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FREEMIUM KILLER APPS: BUSINESS MODEL COMPETITION AND PRODUCT
PERFORMANCE IN THE MARKET FOR DIGITAL PC GAMES ON STEAM
Joost Rietveld1
UCL School of Management
University College London
Level 38, One Canada Square, Canary Wharf,
London E14 5AA
Joe N. Ploog
UCL School of Management
University College London
Level 38, One Canada Square, Canary Wharf,
London E14 5AA
This version: June, 2020
1 Corresponding author: [email protected]
Acknowledgements: This research started when both authors held positions at the Rotterdam School of
Management, Erasmus University. They are grateful for assistance provided by the Erasmus Data Service Centre
(EDSC) in the form of collecting data from the Steam platform. The research benefitted from feedback received
at a seminar presentation at Goldsmiths University as well as from presentations at the London50 conference at
the LBS, the SMS 39th Annual Conference in Minneapolis (MN), and the UCL School of Management reading
group. The authors thank Yiting Deng, JP Eggers, Yongdong Liu, David Nieborg, and Bart Vanneste for their
feedback on earlier versions of the paper. The authors also thank Will Luton for his insights on free-to-play
games and Bill Greene for his helpful feedback on the identification of recursive bivariate probit models.
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FREEMIUM KILLER APPS: BUSINESS MODEL COMPETITION AND PRODUCT
PERFORMANCE IN THE MARKET FOR DIGITAL PC GAMES ON STEAM
Abstract: Freemium products rely on attracting a large user base for their success and are often
“hit or miss”. Which freemium products are best positioned to attain market-leading
performance, and when? Drawing on freemium’s distinguishing features of enhanced
discoverability and rapid diffusion via word-of-mouth, we argue that freemium products that
include many social features and are released when the platform has a large installed base will
have a higher probability of becoming a killer app than freemium products without these
features, or similar paid products. We test our hypotheses on a sample of 9,700 games released
on Steam, the market-leading distribution platform for digital PC games. Our results suggest
that performance heterogeneity between and among competing business models is importantly
associated with product and market-level contingencies.
Running head: Freemium Killer Apps
Keywords: Freemium, business models, network effects, product-market strategy, killer apps.
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1 INTRODUCTION
The freemium business model—wherein a base product is offered for free and consumers can
pay for additional content and features after they have adopted the base product—has gained
rapid popularity on digital distribution platforms. The share of freemium apps on the Apple
App Store, for example, increased from 25% of all apps in 2009 to over 75% in 2018. The rise
of freemium is signified by a handful of extremely successful products, such as the mobile
video game Candy Crush Saga and the online dating application Tinder. Not only do such killer
apps2 rank atop the market by number of downloads, they also generate significant revenues
from microtransactions—such as superior gameplay mechanics or enhanced access to romantic
partners. For every freemium product that attains killer app status, however, there are countless
instances of freemium products that fail. This raises important questions, such as: Which
freemium products are best positioned to becoming a killer app, and under what conditions?
The freemium business model has attracted interest from strategy scholars, who study
how freemium products compete with paid rivals. In a study of apps on the Palm computing
platform, Eckhardt (2016), for example, found that freemium apps expand the overall market
and thus have a positive spillover effect on paid apps. Studying digital PC games on Steam,
Rietveld (2018) found that freemium games are played less and generate less revenues than
paid games. In their study of freemium apps on the Apple App Store, Tidhar and Eisenhardt
(2020) found that the freemium business model complements complex product designs and
heavy marketing, which entice consumers to pay for microtransactions. In their study of
smartphone apps, Bond, He and Wen (2019) found that consumers are more compelled to
recommend freemium apps than paid apps because of feelings of reciprocity toward the
developers. These important findings notwithstanding, we still know relatively little about the
2 Definitions of killer apps generally include two elements: 1) The value of a product or technology spills over to
the platform on which it is released, and 2) the product is superior to rival products on the same platform. Since
our interest is in the adoption of the product itself (rather than its effects on the platform), we follow prior work
(e.g., Yin, Davis and Muzyrya, 2014) and operationalize killer apps by using a measure of relative adoption.
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freemium business model, and why it is that some freemium products become breakout hits
whereas most others fail. Here, we aim to address some of these questions. Particularly, we
investigate the product-level features that are associated with a freemium product’s superior
download performance, and the extent this is contingent on market-level factors.
One of the key benefits of the freemium business model is the ability to generate
network effects. Freemium’s low barriers to adoption can facilitate the rapid accumulation of
a product’s user base, which is particularly beneficial when the value created for consumers is
a function of the number of other consumers using the same product (Katz and Shapiro, 1985).
We relax the commonly held assumption that network effects are a market-level construct that
apply to all products equally (Schilling, 1998; Suarez, 2004). Instead, we argue that the strength
of network effects can vary from one product to the next as a function of a product’s features
and the size of its user base (e.g., Shankar and Bayus, 2003). It is not hard to imagine that it
does: A spreadsheet application that allows for simultaneous editing and formatting of data by
users connected via the Internet will create more value by having a large user base than a
spreadsheet application lacking such functionality. The first part of our argument thus revolves
around the features that are associated with the strength of a product’s network effects.
Our argument would be incomplete, however, without considering the embedded nature
of products released on digital distribution platforms. That is, the adoption potential of any
product released on digital distribution platforms depends on the number of users on the
platform itself; the platform’s installed base (Boudreau and Jeppesen, 2015; Rietveld and
Eggers, 2018). While this is generally true for any embedded adoption decision, it can be a
particularly pertinent factor for those products that rely on accumulating a large user base for
their value propositions to fully materialize. The value created from network effects will be
limited when the market is insufficiently large to seed a product’s user base. Therefore, the
main argument we advance is that the likelihood of a freemium product attaining superior
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market performance—by leveraging network effects—is contingent on its features and on the
size of the platform’s installed base. Put differently, freemium products with social features
will have a higher probability of becoming a killer app when the platform’s installed base is
large rather than small, and this effect will be more pronounced than for paid products.
We test our arguments on a unique dataset of 9,700 games released between 2011 and
2016 on Steam—the market leading platform for digital PC games. So-called free-to-play
games generate revenues exclusively from microtransactions (such as aesthetic enhancements
or additional content) and represent roughly 10% of the sample. We first predict the probability
of a game being free-to-play and then account for this strategic decision in our outcome model,
which estimates the likelihood of a game attaining superior download performance relative to
similar games released in the same year. After controlling for various factors, including game
quality, competition, seasonality, and firm experience, we find that free-to-play games have a
14.38 percentage points higher probability of becoming a killer app than paid games. We further
find that free-to-play games with many social features (e.g., online competitive play) have a
25.58 percentage points higher probability of becoming a killer app when the platform’s
installed base is large rather than small. Moreover, the probability of becoming a killer app is
27.05 percentage points higher for free-to-play games than for paid games when they have
many social features and are released when the platform’s installed base is large. These results
are consistent with our arguments and are robust to a number of alternative specifications.
Our findings may interest scholars across a range of literatures. First, we contribute to
the literature on business models. While there is consensus that the business model can be a
source of performance heterogeneity (Casadesus-Masanell and Ricart, 2010; Lanzolla and
Markides, 2020; Zott and Amit, 2007), less is known about the drivers of heterogeneity, leading
to one firm’s business model advantage over another. This is important given that firms often
operate prototypical business models that are neither wholly unique nor imperfectly imitable
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(Teece, 2010). Our findings suggest that the effect business models have on performance
depends on a combination of factors, thus pointing to the contingent nature of business models
and the importance of having the right fit between business model, product design, and market-
level conditions (also see: Suarez, Cusumano and Kahl, 2013; Zott and Amit, 2008).
Second, we contribute by pointing to some strategic considerations for product-level
network effects. Digitization has afforded firms greater control over how they design and
commercialize their products, particularly with an eye to network effects (Shankar and Bayus,
2003; Zhu and Iansiti, 2012). We show that firms can increase the strength of their products’
network effects by adding such social features as online multiplayer functionality and cross-
platform compatibility (also see: Dou et al., 2013; Niculescu, Wu and Xu, 2018). Our results,
however, caution against unconditionally adding to a product’s social features: We further find
that freemium products with many social features experience subpar performance when there
are insufficient customers in the market to create value from network effects.
Third, we contribute to the burgeoning research on freemium. Scholars across strategic
management, information systems, and marketing are only just beginning to uncover the
distinguishing features of the freemium business model. Much of this research looked at
products’ average performance. This tends to overlook the asymmetric demand distribution
characteristic of many digital distribution platforms, wherein only a fraction of the products
captures a disproportionally large share of the overall demand (e.g., Benner and Waldfogel,
2020; Brynjolfsson et al., 2010). Our results suggest that freemium strategies can have
heterogeneous effects across the demand distribution, and that effective strategies for superior
performance may go unnoticed in models that estimate average performance outcomes.
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2 BACKGROUND AND HYPOTHESES
2.1 The Freemium Business Model
We follow prior work in conceptualizing freemium as a business model (e.g., McGrath, 2010;
Rietveld, 2018; Teece, 2010).3,4 Freemium can be defined as a business model—as opposed to
a revenue model or pricing strategy—because it dictates how a firm creates value for its
customers and the “content, structure, and governance of transactions” that afford the firm to
subsequently capture a portion of that value (Amit and Zott, 2001; p. 511). The implications of
producing and commercializing freemium products are much more consequential than deciding
on a product’s pricing strategy: At a minimum, it requires distinct capabilities in the areas of
user engagement, data analytics, and product life cycle management (Kumar, 2014; Luton, 2013;
Seufert, 2013). That is, the activities required for developing freemium products are distinct
from those required for developing paid products (also see: Lee and Csaszar, 2020).
Firms that operate the freemium business model offer a base product for free and
encourage consumers to pay for additional content or features in the form of microtransactions.
Examples include Skype’s credit for making calls to mobile phones and landlines and Tinder’s
“Super Likes” for signaling interest to potential romantic partners. Prior research has studied
the distinguishing features of the freemium business model and the conditions for when
freemium “works” (Bond et al., 2019; Kumar, 2014; Pauwels and Weiss, 2008). This research
has further looked at the dynamics between freemium and paid products that compete in the
same market—often a digital platform or app ecosystem (Eckhardt, 2016; Kübler et al., 2018;
3 The term freemium was coined in 2006 by Fred Wilson, who used it to describe a new business model where a
firm would “Give your service away for free, possibly ad supported but maybe not, acquire a lot of customers
very efficiently through word of mouth, referral networks, organic search marketing, etc, then offer premium
priced value added services or an enhanced version of your service to your customer base.” See:
https://avc.com/2006/03/my_favorite_bus/; https://avc.com/2006/03/the_freemium_bu/ (last accessed May, 2020) 4 A second stream of research (mostly in marketing and information systems) studies the phenomenon of
feature-limited apps that serve as free trial or sampling instruments for full-fledged paid companions. This
research looks at whether offering a free companion app stimulates the adoption of the paid version of the same
product (Deng, Lambrecht and Liu, 2018; Gu, Kannan and Ma, 2018), and the types of products for which this
strategy is more or less attractive (Arora, ter Hofstede and Mahajan, 2017; Liu, Au and Choi, 2014).
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Rietveld, 2018). Our study contributes to this literature as it attempts to understand whether
freemium products are more likely than paid products to attain market-leading performance,
and the conditions for when freemium products are more (or less) likely to achieve this.
2.2. Differences Between Freemium and Paid Products
There are several differences between freemium and paid products. First, the user base of
freemium products is often characterized by significant heterogeneity (Rietveld, 2018). This is
because freemium products can be adopted free of charge. The lack of adoption barriers entices
users across a wide range of willingness-to-pay to adopt and use freemium products.
Second, freemium products offer a menu of paid items at various price points.
Decomposition of the product bundle into smaller pieces allows heterogeneous users to mix-
and-match a combination of microtransactions that corresponds with their willingness-to-pay
(Langlois and Robertson, 1992). Freemium price menus range from highly decomposed to one-
off payments that unlock all available content in a product, and everything in between.
Third, payments for freemium content are temporally decoupled from consumers’
initial use. That is, consumers first use the base product for some time before they pay for
microtransactions. Temporally decoupling a product’s use from discretionary payments has
been found to negatively impact consumers’ perceived value in use (Datta, Foubert and Van
Heerde, 2015; Gourville and Soman, 1998). Subsequently, only a very small portion of a
freemium product’s user base pays for additional content, and there exists substantial variation
in the amount users spend (Pauwels and Weiss, 2008). In the video game industry, for example,
it is well-established that between two and five percent of freemium gamers spends any money
on microtransactions (Luton, 2013; Seufert, 2013). Freemium products must therefore attract a
disproportionately large user base (relative to paid products) to generate revenues.
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Last, freemium products enjoy stronger word-of-mouth (WOM) than paid products.
Consumers are much more likely to recommend freemium products than paid products (Oh,
Animesh and Pinsonneault, 2016). This is because consumers are more inclined to recommend
products that exhibit low risks to adoption, such as those that are free. Consumers are also more
likely to recommend freemium products because they feel compelled to reciprocate the benefits
they receive by endorsing the firm’s products (Bond et al., 2019).
Given these differences, we expect that the performance of freemium products will be
determined by factors different from those determining the performance of paid products. After
developing our baseline hypothesis about the main effect of freemium, we next develop our
central arguments about the contingent effects of a product’s social features and the size of the
platform’s installed base on the likelihood of a product attaining market-leading performance.
