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Impact of Modularity Design on Mobile App Launch

Success
Moonwon Chung, Luv Sharma, Manoj K. Malhotra

To cite this article:

Moonwon Chung, Luv Sharma, Manoj K. Malhotra (2023) Impact of Modularity Design on Mobile App Launch Success.
Manufacturing & Service Operations Management 25(2):756-774. https://doi.org/10.1287/msom.2022.1181

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 MANUFACTURING & SERVICE OPERATIONS MANAGEMENT
Vol. 25, No. 2, March–April 2023, pp. 756–774
https://pubsonline.informs.org/journal/msom ISSN 1523-4614 (print), ISSN 1526-5498 (online)

Impact of Modularity Design on Mobile App Launch Success

Moonwon Chung,a,* Luv Sharma,b Manoj K. Malhotrac
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a
Department of Operation and Supply Chain Management, Monte Ahuja College of Business, Cleveland State University, Cleveland, Ohio
44114; b Department of Management Science, Darla Moore School of Business, University of South Carolina, Columbia, South Carolina 29208;
c
Department of Operations Management, Weatherhead School of Management, Case Western Reserve University, Cleveland, Ohio 44106
*Corresponding author
Contact: m.chung74@csuohio.edu, https://orcid.org/0000-0002-9579-6026 (MC); luv.sharma@moore.sc.edu,
https://orcid.org/0000-0002-1710-4178 (LS); malhotra@case.edu (MKM)

Received: October 20, 2019 Abstract. Problem definition: Initial product design decisions are critically important for
Revised: April 20, 2021; February 9, 2022; mobile apps, which show a relatively short time from launch to peak usage, thus providing
August 12, 2022; November 29, 2022 a narrow window for success and no time for course correction based on market reaction.
Accepted: November 30, 2022 Mobile apps are designed using a highly modular architecture based on software develop­
Published Online in Articles in Advance: ment kits (SDKs), with SDK choices being sequentially determined along three dimen­
January 13, 2023
sions—multiplicity (total number of SDKs), compatibility (SDK co-occurrence frequency),
and novelty (SDK degree of newness to the developer). We evaluate the consequence of
https://doi.org/10.1287/msom.2022.1181 these decisions on initial market success in the context of mobile gaming app design. Aca­
demic/practical relevance: The resulting conceptual framework aids developers in deter­
Copyright: © 2023 INFORMS
mining the modularity of digital product development. Methodology: We formulate an
instrumental variables least absolute shrinkage and selection operator regression model to
estimate relationships of interest using a proprietary data set extracted from the application
programming interface server of a leading mobile apps intelligence firm. Results: We find
a negative impact of SDK multiplicity on initial success. High SDK compatibility can miti­
gate this negative effect, whereas high SDK novelty can exacerbate the negative effect of
multiplicity. Post hoc analysis shows that business-to-consumer (B2C) communication
features can also mitigate this negative impact. Managerial implications: Prior product
modularity research has predominantly focused on physical products or relied on single-
dimensional modularity measures. Our study conceptualizes modularity as multidimen­
sional and investigates how these multidimensional SDK-based modularity choices impact
the performance of a key category of digital products—mobile apps. We demonstrate that
increasing multiplicity, essential in certain markets, negatively affects initial success. How­
ever, firms can enhance SDK compatibility, reduce SDK novelty, and use B2C communica­
tion channels to mitigate this negative impact.

Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.1181.

Keywords: mobile apps • modularity • software development kits

1. Introduction from 2012 to 2017, statistics show an increase from 107 to

With the nature of output evolving from manufactured 223 minutes (Statista 2018). The mobile app market is
physical products to digital products, the field of ope­ growing exponentially, with iOS apps showing over 37.8
rations management needs to pay more attention to billion downloads and consumer spending of $17.3 billion
how digital products are designed, configured, market in Q2 of 2020 (Curry 2021).
launched, and managed over their unique product life The paid and freemium models are the two com­
cycles. This study takes a step in that direction by investi­ monly used revenue models in the mobile app sector.
gating these product development factors in the context In the paid model, users spend an up-front price before
of an important segment of digital products—mobile downloading the mobile app. In a freemium model, the
apps. Increased penetration of smartphones, combined mobile app download is free, and the user pays for
with higher mobile bandwidth and cellular microproces­ optional value-added services and features. The free­
sor capabilities, is impacting how mobile apps conve­ mium business model helps user-base expansion by
niently organize our daily activities (e.g., fitness, travel, reducing user entry costs. Over time, the preferred
shopping, financial management) and entertainment business model has gravitated toward freemium apps,
(e.g., gaming, social media, multimedia). As a result, con­ which currently account for over 90% of the revenue
sumers are spending more time on their mobile devices. generated from apps listed on the iOS and Android
Comparing the daily time spent among U.S. millennials platforms (Perez 2013). The success of freemium apps

756
 Chung, Sharma, and Malhotra: Impact of Modularity Design on Mobile App Launch
Manufacturing & Service Operations Management, 2023, vol. 25, no. 2, pp. 756–774, © 2023 INFORMS 757

relies on quickly achieving user critical mass, which has mostly focused on the impact of success factors
relates to in-app advertisements and in-app sales, the such as user ratings (Liu et al. 2014), portfolio strategies
two primary sources of revenue for freemium apps. (Lee and Raghu 2014), and pricing (Garg and Telang
One of the most notable examples of a successful free­ 2013, Ghose and Han 2014) on app performance. In con­
mium product is Angry Birds 2, which accumulated 10 trast, by explicitly focusing on product design as a criti­
million downloads in just three days (Grundberg 2012). cal success factor, we contribute to the literature by
The product life cycle for freemium mobile apps dif­ diving deeper and showing how the actual modularity
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fers significantly from traditional product life cycles decisions made by the designers impact the initial suc­
(Downes and Nunes 2014). This segment has a com­ cess of the freemium apps.
pressed two-stage product life cycle with exponential To execute our study, we procured access to a market-
growth in the initial stages, followed by gradual decay. leading mobile app analytics firm’s proprietary applica­
In most apps, this exponential growth and peak num­ tion programming interface (API) server and manually
ber of users are achieved within a few days to weeks extracted the empirical data set, which contains daily
from the app launch. This peak number of users repre­ panel observations of 2,483 top-ranked, free mobile
sents the app’s maximum market penetration, which gaming U.S. iOS apps spanning 3.5 years. These apps
puts an upper limit on the app’s revenue generation are released by 804 developer firms that employ an
potential. Further, the relatively short time from launch average of 11 employees while simultaneously servic­
to the peak provides a narrow window of opportunity ing an average of 2.5 apps with an average of 10 SDKs
and no time for course correction based on market reac­ installed. Based on measures obtained from this data
tion, making prelaunch development decisions critical set, we use two outcome measures to capture initial
to success. Following prior literature, which evaluates success—Bayesian-weighted five-star review ratings and
product performance during early stages in the product the number of daily active users. These metrics capture
lifetime (Song et al. 2011) or takeoff periods (Agarwal different aspects of performance, with review ratings cap­
and Bayus 2002), we refer to this exponential growth turing users’ attitudes toward a product (i.e., satisfaction/
period as the initial stage of the app’s lifetime and conse­ dissatisfaction, perceived quality) and daily active users
quently, retain our focus on ensuring the initial success capturing users’ behavior (Fishbein 1979, Fazio et al.
of an app (i.e., maximizing market penetration in the 1989). On average, the initial peak is reached in 29.8 days
initial stage). since launch. These observations show the resource con­
Product design is often considered a critical opera­ straints for the app development firms (Clutch 2018) and
tional decision that significantly affects the user recep­ the short time to the initial peak for freemium mobile
tion and commercial success of technology products gaming apps. Our context is grounded in mobile gaming
(Hise et al. 1989, Ulrich 1995, Krishnan and Ulrich apps because they provide a rich setting for examining
2001, Ramachandran and Krishnan 2008, Özkan et al. the impact of product design and market-related factors
2015). Mobile apps exhibit a highly modular product because of the highest degree of competition intensity
design, implemented through software development and shortest average life span (Gordon 2018) among all
kits (SDKs), which are nonproprietary, modularized, app categories. There is also a high degree of perfor­
pretested, and reusable codes that enable firms to add mance variance because of this industry’s winner-takes-
specific app features at a low cost (Dalmasso et al. all nature, where popular games generate $2.4 million
2013, Atreyi et al. 2015). Modularity decision refers to per day compared with an average app at $4,000 (Strauss
the choice of these SDK modules in a typical mobile 2013). Within this segment, we focus on top-ranked
app development project and is conceptualized to have mobile apps only because unranked apps are primarily
three dimensions. The first dimension, multiplicity, is composed of nonprofit-focused apps, crude prototypes,
measured as the number of modules in the product and social experiments, which may not have market suc­
architecture (Novak and Eppinger 2001, Jacobs and cess as their primary objective.
Swink 2011, Vickery et al. 2016). The second dimension, We estimate the impact of SDK modularity deci­
compatibility, is measured as the degree of integration sions using an instrumental variables (IV) regression
knowledge and process overlap among modules (Succi model with the strongest instruments selected using
et al. 1998, Gershenson et al. 2003). The third dimension, the least absolute shrinkage and selection operator
novelty, is measured as the degree of preexisting inte­ (LASSO). Our results show that a 1% increase in SDK
gration experience or the newness of the module to the multiplicity leads to a 0.55% decrease in daily active
developer team (Griffin 1997, Novak and Eppinger users and a 0.10% decrease in weighted user ratings.
2001, Tatikonda and Stock 2003). However, we find that this negative effect of multi­
Our study aims to answer the following research plicity can be mitigated through higher SDK compati­
question. How does the product design of mobile apps bility. Specifically, we find that a 1% increase in SDK
along the three dimensions of modularity impact the compatibility significantly mitigates the negative effect
app’s initial stage success? Prior work on mobile apps of SDK multiplicity on weighted user ratings by 0.09%
 Chung, Sharma, and Malhotra: Impact of Modularity Design on Mobile App Launch
758 Manufacturing & Service Operations Management, 2023, vol. 25, no. 2, pp. 756–774, © 2023 INFORMS

