Two Faces of Value Creation in Platform Ecosystems: Leveraging Complementarities and Managing Interdependencies

Shiva Agarwal

McCombs School of Business University of Texas at Austin

2110 Speedway, Austin, TX 78705 Email: shiva.agarwal@mccombs.utexas.edu

Rahul Kapoor

The Wharton School University of Pennsylvania

3620 Locust Walk, Philadelphia PA 19104-6370, USA

Email: kapoorr@wharton.upenn.edu

ABSTRACT

A given innovation often does not create value on its own. Rather it is connected with other elements in the

ecosystem for its value creation. We draw on this premise in a platform-based ecosystem in which

participating firms innovate around a platform. We introduce the notion of connectedness to refer to the

extent to which a given innovation connects with platform components and other complements in the

ecosystem, and explore the implications for its commercialization success. While higher connectedness may

allow the innovation to leverage complementary functionalities, it may subject the innovation to technological

interdependencies that may limit its value creation. Evidence from 244,034 apps launched by software

developers for Apple’s iPhone platform during 2008-2013 highlight these two faces of value creation in

platform-based ecosystems.

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INTRODUCTION

Value creation in many platform-based ecosystems is enabled by the innovations of complementors

firms, who leverage the platform to offer complementary products and services to the users (e.g., Cusumano,

Gawer and Yoffie, 2019; Gawer and Cusumano, 2002; Boudreau, 2010; Zhu and Iansiti, 2012).1 While having a

large number of complementors innovating around a platform is uniformly acknowledged as an important

driver of the platform’s success, the performance implications for complementors themselves participating in

these ecosystems with their innovations remain underexplored. In a recent review of the literature on

platform-based ecosystems, McIntyre and Srinivasan (2017) noted that the perspective of complementors is

an often overlooked but an important area of research (p. 142, 155).

In this study, we explore the commercialization success of a complementor’s innovation in a

platform-based ecosystem. To do so, we draw on the emerging theoretical perspective on business

ecosystems that has emphasized that a focal innovation’s commercialization success is a function of the

technological architecture and complementarities in the ecosystem (e.g., Adner and Kapoor, 2010; Adner,

2017, Kapoor, 2018; Jacobides et al., 2018; Baldwin, 2018a). We contribute to this perspective by developing

a theoretical framework that takes into account the different ways a complementor’s innovation can leverage

complementarities in a platform-based ecosystem, and how the platform’s architectural evolution might

impact the commercialization success of the focal innovation.

We start with the basic premise that a complementor’s innovation on its own does not create any

utility for the user. Rather its utility is created once the innovation is connected with the platform. For

example, a video game creates value for the user only when it is connected to the video game console. We

build on this premise to consider a broad set of connections that the focal innovation can draw upon within a

platform-based ecosystem. We use the notion of connectedness to refer to these connections, differentiating

between connections with respect to platform components (i.e., platform connectedness) and those with

1 In this study, we focus on the innovation platforms that provide technological building blocks which are shared by other firms to create complementary products or services (e.g., Amazon Web Services, Microsoft Xbox, Salesforce, SAP Hana), and not on transaction platforms that facilitate exchange relationships between actors such as those between buyers and sellers (e.g., Amazon Online Shopping, eBay, Airbnb). It is the former type of platform-based ecosystems where innovations by complementors is a key driver of value creation (e.g., Cusumano, Gawer and Yoffie, 2019; Gawer and Cusumano, 2002; Boudreau, 2010).

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respect to other complements (i.e., complement connectedness). For example, in the iPhone ecosystem, every

software application (app) is connected with the core mobile computing module of the iPhone. An app can

have high platform connectedness if it also connects with additional platform components that are integrated

into the iPhone by Apple such as the camera, the Open Graphics Library for Embedded Systems (Open

GLES) and the GPS module. Similarly, an app can have high complement connectedness if it not only

connects with the iPhone but also with apps developed by other firms such as those developed by Alphabet,

Dropbox, and Facebook. In the Amazon Web Services (AWS) ecosystem, complement such as security

software provided by Symantec have high complement connectedness because it not only connects with the

core AWS platform but also with networking applications provided by Cisco.

On the one hand, higher connectedness has the potential to enhance the value of the

complementor’s innovation by bundling the additional functionalities accorded by platform components and

other complements, and by reducing the cost of integration of these functionalities for the user

(Baldwin,2018a; Baldwin, 2018b; Schilling, 2000). On the other hand, higher connectedness can also subject

the innovation to an array of external interdependencies that may limit its value creation. For the innovation

with higher platform connectedness, its interdependencies with the platform components may impose

additional adjustments costs and design-related challenges for both its users and the firm (Kapoor and

Agarwal, 2017; Levinthal, 1997), and thus limiting the desired benefits that the components can provide to

the user. For the innovation with higher complement connectedness, its interdependencies with other

complements can subject the focal innovation to performance bottlenecks such that its performance in-use

may be inadvertently constrained by the performance of the connected complements (e.g., Ethiraj, 2007;

Adner and Kapoor, 2016). These challenges are especially salient when there is a change in the platform

architecture triggered by platform firms’ introduction of a new generation of platform such as a major update

to the iPhone operating system or to AWS (Kapoor and Agarwal, 2017). We consider how platform’s

architectural evolution from one generation to another might buffer the benefits that accrue to highly

connected innovations, and how platform owners and complementors might be able to mitigate these

challenges.

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The empirical setting is the Apple’s iPhone ecosystem between 2008 and 2015 within the U.S.

market. This context provides a relevant and important opportunity to study the commercialization success

of complementors’ innovations in a platform-based ecosystem. The focal firms are app developers that

participate in the iPhone ecosystem. The iPhone ecosystem represents one of the largest and most valuable

business ecosystems with the App Store revenue estimated to be more than $20B in 2016 (Delgado, 2017).

Hundreds of thousands of app developers participate in this ecosystem by frequently launching new apps.

Moreover, apps launched by developers vary in terms of leveraging the components that are integrated by

Apple within the iPhone platform and other complementary apps, providing us with significant variance to

test our predictions with respect to platform and complement connectedness. Finally, we are able to exploit

yearly changes in the iPhone platform through Apple’s introduction of new platform generations to consider

the challenges that app developers may face with the new generation of the platform.

The analysis is performed on a newly assembled dataset of 244,034 iPhone apps launched by 31,466

developers with detailed information on the focal app and the app developer, along with novel measures for

apps’ platform and complement connectedness. An app’s successful commercialization is measured based on

the likelihood of it being listed in the Top 500 list by revenue (e.g., Kapoor and Agarwal, 2017; Davis et al.,

2016). The Top 500 list is an important indicator of an app’s successful commercialization as apps that make

it into this list represent approximately 95 percent of the total revenue generated by apps in the iPhone

ecosystem (SensorTower, 2016). Such a list is keenly followed by industry observers and analysts as a

reference for successful apps.

We find that connecting with platform components in addition to the core platform module (i.e.,

higher platform connectedness) is in general associated with a greater likelihood of the focal innovation’s

successful commercialization. However, this positive effect is weakened by the generational newness of the

platform, and much more so when the higher platform connectedness is with respect to those components

that are not widely used by complementors within the ecosystem. Hence, while higher platform

connectedness can facilitate an innovation’s commercialization success by bundling complementary

functionalities accorded by the platform owner, these additional connections can also impose greater

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challenges for both innovators and users when there is a new platform generation, limiting the innovation’s

value creation. The evidence also suggests that platform owners may be able to help mitigate these challenges

with respect to those components that are widely connected within the ecosystem such as through backward

compatibility and by swiftly responding to the complementors’ and users’ challenges through subsequent

R&D efforts. When it comes to complement connectedness, we find that it has a positive effect on the

innovation’s commercialization success. This effect is weakened by the generational newness, but only when

the connected complements themselves have high platform and complement connectedness. It is under these

conditions that the innovation’s value creation is more likely to be constrained by performance bottlenecks

stemming from connected complements. These results are robust to instrumental variable analyses deploying

several different instruments to account for the potential endogeneity with respect to an innovation’s

connectedness and its commercialization success.

Overall, these findings highlight the two faces of value creation in ecosystems, and the implications

for complementor firms innovating around a platform. Firms in platform-based ecosystems can enhance the

value of their innovations by leveraging a broad array of platform components and other complements.

However, this interconnected architecture of value creation can subject the firm to challenges with respect to

managing the technological interdependencies especially when there is a new platform generation. In so

doing, the study contributes to the emerging literature on business ecosystems in showing how

complementarities and technological interdependencies interact to shape an innovation’s value creation in a

platform-based ecosystem (e.g., Adner and Kapoor, 2010; Adner, 2017, Kapoor, 2018; Jacobides et al., 2018).

Our findings also contribute to the nascent literature stream examining complementors’ performance

(Ceccagnoli et al., 2012; Kapoor and Agarwal, 2017; Rietveld and Eggers, 2018; Wen and Zhu, 2018), by

showing how complementors can benefit from functionalities accorded by platform owners and other

complementors, and how the platform’s evolution might buffer these benefits. More broadly, the study

contributes to the complementary assets framework (Teece, 1986) that has been instrumental in explaining

innovators’ commercialization outcomes. As Teece (2006) points out in his reflection of the original article,

the extant literature has been somewhat limited in its examination of complementarities, confining them to

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enterprise-level value chains (i.e., manufacturing, sales, marketing, and distribution), and not considering

complementarities within the broader ecosystem. Our findings offer compelling evidence of how such

complementarities impact the innovation’s commercial success in a platform-based ecosystem. Moreover,

while the extant literature has emphasized the importance of complementary assets that are specialized to the

innovation (e.g., Arora and Ceccagnoli, 2006), our findings suggest that in platform-based ecosystems, even

generic complementary assets (i.e., platform components, complements) can exert an important influence on

innovators’ value creation and appropriation.