2.3. Main Effect of Freemium on Adoption
As a baseline prediction, we expect that freemium products will be more likely than paid
products to attain market-leading performance (i.e., become a killer app). Low barriers to
adoption give freemium products an edge over paid products when it comes to attracting a large
user base. Freemium’s adoption advantage is further boosted by two additional factors. First,
products that are free are generally perceived as offering markedly higher ex-ante benefits than
products that are paid—even when the price of paid items approaches zero (Shampanier, Mazar
and Ariely, 2007). Second, low barriers to adoption facilitate the discoverability and diffusion
of freemium products. For reasons noted earlier, freemium products enjoy stronger WOM than
paid products (Bond et al., 2019; Oh et al., 2016). This compounds the overall adoption
advantage that freemium products have over paid products. We therefore expect that:
Hypothesis 1: Freemium products will be more likely to become a killer app than paid products.
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2.3. The Contingent Effects of Freemium on Adoption
Similar to variation in the strength of WOM between product types (freemium and paid
products, in our case), variation also exists within product types. That is, when consumers value
a product highly, they will be more inclined to recommend it than when they experience less
value (Berger and Schwartz, 2011). Consumers can value products for a variety of reasons
(Boatwright, Kalra and Zhang, 2008), one them being network effects (Aral and Walker, 2011).
Given their low barriers to adoption and rapid diffusion through WOM, freemium
products are advantageously positioned to create value from network effects (Shi, Zhang, and
Srinivasan, 2019; Sun, Xie and Cao, 2004). Firms can unlock network effects by adding such
social features as online connectivity, multiplayer functionality, or compatibility with other
products or technologies.5 Implementing social features, however, is insufficient for generating
network effects. Without a large user base, network effects will fail to materialize. That is, the
value created from network effects is a function of a product’s social features and the size of
its user base (Shankar and Bayus, 2003). Given that products released on digital distribution
platforms can only be adopted by those consumers who first adopt the platform on which a
product is released, we argue that a product will be more likely to benefit from social features
when it is released on a platform with a large installed base. Products released on a platform
with a small installed base have a limited adoption potential, whereas products released on a
platform with a large installed base have a large adoption potential (Boudreau and Jeppesen,
2015; Rietveld and Eggers, 2018). Even though all products that are released at the same time
will face an equally sized installed base (i.e., adoption potential), the value created from adding
social features will be higher when the platform’s installed base is large rather than small.
5 Prior research in strategy has treated network effects mostly as a market-level construct (e.g., Schilling, 1998; Suarez,
2004), implying that all products enjoy similar network effects. There is precedent, however, to suggest that network effects
can vary between products as a function of their design and features. Basu, Mazumdar and Raj (2003), for example, found
that consumers experience greater value from CD title availability for those CD players that include changer capacity and
oversampling functionality than CD players lacking such functionality (also see: Dou et al., 2013; Niculescu et al., 2018).
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Imagine the extreme case of a platform with an installed base of one. In this scenario,
a product’s social features will have no bearing on the likelihood of it getting adopted, let alone
being recommended. Moreover, if a product relies on network effects for a large portion of its
overall value proposition, it will be at a disadvantage compared to products that do not rely on
network effects. (Our assumption is that there exists a trade-off between a firm’s investments
in social functionality and a product’s standalone value.) In their study on market entry timing
effects, Srinivasan, Lilien and Rangaswamy (2004) found that network effects have a negative
effect on the survival rate of pioneering products—those products that are first to enter a market
when demand is still limited. Products that rely on strong network effects will need a large
adoption potential to seed their user base and take full advantage of their social features.
The above suggests that if firms are to optimally exploit the benefits conferred by the
freemium business model (i.e., rapid diffusion through WOM), they ought to take into account
the size of the platform’s installed base. When a platform has a small installed base, freemium
products have limited potential to create value from social features. Instead, consumers will
derive more value from those products that are fully focused on offering standalone value.
These products can expect stronger WOM and will therefore be more likely to attain market-
leading adoption rates. However, when a platform has a large installed base, freemium products
will be in an opportune position create value from network effects, given the large adoption
potential and low barriers to adoption. In this case, consumers will derive more value from
freemium products that include social functionality. The combination of WOM and strong
network effects will increase the likelihood these products of becoming a killer app:6
6 The logic implies that the likelihood of a freemium product becoming a killer app will be higher when a
product has many social features and is released on a platform with a large installed base than when a product
has no social features and is released on a platform with a small installed base. The combination of strong
network effects and strong WOM can set in motion a virtuous cycle that further boosts a product’s popularity.
While we do not formally test for this we can explore it empirically. Results support this argument.
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Hypothesis 2: Freemium products that are released on a platform with a large (small) installed
base will be more likely to become a killer app when they have more (less) social
product features.
There are two reasons why these effects will be more pronounced for freemium
products than paid products. First, the potential upside of a product with social features on a
platform with a large installed base is larger for freemium products than for paid products. A
product with strong network effects and strong WOM is likely to capture a large share of the
overall market, to the point where it could dominate the entire market (Schilling, 1998; Suarez,
2004). This happened, for instance, on Steam (our empirical setting) with the free-to-play game
Dota 2. Released in 2013 by publisher Valve, Dota 2 quickly became the all-time most
downloaded game on the platform with over 112 million downloads. Dota 2 was highly rated
among gamers and its online multiplayer and cooperative play features created strong network
effects that set in motion a virtuous cycle boosting the game’s overall popularity on the
platform. The second most downloaded game on Steam is Team Fortress 2, another free-to-
play game, with 43 million downloads. Freemium products will thus be more likely than paid
products to attain market-leading performance by implementing social product features.
A second, more nuanced, reason revolves around how freemium and paid products are
consumed. Recall that paid products have a less heterogeneous user base than freemium
products. Because consumers must pay before they can use a paid product, only those
consumers for which a product offers sufficient value are included in the user base. Moreover,
upfront payments can create a sunk cost effect wherein consumers will want to “get their
money’s worth” (e.g., Arkes and Blumer, 1985; Rietveld, 2018). Consumers of paid products
will be more committed to fully experience all of a product’s benefits compared to those of
freemium products. Finally, because a paid product’s use and payment are joined in time—as
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opposed to temporally decoupled—consumers perceive overall greater value in use from paid
products (Datta et al., 2015; Gourville and Soman, 1998). The above suggests that consumers’
average use rates will be higher for paid products than freemium products (something we can
check empirically). For these reasons, the strength of a paid product’s network effects will be
not so much determined by a product’s cumulative adoption as by the amount of time
consumers spend using these products. Combined, we argue that though paid products enjoy
overall lower adoption rates, the size of the platform’s installed base will be less of a
determining factor in generating network effects to attain market-leading performance:
Hypothesis 3: The interaction between the platform’s installed base and a product’s social
features will be stronger for freemium products than paid products: Freemium products
with more (less) social features that are released on a platform with a large (small)
installed base will be more likely to become a killer app than paid products with more
(less) social features on a platform with a large (small) installed base.
3 DATA SAMPLE AND MEASURES
3.1 Free-to-Play PC Games on Steam
We test our hypotheses in the context of Steam, the market-leading platform for digitally
distributed PC games for the Windows, Mac and Linux operating systems. Steam was founded
in 2003 by games publisher Valve as a platform initially for the distribution and support of its
internally developed PC games, Counterstrike and Half-Life 2. Shortly after Steam was
launched, however, Valve recognized an opportunity, as the PC gaming industry was gaining
traction, and started developing tools to facilitate game developers in offering third-party
products on the platform. The first externally developed PC games on Steam were released in
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2005, and the number of games has grown exponentially since. By 2016, Steam listed over
10,000 games—the majority of which (>99%) released by external game developers.
Developers wishing to release games on Steam must design their software to be
compatible with Steamworks, Valve’s proprietary software development kit (SDK). In 2011,
Valve added the microtransactions application programing interface (API) to Steamworks,
allowing developers to forgo charging a price for their games and instead monetize in-game
components. The introduction of this API gave rise to the freemium business model. Free-to-
play games quickly followed and have been among the most popular games on the platform.
Examples of hit free-to-play games include Dota 2 (Valve, 2013), Paladins (Hi-Rez Studios,
2016), and Heroes and Generals (Reto-Moto, 2016). These games all accumulated in excess of
10 million downloads and generate revenues from microtransactions, such as novel game
worlds, aesthetic improvements, and enhanced gameplay functionality. Valve takes a 30%
revenue share on all (micro-) transactions from externally developed games on Steam.
3.2 Data
Data were collected primarily from two sources. First, we collected game-level download data
from Steam Spy. Steam Spy is an online analytics service based on Valve’s web-based API.
Every minute, Steam Spy requests the API to survey a random sample of user profiles and
obtain lists of games these users own. Linking this information to the number of registered
users, Steam Spy estimates the total ownership for each game. Notwithstanding the fact that
Steam Spy provides estimates rather than exact download statistics, game developers are not
allowed to publicly disclose these data and the industry has largely relied on Steam Spy for
access to data from Steam. Steam Spy’s margin of error is less than 10%, and game developers
regularly confirm the accuracy of Steam Spy’s estimates for their games.7 Besides cumulative
7 For example: https://www.pcgamesn.com/steam/steam-spy-accuracy-developers (last accessed May, 2020)
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downloads for every game released between 2005 and 2017, we also collected data on games’
release dates, whether games are free-to-play, and games’ publishers. We further collected data
on the number of registered users on the Steam platform for each year in our sample.
Our second data source is Valve’s web-based public API. Using web scraping
techniques, we requested game-level data on various aspects directly from Steam. These data
include the type and number of social features embedded in a game (discussed below), a game’s
genre(s), the type of publisher and its prior experience on Steam, and several technical elements
such as system requirements and price menu data that we use for robustness checks.
The Steam Spy dataset additionally contains information on game quality as curated by
Metacritic.com. Metacritic is a publicly accessible expert review aggregation database that
collects, transforms, weighs and combines critic review scores and reports from over 180 online
and offline publications (at the time of data collection). For each game, Metacritic publishes a
so-called Metascore, which measures the weighted average of all critic scores, ranging from 0
to 100 (100 indicating a perfect score).8 Metacritic assigns different colors to its Metascores to
distinguish between “good” Metascores (green; ranging from 75 to 100), “average” Metascores
(orange; ranging from 50 to 74), and “bad” Metascores (red; scores below 50). Metascores are
a good way to control for quality given the aggregated and independent nature of this data.
We collected all our data in December, 2017. We excluded games released in 2017 from
our sample to allow games a minimum of one year to accumulate downloads on Steam.
We note that since we collected our data, Valve implemented a number of important changes to its API (in April,
2018), and also updated its terms for developers (in November, 2018). As a result of these changes, developers
may now disclose download data. Furthermore, citing GDPR legislation, Valve restricted its API functionality,
which has significantly hampered the accuracy of Steam Spy’s downloads estimates after November 2018.
For more information, see: https://www.pcgamesn.com/steam-spy-shut-down (last accessed May, 2020) 8 For more information, see: https://www.metacritic.com/about-metascores (last accessed May, 2020)
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3.3 Estimation Sample
We start our sample by considering all games released between 2011 and 2016. We excluded 142
observations that are non-game software (mostly software development tools), 119 observations
for which we do not observe any information at the publisher level, and 514 observations that
are either games bundles, demo versions, add-on packages released as standalone games, or
games that were removed from Steam during our data collection period. Our final sample for
analysis includes 9,700 digital PC games, of which 771 games are free-to-play.
---INSERT TABLE 1 HERE---
Table 1 provides an overview for some of the key measures in our data. The distribution
of downloads is heavily skewed as killer apps (defined below) generate on average 2.6 million
downloads per game, while non-killer apps generate 107 thousand downloads on average. The
asymmetry between killer apps and non-killer apps has grown over time. In 2011, killer apps
enjoyed 12.55 times as many downloads as non-killer apps, whereas in 2016 this increased to
31.14 times as many downloads.9 The share of free-to-play games for the entire population
varies between 0.05 and 0.10 each year. The share of free-to-play games within the subsample
of killer apps is 0.16. Steam’s installed base grew exponentially during our study timeframe,
from over 38 million registered users in 2011 to nearly 223 million registered users in 2016.
3.4 Variable Definitions
Dependent variable. Despite widespread use of the term killer app, there is no universal
definition—let alone operationalization—of what a killer app is. Dictionary definitions point
to the overall popularity of a product or technology feature and generally emphasize two
attributes: 1) the value of the product spills over to the platform or technology on which it is
released, and 2) the product is substantially superior compared to rival products in the same
9 The download statistics for killer apps released in 2013 are distorted by the release of Dota 2, the all-time most
downloaded (free-to-play) game on Steam with more than 112 million downloads.
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market. Prior research has looked at products’ absolute market performance (Binken and
Stremersch, 2009), as well as their relative standing (Yin et al., 2014). These studies applied
contextualized thresholds (e.g., one million units sold, being ranked among the Top 300 most
downloaded apps) to delineate killer apps from the rest of the market. One problem with using
an absolute measure of performance is that what constitutes superior performance on digital
distribution platforms evolves as the installed base grows and additional products enter the
market (Boudreau, 2012; Rietveld and Eggers, 2018). Over time, the difference between the best
and the worst-performing products becomes markedly asymmetric with the top-performing
products increasing their market share over laggard products (per Table 1 statistics).
To address this issue, we deploy a standardized measure of superior download
performance based on the subsample of all games released in the same year as a focal game.
