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Mobile application service networks: Apple’s App Store
Article in Service Business · March 2014
DOI: 10.1007/s11628-013-0184-z
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EMPI RICAL ARTICLE
Mobile application service networks: Apple’s App Store
Jieun Kim • Yongtae Park • Chulhyun Kim •
Hakyeon Lee
Received: 8 August 2012 / Accepted: 22 January 2013
� Springer-Verlag Berlin Heidelberg 2013
Abstract In fast-moving and complex App Store, there is a need for exploring the
content of mobile application services themselves. Thus, this research empirically
analyzes the relationships among mobile application services to identify their
structures and positions through a text-mining-based network analysis. Associations
among categories and applications are visualized as macro-level category network
and micro-level app network; network indexes gauge the structural properties and
positional characteristics of each network. Mobile service categories are compared
according to their values and grouped according to network properties using cluster
analysis, offering implications for the sectoral characteristics of mobile services in
App Store.
Keywords Mobile application � Mobile services � App Store �Network analysis � Cluster analysis
J. Kim � Y. Park
Department of Industrial Engineering, School of Engineering, Seoul National University,
1 Gwanak-ro, Gwanak-gu, Seoul 151-742, Republic of Korea
e-mail: [email protected]
Y. Park
e-mail: [email protected]
C. Kim
Department of Technology and Systems Management, Induk University, 14 Choansan-gil,
Nowon-gu, Seoul 139-749, Republic of Korea
e-mail: [email protected]
H. Lee (&)
The Graduate School of Public Policy and Information Technology, Seoul National University
of Science and Technology, 232 Gongneung ro, Nowon-gu, Seoul 139-743, Republic of Korea
e-mail: [email protected]
123
Serv Bus
DOI 10.1007/s11628-013-0184-z
1 Introduction
Mobile services and platforms have indisputably achieved critical mass in the
information and communications technology (ICT) industry (Kim et al. 2010; Lee
et al. 2012). Especially, mobile service business has moved into a new epoch due to
the emergence of new mobile devices and the explosive growth in mobile
application (‘‘app’’) services available at ‘‘App Stores’’. New smart computing
devices such as smartphones and tablet PCs offering traditional wireless voice
services and Internet access have recently gained prominence by replacing
traditional PCs. Almost 1.8 billion mobile phone handsets were being sold annually
by 2011, and smartphone sales had reached 472 million units, representing 31 % of
total sales and an annual increase of over 50 % (Gartner 2012). The key to their
success has been mobile app services, including naive software or content and
primary channels for connecting to Internet-based services that offer good
smartphone user experiences (Kenney and Pon 2011).
Mobile app services have proliferated since the Apple App Store launched on
July 10, 2008. Due to the store’s open concept, any developer with expertise can
freely create a mobile app service (Laudon and Traver 2010; Suh et al. 2012). Thus,
full-scale innovation has occurred in various mobile service sectors, such as content
services (e.g., e-book, news) and traditional offline services (e.g., banking,
healthcare) (Murray et al. 2010), as indicated by the many categories used in
App Stores. Companies can now deliver a wide range of businesses and services
(including e-mail, streaming video, social networking, and location-based services)
through mobile app services and thus strive for competitive edges in the mobile
service marketplace (Wang et al. 2006; Murray et al. 2010). Therefore, the value of
smartphones and the App Store is believed to be a significant key to future growth
and profits for all players in the mobile ecosystem, not only for mobile telephony
and network operators but also for device vendors, platform owners, service
providers, content providers, and others.
Its current importance has prompted various discussions in the literature on App
Store issues, such as its market outlook and possible strategies (Kimbler 2010;
White 2010), changes in the mobile ecosystem and in the industry-level business
model driven by the App Store (Goncalves et al. 2010; Holzer and Ondrus 2011;
Muller et al. 2011), and the diffusion and adoption of user-level mobile innovations
(Verkasalo et al. 2010). However, empirical investigations of the structures and
contents of mobile app services, especially those focusing on mobile apps as such,
are few. The open platform structure of mobile app service development allows
services to be indiscriminately and instantaneously created by third parties (Danado
et al. 2010). Amidst the vast number of mobile app services that are continuously
emerging and quickly changing, service developers and providers can lose their
position or ignore important competitive and complementary services because of
their complexity. Therefore, we need to identify the structures and contents of
mobile app services. Mobile app services per se are a significant data source for
understanding mobile service characteristics. A number of their important aspects
can be subjected to analysis—including mobile app services’ attributes and
contents, a comparison of service features among mobile app service fields, and
J. Kim et al.
123
relationships among mobile app services—in order to identify their relative
importance. Studies on the App Store have relied on qualitative or simple statistical
approaches to their survey data (Agarwal et al. 2010; Copeland 2010), which is
inadequate for analyzing the contents and structures of mobile app services. A more
quantitative and systematic technique should be applied to web documents
describing the app services of App Stores to identify which of the services have
been developed.
Thus, the objective of this research is to identify the structures of mobile app
services and influential categories and services among them through mobile app
service networks. Especially, this paper applies a text-mining-based network
analysis to the descriptions of the mobile app services available from the App Store.
Text mining, the automated discovery of knowledge in unstructured texts (Berry
2003), helps transform unstructured service documents into analyzable structured
keyword vectors. Network analysis, a quantitative technique derived from graph
theory, facilitates the analysis of interactions, or ‘‘edges,’’ between actors, or
‘‘nodes’’ (Gelsing 1992), and shows the relationships among services as a visual
network. The structure of relations among actors and the locations of individual
actors in a network provide rich information on the behavioral, perceptual, and
attitudinal aspects of individual units and the system (Knoke and Kuklinski 1982;
Marseden and Laumann 1984). In the studies on patent or literature related analysis,
researchers have utilized a text-mining-based network analysis to observe the
technological structures and trends engaged in patents or literatures by identifying
information such as the main clusters of technological fields, the affinities of them,
the technological periphery, or the significant technological topics (Callon et al.