INNOVATION IN PLATFORM-BASED ECOSYSTEMS

Many platform-based ecosystems encompass a central platform firm and a number of complementor

firms who leverage the platform to create value from their innovations. The platform represents an

underlying technical architecture that acts as a foundation upon which complementor firms can build their

products, and offer them to the users of the platform. Gawer (2014) highlights two distinct approaches to

studying such platforms in the extant literature. One approach focusses on platforms as creating value

through network effects or multisided markets (e.g., Katz and Shapiro, 1986; Schilling, 2002; Eisenmann et

al., 2006; Rochet and Tirole, 2006; Armstrong, 2006). The other approach focusses on platforms as modular

architectures that facilitate innovation by complementors within the ecosystem (e.g., Gawer and Cusumano,

2002; Baldwin and Woodard, 2009; Boudreau, 2010). Scholars from both streams of research have considered

the focal platform or the focal platform firm as the primary object of attention, while leaving the perspective

of complementors somewhat underexplored (cf. McIntyre and Srinivasan (2017) for a detailed review of the

extant literature).

A nascent stream of research has considered the implications of platform firms’ choices on how

complementors innovate and participate in the ecosystem. This stream has explored the effect of platform’s

openness (Boudreau, 2012), and platform firm’s entry into complementary niches (Gawer and Henderson,

2007; Cennamo, Gu and Zhu, 2018; Wen and Zhu, 2018). Scholars have also studied how participation in

platform-based ecosystems impact complementors’ performance, highlighting the role of complementary

assets (Ceccagnoli et al., 2012), demand-heterogeneity (Rietveld and Eggers, 2018), selective promotion by

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platform firms (Rietveld, Schilling and Bellavitis, 2018), and ecosystem complexity (Kapoor and Agarwal,

2017).

We contribute to this nascent stream by bringing an emerging ecosystem perspective to study

complementors’ performance. Specifically, the ecosystem perspective has emphasized that a focal firm’s

performance is a function of the technological architecture and complementarities in the ecosystem (e.g.,

Adner and Kapoor, 2010; Adner, 2017, Kapoor, 2018; Jacobides et al., 2018, Baldwin, 2018a; Baldwin,

2018c). We draw on this perspective and take into account the different ways a complementor’s innovation

can leverage complementarities in a platform-based ecosystem, and the architectural evolution of the platform

via generational transitions. This allows us to show how firms can benefit from the myriad of

complementarities but that these benefits can be offset by the challenges of managing technological

interdependencies. By considering the architectural evolution of the platform, the study is also among the first

to incorporate platform-level technology dynamics in examining complementors’ performance (McIntyre and

Srinivasan, 2017).2

Theoretical Framework

Modularization and complementarities are necessary conditions for the functioning of platform-

based ecosystems (Jacobides et al., 2018; Baldwin, 2018b; Baldwin, 2018c). Modular architectures allow

different actors to innovate and produce different components in the ecosystem without extensive

requirements for coordination. The interactions between these components can be subject to different types

of complementarities. Complementarities between two components can be strong or strict such that the focal

component has no standalone value in the absence of the other component (Hart and Moore, 1990).

Complementarities between two components can also be supermodular such that the value of the focal

component increases with availability or improvement in other components (Milgrom and Roberts, 1990).

2 An exception is the study by Kapoor and Agarwal (2017) which also considers the impact of platform-level generational transitions on complementors. However, the emphasis in that study is on complementor-level adaptation, conditional on achieving superior performance. In contrast, the emphasis in this study is on complementors pursuing different structural connections to leverage complementarities for their focal innovations, and how the commercial success of the innovation is impacted by the platform’s generational evolution

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We start with the basic premise that the modular architecture underlying a platform-based

ecosystem comprises of the core platform module. All of complementor’s innovations in the ecosystem are

connected to the core platform module, and these connections are subject to strict complementarities for the

complementor. That is, complementor’s innovation has no standalone value in the absence of the core

platform module. Accordingly, connecting with the core module is a prerequisite for complementor’s

participation in a platform-based ecosystem. In contrast, complementor’s innovations exhibit supermodular

complementarities for the platform such that the value of the platform increases with more and higher

performing complements.

Complementor’s innovation can also be connected to additional components that are integrated

with the core by the platform firm but these components may not exhibit strict complementarities with the

focal innovation. Hence, connecting to them is not a prerequisite for participation in the ecosystem. Rather

these are optional platform components that a complementor’s innovation can connect with. Platform firm

integrates these additional components along with the core module to enhance the utility of their platform for

the end-user and to differentiate it from other platforms (e.g., Eisenmann et al, 2011). Platform firm can also

use this design approach to experiment with new functionalities which can over time be incorporated into the

core module. For example, in video game ecosystems, a video game console (i.e., the platform) consists of a

core module that includes the central processing unit (CPU), the graphics processing unit, the memory

controller, and the video decoder, and a number of additional components such as motion detectors, camera,

and Bluetooth that are integrated within the console by the platform firm such as Microsoft or Sony. Game

developers participating in the Microsoft or Sony ecosystem need to connect their games with the core

module for the games to be played on the console. They can also leverage the functionalities accorded by the

additional components while managing the design tradeoffs that those optional functionalities may present.

Such a platform architecture that comprises a core module and a number of additional components that are

integrated within the platform exists in many settings such as computing hardware, enterprise software,

genomics technologies and mobile payments.

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Finally, the modular architecture underlying platform-based ecosystems may not only entail “hub-

and-spoke” modular interfaces between the central platform and the peripheral complements but can also

entail modular interfaces among the complements themselves. Such modularization can enable innovations

by complementors to connect with other complements, generating supermodular complementarities. For

example, in the iPhone ecosystem, several apps are connected with the Google Maps app for the navigation

functionality. Similarly, in the Amazon Web Services ecosystem, complementors leverage each other’s’ offers

in security, networking, storage, database, and business intelligence to provide users with an integrated

solution.

In this paper, we offer a framework to analyze how an innovation’s value creation is impacted by its

connections in a platform-based ecosystem. We use the notion of connectedness to refer to these

connections, differentiating between connections with respect to platform components (i.e., platform

connectedness) and those with respect to other complements (i.e., complement connectedness). Figure 1

illustrates the two types of connectedness using a simplified schema that we explore in this paper. An

innovation (I) can have low platform and low complement connectedness by only connecting with the core

module of the platform. Alternatively, it can have high platform connectedness by connecting with additional

(or optional) platform components (OC1, OC2), and it can have high complement connectedness by

connecting with other complements (C).

(Insert Figure 1 about here)

We expect that high platform connectedness can enhance the utility of the focal innovation for the

user by bundling complementary functionalities accorded by the platform components. In other words, high

platform connectedness can supplement strict complementarities with supermodular complementarities. The

realization of these supermodular complementarities for the focal complementor, however, can be subject to

the adoption challenges by the user (Adner and Kapoor, 2010). Because platform components are integrated

with the core module by the platform firm, users are generally familiar with these components, making it

easier for them to adopt and benefit from the complementary functionality (Rogers, 2003; Hall, 2004). Hence,

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ceteris paribus, we expect that innovations with high platform connectedness will be more likely to achieve

successful commercialization than those with low platform connectedness:

H1 - Innovations that connect with optional platform components will have a greater likelihood of successful commercialization than those that only connect with the core platform module.

An innovation has high complement connectedness if it connects with other available complements

in the ecosystem. The connections between the focal innovation and other complements can unfold

functionalities and additional benefits that otherwise might not be available to the users of the focal

innovation (i.e., the condition of supermodularity). For example, the users of a note-taking app for mobile

devices, Notability, benefit when the app is connected to other cloud-based storage apps like Dropbox as

they can not only create notes, the main functionality accorded by the app, but can also share the notes with

other users and across devices as additional bundled functionalities.

Further, having access to the specialized technologies provided by external complements can increase

the combinatorial set for experimentation and learning for the focal innovation and thus, can facilitate

commercialization (Baldwin and Clark, 2000; Boudreau, 2012). Finally, developing the complementary

technologies on one’s own can be costly and uncertain. By leveraging the readily available complementary

technologies provided by other firms, firms can also avoid commercialization setbacks that can be associated

with the launch of new innovations within an ecosystem (Adner and Kapoor, 2010; Kapoor and Furr, 2015).

Accordingly, we suggest that:

H2 - Innovations that connect with other complements will have a greater likelihood of successful commercialization than those that only connect with the platform.

Effect of platform evolution (generational change)

We now consider how the effect of platform and complement connectedness on an innovation’s

commercialization success might be impacted by the introduction of the new generation of the platform.

Transitioning to a new platform generation is an important mode by which platform firms compete and

create value over time. New platform generations typically offer improvements in existing functionality as

well as add new functionality. They represent instances of architectural changes as discussed by Henderson

and Clark (1990) but at the level of the ecosystem such that the core design concepts and the associated

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knowledge are not overturned but there is a change in the nature of the interactions between the platform

and the complements (Kapoor and Agarwal, 2017; McIntyre and Srinivasan, 2017). In so doing, both users

and complementors need to adapt to the new architecture. For example, iPhone users and app developers

face significant challenges whenever Apple introduces new generations of the platform. This is evident in

Figure 2, which plots the normalized monthly trend of U.S. search volume on Google for the search term

“app not working” from January 2010 to December 2015. There are significant spikes in the search volume

during the months when the new platform generation is launched by Apple. Hence, while the introduction of

new platform generations is an important mode by which platform firms compete and create value, these

generations can impose significant adjustment costs for both users and complementors due to ecosystem-

level architectural changes.

( Insert Figure 2 about here)

These adjustment costs underlie what Rosenberg (1982) referred to as a process of learning-by-using.

This mode of learning, in contrast to learning-by-doing, only takes place once the innovation is adopted by

the users, and is especially important when the focal product creates value through interactions with other

elements in an ecosystem as in the case of aerospace (Mowery and Rosenberg,1981) and smartphone (Kapoor

and Agarwal, 2017) ecosystems. Therefore, an innovation’s successful commercialization when the platform

has undergone a generational transition would be impacted by the adoption challenges faced by the users and

the accompanying uncertainty around design tradeoffs and the pattern of use faced by the complementors.

Higher platform connectedness will increase the degree of technological interdependence between

the innovation and the platform. Accordingly, it will exacerbate the extent of the adjustments that users

would have to make to adopt the innovation while the platform is transitioning through a new generational

architecture. For complementors choosing to leverage the functionality accorded by the new platform

generation through high platform connectedness, this will also impose greater uncertainty with respect to

design tradeoffs and how their innovations would be adopted in the new platform generation.

