Software Ecosystems Governance

A Systematic Literature Review and Research Agenda

Carina Alves

, Joyce Oliveira

and Slinger Jansen

Centro de Informática, Universidade Federal de Pernambuco, Recife, Brazil

Department of Information and Computing Sciences, Utrecht University, Utrecht, The Netherlands

Keywords: Software Ecosystems, Governance, Health, and Systematic Literature Review.

Abstract: The field of Software ecosystems is a growing discipline that has been investigated from managerial, social,

and technological perspectives. The governance of software ecosystems requires a careful balance of control

and autonomy given to players. Orchestrators that are able to balance their own interests by bringing joint

benefits for other players are likely to create healthy ecosystems. Selecting appropriate governance

mechanisms is a key problem involved in the management of proprietary and open source ecosystems. This

article summarizes current literature on software ecosystem governance by framing prevalent definitions,

classifying governance mechanisms, and proposing a research agenda. We performed a systematic literature

review of 63 primary studies. Several studies describe governance mechanisms, which were classified in

three categories: value creation, coordination of players, and organizational openness and control. The

number of studies indicates that the domain of software ecosystems and their governance is maturing.

However, further studies are needed to address central challenges involved on the implementation of

appropriate governance mechanisms that can nurture the health of ecosystems. We present a research

agenda with several opportunities for researchers and practitioners to explore these issues.

1 INTRODUCTION

In the last decade, a large amount of research has

been devoted to investigate the field of software

ecosystems from managerial, social, and

technological perspectives (Barbosa et al., 2013),

(Bosch, 2014). Software ecosystems are sets of

actors functioning as a unit and interacting with a

shared market for software and services, together

with relationships among them (Jansen et al., 2009).

A software ecosystem frequently relies on a

platform on which extenders can build specific

solutions to create complementary value (Jansen et

al., 2012). Independent developers can extend and

enrich the platform while sharing costs and risks

with the platform owner. Examples of successful

software platforms are Apple’s iOS, Google Apps,

and the Mozilla Firefox browser. The leading firm,

typically called the orchestrator (or keystone) firm,

must promote the sustainable development of the

ecosystem by defining strategies and orchestrating

the activities of players. The orchestrator is

responsible for managing the evolution of the

enterprise architecture (Iyer et al., 2007) and the

interactions among all actors within the ecosystem

(Manikas and Hansen, 2013a). The governance of

software ecosystems requires a careful balance of

control and autonomy given to players.

Orchestrators that are able to balance their own

interests by bringing joint benefits for other players

are likely to create healthy ecosystems. Software

ecosystems governance has become a crucial

managerial aspect for proprietary platform owners

and open source communities.

According to Tiwana (2010), governance

mechanisms are employed to establish the level of

control, decisions rights, and scope of proprietary

versus shared ownership. There are several models

to govern software ecosystems. For instance, GNU

Linux is an open source ecosystem with a thriving

community of developers. Apple’s iOS is a

prosperous example of proprietary ecosystem with

tight control mechanisms. Google built a lively

ecosystem around its Android open source

community named the “Open handset Alliance”. On

the other hand, Nokia’s Symbian is an open source

operating system that failed to create a vibrant

ecosystem due to its inability to attract partners and

develop a rich set of apps (West and Wood, 2008).

Alves, C., Oliveira, J. and Jansen, S.

Software Ecosystems Governance - A Systematic Literature Review and Research Agenda.

DOI: 10.5220/0006269402150226

In Proceedings of the 19th International Conference on Enterprise Information Systems (ICEIS 2017) - Volume 3, pages 215-226

ISBN: 978-989-758-249-3

215

The examples above show that choosing the right

ecosystem strategies and governance mechanisms

are life-or-death decisions for orchestrator

organizations. In fact, companies engaging in an

ecosystem are mutually dependent on each other for

survival.

