Abstract
In this conceptual article, we highlight group modeling and crowd-based modeling as an approach for collectively constructing business ecosystem models. Based on case study examples, we showcase how engaging in the collective activity of crowd-based modeling supports the creation of value propositions in business ecosystems. Such collective activity creates shared, IS-embedded resources on the network level that align the diverse set of ecosystem stakeholders such as firms, public actors, citizens, and other types of organizations. Based on extant research, we describe the roles involved in building ecosystem models that inform and reconfigure shared infrastructures and platforms.
1 Introduction
Business ecosystems have gained interest from researchers and practitioners as companies as well as public organizations increasingly recognize the relevance of their complex business environment. This environment consists of all value creation activities related to development, production and distribution of services and products and comprises suppliers, manufacturers, customers, and entrepreneurs. Coping with the challenges and opening up the opportunities that arise in these business ecosystems is a reality for most companies nowadays [1]. The growing relevance of business ecosystems substantiates through the perceived shift of the competitive environment from single companies and their supply chains towards ecosystems competing against each other [2].
We define business ecosystems as the holistic environment of an organization covering current and potential future business partners, such as customers, suppliers, competitors, regulatory institutions and innovative start-ups. As such entities continuously enter and leave the ecosystem, or change their role within the ecosystem, ecosystems exhibit high dynamics. Peltoniemi and Vuori [1] provide a comprehensive definition of business ecosystems that emphasizes this adaptive characteristic. Moore [3] uses the metaphor of biological ecosystems as a basis for his initial definition of business ecosystems. Metaphorically, as in natural ecosystems, the economic success of an enterprise can therefore depend on the individual ‘health’ and capability to evolve with their business ecosystem. In the ecosystem, the participating companies as individuals adopt varying levels of influence on the overall health of the ecosystem, taking up roles as keystone or niche player [4].
The types of business ecosystems described in extant literature vary between ecosystems around one focal firm, such as Wal-Mart or Microsoft [4], ecosystems of a specific market exploiting specific digital technologies, such as application programming interfaces (API) [5] or mobile phones and platforms [6] or ecosystems established around a singular technology platform, such as Google and Apple [7].
Thus, because ecosystems potentially influence the economic success of businesses, enterprises increasingly realize the need to analyze their business ecosystem. Through continuous monitoring, changes within an enterprises’ ecosystem might be identified and addressed through dedicated strategies or adaptations [8]. Enterprises aim to “learn what makes the environment tick” [9] and improve or adapt one’s own business activities accordingly.
However, analyzing business ecosystems is principally impossible for one single stakeholder to achieve because of the abundancy and complexity of processes and data that would need to be observed, recorded, documented or otherwise be made visible. This is why in this conceptual paper we acclaim for an approach to use crowdsourcing of ecosystem-related data in order to create ecosystem models that can be exploited to learn about the ecosystem and to predict future developments [10] and that inform and configure shared platforms and infrastructures and as such become valuable for the entire ecosystem [11]. As case studies from our previous research suggest, such ecosystem models can be considered as IS-embedded network resources [12], i.e., network-level, shared resources that support the creation of value propositions for all involved stakeholders as users of the ecosystem model.
However, crowdsourcing approaches to model business ecosystems so far have not been implemented or observed in practice. This is why in this article we discuss the boundary conditions for such an approach and its principle benefits.
2 Related Work
2.1 Business Ecosystem Modeling and Ecosystem Data
Business ecosystems have early been defined as collection of interacting firms [3]. Until today, research on this concept has been extensive [13]. Sako [7] defined three meta-characteristics of business ecosystems—sustainability, self-governance, and evolution—to contribute to a better distinction of the ecosystem concept from clusters or networks. Thereby, he focuses on “value-creating process (…) rather than… industrial sector”. Basole et al. [14] characterized business ecosystems as an interconnected, complex, global network of relationships between companies, which can take on different roles, such as suppliers, distributors, outsourcing firms, makers of related products or services, technology providers, and a host of other organizations [4]. The boundaries, characteristics and the evolving, dynamic structure [1] of a business ecosystem are not only affected by these different roles, but by the fact that firms continuously enter and leave the ecosystem [15]. Recently, researchers have focused their efforts on the challenges for ecosystem formation that derive from various contexts, such as technology, e.g., the Internet of Things (IoT) [15], or policy, e.g., emerging smart cities [16]. This led to emergence of a discourse about identifying appropriate ways to model business ecosystems [17], such as frameworks to grasp the scope of ecosystem complexity [16, 18] or visualizations, which are developed to aid at understanding the topology of such an ecosystem, as well as emerging structures and patterns [18, 19].
