=Paper=
{{Paper
|id=Vol-1808/IWSECO16-paper4-Hyrynsalmi-p56-69
|storemode=property
|title=Multi-homing in Ecosystems and Firm Performance: Does it Improve Software Companies' ROA?
|pdfUrl=https://ceur-ws.org/Vol-1808/IWSECO16-paper4-Hyrynsalmi-p56-69.pdf
|volume=Vol-1808
|authors=Sami Hyrynsalmi,Arho Suominen,Slinger Jansen,Katariina Yrjonkoski
|dblpUrl=https://dblp.org/rec/conf/icis/HyrynsalmiSJY16
}}
==Multi-homing in Ecosystems and Firm Performance: Does it Improve Software Companies' ROA?==
https://ceur-ws.org/Vol-1808/IWSECO16-paper4-Hyrynsalmi-p56-69.pdf
Multi-homing in ecosystems and firm performance:
Does it improve software companies’ ROA?
Sami Hyrynsalmi1 , Arho Suominen2 , Slinger Jansen3 , and Katariina Yrjönkoski1
1
Tampere University of Technology, Department of Pori, Pori, Finland
{sami.hyrynsalmi,katariina.yrjonkoski}@tut.fi
2
VTT Technical Research Centre of Finland, Espoo, Finland
arho.suominen@vtt.fi
3
Utrecht University, Department of Information and Computer Sciences, the Netherlands
slingerjansen@uu.nl
Abstract. Joining or leaving a platform ecosystem is a crucial strategic decision
for a software producing organization. Multi-homing is strategy where a company
participate more than one platform ecosystem. A decision to multi-home entails
considerable impact for companies, as entering into a new ecosystem always
requires investments in, e.g., developing, maintaining and marketing a platform
specific extension. While a costly decision, it also opens new markets in domains
where customers seldom multi-home, i.e., it is a reliable way to address new
markets. Multi-homing strategies are infrequently addressed topic in the literature
and their impacts on the performance of the companies are rarely analyzed. In
this paper, we study how the decision to multi-home affects to the performance of
Finnish game companies. Our results question previous assumptions that multi-
homing has a positive impact on firm performance, as our study finds is unable
to find support for the differences in performance for single- or multi-homing
companies. This might be due to a development in which it is a norm in the market
is to publish simultaneously for all ecosystem in order to consolidate their position
in the market.
Key words: multi-homing, software ecosystems, strategic management, platforms,
two-sided markets
1 Introduction
The main difference between artificial ecosystems, such as business ecosystems, and
natural ecosystems is that the actors of the former are aware of their existence and can
make deliberate actions [1]. The conscious action can be, e.g., the selection of the best
fitting environment for an individual actor. The actors of the former—the famous lion
and antelope from Moore’s [2] metaphor—are not able to change the savanna to more
suitable environment.
The actors have the capability to make conscious decisions, allowing them to select
in which of the competing ecosystems to join. The actors can even decide to join more
than one of the competing ecosystems — a behaviour that is referred to as ‘multi-homing’
[3, 4]. Multi-homing is a significant factor in the competition between ecosystems [4, 5, 6].
As an example, a mobile software developer is multi-homing when it is offering the same
Copyright © 2016 for the individual papers by the papers' authors. Copying permitted for
private and academic purposes. This volume is published and copyrighted by the editors
of IWSECO 2016: The 8th International Workshop on Software Ecosystems.
2 Sami Hyrynsalmi et al.
or different products for Google’s Android as well as for iOS. The opposite, participating
only on one ecosystem is called ’single-homing’ [3].
As each ecosystem has an entry barrier [7], the multi-homing strategy incurs new costs
for the developing company. In the software ecosystem domain, entry barrier can be, for
example, a fee of participation or ecosystem specific development tools or skills needed.
For instance, there is a registration fee of USD 25 $ in Google Play1 and the official
development tool is Java-based Android SDK. A membership allowing publication in
Apple’s App Store costs USD 99 $2 and iOS applications official development tool iOS
SDK supports Swift and Objective-C languages.
In the software context, there are tools to reduce the cost of cross-ecosystem
development [c.f. 8, 9, 10]; however, multi-homing is never free even in software domain.
Cross-ecosystem development insures cost that are not only technology related, e.g.,
product management, maintenance, marketing. In addition, the technical cost of using
different programming languages and environment used in the different ecosystems also
increase the multi-homing costs [11]. Thus, the decision to contribute more than one
ecosystem is balancing between gaining a potentially larger market share [5] as well as
reducing dependency on a single orchestrator [12] against to the increased costs [13].
