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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: hsns1234@snu.ac.kr

Y. Park

e-mail: parkyt1@snu.ac.kr

C. Kim

Department of Technology and Systems Management, Induk University, 14 Choansan-gil,

Nowon-gu, Seoul 139-749, Republic of Korea

e-mail: stddevs@induk.ac.kr

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: hylee@seoultech.ac.kr

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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