and daily active users by 0.52%. We also find that a 1% research on modularity focuses on product design and
increase in SDK novelty significantly exacerbates the associated production challenges (Vickery et al. 2016,
negative effect of multiplicity on weighted user ratings Dooley et al. 2019), reorganizing/grouping tasks into
by 0.08% but not the effect on daily active users. Choos­ production cells (Baldwin and Clark 2000, Browning
ing SDKs with high compatibility and low novelty may 2001, Danilovic and Browning 2007), and modularity in
thus partially mitigate the negative impact of multiplic­ use (Christensen and Rosenbloom 1995, Baldwin and
ity, thus enhancing the app’s performance. We conduct Clark 2000). Modularity research for digital products
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a post hoc analysis to examine the impact of another focuses on its implication for the firm and software
design decision—business-to-consumer (B2C) commu­ performance-enhancing flexibility (Nambisan 2002),
nication SDKs. This analysis reveals that including B2C enhancing innovativeness (Sullivan et al. 2001), reduc­
communication SDKs can mitigate the negative impact ing cost (MacCormack et al. 2006), and affecting imitability
of multiplicity by 0.01% on weighted user ratings and (Ethiraj et al. 2008). All these prior studies conceptualize
0.19% on daily active users. modularity decisions along the single dimension of the
Our results have multiple implications for theory and degree of modularity and treat modules as homogenous.
practice. First, we propose a multidimensional frame­ However, this is an oversimplification of reality because
work for modularity decisions, thus extending the lit­ the modules’ integration difficulty, functional fit with
erature on product modularity that relies on a single other modules, and knowledge requirements are hetero­
measure (Nambisan 2002, Lau Antonio et al. 2007, geneous. Accounting for this heterogeneity of modules is
Danese and Filippini 2012, Vickery et al. 2016). Second, important as the integration challenges developers face
we extend the modularity literature into the digital may affect the entire system’s performance.
product design domain, where multiplicity’s negative Although prior research on modularity predomi­
effect is more pronounced. Our findings are aligned nantly focused on highlighting the positive effects of
with industry reports (Shoavi 2017) that express con­ modular design, some studies mention that increased
cerns about excessive modularization in mobile app modularization can lead to reduced innovation, lower
development because of the low cost of adoption for responsiveness to market needs, and suboptimal pro­
SDKs and the abundance of SDK options. Third, we duct configurations (Brusoni and Prencipe 2001, Ches­
delineate the significance of using multiple performance brough and Kusunoki 2001, Ethiraj and Levinthal
2004, Ernst 2005). This may be exacerbated in mobile
metrics. Although extant literature has primarily relied
apps because developers face challenges that differ
on user ratings only to evaluate app performance, we
from other settings. Most app developers are small
show the implications of modularity choices on both the
start-ups with fewer than 50 employees (Clutch 2018).
attitude of users (captured through weighted user ratings)
Correspondingly, app development budgets are tight
and their usage behaviors (captured through daily active
(generally below $250,000), with development times
users). Finally, our results yield a framework that aids
often less than three months to ensure quick time to
developers in making their modularity decisions. We first
market (Panko 2017). Given these constraints, app
note that the decision to expand the app feature set and
developers rely on SDKs—modularized solutions or
the resulting multiplicity should be carefully made
collections of software code libraries that quickly add
because of the multiplicity’s negative impacts. For low-
desired features to apps (see Online Appendix Table
multiplicity apps, the negative impacts are insignifi­ WA2) to optimize their effort and return on invest­
cant; hence, there is less need for a mitigation strategy. ment. Although modular design enabled by SDKs
However, high multiplicity can be inevitable, espe­ promotes knowledge reuse, which has been shown to
cially when developing feature-rich apps that target reduce development cost and time (Barnes et al. 1988,
more advanced users. In such situations, we recom­ Banker and Kauffman 1991) and improve final prod­
mend developers prioritize choosing highly compati­ uct quality (Knight and Dunn 1998), it can also lead to
ble SDKs and then, further supplement them with reduced innovation, lower responsiveness to market
low-novelty SDKs to alleviate the negative impacts of needs, and suboptimal product configurations (Brusoni
multiplicity. Adding B2C communication SDKs will and Prencipe 2001, Chesbrough and Kusunoki 2001,
also help improve app performance in such situations. Ethiraj and Levinthal 2004, Ernst 2005) because of
increased knowledge requirements of the functionality
2. Literature Review of individual SDKs and on calibrating performance at
2.1. Physical and Digital Product the SDK and app level. There may also be a lack of fit
Modularity Research of SDKs within the app architecture or between SDK
Prior literature has extensively studied product mod­ interfaces, which may severely affect app performance.
ularity for physical and digital products. A review of Therefore, although using SDKs has increased the ease of
research on product modularity is presented in Online integrating features, the resulting feature proliferation
Appendix Table WA1. Regarding physical products, has also increased crashes, viruses, malware, privacy
 Chung, Sharma, and Malhotra: Impact of Modularity Design on Mobile App Launch
Manufacturing & Service Operations Management, 2023, vol. 25, no. 2, pp. 756–774, © 2023 INFORMS 759

breaches, battery drain, and lags (Shoavi 2017, SafeDK 1, steps 1 and 2). This decision determines the total
2018). To aid developers’ app modularity decisions, number of features to be implemented in the app and
there is a need to disentangle the complex effects of defines the multiplicity of the modular system structure.
modularity on a product’s performance and provide Our first modularity dimension, multiplicity, is therefore
prescriptions for maximizing its benefits. measured by the total number of SDKs installed in the
app. Each SDK contributes to an app’s feature. Hence,
2.2. Modularity Dimensions and Sequential increased multiplicity contributes to building a feature-
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Decision Making in App Development rich app. Feature-rich apps generally attract more en­
We propose a multidimensional modularity decision gaged users with a higher lifetime value (Venkatesan
framework that disentangles these opposing effects and Kumar 2004, Dahana et al. 2019). At the same time,
on product performance. Specifically and as already higher multiplicity increases the complications of ensur­
mentioned before, we conceptualize modularity to ing fit between SDKs and integration of a more exten­
have three dimensions—multiplicity, compatibility, and sive feature set, thus requiring additional resources and
novelty, which are determined sequentially at different development time (Strong and Volkoff 2010, Berente
stages of the app development cycle. To better illustrate et al. 2016). This is especially true in the mobile app
this sequential set of decisions, we present in Table 1 a domain, where third parties, including open-source
typical mobile app development process, along with communities, often develop these SDKs—resulting in
the decisions regarding dimensions and stakeholders poor functional fit, which often necessitates the devel­
involved in each critical step of planning, design, devel­ opment of wrappers and interfaces to ensure seamless
opment, testing, and maintenance/support. This devel­ integration. High multiplicity also implies more possi­
opment process is compiled from case studies (Flora ble interdependencies to manage (Vickery et al. 2016).
et al. 2014, Ghandi et al. 2017), meta-analyses (Jabangwe These interdependencies may cause design changes to
et al. 2018), and anecdotal evidence (Invonto 2022). impact multiple modules simultaneously and impose
Decisions in the planning phase (strategy/analysis further challenges for developers, eventually affecting
and planning) involve determining the app platform the app’s quality. Too many features may also nega­
and core feature requirements of the app, which are tively impact the users’ experience because of a steeper
made by the marketing/management of the firm (Table learning curve associated with app usage (Thompson

