Building Network Effects via Business Model Design: A Study ......freemium strategies were...
Transcript of Building Network Effects via Business Model Design: A Study ......freemium strategies were...
Building Network Effects via Business Model Design: A Study of
Game Center and Freemium Models on the App Store
Abstract
The “Game Center” was instituted on the Apple App Store in 2010, enabling users of games
apps to interact, communicate and play games with one another, opening up the possibility
that network effects could take root. This paper theorizes that instituting social interactions
and network effects, and even having a leading share of one’s own market or niche, will not
on their own necessarily translate to higher revenues and economic performance, without a
more careful consideration of how those strategies interact with one’s broader business model in
both creating and capturing value. We study here the case of instituting a “freemium” model,
clarifying that this should be complementary with network effects strategies on its impact on
revenues. Consistent with our theoretical predictions, we find that across data on leaders across
distinct categories of apps on the Apple App Store, the establishment of Game Center was not
generally associated with a statistical increase in revenues share. However, market leaders who
had previously instituted a freemium strategy, saw their revenues market share lead on follower
apps grow by 380 percent in the window immediately following the creation of Game Center.
We discuss implications for theory and strategy of network effects and digital business model
design.
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1 Introduction
As companies in digital industries vie for advantage relative to competitors, many of them seek to
foster social interactions and create network effects around their products (Aral and Walker, 2011;
Zeng and Wei, 2013; Dou et al., 2013). Competing on the basis of network effects has traditionally
been cast as a “race” to grow faster than competitors, as market leaders will have advantages over
followers (Katz and Shapiro, 1986, 1994; Farrell and Saloner, 1986; Gallaugher and Wang, 2002; Noe
and Parker, 2005; Zhu et al., 2006; Shankar and Bayus, 2003; Cennamo and Santalo, 2013; Gawer
and Cusumano, 2008). Research has begun to evaluate too how a variety of investment and tactical
approaches from aggressive marketing, to influencing consumer expectations, to penetration pric-
ing, to forming strategic partnerships, to innovating the quality of products, and other maneuvers
might be used to win such a race (Anderson et al., 2014; Tanriverdi and Lee, 2008; Tiwana, 2015).
However, as network effects strategies become more common, we witness many prominent examples
where these tactics and even market leading scale are not sufficient to guarantee that value is both
created and captured. For example, the company that has come to define buyer syndicate plat-
forms, Groupon, has long struggled to achieve sustained profitability. Another iconic organization,
Craigslist, faces growing competitive pressure while it continues to generate little income. More
generally, even highly successful companies and products whose success is built on network effects
such as Dropbox, Spotify, Pokemon Go, and Clash of Clans, can hardly claim to have locked-in to an
indomitable position that allows them to reap monopoly profits without facing competitive threats.
Therefore, the theoretical starting point of this paper is to consider this possibility that boosting
consumer utility through positive social interactions and network effects is not in itself a sufficient
condition for profitability and economic performance. Business models designed to productively
harness, build and exploit network effects must, therefore, contemplate these business model chal-
lenges. To both exemplify and explore this point, in this paper, we investigate interactions between
network effects strategies and the use of so-called “freemium business models” (Kumar, 2014; Cheng
and Liu, 2012; Niculescu and Wu, 2014). We theorize and present evidence of complementarities
between network effects strategies and freemium business models, in terms of their impact on income
generation and economic performance.
Freemium models have quickly become widespread in use across digital industries and involve
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releasing a basic version of a product for free and charging users for either premium features or
versions of the product. The possibility of offering multiple versions and quality levels of a product
with gradation of prices, including the particular possibility of a free version, has long been studied
(Ellison, 2005; Deneckere and McAfee, 1996; Sundararajan, 2004). Digital and information goods
have been a particular area of interest, given the plastic and extensible nature of these technologies
allows for versioning or add-ons (Shapiro and Varian, 1998). Marginal costs of these goods also
often approach zero (Farrell and Klemperer, 2007; Katz and Shapiro, 1986; Shapiro and Varian,
1999). Earlier studies have examined how careful design of these schemes can potentially optimize
second-order price-discrimination (Shapiro and Varian, 1998; Pang and Etzion, 2012; Cheng and
Tang, 2010) or help overcome uncertainty regarding the nature or quality of a product (Dou et al.,
2013; Cheng and Liu, 2012). The ability to increment quality and expenditures higher can plausibly
also reduce switching away from the product and extend its commercial life. Thus much of the
literature focuses on how these benefits might weigh against a multi-tier pricing scheme could also
cannibalize revenues (Varian, 1989).
Niculescu and Wu (2014) take further strides to considering these tradeoffs in revenue generation,
relating freemium models and to possible externalities and interactions that can exist among cus-
tomers. In particular, their argumentation focuses on learning externalities and growing awareness
and diffusion that results from product adoption. In such cases, “giving away” the product could lift
overall adoption sufficiently to outweigh lost revenues from self-cannibalization with the zero price
offer. Thus, again, these information externalities entail a tradeoff between giving away a product
for free and associated cannibalization, and the benefits of boosting demand.
At the highest level, these tradeoffs created in offering free versions to build awareness, an
information externality, might be loosely analogized to tradeoffs in the case of network externalities
and social interactions (Niculescu and Wu, 2014). However, the positive externality is most likely to
be positive and enduring in the case of market leaders, perhaps creating greater scope for a positive
complementarity between network effects strategies and freemium models. Raising awareness and
positive information externalities might instead be ephemeral and create greatest benefits for lesser
known products. The positive revenue-enhancing potential for freemium for market leaders with
network effects might be greater still in modern digital industries where prevailing competitive
prices are often free, zero (Shapiro and Varian, 1998, 1999). Lost revenue from a free version might
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be much smaller when the consumer’s outside option would be to acquire a similar product from a
competitor for zero price, if the focal company fails to make a free offer, itself. Therefore, to not
offer a free version is de facto to be priced out of the market; failing to at once offer a premium
version is to fail to capture any revenues at all, at least from those customers willing to pay for the
premium version.1
Therefore, here we argue and test that there exist complementarities between network effects
and freemium business models and that their combined use has a far greater impact on firm revenues
than network effects alone. We exploit a rare instance in which it is possible to observe network
effects suddenly “switched on”. In 2010, Apple updated the functionality of its platform to allow
developers to include social interactions and multi-player game play.2 This allowed individual app
developers might themselves seek to build network effects and to compete on social interactions
around apps. We find that the institution of network effects and game center, on its own, was not
associated with a statistically discernible increase in revenues of market leaders in each app category.
