Paper to be presented at the DRUID Academy Conference 2020 at University of Southern Denmark, Odense, Denmark

January 16-17, 2020

The convergence of business model and ecosystem literature

Leticia FoersterUniversity Cote d'Azur

GREDEG/ CNRSleticia.foerster@univ-cotedazur.fr

AbstractThe convergence of business mode and ecosystem literature Leticia FOERSTER*, **, Amel ATTOUR**Universit? Cote d’Azur, CNRS, GREDEG **Enrolment from 2019 to 2021**leticia.foerster@univ-cotedazur.fr

Abstract: Both business models and ecosystems have increasingly gained ground in the literature onstrategy, innovation and entrepreneurship. Indeed, these two concepts foster two parallel currents ofimportant research in strategic management; and each current of research led to recent literaturereviews and essays that aimed to synthetize the development made, respectively, on the two concepts(Belussi, Orsi & Saverese, 2019; Maucuer & Renaud, 2019; De Vasconelos Gomes et al. 2018; Tsujimotoet al. 2018). In both currents of research, authors have identified the main trends that are beinginvestigated by the literature in order to anticipate the future research agenda. However, this effort isdone separately. More recently, key authors in strategic and innovation management, such as Teece(2018) and Demil et al. (2018) have stressed the importance of the interconnections between thesetwo concepts. In a similar manner, a co-occurrence keyword analysis, developed in this study, confirmeda strong link between business models and ecosystems. It also suggests that both concepts are equallylinked to: innovation, business modelling, value creation, internet of things and platforms. However,how the two concepts are inter-connected and why they are both linked to the cited concepts is yet notclear. From there, to better understand the relationship between business models and ecosystems, thispaper aims to answer the following research question: how business models and ecosystems have beencorelated in the strategic management literature and for what theoretical and managerial purposes.

To answer this research question, we employed a systematic literature review that adopts a hybridmethodology combining descriptive, bibliometric and content analysis in a sample of articles that relatethe concepts of business models and ecosystems published in academic journals of different fields.Results demonstrate that, while scholars’ interest is increasing towards understanding the relationsbetween the two concepts; they are still very dispersed amongst journals, theoretical lenses, andtopics; furthermore, they are mostly guided by the researcher’s personal agenda. Additionally, thebibliometric analysis revealed that the business model concept is widely used to finely examine valuecreation and capture mechanisms within innovation ecosystems. Moreover, while the business modelconcept is applied as the unit of analysis, ecosystems are seen as the theoretical framework. These twoviews are promptly observed in four main streams of research: sustainable business model innovation;smart mobility services business models, convergent ecosystems, and entrepreneurial ecosystems.Finally, it was identified how business model and business ecosystem constructs are mobilized by thefour identified streams of research, leading us to suggest topics, such as co-evolution processes,

knowledge management processes and dynamic capabilities as future research agendas in order tosolidify scientific knowledge related to the interrelation mechanisms between business models andecosystems.

Main references ?Belussi, F., Orsi, L., & Savarese, M. (2019). Mapping Business Model Research: A Document BibliometricAnalysis. Scandinavian Journal of Management, 101048.?Demil, B., Lecocq, X., & Warnier, V. (2018). ?Business model thinking?, business ecosystems andplatforms: the new perspective on the environment of the organization. M@ n@ gement, 21(4),1213-1228.?de Vasconcelos Gomes, L. A., Facin, A. L. F., Salerno, M. S., & Ikenami, R. K. (2018). Unpacking theinnovation ecosystem construct: Evolution, gaps and trends. Technological Forecasting and SocialChange, 136, 30-48.?Maucuer, R., & Renaud, A. (2019). Business Model Research: A Bibliometric Analysis of Origins andTrends.?Teece, D. J. (2018). Business models and dynamic capabilities. Long Range Planning, 51(1), 40-49.

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WORKING PAPER – WORK IN PROGRESS

The convergence of business model and ecosystem literature

Amel ATTOUR*, Leticia FOERSTER*

*Université Cote d’Azur, CNRS, GREDEG

Abstract: Both business models and ecosystems have increasingly gained ground in the

literature on strategy, innovation and entrepreneurship. Each current of research led to recent

literature reviews and essays that aimed to synthetize the development made, respectively, on the

two concepts. However, how and why the two concepts are inter-connected is yet not clear. From

there, this paper aims to examine how business models and ecosystems have been corelated in the

strategic management literature and for what theoretical and managerial purposes. For that, we

employed a systematic literature review that adopts a hybrid methodology combining descriptive,

bibliometric and content analysis in a sample of articles published in academic journals of different

fields. Results demonstrate that, while scholars’ interest is increasing towards understanding the

relations between the two concepts; they are still very dispersed amongst journals, theoretical

lenses, and topics; furthermore, they are mostly guided by the researcher’s personal agenda.

Results identified four streams of research that jointly mobilize business model and business

ecosystem constructs. From these four streams of research, we suggest topics, such as co-evolution

processes, knowledge management processes and dynamic capabilities as future research agendas

of the two convergent (business models and business ecosystems) literature.

Keywords: Business models, business ecosystems, innovation ecosystems, entrepreneurial

ecosystems, innovation, systematic literature review.

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WORKING PAPER – WORK IN PROGRESS

The convergence of business model and ecosystem literature

1. Introduction

The concept of business model emerged with the advent of the internet, that led companies to

realize new forms of doing business besides the traditional way (Demil et al., 2018). Because it

was an innovation that gave rise to this new way of doing business, this might be one of the reasons

why the business model concept is often associated as a vector or as a source of innovation

(Chesbrough & Rosenbloom, 2002; Massa & Tucci, 2014). This phenomenon, added to the context

of globalization, higher level of competitiveness, and nowadays the digital transformation of the

economy, drove companies to look at their business in a strategic manner and to depart from the

traditional approach of doing business. To face these challenges, the business model construct

appears as very helpful tool as it integrates the various ways to deploy resources, to create and

capture value and, as a consequence it has induced major changes in strategic management over

the last years (Massa, Tucci & Afuah, 2017). One of those changes is the awareness of the

environment on a firm’s business model design.

To explain this environment, business studies bowered the concept of ecosystem from ecology.

The term ecosystem was coined by A. G. Tansley in 1935 who defined ecosystem as: “a particular

category among the physical systems that make up the universe. In an ecosystem the organisms

and the inorganic factors alike are components which are in relatively stable dynamic equilibrium.”

(p. 306). Moore (1993) drew a parallel between the ecological system and employed it to the

business field. Since then, scholars have attempted to articulate and develop the ecosystem concept.

Through a bibliometric analysis Gomes, Facin, Salerno & Ikenami (2018) have done a thorough

examination of the development of the ecosystem concept in the fields of strategy and innovation.

This study has helped consolidate knowledge regarding the ecosystem concept. Other authors also

have contributed to the field by analysing the relationship of ecosystems with other theoretical

streams of research such as business models (Demil, Lecocq & Warnier, 2018; Teece, 2018; Snihur,

Thomas & Burgelman, 2018); entrepreneurship (Neumeyer & Santos, 2018; Autio, Nambisan,

Thomas & Wright, 2018; Calmé, Onnée & Zoukoua, 2016) and digital platforms (Jacobides,

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Cennamo & Gawer 2018; Watanabe, Naveed & Neittaanmäki, 2016 and Gawer & Cusumano,

2014).

In parallel, and due to the large body of work in this research domain, several literature reviews

of business models have been published. The more recent ones used systematic, bibliometric or

scientometrics analysis methods to explore trends applied by management disciplines (strategic

management, innovation, entrepreneurship) to understand the current state of business model

research and its theoretical foundations (Belussi, Orsi & Saverese, 2019; Maucuer & Renaud,

2019). Belussi et al. (2019) study reveals that, in the majority of articles, business model

application has both a conceptual and an empirical validity. In addition, authors commonly noted

that the term is largely employed (Foss & Saebi, 2018) in the domains of Strategic Management

and Innovation & Entrepreneurship literatures, and as a useful matrix for practitioners. The authors

also tried to anticipate the future of the business model literature. Belussi et al. (2019), notably,

examined the emergent trends from the most recent works through a bibliographic coupling

analysis. Among the more important topics identified by the authors, two – Business models and

open innovation & business models and social innovation – clearly consider the key role of

external partners or ecosystems in the process of business model innovation or in the development

of new business models.

More recently, key authors of the business model literature, as David Teece and Benoît Demil

& Xavier Lecocq, stress interconnections between the concepts of business model and ecosystem.

For example, considering the impact of digital convergence and the amplified problems associated

with enabling technologies, Teece (2018) extends his 2006 profiting from innovation (PFI)

framework1 by an additional component to the initial four: the ecosystem strength. On their side,

Demil, Lecocq & Warnier (2018) claim that business model thinking shouldn’t be conducted

without considering or including an ecosystem perspective, even if the latter could not be

deterministic. Beyond the digital transformation of our economy, it is the inter-organizational

landscape that leads to revisit or extend some of the strategic management theories or frameworks.