Ensuing previous research, our conceptualization of business ecosystem models takes into account both, the static network of entities, i.e., firms, technologies, and the dynamic network characteristics, i.e., the relationships between entities and activities, all changing over time. Entities include companies of every size as well as corporations, public sector organizations, universities and research facilities, and other parties that influence the ecosystem [1], all linked via different kinds of relationships. All these elements need to be incorporated into the business ecosystem model. Which entities and relationship types need to be modeled depends decidedly on the requirements put forward by the (business) stakeholders using the business ecosystem model for their ecosystem-related decisions. Their needs and demands that define which (visual) views are relevant, and which insights are vital for generating and adapting the model.
Past research has shown that visualizations of business ecosystems on basis of such models indeed support decision-makers in their ecosystem-related tasks and decisions [5, 8, 20]. In order to spot anomalies, identify keystone and niche players of the ecosystem, or recognize change patterns and trends, visualizing data can help to derive value from ecosystem data [21].
‘Ecosystem data’ as the basis for modeling is diverse and ranges from technology-related information about applied standards and platforms to market information and legal regulations. Basole et al. [14] characterized it to be ‘large and heterogeneous’. Relevant for the business aspect of an enterprise’s business ecosystem as we have observed in our case studies, is information about business partners, competitors, partnerships and offered solutions, cooperative initiatives, as well as start-ups and their strategies [22]. This information can be obtained from a wide range of sources, such as publicly accessible databases, enterprise or institutional presences and publications, or blogs and news articles. Successfully collecting ecosystem data sets distinct limits concerning the value and usefulness of visualizations in the ecosystem analysis or business development [12]. However, no solution has been determined so far that might resolve the issue of how comprehensive amounts of ecosystem data can be obtained and validated for their usefulness and efficacy towards ecosystem-related tasks and decisions [18, 23].
In addition, to include a broad perspective and involve diverse aspects of the ecosystem, not only various data sources but also various types of stakeholder groups need to be taken into account. These groups provide for both, diverse ways to access ecosystem data, and own interests to use the ecosystem model. Depending on the ecosystem in focus, these groups can range from company representatives in case of a company-internal business ecosystem modeling approach, to boards, associations’ interest group, or online communities. Including these stakeholder groups into joint model creation and model evaluation is generally discussed under the header of collaborative modeling. As related literature has vividly discussed, collaborative modeling processes enhance the quality and scope of achievable results through iterative interactions and collaborative knowledge exchanges [24].
2.2 Business Ecosystem Visualization in Visual Analytic Systems (VAS)
Park et al. [25] presented a Visual Analytic System (VAS) to nurture the perception of business ecosystems. It addresses three salient design requirements related to distinct complications in the context of supply chain ecosystems. After extensive research on modeling and visualizing ecosystems, and analyzing different types of business ecosystems [1, 5, 8, 15, 16, 18] the VAS empowers its users to interactively explore the supply network by offering multiple views within an integrated interface as well as data-driven analytic features. The authors suggest and test five visualization types (layouts) to visualize the dynamic networked structures of their problem context.
These layouts include Force-directed Layout (FDL), Tree Map Layout (TML), Matrix Layout (MXL), Radial Network/ Chord Diagram (RCD), and Modified Ego-Network Layout (MEL). Interactive features, such as clicking, dragging, hovering, and filtering, are essential parts of the visualizations. These layouts are used by us as the baseline for the design of our own VAS in our problem context. Nonetheless, further designs exist, such as bi-centric diagrams that visualize the relative positioning of two focal firms [8, 15] or cumulative network visualization [5].
Current research on ecosystems at large uses data-driven approaches, i.e., sets of data are collected from commercial databases on business and economic data, or drawn from social or business media [5, 15]. Implications of this approach are, first, the VAS users need to understand the relevance and quality of sources that can provide data for the ecosystem model, and second, the guiding questions and rules for the visualizations are clear. When both the model and the visualizations can be adapted to host diverse business perspectives and intentions, it is possible that different VAS users can create their own VAS instances in order to facilitate setting the focus on distinct data sources and structures.
