An Agile Framework for Modeling Smart City Business Ecosystems

Anne Faber

, Adrian Hernandez-Mendez

, Sven-Volker Rehm

and Florian Matthes

Technical University of Munich, Boltzmannstrasse 3, 85748 Garching, Germany

WHU - Otto Beisheim School of Management, Burgplatz 2, 56179 Vallendar, Germany

Keywords:

Agile, Adaptation, Business Ecosystem, Smart City, Visualization.

Abstract:

Modeling business ecosystems enables ecosystem stakeholders to take better-informed decisions. In this paper

we present an agile framework for modeling a smart city business ecosystem. We follow a design science re-

search approach to conceptualize the agile approach to manage ecosystem models and present the architecture

of our framework as design artefact. During the design process, we evaluated ecosystem models that need to

adapt with the emerging structures of the business ecosystem. The platform aims at a collaborative modeling

process, which empowers end-users to manage the business ecosystem models and underlying data. The

evaluation of the platform was conducted with industry partners as part of the presented smart city initative,

indicating it usefulness when fulﬁlling modeling related tasks.

1 INTRODUCTION

Digitalization has long reached cities and is changing

urban mobility. Digital technologies are progressively

integrated to vehicles, trafﬁc systems, and infrastruc-

ture. Internet of Things (IoT) devices for instance

include sensors that provide information about occu-

pied or available parking slots. The created techno-

logical affordances are thereby changing the mobi-

lity opportunities and demands of travelers (Mitchell,

2010). The variety of services enabled by digital

technologies currently ranges from providing timely

information on the trafﬁc situation, to buying tic-

kets for public transportation online, to car or bike

sharing services, etc. The services usually comprise

consumer-facing mobile applications that rest on digi-

tal platforms integrating various underlying services.

This technology-driven opportunity space creates

a growing market that challenges established mobi-

lity providers such as automotive OEMs, their tier

1 to 3 parts suppliers, but also public transportation

providers. One challenge originates from technology

startups deploying new IoT technologies such as sen-

sor technology, augmented reality or artiﬁcial intelli-

gence to urban mobility. Tech giants such as Google

and Apple are also entering the mobility markets glo-

bally, by developing self-driving cars and pushing au-

tonomous driving (Etherington and Kolodny, 2016),

(Taylor, 2016). As a result, new business ecosystems

are currently emerging around mobility markets that

are geographically focused on speciﬁc metropolitan

areas. Besides commercial mobility providers, cities,

public institutions and their governments are addres-

sing these challenges as part of their urban develop-

ment policies concerning smart city concepts. They

increasingly become actors within the emerging mo-

bility business ecosystems.

Understanding the evolution process of such mo-

bility ecosystems is instrumental for developing pu-

blic policies, for taking strategic decisions about bu-

siness and technology partnerships, or for identifying

gaps in the service provision to consumers (Basole

et al., 2015a). Hence, the proactive management of

the business ecosystem is gaining relevance for ﬁrms

as well as city authorities (Basole et al., 2015a). Par-

ticularly, ﬁrms have to adapt their own competencies

to their speciﬁc (part of the) ecosystem to achieve

complementarity (Leonardi, 2011), (Rehm and Goel,

2017).

Our research contributes to this issue by providing

a community-based, agile approach to model and vi-

sualize smart city mobility business ecosystems. We

base our insights on own software engineering design

work and a ﬁeld test of the developed system. The

case study is part of a smart city initiative pursued by

a European city with a population of more than 2.5m

in its urban area and more than 5.5m in its metropo-

litan region. The mobility ecosystem is anticipated

to embrace more than 3.000 ﬁrms in the automotive,

trafﬁc and logistics sectors residing in the urban area

Faber, A., Hernandez-Mendez, A., Rehm, S. and Matthes, F.

An Agile Framework for Modeling Smart City Business Ecosystems.

DOI: 10.5220/0006696400390050

In Proceedings of the 20th International Conference on Enterprise Information Systems (ICEIS 2018), pages 39-50

ISBN: 978-989-758-298-1

and more than 18.000 ﬁrms in these sectors in the me-

tropolitan region.

