through their remote coordination and centralizes their integrated control through that same coordination” provides an insight into how exactly platforms

enable and control actions that take place on them: through the standardization of their components and providing different interfaces to different kinds of users.

2.2.2 Manipulation in modern user interface design

There are three reasons why the discussion of platform capitalism is important for the development of my argument. The first reason is the opportunities for exerting power over users that UI designers achieve by utilizing the ubiquitous datafication. One of the main contributors to the studies of interface power is James Ash, who provided several methodological and theoretical frameworks suited for assessing interfaces’ influence on user behavior (Ash, 2015; Ash et al., 2018a, 2018b). In my analysis, I will draw from Ash et al.’s (2018b) latest research on interface power, as it will allow spotting similarities between modern UI design practices and the choice architecture approach.

In the web domain data extraction is mainly performed through the use of cookies – bits of code that are sent from a server to users’ browsers to manage their sessions on the site, personalize their experience, and, most importantly, track their behavior (MDN Web Docs, 2020). In combination with cookies, analytical software is employed by user experience (UX) designers1_{to measure how}

much time users spend on each part of their website, track at which page users perform valuable actions, and collect data about users, like their geographical location or operating system and browser (Ash et al., 2018b, p. 8). Google Analytics, for example, can create behavior reports that can showcase users’ complete paths around the site, sources of traffic, and the site’s most engaging content (Google, n.d.). All of these types of data can be further used to change and optimize the aspects of the interface, which efficiency is later measured through A/B testing – a technique that randomly divides the users into groups which receive different versions of the UI (Ash et al., 2018b, p. 8).

In their analysis of how power is enabled through UI, Ash et al. argue that interface design constitutes a form of a two-fold modulation (Deleuze, 1992), in which interfaces influence user’s behavior, and user, in turn, influences UI design by providing behavioral data that bring new insights. Basing on interviews conducted with real UX designers, Ash et al. (2018b, p. 5) introduce a vocabulary of frictions, thresholds and transitions to describe the process of this modulation.

1_{UX designers are responsible not only for the look and feel of an object, but design and test the complete user path of} interaction with the it (see Forlizzi & Ford, 2000; Hassenzahl, 2008). Thus, the field of UX incorporates UI design and combines it with the analysis of its effectiveness.

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According to Ash et al., UI designers manipulate users through the management of frictions, or “interruptions that delay, stop, halt or defer some kind of desired interface effect” and that are increased or smoothed around thresholds, or points in the interface where a user has to make a

transition – to move further on the path to a valuable action set by designers (Ash et al., 2018b, p. 6).

Management of friction, then, relies on steering users towards desirable behavior through controlling the amount of effort needed from the user to follow different lines of action, and, therefore, operates by the same logic as nudging, even if it does not utilize the same conceptual framework. Just as choice architects present all options and employ nudging techniques based on behavioral experiments to guide decision-makers towards the preferred choices, UX designers utilize the constant stream of behavioral data to adjust and position the units, or “modular piece[s] of an interface, such as an image, text box, or button” (Ash et al., 2018b, p. 7), to direct users towards a needed action. Thus, Ash et al.’s (2018b) account of interface design as two-fold modulation provides a vocabulary for describing the nudging approaches in the modern UI design that will be utilized in my analysis of video games’ interfaces.

2.2.3 The data-charged nudge

The second reason for platform capitalism’s relevance to my argument is the conditions for the decision-guiding techniques like nudging that are, again, provided by datafication and data imperative. Every day public organizations and private companies generate more than 2.5 quintillion bytes of data (Marr, 2018), a considerable part of which are behavior data extracted by tracking users (Guszcza, 2015, p. 73), and with our current pace, this number will double every year (Helbing et al., 2019, p. 74). While the original experiments on the workings of heuristics and biases in decision-making were conducted on sizable, yet limited sample groups (Tversky & Kahneman, 1973, 1974, 1981; Tversky & Thaler, 1990), this constant stream of data allows to gain new insights about human behavior, build predictive models and, therefore, test and implement nudges on a much wider scale.