3 Agile Modeling Framework for Business Ecosystem Modeling
3.1 Business Ecosystem Explorer (BEEx): Visual Analytic System (VAS) Implementation
In order to engage with business ecosystem modeling, we propose an agile modeling framework, which technically resides upon the ‘Hybrid Wiki’ approach suggested by Reschenhofer et al. [26], and which from a use perspective allows to follow an agile modeling process (see Sect. 5). This framework addresses the dynamic structure of business ecosystems as it supports the evolution of the model as well as its instances at runtime by stakeholders and ecosystem experts, i.e., users without programming knowledge or skills. We have implemented the framework as Business Ecosystem Explorer (BEEx) on basis of an existing integrated, adaptive collaborative Hybrid Wiki system. The latter system not only serves as a Knowledge Management System application development platform, including features necessary for collaboration, data management, and decision support, but which also implements other features such as tracing back changes to the responsible user, including the time and date the change was made. In our case studies, we have used its underlying Hybrid Wiki metamodel to create business ecosystem models.
The Hybrid Wiki metamodel comprises the following model building blocks: Workspace, Entity, EntityType, Attribute, and AttributeDefinition. These concepts structure the model inside a Workspace and capture its current snapshot in a data-driven process (i.e., as a bottom-up process). An Entity consists of Attributes, which have a name, can be of different data types (i.e., strings, numbers, references on other Entities), and are stored as key-value pairs. Attributes can be instantiated at runtime, and this helps to seize structured information about an Entity. The EntityType facilitates grouping related Entities, such as organizations or persons. It consists of several AttributeDefinitions, which can define validators for the Attributes of the corresponding Entities, like multiplicity or link value validators, which in turn leads to increased cohesion among the Attribute values.
3.2 Business Ecosystem Explorer Model
Our agile framework relies on two models that each provide features for creation and adaption, first, the ecosystem data model, and second, the ecosystem view model. Both models are encoded using the Hybrid Wiki metamodel.
The ecosystem data model contains the EntityTypes of relevance for the business ecosystem in focus. The ecosystem view model is encoded as one EntityType called visualizations. Each visualization has two elements: the first element is the link between the data model and the visualizations. The second element is the specification of the visualizations using a declarative language. Five main building blocks enable static and dynamic visualization features; these are (a) data, including data but also all data transformations; (b) marks, covering the basic description of the visualized symbols, e.g., shape and size of a node; (c) scales, containing visual variables, such as the color coding; (d) signals, including the different interaction options, e.g., dragging and dropping of entities; and in some instances (e) legends.
This approach allows making changes to the models at runtime, thus updating the visualizations instantly when the data model is changed by, e.g., adding new categories or changing or deleting categories. Figure 1 gives an example of categories of organizations and their types, which both can be adapted at runtime.
3.3 Business Ecosystem Explorer Views
Currently, the framework comprises six different views: a landing page, a list of all entities, a relation view, a detail view with entity information, a visualization overview, and several visualizations. All views include a menu bar at the top of the page, which provide links to the other views, as illustrated in Fig. 2.
4 Case Studies Informing Our Concept of Crowdsourcing
The design-oriented research results presented here are based on our insights on own software engineering design work, a field test of the developed system and two case studies we conducted in close collaboration with industry partners.
The research was initiated within a smart city initiative pursued by a European city with a population of more than 2.5 m in its urban area and more than 5.5 m in its metropolitan region. The business ecosystem of focus is mobility ecosystem is anticipated to embrace more than 3.000 firms in the automotive, traffic and logistics sectors residing in the urban area and more than 18.000 firms in these sectors in the metropolitan region.
Within this initiative, the agile modeling framework was used to model the mobility business ecosystem relevant for the initiative. As a first evaluation, we conducted two rounds of interviews. Within the first round, we conducted nine interviews in semi-structured form with nine different companies within two months presenting a pre-defined business ecosystem model using a force-directed layout. All interviewees stated that the business ecosystem model supported them in understanding the relations within the presented business ecosystem and that their knowledge of this ecosystem was increased. We used the interview results to update the existing prototype.
In the second interview round, we conducted three in-depth interviews with three additional companies. To obtain a wider range of opinions, we selected three companies from different fields of activity. Namely, an automotive OEM, a publicly funded non-research institution and a software company whose main business area addresses the connected mobility ecosystem. The prototype as visualized in Fig. 2 was used in this interview round. All companies agreed that the prototype fosters the understanding of the presented ecosystem and two emphasized that it would be interesting to use such a tool within their enterprise to collaboratively manage the business ecosystem evolution.