1991; Engelsman and van Raan 1994; Yoon and Park 2004; Kim 2008; Su and Lee
2010). Like in these previous literatures, mobile app service network is based on an
assumption that the sharing of same keyword between app services implies the
overlap each other and this overlap can be interpreted as the relationship. Thus, the
relationship (links) between mobile app services (node) is examined by the
similarity between service descriptions, which includes information of the contents
and functions of services. Mobile app service networks can help service providers
and developers intuitively explore industry overview and strategic cues and can
enrich the potential utility of analyses, as it considers many diverse keywords and
produces meaningful indicators.
This paper regards mobile App Store categories as representative of mobile
service sectors. Thus, networks are constructed at the category-level to identify the
interactions between mobile service sectors and at the app-level to examine the
structural characteristics of each sector and identify the local positions of the
services in each sector. This study compares network structures according to their
‘‘mobile service value’’ and explores the relationships among them to understand
mobile service characteristics more effectively. Mobile services can be divided into
utilitarian and hedonic categories according to their value—the purpose, motivation,
and result of consuming services as perceived by users (Pihlstrom 2008; Kim and
Han 2011). Network indexes are used to gauge the structural information of and
identify the influential services in networks. We apply a Mann–Whitney U test to
the index values from each category to assess whether network indexes differ
Mobile application service networks: Apple’s App Store
123
according to the mobile services’ value. We then group the mobile service
categories according to their network properties using a cluster analysis.
This study is organized as follows. Section 2 discusses the concepts related to the
App Store, mobile app services, and mobile app service value. Next, Sect. 3
introduces the mobile app services of the Apple App Store based on the descriptive
statistics. Then, Sect. 4 outlines text-mining-based network analysis methods and
the overall research process. Section 5 explains the results concerning the mobile
app service network, including the macro-level and micro-level app networks.
Section 6 presents the cluster analysis of the mobile app service categories. Finally,
we present conclusions while discussing the contributions and limitations of our
research in Sect. 7.
2 Background
2.1 App Store and mobile app service
The App Store is a logical extension of the mobile content market that has existed
for more than a decade (Kimbler 2010). In ‘‘pre-App Stores,’’ applications were
primarily distributed through multi-modal portals operated by handset vendors,
mobile network operators, hundreds of independent mobile content providers, and
brand and app developers themselves. However, the App Store in the smartphone
era has a new value proposition. The Apple App Store is a digital application
distribution platform or open market for iOS developed and maintained by Apple.
The service allows users to browse and download apps from the iTunes Store that
were developed with the iOS Software Development Kit (SDK) or Mac SDK and
published through Apple Inc. It includes an ‘‘ecosystem’’ that has attracted
numerous developers and has generated many apps based on the open platform,
open application program interface (API), and open market concepts (Jang and Lee
2009; Holzer and Ondrus 2011; Dixon 2011). The open concept plays critical roles
in the simplification of users’ app development. Mobile app services tend to be
freely created by users with varying levels of expertise (Laudon and Traver 2010;
Suh et al. 2012). The App Store’s open concept has created a new competition
landscape in the mobile industry. The mobile industry had been on an innovation
path that had led to improved device performance and faster data transmission
speeds. Now, though, all the rage is around mobile platforms, content, and app
services as devices become increasingly commoditized (Feijoo et al. 2008; Dixon
2011). Thus, the mobile ecosystem has evolved from network operator-centered to
platform-centered (Basole 2009). While other platforms such as Android, Symbian,
RIM, Bada, and Microsoft release their stores, the App Store can generally refer to
online application distribution systems. App Stores allow smartphone and tablet
users to personalize and customize their mobile devices.
Mobile apps are software programs that can interrogate a web server and present
users with formatted information. They exploit the technical functionality of
smartphones (such as touch sensitive screens) and web features such as information
and the functionality of hyper text markup language (HTML) pages. Generically,
J. Kim et al.
123
some mobile app web features can be directly implemented through an app code;
some web features can be partially emulated; some web features cannot yet be
implemented or emulated (e.g., multiple windows), and apps can provide function-
alities not available to HTML browser users (White 2010). Of course, mobile apps
represent a major revenue source for the mobile phone and software industries. For
instance, by October 4, 2011, there were at least 500,000 third-party apps officially
available from the App Store, and 250 million iOS users had downloaded over 18
billion apps (Apple 2011). Companies typically take 70 % of apps sale revenues and
pass on 30 % to developers. Another benefit of mobile app services is that they tend to
lock-in mobile phone users to a particular set of apps, which is why Apple in
particular has made a significant effort to support apps development for its range of
handsets. Mobile app services play a crucial role in providing a value differential for
mobile phones, as they can aggregate data from multiple databases; thus (for
example), a current share price can be presented together with a summary of the
annual accounts, the views of investment analysts, and the latest news stories,
something that would be challenging for a laptop (White 2010).
2.2 Previous research on mobile app service
Previous research on smartphones, the App Store, and mobile app services (mostly of
it still in progress) can be categorized as shown in Table 1. A major research stream
has focused on changes in the mobile ecosystem and business model driven by the
App Store at the industry level. For example, (Holzer and Ondrus 2011) examine the
dramatic changes in six mobile app platforms provided by Nokia, RIM, Microsoft,
Apple, LiMo, and Google and identify a trend toward more open platforms,
centralization, device diversity, and a higher degree of integration. Other papers have
examined the market outlooks and strategies of the App Store. (White 2010) reviews
the role of smartphones and the App Store in the delivery of information to businesses
and their technological trends. Some studies have examined the diffusion and
adoption of mobile innovation at the user level. (Verkasalo et al. 2010) studied the
users and non-users of mobile apps and discovered the important drivers of the
intention to use, such as behavioral control, perceived usefulness, and enjoyment.
