competing across platforms: antecedents of …...platform has attracted more women and young girls...

COMPETING ACROSS PLATFORMS: ANTECEDENTS OF PLATFORM MOBILITY

Yongzhi Wang Assistant Professor of Strategic Management

Department of Management & Human Resources Fisher College of Business

Fisher Hall 856 Ohio State University Columbus, OH, 43210

Email: [email protected]

Nandini Rajagopalan Professor of Management and Organization

Department of Management and Organization Marshall School of Business

Bridge Hall 101 University of Southern California

Email: [email protected]

Lori Qingyuan Yue Associate Professor of Management and Organization

Department of Management and Organization Marshall School of Business

Hoffman Hall 513 University of Southern California Email: [email protected]

Version: March 7, 2018

The manuscript is under review. Please do not quote, cite, or circulate without the first author’s written permission.

Ó Wang, Rajagopalan, and Yue, 2018

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COMPETING ACROSS PLATFORMS: ANTECEDENTS OF PLATFORM MOBILITY

ABSTRACT

The surpassing of a competing platform over the focal platform in installed bases creates

uncertainty surrounding the emergence of a dominant platform. This in turn triggers

complementors’ anxiety and thus increases their migration to the competing platform. However,

as platform competition evolves and demonstrates that multiple platforms with strategic

differentiation (e.g., quality- versus quantity-driven) could coexist, the migration tendency

recedes. Consistent with our argument, we find that the increasing gap between installed bases of

Android and iOS has an inverted-U effect on iOS developers’ likelihood of moving to Android,

and that low performers and free-app developers have been the primary contributors to the

inverted-U shaped pattern. App developers that migrate to Android at a highly uncertain time

also experience a high rate of product failure.

Keywords: platform competition, mobility, multihoming, app developers, quality-driven strategy, quantity-driven strategy.


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INTRODUCTION

A critical question in platform-based markets is whether market competition could tip and lead to

the emergence of a single dominant platform (e.g., Hossain, Minor, and Morgan, 2011; Shapiro

and Varian, 1999). Market tipping is often associated with tremendous uncertainty (Anderson

and Tushman, 1990; Tushman and Anderson, 1986), high rates of technological shakeouts

(Schilling, 2002), and high rates of failure for organizations and their products (Agarwal, Sarkar,

and Echambadi, 2002). As a result, betting on which platform will emerge as the dominant one

and swiftly moving to avoid being shaken out are important strategic decisions for

complementors that produce goods for consumers they share with their hosting platform.

Part of the reason why the emergence of a dominant platform is so consequential is

because of the network effects in platform-based competition (Chen, Qian, and Narayanan,

2017). Either because consumers value direct links with other consumers or the platform with a

larger installed base offers a wider variety of complementary products and services, platforms

with a large number of end users and complementary products are able to acquire an even bigger

market share, resulting in a “winner-take-all” type of market dynamics. Market tipping,

therefore, means that the dominant platform will drain resources and consumers away from the

other (losing) platforms (Besen and Farrell, 1994; Caillaud and Jullien, 2003; Katz and Shapiro,

1985; Shapiro and Varian, 1999). For an individual complementor, remaining on a losing

platform is akin to betting on the wrong side of history. A complementor’s anxiety about moving

to a competing platform could be especially high when the competing platform shows a tendency

to become the dominant one.

However, not all platform-based markets will tip. For example, there are multiple credit-

card platforms. In the gaming console market, although Sony controls over 55% of worldwide


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consumer spending, Microsoft has retained 30%, and Nintendo nearly 15% (Harding-Rolls,

2017). Similarly, in the U.S. online dating market, although Tinder has been the market leader,

by the end of 2016, it only controlled 25% of the market while its closest competitors—Plenty of

Fish and OkCupid—had 19% and 10% of the market, respectively.1 Reconciling with these facts,

some recent research has quantified the application of network effects by pointing out that, if a

platform is able to sufficiently differentiate itself from competitors with larger installed bases, it

can maintain competitive advantage by being the “right” platform for a niche segment of the

market (e.g., Armstrong and Wright, 2007; Cennamo and Santalo, 2013; Eisenmann, Parker, and

Van Alstyne, 2006; Hossain et al., 2011; Hossain and Morgan, 2013).

While differentiation can prevent the platform-based competition from tipping, predicting

accurately whether competition will tip in a pre-hoc fashion is difficult. Researchers that

investigate the emergence of dominant design in the technology domain have long noted that the

emergence of a dominant technology is driven by many complex factors, and the technology

with superior design will not necessarily dominate (Anderson and Tushman, 1990; Suarez,

2004). Similarly, competition in the platform-based market is an evolving process, and

uncertainties exist regarding whether cross-platform differentiation would be sufficient to sustain

the co-existence of multiple platforms. When a platform is able to rapidly expand its installed

base of users and offers a large number of complementary products, it is more capable of locking

in users, attracting resources, and undermining the ability of rival platforms to do the same.

Consequently, it may eventually corner more segments of the market. Thus, the evolution of

macro-level uncertainties regarding the emergence of a dominant platform is likely to influence

complementors’ cross-platform mobility decisions.

1 Source: https://www.statista.com/statistics/737081/popular-online-dating-market-share-users-monthly/, accessed on February 22, 2018.


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Moving across platforms can also be risky when the mobility decision is made under the

anxiety of avoiding a shake out. Platforms tend to be not entirely compatible, and developing

products for multiple platforms require different technical skills (e.g., programming language

and expertise) that can be costly to acquire and cumulate (Dierickx and Cool, 1989).

Simultaneously pursuing markets on multiple platforms could subject a complementor to the risk

of spreading resources too thinly and consequently to suffer from a quality decline in products

and services. In addition, consumers’ tastes can also be different, especially on platforms that are

different from the focal one. Without knowing whether a competing platform market would be a

good fit for its own products, a complementor may make the wrong decision of market entry and

experience a higher failure rate.

In this paper, we test the impact of the macro-level uncertainty regarding the emergence

of a dominant platform on complementors’ micro-level decision to move across platforms. Our

research setting is the competition between Apple’s iOS and Google’s Android mobile operation

systems. Launched in January 2007, iOS initially enjoyed substantial advantages due to its early

mover advantages and superior proprietary hardware and was able to draw a large number of

users in the early stages of the competition. However, the open nature of Android enabled it to

quickly catch up. Android’s hardware installed base surpassed that of iOS in early 2011, and the

number of apps available on Android surpassed those on iOS in early 2013. Around that time,

there were many media reports speculating that Android was going to overtake iOS as “the

supreme and dominant” platform, and as a result, “the pace at which mobile app developers have

shifted their allegiance from iOS to Android has accelerated markedly.”2 Yet as the competition

evolved, it became gradually clear that iOS and Android are differentiated, and iOS is able to

2 Source: http://www.makingmoneywithandroid.com/2013/08/why-android-may-soon-be-the-dominant-platform-for-mobile-advertising/, accessed on February 22, 2018.


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maintain a group of loyal consumers with a relatively high willingness-to-pay despite the fact

that the platform has far fewer users than Android. Thus, the tendency of iOS app developers to

migrate to Android should be attenuated, leading to an inverted-U relationship between the gap

of Android and iOS installed bases and the hazard of migrating to Android.

THEORY AND HYPOTHESES

Uncertainty of Emergence of a Dominant Platform

The emergence of a dominant platform is a watershed event in platform-based competition. As a

single dominant platform becomes more popular, its user value increases, resulting in a virtuous

cycle of “winner-take-all” and market coalescence. The dominant platform can provide

consumers with higher benefits such as lower prices and access to more data-driven services,

more connectivity to other users, and more complementary products and services. In such

winner-take-all dynamics, the dominant platform can become a powerful monopoly by draining

resources away from existent platforms that are losing ground, as well as preventing new

entrants. Thus, the emergence of a dominant platform is often associated with tremendous

technological turmoil, and third-party complementors that develop services and products for the

losing platforms face the immediate risk of being shaken out (Agarwal et al., 2002; Anderson

and Tushman, 1990; Chen et al., 2017; Schilling, 2002). Market tipping in platform competition

is not rare. The war of personal-computer operating systems resulted in Windows controlling

more than 85% of the market share for decades. Similarly, the search giant Google currently

controls nearly 75% of the worldwide search market, and Facebook retains a huge lead on the

competition among social networking platforms with 89% of U.S. internet users in 2017.