We define software ecosystem governance

mechanisms as managerial tools of participants in

software ecosystems, i.e., orchestrators and platform

extenders that have the goal of influencing an

ecosystem's health. Ecosystems are healthy when

they exhibit longevity and propensity for growth

(Den Hartigh and Visscher, 2006).

Selecting appropriate governance mechanisms is

not a trivial task. The challenge is to bound players

actions without excessively constraining the desired

level of innovation and value creation in the

ecosystem. This situation creates fine tension

between control and autonomy. Balancing these

tensions is one of the main goals of software

ecosystem governance. The correct implementation

of governance mechanisms can accommodate these

tensions towards a sustainable and healthy

ecosystem. On the other hand, ineffective

governance can result in a declining growth of the

ecosystem (Wareham et al., 2014). The challenge of

selecting ecosystem governance strategies that

contributes towards the ecosystem health has driven

us to conduct a systematic literature review. Our

review aims at synthetizing the increasing number of

studies in the field of software ecosystem

governance.

This article is organized as follows. Section 2

describes the research method. The results of the

review are presented in Section 3. To discuss the

results of our review and propose future areas for

investigation, a research agenda containing six areas

of interest is proposed in Section 4. Then, we discuss

threats to validity in Section 5. Finally, Section 6

concludes this article.

2 RESEARCH METHOD

A Systematic Literature Review (SLR) is a means

for answering specific research questions, examining

a particular research topic, or phenomenon of

interest by systematically identifying, evaluating,

and interpreting available relevant research. Our

review protocol follows guidelines from Kitchenham

and Charters (2007). We undertook the review of

studies following these activities: defining research

questions, searching relevant studies, applying

inclusion/exclusion criteria, assessing the quality of

studies, analysing data, and synthesis.

2.1 Research Questions

We specified two research questions to guide our

study:

 RQ1. How is governance characterized in

software ecosystems literature?

 RQ2. What are the mechanisms proposed to

govern software ecosystems?

Governance is a well-established concept

primarily associated with the needs to protect

investment and ensure the sustainability of

businesses through time (Hoogervorst, 2009).

Corporate governance refers to the mechanisms,

processes, and relations by which corporations are

controlled and governed (OECD, 2004). Governance

involves a set of principles to direct the distribution

of rights and responsibilities among stakeholders. In

RQ1, we present and discuss available definitions

for software ecosystems governance proposed by

primary studies. Then, we compare the definitions

available and propose an integrated definition for the

term.

Traditional corporate governance mechanisms

include monitoring actions, policies, and decisions

by aligning the interests of different stakeholders.

According to Croteau et al. (2013), IT governance

can be organized by the attributes: structure, process,

and participants. In this SLR, we opted not to follow

a pre-existing classification. Instead, we classify the

governance mechanisms based on the data gathered

from the primary studies following a thematic

analysis approach (Cruzes and Dybå, 2011). Our

goal to answer the second question (RQ2) is to

identify the mechanisms proposed by current

literature to govern software ecosystems.

2.2 Search Process

To guide the systematic literature review, a protocol

was developed to specify the steps and criteria to

undertake the review. The review protocol includes

details of how different types of studies will be

located, appraised, and synthesized (Brereton et al.,

2007).

The strategy to collect studies included the

following steps: (i) automatic search of electronic

databases (ii) manual search of journals,

conferences, and workshops (iii) analysis of

reference lists from other secondary studies in

software ecosystems. The automatic search was

executed on the following databases: ACM Digital

Library, IEEE Xplore Digital Library, Science

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Direct, and SpringerLink. We used two independent

search strings: “software ecosystem”, “platform

ecosystem”. We opted to use generic terms to avoid

over restricting the search process. In the early

stages of our research we tried to use the search

string “software ecosystem” AND “governance”.

However, we considered that using these combined

keywords the results retrieved from the search

engines were very limited. In addition, we conducted

a manual search in the following journals,

conferences, and workshops:

 Information and Software Technology;

 Journal of Software Systems;

 International Conference on Software Business;

 International Workshop on Software

Ecosystems.

