Modeling Platform Ecosystems?

Abstract

Platforms facilitate the creation of complementary modules by third parties and act as intermediaries between di erent groups of actors. Due to the high degree of collaboration between actors, ecosystems evolve around such platforms. As a result of digitalization, platform business models are becoming viable in more and more domains. Despite the increasing variety of di erent platform ecosystems, means to clearly specify them are scarce. This conceptual ambiguity impedes comparability of research and knowledge accumulation. To solve this problem, we propose a domain-speci c platform ecosystems modeling language (PEML) which builds on seminal platform ecosystem literature. We demonstrate PEML by modeling two real-life platform ecosystems based on online case studies. Our results support researchers and practitioners alike in clearly specifying platform ecosystems.

Keywords

• Platform
• Ecosystem
• Domain-Speci c Modeling Language

Introduction

Over the course of the last few years, platform rms have dominated the economy in many branches, evident from the examples of Amazon in the retail sector or Airbnb in the hotel industry. A signi cant factor in the success of platforms is their ability to leverage an ecosystem of actors who contribute to the platform in various ways.

Although all platforms share common characteristics, they still seem rather heterogeneous. Considering and understanding their di erences and \complexity is essential in balancing the need on one hand to render platforms a researchable unit of analysis while on the other hand avoiding oversimpli cation of the phenomenon" [32, p. 4625]. The same holds true for their surrounding ecosystems.

Despite the multitude of platform and platform ecosystem research papers, we miss methods to clearly describe and distinguish platform ecosystems. Consequently, researchers refer to platform ecosystems as a rather vague concept without clearly specifying their unit of analysis. The resulting conceptual ambiguity impedes comparability of research and knowledge accumulation [ 24 ]. Additionally, as the \platformization" a ects more and more areas that have previously been dominated by non-platform business models [ 23 ], practitioners need tools ? Supported by the German Federal Ministry of Education and Research (FKZ 01jS17045) as part of the Software Campus project \I4.oT" to capture the peculiarities of platform ecosystems in their domain. This is necessary to assess established insights from other domains. Thereafter, practitioners can decide whether these might prove applicable in their case.

Conceptual modeling is well-suited to reduce the physical and social world's complexity by accurately describing instances of real-world phenomena. In related disciplines, modeling facilitated general comprehension of, e. g., value creation (e3value), business processes (BPMN), systems (SysML), and software (UML). Even though these modeling languages aid in de ning facets of a platform ecosystem, they do not cover platform-speci c aspects. While cross-organizational multi-level modeling can depict an ecosystem in a very detailed manner, we argue for the necessity of high-level comprehensive modeling of platformecosystem speci cs to facilitate comprehensibility for non-experts in modeling.

Therefore, in the paper at hand, we strive to provide an answer to the following research question: How can we create comprehensive conceptual models for platform ecosystems to achieve general consensus about their structure, actors and functionality? To this end, using Frank's [ 8 ] method and guidelines for designing domain speci c modeling languages, we create the platform ecosystems modeling language (PEML).

The remainder of this paper is structured as follows. In Section 2, we provide the theoretical foundations regarding platform ecosystems on which we base our modeling language. Section 3 describes our methodological approach to designing the modeling language. Afterwards, Section 4 presents the modeling language. In Section 5, we demonstrate the usage of the PEML by modeling two platform ecosystems from di erent domains. Section 6 frames the modeling language in its related works. Finally, Section 7 rounds o the paper by outlining both the contributions and limitations. 2

Platform Ecosystems

The concept of platforms originates in the literature on internal product families and has since moved on towards a broader understanding as external industry platforms [ 9, 31 ]. We focus on these external platforms, as they lead to the emergence of ecosystems in two ways. From an architectural perspective, platforms can be de ned as \products, services, or technologies that [...] provide the foundation upon which outside rms [...] can develop their own complementary products, technologies, or services" [9, p.418]. From a market perspective, platforms represent two- or multi-sided marketplaces by facilitating transactions between di erent groups of actors [ 25 ].

The resulting collection of actors who use the platform as technological basis or marketplace forms the platform ecosystem [ 24 ]. This platform ecosystem as the collection of actors \a liated" with each other through the common platform corresponds to Adner's [ 1 ] platform-as-a liation perspective. However, based on the activity-centric structure perspective, ecosystems can also be de ned based on the activities that contribute to a speci c value proposition [ 1 ]. Consequently, the ecosystem-as-structure perspective stresses the necessary alignment of actors and their activities to create value.