We treat each year as a separate market, and we calculate a game’s z-score such that:
where represents game ’s standardized performance as a function of the difference between
its own cumulative downloads ( ) and the mean cumulative downloads of those games
released in the same year as game ( ), divided by the standard deviation of all games
released in the same year as game ( ). Though we lack time-varying data at the game level,
by standardizing a game’s performance, we negate potential concerns about right-censoring
our data: The z-score of a game released in 2016 is based on all other games released in 2016,
which would all be subject to the same bias (if any). A game’s z-score is contextual given that
it is based on a subset of games that are released around the same time, and therefore face
similar market conditions in terms of installed base size and competitive crowding (Boudreau,
2012; Rietveld and Eggers, 2018). Nevertheless, because it is a standardized measure, we can
meaningfully compare the z-score of a game released in 2016 to that of a game released in 2011.
17
Since freemium products rely on attracting a disproportionately large user base to
generate revenues, we believe it is imperative to categorize games as killer apps and otherwise.
Here, we determine a game’s status as a killer app by applying the following rubric:
where the variable killer app takes the value of 1 if game has a z-score ( ) equal to or greater
than 1, and 0 otherwise. It should be noted that downloads on Steam are not normally distributed
and that a z-score of 1 does not correspond with the standard normal cumulative density
function. Instead, 3.58% of the games in our sample are coded as killer apps (n=347). Prior
research on blockbuster products and breakthrough innovations, including patents, drugs and
motion pictures, either found or applied similar cut-off thresholds (e.g., Basuroy, Chatterjee
and Ravid, 2003; Kaplan and Vakili, 2015; Munos, 2009; Kneeland, Schilling and Aharonson,
2020). Our measure for market-leading performance thus is generally representative, while also
giving us sufficient power for statistical analysis. (We validate our results by applying different
thresholds as well as alternative measures for killer apps in the Robustness Tests section.)
Independent variables. First, free-to-play captures the main effect of the freemium
business model, which we use to test H1, that freemium products have an overall higher
likelihood of becoming a killer app than paid products. Free-to-play is a dummy variable that
takes the value of 1 for PC games released with the freemium business model, and 0 for paid
games. It is worth pointing out that free-to-play games on Steam generate revenues exclusively
from microtransactions given that all forms of advertising are banned from the platform.
Second, social features in our industry setting embody whether a game on Steam can
be played by more than one person, the kind of (online) connectivity between players, and the
type of multiplayer functionality. Some games on Steam are built exclusively around single-
player experiences while others include multiplayer functionality involving two or more than
two players at the same time. The potential pool of players for any multiplayer game depends
18
on the kind of connectivity a game offers, which can be either local (including players using
the same computer or those connected to a Local Area Network [LAN]) or online (including
any player that owns the game). A further distinction can be made in terms of the type of
multiplayer functionality. Some games include cooperative gameplay while others offer
competitive multiplayer functionality, or a combination of both. Finally, some multiplayer
games can only be played by players on the same operating system (i.e., Windows, Mac or
Linux), whereas others can be played across operating systems, and sometimes even by players
on different platforms altogether, such as Sony’s PlayStation 3 or Microsoft’s Xbox 360. These
options are not mutually exclusive and developers can implement them as they see fit.
---INSERT TABLE 2 HERE---
Valve requires game developers to disclose information on these design features upon
submitting their games for approval. We collected data on whether a game includes any of the
following five features: local cooperative play, online cooperative play, local competitive play,
online competitive play, and cross-platform multiplayer. We treat each of these as an equally
weighted feature that enhances the value of a game—provided that the game has a sufficiently
large user base.10 Table 2 provides distributions based on our sample, broken out by free-to-
play and paid games. While all features are equally represented between both groups, the
subsample of free-to-play games, on average, has a higher number of social features per game.
Our reported results are robust to using the log-transformation of social features as well as
using a binary measure that takes the value of 1 if a game includes any social features.
Last, installed base measures the number of registered users on Steam (in millions).
Since there is extensive research showing that the number of products on a platform—and
10 We abstain from imposing a rank order on the social features given that we do not observe the actual number
of players a game can accommodate. Moreover, cooperative play is not restricted to coordination between two
players only, and can, in fact, include coordination between large groups of players in online settings. While
local multiplayer on a single computer generally involves a limited number of players, the same does not
unequivocally hold for multiplayer via LANs, which Steam combines into a single category. To the best of our
knowledge, developers do not add or remove any social features after their games have been released.
19
especially the number of killer apps (Binken and Stremersch, 2009)—positively impacts the
adoption of the platform itself (e.g., Zhu and Iansiti, 2012), we lag installed base by one year
to avoid concerns of reverse causality. In alternative specifications we estimate the lagged
number of yearly new platform adopters (in millions) as well as the platform’s age (in months).
For H2 be supported, we expect that the two-way interaction between social features
and installed base is positive for the subsample of free-to-play games. For H3 to be supported,
we either expect a positive coefficient on the three-way interaction between free-to-play, social
features and installed base, or that the two-way interaction between social features and
installed base has a stronger positive effect for free-to-play games than paid games.
Control variables. We include a number of control variables at the market, publisher,
and game level. While the overall effect of competitive crowding in multisided platforms is
ambiguous due to the positive spillover effect of product variety on platform growth (Parker
and Van Alstyne, 2005), it is well-established that entry by similar products has a negative effect
on performance (Boudreau, 2012), especially when rival products enter the platform at the same
time as a focal product (Rietveld and Eggers, 2018). We thus control for competitive crowding
by including the variable genre competition. For each game , genre competition sums the
number of newly released games within the same genre(s) as game , 30 days before and 30
days after the game’s release, divided by the number of genres game lists on Steam. We apply
a 30-day window because video games typically have very short lifecycles and generate the
bulk of their downloads or sales shortly after their release (Nair, 2007). Genre competition can
be interpreted as the mean competition a game faces across all the genres it competes in, and
we expect it to have a negative effect on the probability of becoming a killer app.
We include two measures to account for heterogeneous capabilities and access to
resources at the firm level. First, we control for the type of publisher by including the variable
indie publisher. The video game industry broadly differentiates between independent—or,
20
indie—publishers who are smaller in size, focus their product development efforts on creative
and innovative output, and tend to have less (financial) resources available for developing and
marketing their games. Incumbent publishers, on the other hand, are larger in size, focus on
exploiting existing intellectual properties, and are usually flush with resources, financially and
otherwise. The variable indie publisher takes the value of 1 for games released by indie
publishers, and 0 for games by incumbent publishers. Second, we control for publishers’ prior
experience on Steam. Not all incumbent publishers embraced Steam when it was first launched,
while some indie publishers are Steam specialists. The variable past releases publisher counts
the number of games a publisher released on Steam over a rolling window of five years dating
back from game ’s release. We chose a rolling window rather than publishers’ cumulative
experience because prior experience may become obsolete due to the dynamic and evolving
character of the platform. We log-transform past releases publisher to account for the skew in
the data. We expect indie publisher to have a negative effect on the probability of becoming a
killer app, and past releases publisher to have a positive effect on becoming a killer app.
Finally, we control for a number of game-level factors. First, we control for game
quality by including Metacritic’s Metascore in our models. In line with Metacritic’s color-
coded schema of green, orange and red Metascores, we distinguish between games with good
Metascores, games with average Metascores and games with bad Metascores. Following
Rietveld and Eggers (2018), we include Metacritic’s review classification as a vector of
dummies and we use as the base category PC games without a score listed on Metacritic—
which typically denotes extremely poor quality. We thus expect all of the included dummies to
have a positive effect on the probability of becoming a killer app, with stronger effects for
games with good and, to lesser extent, average Metascores. Second, we control for games’
genres given that players may have heterogeneous preferences for different types of games.
Genres are the primary method used to differentiate between different types of games. On
21
Steam, games can list one or more of the following genres: Action, Adventure, Casual,
Massively Multiplayer, Racing, Role Playing Game (RPG), Simulation, Sports, and Strategy.
We include each of these as a dummy variable in our models. Third, and last, we control for
seasonality by including 11 calendar month of release fixed effects, and we exclude January as
the base category. The video game industry is characterized by strong seasonal fluctuations
both on the demand and on the supply-side, with some of the most anticipated titles being
released right before Christmas, while releases generally slump during the summer season.
4 METHODS
We aim to estimate the effect of free-to-play on a binary outcome variable; the likelihood of a
game becoming a killer app on Steam. Since we rely on archival data and cannot take advantage
of some quasi-exogenous shock, we are faced with a potential endogeneity problem: The
existence of unobserved factors that are correlated with the free-to-play variable and with our
outcome variable. Structural differences between the developers of free-to-play games and the
developers of paid games may bias our results (Rietveld, 2018; Tidhar and Eisenhardt, 2020).
Moreover, shrewd developers may refrain from releasing their best free-to-play games when
Steam’s installed base is small, while being more motivated to do so when the installed base is
large, anticipating a larger user base for their products. While we cannot fully address these
potential issues, we take a number of precautionary steps to account for the choice of business
model as well as for the potentially endogenous timing of game releases on Steam.
We control for the choice of business model by fitting a treatment effects model in
which both the treatment and the outcome are binary, also known as a recursive bivariate probit
model (Greene, 2018). This model is akin to the Heckman two-step control function, such that:
Outcome equation: ,
Treatment equation: , if , and otherwise
22
and
where is a vector of exogenous variables determining a binary outcome (i.e., killer app),
and is an endogenous dummy variable indicating the treatment condition. Note that contrary
to the Heckman two-step control function model, the outcome is observed for both
(i.e., free-to-play) and (i.e., paid). In contrast to estimators with continuous endogenous
covariates, in recursive bivariate probit models there need not be any exclusion restriction—
granted the exogenous variables provide sufficient variation—for the model to be identified.11
We include several variables in our treatment equation. Since we expect that some
genres will be better suited for the freemium business model than others, we include the vector
of genre dummies. We also include the number of social features since we expect free-to-play
games to structurally feature more social features than paid games (per Table 2). We further
include the firm-level covariates indie publisher and past releases publisher to control for
variation at the publisher-level. At the platform level, we control for the size of the installed
base and we additionally include a measure counting the number of past freemium killer apps
measured over a rolling window of three years prior to a game’s release up to one year before
a game’s release, to reflect a typical video game development cycle. We expect that at the time
games go into development, publishers will be guided by the success of the freemium business
model on Steam at the time to determine whether their games should be free-to-play or paid.
First-stage results are reported in Table A1 in the Online Appendix.
11 Formal proofs for why exclusion restrictions are not required in recursive bivariate probit models are beyond
the scope of this paper. The general intuition is that the identification of such models relies on the nonlinearity
of the function and variation in the derivatives of the probability that = 1 with respect to the covariates in
and . The treatment correction hazard ( ) is linearly independent of , even if = 1; all that is required is that
there is variation in and in . That is, as long as and are more than just constant terms, the model is
identified, even if (Greene, personal communication, March 2019).
23
The treatment effects model accounts for the nonrandom assignment of games into the
free-to-play and paid conditions. We are additionally concerned, however, that developers may
release different types of games on Steam as the platform becomes increasingly popular with
time. Such temporal variation would be problematic for our estimations if it affected free-to-
play games differently than paid games. For example, observant developers may invest in
developing multiplayer capabilities as the platform’s installed base grows over time. As such,
we implement a coarsened exact matching (CEM) algorithm to reduce the imbalance in the
empirical distribution of our covariates ( ) between free-to-play and paid PC games (Iacus,
King and Porro, 2012). Based on a set of variables (in our case: social features, installed base,
indie publisher, and past releases publisher), CEM prunes observations from the sample so
that the remaining data have better balance between the treatment and the control groups. For
each free-to-play game, the algorithm finds at least one paid game that is similar on the
matching covariates. The algorithm assigns weights based on the number of control group
observations for each treated observation, which can be used by subsequent estimators to
improve the quality of the inferences drawn (Blackwell, Iacus, King and Porro, 2009).
In sum, while our results are ultimately correlational, we attempt to account for
endogeneity that is both structural (the choice of business model) and time varying (the release
timing of games) by estimating a treatment effects model on a matched sample via CEM.
5 RESULTS
5.1 Main Results
Table 3 lists descriptive statistics and pairwise correlations for the variables in our sample. We
note a strong positive correlation between the installed base and the genre competition
variables (ρ = 0.74). Although the overall variance inflation factor (VIF) is below conventional
24
thresholds, as a robustness check we will estimate results on a model that drops the genre
competition variable to ensure that our findings are not affected by multicolinearity.
Main results are reported in Table 4. Model 1 estimates control variables, Model 2 adds
the main effect of free-to-play, Model 3 adds the various interaction effects between free-to-
play, social features, and installed base, 12 Model 4 accounts for the nonrandom treatment
assignment into free-to-play, Model 5 prunes and rebalances the sample using CEM, and Model
6 re-estimates the endogenous treatment effects model on the pruned and rebalanced sample.
--- INSERT TABLES 3 AND 4 HERE ---
The results lend support to our hypotheses. In Model 6, the most conservative of our
models, the effect on free-to-play is positive and significant (β = 2.05, p = 0.00). Consistent
with H1, we find that free-to-play games have a 14.38 percentage points higher probability of
becoming a killer app than paid games—after we account for the choice of business model and
rebalance the sample. Model 6 further lends support to H2 and H3: Consistent with H2, the
interaction between free-to-play and social features is negative and significant (β = -0.61, p =
0.01), suggesting that free-to-play games with many social features on a platform with a small
installed base are at a disadvantage. This supports the notion that free-to-play games are best
positioned by focusing on standalone value, instead of network value, when the installed base
is small. Consistent with H3, the coefficient on the three-way interaction between free-to-play,
social features and installed base is positive and significant (β = 0.004, p = 0.023).