1.1 Problem Statement

Visualizing data is a widely used approach to derive

value from data by spotting anomalies and correlati-

ons or identifying patterns and trends (Vartak et al.,

2017). This holds true for the context of business

ecosystems, as visualizations of ecosystems have pro-

ven to enable ecosystem stakeholders to take better

informed decisions (Basole et al., 2016), (Huhtamaki

and Rubens, 2016), (Evans and Basole, 2016). In the

context of visual decision support, visual analytic sy-

stems (VAS) have been proposed and evaluated to le-

verage related beneﬁts (Park and Basole, 2016), (Park

et al., 2016). These systems allow addressing needs

and demands of diverse user groups through different

views and types of visualizations (layouts). VAS sy-

stem architecture comprises elements for interaction

of users, for interpreting the visual input, and for ge-

nerating meaningful reports (Park et al., 2016).

One success factor of visualizing ecosystems is

the availability of ecosystem data used for these vi-

sualizations (Park et al., 2016). Ecosystem data com-

prises (a) technology-related data, such as available

services, technological standards and platforms, mo-

nitoring data sources, (b) business-related data, such

as information about service providers, their strate-

gies, partnerships and offered solutions, cooperative

initiatives, as well as (c) market-related data, such as

regional coverage of services, user types (commuter,

tourist etc.), or use patterns of mobile service apps.

Ecosystem experts and data scientists together evalu-

ate and interpret the data to create tailored visualiza-

tions.

Research addressing ecosystem models and visu-

alizations has used sets of data collected from com-

mercial databases on business and economic data or

drawn from social or business media (Basole et al.,

2015a), (Basole et al., 2015b). The required vari-

ety of data sources effects extensive data collection

efforts, also requiring editorial revision of collected

data. Data evaluation is therefore often executed only

for a speciﬁc timeframe, resting on static data sets.

As the structure of the mobility ecosystem in fo-

cus is just emerging, the VAS ecosystem model, com-

prising data model and view model which are used

to generate and visualize structures, must be adapta-

ble to address changing data sets. Regarding the data

model, e.g., new service providers must be linked to

the right types of services or positioned in the market

but can also constitute new types of ﬁrms or exhibit

new types of relationships that subsequently need to

be created in the data model. Regarding the view mo-

del, in general-purpose VAS, visualization are often

not adaptable without high effort. Thus, the view mo-

del needs to have the capability to include new struc-

tures from the data model.

In addition to these aspects concerning data sour-

ces and technical requirements, the data collection

and editing process as well as the visualization pro-

cess face further challenges. For editing data, team-

oriented approaches provide a way to cope with the

complexity and heterogeneity of data sources and bu-

siness/technology contexts to cover. As this editing

process generates the input to the visualization pro-

cess, both processes need to be linked within the VAS

in order to provide high ﬂexibility for interacting and

interpreting with help of the visualization user inter-

face, to deﬁne relevant key indicators and to create

tailored reports.

1.2 Contribution

Our approach addresses the aforementioned challen-

ges by suggesting an agile approach to collaboratively

manage and adapt business ecosystem models and vi-

sualizations. We have developed a VAS we refer to

as Business Ecosystem Explorer (BEEx), intended as

a framework for understanding emerging structures

of smart city business ecosystems. This agile fra-

mework allows to collaboratively aggregate and map

data about the ecosystem, deﬁne analytic structures

and create multiple types of views, thus providing a

customizable instrument for different ecosystem sta-

keholders as users of the system. Data about the

ecosystem allows to visualize the past development of

the considered ecosystem and enables stakeholders to

analyze present structures, e.g., which company posi-

tioned itself as key player within the ecosystem (Ba-

sole et al., 2015b).

We provide insights from the prototypical use of

the framework developed by us in our case study of a

large European smart city initiative. We focus on the

iterative and emergent character of the process to col-

lect, edit and visualize data, including the feedback

loop stimulating review of gathered data and structu-

res, and re-formatting of visualizations to customize

them for different stakeholder needs. This feedback

loop is the source of several requirements concerning

adaptability and collaborative use of the VAS (Leo-

nardi, 2011), (Majchrzak et al., 2002). In response to

that, we conceptualize the ecosystem modeling pro-

cess by focusing on stakeholder roles as well as visua-

lization and interactivity aspects. After describing our

methodological approach, we present related research

and basic features of our tools data and visualization

ICEIS 2018 - 20th International Conference on Enterprise Information Systems

models. Then we describe the iterative approach to

collaboratively manage and adapt business ecosystem

models, highlighting features that allow for an agile

data management. We lastly provide considerations

on limits and future research with respect to two pos-

sible use cases for ecosystem VAS.