Although these studies focus on the smartphone-based mobile industry, they have not
deeply understood the various mobile apps available at the App Store or the
smartphone. Those focusing on the phenomenological or descriptive features of the
App Store (Agarwal et al. 2010; Copeland 2010; Lee and Raghu 2011) have relied on
qualitative or simple statistical approaches to their survey data.
As shown in the last two items of Table 1, some studies have focused on mobile
app services, suggesting design and development methods or investigating content
and evolution. Kim and Park (2010) have proposed a user-centric service map
framework by which to incorporate potential user needs into a new service ideation.
Jang and Lee (2009) have suggested a reliable mobile app modeling based on open
API, including the definition of constraints and a code generation technique for
reliability verification, and have validated the methodology for MapViewer
application. Suh et al. (2012) use text mining and a set-covering algorithm to
identify representative services and visualize the structure of application services and
Mobile application service networks: Apple’s App Store
123
illustrate the applications in the ‘‘Utility’’ category. All these studies use mobile apps
as an illustrative case study to describe their approaches and analyze an individual
service or sector, such as banking (Dohmen et al. 2009), healthcare (Gasser et al.
2006; Marshall et al. 2008), utility (Kwak et al. 2010; Suh et al. 2012), and lifestyle
(Kim and Park 2010; Geum et al. 2011; Kim and Park 2011), in which most services
are utilitarian in nature. Consequently, the need to explore the whole structure and
content of mobile apps in all categories continues to exist.
2.3 Mobile app service value
Mobile users obtain ‘‘mobile value’’ created through the use of mobile app services
containing Internet content and services (Anckar and D’Incau 2002; Hur et al.
2012). Mobile services differ from traditional services in their ability to provide
service offerings regardless of temporal and spatial constraints. Due to the
distinctive features of mobile services, several studies have identified mobile service
values such as ubiquity, time-criticality, spontaneity or immediacy, accessibility,
convenience, localization, and personalization (Anckar and D’Incau 2002; Clarke
2008). They are focused on mobile value based on the unique features of mobile
technology.
However, mobile service value can be understood in terms of the offering
consumed and experienced by users in the context of the motivations for, or purpose
of, consumption (Park 2006). Understanding a good’s or service’s value from the
perspective of users has long been recognized as a primary element of a customer-
oriented strategy (Desarbo et al. 2001). Thus, in the mobile service, the customer-
perceived or consumption value of a mobile service is important. Value is often
divided into utilitarian and hedonic values (Park 2006; Pihlstrom 2008; Kim and
Han 2011). Utilitarian value comprises the extrinsic motivation of a goal-directed
service use. It is closely related to the effectiveness and efficiency resulting from
using a service (Venkatesh and Brown 2001). Hedonic value comprises the intrinsic
motivation in experiential, fun, and enjoyable service use. It is primarily non-
instrumental, experiential, and affective (Sweeney and Soutar 2001; Novak et al.
Table 1 Previous research on smartphone, App Store, and mobile app services
Issues Research
Mobile ecosystem and business
model regarding App Store
Basole (2009), Liu (2009), Goncalves et al. (2010), Muller et al.
(2011), Holzer and Ondrus (2011)
Market outlooks and strategies of
App Store
Agarwal et al. (2010), Copeland (2010), Kimbler (2010), White
(2010), Lee and Raghu (2011), Kenney and Pon (2011)
Diffusion or adoption of smartphone
and App Store
(Gasser et al. (2006), Park and Chen (2007), Verkasalo et al.
(2010), Etoh and Katagiri (2011), Verkasalo (2011)
Design and development of mobile
app services
Marshall et al. (2008), Dohmen et al. (2009), Jang and Lee
(2009), Kwak et al. (2010), Kim and Park (2010, 2011),
Akbulut (2011), Geum et al. (2011), Song and Park (2011)
Content and evolution of mobile app
services
Han and Park (2010), Song et al. (2010), Suh et al. (2012), Kim
et al. (2012)
J. Kim et al.
123
2003). Since most research on mobile services has focused on those (such as
banking) that are basically utilitarian (Laukkanen and Lauronen 2005; Kleijnen
et al. 2007), more research has been encouraged on the aspects that facilitate
comparisons of mobile services in terms of their values (Okazaki 2005; Pihlstrom
2008).
This study investigates the utilitarian and hedonic values of mobile services and
the relationships among them. Apple’s App Store has 20 categories: books, business,
education, entertainment, finance, games, healthcare and fitness, lifestyle, medical,
music, navigation, news, photography, productivity, reference, social networking,
sports, travel, utilities, and weather. These can be divided into utilitarian and hedonic
segments according to their inherent value (Okazaki 2005; Nysveen et al. 2005);
Heinonen and Pura 2006; Kim et al. (2010, 2012), as shown in Table 2. Information-
based services such as in education, healthcare and fitness, medical, news, reference,
and weather, search services such as in navigation, and efficient function-based
services such as in business, finance, productivity, and utilities are all examples of
services that create high utilitarian value and help users achieve a goal effectively and
conveniently. Entertainment-oriented services such as entertainment, games, books,
and music, leisure-related services such as lifestyle, photography, sports, and travel,
and social services such as social networking all belong to the hedonic group of
services that create fun experiences and are used purely for the sake of the experience.
3 Mobile app services in the Apple App Store
This paper uses data from the Apple iTunes App Store; it employs iPhone Apps Plus
(http://www.iphoneappsplus.com), which tracks all the apps in the 70 iTunes App
Stores worldwide, to collect the raw data. Because app information such as cate-
gory, rating, price, size, launch and update dates, detailed description, and reviews
are provided for each app on this website, we scraped each HTML webpage to
gather data on 100,830 apps. Then, data preprocessing, including transformation
into a text file format and crawling description parts, was implemented on the
extracted documents to construct a database.
As shown in Fig. 1, apps in 20 categories were investigated. The highest number
of apps was found in game (14,390) and the least in weather (305). Game and
entertainment dominate the others, accounting for a total of 27 % (14 and 13 %
respectively); followed by books, education, travel, lifestyle, and utilities ranging
from 10 to 6 %. The above-mentioned seven categories account for 66 % of the
total number of apps, with the rest distributed with similar weights.