Interestingly, the impacts of multi-homing strategy for a company are rarely addressed
topic [14]. Most, if not all, of the extant literature of multi-homing focuses on the ecosystem
level analyses and bypasses company level investigations. Nevertheless, multi-homing
seems to be an ever-increasing strategy due growing importance of ecosystems and
platforms in the software business [15], and it will play an in important role in software
companies’ strategic actions. Therefore, this study takes an empirical approach to study
how the multi-homing decision affects the performance of individual actors — posing
the research question how does the multi-homing strategy impact the performance of a
company? This approach differs remarkably from those of previous studies.
In this study, we analyze the performance of Finnish game companies based on
their public financial information. The empirical sample is data of 208 Finnish game
companies. We utilize the categorization of developed by Still et al. [16] that created four
performance classes for Finnish game companies. In addition we gathered information
on the companies’ software products, if a company is developing products to the mobile
domain and if so, do the companies single- or multi-home. We go on to incorporate
additional financial data from Orbis database and analyze if multi-homing game developers
differ from the population.
The remainder of the paper is structured as follows. Section 2 presents previous work
of multi-homing of software companies. It is followed by a description of the research
approach, sampling and research process in Section 3. The following section presents the
results while Section 5 discusses on the findings. The final section concludes the study
with proposals for future work.
1
Android Developers – Get Started with Publishing. https://developer.android.com/
distribute/googleplay/start.html
2
Apple Developer – Choosing a Membership. https://developer.apple.com/support/
compare-memberships/
Multi-homing in ecosystems and firm performance 3
2 Related work
In the forthcoming, we will go trough previous studies regarding multi-homing. The
literature review is divided into two parts: 1) Surveys on multi-homing in software
business domain. 2) Theoretical studies on multi-homing and its impacts.
2.1 Multi-homing surveys
The mobile application ecosystems have been a regular study object for multi-homing
studies. Several studies have all analyzed multi-homing in these markets [c.f. 12, 15, 17,
18, 19] . However, all these studies have considered ecosystems at different life-cycle
phases, making it difficult to compare the results. Boudreau [17] found that multi-homing
companies were rare in his dataset of mobile applications during 1999–2004. A decade
later, Hyrynsalmi, Suominen and Mäntymäki [15] found also small general levels of
multi-homing: only 1.7–3.2% of applications and 5.8–7.2% developers were multi-
homing. However, they showed also that from the most popular applications 41–58%
and 42–69% from the most important developer were multi-homing. In addition, there
seem to be an overall trend that more developers and applications are multi-homed.
In addition to the mobile domain, multi-homing has been studied in a few areas.
For example Burkard et al. [20, 21] have studied multi-homing in SaaS CRM solution
ecosystems, finding that a small level of developers multi-home in these market. In
other industrial domains, a considerable amount of studies has also been published. For
example, Rysman [22] has empirically analyzed multi-homing in payment card markets
in USA. He showed that customers’ concentrate their using on a single card whereas
over 60% of the customer have several credit cards.
2.2 Theoretical analysis of multi-homing’s impact
From theoretical point-of-view, multi-homing effects on the whole ecosystem and on
the competition between the ecosystems have been addressed frequently [see 14, 23, for
literature reviews]. Sun and Tse [24, 25], for example, presented that the fate of competing
ecosystems depends on the single- and multi-homing patterns of the developers. If among
competing ecosystems, most of the developers multi-home, it supports the existence of
several competing ecosystem. If, however, the developers between competing ecosystems
most often single-home, Sun and Tse [24] results suggest that one ecosystem emerges a
dominant ecosystem — a monopoly. Hyrynsalmi [14], e.g., used their theory to forecast
that several mobile application ecosystems — e.g., Apple’s App Store and Google Play
[26] — can survive and compete also in the future.
Sun and Tse’s [24] theory also states that if the business segment is single-homing
market, only one dominant ecosystem will survive. As an example, Microsoft Windows
operating system and its dominance in the market can be considered. While competing
operating systems have survived in niche customer segments, different versions of
Windows operating system control over 90%3 of the desktop operating system market.
