Personas and Tasks for International Data Space-based Ecosystems
Torsten Werkmeister
Technical University Ilmenau, Institute for Media Technology, Germany
Keywords:
Persona, Tasks, User-centered Interaction Design, International Data Space.
Abstract:
The International Data Space (IDS) is a platform developed for a global data ecosystem, composed of inter-
connected devices which gather, process, exchange and trade data. IDS and industry data are the research
focus of many scientific papers dealing with analysis from a technical and economic point of view (Brost
et al., 2018; Graube, 2018). This paper examines the users of the IDS as personas, a method for presenting
and communicating stakeholder needs for IDS. By applying a user picture and name, properties and descrip-
tion a persona provides product users and developers with a representation of the design target. Using this
research approach, the industry-wide requirements of potential users are analyzed in a task-oriented scientific
approach and presented in personas. Applying this task-oriented approach, the new roles and associated new
tasks of this domain are essential, as the people and tasks are extremely risky, hard to access and complex.
The results provide the prerequisites for designing user-centric interfaces for the IDS.
1 INTRODUCTION
The intelligent use of data in IDS enables the creation
of new products, services and the reinforcement of re-
search and development. ”The data value chain is at
the center of the future knowledge economy, bringing
the opportunities of the digital developments to the
more traditional domains (e.g. transport, financial ser-
vices, health, manufacturing, retail)” (European Com-
mission, 2013). In this context data is becoming a
valuable economic and commercial asset.
The IDS
1
is a platform designed for the industry-
wide use, composed of interconnected devices which
gather, process, exchange and trade data. It was initi-
ated and developed by various researchers from eco-
nomics, law and computer science with a focus on
service architecture and security (Otto et al., 2016;
Teuscher, 2018; Otto et al., 2019).
Against the background of human-computer inter-
action, this brings with it new roles and tasks and as-
sociated new user interfaces for users. For this reason,
potential users respective roles, as well as business
critical and high risk tasks connected with the sys-
tem, must be researched. In this respect, an error-free
performance must be enabled to ensure successful in-
teraction. The analysis focuses for instance on ac-
1
The title ”Industrial Data Space” has been replaced by
”International Data Space”. At the beginning of the project,
the focus was limited to industrial domains and expanded in
the course of the project.
tivities (What does the user do?), settings (How does
the user think about the system?) and skills (What is
the existing experience with the system?). In order to
merge and present the extensive and complex connec-
tions on the user level intuitively, high demands are
placed on the analysis. Topic-centred interviews with
experts from the domain were conducted to identify
business-critical, high-risk tasks and user needs. In
the subsequent analysis, the insights gained are de-
rived in user models and provided with, for example,
a name, a function, a usage context as well as tasks
and activities, transferred into personas. By provid-
ing personas, for instance, a development team can
put itself in the position of a potential user and thus
more easily understand its perspective during the de-
velopment process. Segmentation and cluster analysis
methods are used to develop primary and secondary
personas, which represent the user groups. Using a
hierarchical task analysis (HTA) in the analysis of so-
lution independent tasks, the goals, tasks and actions
of users are determined and structured (Diaper and
Stanton, 2003; Sharp et al., 2019). Summarizing, the
research work aims at the creation of generic require-
ments, which are based on a cross-domain task analy-
sis. In this context, the article addresses the following
research questions:
1. How can the users of the IDS be characterized?
2. What tasks do these users have perform?
These persons and their tasks are the basis for a user-
202
Werkmeister, T.
Personas and Tasks for International Data Space-based Ecosystems.
DOI: 10.5220/0010146902020209
In Proceedings of the 4th International Conference on Computer-Human Interaction Research and Applications (CHIRA 2020), pages 202-209
ISBN: 978-989-758-480-0
Copyright
c
2020 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
centered development of the user interface of IDS sys-
tems.
2 RELATED WORK
2.1 International Data Space
The IDS represents a reference architecture model
2
based on a high level of abstraction (Otto et al., 2019).