The interpretation of three-way interaction effects, particularly for models that are non-
linear, is anything but straightforward (Hoetker, 2007; Wiersema and Bowen, 2009; Zelner,
2009). Therefore, and to further assess support for H2 and H3, we conduct two additional
analyses. First, we perform a cross-model hypothesis test on the two-way interaction between
social features and installed base for the subsamples of free-to-play and paid games. We first
12 Refer to Table A2 in the Online Appendix for stepwise regression results reporting all partial interactions
25
jointly estimate the probability of killer app for both subsamples and then perform a Chow test
to assess the difference on the interaction effect between both samples (Chow, 1960). Results
reported in Table 5 lend support to both H2 and H3. The two-way interaction between social
features and installed base is positive and significant for the subsample of free-to-play games
(β = 0.002, p = 0.060). This suggest that free-to-play games benefit from adding social features
when the platform’s installed base is large. Moreover, we note a statistically significant
difference on the interaction coefficient between the subsamples of free-to-play and paid games
(χ² = 3.91, p = 0.04), which can be explained by the absence of a significant interaction between
social features and installed base for the subsample of paid games (β = -0.001, p = 0.421).
Consistent with our arguments, this implies that paid games do not benefit from adding social
features when the installed base is large, presumably due to higher barriers to adoption.
--- INSERT TABLE 5 AND FIGURE 1 HERE ---
Second, we separately estimate the marginal effects on killer app at different values of
the social features and installed base variables for the subsamples of free-to-play and paid
games (Hoetker, 2007; Zelner, 2009). We first standardize all covariates relative to their
subsample means to facilitate interpretation and comparison of the predicted probabilities.
Based on the distribution of the social features variable in both subsamples, we chose values
of +3 and -1 standard deviations to represent games with many and few social features,
respectively. Figure 1 depicts the margins slopes. Supporting H2, the left-hand panel in the
figure suggests that free-to-play games with many social features have a 25.58 percentage
points higher probability of becoming a killer app when the platform’s installed base is large
rather than small. Moreover, free-to-play games released on a platform with a small installed
base are 21.60 percentage points more likely to become a killer app when they feature few,
rather than many, social features. We assess H3 by comparing the marginal effects between
both subsamples: Free-to-play games with many social features released on a platform with a
26
large installed base have a 27.05 percentage points higher probability of becoming a killer app
than paid games with many social features that are released on a platform with a large installed
base. In sum, between the full-sample regression results, the split-sample coefficient tests, and
the marginal effects analyses, we note evidence that is fully consistent with our hypotheses.
To interpret the coefficients on our control variables, we estimate a separate model that
does not include interaction effects nor any variable transformations (Wiersema and Bowen,
2009). We find that one additional social feature increases the probability of becoming a killer
app by 1.31 percentage points. One million additional platform users (i.e., installed base)
increases the probability of becoming a killer app by 0.02 percentage points. One hundred extra
same-genre games on the platform (i.e., genre competition) reduces the probability of
becoming a killer app by 1.18 percentage points. Games released by indie publishers have a
2.02 percentage points lower probability of being a killer app than games by incumbent
publishers. Adding ten games to a publisher’s past releases, is associated with a 0.32 percentage
points higher probability of becoming a killer app. And, games with good Metascores have a
11.16 percentage points higher probability of becoming a killer app than games with medium
Metascores, and a 12.80 percentage points higher probability than games with bad Metascores.
5.2 Robustness Tests and Mechanism Checks
The reported results are fully consistent with our hypotheses. After accounting for various
product, firm, and market-level factors, as well as correcting for the nonrandom assignment of
games into the free-to-play and paid conditions, we find that free-to-play games have an overall
higher probability of becoming a killer app, and that this effect is stronger for those free-to-
play games with many social features that are also released when the platform has a larger,
rather than smaller, installed base. That being said, there are some alternative explanations and
potential concerns that we wish to address. Below we report a number of robustness checks
and mechanism checks. Fully tabulated results are reported in the Online Appendix.
27
First, one of the arguments presented for H3 is that consumers spend less time on
freemium products than paid products. In order to create network effects, freemium products
thus need a larger user base than paid products, which enjoy higher average concurrent use
rates. To check for this, in Table A3 we re-estimate our results on games’ median playing time,
or the median minutes spent playing a game per user, as dependent variable. Results from a
linear regression with endogenous treatment effects suggest that free-to-play games indeed
have lower use rates. The median playing time for free-to-play games is 98 percentage points
lower than paid games (β = -3.92, p = 0.00). Thus, while we cannot fully tease out the
mechanisms driving our findings, these results are consistent with our suggested mechanisms.
Second, we implement a number of alternative ways to measure whether a game is a
killer app. Rather than basing a game’s z-score on the sample of games released within the
same year, we create a measure at the level of a game’s genre. In Table A4, we take the mean
of the z-scores for each genre that a game competes in, and we categorize games as killer apps
when the average of the genre-based z-scores is equal to or greater than 1. In Table A5, we
apply different thresholds for our main outcome variable by increasing the cut-off for killer
apps to be equal to or greater than 1.6 (213 games are coded as killer app under this threshold).
In Table A6, we estimate results by using a less constructed measure of killer apps, namely
whether a game is among the top 5% of most downloaded titles within a given year. Results
from all these alternative variable measurements are consistent with our main results.
Third, we estimate two alternative operationalizations for the social features measure.
The first measure, reported in Table A7, takes the log-transformation of the number of social
features to account for some skewness in this variable. The second measure, reported in Table
A8, takes the value of 1 if a game has any social features, and 0 if a game lacks any social
features. Results from both these robustness checks are fully consistent our main results.
28
Fourth, we address concerns related to the installed base variable. It could be argued
that by looking at the cumulative installed base we are overstating the adoption potential for a
game (Nair, Chintagunta and Dubé, 2004). The reason is that consumers typically are most
active in their engagement with the products available on a platform, shortly after adopting the
platform. After some time, despite still being included in the installed base, consumers may
become disengaged. To address this issue, we estimate the effect of the lagged new platform
adoption (in millions) in Table A9 as an alternative measure for installed base. Our results
remain fully supported. We also look at platform age (in months) as another way to measure
the effect of the platform’s installed base. In this robustness check, we fail to fully reject the
null hypotheses: the three-way interaction for our full-sample results, reported in Table A10, is
not statistically significant. That said, the two-way interaction in the split sample analysis
remains significant at conventional levels. One potential explanation for not fully supporting
our hypotheses might be that platform age fails to take into account the non-linear—s-shaped—
accumulation of the platform’s installed base over time (e.g., Adner and Kapoor, 2016).
Fifth, we add and remove some of our control variables to further assess the sensitivity
of our results. To address concerns relating to potential multicolinearity arising from the strong
correlation between installed base and genre competition (per Table 3), we re-estimate our
results by excluding genre competition in Table A11. In Table A12, we replace publisher age (in
months) for past releases publisher, as an alternative proxy for firm experience and access to
resources. We also add a number of additional control variables to our models. First, in Table
A13 we add games’ system requirements in the form of random access memory (in gigabytes),
hard-disk space (in gigabytes), and processor (in gigahertz) requirements (Ghose and Han,
2014). In Table A14, we control for games’ price menu variety by adding the log-transformation
of the count of downloadable items as well as a dummy indicating whether a game offers any
microtransactions. Finally, in Table A15, we control for whether a game is published by Valve.
29
We expect Valve to hold superior market intelligence, which may structurally increase the
firm’s probability of releasing a hit game. The variable’s positive coefficient seems to suggest
that it does. In all of these alternate specifications, we note full support for our hypotheses.
--- INSERT FIGURE 2 HERE ---
Last, we assess the effects of operating the freemium business model by taking into
consideration the entire download performance distribution. If free-to-play games are
overrepresented in both tails of the distribution, this would imply that not only the potential
upside, but also the risks from operating the freemium business model are disproportional. That
said, if free-to-play exclusively has a strong positive effect in the head of the performance
distribution, this would imply that there is no apparent downside to operating the freemium
business model (on Steam), and also that our results are specific to the head of the performance
distribution (i.e., killer apps). To test for this, we jointly estimate the effect of free-to-play on
games’ z-scores at several points in the distribution via simultaneous quantile regressions with
bootstrapped standard errors. The results depicted in Figure 2 suggest that with the exception
of the highest quantiles (i.e., Q90 and Q99), the free-to-play variable only has a marginally
positive effect on games’ download performance on Steam. For the highest quantiles, however,
the effect of free-to-play is highly positive as well as statistically significant, suggesting that
free-to-play games are particularly more likely to become a killer app, which is consistent with
our main analyses and also with the arguments presented to build our hypotheses.
6 DISCUSSION
Recognizing the growing importance and prevalence of the freemium business model in the
digital economy, this study asked: Which freemium products are best positioned to becoming a
killer app, and under what conditions? We addressed these questions by analyzing a unique
sample of 9,700 games released between 2011 and 2016 on Steam, the market-leading platform
30
for digital PC games. We found that freemium games have a 14.38 percentage points higher
probability of attaining superior performance than paid games. Furthermore, we found 1) that
freemium games with many social features, such as online multiplayer or cross-platform
compatibility, have a 25.58 percentage points higher probability of becoming a killer app when
the platform’s installed base is large rather than small, 2) that freemium games on a platform
with a large installed base are 18.80 percentage points more likely to become a killer app when
they have many rather than few social features, and 3) that freemium games with many social
features on a platform with a large installed base have a 27.05 percentage points higher
probability of becoming a killer app than paid games with many social features on a platform
with a large installed base. Our findings hold implications for how the freemium business
model can compete with paid products, and how the design of freemium products interacts with
market-level factors to shape superior performance in the presence of network effects.
Our findings contribute to the business model literature by pointing to the contingent
nature of business models as a source of performance heterogeneity. There is a growing body
of research stating that the business model itself can create and capture value (e.g., Casadesus-
Masanell and Ricart, 2010; Lanzolla and Markides, 2020; Zott and Amit, 2007). However, this
tends to overlook that firms often operate generic business models that are neither wholly
unique nor imperfectly imitable (Teece, 2010). It is perhaps for this reason that prior research
has started to identify contingencies that can hinder or strengthen the value creation potential
of a business model, such as the maturity of an industry (Suarez et al., 2013), or a firm’s
product-market strategy (Zott and Amit, 2008). Our work confirms these findings and
contributes by demonstrating that the likelihood of attaining superior performance is contingent
on a combination of market and product-level factors. In the context of PC games released on
the Steam platform, we found that the freemium business model is more likely to be associated
31
with superior market performance (than the traditional paid model) when it is linked to a
product with many social features and launched on a platform with a large installed base.
Our research further contributes to the literature on network effects. We explored the
implications of allowing for variation in the strength of network effects across products
competing in the same market. Our results suggest that firms can indeed increase the value
created from network effects by adding social features to their product designs (also see: Dou
et al., 2013; Niculescu et al., 2018). Adding social features, however, can be a double edged
sword. One the one hand, we found that freemium products with many social features that are
released on a platform with a large installed base were among the products (freemium and paid
products combined) most likely to attain superior market performance. On the other hand, we
also found that when freemium products with many social features are released on a platform
with a small installed base, they were among the products least likely to become a killer app.
Compromising a product’s standalone value for network effects thus can be perilous when the
downstream market is insufficiently large to seed a product’s user base and take full advantage
of social features (Srinivasan et al., 2004)—even when the product is free (Shi et al., 2019; Sun
et al., 2004). These findings were less pronounced for paid products, because, we argue,
network effects for paid products are derived to a lesser extent from a product’s cumulative
adoption and more so from its higher use rates. Taken together, our findings suggest that there
are important trade-offs firms need to consider when it comes to designing their products to
take advantage from network effects versus relying on standalone product value.
Our third and last contribution is to the literature on freemium strategies and digital
distribution platforms more broadly. Together with Eckhardt (2016) and Kübler et al. (2018),
our work is among the first to explicitly consider freemium strategies in the context of a
multisided platform setting. While several prior studies are based in platform settings (e.g.,
Arora et al., 2017; Deng et al., 2018), these do not take into account the embedded nature of
32
apps or games released in such contexts. Digital platforms are characterized by a rather uneven
distribution of demand, wherein only a fraction of the products captures a disproportionally
large share of the overall demand (Brynjolfsson et al., 2010). This has given rise to the long-
tail phenomenon, and has prompted researchers to look for effective product strategies at
different points in the demand distribution (Benner and Waldfogel, 2020). Our empirical
strategy of estimating the likelihood of a freemium product becoming a killer app was directly
informed by the fact that products on digital distribution platforms can be “hit or miss”. Our
results suggest that freemium strategies can have heterogeneous performance effects across the
demand distribution, and some of our empirical results imply that the positive effects of adding
social features to freemium products would have gone unnoticed if we had estimated our results
using conventional estimation techniques. Though freemium products may have lower average
use and adoption rates (cf. Rietveld, 2018), they are rather well-positioned to attain market-
leading performance by virtue of combining network effects with strong WOM.