Actors in platform ecosystems comprise complementors, users and a platform owner. In contrast to classic suppliers who enable a product's creation, complementors enable a product's use [ 2 ]. In the context of platforms, the complementors provide \complementary" solutions to and via the platform to the users. To facilitate the development of modules, platform owners provide boundary resources such as application programming interfaces (APIs) [ 10 ]. At the same time, boundary resources serve as a governance instrument to exercise control over complement quality.

Based on the characteristics described above, a variety of di erent platforms can be distinguished. Some platforms enable the creation of complementary modules (e.g. Sony's PlayStation), while others merely act as a marketplace (e.g. Airbnb). Many platforms provide both of these functionalities (e.g. iOS and the App Store).

These di erences also bear implications for the respective platform ecosystems. The ecosystems around market platforms might resemble an ecosystem as a liation, whereas technology platforms predominantly cause the evolution of an ecosystem as structure around their speci c complementary solutions. The creation of applications on mobile platforms (e.g. iOS) might not be subject to speci c complementarities between actors. However, the development of more complex technological solutions on digital industrial platforms (e.g. MindSphere) requires close alignment of many actors in di erent roles.

Some platforms acting as a marketplace (e.g. Airbnb) will be primarily driven by positive cross-side network e ects. These occur if the value of the platform for a certain group of actors increases as the number of actors on the other side grows [ 17 ]. The more apartments there are available at Airbnb, the more prospective guests will use the platform and vice versa. On the contrary, other platforms (e.g. Facebook) are built on the premise of same-side network e ects: A higher number of facebook users increases the platform's value for other users. As the heterogeneity of platforms grows, providing means to model and distinguish di erent platform ecosystems is therefore critical for a meaningful analysis. 3

Method

For the development of PEML we follow the domain-speci c modeling language engineering method proposed by Frank [ 8 ]. The purpose of PEML is to provide means for the comprehensive description of di erent kinds of platform ecosystems. The scope thereby includes all types of platform ecosystems. However, we mainly focus on digital platforms as they currently are the most relevant type in research and practice.

PEML consists of an abstract syntax describing the semantics of the modeling language, and a concrete syntax comprising the visual notation [ 20, 26 ]. Additionally, we provide instructions to simplify entry into modelling and to enable comparison of di erent models. To create the abstract syntax we extracted key concepts from seminal literature on platforms and ecosystems. As requirements such as ontological completeness and clarity may be relaxed on purpose for domain-speci c modeling languages, we do not refer to a speci c ontology as a basis [ 8 ]. The concrete syntax is an important part of any modeling language, as humans develop and interpret models via graphical and textual elements [ 8 ]. Thus, creating a cognitively meaningful visual notation is critical to facilitate communication and problem solving in the later use of a modeling language [ 19 ]. To ensure this, we stick to the design principles in [ 19 ] and [ 8 ] to develop the concrete syntax for PEML. Throughout the development process, we evaluated both the abstract and concrete syntax iteratively. To this end, we modeled different scenarios. Every time we realized that a real-world concept could not be represented by PEML or if discussions arose that indicated ambiguity of chosen concepts, we went back and re ned our language.

Consequently, to demonstrate the applicability and contribution of PEML, we show two di erent platform ecosystem models based on online case studies. The goal was not necessarily to create exhaustive models, but rather to present the application of PEML. First, we modeled the Airbnb platform, as its high degree of popularity and relatively simple structures make it ideal for demonstrating the intuitive use of PEML. Second, we modeled the industrial internet of things (IIoT) platform Siemens MindSphere to illustrate PEML in the context of highly diverse business to business (B2B) ecosystems with a multitude of platform components, resulting in high complexity. 4 4.1

Platform Ecosystems Modeling Language (PEML)

With the development of PEML, we aim for a comprehensive depiction of platform ecosystems while at the same time limiting the number of used concepts. The rationale behind this is to ensure familiarity of a wide range of users with the included concepts [ 8 ]. To this end, based on the understanding of platforms presented in section 2, we identi ed key concepts from platform and ecosystem literature that need to be re ected in PEML. In the following lines, we describe the selected concepts and their semantics.