Farther, the IDS is to be understood as a global virtual
data room, in which the participants are enabled to ex-
change and link data. The human-computer interac-
tion is technically established by the IDS-connectors
which connect data of machines, processes and sys-
tems and can be controlled by the participants via user
interfaces. This network
3
involves different partici-
pants in specific roles who interact with each other,
Figure 1 shows examples. The functions of the role
enable data to be integrated and controlled, data to be
made available for exchange, and data to be received
from the data provider. In the exemplary scenario, the
company (1) provides or exchanges data with the par-
ticipant/s in other companies (n) via a connector. The
associated data usage restrictions (UR) of the appli-
cation case regulate the contractual framework condi-
tions. The configuration and management of data ex-
change is controlled via user interfaces (UI). Broker
and Appstore are optional factors. There are currently
-Usage
restrictions
-IDS Connector
Company 1
Company n
UI
UI
-Essential role-Participants
UI
-User Interface
-Optional role
Broker
Data
Owner
Data
Provider
Data User
Data
Consumer
UR
App
Store
Figure 1: Roles and interactions in the Industrial Data
Space.
a few company-specific developments of connectors,
but there are no generally valid user interfaces that
allow cross-domain use. The IDS enables the partici-
pants to exchange controlled data. The establishment
2
This research topic will be examined from various per-
spectives in further scientific papers (Brost et al., 2018;
Graube, 2018).
3
also referred to as ”Business Ecosystem”
of contact can be made directly or via intermediaries.
If the data link is established, intervention by the par-
ticipants is optional. In this all-embracing scenario,
the human being as a participant is essential for es-
tablishing and controlling the connections. Each par-
ticipant has to take on different roles in which he has
to perform specific tasks. These participants or per-
sons and their tasks must be identified.
2.2 Analysis of Users
The persona originates from psychology and is de-
scribed as the outwardly shown attitude of a person.
In the field of psychology, the persona serves social
adaptation and is sometimes also identical with a self-
image (Jung, 2011). In the field of Human Com-
puter Interaction this procedure was transferred and
developed further by a multitude of authors
4
. A per-
sona is an abstractions of groups of real persons who
share common characteristics and needs. Further, per-
sonas are hypothetical stereotypes constructed from
the characteristics and behavior of real people, as fig-
ure 2 illustrates. A persona description is fictitious,
Position: skilled worker
Name: Felix Seidel
Age: 24 years
Marital status: single, no children
Company: MNI-Systems GmbH
Department: Production and
Manufacturing
Characteristics: motivated, precise,
tech-savvy
descriptive representations
quickly graspable knowledge
good usabiblity
"My
motto"
Personel
Expectations
Figure 2: Persona example.
but precise and specific in order to encourage the
empathy of the development team (Pruitt and Adlin,
2005; Pruitt and Adlin, 2006). By dropping redundant
or unessential information personas keep their stereo-
typical characters and become clearly distinguishable
from each other. When comparing the persona de-
velopment process according to Cooper (2015) and
Pruitt (2006) it should be noted that the basic process
at the beginning is similar in data collection, process-
ing and analysis. However, Cooper (2015) focuses
on the behavioral variables from the beginning. The
comparison is shown in the table 1.
4
(Pruitt and Adlin, 2005; Pruitt and Adlin, 2006; Mul-
der, 2006; Sears and Jacko, 2009; Mayas et al., 2012;
Cooper et al., 2015)
Personas and Tasks for International Data Space-based Ecosystems
203
Table 1: Identification variables and values and personas
Persona procedure models of Cooper (2015), Pruitt (2006).
Artifact Cooper Pruitt
Variables
and
values
Identify behavioral
variables, Map
interview subjects
to behavioral
variables.
Discuss categories
of users,
Process data.
Personas
Expand description
of attributes
and behaviors,
Designate persona
types.
Develop skeletons
into personas,
Validate the
personas.
Referring to Cooper (2015) different roles are in-
terviewed, from which behavioral variables are to be
derived. Following this, the variables are assigned to
the interview partners and significant behavioral pat-
terns are identified. The individual components form
a new unit through their composition. After checking
for rendudance and completeness, the stereotypes can
be named. In the further course these are subject to
a constant expansion. The procedure is shown in the
figure 3.