To complement our manual search, we analysed

the references of the following reviews in the field

of software ecosystems: (Barbosa et al., 2013;

Franco-Bedoya et al, 2014; Manikas and Hansen,

2013a; Manikas, 2016). Although the scope and

research questions of these reviews are different

from ours, we examined the list of articles to correct

any eventual omission of studies from the other

search procedures. 7 studies [S13, S17, S18, S25,

S26, S41, S61] were obtained from the analysis of

secondary studies described above. Finally, we

collected additional three studies [S31, S53, S57]

that were recommended by experts in the field.

2.3 Inclusion and Exclusion Criteria

We adopted the following inclusion criteria to select

articles: (i) studies written in English, (ii) studies

that answer at least one research question. The

exclusion criteria adopted was: (i) secondary studies

(e.g. mapping studies and systematic literature

reviews), (ii) technical reports, abstracts, and

whitepapers, (iii) duplicate reports of the same

study.

The literature collection started with 997 articles

returned from the electronic and manual search. The

automatic search was conducted on the 5th of

January 2016. We did not restrict year range in our

search. Then, we excluded articles based on titles

and abstract that did not satisfy our inclusion

criteria. In practice, we assessed if the title and

abstract are likely to answer at least one RQ.

Whenever we were in doubt we included the article

for further analysis of its full content. After this step,

we included 592 studies. Then we read the full

content of the articles and selected 67 primary

studies. In a final step, a quality assessment of each

article was conducted and we finally selected 63

articles (studies are listed in the Appendix).

The quality assessment criteria based on Brhel et

al., (2015) are described below:

 Is there a clear statement of the research goals,

e.g. in an explicitly verbalized research question?

 Is there an adequate description of the context in

which the research was carried out?

 Only applicable to empirical research articles:

- Is the research method explicitly stated?

- Which research method was chosen?

2.4 Data Extraction and Analysis

We used a database to store data from the selected

studies. Two researchers extracted data from the

studies. Several discussion meetings were held with

all authors to compare extractions, clarify

uncertainties, agree on discrepancies, and perform

sanity checks. To answer RQ1, we simply searched

the term “governance” in the primary studies and

checked if the article provided a definition for

governance in the context of software ecosystems.

To answer RQ2, we used thematic analysis as

synthesis method, following the recommended steps

proposed by (Cruzes and Dybå, 2011). We identified

the relevant codes from primary studies. Then, we

merged the codes into key themes. We considered

that governance mechanisms were encapsulated in

related terms, such as: “manage”, “govern”,

“control”, “strategy”, “orchestration” and “critical

factors”.

3 RESULTS

3.1 Overview of Studies

63 studies were identified in our systematic literature

review, as listed in the Appendix. Our final list

included articles published between 2002 and 2016.

We observed an increasing number of studies

published over the last few years, where the peak

publication period is from 2012 to 2015 (36 articles,

57%).

The most popular publication channels are:

ICSOB (8 articles, 13%), IWSECO (5 articles, 8%),

ECSA (3 articles, 5%), IST (5 articles, 8%), JSS (2

articles, 3%). In particular, the following events are

dedicated to the field of ecosystems: IWSECO,

MEDES, EWSECO, WEA, DEST. These results

confirm that the field of software ecosystems has

been receiving a growing attention from the

academic communities of information systems,

software business, and software engineering.

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Figure 1 presents the main software ecosystems

investigated by the primary studies. Apple and

Android are the most frequent ecosystems examined

(with 4 studies both ecosystems), followed by

Siemens, IBM, GX Software and Eclipse (3 studies

each ecosystem).

Figure 1: Software ecosystems investigated by the studies.