Hence, while higher platform connectedness can help an innovation to leverage complementary

functionalities accorded by the platform owner, it also increases technological interdependence between the

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innovation and the platform. The higher degree of interdependence can exacerbate the adoption challenges

for the users and the design challenges for the complementors when the platform generation is new,

offsetting the benefit that the innovation derives from high platform connectedness:

H3 - The positive effect of innovation’s connectedness with optional platform components on the likelihood of successful commercialization will be lower when the platform generation is new than when it is mature.

Platform’s generational evolution can also have an impact on the benefit that an innovation might

derive from connectedness with other complements. While the innovation’s connectedness with optional

platform components imposes direct adjustment costs for focal innovator when the platform generation is

new as discussed above, the innovation’s connectedness with other complements creates indirect adjustment

costs for the focal innovator. These indirect costs arise when the connected complement might act as a

performance bottleneck, constraining the innovation’s value creation during periods of generational

transitions (Adner and Kapoor, 2010; 2016). Performance bottleneck is a constraint imposed on the

functionality of the focal innovation as-designed by other complements in the ecosystem (Baldwin, 2015). A

generational change in platform changes the interactions among the platform and the connected

complements. These changes can adversely affect the functionality of the connected complement on its own

or in combination with the focal innovation, undermining the value that the focal innovation derives from the

connected complement. For example, in the semiconductor lithography equipment industry, the

commercialization success of innovations was negatively impacted by performance bottlenecks due to the

mask and the resist complements (Adner and Kapoor, 2010).

It is possible that firms commercializing their innovations can identify potential performance

bottlenecks stemming from connected complements, and can take steps to resolve them prior to the

commercialization. However, in platform-based ecosystems characterized by multiplicity of interactions

between the platform, the users, and the complements, the search and the discovery of bottlenecks is difficult

before the innovation is commercialized. Moreover, resolving those bottlenecks require explicit coordination

between firms commercializing their innovations and those offering the connected complements. In the

presence of significant uncertainty during periods of platform transitions, such coordination among

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complementors is difficult to achieve. It is also likely that the connected complements would themselves

change in the face of the platform transition. Changes in connected complements may alter their

technological interactions with the focal innovation, resulting in additional challenges during the innovation’s

commercialization.

Hence, while connectedness with other complements can enhance the innovation’s value creation, it

can also increase the likelihood of those complements creating performance bottlenecks when the platform

generation is new. Accordingly, we expect the benefits from complement connectedness to be weaker when

the platform generation is new than when it is mature:

H4 - The positive effect of innovation’s connectedness with other complements on the likelihood of successful commercialization will be lower when the platform generation is new than when it is mature. Heterogeneity among connected platform components and complements

Finally, we consider an important source of heterogeneity among connected platform components

and complements. Connectivity arising due to technological interactions among elements in platform-based

ecosystems, like most complex networks, is subject to a pareto distribution or the power law (Simon, 1955;

Strogatz, 2001). That is, a handful of platform components and complements may represent a

disproportionately high frequency of connectivity while most platform components and complements may

have relatively few connections. We refer to these latter elements as niche elements, and differentiate them

theoretically from widely connected elements.

We have argued that the introduction of a new platform generation can impose significant challenges

for both users and complementors due to ecosystem-level architectural changes. These challenges would get

exacerbated in the case of innovations with high platform and high complement connectedness. From the

perspective of a platform firm, while it is difficult to account for all possible interactions among the platform,

the complements and the users that will be impacted by the generational transition, the platform firm may

take steps to mitigate these challenges. This could be through the design of the new generation itself such that

the existing interfaces with complements and users are not fully overturned (e.g., preserve the previous

interactions through backward compatibility) or through subsequent R&D efforts to resolve those challenges

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(e.g., bug fixes in the case of software). In an environment of high complexity and rapid technological change

that characterizes platform-based ecosystems, the platform firm may only be able to mitigate these challenges

for widely connected platform components than for the niche components. That is, it may tradeoff

performance improvement for backward compatibility for widely connected components, and it may also

expend subsequent R&D resources towards resolving challenges with respect to the widely connected

components. Therefore, we predict that for innovations that connect with optional platform components, the

negative effect of platform newness will be higher for those innovations that only connect with niche

components than those that connect with widely connected components:

H5 - For innovations that connect with optional platform components, the negative effect of platform newness on the likelihood of successful commercialization will be higher for those that only connect with niche components than those that connect with widely connected components.

From the perspective of a focal innovator that is interdependent on another complementor, it may

also take steps to manage interdependencies and resolve performance bottlenecks arising from platform’s

generational evolution. This could be through coordinated R&D efforts or through information sharing

(Ethiraj, 2007; Kapoor, 2013). Such steps are likely to be more effective with respect to niche complements

involving a small sect of actors where the technological interdependencies are relatively simple than with

widely connected complements where the technological interdependencies are relative complex involving

many actors and interactions. Accordingly, we expect that for innovations that connect with other

complements, the negative effect of platform newness will be lower for those innovations that only connect

with niche complements than those that do not:

H6 - For innovations that connect with other complements, the negative effect of platform newness on the likelihood of successful commercialization will be lower for those that only connect with niche complements than those that connect with widely connected complements. METHODOLOGY

The empirical setting for the study is Apple’s iPhone ecosystem, and the focal complementor firms

are application software (app) developers who participated in the ecosystem from 2008 to 2015 within the

U.S. market. Apple launched its first generation of iPhone in January 2007, and it developed its own apps for

this generation. However, in March 2008, Apple released the first software development kit that allowed

external software developers to build apps for the iPhone, and their apps were made available to iPhone user

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via its App Store in July 2008. Since this shift towards a platform-based strategy, the number of application

developers building apps and the number of apps for the iPhone have grown exponentially, and this has been

a key enabler of iPhone’s success over the past decade. By 2015, there were more than 1.5 million apps

offered in the App Store by more than a hundred thousand app developers.

The setting provides an important and relevant context to study how the commercialization success

of complementor’s product innovation is shaped by the architecture of connections with respect to the

platform components and other complements. The iPhone ecosystem represents one of the largest and most

valuable business ecosystems with App Store revenue estimated to be more than $20B in 2016. Hundreds of

thousands of app developers participate in this ecosystem by frequently launching new apps, their focal

product innovations. Moreover, apps launched by developers vary in terms of their connectedness with

respect to platform components that are integrated within the iPhone (e.g., camera, accelerometer), and with

respect to other complementary apps. Finally, between 2008 and 2015, there were six generational transitions

within the iPhone platform when Apple launched new versions of the smartphone operating system and the

handset, allowing us to observe the impact of platform’s evolution on the commercialization success of apps.

Data The primary sources of data are App Annie (www.appannie.com) and AppShopper

(www.appshopper.com) which are the leading data aggregating and archiving sources for information on

iPhone apps since 2008. Using two different sources helped us to ensure the accuracy of the information and

minimize missing data. We identified 796,876 unique apps that were launched between July 2008 and March

2013. For each of these apps, we collected information on the app-category, the launch date, textual

description of the app, content rating, language, app size, download price, in-app purchases and average user

rating. We supplemented this with additional information from iTunes on platform components leveraged by

the focal app, and all of the version updates up to December 2015.3

In the analysis, we only consider those apps whose primary source of revenue is from the App Store

through either paid downloads or in-app purchases. We did that for two reasons. First, firms from many

industries such as retail and financial services offer iPhone apps as an additional channel to support their

3 App developers have to disclose to Apple the list of platform components that the app is connected to.

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existing business. Hence, the app on its own is not their focal product innovation. Second, many firms also

offer apps for free and rely on ad-based revenue model. In such cases, apps are the main source of ad-based

revenue, but these revenues are not captured by the App Store and, hence, do not allow us to draw inferences

with respect to their commercialization success. We note that during the period of study, ad-based revenue

was estimated to be less than 10% of the total app revenue (Dogtiev, 2017). In parallel, we identified all apps

that were included in Apple’s daily list of Top grossing apps by revenues. These apps are among the Top 500

apps in terms of total daily revenue of the App Store. App Annie has archived this information from Apple

going back to February of 2010. To avoid any left censoring in the data, we excluded 127,703 iPhone apps

that were introduced before February 2010. Finally, since we identify the extent of complement

connectedness from the app’s textual description using a keyword-based approach, we excluded apps that

were offered in languages other than English. We also excluded books, news, and reference apps, whose

description typically include portions of the actual content, which made the keyword-based approach less

effective. The final sample, after all these exclusions, comprises of a total of 244,034 apps launched by 31,446

app developers. We constructed a panel dataset of these apps with monthly observations in order to account

for the hypercompetitive nature of the setting, and to explore the effect of new iPhone generations that are

launched every year (e.g., Kapoor and Agarwal, 2017; Davis et al., 2016).

Measures Dependent variable: We measure successful commercialization of an innovation by examining whether

the focal app made it to the Top 500 apps list by revenue (e.g., Kapoor and Agarwal, 2017; Davis et al., 2016).

The revenue distribution for smartphone apps is heavily skewed. According to Sensor Tower, a leading

vendor for App Store marketing and sales tracking software, the top 1 percent of the app developers in the

iPhone ecosystem represent approximately 95 percent of total ecosystem-level revenue (SensorTower, 2016).

Therefore, having an app in the Top 500 list offers clear evidence of successful commercialization among

hundreds of thousands of apps. Such a list is also keenly followed by industry observers and analysts as a

reference for successful apps.

Platform connectedness: We measured an app’s platform connectedness using a variable that takes a value of 1

if the focal app is connected to any optional platform component in addition to the core mobile computing

16

module of the iPhone, and 0 if the focal app is only connected to the core module. This information is

extracted from the iTunes website.4 We also examined the version history updates to identify connections

that were introduced as part of the version update and coded the platform connectedness variable as 0 if the

connection was introduced after the app became commercially successful. In the data, about 50 percent of

apps were connected to at least one optional component. Two platform components, Open GLES and

Location Services, were connected with a disproportionately large number of focal apps (64.76 percent of all

apps that connect to any optional platform component), whereas components such as GPS, camera,

telephony were only connected with a small number of apps. We characterized the later components as niche

components. The variable, Platform Connectedness with Niche Components, takes the value of 1 if the focal app is

connected only to niche components and 0 if it is connected to OpenGLES and Location Services.