Group interview subjects by role
Identify behavioral variables
Map interview subjects to behavioral variables
Identify significant behavior patterns
Synthesize characteristics and define goals
Check for redundancy and completness
Designate persona types
Expand description of of attributes and behaviors
Figure 3: Persona creation process according to Cooper
(2015).
According to Cooper (2015) and Mayas (2012)
personas can be understood as a ”design tool” used
early in the development process. They offer the fol-
lowing strengths (Mayas et al., 2012; Cooper et al.,
2015)
• User-centered determination of goals and tasks in
dealing with a system, product or service.
• Creation of a common communication system for
the development team and stakeholders involved.
• Mediation of a design consensus.
• An early evaluation of design decisions using per-
sonas instead of real users.
• Support of product marketing.
• Support the identification of actors and scenarios
in IDS.
• The prioritization of system requirements.
• Reflect the ideated tasks and activities of a role.
• In the requirements review or test phases, the use
of personas ensures that the user perspective is
taken into account and ensured.
• By integrating the user perspective Personas sup-
port in this case by decision making, evaluation or
even compliance with requirements.
• Additionally, the persona method closes the gap
between requirement-oriented, user-centered soft-
ware development and human-computer interac-
tion.
The approach according to Cooper (2015) is very
suitable for the present research project, as it se-
lects the observable characteristics of people and later
condenses these into personas. Furthermore, the re-
quirements elicitation and persona development pro-
vides the opportunity to derive context scenarios from
which interaction design principles and patterns can
be created.
2.3 Analysis of the Tasks
Task analysis is primarily concerned with the analy-
sis of processes (procedural knowledge). To be able
to analyze procedural knowledge, it is necessary that
a user has structured knowledge about the system and
the application domain. This is where mental models
are used for task analysis. These are formed by in-
teraction with a system, by observing the relationship
between one’s own actions and subsequent system re-
actions (Benyon, 2013). If the structured knowledge
is available, the analysis of the procedural knowledge
(user, system, application domain) can be performed.
The task analysis deals with the performance of a
”work system” which is related to the user and the
technology of a certain application domain. In the
figure 4 the relationship of the ”performance of work”
between the work system (consisting of the human –
people or agents and the technology) and the applica-
tion is visualized. A task is seen as a goal in combi-
nation with a certain amount of actions. The goals,
tasks and actions of the users are the focus of the con-
sideration (Benyon, 2013). The methods of task anal-
Work System
People or agents
Technologies
Application
Performace of
work
Figure 4: Task analysis according to Benyon (2013).
ysis can be divided into two superordinate categories.
On the one hand, there is the method of task logic, in
which the sequences of work steps that the work sys-
tem must go through to achieve a goal are defined. A
CHIRA 2020 - 4th International Conference on Computer-Human Interaction Research and Applications
204
typical example of task logic is hierarchical task anal-
ysis (HTA). On the other hand, there is the method
that deals with the understanding of cognitive aspects.
In this method, the cognitive processes that a user has
to go through in the work system in order to reach a
goal are analysed. A typical example for the analy-
sis of cognitive aspects is GOMS – Goals, Operators,
Methods, Selection Rules (Benyon, 2013). However,
this method is based on already existing interaction
solutions. The purpose of an HTA is to concretise
and fix the purpose of the analysis as far as possible.
Subsequently, the task objectives are to be defined.
Furthermore, the data for the preparation of the task
modelling shall be collected. In the following, a val-
idation is carried out, in which the tasks are broken
down and checked with, for example, a development
team or users. This identifies significant paths or ac-
tions. This step enables the development of possible
hypotheses about user performance, which are to be
tested. The figure shows an example for the program-
ming of a video recorder. In the sequence of tasks of
0. Record a TV
programme
Figure 5: HTA according to Benyon (2013), Basic example:
HTA for programming a video recorder.
the main task 0. Record a TV programme, the tasks
with subtasks, 1. Put tape into VCR, 2. Program the
TV Channel, etc. and actions 1.1 Find suitable tape
and 2.1 Press channel up illustrated in a hierarchy.