However, in studying variation across market leaders with and without freemium models, in a narrow
time window during which none could adjust their strategies, we find that those market leaders with
freemium strategies were associated with a boost of 380% in their revenues, relative to those market
leaders that did not use freemium models. We interpret results as consistent with earlier arguments
concerning complementarities between freemium models and network effect strategies.
The paper advances theories of network effects, value-creating social interactions, and links to
business models and tactics, particularly in digital settings (Lee et al., 2006; Chen, 2009; Dube et al.,
2010; Boudreau and Hagiu, 2009; Boudreau, 2010; Cennamo and Santalo, 2013; Dou et al., 2013).
Our study fundamentally departs from the main of past literature on network effects, which has
tended to implicitly assume a link between network effects, growing share of users and economic
performance (Shankar and Bayus, 2003; Corts and Lederman, 2009). Our paper is arguably closer
to papers studying tactical interventions and maneuvering in network effects industries (Cennamo
and Santalo, 2013; Clements and Ohashi, 2005; Gawer and Cusumano, 2008). But, again, we step
closer to not just value creation and adoption, but also to value capture, in our study of revenues.
1Vendors of free goods might still capture value via complementary products, a contingency not studied here (seeCeccagnoli et al., 2012, for example.)
2(Thus, this is a case of network effects at the level of applications, rather than just at the level of the overallplatform, a case highlighted, for example, by Casadesus-Masanell and Halaburda (2014).
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In this latter regard, our paper might be related to early hedonic studies of software products with
network effects (e.g. Gallaugher and Wang 2002, Brynjolfsson and Kemerer 1996).
Finally, relative to (the yet little) empirical research on freemium models, special mention should
be made of the structural model used by Ghose and Han (2014) of demand for apps. This study
finds an association between revealed preferences for apps and those available with freemium. Here
we find that this preference also translates to boosted revenues and complementarities with network
effects.
2 Literature Review
One of the defining characteristics of digital industries is the presence of demand-side economies
of scale or “direct network effects” (Shapiro and Varian, 1999). While network effects may exist
in a variety of non-digital settings, they are a prevalent feature in most digital industries and a
key determinant of market outcomes (Church and Gandal, 1992; Shankar and Bayus, 2003). In
the presence of direct network effects, the value of a product depends on the size of its user base
(Katz and Shapiro, 1985; Farrell and Saloner, 1986). Under such conditions, the products with the
largest user base create the most value, allowing them to attract even more users. This virtuous
circle can ultimately lead to a single firm dominating an entire market segment. Numerous studies
have looked at how firms can benefit from the presence of network effects and create the types of
winner-take-all dynamics that can be found in many digital industries (Parker and Van Alstyne,
2005; Rochet and Tirole, 2006; Boudreau, 2010). However, these studies have generally focused on
how the characteristics of industries may affect market structure and have not considered whether
the strategies used by firms can allow them to exploit the presence of network effects and what
this may mean for market outcomes. Some studies have looked at how technology platforms can
optimize their product offering to best leverage the presence of direct and indirect network effects
(Cennamo and Santalo, 2013; Corts and Lederman, 2009; Markovich and Moenius, 2009). While
these studies have contributed to our understanding of the decision of firms to offer complementary
products, there is still a need to understand how a company’s business model can allow it to benefit
from the presence of network effects.
There is a vast literature looking at the how the business models of firms influence their ability
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of firms to create and capture value (Chesbrough and Rosenbloom, 2002; Casadesus-Masanell and
Ricart, 2010; Zott et al., 2011). A number of studies have looked at the evolution and emergence of
new types of business models (Chesbrough, 2010; Zott and Amit, 2010), particularly those suited to
digital industries (Amit and Zott, 2001; Mithas et al., 2013; Bharadwaj et al., 2013). In addition to
this theoretical discussion of business models, several studies have looked specifically at freemium
business models and how they influence competitive dynamics in industries where they are used
(Pauwels and Weiss, 2008; Niculescu and Wu, 2014; Ghose and Han, 2014). A number of studies
have looked at the performance effects of freemium business models and how they can reduce piracy
(Chellappa and Shivendu, 2005; Wu and Chen, 2008; Chen and Png, 2003; Sundararajan, 2004),
create user awareness and familiarity with products (Bhargava and Choudhary, 2008; Cheng and
Liu, 2012) and minimize consumer surplus (Bhargava and Choudhary, 2001). These studies have
suggested that freemium business models can allow firms to build a larger user base than conventional
business models. However, they have not considered whether freemium business models can allow
firms to somehow benefit from the presence of network effects. It is important to mention that some
studies have looked at how other strategic decisions may allow companies to exploit the presence
of network effects (Dou et al., 2013; Niculescu and Wu, 2014; Cheng and Tang, 2010), but they
have not focused on how freemium business models specifically can exploit the presence of network
effects.
It is also important to mention that there is a considerable literature within economics that
has looked at quality differences and how companies can release multiple products in order to
price discriminate across different quality tiers (see Varian, 1989 for a review). Deneckere and
McAfee (1996) look at a particular case of such price discrimination where companies deliberately
sell an inferior (feature limited) version of their products, alongside a superior (full featured) version.
Freemium business models are a unique subset of this type of price discrimination, where the feature
limited version of the product is released for free, while premium features are sold for an additional
fee (Shapiro and Varian, 1998, 1999). However, studies that have looked at price discrimination,
including those that have looked at price discrimination with a zero price version, have not looked
specifically at how this may be affected by the presence of network effects.
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3 Theory and Hypotheses
While there are many subtle variations on freemium business models, the basic idea involves releasing
a free version with limited functionality along with a full featured (premium) version that customers
can buy. Traditionally, software vendors would implement these freemium business models by re-
leasing several versions of their software: a light (free) version and a professional (premium) version.
However, increasingly software vendors are only releasing a single free version and allowing customers
to purchase premium features or add-ons through what are called “in-app purchases”. Prominent
examples of products that use such an approach include Dropbox, Spotify or Candy Crush Saga.
In addition, this freemium approach involving in-app purchases is one of the most widely used and
lucrative strategies on platforms such as the Apple App Store or Android Marketplace.