Table 2 Segments of Apps Store categories
Segment Category
Utilitarian Business, education, finance, healthcare and fitness, medical, navigation, news, productivity,
reference, utilities, weather
Hedonic Books, entertainment, games, lifestyle, music, photography, social networking, sports, travel
Mobile application service networks: Apple’s App Store
123
Next, to identify the growth pattern of the mobile service sector, the numbers of
apps accumulated over time was examined for each category (see Fig. 2). Game has
increased exponentially since the App Store opened; Entertainment and Books
follow, with a roughly one-month time lag. Education, lifestyle, and utilities have
been rising since early 2009 and the others since mid-2009. Unlike other categories,
Travel began to grow suddenly and rapidly after May 2009. Thus, game and
entertainment services have played trigger roles in the expansion of mobile apps,
but books and education and leisure and life-relevant services are also becoming
important mobile app services. The other sectors (except weather) are in similar
positions—growing and reaching around 2,000.
In terms of pricing, 30 % of all apps are free, with an average price of $2.56.
More than 50 % of paid apps are fixed at low prices, under $0.99 in all categories.
However, the distributions of app prices are not identical among categories.
Services that provide simple information, such as news and weather, or have free
business models, such as social networking, entertainment, and games, have a
greater proportion (about 80 %) of free or cheap apps. On the contrary, services
dealing with professional information, such as medical, navigation, and reference,
or charged contents such as books have a relatively large proportion (about 10 %) of
paid apps costing more than $10. Medical, navigation, and business have expensive
apps, costing over $50. There are some extremely expensive apps, some costing
$999.99; MobiGage NDI in business, for instance, is a metrology iPhone app used in
Fig. 1 Total number and proportion of mobile app services
J. Kim et al.
123
the inspection of manufactured parts and assemblies and has very specialized
functions such as measurement methodologies and industry-standard fitting
algorithms. The distribution of expensive and cheaper apps is also reflected in
their average prices: the category with the highest average price is medical ($7.9),
and that with the lowest is news ($0.8).
With regard to ratings, out of a possible 10 points (5 stars), all apps have a low
average rating (2.78 points), and most are rated as 0 stars. User ratings are often
higher than three stars. This could mean that users are seldom satisfied with their
apps or tend to evaluate them after downloading them (producing a low response
rate). The distributions of the app ratings are somewhat different among categories.
Games has a significantly higher rating than the others (about 70 % of its scores are
higher than 3 stars, and the average is 5.41 points), whereas productivity (with a
1.13 average), books, and travel have a greater proportion of 0 stars (about 80 %).
Both price and rating affect developers’ revenues and users’ satisfaction, but they
contribute differently across service sectors. It is thus worth mapping the categories in
a price-rating matrix, as in Fig. 3. The price-rating matrix indicates a clear negative
relationship between rating and price. When price is higher, the rating lowers almost
linearly (as in games, finance, education, and books). However, several categories
have comparatively high ratings despite their high price (as in medical, navigation,
business, and reference), low ratings despite their medium price (as in travel and
productivity), or medium ratings despite their low price (as in the rest).
So far, the descriptive statistics have shown the market trends in the Apple App
Store, which can be helpful in identifying the App Store’s general phenomena.
However, they are not enough for understanding the characteristics of the store’s
‘‘mobile app services’’. This paper focuses on the relational characteristics among
the mobile service sectors and mobile app services; a network analysis identifies
Fig. 2 Service growths in category
Mobile application service networks: Apple’s App Store
123
structural characteristics such as the cohesion of services in a certain mobile service
sector and positional characteristics such as the App Store’s most influential service
sector.
4 Research framework
4.1 Method
This paper develops the mobile app service networks through text-mining-based
network analysis approach. Much of the literature on network analysis investigates
links typically through citation or co-citation. However, citation analysis has
fundamentally two main limitations: new patents tend to be less cited than old ones
and may miss citations to contemporary patents; citation-based analysis cannot be
used for patents in databases which do not require citations (Yoon and Kim 2012).
Responding these problems, several literatures have attempted to construct network
through other bibliometric techniques which uses the sharing of keywords or the
similarities of properties between documents. The examples of keyword-based
approaches are text-mining-based patent network (Yoon and Park 2004), property-
function-based patent network (Yoon and Kim 2011, 2012), a semantic networks of
keywords (Kim 2008), and co-word network (Callon et al. 1991; Engelsman and
van Raan 1994; Su and Lee 2010). These papers are based on an assumption that
sharing the same keyword implies these two documents (patent or research)
partially overlap each other. In the same manner, this paper assumes that the sharing
of same keyword corresponds to the overlap between app services, and the
similarity based on this overlap can be interpreted as the relationship. To produce a
Fig. 3 Price-rating matrix
J. Kim et al.
123
database (including an app-by-app description), a text-mining-based network
analysis was applied in the following four stages:
1. Setting of keyword vector: since the detailed descriptions of each app are
expressed in natural language, a text mining that extracts keywords from
documents was used to transform the unstructured data into analyzable
structured data.
2. Construction of association matrix: based on the keyword vector, an association
matrix was constructed using the relationship among services, quantified in
terms of distance or similarity.
3. Development of service network: by applying the association matrix as input, a
service network was generated with nodes (categories or apps) and links
rendered visually.
4. Analysis of interrelationships: the interrelationships among categories or
services were analyzed based on quantitative indexes.
4.1.1 Setting of keyword vector
Text mining, the process of finding interesting patterns, models, directions, trends,
or rules from unstructured text, is an automated discovery of knowledge from texts
(Berry 2003). Structuring the input text usually involves parsing, along with the
addition and removal of derived linguistic features, and subsequent insertion into a
database. In text mining, a keyword vector is the general method of handling large
amounts of unstructured text to extract information from structured data (Yoon and
Park 2004).