Yet, not all platforms tip. The market for gaming consoles is split among Microsoft,


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Nintendo, and Sony. Similarly, the online dating sites are even less concentrated with the market

leader controlling only a 25% share. Addressing the question of whether platform-based

competition will tip or multiple competing platforms can coexist, some theoretical models have

demonstrated the role of switching costs in preventing the emergence of a dominate platform

(e.g., Carrillo and Tan, 2015), while more recent studies have suggested that horizontal

differentiation can be sufficient to offset network effects (e.g., Armstrong and Wright, 2007;

Cennamo and Santalo, 2013; Eisenmann et al., 2006; Hossain et al., 2011; Hossain and Morgan,

2013). For example, Hossain, Minor, and Morgan (2011) used experimental evidence to show

that,when platforms are undifferentiated, markets inevitably tip to the more efficient platform;

however, when platforms are horizontally differentiated, there is no single efficient platform,

which leads to multiple coexistence. The rationale behind the coexistence of multiple

horizontally differentiated platforms involves a trade-off between efficiency and taste in that the

“right” platform may well differ between users. For the gaming console market, Nintendo’s Wii

platform has attracted more women and young girls to video games while rivals, such as

Microsoft’s Xbox 360 and Sony’s PlayStation 3, primarily appeal to males.3 Similarly, the online

dating markets are also segmented by geographic and demographic characteristics, with some

sites offering singles a more culturally specific experience based on ethnicity and allowing a

search for those that align with their cultural practices and preferences.

Along this line, we argue that horizontal differentiation includes not only consumers’

demographical characteristics, such as gender and ethnicity, but also includes product quality and

consumers’ willingness to pay. In the smartphone mobile platforms, iOS pursues a quality-driven

strategy that emphasizes customer satisfaction in order to improve its customers’ willingness to

3 Source: http://www.telegraph.co.uk/technology/video-games/nintendo/6672402/Nintendo-Wii-most-popular-console-among-female-gamers.html, accessed on February 22, 2018.


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pay whereas Android pursues a quantity-driven strategy that seeks to leverage the benefits

associated with a large number of users.4 iOS’s high quality originates from Apple’s closed

system and it has more restrictive governance rules for app developers. The tight control over the

App Store is consistent with Steve Jobs’ belief in closed systems that help ensure the

seamlessness and simplicity of user experience. The integrated hardware and operating system

has also eased app developers’ efforts of product development, and developers tend to first

launch their products on iOS to avoid development complexity.5

In contrast, Google’s Android operating system appears to stress quantity,6 with a

platform that is more open to potential app developers through less-restrictive governance rules.

Android is also open on the hardware side (i.e., device manufacturers).7 The platform’s open

source code, enabled by the Android Open Source Platform (Parker, Van Alstyne, and Choudary,

2016),8 is available to third-party hardware manufacturers. Being open apparently facilitates the

quantity-driven nature of the platform, as it can leverage a vast pool of potential complementors

and stimulate market competition among them (Cennamo and Santalo, 2013), and also attract a

large number of consumers. However, the strategy of openness on both the hardware and

software ends has resulted in a tradeoff on the quality of service offered by Android. App

developers have complained about difficulties of developing and maintaining software

4 Source: https://www.makeuseof.com/tag/ios-apps-still-better-android-apps/, accessed on February 22, 2018. 5 Source: https://www.theguardian.com/technology/appsblog/2013/aug/15/android-v-ios-apps-apple-google, accessed on February 22, 2018. 6 Android’s quantity of apps overtook that of iOS but with lower quality, because apps published on Android are not required to pass the rigorous quality checks applied to iOS apps. (http://www.techaheadcorp.com/blog/mobile/googles-android-quantity-quality-apps.php, accessed on July 25, 2014). 7 A formal definition of open platform can be found in Parker et al. (2016), in which the authors argued that, “A platform is ‘open’ to the extent that (1) no restrictions are placed on participation in its development, commercialization, or use; or (2) any restrictions—for example, requirements to conform with technical standards or pay licensing fees—are reasonable and non-discriminatory, that is, applied uniformly to all potential platform participants” (p.130). 8 For instance, “[Android Open Source Platform] is the platform used by Amazon in its Kindle Fire and by China’s Xiaomi in its mobile phones” (Parker et al., 2016: 140).


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applications for the platform’s fragmented devices (see Appendix A for a small case).

Differentiation between iOS and Android clearly affects app developers’ decisions of

which platform to launch their products. As one developer said (Swanner, 2015),

[F]or my target demographic and audience, the people who have iPhones are far

more desirable than their peers with Android phones. When talking with teens and

20 year olds, having a big, gold iPhone 6 Plus is like carrying a Prada bag or

driving a BMW. It signifies a social status, a place at the top of their social circle.

You buy an iPhone because you want the best. Young people I talk to with

Android phones have them because it was cheap, or free, and their goal is to buy

an iPhone a year or two from now when they have the money. They settled with

an Android phone and now they’re settling with clunky implementations of the

apps their friends had before them on their iPhones. I’m building for iOS because

I want to target users with taste and buying power.

Although differences between iOS and Android have become clearer over time, there was

substantial uncertainty over whether Android would emerge as the dominant platform,

particularly when the amount of Android device shipments, and Android apps, surpassed those

on iOS. Around this time, media stories reported on, “Who’s winning the mobile platform wars,

Apple’s iOS or Google’s Android?”9 Many people believed that Android was going to overtake

iOS as “the supreme and dominant” platform, as described by a senior editor of a media

company, “[i]n the last seven months alone, the pace at which mobile app developers have

shifted their allegiance from iOS to Android has accelerated markedly” (Essany, 2013, Italics

added). Repositioning is an optional strategy when facing the emergence of a dominant platform

(Suarez, Grodal, and Gotsopoulos, 2015). Given the importance of market tipping, it is 9 Source: http://techland.time.com/2013/04/16/ios-vs-android/, accessed on February 22, 2018.


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understandable that app developers that did not initially adopt Android would then choose to

launch their products on Android.

Such changes, however, are risky and can hurt developers’ market performance and

survival chances (Hannan and Freeman, 1984; Haveman, 1992). Thus, when competition

between Android and iOS gradually revealed that iOS and Android are two differentiated

platforms, iOS developers’ tendency to move to Android should be attenuated. The better user

experience of iOS coupled with the premium status of Apple devices have created a group of

loyal consumers with a relatively high willingness-to-pay, whereas Android’s open system is

more attractive to the large market of consumers willing to accept free apps with less desirable

quality, exchange free services with their private information, or be willing to watch

advertisements. At the beginning of 2017, although iOS had only half as many users as Android,

the Apple App Store brought in almost 64% more revenue.10 Thus, a further widening of the

installed-base gap between iOS and Android did not indicate a takeover of Android, but rather

validated the co-existence of the quality- and the quantity-oriented platforms. Thus, the tendency

of iOS-only developers to migrate to Android should be attenuated, leading to an inverted-U

shaped relationship between the Android and iOS installed-base gap and the hazard of migration

to Android.

Hypothesis 1 (H1): The installed-base gap between Android and iOS will have an

inverted-U effect on the likelihood of an iOS-developer firm moving to Android.

Developer Heterogeneities in Cross-platform Mobility

The impact of the installed-base gap between Android and iOS on app developers’ tendency to

move reflects the macro-level platform competition on micro-level complementor strategies. 10 Source: https://www.upwork.com/hiring/for-clients/android-vs-ios-development/, accessed on February 22, 2018.


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Besides the macro-level influence, complementors also differ in the ways they are influenced by

such a force. In particular, the macro force should exert less influence over app developers that

have been doing well or have a good fit with their home platforms. We next unpack developer

heterogeneities along the dimensions of their performance on the home platform and

monetization strategy.