1 Teece 2006 framework components are: dominant design, timing, and learning; appropriability regime;

complementary assets and cospecialization; and intellectual property.

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Furthermore, such inter-organizational context systematically imposes to consider the firm’s

ecosystem in any business model analysis.

To further understand the relationship between business model and ecosystem constructs, this

paper aims to answer the following research question: how business models and ecosystems have

been corelated in the strategic management literature and for what theoretical and managerial

purposes? By answering this question our aim is to clarify how business models and ecosystems

could be a complementary unit of analysis on the one hand, and to suggest new avenues for

researchers to contribute to the new research agenda of strategic management literature on the

other hand. For that purpose, we employed a systematic literature review to understand what

scholars have uncovered related to the relationship between business models and ecosystems in

the management field. To do so, this study adopts a hybrid methodology combining descriptive,

bibliometric and content analysis in a sample of articles that relate the concepts of business models

and ecosystems published in academic journals of different fields. This study provides numerous

contributions to the strategic management literature. First, it highlights the emergence of a new

and recent research agenda, that is an increase interest in understanding the relationship between

a firm and its environment. It demonstrates this by two very important perspectives. First by

exploring the theoretical pillars that gave rise to this interest (co-citation analysis) and to where

scholars’ interest is moving towards to (bibliographic coupling).

The paper is structured as follows. Section two describes the methods used to select and analyse

the data. Section three reports the main findings of the descriptive, the bibliometric and the content

analysis. Lastly, section four draws conclusions and indicates the limitations and implications for

future research.

2. Research Methods

This study presents a systematic literature review on the emerging research agenda that is the

convergence of business models and ecosystems. Bibliometric analysis refers to a set of conceptual

tools used to quantitatively analyse academic literature (White & McCain, 1998); it introduces

objective measures of evaluation of scientific publications that contrast the potential bias

embedded in subjective evaluation (Appio, Cesaroni, & Di Minin, 2014). Bibliometric studies are

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gaining relevance, considering the growing number of scientific publications and the ability to use

techniques to quantify the written communication process (Perianes-Rodriguez, Waltman & van

Eck, 2016; Ikpaahindi, 1985). Furthermore, bibliometric analysis can be used to identify important

scientific papers, as well as their interrelationships (Chai & Xiao, 2012). It allows the identification

of the most relevant topics, approaches and methods, as well as the most important definitions of

a theme (Carvalho, Fleury & Lopes, 2013). It cans also be employed in order to identify future

research agendas. However, no method is perfect, this approach loses detail and presents only a

macro view on the evolution of the literature. To complement this shortfall, this study includes a

descriptive and a content analysis. The descriptive analysis provides an overall perspective of the

research field and the content analysis completes the first two steps by adding a qualitative

perspective to the results.

2.1. Sample description

The data was collected through a multi-step process (Figure. 1). The bibliometric dataset was

extracted from Scopus. There are a few databases that are references in the market, one being

Scopus and another one being Web of Science. Scopus was the first choice for this study. Reason

for that is explained by the intention of this study, which is to deeply analyse where the debate

regarding business models and ecosystems are taking place and how it is progressing. Scopus

allows for such view given that it encompasses several disciplines and document types.

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In order to obtain the database, a research was run on Scopus on documents that contained

either in their title, abstract or keywords both terms “business model” and “ecosystem”. The

employment of those two terms were used for a number of reasons. The intention of this study is

to prove that - implicitly or explicitly - scholars are converging the literature of business models

and ecosystems. This study investigates when this process started, who is taking part (main authors,

main field, etc.) and what is being debated. The database search resulted in a sample of 1049

documents. To have an overall understanding of the research arena regarding business models and

ecosystems, a descriptive analysis was conducted on that entire sample and presented in the

following section of this study. Secondly descriptive analysis was conducted, this time focused on

the domain of business and management, which is the domain of interest of this study. This second

step narrowed down the sample to 333 documents. For the document types, “articles” were

selected as these types of documents undergo peer review, resulting in a sample of 173 articles.

Finally, a third step was taken to make sure that every article was relevant to the study. All abstracts

were read and as a criterion for exclusion, it was considered the fact that certain articles, despite

containing the term “business model” and “ecosystem” either in the title or as a topic, did not

address the relations between the two terms as their central theme or as part of their theoretical

contribution. After reading the abstracts, 47 articles were excluded, leaving a final sample of 126

articles published in 84 journals, from the years of 1997 to 2019.

Figure 1:Multi-step systematic review

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2.2. Bibliometric analysis process

The data was examined through a bibliometric analysis process. Bibliometric analysis refers

to a set of methods that statistically analyse a scientific field by its publications (Belussi, Orsi, &

Savarese, 2018; Walsh & Renaud, 2017). Bibliometric techniques introduce objective measures of

evaluation of scientific publications that contrast the potential bias embedded in subjective

evaluation (Appio, Cesaroni & Di Minin, 2014). Through bibliometric analysis, it is possible to

create a map of science in a specific field or discipline. In the scientific literature, the map of

science can facilitate the understanding of the past and current state of the art, considered by Holton

(2000) as the first requirement of a good history of science, and facilitate the understanding of

conceptual relationships (Small, 1999). Furthermore, it can bring the researcher to new discoveries

(Swanson, 1987). The most common methods employed are: (1) citation relation, (2) keyword co-

occurrence relations, and (3) co-authorship relations (Van Eck & Waltman, 2014). In the case of

citation relations, a further distinction can be made between direct citation relations, co-citation

relations, and bibliographic coupling relations (Van Eck & Waltman, 2014). For the purpose of this

study, two bibliometric techniques were employed: bibliographic coupling and co-citation

relations (table 1).

Table 1: Definitions and attributes of bibliometric techniques

Bibliographic coupling was first introduced by Kessler (1963) making it one of the first

citations analysis methods employed in academic research. Papers are bibliographically coupled

when different authors cite one or more articles (Garfield, 2001). Documents are linked because

Bibliometric technique Definition Purpose

Co-citation relations

The number of identical citing items defines the

strength of co-citation between the two cited

papers. Co-citation is the frequency with which

two items of earlier literature are cited together

by the later literature (Small, 1973).

Because the citations a paper receives in

the future depend on the evolution of an

academic field, it is said that co-citation is

a dynamic document classification system

(Garfield, 2001).

Bibliographic coupling

Papers are bibliographically coupled when

different authors cite one or more papers in

common (Garfield, 2001). Bibliographic

coupling is about the overlap in the reference

lists of publications.

Because bibliographic coupling is said to

be stable over time, it can help researchers

find related documents (Garfield, 2001)

that cover the same research theme or

trend (Walsh & Renaud, 2018).

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they cite the same publications. Two documents are bibliographically coupled when they both cite

a third document. The more cited documents there are, the stronger the coupling. This means that,

bibliographic coupling provides a measure of the extent to which different documents draw on the

same set of an intellectual set. Bibliographic coupling depends on references contained in the

coupled documents, meaning it is fixed (Small, 1973; Garfield, 2001). It can support the

understanding of the key concepts, methods, or the current “state of the art” of a specific research

area. It defers of co-citation which is referred to when two publications are cited together by

another paper, both appearing in the paper’s list of cited references (Chen, 2006). According to

Small (1973), co-citation is the frequency with which two items of earlier literature are cited

together by the later literature, thus this pattern can change over time (Garfield, 2001).

The quantitative data for descriptive analysis was obtained from Scopus. VOSviewer software

(Waltman, Van Eck & Noyons, 2010) was employed to obtain the bibliographic coupling analysis

and co-citation analysis, which it assigned respectively to clusters. The clusters link the most cited

documents within the network. They can present other types of references such as books,

conferences papers or papers that were not indexed by Scopus, however they are incorporated into

the analysis as they are mentioned by the documents of the selected sample.

2.3. Content analysis process

The content analysis comes to complement the bibliometrics analysis by (1) “translating” the

clusters, (2) detecting similarities in researchers’ agendas and (3) identifying new trends related to

the understanding of the relationship between the evolution of business model and ecosystem

concepts. In order to do that, each article in each cluster was carefully read. Special attention was

given to the questions each article was trying to answer, what were their contribution and

limitations. By doing so, it was possible to label each cluster and attribute to them a

definition/description. The bibliographic coupling is a good source to understand how researchers

first started to notice the links between the two concepts. On the other hand, the co-citation clusters

analysis highlights scholar’s intention to explain the different levels of interconnection between

the two concepts. In other words, the first is the pillar, and the later a new research agenda.

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3. Results

3.1. Descriptive analysis

Results from the descriptive analysis demonstrate an increase, over the years, in the

employment of the two terms; and that this use is very diverse among domains of research (Table

2). Documents jointly utilizing both terms as a keyword, title or in the abstract totalled 1049.