This study’s text mining extracted 2,357 keywords from the documents, but only
563 keywords were selected after the elimination of redundant words and the
consideration of total occurrence and semantic meaning. Using the selected
keywords, documents with no occurrences were eliminated from the 100,830 apps;
some apps were not relevant to the keywords because they had very few keywords
by which they analyze the characteristics. Then, the frequencies of the keywords’
appearance in individual documents were entered into the keyword vector, resulting
in the keyword vectors of 1,919 apps, with 563 keywords ultimately constituted.
Table 3 shows the frequencies of the keyword vectors resulting from the text
mining. The keyword vectors were used to construct an association matrix and to
conduct the network analysis.
4.1.2 Construction of association matrix
The association matrix was constructed by quantifying the degree of similarity
between keyword vectors. This study used cosine similarity, a representative
measurement for the similarity between two vectors of n dimension, and calculated
the cosine of the angle between them. The cosine similarity is represented using a
dot product and magnitude, as below:
Mobile application service networks: Apple’s App Store
123
Vi � Vj
Vij j Vj
����
where Vi and Vj are the keyword vectors of mobile apps or categories.
The resulting cosine similarity ranges from -1 (exactly opposite) to 1 (exactly
the same), with 0 usually indicating independence and the in-between values
indicating intermediate similarity or dissimilarity. In the case of information
retrieval, the cosine similarity of two mobile app services ranges from 0 to 1
because the frequency of keywords is positive.
4.1.3 Development of service network
The degree of connectivity was decided based on the threshold value that the analyzer
is supposed to determine. The connectivity between Vi and Vj was set to 1 in the
association matrix if the cosine similarity was larger than the selected threshold value.
Otherwise, the connectivity was set to 0 and considered a weak relationship.
Determining the threshold value was subjective, and the results may be strongly
dependent on the threshold value. A few representative services can be selected if we
set a higher threshold values, while many representative services can be identified if
we set a lower threshold value. There were two alternatives. At an intermediate level,
the decision maker could select a reasonable threshold value so that the number of
representative services would become clearly relevant, or multiple threshold values
could have been applied in a sensitivity analysis. In this paper, the threshold values
were selected to yield the relevant number of representative services. After deciding
on the threshold value, service networks were developed using visualization software.
The networking software package UCINET 6, a popular network analysis program,
was used to depict the network and compute the quantitative indexes.
4.1.4 Analysis of interrelationships
Structure and interrelationship characteristics were identified through the quantitative
indexes drawn from network theory as complements to visualization for effective
description. This paper considers two network characteristic types, network structure
property, and node centrality, measured using metrics such as density, centralization,
degree centrality, closeness centrality, and betweenness centrality, among the primary
methods of understanding networks and their participants.
Regarding network structure property, we examined the shape of the network by
density and centralization. First, network density represents the degree of interaction
Table 3 Example of keyword
vectorCity Video Story Online TV Upload
L152 1 0 1 0 0 1
L54 1 0 1 0 0 1
L263 1 1 0 1 1 0
L109 0 1 1 0 0 1
L205 0 1 1 0 0 1
J. Kim et al.
123
in a network (Meagher and Rogers 2004), calculated as the proportion of the
number of actual relations between categories divided by the maximum possible
number of ties that would be present if the network were complete (Scott 2000).
This is based on the idea that the more the actors are connected to one another, the
more cohesive their network is. Second, network centralization indicates whether
the interaction is equally distributed or centralized on a few nodes. While
centralization provides information about the compactness of the overall structure of
the network (like its density), while density indicates the overall level of network
cohesion, centralization measures the degree to which an entire network is focused
around a few central nodes (Wasserman and Faust 1994). Thus, density corresponds
to the concept of ‘‘mean’’ inferred from the number of relationships between actors,
while centralization corresponds to the ‘‘variance’’ in the interrelationships.
Consequently, density and centralization can provide important clues to the nature
of network structures. For instance, if a network is a complete type in which all
nodes are linked, its density will be 1 and its centralization 0. If a network is a circle
type in which the actors form a big circle, its density will be 0.5 and its
centralization 0. If a network is a star type, in which the actors are related to only
one central hub, its density will be 0.5 but its centralization 1.
Node centrality is a primary measure used to evaluate the position of actors in a
network. It is divided into three types of centrality: degree, betweenness, and
closeness (Freeman 1979). It has been argued that centrally located services occupy
strategic positions that allow them greater access to information, knowledge, and
resources. This applies particularly to the context of the mobile App Store, where
potentially complementary technologies, information, and services are dispersed
among numerous firms. First, degree centrality refers to the number of ties the actors
have. This paper does not distinguish between in-degree or out-degree centrality
(Scott 2000) because we assume that the relationship between two actors does not
have a direction. Actors with a high relationship degree are generally connected or
adjacent to many actors and should be considered as being in a prominent location
where ‘‘value’’ flows. A low degree tends to characterize actors at the periphery.
However, degree centrality may be criticized because it takes into account only the
immediate ties an actor has and ignores the indirect ties to others. In many instances,
a firm may be tied to a large number of other firms that are rather disconnected from
the network as a whole. Second, in response to this deficiency, closeness centralityhas been used to emphasize the ‘‘nearness’’ of an actor to all others in the network
using the reciprocal of the geodesic distances (Scott 2000). Lastly, betweennesscentrality measures the extent to which actor k lies on the path ‘‘between’’ the other
actors in the network: an actor of relatively low degree may play an important
intermediary role and so be very central in the network. The existence of such a
structural hole allows the relevant actor to act as a broker (Freeman 1979). Table 4
summarizes the indexes used to analyze network interrelationships.