First, we propose that the inverted-U-shaped market trend is more likely to influence low-

performers’ mobility decisions. Moving across platforms is risky. In the mobile platform context,

apps need to be implemented for both iOS and Android, which means each version will have its

own bugs to fix, versions to maintain, and features to roll out. App developers that perform

poorly on their home platforms tend to look for changes and are thus more likely to take such

risks (Greve, 1998). For them, moving to a competing platform can also be a high-rewarding

action. When making sense of market failures, managers tend to blame external factors (Eggers

and Song, 2015). A relatively poor performance may similarly be attributed to home platform

problems or positioning, or a lack of fit between strategies. Thus, managers may believe that they

would benefit from moving to a target platform that potentially fits them better and is even likely

to emerge as the dominant one. In the mobile app market, app developers’ financial performance

is tightly related to rankings on the revenue-based lists (e.g., “grossing ranking” in the App Store

and Play Store). Prior research has found that apps’ grossing ranking and revenue is related via a

power law distribution and the correlation is positive (in the sense that highly ranked apps accrue

better financial performance) (Garg and Telang, 2013; Kapoor and Agarwal, 2017). A simple

way to categorize app developers is whether they are top- or non-top-ranked ones. Accordingly,

we propose:

Hypothesis 2a (H2a): Whether an app developer firm is top-ranked will moderate


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the inverted-U effect in H1, such that non-top-ranked developers are more likely

to exhibit the inverted-U pattern.

The second dimension that we use to gauge developers’ fit to a quality-driven platform is

their monetization strategy. One key monetization decision for app developers is whether to offer

free or paid apps—i.e., apps that customers have to buy in order to download. Because the price

can scare off some prospective consumers, these types of apps are targeted mostly at those who

expect high performance and have a willingness to pay. In contrast, free apps are completely free

to download, and the main sources of revenue are in-app ads and purchases.11 Clearly, the

quantity-oriented Android fits better with the free app strategy, because its huge user bases help

to speed up business exposure and leverage the benefits associated with installed bases.

Consumer expectations of free apps are not as high as those for paid apps. If a developer has a

higher percentage of paid apps in its product portfolio, it should fit better with the iOS’s quality-

oriented platform and is therefore less influenced by the macro environmental force. In contrast,

if a developer offers primarily free apps and profits from advertising revenue or selling consumer

data, it should benefit more from locating on a quantity-oriented platform that is likely to emerge

as the dominant one. Therefore,

Hypothesis 2b (H2b): A developer’s percentage of paid apps in its app portfolio

will moderate the inverted-U effect in H1, such that developers with a smaller

percentage of paid apps are more likely to exhibit the effect.

As predicted, if the anxiety of avoiding been shaken out is a driving force behind an iOS 11 There is another type called “freemium” apps that are a modified version of the free apps. Customers download and use such apps for free, but premium features and advanced functions are available for purchase. Due to our available data, we do not further differentiate free apps as those with and without in-app-purchases.


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app developer’s move to Android, the developer may not have made a high-quality decision

when the uncertainty surrounding Android’s emergence of a dominant platform is high. We

therefore expect that the relationship between the installed base gap between Android and iOS

and the failure rates of apps that movers introduce on Android will similarly show an inverted-U

shape. Due to limited space, although we do not formally hypothesize the proposition, we test it

in the following section describing our empirical setting and empirical approaches.

METHODOLOGY

Data

The data include 1.44 million iOS apps and corresponding developers (nearly the whole

population of iOS developers and apps in late 2015)12 and 2.5 million Android apps and

corresponding developers (close to the whole population of Android developers and apps in early

2016). The data were compiled from laborious web scraping from the App Store of the iOS

platform and a few commercial sources of app data. We collected platform-specific information

including: (1) hardware data of the two platforms (revenue, price, and shipments) from

Bloomberg terminals, and (2) platform major events (operating system updates and developer

conferences) that were manually collected from platform owners/sponsors’ websites and media

websites. We used hardware sales data to code the hardware dimension of the installed-base gap

between Android and iOS, and controlling for platform events, we could account for shocks

induced by such events (Dranove and Gandal, 2003; Kapoor and Agarwal, 2017).

To determine whether and when a developer of a platform moves to the competing

platform, we first matched developers across platforms via Python programming. The purpose

12 The number is comparable to the number of apps (1.5 million) announced by Apple at its 2015 Worldwide Developers Conference.


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was to overcome the challenge of the different templates used by the two platforms to assign app

identification numbers (IDs) and/or developer IDs. Such IDs are used to distinguish developers

into the following types: iOS-only, Android-only, and iOS-and-Android developers.13 We used a

developer’s earliest released app(s) to determine the app’s platform entrance date. For developers

that have adopted both platforms, we were able to determine the mobility time by comparing

entry dates on both platforms.

We exclusively focused on iOS developers’ mobility to Android—not vice versa (i.e.,

from Android to iOS)—for two reasons. First, according to industry experts, it is a common

practice that, after first adopting the iOS platform and releasing a product in the App Store,

developers then decide whether to port existing products on Google Play or develop new ones

for Android. Second, because Android was the second mover (compared to iOS), it did not face

the challenges and associated uncertainties of a rising competing platform that could become the

dominant technology.

To test iOS developers’ mobility decisions, we compiled the platform’s sample of

developers as consisting of (1) those who eventually moved to the other platform (i.e., movers),

and (2) those who initially adopted the platform and have since stayed on the platform (i.e.,

stayers) until the time of observation. Admittedly, there are cases in which developers

simultaneously adopted both platforms in the same month, a group that accounts for only a very

small percentage of the entire sample (e.g., less than 0.2% of iOS developers) so we chose to

exclude this group from our analyses. The initial sample is an unbalanced panel of 5,890,129

developer-month observations. The panel data span a time range of 76 months (from July 2008

to October 2014) and include a total of 244,387 iOS developers, among which 23,214 made

13 Given the technological complexity of the matching algorithm, we described it in detail as an Appendix in an elaborative version of the paper.


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cross-platform mobility decisions. After dropping observations when variables have missing

values, the final sample for the regression analyses includes about 4.3 million observations of

198,316 iOS developers, among which 20,275 made mobility decisions.

Variables and Cox Proportional Hazard Models Predicting Mobility

We applied proportional hazard models to estimate the hazard that an app developer moved from

iOS to Android, the competing platform, treating the hazard rate as continuous and in the

following form (Hsiao, 2014):

!"# = lim∆#→*

+,-. #/012#3∆#|015#

∆#,

where 6" is the time spent by the developer in the state of not moving, and !"# is the hazard

function of the duration variable 6". The hazard rate gives the instantaneous conditional

probability of the app developer leaving the state of not moving and thereby entering the

competing platform. We assumed the hazard rate !"# as a function of platform and developer

attributes—789—that explain platform mobility.

The proportional hazard model takes the form of !"# = !(;)exp(789A B), where !(;) is

the baseline hazard function. In a Cox proportional hazard model, the baseline hazard does not

need to be estimated, as we only need to estimate the part of the likelihood function that contains

the coefficient vector B. An app developer is in the risk set of moving to the competing (target)

platform starting from the month that the developer adopts an initial (home) platform. The

developer exits the risk set (1) when the developer makes the mobility decision, or (2) when the

right censoring month is reached (i.e., 2014 October, when comprehensive data could be

ensured). The unit of analysis is a developer and the time interval of the panel structure is a

month. Depending on when the focal developer adopted a home platform, the time range of the


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unbalanced developer-month panel could be from July of 2008 to October 2014.

Dependent variable: platform mobility. The dependent variable, platform mobility, is a

dummy variable indicating whether a developer shifts to the competing platform. Therefore, the

variable platform mobility equals to 0 if the developer stayed with the initial (home) platform,

and changes to 1 in the month the developer moved to (i.e., subsequently adopted) the competing

(target) platform.

Installed-base gaps between Android and iOS. These variables intended to capture the

difference between the two platforms’ installed bases of hardware and software. Because

Android eventually possessed larger installed bases during the time investigated, we therefore

subtracted the corresponding iOS installed-base variables from the Android installed bases. The

hardware data that we obtained from the Bloomberg Terminal were at either the yearly or

quarterly level, thus we broke the data down to the monthly level, assuming that within such

quarters (or years) shipments were evenly distributed.14 For each platform-month, therefore, we

were able to calculate the cumulative smartphone shipments until the end of the month, which

we referred to as the platform’s installed base of smartphones. By denoting the installed bases of

phones as CD_FGHI;JℎLMN"OP,# and CD_FGHI;JℎLMNRST,-"T,#, then the Android-iOS installed-

base gap of smartphone shipments is equal to (CD_FGHI;JℎLMNRST,-"T,# −

CD_FGHI;JℎLMN"OP,#). Similarly, we also coded a variable to reflect installed-base differences in

software. We first calculated the total number of apps for a platform-month and then subtracted

the cumulative iOS apps from the cumulative value of Android. That is, the Android-iOS

installed base gap of apps is equal to (CD_VJJWRST,-"T,# − CD_VJJW"OP,#).