Table 1. Mobile App Development Process

Step Decisions Stakeholders Modularity decisions

1. Strategy Identify app target segment Marketing/management Multiplicity

Analyze competition
Establish goals and budgets
Identify the required feature set for the app
2. Analysis and planning Build project road map Project manager Multiplicity
Define minimum viable product
Identify technology and resource requirements
3. User interface (UI)/ Determine data to display and collect UX/UI designer Compatibility, novelty
user experience (UX) design Build menu structures (wire frame)
Determine the app’s design standards
Create interactive mockups and prototypes
4. App development Define the technical architecture Development engineer Compatibility, novelty
API, backend server technology configuration
Deploy agile development using SDKs
SDK module selection to realize feature set
Crossplatform design
Define programming language
5. Testing User experience testing Quality assurance team
Functional testing
Performance testing
Security testing
Device and platform testing
6. Deployment and support App store metadata preparation and release Service operation team
Monitor key performance indicators and
crash reports
Enhancement and maintenance activities
Source. Flora et al. (2014), Ghandi et al. (2017), Jabangwe et al. (2018), Invonto (2022).
 Chung, Sharma, and Malhotra: Impact of Modularity Design on Mobile App Launch
760 Manufacturing & Service Operations Management, 2023, vol. 25, no. 2, pp. 756–774, © 2023 INFORMS

et al. 2005). Finally, heavy reliance on third-party mod­ recommend integrating other supporting SDKs, such as
ules may also reduce the innovativeness of the app and OFX, which add capabilities to sell virtual goods, virtual
make them look generic in its appearance and function­ currency, and downloadable content. As a result, these
ality (Xue et al. 2019). SDK pairs are frequently installed together in an app.
Given the number of features for the app under Another decision developers can consider when
development, developers can influence the ease of SDK coping with SDK integration challenges during the
integration and app performance through a deliberate design and development phase (Table 1, steps 3 and
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choice of SDKs. During the design and development 4) is to rely on familiar SDKs. Our third dimension of
phase, the design and coding engineers translate these modularity, novelty, captures the developer’s degree
feature requirements into technological code structures of newness of the SDKs installed in an app. Because
and choose/implement the corresponding SDKs (Table 1, novelty reflects the developer’s lack of experience with
steps 3 and 4) (Masi et al. 2012). Integration or coordina­ using an SDK, it measures the integration challenges
tion of an SDK refers to installing the SDK package resulting in increased development lead time and
library to the integrated development environment and reduced product quality (Clark and Fujimoto 1991,
configuring the various SDK component parameters to Griffin 1997, Novak and Eppinger 2001, Tatikonda
add a particular feature to the app under development. and Stock 2003) when integrating modules into the
The available SDK component parameters and the re­ product. Reducing novelty means developers tap into
sulting features that can be built vary from SDK to SDK. their prior development experience and choose SDK
This variation in configuration parameters requires devel­ pairs that they are already familiar with. Even if the
opers to study and learn each SDK to integrate it properly developers assigned to the integration task do not
(Murkin 2021). For example, integration effort may be possess the knowledge, they can interact with their
lower for modules with relatively more standardized colleagues in the firm to indirectly access the required
interfaces—which refers to common, agreed-upon me­ know-how (Narayanan et al. 2009). However, SDKs
chanisms for interaction (Baldwin and Clark 2000) and with a high degree of novelty will not have the
integration processes (Succi et al. 1998, Gershenson required know-how even among the pooled group of
et al. 1999). An overlap may exist in the configuration developers in a team.
parameters or the configuration user interface between
SDKs, so that knowledge for integration of one SDK
may be similarly applied to another SDK. We refer to
3. Research Hypotheses
this degree of the integration process and interface 3.1. Main Effect of SDK Multiplicity
overlap between a given SDK pair as SDK compatibil­ SDK multiplicity influences the feature richness of an
ity. Highly compatible SDKs may contribute to the app. Although offering higher multiplicity is viewed to
same app feature cohesively (i.e., perform similar SDK positively impact the users’ perceived quality (Carpenter
logic). These SDKs are often installed together as a et al. 1994, Brown and Carpenter 2000) and achieve dif­
standard best practice in building certain app features. ferentiation from competitors (Nowlis and Simonson
Relying on this observation, we capture SDK compati­ 1996), the negative impacts listed earlier may outweigh
bility as the average degree of SDK pair co-occurrence these benefits for mobile apps. Still, high levels of multi­
frequency in the app market for all SDK pairs installed plicity may occur for feature-rich apps that target more
in an app. When a developer chooses highly compati­ advanced users. From the user perspective, higher mul­
ble SDKs, the existence of best practices and tools (e.g., tiplicity requires them to learn about these new fea­
interface bridges) to integrate them seamlessly can poten­ tures, which may cause feature fatigue (Thompson
tially reduce software glitches and required development et al. 2005). Because higher multiplicity increases the
resources. required knowledge and skill levels of the users, it may
As an example of compatibility, chat server support also limit the product appeal to a limited set of users who
SDKs are commonly installed with multiplayer sup­ are more experienced and have the time and commitment
port SDKs because they jointly facilitate the multi­ to understanding the complicated interactions between
player environment with user interactions. Because the features (Qiu et al. 2017).
player communication features are essential in a mul­ From the developer perspective, although using
tiplayer environment, most multiplayer apps also install third-party SDKs allow developers to add features
chat support SDKs, with integration best practices being conveniently, they also result in a low functional fit
well documented. SDK developers even recommend with the developers’ unique needs (Strong and Volk­
some SDK combinations with high compatibility. For off 2010, Berente et al. 2016). Additionally, higher mul­
example, OpenFeint is a popular mobile app develop­ tiplicity can potentially compromise product performance
ment SDK that adds multiplayer support such as lead­ and represent other challenges at the development end.
erboards, achievements, forums, community space, and Even though some end users may prefer apps with many
real-time player communication. OpenFeint developers features and higher SDK multiplicity, they may not like
 Chung, Sharma, and Malhotra: Impact of Modularity Design on Mobile App Launch
Manufacturing & Service Operations Management, 2023, vol. 25, no. 2, pp. 756–774, © 2023 INFORMS 761

the product if it is clunky or slow functioning. Further, high-compatibility SDKs will make the integration
integrated SDKs could have differential user preferences, challenges less pronounced than the former, as develo­
data and device resource requirements, and interactions pers will be able to leverage existing best practices and
with other SDKs. Increasing SDK installations without an tools to integrate compatible SDKs seamlessly. Hence,
in-depth understanding of their integration challenges compatibility should have a beneficial moderating impact
may lead to increased crashes, viruses, malware, privacy on the relationship between multiplicity and initial market
breaches, battery drain, and lags (Shoavi 2017, SafeDK success.
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2018). Increasing multiplicity makes this comprehension

Hypothesis 2. SDK compatibility positively moderates the
of the unique features of many different SDKs more chal­
relationship between multiplicity and initial market success.
lenging for developers. Hence, we argue that develop­
ment challenges associated with multiplicity and the Novelty measures the developer’s degree of newness
associated performance issues will outweigh the incre­ of the SDKs installed in an app. An SDK is deemed
mental benefits of a richer set of features, leading to our new when the developer firm has no prior experience
first hypothesis. using the SDK for mobile app development. The in­
creased novelty comes with significant development
Hypothesis 1. Increased SDK multiplicity is negatively
challenges (Novak and Eppinger 2001, Tatikonda and
associated with initial market success. Stock 2003), resulting from high task uncertainty associ­
ated with SDK integration. Additionally, with increasing
3.2. Interaction Effects of SDK Compatibility
novelty, app performance issues may also arise from
and Novelty unanticipated interactions between the novel and mature
In addition to their main effect, app developers’ SDK
SDKs. This will necessitate increased testing and develop­
choices concerning compatibility and novelty may in­ ment time, a luxury not available to most app developers.
fluence the relationship between multiplicity and the Hence, the increased novelty of added SDKs may
app’s initial market success. Increased compatibility adversely impact the app quality and initial market
has significant benefits for the app developer. SDKs success in the hypercompetitive mobile app domain.
for mobile apps, in general, have a less optimal fit We argue that as multiplicity increases for apps
than modules for physical products. This nonideal fit with high novelty, the development challenges of inte­
often requires additional effort in understanding the grating multiple untested SDKs with potentially poor
functionality of SDKs, their functional fit within the functional fit will compound the negative impacts of
app architecture, and their interaction with other SDKs. It multiplicity on app performance. Integrating multiple
may also require additional coding to ensure seamless SDKs and managing the interactions between distinct
information exchange between SDKs through their SDK features can get challenging for new SDKs unfa­
(generic) standardized interfaces. Compatible SDKs miliar to the developers because they generally do not
often come from the same third-party developer or are have a stable, well-understood set of interactions
used together extensively, resulting in more compati­ (Tatikonda and Stock 2003). Further, because of their
ble interfaces for information exchange, better integra­ third-party development, the nonideal functional fit
tion documentation, and tools to facilitate seamless between SDKs further exacerbates the integration
integration (e.g., interface bridges, SDK wrappers, etc.), and coordination challenges for a seamless fit in the
which increase the cohesiveness of the modules (Gershen­ product architecture and information flow for novel
son et al. 2003). We see these factors as positives for SDKs. On the other hand, development challenges
end-user experience and hence, market performance. associated with integrating SDKs into the app will be
We hypothesize that compatibility will positively less pronounced as multiplicity increases for low-novelty
influence the direct relationship between multiplicity SDKs. Hence, novelty should negatively moderate the
and app performance because high compatibility en­ relationship between modularity and initial market suc­
hances app quality by reducing performance issues of cess, which leads to Hypothesis 3.
the final product and allows the developer to cope with
a greater degree of multiplicity. High-compatibility Hypothesis 3. SDK novelty negatively moderates the rela­
SDKs will have well-documented integration know- tionship between multiplicity and initial market success.
how and tools (like interface bridges) to ensure seam­
less integration (Gershenson et al. 1999). This reuse of 4. Sample Construction and Data
integration process know-how accumulates propor­ Description
tionally with multiplicity. If multiplicity increases with To test the relationships, we extracted mobile app mar­
low compatibility, development challenges associated ket data from a leading app store intelligence company’s
with integrating untested SDK pairs will increase, proprietary API server. At the time of data extraction,
resulting in performance issues with the app. On the the API server contained a global (160 countries) daily
other hand, an increase in multiplicity supported by panel database of approximately 2 million apps in the
 Chung, Sharma, and Malhotra: Impact of Modularity Design on Mobile App Launch
762 Manufacturing & Service Operations Management, 2023, vol. 25, no. 2, pp. 756–774, © 2023 INFORMS