However, those terms are still predominantly studied in the fields of computer science; business,

management & accounting and engineering (representing 59% of total documents). In terms of

documents types, 46% are concerning conference papers. The number is superior to articles

published by 10% ( Table 2). This is an indication that, indeed, this research field is recent and that

is seeing an increase interest by scholars.

Another way of looking at this fact is through the evolution of publications by year. As it can

be attested, the number of publications since 1997 are constantly increasing. Considering that the

number of conference papers are superior to articles, one can expect that this trend will continue

to increase at least for the upcoming year. When isolating the number to the field of interest of this

study (business and management) the results are replicated, demonstrating almost the exactly same

Table 2: Distribution of documents per subject areas and by document type

Subject area Number of documents Percentage of total Document by type

Computer Science 535 27% Conference Paper 480 46%

Business, Management and Accounting 333 17% Article 377 36%

Engineering 313 16% Conference Review 64 6%

Social Sciences 174 9% Book Chapter 52 5%

Decision Sciences 141 7% Review 36 3%

Mathematics 102 5% Book 19 2%

Economics, Econometrics and Finance 97 5% Note 4 0%

Environmental Science 87 4% Short Survey 3 0%

Energy 45 2% Editorial 1 0%

Medicine 28 1% Undefined 13 1%

Physics and Astronomy 21 1% total 1049 100%

Agricultural and Biological Sciences 19 1%

Earth and Planetary Sciences 15 1%

Health Professions 15 1%

Materials Science 15 1%

Psychology 14 1%

Arts and Humanities 12 1%

Biochemistry, Genetics and Molecular Biology 12 1%

Chemical Engineering 4 0%

Multidisciplinary 3 0%

Pharmacology, Toxicology and Pharmaceutics 3 0%

Nursing 2 0%

Veterinary 1 0%

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pattern (Figure 2). This result can be explained by the fact that since the year 2000, the growth of

the number of publications related to business models has been exponential (Belussi et al., 2019).

Equally important, is the ongoing and fundamental debate around organization’s activities inside

and outside the boundaries of the firm (Kapoor & Lee, 2013). Traditionally, this debate has focused

mainly on the outsourcing of production activities (Rong, Shi & Yu, 2013). However, many

scholars turned their attention to the phenomenon of the network of actors involved in developing

and in commercializing innovations. This phenomenon received different labels, such as open

innovation (e.g., Chesbrough, 2003) or innovation networks (e.g. Ewe, Yap & Lee, 2015). Both of

those debates have mostly been treated separately, when in fact they are intertwined. The logic is

simple, a successful business model depends on the ecosystem to be developed, and inversely an

ecosystem is impacted by a leading firm’s business model (Demil et al. 2018).

By concentrating on the domains of management and business, the results add-up to 333

documents. Similar to the results of the general descriptive analysis, it is seen that scholars in

management and business are increasing their interest in the relations of the concepts of business

models and ecosystems (Figure 2), the last 4 years represent 70% of articles published mobilizing

the 2 concepts. In the domain of business & management, most of those documents are articles

(173 of the total, or 52%) and conference papers (represented by 81 documents, or 24%). For the

purpose of this study and to deepen the analysis, closer attention was given to documents labelled

0

20

40

60

80

100

120

140

160

180

200

1995 2000 2005 2010 2015 2020

Evolution of documents found by year

All domains Business and Management

Figure 2:Evolution of documents dealing with business models and ecosystems per year

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as articles, articles have been peer reviewed, thus validating the research in the field. All abstracts

were read to make sure that the data set indeed mobilized both business models and ecosystems

construct in an intertwined way. After this thorough reading the final sample is composed of 126

articles, or in other words, the final sample of this study.

Once the final sample was identified, it was easy to identify the journals that are publishing

studies on the matter and their evolution over the years. The results are very revealing, publications

are still very dispersed amongst journals (84 in total) indicating that even though the interest

(implicit or explicit) to connect business models and ecosystems is gaining ground they are still

treated through a multitude of lenses and topics, mostly guided by the researcher personal agenda.

Amongst those journals, however, there is one that seems to be paving the way: Technological

Forecasting and Social Change, which holds 9 publications (Table 3). From all those 126

publications, 88% are qualitative studies. In terms of domains, almost half of total publications,

are concentrated in two domains: general management (25%) and information technology

management (22%). This result corroborates with the previous one, meaning that, indeed there is

an increased interest by scholars to articulate business models and ecosystems concept together,

however it also demonstrates that for now this effort is scattered amongst domains of research,

source of publication and methods of research.

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Source title

Articles 2000 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 Total

Academy of Management Review 1 1

Accounting Forum 2 2

Asia Pacific Journal of Management 1 1

Asian Journal of Technology Innovation 1 1

Baltic Journal of Management 1 1

Business Horizons 1 1 2

Business Strategy and the Environment 1 2 3

California Management Review 1 1

Creative Industries Journal 1 1

Digital Policy, Regulation and Governance 1 1

DLSU Business & Economics Review 1 1

Emerald Group Publishing Limited. 1 1

Enterprise Information Systems 1 1

Entrepreneurship and Sustainability Issues 1 1

European management review 1 1

IEEE Engineering Management Review 1 1

IEEE Transactions on Engineering Management 2 2

Industrial Marketing Management 1 1 2

Industry and Innovation 1 1

Information Resources Management Journal 1 1

Innovation 1 1

International Entrepreneurship and Management Journal 1 1

International Journal of Applied Business and Economic Research 1 1

International Journal of Automotive Technology and Management 1 1 2

International Journal of Business and Globalisation 1 1 2

International Journal of Business Data Communications and Networking 1 1

International Journal of Business Excellence 1 1

International journal of business information systems 1 1

International Journal of E-Services and Mobile Applications 1 1

International Journal of Entrepreneurship and Small Business 1 1

International Journal of Knowledge Management 1 1

International Journal of Production Economics 1 1

International Journal of Production Research 2 2

International Journal of Project Management 1 1

International Journal of Service Science, Management, Engineering, and Technology 1 1

International Journal of Services, Economics and Management 1 1

International Journal of Technology Management 1 2 3

International Journal on Food System Dynamics 1 1

Journal of Brand Management 1 1

Journal of Business & Industrial Marketing 2 2

Journal of Business Ethics 1 1

Journal of Business Research 2 2

Journal of Business Venturing Insights 1 1

Journal of Cleaner Production 1 1 2 2 6

Journal of Commercial Biotechnology 2 2

Journal of Entrepreneurship Education 1 1

Journal of Facilities Management 1 1

Journal of Hospitality and Tourism Technology 1 1

Journal of information Technology 1 1

Journal of intellectual capital 1 1

Journal of management 1 1

Journal of Management Studies 2 1 3

Journal of Marketing Education 1 1

Journal of Media Business Studies 1 1 2

Journal of Research in Marketing and Entrepreneurship 1 1

Journal of Service Management. 1 1

Journal of Strategic Marketing 1 1

Journal of theoretical and applied electronic commerce research 3 3

Latin American Business Review 1 1

M@ n@ gement 1 1 2

Management & Marketing. Challenges for the Knowledge Society 1 1

Management Decision 1 1

Quality-Access to Success, 1 1

R&D Management 1 1 1 3

Research Policy 1 2 3

Research-Technology Management 1 1 1 3

Revue d'économie industrielle 1 1

Revue française de gestion 1 1 2 4

Service Business 1 1

Strategic Entrepreneurship Journal 1 1

Strategic Organization 1 1

Strategy & Leadership 1 1

Systems Research and Behavioral Science 1 1

Technological Forecasting and Social Change 1 1 4 3 9

Technology in Society 1 1

Technovation 1 1

The International Journal of Entrepreneurship and Innovation 1 1

The Journal of High Technology Management Research 1 1

The Journal of Technology Transfer 1 1

Thunderbird International Business Review 1 1

Tourism Management Perspectives 1 1

Transformations in Business & Economics 1 1

Universia Business Review 1 1

Technology Analysis & Strategic Management 1 1

Grand Total 1 3 1 3 6 6 12 7 7 13 15 32 20 126

Publication year

Table 3: Number of articles published per journal and per year

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3.2. Co-citation analysis

The co-citation method was employed by reading data from the Scopus bibliographic database.

It used the fractional counting of cited references. A minimum number of cited references of 3 was

applied on a total set of 7085 cited references. The result groups a total of 58 items clustered into

5 groups. Figure 3 shows the graphical results expressed. The size of the label and the dot of an

item is determined by the weight of the item. The higher the weight of an item, the larger the label

and the dot of the item (VOSviewer Manual, 2019).