We noted that both open source and proprietary

ecosystems have been equally analysed. It is a good

sign that researchers are focusing on the specific

managerial issues faced by open source and

proprietary ecosystems. The diversity of ecosystems

being investigated reinforces the wide perspective

that the software ecosystems community is gaining

regarding the differences between open source and

proprietary ecosystems.

Figure 2 shows the research type followed by the

studies. We adopted the classification proposed by

Wieringa et al., (2006). Petersen et al., (2015)

provides helpful decision criteria on how to classify

the studies. According to the classification, studies

can be classified into six research types, namely:

Solution Proposal – The article proposes a novel

solution or a significant improvement of an existing

technique without a full validation.

Philosophical Paper – The paper proposes a

conceptual framework and a new way to look at

things.

Opinion Papers – The paper presents the

author’s opinion about something.

Experience Paper – The paper describes the

practical experience of the author who is generally

an industry practitioner.

Validation Research – The paper describes an

empirical validation of a solution done in the lab.

Examples of studies include: student case study,

mathematical analysis, prototyping, laboratory

experiments, and simulation.

Evaluation Research – The paper presents a real-

world industrial evaluation of a solution. It includes:

industrial case study, controlled experiments with

practitioners, practitioner targeted survey, action

research, and ethnography.

Figure 2: Research design adopted by the studies.

The most common type of study identified in our

review is evaluation research (27 studies). This

result suggests that the majority of studies explore

the field from a practical perspective by conducting

empirical studies of real ecosystems. 21 studies are

classified as philosophical papers. 11 studies

propose a solution such as new techniques, models,

and methods. 2 studies present validation research.

Finally, we identified 2 experience papers. We did

not include any opinion paper in our list of primary

studies.

3.2 Answering RQ1: How is

Governance Characterized in

Software Ecosystems Literature?

Our results show that the concept of governance is

gaining importance in software ecosystem literature.

9 studies [S1, S4, S20, S21, S28, S32, S51, S54,

S62] explicitly define what is software ecosystem

governance. Jansen [S34] proposes that governance

is one of the key domains of the Open Software

Enterprise Model. The study adopts the definition of

governance given by Dubinsky and Kruchten [S12],

who consider governance as “the way an

organization is managed, including its powers,

responsibilities, and decision-making processes”.

According to Jansen et al. [S32], it also involves the

assignment of roles and decision rights, measures,

and policies. A fundamental governance decision

that orchestrators must make is how much power is

given to the community and how much control it

keeps for itself. Jansen and Cusmano [28] and van

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Angeren et al. [S54] consider that ecosystem

governance “involves the use of strategic procedures

and processes to control, maintain, or change the

ecosystem”. Study [S54] also states that software

ecosystems governance “encompasses both

technical and managerial aspects, including the

management of the software platform and its

interfaces, definition of business and partnership

models, and establishment of entry barriers”.

Baars and Jansen [S4] propose a framework for

software ecosystems governance. They define

software ecosystems governance as: “procedures

and processes by which a company controls,

changes or maintains its current and future position

in a software ecosystem on all different scope

levels”. Studies [S1], [28] and [S62] adopt the same

definition. Albert and colleagues [S1] present a

software ecosystem governance approach for

enabling IT architecture based on software asset

management. In [S28], Jansen and Cusumano

propose a governance model for ecosystem health

preservation and improvement. Wnuk et al., [S62]

evaluate the model proposed in [S28] by means of a

case study in a hardware-dependent software

ecosystem.

We noted that several primary studies discuss the

classical tension between open and closed

governance models. Jansen et al., [S32] suggest that

companies benefit from opening-up their business

models. It includes sharing strategic knowledge,

making the ecosystem strategy explicit to all players,

and coordinating actions. The authors propose an

openness degree to assess how open a company is.

In [S13], the authors do not define what is

governance in the context of ecosystems, but they

provide a rich discussion on the tensions between

open and closed governance models as platforms

mature. The study proposes that hybrid governance

models are more suitable for both proprietary and

shared platforms. Such model is characterized by the

centralized control over platform technology and

shared responsibilities for the ecosystem

community.

