An Agile Framework for Modeling Smart City Business Ecosystems
Anne Faber
1
, Adrian Hernandez-Mendez
1
, Sven-Volker Rehm
2
and Florian Matthes
1
1
Technical University of Munich, Boltzmannstrasse 3, 85748 Garching, Germany
2
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
Copyright
c
2019 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
39
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
40
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
41
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
42
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
1
or Anglelist
2
, and nati-
1
https://www.crunchbase.com
2
https://angel.co/
An Agile Framework for Modeling Smart City Business Ecosystems
43
onal, for Germany e.g. Grunderszene
3
or Bayern-
International
4
, 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
3
http://www.gruenderszene.de/
4
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
44
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
45
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
46
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
5
, 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.
5
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
47
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
48
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