Complement connectedness: We identified an app’s complement connectedness based on whether the app

is leveraging functionalities of other apps in the iPhone ecosystem. This was done by searching through the

description of each of the focal app and capturing the names of any other apps that were included in the

description. The main assumption here is that if an app developer is mentioning other apps in the app’s

description, it’s leveraging the functionalities accorded by those apps. We validated this assumption by

scanning through more than hundred randomly selected app descriptions spanning across different app-

categories. For example, one of the apps, ReaddleDocs, describes its connectedness with other apps such as

MobileMe iDisk, Dropbox, and Google Docs in its description:

“…readdleDocs is all-in-one document reader for iPhone and iPod touch...readdleDocs allows you to download and

upload files from MobileMe iDisk, Dropbox, Google Docs...”

Almost 90% of apps were connected with apps in a different app-category, suggesting that

complement connectedness is associated with app developers leveraging supermodular complementarity

(Baldwin 2018a).5 The variable, complement connectedness, is operationalized based on whether the focal app is

4 In the sample of apps, there were a total of 17 iPhone components that the apps were connected to. These components were accelerometer, bluetooth, camera, flash, gamekit, GPS, gyroscope, healthkit, location services, magnetometer, metal, microphone, OpenGLES, SMS, telephony, video, WiFi. 5 A related consideration is whether the connected app is offered by the same app developer. We don’t find much occurrence of this in our dataset. Only 2.3% of apps with a connected complement referred to a connected complement that was launched by the same app developer, and the majority of developers specialize in a single app-category.

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connected to any other app in the iPhone ecosystem or not. The variable, complement connectedness, takes the

value of 1 if the focal app is connected to at least one other app, and 0 otherwise. In our dataset, about 23

percent of apps were connected to at least one other app. In some cases, apps introduced connections with

other apps as part of the “version update.” Hence, in addition to searching through the app description, we

also searched through the version update history to identify changes in an app’s complement connectedness.

We code the complement connectedness variable to be 0 for apps that introduced a connection with a complement

after becoming commercially successful. As a robustness check, we excluded these apps from the analysis,

and found very similar results.

Table 1 tabulates the number of apps based on their platform and complement connectedness for

the entire sample and only for those apps that made it into the Top 500 list by revenue. It is organized into

four quadrants based on whether the platform and complement connectedness of the focal app is high or

low. An app is considered to have high connectedness if it is connected to at least one optional platform

component or a complementary app, and low otherwise. 98,105 apps had low platform and low complement

connectedness in Quadrant 1 and only 0.8% of these apps made it to the Top 500 apps. 27,004 apps had high

platform and high complement connectedness in Quadrant 4, and 4.4% of these apps made it to the Top 500

apps. Further, 1.8% and 2.3% of apps in Quadrants 2 and 3 made it to the Top 500 apps. Taken together,

this pattern is consistent with our predictions in Hypothesis 1-2 that high platform and complement

connectedness will be associated with a greater likelihood of successful commercialization.

(Insert Table 1 about here)

In our data, we found clear evidence of pareto distribution with respect to connected complements.

That is, there were a handful of widely connected complements such as Facebook, Twitter, Google Maps,

Dropbox, and numerous niche complements that were connected with a small number of apps. The variable,

Complement Connectedness with Niche Complements, takes the value of 1 if the focal app is connected only to niche

complements and 0 if it is connected to widely connected complements.

Generational Newness: Between 2008 and 2015, the iPhone platform underwent six episodes of

generational transitions. These transitions included changes in the operating system (iOS) and in the handset.

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More than 70% of iPhone users have been shown to migrate to the new operating system within the first

three months whereas the migration to the new handset is much more gradual (Mixpanel, 2017). From a

perspective of an app developer, changes in iOS are a major consideration as it impacts almost the entire

iPhone user base. The variable, generational newness, is the number of months between the observation month

and the month in which the latest generation of the iPhone platform was launched. We multiplied this

measure by -1 for ease of interpretation with respect to the hypotheses. Hence, higher values correspond to

an early period of a new platform generation.

Control variables: We control for a number of firm-level and app-level characteristics that can influence

the likelihood of focal app’s successful commercialization. First, we control for firms’ experience in the

ecosystem using the variable firm experience, which is the total number of months that a firm has been

participating in the ecosystem. To obtain this measure, we first identified the month in which the firm

introduced its first app in the ecosystem (i.e., the month of entry) and then calculated the number of months

between the observation month and the month of entry. Second, app developers often try to gain visibility

among their potential users by providing free apps. We controlled for this firm-level effect through a dummy

variable top 500 free that takes a value of 1 if any of the apps developed by the firm were also part of the Top

500 ranking based on the number of downloads for free apps in a given month. Further, an app’s successful

commercialization is likely to be influenced by the overall demand for its app-category (e.g., Games,

Productivity, Utility, and Business). An app in a high-demand category might find it relatively easier to

achieve successful commercialization. We account for this possibility using the proxy variable category demand,

which is the total number of apps in the Top 500 list in a given month in the same category as the focal app.

In addition, we also control for any category-level differences by using category fixed effects.

We control for the quality of the app based on consumer ratings received by the focal app. Users can

rate an app from 1 to 5, with 5 being the highest quality. We only observe the cumulative rating offered by

the users for a given app as of March 2013, but not the changes in the rating over time. The variable app rating

is the cumulative rating received by the focal app as of March 2013. We also control for the extent of efforts

by firms to improve their apps. We did this by using two different variables. The variable 3-month updates is the

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number of updates to the focal app in the past three months, and the variable Total updates is the total number

of updates since the app was launched. To account for differences in the cost of development across apps,

we used the app’s file size as a proxy for an app’s complexity and development cost (Boudreau, 2018; Boehm et

al., 2000). Further, we control for the costs undertaken by firms to resolve software-related errors (commonly

referred to as bug fixes) after the launch of the app via the variable total bug fixes. The variable is the total

count of updates that correspond to bug fixes but without the addition of new functionality. 6 Platform

owners have also been shown to selectively promote complementors, and such a promotion can have a

significant impact on complementors’ commercialization success (Rietveld, Schilling and Bellavitis, 2018). In

May 2012, Apple introduced a feature called “Editor’s Choice” in its App Store to promote a small number

of apps. We account for this effect of Apple’s promotion on an app’s commercialization success through a

categorical variable called Apple promoted app that takes a value of 1 if an app is promoted by Apple, and 0

otherwise. Additionally, we controlled for other app-level characteristics like the price for download (download

price), recommended age rating for the app (content rating), whether the app has an in-app purchase option or

not (in-app purchase), and whether the app is only available for iPhone or also for Android smartphones (multi-

homing app). Finally, we control for platform age through the number of months since the launch of the iPhone

in 2007.

Analysis The hypotheses are tested using continous time event history models, which estimate the hazard rate

of an app achieving successful commercialization. We constructed the data in the long form to account for

time-varying covariates. We started analyzing all the apps since their first month of launch on the iPhone

platform. For the apps that made it to the Top 500 list by revenue, we included information for the months

from their launch to when they first appeared in the Top 500 list. For those apps that did not appear in the

Top 500 list until December 2015 (i.e., the last month of observation), we classified their last month’s

6 To capture the updates that only cater to bug fixes and not new functionality, we identified several keywords that are typically used by app developers to refer to bug fixes in describing the changes in the new version of an existing app. The keywords used were bug, fix, issue, crash, problem, error and glitch. We excluded those updates where the description also included the keywords new, introduc, featur, add, support, performance, improv, upgrad, enabl, updat, enhan, modif, optimiz, fast, adjust, multitask, and where the description of update was more than 200 characters (longer descriptions are often used to describe new functionality).

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observation as censored observation only if there was an update to that app after December 2014. This is

because while apps continue to be available in the App Store over a prolonged period of time, many of these

apps represent the case of a ‘living dead’ phenomenon (Bourgeois and Eisenhardt, 1987) with app developers

not expending any efforts to improve them. Censoring these apps based on the last month of observation

might be problematic because the likelihood of them making it to the Top 500 list in the future may be very

low. To account for this possibility, we only include monthly observations for these apps until 12 months

after their last update. As an additional robustness check, we also estimated a model where we only include

observations for these apps until six months after their last update. We report this additional analysis in the

robustness checks section after presenting our main results.

We used the Cox proportional hazard model, a robust technique for hazard rate analysis that does

not require making an additional assumption about the shape of the baseline hazard, which may be increasing,

decreasing, constant, or non-monotonous (Cox, 1975). This helps address concerns about the incorrect

distributional assumptions yielding biased estimates and the choice of parametric specification based on

observed data generating inconsistent results (Blossfeld 2001). Further, we tested for the proportionality

hazard assumption by checking if the slope of the regression equation of scaled Schoenfeld residuals on time

is nonzero for the full model as well as for all predictor variables (Grambsch and Thermeau, 1994). The

proportional hazard assumption was not violated for the full model and all predictor variables. Finally, apps

introduced by the same firm often differed with respect to their connectedness within the ecosystem, allowing

us to control for unobserved firm-level heterogeneity by treating each firm as a separate stratum (Allison,

1996).

RESULTS We report the summary statistics and correlations between our covariates in Table 2. The results

from the Cox model are reported in Table 3. The model estimates the hazard of an app achieving successful

commercialization. The reported coefficients can be exponentiated to obtain hazard ratios, which are

interpreted as the multiplier of the baseline hazard for the app being included in the Top 500 list when the

variables increase by one unit (Allison, 2010). An increase in hazard can also be interpreted as an increase in

the likelihood of an app achieving successful commercialization. All standard errors reported were corrected

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for non-independence across multiple observations for the same app by clustering observations for each app.