Dots appear to have similar sizes, indicating that, thus far, there is not a reference or a leading

paper in the domain. That being said, is important to point out that, 4 papers are slightly

predominant. Zott, Amit & Massa (2011), which is a reference paper regarding the evolution of

the business model concept. Eisenhardt (1989) a paper that guides researchers on how to build

theories from case study research, this is explained by the fact that that most of the articles

identified are qualitative case studies. Moore (1993), which is a reference paper related to the

Figure 3: Co-citation graphical analysis source VOSviewer

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importance of external sources for the success of innovative business and it is also a reference

paper in the innovation and business ecosystem literature. And finally, Teece (2010) is a paper that

explains how the development of the global economy has changed the traditional balance between

customer and supplier leading to new forms of business models. Another interesting fact is the

proximity and overlaps between some of the clusters. In this case, cluster 2 is clearly more distant

to the others. Inversely there is close proximity and overlap between clusters 1, 3 and 5. The sub-

section analysis all five clusters in detail.

3.2.1. Co-citation Cluster 1: Platform strategies for facing servization business challenges

This cluster is one of the most important ones in the group, it sheds light on the importance of

innovating business models in order to say competitive in the current global economy by

considering the importance of collaboration amongst the various actors of the ecosystem. Indeed,

papers of this first co-citation cluster are either focused on the servization of businesses

(Vandermerwe & Rada, 1988; Lusch & Nambisan, 2015, Porter & Heppelmann, 2014) or on the

role of platforms in achieving this (Gawer, 2014; Gawer, Cusumano, 2014; Lusch & Nambisan,

2015; Ritala et al. 2014, Thomas et al. 2014) in networks where firms’ performance depends on

aligning structurally heterogenous actors (Adner & Kapoor, 2010). The development of the global

economy shifted the balance between customer and supplier leading to new forms of collaboration

between firms and new forms of business models (Teece, 2010). More generally, the main idea of

these works is that shifting from a product centric economy to a service-dominant logic (Lusch &

Nambisan, 2015) to attaint client’s expectations and demands, firms need to engage and interact

(Lusch & Nambisan, 2015). For this, firms should resolve internal innovations challenges within

firms, but also succeed in aligning structurally heterogenous actors in order to create value (Adner

& Kapoor, 2010). Platform strategy appears then as a suitable solution that facilitate creating and

capturing value collectively. To illustrate this, authors examined the case of multisided platforms

such as Google, Amazon, etc. (Gawer, 2014; Gawer & Cusumano, 2014; Ritala et al. 2014) or

smart services (Porter & Heppelmann, 2014).

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3.2.2. Co-citation Cluster 2: Designing open business model

Cluster 2 perceives the impact and the influence of the external environment to explain how

external innovators affect the focal firm's outcome (Adner & Kapoor, 2010). In this cluster authors

are interested in addressing a pragmatistic perspective through the lenses of the business model

concept in order to explain this phenomenon. That is why in this cluster we find many important

works related to the business model literature, such as the articles from Zott, Amit & Massa (2011);

Teece (2010) and Zott & Amit (2010). This cluster in anchored in the idea that the influence or the

relationship between a firm's and its ecosystem - in the ever-changing dynamic of the global

economy – has a great impact on which business models a firm should adopt. Furthermore, it calls

attention to the innovator trap, meaning not necessarily the first to innovate is the one to profit

from the innovation itself, and thus the importance of connecting business model design to internal

and external innovation (Chesbrough, 2003; Chesbrough & Rosenbloom, 2002). Given that today's

marketplace is unpredictable, companies find themselves looking for industry architectural

advantages by enhancing complementarity and mobility in parts of their value chain process

(Jacobides, Knudsend & Augier, 2006). This shift requires a lot from a firm's business model -

what that entails is not clear. Authors in this clusters, they do a good job on raising several

reflexions and pertinent points, however the answer to those reflexions are yet to be fully

understood.

3.2.3. Co-citation Cluster 3: Business model as a tool

The main idea, of this cluster, is related to business model innovation as a response to external

changes in the environment caused by technological advancements (Amit & Zott, 2001;

Chesbrough & Rosenbloom, 2002; Martins, Rindova, Greenbaum, 2015). In short, this cluster

deals with the impact of technology on firm's business models. It claims, that business models are

contingencies to deal with those external change in order to adapt. In a highly uncertain, complex

and fast-paced environment business model innovation is perceived as key in order for companies

to remain competitive. In fact, some authors highlight that in this context, a business model is a

"locus of innovation" (Amit & Zott, 2001) and that the role of business models is to capture value

from innovation (Chesbrough & Rosenbloom, 2002). When perceived that way, business models

become a reflection of a firm's strategy (Casadeus-Masanell & Ricart, 2010; McGrath, 2010). As

16

such, these authors place the business model concept as a strategic tool, that indeed can change the

track of a company if dealt with properly. However, companies have to be in a constant state of

alert. Successful business model innovation is a process, as pointed out by Chesbrough &

Rosenbloom (2002), the same way business models unlocks the latent value from a technology, its

logic might constrain the subsequent search for new, alternative models for other technologies later

on. In a less latent level, this cluster also points out that the technological shift is not all about

technology. It is also about expanding the boundaries of the firm and pushing firms to innovate the

way strategies are conceived (Zott & Amit, 2010, 2008; Teece, 2010; McGrath, 2010).

3.2.4. Co-citation Cluster 4: The end of value chain, the rise of value networks

This cluster reinforces the importance of working with and for the ecosystem. It defends the

idea that a firm has to match its innovation strategy process with the pace of the innovation

ecosystem in order to succeed (Adner, 2006). Authors in this cluster defend the idea of "team

playing". Even more so, they claim that players of an ecosystem are equally important even if the

figure of the focal firm is present (Dhanaraj & Parkhe, 2006; Iansiti & Levien, 2004; Moore, 1993).

However, actors of an ecosystem can adopt one among three strategic positions - keystone, niche

player or physical dominator – depending on the kind of company they are, and the business

context in which they operate (the general level of turbulence and the complexity of its

relationships with members) (Iansiti & Levien, 2004). With the exception of Kandiah & Gossain

(1998) who see business ecosystem basically similar to integrated value chain, the idea behind this

cluster logic is simple, adopting a network perspective is more suited than the concept of value

chain for analysing business ecosystem and, more generally, the new economy organisation

(Peppard & Rylander, 2006; Li and Whalley, 2002). Dhanaraj & Parkhe (2006) for example,

assimilate business ecosystem to an innovation network that evolves in a dual context (an

innovation context and interfirm network context) and where orchestration activities 2 help to

understand how a platform should facilitate both value creation and value appropriation. In sum,

2 Orchestration activities involve insuring five processes: managing innovation leverage, managing innovation

coherence, managing knowledge flows, managing network membership stability, managing network stability and

managing innovation appropriability (Nambisan and Sawhney, 2011).

17

papers of this cluster have mainly contributed to build one of the foundational of the business

ecosystem concept: innovation and value network approach.

3.2.5. Co-citation Cluster 5: Dynamic capabilities and business model innovation

Cluster 5 articles highlight the importance of adaptation. Scholars look for internal answers to

respond to the changes in the external environment. They highlight the importance of adaptation

either by developing dynamic capabilities (Teece, 1986, 2007; Teece, Pisano & Shuen, 1997) or

though the innovation of business models (Amit & Zott, 2012; Chesbrough, 2007). However, those

two solutions are still interfirm. They are less concerned with how firms integrate to their

ecosystems and more concerned on how they will respond to the external changes through internal

adaptation. For example, regarding dynamic capabilities framework, the basic assumption is that

core competencies should be used to modify short-term competitive positions that can be used to

build longer-term competitive advantage. Indeed, dynamic capabilities refer to “the firm’s ability

to integrate, build, and reconfigure internal and external competences to address rapidly changing

environments” (Teece, Pisano & Shuen, 1997, p. 516). Developing dynamic capabilities allow

firms to modify their resource base to adapt to changing conditions and to assure competitive

advantage (performance). It has been developed for examining rapidly changing ecosystems

(Teece, Pisano & Shuen, 1997; Teece, 2007), and applied also when change rates are lower (Zollo

& Winter, 2002; Eisenhardt & Martin, 2000). It has also inspired Chesbrough (2007)3 to clearly

explain why open business models enable an organization to be more effective in creating and

capturing value. In this exercise, Chesbrough begin by reminding that a business model performs

two main functions: value creation and value capture. Capturing value requires the establishing of

a unique resource, asset or position within a series of activities. But as companies possess different

assets, resources and market position, and each has a unique history (Teece, Pisano & Shuen, 1997),

they will quickly recognize ideas that fit with their past successful pattern and struggle the ones

that are unfamiliar. Nothing however prevents the exploitation and exploration of these unfamiliar

configuration of assets, resources and positions outside the companies’ boundaries. They can find

3 Henry Chensbrough explicitly wrote in a footnote “the ideas in this paragraph are inspired by D. Teece, G.Pisano

and A. Shuen” (1997) (Chesbrough, 2007, p.27).

18

“homes” in other companies (Chesbrough, 2007, p.23). represent an important source of value

creation and capture in an open business model.