Freemium business models have several important advantages over more conventional, “paid
only” business models. First, by using freemium business models, companies charge a zero price for
the most basic (or feature limited) version of their products. This allows them to foster adoption by
enabling even consumers with low willingness-to-pay to adopt their products (Shapiro and Varian,
1998). As a result, products that are sold through freemium business models have a larger user
base than those sold through “paid only” business models. Second, by using freemium business
models, companies price discriminate and offer “premium features” to the subset of consumers that
have a higher willingness to pay. If done properly, this type of price discrimination allows them to
generate greater revenue than selling their products at a single price (Varian, 1989).3 An additional
benefit of this type of price discrimination is that it allows companies to charge higher prices to
paying consumers, than they would with a single price, “paid only” business model.4 This, in turn,
implies that freemium business models create higher switching and multi-homing costs for consumers,
compared to a single price, “paid only” business model. Thus, other things being equal freemium
business models allow companies to build up a larger base of customers, and appropriate value from
customers better than conventional “paid only” business models.
An important factor to consider is that while freemium business models have some advantages,
3Additionally, this type of price discrimination may allow companies to adapt product offerings to enhance con-sumer’s perceptions of their products (Simonson and Tversky, 1992; Smith and Nagle, 1995).
4For instance, the average price of a game that is sold for a set fee on the Apple App Store is less than $1.00.Alternatively, Candy Crush, a popular game that is available for free but allows users to purchase add-ons such asadditional in-game credit or lives generates more than $20.00 per month per paying user, according to analyst reports.
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they are not suitable for all types of products (Shapiro and Varian, 1998). In this paper, we do
not try to understand why freemium business models are used in some markets and not in others.
Instead, we focus on whether direct network effects affect market outcomes differently in settings
where freemium business models are used, in comparison to those where they are not.
In the presence of direct network effects, the product with the largest user base, provides the most
value to consumers and is able to attract the largest number of new users (Katz and Shapiro, 1985;
Farrell and Saloner, 1986). Over time, this process (or virtuous circle, as it is sometimes called), can
bestow considerable advantages on the leading firm, particularly in settings where switching costs
are high and consumers are locked in to a specific product (Farrell and Shapiro, 1988; Klemperer,
1995; Viard, 2007). Given that freemium business models allow companies to build up a larger user
base and appropriate greater value than conventional paid-only business models, we would expect
that that network effects would provide greater advantages to market leaders in those markets where
freemium business models are used. As a result, we would expect that network effects are likely
to bestow a greater advantage on market leaders in industries where freemium business models are
used, compared to industries where they are not used.
Empirically, this implies that when network effects are strengthened in an industry, market
leaders will become more dominant in those industries where freemium business models are used,
relative to those industries where they are not. This motivates our baseline and main hypotheses.
Hypothesis 1. Stronger network effects lead to greater market dominance.
Hypothesis 2. Stronger network effects lead to even greater market dominance where
freemium models are used.
4 Empirical Context & Data
Apps are computer programs that run on top of an operating system, using the technological compo-
nents of the operating system and hardware to provide additional functionality to users (i.e., games,
word processors, social media). The Apple App Store is a marketplace for third-party software
applications (or “apps”) designed to run on Apple handheld devices (i.e., iPhone, iPad & iPod). The
Apple App Store is one of the largest marketplaces for mobile applications in existence hosting more
than 1.4 million unique software titles and generating more than 25 billion USD in revenue since its
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inception (Apple Inc., 2015). The App Store acts as a sales and distribution channel for third-party
developers to create products and bring them to market.
There are 25 broad categories on the Apple App Store (i.e., games, health applications, produc-
tivity software), but there are more than a thousand different narrow market niches, according to
analyst reports.5 In some market niches there are thousands of unique product titles, while in others
there are only a few dozen unique titles. Firms on the Apple App Store use a number of different
strategies to generate revenue from their applications. Many firms simply sell their applications to
end users for a flat fee, while others use a freemium model, releasing a light version of their products
and charging users to buy additional or premium features. In freemium applications, customers can
purchase these premium features directly within the application through in-app purchases). Some
firms also use advertising to generate revenue from their applications, but this is only the case for a
small subset of products.
The basic tools necessary to develop an application are provided by Apple and include a de-
velopment environment in which developers can code their applications, and interface builder in
which they can design the layout of their products, an emulator in which they can simulate their
application and test its functionality and software libraries that allow them to reuse existing code.
Apple also provides tools to help developers learn how to develop software applications and learn the
programming language (objective C). While the basic tools to develop an application are provided
by Apple, developers have the discretion to use more sophisticated tools and components to develop
more detailed and intricate applications.
In addition to providing the tools and acting as a distribution channel, Apple also regulates
the application marketplace. Apple ensures that applications on the marketplace do not contain
content that is illegal, lewd or offensive. All applications on the Apple App Store have to adhere to
U.S. patent, copyright, and trademark law. In addition, applications have to adhere to very specific
technical requirements that specify how applications can access internet or web content, and how
they allow users to interact with one another.
5According to Priori Data GmbH, there are more than one thousand narrow market niches on the Apple AppStore.
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5 Analysis and Results
In the analysis, we exploit a policy change that led to the strengthening of network effects in a a subset
of categories (or market niches), and test how this affected the relative and absolute market share
generated by the market leader. We use a difference-in-difference-in-differences (DDD) approach
to analyze how this policy change affected categories where freemium business models were used,
compared to categories where freemium business models were not used, and categories not affected
by the shock. In the following section we describe in detail our research design.
5.1 Introduction of Game Center and Strengthening of Network Effects
Apple imposes strict technical requirements for all products which are sold through the App Store.
One requirement is that applications only use approved channels to allow users to communicate
with external parties or allow users to interact with one another through their application. In the
past, games in particular, were highly regulated and were restricted in their ability to allow users to
interact with one within a game. This made it difficult for developers to introduce leaderboards or
multiplayer functionality into their applications.
In the summer of 2010, Apple released the iPhone 4 and the corresponding operating system
(iOS 4.0). In the fall of that year, Apple released an update for its operating system (iOS 4.1) that
introduced a piece of software called Game Center into the operating system, along with a number of
minor bug fixes and tweaks. From the perspective of the technology, the Game Center components
allowed developers to allow users to interact with one another either through competitive or co-
operative multiplayer features or by allowing users to share accomplishments through leaderboards.