All the models include category-fixed effects. Model 1 is a baseline model with only control variables. In

Models 2 and 3, we include the variables platform connectedness and complement connectedness to test Hypotheses 1

and 2, respectively. In Model 4, we include the interaction term between platform connectedness and generational

newness to test Hypothesis 3. Similarly, we include the interaction term between complement connectedness and

generational newness in Model 5 to test Hypothesis 4. Model 6 is the fully specified model with all independent

variables and interaction terms for testing Hypotheses 1-4. Models 7 and 8 are used to test Hypotheses 5 and

6 using only the sample of apps with high platform and high complement connectedness respectively.

(Insert Tables 2 and 3 about here)

The results from the baseline model (Model 1) suggest that apps that have higher consumer

rating, those in categories with high demand, those offering an in-app purchase, those promoted by Apple,

those where developers are being responsive to user experiences by resolving any software related errors, and

those which are being frequently updated are more likely to make it to the Top 500 list. At the firm-level, app

developers who are new to the iOS ecosystem, and who are visible in terms of their apps making it to the

Top 500 free app list based on the number of downloads are more likely to have an app in the Top 500 list by

revenue.

In Hypothesis 1, we predicted that innovations that connect with optional platform components will

have a greater likelihood of achieving successful commercialization than those that only connect with the core

platform module. We find support for this prediction in Models 2, 4, 6 and 8. The estimated coefficient for

platform connectedness is positive with a p-value of 0.000 (Model 2). In considering the magnitude of the

estimated coefficient in Model 2, we find that a connection with at least one platform component is

associated with a 53.4 percent higher likelihood of the focal app making it into the Top 500 list by revenue. In

Hypothesis 2, we predicted that the innovations that are connected with other complements have higher

likelihood of successful commercialization than those that only connect with the platform. We find support

for this prediction in Models 3, 5, 6 and 7. The estimated coefficient for the variable complement connectedness is

positive with a p-value of 0.000 (Model 3). Based on the estimated coefficient in Model 3, apps that are

22

connected to at least one complement have 17.9 percent higher likelihood of making into the list of Top 500

apps by revenue than those that only connect with the platform.

In Hypothesis 3, we predicted that the positive effect of innovation’s platform connectedness on its

successful commercialization will be weaker when the platform generation is new than when it is mature. The

results from Models 4 and 6 support the prediction. The estimated coefficient for the interaction term

between platform connectedness and generational newness is negative with a p-value of 0.016 (Model 4). This suggests

that the benefits of platform-level complementarities that accrue to app developers whose apps have high

platform connectedness may be buffered by the challenges of managing additional technological

interdependencies between their apps and the new generation of the iPhone platform.

In Hypothesis 4, we predicted that the effect of complement connectedness on the successful

commercialization of an innovation will be weaker when the platform generation is new than when it is

mature. The estimated coefficient for the interaction term between complement connectedness and generational

newness in Models 5 and 6 is positive with a p-value of 0.646 (Model 5). Hence, we did not find support for

this prediction.

In Hypothesis 5, we predicted that for innovations that connect with platform components, the

negative effect of platform newness on the likelihood of successful commercialization will be higher for those

that only connect with niche components than those that do not. We find support for this prediction as the

estimated coefficient for the interaction term between platform connectedness with niche component and generational

newness in Model 7 is negative with a p-value of 0.035. Finally, in Hypothesis 6, we predicted that the negative

effect of platform newness on the likelihood of successful commercialization will be lower for those

innovations that only connect with niche complements than those that connect with widely used

complements. We find support for this hypothesis as the estimated coefficient for the interaction term

between complement connectedness with niche complement and generational newness in Model 8 is positive with a p-value

of 0.011. We illustrate the different interaction effects graphically in Figure 3.

(Insert Figure 3 about here) We explored the lack of support for Hypothesis 4 through a post hoc analysis. Our theory underlying

this hypothesis is based on the existence of performance bottlenecks with respect to the connected

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complements when the platform goes through a generational transition. Given that the connected

complements might themselves differ in terms of platform connectedness, we expect that performance

bottlenecks will more likely arise in the case of connected complements with high platform connectedness

themselves. This is because of the greater degree of required adjustments for connected complements that

also connect with optional platform components.

(Insert Table 4 about here)

We identified the connected complements with high platform connectedness. Apps such as Google

Maps, Waze, and YouTube use one or more additional platform components whereas apps like Dropbox and

Google Drive use only the core mobile computing module. In Models 9, 10 and 11, we explored this

heterogeneity within connected complements by splitting the main measure of complement connectedness

into two measures depending on whether the connected complement itself has high or low platform

connectedness. The coefficients for the direct effect of both types of complement connectedness are positive

with p-values of 0.009 and 0.017 (Model 11), consistent with our prediction. However, the coefficients for the

interaction terms with generational newness illustrate important differences. The interaction term is positive

with a p-value of 0.009 (Model 11) when the connected complement has low platform connectedness

whereas it is negative with a p-value of 0.035 (Model 11) when the connected complement has high platform

connectedness. Hence, our argument with respect to performance bottleneck seems to be particularly relevant

when the connected complement itself has high platform connectedness.

Robustness Checks

We conduct a number of additional checks to establish the robustness of our findings. First, we

consider the potential endogeneity with respect to an app’s connectedness and its successful

commercialization, and use instrumental variable approach to assess the robustness of our results. Second, we

evaluate several alternative explanations.

Instrument variable analysis

For this analysis, we needed to identify instruments that are correlated with an app’s platform or

complement connectedness, but uncorrelated with the app’s commercialization success beyond its effect on

the endogenous regressor (Angrist and Pischke, 2008). To do so, we drew upon Stack Overflow

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(stackoverflow.com), the largest online community for software developers. A key feature of this website is

that developers post and respond to queries about software development. Because of the high complexity and

breadth of the queries asked, Stack Overflow has created a sophisticated tagging schema where each query is

tagged with multiple keywords. We started with downloading all the queries from the website that were

tagged “iOS.” This provided us with a large-scale database of app developers’ public queries with respect to

iPhone app development going back to July 2008. Within these queries, we then identified tags with respect

to specific iPhone components and the connected apps in our dataset.

We leveraged two features of this online community to identify instrumental variables. First, queries

posted by developers were tagged to indicate whether developers were able to successfully resolve them or

not. Figure 4 plots the trend in the total number of queries and the total number of queries that were resolved

for iPhone components and connected apps respectively. The proportion of queries for platform

components or connected apps that were unresolved among developers is a good proxy for the challenges

that developers might be facing in connecting their focal innovation with a given platform component or app

(complement). The extent of challenges faced by the developer community is likely to be negatively correlated

with the likelihood of a focal app having the connection with the given platform component or complement.

However, the proportion of unresolved queries with the developer community is unlikely to be correlated

with the focal app’s commercialization success, for reasons beyond its effect on app’s platform or

complement connectedness.

(Insert Figure 4 about here)

Stack Overflow also tracks the number of views received for each query. The number of views

received by queries for platform components or connected apps can be a good proxy for the awareness

among developers regarding the opportunity to connect with a given platform component or an app. The

higher the level of awareness that the developer community has, the greater the likelihood that a focal app

might connect with that component or app. Again, this developer community-level measure is unlikely to be

correlated with the focal app’s commercialization success, for reasons beyond its effect on app’s platform or

complement connectedness.

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The first set of instruments, Platform Component (Complement) Queries Unresolved , are operationalized as

the proportion of queries related to focal platform components (complements) that were not resolved within

three months prior to the launch of the app. The second set of instruments, Platform Component (Complement)

Queries Views, are operationalized as the number of views received by queries about the focal platform

components (complements) within three months prior to the launch of the app. It is divided by the total

number of “iOS” queries views to account for the increasing trend in the number of queries and views over

time. It is important to note that that both of these instruments are operationalized based on the total

number of queries by all developers on stackoverflow.com with respect to the focal platform component

(e.g., camera) or the focal connected complement (e.g., Facebook), and not the queries by the developer of

the focal app.

The estimates from the instrumental variable analysis are reported in Table 5 using the ivreg2

procedure in STATA on the cross-section of all apps and using the last month of observation for the control

variables.7 Models 16 and 19 include estimates for the full sample. The results from each model comprise of

both the first-stage and the second-stage estimates. In order to explore the effect of platform generational

newness, we divide the sample into apps that are launched within the first six months of the introduction of

the new platform generation (Models 17, 20), and those that are launched after the six months (Models 18,

21). It the first-stage (Models 16a-21a), the estimated coefficients for Platform Component (Complement) Queries

Unresolved are negative and statistically significant (p-value < 0.001), suggesting that greater proportion of

unresolved queries about platform components and complements is associated with lower likelihood of apps

having platform and complement connectedness, respectively. The estimated coefficients for the second set

of instrumental variables, Platform Component (Complement) Queries Views, are positive and statistically significant

(p-value < 0.001), suggesting that the higher number of views received by queries about platform

components and complements is associated with higher likelihood of apps having platform and complement

connectedness, respectively. These results offer support for the validity of the different instruments. We also

7 An alternative estimation approach would have been to use the instrumental variable probit model (ivprobit procedure in STATA). However, since the endogenous regressors are dichotomous in nature, the ivprobit model may not provide efficient estimates (Angrist, 2001).

26

ran a series of tests to ascertain the quality of the instruments (Kennedy, 2008; Semadeni et al., 2014). First,

the Cragg Donald statistics for the second-stage models are greater than 10% maximal IV size, the

recommended threshold provided by Stock and Yogo (2004) to satisfy instrument relevance condition.

Second, for all models, the Anderson LM test rejects the null hypothesis of under-identification. Finally, the

Sargan test statistics cannot be rejected for all the models providing us additional confidence that both sets of

instruments are exogenous and affect an app’s successful commercialization only through platform or

complement connectedness respectively.

The estimates from the second-stage continue to offer support for our predictions. Consistent with

the predictions for Hypotheses 1 and 2, the coefficients for Platform Connectedness and Complement Connectedness

are positive and statistically significant (Models 16-21). Further, the magnitude of the coefficient for Platform

Connectedness is much higher in Model 18 (i.e., for apps launched after the first six months of the new platform

generation) than in Model 17 (i.e., for apps launched during the first six months of the new platform

generation), offering support for Hypothesis 3. Consistent with the main analysis, the estimates for

Complement Connectedness in Models 20 and 21 are qualitatively similar. These results provide greater confidence

that our findings are not impacted by the endogeneity with respect to an app’s platform and complement

connectedness.