2 APPROACH

We have adopted a design science research appro-

ach (Hevner et al., 2004), (Hevner and Chatterjee,

2010). This perspective suits our research context of

a case study that is part of a European smart city ini-

tiative. In the metropolitan region in focus, the struc-

tures of the mobility ecosystems are currently emer-

ging as effected by public authorities re-evaluating

their policy and regional development activities, and

businesses trying to co-shape the evolving mobility

ecosystem. Our design artefact is a visual analytic sy-

stem (VAS), the Business Ecosystem Explorer, which

is particularly instrumental to provide tailored visu-

alizations to different stakeholders supporting them

in their business- or policy-related tasks and decisi-

ons. According to Hevners three cycle view of design

science (Hevner et al., 2004), (Hevner, 2007), our re-

search contributes in the following ways to each of

the cycles.

Relevance Cycle: The aforementioned challenges

linked to smart city and urban mobility provide the

boundary conditions to our case study. In the evol-

ving ecosystem, application of the Business Ecosy-

stem Explorer is expected to improve the visibility

and understanding of ecosystem structures. We pro-

vide data from its prototypical use including expert

(user) feedback, in order to assess its utility in practice

as well as the viability of our approach.

Design Cycle: We use an established Knowledge

Management System application development plat-

form to implement the VAS as design artefact. This

platform contains functionality for data management,

collaboration, and decision support. The system par-

ticularly provides features to address both data and

view model adaptation. We draw on recent visuali-

zation research to identify and implement ﬁve apt vi-

sualization types. As in its current stage, the system

is able to adapt data and view models to implement

further visualizations, we seek evaluation in the re-

levance cycle by conducting a prototypical ﬁeld test

and including expert feedback which might stimulate

further developments.

Rigor Cycle: We have studied and evaluated lite-

rature and existing artefacts in the domain of visua-

lizing and particularly, business ecosystem analysis.

We draw on the current state of the art in modelling

approaches for data visualization and modeling. In

addition to the Business Ecosystem Explorer as de-

sign artefact, we contribute to the knowledge space by

proposing an agile and collaborative process to ma-

nage and adapt business ecosystem models.

3 RELATED WORK

3.1 Business Ecosystem Modeling

Since the conceptualization of business ecosystems

by James Moore in the mid-1990s, who deﬁned it as

a collection of interacting companies (Moore, 1996),

the concept has been widely studied (Guittard et al.,

2015). Ecosystems are interconnected through a com-

plex, global network of relationships (Basole et al.,

2015b). In a business ecosystem, ﬁrms take on roles

such as suppliers, distributors, outsourcing ﬁrms, ma-

kers of related products or services, technology provi-

ders, and a host of other organizations (Iansiti and Le-

vien, 2004), all affecting the characteristics and boun-

daries of the ecosystem. As ﬁrms continuously en-

ter and leave the ecosystem (Park and Basole, 2016),

they constantly evolve and exhibit a dynamic struc-

ture (Peltoniemi and Vuori, 2004). Research on bu-

siness ecosystems has recently highlighted the role

of novel challenges for ecosystem formation, inclu-

ding technology contexts, e.g., the Internet of Things

(IoT) (Iyer and Basole, 2016) or policy contexts, e.g.,

smart city (Visnjic et al., 2016). This has focused

researchers attention on ecosystem modeling (Uchi-

hira et al., 2016). Current approaches focus on fra-

meworks to grasp the scope of ecosystem complexity

(Iyer and Basole, 2016), (Visnjic et al., 2016), or on

visualization to understand emerging structures and

patterns (Leonardi, 2011), (Iyer and Basole, 2016).