For example, Guszcza (2015) discusses the 2012 US presidential campaign as an example of a combination of data science and nudging. In their campaign, Obama’s team used data-driven predictive models to identify the most valuable, undecided voters and focused on prompting a change in their voting behavior through the use of nudges (Guszcza, 2015, pp. 67–68). Helbing et al. (2019) consider how the combination of datafication and nudging will shape our society. They argue that application of these two measures, or big nudging, in public policy allows massive scale programming of people’s decision-making behavior that contradicts the very core values of democracy, and warn for possible abuse of such widespread behavioral manipulation by criminals and terrorists (Helbing et al., 2019, p. 76). However, it is Yeung’s (2017) assessment of datafication influence on digital

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11 decision-guidance techniques and regulation through UI design, which is most valuable for my argument.

According to Yeung (2017), Big-data-driven nudges employed in digital environments today constitute a new form of behavior-altering technique which she calls hypernudge. She highlights three directions in which UI hypernudges outperform traditional static nudges (Yeung, 2017, p. 122). Firstly, similarly to Helbing et al. (2019), she notes the population-wide scale of hypernudges’ operation, supplied by the scope of datafication. Distributed on a one-to-many basis (Yeung, 2017, p. 123), hypernudges initiated by Google or Facebook instantly reach a significantly larger audience than flies drawn on urinals or food arrangements in a cafeteria, described by Thaler and Sunstein as perfect nudges (Thaler & Sunstein, 2008, pp. 1–4). Secondly, as also demonstrated by Ash et al. (2018b), hypernudges rely on constant data feedback that allows designers to dynamically tune them for higher efficiency. Lastly, hypernudges allow to create and refine individual ‘choice environments’ in which decision guidance is not only dynamic but can also be highly personalized (Yeung, 2017, p. 122).

Overall, I find Yeung’s account of data-driven digital-guidance techniques to be the most relevant for my analysis, because in its consideration of the datafication effects on behavioral manipulation hypernudge provides a perfect lens for highlighting the grand power asymmetry between interface designers and individual users (Yeung, 2017, p. 123). However, I’d like to clarify that personalization, or at least the high level of personalization found in social media platforms, should not be treated as an essential characteristic of hypernudges for two reasons. The first reason is that in many cases hypernudges are initiated by the same algorithms and therefore generate the same type of behavioral intervention, rather than a personalized one. The second reason is that massive behavior tracking can be still be used to design one-size-fits-all solutions, which efficiency is proven by the traditional accounts of nudging. Thus, with this addition, in my analysis, I will utilize the narrow scope of the hypernudge to assess the power relations between game designers and players.

As evidenced by the interviews conducted by Ash et al (2018b), while UI designers realize the power of the interfaces over user behavior, they do not necessarily actively employ the terminology of choice architecture. However, there is a group of scholars that attempt to change that by developing the concept of digital nudge to “provide valuable guidance for improved interface design” (Weinmann et al., 2016, p. 435). In their uncritical adaptation of the nudge theory, Weinmann, Schneider, and Brocke promote nudging efficiency in UI design and provide a framework for digital nudge design and implementation (Schneider et al., 2018). They argue that all UI designers today become choice architects and influence users’ choices, so it is important for them to be aware of the erroneous nature of human decision-making and prevent unintentional nudges. This approach

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is already gaining traction, as its followers are occupied by making a list of known heuristics and biases (Mirsch et al., 2017).

The problem of this approach lies in the lack of serious ethical consideration, similar to Thaler and Sunstein’s (2008) original book, as Schneider et al. provide a limited assessment of digital behavior manipulation’s moral aspect. They abruptly note that, besides the organization’s goals, choice architects should also take into account the ethical implications when implementing nudges (Schneider et al. 2018, pp. 70–71). In a sense, digital nudge and hypernudge represent two competing approaches to the analysis of UI design on the grounds of ethical considerations. While proponents of digital nudge praise behavioral interventions for their efficiency for organizations (Mirsch et al., 2017; Schneider et al., 2018), Yeung (2017) criticizes hypernudging, defends people’s right not to be deceived and calls for transparency in situations when nudging is inevitable. In this conceptual debate, I take a critical stance and join Yeung in arguing for nudging’s manipulative and unfair nature.