Actor. Actors are parties that contribute resources and activities to value creation. There are three types of actors in platform ecosystems: platform owners, complementors and users [ 16 ]. The platform owner is the actor who provides the platform around which the ecosystem emerges [ 16 ]. The owner governs the platform, provides interfaces and rules to complementors and manages the exchange of services. Complementors are actors who o er compatible modules that contribute to the value creation of the focal o er [ 16 ]. Actors that consume a platform's value proposition are users. Both users and complementors can either be businesses or consumers. While a real actor entity can operate in di erent functions on a platform, we focus on their current function in the platformecosystem. A traveler using AirBnB to nd accommodation, for example, may at the other side of the platform also o er accommodation to others. For PEML, we separate the actual actors from their functions, so that actors only exist as their function in the ecosystem in PEML.

Role. Actors can perform di erent roles. In PEML, role is a de ning construct for a set of actors who possess similar resources or perform similar activities with regard to value creation and capture in relation to the platform. Thus, role is an attribute of actors.

Side. From a market perspective, platforms are two- or multi-sided markets [ 25 ]. Accordingly, in the simplest form, sides structure the platform ecosystem based on supply and demand [ 33 ]. However, in the case of multi-sided platforms a more di erentiated consideration of sides is necessary. YouTube, for example, has a content provider side, a user side, and an advertiser side, besides others. Thus, PEML de nes the sides of a platform ecosystem dependent on each actors' links to the platform's high-level value proposition. Hence, sides are attributes of the whole platform ecosystem, whereas roles are actors' attributes.

Value Proposition. The value proposition describes a bundle of products and services that creates value for the customer by satisfying a need or solving a problem [ 22 ]. As most complex platforms can not be reduced to a single value proposition, PEML distinguishes two levels of value propositions. On a high level, a platform provides a main value proposition to its customers. For instance, in the case of digital industrial platforms such as Siemens MindSphere, a high-level value proposition would be the connection of products, plants, systems, and machines to harness the wealth of data. Value propositions on a lower level directed at individual customers or customer segments are the de ning element for ecosystem boundaries from an ecosystem-as-structure perspective [ 1 ]. In digital industrial platforms, such a value proposition could be a speci c predictive maintenance solution. Such value propositions are the focal point of sub-ecosystems forming in the overall platform ecosystem.

Link. Actors are connected through links. More speci cally, links describe transfers of resources and activities between actors. The speci c composition of such transfers may vary and comprise, e.g., material, information, value, funds, or even in uence [ 1 ]. Additionally, the links relate actors to the value proposition by specifying what they contribute to which value proposition.

Activity. Based on Kapoor [16, p. 6] we de ne activities as \tasks that underlie the di erent o ers that contribute to the focal o er's user value proposition". Simply spoken, this includes all actions performed by actors that directly or indirectly contribute to the creation of a value proposition on the platform.

Resource. Resources in the context of PEML comprise assets, funds, technologies, intellectual property and other means required for the creation of a certain value proposition on the platform.

Boundary Resource. Independent of resources in a general sense, boundary resources (BR) are interfaces of various kinds that enable interaction of complementors with the platform [ 10 ]. Bianco et al. [ 6 ] distinguish between application BR, development BR and social BR. Application BR such as application programming interfaces enable the interaction of third party modules with the platform. Development BR such as software development kits support the development of complementary solutions. Social BR foster (knowledge) exchange between ecosystem participants, for example in the form of partner programs or workshops.

Leverage. Leverage describes how actors bene t from participating in the platform ecosystem in the sense of \exercising an in uence that is disproportionate to one's size" [31, p. 206]. There are three types of leverage: production leverage, innovation leverage, and transaction leverage [ 31 ]. Production leverage refers to economies of scale achieved through the re-use of standardized components. Innovation leverage describes the use of the platform as a basis to create new solutions. The platform's market intermediary character enables the transaction leverage, as it simpli es and fosters transactions among actors. In the context of PEML, leverage is an attribute that characterizes the association between an actor's activities and the platform. 4.2

Concrete Syntax

The design of the concrete syntax was guided by the principles for constructing visual notations proposed by Moody et al. [ 19 ] and Frank [ 8 ]. In line with Lusch et al. [ 18 ], we split the elements in the concrete syntax into the three categories ecosystem, platform, and value proposition. The Figures 1-3 show the concrete syntax elements of PEML.