iOS market over 3.5 years from 2015 to 2018. To obtain 4.1. Variable Description
the sample for our research purpose, we restricted the For each variable description, we include subscripts i
app category to gaming apps in the U.S. app market to denote apps, j for app developers, c for the subcate­
(252,568 apps). Then, we selected freemium apps (i.e., gory, t for the initial success date, and l for the app
apps with no download fees), which still account for launch date.
92% of the apps in the category (232,362 apps). We fur­
ther narrowed it down to the top 1,000 gross down­ 4.1.1. Dependent Variable. We argue that initial suc­
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loaded apps. To the top 1,000 ranked apps in 2015, we cess is critical to the app’s overall success. The mobile
added 2,192 apps that entered the ranking between 2016 app market has a compressed two-stage product life
and 2017. Finally, to calculate some of the instruments cycle with rapid exponential growth followed by grad­
included in our model, we only included apps devel­ ual decay. The peak number of daily users achieved
oped by developers that had launched at least two or during this initial stage most likely represents the
more apps in the market during our time frame. This app’s maximum market penetration, putting an upper
dropped 709 apps from our sample. The resulting sam­ limit on the revenue generation potential for the app. We
ple size consists of 2,483 apps in total. For all apps, we propose a way to identify an app’s initial success and
extracted general information such as file size, in-app define two performance measures—review rating, which
purchase availability, and release dates at the time of captures the users’ satisfaction with the app’s quality, and
launch. As for app performance metrics, we collected logged daily active user, which captures the users’ level
daily/monthly active users and five-star user ratings. of engagement based on actual app execution behavior.
Information on SDKs is compiled by the proprietary mar­ Quality perceptions or satisfaction toward a product
ket intelligence firm, which downloads each new app form the basis of the users’ attitude. Prior research has
and then decompiles, decrypts, and analyzes the source shown that attitude is a strong precursor of behavior but
code to see which SDKs and development tools are not a sufficient condition (Ajzen and Fishbein 1977, Fazio
installed/uninstalled daily. After deleting apps with et al. 1989). Similarly, the quality of a service or product
missing data, our final sample had 2,282 apps from may be a factor that causes user churn, but there can be
804 developers. other factors (e.g., switching cost) that can prevent the
In this study, we restrict our focus to iOS gaming user from churning (Bolton 1998, Ahn et al. 2006). Using
apps in the United States that attained a top 1,000 the two dependent variables, we can assess whether a
rank at least once during their lifetime. This is done specific modularity decision impacts only attitude or
because the iOS market provides a user experience on both attitude and behavior.
relatively homogeneous iPhone devices (Cuadrado We use the time series of daily active users to iden­
and Dueñas 2012). This eliminates concerns about tify the app’s initial success. However, the noisy daily
unobserved user device characteristics from our model fluctuations in time series make identification diffi­
estimations. The iOS platform also has extensive guide­ cult. Therefore, to find the first peak in performance
lines that SDKs must conform to. This results in a highly from the underlying trend in daily active users, we
integrated SDK environment that provides a more ho­ rely on a commonly used technique in finance where
mogenous user experience. Focusing on a single country the shift in stock price trends is detected by moving
allows us to reduce country-level confounds and over­ average crossover points (Murphy 1999, Pätäri and
come language barriers in the data. Additionally, by con­ Vilska 2014). The crossover point is defined as the time
centrating on top-ranked apps, we eliminate those apps point when the short-term moving average crosses a
that tend to be social experiments or crude prototypes long-term average. This crossover signals that the
that are not aiming for commercial success. We focus on momentum is shifting in one direction—signaling a
mobile gaming apps because developers in this category growth or decline for the underlying trend. We define
continuously experiment with novel development pro­ the maximum before the first crossover that signals a
cesses and adopt cutting-edge technologies to expand the declining trend as the initial success. The time of initial
user base and prolong app life span. This is evident in success t is identified by calculating a short-term 3-day
the average app development cost (Dogtiev 2018) and the moving average and a relatively long-term 10-day
average number of SDKs embedded in gaming apps, moving average of daily active users and then finding
which are the highest across all app categories (Shoavi the first crossover points. The peak is the maximum
2017). Additionally, gaming apps are the revenue-driving daily active user of the raw variable that occurs right
category for the entire market, and there is a high degree before the first moving average crossover point. To
of performance heterogeneity because of this industry’s choose the appropriate moving average lengths, we
winner-takes-all nature. Finally, we identify our time search for the earliest global maximum in the apps’
point of initial success for each app using the method daily active user time series. The app that reached the
described in Section 4.1.1 and convert it into cross- global maximum the earliest was 15 days after launch.
sectional data to conduct the analysis. If we set the long-term moving average to longer than 15
 Chung, Sharma, and Malhotra: Impact of Modularity Design on Mobile App Launch
Manufacturing & Service Operations Management, 2023, vol. 25, no. 2, pp. 756–774, © 2023 INFORMS 763

days, we would lose these observations from our sample. where S(n1 , : : : , n5 ) is the adjusted star score based on
We then tested multiple moving average lengths within the number of k star ratings (nk ), sk is the point value
15 days that accurately capture this earliest global maxi­ for k stars (i.e., one point for one star, five points for
mum. The maximum identification was robust to 62 days five stars), N is the total number of ratings, K is the
in moving average windows. Contrary to our concerns maximum number of stars (i.e., K � 5 for a five-star
about time series fluctuations, there were minimal fluctua­ rating system), and zα=2 is the 1 � α=2 quantile of a
tions in the early stage. An alternative method of using first normal distribution (i.e., we use 1.65 for 95% confi­
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differencing (i.e., the first-order derivative of the raw daily dence). We also provide estimation results using the
active user variable) and choosing the peak point where raw review score as a robustness check.
the slope changes from positive to negative yielded consis­ To capture the user engagement levels, we use
tent results. An example of identifying the initial peak lndaui,t, an app i’s logged daily active users at the time
using two moving average lines is shown in Figure 1. of initial success t. Variable daily active users are users
From the initial success, we define two dependent that open the app at least once during a particular
variables of interest to estimate the impact of the day. In prior literature, the number of downloads
developers’ SDK choice on the user-perceived quality (Garg and Telang 2013, Ghose and Han 2014) and app
of the app and user engagement using wratingsi,t and rankings (Lee and Raghu 2014) were primarily used
lndaui,t. wratingsi,t is the app i’s Bayesian-weighted to measure app demand. In contrast, daily active users
average five-star user rating score at the initial success measure user engagement (i.e., stickiness) of the app
date, t. A five-star user review rating, a commonly used because it captures revisiting users and drops churned
measure for the user-perceived quality of a product, is users. Therefore, daily active users is a performance
used to capture the users’ perception of the app’s quality metric that mobile app practitioners carefully moni­
(Lee and Raghu 2014, Wang et al. 2018). We acknowledge tor. Market-leading mobile app analytics tools such
the possibility of measurement error in the raw five-star as Flurry Analytics and Google Play Developer Con­
review ratings. Ratings with a small number of reviews sole collect and provide this metric to developers
cannot represent the true quality of the app, and such (Del Gallego et al. 2016).
review scores can be easily swayed by outliers (i.e.,
extremely satisfied dissatisfied users) (Talton et al. 2019). 4.1.2. Independent Variables of Interest. The variable
Hence, we estimate the main effects using the Bayesian multiplicityi,l is the total number of app i’s installed
adjusted review score measure (Miller 2014, Li and Hecht SDKs at launch l. We measure compatibilityi,l as the
2021), adopted by Amazon and several other e-commerce average degree of co-occurrence frequency between
sites (Zhang et al. 2011). This method calculates the Bayes­ all SDK pairs installed in app i at the time of launch l.
ian adjusted rating score as follows: The concept of co-occurrence is often used in research
S(n1 , n2 , n3 , n4 , n5 )
to infer the degree of skill overlap or relatedness
among individuals, organizations, and industries
XK
nk + 1 based on the outcome state (Teece et al. 1994, Neffke
� sk
k�1
N +K and Henning 2013). We assume that SDKs endorsed
vffi�
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi�
ffiffiffi as compatible pairs by the SDK developers or known
u �
u XK 2 nk +1� �PK nk +1�2 to be compatible combinations to implement a cohe­
u s � k�1 sk N+K
t k�1 k N+K
sive app feature will frequently co-occur in apps. First,
� zα=2 , (1)
N+K+1 we calculate the co-occurrence frequency matrix
between all SDK pairs for the entire sample. Second,
all pairwise frequencies are normalized. Third, we cre­
Figure 1. (Color online) Initial Peak Identification of ate a pairwise matrix for each app that indicates
“Cinderella Fall” by Disney whether an SDK pair is installed. Finally, the normal­
ized frequencies are added and averaged per app for
all existing SDK pairs to calculate compatibility.
The variable noveltyi,l is defined as the developer’s
average degree of newness of the SDKs installed in an
app i at the time of launch l. We use a binary variable
to indicate whether a specific SDK is new to the devel­
oper during the app development. Next, we calculate
the focal app’s average newness of all SDKs. One par­
ticular concern regarding noveltyi,l is the potential con­
founding with the novelty of the SDK to the users.
This would happen mainly when the developer intro­
duces a novel SDK in their app, which is also a novel
 Chung, Sharma, and Malhotra: Impact of Modularity Design on Mobile App Launch
764 Manufacturing & Service Operations Management, 2023, vol. 25, no. 2, pp. 756–774, © 2023 INFORMS