However, in a more abstract sense this created greater demand-side economies of scale or direct
network effects, as having a large user base created more value for consumers after the introduction
of Game Center than before. This is not to say that same-side network effects did not exist at all
prior to the introduction of Game Center, but that they were strengthened following its introduction.
One obvious concern is that individual firms had to make a deliberate choice on whether to
make use of the Game Center functionality. Moreover, there is a clear cost associated with using
the Game Center functionality that may affect the decision of firms to use Game Center. Similarly,
some firms may have anticipated the use of Game Center and integrated these components right
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away while others may have been slower to adopt these features. From the data, it is clear that
not all firms decided to make use of Game Center, and not all firms were quick to adopt the Game
Center functionality. That said, the firms with the largest market share (i.e., firms that were in the
Top 5 or Top 10 in any market niche), overwhelmingly made use of Game Center and were quick
to adopt this feature. Since this analysis is focused on market dominance and, in particular, the
performance of the top firms in a particular market niche, the decision of firms with lower market
share to delay implementing Game Center should not bias the results.
5.2 Non-Games as a Control Group
The introduction of Game Center leads to stronger network effects in games categories. In the
analysis, we make use of non-games categories as a control group which was not affected by the
introduction of Game Center. The introduction of Game Center did not have any direct influence
on market conditions in non-game categories, and there is no evidence that any of the additional
minor tweaks that coincided with the introduction of Game Center in iOS 4.1 had any direct effect
on market structure in non-game categories.
5.3 Measuring Performance
In the analysis we use two different measures of market leader performance.
Performance Measure 1: Daily Product Revenues. We measure performance based on the
daily revenues generated by the leading firm within a given niche, on a given day. Given the highly
skewed nature of daily revenues and the large number of zero (or close to zero) values, we use the loga-
rithm of revenues, plus one, as our first measure of performance (i.e. ln(TotalDailyRevenueLeader+
1)).
Performance Measure 2: Relative Dominance of Market Leader Our key theoretical
argument is that the combination of network effects and freemium business models can amplify the
revenues of the market leader and lead towards market dominance. While it is subjective what
market dominance may actually mean, increasing market dominance implies larger difference in
revenue between the market share of leading and following firms in a market segment. As a result,
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we construct a second performance measure that attempts to capture market dominance as the
difference in revenue between the firm with the largest market share and the firm with the second
largest market share. We construct this measure as follows:
ln(Total Daily RevenueLeader/Total Daily RevenueFollower)
An attractive feature of this measure is that it reflects cases of market dominance where the
revenues of leading firms are several orders of magnitude greater than those of laggard firms. This
variable is similar to measures that have been used in earlier studies (Ferrier et al., 1999; Caves and
Ghemawat, 1992; Davies and Geroski, 1997).
5.4 Measuring The Use of Freemium Business Models
On the Apple App Store the overwhelming majority of firms use either paid, free or freemium
models to generate revenue from their products. With free models, firms release their products for
free and either generate no revenue or generate small amounts of revenue through advertising. With
paid models, firms charge for the basic version of their application and may charge for additional
features in addition to the basic version of the software. Finally, with freemium business models,
firms release a free version of their product and allow customers to purchase premium features or
functionality. Firms can purchase these premium features through the use of in-app purchases,
which is a feature that developers can include in their applications. In the theoretical framing of
the paper, we discuss the difference between paid and freemium models since free revenue models
generally seldom generate considerable revenue and become dominant players in a market.6
We distinguish between freemium and paid revenue models based on the price that firms charge
for their products and whether they make use of in-app purchases. We define freemium products as
those that are released for free (i.e., the price of their products is zero), but use in-app purchases to
sell premium features. We define paid products as those that are sold for a fee (i.e., the price of their
products is greater than zero). Some paid developers also use in-app purchases to sell additional
features to consumers, while many do not use in-app purchases and only sell the basic version of
6While there are thousands of free and ad-based applications on the Apple App Store, these products are generallynot the top products in any given category. Unlike in settings such as OSS, the Apple App Store is dominated byfirms that generate revenue, either by selling their applications or using a freemium model.
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their product.
We use a dummy variable to indicate whether the leading firm within a narrowly defined market
niche uses a freemium business model. Generally speaking, when freemium business models are
used they are used by most of the top firms within a particular category. However, there may
be unobserved factors that correlated with the use of freemium business models that may end up
biasing our results. We designed our analysis to address these issues. Moreover, in this paper, we
do not focus on why freemium business models may be used by individual firms, but treat the use
of freemium business models as a feature of a particular category. Instead, we simply look at how
the use of freemium combined with the presence of network effects influences the performance of the
market leader.
5.5 Sources of Bias
There are a number of factors that may introduce bias into the analysis. Here we discuss each
potential source of bias and describe the steps that we take to address them.
Unobserved Category and Time Effects. Given that we are looking at wide range of categories
in the Apple App Store over a period of several weeks, it is likely that there are unobserved differences
across both categories and time periods being studied. To address this, we include category dummies
in all of our regressions, as well as either time trends or time dummies in our regressions to control
for these unobserved factors.
Unobserved Factors Correlated with the Use of Freemium. Perhaps the most obvious
source of bias is that the use of freemium is non-random and may be influenced by unobserved
factors. For instance, companies that use freemium business models may have more sophisticated
managers or make larger investments in marketing, than companies that use paid-only business
models. Consider prominent freemium based apps developers such as King, the maker of Candy
Crush Saga, or Nintendo, the maker of Pokemon Go. These firms often have considerable marketing
resources, in terms of both financial and human assets (community managers, growth hackers and
marketing staff), that are dedicated to promoting a single product. In comparison, developers that
create paid-only apps spend little promoting their applications to consumers once they are released.
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This may introduce bias into our analysis as companies with freemium business models may be better
able to benefit from the introduction of network effects due to their superior marketing capacity.
We have designed our analysis to ameliorate these issues. We perform the analysis at the level of
a narrowly defined market segment or “niche.” In some niches, freemium business models are used
by virtually all firms, while in others they are not used at all. Thus, in our setting, if the leading
company within a particular market niche is using freemium business models, than competing firms
are generally using freemium business models as well. As a result, if companies that used freemium
business models were more sophisticated or had better marketing assets, then this would apply
for both the market leader and the first placed follower within a particular market niche. While
the introduction of network effects may increase absolute demand for these products (creating an
upward bias in terms of daily revenues), it would not affect the relative demand for these products.