This initial evaluation was followed by two case studies conducted with two industry partners targeting different business ecosystems. One an automotive company, the other a publishing company, both headquartered in Europe with a high interest in modeling and visualizing business ecosystems of their specific focus but no modeling activities in place before the studies. With both organizations, several workshops were conducted in the period from December 2017 to June 2018. All involved stakeholders from both companies had access to the provided BEEx framework (see Sect. 3) and had used it to instantiate and model their business ecosystem of focus. After performing the agile collaborative modeling process as presented in Sect. 5, for each study, the existing prototype was adapted comprising two tailored visualizations of the business ecosystem.
5 Agile Collaborative Modeling of Business Ecosystems
5.1 Agile Modeling Process
Based on insights and experience from our case studies, we propose the generic, agile modeling process depicted in Fig. 3 to model business ecosystems in a collaborative process. The process consists of five steps overall. Three teams are involved in the process, the Ecosystem Editorial Team, the Modeler Team, and a team of Management Stakeholders.
In a first phase (process step no. 1 in Fig. 3), the focus of the business ecosystem is defined, e.g., an ecosystem established around a technology platform, an ecosystem of a specific market exploiting specific digital technologies [7] or ecosystems around one focal firm, and the model instantiated, for which both the data model and the view model are set up. When the data model is instantiated, the relevant entities of the ecosystem are defined, along with corresponding attributes. Additionally, the preliminary relation types between the entities need to be identified and set. When the view model is set up, the type of visualization including the specifications for this visualization are established, guided by the Ecosystem Editorial and Modeler Team. Further, all stakeholders with various roles should be included into the requirements elicitation of the models to ensure tailored visualizations in the upcoming phases of the process.
The next phase of the process is threefold and repeated iteratively. It consists of gathering the data about the ecosystem according to the data model (process step 2), the provision of tailored visualization according to the view model (process step 3) and the adaption steps in which both models are modified using feedback collected from involved stakeholders (process step 4). This step finishes as soon as the stakeholders’ requirements and needs are satisfied.
In the final process step, the created visualizations are used to extract knowledge about the ecosystem, which contributes to a better understanding of the ecosystem in focus.
5.2 Agility Through Collaborative and Group Modeling
Agility particularly becomes evident in short-term iterative cycles of process steps 2 to 4, by collaboratively prototyping and consolidating tailored visualizations. Insights achieved about the ecosystem inform conceptualization of adapted business strategies, thus creating new questions towards the ecosystem and motivating formulation of modified decisions or amended tasks, which in turn create new impetus to change the model, collect data, and improve or alter visualizations.
The concept of collaborative modeling arose in the 70’s and since gained increased popularity together with the increased need for collaboration among experts [27, 28]. However, collaborative processes for business ecosystem modeling and instantiation of such models have not been discussed in existing literature, but in various other fields, such as group decision support system modeling [29], business process modeling [24], and enterprise architecture modeling [30].
According to Richardson and Andersen [31], essential roles for collaborative modeling are the facilitator, the process coach, the recorder, and the gatekeeper. They are described as follows: (a) facilitator, monitoring the group process and stimulating the model building effort; (b) modeler, focusing on the model out-come; (c) process coach, observing the process and the dynamics of the participants; (d) recorder, documenting the modeling process; and (e) gatekeeper, responsible for the process and major decision maker. These roles may be associated to different persons, but one person might also incorporate several roles at once [27].
In the following sections, we detail the activities of each of the three teams involved in our agile modeling process with regard to the roles in collaborative modeling as described in extant literature.
5.3 Ecosystem Editorial Team
The Ecosystem Editorial Team is present and active in all process steps and contributes highly to the outcome of the modeling initiative and the overall perceived success. This group integrates several roles for collaborative modeling in addition to roles specific for the software development. We map the generic roles to business ecosystem specific activities of the Ecosystem Editorial Team in Table 1.
5.4 Modeler Team
The members of the Modeler Team contribute their knowledge about the ecosystem in focus and are vital for creating and continuous updating the model. The facilitator, acting as a bridge between the management stakeholders, the Ecosystem Editorial Team and the modelers. Group members acting as modelers identify suitable data sources to use, both company internal and external, relevant entities, attributes and relations for the data model. With the usage of a declarative visual language, the modelers are also enabled to adapt the view model.
We mapped these two generic roles to business ecosystem specific activities of the Modeler Team in Table 2.
