ArticlePDF Available

Protecting their digital assets: The use of formal & informal appropriability strategies by App developers

February 2019
Research Policy 48(8)

DOI:10.1016/j.respol.2019.01.012

Authors:

Milan Miric

University of Southern California

Kevin J. Boudreau

Lars Bo Jeppesen

Copenhagen Business School

Innovators and entrepreneurs developing products and competing “on top” of digital platforms face different conditions than do those in more traditional industries. In this paper, we explore how this affects appropriability strategies in novel data on mobile app developers’ appropriability strategies. We find that the many smallest developers in the “long tail”— the vast majority of all developers – do in fact take actions to capture value and to protect their intellectual property, but do so only through informal mechanisms. By contrast, larger developers exploit a combination of both informal mechanisms and formal intellectual property rights, using copyright, patents, and trademarks. Several strategies particular to digital platforms are also documented. We link this pattern of different strategies pursued by different competitor types to the structural features of digital competition.

Comparison of Sample and Population across observed variables.

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Descriptive statistics.

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Comparison of Sample and Population Means

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Marginal effects for incidence of formal and informal strategies.

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Marginal effects for multinomial logit regressions.

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Research Policy
journal homepage: www.elsevier.com/locate/respol
Protecting their digital assets: The use of formal & informal appropriability
strategies by App developers
☆
Milan Miric
a,⁎
, Kevin J. Boudreau
b
, Lars Bo Jeppesen
c
a
University of Southern California, Los Angeles, CA, United States
b
Northeastern DMSB and National Bureau of Economic Research, Boston, MA, United States
c
Copenhagen Business School, Frederiksberg, Denmark
ARTICLE INFO
Keywords:
Appropriability mechanisms
Intellectual property
Digitization
Platforms
Entrepreneurship
ABSTRACT
Innovators and entrepreneurs developing products and competing “on top” of digital platforms face diﬀerent
conditions than do those in more traditional industries. In this paper, we explore how this aﬀects appropriability
strategies in novel data on mobile app developers’ appropriability strategies. We ﬁnd that the many smallest
developers in the “long tail”— the vast majority of all developers – do in fact take actions to capture value and to
protect their intellectual property, but do so only through informal mechanisms. By contrast, larger developers
exploit a combination of both informal mechanisms and formal intellectual property rights, using copyright,
patents, and trademarks. Several strategies particular to digital platforms are also documented. We link this
pattern of diﬀerent strategies pursued by diﬀerent competitor types to the structural features of digital com-
petition.
1. Introduction
A central question in the study of innovation is appropriability, that
is, how innovators are able to protect and proﬁt from their innovations
so that they have an incentive to undertake innovation in the ﬁrst place
(Arrow, 1962; Levin et al., 1987; Laursen and Salter, 2014; Teece,
1986). Over the past decade, the emergence of digital platforms has
given rise to a new mode of production (Gawer and Cusumano, 2002;
Tiwana, 2004; Parker et al., 2016), allowing smaller ﬁrms to create
complementary innovations on top of platforms to bring them to
market. While the importance of third-party developers appropriating
value has been established (Huang et al., 2012; Parker and Alstyne,
2017; Gawer and Henderson, 2007), little of the research on appro-
priability mechanisms and strategies (Cohen et al., 2000; Levin et al.,
1987; Hall and Ziedonis, 2001) has yet examined touched on the stra-
tegies of these innovators working on digital platforms. In this paper,
we seek to provide more insights into this under-studied question, and
particularly how the smallest developers on digital platforms, protect
and appropriate value from their innovations.
The value of digital platforms depends on the availability of com-
plements (Parker and Alstyne, 2005; Rochet and Tirole, 2006). For
instance, the value of computer operating systems depends on the
availability of complementary third-party software. As a result, plat-
form owners have tried to increase ease of access and lower the costs of
developing for their platforms, to allow even small ﬁrms or individuals
to do so, and for a “long tail”of developers to join, including hobbyists
and amateurs. While these small-scale innovations may individually
account for a tiny share of revenue, collectively the long tail can
sometimes represent a considerable share of economic activity. Take,
for example, that most developers on both the Unity asset store and the
Salesforce app market are small 3rd-party developers generating bil-
lions of dollars in revenue. For many of these innovators, the ability to
appropriate value and proﬁt may be important to motivating them to
join the platform in the ﬁrst place (Huang et al., 2012; Parker and
Alstyne, 2017; Gawer and Henderson, 2007). Little is yet known about
how such small developers protect their innovations, including whether
https://doi.org/10.1016/j.respol.2019.01.012
Received 9 December 2017; Received in revised form 16 January 2019; Accepted 16 January 2019
☆
We appreciate helpful comments and reactions from seminar participants at Copenhagen Business School, INSEAD, London Business School, National University
of Singapore, Singapore Management University, Robert Austin, Shane Greenstein, H.C. Kongsted, Toke Reichstein, Keld Laursen, Thomas Dahl Jensen, Alfonso
Gambardella, Claudio Panico, Phanish Puranam, Juan Santalo, Sampsa Samila, Thomas Rønde, Chris Tucci, Feng Zhu. We are grateful to Richard Nelson and Wes
Cohen for providing us with the original Carnegie Mellon Survey. We are grateful to Copenhagen Business School, London Business School, Bocconi University,
Harvard University, Northeastern University and the University of Southern California for ﬁnancial. London Business School provided IT, server, and operational
support. All errors are our own.
⁎
Corresponding author.
E-mail addresses: mmiric@marshall.usc.edu (M. Miric), kevin@northeastern.edu (K.J. Boudreau), lbj.si@cbs.dk (L.B. Jeppesen).
Research Policy 48 (2019) 103738
Available online 10 February 2019
0048-7333/ © 2019 Elsevier B.V. All rights reserved.
T

they rely on intellectual property rights (IPR) to protect their innova-

tions the same way that larger ﬁrms typically choose to do. For in-

stance, there has been little exploration of how developers might pro-

tect their innovation in contexts where the cost of getting a patent,

itself, might often exceed the expected revenues from selling the pro-

duct, or where it is uncertain whether property rights are practically

enforceable (Graham et al., 2009).

The question of appropriability is critical to the study of innovation

and innovation policy; it helps policymakers to create conditions to

incentivize innovation (Arrow, 1962). In this context, the question of

how third-party developers appropriate value is perhaps even more

important, because the success of the platform depends on the ability of

the platform regulator to attract complementers to create third-party

products (Boudreau, 2010; Gawer and Cusumano, 2014; Parker and

Alstyne, 2005). As Gawer and Cusumano (2014) noted, “platform lea-

ders tend to successfully stimulate a certain kind of externally devel-

oped innovation (that would complement the platform).”Earlier studies

have provided insights into how ﬁrms appropriate value from their

innovations (Levin et al., 1987; Cohen et al., 2000; Laursen et al., 2014;

Hall and Ziedonis, 2001; Graham et al., 2009; Hall and Sena, 2017;

Arundel, 2001; Huang et al., 2012) and the broad strategies that ﬁrms

(can) use to protect their assets resulting from their innovation (Teece,

1986). Out of this interest has emerged a body of empirical studies

documenting appropriability strategies by large ﬁrms, predominantly in

manufacturing industries (Cohen et al., 2000; Levin et al., 1987). More

recently, studies have looked at forms of appropriability in traditional

software development (Cockburn et al., 2011; Bessen and Hunt, 2007;

Hall and Ziedonis, 2010), but these still generally focuses on larger

ﬁrms. The literature regarding appropriability by smaller innovators

has focused often on software developers that may freely reveal their

innovations to derive indirect monetary beneﬁts (Harhoﬀet al., 2003).

However, many small developers may want to proﬁt from their in-

novations, particularly on digital platforms. None of these earlier stu-

dies have considered how smaller innovators that seek to proﬁt from

their innovations by selling or commercializing their products will

choose to appropriate value from their innovations.

To make progress in understanding how innovators protect their

innovations on digital platforms, we study appropriability strategies of

innovators on the Apple App Store—the largest and economically most

important case of competing innovators working on a digital platforms,

today. Beyond its sheer economic magnitude, the Apple App Store

presents a useful case to study in that its products cover a wide range of

particular digital product types (e.g., productivity, gaming, education,

and ﬁnance apps, and many more). Studying the Apple App Store also

permits us to study a wide cross-section of types of innovators, in-

cluding both large and small developers, including independent de-

velopers, micro-enterprises, and part-time developers. This is also a

context to observe strategies in an increasingly typical cases where the

platform owner itself, Apple, takes actions to support enforceability of

US patent, copyright, and trademark laws.

In this study, we begin by framing the use of appropriability stra-

tegies along the lines as has been identiﬁed in earlier studies (i.e., pa-

tents, copyrights, trademarks, lead time, and rapid innovation). We are

guided, in particular, by the framework established in Yale and later

Carnegie Mellon surveys (Cohen et al., 2000; Levin et al., 1987) for our

data collection on the use of formal and informal mechanisms. Apart

from the use of appropriability mechanisms, our global app developer

survey also collected a range of information on developer attributes,

strategies, perceptions of competition, and appropriability concerns.

We match our survey data to an observational data set covering the full

population of app developers, in which we observe all titles and ver-

sions developed by all developers, across all distinct categories or

genres of apps (e.g., games, travel, references). We use the observa-

tional data to reweigh our survey responses to ensure representative-

ness of the survey sample and an accurate estimation of population-

level patterns. (Fortunately, the survey responses are themselves highly

representative of the population, and reweighing to reﬂect the popu-

lation does not substantially alter results.)