4.2 Overall procedure
The objective of this paper is to gain a deeper understanding of the structure and
complexity of the relationships among the mobile app services of the App Store. Its
Mobile application service networks: Apple’s App Store
123
overall procedure is organized as shown in Fig. 4. Network visualizations were
generated in a top-down manner by creating first a macro view of the mobile app
categories and then a micro view of the mobile app services. Thus, the networks
were developed in two levels—a category network for macro-level analysis and an
app network for micro-level analysis. In the category network, the centrality
measures (the degree, closeness, and betweenness) of each category, representing
the position and influence of the category in the macro-level network, were derived.
We divided the mobile app categories into utilitarian and hedonic segments based
on their values; thus, whether the centrality measures are different between the two
segments can be assessed. This was analyzed by a Mann–Whitney U test, a non-
parametric statistical test for two unpaired groups. We selected a non-parametric
test because there are only small centrality data samples from non-Gaussian
populations for the two segments. An app network was constructed for each
category, and the network structure property measures (of density and centraliza-
tion), indicating the structural shape and compactness of each network, were
computed. Then, a Mann–Whitney test was conducted on these measures. Finally, a
cluster analysis on the mobile app categories was performed to identify the new
taxonomy based on the relationships among the mobile service sectors.
5 Networks of mobile app services
This section presents the results of the visualization and analysis of the mobile app
service networks of the two sub sections—the macro-level category and micro-level
app networks—while also incorporating the Mann–Whitney test for comparing the
network indexes of the utilitarian and hedonic segments.
5.1 Macro-level analysis: category network
In the macro view, the categories the apps belong to are represented as a single
vertex. Figure 5 shows the global relations among all categories. In the macro-level
analysis, 1,919 apps’ keyword vectors were merged according to their categories,
and the average frequency was filled with the keyword vectors of 20 categories.
Some categories may have consisted of more apps than others, and a summation of
Table 4 Definition of network indexes
Class Index Definition
Network structure
property
Density The degree of the overall level of network cohesion and
interaction
Centralization The degree to which an entire network is focused
around a few central nodes
Node centrality Degree The number of direct edges nodes has
Closeness The nearness of an node to all (direct and indirect) other nodes
Betweenness The extent to which node lies on the path between the various
other nodes
J. Kim et al.
123
the links could have reflected tie strengths; thus, we normalized the cosine similarity
with the overall number of apps in the categories. As shown in Fig. 4, the thickness
of their edges is proportional to the degree of linkage between nodes. To visually
differentiate between the segments’ categories, colors were used for the nodes.
Utilitarian segments were depicted with black spheres, whereas hedonic segments
were depicted with white spheres.
The visualization prompts several key observations. First, several central
categories appear in the utilitarian and hedonic segments of the App Store
structure. Utilities in the utilitarian segment and entertainment in the hedonic
segment seem to be the most central categories. Meanwhile, specialized categories
from the utilitarian segment such as navigation, medical, finance, and healthcare and
fitness appear to be relatively peripheral to the rest of the App Store. Second, there
seems to be a strong relationship among education and reference, utilities and
finance, and utilities and business. The reference category in the App Store is a
portal to new offerings that not only fit under books and guides but also include
interactive graphics and audio for both children and adults. It is thus natural for
reference to be associated with educational services. The contributing keywords are
‘‘dictionary,’’ ‘‘thesaurus,’’ ‘‘bible,’’ ‘‘language,’’ ‘‘translator,’’ ‘‘navigation,’’
‘‘video,’’ and ‘‘audio.’’ The utility category covers many simple instruments that
help people in their daily lives, but the relationship shows that the functions are
concentrated on the finance and business sectors, with keywords like ‘‘calculator,’’
‘‘system,’’ ‘‘converter,’’ ‘‘currency,’’ ‘‘unit,’’ ‘‘tracker,’’ ‘‘timer,’’ and ‘‘auto.’’
To complement the visual evaluation and gain further insight into the structure of
mobile apps, network metrics were computed. The first measurements of the
network’s structural property yielded a density of 0.1563 and a centralization of
0.1696. Accordingly, the App Store’s network structure is somewhat loosely netted
and barely centralized. The second measurements, of node centrality, are shown in
Table 5. (Note that the top six values are represented in bold strokes for each index).
Entertainment scores highest in every node centrality measurement and can thus be
identified as a network hub. The other significant categories with higher centralities
Network analysis
Constructing a macro-level network(category network)
Constructing micro-level networks(app network)
Deriving node centrality(Degree, Closeness, Betweenness)
Computing network structure property(Density, Centralization)
Cluster analysis
Fig. 4 Overall research procedure
Mobile application service networks: Apple’s App Store
123
are utilities, lifestyle, games, education, and business; however, their index values
differ slightly. For example, education, games, and lifestyle have the same degree
but occur as game, education, and lifestyle of closeness; hence, the indirect
interaction is active in game but inactive in lifestyle. The betweenness of business
and travel is comparatively high; thus, they seem to have an intermediary role in the
whole network. The results also show that relative importance does not depend on
the scale of the category.
Lastly, to assess whether the node centralities differ between utilitarian and
hedonic segments, a Mann–Whitney U test was applied to the data in Table 5 using
SPSS 12.0K software. The p values yielded were 0.220 in degree, 0.046 in
closeness, and 0.422 in betweenness. At the 5 % significance level, the null
hypothesis was rejected only in closeness centrality. Consequently, the closeness
centralities are likely to differ but the degree and betweenness centralities do not
significantly differ between the utilitarian and hedonic segments. The closeness
centrality of the hedonic segment is larger than that of the utilitarian segment. Thus,
the hedonic segment appears to be more central than the utilitarian segment in terms
of the direct and indirect nearness. A plausible explanation for this is that the
categories of the hedonic segment have more general content and functionalities
than do those of the utilitarian segment and thus have more services in common
with other categories. In other words, the categories of the utilitarian segment are
constituted by more specialized services.