(Insert Table 5 about here) Additional robustness checks

We conducted a number of additional checks to establish the robustness of our findings with respect

to several alternative explanations. We performed an analysis that included firm fixed-effects to account for

unobserved differences across firms such as the quality of the developer teams and the nature of

complementary assets. For this analysis, we used a linear probability model because proportional hazard

models are not amenable for explicitly incorporating fixed effects for a large number of firms that we have in

our sample (Allison,1996). In order to meet the i.i.d. assumption for the error term, we conducted this

analysis on a cross-sectional data of all apps, using the last month of observation for the control and

connectedness variables. The variable Generational Newnesss takes the value of 1 if the app is launched within

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six month of the launch of the new generation, and 0 otherwise. The results are reported in Model 12 in

Table 4 and are qualitatively similar to the main results. We tested whether our results are sensitive to the

choice of using a 12-month window to identify the “living dead” apps. As an additional check, Model 13

reports results using the 6-month window and the estimates are consistent with our main analysis. We also

explored our model’s sensitivity towards Apple’s promotion of selects apps by excluding the apps that were

promote by Apple from the analysis. The results are reported in Model 14 and are consistent with our main

findings. Moreover, to rule out any issues related to reverse causality between connectedness and

performance, we ran an analysis by excluding apps that introduced a platform or a complement connection

after becoming commercially successful. The results are reported in Model 15 and continue to support our

hypotheses.

Finally, since our dependent variable is operationized from revenue-based ranking, it might not

capture differences in cost associated with developing apps with high levels of connectedness, potentially

making inferences around commercialization success problematic. Although we accounted for this issue by

including proxies for the cost of app development (app’s file size, total bug fixes), we also explored other ways to

get at this issue. We were able to gather granular technical data of software “teardowns” of 375 iPhone apps.

The data provided us with the information about the first time that any of the code files for the focal app

were modified. According to our understanding from the firm that performed the teardown, this time closely

corresponds to the beginning of the app’s coding process once the initial specification is defined. Hence, the

time difference between the app’s initial launch and the first modification to a code file could be a useful

proxy for the app’s time-to-market, and its cost of development.8 Based on the data from the teardowns, we

did observe that apps with high level of platform and complement connectedness took an average of 7

additional days during the coding process. Specifically, the coding period for an app in the sample that had

high platform connectedness was 14 days as compared to 7 days for the ones that had low platform

connectedness. The coding period for an app with high complement connectedness was 15 days as compared

8 This time-period will not include the upfront time to come up with the idea and the specification of the app, which often takes several weeks or even months. It will only includes the time-period when the app specification is translated into the actual app through software codes.

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to 8 days for the app that had low complement connectedness. This finding is consistent with what we also

heard from the app developers in the interviews. Here is a quote from a senior developer at Fitbit:

“The amount of time required to connect with other app really depends on the level of documentation provided about interfaces provided by that app. If the documentation is good, which is mostly the case, I can develop and test the feature that connects with other app in roughly 15-20 days.” “If I have to compare two apps with almost identical features except that one uses the iPhone component and other one does not, I would think that I would roughly take additional 7-10 days to develop the app that uses iPhone component.”

This additional analysis provides some suggestive evidence that high level of interdependency (i.e.

high platform or complement connectedness) can also lead to increased cost or delayed time-to-market for an

iPhone app by several days. However, this difference in terms of time seems rather short, in contrast to the

much longer period of upfront app design and specification that typically spans several weeks or months.

DISCUSSION

A given innovation often does not create value on its own. Rather it is connected with other

elements in the ecosystem that impacts its value creation. In this study, we draw on this premise in a

platform-based ecosystem in which participating complementor firms innovate around a platform to explain

the commercial success of their innovations. We introduce the notion of connectedness to refer to the extent

to which a given innovation interacts with the platform (i.e., platform connectedness) and also with other

complements in the ecosystem (i.e., complement connectedness). On the one hand, higher connectedness

may allow the innovation to leverage a broader set of complementarities in the ecosystem. On the other hand,

it may subject the innovation to an array of interdependencies that may limits its value creation especially

when a generational transition triggered by the platform firm changes the underlying platform architecture.

We explore these arguments on app developers that participated in Apple’s iPhone ecosystem

between 2008 and 2015 in the U.S. market. We find that higher platform connectedness and higher

complement connectedness is associated with a higher likelihood of app’s successful commercialization.

However, the benefit of high platform connectedness is negated during the initial period of the new

generation of platform, and much more so when the innovation only connects with those components that

29

are not widely used by complementors within the ecosystem. Hence, while higher platform connectedness

can facilitate an innovation’s commercialization success by bundling complementary functionalities accorded

by the platform owner, these additional connections can also impose additional adjustment costs for both

innovators and users when there is a new platform generation, limiting the innovation’s value creation. Our

evidence indicates that platform owners may mitigate these costs with respect to those components that are

widely connected within the ecosystem such as through maintaining backward compatibility and undertaking

subsequent R&D efforts to resolve challenges for both users and complementors. When it comes to

complement connectedness, we find that the positive effect on the innovation’s commercialization success is

weakened by the generational newness, but only when the connected complements themselves have high

platform and high complement connectedness. As we argue, it is under these conditions that the innovation’s

value creation is more likely to be constrained by performance bottlenecks stemming from connected

complements (Ethiraj, 2007; Adner and Kapoor, 2010). Innovators may not be subject to performance

bottlenecks when they are connected to complements with low platform connectedness, and they may be able

to resolve performance bottlenecks through coordinated R&D efforts and information sharing when they are

connected to complements with few connections.

The study contributes to the emerging theoretical perspective on business ecosystems in which focal

firm’s value creation is shaped by the technological architecture and complementarities in the ecosystem ( e.g.,

Adner and Kapoor, 2010; Adner, 2017, Kapoor, 2018; Jacobides et al., 2018; Baldwin, 2018a). It does so by

considering the different types of connections that complementors can deploy to leverage complementarities

in a platform-based ecosystem, and the architectural changes that are triggered by the platform’s generational

transition. Accordingly, it presents a novel synthesis of how structure and dynamics interact to shape focal

innovation’s value creation in a platform-based ecosystem.

The study also contributes to the nascent literature examining complementors’ performance in a

platform-based ecosystem (Ceccagnoli et al., 2012; Kapoor and Agarwal, 2017; Reitveld and Eggers, 2018).

This literature has highlighted the role of complementary assets, demand-heterogeneity, and ecosystem

complexity in impacting complementors’ performance. In so doing, complements are treated as nodes in a

30

network with all of the nodes connected in the same way within a platform-based ecosystem. Accordingly,

platform-based ecosystems are treated somewhat of an exogenous environment for complementors to

leverage the platform and to compete with other complementors. We offer a theoretical framework in which

we decompose the platform architecture into core module and optional components (Baldwin, 2018b), and

consider that complementors may vary in terms of how they leverage the platform architecture and other

complements. We use this framework to highlight how complementor’s connectedness with platform and

other complements can not only facilitate their commercialization success but can also subject them to

additional adjustment costs and performance bottlenecks. Further, our findings suggest that platform firms

may help mitigate adjustments costs with respect to widely connected platform components, and that

complementors themselves may be able to resolve performance bottlenecks through coordinated R&D

efforts and information sharing when the complement is not widely connected.

More generally, the study contributes to the strategy literature on complementary assets. It has long

been recognized that complementary assets especially those that are specialized to the focal innovation play

an important role in its commercial success (Teece, 1986). However, the bulk of the attention in this literature

has been on complementarities that lie within enterprise-level value chains such as those with respect to

manufacturing, marketing, sales and distribution. The role of complementary technologies that reside in the

external business ecosystem has remained relatively underexplored (Teece, 2006; Kapoor and Furr, 2015). We

show how complementary assets that lie outside the firm’s value chain can affect the success of the

innovation. Moreover, we show that in the context of platform-based ecosystems, even generic

complementary assets (i.e., platform components, complements) can have a significant impact on innovators’

value creation.

The findings of the study are subject to a number of limitations that provide opportunities for future

research. First, they are based on a single platform. While the iPhone platform is one of the most valuable

platforms in the world, the validity of our findings needs to be established through explorations in other

settings. Second, our measure for successful commercialization is based on whether the focal app is ranked

within Top 500 apps in terms of revenue in the iPhone ecosystem. Although this measure is consistent with

31

our theory and is widely accepted as a proxy for successful commercialization, it may not represent superior

economic performance for firms in general. Third, while we have attempted to address the potential

endogeneity concerns with respect to an innovation’s connectedness through a series of robustness checks,

we cannot completely rule out these concerns. Despite these and other limitations, the study sheds light on

the two faces of value creation for firms innovating in platform-based ecosystems -- the opportunities

associated with leveraging complementarities and the challenges associated with managing technological

interdependencies. In so doing, it offers an imagery of platform-based ecosystems in which complementor

firms can innovate around the platform and can create their own architecture of value creation. We hope that

such a perspective can yield valuable insights regarding how complementor firms compete and create value in

platform-based ecosystems.

32

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Figure 1: Different types of connectedness for an innovation in a platform-based ecosystem (I –

Innovation; C – Complement; OC1, OC2 – Optional Platform Components)

Figure 2: Normalized weekly trend of web search in the U.S. on Google for the term “app not working.”a

a Data source: Google Trends; http://www.google.com/trends/; Last accessed 8/23/2017. The additional peaks during March

2011, March 2012, December 2012, January 2014, December 2014 correspond to new Android platform generations (i.e. Honeycomb, Ice cream Sandwich, Jellybean, Kitkat and Lollipop). The timing for the generation releases is determined based on when the new platform generations were made available by the wireless carriers in the US market.