Platform capitalism, with its underlying datafication logic and data imperative it imposes on organizations, has a significant influence on both UI-design and nudging, and this influence results in the UI designers unwarily adoption of the behavior-changing approaches of the nudge, which are all reasons why I discuss it in this section. However, I have mentioned three reasons for platform capitalism’s importance to my argument. The third and last reason is the relevance of both the platform business model and datafication to the game design industry. As the platform capitalism logic’s influence on the game production process is versatile, I will expand on it in the following section.

2.3 Player Telemetry and Triple-A Game Design

This final section of my theoretical framework is devoted to introducing the object of my analysis – the modern Triple-A video game, – and framing it as a contingent cultural commodity (Nieborg & Poell, 2018) that is constantly economically and technically reshaped by the platforms of its distribution and behavior of its players. Here I closely inspect the influence of platform capitalism on the modern gaming industry by showing the nativity of platform business model to the gaming economy (Nieborg, 2014, 2016b) and exploring the modern game design approach based on datafication (Drachen et al., 2013). By drawing from the literature on game analytics (Seif El-Nasr et al., 2013a; Wallner, 2019), I show how players’ behavioral data collection becomes the central principle of modern game development. I synthesize these key insights to identify three value-generating, or monetizable, lines of player action, which I will examine in the analysis that follows this chapter.

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2.3.1 Platforms in the gaming industry evolution

Platform, both as a business model and a more abstract system, is native to the video game industry, as, since the introduction of the Magnavox Odyssey game console in 1972, this segment of cultural production has been continuously impacted and by a series of cycles related to the development of new generations of gaming consoles (Nieborg, 2014, p. 47). Gaming consoles can be rightfully considered as platforms. Firstly, they connect different types of users, or developers and players; through the use of different interfaces – software development kits (dev kits) for developers (Nieborg, 2016b, p. 230) and graphical UI for players. Secondly, by standardizing the core components, which can range from internal hardware, installed software and external hardware: controllers, accessories and storage formats used for physical distribution of video games. In fact, the very academic field of platform studies originated from the analysis of video game consoles, as Montfort and Bogost (2009, p. 147) introduced it in their examination of 1977 Atari Video Computer System.

However, the platform logic of the video game industry’s economy is not limited to the hardware platforms. Today, the major console manufacturers, namely Sony, Nintendo, and Microsoft, do not only produce new hardware and hand out dev kits to game developers but also host large digital console-exclusive marketplaces for them to distribute the games – PlayStation Store, Nintendo Game Store, and Xbox Games Store. On the PC gaming market, most of such digital platforms are owned by large game publishers, rather than hardware manufacturers. Following the success of Valve’s Steam Store, other publishers have introduced their marketplaces: Origin by EA, Uplay by Ubisoft, and Battle.Net by Activision Blizzard. Lately, even smaller publishers, like Epic Games and Bethesda Softworks follow the trend and open digital marketplaces of their own.

According to David Nieborg (2014), the console and PC segments of gaming operate by following the blockbuster, or Triple-A2_{approach of game publishing, characterized by high-risk,}

high-return strategy, large budgets, intensive marketing, and a premium price paid up-front by consumers. Following Bill Ryan’s (1991, pp. 178, 184) theorization of capitalist cultural production, Nieborg (2014, p. 49) conceptualizes the Triple-A game as a cultural commodity, which provides an analytical frame apt to study how such cultural products are produced, circulated, and monetized. In particular, to unpack this commodity form Nieborg (2014, p. 51) introduces the notion of the

formatting strategy. These strategies represent the pervasive governing systems of creative control

that depend on the technical and economic affordances of platforms and directs Triple-A game developers towards repetition and reproduction of successful revenue models and gameplay elements.