3
NetMarketShare – Desktop Operating System Market Share. https://www.netmarketshare.
com/operating-system-market-share.aspx
4 Sami Hyrynsalmi et al.
Eisenmann, Parker and van Alstyne [13] as well as Cusumano [27] have continued
this discussion by presenting the effects of multi-homing to the competition of different
kinds of business ecosystem. For example, Cusumano [27] notes that customers’ strategy
to single-home was among the main reasons why VHS versus Betamax battle ended in
the extinction of the alternative standard.
Landsman and Stremersch [23] have followed the same line of research. They studied
the multi-homing in a game console market and showed that initially a multi-homing
strategy hurts the sales of the hardware consoles but this effect melts away when
the ecosystem ages. They also divided multi-homing into two different categories: 1)
Seller-level multi-homing refers to a situation where the same producer works several
ecosystem. 2) Platform-level multi-homing refers to a case where the product is offered
for several ecosystem. While these two are naturally correlated, it is also possible that a
produce offers different products for different ecosystem — and that the same product
is implemented by different parties to different ecosystem. As an example of the latter,
please consider Facebook application that has been produced to Google Play and Apple’s
AppStore by Facebook, Inc. whereas Microsoft Corporation is the publisher of the official
Facebook application for Windows Phone.
As previously discussed, Hyrynsalmi et al. [15] studied multi-homing patterns in
mobile application ecosystems. They showed that while overall levels of multi-homing
are small, the most used applications are available in all competing software ecosystems
and the most important software vendors multi-home. They called these kinds of domains
as multi-level two-sided markets to depict the difference between the general level of
multi-homing and the superstar products’ multi-homing rates. They showed that the best
performing applications are multi-homed whereas average perfoming applications often
are not.
To summarize the literature review, studies empirically surveying multi-homing
strategy of software companies are scarce. Furthermore, the multi-homing research
often focuses on the ecosystem level by addressing the amount of multi-homers and
single-homers [e.g. 12, 15, 23], and analyses on a company-level are rare. There have
been previous studies focusing on structuring the impacts of multi-homing on ecosystem
as a whole as well as on competition of several ecosystems. However, there seems to
be a lack of company level studies analyzing the impact of multi-homing on company
performance.
3 Research approach
In the following, we will present and motivate the research question of this study. It is
followed by a short presentation about the Finnish game industry, which serves as a
setting for our study. This section ends in a description of data collection procedures
used.
3.1 Research objective
Previously, Hyrynsalmi et al. [15] showed that the majority of the most popular ap-
plications in the mobile application marketplaces are multi-homed. On the contrary,
Multi-homing in ecosystems and firm performance 5
they also showed that most of the applications that have not succeeded, measured by
the number of downloads or revenue gathered, are single-homed. This leads us to our
working hypothesis that multi-homing companies perform financially better compared
to single-homers. The hypothesis, however, does not take a stance on correlation and
causality. That is, in this study we do not assess whether multi-homing leads to a better
performance or do well-performing companies decide to multi-home. To summarize,
this study focuses on the question:
RQ Does a multi-homing strategy impact the performance of a company
Finnish game scene has had a long tradition on studios publishing for video games for
several different platforms from feature phones to game consoles. Only with the growth of
mobile ecosystems have we seen a strong increase in companies developing to the mobile
domain. It has been noted that the app economy is growing extremely fast, c.f. [14], and
we expect this transition have led to higher performance of mobile game companies when
compared to non-mobile ones. In addition, competition of mobile ecosystems has been
tight, there have been several different ecosystem to which join (e.g. Nokia Ovi) and that
multi-homing costs for a mobile application be assumed to smaller than, for example,
for a console or PC game. That is, developing a game for several mobile ecosystems is
assumed to be cheaper than developing a game for several console ecosystems. Therefore,
we pay a special attention in the following analysis on the mobile game companies as we
are expecting to see a higher level of multi-homing in this market.
As the second point we focus specifically on the impact of multi-homing. Previous
studies have suggested that multi-homing companies would perform better than single-
homing companies [15]. These finding are however based on an ecosystem level evaluation,
leaving open the question if at a company level publishers that multi-home would perform
better than single-homing companies. In this study, we analyze seller-level multi-homing
— i.e., do a company work for several ecosystem with same or different products — and
performance of multi-homing companies.
3.2 Industrial context — Finnish game industry
The game industry in Finland has been in the spotlight due to fast growing companies and
even unicorns, technology companies valued over $1 billion, like Supercell and Rovio
Entertainment. Dating back to the 1980s, game developers have been more noticeable in
the Finnish industrial landscape through demo scenes and early video game developers,
such as Remedy Entertainment Oy with its Max Payne series. Not only the international
success of a few game developers, but the information technology industry has been the
significant growth agent in the Finnish industrial landscape during the 1990s and 2000s.