Performance. In this empirical context, it is very challenging to obtain revenue and app-

14 Given that the installed-base gap variable is based on cumulative hardware installments, we believe the assumption is appropriate for the following analyses.


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download information from app developers. However, prior studies suggest that app revenue and

app ranking (in top-ranking lists) is “assumed to be related via the power law (or Pareto

distribution)” in the following form (Garg and Telang, 2013; Kapoor and Agarwal, 2017):

INXNM!N = Y×(IHM[)\] + _,

where Y is the scale parameter and H the shape parameter. Garg and Telang (2013) estimated that

the shape parameter is 0.860 for iOS apps that are top-ranked in grossing ranking lists and 1.165

for Android. We adopted these estimated shape parameters. Regarding the scale parameter, we

followed Kapoor and Agarwal (2017) and assumed that it is constant within a month but can

vary across months. At the developer-level, revenues are aggregated across all apps that are top-

ranked, thus the shape parameter can be moved outside the summation (that is, Y# ∙ IHM[",a\]S1

abc ,

where Y# is the scale parameter, d the developer, e an app of the developer, M" the total number of

apps). Taking log of this math expression, the estimated revenue can therefore be expressed as

two elements: (1) one that is constant within a month (ln(Y#)) and can therefore be absorbed by

month fixed effects, and (2) one that can proxy the extent of the developer’s revenue in the

month (i.e., ln IHM[",a\]S1

abc ). Thus, one measure of developer performance that we used is the

second element, which we referred to as revenue proxy. Another measure is a dummy variable,

top-ranked, indicating whether the developer has entered top-grossing-ranking lists in a month

that was adopted from Kapoor and Agarwal (2017). Although we mainly used the dummy

variable, top-ranked, for hypothesis testing following prior research, we also conducted

robustness tests using the continuous variable, revenue proxy, after controlling for month fixed

effects. Results are qualitatively the same.15

Monetization. The other aspect in which we probe developer heterogeneities is to what

15 We report the results in Model 1 and Model 2 of the robustness-check table (Table 3).


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extent they stress monetization on the iOS platform. In our data, we were able to differentiate

apps in terms of “paid” versus “free” to download. Although our data do not allow us to further

categorize free apps in terms of whether apps have “in-app-purchase” options, we suggest that

developers with greater proportions of paid apps are more likely to target on consumers that

expect high-quality apps and have a high willingness-to-pay. Consequently, we calculate the

percentage of paid apps in the product portfolio of a developer-month.16

Controls. We also identified a set of controls that could potentially affect a developer’s

decision to move across platforms, and we categorized these controls as developer attributes,

product-category attributes, and platform attributes. At the developer level, we controlled for the

number of apps, or portfolio size, on the home platform (iOS). We also controlled for the level of

diversification within a developer’s product portfolio. Diversification index is an adaptation of

the Herfindahl Index and is in the form of 1 − h"i, where h" is the developer’s percentage of

products in category d (Teodoridis, 2016). The higher the value, the more diversified the

developer’s product portfolio, while a lower value indicates a more focused product portfolio.

At the product-category level, too much competition on the home platform may lower

developers’ incentive to develop quality products locally (Hagiu, 2014) and thus push them to

move to the rival platform. We first calculated the share of a category out of a developer’s

product portfolio and then identified the focal developer’s specialized category(ies), which was

defined as at least 50% of product assignments to the category. Because a developer could

specialize in multiple categories, we therefore calculated the averages of the following category

attributes. The first is the average size of product categories (or, category size), which previous

16 In addition to the continuous variable, we have also coded a dummy variable to indicate whether a developer’s product portfolio has any “paid” apps. Results based on the dummy variable are qualitatively consistent with that of the continuous measure.


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research has found to affect developers’ product releases (Boudreau, 2012). The second is the

average concentration of a developer’s specialized category(ies). Within a single category,

market concentration was calculated as the percentage of products released by the highest ten

developers in the category.

The third category-specific control variable is average market growth, which presumably

would function as a retaining force for a developer to stay on the home platform. We used a flow

variable—the number of apps released in a category-month—as the building block to construct

the market growth measure. Based on Dess and Beard (1984), we first regressed the flow

variable (i.e., the number of released apps in a category-month) on dummy variables of years in a

random-coefficient maximum likelihood model. The model is in the form of j =

ki**lm +ki**nm Ci**n + ⋯+ ki*cp

m Ci*cp + _, where each coefficient kmhas its own mean and

standard deviation and can vary across categories. Ci**n through Ci*cp are indicator variables of

years; the year 2008 was set as the baseline (as reflected by the intercept). We obtained the

estimated coefficients for each category; for instance, the “Games” category has a set of

predicted regression coefficients: ki**lq]rst through ki*cpq]rst. We then used the predicted

coefficients to divide the average value of the flow variable (monthly app releases) during the

entire 2008–2014 period (i.e., ki**lRuv\i*cpOm#q]rst for the “Games” category). Thus, the yearly

market growth measure is in the form of a predicted regression coefficient divided by a

corresponding average value. For instance, the market growth for the games category in 2010 is

equal to wxyzy{|}~�

wxyyÄÅÇÉÑxyzÖÜ|á{|}~� .

In addition to the competition at the home platform, we also control for the competition

effect at the target platform. We controlled for market concentration on the target platform,


19

which is operationalized as the percentage of apps launched by the fifty-highest developers (with

the largest app portfolios) on Android. This variable reflects competitive intensity on the target

platform and is therefore expected to have a deterring effect on developers’ migration intention.

At the platform level, we controlled for platform major events—namely, iOS version updates

and Android version updates, which equals to 1 if, in the month, the corresponding platform

released an update of the platform, and 0 otherwise. Research suggests that major updates of

platforms could both be disruptive to developers’ existing products (Kapoor and Agarwal, 2017)

and enabling for future releases.

Table 1 provides descriptive statistics and pairwise correlations of the variables. We also

used several key variables to graphically describe the phenomenon of our interest—that is, as

Android initially surpassed iOS in hardware and then in software, iOS developers’ mobility to

Android first increased, and then declined. Figure 1 depicts the installed base gaps between

Android and iOS, with below 0 indicating a larger installed base for iOS and above 0 suggesting

a larger installed base for Android. As can be seen, Android overtook iOS in terms of installed

base of smartphone shipments (the blue solid line) around January of 2011, and then in terms of

installed base of apps (the red dashed line) around February of 2013. Figure 2, which graphs the

monthly percentage of movers, shows that iOS developers were most likely to move immediately

after Android overtook iOS in the number apps. This graphical presentation thus provides initial

evidence to support our theory. As this is a brief description at the market level, we next test

hypotheses regarding individual developers’ mobility and heterogeneous decisions across

developers.

-------------------------Insert Table 1, Figure 1, and Figure 2 about here-------------------------

Results


20

Our theory predicts that (1) installed-base gaps between Android and iOS will have an inverted-

U effect on the likelihood that an iOS developer will move to Android (H1), and (2) that this

general trend is more salient for low performers (H2a) and developers with a smaller percentage

of paid apps in their portfolio (H2b).