SDK to the users. If we do not control for the con­ visual appeal is an essential factor influencing user
founder, market SDK novelty, omitted variable bias adoption. An app’s file size with heavy graphical fea­
may be an issue. We address this endogeneity by (1) tures and three-dimensional modeling can go up to six
adding a control variable for the market-level novelty gigabytes. These visual resources account for most
of the SDK; (2) accounting for developer unobserved of the app’s file size. Therefore, controlling the app’s
heterogeneity that may influence the innovativeness file size allows us to control the aesthetical aspect of
of the developer, such as developer fixed effects, the app (Liu et al. 2014, Wang et al. 2018). We control
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developer app portfolio size, and number of employ­ the market hype for the app that may build up before
ees that proxies the resources of the developer; and the app launch by including firsthypei,l, which is the
(3) instrumenting novelty with instruments that are logged number of downloads of an app i during the
orthogonal to user SDK novelty. first launch date l (Lee and Raghu 2014). To control
developer effects, we include empnoj,l, which is the
4.1.3. Control Variables. For control variables, com­ number of employees at developer j at the time of app
petitionc,t is measured as the Herfindahl–Hirschman launch l. This variable is a proxy for the developer’s
Index in a subcategory c at the initial success t. This resources, potentially affecting app innovativeness
controls the increase in market competition, which and SDK decisions. We also include appnumj,l, which
can affect the demand and quality perceptions (Roma is the number of apps that developer j is operating at
and Vasi 2019). Variable devexpj,l is the cumulative the time of focal app launch l. This variable accounts
number of apps developed and launched by the de­ for the resource constraints and level of managerial
veloper j at the time of launch l. This variable controls attention that the developer can spend on the newly
for developer experience and brand effects that may launched app (Lee and Raghu 2014, Allon et al.
influence the initial performance of the app (Ghose 2022). To account for market-level novelty, we create
and Han 2014, Lee et al. 2020). Variable ageresi,l is the mktnoveltyi,l by first using a binary variable for each
four classification age-restriction levels coded as one SDK that indicates the first app that adopted the SDK
for “ages 4+,” two for “ages 9+,” three for “ages 12+,” in our sample and then averaging the binary variable
and four for “ages 17+” for an app i at time of launch for each app i to calculate the average market-level nov­
l, which is treated as a continuous variable in our anal­ elty at launch l. We include subcategory fixed effects to
yses. Apps with different age restriction levels target control for potential user behavior heterogeneity. We
different user segments in the market, which may add year and month fixed effects to control for unob­
vary in size and preferences (Ghose and Han 2014). served time-correlated shocks. We include developer
Variable multicategoryi,l is the number of subcategories fixed effects to account for developer unobserved
that app i is enlisted at the time of launch l. As the heterogeneity.
number of enlisted subcategories increases, the app Tables 2 and 3 show the summary statistics and corre­
can get exposure to a broader user pool through cross­ lations of the key variables used in our model. All vari­
genre listings (Ghose and Han 2014). Variable multi­ able correlations are below 0.7, and the mean variance
platformi,l is an indicator variable coded as one for an inflation factor (VIF) scores of all estimated models are
app i launched in multiple operating platforms at the below 10. For focal independent variables (i.e., multiplic­
time of launch l to control potential spillover effects ity, compatibility, novelty), mean VIF scores are below
across app stores (Ghose and Han 2014). Similarly, five (Kutner et al. 2004). Therefore, we do not find strong
the variable adsi,t controls the developer’s marketing evidence of multicollinearity in the data.
efforts by measuring the number of impressions (expo­
sures) made for the app i in the advertisement network 5. Econometric Model
at initial success t. We control the app i’s file size mea­ We present instrument variables to address endogene­
sured in bytes at launch l using appsizei,l. The app’s ity in SDK multiplicity, compatibility, and novelty.

Table 2. Descriptive Statistics

Variable Mean Standard deviation Min Max Variable Mean Standard deviation Min Max

ln(DAU) 6.74 2.76 0 13.53 Multiplatform 0.21 0.41 0 1

Wratings 2.4 0.73 1.31 4.87 Multicategory 3.56 0.58 2 4
Multiplicity 9.4 6.61 1 38 Ads 0.11 0.88 0 11.08
Compatibility 0.13 0.09 0 0.32 Appsize 4.67 0.91 1.75 8.3
Novelty 2.3 3.47 0 15 Competition 0.08 0.12 0 1
Mktnovelty 0 0.01 0 0.26 Empno 11.59 16.76 1 358
Devexp 7.49 11.95 1 87 Appnum 5.73 10 1 76
Firsthype 4.06 2.19 0 11.93 Ageres 1.80 0.99 1 4
 Chung, Sharma, and Malhotra: Impact of Modularity Design on Mobile App Launch
Manufacturing & Service Operations Management, 2023, vol. 25, no. 2, pp. 756–774, © 2023 INFORMS 765

Table 3. Correlation Matrix

Variables (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15)

Dependent variable
(1) ln(DAU)
(2) Wratings 0.34*
Independent variable
(3) Multiplicity 0.06* 0.02
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(4) Compatibility 0.20* 0.12* 0.67*

(5) Novelty 0.12* 0.06* 0.03 0.20*
Controls
(6) Mktnovelty 0.02 0.02 0.04 0.02 0.09*
(7) Devexp 0.03 �0.05* 0.12* 0.05* �0.33* �0.03
(8) Firsthype 0.74* 0.22* 0.05* 0.15* 0.04* 0.01 0.09*
(9) Multiplatform 0.19* 0.11* �0.03 0.12* 0.18* 0.01 �0.11* 0.11*
(10) Multicategory �0.11* 0.00 0.03 0.02 �0.08* 0.02 �0.03 �0.08* �0.01
(11) Ads 0.12* 0.04* 0.05* 0.06* 0.00 �0.01 0.07* 0.10* �0.05* �0.01
(12) Appsize 0.35* 0.12* 0.06* 0.10* 0.05* 0.02 0.01 0.30* 0.15* �0.10* 0.08*
(13) Competition �0.01 0.06* �0.02 0.03 0.06* 0.14* �0.14* �0.05* 0.07* 0.05* �0.05* �0.08*
(14) Empno 0.21* 0.04* 0.03 0.01 �0.01 0.09* 0.05* 0.13* 0.07* �0.12* 0.03 0.20* 0.04
(15) Appnum 0.00 �0.06* 0.05* 0.01 �0.31* �0.03 0.95* 0.06* �0.10* �0.05* 0.06* �0.01 �0.12* 0.01
(16) Ageres �0.15* �0.06* �0.17* �0.15* �0.02 �0.02 �0.20* �0.14* �0.05* 0.07* �0.03 0.00 �0.06* �0.01 �0.18*
*p < 0.05.