In this growth, the impact of Nokia was significant, not only creating mobile devices,
but opening the way for mobile games. The first significant mobile game, Snake, was
developed by Nokia and included into several early mobile phone models.
The advent of smart phones changed the digital distribution channels for third party
content, with the introduction of application (App) stores, opening opportunities for game
developers to create content to mobile devices. Early success stories, such as Remedy
Entertainment and, more recent, Rovio Entertainment and Supercell have attracted
6 Sami Hyrynsalmi et al.
venture capital resulting in a strong increase in game industry, specifically companies
developing game content to mobile devices.
The not-for-profit hub for the Finnish game industry, Neogames, estimates that the
revenue of the Finnish game industry is in the range of 2.4 billion euros in 2015. This
is a 33 percent growth compared to 2014. Neogames estimates that the majority of the
revenues of the industry comes from exports.
The amount of foreign investment in the Finnish game companies has been significant.
In 2014, 11 game studios received capital investment in the sum of 33 million euros
and in 2015 Finnish companies receives 36 million euros divided into Eight game
studios. In 2015 the Finnish game industry released a total of 150 commercial games,
published in several platforms. To date, the Finnish game industry remains mobile driven,
where Finnish companies have also resulted in the most successes. In addition to the
mobile platforms, most significantly Apple and Android, Steam, the Internet-based
digital distribution platform has received significant interest. In addition the Finnish
game companies, taking to its roots, published 30 PC or console games in 2015. [28]
3.3 Data collection
For this study, we selected a population of a specific type: Finnish video game developers.
The game developers were chosen due to two reasons: First, games are products that are
frequently offered for several ecosystems. Thus, the video game industry should offer a
good amount of information from both single- and multi-homing developers. Second, a
crosscut of an industry allows us to see differences in different market domains.
The sample was constructed by downloading a list of all game producing organizations
provided by Neogames Finland4, the not-for-profit hub of the Finnish game industry. The
list of companies was downloaded in the end of October 2016. The list contained 277
organizations categorized into the group ‘Game development and publishing’. Neogames’
list also provides a rough categorization in which market the company operates. As 197
(71.2%) of the companies in the list declares working in the mobile market, we pay a
special attention in the analysis to the mobile application ecosystems.
The Orbis database was used to gather financial data for all companies. Compa-
nies were identified in the database by name, manually checking for possible miss-
classifications during the search. For each company we retrieved; number of employees
(last available year), Total assets (10 years), Operating revenue (10 years), Return on
Assets (10 years). In addition we had control variables ISO country code and NACE rev.
2 Core industry classification code which were used to check that the financial data was
from the correct company.
The companies were individually screened by the authors in order to evaluate each
company’s focus and strategic options. Each of the companies were checked by following
an URL included into Neogames’ list, companies were searched online to find publicly
available news articles or other data that can highlight if the company is active and if so,
whether the company has decided to develop their offering to only one or more than one
ecosystem. Going through all of the companies, the authors we able to operationalize if
4
Neogames – Operators. http://www.neogames.fi/en/industry-info/operators/
Multi-homing in ecosystems and firm performance 7
Assets average
GROUP 2 GROUP 4
No revenue, above aver- Above average revenue,
age assets above average assets
average
GROUP 1 GROUP 3
No revenue, below aver- Positive revenue, no as-
age assets sumptions on assets
Revenue
Fig. 1. Still et al.’s [16] taxonomy for Finnish game companies
the company was active in mobile, PC or console game market as well as in how many
ecosystems.
Out of 277 companies in the list, only 208 were found to be active game developers.
We excluded organizations that were closed down, acquired by another company, startups
that have not published, yet, as well as companies that were not actually game producing
organizations but rather, e.g., animation houses. For all of the 208 companies, we gathered
information to which ecosystems they have published. Out of the 208 companies, 158
(76.0%) had published at least one mobile application for modern smart phone operating
systems.
Finally, we classified all companies based on the taxonomy presented by Still et al.
[16] for Finnish game producing organizations. They used classic fourfold table for their
taxonomy: the vertical axis is the amount of total assets and the horizontal axis is revenue.
The emerged four groups are presented in Figure 1. The taxonomy is used for allowing
us to compare and study companies who are in different phases.