Table 2 presents regression results of the Cox proportional hazard models. Model 1 is the

baseline model with only control variables. Model 2 and Model 3 test H1 with the two measures

of installed-base gaps—Android-iOS installed base gap in terms of smartphone shipments

(Model 2) and apps (Model 3). As can be seen, the first-order terms are positive and significant

(k = 1.866, J = 0.000 in Model 2; k = 0.556, J = 0.000 in Model 3), and the second order

terms are significantly negative (k = −1.132, J = 0.000 in Model 2; k = −2.375, J = 0.000 in

Model 3), providing initial evidence of inverted-U-shaped effects. To further verify that the

effects are indeed inverted-U shaped rather than just decreasing in effect magnitudes, we

conducted two additional tests. The first was to test whether the slopes at the two ends (i.e., the

minimum and maximum of an installed-base gap variable) support an inverted-U pattern; that is,

whether the slope on the left-hand side (i.e., the minimum) is positive and whether the slope on

the right-hand side (i.e., the maximum) is negative. Based on the estimated coefficients of Model

2 and Model 3 in Table 2, Chi-square tests suggest that the slope on the left side (minimum) is

indeed positive and statistically significant (slope = 1.932, chi-square = 540.31, p-value = 0.000

based on estimates of Model 2; slope = 1.178, chi-square = 158.15, p-value = 0.000 based on

estimates of Model 3), and the slope on the right side is significantly negative (slope = -2.375,

chi-square = 1448.41, p-value = 0.000 based on estimates of Model 2; slope = -3.263, chi-square

= 1076.43, p-value = 0.000 based on estimates of Model 3). The second analyses that we

conducted to verify the inverted-U effect was to graph the effect based on logit-model estimates,


21

in which we modified the data structure following Allison (1982) and applied pooled logit

models with the error term clustered at the developer level.17 As an example, Figure 3 presents

the effect of the installed-base gap in smartphone shipments on mobility likelihood. The graph

provides visual support to the hypothesized inverted-U effect. Hence, we conclude that

Hypothesis 1 is supported.

-------------------------Insert Figures 3 about here-------------------------

Model 4 and Model 5 further test interaction effects between installed-base-gap variables

and the top-ranked dummy to examine Hypothesis 2a, which predicts that non-top-ranked app

developers are more likely to embark on the inverted-U shaped market trend. As can be seen,

interactions between the first-order term of installed-base gaps and the top-ranked dummy are

negative and significant (k = −2.400, J = 0.000 in Model 4; k = −1.750, J = 0.000 in Model

5), an opposite direction from the main effect of installed-base-gap variables. The interactions

between the second-order term of installed-base gaps and the top-ranked dummy are positive and

significant (k = 1.283, J = 0.000 in Model 4; k = 3.506, J = 0.000 in Model 5), also

suggesting an opposite direction from the squared term. These findings suggest that the inverted-

U effect is weakened for top-ranked developers, providing initial support to H2a. In order to

provide a visual aid when interpreting such findings, we conducted alternative tests with logit

models by using the data structure suggested by Allison (1982) and then drew graphs of

interaction effects based on the estimated results.18 Figure 4a presents the interaction effect with

installed-base gap in smartphone shipment. As can be seen, installed-based-gap in smartphone

shipments has an inverted-U effect on a non-top-ranked iOS developer’s mobility likelihood, but

17 Due to page limit constraints, we do not provide the regression results of the logit models in the manuscript, but these are available upon request. 18 Results of logit models are qualitatively the same as those reported in the proportional hazard models, although they are not reported to save space.


22

a negative effect on top-ranked developers’ mobility likelihood. Hence, H2a, which predicts that

the inverted-U pattern will be more salient for non-top-ranked performers, is supported.

-------------------------Insert Table 2 and Figure 4a about here-------------------------

Finally, in Hypothesis 2b we predicted that the inverted-U effect would be more salient

for developers with smaller percentages of paid apps. Regression results are presented in Model

6 and Model 7 in Table 2, which show that the variable’s (percent of paid apps) interactions with

the first-order terms of installed-base-gap variables are significantly negative (k = −1.136, J =

0.000 in Model 6; k = −0.953, J = 0.000in Model 7), while interactions with the second-

order terms of installed-base-gap variables are significantly positive (k = 0.620, J = 0.000 in

Model 6; k = 1.786, J = 0.000 in Model 7). The results suggest that the inverted-U effect is

weaker for developers with a greater percentage of paid apps in their portfolios (i.e., more salient

for those with a smaller percentage of paid apps), thus providing support to H2b. Figure 4b,

which is based on estimates of pooled logit models, provides further visual support for the

prediction.

-------------------------Insert Figure 4b about here-------------------------

Effect sizes. We can interpret effect sizes in hazard models in terms of hazard ratios. For

instance, the coefficient on the top-ranked dummy in Model 8 of Table 2 is 0.663. This equals a

hazard ratio of 1.941 (i.e., exp(0.663)), or intuitively, that the chance a top-ranked iOS

developer moves to Android is 1.941 times of the chance that a non-top-ranked iOS developer

moves. Similarly, we can interpret effect size of installed-base gap in smartphone shipment as

follows. In Model 2, the first-order term has a coefficient of 1.866 and the second-order term has

a coefficient of -1.132. Therefore, first-order differencing gives us the “slope” in the form of

1.866 − 1.132x, where x represents the installed-base gap variable. We know that in October of


23

2012, the variable took a value of 0.434 (billion smartphone shipments), so the “slope” at this

value is 1.375 (i.e.,1.705 − 1.062 ∗ 0.434). This means that increasing Android-iOS installed-

base gap in smartphone shipments by one standard deviation (0.602) from this point is

associated with a hazard ratio of 2.288 (i.e., exp(1.375 ∗ 0.602)), or 2.288 times the chance that

an iOS developer moves to Android if the installed-base gap in smartphone shipment remained at

the original value (0.434 billion).

Ex-Post Performance of iOS Movers’ First Products on Android

If iOS developers’ mobility decisions are driven by macro platform-level uncertainties regarding

the emergence of a dominant platform, it stands to reason that market entry decisions made under

the anxiety of avoiding been shaken out are not as well deliberated as those made under normal

conditions. We should therefore expect the failure rates of the app products that iOS developers

initially launched on Android to similarly exert an inverted-U shaped relationship with the

installed-base gap variables. We identified the sample of products launched on Android in the

first month after an iOS developer moved there. The reason that we chose to focus on first

products is to avoid the concern over learning effects—that is, products launched after the first

month may have performed better because the developer learned about the local environment on

the target platform. The final sample consists of 26,735 first products by 20,713 iOS movers.

In our data, we were able to identify whether an app was eventually removed from the

Play Store of the Android platform. We therefore coded a first product as being failed if it was

eventually removed from the Play Store. Given that the dependent variable, app failure, is a

dummy variable, with 1 indicating removal from the store and 0 otherwise, we applied pooled

logit models. We controlled for a continuous time trend (because, in general, older products are


24

more likely to be removed), developer attributes (i.e., portfolio size and diversification index on

the home platform iOS), target platform attributes (i.e., Android updates), and market attributes

of the corresponding developer’s specialized categories on the target platform (i.e., size,

concentration, and growth). We also clustered the error term at the developer level so to provide

robust standard errors. We regressed the dependent variable app failure on each installed-base-

gap variable. The regression results, and graphs based on the estimation, are presented as

Appendix B. As is shown, the first-order terms of the installed-base-gap variables are positive

and statistically significant (k = 2.528, J = 0.009 in Model 1 as a function of smartphone

shipments; k = 1.532, J = 0.007 in Model 2 as a function of apps), and the second-order terms

are significantly negative (k = −0.852, J = 0.013 in Model 1; k = −2.041, J = 0.013 in

Model 2). The graphs based on the logit model estimation show that, indeed, the relationship

between the installed-base-gap variables and the likelihood that a first product fails depicts an

inverted-U pattern, supporting our prediction that first products launched during the peak era of

migration are more likely to fail. In sum, the ex-post performance analyses provide further

evidence suggesting that iOS developers’ strategic mobility decisions to avoid being shaken out

of the platform dominance war unexpectedly lead to worse results.

Robustness Checks

We conducted multiple additional tests to ensure that our main findings are robust. The first

robustness test concerns H2a, which unpacks developer heterogeneities in terms of performance.

Here we used the continuous performance measure, revenue proxy, to test the hypothesis.

According to the previous description of the variable, it could reflect a developer’s revenue

strength once we accounted for month fixed-effects, which could absorb the element of the


25

revenue measure that is constant within a month (i.e.,ln(Y#), where Y# is the scale parameter,

and ; represents a month). In addition, our macro-level variables, the gaps of installed bases

between iOS and Android, may covariate with some unobserved time effects. Adopting the

monthly fixed effects helps to tease out these confounding effects. Regression results with this

alternative performance measure are presented in Model 1 and Model 2 of Table 3. As can be

seen, due to the inclusion of month fixed effects, variables that only vary across months are

absorbed, including the installed-base-gap variables. The interaction terms between the first

order term of installed-base variables and revenue proxy are negative and significant (k =

−1.898, J = 0.000 in Model 1; k = −1.817, J = 0.000 in Model 2), and the interaction terms

between squared installed-base-gap variables and revenue proxy are significantly positive (k =

0.867, J = 0.000 in Model 1; k = 2.703, J = 0.000 in Model 2). Directions of the effects are

consistent with those of the findings with the dummy performance measure (top ranked). The

findings thus lend further support to Hypothesis 2a (H2a), which predicts that the inverted-U

effect of installed-base gaps on mobility likelihood is weakened for high performers (or, more

salient for low performers).