Ecosystem. The ecosystem modeling elements in Figure 1 comprise actors and links. Actors themselves consist of a role, their resources and the activities that they contribute to the platform ecosystem. Actors can either be singleinstance or multi-instance entities (cf. 1a and 1b). Actors who contribute similar activities and resources to the ecosystem constitute one multi-instance actor. While single-instance actors are labeled with their name, multi-instance actors are labeled with their collective role. As activities and resources are provided by actors, they are always attached to an actor.

Links specify the connections between actors and other entities. To improve the visual feedback of platform interactions, PEML introduces curved edges as platform-actor links and straight edges as actor-platform links (cf. 1c and 1d). The distinction of the link type depends on the active entity in a transaction.

Further, we distinguish peripheral links from the platform-related links by adding the visual cue of dashed arrows. Peripheral links are those links that have an indirect in uence on the platform. Hence, peripheral links display relations in the ecosystem surrounding a platform. Comment boxes (cf. 1f) enable

Actor Resources Activities

RRRooollee Resources Activities 1c) Actor-Platform Link 1d) Platform-Actor Link

Comment 1a) Single-Instance Actor 1b) Multi-Instance Actor 1e) Peripheral Link

1f) Comment Box Fig. 1. 1) Ecosystem elements Side Type

PO 2ci) Platform Owner C 2cii) Complementor

Boundary Resource 2di) Social 2dii) Development 2a) Platform 2b) Side + 2c) Actor Type U 2ciii) User 2d) BoundaryResource 2diii) Application the modeler to make comments when necessary, especially to clarify the links' composition of transfer.

Platform. The platform elements, depicted in Fig. 2, constitute further syntax elements of PEML. The platform itself (cf. 2a) is a rectangle with rounded corners. It wraps around value propositions (cf. 2d) and boundary resources. A platform generally arbitrates between di erent groups of actors. These groups of actors are arranged around the platform in the form of sides. PEML depicts sides (cf. 2b) as rectangular solid wrappers that contain all actors belonging to the particular side. As a reminder, sides are di erent from roles, as they classify actors according to their position in relation to the platform, not based on a similar set of activities and resources. Optionally, sides can be labeled, if further clari cation is necessary. However, the speci cation of the actor types within the side is mandatory. Actor types are speci ed at the level of sides instead of actors, as all actors belonging to the same side always possess the same type. In the square at the top left corner modelers may either add a U for user, C for complementor, or PO for platform owner (cf. 2c). In some cases, complementors and users may be the same real-world entities. At the same time, platform owners might also act as complementors. We propose to split these real world entities into their respective types and assign them to several sides if necessary for the modeling with PEML.

Value proposition. Figure 3 presents all symbols that relate to the value proposition when modeling platform ecosystems. The value proposition is a box with rounded corners (cf. 3a, 3b) with leverage ports (3c). The modeling of functions in control engineering inspires our approach to model value propositions. The value proposition receives at least one input and delivers at least one output. Inputs are speci ed using the platform-actor and actor-platform links. PEML emphasizes the platform's general value proposition via bold borders and text. The leverage ports de ne which kind of in- or output the value proposition provides. A leverage port can contain either innovation, production, or transaction Main Value Proposition

Value Proposition 3a) MainValue Proposition 3b) Value Proposition 3c) Leverage Ports

3g) Empty Fig. 3. 3) Value proposition elements 3d) InnovationLeverage 3e) ProductionLeverage 3f) TransactionLeverage leverage (cf. 3d-3f). If the value proposition does not build on leverage by or provide leverage for any of its participants, the leverage ports remain empty. 4.3

Instructions

The complexity of platform ecosystems usually takes on considerable dimensions and thus modeling becomes a rather challenging task. By introducing a set of basic instructions, we guide the modeler through a standardized way of reasoning to enhance applicability and guarantee comparability across di erent models.

Depending on the speci c task, we recommend to choose an appropriate perspective of interest. Such perspectives can be a clear representation of the entire platform ecosystem or a detailed description of individual actors, or value propositions. If, for example, the modeler's focus is on providing a high-level overview of the actors in the platform ecosystem, he or she can model from an ecosystem-as-a liation perspective [ 1 ]. As a result, resources, activities and value propositions move out of focus. This may make sense for platforms geared towards a single value proposition where success is largely based on the quantity of actors on each side.