5.5 Management Stakeholders
The Management Stakeholders are those using the business ecosystem model created in the process for their business decisions. Thus, they receive the tailored visualizations and provide feedback on how to adapt these for future use. This group is mainly responsible for the business ecosystem focus set in the beginning. They provide resources for the business ecosystem modeling process.
We mapped the generic roles to business ecosystem specific activities of the Management Stakeholders in Table 3.
6 The Idea of Crow-Based Modeling of Business Ecosystems
As from our case study experience, the insights that can be gained from an agile modeling process – apart from the quality of visualizations – significantly depend on the availability of ecosystem data and stakeholder feedback. Hence, the data available during step 2 (data gathering) of our generic agile modeling process (see Fig. 3) delimits the reliability and efficacy of visualizations. Similarly, the feedback obtainable during step 4 (feedback collection) defines which novel insights or stimuli potentially originate, motivating to refine the model focus and broaden the support to ecosystem-related tasks and decisions.
In our case studies, we have also observed that a significant number of today’s ecosystem-related tasks and decisions—that affect multiple levels or stages of value creation across larger areas—cannot be accomplished on basis of existing corporate information repositories or limited data sources. Crowd-based modeling provides an approach to integrate diverse views and create more realistic models including more relevant factors.
This is why we claim for extending the base of involved modelers to a crowd-based approach. Our case studies have pointed us towards thinking about the ‘crowd’ as to overcome the difficulty of collecting substantially meaningful and comprehensive amounts of data. What the ‘crowd’ is depends on the usage of visualizations in the respective ecosystem use case. In corporate settings, these uses might lie in defining corporate strategies in face of competitor movements. In smart city contexts, as we have witnessed in one of our case studies, the ‘crowd’ was established through public authorities as well as corporate firms and citizens (see also the discussion on crowd energy in [32]). Looking at the heterogeneity of business ecosystems and related strategic and entrepreneurial challenges, it seems plausible to engage in a more intensive involvement of the diverse set of stakeholders to business ecosystem modeling. Depending on the context, citizens, corporate employees, protagonists of start-up communities, local authorities, but also IoT providers, social network service providers, and others, could all benefit from contributing to building open, shared ecosystem models and visualizations.
In this respect, the question of whether actors are motivated to contribute freely, i.e., without direct remuneration, to building shared, and eventually publically available resources, must be further investigated. Earlier research has extensively studied and theoretically framed the boundary conditions and quality of outcomes in cooperating on shared resources [33,34,35]. We believe that a viable approach might lie in the provision of visualization services to core ecosystem stakeholders that deliver a helpful tool for ecosystem-related tasks in addition to freely available visualizations, in order to achieve reciprocity in contributing and profiting from ecosystem models [36]. Such visualization services could take over the task to moderate between the roles we have identified from previous literature and instantiated in our agile modeling process. Contributing stakeholders might assume several different roles during the interaction with a visualization service—a practice we have observed and exercised in our case studies.
A future advancement will lie in the automated inclusion of data into modeling and visualizing. The—ethically responsible—evaluation of social network data or of IoT data for instance might hold unseen value propositions for understanding such contexts as traffic organization in metropolitan areas to adapt mobility services, or as start-up communities to stimulate innovation by explicating relationship patterns that arise from the plethora of interactions at the individual entrepreneurs’ level.
Overall, from a modeling perspective, tackling business ecosystem related challenges will see a shift from enterprise-level IS, or ‘enterprise systems’ to network-level IS-embedded resources. Such network-level resources will require to obtain buy-in from diverse stakeholder groups of an ecosystem as a smart city or a local start-up community or others. Then the ‘critical mass’ to obtain reliable and effective ecosystem data might be met, being more comprehensive than currently existing corporate or consulting agencies’ information repositories.
Lastly, in all use cases for ecosystem models and visualizations, ecosystem data governance becomes a vital challenge—even more so than in the current debate on user data in social networks and the like. We believe that cooperative societies founded by ecosystem members or local citizens might be a way to moderate in these issues as the provision of data for modeling will encompass purposeful individual action and willful sharing of knowledge for the common good.
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Faber, A., Rehm, SV., Hernandez-Mendez, A., Matthes, F. (2019). Collectively Constructing the Business Ecosystem: Towards Crowd-Based Modeling for Platforms and Infrastructures. In: Hammoudi, S., Śmiałek, M., Camp, O., Filipe, J. (eds) Enterprise Information Systems. ICEIS 2018. Lecture Notes in Business Information Processing, vol 363. Springer, Cham. https://doi.org/10.1007/978-3-030-26169-6_8
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