Furthermore, one potential concern of our main findings is that they may be driven by

fine-grained product-category-level competition on the target platform (Android). By controlling

for attributes of the category(ies) in which a developer intends to release products, we should be

able to tease out such effects. To do that, ideally, we should have perfect ex-ante knowledge

regarding which category(ies) a developer firm considers. However, we do not have such perfect

information, particularly because categories across the two platforms do not map perfectly onto

each other. We devised a method of selecting a subset of categories that we were quite confident

matched across platforms, assuming that when a developer decides to launch products on the


26

competing platform, it prioritizes its existing specialized category(ies) and identifies the same

category(ies) on the target platform. This research design therefore required us to keep only

developer-month observations when developers’ specialized categories fall into our subset of

category pairs.19 We then leveraged category attributes on the target platform and coded the

following variables for a developer: the average size, concentration, and growth of the

developer’s specialized category(ies) on the target platform (Android).20 Regression results of

models that control for the three variables are reported in Models 3 and 4 of Table 3.

Interestingly, we can see that, while average category size and concentration on the target

platform deter mobility, category growth facilitates mobility. We can also see that our main

hypothesis (H1) is supported by results in Model 3, where the predictor is installed-base-gap in

smartphone shipments, but not in Model 4, where the alternative measure is installed-base-gap

in apps and instead suggests a linear negative effect on mobility likelihood. The linear negative

effect found in Model 4 may be due to the subsample not fully representing the population or the

fact that Android surpassed iOS in software at a later time, thus passing the stage when the effect

is positive.

-------------------------Insert Table 3 about here-------------------------

DISCUSSION AND CONCLUSION

Our paper shows that platform-level competition dynamics regarding the emergence of a

dominant platform affects complementors’ decisions to move across platforms. We specifically

study app developers’ mobility across two major mobile platforms—iOS, which is more quality-

19 The list of category pairs that we were confident matched across platforms included: (1) Games (on both iOS and Android), (2) Business (iOS and Android), (3) Education (iOS and Android), (4) Finance (iOS and Android), (5) Health & Fitness (iOS and Android), (6) Magazines & Newspapers (iOS) and News & Magazines (Android), (7) Medical (iOS and Android), (8) Music (iOS) and Music & Audio (Android), and (9) Sports (iOS and Android). 20 To understand how we coded the building blocks of these composite variables, please refer to the previous section describing the three types of variables.


27

driven, and Android, which is more quantity-driven. The evolution of market competition

between the two competing mobile platforms can be summarized as follows. The quality-driven

platform (iOS) was the first mover and had initial advantages in installed bases; however, it has

pursued a closed-system strategy, which has resulted in a lower speed of growth. The quantity-

driven platform (Android) was the follower, and despite its smaller installed bases, its open-

system strategy has enabled rapid growth that eventually surpassed iOS in installed bases.

During this process of market evolution, as the amount of hardware and software associated with

the quantity-driven platform surpassed those of the quality-driven platform, uncertainties arose

as to which platform would emerge as the single dominant one. The anxiety of avoiding being

shaken out drove complementors that initially adopted the quality-driven platform to move to the

emerging rival platform.

We capture the dynamics of platform competition by comparing installed bases of the

two platforms (in terms of both hardware and software), as prior literature has suggested that

changes in installed bases could indicate strength of network effects and reflect market dynamics

of competing platforms (Cennamo and Santalo, 2013; McIntyre and Srinivasan, 2017; Tellis,

Yin, and Niraj, 2009; Zhu and Iansiti, 2012). Our theory predicts, and we empirically verify that,

during the process of Android catching up to iOS and then eventually surpassing it, app

developers on the iOS platform initially exhibit a greater likelihood of moving to the competing

platform (due to increased uncertainties regarding the emergence of Android as the dominant

platform). However, such developers then show a reduced tendency to move (due to receding

uncertainties that both platforms seem to co-exist). The tendency to move is especially high

among complementors that have poor performance on the quality-driven iOS platform and those

whose business models may fit better with the quality-driven platform than for who are not top-


28

ranked and have weaker monetization incentives (reflected by possessing a smaller percentage of

paid apps in the portfolio).

Theoretical Contributions

Our research contributes to the platform literature in several significant ways. First, our study

bridges macro-level platform competition with micro-level complementor strategic choices. In

the platform-based market, competition at the platform-level and complementors’ mobility

decisions coevolve. On the one hand, micro-level decisions by individual complementors to enter

and launch products on different platforms combine to influence the quality of products and

services that a platform offers, consequently affecting relative competitive advantages of the

platform. On the other hand, macro-level competitive dynamics between platforms also shape

long-term prospects of complementors. While prior platform literature has predominantly

focused on platform-level strategies (e.g., Cennamo and Santalo, 2013; Eisenmann, Parker, and

Van Alstyne, 2011; Hagiu and Eisenmann, 2007; Zhu and Liu, 2017), our paper contributes to

the literature by painting a more complete picture of the coevolution between complementors’

micro-level strategies and macro-level competition between platforms.

Second, through emphasizing the macro platform-level influence on micro

complementor-level strategies, we show that the complementors’ rational decisions in response

to uncertainties in the platform-based competition can unexpectedly lead to inferior strategic

outcomes. In our context, the uncertainties regarding the emergence of a single dominant

platform lead app developers to jump ship to avoid being shaken out; however, developing apps

across platforms significantly increases task complexity and accelerates the rates of product

failure. By studying the macro-level influence of platform competition and the associated


29

consequences, our paper joins the most recent stream of research in the platform literature that

examines the relationship between platforms and complementors (e.g., Edelman, 2014;

Eisenmann et al., 2011; Zhu and Liu, 2017) and suggests that high uncertainties in the platform-

based market can lead to unexpected competition results.

Third, our paper sheds light on the platform literature regarding the positioning and the

emergence of a dominant platform. Prior literature has emphasized the horizontal differentiation

between platforms (Hagiu, 2011, 2014; Hossain et al., 2011) and suggested that platforms

differentiated from competitors in user demographical characteristics—such as gender and

ethnicity—can sustain competitive advantages by setting back the network effects and serving

particular niches of the market. Our research suggests that quantity- vs. quality- positioning (i.e.,

a closed-system associated with superior product quality and customers’ high willingness-to-pay)

is an important differentiation strategy for platforms to sustain their competitive advantages. Our

conceptualization of a quantity-driven platform versus a quality-driven platform builds on

previous studies (e.g., Hagiu, 2011, 2014; Zhu and Iansiti, 2012) and echoes Shapiro and

Varian’s (1999) proposition that, in network economies, a platform could choose either to

increase the total value added to the industry (which aligns with a quantity-driven orientation) or

improve its own share of industry value (which aligns with a quality-driven orientation). We

studied a market in which the quality-driven platform as the first mover was then followed and

eventually surpassed by the quantity-driven platform in terms of installed bases. To some extent,

the evolution path that we propose is akin to the theory of disruptive innovation (Christensen,

1997) and can be mapped onto businesses histories, such as the competition between Windows

and Macintosh in the 1990s, keyboard standards competition, and the rivalry between video-

recorder formats. A common theme across these examples is that inferior technologies were the


30

late entrants but ultimately won. We believe, therefore, that our framework of market evolution

of two competing platforms with distinct strategies can be informative for many other industry

settings.