3.3. Bibliographic coupling network analysis

The bibliographic coupling analysis was employed by reading data from the Scopus

bibliographic database. As per the method, it was used the fractional counting of cited documents

as this approach has been shown to be preferable (Perianes-Rodriguez, Waltman and van Eck,

2016). After different trials on the 126 documents first-order sample, 38 documents were selected

as the second-order sample. The article from Murray, Skene & Haynes (2017) is the most cited.

The article explores the concept of Circular Economy as an attempt to conceptualize the integration

of economic activity and environmental wellbeing in a sustainable way, but more than that, the

article centres around the idea of how to purposely maximize ecosystem functioning. Cluster 4,

even though is the smallest one in size, it hosts the most cited articles in the set, such as the works

Figure 4: Bibliometric coupling graphical analysis source VOSviewer

19

of Basole (2009), Adner (2017); Zott & Amit (2013) and Maglio & Spohrer (2013). Figure 3 shows

the graphical results expressed.

3.3.1. Bibliographic Coupling Cluster 1: Ecosystem Business models

Articles in this cluster acknowledges the importance and the influence of ecosystems on firms’

different theoretical levers (innovation, business models, strategic positioning, etc). Generally,

papers in this cluster use business models as a theoretical or empirical construct where (innovation

or business) ecosystems are the unit of analysis. Business models appear as a key tool to measure

firms’ interactions within an ecosystem. More precisely, articles in this cluster might be organized

into the following three groups: (i) ecosystems’ convergence, (ii) value creation and capture within

ecosystems, and (iii) business model innovation at inter-organizational level. In the first group, the

authors examine structural changes in high technology industries, such as the mobile ecosystem

(Basole, 2009), music ecosystem (Evens, 2010), media ecosystem (Daidj & Jung, 2011) or the app

store ecosystem (Müller, Kijl & Martens, 2011). These changes are mainly caused by the entry of

new actors into or from the telecommunications and IT industries. They impact the value chains

and business models of both the traditional actors and the new entrants of the studied ecosystem.

Value network creation and platform strategies appear then as key elements that structure firms’

interactions and, more generally, the ecosystem’s business model. Based on Chesbrough (2006)

definition of business model, articles in this cluster use the concept to examine the structure of the

ecosystem’s value chain and to describe firms’ position in the value network.

The emergence of converging ecosystem encourages to consolidate how the concept of

business model could be used to examine the process of value creation and capture within a

complex network. For that purpose, in the second group of works of our first bibliographic

coupling cluster, scholars recognize that ecosystems do play an important role on firms’ modus

operandi, however it does not always happen in the same way or with the same level of impact.

Indeed, the level of maturity of an ecosystem may ask (or not) for different levels of synergies.

Adner (2017) points out to the confusion regarding how these ideas relate to each other in terms

of boundaries, overlaps, redundancies, applicability, unit and focus of analysis. To solve the

problem authors have tried to introduce new concepts of ecosystems. For example, Adner (2017)

introduces the distinctions between ecosystem-as-affiliation (communities defined by their

20

network) and ecosystem-as-structure (configuration of activity defined by a value proposition).

Van der Borgh, Cloodt & Romme (2012) points to the co-evolution of the ecosystem's business

models versus the firm-level business models, a concept that explains why technology-based firms

join, stay in, or leave the ecosystem at a certain point in time. Authors in this group explicitly

explain business model as a key concept that could be used to examine, in a fine way, how members

of an ecosystem chose to create and capture value (Lecocq, Demil & Ventura, 2010; Zott & Amitt,

2013).

Being member of a business, industrial or innovation-based ecosystem, implies that a firm

needs to adapt its business model to the inter-organizational context or, inversely, to integrate the

inter-organizational dimension to its initial business model. To conduct this analysis, authors of

the third group of this cluster, mobilized additional concepts to business models, such as: dynamic

capabilities (Helfat & Rawbitschek, 2018), modularity (Tsvetkova & Gustafsson, 2012), and open

innovation (Munsch, 2009; Raivio & Luukkainen, 2011; West & Bogers, 2017). However, if in an

industry convergence context, firms need to adapt their business models to an inter-organizational

level; as shown by Maglio & Spohrer (2013), business model innovation could in turn impact

ecosystem’s configuration. Hence, the transformation process must not be examined in only one

sense. There is a co-evolution process between business model innovation and the interaction

within a business, an industrial or an innovation ecosystem.

3.3.2. Bibliographic Coupling Cluster 2: Business models and sustainability in ecosystems

Bibliographic coupling Cluster 2 has on its centre the uncertainties brought upon the complex

modern economy. In this cluster, authors explore how concepts as circular economy (Murray,

Skene & Haynes, 2017) green transformation (Rajala, Westerlund & Lampikoski, 2016) or

sustainability (Carayannis, Sindakis & Walter, 2015; Goyal, Sergi & Jaiswal, 2016) are

operationalized in business strategy. Business model is then here the unit of analysis examined by

the authors; while in bibliographic coupling cluster 1 it is a theoretical concept. For example,

Murray, Skene & Haynes (2017) show that circular economy is a way to maximise ecosystems

functioning and business models’ sustainability. Similarly, Rajala, Westerlund & Lampikoski

(2016) explores how companies can renew their business models to take better account of the

environmental sustainability at the organizational level in their business ecosystems. In their work,

21

the authors identify links between organizational identity, business model innovation and its

implication to the business model ecosystem when a firm drives environmental sustainability. To

lever organizational sustainability within an ecosystem, business model innovation requires the

application of both organization design and governance competences such as: resources, dynamic

capabilities and entrepreneurship (Carayannis, Sindakis & Walter, 2015). Acquiring support from

the innovation ecosystem is one, among the six challenges a firm must face to achieve

organizational sustainability (ibid).

Special attention is given to the business model of specific ecosystems such as cybercrime

ecosystems (Kraemer-Mbula, Tang & Rush, 2013), waste management business ecosystems

(Peltola, Aarikka-Stenroos, Viana & Mäkinen, 2016), and low-income markets (Sanchez & Ricart,

2010). Papers in this cluster provide a holistic framework for digital business ecosystems in order

to analyse a specific industry. Business ecosystem perspective is indeed for Kraemer-Mbula, Tang

& Rush (2013) the most suitable perspective to obtain a clear view of the multiple actors within

the cybercrime universe on the one hand, and a better understanding on value creation and capture

in such ecosystems on the other hand. Such framework allowed Peltola, Aarikka-Stenroos, Viana

& Mäkinen (2016) to show that despite a strong role of regulators in waste management business

ecosystems, some private actors engage in value capture. By comparing isolated business models

and interactive business models, Sanchez & Ricart (2010) demonstrate that the intensity of

interdependencies amongst competitors within an ecosystem has a significant effect on value

creation. In other words, isolated business models, interdependencies with competitors are high

and negative as they intensify competition. However, in interactive business models,

interdependencies with competitors are low and positive, stimulating cooperation between actors

and value creation. Both Bergvall-Kåreborn & Howcroft (2013) and Gillier, Hooge, & Piat (2015)

highlight how it is possible to create value from creative projects while adopting a strategy for

human wellbeing and sustainability. Inversely, the example brought by Bergvall-Kåreborn &

Howcroft (2013) sheds light into how a large corporation can capitalize on the mass production of

digital products in developing countries while simultaneously managing to sidestep the incurred

costs and responsibilities associated with directly employing a high-tech workforce.

22

3.3.3. Bibliographic Coupling Cluster 3: Co-evolution of business models and ecosystems

The third bibliographic coupling cluster is the one where business models and ecosystems

appear clearly as two theoretical concepts that are mobilized together establishing a new agenda

of strategic management research. Two main streams seem to emerge. The first ones mostly focus

on the several types of existing ecosystems and their differentiation. They do not only clarify the

distinctions between business and innovation ecosystems, but they especially highlight a co-

evolution process between them that facilitates the elaboration of innovative business models. For

example, identifying a turning point in the business ecosystem literature (the transition from

business to innovation ecosystem). De Vasconcelos Gomes, Facin, Salerno & Ikenami (2018)

explain that business ecosystems relate mainly to value capture, while innovation ecosystems

relate more to value creation. But according to Ma, Rong, Mangagliu, Thornton & Zhu (2018)

analysing what are business ecosystems, or how they differ from innovation ecosystems leads to a

static view; while adopting a more dynamic view could be more helpful to understand business

ecosystem innovation, i.e. how a business ecosystem is in itself the subject of innovation. Indeed,

as explained by the authors, business ecosystem innovation relies on a co-evolutionary mechanism

between the value network and the resource pool that nurture and grow an emerging industry. More

precisely, through the case of the sharing mobility sector, the authors show a co-evolution process

between the macro, meso and micro level of an ecosystem. They conclude that a mutual influence

exists between business and innovation ecosystems that reinforce sustainable values and practices.