2_{The Triple-A, or AAA-rank games here refer to the bond credit classification system utilized by the largest American} credit rating agencies – Moody’s, S&P and Fitch (Finney, 2019). On their scale, the AAA is the highest mark, assigned for the safest bonds that have the strongest capacity to meet financial expectations (S&P Global, 2020). The game publishers borrowed the AAA grade to classify the titles with the largest budgets.

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Regarding the platform-dependent logic of Triple-A game development, Nieborg (2014, p. 49) notes that new console cycles, usually lasting five to seven years (Schilling, 2003), bring new standardized hardware and software, therefore enforcing platform-specific modality of production and circulation. New console generations do not only improve the technical aspect of Triple-A games, by providing developers with more powerful hardware and software with more features, but, what is more important for my thesis, also introduce new ways for monetizing the games (Nieborg, 2014). While for the first six generations of consoles the Triple-A games were distributed through one-time upfront purchases of physical, packaged goods, the seventh cycle of consoles, consisting of PlayStation 3, Wii and Xbox 360, have shifted towards distribution through the aforementioned digital marketplaces (Nieborg, 2016b, pp. 229–230). These digital platforms were used by game publishers to expand their revenue model through introducing downloadable content (DLC) – post-launch paid extensions to existing games (Nieborg, 2014, p. 48).

Nieborg (2014, p. 55) argues that DLCs have redefined the Triple-A game industry, as they allowed game publishers to extend the shelf life of their shipped games, decrease trade-in and disk-sharing practices among players and holding the initial premium pricing for longer by countering the price drops for the game with additional purchases. Overall, he identifies DLCs as one of the key formatting strategies of the seventh generation Triple-A game commodity, accompanied by the second strategy of franchising, or the seriality of the publishing framework that pushes developers to make multiple sequels for successful titles (Nieborg 2014, pp. 51–52).

In his later assessment of the mobile game industry, Nieborg (2016a, p. 6, 2016b) argues that the emergence of iOS and Android platforms, and especially the advent of integrated application stores – App Store and Google Play Market, – have also resulted in the evolution of gaming, as they introduced or popularized revenue models different from the existing premium one. The new

free-to-play models are characterized by a lack of up-front payment from consumers and divide into distinct

approaches, which can be combined in a single game (Nieborg, 2016b, p. 233). These approaches comprise the freemium model, where consumers can access a free version of the game unlock the full one by paying, the advertising-supported model, where revenue is made through commodifying the audiences of the game and selling them to advertisers; the subscription model, that requires consumers to pay a fee to have continuous access to the game, and, the model driven by additional in-game purchases of virtual items called microtransactions (Perks, 2019, p. 7).

While Nieborg contrasts these models to the transaction-based model of the traditional console gaming segment, I argue that this shift towards free-to-play monetization practices is symptomatic for the Triple-A game industry. Motivated by players’, critics’, and developers’ frustration with this traditional model, and inspired by the success of mobile gaming industry, which have generated a $68.5 billion revenue in the last year (Luz, 2019), console game publishers

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15 increasingly implement new revenue models to the triple-A games (Perks, 2019, pp. 4–8). Therefore, platforms, be it a hardware unit or a digital marketplace, shape the economic and technical aspects of the Triple-A game, which Nieborg (2014), frames as a form of cultural commodity.

2.3.2 Datafication in modern game production

Following the datafication trend of platform capitalism, modern Triple-A game development relies heavily on the data-mining practices summarized as game analytics (Seif El-Nasr et al., 2013a; Wallner, 2019). According to Seif El-Nasr et al. (2013b, pp. 5–6), since the seventh generation of consoles, the game industry has started to increasingly extract game telemetry – players’ behavioral data transmitted online from installed games’ clients. As Drachen et al. (2013, p. 16) explain, every aspect of players’ interaction with the game, ranging from in-game purchasing behavior and settings’ adjustments to physical movement on game’s levels and communication with other players, can be constantly collected from the game client. This telemetry is later interpreted into game metrics, or more concrete quantitative measures of the game’s technical performance, like server stability, and players’ behavior, like average game session length, most purchasable items or players’ shooting accuracy; which are utilized by stakeholders from all levels of the game production processes (Canossa et al., 2013; Drachen et al., 2013, pp. 15, 17–19)3_.

