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)