Ghazawne et al., [S20] argue that the governance

of platform ecosystems involves “a delicate balance

act of the platform owner, trying to keep control of

the platform while simultaneously seeking to expand

the diversity of potential developers”. According to

Tiwana et al. [S51], governance broadly refers to

“who decides what in an ecosystem”. Study [S51]

investigates the evolution of platform-centric

ecosystems and proposes that governance can be

analyzed from three facets: i) how decision rights

are divided between the platform owner and app

developers, ii) what types of formal and informal

control mechanisms are used by the platform owner,

and iii) how ownership is regulated if the platform is

property of a single company or shared by multiple

owners. [S51] also states that ecosystem governance

“involves sharing responsibilities and authority,

aligning incentives, and sharing stakes”. Goldbach

and Kemper [S21] adopt the same definition of

platform governance given by study [S51] to

understand how control mechanisms imposed by the

platform owner affects the platform stickiness. All

primary studies that answer this research question

suggest that a key challenge faced by platform

owners is balancing their own strategic objectives

with the goals and activities of players within the

platform. Such delicate balance becomes critical for

software ecosystems to thrive.

7 studies [S10, S28, S32, S41, S54, S55, S62]

indicate that ecosystem governance influences the

health and sustainability of ecosystems. This means

that governance strategies and managerial decisions

taken by orchestrators will affect the healthy

evolution of the entire ecosystem. The primary

studies suggest that health metrics provide

operational indicators on how software ecosystems

are governed. For instance, if an open governance

model is adopted by the ecosystem, more autonomy

will be given to players to shape their future growth

and expansion. Otherwise, in a closed governance

model, the orchestrator holds substantial power and

control over the players. Consequently, the

orchestrator has more responsibility towards the

prosperity and overall health of the ecosystem.

Defining the openness strategy is an important

decision that orchestrators must make when

structuring the governance model for their software

ecosystems. This decision will have a significant

impact on the evolution of the enterprise architecture

of integrated systems. In particular, players must

decide if their enterprise architecture will follow a

centralized, federated or decentralized organizational

structure (Rychkova et al., 2013). We conclude this

section by proposing an integrated definition for

software ecosystems governance: all processes by

which a player creates value, coordinates

relationships, and defines controls.

3.3 RQ2: What are the Mechanisms

Proposed to Govern Software

Ecosystems?

We define software ecosystem governance

mechanisms as managerial tools of players in

software ecosystems that have the goal of

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influencing an ecosystem's health. We observed that

frequently authors use terms such as “orchestration”

and “management” to refer to what can be

understood as a governance mechanism. To classify

the 63 studies, we propose three main categories of

governance mechanisms:

Value Creation – involve mechanisms to

generate and distribute value for the whole

ecosystem. Value creation mechanisms are generally

proposed and nurtured by the orchestrator (i.e.

platform and/or marketplace owner), who must

understand how to create value that is appreciated

both by partners and customers. In this context, it is

important to identify sources of value (such as

licenses and revenue models), and stimulate the co-

creation of value among players, by means of

innovation, investments, and cost sharing. As a

result, the ecosystem can attract and retain partners

who will mutually benefit from the value distributed

within the ecosystem. This category covers all the

incentives and benefits that players can gain from a

software ecosystem.

Coordination of Players – describe mechanisms

to maintain the consistency and integration of

activities, relationships, and structures of the

ecosystem, for both customers and partners, leading

to a harmonious and effective coordination with

players in the ecosystem. We identified mechanisms

to stimulate partnership models, define roles and

responsibilities for players, improve communication

channels within the ecosystem, and nurture

collaborations. In addition, primary studies propose

mechanisms to manage critical issues, such as:

conflicts, resources, risks, and expectations. This

category focuses on the coordination aspects of

governance, whereas the next focuses on strategic

decisions of openness and control.