We ﬁnd that appropriability strategies used by developers on the

Apple App Store cluster onto several combinations of approaches. Of

those ﬁrms that attempt to protect their innovations (70.59% of ﬁrms),

the majority use either only informal strategies (36.76%) or a combi-

nation of formal and informal strategies (24.12%). Only a small pro-

portion of all ﬁrms (9.71%) utilize only formal protections. A con-

siderable share of developers do not report using any form of

appropriability strategy (29.5%),

To identify key correlates with these clusters of appropriability

strategies, we use a variable selection model commonly used in ma-

chine learning applications that has been adapted to econometric ap-

plications (Double LASSO by Belloni et al., 2013, 2014). A main ﬁnding

is that a ﬁrst-order distinction exists between strategies used by large

and small developers. Our analysis shows that informal IPR protections

are used by very small ﬁrms and part-time developers, while larger

ﬁrms use formal IPR protections alongside informal strategies. The

ﬁndings are robust to including in the analysis controls for developer

motivations, revenue models, sources of innovation, and products

characteristics.

Our study most directly contributes to the extensive literature on

appropriability strategies (Teece, 1986; Cohen et al., 2000; Levin et al.,

1987), where here we take an early step toward understanding these

issues in the increasingly important context of digital platforms, where

structural conditions fundamentally diﬀer (Greenstein et al., 2013;

Goldfarb et al., 2015; Yoo et al., 2010; Nambisan et al., 2017) from

those in previously-studied manufacturing and bricks-and-mortar in-

dustries, reviewed herein. For instance, this includes a relative ease of

developing innovations “on top” of platforms, and relative ease of

copying or replicating digital innovations.

This paper also contributes to research on platforms that has con-

sidered how third-party developers can protect themselves from having

the value of their innovations expropriated. Whereas the past literature

has tended to study the possibility of the platform owner engage in

proﬁt-squeezing or vertical integration into a given complement

(Gawer and Henderson, 2007; Parker and Alstyne, 2017; Huang et al.,

2012; Foerderer et al., 2018; Zhu and Liu, 2018; Boudreau, 2010),

whereas here we focus on the threat of peer developers.

2. Literature review

As a backdrop for our study of appropriability on digital platforms,

we provide an overview of appropriability as it relates to the literature

on the economics of innovation.

The patent's role as a legal/formal protection mechanism has been

prominent in studies of appropriability. The purpose of patent rights is

to provide innovators with exclusive rights to use an innovation in

exchange for disclosing the inner workings of the technology. Existing

studies have found that innovators report using a wide range of stra-

tegies in addition to or instead of patents to limit competition and ap-

propriate value from their innovations, including other formal property

rights (e.g., copyrights and trademarks) and informal strategies, such as

ﬁrst-mover advantages and design complexity (Levin et al., 1987;

Cohen et al., 2000). In some cases, ﬁrms may rely on proprietary

complementary assets in manufacturing, distribution, and marketing,

sales, and service (Teece, 1986). At times, innovators choose to forgo

patent protection to avoid disclosing the required inner workings of

their innovation (Png, 2017; Arundel, 2001). In other cases, ﬁrms use a

combination of patents to protect certain elements of their innovation

and secrecy to protect other elements (Levin et al., 1987). The decision

of whether ﬁrms will use patents, combined with or instead of some

other form of protection, depends largely on the eﬀectiveness of the IPR

regime for that particular innovation, the characteristics of the focal

ﬁrm, and the cost of deploying the mechanism (Teece, 1986; Graham

et al., 2009).

M. Miric, et al. Research Policy 48 (2019) 103738

2.1. Eﬀectiveness of IPR and appropriability strategies

An important feature of whether ﬁrms protect through patents or

alternative strategies depends largely on the eﬀectiveness of patent

protection (Teece, 1986). Prior studies have attempted to determine

this eﬀectiveness in a variety of ways. Several papers have explored

how the introduction of patents or trade secrets protection inﬂuences

innovative activity (Moser, 2005, 2013; Png, 2017). These studies have

found that patent protection leads to more innovation, suggesting that

patents are eﬀective at protecting innovation. Similar approaches have

been used to test the eﬀectiveness of copyrights and trademarks (Moser,

2013; Png, 2017). Other studies have investigated patent ﬁling and

renewal decisions and used this to infer whether patent rights are an

eﬀective means of appropriating value and what this means for in-

novation (Hall and Ziedonis, 2001; Bessen and Hunt, 2007; Lanjouw

and Schankerman, 2001). Several studies have focused on the re-

lationship between holding patents and ﬁrm performance in a compe-

titive market. These studies ﬁnd that patents are an eﬀective means of

appropriating value from innovation (Cockburn and MacGarvie, 2011;

Cockburn and Griliches, 1987). Some studies have attempted to quan-

tify the “patent premium” or boost to revenues that results from in-

novators using patents or copyrights to protect their innovation (Arora

et al., 2008). Other studies have surveyed companies, asking them

whether they use formal IPR and whether they perceive IPR to be an

eﬀective means of appropriating value (Levin et al., 1987; Cohen et al.,

2000).

The consensus from this literature is that patents can provide an

eﬀective means of appropriating value from an innovation. This holds

true for software industries, where patents may not necessarily be as

widely used as in other settings but have still been found to be eﬀective

at protecting innovations and in strategic maneuvering (Bessen and

Hunt, 2007). Interestingly, the incentives to use patents have been

found to be lower for small ﬁrms (See Graham et al., 2009). It is also

clear that patents are not the only means through which ﬁrms are able

to appropriate returns. In many cases, ﬁrms use a variety of other

strategies in addition to patents to protect their innovation, and they

often do so through a combination of formal and informal means

(Cohen et al., 2000). For instance, patents are often a natural comple-

ment to early entry or lead-time advantages (Graham et al., 2009).

Existing studies have not empirically explored how innovators might

combine diﬀerent appropriability strategies or how those combinations

vary by industry or ﬁrm characteristics (James et al., 2013).

Innovators may combine formal and informal appropriability stra-

tegies. For instance, ﬁrms that have lead-time advantages from entering

early may want to protect those advantages by patenting their tech-

nology. Alternatively, a ﬁrm may patent part of its technology while

protecting another part through informal means (Levin et al., 1987) or

may delay it's entry on account of the time it takes to acquire a patent

(Gans and Stern, 2017). While there is an expectation that formal ap-

propriability is often complemented with informal protections, existing

studies have not extensively investigated how these diﬀerent strategies

are combined. Several studies have documented the importance of in-

formal strategies such as lead time and rapid innovation (versioning), in

allowing ﬁrms to appropriate value. To our knowledge, there are no

extant studies that focus on the combination of diﬀerent strategies in

digital markets, and in particular how the use of these combinations

varies across ﬁrms of diﬀerent sizes.

2.2. The nature of digital goods and protection strategies

The discussion above outlines the broader literature within the

settings where appropriability strategies have traditionally been stu-

died, such as manufacturing. However, digitization and the emergence

of digital platforms has shifted a considerable share of innovation to

these settings. Given that formal IPR was not designed to protect digital

technologies, which are easily copied, reproduced, and imitated

(Shapiro and Varian, 1998; Greenstein et al., 2013; Goldfarb et al.,

2015), we expect that appropriability strategies diﬀer considerably in

digital industries and on digital platforms.

The shift to digitalization is largely driven by increasingly low

computing and development costs. For instance, small mobile devices,

such as smartphones and other smart devices (i.e., wearable watches

and smart thermostats), have computing power comparable to com-

puters from just ﬁfteen years ago. This growth in the availability of

computing power, in a range of diﬀerent settings, has enabled a greater

scope for innovation than what has been previously possible. For in-

stance, smartphones and smart thermostats are now capable of per-

forming new and complex functions.

The scope for developing digital innovations has been accelerated

by the emergence of digital platforms, such as the Apple App store and

Android market, which provide an infrastructure for third-party de-

velopers to create innovations for these smart devices. These platforms

provide access for third-party complementors to develop software apps,

as well as supporting distribution, marketing and sales functionality.

An important feature of these platforms is that they greatly reduce

the costs of developing digital innovations. This lowers the cost of

creating a “minimum viable product”and, in turn increases the variety

of diﬀerent innovators that may want to enter onto the platform. Digital

platforms allow developers to create and distribute products even if

they are expecting to generate low, or even in extreme cases zero, direct

monetary rewards. This can create a seemingly unending variety or

long tail of products (Brynjolfsson et al., 2010, 2011). However, low-

ering the barrier to entry can greatly increase the pool of potential

competitors and so create considerable scope for especially intensive

competition, copying, and “business stealing” among digital innovators

(Sundararajan, 2004; Tunca and Wu, 2013).

The low costs of innovating on digital platforms are further reduced

by the knowledge, data, and content that is made readily available

through digital technologies. For instance, through APIs individuals

have access to a “ﬁrehose” of data that at low cost can be used to create

complex and data-rich innovations. Readily available programing tools

and libraries can be used to create complex technologies with relative

ease. This creates scope for rapid innovation and experimentation. At

the same time, the low costs of innovating on digital platforms imply

lower replication costs, which allow competitors to imitate and re-

plicate existing innovations (Shapiro and Varian, 1998). As a result, it

becomes important to understand how developers can protect and ap-

propriate value from digital innovations when these are so easily copied

and replicated (Greenstein et al., 2013; Goldfarb et al., 2015).

2.3. Importance of IP for digital platforms

A deﬁning characteristic of platforms and platform companies is

that the value of the platform increases with the availability of com-

plementary products (Rochet and Tirole, 2006; Parker and Alstyne,

2005). For instance, the value of social media sites grows with the

number of users and the value of a smartphone operating system grows

with the number of complementary apps. A central, but largely un-

explored, element for a platform to attract such third parties to develop

complementary products is for the platform to create conditions for

complementors to be able to proﬁt, or appropriate value, from their

innovations, providing incentives to develop complementary products

(Boudreau, 2012; Boudreau and Hagiu, 2008). In the broader economy,

the ability for innovators to appropriate value is facilitated through

property rights such as patents, copyrights, and trademarks. However,

there may be informal strategies in use, as has been discussed. On di-

gital platforms, the use of such property rights may be controlled and

regulated, or at least shaped, by the platform owner (Parker and

Alstyne, 2017). For instance, many platforms choose to deﬁne the legal

rights that govern their marketplace (Boudreau and Hagiu, 2008). Si-

milarly, platforms may choose to deﬁne the conditions in which a third

party has exclusive rights to enter a marketplace.