Fig. 5 Category network (cutoff value = 0.17)
J. Kim et al.
123
5.2 Micro-level analysis: app network
At the micro level, we constructed app networks for each category to examine the
characteristics of each mobile service sector in more detail. For example, to develop
the utility app network, 103 apps’ keyword vectors were used to yield an association
matrix; the resulting visualization is shown in Fig. 6. Network metrics were also
computed: the network density is 0.0305 and the centralization 0.0725; the top five
services with high centrality are listed in Table 6.
First, regarding network structure property, both density and centralization are
lower than those of the previous networks. The degree of interaction between apps
in Utility and the degree to which a few apps dominate the relationship seem not
much greater than in other categories. Second, regarding node centrality, the service
listed in Table 6 turns out to be the most influential service as intuitively identified
by the visual network (and depicted by red spheres). The service with the highest
average rank of three index values is HodgePodge (S601), which is a utility
collection with a clean, user-friendly interface. The nine utilities include location,
battery, alarm clock, tip calc, converter, flashlight, ruler, level, and random number.
These utilities appear to be representative of the basic functions that help us in our
daily lives and are thus influenced by many apps, such as Battery God Lite (S56),
Battery–Flashlight (S58), and Utilitybox (S248). Thus, they naturally exhibit an
active flow of knowledge across other services.
Table 5 Node centrality of category network
Segment Category Degree Closeness Betweenness
Utilitarian Business 0.3158 0.3725 0.0684
Education 0.3684 0.3800 0.1268
Finance 0.1579 0.3220 0.0058
Healthcare and fitness 0.1579 0.3167 0.0049
Medical 0.0526 0.2836 0
Navigation 0.1053 0.2969 0.0039
News 0.1053 0.3167 0
Productivity 0.2632 0.3257 0.0194
Reference 0.2105 0.3276 0.0093
Utilities 0.5263 0.4043 0.1615
Weather 0 0.0500 0
Hedonic Books 0.2632 0.3585 0.0251
Entertainment 0.6316 0.4318 0.3018
Games 0.3684 0.3878 0.0476
Lifestyle 0.3684 0.3725 0.0505
Music 0.1579 0.3333 0
Photography 0.2632 0.3585 0.0085
Social networking 0.2105 0.3519 0.0468
Sports 0.0526 0.3245 0
Travel 0.2632 0.3585 0.0611
Mobile application service networks: Apple’s App Store
123
The same micro-level analysis was conducted on the remaining 19 categories. Thus,
20 individual app networks were developed and their network metrics computed.
Table 7 shows the results of the network structure properties (Note that the top five
values are represented in bold strokes for each index), and Table 8 represents the most
central apps selected by the average rank of the three node centrality measures. Density
and centralization show similar patterns: if the density is high, centralization is high,
meaning that most app networks are star-shaped, not circular. However, the overall level
of density and centralization is quite low, and absolute connectivity is not frequent. The
categories with relatively high density and centralization are photography, music,
reference, and navigation; thus, they have influential leader services that dominate the
others, such as Felaur PDF Reader (Photography), Guitar Jam (Music), EnglishDictionary and Thesaurus by Ultralingua (Reference), and Sygic Mobile Maps SEAsia—Turn-by-Turn Voice Guided GPS Navigation (Navigation). Since most of the
services in all categories flock to these central services, they develop into similar classes
in terms of content and functionality. Thus, central apps in highly centralized networks
will be representative of most of those service sectors.
In the Mann–Whitney U test for the data in Table 7 assessing whether the
network structure properties differ between utilitarian and hedonic segments, the
p values yielded 0.766 in density and 0.970 in centralization. Consequently, all of
the null hypotheses are accepted, and density and centralization are not likely to
differ between the segments.
Fig. 6 App network of Utilities (cutoff value = 0.2)
J. Kim et al.
123
6 Clusters of mobile app services
As shown in the previous networks, the network structure and importance properties
vary across the 20 mobile App Store categories. This study has attempted to
compare between the utilitarian and hedonic segments’ network indexes using a
value-based typology proactively defined based on mobile service value, but a
significantly different measure, closeness centrality, appeared. Mobile app service
categories can thus be re-categorized according to a relationship-based taxonomy.
In order to group the mobile app service categories based on the pattern of network
characteristics, a cluster analysis was implemented using five network indexes.
Network structure property measures were gathered from app networks (see
Table 7) to include the internal cohesion of app services in categories, and node
Table 6 Top five node centrality of app network of utilities
Rank Degree Closeness Betweenness
1 S324 0.9141 S601 0.1686 S601 0.1935
2 S128 0.7408 S385 0.1667 S385 0.1315
3 S601 0.7354 S617 0.1643 S168 0.0763
4 S22 0.7293 S260 0.1637 S260 0.0755
5 S172 0.7262 S324 0.1635 S98 0.0655
Table 7 Network structure
property of app networksCategory Density Centralization
Books 0.0575 0.0862
Business 0.0477 0.0707
Education 0.0591 0.1011
Entertainment 0.0246 0.0516
Finance 0.1251 0.1439
Games 0.0373 0.0746
Healthcare and Fitness 0.0440 0.0770
Lifestyle 0.0210 0.0414
Medical 0.0619 0.0621
Music 0.1708 0.1954
Navigation 0.1222 0.1712
News 0.1016 0.1015
Photography 0.1809 0.1956
Productivity 0.0440 0.0821
Reference 0.1494 0.1758
Social networking 0.0988 0.1531
Sports 0.1058 0.1574
Travel 0.0586 0.0918
Utilities 0.0305 0.0725
Weather 0.1050 0.2100
Mobile application service networks: Apple’s App Store
123
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Mobile application service networks: Apple’s App Store
123
centrality measures were collected from category network (see Table 5) to
incorporate the roles of categories in the overall App Store. A two-step cluster
analysis was performed using the SPSS 12.0K program. First, through hierarchical
clustering, the dendrogram was used to identify the appropriate number of clusters,
determined to be three. Then, a K-means clustering was executed to classify the
categories into their most homogeneous groups. The center of cluster and p value
from the ANOVA are shown in Table 9. The null hypotheses were all rejected at the
10 % significance level; thus, the clusters are different in all five indexes. The
names of the clusters and the classification of the categories are suggested in
Table 10.