Other platforms, for example, act as marketplaces to distribute numerous applications. Such platforms might bene t from modeling them from an ecosystemas-structure perspective, focusing on the links between actors and value propositions and the activities and resources they contribute [ 1 ]. However, with every application having its own value proposition and relying on speci c boundary resources, representing each can get out of hand fairly quickly. The result is an overwhelming model. For some modelers, this degree of detail is not necessary and thus can be omitted without signi cant loss of information. Others, who strive to visualize all actors and their links on a speci c app, should delve in considerably deeper while simultaneously neglecting not involved actors and boundary resources.

As such, PEML can be used to model platform ecosystems on multiple layers. Beginning with a high-level model, the modeler describes all basic components such as actors, sides, boundary resources, and general links that constitute a platform ecosystem. PEML predominantly focuses on this high level of consideration, but also o ers components to enable a much more detailed elaboration. Thus, the models can be augmented with additional sub-models, depending on speci c purposes. In these sub-layers, the modelers can zoom in on elements of particular interest and take a more detailed perspective as already discussed earlier. 5 5.1

Demonstration

Airbnb

Airbnb is a two-sided platform that enables private persons to o er accommodation and activities to travelers [ 3 ]. To create a simple and comprehensible initial C

Providers

RRHoololeest Resources • Accommodation Activity • Prepare rooms • Care for guest • Add descriptions

ActivRiRtyoolHleeandlers Resources • (Tourist) activities Activity • Plan activities • Add description • Provide experience Guests

TRRroaovlleeler Resources • Time • Money Activity • Take part in activity • Rent accommodation

U example, we only focus on direct actors of the platform. Fig. 4 depicts the model we created for the Airbnb platform.

The rst glance at the model shows the two sides of the Airbnb platform. Furthermore, the model unveils two value propositions. Providing accommodation via the platform is the original idea of the platform [ 4 ], wherefore we mark it as the main value proposition. Later, Airbnb added the distribution and organization of activities to the platform. By enabling its complementors to sublet their own property to travelers, Airbnb could grow at a great pace and was by 2017 already o ering more rooms than the biggest ve hotel brands worldwide combined [ 12 ]. Additionally, Airbnb also leverages the innovativeness of their ecosystem, resulting in a large variety of accommodations o ered, ranging from mansions to tree houses. The same is true for activities, where the number and variety are also high because of the reliance on many complementors. However, the main purpose of Airbnb is to connect hosts with guests and facilitate easy transactions. As evident from the description, both value propositions provide production, innovation and transaction leverage.

The value propositions share the same boundary resources, i. e., the marketplace application as well as the communication services. Airbnb as platform owner provides access to the platform, orchestrates the interaction on the platform, and acquires new users. The users either rent property or attend certain activities. This simplistic demonstration explains the basic idea of PEML to put the platform in the middle of the surrounding actors and only include actors with direct links to the platform.

Pauli et

al.

InfrRaRosotlrleuecture Resources • IT infrastructure

Activity C C

Platform enabler

CoRnRnooellecetivity •ResoPuorrctfeoslio of connectivity products •ActiCviotynnect assets to platform

SysteRmRooIlnleetegrator Resources ••ActiiPCvmirotopynvlneiedmceetcnoottanhtneiorencetnsiveteirtrvypicareinssde systems

Solution provider

TecRhonloelogy •ResoaKunnrdocBewsilgedDgaetaabout analytics, AI, Activity

HyRborlied OT •ResoiEnuxsrptcreeursmtiseentiantiaountomation and ••ActiPDviretoyvveildoepsIeorTviacpesps

AppRDoelveeloper Resources •ActiDvietyvelop vertical apps

ConsulRtinogle/ Strategy Resources ••ActisPDveiretroyvvveiicldoeespdviegrittiaclatlraapnpsfsormation

PO Siemens AG is a multinational technology corporation providing products and services for manufacturing. In 2017, Siemens established its platform MindSphere to harness the vast amount of data created by their customers [ 29, 27, 28 ]. Since then, MindSphere has become a leading platform in the eld of Industrial IoT. Figure 5 displays the platform MindSphere using PEML. MindSphere's features allow for the collection of data from various products, plants, systems, and machines. Furthermore, it enables transferring and processing of data either in the cloud or the edge. Siemens provides boundary resources, like application programming interfaces (APIs), and software development kits (SDKs) to leverage the development of custom applications. The MindSphere app store o ers third parties to create, test and sell applications, and to access data.