Ensuing previous research, our business ecosy-

stem model takes into account both the static network

of entities (ﬁrms, technologies), and the dynamic net-

work characteristics, i.e., the relationships between

entities, and activities, all changing over time. Enti-

ties comprise small ﬁrms, large corporations, univer-

sities, research centers, public sector organizations,

(...) other parties [and human actors], which inﬂu-

ence the system (Peltoniemi and Vuori, 2004). They

are linked through a variety of different relationship

types. All elements are to be integrated into the vi-

sual analytic system (VAS). Which entities and relati-

onship types need to modelled depends on the requi-

rements put forward by the VAS users, i.e., the (bu-

siness) stakeholders. Their needs and demands that

deﬁne which (visual) views are relevant, and which

An Agile Framework for Modeling Smart City Business Ecosystems

Figure 1: Visualization types of the Business Ecosystem Explorer (BEEx): a) Force-Directed Layout (FDL), b) Matrix Layout

(MXL), c) Modiﬁed Ego-Network Layout (MEL), d) Radial Network / Chord Diagram (RCD), and e) Tree Map Layout

(TML).

insights are vital, are fundamental for generating and

adapting the model.

3.2 Business Ecosystem Visualization

To foster understanding of business ecosystems, Park

et al. have presented a VAS (Park et al., 2016) that ad-

dresses three salient design requirements of the par-

ticular problem context of supply chain ecosystems.

Their work includes extensive research in the areas of

modeling, visualizing and analyzing across different

types of business ecosystems (Peltoniemi and Vuori,

2004), (Iyer and Basole, 2016), (Visnjic et al., 2016),

(Evans and Basole, 2016), (Park and Basole, 2016),

(Basole et al., 2016). The described VAS offers mul-

tiple views within an integrated interface, which ena-

ble users to interactively explore the supply network.

The system additionally provides data-driven analytic

capabilities. The authors suggest and test ﬁve visua-

lization types (layouts) to visualize the dynamic net-

worked structures of their problem context (Fig. 1).

These layouts comprise Force-directed Layout

(FDL), Tree Map Layout (TML), Matrix Layout

(MXL), Radial Network/ Chord Diagram (RCD),

and Modiﬁed Ego-Network Layout (MEL). Each of

these layouts provides for interactive features, such

as clicking, dragging, hovering, and ﬁltering. We use

these layouts suggested by previous research to ap-

proach the design of the VAS in our problem con-

text. In addition to these visualization types further

ones exist, which focus on particular aspects and per-

spectives on ecosystems, such as cumulative network

visualization (Evans and Basole, 2016), or bi-centric

diagrams that visualize the relative positioning of two

focal ﬁrms (Park and Basole, 2016), (Basole et al.,

2016).

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 (Evans and

Basole, 2016), (Park and Basole, 2016). This ap-

proach implies that the VAS user, e.g., the strategy

team of a company, needs to understand requirements

for relevant sources of data that inform the ecosystem

model, as well as questions that guide visualizations.

Accordingly, both model and visualizations need to

be adaptive to host diverse business perspectives and

intentions. For the use of the resulting VAS it is thus

plausible to assume that business or public authority

users might create their own VAS instance that focu-

ICEIS 2018 - 20th International Conference on Enterprise Information Systems

Figure 2: Agile process to collaboratively manage and adapt the business ecosystem model.

ses on distinct data sources and structures. In the fol-

lowing, we therefore highlight the process of ecosy-

stem modelling.

4 AGILE APPROACH TO

ECOSYSTEM MODELING

This section describes the process and roles necessary

to collaboratively manage and adapt business ecosy-

stem models, and to visualize the models. We envi-

sion a process that is adaptable to different types of

teams, or communities. On the one hand, enterprise

internal working groups can constitute the stakehol-

ders of an ecosystem modelling initiative. On the ot-

her hand, a public ecosystem model and VAS can be

conceived that serves both the public and policy ma-

kers. Independent of these use scenarios, several roles

need to be represented that we present in the follo-

wing.

4.1 Agile Modeling Process and Roles

Our approach comprises three phases that constitute

an iterative procedure to initially build, use, and revise

an ecosystem model (Fig. 2) (Roth et al., 2014).

First, the build phase comprises activities to mo-

tivate creation and use of the VAS, collection of data

about/from the ecosystem, and carry out the model-

ling. Basic requirements for engaging into an ecosy-

stem modelling initiative stem from the core stakehol-

ders such as an enterprises top management or stra-

tegy team. Together with domain experts, e.g., busi-

ness case owners, speciﬁc questions about the ecosy-

stem, its development or structures are formulated.