Sometimes, these metrics are displayed back to the players to empower them and foster community building (Wallner, 2019, p. 6), for example, in form of in-depth statistics of players’ gameplay performance in online games, used to provide insights that will improve their efficiency (Egliston, 2019). However, most game metrics are exclusively operationalized in-house in the cycle of game development, publishing, and maintenance: in particular, to improve game design and player experience, inform business decisions, and innovate and optimize game technology (Wallner, 2019, pp. 4–6).

Out of all types of game metrics, the most relevant for my analysis are user metrics, which are related to tracking users both as customers, and as players, since they provide insights about players’ engagement and efficiency of monetization models that drive game design and publishing strategy (Drachen et al., 2013, pp. 19–24). The customer-oriented user metrics afford game developers to build the demographics of their customer base, perform microtransaction analysis and predict average customer lifetime value (CLV), or the revenue that the user has generated via in-game

3_{Throughout this paragraph I refer to sources by Seif El-Nasr et al., Drachen et al. and Canossa et al. that come from a} book that is now seven years old, which is a long enough time to render a source irrelevant for the discussion of platform capitalism. However, this book constitutes an irreplaceable source for my inquiry for two reasons. Firstly, it incorporates inside information about game design and other organizational processes derived from the industry experts employed at large game publishers and development studios like EA, Ubisoft, and Sony. Secondly, 2013 is the release year of the most recent, eighth generation of gaming consoles, which means that the accounts of game analytics’ application featured in this book already consider the technical and software affordances of the latest cycle of gaming platforms.

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purchases up to the current point in time (Voigt & Hinz, 2016, p. 108). Player-oriented metrics, on the other hand, are used to identify the most problematic, boring and engaging parts of the gameplay and improve user experience. Representing the opposite poles in the revenue chain in the game development process, these two directions of user metrics are important to be balanced out, as any game has both to be enjoyable and to make profits (Drachen et al., 2013, p. 32).

The application of game analytics, therefore, creates a constant feedback loop, similar to Ash et al.’s (2018b) modulation, where players’ behavior data provide the basis for game design innovations focused on nudging the very players’ behavior towards more valuable actions. From this perspective, I will further narrow the framing of Triple-A games in my analysis to Nieborg’s (2016a, p. 29) later concept of the contingent cultural commodity, which views the game as “constantly altered, sometimes in real-time, to improve KPIs based on player actions.” Introduced first to describe the realities of mobile gaming development, this concept was later extended to all domains of cultural production and connected to the growth of digital platforms (Nieborg & Poell, 2018, p. 2). This framing provides a vantage point from which modern Triple-A games can be analyzed not as static entities governed by the industry-predefined logic, but as highly dynamic commodities, shaped both by platforms on which they operate and players that engage with them.

2.3.3 Game analytics’ impact on the industry

The data-driven game design approach, widespread in the industry, renders player retention (Weber et al., 2011) as a crucial part of the game monetization for both traditional Triple-A transaction-based and free-to-play revenue models. As Voigt and Hinz’s (2016, pp. 113–115) study shows, three signs can predict a higher CLV for a player: a quick conversion from a free to a paying customer, high initial purchase amount and paying with a credit card. It was also found that players who pay more money per each transaction tend to make more transactions overall and that the average transaction amount usually stays the same throughout the customer lifetime. These insights render the importance of targeting the customer segment of players with high ‘willingness to pay’ (Voigt & Hinz, 2016, p. 114) and to identify the gameplay aspects that keep them in the game, both of which can be achieved through user metrics, to retain such high-paying players.