The MindSphere ecosystem bears considerable complexity due to the quantity of complementors and the high variance of their functions and interests [ 28, 30 ]. We have identi ed three basic sides, two of which are complementors. There are two types of customers on the users side: Producers and Machine and Plant Manufacturers, which in turn supply the Producers. The platform enabler side comprises the complementors Connectivity Partners and System Integrator Partners which ensure the technical operability of MindSphere for the customer. Together, Technology Partners, Hybrid OT Partners, App Developers and Consulting/ Strategy Partners form the side of solution providers. They create value by developing apps, implementing use cases or making their domain-speci c knowledge available. Additionally, the MindSphere ecosystem also includes actors that do not leverage the platform directly, but are indirectly linked with another actor: Infrastructure Partners, Marketing Partners, and End Consumers.

Of course, this demonstration only shows a part of the overall MindSphere ecosystem. As described in the instructions, the modeler can choose to apply a wider or narrower lens depending on his or her needs. While a narrower lens might enable a more thorough analysis of a small number of value propositions and actors, a wider lens will yield a more high-level overview. 6

Related Works

Various other methods have been proposed or used to model platform ecosystems in a broader sense, especially in the software business [ 11 ].

Domain speci c languages include, for example, Software Supply Network Diagrams (SSN). As the name suggests, SSN was developed to model networks of organizations that cooperate to provide an integrated software product or service to the market [ 15, 7 ]. Moving away from linear supply chains, SSN considers the prevalence of horizontal networks of actors also re ected in ecosystems. However, based on the underlying concept of supply networks, the focus of this modeling approach is on the supply side as opposed to a balanced consideration of all sides of a platform ecosystem.

The majority of languages employed for the modeling of platform ecosystems are not domain-speci c. Examples include the i* modeling technique [ 34 ] or value network diagrams [ 5 ]. The most widely used modeling language is e3value [ 14 ]. e3value is very powerful and versatile and can also be used to model platform ecosystems. A similarly comprehensive modeling approach is o ered by the value delivery modeling language (VDML) [ 21 ]. However, as generic modeling languages, they lack functionality to model some platform-speci c features such as sides or boundary resources that are critical for understanding value creation in platform ecosystems, e.g. in terms of network-e ects and governance.

Nevertheless, other languages and techniques can and should be used to complement our approach and mitigate some of its shortcomings. For example, e3value can serve to model interactions and especially ows of money and resources within sub-ecosystems around a certain value proposition. Similarly, in case of service o erings involving smart devices and machines (e. g., in the case of Siemens MindSphere), the smart service systems modeling language proposed by Huber et al. [ 13 ] might be helpful to capture service-speci c aspects. 7

Conclusion

This paper presents the domain-speci c modeling language PEML which comprises abstract and concrete syntax, and instructional guidelines to model platform ecosystems. We demonstrate the modeling language using two di erent platforms to show that it allows for an appropriate speci cation of the realworld phenomenon across various domains.

This paper entails two contributions. First, we contribute to academia by providing researchers with means to specify their objects of interest when referring to platform ecosystems. In doing so, we answer the recent call for the development of means to \visualise structure [and dynamics] of ecosystems" [24, p. 129]. As a limitation, however, PEML focuses on the static structure and does not necessarily consider platform ecosystem dynamics.

Second, we enable practitioners to model platform ecosystems. This is important in light of the increasing prevalence of platforms in traditionally nonplatform domains. As a result, vertical supplier-customer relationships are transformed into horizontal relationships with complementors [ 23 ]. Practitioners can use PEML to make sense of this new reality. However, as a caveat, as the focus of PEML lies on digital platforms, its applicability for the modeling of non-digital platforms might be impeded to some extent. Nevertheless, as all core aspects of platforms are represented, it will still be applicable in these cases.