Thus, while our absolute performance measure (ln(RLeader)) may have an upwards bias, our relative
measure of performance (ln(RLeader/RFollower)) is not affected. We perform the analysis using both
variables to demonstrate the robustness of our results. We present an additional set of regressions
using our relative measure of performance and an alternative measure of freemium business models
to further test the robustness of these results.
Another potentially unobserved source of bias arises from the fact that freemium-based games
have been steadily growing in popularity over the past several years. This may create an upward
bias in our analysis, since the growing popularity of freemium games may increase demand in these
categories. There are several aspects of our analysis that help to address this issue. First, the time
window that we study is quite narrow (70 days) which limits the extent to which this trend may
bias our results. Second, in our regressions we directly control for these trends in the data, including
overall time trends, as well as time trends in freemium categories and in games categories. Finally,
in our regressions we perform placebo tests to directly test whether our results are not being driven
by unobserved trends in the data.
Unobserved Factors Correlated with the Introduction of Network Effects. Another ob-
vious source of bias is that the there may exist a number of unobserved factors that are correlated
with the introduction of network effects. For instance, the introduction of network effects may have
attracted larger or more sophisticated developer firms to enter the marketplace. Alternatively, the
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introduction of network effects may have inspired existing developers to revamp their product of-
ferings or otherwise improve the quality of their products. Similarly, the use of freemium business
models may have been more common after the introduction of network effects, than prior to the
policy change. These issues would create an upward bias in our estimates.
Once again, we designed our analysis to account for these potential sources of bias. We restricted
our analysis to a very narrow time window (70 days) before and after the introduction of Game
Center. Thus, while over time more sophisticated firms may have entered the marketplace, and
freemium business models became more widespread, our narrow time window limits the possibility
of this biasing our results. Second, by using a policy change introduced by the platform itself,
we limit the possibility that individual developers could themselves influence the introduction of
network effects.
5.6 Sample Construction and Model Specification
To test our hypotheses we will use a difference in differences approach. Since, we construct our
sample specifically for the purpose of using the difference in differences approach, we describe both
the sample construction and econometric specification in the same section.
We construct our sample around the introduction of Game Center with the release of iOS 4.
We exclude the data two days before and after the shock to avoid the most mechanical forms of
correlation that may arise from the introduction of iOS 4.1. Similarly, to avoid having the release of
iOS 4.0 bias the results, we begin the sample several days after the release of iOS 4.0. This leaves
us with a period of 70 days prior to the release of Game Center to use in the analysis. Thus, for
our sample we use a period of 70 days before and 70 days after the introduction of Game Center.
While a sample of 140 days may not seem a long time in conventional industries, app markets are
particularly dynamic and on average a market niche would undergo dozens of leadership changes
within this short time window.
The data is divided into 1005 narrowly defined market niches. While we have information about
the characteristics of the entire population of firms, we only have performance (revenue) data on the
upper echelon of firms on the App Store (revenue data on unranked and low grossing firms on the
App Store is difficult to acquire). This leaves us with a total of 243 categories to use in the analysis.
The unit of observation for the analysis is the category-day pair.
15
To test the hypothesized relationship we use a basic difference-in-difference-in-differences (DDD)
specification. The DDD approach allows us to account for a number of potential sources of bias. For
instance, there may be inherent differences between categories that use freemium business models
and categories that do not. Alternatively, some categories may be subject to greater network effects
or have higher switching costs at the outset. Finally, there may be unobserved trends moving towards
market dominance. Using the DDD approach allows us to control for these potential biases. The
basic model can be represented as follows:
Leader Performance = α+ β1GC Shock + β2Freemium+ β3GC Shock × Freemium
+β4Freemium×Games+ γ GC Shock ×Games
+δ GC Shock × Freemium×Games + C−1ψ + T−1ρ+ ϵ
where C is a vector of industry dummies, T is a vector of time trends, and ϵ represents an
idiosyncratic error term. GCShock is a dummy variable indicating the periods after the introduction
of Game Center. Games is a dummy variable indicating games categories. These were the categories
affected by the introduction of Game Center. Since category dummies are included in the analysis,
there is no un-interacted Games term in the basic model. Freemium is an dummy variable that
indicates whether the market leader in a particular category is using a freemium business model.
The key parameters of interest in this model are γ and δ. The first hypothesis predicts that the
parameter for γ will be positive and significant. The second hypothesis predicts that the parameter
for δ will also be positive and significant. Including category dummies allows us to control for
unobserved factors across different categories. Using the DDD approach provides an additional level
of robustness. In addition to controlling for potential trends that may be influencing the apps market
(by including non-games), it allows us to control for the possibility that there are unobserved factors
that affect only games industries after the shock (by including GC Shock × Games). This allows
us to disentangle the specific effect that the hacking shock and subsequent strengthening of network
effects had on market dominance in categories where freemium business models were used.
Additional Specifications Alongside our basic regressions, we include a number of additional
specifications to demonstrate the robustness of our results.
One potential concern, as mentioned above, is that the use of freemium business models is
16
non-random and may be correlated with unobserved factors. To address this issue, we utilize the
proportion of developers that use freemium business models as an instrumental variable to predict the
use of freemium business models by market leaders. The average use of freemium business models
in a category is correlated with the probability of the market leader using a freemium business
model, but does not have any no direct effect on the revenues of the market leader, particularly
when category dummies are included in the regressions.7 We include the instrumental variable
specification alongside our main regression results for both outcome variables.
Another concern is that by including time trends in our regression, we cannot include time dum-
mies to control for unobserved differences across time periods. Thus, alongside our main regression
results, we present our results, omitting time trends and including time dummies in the regressions.
A final concern may be that there exist unobserved differences between our treatment and control
groups, and that this may be somehow influencing the results. The intuition behind the placebo
test is to include a “fake (or placebo) shock” prior to the actual shock in the regressions and interact
it with the other variables of interest. If the three-way interaction is significant, this indicates that
the results are likely being driven by unobserved differences in time trends between the treatment
and control samples. Alongside our main regression results we also include a placebo test for both
outcome variables.
6 Regression Results
In Table 1, we present descriptive statistics for the variables used in our analysis. The unit of
observation of the data is the category-day pair. Consistent with our arguments about market
dominance, it is clear from the descriptive statistics that the revenue generated by the top firm in
each category is several orders of magnitude greater than that generated by the followers. This is
evidenced by the scale of the outcome variable (Mean = 1.98, Max = 16.58).