The HTA method is based on a graphical represen-
tation in one of the structure chart notation forms. In
addition, task sequences with subtasks and actions are
displayed in a hierarchy. This form of presentation
allows an overview of all tasks and also serves for
quick validation with involed persons. The method
focuses on the task objectives and discloses the user’s
task solution process. The user and his or her needs
are placed in the foreground. Furthermore, signifi-
cant paths or actions can be identified. HTA also al-
lows information to be given on whether an iteration
or selection of tasks is present. Structured paths in
the form of a ”plan” are possible via the hierarchy.
Due to the high novelty value of the system for the
present research question, both the personas and the
task analysis must be carried out with theme-centred
interviews.
2.4 Theme-centred Interviews
Theme-centred interviews are characterised by the
setting of relevance by guiding the interview partic-
ipants to a certain degree. The conduct of certain the-
matic areas is specified in the form of guiding ques-
tions. Furthermore, narrative generating questions
and structuring questions can be combined (Schorn,
2000; Witzel, 2000; Bohnsack et al., 2018). Simi-
lar to the expert interview, the criteria of restriction to
thematic complexes are set, which presupposes exist-
ing knowledge of the subject area. In the execution of
the interview the interviewer steers by means of open
questions. Here the narrative potential of the inter-
view partner within the given thematic complex is to
be exploited as much as possible. The procedure of-
fers the possibility to quickly identify initial insight in
a largely unknown field of knowledge. The extensive
information from the theme-centered interviews can
be evaluated with a qualitative content analysis.
2.5 Qualitative Content Analysis
Qualitative content analysis is a method of data eval-
uation. This method originates from empirical social
research, which aims to order and structure unam-
biguous and latent content (Mathes, 1992).
The method was applied in Human Computer In-
teraction and has been further developed by many re-
searchers
5
. Data material shall be evaluated induc-
tively, deductively or inductive-deductively. Inductive
coding – categories used are derived from the data
material. Deductive coding – categories are derived
from one or more theories. A mixed form of both
codings is possible.
The figure 6 shows - according to Mayring (2010)
- the applied method. The procedure begins with the
determination of units of analysis. In the second step,
the text is divided into individual text sections and re-
produced in your own words. The next step is a gener-
alization of a defined level of abstraction. Afterwards
a first reduction by selection and deletion of para-
phrases with the same meaning takes place, a second
reduction by bundling, construction as well as the in-
tegration of paraphrases takes place on a desired level
of abstraction. The combined category system is then
verified together with the source material. The proce-
dure according to Mayring (2010) enables the extrac-
tion of results by a successive abstraction. Further-
5
(Mayring, 2010; Gl
¨
aser and Laudel, 2010)
Personas and Tasks for International Data Space-based Ecosystems
205
1. Determination of the analysis units
2. Paraphrase of text passages
3. Determination of the desired level of abstraction
Generalization of the paraphrases below this level of abstraction
4. First reduction by selection, deletion of paraphrases with the
same meaning
5. Second reduction through bundling, construction, integration of
paraphrases at the desired level of abstraction
6. Collation of the new statements as a category system
7. Checkability of the summarizing category system on the source
material
Figure 6: Process model summarizing content analysis
(Mayring, 2010).
more, the procedure enables the categories that ap-
pear in the further course of the project to be assigned
to a theory. These can additionally form a theoretical
framework. The Mayering method allows a high de-
gree of transparency despite a high degree of subjec-
tive interpretation. Thus, the possibility of traceability
and verifiability of decisions is given by coding.
3 RESEARCH IDEA
3.1 Research Question
This paper examines potential users and their needs
towards a new and still widely unknown system (IDS)
to answer the research questions:
1. How can the users of the IDS be characterized?
2. What tasks do these users have perform?
In the exploration of potential users, the goals, tasks
and actions of cross-domains are in the focus of a
completely new system. This task-oriented approach
provides essential basic information that can later be
used to create user-centered interaction design pat-
terns for the IDS.
3.2 Research Method
The persona method according to Cooper was used
to characterize potential users of the IDS, because the
field of application has to be newly discovered. No
target groups have yet been defined, only behavioural
variables can be extracted from the analyses, from
which personas can then be defined. The personas and
their tasks were surveyed with a qualitative content
analysis of the structured theme-centred interviews.
The tasks were also described with an HTA to extract
the task logic. This is of particular relevance for the
design of subsequent interaction.