M. Miric, et al. Research Policy 48 (2019) 103738

Existing studies of appropriability on platforms have so far con-

sidered the competitive threats of the platform itself in appropriating

the value from a third-party complementor (Gawer and Henderson,

2007; Huang et al., 2012; Parker and Alstyne, 2017) and how com-

plementors may respond to such an action (Foerderer et al., 2018; Wen

and Zhu, 2017). However, a perhaps greater concern facing com-

plementers on a digital platform is copying or imitation of successful

innovators that may occur as competitors enter the market. While de-

velopers on a digital platform may utilize the same types of appro-

priability strategies that are commonly used in other settings (Cohen

et al., 2000), the degree to which diﬀerent strategies are used may

diﬀer considerably, particularly because of the large number of small

ﬁrms that are present in the digital platforms context.

2.4. Appropriability by small ﬁrms on digital platforms

The literature on appropriability has identiﬁed a menu of diﬀerent

appropriability strategies that are thought to be the primary means

through which innovators may limit competition and appropriate value

from their innovations. We focus on the strategies identiﬁed by earlier

studies (Teece, 1986; Cohen et al., 2000; Levin et al., 1987), namely,

formal protections, such as patents, registered copyright, and trade-

marks, and informal protections, such as rapid innovation and early-

entry advantages. These strategies are often characterized as formal and

informal; formal protections often rely on some form of legal in-

tellectual property right, while informal protections are a form of

strategic maneuvering relative to competitors (Teece, 1986).

An important determinant of which strategies are used is the re-

lative cost of implementing these strategies. For instance, formal pro-

tection strategies, such as patents, registered copyrights, and trade-

marks, are costly to acquire and enforce. Alternatively, informal

strategies, such as lead time and rapid innovation, are less costly to

implement, particularly in a digital setting, where the costs of in-

novating are low. Therefore, we expect that on average, formal IPR,

such as patents, copyrights, and trademarks might be observed to be

less used than informal protections such as rapid innovation or early-

entry advantages. We also expect that the use of formal and informal

protections varies considerably across ﬁrms, in particular between ﬁrms

that are large and have considerable resources to acquire and enforce

IPR and smaller ﬁrms that have more limited means to acquire and

enforce IP (Graham et al., 2009; Leiponen and Byma, 2009).

Here we ask how the use of diﬀerent appropriability strategies may

correlate with diﬀerent ﬁrm characteristics, particularly ﬁrm size.

Existing studies have found that ﬁrm size is an important determinant

of how ﬁrms can protect their innovations (Leiponen and Byma, 2009;

Graham et al., 2009), largely because ﬁrm size is indicative of ﬁrm

assets. For instance, smaller ﬁrms have less access to ﬁnancial, product

development, and marketing assets. This may be particularly true for

digital marketplaces, where competition is intense and expected returns

from innovation are low. Weighed against the relatively high cost of

acquiring and enforcing IPR, this suggests that patents, copyrights, and

trademarks may not be a suitable means for these smaller developers to

protect and appropriate value from their innovations. A plausible

consequence would be that these smaller developers who are unlikely

to protect their innovation through formal IP (patents, copyrights &

trademarks), will choose to do so only through informal means –if any

at all.

While we would expect that formal protections (or IPR) are more

commonly used by larger companies, these companies may also make

use of informal strategies. For instance, ﬁrms that pursue patents or

copyrights may naturally also enjoy lead-time advantages resulting

from early entry. As a result, appropriability strategies may be used in

combination. On the basis of the above reasoning, we therefore broadly

expect that small-scale developers will be less likely to rely on formal

intellectual property rights than larger developers but that both may

rely on informal appropriability strategies.

In terms of the overall use of diﬀerent appropriability strategies at

the level of the platform as a whole, where we expect that the over-

whelming majority of developers are small ﬁrms (many even with only

one or two employees), our earlier arguments would imply that the

overall use of formal protection strategies may be considerably lower

than what has been found in earlier studies (e.g., Cohen et al., 2000).

Additionally, the predominant protection strategies in this context are

likely to be informal. However, to what extent informal strategies are

used and the extent to which diﬀerent appropriability strategies are

used in combination are empirical questions.

3. Empirical context

In the analysis that follows, we will use data on appropriability

strategies from third-party developers creating apps for the Apple iOS

platform. The Apple iOS platform is a software operating system that

runs on Apple devices (iPhone, iPad, iPod). Apple only allows third-

party software to be installed on these devices through its oﬃcial

storefront, the Apple App Store. Since the App Store was created in

2008, more than 2.4 million apps have launched on the storefront, and

developers have generated more than US$130 Billion in revenue

through either the sales of their apps or in-app purchases and sub-

scription sales. In addition to the sheer volume of sales on the Apple

App Store, this storefront includes apps by companies from a wide

range of the broader economy. For instance, there are many companies

that do not consider themselves to be software developers but rather

social networks (Twitter, Instagram, etc.), which release their software

on the Apple App Store and for whom the iOS platform is an important

outlet for their products. There are of course many pure developers,

which create only software apps such as games and productivity tools.

Several features make the Apple App Store an important context for

studying the appropriability strategies of digital innovators. First, it

hosts a wide range of diﬀerent categories, from productivity to gaming

and internet services. As a result, the Apple App Store is representative

of digital innovators in the digital economy and an important context

for studying digital innovation (Bresnahan et al., 2015; Yin et al.,

2014). Second, this storefront allows developers to use US patent,

the case of international developers, the rules of the marketplace re-

quire that developers comply with US property right laws. Therefore,

the App Store has relatively consistent legal conditions for all devel-

opers to acquire legal protection. Third, it is possible to observe the full

population of developers and individual product titles for the entire app

store. This makes it possible to account for biased sampling and ensure

that the analysis is reﬂective of the overall sample.

3.1. Data collection

The challenge in studying appropriability strategies is that, with the

exception of patents or trademarks, it is diﬃcult to directly observe the

appropriability strategies that are being used by ﬁrms in an industry. As

a result, the primary approach for measuring the appropriability

strategy of developers in marketplaces has been through surveys (Levin

et al., 1987; Cohen et al., 2000; Graham et al., 2009). We ﬁrst collected

information about the entire population of developers and apps on the

Apple App Store between 2009 and 2014. This allowed us to obtain

information about more than 800,000 app developers (approximately

30,000 of which provided email contact information). This observa-

tional information not only provided contact information but also al-

lowed us to account for population-level diﬀerences in the use of ap-

propriability strategies (as described at the end of this section).

A typical limitation of surveys is partial, non-random sampling. A

ﬁrst, if imperfect, step we take toward reducing this problem is simply

sampling as broadly as possible. We contacted roughly thirty thousand

app developers via email. This subset was simply according to those app

developers who listed an email address on the Apple App Store website.

M. Miric, et al. Research Policy 48 (2019) 103738

Of these, we received completed surveys from 809 developer ﬁrms. The

group represents 9,152 individual apps across 24 app categories. This is

a relatively high number of respondents in comparison to many survey

studies in management and social science and those studying appro-

priability strategies (Levin et al., 1987; Cohen et al., 2000; Graham

et al., 2009). However, the arguably larger and more important point is

that this sample size reﬂects only a small proportion (Approx. 3%) of

those invited to participate and a even smaller proportion of the overall

population of app developers (0.6%), similar to other survey-based

research of appropriability strategies used across the economy or stu-

dies of online activity within a larger population. Therefore, our ap-

proach must recognize that there is abundant scope for nonrandom

sampling in the initial harvesting of email addresses in relation to the

population as well as in the choice to respond to the survey. In Section

4.1, we describe how we account for this bias in sampling.

3.2. Variable construction and deﬁnitions

We consider the appropriability strategies based on the mechanisms

that were used in the highly inﬂuential papers (Cohen et al., 2000;

Levin et al., 1987; Graham et al., 2009) that have established the lit-

erature on “how ﬁrms protect their intellectual assets.” Doing so allows

us to compare our results to those of earlier studies. From the survey

responses, we observe whether a ﬁrm used patents, copyrights, trade-

marks, early entry, or versioning. Versioning is the process of revising

and re-releasing software apps and can be thought of as the digital

analog to rapid innovation. We use these terms interchangeably.

We deﬁne the key covariates to be used in the analysis as follows.

These are referred to as BASIC CONTROLS in later sections of the paper.

We deﬁne Market Tenure as the number of months that a developer has

been in the marketplace since its initial product launch. We deﬁne

Promotion Channel as an indicator for whether a ﬁrm uses its app on the

Apple App Store as a promotion channel for an alternative business,

such as an airline app or mobile banking app. We deﬁne US Based as an

indicator for whether the ﬁrm is based primarily in the United States.

We deﬁne Hobbyist Motivation as an indicator for whether the developer

reported that they develop software as a hobby rather than as their

profession. This also serves as a proxy for whether a developer is

looking to capture monetary proﬁts. We deﬁne BM: Licensing and BM:

App Revenue as indicators for whether a developer has reported that

they generate revenue from licensing the technology in their app to

others and an indicator for whether they attempt to generate revenue

from selling or monetizing their application, respectively. These vari-

ables capture whether developers have an intention to proﬁt from their

innovations.

We also identify key controls using the Double LASSO procedure

inspired by Belloni et al. (2013, 2014) as described in later sections. The

key control variables as identiﬁed by that procedure are deﬁned as

follows. We deﬁne Source of Innovation - Users as an indicator for

whether the developer reported using “users” as a source of inspiration

for their innovative ideas. We deﬁne Diﬀ: Network Eﬀects and Diﬀ:

Special Tech as indicators for whether the developer reported “at-

tempting to foster network eﬀects” or “making use of special technol-

ogies” as strategies to diﬀerentiate their products from competitors,

respectively.

Descriptive statistics for these variables are shown in Table 1.

4. Empirical analysis and results

4.1. Estimating the overall use of appropriability strategies

A key contribution of earlier papers (Levin et al., 1987; Cohen et al.,

2000; Graham et al., 2009; Hall and Sena, 2017) has been documenting

the incidence at which diﬀerent types of intellectual property rights are

used. We begin by estimating the incidence of IPR strategies within the

Apple App Store.