Organizational Openness and Control – these

mechanisms capture the notorious tension between

open versus closed organizational models and

represent how control will be retained by the

orchestrator to guarantee its power position and how

autonomy will be given for the community to make

their own decisions independently. On the one side,

orchestrators can support autonomy, distribute

power, and share knowledge regarding technological

roadmaps and architectural decisions. On the other

side, orchestrators can keep control by defining

entry requirements, establishing quality standards,

and through certifications.

Table 1 shows the classification of governance

mechanisms proposed by the primary studies. We

observed that the most cited mechanisms are: attract

and maintain partners (28 articles, 44%), share

knowledge (20 articles, 31%), promote innovation

(25 articles, 39%), manage licenses (21 articles,

33%). We do not claim that these are the most

important governance mechanisms, as several

studies suggest that the governance must match the

specific context and market drivers involved in the

ecosystem [S4, S13, S25 S32, S43].

4 DISCUSSION AND RESEARCH

AGENDA

The synthesis provided by our literature review

enables further analysis and insights regarding the

future role of software ecosystems governance in

software producing organizations. We express the

following needs, which are requirements that should

to be met to advance the field of software

ecosystems governance. The following statements

can contribute to the overall research agenda on

software ecosystems and serve as an addendum to

the works of Jansen et al (2009), Barbosa et al

(2013), Manikas (2016), and Axelsson and Skoglund

(2016).

1. The Need for a Common Vocabulary in

Software Ecosystems Governance - The numbers

of publications in this domain emphasize that the

field of software ecosystems governance is

maturing. Increasing numbers of work take positions

on definitions of the concepts central to software

ecosystems: health (Manikas and Hansen, 2013b),

governance (this work), open source ecosystems

[S34], developer ecosystems [S33], and quality in

software ecosystems (Axelsson and Skoglund,

2016), each of these concepts is settling in as an

established term in the ecosystems discourse. We

identified that several studies adopt related terms

such as management and orchestration to refer to

governance mechanisms. Therefore, we suggest the

need to establish a common glossary and conceptual

framework that collects these definitions into one

tome of ecosystems governance knowledge.

2. The Need for Practical Governance Guidance

- Even though there exists an extensive body of

knowledge on software ecosystems governance, it is

hard for practitioners to extract practical and

strategic guidance from the works under study.

There is a need for more consumable and practical

knowledge for practitioners. Other relevant studies

for practitioners interested on creating health

ecosystems dashboards, include Goeminne and

Mens (2013) on GitHub analysis, collecting

intelligence on the progress of particular ecosystems.

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Table 1: Governance Mechanisms in Software Ecosystems.

Governance Mechanisms Studies Number of Studies

Value Creation

Promote innovation S61, S7, S32, S50, S40, S48, S52, S9, S47, S3, S7, S45, S10, S8, S35,

S27, S18, S17, S19, S61, S38, S57, S24, S43, S44

25 (39%)

Manage licenses S16, S32, S41, S40, S1, S3, S6, S58, S28, S2, S51, S63, S8, S27, S18,

S13, S17, S57, S31, S22, S24

21(33%)

Create revenue

models

S7, S3, S45, S58, S4, S5, S7S6, S28, S10, S62, S30, S23, S27, S61, S38,

S53, S57, S23, S36, S39

20 (31%)

Attract and maintain

varied partners

S61, S32, S29, S52, S47, S45, S15, S58, S4, S6, S10, S62, S55, S46, S63,

S35, S27, S18, S17, S61, S38, S53, S57, S42, S19, S23, S26, S36

28(44%)

Stimulate partner

investments and

share costs

S61, S56, S3, S45, S8, S27, S22, S23, S43 9 (14%)

Coordination of Players

Create partnership

models

S32, S56, S54, S4, 28, S62, S55, S49, S30, S8, S27, S53, S31, S19, S24 15 (23%)