We highlight a number of areas for further research. First, our work has certain
limitations, including a lack of longitudinal data at the game level, the absence of revenue data
from microtransactions, and the archival nature of our data, preventing us from tightly
identifying effects. We invite future research to address these shortcomings anticipating that it
will shed additional light on how the freemium business model works. For example, it is
important to gain a better understanding about the life cycle dynamics of freemium products.
How do adoption patterns for freemium products differ from those of paid products? At what
point do freemium killer apps fail to retain and engage their users, and why? These questions
are particularly relevant as consumers seem to lose interest in freemium killer apps virtually
overnight—as witnessed by H1Z1, a once dominant freemium game on Steam that got abruptly
dethroned by PlayerUnknown’s Battleground, another freemium game in the same genre.
Future research may look into the monetization of freemium products. Little is known about
33
the design and strategies for microtransactions in freemium products (for an exception, see:
Tidhar and Eisenhardt, 2020). This is a complex issue at the intersection of product
development and consumer psychology, given the modular nature of freemium products and
the heterogeneity in these products’ user bases. Archival data is unlikely to generate conclusive
results, which may be better off addressed via experiments or machine learning techniques.
Finally, we point to some interesting ancillary results from our analyses. Publishers
strategically chose the freemium business model based on a number of platform, firm, and
product-level factors. In our first-stage regressions, we noted that independent publishers and
publishers with more experience on Steam were less likely to operate the freemium business
model. Remarkably, in our outcome regressions we found that when such publishers do operate
the freemium business model, they have a higher likelihood of their games becoming a killer
app than incumbent publishers and publishers with less experience. Furthermore, games by
independent publishers were marginally more likely to become a killer app when they are free-
to-play rather than paid. Herein seemingly lies a discrepancy between the choices managers
make and the consequences of these choices. When and why do managers make suboptimal
decisions in terms of their choice of business model? Another interesting result is the
differential effect of competition. We found that competition had a much stronger effect on the
likelihood of becoming a killer app for freemium games than paid games. One potential
explanation for this result is that consumers are more likely to use a sampling approach when
it comes to gauging their willingness-to-pay for freemium products, rather than relying on such
factors as expert reviews or other sources of external information as is the case for paid
products. A greater number of products to choose from can spread consumers’ time and
attention thin, which is problematic for freemium products that already have a less active and
less engaged user base compared to paid products. Future research may want to investigate the
heterogeneous effects of competition on the performance of competing business models.
34
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37
TABLES AND FIGURES
Table 1. Steam Game and Platform Statistics by Year
Table 2. Distribution of Social Features by Game Type
Table 3. Descriptive Statistics and Pairwise Correlations
Year 2011 2012 2013 2014 2015 2016 All
All games 256 332 462 1,609 2,661 4,380 9,700
Free-to-play games 12 21 28 81 190 439 771
Share of killer apps in
Paid 0.07 0.05 0.00 0.04 0.02 0.02 0.03
Free-to-play 0.25 0.19 0.18 0.36 0.22 0.09 0.16
Mean downloads
Killer app = 1 4,598,066 5,859,613 31,996,919 1,735,460 1,633,832 1,470,115 2,633,332
Killer app = 0 366,269 403,848 472,266 94,787 56,840 38,543 106,677
Platform statistics
New platform adoption 10,708,000 13,747,000 25,579,000 36,915,000 47,682,000 60,317,000 32,491,333
Installed base 38,738,000 52,485,000 78,064,000 114,979,000 162,661,000 222,978,000 222,978,000
Notes. Based on estimation sample.
Social features (type) Net difference
Local cooperative play 115 628 7.88%
Online cooperative play 50 196 4.29%
Local competitive play 288 1,395 21.73%
Online competitive play 118 367 11.19%
Cross-platform multiplayer 120 391 11.19%
Social features (count) Net difference
0 428 7,328 -26.56%
1 122 730 7.65%
2 139 539 11.99%
3 50 202 4.22%
4 19 87 1.49%
5 13 43 1.20%
Free-to-play games Paid games
Free-to-play games Paid games
Notes. Based on estimation sample. Local play includes multiplayer functionality on the same PC as well as over
multiple PCs connected to the same local network (local area network, or LAN). Cross-platform multiplayer facilitates
online multiplayer functionality between Steam accounts from different operating systems (i.e., Windows, Mac and
Linux) as well as non-Steam platforms (e.g., PlayStation 3 or Xbox 360), and includes either cooperative or competitive
play. The average free-to-play game has 0.90 social features and the average paid game has 0.33 social features.
Mean St Dev Min Max 1 2 3 4 5 6
1 Killer app 0.04 0.19 0.00 1.00
2 Free-to-play 0.08 0.27 0.00 1.00 0.19
3 Social features 0.38 0.88 0.00 5.00 0.17 0.17
4 Installed base t-1 122.51 42.20 28.03 162.66 -0.06 0.07 -0.02
5 Genre competition 159.90 100.59 1.00 456.00 -0.08 -0.11 -0.06 0.74
6 Indie publisher 0.67 0.47 0.00 1.00 -0.10 -0.07 -0.03 0.16 0.21
7 ln(Past releases publisher) 1.05 1.32 0.00 4.82 0.05 -0.13 -0.06 -0.12 -0.08 -0.37
Variable
Notes. Based on estimation sample (n=9,700). Mean variance inflation factor (VIF) = 2.87.
38
Table 4. Probit Regressions Estimating the Probability of Killer App
Variable 1 2 3 4 5 6
Free-to-play 1.07
[0.09]
2.30
[0.33]
1.06
[0.55]
2.41
[0.41]
2.05
[0.57]
Free-to-play x Social features -0.48
[0.18]
-0.39
[0.18]
-0.60
[0.25]
-0.61
[0.25]
Free-to-play x Installed base t-1 -0.009
[0.002]
-0.007
[0.002]
-0.008
[0.003]
-0.008
[0.003]
Social features x Installed base t-1 -0.0005
[0.0006]
-0.0004
[0.0005]
-0.006
[0.009]
-0.006
[0.009]
Free-to-play x Social features x
Installed base t-1
0.003
[0.001]
0.003
[0.001]
0.004
[0.002]
0.004
[0.002]
Social features 0.25
[0.02]
0.23
[0.03]
0.31
[0.08]
0.31
[0.07]
0.34
[0.13]
0.33
[0.13]
Installed base t-1 0.008
[0.001]
0.004
[0.001]
0.006
[0.001]
0.006
[0.001]
0.002
[0.002]
0.002
[0.002]
Genre competition -0.004
[0.001]
-0.002
[0.001]
-0.003
[0.001]
-0.003
[0.001]
-0.001
[0.001]
-0.001
[0.001]
Indie publisher -0.35
[0.06]
-0.27
[0.06]
-0.29
[0.06]
-0.36
[0.06]
-0.17
[0.10]
-0.16
[0.11]
ln(Past releases publisher) 0.05
[0.02]
0.10
[0.02]
0.10
[0.02]
0.05
[0.03]
0.09
[0.05]
0.09
[0.05]
Quality dummies (3) Yes Yes Yes Yes Yes Yes
Genre dummies (9) Yes Yes Yes Yes Yes Yes
Month of release dummies (11) Yes Yes Yes Yes Yes Yes
Endogenous treatment correction No No No Yes No Yes
Matched and rebalanced sample No No No No Yes Yes
Constant -2.57
[0.18]
-2.59
[0.18]
-2.83
[0.21]
-2.57
[0.25]
-2.98
[0.32]
-2.82
[0.33]
McFadden's Pseudo R2 0.24 0.29 0.29 0.33
Observations 9,700 9,700 9,700 9,700 7,680 7,680
Notes. Heteroskedasticity robust standard errors in parentheses. Model 4 estimates an endogenous treatment
effects, or recursive bivariate probit, model (STATA 15 command: eprobit ). First stage results for assignment
to free-to-play are reported in the Online Appendix (A1). The correlation between the error terms for both
models is 0.49 (p = 0.001). Model 5 prunes and rebalances the sample based on coarsened exact matching
strata (STATA 15 command: cem). The matching for free-to-play is based on Social features , Installed
base , Indie publisher, and Past releases publisher (155 matched strata). After matching, the overall
imbalance statistic, or L1 distance, improves from 0.44 to 0.05, indicating smaller imbalance. Model 6 re-
estimates the endogenous treatment effects model based on the matched and rebalanced sample.
Killer app
39
Table 5. Subsample Regressions Estimating the Probability of Killer App
Variable Free-to-play Paid Chow (χ²)
Social features x Installed base t-1 0.002
[0.001]
-0.001
[0.001]
3.91
Social features -0.35
[0.20]
0.34
[0.16]
7.85
Installed base t-1 -0.003
[0.003]
-0.002
[0.002]
0.06
Genre competition -0.005
[0.001]
0.001
[0.001]
11.83
Indie publisher 0.38
[0.22]
-0.14
[0.16]
3.68
Past releases publisher 0.17
[0.08]
0.09
[0.06]
0.70
Quality dummies (2) Yes Yes -
Genre dummies (9) Yes Yes -
Month of release dummies (11) Yes Yes -
Endogenous treatment correction Yes Yes -
Matched and rebalanced sample Yes Yes -
Constant 10.13
[3.84]
-2.10
[0.70]
4.23
Observations 758 6,922
Killer app
Notes. Heteroskedasticity robust standard errors in parentheses. Seemingly unrelated
regressions (STATA 15 command: suest ) estimating the probability of killer app for
free-to-play and paid games based on the matched and rebalanced sample
(n=7,680). The last column reports cross-model hypotheses, or Chow, tests estimating
the difference in a covariate's coefficients across both models (χ² statistic).
40
Figure 1. Predicted Probability Plots Based on Subsample Analyses
Notes. Covariates have been mean-standardized based on their respective subsamples to facilitate
comparison of results. The values for social features (SF) were chosen based on the variable’s
distribution. Further note that although the 95% confidence intervals overlap for some of the estimates
in the subsample of free-to-play games, confidence intervals provide only partial reliability for judging
the significance of differences between assumed values of a variable (Krzywinski and Altman, 2013).
Figure 2. The Effect of Free-to-Play Across the Distribution of Games’ Z-Scores
Notes. Each dot represents the effect of free-to-play on games’ z-score (or, their standardized download
performance) at a respective quantile, while controlling for the same covariates as those reported in
Model 2 of Table 4 (our main results). The capped bars represent 95% confidence intervals. Results
suggest that there is no statistically significant effect of free-to-play on games’ z-score for most part of
the performance distribution, with the exception of the highest quantiles—games we label killer apps.
41
ONLINE APPENDIX FOR FREEMIUM KILLER APPS
Contents:
A1. First-Stage Results for Treatment Effects Model
A2. Stepwise Models for Main Results
A3. Median Playing Time as Outcome Variable
A4. Genre-based DV (average Z-score across genres; Z ≥ 1)
A5. Alternative threshold for Z-score (z ≥ 1.6)
A6. DV based on Top 5% of most downloaded games in year
A7. Log of games’ social features
A8. Social features operationalized as a binary construct
A9. New platform adoption as measure of installed base
A10. Platform age as measure of installed base
A11. Drop competition to reduce multicollinearity
A12. Publisher age as measure of publisher experience
A13. Add system requirements (HD, RAM, processor) as additional controls
A14. Add price menu variety (DLC, microtransactions) as additional controls
A15. Add published by Valve as additional control
42
A1. First-Stage Results for Treatment Effects Model
Free-to-play
Variable 1
Past freemium killer apps 0.005
[0.002]
Social features 0.18
[0.02]
Installed base t-1 0.001
[0.001]
Indie publisher -0.41
[0.05]
Past releases publisher -0.27
[0.02]
Genre dummies
Action -0.35
[0.05]
Adventure -0.22
[0.05]
Casual -0.0001
[0.0466]
Massively multiplayer online 1.54
[0.09]
Racing -0.38
[0.13]
Role playing -0.005
[0.056]
Simulation -0.19
[0.06]
Sports -0.08
[0.10]
Strategy -0.11
[0.05]
Constant -1.15
[0.10]
McFadden's Pseudo R2
0.19
Observations 9,700
Notes . Heteroskedasticity robust standard errors
in parentheses. Mean model VIF = 3.71.
43
A2. Stepwise Models for Main Results (Partial Interactions for Table 4)
Variable 1 2 3 4 5 6 7
Free-to-play 1.07
[0.09]
1.14
[0.10]
1.70
[0.23]
1.77
[0.24]
1.06
[0.09]
2.30
[0.33]
Free-to-play x Social features -0.07
[0.05]
-0.07
[0.05]
-0.48
[0.18]
Free-to-play x Installed base t-1 -0.005
[0.002]
-0.005
[0.002]
-0.009
[0.002]
Social features x Installed base t-1 -0.0003
[0.0005]
-0.0005
[0.0006]
Free-to-play x Social features x
Installed base t-1
0.003
[0.001]
Social features 0.25
[0.02]
0.23
[0.03]
0.25
[0.03]
0.23
[0.03]
0.25
[0.03]
0.26
[0.07]
0.31
[0.08]
Installed base t-1 0.008
[0.001]
0.004
[0.001]
0.004
[0.001]
0.006
[0.001]
0.006
[0.001]
0.004
[0.001]
0.006
[0.001]
Genre competition -0.004
[0.001]
-0.002
[0.001]
-0.002
[0.001]
-0.003
[0.001]
-0.003
[0.001]
-0.002
[0.001]
-0.003
[0.001]
Indie publisher -0.35
[0.06]
-0.27
[0.06]
-0.27
[0.06]
-0.28
[0.06]
-0.28
[0.06]
-0.27
[0.06]
-0.29
[0.06]
ln(Past releases publisher) 0.05
[0.02]
0.10
[0.02]
0.10
[0.02]
0.10
[0.02]
0.10
[0.02]
0.10
[0.02]
0.10
[0.02]
Quality dummies (3) Yes Yes Yes Yes Yes Yes Yes
Genre dummies (9) Yes Yes Yes Yes Yes Yes Yes
Month of release dummies (11) Yes Yes Yes Yes Yes Yes Yes
Constant -2.57
[0.18]
-2.59
[0.18]
-2.61
[0.18]
-2.77
[0.19]
-2.79
[0.19]
-2.62
[0.20]
-2.83
[0.21]
McFadden's Pseudo R2 0.24 0.28 0.29 0.29 0.29 0.29 0.29
Observations 9,700 9,700 9,700 9,700 9,700 9,700 9,700
Notes. Heteroskedasticity robust standard errors in parentheses.