Correcting for Bias in Sampling and Sample Construction. We

have information about the 1.2 million software titles available on the

Apple App Store as of 2013 and information about appropriability

strategies from those developers that responded to our survey. There is,

of course, a concern that sample responses are nonrandom and not

representative of the overall population. In many contexts where re-

searchers cannot observe the entire population of potential ﬁrms, re-

searchers ensure that their survey is randomly allocated to all ﬁrms and

hope to receive a suﬃciently large number of responses to be able to

generalize their results. In settings such as this, where, by design, it is

diﬃcult to approach all respondents in such a way that they are likely

to respond, but where there is data on the broader population (e.g.,

Manski and Lerman, 1977), it is possible to use sampling weights to

correct for the fact that the sample may be nonrandom, therefore

biasing our resulting analysis (Wooldridge, 2010). We use sampling

weights calculated based on the category (genre), price of the apps,

average rating of the products, number of products the developer has

released, and the market tenure (the number of days since ﬁrst release)

of the developer. The distribution of each of these variables over the

sample and population are shown in Fig. 1.

The key requirement for our reweighing strategy is that our sample

covers the same types of ﬁrms as the population, even though there may

be diﬀerent densities.

The graphs in Fig. 1 demonstrate that that our samples exhibits

coverage in terms of product and developer characteristics comparable

to that of the overall population. For instance, in terms of average

product rating, our sample covers product with ratings ranging from

one to ﬁve. Nevertheless, it is clear that the distributions of the sample

and population are not identical. For instance, the distribution of the

Number of Applications suggests that we are over-sampling developers

that release multiple titles and possibly larger companies. Similarly, our

distribution for market tenure (N Days in the Market) has a higher share

of younger ﬁrms than the overall population. However, because we

have information about how our sample corresponds to the distribution

of the overall population, we are able to weight our sampling (as de-

scribed above) to correct for our nonrandom sample and to ensure that

our analysis is generalizable to the broader population. Reweighing

allows us to generate bias-corrected population-level estimates of the

incidence of diﬀerent appropriability mechanisms.

Overall Use of Appropriability Strategies. In Fig. 2, we present

the raw results of our survey, alongside the reweighed population-level

estimates. The sample proportions and population estimates are com-

parable, suggesting that sampling bias does not greatly aﬀect our re-

sults. The population-level estimates in Fig. 2 show that patents and

copyrights are used infrequently (13% and 23%, respectively) com-

pared to other strategies, such as trademarks, versioning, and early-

entry (28%, 43%, and 48%, respectively). The use of patents is con-

siderably lower than the rates found by earlier studies on manu-

facturing settings (e.g., Cohen et al. 2000 ﬁnd that 34.8% of ﬁrms use

patents to protect their product innovations). Although, earlier studies

have not reported the exact incidence of copyrights and trademarks, in

earlier studies, the aggregate report for the use of other (non-patent)

legal protections (20.7% in Cohen et al., 2000) is slightly lower but

comparable to our ﬁndings in the digital platform context (31%). Per-

haps most important is that most ﬁrms (67%) do not use legal protec-

tions for their innovations. Instead, ﬁrms appear to rely more heavily

on informal protection mechanisms, such as early entry and rapid

The use of patents, copyrights and trademarks follows use in what was

surveyed in earlier studies. Early entry were often referred to as lead-time ad-

vantages in the earlier surveys. Versioning is analogous to the “rapid” or

“continuous” innovation constructs used in earlier surveys. We adapted this to

be versioning because it is more indicative of the digital phenomenon that we

are studying.

The corresponding survey questions are responses to Question 12 from the

survey instrument, shown in the Appendix C.

M. Miric, et al. Research Policy 48 (2019) 103738

innovation. It is important to highlight that the numbers that we ob-

serve here for the use of informal strategies are comparable to the

numbers found in earlier studies (52.8% in Cohen et al. 2000). This

suggests that informal strategies may be as important for small ﬁrms, in

a setting such as the Apple App Store, as they are for large manu-

facturing ﬁrms.

4.2. Clustering of formal and informal strategies

While these strategies may be important individually, we expect

that developers use multiple appropriability strategies in concert to

protect their innovations. To explore this, we analyzed how the stra-

tegies that we study (patents, copyrights, trademarks, early-entry, and

versioning) cluster in the data.

Table 1

Descriptive statistics.

Variables Mean S.D. (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13)

(1) Patents 0.11 0.31 1.00

(2) Copyrights 0.17 0.38 0.32 1.00

(3) Trademarks 0.26 0.44 0.36 0.47 1.00

(4) Early Entry 0.41 0.49 0.14 0.14 0.17 1.00

(5) Versioning (Rapid Innovation) 0.39 0.49 0.10 0.17 0.09 0.15 1.00

(6) FORMAL 0.34 0.47 0.49 0.63 0.84 0.19 0.12 1.00

(7) INFORMAL 0.61 0.49 0.08 0.13 0.14 0.67 0.65 0.15 1.00

(8) Size: 1 Employee Firm 0.23 0.42 −0.13 −0.13 −0.15 −0.09 0.01 −0.18 −0.03 1.00

(9) Size: 2 Employee Firm 0.13 0.34 −0.05 −0.07 −0.03 0.04 −0.01 −0.05 0.05 −0.21 1.00

(10) Size: 3 −10 Employee Firm 0.30 0.46 0.10 0.08 0.11 0.07 0.04 0.16 0.07 −0.35 −0.25 1.00

(11) Size: > 10 Employee Firm 0.14 0.35 0.24 0.29 0.27 0.14 0.13 0.29 0.09 −0.22 −0.16 −0.26 1.00

(12) Market Tenure (Months) 32.38 13.66 −0.03 −0.09 −0.01 0.06 0.04 −0.03 0.09 0.12 0.01 −0.14 0.02 1.00

(13) Promotion Channel 0.11 0.31 0.06 0.08 0.09 0.02 −0.01 0.13 0.00 0.00 0.02 −0.02 0.04 −0.09 1.00

(14) US Based Company 0.37 0.48 0.01 0.03 0.04 0.05 −0.03 0.01 −0.02 0.03 −0.01 −0.06 −0.06 0.14 −0.05

(15) Hobbyist Motivation 0.11 0.31 −0.11 −0.12 −0.15 −0.08 −0.10 −0.18 −0.11 −0.19 −0.14 −0.23 −0.14 −0.01 −0.06

(16) BM: Licensing 0.11 0.31 0.20 0.10 0.16 0.15 0.07 0.16 0.11 −0.10 −0.09 0.16 0.16 −0.12 0.13

(17) BM: App Revenue 0.76 0.42 −0.15 −0.09 −0.09 −0.05 0.11 −0.09 0.04 0.15 0.05 0.00 −0.22 0.11 −0.11

(18) Source of Innovation - Users 0.46 0.50 0.08 0.04 0.09 0.11 0.20 0.08 0.21 0.04 −0.02 −0.03 0.08 0.06 0.05

(19) Diﬀ: Network Eﬀects 0.18 0.39 0.11 0.18 0.20 0.17 0.22 0.21 0.18 −0.08 −0.03 0.12 0.14 −0.03 0.12

(20) Diﬀ: Special Tech 0.15 0.36 0.09 0.10 0.09 0.27 0.05 0.09 0.16 0.01 0.00 0.03 0.15 0.02 0.06

(14) (15) (16) (17) (18) (19) (20) (21) (22) (23) (24) (25)

(14) USA based 1.00

(15) Hobbyist Motivation 0.09 1.00

(16) BM: Licensing −0.06 −0.10 1.00

(17) BM: App Revenue −0.02 0.00 −0.11 1.00

(18) Source of Innovation - Users 0.07 −0.01 0.10 0.06 1.00

(19) Diﬀ: Network Eﬀects 0.00 −0.12 0.13 0.01 0.11 1.00

(20) Diﬀ: Special Tech 0.06 −0.11 0.13 −0.04 0.13 0.14 1.00

Fig. 1. Comparison of Sample and Population across observed variables.

M. Miric, et al. Research Policy 48 (2019) 103738

Using principal component analysis, we ﬁnd that there are two

overall clusters of appropriability strategies: ﬁrms that use only in-

formal (lead time and versioning) strategies and ﬁrms that use a com-

bination of formal (patents, copyrights, and trademarks) and informal

strategies. (Screen plot and cluster weights are shown in Appendix B.)

Given this clustering of formal and informal strategies, we create two

outcome variables to use in our analysis. FORMAL is a dummy variable

that indicates whether a ﬁrm uses patents, copyrights, or trademarks.

INFORMAL is a dummy variable that indicates whether a ﬁrm uses lead

time or secrecy. Approximately 36% of developer ﬁrms use only INF-

ORMAL strategies, while only 9% use only FORMAL strategies; 24% of

ﬁrms use a combination of both strategies.

4.3. Appropriability strategies by ﬁrm size

We model the choice of using a protection mode as a function of the

size of the ﬁrm. The basic model guiding our empirical analysis is as

follows.

=+ + + +Pr(PROTECTION) SIZE CONTROLS CATEGORIES

(1)

The outcome variable is an indicator for FORMAL or INFORMAL

strategies or both. SIZE is a vector of dummy variables that indicate the

size of the ﬁrm from the set of potential categories (i.e., < 1 full-time

employee, 1 full-time employee, 2 full-time employees, 3–10 full-time

employees, and > 10 full-time employees). Our survey also captures

whether ﬁrms have more than ﬁfty employees. However, since there

are no ﬁrms of that size that use only informal strategies, we could be

concerned that this lack of variation would bias our results. As a result,

we construct our size variable to indicate whether ﬁrms have ten em-

ployees or more.