For interactions, we include the interactions of in­ multiplicity, compatibility, and novelty levels in each
struments to the instrumental variable set. Next, we mean calculation. The logic is derived from previous
address the resulting high dimensionality of the works that identify instruments from average charac­
instruments by using the LASSO (Tibshirani 1996) teristics of other products supplied by the same firm
with postdouble-selection method (Belloni et al. 2012, (i.e., capability and resources within the firm), pro­
2013), which penalizes weak instruments and instru­ ducts provided by other firms within the same market
ments correlated with the error term to refine the (i.e., industry standards or conventions) (Berry et al.
instruments. 1995), or studies that use level of market power or
competitive pressure within the market (Berry and Jia
5.1. Endogenous SDK Multiplicity, Compatibility, 2010, Sharma et al. 2020). Mobile app developers’
and Novelty decision on SDK installation can be affected by the
Developers’ SDK choice may depend on unobserved degree of multiplicity, compatibility, and novelty the
app-specific characteristics such as the quality of the developer is comfortable handling, competitive pres­
idea, monetization structure, addictiveness, etc. These sures in the market, and cultural/local development
additional characteristics are shaped with the support conventions. However, these capabilities and external
of features enabled by SDKs. However, these charac­ environments are less likely to be correlated with the
teristics can also directly impact the users’ perceived app-specific unobserved characteristics, which are the
quality and engagement levels. Because we do not sources of endogeneity.
have controls for these characteristics, the estimation
for our variables of interest, multiplicityi,l, compatibilityi,t, 5.2. IV Regression with LASSO Selection
and noveltyi,t, may be biased because of omitted factors We evaluate the impact of SDK modularity decisions
(Wooldridge 2010). To address this endogeneity con­ on an app’s market performance using an IV regres­
cern, we rely on instruments that explain the develo­ sion model. For app i, subcategory c, developer j at
per’s SDK decisions at the time of launch that are less the time of launch l and time of initial success t, we
likely to be correlated with the omitted factors. We formulate the following system of equations:
report both instrumental variables regression and stan­ yi,l � β0 + B1 X + B2 Γ + δc + ξj + τl + ɛi,l (2)
dard ordinary least squares (OLS) estimation results for
xi,l � β10 + Θ1 Z + Θ2 Γ + δc + ξj + τl + ui,l , (3)
comparison.
For endogenous SDK choice, we use instruments where yi,l denotes the dependent variables wratings
devaj,l , subcatac,l , and countryak,l , which are defined as the and lndau and B1 is a vector of estimated coefficients
mean values of the endogenous variable a of installed for our endogenous variables of multiplicity, compatibility,
SDKs in apps within the same developer j, subcate­ and novelty. The vector B2 contains the estimated coeffi­
gory c and apps developed in the same country k at cients for control covariates in the vector G. We include
the time of launch l. We exclude the focal app’s subcategory fixed effects, dc , developer fixed effects, jj ,
 Chung, Sharma, and Malhotra: Impact of Modularity Design on Mobile App Launch
766 Manufacturing & Service Operations Management, 2023, vol. 25, no. 2, pp. 756–774, © 2023 INFORMS

and time fixed effects (i.e., year, month), tl . Errors are As our IV regression model is overidentified, we can
represented by the term, ei,l . For each endogenous vari­ test whether the excluded instruments are appropriately
able xi,l in vector X, we estimate the first-stage model independent of the error process. We report the test re­
where Z is a vector of instruments including devaj,l , sult of overidentifying restrictions using the Hansen J,
subcatac,l , and countryak,l . Coefficients of the instruments which regresses the residuals from the 2-stage least
and control covariates are represented by Q1 and Q2 , squares equation on all instruments (Hansen 1982). We
respectively. For interaction models, all interactions are fail to reject the null hypothesis that all instruments are
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treated as endogenous. We include interactions of the uncorrelated with the error term and that the overidenti­
instrumental variables in the vector of Z (Bun and Harri­ fying restrictions are valid for all models (p > 0.10).
son 2019). All models are estimated with robust standard Overall, the test results reported in Table 4 jointly sup­
errors clustered at the developer level. port the validity of our instruments.
For each endogenous variable, we have three in­
struments. For the interaction models, we have nine 6. Empirical Results
instruments for each interaction term. With the high This section presents our results from estimating the
dimensionality of instruments, the validity assumptions instrumental variable regression model and demon­
can be easily violated. Therefore, we apply the LASSO strates robustness with alternative specifications.
on the first-stage regression (Tibshirani 1996) using the
postdouble-selection method (Belloni et al. 2012, 2013), 6.1. Estimation Results
which selects strong instruments among the available The results of the estimates are shown in Table 4. The
set. When instruments are added to the first-stage first two columns show effects on weighted user rat­
model, LASSO penalizes each addition of an instrument ings, whereas the following two show effects on
by minimizing the following objective function, Q(B, Θ): logged daily active users. In Models (1A) and (2A),
X
n we present estimates of the direct effect of SDK multi­
Q(B, Θ) � ‖multiplicityi,l � ΓB � ZΘ‖2 + λ θj , (4) plicity. Compatibility and novelty are included but
j�1
are considered control covariates because their effects
where G is the vector of exogenous regressors with depend on multiplicity, which needs to be examined
estimated coefficient vector B and Z is the vector of in an interaction model. We calculate the elasticities
instruments with estimated coefficient vector Q. The for the estimated effects. We find that a 1% increase in
last term in Equation (4) is the penalization term on SDK multiplicity leads to a decrease in weighted user
each instrument coefficient, θ, with a weight of λ. The ratings by 0.10% (β � �0.024, p < 0.01) and daily active
selection process chooses instruments that are strongly users by 0.55% (β � �0.058, p < 0.01). These results
correlated with the endogenous variable. We report provide support for Hypothesis 1. Despite the benefits
the first-stage estimation results in Online Appendix of increased app features, SDK multiplicity leads to
Table WA3, showing the LASSO-selected instruments. reduced app performance during its initial period.
For the IV regression to produce an unbiased esti­ Next, we introduce interactions into the model
mate, the instruments must satisfy two validity as­ to estimate the interaction effects between the focal
sumptions. First, the instrument must be relevant independent variables. As shown in Models (1B) and
such that it is strongly correlated with SDK multiplic­ (2B), we find a significant positive moderation effect
ity, compatibility, and novelty. We can verify this by of compatibility on the relationship between SDK
analyzing the first-stage regression F statistics of the multiplicity and app performance. Specifically, a 1%
instruments on the endogenous variables (Staiger and increase in compatibility mitigates the negative rela­
Stock, 1994). The robust rk Wald F statistics for the tionship between multiplicity and weighted user rat­
first-stage regression are 76.53 for the main effect ings by 0.09% (β � 0.130, p < 0.05) and the negative
model and 17.77 for interaction models. These values relationship between multiplicity and daily active
are larger than the critical values of 12.20 for 5% maxi­ users by 0.52% (β � 0.314, p < 0.05). Thus, Hypothe­
mal IV relative bias and 12.33 for 15% maximal IV sis 2 is supported. On the other hand, the modera­
size, which suggests that the instruments are not tion effect of novelty is significant and negative on
weakly associated with the endogenous regressor the relationship between multiplicity and weighted
(Stock and Yogo 2005). The significant underidentifi­ user ratings but insignificant on the relationship
cation of test results for all models (p < 0.01) further between multiplicity and logged daily active users.
support the relevance of the instruments (Kleibergen Specifically, a 1% increase in novelty exacerbates the
and Paap 2006). Second, the instrument must plausi­ negative effect of multiplicity on weighted user ratings
bly satisfy the exclusion restriction, which means that by 0.08% (β � �0.002, p < 0.05). Therefore, Hypothesis 3
the instruments do not directly influence the weighted is only partially supported. We present the interaction
review ratings and daily active users at the initial peak. plots in Figure 2 with low and high levels of the
 Chung, Sharma, and Malhotra: Impact of Modularity Design on Mobile App Launch
Manufacturing & Service Operations Management, 2023, vol. 25, no. 2, pp. 756–774, © 2023 INFORMS 767

Table 4. Main IV Regression Results

(1A) (1B) (2A) (2B)

Model
Dependent variable Wratings ln(DAU)

Key independent variables

Multiplicity �0.024*** �0.044*** �0.058*** �0.129***
(0.007) (0.013) (0.015) (0.030)
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Compatibility 1.666*** 0.392 4.816*** 2.028

(0.481) (0.716) (0.955) (1.422)
Novelty �0.016** 0.016 �0.000 �0.035
(0.008) (0.016) (0.018) (0.032)
Multiplicity × Compatibility 0.130** 0.314**
(0.065) (0.133)
Multiplicity × Novelty �0.002** 0.004
(0.001) (0.002)
Controls
Devexp 0.017* 0.016* 0.015 0.020
(0.010) (0.010) (0.022) (0.023)
Firsthype 0.019* 0.024** 0.515*** 0.519***
(0.012) (0.012) (0.029) (0.029)
Multiplatform 0.015 0.011 0.184 0.187
(0.060) (0.060) (0.125) (0.125)
Multicategory 0.159*** 0.141*** 0.135 0.109
(0.047) (0.046) (0.113) (0.115)
Ads 0.013 0.013 0.109** 0.112**
(0.021) (0.021) (0.049) (0.050)
Appsize �0.015 0.007 0.166** 0.182**
(0.035) (0.034) (0.084) (0.086)
Competition �0.105 �0.065 0.377 0.370
(0.198) (0.200) (0.417) (0.454)
Ageres �0.036 �0.029 �0.116* �0.119*
(0.026) (0.026) (0.062) (0.063)
Empno �0.028*** �0.023*** 0.039 0.048
(0.007) (0.007) (0.024) (0.031)
Appnum �0.021* �0.020* �0.014 �0.015
(0.011) (0.011) (0.025) (0.025)
Mktnovelty �5.036 �0.252 0.861 2.029
(4.353) (2.839) (6.441) (10.848)
Year/month FE Yes Yes Yes Yes
Subcategory/developer FE Yes Yes Yes Yes
Observations 2,282 2,274 2,282 2,274
R2 0.016 0.009 0.283 0.280
Number of instruments 6 11 6 11
Weak identification (F statistic) 76.53 17.77 76.53 17.77
Under identification (p-value) <0.001 <0.001 <0.001 <0.001
Overidentification (p-value) 0.163 0.234 0.450 0.191
Notes. Robust standard errors clustered by developer are in parentheses. Five percent maximal IV relative bias critical value: 12.20 (three
endogenous variables); 15% maximal IV size critical value: 12.33 (two endogenous variables) (Stock and Yogo 2005). FE, fixed effect.
*p < 0.1; **p < 0.05; ***p < 0.01.