Furthermore, our study also contributes to the broader strategic management literature by

addressing a major question of the field: How do firms make strategic choices? We map this

major topic onto the specific research question: What are the antecedents of a developer’s cross-

platform mobility decision? Although our theory is anchored in the field’s traditional emphases

on the role of market structures and firm heterogeneities, it also extends to information industries

and network economies (Parker et al., 2016; Shapiro and Varian, 1999). Regarding market

structure, we integrate insights from the platform literature and find that gaps in the two

competing platforms’ installed bases create uncertainties and thus shape developers’ mobility

decisions. Such external forces are further complemented by internal ones in the sense that not

all firms respond to the general market trend in the same fashion. Heterogeneities in

complementors’ commitment to their home platform result in some being more sensitive to

external market trends—in our study, for instance, app developers that are not top-ranked and

pursue the free-app model on the iOS platform.

Finally, our topic on cross-platform mobility also sheds light on a central question of

strategic management: What determines long-term competitive advantages? Through this

research we explain how platforms accrue (or lose) competitive advantages due to

complementors’ mobility. We echo Dierickx and Cool’s (1989) proposition that, although

strategic stocks (e.g., platforms’ stocks of complementors) determine competitive advantages in

a snapshot of time, it is ultimately strategic flows (e.g., complementors’ cross-platform mobility)

that shape long-term competitive advantages of competing platforms.


31

From a practical perspective of platform sponsors and owners, our research suggests that

understanding complementors’ migration flows and managing such flows could be essential in

protecting and improving a platform’s competitive position. Business history is littered with

failures of technological platforms (or standards) because they failed to attract or retain users and

complementors. A recent such example might be the mobile platform, Blackberry, which has

started to lose software developers as complementors. “As users began to flee the platform, the

loss of network nodes caused the value of the network itself to plummet, encouraging still more

people to abandon Blackberry for other devices” (Parker et al., 2016: 20). If practitioners such as

the management of Blackberry could better understand complementors’ cross-platform mobility,

they might do a better job in managing—and perhaps even reshaping—the market rather than

passively responding to consequences of complementors’ migration to competing platforms.

Limitations and Future Research Avenues

Admittedly, several limitations of our research are worth mentioning, through which we hope to

guide future research. The first limitation pertains to matching app developers across iOS and

Android platforms. The set of developers for which we found matches on both platforms is not

likely to be 100% accurate or 100% comprehensive. As mentioned in the description of our

matching algorithms, the matching approach attempts to strike a balance between

comprehensiveness (if we used the least-restrictive decision rules when selecting matched

developer pairs) and accuracy (if we constrained the sample to the most-restrictive decision

rules). Although such a limitation is unavoidable (per experts from app analytics companies),

theoretical predictions of this paper have validity as long as the findings hold across samples

selected based on different decision rules.


32

Another limitation of our paper originates from the big data nature of this study. Mobile

apps affect almost all aspects of people’s daily lives and span many industries. As a result, not all

apps are created with the same purpose. Consistent with our focus on the strategy audience that

are interested in platform competition, we focus on profitability when defining the performance

of app developers in cases where we used revenue-based app ranking to code performance

measures. However, many other (non-monetary) motivations could exist when an organization or

individual releases apps on a mobile platform. Given the scope of our research, we did not probe

in-depth alternative incentives in app development, but just touched briefly on the topic when

examining developer differences in monetization strategies. This limitation of our research

therefore points to fruitful future research avenues, as demonstrated by a few interesting recent

studies (Boudreau, 2015; Miric, 2016).

Another limitation resides in our conceptualization of platform-level competition, as we

have only examined the condition under which the quality-driven platform is the first mover. We

focused on this scenario because we could imagine that winner-take-all is more likely to happen

under alternative setups (e.g., the quantity-driven platform being the first mover), thus making

the theory less interesting. In addition, although the scenario that we conceptualize echoes many

historical technological and standards competition, theoretically, alternative scenarios could also

occur. It could be that the quantity-driven platform is the first mover while the quality-driven

platform follows. It could also be the case that both platforms initiate at the same time. Future

research could build on some previous work (e.g., Zhu and Iansiti, 2012) to examine these

alternative scenarios of platform competition.

Finally, we only studied one direction of mobility: from iOS to Android. A reason that

we did not examine the other mobility direction (from Android to iOS) is that the host platform


33

in this case (Android) does not face the competitive threat and uncertainty from a newly rising

rival platform. In fact, a lack of market uncertainty for this reverse direction could serve as a

counterfactual case for our mobility study—for instance, the mobility trend from Android to iOS

tends to be monotonically positive in the studied period (as shown in Figure 2). Nevertheless, it

could be fruitful for future research to study both directions of mobility, for instance, by

examining how shifting platforms leads to a better strategic fit between a mover and the platform

environment.

Conclusion

In this research, we address an important topic in the strategy and platform literatures:

competition for dominance between technological standards/platforms. Our research examines

an interesting phenomenon—complementors’ cross-platform mobility—that we believe could

determine long-term competitive advantages of competing platforms. Conceptualizing the

competition between a quality-driven platform (iOS) and a quantity-driven platform (Android),

we proposed and found that uncertainties associated with macro-level platform competition

shape micro-level complementors’ mobility decisions. We hope our research serves as a

foundation for a research program on the coevolution of platform competition and complementor

strategies.

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FIGURE 1: Android Overtook iOS, First in Hardware, Then in Software

FIGURE 2: Monthly Percentage of Developers Moving across Platforms and Different Phases of Platform Competition


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FIGURE 3: Effect of Android-iOS Installed-base Gap in Smartphone Shipment on the Likelihood that an iOS Developer Moves to Android

FIGURE 4a: Interaction Effect between Installed-base Gap in Smartphone Shipment and Top-ranked Dummy

FIGURE 4b: Interaction Effect between Installed-base Gap in Smartphone Shipment and Percentage of Paid Apps


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TABLE 1: Descriptive Statistics and Correlations Mean S.D. Min Max 1 2 3 4 5 6 7 8 9 10 11 12 13 14

1 Mobility 0.004 0.063 0 1 1.000 2 Android-iOS Installed-base Gap in Smartphone Shipment (billion) 0.891 0.602 -0.029 1.874 -0.003 1.000

3 Android-iOS Installed-base Gap in Developers (million) 0.067 0.070 -0.022 0.177 -0.004 0.993 1.000 4 Android-iOS Installed-base Gap in Apps (million) 0.256 0.339 -0.132 0.804 -0.007 0.978 0.993 1.000

5 Dummy = 1 if top-ranked on grossing ranking lists 0.082 0.274 0 1 0.001 -0.062 -0.063 -0.062 1.000 6 Revenue Proxy, which is without adding log(Scale parameter) 0.044 0.263 0 6.825 0.002 -0.047 -0.048 -0.047 0.561 1.000 7 Portfolio Size 3.988 13.417 1 2262 -0.003 0.007 0.007 0.007 0.178 0.155 1.000

8 Diversification Index 0.225 0.125 0 6.95E-01 -0.003 -0.076 -0.073 -0.069 0.052 0.023 0.013 1.000 9 Avg. SIZE of the Developer's Specialized Categories 61883.344 47286.739 0 217000 -0.005 0.585 0.574 0.559 -0.006 0.002 0.062 -0.107 1.000

10 Avg. CONCENTRATION of the Developer's Specialized Categories 0.038 0.030 0 1 -0.008 -0.225 -0.174 -0.130 0.055 0.036 0.001 0.077 -0.348 1.000 11 Avg. GROWTH of the Developer's Specialized Categories 1.347 0.566 -2.791 4.7 0.007 0.764 0.744 0.717 -0.020 -0.011 -0.010 -0.072 0.268 -0.072 1.000

12 iOS Updates 0.575 0.494 0 1 -0.002 0.144 0.144 0.135 -0.001 -0.004 0.001 -0.010 0.078 0.003 0.125 1.000 13 Android Updates 0.412 0.492 0 1 -0.007 -0.088 -0.096 -0.077 -0.004 -0.004 0 0.009 -0.05 -0.011 -0.151 0.005 1.000

14 Android Market Concentration (by highest-50 developers) 0.055 0.052 0.024 1 -0.012 -0.677 -0.556 -0.489 0.06 0.046 -0.008 0.066 -0.418 0.508 -0.605 -0.07 0.081 1.000 Notes: The sample consists of iOS developer-month observations. The number of observations is 5,890,129 for most variables (e.g., Mobility), except for the performance variables (i.e., Top-ranked Dummy and Revenue Proxy) which have 4,672,199 observations.