In Table 2, we present the results of the DDD regressions using the simple revenue measure
(ln(Revenue + 1)). Standard errors for all models are bootstrapped due to the large number of
7Given that we include category dummies in all of our regressions our instrumental variable must not be timeinvariant. Thus, as our instrument we include the proportion of applicatons that use freemium business modelswithin a market niche, that use freemium business models within a given category, on a given day. The results arerobust to alternative specifications of this instrumental variable including limiting the variable to the proportion ofapplications within the top ten, or changing the length of the time window from one day, to one week.
17
observations. In Column 1, the freemium and game center variables are introduced. In Column 2,
the two-way interactions of the variables are introduced. In Column 3, the three-way interaction is
introduced. Category dummies and trend controls are included in all of the regressions. To test our
hypotheses, we look at the parameter estimates of GCShock×Games and GCShock×Freemium×
Games. In columns 2 and 3, the parameter estimates for the two-way interaction are positive, but
they are not significant at the 90% level. The parameter estimate for the three-way interaction that
is introduced in Column 3 is both positive and significant (p < 0.001). In Columns 4, 5 and 6 we
present additional specifications to demonstrate the robustness of these results. In Column 4, we
present the results of the instrumental variable specification as described above. In Columns 5, we
omit time trends and instead introduce time dummies to control for unobserved differences that
may have occurred in different time periods. In Column 6, we introduce placebo dummies to test
if the results are being driven by an unobserved trend in the data. For the two way interaction,
when we remove the trend controls and add time dummies, we observe that the parameter estimate
becomes negative in sign. However, when we introduce the placebo test the parameter estimates
again become positive in sign, which suggests that the results may be driven by an unobserved
trend in the data. Regardless, the parameter estimates in both cases are minuscule. As a result, we
cannot find evidence that the introduction of network effects led to higher revenues for the leading
developers in affected categories. For the three-way interaction, the parameter estimate remains
significant and positive (at different levels) in columns 4 through 6. This provides evidence that
the introduction of network effects did lead to higher revenues for the leading developers in those
categories where freemium was used.
In Table 3, we present the results of the DDD regressions using the second outcome variable,
the ratio between revenue of the market leader and the market follower (ln(RevenueL/RevenueF )).
The results are presented in the same order as they were in Table 2. In columns 1 through 3, the key
variables of interest are introduced. In columns 2 and 3, the parameter estimates for the two-way
interaction are positive, but they are not significant at the 90% level. In column 3, the three-way
interaction is positive and significant at the 99% level (p < 0.001). This, again, suggests that the
introduction of network effects did not affect the revenues or the market dominance of the leading
firm, except in those instances where freemium business models were used. As before, in columns 4
through 6, we present a number of additional robustness tests. In column 4, we present the results
18
of the instrumental variable specification. In columns 5, we omit time trends and instead introduce
time dummies to control for unobserved differences over time. In column 6, we omit time trends
and introduce placebo dummies to test if the results are being driven by an unobserved trend in
the data. In columns 4 through 6, the three-way interaction term remains significant and positive
across all of the models. However, the two-way interaction of GC Shock×Games is significant and
negative in columns 5 and 6 once the time trend controls are omitted. This suggests that the results
are being driven by unobserved time trends. Thus, once again we do not find that the introduction
of network effects led to an increase in relative market dominance overall. However, we do find that
the introduction of network effects led to an increase in market dominance in those categories where
freemium business models are used.
6.1 Alternative Freemium Measure
As previously mentioned, we designed our regression analysis to minimize the potential for unob-
served factors to bias our analysis. However, one concern that remains is that firms which use
freemium may somehow be superior to those that do not, and that this may create an upwards
bias our results. To address this issue we perform an additional set of regressions using a different
variable to indicate the use of freemium business models.
If companies that use freemium business models do have superior managers or superior marketing
ability, then the same would be true for both the first and second ranked companies within a
particular market niche. We construct our alternative freemium measure to equal one if both the
market leader and follower within a niche use freemium business models, and zero otherwise. If
the results are being driven by the fact that companies that use freemium are somehow superior to
those that don’t, then the introduction of network effects would not affect the relative revenue of the
market leader, compared to the follower (ln(RevenueL/RevenueF )) if both the leader and follower
use freemium business models. We present the regression results using this alternative freemium
variable in Table 4. As before, we introduce the two-way interactions in Column 2, and the three
way interactions in Column 3. The coefficient for the three-way interaction in Column 3 is positive
and significant, suggesting that the results are not being driven by unobserved quality differences
between firms that use freemium, compared to those that do not. This provides further support for
Hypothesis 2.
19
6.2 Matching Treatment and Control Categories
From the regressions in tables 2 and 3, it appears that the treatment and control groups follow
different trends over time. While we attempt to control for these differences in our regressions, this
difference in trends violates the parallel trend assumption and undermines the validity of our DDD
regressions. To address this issue, we match control and treatment groups based on the trend of
the outcome variable prior to the introduction of Game Center. For matching, we use the CEM
algorithm developed by Blackwell et al. (2009). This ensures that the treatment and control groups
follow a similar trend prior to the shock. We reweigh our sample based on the weights generated
by the CEM algorithm and present the results of the DDD regressions for both outcome variables
in Table 5. For the regressions in Table 5, we do not include time trend controls. The results are
consistent with those presented in earlier tables. The results do not provide support for Hypothesis
1, but do provide support for Hypothesis 2.
6.3 Summary of Results
Across all of the results, we find that the introduction of Game Center and strengthening of network
effects did not have a significant effect on the revenues or dominance of market leaders overall.
However, we do find that the introduction of network effects led to higher revenues and greater market
dominance of the market leader in categories where freemium business models were used. In terms
of the magnitude of these effects, the introduction of network effects in categories where freemium
business models were used corresponds to approximately 2.3 times higher revenues for the market
leader8, relative to their revenues prior to the shock. In terms of our second performance measure
(ln(RevenueL/RevenueF )), the introduction of network effects corresponds to approximately 3.8
times higher ratio in the revenue of the market leader compared to the market follower.9 The
magnitude of these effects suggests that the combination of network effects and freemium business
models has considerable implications for market outcomes in digital industries.
8Based on the coefficients in Table 3, Column 3.9Based on the coefficients in Table 4, Column 3.