In our model, we control for unobserved diﬀerences across diﬀerent

types of apps by including a vector of dummies for each category where

a developer is present, indicated by CATEGORIES. In addition, we in-

troduce several controls (referred to as BASIC CONTROLS in the results

tables) to account for additional factors that may confound our analysis

(as described in Section 4.1). We control for the fact that a developer

may be a hobbyist and may not be attempting to protect his or her

innovation by controlling for whether a developer reported that “de-

veloping software as a hobby” was an important motivation. We control

for the fact that a ﬁrm may not be purely a software developer and may

instead use the app as a promotion channel for an oﬄine business. We

control for the market tenure of ﬁrms by controlling for the number of

days since the ﬁrm ﬁrst entered the marketplace. We also control for

whether a ﬁrm is attempting to generate revenue by licensing their

technology or whether a developer wants to generate revenue at all, as

these may determine whether a developer has any desire to protect its

innovation.

An important factor to consider is that there are likely to be several

additional controls that may inﬂuence appropriability strategy choices.

For example, motivation to proﬁt may inﬂuence the choice of how ﬁrms

protect (Lakhani and Wolf, 2005; Harhoﬀet al., 2003) or the source of

innovation may correlate with the need to protect (Laursen and Salter,

2014). While these factors are not the direct focus of our study, it may

be important to control for them, and the survey data allows us to

capture much of this information. However, simply introducing a full

battery of potential covariates may greatly reduce the accuracy of our

estimates while not adequately accounting for potential biases.

To select an appropriate subset of control variables, we implement

the Double LASSO algorithm developed by Belloni et al. (2014). The

LASSO algorithm is a variation of the least squares regression approach

that performs shrinkage (reduction of coeﬃcient size) and selection

(removal of variables with low statistical power). The conventional

LASSO approach (Tibshriani, 1996) results in a linear regression with a

subset of the most statistically important variables; however, this also

aﬀects the magnitude of the coeﬃcients and we would not be able to

directly interpret the coeﬃcients or standard errors of such a model.

The Double LASSO is a two-stage regression approach that uses the

LASSO regression to regress the outcome variable and main variable(s)

of interest against the set of potential control variables. The selected

coeﬃcients are then included in a second stage (regular OLS or Probit)

regression.

This approach provides the beneﬁt of variable selection

Fig. 2. Reported use of diﬀerent appropriability strategies (sample & population estimate).

The LASSO regression is a variant of the OLS regression that deliberately

reduces the coeﬃcients (often referred to as a shrinkage estimator) based on a

pre-speciﬁed shrinkage factor (often denoted λ). In our case we allow the λis

determined by the DOUBLE LASSO algorithm (Belloni et al., 2013, 2014).

Unlike OLS regression where the objective function of the estimator is to

minimize the residual sum of squares (RSS), the LASSO estimator includes an

additional term in the objective function (

=wOBJECTIVE RSS i

where

is the coeﬃcient for each of M variables included in the model. As a

result, the objective function includes a penalty for the size and number of

M. Miric, et al. Research Policy 48 (2019) 103738

generated by the LASSO, but it does not aﬀect the parameters of the

standard errors and coeﬃcients (via shrinkage). This approach is pro-

posed as a way of controlling for potentially important covariates

without running the risk of data mining or selectively introducing

covariates.

Tables 2 and 3

In our LASSO regression, we include the full set of potential controls

from our survey and observational data, including the number of apps

the developer has released, as well as variables that indicate the source

of the developers’ ideas, their product diﬀerentiation strategy, their

revenue model, their “openness” in terms of selectively revealing code

or releasing their code as open source, and their personal motivation.

This leads us to a set of forty-two potential control variables that we

may want to include in the model in addition to our basic controls. We

deﬁne these variables that are selected by the LASSO model as ADDI-

TIONAL CONTROLS.

Multivariate probit regression results

Here we separately consider the use of formal and informal pro-

tection strategies. Since these two groups are not mutually exclusive,

we use a multivariate probit regression to account for the potential

correlation between these two outcome variables.

The results are reported in Table 4. We present the results for formal

strategies in columns 1–4 and informal strategies in columns 5–8. In the

ﬁrst column for each outcome, we include only the ﬁrm size variables

and category dummies. In the second column, we introduce the BASIC

CONTROL variables. For those variables that are signiﬁcant, we also

report the point estimates and standard errors to show which variables

are correlated with the use of diﬀerent strategies. In the third column,

we include the ADDITIONAL CONTROLS that were selected using the

double LASSO method mentioned above. In the fourth column, we

present the results reweighed by our sampling weights to ensure that

our regression results are not drastically inﬂuenced by our sampling

procedure.

Across columns 1 through 4, the results show that dummies larger

ﬁrms (with 3–10 and more than 10 employees) are signiﬁcant and

positive relative to the baseline (ﬁrms with less than one full-time

employee) for formal protection. However, for informal protections, the

indicator variables for larger ﬁrms are not signiﬁcant, and the coeﬃ-

cients are smaller in comparison. To interpret the magnitude of these

eﬀects, we present marginal eﬀects of these models (columns 3 and 7)

in Fig. 3. The use of informal strategies does not increase signiﬁcantly

with ﬁrm size. Approximately 53% of ﬁrms with less than one full-time

employee use informal strategies, while 65% of ﬁrms with more than

ten employees use informal strategies. However, for formal strategies,

this increase is considerable. Approximately 22% of ﬁrms with less than

one full-time employee use informal strategies, while 62% of ﬁrms with

more than ten employees use informal strategies. This corresponds to an

increase of 2.8 times the likelihood of using formal protection.

What is perhaps most important is that informal strategies are an

important protection strategy for both small and large ﬁrms. Moreover,

for larger ﬁrms, informal protection strategies appear to be used just as

frequently as formal protection strategies. This further reinforces the

notion that there exists a cluster of ﬁrms that use only informal stra-

tegies (seemingly smaller ﬁrms) and a cluster of ﬁrms that use a com-

bination of formal and informal strategies (larger ﬁrms).

Multinomial Logit Regression Results

To speciﬁcally address the use of a combination of diﬀerent stra-

tegies, we repeat the analysis by looking at mutually exclusive groups of

protection strategies, namely, FORMAL only, INFORMAL only, both

FORMAL and INFORMAL, and ﬁrms which do not use any protection.

As the earlier cluster analysis suggests, ﬁrms that choose to protect their

assets generally do so through only informal means or a combination of

formal and informal means.

We re-estimate the model in expression (1) using a multinomial

logit regression using a categorical variable to indicate whether a ﬁrm

uses formal or informal protections only or a combination of the two.

The results are shown in Table 4. As in the earlier models, the results

are split into three groups for each of the outcome variables. The

baseline outcome for these regressions is that the ﬁrm does not use any

protection strategy at all.. Similar to the earlier results, the ﬁrst column

for each outcome introduces only the ﬁrm size variable. The second

column introduces the basic control variables, the third column in-

troduces the control variables selected by the double LASSO algorithm,

and the fourth column presents the regression results, weighted using

sampling weights. Because of the large number of controls that are

included in these models, we do not report all the control variables in

the main tables. Instead, we report only the control variables that are

signiﬁcant, in order to document which variables are correlated with

our outcomes of interest.

The coeﬃcients for dummy variables for larger ﬁrms (ﬁrms with 3

and 3–10 employees) are positive and signiﬁcant (95% level) for both

ﬁrms that use only formal protections and those that use a combination

of formal and informal protections. The coeﬃcient is positive but not

signiﬁcant for ﬁrms that use only informal protections. Regarding the

control variables, ﬁrms that develop apps as a promotion channel for

their non-app business (Deﬁned Promotion Channel in Table 4) are more

likely to use only formal protection. Firms that diﬀerentiate themselves

based on special technology according to their survey responses (De-

ﬁned DiﬀStrategy: Special Tech in Table 5) are more likely to use in-

formal protections or a combination of formal and informal protections

than to not use protecting strategies at all. Finally, those developers that

generate ideas for new products from their users appear to rely more on

informal strategies, while developers that attempt to build network

eﬀects around their products are more likely to use a combination of

formal and informal strategies.

To interpret the magnitude of these eﬀects, we examine the mar-

ginal eﬀects for these models in Fig. 4. From the marginal eﬀects, it is

clear that the use of formal strategies increases with ﬁrm size, both

alone and together with informal strategies. Smaller ﬁrms are more

likely to use only informal strategies. However, with larger ﬁrms, only a

small proportion choose to use formal strategies. Moreover, smaller

ﬁrms are likely not to protect at all, while there is only a small share of

Table 2

Comparison of Sample and Population Means

Variable Sample Population t-Stat. (p - value)

Average Product Rating 3.48 3.55 3.83 (0.00)

Number of Product Ratings 569.20 432.78 −1.03 (0.15)

Days in Top 10 Ranking 1.55 2.71 1.36 (0.08)

Number of Apps Launched (by

Developer)

12.04 4.61 −9.18 (0.00)

Price 2.59 1.80 −6.90 (0.00)

Days Since Initial Entry to Market 1020.68 1171.21 32.00 (0.00)

The above table compares all variables that are available for both the sample

and population. t statistics are reported along with respective signiﬁcance level.

In instances where the t-statistic is positive, the signiﬁcance level indicates the

probability that sample mean is higher than the population mean. Alternatively,

where the t-statistic is negative the signiﬁcance level indicates the probability

that the population mean is higher than the sample mean.

(footnote continued)

coeﬃcients. Therefore, the estimator reduces the size of the coeﬃcients but also

sets the coeﬃcients to zero if they fall below a threshold (this is speciﬁc to

LASSO instead of other shrinkage estimators). The set of variables that are in-

cluded in the LASSO are therefore the variables that are most predictive of the

outcome variable. Because the lasso deliberately alters the magnitude of the

coeﬃcients, the results of the LASSO regression are not directly interpretable.

However, the DOUBLE LASSO indicates which variables are suitable controls to

be included in a regular regression model. The coeﬃcients from that model can

then be interpreted directly.

M. Miric, et al. Research Policy 48 (2019) 103738

Table 3

Results of Multivariate Probit Regressions for Use of Formal & Informal Strategies

DV: Indicator for FORMAL (Patents, Copyrights &Trademarks) and INFORMAL (Early Entry &Versioning).