Killer app
44
A3. Median Playing Time as Outcome Variable
Variable 1 2 3 4 5
Free-to-play -1.76
[0.05]
-3.59
[0.11]
-1.74
[0.07]
-3.92
[0.13]
Social features 0.06
[0.02]
0.10
[0.01]
0.15
[0.02]
0.09
[0.02]
0.07
[0.03]
Installed base t-1 -0.003
[0.001]
0.003
[0.001]
0.004
[0.001]
0.002
[0.002]
0.001
[0.002]
Genre competition 0.003
[0.0004]
0.0001
[0.0003]
0.00002
[0.0003]
-0.001
[0.001]
-0.0005
[0.001]
Indie publisher 0.04
[0.04]
-0.05
[0.03]
-0.17
[0.04]
0.02
[0.07]
0.06
[0.07]
ln(Past releases publisher) 0.13
[0.01]
0.08
[0.01]
0.03
[0.01]
0.18
[0.03]
0.18
[0.03]
Quality dummies (3) Yes Yes Yes Yes Yes
Genre dummies (9) Yes Yes Yes Yes Yes
Month of release dummies (11) Yes Yes Yes Yes Yes
Endogenous treatment correction No No Yes No Yes
Matched and rebalanced sample No No No Yes Yes
Constant 4.31
[0.09]
4.17
[0.09]
4.32
[0.09]
4.30
[0.19]
4.59
[0.19]
McFadden's Pseudo R2 0.08 0.17 0.19
Observations 9,246 9,246 9,246 7,268 7,268
ln(Median playing time)
Notes. Heteroskedasticity robust standard errors in parentheses. Model 3 estimates a linear regression with
endogenous treatment effects, model (STATA 15 command: etregress ). First stage results for assignment
to free-to-play are reported in Table A1. The correlation between the error terms for both models is 0.78
(p = 0.00). Model 4 prunes and rebalances the sample based on Coarsened Exact Matching strata
(STATA 15 command: cem). The matching for free-to-play is based on social features , installed base ,
indie publisher and past releases publisher (155 matched strata). After matching, the overall imbalance
statistic, or L1 distance, improves from 0.44 to 0.05, indicating smaller imbalance. Model 5 re-estimates the
endogenous treatment effects model based on the matched and rebalanced sample.
45
A4. Genre-Based DV (Average Z-Score Across Genres; Z ≥ 1)
Variable 1 2 3 4 5 6
Free-to-play 1.01
[0.08]
2.05
[0.31]
0.99
[0.53]
2.09
[0.39]
1.52
[0.53]
Free-to-play x Social features -0.40
[0.18]
-0.32
[0.17]
-0.44
[0.23]
-0.44
[0.22]
Free-to-play x Installed base t-1 -0.008
[0.002]
-0.007
[0.002]
-0.007
[0.003]
-0.007
[0.003]
Social features x Installed base t-1 -0.0009
[0.0005]
-0.0009
[0.0005]
-0.0015
[0.0008]
-0.0015
[0.0008]
Free-to-play x Social features x
Installed base t-1
0.003
[0.001]
0.003
[0.001]
0.003
[0.002]
0.003
[0.002]
Social features 0.25
[0.02]
0.23
[0.02]
0.34
[0.08]
0.35
[0.07]
0.43
[0.12]
0.42
[0.12]
Installed base t-1 0.006
[0.001]
0.002
[0.001]
0.004
[0.001]
0.005
[0.001]
0.003
[0.002]
0.003
[0.002]
Genre competition -0.003
[0.001]
-0.001
[0.001]
-0.001
[0.001]
-0.001
[0.001]
-0.0005
[0.001]
-0.0004
[0.001]
Indie publisher -0.19
[0.05]
-0.12
[0.06]
-0.13
[0.06]
-0.19
[0.06]
-0.03
[0.09]
-0.02
[0.09]
ln(Past releases publisher) 0.07
[0.02]
0.12
[0.02]
0.12
[0.02]
0.08
[0.03]
0.12
[0.04]
0.12
[0.04]
Quality dummies (3) Yes Yes Yes Yes Yes Yes
Genre dummies (9) Yes Yes Yes Yes Yes Yes
Month of release dummies (11) Yes Yes Yes Yes Yes Yes
Endogenous treatment correction No No No Yes No Yes
Matched and rebalanced sample No No No No Yes Yes
Constant -2.46
[0.16]
-2.45
[0.16]
-2.67
[0.18]
-2.48
[0.21]
-2.97
[0.30]
-2.85
[0.31]
McFadden's Pseudo R2 0.17 0.22 0.22 0.26
Observations 9,700 9,700 9,700 9,700 7,680 7,680
Notes. Heteroskedasticity robust standard errors in parentheses. Model 4 estimates an endogenous treatment effects, or
recursive bivariate probit, model (STATA 15 command: eprobit ). First stage results for assignment to free-to-play are
reported in A1. The correlation between the error terms for both models is 0.43 (p = 0.009). Model 5 prunes and
rebalances the sample based on Coarsened Exact Matching strata (STATA 15 command: cem). The matching for free-to-
play is based on network effects , installed base , indie publisher and past releases publisher (155 matched strata).
After matching, the overall imbalance statistic, or L1 distance, improves from 0.44 to 0.05, indicating smaller imbalance.
Model 6 re-estimates the endogenous treatment effects model based on the matched and rebalanced sample.
Killer app
46
A5. Alternative Threshold for Z-Score (z ≥ 1.6)
Variable 1 2 3 4 5 6
Free-to-play 1.08
[0.10]
2.15
[0.36]
1.17
[0.64]
2.10
[0.45]
1.45
[0.65]
Free-to-play x Social features -0.45
[0.20]
-0.38
[0.20]
-0.49
[0.26]
-0.49
[0.26]
Free-to-play x Installed base t-1 -0.008
[0.003]
-0.006
[0.003]
-0.006
[0.003]
-0.005
[0.003]
Social features x Installed base t-1 -0.0007
[0.0007]
-0.0006
[0.0007]
-0.0009
[0.0011]
-0.0009
[0.0010]
Free-to-play x Social features x
Installed base t-1
0.003
[0.001]
0.003
[0.001]
0.003
[0.002]
0.003
[0.002]
Social features 0.24
[0.03]
0.21
[0.03]
0.32
[0.09]
0.32
[0.09]
0.39
[0.15]
0.37
[0.15]
Installed base t-1 0.009
[0.001]
0.005
[0.001]
0.007
[0.002]
0.007
[0.002]
0.002
[0.002]
0.002
[0.002]
Genre competition -0.005
[0.001]
-0.002
[0.001]
-0.003
[0.001]
-0.003
[0.001]
-0.001
[0.001]
-0.001
[0.001]
Indie publisher -0.43
[0.08]
-0.35
[0.07]
-0.36
[0.08]
-0.43
[0.08]
-0.20
[0.11]
-0.18
[0.12]
ln(Past releases publisher) 0.02
[0.03]
0.08
[0.03]
0.08
[0.03]
0.04
[0.04]
0.10
[0.06]
0.10
[0.05]
Quality dummies (3) Yes Yes Yes Yes Yes Yes
Genre dummies (9) Yes Yes Yes Yes Yes Yes
Month of release dummies (11) Yes Yes Yes Yes Yes Yes
Endogenous treatment correction No No No Yes No Yes
Matched and rebalanced sample No No No No Yes Yes
Constant -2.80
[0.21]
-2.84
[0.22]
-3.08
[0.27]
-2.87
[0.32]
-3.02
[0.36]
-2.87
[0.37]
McFadden's Pseudo R2 0.23 0.29 0.29 0.33
Observations 9,700 9,700 9,700 9,700 7,680 7,680
Notes. Heteroskedasticity robust standard errors in parentheses. Model 4 estimates an endogenous treatment effects, or
recursive bivariate probit, model (STATA 15 command: eprobit ). First stage results for assignment to free-to-play are
reported in A1. The correlation between the error terms for both models is 0.40 (p = 0.035). Model 5 prunes and
rebalances the sample based on Coarsened Exact Matching strata (STATA 15 command: cem ). The matching for free-to-
play is based on social features, installed base, indie publisher and past releases publisher (155 matched strata). After
matching, the overall imbalance statistic, or L 1 distance, improves from 0.44 to 0.05, indicating smaller imbalance. Model
6 re-estimates the endogenous treatment effects model based on the matched and rebalanced sample.
Killer app
47
A6. DV Based on Top 5% of Most Downloaded Games in Year
Variable 1 2 3 4 5 6
Free-to-play 1.03
[0.08]
2.45
[0.31]
1.50
[0.50]
2.37
[0.39]
1.95
[0.53]
Free-to-play x Social features -0.63
[0.18]
-0.55
[0.18]
-0.62
[0.23]
-0.62
[0.23]
Free-to-play x Installed base t-1 -0.01
[0.002]
-0.01
[0.002]
-0.01
[0.003]
-0.01
[0.003]
Social features x Installed base t-1 -0.001
[0.0006]
-0.001
[0.0005]
-0.001
[0.0009]
-0.001
[0.0009]
Free-to-play x Social features x
Installed base t-1
0.005
[0.001]
0.004
[0.001]
0.005
[0.002]
0.005
[0.002]
Social features 0.26
[0.02]
0.23
[0.02]
0.41
[0.07]
0.42
[0.07]
0.40
[0.13]
0.40
[0.13]
Installed base t-1 0.01
[0.001]
0.007
[0.001]
0.01
[0.001]
0.01
[0.001]
0.01
[0.002]
0.01
[0.002]
Genre competition -0.004
[0.001]
-0.002
[0.001]
-0.003
[0.001]
-0.003
[0.001]
-0.001
[0.001]
-0.001
[0.001]
Indie publisher -0.19
[0.06]
-0.11
[0.06]
-0.12
[0.06]
-0.18
[0.06]
-0.05
[0.09]
-0.04
[0.09]
ln(Past releases publisher) 0.07
[0.02]
0.12
[0.02]
0.12
[0.02]
0.08
[0.03]
0.11
[0.04]
0.11
[0.04]
Quality dummies (3) Yes Yes Yes Yes Yes Yes
Genre dummies (9) Yes Yes Yes Yes Yes Yes
Month of release dummies (11) Yes Yes Yes Yes Yes Yes
Endogenous treatment correction No No No Yes No Yes
Matched and rebalanced sample No No No No Yes Yes
Constant -2.98
[0.17]
-2.98
[0.17]
-3.30
[0.20]
-3.12
[0.22]
-3.23
[0.22]
-3.15
[0.31]
McFadden's Pseudo R2 0.20 0.24 0.25 0.28
Observations 9,700 9,700 9,700 9,700 7,680 7,680
Notes. Heteroskedasticity robust standard errors in parentheses. Model 4 estimates an endogenous treatment effects, or
recursive bivariate probit, model (STATA 15 command: eprobit ). First stage results for assignment to free-to-play are
reported in A1. The correlation between the error terms for both models is 0.38 (p = 0.009). Model 5 prunes and rebalances
the sample based on Coarsened Exact Matching strata (STATA 15 command: cem ). The matching for free-to-play is based
on social features, installed base, indie publisher and past releases publisher (155 matched strata). After matching, the
overall imbalance statistic, or L 1 distance, improves from 0.44 to 0.05, indicating smaller imbalance. Model 6 re-estimates
the endogenous treatment effects model based on the matched and rebalanced sample.