Define rules to

manage relationships

S32, S40, S29, S56, S52, S9, S3, S4, S5, S46, S2, S63, S35, S27, S57,

S42, S36

17 (26%)

Establish roles and

responsibilities

S41, S50, S40, S56, S3, S15, S4, S5, S49, S46, S51, S63, S27, S13, S42,

S26, S37

17 (21%)

Enable effective

communication

channels

S41, S29, S48, S52, S9, S3, S11, S14, 28, S16, S27, S31, S37 13 (20%)

Manage conflicts S32, S52, S15, S8, S27, S57, S31, S42, S19 9 (14%)

Manage resources S1, S52, S9, S47, S3, S15, S10, S46, S20, S35, S42, S26, S36, S44 14 (22%)

Manage risks S50, S40, S56, S52, S58, S46 , S30, S8, S18, S17, S57, S22, S39, S43 14 (22%)

Manage expectations S47, S49, S16 3 (4%)

Nurture

collaborations

S61, S50, S52, S46, S58, 28, S62, S55, S49, S46, S35, S17, S42, S44 14(22%)

Organizational Openness

and Control

Support autonomy S7, S50, S52, S3, S48, S4, S7, S46, S20, S51, S35, S18, S17, S61, S42 15(23%)

Share knowledge S16, S32, S50, S40, S29, S48, S52, S3, S4, S11, S62, S30, , S20, S35,

S18, S17, S61, S57, S31, S37

20 (31%)

Distribute power S32, S50, S52, S3, S15, S46, S16, S51, S27, S37 10 (15%)

Define entry

requirements

S54, S45, S4, S28, S62, S30, S18, S38, S53, S24, S36 11 (17%)

Share architectural

decisions

S16, S29, S48, S1, S52, S9, S47, S3, S5, S58, S28, S62, S2, S51, S27,

S11, S14

17 (21%)

Share roadmaps S52, S58, S28, S27, S57, S31 6 (9%)

Define quality

standards and

certifications

S32, S41, S50, S40, S56, S58, S28, S62, S55, S30, S38, S57, S22 13 (20%)

These tools can form the basic groundwork under

mature evaluation mechanisms and tools for large

open and commercial software ecosystems.

3. The Need for Analysing the Interplay between

Governance Mechanisms and Health Metrics –

Our study indicates that health metrics provide

operational indicators on how software ecosystems

are governed. Therefore, by selecting appropriate

health metrics, players can govern the ecosystem

towards a sustainable path. A challenge remains on

how to implement governance to foster innovation

and encourage autonomous behaviour for diversity,

without undermining the quality of software and

accountability of players’ actions [S20]. The tension

between control and autonomy must be

appropriately balanced. Understanding how the

implementation of specific governance mechanisms

affects the success of ecosystems and the underlying

enterprise platform is an exciting problem for

scholars in the field.

4. The Need for Understanding the Governance

of Developer Ecosystems - The developers’ and

niche players’ impacts in ecosystems are amplified

by the success of the ecosystem. Examples like

Farmville for Facebook and Angry Birds for iOS

illustrate how ecosystems grow immensely through

the success of its constituents. The developers are

the starting point for any software ecosystem; hence

the recent increase of interest in developer

ecosystems. There is a need for further

understanding developers interests and behaviours

[S38]. Barriers to entry, platform stickiness, and

developer attraction are factors that require further

research. An extension to this perspective is a need

for further study of enterprise architecture and

delivery mechanisms that enable software

ecosystems [S33]. Orchestrators must understand

developers’ motivations and expectations to adopt

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appropriate governance mechanisms.

5. The Need for Studying Governance in Open

Software Ecosystems - Open source ecosystems

exhibit different properties than more traditional

closed and commercial ecosystems. The openness of

a platform permeates through every aspect of an

ecosystem, whether it is about ownership of the code

or about mechanisms around supporting tools, such

as application stores. These openness questions also

play a part in the architecture of a platform itself:

without an open platform architecture, extenders

cannot extend it. In our SLR, we found no study that

presented a comparative analysis of governance

mechanisms employed by open source versus

proprietary ecosystems. This is a promising line of

research.