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

DV: Formal Protection DV: Informal Protection

Size: 1 Employee Firm 0.17 −0.02 −0.12 0.03 0.46

0.49

0.10 0.29

(0.22) (0.26) (0.25) (0.27) (0.19) (0.25) (0.22) (0.25)

Size: 2 Employee Firm 0.50

0.33 0.15 0.34 0.50

0.56

0.35 0.24

(0.25) (0.28) (0.26) (0.30) (0.23) (0.28) (0.24) (0.28)

Size: 3 - 10 Employee Firm 1.13

***

1.03

***

0.63

0.92

***

0.64

***

0.67

0.29 0.36

(0.20) (0.24) (0.23) (0.26) (0.19) (0.24) (0.21) (0.24)

Size: 10+ Employee Firm 1.67

***

1.51

***

1.11

***

1.19

***

0.73

0.81

0.38 0.30

(0.24) (0.28) (0.28) (0.31) (0.23) (0.28) (0.27) (0.30)

Selected Control Variables

App is Promotion Channel 0.73

***

0.57

0.68

−0.08 −0.09 −0.16

(0.21) (0.19) (0.22) (0.21) (0.19) (0.23)

Diﬀ. Strategy - Network Eﬀects 0.41

0.60

0.31 0.42

(0.17) (0.19) (0.18) (0.22)

Source of Innovation - Users 0.10 0.12 0.24

0.30

(0.13) (0.15) (0.12) (0.14)

Diﬀ. Strategy - Special Tech. −0.02 0.01 0.53

0.49

(0.17) (0.18) (0.18) (0.21)

Controls Variables

Category Dummies Yes Yes Yes Yes Yes Yes Yes Yes

Basic Controls Yes Yes Yes Yes Yes Yes

Additional (LASSO Selected) Controls Yes Yes Yes Yes

Constant −1.08

***

−1.25

***

−1.11

***

−1.14

***

−0.27 −0.52 −1.19

***

−0.79

(0.16) (0.30) (0.29) (0.33) (0.15) (0.29) (0.28) (0.32)

191.99 217.97 242.89 277.45 191.99 217.97 242.89 277.45

(0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00)

log likelihood −559.50 −545.88 −652.64 −505.73 −559.50 −545.88 −652.64 −505.73

* Standard errors in parentheses. p< 0.05.

** p< 0.01.

*** p< 0.001). N= 626.

Table 4

Results of Multinomial Logit Regression for Probability of Using IPR

DV: Indicator for use of Formal, Informal or Both. Baseline: Do Not Protect

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)

Formal Protection Informal Protection Formal &Informal Protection

Size: 1 Employee Firm 0.35 −0.37 −0.32 0.07 0.48 0.48 0.10 0.54 0.80 0.59 0.33 0.64

(0.53) (0.59) (0.63) (0.67) (0.28) (0.37) (0.40) (0.48) (0.48) (0.63) (0.66) (0.68)

Size: 2 Employee Firm 0.49 −0.27 −0.39 −0.24 0.75

0.79 0.28 0.12 1.70

***

1.61

1.20 1.11

(0.63) (0.69) (0.73) (0.80) (0.35) (0.42) (0.46) (0.54) (0.51) (0.65) (0.68) (0.72)

Size: 3 - 10 Employee Firm 1.44

0.98 0.78 1.29

0.67

0.77

0.23 0.42 2.41

***

2.38

***

1.81

2.24

***

(0.46) (0.54) (0.57) (0.63) (0.29) (0.38) (0.42) (0.48) (0.43) (0.60) (0.63) (0.61)

Size: 10+ Employee Firm 2.55

***

2.09

1.33 1.68

0.90 1.09

0.12 0.23 3.62

***

3.71

***

2.60

***

2.48

(0.59) (0.66) (0.74) (0.82) (0.48) (0.54) (0.61) (0.70) (0.54) (0.69) (0.75) (0.77)

Controls Variables

App is Promotion Channel 1.43

1.17

1.42

0.12 −0.10 −0.27 1.04

0.77 0.89

(0.48) (0.52) (0.57) (0.40) (0.45) (0.54) (0.41) (0.47) (0.56)

Diﬀ. Strategy - Network Eﬀects 0.43 0.91 0.41 0.63 1.14

1.65

(0.54) (0.65) (0.40) (0.53) (0.43) (0.54)

Source of Innovation - Users 0.64 0.85 0.58

0.81

0.47 0.70

(0.38) (0.48) (0.25) (0.29) (0.30) (0.38)

Diﬀ. Strategy - Special Tech. 0.63 0.97 1.39

***

1.52

1.11

1.15

(0.58) (0.69) (0.41) (0.51) (0.46) (0.56)

Category Dummies Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

BASIC CONTROLS Yes Yes Yes Yes Yes Yes Yes Yes Yes

ADDITIONAL (LASSO) CONTROLS Yes Yes Yes Yes Yes Yes

Constant −1.72

***

−1.84

−1.73

−1.95

−0.41

−1.13

−1.83

***

−1.34

−2.11

***

−3.30

***

−3.83

***

−3.13

***

(0.39) (0.67) (0.73) (0.87) (0.21) (0.46) (0.55) (0.64) (0.38) (0.70) (0.77) (0.80)

210.01 250.02 358.87 2276.34 210.01 250.02 358.87 2276.34 210.01 250.02 358.87 2276.34

(0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00)

log likelihood −708.17 −688.16 −633.74 −486.98 −708.17 −688.16 −633.74 −486.98 −708.17 −688.16 −633.74 −486.98

* Standard errors in parentheses. p< 0.05.

** p< 0.01.

*** p< 0.001. N= 626.

M. Miric, et al. Research Policy 48 (2019) 103738

larger ﬁrms that do not protect their innovations.

The control variables also suggest that protection strategies are

correlated with several ﬁrm characteristics. For instance, ﬁrms that

attempt to diﬀerentiate themselves through network eﬀects are more

likely to use a combination of formal and informal strategies. Similarly,

ﬁrms that diﬀerentiate themselves through “special technology”, as is

described in the survey, tend to protect their innovation through in-

formal strategies or a combination of formal and informal strategies,

rather than simply using formal protections. Alternatively, ﬁrms that

source ideas for their innovations from users are likely to use only in-

formal strategies to protect their innovations, while there is no sig-

niﬁcant eﬀect for formal protections.

Multivariate Probit Results for Individual Appropriability Strategies

We disaggregate our earlier measures of formal and informal stra-

tegies into individual appropriability strategies (patents, copyrights,

trademarks, lead time and versioning (i.e., Rapid Innovation)). Since

these strategies are not mutually exclusive, we again use a multivariate

regression to estimate their incidence with varying levels of ﬁrm size. In

Fig. 5, we present the marginal eﬀects with respect to ﬁrm size. The

individual regression results are given in Appendix B.

The results for informal strategies (lead time and versioning) is

overall consistent with the clustered results. These strategies are used

slightly more frequently by larger ﬁrms. However, even the smallest

ﬁrms use them relatively infrequently. Alternatively, for formal stra-

tegies (patents, copyrights, and trademarks), there is an increase in the

use of these strategies with ﬁrm size. Smaller ﬁrms are far less likely to

utilize these strategies than are larger ﬁrms.

Additional speciﬁcations and robustness

We performed several additional tests to demonstrate the robustness

of the regression results, including using sampling weights for the re-

gression to correct for biased sampling, for the full sample of 809 ﬁrms

for which we are able to observe both IPR and BASIC CONTROLS and

the smaller subset of 626 ﬁrms for which we observe the full set of

Fig. 3. Marginal eﬀects for incidence of formal and informal strategies.

Fig. 4. Marginal eﬀects for multinomial logit regressions.

M. Miric, et al. Research Policy 48 (2019) 103738

ADDITIONAL CONTROLS (all 18 variables). Similarly, the overall re-

sults of our clustering strategy are robust to alternative clustering

methods (K means, K medians, LDA, etc.). In each case, the clusters

conform to groups of formal and informal clusters, and in some cases,

the intersection of these two sets.

The introduction of the LASSO-selected control variables did not

greatly impact the results of the analysis, as the variable selected were

those that most highly correlated with either the outcome or ex-

planatory variables. For instance, the App is Promotion Channel variable

was correlated with the use of formal protections and only accounts for

11% of apps. These are companies that are not predominantly app

developers but are using the app as a promotion channel for their main

business.

Similarly, the Source of Innovation −Users variables, which

indicates whether the ideas for their innovations come from “user in-

novators,” indicates that 46% of innovators are gaining ideas from their

users. This suggests that innovation in this setting is highly dependent

on interaction between innovators and end users. Approximately 42%

of respondents in our survey report being end-users, although this does

not correlate suﬃciently with appropriability strategies to be included

by the LASSO algorithm.

5. Discussion and conclusion

There has been a long-standing general interest in research on ap-

propriability (Teece, 1986; Laursen and Salter, 2014; Cohen et al.,

2000) running parallel to, but separate from, a rapid expansion of re-

search on digitization of the economy and digital platforms, more

speciﬁcally. The broader literature on digitization (Greenstein et al.,

2013; Yoo et al., 2010; Nambisan et al., 2017) has explored how the

growing shift toward digital innovation is aﬀected by existing institu-

tions, such as IPR. The present paper builds on this stream of work by

empirically examining how innovators on a digital platform appro-

priate value from their innovations. The ability of innovators to ap-

propriate value from their innovation is particularly important in the

context of platforms, because it directly relates to their incentives to

join the platform in the ﬁrst place. Therefore, the question of appro-

priability is at the heart of the issue of platform strategy (Gawer and

Henderson, 2007; Huang et al., 2012; Parker and Alstyne, 2017).

However, the innovators on these digital platforms are often far smaller

ﬁrms than typically found in other settings. For example, more than

43% of the developers in our sample had one or fewer full-time em-

ployee.