variable calculated as 61 standard deviation from We find that this negative impact is mitigated if the
the mean. Across all plots, we see a negative perfor­ developers choose highly compatible SDKs or fewer
mance impact of multiplicity. As the number of SDKs novel SDKs (i.e., familiar SDKs). Interestingly, the nov­
increases, developmental challenges imposed by nov­ elty effect is relatively minimal compared with the com­
elty can impede the integration of the modules and patibility effects. Also, the moderation effect is only
deteriorate the final app quality and user engagement. significant for weighted user ratings. One explanation is
 Chung, Sharma, and Malhotra: Impact of Modularity Design on Mobile App Launch
768 Manufacturing & Service Operations Management, 2023, vol. 25, no. 2, pp. 756–774, © 2023 INFORMS

Figure 2. Main Analysis Interaction Plots

(a) Multiplicity × Compatibility Interaction Effect on Wratings (b) Multiplicity × Novelty Interaction Effect on Wratings
2.6
Low Novelty
2.6 Low Compatibility High Novelty
High Compatibility

Linear Prediction of Wratings

Linear Prediction of Wratings

2.4
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2.4

2.2 2.2

2
2
Low high Low high
Multiplicity Multiplicity

(c) Multiplicity × Compatibility Interaction Effect on ln(DAU)

7.5
Low Compatibility
High Compatibility
Linear Prediction of ln(DAU)

6.5

5.5
Low high
Multiplicity

that user ratings are more sensitive to developer design boards, and support chat features (see Online Appendix
decisions than daily active user numbers. In other Table WA1). Prior research shows that B2C comm­
words, although user ratings closely reflect the quality unications can stimulate customer sales by increasing
implications of the app feature design, a negative im­ the available product attribute information (Jang and
pact on ratings does not always lead to user churn. Chung 2015), thus facilitating user learning of the pro­
Users may tolerate somewhat lower app quality and duct’s functionality and improving engagement levels
will only churn if the quality degradation is severe (Bol­ (De Giovanni 2019). Forming a responsive B2C relation­
ton 1998, Ahn et al. 2006). For this reason, we may ship through active communication is known to build
observe a significant impact on user ratings rather than attraction, which may lead to technology adoption
daily active users. (Campbell et al. 2012).
To test this user-side multiplicity mitigation effect,
6.2. Post Hoc Analysis we estimate the moderating effect of SDK-based B2C
Our main analysis finds a consistently negative im­ communication features on the relationship between
pact of SDK multiplicity. We further find that the nov­ multiplicity and app performance. We operationalize
elty of the installed SDKs can amplify the adverse CommB2C as a binary variable, coded as one if the app
effects. However, these factors only focus on miti­ has B2C communication-related SDKs installed and
gating the developer-side challenges. From a user zero otherwise. The instrumental variables regression
perspective, handling a high-multiplicity app can be estimation results are presented in Table 5. We find
challenging because of feature fatigue caused by steep that B2C communication significantly moderates the
learning curves and experience requirements. There­ SDK multiplicity effect on weighted user ratings and
fore, we explore tools at the developer’s discretion to daily active users (Model (3A): β � 0.007, p < 0.05; Model
ease customer adoption barriers. Among the features (3B): β � 0.022, p < 0.05). The significant positive moder­
enabled by SDKs, we notice the B2C communication ation suggests that communication features can miti­
feature that includes push messaging, announcement gate the negative multiplicity effect on weighted user
 Chung, Sharma, and Malhotra: Impact of Modularity Design on Mobile App Launch
Manufacturing & Service Operations Management, 2023, vol. 25, no. 2, pp. 756–774, © 2023 INFORMS 769

Table 5. Post Hoc IV Regression Results OLS model allows us to examine the direction of the
bias caused by the omitted variable bias and the mag­
Model (3A) (3B)
Dependent variable Wratings ln(DAU)
nitude of the correction. We present the non-IV OLS
regression results in Online Appendix Table WA3.
Key independent variables Most of the estimates are qualitatively consistent with
Multiplicity �0.030*** �0.063***
our main findings regarding their magnitudes and
(0.009) (0.016)
directions. We find the main effect and the interaction
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Compatibility 1.728*** 4.793***

(0.584) (0.955)
terms to be insignificant in contrast to the main model
(Online Appendix Table WA4, Models (1A) and (1B)),
Novelty �0.015* 0.006
(0.009) (0.018) driven by the smaller effect size of multiplicity. This
CommB2C �0.051 �0.393***
confirms the upward bias caused by the omitted vari­
(0.063) (0.145) ables in the negative effect of multiplicity. The source
Multiplicity × CommB2C 0.007** 0.022** of endogeneity is argued to be mostly app characteris­
(0.003) (0.008) tics such as quality of the idea, addictiveness, etc.,
Controls which may be positively correlated with multiplicity
Devexp 0.017 0.013 and with the app’s performance. Therefore, we con­
(0.012) (0.022)
clude that the IV regression model applies the appro­
Firsthype 0.018 0.516*** priate correction in the expected direction.
(0.014) (0.029)
Second, we address the potential concern of sample
Multiplatform 0.010 0.172
selection bias by dropping developers that only have
(0.072) (0.125)
one app released in the market by including the
Multicategory 0.161*** 0.149
(0.053) (0.112)
inverse mills ratio in our models for correction (Heck­
man 1979, Adbi et al. 2021) (Online Appendix Table
Ads 0.013 0.113**
(0.029) (0.050) WA4). For the first-stage Probit estimation (Model
Appsize �0.019 0.156*
(1)), we create a binary variable, multidev, coded as
(0.042) (0.085) one for developers with more than one app released
Competition �0.091 0.391 and zero for developers with only one app released.
(0.238) (0.413) We explain this binary selection with developer-level
Ageres �0.040 �0.121* variables that may explain the likelihood that a de­
(0.031) (0.062) veloper would develop more than one app. We agg­
Empno �0.028*** 0.041* regate the data into developer averages and use variables
(0.006) (0.024) such as average employees per developer, the average
Appnum �0.021* �0.012 number of operating system platforms the developer
(0.013) (0.024) launches their app, the average number of app categories
Mktnovelty �4.961 0.979 per developer, average app size, non-U.S. developer bi­
(4.890) (6.166) nary indicator, and headquarter country fixed effect. These
Year/month FE Yes Yes variables capture the developers’ development capacity,
Subcategory/developer FE Yes Yes workload, and location-specific development conven­
Observations 2,282 2,282
R2 0.016 0.286
tions, which may be correlated with the number of
Number of instruments 6 6 apps the developer launches and maintains but are
Weak identification (F statistic) 28.01 40.56 less likely to be correlated with a specific app’s perfor­
Under identification (p-value) <0.001 <0.001 mance. To empirically test these variables’ relevance,
Overidentification (p-value) 0.609 0.573 we calculate the first-stage Kleibergen–Paap rk Wald
Notes. Robust standard errors clustered by developer are in parentheses. F statistic (Kleibergen and Paap 2006). The F statistic
Five percent maximal IV relative bias critical value: 12.20 (three is 140.5, which is above the weak identification critical
endogenous variables); 15% maximal IV size critical value: 12.33 (two
endogenous variables) (Stock and Yogo 2005). FE, fixed effect.
values for 5% maximal IV relative bias (21.21) and 15%
*p < 0.1; **p < 0.05; ***p < 0.01. maximal IV size (90.48), which gives us confidence that
the selected variables are not weak (Stock and Yogo
ratings by 0.01% and the multiplicity effect on daily 2005). We then calculate the inverse mills ratio and
active users by 0.19%. include it in the main IV regression model. The results are
consistent with our main model and support the robust­
6.3. Robustness Checks ness of our findings to sample selection bias (Online
We demonstrate the robustness of our findings by (1) Appendix Table WA5, Models (2A)–(3B)).
estimating a non-IV OLS model, (2) estimating a selec­ Finally, we check whether the results are robust to
tion bias correction model, and (3) estimating a model user rating weighting schemes. For the direct effect
with the raw user rating score. Estimating a non-IV model (Online Appendix Table WA6, Model (4A)), we
 Chung, Sharma, and Malhotra: Impact of Modularity Design on Mobile App Launch
770 Manufacturing & Service Operations Management, 2023, vol. 25, no. 2, pp. 756–774, © 2023 INFORMS