4 Results
In the following, we first presents descriptive statistics of the studied game development
organization. That is, we illustrate rates of seller-level multi-homing. In the second
subsection, we will focus on the performance of the multi-homing companies.
4.1 Seller-level multi-homing
Out of the all studied 208 Finnish game producing organizations, 114 (54.8%) has
published game or games for more than two ecosystems. That is, a slightly larger
population has adopted the strategy of relying on several ecosystems platform. From
8 Sami Hyrynsalmi et al.
Table 1. Used dataset and subsets
Group N %
‘Game development and publishing’ companies 277
Active game development companies 208
Multi-homers 114 (54.8%)
Single-homers 94 (45.2%)
Active companies with a mobile game 158
Multi-homers 105 (66.5%)
Single-homers 53 (33.5%)
Active companies without a mobile game 49
Multi-homers 9 (18.4%)
Single-homers 40 (81.6%)
the 158 organizations who have produced a mobile game, a large majority (105, 66.5%)
multi-homes. From 49 companies that have not published a mobile game, only nine
(18.4%) multi-homes. Table 1 summarizes the result from the main dataset and the
subsets used.
From 158 companies that have published a mobile game: 53 single-home, 70
companies multi-home to two ecosystems and 24 multi-home to three or more ecosystems.
For single-homing companies, 35 of the 53 companies are releasing products through
Apple’s iOS ecosystem. Twenty developers launch products only through Google’s
Android ecosystem and the remaining eight publish through some other mobile ecosystem.
For companies that are active in two ecosystems, 64 publish through Apple and Google
ecosystems. Six companies have selected Apple or Google ecosystem and a third
ecosystem.
From Orbis database, 272 companies out of the full list of 277 companies were
found. Financial information was registered for only 162 companies. However, revenue
information was found only for 142 companies. The average yearly revenue in this set
is 13,873 ke. However, there is a single company in the dataset which revenue is more
than 92 times bigger than the second biggest one. After removing this one, the average
yearly operating revenue is 852 ke. The average total asset value is 12,004 ke for all
and 959 ke without the biggest company. Median for current ratio is 4. There were only
41 companies with current ratio under 1; that is, these are companies that have more debt
than assets. In the studied set, there were nine companies which book value equals or is
lesser than its basic capital.
4.2 Financial performance
By focusing on the active 208 game producing organizations, we found financial data for
only 110 companies. In the following analysis, we focus only on these. We apply Still et
al.’s [16] taxonomy to the remaining companies. Group 1, Group 2, Group 3 and Group
4 have 34, 32, 72 and 9 members, respectively. The averages and standard deviations of
total assets values and revenues of each group are given in Figure 2.
Multi-homing in ecosystems and firm performance 9
Assets GROUP 2 GROUP 4
Assets Revenue Assets Revenue
AVG 652.3 0 AVG 153,505 36,564
s 1,091 0 s 377,922 48,230
N 32 32 N 9 9
GROUP 1 GROUP 3
Assets Revenue Assets Revenue
AVG 2.8 0 AVG 398.7 251
s 3.2 0 s 798.7 370
N 34 34 N 72 72
Revenue
Fig. 2. Assets and revenue values (in thousands e) for Still et al.’s taxonomy’s classes [adapted 16]
Table 2. Groups of non-mobile and mobile game companies
Group Non-mobile Mobile
AVG Count AVG Count
Group 1 -20.88 10 -12.46 10
Group 2 12.79 12 -22.34 11
Group 3 -5.42 32 -4.78 26
Group 4 -15.77 5 18.45 4
Table 2 gives the averages of Return on Assets (ROA) for non-mobile and mobile
developers, as well as the count of companies in each cell. The number of companies
in the matrix has reduced by 41 due to missing data. Based on a qualitative evaluation,
major difference are present in Group 2 and Group 4. In Group 2, companies with small
or zero revenue but asset value, mobile companies have a negative ROA but non-mobile
have a positive ROA. In the superstar Group 4, mobile companies have a positive ROA
and non-mobile a negative ROA. For Group 2, we can question is metrics calculating the
ROA practical. For Group 4, it seems that the impact of few high performing mobile
developers is significant. We employed the Mann-Whitney U test to analyze if companies
developing mobile applications differ from companies developing other game software
products. We are unable to reject the null hypothesis.
Table 3 reports ROA values for companies that multi-home or single-home. Table 4
isolates the companies that multi-home to two or or more ecosystems. The tables also
give counts of companies in each cell. The number of companies included in the table is
smaller than the sample due to missing ROA data for several of the companies.