While earlier studies have looked at appropriability by smaller

ﬁrms (Leiponen and Byma, 2009; Graham et al., 2009), they have not

explored the protection strategies used by the smallest of ﬁrms, such as

those that can be found on digital platforms.

While these smallest ﬁrms are often given less attention in the re-

search, the results of the present study suggest that they represent a

sizable share of the overall apps marketplace. When we consider that

these developers contribute to the “long tail” of complementary pro-

ducts accounting for almost US$130 Billion in revenue, we can expect

that these small developers constitute an economically important share

of this marketplace. Additionally, the ability of these small ﬁrms to

limit competition and proﬁt from their innovation so that they may

grow into larger ﬁrms relates to a broader set of questions about the

entrepreneurial strategies of ﬁrms in these platform markets. While the

present paper does not explore all possible strategies, our inquiry oﬀers

insights into those strategies that are commonly used and that have

been most commonly studied.

Our study documents the use of appropriability strategies by de-

velopers on a digital platform and how the use of these strategies diﬀers

for smaller ﬁrms, compared to the larger ﬁrms that have been typically

studied. We provide evidence that appropriability strategies used by

developers on the Apple App Store cluster into formal and informal

protections. A large majority of ﬁrms (more than 70%) take measures to

protect their innovations in some way, with many using only informal

strategies (36.76%) and a smaller subset using a combination of formal

and informal strategies (24.12%). We found that merely a fraction of all

ﬁrms (9.71%) employ only formal protections. Looking into speciﬁc

protection strategies, we ﬁnd that patents are seldom used (approxi-

mately 13% of ﬁrms), while versioning (or rapid innovation) and early

entry are used by more than 40% of ﬁrms. This suggests that patents are

not the most important appropriability lever for ﬁrms in this setting but

that there is a non-negligible amount of patenting being done. In

Fig. 5. Marginal eﬀects for multivariate probit regressions (individual protection strategies).

Examples of this include airlines that use apps for check-in, or restaurants

that allow customers to order through their apps.

This is not shown in the tables but is based on the fact that 42% of devel-

opers reported that “use need” was an important motivation for them to de-

velop innovations.

As reported in the descriptive statistics, 20% of developers have less than

one full time employee and 23% have only one full time employee.

M. Miric, et al. Research Policy 48 (2019) 103738

comparison, Graham et al. (2009) found that 24% of software ﬁrms that
do not acquire venture capital funding are likely to patent.
7
In line with our arguments, we ﬁnd that ﬁrm size is an important
factor in determining the choice of protection strategies. We ﬁnd that
informal strategies (early entry and rapid innovation/versioning) are
used extensively by both large and small ﬁrms, while we found that
formal IPR protections (patents, copyrights, and trademarks) are used
mainly by larger ﬁrms. These results hold for both the use of formal and
informal protections, and for individual protection strategies. Our re-
sults show that informal IPR protections are used by very small ﬁrms
and part-time developers, while for larger ﬁrms, informal protections
are important when combined with formal protections.
Theoretical Implications. The results of this paper make several
theoretical contributions. First, the literature on technology platforms
has demonstrated that a vital determinant of the success of technology
platforms is creating conditions for outside innovators to both create
and capture value by joining the platform (Parker and Alstyne, 2005;
Gawer and Cusumano, 2014; Gawer, 2014; Boudreau, 2010). However,
there has been far less inquiry into how these innovators on these
platforms may protect themselves from competitive pressures and ap-
propriate value. The results of this paper help to deepen our under-
standing of how platforms may shape the population of third-party
developers that they attract (Cennamo and Santalo, 2013; Boudreau
and Hagiu, 2008; Corts and Lederman, 2009; Zhu and Iansiti, 2012),
based on the types of appropriability strategies that are available. This
study directly relates to the question of how innovators “stimulate ex-
ternally developed innovation that complements the platform” (Gawer
and Cusumano, 2014).
Second, this paper contributes to the literature on appropriability by
considering the use of appropriability by small companies on digital
platforms.. The literature on appropriability has examined how com-
panies protect their innovations in a variety of settings. Most notably,
this includes the highly inﬂuential studies by Cohen et al. (2000), Levin
et al. (1987), Graham et al. (2009)Cohen et al., 2000Cohen et al.
(2000), Levin et al. (1987), Graham et al. (2009)Cohen et al.,
2000Cohen et al. (2000), Levin et al. (1987), Graham et al. (2009).
These studies have exclusively considered either very large companies,
predominantly in manufacturing and brick-and-mortar settings, or
packaged software. We explore how innovators, predominantly very
small ﬁrms, on a digital platform appropriate value from their in-
novations. As a growing share of economic and innovative activity
shifts to platforms, this study helps to extend our understanding of how
innovators in such a setting appropriate value and how this diﬀers from
what is known about manufacturing and more conventional settings.
Managerial and Policy Implications. Existing research has es-
tablished that allowing innovators to protect their innovation and ap-
propriate proﬁts is an important factor in fostering innovation (Moser,
2005, 2013; Arrow, 1962); it is thus a vital concern for policy makers.
Within a platform setting, this takes on its own set of conditions. Given
that the success of platforms is so heavily predicated on the availability
of complementary innovations (Parker and Alstyne, 2005; Rochet and
Tirole, 2006), a critical concern for the platform is creating conditions
so that third-party developers have the incentive to innovate on their
particular platform. The famous case of Atari, where low barriers to
entry led to intense competition and eventually diminished innovation,
resulting in a collapse of the market, further reinforces this point
(Boudreau and Hagiu, 2008). As a result, it is critical for such platforms
to understand how innovators on their platform are able to appropriate
value, so that platforms may design and enforce appropriate property
rights.
From this perspective, understanding the relative importance and
role of formal and informal property rights in allowing third-party de-
velopers to appropriate value may shape the policies that the platform
chooses to enact. From the perspective of a platform regulator (Gawer
and Cusumano, 2014; Parker and Alstyne, 2005; Cennamo et al., 2018;
Ozalp et al., 2018), the objective is to grow the multiple sides of the
platform. Knowing that formal property rights are used primarily by
larger companies can allow the platform to determine the rules gov-
erning IPR such that they create conditions favorable to larger com-
panies. An example of this may be ensuring that property rights such as
patents, copyrights, and trademarks are strictly enforced.
8
Similarly,
knowing that informal rights are used by both large and small devel-
opers suggests that creating the ability for developers to enter the
platform easily and without frictions or to quickly innovate may be
important for the incentives of both large and small developers to ap-
propriate value on the platform. However, by creating conditions where
informal strategies, such as early entry or versioning, may be easily
implemented, the platform owner may be able to foster innovation by
smaller developers. Such conditions might include easing the costs of
innovating on the platform, or getting new products approved. Re-
latedly, if the platform owner wants to focus exclusively on having a
long tail of very small scale developers, the present results suggest that
formal property rights may not be critical to allowing such innovators
to appropriate value.
Appendix A. Supplementary data
Supplementary data associated with this article can be found, in the
online version, at https://doi.org/10.1016/j.respol.2019.01.012.
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Stanley Akpevwe Onobrakpeya

The study designed a strategic framework to guide high-tech companies on their journey to hypergrowth. The specific objectives are to examine the effects of innovation acceleration, market penetration, and operational scalability on hypergrowth. The study was based on quantitative research method. The cross-sectional survey research design was selected for the study. The population for this study includes staff from high-tech companies in Nigeria. To ensure the sample size is representative of the larger population, Cochran's formula was employed, resulting in a sample size of 384 participants. The judgmental sampling technique was utilized to select the participants. Data collection was carried out using a structured questionnaire with a five-point Likert scale response format. To ensure the validity and reliability of the research instrument, content validity and test-retest reliability were employed. The data acquired were analyzed using both descriptive and inferential statistical methods. Findings showed that innovation acceleration (β = 0.395, p < 0.05), market penetration (β = 0.105, p < 0.05), and operational scalability (β = 0.340, p < 0.05) positively affect hypergrowth. The study concluded that Ignition Model serves as a valuable roadmap for navigating the complexities of business growth, ensuring that organizations remain focused and resilient on their journey to success. Moreover, the study recommended that high-tech companies should enhance their market penetration strategies by leveraging data analytics to identify and target high-potential customer segments effectively.

Role of Artificial Intelligence in Data Protection for Digital Asset Systems: A Review of Recent Development

Article

Full-text available

Nov 2024

AI integration in digital assets and data protection is revolutionizing asset management. Proper AI applications can enhance security, but regulation must balance innovation and misuse, requiring a thorough understanding of technology. Data protection safeguards sensitive data from loss, alteration, or corruption, ensuring compliance with legal and regulatory obligations. It involves business information procedures and data protection mechanisms, particularly in the financial industry. Restricting access to digital asset data is crucial. This paper systematically reviews the recent role of AI in data protection for digital asset systems. Recent studies have reported enhanced algorithms for data protection in digital assets, focusing on transactions, markets, surveillance, and infrastructure assets. Robust algorithms in the digital asset market improve data protection and address issues with traditional crowdfunding. Token financing is a new strategy aimed at solving these issues. However, startups often lack knowledge about risk when choosing new token financing options. In the future, AI may be able to predict potential threats or vulnerabilities to digital assets through data protection trends and patterns. This would allow proactive data protection measures rather than just reactive ones. It will enhance digital asset systems' functionality and accuracy.

Open innovation governance for the Big Tech (BT) platform-ecosystems: a multi-level stakeholder framework

Article

May 2025

Purpose Silicon Valley Big Tech (BT), representing Alphabet, Apple, Meta and Amazon, wields substantial influence over their platform users, leading to calls for more stringent digital regulation. The purpose of this study is to conceptualize “open innovation governance” for the BT platform ecosystems. This involves the balanced use of both incentives and controls to address stakeholder power imbalances at the corporate (BT senior manager), platform (complementor) and ecosystem (end-users) levels to share ecosystem value. Design/methodology/approach A conceptual review methodology systematically examines various academic articles, books and 10 K annual reports on BT firms. This study dissects the business models of each BT firm while drawing on empirical examples from the high-tech sectors to advance general propositions. This research presents a prescriptive open innovation (OI) governance framework based on the literature synthesis. Findings This research advances a “managerial toolkit” leveraging Objectives and Key Results and Key Performance Indicators tied to specific incentives and controls to enable BT senior managers to generate OIs, complementors to absorb and end-users to disseminate open digital platform-ecosystem value. Originality/value This study has implications for both theory and practice. Theoretically, to the best of the authors’ knowledge, this study is the first to conceptualize a prescriptive OI governance framework that BT managers can use to generate shared values. Practically, the conceptual framework has implications for digital policymakers governing BT, representing a middle ground between advocates for breaking up BT platforms and proponents of limited digital regulation.