These mirror the business strategy that underlies the

initiative at large. (For reasons of simplicity, we ap-

ply the use scenario of an enterprise, but roles will

correspond to use in a public policy maker scenario,

too). These requirements are taken up by a team (role)

we named Ecosystem Editorial Team. This group is

responsible for collecting data and modelling. (It is

conceivable that for public use, a separate, public or

third party funded editorial ofﬁce is created that over-

looks and eventually investigates on, ecosystem data

sources).

For the initial instantiation, company internal in-

formation systems are used as data sources, provi-

ding already collected information about competitors,

business partners, etc. Additionally, each stakehol-

der group is motivated to implement their speciﬁc

knowledge documented locally and to communicate

the sources used to gather information. Within the

iterations of the build phase, these sources are used

to collect continuous information, both manually but

also automatically from news feeds, blocks, etc., both

orchestrated by the Ecosystem Editorial Team. Fi-

nally, the stakeholder groups should be kept motiva-

ted to contribute with information whenever possible.

(For public use, available data sources, both interna-

tional, such as Crunchbase

or Anglelist

, and nati-

https://www.crunchbase.com

https://angel.co/

An Agile Framework for Modeling Smart City Business Ecosystems

onal, for Germany e.g. Grunderszene

or Bayern-

International

, are used for the initial build phase.

In the next iterations, the data gathering extends to

crowdsourced data provided by the stakeholder also

using the provided metrics, visualizations, and re-

ports. Also, automatic news feeds evaluation are in-

cluded to enrich the data base. It is within the respon-

sibility of the mentioned editorial ofﬁce to supervise

this process.)

In this phase, each stakeholder group owns spe-

ciﬁc requirements towards both, understanding the

ecosystem as well as the functioning and use of the

VAS. In our case study, for the Business Ecosystem

Explorer prototype, requirements from several stake-

holders groups were collected; each group provided

particular demands with regard to relevant entities,

and creation of views. For instance, legal department

representatives were rather interested in legal forms

and business relationships of business partners, while

a strategy team focused on platforms and technolo-

gies related to ecosystem members and cooperative

initiatives, to inform the search for potential future

business partners. The requirements are inititally col-

lected in workshops lead by the Ecosystem Editorial

Team with each stakeholder group. In a later phase

of the VAS, the requirements are gathered within the

VAS.

Second, the use phase covers presentation (execu-

tion) of the created model within different layouts, in-

teractions between users and the layouts to analyze

the ecosystem, and feedback to the Editorial Team

in order to ﬁne tune or revise the model. In our

case study, the business ecosystem model is presented

through stakeholder-speciﬁc ecosystem views that in-

clude metrics, e.g., key values about centrality or con-

nectedness of an entity, different visualizations, and

reports. Reports play an important role in the com-

munication process (Roth et al., 2014) and for expla-

nation, as they contain the interpretation of data and

visuals by the domain experts, top management or bu-

siness stakeholders as users. This interpretation at a

speciﬁc point of time serves as input to further analy-

sis and revision, and helps to follow the emergence of

structures or patterns at a later stage.

Third, the revision phase comprises the reﬂection

on achieved results and validity of the model/provided

visuals as well as the adaptation of model and requi-

rements. In this phase, additional input from exter-

nal domain experts can be sought depending on up-

coming tasks (Basole et al., 2016). A key role in this

process is assumed by the Ecosystem Editorial Team,

whose modelling expert members particularly require

http://www.gruenderszene.de/

http://www.bayern-international.de/en/

some domain knowledge about modelling. The team

should be capable of managing the various stakehol-

der groups, deliver stakeholder-speciﬁc visualizations

and safeguard the process cycle. The task of ecosy-

stem experts holding domain knowledge about busi-

ness or regional factors etc. is to collect information

from the ecosystem and prepare it in the right format

as content of the ecosystem model.

In our case study, as for the requirements put for-

ward by the modelling process, we have implemen-

ted the ecosystem model and the VAS into an integra-

ted, adaptive collaborative work system. This systems

ecosystem model is capable of integrating all sta-

keholders requirements and visualizations, and thus

grows with increasing demands and solutions, e.g.,

visuals that comprise selected entities and relations-

hips to answer speciﬁc questions about the ecosystem.

This integrative aspect is particularly relevant, if one

unique system is to be developed for use by a larger

ecosystem initiative, as is the case in our case study

context of a smart city initiative. Multiple stakehol-

ders including public and private organizations might

then become users of the ecosystem model.