In Table 3, Group 1 seems to be relatively similar between multi-homing and
non-multi-homing companies. Major differences are seen with Groups 2 and 3 where
multi-homing companies in Group 2 have a negative ROA but in Group 3 a positive ROA,
10 Sami Hyrynsalmi et al.
Table 3. Results for single-homing vs multi-homing game companies
Group Single-homing Multi-homing
AVG Count AVG Count
Group 1 -10.24 3 -13.41 7
Group 2 8.86 4 -40.17 7
Group 3 -28.43 8 5.73 18
Group 4 18.45 4
Table 4. Results for multi-homing for several ecosystems
Group To 2 To 3+
AVG Count AVG Count
Group 1 -15.64 6 0 1
Group 2 -41.47 6 -32.35 1
Group 3 -1.31 10 14.53 8
Group 4 28.15 2 8.74 2
but this situation is vice versa for non-multi-homing companies. All of the superstar
companies multi-home.
For Table 4, the count of companies isolated to the different groups is relatively
small. Specifically the number of companies that would multi-home to three of more
ecosystems is extremely small. Interesting differences due exist for Group 3, where
companies that multi-home to three or more ecosystems have a positive ROA, but for
companies multi-homing in two the ROA value is negative.
We employed the Mann-Whitney U test to test if companies developing mobile
applications to two or more ecosystems differ from companies developing products to
one ecosystem. We are unable to reject the null hypothesis.
5 Discussion
In this study, we examined how a company’s strategic decision to either single-home in
one ecosystem or multi-home for two or more ecosystems affects the performance of the
companies. The performance was measured with the ROA. This type of performance
measurement was suggested by Iansiti and Levien [29] to evaluate the productivity of an
ecosystem. Our key findings are:
– Multi-homing companies have a negative ROA, whereas single-homing companies
have an average positive ROA.
– Multi-homing is far more frequent among mobile game developers (66.5%) than
non-mobile game developers (18.4%).
– While the overall seller-level multi-homing rate is remarkable high (54.8%), it still
lower than one could expect due to availability of plethora of different multi-ecosystem
development tools.
Multi-homing in ecosystems and firm performance 11
The mobile and non-mobile game companies’ ROA are rather similar with a few
distinct differences (c.f. Table 2). First, interestingly, the mobile superstars average ROA
is positive whereas the non-mobile superstars’ average ROA is negative. Overall, as the
‘App Economy’ is now one of firmly growing business areas [30], the result is not a
surprise. Second, there is a remarkable, opposite, difference in Group 2. We interpret
that this result means noteworthy investments in mobile game companies while these
companies have not yet been able to create stable revenue streams from their products.
This observation is in line with the overall argumentation of a fast growth in the App
economy. In contrast, the non-mobile companies have a positive ROA, that could suggest
that these companies would have a higher expectation to revenue gains prior to investment
— this in contrast to the pre-revenue venture capital drawn by mobile game companies.
For game publishers the cross-tabulation of groups and multi-homing patterns reveal
interesting ROA averages. For Group 2, which is still nearly pre-revenue but has gained
assets, companies who multi-home have a clearly negative ROA, whereas single-homing
companies are able to produce on average positive ROA. For Group 3, companies that
have revenue, the tables turn, as multi-homing companies have a positive ROA. This
behaviour is to an extent surprising. One possible explanation is that companies who
have gathered significant investment prior to revenue are expected to multi-home to
increase the user base for their products, the benefits of this broad user base would then
be visible as the companies move to Group 3 — by gaining revenues.
Our data for companies that multi-home to two, three or more struggles with the
low volume of companies in the groups. The performance of companies seem similar,
but interestingly by comparing Table 3 and Table 4 we identify that it is in fact the
companies that multi-home to three or more than impact Group 3 difference between
single- and multi-homing companies. More in depth qualitative analysis should look into
the structure of these companies to identify specific reason in making the strategic choice
of a multi-homing strategy.