6. The Need for Understanding the Interactions

between Ecosystems – Even good governance can

lead to the demise of an ecosystem due to external

factors. When looking at the governance and health

of the Symbian ecosystem in 2007, it would have

been hard to predict its demise. One can speculate

about its poor business support from Nokia and

fundamental faults in the business model of

Symbian. However, it is hard to ignore the

impending doom coming from the iPhone after

2007: its high rate of adoption and superior

technology simply blew the Symbian ecosystem

away. The challenge for governance research in the

next decade will be to analyse and understand the

interplay between large ecosystems. As long as

standards, age-old ecosystems, and settled industry

stacks can be blown away or grow exponentially

through the workings of other ecosystems, we must

develop governance tools and management practices

that focus on the robustness of software ecosystems

that can prepare for surviving in such storms.

5 THREATS TO VALIDITY

Our study faced similar validity threats as any other

systematic literature review. Two of the main

limitations in a review are the bias in selection and

data extraction procedures (Kitchenham and

Charters, 2007). Software ecosystem is a

multidisciplinary field covering studies from

software engineering, information systems,

organization, and management science. To limit the

threat of not including relevant primary studies, we

adopted a search strategy with generic keywords to

retrieve as many articles as possible that were

related to the research topic. We complemented the

automatic search with manual searches in the main

journals, conferences, and workshops where studies

in software ecosystems have been published. In

addition, we also analysed the primary studies of

other literature reviews published in the field.

In order to mitigate the impact of selection bias,

we defined the review protocol with clear inclusion

and exclusion criteria for each selection step. In the

first selection step, a large number of irrelevant

studies were removed by analysing title and abstract.

One author performed this task. In the second

selection step, two authors screened the content of

studies and constantly crosschecked the preliminary

selection results. We also analysed the potential

primary studies against a quality assessment

checklist. With respect to bias in the data extraction,

we had some problems to extract relevant

information from primary studies. This problem was

more critical to answer RQ2. We observed that

studies use different terminologies to describe

aspects related to governance mechanisms and

metrics to operationalize health. This specific

limitation of the software ecosystem literature was

discussed on item 1 of our research agenda

presented in Section 4. In several occasions we had

to interpret the subjective information provided by

the articles. To minimize interpretation bias, we

conducted a very careful reading and had several

discussion meetings among the authors during the

data extraction phase.

6 CONCLUSIONS

The governance of software ecosystems is currently

one of the largest challenges software platform

companies need to deal with for the sake of their

survival. Governance includes technical decisions

regarding the enterprise architecture, social aspects

involving the coordination of players, and business

strategies. Therefore, governance impacts all three

dimensions of software ecosystems (i.e. technical,

social and business).

From 63 studies analysed, we conclude that

software ecosystems governance is defined as all

processes by which a player creates value,

coordinates relationships, and defines controls. An

overview of software ecosystem governance

mechanisms is provided, in which we classify

governance mechanisms as belonging to

mechanisms for value creation, coordination of

players, and organizational openness and control.

We identify approximately 20 governance

mechanisms that can be directly implemented by

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players in software ecosystems and studied by the

research community. To our knowledge there is no

study in the field to systematically structure and

classify governance mechanisms reported in the

literature.

The practical impact of this work is that for

practitioners in the software industry light is shed on

the concept of ecosystem governance. Although we

do not claim that the overview of governance

mechanisms is complete, it provides a useful

strategic tool for practitioners and a conceptual base

for researchers. The scientific impact of the work is

threefold: we provide insight into the concepts of

software ecosystems governance and present

mechanisms to perform governance. In future

research, we aim at developing a governance

conceptual model for software ecosystems.

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