20
7 Discussion and Conclusion
Despite the traditional narrative of “winner-take-all” outcomes in the presence of network effects,
there is a growing realization that firms require a complex and deliberate strategy to benefit from the
presence of network effects (Anderson et al., 2014; Tanriverdi and Lee, 2008; Tiwana, 2015). As a
result, business models must account for the challenges of building up and exploiting network effects
around their specific products. To that end, in this paper, we explore the use of freemium business
models, a commonly used strategy for selling digital goods and services in industries with network
effects. We argue that freemium business models amplify the impact of network effects increasing
demand for leading products far more than network effects alone. We tested this prediction using
a policy change on the Apple App Store that led to a strengthening of network effects in a subset
of categories. Consistent with our arguments, we find that the introduction of network effects did
lead to greater market dominance in those categories where freemium business models were used.
However, importantly, we do not find that the introduction of network effects per se had any influence
on market dominance in those categories where more conventional paid-only business models were
used.
This paper contributes to the literature on network effects and competitive dynamics within
network industries (Lee et al., 2006; Dou et al., 2013; Pang and Etzion, 2012; Ghose and Han, 2014;
Boudreau and Hagiu, 2009; Boudreau, 2010), by considering how different strategies may allow
companies to benefit from the presence of network effects. This paper specifically contributes to the
growing body of research (Cennamo and Santalo, 2013; Clements and Ohashi, 2005; Niculescu and
Wu, 2014) looking at different strategies that firms can employ to gain an advantage over competitors
in the presence of network effects. The paper highlights the importance of freemium business models
and that they complement the presence of network effects. In doing so, this paper also answers calls
for further research into the process of value creation and capture in digital industries (Yoo et al.,
2012; Greenstein et al., 2013). In addition to its theoretical contribution, this paper also contributes
in terms of methodology by providing a novel approach to testing the impact of network effects.
To date, papers that have looked at the impact of network effects have either developed theoretical
models (Katz and Shapiro, 1985; Farrell and Klemperer, 2007), or have used structural econometrics
to measure the strength of network effects (Dube et al., 2010; Corts and Lederman, 2009). In this
21
paper, we use a novel approach by exploiting a policy change that led to a “switching on” of network
effects.
While the focus of this paper is on the implications of network effects and freemium business
models for market leaders, the results have considerable implications for firm strategy more broadly.
Looking at the magnitude of the results, it is clear that the combination of network effects and
freemium business models greatly enhance the demand for the leading product, and in turn the
relative dominance of the leading firm. This may explain why many prominent businesses such as
Dropbox, Spotify, Skype, LinkedIn, Clash of Clans, Candy Crush, and Farmville rely on freemium
business models, while attempting to foster social interactions among users and benefit from the
presence of network effects. This may also explain why digital marketplaces are so often dominated
by such a small number of firms. For example, as mentioned before, in 2012 Apple reported that
only twenty five firms accounted for more than half of all product sales on the Apple App Store.
However, this implies that while this complementarity may provide and advantage for leading firms,
it spells a clear disadvantage for all firms other than the market leader.
Given this insight, we may expect that firm strategies may differ in settings where network effects
exist and freemium business model are used. For instance, new entrants may have an incentive to
focus on industries where freemium business models are not used, simply because market leaders
in freemium industries have such dominant positions. Alternatively, entrants may have to invest
more highly in marketing assets to be able to overcome the dominance of market leaders. While in
this paper we do not evaluate the strategic tradeoffs associated with different business models, the
results suggest that there may be a number of unexplored issues regarding how companies operate
in digital industries characterized by network effects.
That said, the insights from the current paper raise a number of other questions for future
research. For instance, given this complementarity, it would be interesting to understand dynamic
strategic interactions between firms in such a setting. How do competitors respond to a market
leader with a freemium business model? Moreover, it would be interesting to understand to what
extent these dominant firms may be able to retain their advantages in such a setting. Finally, it
would be interesting to understand how add-on’s to freemium goods are optimally priced given the
presence of this complementarity.
One obvious limitation of the current study is that it does not consider why freemium business
22
models are used in some categories and not in others. For instance, some types of digital products,
such as anti-virus software have traditionally been sold using freemium models, while word processors
or spreadsheet software have been sold primarily using a paid-only model. The current paper does
not explore how these industries differ and why freemium business models may be suited for one
type of digital product, but not another. Similarly, the present study does not consider why, within
a given category, there may be some products that are sold using freemium business models and
others that are not. Finally, the present study does not explain how competitive dynamics function
in settings where some companies use freemium business models, and others do not. While these are
all important questions, the present paper does not attempt to resolve any of these issues. Rather, it
tries to argue that in instances where freemium business models are used, there is a greater likelihood
that the presence of network effects will lead to market dominance and a single firm controlling the
market.