36

Figure 3: Interaction graphs for platform and complement connectedness

Platform Connectedness Complement Connectedness

Platform Connectedness (Niche Components) Complement Connectedness (Niche Complements)

Figure 4: Number of iPhone app queries posted and resolved on stackoverflow.com

37

Table 1: Apps with platform and complement connectedness

Complement connectedness

Platform connectedness Low High

Low

Quadrant I All Apps: 98,105 Top Apps: 826 (0.8%*)

Quadrant III All Apps: 26,467 Top Apps: 608 (2.3%)

High

Quadrant II All Apps: 92,458 Top Apps: 1,683 (1.8%)

Quadrant IV All Apps: 27,004 Top Apps: 1,181 (4.4%)

*The value in the brackets next to top apps is the percentage of apps for that quadrant that made it to the Top 500 list by revenue

Table 2: Descriptive statistics and correlations

No. Variables Mean S.D 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

1 Platform connectedness 0.47 0.50 1.000

2 Complement connectedness 0.22 0.42 0.017 1.000

3 Generational newness -6.90 3.80 -0.003 0.002 1.000

4 Apple promoted app 0.00 0.03 0.010 0.017 -0.001 1.000

5 Multi-homing app 0.13 0.33 -0.011 -0.012 0.000 0.015 1.000

6 Total bug fixes 0.23 0.64 0.059 0.036 0.000 -0.002 0.045 1.000

7 App rating 1.46 2.07 0.010 0.137 0.008 0.047 0.051 0.130 1.000

8 App content rating 1.38 1.08 -0.003 0.026 -0.003 0.010 0.014 0.023 0.040 1.000

9 App file size 0.05 0.23 -0.014 0.025 -0.001 0.124 -0.017 -0.024 0.030 0.040 1.000

10 In-app purchase 0.31 0.46 0.185 0.072 -0.003 0.013 0.010 0.105 0.268 0.055 0.006 1.000

11 3- months updates 0.53 0.89 0.046 0.052 0.021 0.025 0.028 0.127 0.133 0.025 -0.005 0.083 1.000

12 Total updates 2.72 2.61 0.038 0.084 0.008 0.023 0.047 0.463 0.261 0.032 -0.015 0.113 0.317 1.000

13 App price 3.19 19.21 -0.162 -0.032 0.002 0.016 -0.029 -0.092 -0.170 -0.033 0.124 -0.587 -0.045 -0.056 1.000

14 Firm experience 26.96 16.44 0.181 0.052 -0.033 0.020 0.010 0.066 0.003 0.024 0.045 0.147 -0.104 0.094 -0.061 1.000

15 Platform age 52.88 15.68 0.413 0.024 -0.028 0.016 0.021 0.106 -0.083 0.025 0.035 0.263 -0.080 0.094 -0.176 0.613 1.000

16 App in Top 500 free 0.02 0.134 0.005 0.085 0.003 0.028 0.004 0.008 0.149 0.016 0.035 0.079 0.029 0.013 -0.048 0.066 -0.030 1.000

17 Category demand 136.11 186.49 0.153 -0.021 0.016 0.005 0.042 0.023 0.173 0.040 0.015 0.285 -0.020 -0.047 -0.249 0.108 0.183 0.084 1

Correlations greater than 0.01 or smaller than -0.01 are significant at p <0.05, N = 3,797,947

38

Table 3: Cox proportional hazard model estimates for the app achieving successful commercialization Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8

Platform connectedness 0.421*** 0.495*** 0.494*** 0.507***

(0.050) (0.064) (0.064) (0.105)

Complement connectedness 0.149*** 0.157*** 0.155*** 0.138**

(0.044) (0.057) (0.057) (0.069)

Platform connect.*Gen. newness -0.275** -0.282** -0.295

(0.131) (0.131) (0.229)

Complement connect.*Gen. newness -0.028 -0.055 -0.042

(0.126) (0.127) (0.152)

Platform connect. (Niche) 0.095

(0.103)

Platform connect. (Niche)*Gen. newness -0.535**

(0.232)

Complement connect.(Niche) -0.102

(0.118)

Complement connect.(Niche)*Gen. newness 0.682***

(0.250)

Generational Newness 0.215*** 0.195*** 0.211*** 0.375*** 0.223*** 0.400*** 0.206** 0.170

(0.064) (0.063) (0.064) (0.107) (0.082) (0.117) (0.104) (0.194)

Apple promoted app 0.624*** 0.611*** 0.618*** 0.608*** 0.618*** 0.601*** 0.524*** 0.805***

(0.063) (0.063) (0.064) (0.063) (0.063) (0.063) (0.063) (0.101)

Multi-homing app 0.350*** 0.350*** 0.347*** 0.349*** 0.347*** 0.346*** 0.321*** 0.250***

(0.042) (0.043) (0.042) (0.043) (0.042) (0.043) (0.051) (0.068)

Total bug fixes 0.095** 0.091** 0.092** 0.090** 0.092** 0.088* 0.144** 0.015

(0.046) (0.046) (0.046) (0.046) (0.046) (0.046) (0.057) (0.087)

App rating 0.833*** 0.838*** 0.833*** 0.837*** 0.833*** 0.837*** 0.970*** 0.753***

(0.076) (0.076) (0.076) (0.076) (0.076) (0.076) (0.112) (0.144)

App rating2 -0.070*** -0.072*** -0.071*** -0.072*** -0.071*** -0.072*** -0.085*** -0.048***

(0.010) (0.010) (0.010) (0.010) (0.010) (0.010) (0.013) (0.018)

App content rating 0.089*** 0.084*** 0.087*** 0.083*** 0.087*** 0.082*** 0.065*** 0.050*

(0.016) (0.016) (0.016) (0.016) (0.016) (0.016) (0.019) (0.026)

App file size -0.159** -0.154** -0.155** -0.154** -0.155** -0.150** 0.050 -0.242

(0.062) (0.066) (0.062) (0.065) (0.062) (0.065) (0.118) (0.176)

In-app purchase 0.376*** 0.344*** 0.367*** 0.343*** 0.367*** 0.334*** 0.180*** 0.363***

(0.054) (0.054) (0.054) (0.054) (0.054) (0.054) (0.064) (0.096)

3-month updates 0.259*** 0.251*** 0.257*** 0.251*** 0.257*** 0.250*** 0.171*** 0.279***

(0.030) (0.029) (0.030) (0.030) (0.030) (0.030) (0.035) (0.057)

Total updates 0.104*** 0.102*** 0.105*** 0.101*** 0.105*** 0.102*** 0.113*** 0.110**

(0.024) (0.024) (0.025) (0.024) (0.025) (0.024) (0.033) (0.054)

App price 0.201*** 0.208*** 0.202*** 0.208*** 0.202*** 0.209*** 0.200*** 0.041

(0.026) (0.026) (0.026) (0.026) (0.026) (0.026) (0.032) (0.044)

Firm experience -0.058*** -0.052*** -0.059*** -0.051*** -0.059*** -0.053*** -0.044** -0.216

(0.018) (0.018) (0.018) (0.018) (0.018) (0.018) (0.018) (0.259)

Platform age 0.041** 0.031* 0.043** 0.031* 0.043** 0.032* 0.027 0.187

(0.018) (0.018) (0.018) (0.018) (0.018) (0.018) (0.018) (0.260)

Top 500 free app 0.826*** 0.825*** 0.827*** 0.827*** 0.827*** 0.828*** 0.815*** 0.967***

(0.071) (0.071) (0.071) (0.071) (0.071) (0.071) (0.083) (0.125)

Category demand 0.004** 0.004* 0.004** 0.004* 0.004** 0.004** 0.003 0.008**

(0.002) (0.002) (0.002) (0.002) (0.002) (0.002) (0.003) (0.004)

Category demand2 -0.000** -0.000* -0.000** -0.000* -0.000** -0.000** -0.000 -0.000**

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) Category fixed-effects Yes Yes Yes Yes Yes Yes Yes Yes

Firm-level stratification Yes Yes Yes Yes Yes Yes Yes Yes

Total observation 3,797,947 3,797,947 3,797,947 3,797,947 3,797,947 3,797,947 1,804,114 848,621

Total apps 244,034 244,034 244,034 244,034 244,034 244,034 119,462 53,471

Total events 4,213 4,213 4,213 4,213 4,213 4,213 2,802 1,736

Log likelihood -6,930.14 -6,905.17 -6,926.26 -6,903.79 -6,926.24 -6,900.30 -3,965.75 -2,384.56

* p<0.1; ** p<0.05; *** p<0.01

39

Table 4: Post-hoc analyses and robustness checks Model 9 Model 10 Model 11 Model 12 Model 13 Model 14 Model 15

Platform connectedness 0.484*** 0.493*** 0.484*** 0.018*** 0.482*** 0.473*** 0.447***

(0.064) (0.064) (0.064) (0.002) (0.064) (0.068) (0.071)

Platform connect.*Gen. newness -0.279** -0.281** -0.290** 0.000 -0.273** -0.278** -0.328**

(0.131) (0.132) (0.132) (0.001) (0.131) (0.140) (0.148)

Complement connectedness 0.003*** 0.144** 0.188*** 0.216***

(0.001) (0.057) (0.062) (0.066)

Comp. connect. #Gen. newness -0.001 -0.048 -0.067 -0.277*

(0.001) (0.127) (0.138) (0.149)

Comp. connect. (Low plat. connect.) 0.100*** 0.083**

(0.034) (0.035)

Comp. connect. (Low plat. connect.)*Gen. newness 0.165** 0.198***

(0.073) (0.076)

Comp. connect. (High plat. connect.) 0.274*** 0.238***

(0.090) (0.091)

Comp. connect. (High plat. connect.)*Gen. newness -0.249 -0.359**

(0.179) (0.171)

Generational newness 0.286** 0.408*** 0.319*** 0.006*** 0.387*** 0.369*** 0.505***

(0.113) (0.110) (0.115) (0.001) (0.117) (0.121) (0.128)

Apple promoted app 0.601*** 0.601*** 0.596*** 0.233*** 0.604*** 0.602***

(0.063) (0.063) (0.063) (0.028) (0.063) (0.076)

Multi-homing app 0.350*** 0.350*** 0.352*** 0.009*** 0.345*** 0.381*** 0.350***

(0.043) (0.043) (0.043) (0.001) (0.043) (0.047) (0.048)

Total bug fixes 0.091** 0.090** 0.090* 0.011*** 0.077* 0.063 0.099*

(0.046) (0.046) (0.046) (0.001) (0.046) (0.049) (0.052)