Patent Law in Islamic Economics Challenges and Opportunities

Article

Full-text available

Dec 2024

One important way to protect intellectual property rights is to use patent law to encourage creativity and innovation. In Islamic economics, the application of patent law raises several issues. This is because there are fundamental differences between the principles of Shari'a and some elements of conventional patent law. Since Islamic economics emphasizes justice, public interest, and equitable distribution of wealth, patent law must reflect these values. This study aims to identify and analyze the challenges and opportunities in the implementation of patent law in Islamic economics. Specifically, this study aims to understand the concepts and principles of patent law from an Islamic economic perspective, identify the main problems faced in the implementation of patent law in Muslim countries, explore opportunities for developing a patent legal framework that is in accordance with sharia principles, and provide. This research was conducted using qualitative methods, using literature studies and document analysis. Data were obtained from various sources, such as scientific journals, books, laws, fatwas, and other documents. The results of the study indicate that patent law in Islamic economics must balance individual rights and societal benefits. Some of the main problems identified include lack of knowledge about the importance of patents, inconsistencies between some aspects of conventional patent law and sharia principles, and resistance to changes in current regulations. According to this study, patent law in Islamic economics faces many challenges and opportunities. By incorporating sharia principles into the patent legal system, innovation and technological progress that benefit society can be increased.

Two decades of research on digitalization, entrepreneurship, and internationalization: What foundations do they provide for research on digital entrepreneurial internationalization?

Article

Apr 2025
INT BUS REV

A rabbit hole to innovation land: An empirical examination of the Alice decision

Article

Apr 2025

Evaluating the challenges of metaverse-enabled digital entrepreneurship: evidence from Saudi Arabia as an emerging economy

Article

Feb 2025

Purpose Although several papers have been published over the past decade on various aspects of digital entrepreneurship, nothing has hitherto been written on the theme of digital entrepreneurship in the metaverse. This paper, therefore, aims to explore the key challenges of digital entrepreneurship in the metaverse, with a view to developing a model to address these challenges. Design/methodology/approach The Decision Making Trial and Evaluation Laboratory approach was adopted in this study to rank the selected challenges in order of importance and establish a cause-and-effect relationship between them. The data were gathered from 10 experts from Saudi Arabia who deploy augmented reality, virtual reality and other immersive technologies in the course of their business. Findings Three challenges, namely, “Market fragmentation (C3)”, “Technical complexity (C1)” and “Monetisation and revenue models (C5)” were highlighted in the findings as the main factors of influence in the Cause group, whereas the remaining five challenges, “Infrastructure and connectivity (C2)”, “Social and ethical considerations (C8)”, “User adoption and engagement (C6)”, “Privacy and security (C7)” and “Intellectual property protection (C4)”, were categorised in the Effect group, being significantly influenced by the challenges in the Cause group. Originality/value To the best of the authors’ knowledge, this is the first study to explore the challenges of metaverse-enabled digital entrepreneurship and classify the identified challenges into groups of Cause and Effect.

Persistence in mobile app ecosystems: App longevity via updates and the role of complementor specialization

Article

Feb 2025
LONG RANGE PLANN

Oleksandr Tsaruk

Secrecy and Patents: Theory and Evidence from the Uniform Trade Secrets Act

Article

Full-text available

Sep 2017

Ipl Png

How should firms use patents and secrecy as appropriability mechanisms? Consider technologies that differ in the likelihood of being invented around or reverse engineered. Here, I develop the profit-maximizing strategy: (i) on the internal margin, the marginal patent balances appropriability relative to cost of patents vis-a-vis secrecy, and (ii) on the external margin, commercialize products that yield non-negative profit. To test the theory, I exploit staggered enactment of the Uniform Trade Secrets Act (UTSA), using other uniform laws as instruments. The Act was associated with 38.6% fewer patents after one year, and smaller effects in later years. The Act was associated with larger effect on companies that earned higher margins, spent more on R&D, and faced weaker enforcement of covenants not to compete. The empirical findings are consistent with businesses actively choosing between patent and secrecy as appropriability mechanisms, and appropriability affecting the number of products commercialized. The online supplement is available at https://doi.org/10.1287/stsc.2017.0035 .

Innovation, Openness, and Platform Control

Article

Full-text available

Aug 2017

Suppose that a firm in charge of a business ecosystem is a firm in charge of a microeconomy. To achieve the highest growth rate, how open should that economy be? To encourage third-party developers, how long should their intellectual property interests last? We develop a sequential innovation model that addresses the trade-offs inherent in these two decisions: (i) Closing the platform increases the sponsor’s ability to charge for access, while opening the platform increases developer ability to build upon it. (ii) The longer third-party developers retain rights to their innovations, the higher the royalties they and the sponsor earn, but the sooner those developers’ rights expire, the sooner their innovations become a public good upon which other developers can build. Our model allows us to characterize the optimal levels of openness and of intellectual property (IP) duration in a platform ecosystem. We use standard Cobb–Douglas production technologies to derive our results. These findings can inform innovation strategy, choice of organizational form, IP noncompete decisions, and regulation policy. This paper was accepted by Chris Forman, information systems.

Regression Shrinkage and Selection Via the Lasso

Article

Jan 1996

Robert Tibshirani

We propose a new method for estimation in linear models. The ‘lasso’ minimizes the residual sum of squares subject to the sum of the absolute value of the coefficients being less than a constant. Because of the nature of this constraint it tends to produce some coefficients that are exactly 0 and hence gives interpretable models. Our simulation studies suggest that the lasso enjoys some of the favourable properties of both subset selection and ridge regression. It produces interpretable models like subset selection and exhibits the stability of ridge regression. There is also an interesting relationship with recent work in adaptive function estimation by Donoho and Johnstone. The lasso idea is quite general and can be applied in a variety of statistical models: extensions to generalized regression models and tree‐based models are briefly described.

Competing with Complementors: An Empirical Look at Amazon.com

Article

Jul 2018

Research summary Platform owners sometimes enter complementors’ product spaces and compete against them. Using data from Amazon.com to study Amazon’s entry pattern into third‐party sellers’ product spaces, we find that Amazon is more likely to target successful product spaces. We also find that Amazon is less likely to enter product spaces that require greater seller efforts to grow, suggesting that complementors’ platform‐specific investments influence platform owners’ entry decisions. While Amazon’s entry discourages affected third‐party sellers from subsequently pursuing growth on the platform, it increases product demand and reduces shipping costs for consumers. We consider the implications of these findings for complementors in platform‐based markets. Managerial summary Platform owners can exert considerable influence over their complementors’ welfare. Many complementors with successful products are pushed out of markets because platform owners enter their product spaces and compete directly with them. To mitigate such risks, complementors could build their businesses by aggregating non‐blockbuster products or focusing on products requiring significant platform‐specific investments to grow. They should also develop capabilities in new product discovery so that they could continually bring innovative products to their platforms. This article is protected by copyright. All rights reserved.

Does Platform Owner’s Entry Crowd Out Innovation? Evidence from Google Photos

Article

May 2018

We study how platform owners’ decision to enter complementary markets affects innovation in the ecosystem surrounding the platform. Despite heated debates on the behavior of platform owners toward complementors, relatively little is known about the mechanisms linking platform owners’ entry and complementary innovation. We exploit Google’s 2015 entry into the market for photography apps on its own Android platform as a quasi-experiment. We conclude based on our analyses of a time-series panel of 6,620 apps that Google’s entry was associated with a substantial increase in complementary innovation. We estimate that the entry caused a 9.6% increase in the likelihood of major updates for apps affected by Google’s entry, compared to similar but not affected apps. Further analyses suggest that Google’s entry triggered complementary innovation because of the increased consumer attention for photography apps, instead of competitive “racing” or “Red Queen” effects. This attention spillover effect was particularly pronounced for larger and more diversified complementors. The study advances our understanding of the effects of platform owner’s entry, explicates the complex mechanisms that shape complementary innovation, and adds empirical evidence to the debate on regulating platforms. The online appendix is available at https://doi.org/10.1287/isre.2018.0787 .

Disruption in Platform-Based Ecosystems

Article

May 2018

We study intergenerational platform‐technology transitions as instances of potentially disruptive innovation at the ecosystem level. Examining the launch of 12 platform technologies in the U.S. videogame industry covering three console generations from 1993 until 2010, we show that incumbents introducing next‐generation platform technologies with advanced capabilities increase the challenges of developing complements for the platform technology, steepening complementors' learning curves and disrupting the very same complementors that platform owners need to thrive in the next‐generation competition. We find that, because of these struggles, platforms with advanced capabilities but high complement‐development challenges show a pattern of defection of complementors toward rival, less challenging platforms. Our study extends mainstream disruptive‐innovation theory to the context of platform‐based ecosystems by offering a systemic view that accounts for disaffection on the part of technology complementors—rather than end users—as the main reason for disruption. This article is protected by copyright. All rights reserved.

Network Isolates: Entrepreneurial Bootstrapping and the Social Disconnection of New Organizations in the Mobile App Ecosystem

Article

Jan 2017

Platform Architecture, Multihoming and Complement Quality

Article

Aug 2017

Entrepreneurial Innovation: Killer Apps in the Iphone Ecosystem

Article

Jan 2014

Entry into Platform-Based Markets

Article

Jan 2009

Protecting their digital assets: The use of formal & informal appropriability strategies by App developers

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