References

1. Adner , R.: Ecosystem as structure . Journal of Management 43 ( 1 ), 39 { 58 ( 2017 ). https://doi.org/10.1177/0149206316678451

2. Adner , R. , Kapoor , R.: Value creation in innovation ecosystems: how the structure of technological interdependence a ects rm performance in new technology generations . Strategic Management Journal 31 ( 3 ), 306 { 333 ( 2010 ). https://doi.org/10.1002/smj.821

3. Airbnb: About us - airbnb newsroom ( 2020 ), https://news.airbnb.com/about-us/, last visited 29.05 .2020

4. Airbnb: Fast facts - airbnb newsroom ( 2020 ), https://news.airbnb.com/fast-facts/, last visited 29.05 .2020

5. Allee , V.: Value network analysis and value conversion of tangible and intangible assets . Journal of Intellectual Capital 9 ( 1 ), 5 { 24 ( 2008 ). https://doi.org/10.1108/14691930810845777

6. Bianco , V.D. , Myllarniemi, V., Komssi , M. , Raatikainen , M.: The role of platform boundary resources isoftware ecosystems: A case study . In: 2014 IEEE/IFIP Conference on Software Architecture . pp. 11 { 20 ( 2014 )

7. Boucharas , V. , Jansen , S. , Brinkkemper , S. : Formalizing software ecosystem modeling . In: Di Cosmo, R. , Inverardi , P. (eds.) Proceedings of the 1st international workshop on Open component ecosystems - IWOCE '09 . p. 41 . ACM Press, New York, New York, USA ( 2009 ). https://doi.org/10.1145/1595800.1595807

8. Frank , U. : Domain-speci c modeling languages: Requirements analysis and design guidelines . In: Reinhartz-Berger , I. , Sturm , A. , Clark , T. , Cohen , S. , Bettin , J . (eds.) Domain Engineering, pp. 133 { 157 . Springer Berlin Heidelberg ( 2013 ). https://doi.org/10.1007/978-3- 642 -36654-3 6

9. Gawer , A. , Cusumano , M.A. : Industry platforms and ecosystem innovation . Journal of Product Innovation Management 31 ( 3 ), 417 { 433 ( 2014 ). https://doi.org/10.1111/jpim.12105

10. Ghazawneh , A. , Henfridsson , O. : Balancing platform control and external contribution in third-party development: the boundary resources model . Information Systems Journal 23 ( 2 ), 173 { 192 ( 2013 ). https://doi.org/10.1111/j.1365- 2575 . 2012 . 00406 .x

11. H. Sadi , M. , Yu , E. : Designing software ecosystems: How can modeling techniques help ? In: Gaaloul, K. , Schmidt , R. , Nurcan , S. , Guerreiro , S. , Ma , Q. (eds.) Enterprise, Business-Process and Information Systems Modeling . pp. 360 { 375 . Springer International Publishing, Cham ( 2015 )

12. Hartmans , A. : Airbnb now has more listings worldwide than the top ve hotel brands combined ( 2017 ), https://www.businessinsider.com/airbnb-totalworldwide-listings -2017-8

13. Huber , R.X.R. , Puschel, L.C. , Roglinger, M.: Capturing smart service systems: Development of a domain{speci c modelling language . Information Systems Journal 29 ( 6 ), 1207 { 1255 ( 2019 ). https://doi.org/10.1111/isj.12269

14. J, G.: e-business value modelling using the e3-value ontology . In: Currie, W.L . (ed.) Value Creation from E-Business Models , pp. 98 { 127 . Butterworth-Heinemann , Oxford ( 2004 ). https://doi.org/10.1016/B978-075066140-9/ 50007 - 2

15. Jansen , S. , Brinkkemper , S. , Finkelstein , A. : Providing transparency in the business of software: A modeling technique for software supply networks . In: CamarinhaMatos, L.M. , Afsarmanesh , H. , Novais , P. , Analide , C. (eds.) Establishing the Foundation of Collaborative Networks . pp. 677 { 686 . Springer US , Boston, MA ( 2007 )

16. Kapoor , R.: Ecosystems: broadening the locus of value creation . Journal of Organization Design 7 ( 1 ) ( 2018 ). https://doi.org/10.1186/s41469-018-0035-4

17. Katz , M.L. , Shapiro , C. : Network externalities, competition, and compatibility . The American Economic Review 75 ( 3 ), 424 { 440 ( 1985 )