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TABLE 2: Cox Proportional Hazard Models Estimating iOS Developers Moving to Android

(1) (2) (3) (4) (5) (6) (7) (8)

H1 H1 H2a H2a H2b H2b

Android-iOS Installed-base Gap in Smartphone Shipment (billion)

1.866

1.492

2.006

(0.000)

(0.000)

(0.000)

Android-iOS Installed-base Gap in Smartphone Shipment (billion) 2

-1.132

-1.006

-1.216

(0.000)

(0.000)

(0.000)

Android-iOS Installed-base Gap in Apps (million)

0.556

0.431

0.699

(0.000)

(0.001)

(0.000)

Android-iOS Installed-base Gap in Apps (million) 2

-2.375

-2.248

-2.634

(0.000)

(0.000)

(0.000)

Top-ranked X Android-iOS Smartphone Shipment

-2.400

(0.000)

Top-ranked X Android-iOS Smartphone Shipment 2

1.283

(0.000)

Top-ranked X Android-iOS Apps

-1.750

(0.000)

Top-ranked X Android-iOS Apps 2

3.506

(0.000)

% of Paid Apps X Android-iOS Smartphone Shipment

-1.136

(0.000)

% of Paid Apps X Android-iOS Smartphone Shipment 2

0.620

(0.000)

% of Paid Apps X Android-iOS Apps

-0.953

(0.000)

% of Paid Apps X Android-iOS Apps 2

1.786

(0.000)

Top-ranked

1.503 0.550

0.663

(0.000) (0.000)

(0.000)

Portfolio's % of Paid Apps on iOS

0.305 -0.095

(0.000) (0.000)

Portfolio Size 0.010 0.010 0.010 0.008 0.008 0.010 0.010 0.008 (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) Diversification Index 0.113 0.140 0.150 0.081 0.085 0.155 0.162 0.057 (0.024) (0.005) (0.003) (0.119) (0.101) (0.002) (0.001) (0.272) Avg. SIZE of the Developer's Specialized Categories -0.000 -0.000 -0.000 -0.000 -0.000 -0.000 -0.000 -0.000 (0.000) (0.000) (0.002) (0.000) (0.000) (0.000) (0.004) (0.000) Avg. CONCENTRATION of the Developer's Specialized Categories -4.115 -3.104 -3.567 -4.132 -4.505 -3.153 -3.549 -4.705 (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) Avg. GROWTH of the Developer's Specialized Categories -0.216 0.084 0.144 0.099 0.126 0.082 0.139 -0.107 (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) iOS Updates -0.085 -0.134 -0.115 -0.154 -0.148 -0.133 -0.114 -0.128 (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) Android Updates -0.142 -0.004 -0.042 -0.001 -0.026 -0.004 -0.043 -0.085 (0.000) (0.765) (0.004) (0.949) (0.090) (0.797) (0.003) (0.000) Android Market Concentration (by highest-50 developers) -16.403 -9.908 -14.937 -16.301 -17.584 -10.685 -14.991 -3.424 (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.002) Developer-month Observations 4.3e+06 4.3e+06 4.3e+06 3.4e+06 3.4e+06 4.3e+06 4.3e+06 3.4e+06 Developers 198316 198314 198316 196065 196065 198314 198316 196065 Movers 20275 20275 20275 18091 18091 20275 20275 18091 Log Likelihood -242958 -242261 -242383 -211768 -211831 -242225 -242359 -212280 Notes: (1) p-values are in parentheses. (2) Standard errors are clustered at the developer-level. (2) The sample includes (a) iOS movers to the competing platform (Android), and (b) iOS stayers.


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TABLE 3: Robustness Checks (1) (2) (3) (4) subsample subsample Android-iOS Installed-base Gap in Smartphone Shipment (billion) -

2.118

(0.000) Android-iOS Installed-base Gap in Smartphone Shipment (billion) ^2 -

-1.253

(0.000) Android-iOS Installed-base Gap in Apps (million)

-

-0.523

(0.000) Android-iOS Installed-base Gap in Apps (million) ^2

-

-1.204

(0.000) Revenue Proxy X Android-iOS Smartphone Shipment -1.898

- -

(0.000) Revenue Proxy X Android-iOS Smartphone Shipment ^2 0.867

- - (0.000)

Revenue Proxy X Android-iOS Apps

-1.817 - -

(0.000)

Revenue Proxy X Android-iOS Apps ^2

2.703 - -

(0.000)

Revenue Proxy without log(Scale Parameter) 1.084 0.273 - - (0.000) (0.000)

Portfolio Size -0.001 -0.001 0.001 0.001 (0.292) (0.294) (0.055) (0.082) Diversification Index -0.184 -0.184 -0.248 -0.236 (0.002) (0.002) (0.000) (0.001) Avg. SIZE of the Developer's Specialized Categories on iOS -0.000 -0.000

(0.000) (0.000) Avg. CONCENTRATION of the Developer's Specialized Categories on iOS -1.996 -2.000 (0.000) (0.000) Avg. GROWTH of the Developer's Specialized Categories on iOS 0.154 0.154 (0.000) (0.000) iOS Updates - - -0.113 -0.118

(0.000) (0.000) Android Updates - - 0.044 0.010

(0.024) (0.598)

Avg. SIZE of the Developer's Specialized Categories on Android

-0.000 -0.000

(0.000) (0.000)

Avg. CONCENTRATION of the Developer's Specialized Categories on Android

-2.047 -3.207

(0.000) (0.000)

Avg. GROWTH of the Developer's Specialized Categories on Android

0.319 0.727

(0.000) (0.000)

Month Fixed Effects Included Included - - Developer-month Observations 3.5e+06 3.5e+06 2.6e+06 2.6e+06 Developers 196210 196210 134350 134410 Movers 18091 18091 12624 12624 Log Likelihood -203793 -203787 -140134 -140325 Notes: (1) Robust standard errors, clustered at developers, are in parentheses. (2) The sample in Model 1 and 2 includes (a) movers from iOS to Android, and (b) iOS stayers. (3) The following time-variant, but not developer-specific, variables in Models 1 and 2 are absorbed by month fixed-effects: Android-iOS Installed-base Gap in Smartphone Shipment, Android-iOS Installed-base Gap in Apps, iOS Updates, Android Updates. (3) The Sample in Models 3 and 4 include (1) iOS-to-Android movers and (2) iOS stayers if they specialize in iOS categories that can be clearly mapped onto corresponding Android categories.


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APPENDIX A: The Development Complexity for iOS and Android

This case shows the records of Infinium, an independent app design and development company, that has developed the same app product for both iOS and Android. On average, Android app costs 38% more lines of coding and 28% more time to develop than its iOS counterpart.

Lines of Code Development Time


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APPENDIX B: Logit Models Estimating Failure Likelihood of iOS Movers’ First Apps on Android, and the Interpretive Graphs

Dependent Variable: App Eventually Removed from Google’s Play Store (1/0) (1) (2)

Android-iOS Installed-base Gap in Smartphone Shipment 2.528 (0.009) Android-iOS Installed-base Gap in Smartphone Shipment 2 -0.852 (0.013) Android-iOS Installed-base Gap in Apps

1.532

(0.007) Android-iOS Installed-base Gap in Apps 2

-2.041

(0.013) Continuous time since 2008-07 -0.066 -0.029 (0.000) (0.000) Portfolio Size on iOS 0.005 0.005 (0.434) (0.438) Diversification Index on iOS 0.289 0.282 (0.390) (0.396) Android Updates -0.020 -0.041 (0.852) (0.693) Avg. SIZE of the Developer's Specialized Categories on Android 0.000 0.000 (0.004) (0.004) Avg. CONCENTRATION of the Developer's Specialized Categories on Android 0.382 0.419 (0.653) (0.630) Avg. GROWTH of the Developer's Specialized Categories on Android -0.127 -0.144 (0.088) (0.050) Number of Apps 26735 26735 Number of Developers 20713 20713 Log Likelihood -15018 -15015 Notes: (1) p-values are in parentheses. (2) Errors are clustered at the developer level; (3) The sample is a pooled app-level data of all the first apps launched by iOS movers on Google Play Store in the first month of entry.

Note: Predictive margins with 95% confidence intervals, based on logit model estimations.

competing across platforms: antecedents of …...platform has attracted more women and young girls...

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