4.2 Adoption and Agility Aspects

From our ﬁeld test, we have seen that it is vital to en-

sure involvement of different stakeholder groups, and

to keep them motivated to discuss and explore the mo-

del across process cycles. In this respect we have ex-

perienced that it is helpful to present an early version

of the business ecosystem model and visualizations

to address speciﬁc demands (Roth et al., 2013). Furt-

her, we have noticed that the availability of varied vi-

sualizations can stimulate cross-contextual thinking,

which might lead to formulation of new key values in

interpreting the ecosystem, e.g. transitive relations-

hips that express the indirect closeness between enti-

ties.

As a result, each of the phases should be imple-

mented as interactive and collaborative processes to

enable early adaptation and validation of formulated

requirements (Roth et al., 2013). Additionally, diffe-

rent stakeholders should be able to adapt and evolve

the business ecosystem model and visualizations wit-

hout having software development skills. Thereby,

the collaborative process must be supported continu-

ously by an information systems, which allows the

end-users (i.e., users without software development

skills) to modify the business ecosystem model and

visualizations at run-time (i.e., without the need to

stop and recompile the system to integrate new functi-

onalities). In this sense, we use the term agile to cha-

racterize the way in which the process from require-

ICEIS 2018 - 20th International Conference on Enterprise Information Systems

ments deﬁnition to visualization and feedback should

be managed.

5 DESIGN ARTEFACT: AN AGILE

FRAMEWORK FOR

MODELING ECOSYSTEMS

5.1 The Hybrid Wiki Approach to

Collaborative Work, and

Architecture

The previously described requirements to the ecosy-

stem modelling process caused us to design an agile

framework for modeling ecosystems as integrated,

adaptive collaborative work system supporting the

evolution of both the model and its instances at run-

time by stakeholders and ecosystem experts (i.e.,

users without programming knowledge or skills). De-

veloping and maintaining such collaborative environ-

ments can be considered a difﬁcult task. Recent rese-

arch has suggested the Hybrid Wiki approach to ad-

dress this challenge (Matthes et al., 2011). The Hy-

brid Wiki approach has been used in different use ca-

ses and domains such as Enterprise Architecture Ma-

nagement (Matthes and Neubert, 2011), (Buckl et al.,

2009) and Collaborative Product Development (Shu-

maiev et al., 2014), (Hauder et al., 2013).

This framework rests on the Hybrid Wiki appro-

ach as presented in (Reschenhofer et al., 2016) that

serves as Knowledge Management System applica-

tion development platform and contains features for

data management as well as collaboration and deci-

sion support. To create the business ecosystem model

we use the Hybrid Wiki metamodel.

The Hybrid Wiki metamodel contains the follo-

wing model building blocks: Workspace, Entity, En-

tityType, Attribute, and AttributeDeﬁnition. These

concepts structure the model inside a Workspace and

capture its current snapshot in a data-driven process

(i.e., bottom-up process). An Entity contains a col-

lection of Attributes, and the Attributes are stored as

a key-value pair. The attributes have a name and can

store multiple values of different types, for example,

strings or references to other Entities. The user can

create an attribute at run-time to capture structured

information about an Entity. An EntityType allows

users to refer to a collection of similar Entities, e.g.,

organizations, persons, amongst others. The Entity-

Type consists of multiple AttributeDeﬁnitions, which

in turn contain multiple validators such as multipli-

city validator, string value validator, and link value

validator. Additionally, an Attribute and its values can

be associated with validators for maintaining integrity

constraints.

The EntityType and AttributeDeﬁnition are loo-

sely coupled with Entity and Attribute respectively

through their name. These elements specify soft-

constraints on the Entities and Attributes. The use

of soft-constraints implies that the users are not re-

strained by strict integrity constraints while capturing

information in Entities and their Attributes. There-

fore, the system can store a value violating integrity

constraints as deﬁned in the current model.

5.2 Business Ecosystem Explorer Views

The agile framework currently consists of ﬁve views;

a landing page, detail view with company informa-

tion, a relation view, a visualization overview, and se-

veral visualizations (Fig. 3). For all views, a menu

bar at the top of the page provides links to the other

views available.