Co-evolution appears not only as a key process that facilitates value exploration within new

ecosystems (Watanabe, Naveed & Neittaanmäki,2016), but it also triggers sustainable value that

results from a mutual influence between the macro, meso and micro level of an ecosystem. Indeed,

as show it Watanabe, Naveed & Neittaanmäki (2016), a co-evolution process between mega-trends

of an industry could contribute to define an innovative business model such as the one of UBER.

The second stream of research in this cluster tackles how firms design new business models

by exploring what is outside its boundaries. For example, Priem, Butler & Li (2013) directly stated

that resource value is determined outside the business-level resource-based-view and that scholars

from the strategy domain must start thinking about strategy differently by expanding the

boundaries of the firm. Wei, Yang, Sun & Gu (2014) and Wei, Song & Wang (2017) explore the

antecedents of business model design. They find that both manufacturing flexibility, exploitative

23

and exploratory innovation significantly impact the performance of a firm and the design of its

business models. Furthermore, the influence of these factors on business model’s design is

significantly weakened or enhanced by environmental factors, such as competitive intensity.

3.3.4. Bibliographic Coupling Cluster 4: Business model innovation in entrepreneurial

ecosystems

The fourth bibliographic coupling cluster is clearly devoted to entrepreneurial ecosystems. Two

main streams are here identified. The first one is the most recent one. The aim for the authors is to

clarify what are entrepreneurial ecosystems and how they differ from knowledge clusters,

industrial districts and systems of innovation. Authors highlight that entrepreneurial ecosystems

emphasis on business model innovation appears as one key element of differentiation (Autio,

Nambisan, Thomas & Wright, 2018; Neumeyer & Santos, 2018). More precisely, Autio, Nambisan,

Thomas & Wright (2018) suggest that entrepreneurial ecosystems can be viewed as structures

specialized in the “stand-up”, “start-up” and “scale-up” of activities. Among the three stages, the

“start-up” one covers the business model experimentation to discover a robust and scalable

business model. Sustainable and conventional business models could however be segregated based

on the types of venture, venture age, type of actors they relate to, gender, ethnicity and race

(Neumeyer & Santos, 2018). As per entrepreneurial ecosystem itself, a differentiation can be made

through the diversity of participants, ventures, business models and support organizations (Roundy,

Brockman, & Bradshaw, 2017).

The second stream of work in this fourth bibliographic coupling cluster suggests introducing

the concept of business model in evolutionary theory; which still now depends mainly on the

notion of routines. Business model approach to strategy is viewed as complementary to the

ecosystem approach (Li & Garnsey, 2014). Adopting such double framework, allowed to Li &

Garnsey (2014) and Garnsey, Lorenzoni & Ferriani (2008) to position entrepreneurial firms as key

actors leveraging from new business models and enabling an innovation ecosystem to adapt, and

by doing so move the technology into new market domains. Indeed, in some case, entrepreneurial

firms form new ecosystems in order to create opportunities for innovation that had not previously

been recognized (Garnsey & Leong, 2008) and that promotes effective partnership in both private

and public spheres (Li & Garnsey, 2014). Knowledge and resources flows is then crucial,

24

implicating a combination between evolutionary and resource-based approaches to examine how

business models are defined and aligned with national policy objectives (Garnsey and Leong,

2008; Li & Garnsey, 2014).

4. Discussions and Conclusions

The aim of this paper is to unveil that academia is witnessing a convergence in the literature

related to two very important topics in strategic and innovation management, that is business

models and ecosystems. To uncover this phenomenon, we applied an original approach where we

utilized scientometrics to investigate our questions. We applied a systematic literature review with

a hybrid methodology composed of bibliometric analysis and content analysis, covering the period

1997–2018. More precisely we combined and compared to bibliometrics analysis (co-citations and

bibliographic coupling). We used the co-citation analysis, which has allowed us to build an

analytical frame-work that helped us identify the ground articles that lead to the convergence of

application of business models and ecosystems.

Two relevant conclusions can be drawn from the bibliographic coupling analysis on the set of

samples of this study. First, indeed joining the bibliographic coupling analysis to the co-citation

one promotes a rich and complementary perspective. By doing so, one can clearly see the evolution

of the intellectual structure related to convergence of business models and ecosystems. Documents

analysed through the bibliographic coupling, very simply put it, demonstrate a state of awareness

regarding the impact of the ecosystems of a firm’s business models. On the other hand, documents

emerging from the co-citation, address the subject starting from such awareness. Because of that,

documents on this set are more interested in how a firm (that is aware of the interrelations between

business models and ecosystems) use that to manipulate their environment in order to attain their

goals. The bibliographic coupling analysis points out to new research trends, such as the one

dedicated exclusively to entrepreneurial ecosystems and their business models. Or how, one can

use the relationship between business models and ecosystems to, either create sustainable

businesses and improve human wellbeing; or inversely, use the same techniques to manipulate or

create business models based on ecosystems that will fraud the system.

25

On the other hand, clusters on the co-citation analysis demonstrates the interest of scholars to

address the success of an innovating firm. It has identified important considerations under which

the dynamics of value creation, and the dependency that innovating firms often have on the efforts

of other innovators in their environment. This perspective, places emphasis on the breakdown of

traditional industry boundaries, the rise of interdependencies, and the potential for symbiotic

relationships in ecosystems (Adner, 2017). It focuses on questions of access and openness,

highlighting measures such as number of partners, network density, and actors’ centrality in their

respective networks. The practical and scholarly literatures are full with studies, prescriptions, and

caveats regarding the influence of the environment on a firm’s value capture and creation (Adner

& Kapoor, 2010). However, this debate, over the influence of the environment in the success of

innovation firms have tended to overlook the nature of intertwined relations between firms’

ecosystems and business models. It has also neglected to register the co-evolutive process they

interpose on each other. More generally, the co-citation analysis classified foundational works in

business model (mostly) and business ecosystem literature in five groups of research that built the

theoretical framework of the fourth stream of researches identified by the bibliographic coupling

analysis (table 4).

Table 4: Theoretical framework and research identified research streams

Clusters from co-citation analysis Clusters used as framework by bibliometric coupling research stream

1. Platform strategies for servization business challenges2. Business models and sustainability in ecosystems

3. Co-evolution of business models and ecosystems

2. Designing open business models

1. Ecosystem Business models

2. Sustainable business models and ecosystems

4. Business model innovation in entrepreneurial ecosystems

4. The rise of value network4. Business model innovation in entrepreneurial ecosystems

3. Co-evolution of business models and ecosystems

3. Business model as a tool 1. Ecosystem Business models

5. Dynamic capabilities and business model innovation Not really used by the recent research streams.

26

From there, we could map a research agenda for strategic management derived from the fifth

co-citation cluster (dynamic capabilities and business model innovation) and three of the four

stream of research highlighted by the bibliometric coupling analysis that are: sustainable business

models and ecosystems (bibliometric coupling cluster 2), the role of co-evolution in business

model and ecosystem innovation (bibliometric coupling cluster 3), business model innovation &

entrepreneurial ecosystems (bibliometric coupling cluster 4). This finding can help researchers

directed their researcher and at the same time help to consolidate their desired area of study.

27

References

Aarikka-Stenroos, L., & Lehtimäki, T. (2014). Commercializing a radical innovation: Probing the

way to the market. Industrial Marketing Management, 43(8), 1372-1384.

Adner, R. (2006). Match your innovation strategy to your innovation ecosystem. Harvard business

review, 84(4), 98.

Adner, R. (2017). Ecosystem as structure: an actionable construct for strategy. Journal of

management, 43(1), 39-58.

Adner, R., & Kapoor, R. (2010). Value creation in innovation ecosystems: How the structure of

technological interdependence affects firm performance in new technology generations.

Strategic management journal, 31(3), 306-333.

Ahuja, G. (2000). Collaboration networks, structural holes, and innovation: A longitudinal study.

Administrative science quarterly, 45(3), 425-455.

Amit, R., & Zott, C. (2001). Value creation in e‐business. Strategic management journal, 22(6‐7),

493-520.

Amit, R., & Zott, C. (2012). Creating value through business model innovation. 2012.

Appio, F. P., Cesaroni, F., & Di Minin, A. (2014). Visualizing the structure and bridges of the

intellectual property management and strategy literature: A document co-citation analysis.

Scientometrics, 101(1), 623-661.

Autio, E., Nambisan, S., Thomas, L. D., & Wright, M. (2018). Digital affordances, spatial

affordances, and the genesis of entrepreneurial ecosystems. Strategic Entrepreneurship

Journal, 12(1), 72-95.

Baden-Fuller, C., & Morgan, M. S. (2010). Business models as models. Long range planning,

43(2-3), 156-171.

Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of management,

17(1), 99-120.

Basole, R. C. (2009). Visualization of interfirm relations in a converging mobile ecosystem.

Journal of information Technology, 24(2), 144-159.

Belussi, F., Orsi, L., & Savarese, M. (2019). Mapping Business Model Research: A Document

Bibliometric Analysis. Scandinavian Journal of Management, 101048.