18. Lusch , R.F. , Nambisan , S. : Service innovation: A servicedominant logic perspective . MIS Quarterly 39 ( 1 ), 155 { 175 ( 2015 ). https://doi.org/10.25300/MISQ/ 2015 /39.1. 07

19. Moody, D.: The \physics" of notations: Toward a scienti c basis for constructing visual notations in software engineering . IEEE Transactions on Software Engineering 35 ( 6 ), 756 { 779 ( 2009 ). https://doi.org/10.1109/TSE. 2009 .67

20. Nordstrom , G. , Sztipanovits , J. , Karsai , G. , Ledeczi , A. : Metamodeling-rapid design and evolution of domain-speci c modeling environments . In: Proceedings ECBS'99. IEEE Conference and Workshop on Engineering of Computer-Based Systems . pp. 68 { 74 . IEEE ( 1999 ). https://doi.org/10.1109/ECBS. 1999 .755863

21. Object Management Group: Value Delivery Modeling Language 1 .1 ( 2018 ), https: //www.omg.org/spec/VDML/1.1/PDF, last visited 12 .05.2020

22. Osterwalder , A. , Pigneur , Y. : Business Model Generation: Ein Handbuch fur Visionare, Spielveranderer und Herausforderer . Campus Verlag ( 2011 )

23. Parker , G.G., van Alstyne, M.W. , Choudary , S.P. : Platform revolution: How networked markets are transforming the economy and how to make them work for you . W W Norton, New York ( 2016 )

24. de Reuver, M., S rensen, C., Basole , R.C. : The digital platform: A research agenda . Journal of Information Technology 33 ( 2 ), 124 { 135 ( 2018 ). https://doi.org/10.1057/s41265-016-0033-3

25. Rochet , J.C. , Tirole , J.: Platform competition in two-sided markets . Journal of the European Economic Association 1 ( 4 ), 990 { 1029 ( 2003 ). https://doi.org/10.1162/154247603322493212

26. Rosemann , M. , Green , P. : Developing a meta model for the bunge{wand{weber ontological constructs . Information Systems 27 ( 2 ), 75 { 91 ( 2002 ). https://doi.org/https://doi.org/10.1016/S0306- 4379 ( 01 ) 00048 - 5

27. Siemens AG : MindSphere: So treibt die Industrie weltweit ihre digitalen Transformationen voran ( 2018 ), https://www.plm.automation.siemens.com/media/global/ de/Siemens-MindSphere -Overview-DE-wp- 73922 -A21n tcm53- 58983 .pdf, last visited 12.05 .2020

28. Siemens AG : Pressekonferenz Grundung MindSphere World: Press release No. 4 ( 2018 ), https://mindsphereworld.de/pressemeldung-4/, last visited 12 .05.2020

29. Siemens AG : MindSphere: The cloud-based, open IoT operating system ( 2020 ), https://siemens.mindsphere.io/en, last visited 12.05 .2020

30. Smith , D. : Mindsphere partner program introduction ( 2019 ), https: //siemens.mindsphere.io/content/dam/mindsphere/partners/program/MindSpheren Partnern Programn Introductionn 2019 .pdf, last visited 25.05 .2020

31. Thomas , L.D.W. , Autio , E. , Gann , D.M.: Architectural leverage: Putting platforms in context . Academy of Management Perspectives 28 ( 2 ), 198 { 219 ( 2014 ). https://doi.org/10.5465/amp. 2011 .0105

32. Tilson , D. , Sorensen , C. , Lyytinen , K. : Platform complexity: Lessons from the music industry . In: 46th Hawaii International Conference on System Sciences . pp. 4625 { 4634 . IEEE ( 2013 ). https://doi.org/10.1109/HICSS. 2013 .449

33. Trabucchi , D. , Buganza , T. : Fostering digital platform innovation: From two to multi{sided platforms . Creativity and Innovation Management ( 2019 ). https://doi.org/10.1111/caim.12320

34. Yu , E.S.K. , Deng , S. : Understanding software ecosystems: A strategic modeling approach . In: Proceedings of the Third International Workshop on Software Ecosystems , Brussels, Belgium, June 7th, 2011 . pp. 65 { 76 ( 2011 )