23
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Table 1: Descriptive Statistics of Key Variables
Mean Std. Dev. Min. Max
ln(Revenue
Leader
+ 1) 1.98 1.92 0.00 16.58
ln(Revenue
Leader
/Revenue
Follower
) 2.16 2.15 0.00 11.80
GC Shock 0.50 0.50 0.00 1.00
GC Shock ⇥Games 0.13 0.33 0.00 1.00
Freemium 0.05 0.22 0.00 1.00
Games⇥ Freemium 0.01 0.08 0.00 1.00
GC Shock ⇥ Freemium 0.03 0.17 0.00 1.00
GC Shock ⇥Games⇥ Freemium 0.00 0.06 0.00 1.00
T ime Trend (Days Since Start of Sample) 69.95 40.06 1.00 139.00
T ime Trend
26497.79 5786.60 1.00 19321.00
T ime Trend ⇥Games 17.61 36.45 0.00 139.00
T ime Trend ⇥ Freemium 3.90 19.32 0.00 139.00
N = 33,543
Table 2: DDD Regression Results for Absolute Market Leader Revenue
DV: ln(Market Leader Revenue+ 1)
(1) (2) (3) (4) (5) (6)
Main Results IV Time Dummies Placebo
GC Shock 0.12*** 0.10*** 0.11*** -0.08 0.09 -0.20***
(0.02) (0.03) (0.03) (0.05) (0.07) (0.02)
GC Shock ⇥Games 0.06 0.04 0.11 -0.06* 0.09**
(0.05) (0.05) (0.06) (0.02) (0.03)
Freemium 0.32*** 0.47*** 0.50*** 3.42*** 0.55*** 0.58***
(0.07) (0.08) (0.08) (0.61) (0.06) (0.07)
Games⇥ Freemium -0.82*** -1.10*** -3.05*** -1.14*** -1.11***
(0.10) (0.10) (0.59) (0.09) (0.15)
GC Shock ⇥ Freemium 0.03 -0.05 3.36*** 0.09 0.08
(0.12) (0.12) (0.87) (0.06) (0.08)
GC Shock ⇥Games⇥ Freemium 0.44*** 1.01* 0.44*** 0.36**
(0.11) (0.39) (0.10) (0.13)
Placebo Shock 0.09***
(0.02)
Placebo Shock ⇥Games -0.29***
(0.04)
Placebo Shock ⇥ Freemium -0.04
(0.10)
Placebo Shock ⇥Games⇥ Freemium 0.10
⇥Freemium (0.19)
Category Dummies Yes Yes Yes Yes Yes Yes
T ime Trends Yes Yes Yes Yes No No
T ime Dummies No No No No Yes No
Constant 2.26*** 2.26*** 2.26*** 1.62*** 2.33*** 2.21***
(0.02) (0.02) (0.03) (0.28) (0.03) (0.02)
N 33543 33543 33543 33543 33543 33543
R
20.76 0.76 0.76 0.74 0.76 0.76
�
2797.34 408.82 798.80 2700.22 562.56
(0.00) (0.00) (0.00) (0.00) (0.00)
F 808.38
(0.00)
Robust standard errors in parentheses (* p < 0.05, ** p < 0.01, *** p < 0.001)
Table 3: DDD Regression Results for Relative Market Leader Revenue
DV: ln(Market Leader Revenue/Market Follower Revenue)
(1) (2) (3) (4) (5) (6)
Main Results IV Time Dummies Placebo
GC Shock 0.13*** 0.18*** 0.19*** -0.01 0.18 0.05*
(0.03) (0.03) (0.03) (0.06) (0.10) (0.02)
GC Shock ⇥Games -0.13 -0.16* -0.14 -0.24*** -0.15**
(0.08) (0.08) (0.08) (0.04) (0.05)
Freemium 0.20* 0.18* 0.25** 3.85*** 0.50*** 0.44***
(0.09) (0.08) (0.09) (0.62) (0.09) (0.10)
Games⇥ Freemium -0.52*** -1.25*** -5.46*** -1.16*** -1.12***
(0.13) (0.18) (0.84) (0.17) (0.27)
GC Shock ⇥ Freemium -0.51*** -0.72*** 3.04** -0.16* -0.23*
(0.14) (0.14) (0.93) (0.08) (0.11)
GC Shock ⇥Games⇥ Freemium 1.15*** 2.89*** 1.06*** 1.10***
(0.18) (0.54) (0.16) (0.21)
Placebo Shock 0.00
(0.03)
Placebo Shock ⇥Games -0.17***
(0.05)
Placebo Shock ⇥ Freemium 0.12
(0.12)
Placebo Shock ⇥Games⇥ Freemium -0.08
(0.32)
Category Dummies Yes Yes Yes Yes Yes Yes
Trend Dummies Yes Yes Yes Yes No No
T ime Dummies No No No No Yes No
Constant 2.02*** 2.02*** 2.02*** 2.45*** 2.00*** 1.99***
(0.03) (0.03) (0.02) (0.39) (0.04) (0.02)
N 33543 33543 33543 33543 33543 33543
R
20.51 0.51 0.51 0.47 0.51 0.51
�
282.16 203.17 215.29 361.82 172.77
(0.00) (0.00) (0.00) (0.00) (0.00)
F
2190.08
(0.00)
Robust standard errors in parentheses (* p < 0.05, ** p < 0.01, *** p < 0.001)
Table 4: DDD Regression Results with Alternative Freemium Measure
DV: ln(Market Leader Revenue/Market Follower Revenue)
(1) (2) (3)
GC Shock 0.13*** 0.16*** 0.16***
(0.03) (0.03) (0.03)
GC Shock ⇥Games -0.12 -0.13
(0.08) (0.07)
Freemium -0.27** -0.07 0.03
(0.10) (0.11) (0.14)
Games⇥ Freemium -0.55** -0.96***
(0.19) (0.19)
GC Shock ⇥ Freemium -0.23 -0.39*
(0.13) (0.18)
GC Shock ⇥Games⇥ Freemium 0.86***
(0.22)
Category Dummies Yes Yes Yes
Trend Dummies Yes Yes Yes
T ime Dummies No No No
Constant 2.03*** 2.03*** 2.03***
(0.03) (0.03) (0.03)
N 33543 33543 33543
R
20.51 0.51 0.51
�
283.18 108.39 133.57
(0.00) (0.00) (0.00)
Robust standard errors in parentheses
(† p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001)
Table 5: DDD Regression Results with Matched Sample
(1) (2) (3) (4) (5) (6)
DV : ln(Rev
L
+ 1) DV : ln(Rev
L
/Rev
F
)
GC Shock -0.20*** -0.21*** -0.21*** -0.03* 0.01 0.01
(0.01) (0.01) (0.01) (0.02) (0.02) (0.02)
GC Shock ⇥Games 0.05* 0.04 -0.14*** -0.17***
(0.03) (0.03) (0.04) (0.04)
Freemium 0.48*** 0.59*** 0.62*** 0.24*** 0.36*** 0.44***
(0.05) (0.07) (0.07) (0.05) (0.07) (0.08)
Games⇥ Freemium -0.88*** -1.15*** -0.44*** -1.12***
(0.09) (0.11) (0.13) (0.16)
GC Shock ⇥ Freemium 0.10 0.04 -0.05 -0.18*
(0.06) (0.07) (0.07) (0.07)
GC Shock ⇥Games⇥ Freemium 0.43*** 1.07***
(0.11) (0.19)
Constant 2.24*** 2.24*** 2.24*** 1.99*** 1.99*** 1.99***
(0.01) (0.01) (0.01) (0.01) (0.01) (0.01)
N 33130 33130 33130 33130 33130 33130
R
20.78 0.78 0.78 0.53 0.53 0.53
F
2170.14 78.93 69.61 12.88 10.11 13.63
(0.00) (0.00) (0.00) (0.00) (0.00) (0.00)
Robust standard errors in parentheses
(* p < 0.05, ** p < 0.01, *** p < 0.001)