The landing page displays a list of top-level enti-

ties as deﬁned in the ecosystem model. For our case

study, we have for instance used organizations that

are part of the smart city ecosystem. The detail view

lists a short text for entity description, attributes such

as location, key personnel, legal form, etc., and rela-

tions of the entity such as group afﬁliation, ﬁnancial

or contractual dependencies, etc. The relation view is

an implementation of the Radial Network/Chord Dia-

gram (RCD). For a selected range of entities, it high-

lights different types of relationships. When hovering

over an entity on the arc of the circle this entity and

all associated relations and entities are emphasized by

a bold type, whereas the remaining relations and en-

tities are grayed out. The visualization overview pre-

sents various options to select other speciﬁc visuali-

zation types as separate, more detailed visualization

views (Fig. 3).

5.3 Business Ecosystem Model

The agile framework relies on (a) ecosystem data mo-

del, and (b) ecosystem view model, each with re-

spective features for creation and adaption. Both mo-

dels are encoded using the Hybrid Wiki metamodel.

The ecosystem data model contains two EntityTy-

pes within the Hybrid Wiki metamodel: organizations

and relations. Thereby, organizations are automotive

OEMs, their suppliers, public institutions, mobility

related projects, etc. The AttributeTypes in the orga-

nization are company name, abbreviation, logo, URL,

short description, headquarter, CEO, category, and le-

gal form. Additionally, the relations describe different

An Agile Framework for Modeling Smart City Business Ecosystems

Figure 3: Architecture of the Business Ecosystem Explorer (BEEx): a) A list of all companies, b) Detailed information of

the ecosystem entities, c) One view focusing on different types of relations between ecosystem entities, and d) Overview of

available visualizations (see also Fig. 1 ).

ICEIS 2018 - 20th International Conference on Enterprise Information Systems

Figure 4: List of Categories and Types in BEEx and conversion to Tree Map Layout (TML).

types of interaction between organizations, as infor-

mation coming from companies webpages, newsfeed,

social media, etc. Therefore, the attributes of the re-

lation are the type of relation, e.g., cooperation, (par-

tially) ownership, funding, etc., involved companies,

the date, and the source.

The ecosystem view model is encoded as one En-

tityType called visualizations. Each visualization has

two elements: the ﬁrst element is the link between the

data model and the visualizations. The second ele-

ment is the speciﬁcation of the visualizations, which

are described using the visual encodings of the visual

grammar Vega

, as presented and described in (Heer

and Bostock, 2010) and (Satyanarayan et al., 2016).

The Vega visualization grammar introduces a decla-

rative language to describe visualizations in a JSON

format. The main building blocks, which enable static

and dynamic visualization features, are a) data, inclu-

ding data but also all data transformations; b) marks,

covering the basic description of the visualized sym-

bols, e.g., shape and size of a node; c) scales, contai-

ning visual variables, such as the color coding; d) sig-

nals, including the different interaction options, e.g.,

dragging and dropping of entities; and in some instan-

ces e) legends.

The proposed approach provides the feature of

adapting the models at runtime. An example within

the business ecosystem scenario is the categorization

of organizations. The initial grouping into Car Ma-

nufacturers, Map Provider, Mobility Platforms, etc.

is pictured in Fig. 4 in the background screenshot.

https://vega.github.io/vega/

This list shows the representation within the collabo-

rative work system. The system provides the feature

to adapt the categories at runtime: adding a new ca-

tegory, changing or deleting existing categories. All

visualizations using the groups of companies, such as

the tree map layout (as pictured in Fig. 4), the chord

diagram layout or the modiﬁed ego-network layout,

are adapted at runtime as well.

5.4 First Evaluation of the Agile

Framework

For the evaluation process, the agile framework is

hosted on a University server accessible on two In-

ternet sites.

Initially, we conducted nine interviews in semi-

structured form with nine different companies within

two months, following (Weiss, 1995). We aimed at

a balance between receiving a quantiﬁable evaluation

but also enabling interviewees to vary the depth of

answer depending on own capabilities and willing-

ness (Gl

aser and Laudel, 2010). The focus of these

interviews was to receive feedback regarding the ex-

isting business ecosystem model. Therefore, a sam-

ple of attributes was presented, as well as some ty-

pes of relations and a visualization of the combina-

tion of entities through relations in the form of a

force-directed layout. All interviewees stated that the

business ecosystem model supported them in under-

standing the relations within the presented business

ecosystem and that their knowledge of this ecosystem

was increased. Some suggested immediately additio-

An Agile Framework for Modeling Smart City Business Ecosystems

nal scenarios, for example, the patent management in

the pharmaceutical industry.