Killer app
48
A7. Log of Games’ Social Features
Variable 1 2 3 4 5 6
Free-to-play 1.05
[0.09]
2.55
[0.31]
1.27
[0.61]
2.69
[0.44]
2.35
[0.59]
Free-to-play x ln(Social features) -1.27
[0.40]
-1.05
[0.40]
-1.47
[0.53]
-1.48
[0.53]
Free-to-play x Installed base t-1 -0.011
[0.003]
-0.009
[0.003]
-0.010
[0.003]
-0.010
[0.003]
ln(Social features) x Installed base t-1 -0.0004
[0.0013]
-0.0002
[0.0012]
0.0001
[0.0020]
0.00004
[0.00205]
Free-to-play x ln (Social features) x
Installed base t-1
0.009
[0.003]
0.008
[0.003]
0.009
[0.004]
0.009
[0.004]
ln(Social features) 0.60
[0.06]
0.54
[0.06]
0.62
[0.16]
0.64
[0.16]
0.64
[0.30]
0.64
[0.30]
Installed base t-1 0.008
[0.001]
0.004
[0.001]
0.006
[0.001]
0.006
[0.001]
0.002
[0.002]
0.002
[0.002]
Genre competition -0.004
[0.001]
-0.002
[0.001]
-0.003
[0.001]
-0.002
[0.001]
-0.001
[0.001]
-0.001
[0.001]
Indie publisher -0.33
[0.06]
-0.25
[0.06]
-0.27
[0.06]
-0.34
[0.06]
-0.14
[0.10]
-0.13
[0.10]
ln(Past releases publisher) 0.05
[0.02]
0.10
[0.02]
0.10
[0.02]
0.05
[0.03]
0.09
[0.05]
0.09
[0.05]
Quality dummies (3) Yes Yes Yes Yes Yes Yes
Genre dummies (9) Yes Yes Yes Yes Yes Yes
Month of release dummies (11) Yes Yes Yes Yes Yes Yes
Endogenous treatment correction No No No Yes No Yes
Matched and rebalanced sample No No No No Yes Yes
Constant -2.63
[0.18]
-2.65
[0.18]
-2.85
[0.21]
-2.60
[0.25]
-2.94
[0.34]
-2.87
[0.25]
McFadden's Pseudo R2 0.24 0.29 0.30 0.34
Observations 9,700 9,700 9,700 9,700 7,680 7,680
Notes. Heteroskedasticity robust standard errors in parentheses. Model 4 estimates an endogenous treatment effects, or
recursive bivariate probit, model (STATA 15 command: eprobit ). First stage results for assignment to free-to-play are
reported in A1. The correlation between the error terms for both models is 0.49 (p = 0.004). Model 5 prunes and rebalances
the sample based on Coarsened Exact Matching strata (STATA 15 command: cem ). The matching for free-to-play is based
on social features, installed base, indie publisher and past releases publisher (155 matched strata). After matching, the
overall imbalance statistic, or L 1 distance, improves from 0.44 to 0.05, indicating smaller imbalance. Model 6 re-estimates
the endogenous treatment effects model based on the matched and rebalanced sample.
Killer app
49
A8. Social Features Operationalized as a Binary Construct
Variable 1 2 3 4 5 6
Free-to-play 1.05
[0.09]
2.78
[0.39]
1.54
[0.62]
2.76
[0.45]
2.50
[0.59]
Free-to-play x Social features (dummy) -1.63
[0.46]
-1.35
[0.47]
-1.53
[0.54]
-1.54
[0.54]
Free-to-play x Installed base t-1 -0.01
[0.003]
-0.01
[0.003]
-0.01
[0.003]
-0.01
[0.003]
Social features (dummy) x Installed base t-1 0.001
[0.001]
0.001
[0.001]
0.004
[0.002]
0.004
[0.002]
Free-to-play x Social features (Dummy) x
Installed base t-1
0.011
[0.003]
0.010
[0.003]
0.010
[0.004]
0.010
[0.004]
Social features (dummy) 0.65
[0.06]
0.58
[0.07]
0.53
[0.17]
0.56
[0.17]
0.28
[0.29]
0.28
[0.29]
Installed base t-1 0.008
[0.001]
0.004
[0.001]
0.005
[0.001]
0.006
[0.001]
0.001
[0.002]
0.001
[0.002]
Genre competition -0.004
[0.001]
-0.002
[0.001]
-0.003
[0.001]
-0.002
[0.001]
-0.001
[0.001]
-0.001
[0.001]
Indie publisher -0.30
[0.06]
-0.22
[0.06]
-0.25
[0.06]
-0.32
[0.06]
-0.15
[0.10]
-0.14
[0.10]
ln(Past releases publisher) 0.04
[0.02]
0.10
[0.02]
0.09
[0.02]
0.04
[0.03]
0.06
[0.04]
0.06
[0.04]
Quality dummies (3) Yes Yes Yes Yes Yes Yes
Genre dummies (9) Yes Yes Yes Yes Yes Yes
Month of release dummies (11) Yes Yes Yes Yes Yes Yes
Endogenous treatment correction No No No Yes No Yes
Matched and rebalanced sample No No No No Yes Yes
Constant -2.72
[0.18]
-2.72
[0.17]
-2.86
[0.22]
-2.64
[0.25]
-2.90
[0.31]
-2.65
[0.32]
McFadden's Pseudo R2 0.24 0.29 0.29 0.32
Observations 9,700 9,700 9,700 9,700 7,922 7,922
Notes. Heteroskedasticity robust standard errors in parentheses. Model 4 estimates an endogenous treatment effects, or
recursive bivariate probit, model (STATA 15 command: eprobit ). First stage results for assignment to free-to-play are
reported in A1. The correlation between the error terms for both models is 0.46 (p = 0.003). Model 5 prunes and rebalances
the sample based on Coarsened Exact Matching strata (STATA 15 command: cem ). The matching for free-to-play is based
on social features, installed base, indie publisher and past releases publisher (112 matched strata). After matching, the
overall imbalance statistic, or L 1 distance, improves from 0.42 to 0.05, indicating smaller imbalance. Model 6 re-estimates
the endogenous treatment effects model based on the matched and rebalanced sample.
Killer app
50
A9. New Platform Adoption as Measure of Installed Base
Variable 1 2 3 4 5 6
Free-to-play 1.03
[0.09]
2.27
[0.36]
0.86
[0.53]
2.44
[0.44]
2.06
[0.61]
Free-to-play x Social features -0.46
[0.20]
-0.36
[0.19]
-0.67
[0.27]
-0.67
[0.27]
Free-to-play x Platform adoption t-1 -0.03
[0.008]
-0.02
[0.008]
-0.03
[0.010]
-0.03
[0.010]
Social features x Platform adoption t-1 -0.001
[0.002]
-0.001
[0.002]
-0.003
[0.003]
-0.003
[0.003]
Free-to-play x Social features x
Platform adoption t-1
0.010
[0.005]
0.008
[0.004]
0.014
[0.006]
0.014
[0.006]
Social features 0.25
[0.02]
0.23
[0.03]
0.29
[0.08]
0.29
[0.07]
0.39
[0.13]
0.39
[0.13]
Platform adoption t-1 0.033
[0.004]
0.021
[0.004]
0.027
[0.005]
0.028
[0.005]
0.021
[0.007]
0.021
[0.007]
Genre competition -0.005
[0.001]
-0.003
[0.001]
-0.004
[0.001]
-0.003
[0.001]
-0.002
[0.001]
-0.003
[0.001]
Indie publisher -0.36
[0.06]
-0.28
[0.06]
-0.29
[0.06]
-0.38
[0.06]
-0.17
[0.10]
-0.16
[0.11]
ln(Past releases publisher) 0.05
[0.02]
0.10
[0.02]
0.10
[0.02]
0.04
[0.03]
0.10
[0.05]
0.10
[0.05]
Quality dummies (3) Yes Yes Yes Yes Yes Yes
Genre dummies (9) Yes Yes Yes Yes Yes Yes
Month of release dummies (11) Yes Yes Yes Yes Yes Yes
Endogenous treatment correction No No No Yes No Yes
Matched and rebalanced sample No No No No Yes Yes
Constant -2.83
[0.19]
-2.85
[0.17]
-3.06
[0.22]
-2.73
[0.26]
-3.33
[0.33]
-3.25
[0.26]
McFadden's Pseudo R2 0.24 0.29 0.30 0.34
Observations 9,700 9,700 9,700 9,700 7,750 7,750
Notes. Heteroskedasticity robust standard errors in parentheses. Model 4 estimates an endogenous treatment effects, or
recursive bivariate probit, model (STATA 15 command: eprobit ). First stage results for assignment to free-to-play are
reported in A1. The correlation between the error terms for both models is 0.55 (p = 0.000). Model 5 prunes and rebalances
the sample based on Coarsened Exact Matching strata (STATA 15 command: cem ). The matching for free-to-play is based on
social features, platform adoption, indie publisher and past releases publisher (154 matched strata). After matching, the
overall imbalance statistic, or L 1 distance, improves from 0.44 to 0.06, indicating smaller imbalance. Model 6 re-estimates the
endogenous treatment effects model based on the matched and rebalanced sample.
Killer app
51
A10. Platform Age as Measure of Installed Base
Variable 1 2 3 4 4 4
Free-to-play 1.04
[0.09]
4.12
[0.97]
2.24
[1.11]
4.48
[1.33]
2.07
[2.70]
Free-to-play x Social features -1.19
[0.52]
-0.96
[0.50]
-2.26
[0.90]
-1.83
[1.21]
Free-to-play x Platform age -0.02
[0.007]
-0.02
[0.007]
-0.02
[0.009]
-0.02
[0.01]
Social features x Platform age -0.001
[0.001]
-0.001
[0.002]
-0.003
[0.004]
-0.003
[0.003]
Free-to-play x Social features x
Platform age
0.008
[0.004]
0.006
[0.003]
0.015
[0.006]
0.012
[0.008]
Social features 0.25
[0.02]
0.23
[0.03]
0.36
[0.23]
0.34
[0.07]
0.77
[0.54]
0.64
[0.55]
Platform age 0.023
[0.003]
0.015
[0.003]
0.018
[0.004]
0.018
[0.004]
0.016
[0.007]
0.013
[0.009]
Genre competition -0.005
[0.001]
-0.003
[0.001]
-0.003
[0.001]
-0.003
[0.001]
-0.003
[0.001]
-0.003
[0.001]
Indie publisher -0.37
[0.06]
-0.29
[0.06]
-0.29
[0.06]
-0.38
[0.06]
-0.10
[0.11]
-0.02
[0.15]
ln(Past releases publisher) 0.05
[0.02]
0.10
[0.02]
0.10
[0.02]
0.04
[0.03]
0.07
[0.06]
0.07
[0.05]
Quality dummies (3) Yes Yes Yes Yes Yes Yes
Genre dummies (9) Yes Yes Yes Yes Yes Yes
Month of release dummies (11) Yes Yes Yes Yes Yes Yes
Endogenous treatment correction No No No Yes No Yes
Matched and rebalanced sample No No No No Yes Yes
Constant -4.80
[0.45]
-4.08
[0.17]
-4.55
[0.20]
-4.25
[0.56]
-4.50
[1.04]
-3.46
[1.81]
McFadden's Pseudo R2 0.24 0.29 0.29 0.33
Observations 9,700 9,700 9,700 9,700 6,883 6,883
Notes. Heteroskedasticity robust standard errors in parentheses. Model 4 estimates an endogenous treatment effects, or
recursive bivariate probit, model (STATA 15 command: eprobit ). First stage results for assignment to free-to-play are reported
in A1. The correlation between the error terms for both models is 0.53 (p = 0.000). Model 5 prunes and rebalances the sample
based on Coarsened Exact Matching strata (STATA 15 command: cem ). The matching for free-to-play is based on social
features, platforma age, indie publisher and past releases publisher (213 matched strata). After matching, the overall
imbalance statistic, or L 1 distance, improves from 0.53 to 0.29, indicating smaller imbalance. Model 6 re-estimates the
endogenous treatment effects model based on the matched and rebalanced sample.
Killer app
52
A11. Drop Competition to Reduce Multicollinearity
Variable 1 2 3 4 5 6
Free-to-play 1.15
[0.08]
2.23
[0.33]
1.09
[0.57]
2.39
[0.41]
2.04
[0.55]
Free-to-play x Social features -0.49
[0.19]
-0.41
[0.18]
-0.61
[0.25]
-0.62
[0.25]
Free-to-play x Installed base t-1 -0.008
[0.002]
-0.006
[0.002]
-0.008
[0.003]
-0.008
[0.003]
Social features x Installed base t-1 -0.0005
[0.0005]
-0.0004
[0.0005]
-0.0007
[0.0008]
-0.0007
[0.0009]
Free-to-play x Social features x
Installed base t-1
0.003
[0.001]
0.003
[0.001]
0.004
[0.001]
0.004
[0.002]
Social features 0.25
[0.02]
0.23
[0.02]
0.31
[0.08]
0.31
[0.07]
0.35
[0.13]
0.34
[0.13]
Installed base t-1 0.0005
[0.0007]
0.0002
[0.0007]
0.0014
[0.0009]
0.0017
[0.0009]
0.0005
[0.0015]
0.0005
[0.0015]
Indie publisher -0.39
[0.06]
-0.28
[0.06]
-0.29
[0.06]
-0.37
[0.06]
-0.18
[0.10]
-0.17
[0.11]
ln(Past releases publisher) 0.03
[0.02]
0.10
[0.02]
0.10
[0.02]
0.05
[0.03]
0.10
[0.05]
0.10
[0.05]
Quality dummies (3) Yes Yes Yes Yes Yes Yes
Genre dummies (9) Yes Yes Yes Yes Yes Yes
Month of release dummies (11) Yes Yes Yes Yes Yes Yes
Endogenous treatment correction No No No Yes No Yes
Matched and rebalanced sample No No No No Yes Yes
Constant -1.98
[0.15]
-2.34
[0.16]
-2.48
[0.21]
-2.26
[0.22]
-2.75
[0.31]
-2.68
[0.31]
McFadden's Pseudo R2 0.22 0.28 0.29 0.33
Observations 9,700 9,700 9,700 9,700 7,680 7,680
Notes. Heteroskedasticity robust standard errors in parentheses. Model 4 estimates an endogenous treatment effects, or recursive
bivariate probit, model (STATA 15 command: eprobit ). First stage results for assignment to free-to-play are reported in A1.