App rating 0.838*** 0.836*** 0.837*** -0.008*** 0.835*** 0.800*** 0.901***

(0.076) (0.076) (0.076) (0.001) (0.077) (0.077) (0.088)

App rating2 -0.073*** -0.072*** -0.073*** 0.002*** -0.072*** -0.066*** -0.080***

(0.010) (0.010) (0.010) (0.000) (0.010) (0.010) (0.011)

App content rating 0.083*** 0.082*** 0.082*** 0.002*** 0.082*** 0.084*** 0.083***

(0.017) (0.017) (0.017) (0.000) (0.017) (0.018) (0.019)

App file size -0.145** -0.153** -0.146** -0.005* -0.143** -0.125 -0.192**

(0.064) (0.066) (0.064) (0.003) (0.063) (0.079) (0.083)

In-app purchase 0.339*** 0.340*** 0.337*** 0.017*** 0.324*** 0.362*** 0.260***

(0.054) (0.054) (0.054) (0.001) (0.054) (0.058) (0.060)

3-month updates 0.247*** 0.251*** 0.247*** 0.049*** 0.262*** 0.261*** 0.242***

(0.029) (0.030) (0.029) (0.002) (0.029) (0.032) (0.032)

Total updates 0.102*** 0.101*** 0.102*** -0.007*** 0.079*** 0.103*** 0.110***

(0.024) (0.024) (0.024) (0.000) (0.023) (0.025) (0.027)

App price 0.210*** 0.208*** 0.209*** 0.005*** 0.207*** 0.218*** 0.179***

(0.026) (0.026) (0.026) (0.001) (0.026) (0.028) (0.031)

Firm experience -0.052*** -0.051*** -0.052*** -0.003 -0.052*** -0.053*** -0.050***

(0.018) (0.018) (0.018) (0.002) (0.018) (0.019) (0.018)

Platform age 0.032* 0.031* 0.032* 0.001 0.032* 0.030 0.032*

(0.018) (0.018) (0.018) (0.002) (0.018) (0.019) (0.018)

Top 500 free app 0.827*** 0.828*** 0.828*** 0.120*** 0.831*** 0.850*** 0.866***

(0.071) (0.071) (0.071) (0.014) (0.071) (0.076) (0.080)

Category demand 0.004* 0.004** 0.004* -0.001*** 0.004** 0.004* 0.002

(0.002) (0.002) (0.002) (0.000) (0.002) (0.002) (0.002)

Category demand2 -0.000* -0.000** -0.000* 0.000*** -0.000** -0.000* -0.000

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

Total observation 3,797,947 3,797,947 3,797,947 236,911 2,925,262 3,793,921 3,796,389

Total apps 244,034 244,034 244,034 239,911 243,917 243,497 243,445

Total events 4,213 4,213 4,213 2413 4,213 3,858 3,642

Log likelihood -6,892.36 -6,900.60 -6,889.99 269,429.95 -6,869.35 -6,179.57 -5,650.96

* p<0.1; ** p<0.05; *** p<0.01

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Table 5: First and Second stage instrumental variable analysis (Models labelled “a” refer to First stage)

Model 16a

Model 16

Model 17a

Model 17

Model 18a

Model 18

Model 19a

Model 19

Model 20a

Model 20

Model 21a

Model 21

Platform Component Queries Unresolved

-0.164*** -0.156*** -0.421***

(0.028) (0.034) (0.056)

Platform Component Queries Views

0.128*** 0.192*** 0.108***

(0.009) (0.012) (0.016)

Platform Connectedness 0.594*** 0.418*** 2.255*** 0.015*** 0.020*** 0.011*** 0.023*** 0.009*** 0.027***

(0.044) (0.029) (0.288) (0.002) (0.002) (0.002) (0.002) (0.003) (0.008)

Complement Queries Unresolved

-0.126*** -0.157*** -0.300***

(0.027) (0.036) (0.029)

Complement Queries Views 0.155*** 0.207*** 0.078***

(0.012) (0.015) (0.023)

Complement Connectedness 0.001 0.009*** 0.010*** 0.010*** 0.013*** 0.018** 1.208*** 0.707*** 4.467***

(0.002) (0.001) (0.003) (0.001) (0.004) (0.009) (0.093) (0.054) (1.217)

Apple promoted app 0.203*** 0.344*** 0.205*** 0.367*** 0.167*** 0.044 0.059*** 0.384 0.068*** 0.400*** 0.042*** 0.243

(0.018) (0.019) (0.024) (0.023) (0.027) (0.079) (0.021) (0.030)*** (0.028) (0.031) (0.031) (0.149)

Multi-homing app -0.041*** 0.047*** -0.041*** 0.040*** -0.041*** 0.114*** -0.015*** 0.041*** -0.021*** 0.037*** -0.007* 0.054**

(0.003) (0.003) (0.004) (0.002) (0.004) (0.015) (0.003) (0.003) (0.003) (0.003) (0.004) (0.020)

Total bug fixes 0.015*** 0.000 0.016*** 0.003** 0.014*** -0.022*** -0.002** 0.012*** 0.001 0.008*** -0.007*** 0.039***

(0.001) (0.001) (0.002) (0.001) (0.002) (0.006) (0.001) (0.001) (0.001) (0.001) (0.002) (0.012)

App rating -0.009*** 0.004** -0.008** 0.002 -0.010*** 0.024** 0.020*** -0.025*** 0.020*** -0.014*** 0.021*** -0.093***

(0.002) (0.002) (0.003) (0.002) (0.003) (0.008) (0.002) (0.003) (0.003) (0.003) (0.003) (0.028)

App rating2 0.003*** 0.001* 0.003*** 0.001*** 0.003*** -0.004** 0.000*** 0.002** 0.000 0.002*** 0.000 0.001

(0.000) (0.000) (0.001) (0.000) (0.001) (0.002) (0.000) (0.001) (0.001) (0.000) (0.001) (0.003)

App content rating -0.007*** 0.008*** -0.008*** 0.008*** -0.005*** 0.014*** 0.003*** 0.001 0.007*** 0.000 -0.001 0.010**

(0.001) (0.001) (0.001) (0.001) (0.001) (0.003) (0.001) (0.001) (0.001) (0.001) (0.001) (0.005)

App file size -0.057*** 0.025*** -0.061*** 0.018*** -0.050*** 0.102*** 0.037*** -0.053*** 0.035 -0.032*** 0.038*** -0.180**

(0.007) (0.006) (0.007) (0.003) (0.010) (0.028) (0.006) (0.008) (0.009) (0.008) (0.009) (0.060)

In-app purchase 0.021*** 0.016*** 0.021*** 0.016*** 0.017*** -0.009 0.047*** -0.028*** 0.056 -0.015*** 0.032*** -0.114***

(0.003) (0.002) (0.004) (0.002) (0.004) (0.011) (0.002) (0.005) (0.003) (0.004) (0.004) (0.043)

3-month updates 0.019*** 0.081*** 0.013*** 0.064*** 0.033*** 0.057*** 0.000 0.091*** -0.006 0.073*** 0.013*** 0.075***

(0.002) (0.002) (0.002) (0.002) (0.003) (0.012) (0.002) (0.002) (0.002) (0.002) (0.003) (0.022)

Total updates -0.002*** -0.010*** -0.001* -0.010*** -0.004*** -0.002 0.006*** -0.019*** 0.005 -0.013*** 0.008*** -0.048***

(0.000) (0.000) (0.001) (0.000) (0.001) (0.002) (0.000) (0.001) (0.001) (0.000) (0.001) (0.010)

App price -0.029*** 0.029*** -0.029*** 0.022*** -0.030*** 0.080*** 0.007*** 0.004** 0.006 0.006*** 0.007*** -0.017

(0.001) (0.002) (0.002) (0.001) (0.002) (0.010) (0.001) (0.002) (0.002) (0.001) (0.002) (0.012)

Firm experience -0.002*** 0.000** -0.002*** -0.000 -0.002*** 0.004*** 0.001*** -0.002*** 0.001 -0.001*** 0.001*** -0.007***

(0.000) (0.000) (0.000) (0.000) (0.000) (0.001) (0.000) (0.000) (0.000) (0.000) (0.000) (0.002)

Platform age 0.014*** -0.009*** 0.013*** -0.007*** 0.014*** -0.034*** -0.001*** -0.001*** -0.001 -0.001*** -0.001*** -0.001

(0.000) (0.001) (0.000) (0.000) (0.000) (0.004) (0.000) (0.000) (0.000) (0.000) (0.000) (0.001)

Top 500 free app 0.087*** 0.101*** 0.067*** 0.113*** 0.110*** -0.095** 0.240*** -0.140*** 0.259 -0.044*** 0.213*** -0.801***

(0.007) (0.007) (0.009) (0.007) (0.010) (0.040) (0.007) (0.025) (0.010) (0.017) (0.011) (0.268)

Category demand -0.001** -0.001*** -0.000 -0.002*** -0.000* -0.000 -0.002*** 0.001*** -0.001 -0.001*** -0.003*** 0.010***

(0.000) (0.000) (0.000) (0.000) (0.000) (0.001) (0.000) (0.000) (0.000) (0.000) (0.000) (0.003)

Category demand2 0.000*** 0.000*** 0.000** 0.000*** 0.000 0.000 0.000*** -0.000*** 0.000*** 0.000*** 0.000*** -0.000***

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

_cons -0.216*** 0.310*** -0.234*** 0.256*** 0.071*** 0.948*** -0.017 -0.006*** -0.024 0.031*** 0.056 -0.215**

(0.032) (0.018) (0.040) (0.013) (0.062) (0.116) (0.030) (0.010) (0.041) (0.008) (0.051) (0.089)

Total observation 237521 237521 136931 136931 100,590 100,590 237,521 237,521 136,931 136,931 100,590 100,590

Log likelihood -31115.3 -20867.1 -139513.9 -167344.2 -29933.3 -198926.3 Cragg-Donald Test Statistics 110.36 150.24 34.06 91.09 106.77 84.38 Anderson LM statistic 220.56 299.91 68.10 182.01 213.23 168.53 Sargan statistic 0.15 2.23 0.85 1.17 0.53 1.58