Interestingly, the overall rate of seller-level multi-homing found in this study is
surprisingly small. Only a small majority (54.8%) were found to publish more than
one ecosystem. When we omit the mobile application developers, the multi-homing
rate is small (18.4%). For the developers working with mobile domain, the seller-level
multi-homing rate was a considerable higher (66.5%). While this is sharp contrast to the
overall levels (5.8–7.2%) reported by Hyrynsalmi et al. [15] in the mobile application
markets in the end of 2012, we were expecting to see a higher level of multi-homing in
the end of 2016. First, mobile application ecosystems have matured during the previous
years and maturation has been seen in growing number of multi-ecosystem development
tool and method [10, c.f.]. Thus, multi-ecosystem development should be easier than
four years ago. Second, we were focusing specifically on the game domain where one
can expected that, e.g., brand value and game flow are far more important than in utility
software such as e-mail or flashlight applications.
One possible explanation could be that the requirements of multi-ecosystem develop-
ment are more more demanding in the game domain that in utility software domain. That
is, multi-homing can be more challenging in the gaming due to, e.g., higher performance
requirements on operating system and platform compatibility.
12 Sami Hyrynsalmi et al.
To summarize, there has been lots of discussion on multi-homing whereas this study
is among the first one to take a look on the performance of multi-homing companies. Our
results show that statistically multi-homing game companies do not perform better than
single-homing companies. However, there are a few noteworthy limitations that should be
taken into account. First, we focused only on the Finnish game companies and assumed
that our sample is a convenient for the analysis as it contains new small companies, older
ones as well as superstars. However, Finland’s game companies might have some specific
characteristics that differentiate them from the rest. This puts into question, to which
extent the results from our sample is generalizable to the whole population. Second, the
companies have been founded at different time points and we do not assess their maturity
at all. This limitation should be tackled by a longitudinal approach that would give a
more comprehensive picture of the impact of multi-homing. Third, we used quantitative
approach that does not pay attention to the nature of multi-homed content. For example,
an application vendor might have decided to stop supporting an ecosystem but their old
products are still available there whereas the vendor has decided to publish the new games
only for another ecosystem. In our dataset, this would still be treated as a multi-homer.
Therefore, more qualitative work is need to support the findings of this study and pave a
way for further inquiries.
6 Conclusion
This paper offers a company level analysis of the impact of multi-homing on firm
performance. The study uses the Finnish game companies as the population. The dataset
contains a variety of companies from pre-revenue start-ups to unicorns with million
dollars yearly revenue. We show that most of the game companies multi-home whether
they are well-performing superstars or not. These remarkably differ from the results of
Hyrynsalmi et al. [15] who found that most of the superstars multi-home whereas the
majority of the application developers single-home. Our findings open the avenue to
further analyze the impact of multi-homing. Broadening the number of delivery chains,
in the case of mobile companies, would increase the base of potential customer. Thus
it would seem that it would be beneficial to multi-home if the entry barrier remains
manageable. For further work, we propose that studies should consider the maturity of
the company has a variable that might explain changes in the single- or multi-homing
choices of companies. Furthermore, a qualitative longitudinal study on the impacts of
multi-homing on a company would strengthen the arguments raised from the benefits
and challenges of multi-homing.
References
[1] Stallman, R.M.: Free Software, Free Society: Selected Essays of Richard M.
Stallman. Second edn. Free Software Foundation, Inc., Boston, MA, USA (2010)
[2] Moore, J.F.: Predators and prey: A new ecology of competition. Harvard Business
Review 71(3) (May-June 1993) 75–86
Multi-homing in ecosystems and firm performance 13
[3] Caillaud, B., Jullien, B.: Chicken & egg: Competition among intermediation service
providers. The RAND Journal of Economics 34(2) (Summer 2003) 309–328
[4] Rochet, J.C., Tirole, J.: Platform competition in two-sided markets. Journal of the
European Economic Association 1(4) (June 2003) 990–1029
[5] Rochet, J.C., Tirole, J.: Two-sided markets: A progress report. The RAND Journal
of Economics 37(3) (September 2006) 645–667
[6] Armstrong, M.: Competition in two-sided markets. The RAND Journal of
Economics 37(3) (September 2006) 668–691
[7] McAfee, P., Mialon, H.M., Williams, M.A.: What is a barrier to entry? The
American Economic Review 94(2) (2004) 461–465
[8] Dhillon, S., Mahmoud, Q.H.: An evaluation framework for cross-platform mobile
application development tools. Software: Practice and Experience 45(10) (October
2015) 1331–1357
[9] Ohrt, J., Turau, V.: Cross-platform development tools for smartphone applications.