However, retention of players with low willingness to pay or even free players is also important for game developers. Firstly, as Voigt and Hinz (2016, p. 116) note, due to the competitive and networked effect of most online games, these players can stimulate the purchasing behavior of players with high predicted CLV. Secondly, as discussed before, players’ interaction with the game provides telemetry, which lays the basis of game analytics that inform game design and publishing strategy. In cases when developers have to test the performance of new updates and server stability, to find the favorite levels or multiplayer maps of their customer base, or to balance out the power of

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17 in-game items, the amount of revenue generated by the players becomes less important than their gameplay behavior.

In essence, game analytics make not only players’ purchases, but also their attention monetizable, or value-generating. While not necessarily producing revenue by itself, game telemetry created throughout gameplay becomes a part of game developing companies’ business intelligence that drives the decision-making of game designers, marketers, project managers, and other types of stakeholders in the company (Canossa et al., 2013; Drachen et al., 2013, pp. 14–15). Thus, not only how much customers pay, but also how long they play and how often they return to the game become the deciding factors of evaluating a game’s economy, so the developers need to achieve an increase in all of these metrics. My analysis is aimed at identifying the techniques implemented in games’ interfaces by game designers to foster these three lines of monetizable action and to compare these techniques with the principles of hypernudging.

Datafication provides game designers with great power to change players’ behavior, “[a]nd, where power lies, there also lies the potential for overreaching, exploitation and abuse” (Yeung, 2017, p. 123). Natasha Dow Schüll’s (2012) investigation of the American gambling industry demonstrates how data-driven user-centered design can be used to take advantage of customers. In her book, Schüll exposes how the casino owners utilize patrons’ behavior tracking to adjust the casino floors’ layouts and slot machines’ design to increase their revenue per available customer (REVPAC) since as early as 1985 (Schüll, 2012, pp. 21, 144).

In particular, she found that gambling devices’ designers used management of friction (and at times, literal physical friction) to facilitate faster and longer playing sessions, as well as to increase the average amount of money spent during them, and analyzed behavioral data to match the games’ visual appearance and audial accompaniment to the patrons’ preferences (Schüll, 2012, pp. 51–75). In combination, while harmlessly presented as an attempt to give players what they want (Schüll, 2012, p. 53), these design practices created a powerful hypernudge that manipulated gamblers into spending more time and money in the casino. These findings are very relevant for the modern Triple-A video game industry, where a similar trend of data-driven design is combined with continual free-to-play monetization approaches to extract maximum value from the players.

In this chapter, I outlined the theoretical framework that will be utilized in my analysis of the modern Triple-A game. Building on this literature, I will analyze the manipulative game design practices in relation to the broader context of new monetization and publishing approaches and ultimately assess the political economy of the Triple-A gaming industry. In the following chapter, I provide a detailed description of the methodology used to achieve this goal.

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3. Methodology: Technical Walkthrough of Battle Royale Games

In this thesis, I aim to continue the theorization of the video game development as cultural production by analyzing the formatting strategies of the eighth generation of Triple-A games, published for the latest cycle of consoles – PlayStation 4, Xbox One and Nintendo Switch. While Nieborg contrasts the Triple-A game commodity against that of a mobile game (Nieborg, 2016b), I attempt to show the growing similarity between these two and investigate and problematize the tactics employed by game designers to extract value from the players rather than through up-front purchases. Thus, to understand the political economy of the contemporary console game industry and assess its relation to hypernudging, I use the following research question in my analysis:

In what ways do the free-to-play monetization models steer the design of the eighth-generation Triple-A game towards manipulating the players, and how do they reshape the modern game industry?