Bergvall-Kåreborn, B., & Howcroft, D. (2013, December). The Apple business model:

Crowdsourcing mobile applications. In Accounting Forum (Vol. 37, No. 4, pp. 280-289).

Taylor & Francis.

Boyack, K. W., & Klavans, R. (2010). Co‐citation analysis, bibliographic coupling, and direct

citation: Which citation approach represents the research front most accurately?. Journal

of the American Society for Information Science and Technology, 61(12), 2389-2404.

Calmé, I., Onnée, S., & Zoukoua, É. A. (2016). Plateformes de crowdfunding et acteurs de

l’écosystème entrepreneurial-Quelle (s) coopération (s), quels business models? Revue

française de gestion, 42(259), 139-154.

Carayannis, E. G., Sindakis, S., & Walter, C. (2015). Business model innovation as lever of

organizational sustainability. The Journal of Technology Transfer, 40(1), 85-104.

Carvalho, M. M., Fleury, A., & Lopes, A. P. (2013). An overview of the literature on technology

roadmapping (TRM): Contributions and trends. Technological Forecasting and Social

Change, 80(7), 1418-1437.

Casadesus-Masanell, R., & Ricart, J. E. (2010). From strategy to business models and onto tactics.

Long range planning, 43(2-3), 195-215.

28

Chai, K. H., & Xiao, X. (2012). Understanding design research: A bibliometric analysis of Design

Studies (1996–2010). Design Studies, 33(1), 24-43.

Chen, C. (2005, January). The centrality of pivotal points in the evolution of scientific networks.

In Proceedings of the 10th international conference on Intelligent user interfaces (pp. 98-

105). ACM.

Chen, C. (2006). CiteSpace II: Detecting and visualizing emerging trends and transient patterns in

scientific literature. Journal of the American Society for information Science and

Technology, 57(3), 359-377.

Chesbrough, H. W. (2003). Open innovation: The new imperative for creating and profiting from

technology. Harvard Business Press.

Chesbrough, H. (2006). Open business models: How to thrive in the new innovation landscape.

Harvard Business Press.

Chesbrough, H. W. (2007). Why companies should have open business models. MIT Sloan

management review, 48(2), 22.

Chesbrough, H., & Rosenbloom, R. S. (2002). The role of the business model in capturing value

from innovation: evidence from Xerox Corporation's technology spin‐off companies.

Industrial and corporate change, 11(3), 529-555.

Daidj, N., & Jung, J. (2011). Strategies in the media industry: towards the development of co-

opetition practices? Journal of Media Business Studies, 8(4), 37-57.

Davies, A., Brady, T., & Hobday, M. (2007). Organizing for solutions: Systems seller vs. systems

integrator. Industrial marketing management, 36(2), 183-193.

de Vasconcelos Gomes, L. A., Facin, A. L. F., Salerno, M. S., & Ikenami, R. K. (2018). Unpacking

the innovation ecosystem construct: Evolution, gaps and trends. Technological Forecasting

and Social Change, 136, 30-48.

Demil, B., Lecocq, X., & Warnier, V. (2018). “Business model thinking”, business ecosystems and

platforms: the new perspective on the environment of the organization. M@ n@

gement, 21(4), 1213-1228.

Dhanaraj, C., & Parkhe, A. (2006). Orchestrating innovation networks. Academy of management

review, 31(3), 659-669.

Eisenhardt, K. M. (1989). Building theories from case study research. Academy of management

review, 14(4), 532-550.

Eisenhardt, K. M., & Graebner, M. E. (2007). Theory building from cases: Opportunities and

challenges. Academy of management journal, 50(1), 25-32.

Eisenhardt, K. M., & Martin, J. A. (2000). Dynamic capabilities: what are they?. Strategic

management journal, 21(10‐11), 1105-1121.

Evens, T. (2010). Value networks and changing business models for the digital television industry.

Journal of media business studies, 7(4), 41-58.

Ewe, S. Y., Yap, S. F., & Lee, C. K. C. (2015). Network externalities and the perception of

innovation characteristics: mobile banking. Marketing Intelligence & Planning, 33(4), 592-

611.

Foss, N. J., & Saebi, T. (2018). Business models and business model innovation: Between wicked

and paradigmatic problems. Long Range Planning, 51(1), 9-21.

Garfield, E. (2001). From bibliographic coupling to co-citation analysis via algorithmic historio-

bibliography: A citationist’s tribute to Belver C. Griffith, Lazerow Lecture presented at

Drexel University, Philadelphia, PA. November 27, 2001. PA. Retrieved March, 5, 2015.

29

Garnsey, E., & Leong, Y. Y. (2008). Combining Resource‐Based and Evolutionary Theory to

Explain the Genesis of Bio‐networks. Industry and Innovation, 15(6), 669-686.

Garnsey, E., Lorenzoni, G., & Ferriani, S. (2008). Speciation through entrepreneurial spin-off: The

Acorn-ARM story. Research Policy, 37(2), 210-224.

Gawer, A. (2014). Bridging differing perspectives on technological platforms: Toward an

integrative framework. Research policy, 43(7), 1239-1249.

Gawer, A., & Cusumano, M. A. (2014). Industry platforms and ecosystem innovation. Journal of

product innovation management, 31(3), 417-433.

George, G., & Bock, A. J. (2011). The business model in practice and its implications for

entrepreneurship research. Entrepreneurship theory and practice, 35(1), 83-111.

Gillier, T., Hooge, S., & Piat, G. (2015). Framing value management for creative projects: An

expansive perspective. International Journal of Project Management, 33(4), 947-960.

Goyal, S., Sergi, B. S., & Jaiswal, M. P. (2016). Understanding the challenges and strategic actions

of social entrepreneurship at base of the pyramid. Management Decision, 54(2), 418-440.

Gulati, R. (1998). Alliances and networks. Strategic management journal, 19(4), 293-317.

Hagiu, A., & Wright, J. (2015). Multi-sided platforms. International Journal of Industrial

Organization, 43, 162-174.

Helfat, C. E., & Raubitschek, R. S. (2018). Dynamic and integrative capabilities for profiting from

innovation in digital platform-based ecosystems. Research Policy, 47(8), 1391-1399.

Hellström, M., Tsvetkova, A., Gustafsson, M., & Wikström, K. (2015). Collaboration mechanisms

for business models in distributed energy ecosystems. Journal of Cleaner Production, 102,

226-236.

Holton, G. J. (2000). Einstein, history, and other passions: The rebellion against science at the end

of the twentieth century. Harvard university press.

Iansiti, M., & Levien, R. (2004). Strategy as ecology. Harvard business review, 82(3), 68-81.

Ikpaahindi, L. (1985). An overview of bibliometrics: its measurements, laws and their applications.

Libri, 35, 163.

Jacobides, M. G., Knudsen, T., & Augier, M. (2006). Benefiting from innovation: Value creation,

value appropriation and the role of industry architectures. Research policy, 35(8), 1200-

1221.

Jacobides, M. G., Cennamo, C., & Gawer, A. (2018). Towards a theory of ecosystems. Strategic

Management Journal, 39(8), 2255-2276.

Kandiah, G., & Gossain, S. (1998). Reinventing value: The new business ecosystem. Strategy &

leadership, 26(5), 28-33.

Kapoor, R., & Lee, J. M. (2013). Coordinating and competing in ecosystems: How organizational

forms shape new technology investments. Strategic management journal, 34(3), 274-296.

Kessler, M. M. (1963). Bibliographic coupling between scientific papers. American documentation,

14(1), 10-25.

Kraemer-Mbula, E., Tang, P., & Rush, H. (2013). The cybercrime ecosystem: Online innovation

in the shadows? Technological Forecasting and Social Change, 80(3), 541-555.

Lecocq, X., Demil, B., & Ventura, J. (2010). Business models as a research program in strategic

management: an appraisal based on Lakatos. M@ n@ gement, 13(4), 214-225.

Li, F., & Whalley, J. (2002). Deconstruction of the telecommunications industry: from value chains

to value networks. Telecommunications policy, 26(9-10), 451-472.

Li, J. F., & Garnsey, E. (2014). Policy-driven ecosystems for new vaccine development.

Technovation, 34(12), 762-772.

30

Lusch, R. F., & Nambisan, S. (2015). Service innovation: A service-dominant logic perspective.

MIS quarterly, 39(1).

Ma, Y., Rong, K., Mangalagiu, D., Thornton, T. F., & Zhu, D. (2018). Co-evolution between urban

sustainability and business ecosystem innovation: Evidence from the sharing mobility

sector in Shanghai. Journal of Cleaner Production, 188, 942-953.

Maglio, P. P., & Spohrer, J. (2013). A service science perspective on business model innovation.

Industrial Marketing Management, 42(5), 665-670.

March, J. G. (1991). Exploration and exploitation in organizational learning. Organization science,

2(1), 71-87.