The correlation between the error terms for both models is 0.45 (p = 0.006). Model 5 prunes and rebalances the sample based on
Coarsened Exact Matching strata (STATA 15 command: cem ). The matching for free-to-play is based on social features,
installed base, indie publisher and past releases publisher (155 matched strata). After matching, the overall imbalance statistic,
or L 1 distance, improves from 0.44 to 0.05, indicating smaller imbalance. Model 6 re-estimates the endogenous treatment effects
model based on the matched and rebalanced sample.
Killer app
53
A12. Publisher Age as Measure of Publisher Experience
Variable 1 2 3 4 5 6
Free-to-play 1.14
[0.09]
2.28
[0.33]
1.39
[0.54]
2.42
[0.40]
1.79
[0.94]
Free-to-play x Social features -0.47
[0.18]
-0.41
[0.18]
-0.11
[0.24]
-0.52
[0.23]
Free-to-play x Installed base t-1 -0.009
[0.002]
-0.007
[0.002]
-0.009
[0.003]
-0.009
[0.003]
Social features x Installed base t-1 -0.001
[0.001]
-0.001
[0.001]
-0.001
[0.001]
-0.001
[0.001]
Free-to-play x Social features x
Installed base t-1
0.003
[0.001]
0.003
[0.001]
0.004
[0.002]
0.004
[0.002]
Social features 0.26
[0.02]
0.23
[0.03]
0.33
[0.08]
0.34
[0.08]
0.34
[0.12]
0.33
[0.12]
Installed base t-1 0.01
[0.001]
0.003
[0.001]
0.005
[0.001]
0.006
[0.001]
0.006
[0.002]
0.006
[0.002]
Genre competition -0.005
[0.001]
-0.002
[0.001]
-0.003
[0.001]
-0.003
[0.001]
-0.003
[0.001]
-0.003
[0.002]
Indie publisher -0.23
[0.06]
-0.14
[0.06]
-0.15
[0.06]
-0.21
[0.07]
-0.18
[0.10]
-0.16
[0.12]
Publisher age 0.006
[0.001]
0.008
[0.001]
0.008
[0.001]
0.007
[0.001]
0.005
[0.002]
0.005
[0.002]
Quality dummies (3) Yes Yes Yes Yes Yes Yes
Genre dummies (9) Yes Yes Yes Yes Yes Yes
Month of release dummies (11) Yes Yes Yes Yes Yes Yes
Endogenous treatment correction No No No Yes No Yes
Matched and rebalanced sample No No No No Yes Yes
Constant -2.68
[0.18]
-2.98
[0.17]
-2.91
[0.21]
-2.78
[0.23]
-3.07
[0.35]
-2.93
[0.43]
McFadden's Pseudo R2 0.25 0.30 0.31 0.32
Observations 9,700 9,700 9,700 9,700 7,944 7,944
Notes. Heteroskedasticity robust standard errors in parentheses. Model 4 estimates an endogenous treatment effects, or
recursive bivariate probit, model (STATA 15 command: eprobit ). First stage results for assignment to free-to-play are reported
in A1. The correlation between the error terms for both models is 0.35 (p = 0.024). Model 5 prunes and rebalances the sample
based on Coarsened Exact Matching strata (STATA 15 command: cem ). The matching for free-to-play is based on social
features, installed base, indie publisher and publisher age (131 matched strata). After matching, the overall imbalance
statistic, or L 1 distance, improves from 0.45 to 0.17, indicating smaller imbalance. Model 6 re-estimates the endogenous
treatment effects model based on the matched and rebalanced sample.
Killer app
54
A13. Add System Requirements (HD, RAM, Processor) as Additional Controls
Variable 1 2 3 4 5 6
Free-to-play 1.01
[0.10]
2.11
[0.38]
1.01
[0.60]
2.34
[0.47]
1.94
[0.62]
Free-to-play x Social features -0.45
[0.20]
-0.37
[0.20]
-0.59
[0.28]
-0.59
[0.28]
Free-to-play x Installed base t-1 -0.009
[0.002]
-0.007
[0.003]
-0.008
[0.003]
-0.008
[0.003]
Social features x Installed base t-1 -0.0002
[0.0006]
-0.0002
[0.0006]
-0.001
[0.0010]
-0.001
[0.0010]
Free-to-play x Social features x
Installed base t-1
0.003
[0.001]
0.003
[0.001]
0.004
[0.002]
0.004
[0.002]
Social features 0.25
[0.03]
0.23
[0.03]
0.25
[0.08]
0.26
[0.08]
0.41
[0.14]
0.40
[0.14]
Installed base t-1 0.008
[0.001]
0.004
[0.001]
0.006
[0.002]
0.006
[0.001]
0.004
[0.002]
0.004
[0.002]
Genre competition -0.005
[0.001]
-0.003
[0.001]
-0.003
[0.001]
-0.003
[0.001]
-0.001
[0.001]
-0.001
[0.001]
Indie publisher -0.16
[0.07]
-0.07
[0.07]
-0.08
[0.07]
-0.15
[0.08]
-0.01
[0.12]
-0.002
[0.12]
ln(Past releases publisher) 0.02
[0.03]
0.07
[0.03]
0.07
[0.03]
0.03
[0.03]
0.03
[0.05]
0.03
[0.05]
Storage (HD) requirement 0.046
[0.005]
0.047
[0.005]
0.046
[0.005]
0.043
[0.005]
0.046
[0.008]
0.045
[0.008]
Memory (RAM) requirement 0.04
[0.02]
0.031
[0.018]
0.030
[0.018]
0.036
[0.018]
-0.001
[0.031]
0.002
[0.030]
Processor (GHz) requirement -0.01
[0.01]
-0.01
[0.01]
-0.01
[0.01]
-0.007
[0.01]
-0.03
[0.02]
-0.03
[0.02]
Quality dummies (3) Yes Yes Yes Yes Yes Yes
Genre dummies (9) Yes Yes Yes Yes Yes Yes
Month of release dummies (11) Yes Yes Yes Yes Yes Yes
Endogenous treatment correction No No No Yes No Yes
Matched and rebalanced sample No No No No Yes Yes
Constant -2.74
[0.20]
-2.80
[0.21]
-2.94
[0.20]
-2.74
[0.26]
-3.18
[0.34]
-3.10
[0.34]
McFadden's Pseudo R2 0.30 0.34 0.34 0.37
Observations 8,251 8,251 8,251 8,251 6,481 6,481
Notes. Heteroskedasticity robust standard errors in parentheses. Model 4 estimates an endogenous treatment effects, or
recursive bivariate probit, model (STATA 15 command: eprobit ). First stage results for assignment to free-to-play are reported
in A1. The correlation between the error terms for both models is 0.44 (p = 0.008). Model 5 prunes and rebalances the sample
based on Coarsened Exact Matching strata (STATA 15 command: cem ). The matching for free-to-play is based on social
features, installed base, indie publisher and past releases publisher (155 matched strata). After matching, the overall
imbalance statistic, or L 1 distance, improves from 0.44 to 0.05, indicating smaller imbalance. Model 6 re-estimates the
endogenous treatment effects model based on the matched and rebalanced sample.
Killer app
55
A14. Add Price Menu Variety (DLC, microtransactions) as Additional Controls
Variable 1 2 3 4 5 6
Free-to-play 0.95
[0.09]
2.26
[0.34]
1.53
[0.53]
2.54
[0.42]
2.21
[0.53]
Free-to-play x Social features -0.50
[0.19]
-0.45
[0.19]
-0.69
[0.26]
-0.70
[0.25]
Free-to-play x Installed base t-1 -0.010
[0.002]
-0.009
[0.002]
-0.010
[0.002]
-0.010
[0.003]
Social features x Installed base t-1 -0.0003
[0.0006]
-0.0002
[0.0006]
-0.0010
[0.0010]
-0.0010
[0.0010]
Free-to-play x Social features x
Installed base t-1
0.003
[0.001]
0.003
[0.001]
0.004
[0.002]
0.004
[0.002]
Social features 0.20
[0.03]
0.19
[0.03]
0.25
[0.08]
0.24
[0.08]
0.36
[0.13]
0.36
[0.14]
Installed base t-1 0.007
[0.001]
0.004
[0.001]
0.006
[0.001]
0.006
[0.001]
0.003
[0.002]
0.002
[0.002]
Genre competition -0.004
[0.001]
-0.002
[0.001]
-0.003
[0.001]
-0.003
[0.001]
-0.001
[0.001]
-0.001
[0.001]
Indie publisher -0.26
[0.06]
-0.19
[0.06]
-0.20
[0.06]
-0.25
[0.07]
-0.09
[0.10]
-0.09
[0.11]
ln(Past releases publisher) 0.03
[0.02]
0.08
[0.02]
0.08
[0.02]
0.05
[0.03]
0.05
[0.05]
0.05
[0.05]
ln(Downloadable items) 0.42
[0.04]
0.40
[0.04]
0.39
[0.04]
0.41
[0.04]
0.41
[0.06]
0.42
[0.06]
Microtransactions 0.70
[0.14]
0.35
[0.14]
0.50
[0.14]
0.78
[0.17]
0.71
[0.19]
0.86
[0.24]
Quality dummies (3) Yes Yes Yes Yes Yes Yes
Genre dummies (9) Yes Yes Yes Yes Yes Yes
Month of release dummies (11) Yes Yes Yes Yes Yes Yes
Endogenous treatment correction No No No Yes No Yes
Matched and rebalanced sample No No No No Yes Yes
Constant -2.57
[0.18]
-2.65
[0.19]
-2.87
[0.21]
-2.74
[0.23]
-2.89
[0.31]
-2.82
[0.33]
McFadden's Pseudo R2 0.30 0.33 0.34 0.38
Observations 9,700 9,700 9,700 9,700 7,680 7,680
Notes. Heteroskedasticity robust standard errors in parentheses. Model 4 estimates an endogenous treatment effects, or
recursive bivariate probit, model (STATA 15 command: eprobit ). First stage results for assignment to free-to-play are reported
in A1. The correlation between the error terms for both models is 0.32 (p = 0.037). Model 5 prunes and rebalances the sample
based on Coarsened Exact Matching strata (STATA 15 command: cem ). The matching for free-to-play is based on social
features, installed base, indie publisher and past releases publisher (155 matched strata). After matching, the overall
imbalance statistic, or L1 distance, improves from 0.44 to 0.05, indicating smaller imbalance. Model 6 re-estimates the
endogenous treatment effects model based on the matched and rebalanced sample.
Killer app
56
A15. Add Published by Valve as Additional Control
Variable 1 2 3 4 5 6
Free-to-play 1.06
[0.09]
2.27
[0.30]
0.99
[0.55]
2.38
[0.41]
2.02
[0.57]
Free-to-play x Social features -0.46
[0.18]
-0.37
[0.18]
-0.57
[0.25]
-0.59
[0.25]
Free-to-play x Installed base t-1 -0.009
[0.002]
-0.007
[0.002]
-0.008
[0.003]
-0.008
[0.003]
Social features x Installed base t-1 -0.0005
[0.0006]
-0.0004
[0.0005]
-0.0006
[0.0009]
-0.0006
[0.0009]
Free-to-play x Social features x
Installed base t-1
0.003
[0.001]
0.003
[0.001]
0.004
[0.002]
0.004
[0.002]
Social features 0.25
[0.02]
0.23
[0.03]
0.30
[0.08]
0.31
[0.07]
0.34
[0.13]
0.33
[0.13]
Installed base t-1 0.007
[0.001]
0.004
[0.001]
0.006
[0.001]
0.006
[0.001]
0.002
[0.002]
0.002
[0.002]
Genre competition -0.004
[0.001]
-0.002
[0.001]
-0.003
[0.001]
-0.002
[0.001]
-0.001
[0.001]
-0.001
[0.001]
Indie publisher -0.35
[0.06]
-0.26
[0.06]
-0.28
[0.06]
-0.36
[0.06]
-0.17
[0.10]
-0.16
[0.10]
ln(Past releases publisher) 0.05
[0.02]
0.10
[0.02]
0.10
[0.02]
0.04
[0.03]
0.09
[0.05]
0.09
[0.05]
Published by Valve 2.12
[0.54]
1.74
[0.58]
1.52
[0.68]
2.00
[0.58]
1.24
[0.99]
1.53
[1.03]
Quality dummies (3) Yes Yes Yes Yes Yes Yes
Genre dummies (9) Yes Yes Yes Yes Yes Yes
Month of release dummies (11) Yes Yes Yes Yes Yes Yes
Endogenous treatment correction No No No Yes No Yes
Matched and rebalanced sample No No No No Yes Yes
Constant -2.60
[0.18]
-2.60
[0.18]
-2.83
[0.21]
-2.56
[0.25]
-2.89
[0.32]
-2.82
[0.33]
McFadden's Pseudo R2 0.24 0.29 0.29 0.33
Observations 9,700 9,700 9,700 9,700 7,680 7,680
Notes. Heteroskedasticity robust standard errors in parentheses. Model 4 estimates an endogenous treatment effects, or
recursive bivariate probit, model (STATA 15 command: eprobit ). First stage results for assignment to free-to-play are reported
in A1. The correlation between the error terms for both models is 0.50 (p = 0.001). Model 5 prunes and rebalances the sample
based on Coarsened Exact Matching strata (STATA 15 command: cem ). The matching for free-to-play is based on social
features, installed base, indie publisher and past releases publisher (155 matched strata). After matching, the overall
imbalance statistic, or L 1 distance, improves from 0.44 to 0.05, indicating smaller imbalance. Model 6 re-estimates the
endogenous treatment effects model based on the matched and rebalanced sample.
Killer app