3.1 Analytical play as research method

In order to answer the posed question, my research aims to produce knowledge about three aspects of the eighth generation Triple-A game. Firstly, obtaining an understanding of the gameplay mechanics that motivate players to follow behavior valuable for game developers allows me to draw parallels between modern game design and choice architecture design, and thus highlight the manipulative aspects of the former. Secondly, the stated question requires to gain an understanding of the practices of game telemetry application in modern Triple-A game design. Doing so allows me to demonstrate the contingent nature of the Triple-A game and show the additional value generated through new revenue models. Lastly, I need to provide an overview of the free-to-play revenue models and publishing strategies utilized in the modern Triple-A game industry. Originating from the mobile app ecosystem, these new monetization practices constitute an important new step for the production of Triple-A games; therefore, their analysis provides insights about the political economy of the state-of-the-art game development industry and its future. Thus, by producing these three bodies of knowledge, I am able to identify and problematize the formatting strategies of the eighth generation of the Triple-A game and thereby answer my research question.

The stated research question and the bodies of knowledge necessary to answer it call for a qualitative research approach. However, there are several methodological challenges associated with the object and scope of my inquiry.

First of all, video games come as technically-closed digital products, as their source-code is protected as a part of the game developer company business intelligence (Seif El-Nasr et al., 2013b),

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19 which prohibits any ‘back-end’ analysis of their underlying structure. Secondly, while traditional ethnographic methods like interviews or participant observation could provide me insights into players’ behavior and opinions about gameplay mechanics, they would not allow me to answer the stated research question, as they are not able to illuminate the underlying political economy of the studied games. Additionally, due to the obscure and hidden nature of hypernudges, research participants and interviewees could fail to discern the behavioral interventions embedded into the gameplay and UIs, thus requiring the analytical eye of the researcher for the inspection of the games. This problem could potentially be surmounted by interviewing ex- or current game designers or other employees of the large game publishers. However, my inquiry is directly related to information that may fall into the business intelligence category, such as utilized monetization models, manipulative game design practices and in-house operationalization of user data. Therefore, such interviewees could be bounded by the signed nondisclosure agreements, and therefore not give away the information crucial for my analysis.

Thus, these considerations render it essential for my analysis to directly engage with the studied video games to experience their gameplay first-hand. Given the fact that video games comprise natively-digital artifacts (Rogers, 2009, p. 5), following the method of the medium (Rogers, 2019, p. 10), which in this case consists of playing the games, provides new opportunities for analysis and bypasses most of the identified challenges.

However, here comes the last challenge: how can one play analytically? Consalvo and Dutton (2006, para. 3) note a lack of clear methodological system in qualitative game studies, which in such works is often replaced by “[an] assumption that [the games are] played and carefully thought about by the author”. Similarly, in his search for a style of play appropriate for game studies research, Espen Aerseth (2007, p. 7) does not come to a definite conclusion, instead suggesting that “there must […] be a balance between free play, analytical play, and non- play”. The methodologies of play that these authors introduce, while perfectly suitable for critical game analysis, devote too much attention to the assessment of every gameplay element in order to analyze a game in its entirety. My inquiry, on the other hand, is focused on a narrower inspection of gameplay mechanics and UI features that are used by game designers to generate revenue.

A solution to all of these methodological challenges comes in the form of the qualitative walkthrough method, initially developed by Light et al. (2018) for mobile app studies. This method of direct engagement with the digital artifact mobilizes the concepts from cultural studies and the field of science and technology studies (STS) to analyze its political-economic context, creators’ intentions and cultural values, and the ways of its’ mediation of the user, which suits the needs of my research.

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The walkthrough method consists of three essential parts. The first step is establishing the app’s environment of expected use, or “how app provider anticipates it will be received, generate profit or other forms of benefit and regulate user activity” (Light et al., 2018, p. 3). This step requires the investigation of the app’s Terms of Service documents (ToS), app-generated materials, industry media and public market information for information about the app creators’ vision, revenue model and methods of regulating user activity (Light et al., 2018, p. 9). The second step, which constitutes the main point of data collection, is the actual technical walkthrough, or continuous engagement with the app with the adoption of the user’s position, documented through detailed field notes and screenshots (Light et al., 2018, pp. 11–12). During this step, attention is paid to three stages of app use: registration or entry, everyday use, and the app’s suspension. Finally, during the third step, the results of the analysis of the environment of expected are combined with the field notes generated through the technical walkthrough to conclude about the developers’ intentions behind certain features and overall app’s technical and cultural influence on the user.