The results show explicit differences. First, cluster 1 shows a high level of
density and centralization but a low node centrality. Thus, the categories in this
cluster are specialized and dominated by a few leader services within their service
boundary, due to a lack of content variety, and have little information, content,
function, or knowledge interaction with other service areas. Although they may not
help to mediate the convergence among mobile service areas in the network
category, the overall network is fertilized by the insuring of the internal stability of
the service sector. We named the cluster solitary specialist, and the utilitarian
categories peripheral in the category network correspond mainly to it, as Table 10
shows. Music and sports in the hedonic segment also seem to have specialized
features in relational patterns. Second, cluster 2 is the opposite of cluster 1,
including its low level of network structure property but high node centrality. They
are very comprehensive in their coverage of various relationships between other
service areas, but their internal concentration is inactive. Even though the three
measures of node centrality are high, their degree is higher than their closeness;
thus, their interrelationship is more direct than indirect. Due to their particularly
Table 9 Center of clusters
Cluster Network structure property Node centrality
Density Centralization Degree Closeness Betweenness
1 0.1095 0.1438 0.1111 0.2842 0.0026
2 0.0276 0.0621 0.5789 0.4180 0.2316
3 0.0672 0.0996 0.29825 0.3665 0.0505
p value 0.035 0.063 0.000 0.016 0.000
Table 10 Result of clustering
Cluster Category
1 Solitary specialist Finance, healthcare and fitness, medical, music, navigation,
news, reference, sports, weather
2 Mediating center Entertainment, utilities
3 Interim niche Books, business, education, games, lifestyle, photography,
productivity, social network, travel
J. Kim et al.
123
high value of betweenness, the categories in this cluster appear to be intermediary:
they mediate the flow of information between the remaining clusters. Therefore, the
broad coverage not only helps to mediate the convergence of various mobile service
areas but also makes their network sparse. We named this the mediating center, and
the cluster contains Entertainment and Utilities, the unrivaled central services.
Lastly, cluster 3 has medium-level density, centralization, and node centrality,
indicating a moderate level of both intra- and inter-relationships. However, since
their closeness is higher, they are centralized due to their indirect interactions with
other service sectors. Thus, it is named interim niche; here, actors can access
resources from mediating centers and actively contribute to the evolution of the
overall networks. Most of the hedonic segment categories are clustered in it.
7 Conclusion
The mobile industry is undergoing a tremendous transformation facilitated by
mobile app services, creating both opportunities and challenges for utilitarian and
hedonic service values. This research has identified the relational characteristics of
the mobile app services of macro-level category and micro-level app networks in
the App Store’s mobile app service sectors. Text-mining-based network analysis
was used to visualize the similarities among descriptions of mobile app services.
Several network indexes, including network structure metrics and node centrality,
identified the structural cohesion and central services in each network. A Mann–
Whitney U test showed that the network indexes did not differ according to
utilitarian and hedonic segments (except in closeness), indicating the need to re-
classify the mobile app service categories based on their relational characteristics.
Using a cluster analysis, the mobile app service categories were grouped into three
clusters—solitary specialist, mediating center, and interim niche.
This study contributes to the literature using the contents of mobile app services
in every category of App Store to investigate the characteristics of the overall
mobile app service sectors. The results reveal the sectoral characteristics of mobile
service innovation, indicating that the mobile apps in each sector differ in their
impact and association with other fields and services. Thus, this paper is an
important first step in understanding the patterns and structures of mobile apps and
provides implications for mobile ecosystem participants such as service providers,
app developers, mobile network operators, device manufacturers, and policy
makers. Our text-mining network analysis showed the relationships among services
as a visual network and therefore helped to grasp the overall structure of a service
database intuitively. As the process transforms original documents into structured
data through text mining, tracking the topics and keywords contributing to the
relationships can produce time and cost efficiencies. Because developers should
generate the service value more effective for users, the patterns according to value
can be helpful for developers to adjust and enhance the features of app development.
The main clusters found in mobile app services and their roles are important clue to
vitalize mobile ecosystems for platform operators and policy makers. Furthermore,
the network approach presented in this study will help actors construct more
Mobile application service networks: Apple’s App Store
123
valuable strategies. In the highly competitive environment of the mobile industry,
mobile ecosystem actors will have to learn to adapt to a network-centric mindset to
compete and survive in today’s global market. Information about what services are
similar and related can help them identify adjacent services, a potentially
competitive service competing in a similar market segment, or a complementary
service that can be grouped into service bundles. The analyzer can access the
resources of the adjacent service identified in a network, through collaborations
designed to improve firm performance and innovation.
However, like any other exploratory research, this study has a number of
limitations and future research themes. First, it was assumed that each category
belongs only to one segment of mobile value. This may have biased the results,
because the value of each service can differ from that of its category. Moreover, the
utilitarian and hedonic models can be replaced by a multi-dimensional classification
that incorporates the context or social dimension (Pihlstrom 2008). Second, the
accuracy of the visualization depends largely on the quality of the underlying data
and data processing. A network depends on the keywords extracted and selected by
the analyzer. Although the topics the analyzer wants to investigate are reflected in
the keywords, the selected keywords are used in an association matrix and can thus
lose the important relationships among the services. Lastly, the network analysis can
be elaborated through visualization techniques and quantitative indexes to provide
further insight into how the services can compete or collaborate. In the
visualization, networks can be differentiated according to purpose. For instance,
for the app network, multiple relationships between apps can be collapsed into one
type of tie by representing multiple apps as a sub-category node. For the quantitative
indexes, network indexes other than density, centralization, degree, closeness, and
betweenness could be utilized or developed to diversify the scope of analysis.
Acknowledgments This study was supported by the National Research Foundation of Korea (NRF)
grant funded by the Korea government (MEST) (No. 2011-0012759).
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