IEEE Computer 45(9) (September 2012) 72–79
[10] Ahti, V., Hyrynsalmi, S., Nevalainen, O.: An evaluation framework for cross-
platform mobile app development tools: A case analysis of adobe phonegap
framework. In Rachev, B., Tegolo, D., Kalmukov, Y., Smrikarova, S., Smrikarov,
A., eds.: Compsystech’16. (2016)
[11] Kouris, I., Kleer, R.: Business models in two-sided markets: An assessment of
strategies for app platforms. In: International Conference on Mobile Business 2012.
ICMB’2012, Association for Information Systems (January 2012) 1–12 Paper 22.
[12] Idu, A., van de Zande, T., Jansen, S.: Multi-homing in the Apple ecosystem:
Why and how developers target multiple Apple App Stores. In: Proceedings of
the International Conference on Management of Emergent Digital EcoSystems.
MEDES ’11, New York, NY, USA, ACM (2011) 122–128
[13] Eisenmann, T., Parker, G., Van Alstyne, M.W.: Strategies for two-sided markets.
Harvard Business Review 84(10) (2006) 92–101
[14] Hyrynsalmi, S.: Letters from the War of Ecosystems — An Analysis of Independent
Software Vendors in Mobile Application Marketplaces. Doctoral dissertation,
University of Turku, Turku, Finland (December 2014) TUCS Dissertations No 188.
[15] Hyrynsalmi, S., Suominen, A., Mäntymäki, M.: The influence of developer multi-
homing on competition between software ecosystems. The Journal of Systems and
Software 111 (January 2016) 119–127
[16] Still, K., Suominen, A., Rilla, N.: Board interconnections and company perfor-
mance: An analysis of the finnish game developer companies. In: 22nd ICE/IEEE
International Technology Management Conference. (2016)
[17] Boudreau, K.: Too many complementors? Evidence on software developers.
Technical report, HEC-Paris School of Management (January 2007) Working Paper
Series, Social Science Research Network.
[18] Boudreau, K.J.: Let a thousand flowers bloom? An early look at large numbers of
software app developers and patterns of innovation. Organization Science 23(5)
(September 2012) 1409–1427
[19] Hyrynsalmi, S., Mäkilä, T., Järvi, A., Suominen, A., Seppänen, M., Knuutila, T.:
App Store, Marketplace, Play! An analysis of multi-homing in mobile software
14 Sami Hyrynsalmi et al.
ecosystems. In Jansen, S., Bosch, J., Alves, C.F., eds.: Proceedings of the Fourth
International Workshop on Software Ecosystems. Volume 879 of CEUR Workshop
Proceedings., MIT Sloan School of Management, Cambridge, MA, USA, CEUR-WS
(June 2012) 55–68
[20] Burkard, C., Draisbach, T., Widjaja, T., Buxmann, P.: Software ecosystems: Vendor-
sided characteristics of online marketplaces. In: Lecture Notes in Informatics —
Informatik 2011. (October 2011)
[21] Burkard, C., Widjaja, T., Buxmann, P.: Software ecosystems. Business & Informa-
tion Systems Engineering 4(1) (2012) 41–44
[22] Rysman, M.: An empirical analysis of payment card usage. The Journal of Industrial
Economics 55(1) (March 2007) 1–36
[23] Landsman, V., Stremersch, S.: Multi-homing in two-sided markets: An empirical
inquiry in the video game console industry. Journal of Marketing 75(6) (2011)
39–54
[24] Sun, M., Tse, E.: When does the winner take all in two-sided markets?. Review of
Network Economics 6(1) (March 2007) 16–40
[25] Sun, M., Tse, E.: The resource-based view of competitive advantage in two-sided
markets. Journal of Management Studies 46(1) (2009) 45–64
[26] Hyrynsalmi, S., Seppänen, M., Suominen, A.: Sources of value in application
ecosystems. The Journal of Systems and Software 96 (October 2014) 61–72
[27] Cusumano, M.A.: Staying Power: Six Enduring Principles for Managing Strategy
and Innovation in an Uncertain World (Lessons from Microsoft, Apple, Intel,
Google, Toyota and More). Oxford University Press, Oxford, UK (2010)
[28] Neogames ry: The game industry in finland 2015 (2015)
[29] Iansiti, M., Levien, R.: The Keystone Advantage: What the New Dynamics of
Business Ecosystems Mean for Strategy, Innovation, and Sustainability. Harvard
Business School Press, Boston, MA, USA (2004)
[30] Basole, R.C., Karla, J.: Value transformation in the mobile service ecosystem: A
study of App Store emergence and growth. Service Science 4(1) (2012) 24–41