This method resolves the methodological problems, outlined above, as it provides the means of studying a video game through direct engagement, and structures and systematizes this engagement to produce knowledge. Another strength of the walkthrough method relevant to my analysis lies in its requirement of examination of ancillary sources in order to consider the game’s underlying political economy. By synthesizing publicly available information about the game with the field notes about the embedded manipulative gameplay mechanics and UI features, this method will allow me to build a complete picture of the eighth generation Triple-A game and the formatting strategies that govern its production.

However, considering that this analytical approach was tailored for studying mobile apps, and that Triple-A video games, while adopting some of their revenue models, still constitute a different cultural and technical object, the walkthrough method requires some modifications to better suit the purposes of this thesis. Thus, following Light et al.’s (2018, p. 4) advice to use the walkthrough method “flexibly and in conjunction with other methods”, I will incorporate the theoretical lens introduced in the previous chapter into the chosen method and adjust its focus to fit my inquiry.

Firstly, while analyzing the UIs of studied games, I will adopt Ash et al.’s (2018b) vocabulary of friction, threshold, and transition, since it provides a better opportunity to illuminate the intentional modulation of the player than the mediators’ analysis, proposed by Light et al. (2018, p. 11).

Secondly, since a suspended video game account that does not make any purchases or produces any telemetry generates no value for the game developers, the last step of the technical walkthrough dedicated to analyzing the closure and leaving of the app is omitted in my research.

Thirdly, during the first step of establishing the environment of expected use, instead of analyzing the game creators’ vision of their product and assessing the formal signs of their

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21 governance, I investigate their publishing strategies and practices of game analytics’ application, while also paying more attention to the revenue model of the studied games, since these aspects are crucial for the purpose of this research.

Fourthly, since my inquiry is concerned with the possibilities for players’ manipulation through game developers’ monetization practices, in the technical walkthrough I use a narrow scope of identifying specific gameplay and UI features that steer the player towards value-making behavior. While the walkthrough method is introduced in the context of the analysis of cultural connotations embedded in the object of study (like race or gender representations), and exploration of unexpected user practices (Light et al., 2018, pp. 11, 15), both of these issues lie outside of the scope of this thesis.

Lastly, the findings from ancillary media investigation and the games’ technical walkthrough are synthesized to judge whether the determined monetization practices correspond to the principles of hypernudging. In this adjusted form, the walkthrough method allows me to produce the outlined bodies of knowledge and answer the posed research question.

A more detailed explanation of my application of the walkthrough method is unfolded in the third section of this chapter. However, before explaining the steps of my data collection, in the next section I introduce the sample of Triple-A video games that I have chosen for analysis.

3.2 Sample selection

As was mentioned previously in Chapter 2, according to Nieborg (2014, pp. 50–52), the production of Triple-A video game cultural commodity is largely influenced by formatting strategies, which make game developers follow the path of reproduction of successful gameplay mechanics and revenue models. This is one of the key insights on which this thesis draws from, and that also helps to establish the right sample size for the analysis.

Since Nieborg’s (2014) conception of the Triple-A game that I adopt for my inquiry considers replication of moneymaking tactics and techniques inherent to its production cycle, an analysis of a rather small sample of titles is sufficient to diagnose the trends in the industry. In own study of the political economy of the seventh generation Triple-A game, Nieborg (2014), while noting that a single game cannot satisfy the needs of the cultural commodity analysis, chooses a limited sample of just one game franchise – Activision’s Call of Duty series. In my analysis, I partly follow this approach. However, since I am interested in the latest practices established in the Triple-A industry, instead of choosing one game franchise, I pick one game genre that has emerged and gained popularity in the last three years for my analysis – the battle royale genre.

This genre originates from a dystopian Japanese movie called Battle Royale (2000), which tells about a violent competition for high-schoolers sought to restrain the overpopulation, in which

From a Commodity to an Asset: Reassessing the Political Economy of the Triple-A Game