We used the interviews’ results to update the ex-

isting prototype. As a next step, we conducted three

in-depth interviews with additional three companies.

To obtain a wider range of opinions, we selected three

companies of different ﬁelds of activity. Namely, an

automotive OEM, a publicly funded non-research in-

stitution and a software company with main business

area addresses the connected mobility ecosystem. All

interview partners were actively modeling their busi-

ness ecosystem and all stated their perceived limita-

tions with the current in use tools (mainly Microsoft

products in connection with CRM tools in use). Addi-

tionally, two companies conﬁrmed that different sta-

keholders within in the enterprise have different vie-

wpoints towards the enterprise’s business ecosystem.

All companies agreed that the prototype fosters the

understanding of the presented ecosystem and two

continued that it would be interesting to use such a

tool within in their enterprise to collaboratively ma-

nage the business ecosystem evolution.

6 CRITICAL REFLEXION

As visualizations help stakeholders to better under-

stand data, the here presented visual analytic system

links the data model and the view model. This enables

a dynamic mapping of the view model and the ever

changing data model without high developing efforts.

Nevertheless, within the VAS different stakehol-

ders performing various roles are neccessary. This

means, on the one hand, the business owner and dom-

ain experts have to provide their expert knowledge by

providing their data sources and their requirements,

but also technological experts within the Ecosystem

Editiorial Team providing visualisations, reports and

metrics. The quality of the VAS depends highly on

the inclusing of these different stakeholders.

Additionally, as visualizations are data-driven, the

business ecosystem visualizations rely heavily on the

availability and quality of data. The data collection

must therefore fulﬁll quality requirements, which

need to be more clearly deﬁned in future research to

assure data reliability and validity. To address this de-

mand for structured data collection processes, appro-

aches to data governance are needed. In case of ﬁrm-

internal usage, this is accomplished by the Ecosystem

Editorial Team. In public usage, the data governance

role could be assumed by an Urban Data Governance

Board.

An extension to the visualization of ecosystems is

inclusion of data about the actual use of services from

mobile devices and digital platforms, and sensor data

from the mobility infrastructure. This might open up

new options for identifying missing consumer-facing

services as for instance speciﬁc mobility services that

are not available in a particular region and identiﬁca-

tion of mobility providers and solutions that can close

this gap.

As a ﬁnal limiting factor, it should be noticed that

the presented agile framework prototype is not in its

ﬁnal version as it iteratively evolves.

7 CONCLUSION

In this paper, we report from the ﬁeld test of a pro-

totypical visual analytic system (VAS) in context of a

smart city mobility business ecosystem. We propose

an agile approach to collaboratively manage and adapt

business ecosystem models and their visualizations.

We provide insights from the use of our VAS design

artefact, an agile framwork for modeling ecosystems,

developed by us as part of a case study of a large Eu-

ropean smart city initiative. The agile framwork is

based on an application development platform, which

provides the feature of adapting the underlying data

and view models at runtime. Thereby, we address the

need to change the data and the view model according

to the emerging structures of the ecosystem.

The results of our ﬁeld test indicate that additional

iterations of the design cycle are required to extend

the functionality of the developed VAS tool. There-

fore, we envision evaluating the proposed approach

within further business ecosystem scenarios, such as

the medical and pharmaceutical ecosystem or the le-

gal informatics ecosystem. As a tool improvement

step, we envision to adapt existing visualization and

add new ones based on feedback from industry part-

ners. In addition, acceptance criteria for ecosystem

VAS need to be deﬁned, having in mind the two ma-

jor use cases mentioned. Measuring the success of

ecosystem visualizations in general is a further chal-

lenge that will require a broader study of VAS adop-

tion throughout various ecosystems.

ACKNOWLEDGEMENTS

This work is part of the TUM Living Lab Connected

Mobility (TUM LLCM) project and has been funded

by the Bavarian Ministry of Economic Affairs and

Media, Energy and Technology (StMWi) through the

Center Digitisation.Bavaria, an initiative of the Bava-

rian State Government.

ICEIS 2018 - 20th International Conference on Enterprise Information Systems

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