Martins, L. L., Rindova, V. P., & Greenbaum, B. E. (2015). Unlocking the hidden value of

concepts: a cognitive approach to business model innovation. Strategic Entrepreneurship

Journal, 9(1), 99-117.

Massa, L., Tucci, C. L., & Afuah, A. (2017). A critical assessment of business model research.

Academy of Management Annals, 11(1), 73-104.

Massa, L., & Tucci, C. L. (2013). Business model innovation. The Oxford handbook of innovation

management, 20(18), 420-441.

Maucuer, R., & Renaud, A. (2019). Business Model Research: A Bibliometric Analysis of Origins

and Trends.

McGrath, R. G. (2010). Business models: A discovery driven approach. Long range planning,

43(2-3), 247-261.

Moore, J. F. (1993). Predators and prey: a new ecology of competition. Harvard business review,

71(3), 75-86.

Morris, M., Schindehutte, M., & Allen, J. (2005). The entrepreneur's business model: toward a

unified perspective. Journal of business research, 58(6), 726-735.

Müller, R. M., Kijl, B., & Martens, J. K. (2011). A comparison of inter-organizational business

models of mobile app stores: There is more than open vs. closed. Journal of theoretical and

applied electronic commerce research, 6(2), 63-76.

Munsch, K. (2009). Open model innovation. Research-Technology Management, 52(3), 48-52.

Murray, A., Skene, K., & Haynes, K. (2017). The circular economy: an interdisciplinary

exploration of the concept and application in a global context. Journal of Business Ethics,

140(3), 369-380.

Neumeyer, X., & Santos, S. C. (2018). Sustainable business models, venture typologies, and

entrepreneurial ecosystems: A social network perspective. Journal of cleaner production,

172, 4565-4579.

Peltola, T., Aarikka-Stenroos, L., Viana, E., & Mäkinen, S. (2016). Value capture in business

ecosystems for municipal solid waste management: Comparison between two local

environments. Journal of cleaner production, 137, 1270-1279

Peppard, J., & Rylander, A. (2006). From value chain to value network: Insights for mobile

operators. European management journal, 24(2-3), 128-141.

Perianes-Rodriguez, A., Waltman, L., & van Eck, N. J. (2016). Constructing bibliometric

networks: A comparison between full and fractional counting. Journal of Informetrics,

10(4), 1178-1195.

Porter, M. E., & Heppelmann, J. E. (2014). How smart, connected products are transforming

competition. Harvard business review, 92(11), 64-88.

31

Priem, R. L., Butler, J. E., & Li, S. (2013). Toward reimagining strategy research: retrospection

and prospection on the 2011 AMR decade award article. Academy of Management Review,

38(4), 471-489.

Raivio, Y., & Luukkainen, S. (2011). Mobile networks as a two-sided platform-case Open Telco.

Journal of theoretical and applied electronic commerce research, 6(2), 77-89.

Rajala, R., Westerlund, M., & Lampikoski, T. (2016). Environmental sustainability in industrial

manufacturing: re-examining the greening of Interface's business model. Journal of

Cleaner Production, 115, 52-61.

Ritala, P., Golnam, A., & Wegmann, A. (2014). Coopetition-based business models: The case of

Amazon. com. Industrial Marketing Management, 43(2), 236-249.

Rochet, J. C., & Tirole, J. (2003). Platform competition in two-sided markets. Journal of the

european economic association, 1(4), 990-1029.

Rong, K., Shi, Y., & Yu, J. (2013). Nurturing business ecosystems to deal with industry

uncertainties. Industrial management & data systems, 113(3), 385-402.

Roundy, P. T., Brockman, B. K., & Bradshaw, M. (2017). The resilience of entrepreneurial

ecosystems. Journal of Business Venturing Insights, 8, 99-104.

Sanchez, P., & Ricart, J. E. (2010). Business model innovation and sources of value creation in

low‐income markets. European management review, 7(3), 138-154.

Small, H. (1973). Co‐citation in the scientific literature: A new measure of the relationship

between two documents. Journal of the American Society for information Science, 24(4),

265-269.

Small, H. (1999). Visualizing science by citation mapping. Journal of the American society for

Information Science, 50(9), 799-813.

Snihur, Y., Thomas, L. D., & Burgelman, R. A. (2018). An Ecosystem‐Level Process Model of

Business Model Disruption: The Disruptor's Gambit. Journal of Management Studies,

55(7), 1278-1316.

Swanson, D. R. (1987). Two medical literatures that are logically but not bibliographically

connected. Journal of the American Society for Information Science, 38(4), 228-233.

Tansley, A. G. (1935). The use and abuse of vegetational concepts and terms. Ecology, 16(3), 284-

307.

Teece, D. J. (1986). Profiting from technological innovation: Implications for integration,

collaboration, licensing and public policy. Research policy, 15(6), 285-305.

Teece, D. J. (2007). Explicating dynamic capabilities: the nature and microfoundations of

(sustainable) enterprise performance. Strategic management journal, 28(13), 1319-1350.

Teece, D. J. (2009). Dynamic capabilities and strategic management: Organizing for innovation

and growth. Oxford University Press on Demand.

Teece, D. J. (2010). Business models, business strategy and innovation. Long range planning, 43(2-

3), 172-194.

Teece, D. J. (2018). Profiting from innovation in the digital economy: Enabling technologies,

standards, and licensing models in the wireless world. Research Policy, 47(8), 1367-1387.

Teece, D. J., Pisano, G., & Shuen, A. (1997). Dynamic capabilities and strategic management.

Strategic management journal, 18(7), 509-533.

Thomas, L. D., Autio, E., & Gann, D. M. (2014). Architectural leverage: putting platforms in

context. Academy of management perspectives, 28(2), 198-219.

Timmers, P. 1998. Business models for electronic markets. Electronic Markets, 8(2): 3-8.

32

Tsujimoto, M., Kajikawa, Y., Tomita, J., & Matsumoto, Y. (2018). A review of the ecosystem

concept—Towards coherent ecosystem design. Technological Forecasting and Social

Change, 136, 49-58.

Tsvetkova, A., & Gustafsson, M. (2012). Business models for industrial ecosystems: a modular

approach. Journal of Cleaner Production, 29, 246-254.

Van der Borgh, M., Cloodt, M., & Romme, A. G. L. (2012). Value creation by knowledge‐based

ecosystems: evidence from a field study. R&D Management, 42(2), 150-169.

Van Eck, N., & Waltman, L. (2009). Software survey: VOSviewer, a computer program for

bibliometric mapping. Scientometrics, 84(2), 523-538.

Van Eck, N. J., & Waltman, L. (2014). Visualizing bibliometric networks. In Measuring scholarly

impact (pp. 285-320). Springer, Cham.

Vandermerwe, S., & Rada, J. (1988). Servitization of business: adding value by adding services.

European management journal, 6(4), 314-324.

Walsh, I., & Renaud, A. (2017). Reviewing the literature in the IS field: Two bibliometric

techniques to guide readings and help the interpretation of the literature. Systemes

d'information management, 22(3), 75-115.

Waltman, L., Van Eck, N. J., & Noyons, E. C. (2010). A unified approach to mapping and clustering

of bibliometric networks. Journal of Informetrics, 4(4), 629-635.

Watanabe, C., Naveed, K., & Neittaanmäki, P. (2016). Co-evolution of three mega-trends nurtures

un-captured GDP–Uber’s ride-sharing revolution. Technology in Society, 46, 164-185.

Wei, Z., Song, X., & Wang, D. (2017). Manufacturing flexibility, business model design, and firm

performance. International Journal of Production Economics, 193, 87-97.

Wei, Z., Yang, D., Sun, B., & Gu, M. (2014). The fit between technological innovation and business

model design for firm growth: evidence from C hina. R&D Management, 44(3), 288-305.

West, J., & Bogers, M. (2017). Open innovation: current status and research opportunities.

Innovation, 19(1), 43-50.

White, H. D., & McCain, K. W. (1998). Visualizing a discipline: An author co‐citation analysis

of information science, 1972–1995. Journal of the American society for information

science, 49(4), 327-355.

Zollo, M., & Winter, S. G. (2002). Deliberate learning and the evolution of dynamic capabilities.

Organization science, 13(3), 339-351.

Zott, C., & Amit, R. (2007). Business model design and the performance of entrepreneurial firms.

Organization science, 18(2), 181-199.

Zott, C., & Amit, R. (2008). The fit between product market strategy and business model:

implications for firm performance. Strategic management journal, 29(1), 1-26.

Zott, C., & Amit, R. (2010). Business model design: an activity system perspective. Long range

planning, 43(2-3), 216-226.

Zott, C., & Amit, R. (2013). The business model: A theoretically anchored robust construct for

strategic analysis. Strategic Organization, 11(4), 403-411.

Zott, C., Amit, R., & Massa, L. (2011). The business model: recent developments and future

research. Journal of management, 37(4), 1019-1042.