focus on multiplicity only. Although the main effect of challenges of integration, feature fatigue, and limited
multiplicity is not significant, its magnitude and direc­ market appeal dominate the user-side benefits of having
tion are consistent with our main findings. This differ­ more features from SDK multiplicity. However, reducing
ence with the main analysis may be because of the multiplicity may be hard for feature-rich apps that target
meaningful correction from the Bayesian weighting more advanced users. For this situation, we identify that
and the fact that the model does not fully satisfy the the negative impact of multiplicity is mitigated by an in­
instrument validity concerning the overidentification crease in compatibility and exacerbated by an increase
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test. However, the results are much more consistent in in novelty. Furthermore, enabling B2C communication
the interaction model (Online Appendix Table WA6, can further reduce the negative impacts of multiplicity
Model (4B)). The first-order effect of multiplicity and through improved user engagement by keeping them
the interaction effects of compatibility and novelty are abreast of the latest developments in the game, up­
consistent with the main model findings from the grades, events, and updates from fellow gamers.
weighted user rating model. Finally, we comment on the significance of the de­
pendent variables deployed in this study—daily active
7. Discussion and Conclusion users and user ratings. First, we notice slight differences
in the results between the two dependent variables. In
7.1. Theoretical Implications
particular, we find the moderation effect of novelty to
Extant studies have primarily captured product mod­
be significant only in the relationship between multi­
ularity along a single composite measure (Nambisan
plicity and user rating. We think this difference can be
2002, Lau Antonio et al. 2007, Danese and Filippini
attributed to the fact that user rating measures the users’
2012, Vickery et al. 2016). In contrast, we capture the
perceptions and attitudes toward a certain app. In con­
impact of modular components along the three dimen­
trast, daily active users capture users’ actual behavior,
sions of multiplicity, compatibility, and novelty. Instead
such as sustained use. Although attitudes and percep­
of treating each module as homogenous, we believe a
tions are significant antecedents of behavior, they may
useful framework for understanding modular design’s
not be a sufficient condition (Fishbein 1979, Fazio et al.
impact on performance lies in conceptualizing each
1989). Unless the decrease in perceived quality is suffi­
modular installation as increasing multiplicity while at
ciently low or critical to the app’s performance, users
the same time, influencing joint compatibility and nov­ may still tolerate the inconvenience and show sus­
elty of modules. Our results demonstrate the differen­ tained use of an app. Second, instead of using the raw
tial impacts of these dimensions, highlighting the need user rating measure, we recommend that researchers
to consider modularity as a multidimensional construct. should make an adjustment because users generally
Second, our proposed framework extends the discus­ provide ratings when they are either highly satisfied or
sion of modularity, which has primarily focused in the extremely dissatisfied with the app—making raw user rat­
past on physical products (Novak and Eppinger 2001, ings a biased estimate of app performance. Also, the infer­
Danilovic and Browning 2007, Danese and Filippini ence of app quality may be inaccurate when the app does
2012, Vickery et al. 2016), to the unique context of design­ not have many reviews. Daily active users, on the other
ing digital products—specifically, mobile apps using hand, captures user engagement with the app, making it
SDKs. Increased penetration of mobile devices is result­ a more accurate representation of the relationship bet­
ing in rapid growth for the mobile apps sector, which ween the choice of an app’s portfolio of SDKs and initial
has unique characteristics that differentiate it from other success. The weighting of the ratings variable and using
software and physical product development contexts. multiple outcomes allow us to triangulate the effects of
The use of SDKs has increased the ease of integrating modularity decisions. Therefore, we urge future research­
features to such an extent that apps are witnessing ers and practitioners to assess app performance using
feature proliferation, resulting in increased crashes, multiple metrics, including daily active users.
viruses, malware, privacy breaches, battery drain, and
lags (Shoavi 2017). With limited guidance from the lit­ 7.2. Managerial Implications
erature, SDK integration in this important sector is To highlight the performance implications of the mo­
primarily based on anecdotal evidence. Developing dularity decisions, we present a 2 × 2 framework in
and testing an SDK modularity framework for this
novel context provide much-needed empirical evidence Figure 3. Framework for SDK Compatibility and Novelty
on the link between SDK choice and performance. Combinations
Third, our results shed light on the interrelation­
Low Novelty High Novelty
ships and impact of the three modularity dimensions (-1 ) (+1 )
on mobile app performance. Specifically, we find a ne­
High Compatibility (+1 ) Quadrant 4 Quadrant 3
gative impact of multiplicity on app performance. As
hypothesized, in the context of SDKs, the developmental Low Compatibility (-1 ) Quadrant 1 Quadrant 2
 Chung, Sharma, and Malhotra: Impact of Modularity Design on Mobile App Launch
Manufacturing & Service Operations Management, 2023, vol. 25, no. 2, pp. 756–774, © 2023 INFORMS 771

Figure 3 that depicts four possible scenarios of combining and novelty. Choosing SDKs without these consid­
compatibility and novelty for a given multiplicity level. erations can lead to detrimental outcomes. Moreover,
Each quadrant represents a modularity scenario based on based on the defining role of compatibility, we recom­
mean 61 standard deviation split for high and low mend that developers first prioritize choosing high-
levels of compatibility and novelty. Quadrant 1 re­ compatibility SDKs during the development of high
presents developers that rely on low-novelty SDKs multiplicity apps and then consider reducing the nov­
from prior development experience to handle low- elty of chosen SDKs.
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compatibility SDKs. Quadrant 2 represents develo­

pers that choose low-compatibility, high-novelty
SDKs. In other words, this quadrant lacks any mitiga­ 7.3. Future Work
tion toward increased multiplicity. Quadrant 3 repre­ The scope of our study pertains to iOS gaming apps
sents developers choosing highly compatible SDKs, only. This focused scope allowed us to reduce con­
even at the cost of high novelty. Quadrant 4 represents cerns about unobserved influences from mobile device
developers simultaneously choosing highly compati­ characteristics and user demographics. We believe that
ble and low-novelty SDKs. because of its high competition intensity and short
Figure 4 presents the linear predictions of app per­ innovation cycles, the mobile app economy generally
formance for the four quadrants across all observed shares the gaming context to differing degrees with
levels of multiplicity. The plot shows that the impacts other types of contexts. Therefore, even though mobile
of compatibility and novelty decisions are proporti­ gaming is the largest app segment by revenue, our find­
onal to the degree of multiplicity, leading to significant ings should apply to other app categories that use modu­
performance differences when more than 10 SDKs are larized software development kits. Second, we do not
installed. Regarding weighted user ratings (panel (a) in have more detailed performance measures that capture
Figure 4), using high-compatibility and low-novelty the app’s actual duration of use, which would be ideal
apps (Quadrant 4) shows the strongest multiplicity mit­ for capturing user engagement with the mobile app.
igation. By comparing quadrants 3 and 1, we see that Future research can investigate user-level behavior
increasing compatibility is more effective in mitigating data to strengthen the link between app features and
multiplicity than reducing novelty. Regarding daily performance outcomes and thereby, build upon this
active users (panel (b) in Figure 4), the performance gap study, which sheds light on the theory and practice of
is only significant between quadrant 2 and quadrant 4. designing mobile app products and the role played by
Increasing compatibility is the primary mitigating factor their ecosystems. Finally, future research can examine
for multiplicity, whereas reducing novelty plays a mini­ the relationship between user ratings and daily active
mal role. Collectively, our findings show how the three users in greater detail. Differences in our estimated
dimensions of modularity impact app performance impacts between the two performance measures sug­
while detailing the asymmetric impacts of compatibility gest that other mediating factors may also exist.

Figure 4. Modularity Impact on App Performance by Modularity Strategy Quadrants

(a) (b)
8
Quadrant 4 Quadrant 4
2.5 Quadrant 3 Quadrant 3
Quadrant 1 Quadrant 1
Linear Prediction of ln(DAU)
Linear Prediction of Wratings

7
Quadrant 2 Quadrant 2
2
6

1.5
5

1 4

3
.5
0 10 20 30 40 0 10 20 30 40
Multiplicity Multiplicity

Notes. (a) Multiplicity impact on wratings by quadrants. (b) Multiplicity impact on daily active users by quadrants. The difference between quad­
rants 3 and 4 is not significant (p > 0.10) at all levels of multiplicity in panel (b). The difference between quadrants 1 and 2 is not significant (p >
0.10) at all levels of multiplicity in panel (b).
 Chung, Sharma, and Malhotra: Impact of Modularity Design on Mobile App Launch
772 Manufacturing & Service Operations Management, 2023, vol. 25, no. 2, pp. 756–774, © 2023 INFORMS

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