3.3 Research Design
Structured, theme-centred interviews were conducted
with 15 experts from different sectors and functions,
for example from the automotive industry or agricul-
ture. Decisive for the selection of the participants
were:
• previous knowledge of the IDS,
• domain-specific knowledge,
• competence of their roles,
• years of experience in a domain as well as
• representativeness for different domains.
The interviews were conducted over a 14-week period
from 03 April to 08 July 2020. A written document
was produced which served as a guideline. The first
part analyzed information about the experience and
use of business ecosystems and included questions
about the industry and function of the Interviewpart-
ner, the number of employees in the company it repre-
sents and the use of business ecosystems. In the sec-
ond part, information was asked for, which served to
derive personas. For example, questions were asked
about activities, attitudes and skills. In the interviews
a simple form was chosen for the formulation of the
questions and their suitability was tested in a pre-test.
If the participant agreed to the expert discussion, he or
she received a written cover letter and the document in
advance. In principle, the interviews were conducted
by telephone, were usually held in 30-45 minutes and
were transcribed. Afterwards, inductive data analysis
was applied in a schema-based processing to deter-
mine the procedural knowledge. In the course of the
analysis, categories were developed from codes, from
which theories were derived and assigned.
3.4 Research Results
Identification of Domains
From the collection of the identified domains a
structure could be derived in figure 7, which allows
a first assignment of the users. From all identified
domains, from automotive, to energy, to overarching
domains, there are similar corporate functions rang-
ing from production and logistics to data security
and legal issues. In addition, the table 2 contains
information, which provides insights into the sizes
of companies represented by the interviewees. It
can be deduced that the use of ecosystems is not
dependent on the size of a company. Experience with
ecosystems is usually limited to 2 to 5 years, which
confirms the low number of connected actors in an
ecosystem. Another conclusion is that usually CEOs
CHIRA 2020 - 4th International Conference on Computer-Human Interaction Research and Applications
206
D1 Automobile
D3 Trade
D2 Agriculture
CF 1 Production
CF 2 Logistics
CF 3 Security
CF 4 Law
UF 0 Information
technology / development
D4 Energy
D5 Health
D6 Metal
D7 Digital banking
D8 Cross
Corporate functions
Domains
Figure 7: Overview of identified corporate functions and
domains.
Table 2: Business insights, experience and roles and sug-
gested roles for business ecosystems.
Company size
1-49 50-999 5000+
Number of
employees
4 3 3
Years of
experience
no
state.
< 1 1-2 2-5 5-10 10-15 15-20 20-30 >30
Years worked
with business
ecosystems
5 0 0 6 2 0 0 1 0
Interacting
actors
no
state.
1 1-2 2-5 5-10 10-15 15-20 >20 >300
Actors do you
interact with
5 0 0 0 1 1 0 4 1
Position/
Role/Function
CEO
CIO
Lawyer
Head of
Education
Managing
Director
Head of
Dept.
Service &
App. Mgr.
Solution
Arch.
Project
Manager
Security
Consultant
Experts w
i
th a
specific
position/ role
3 1 1 1 1 3 1 1 1 1
S
pec
i
f
i
e
d
ro
l
es
for IDS users
Num
b
er o
f
suggestions
1 2 3 4 7
S
tr
a
te
g
i
s
t
Co
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pl
i
a
nce
M
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.
Knowle
dg
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Eng.
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e
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tif
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tics
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pe
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ts
1000-4999
1
Ma
r
k
e
tin
g
Sal
e
s
D
a
ta
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g
e
r
D
a
ta
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Domain ex
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e
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Offi
c
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Br
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k
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r
and heads of departments are familiar with the topic.
However, users could be identified who will take on
a systemically relevant role in the ecosystem.
Table 3: Behavioral variables for the IDS.
Main activities of the user Main capabilities of the user
Data- Knowledge of. . .
exchange Domain-specific
provide Business data and processes
brokering IDS ecosystem
monitor Consulting
analyze IT knowledge
control Certification
sharing Project management
communicate Understanding of...
retrieve Analytical
track Continuous knowledge enhancement
Expectations Main motivation of the user
Trust in the ecosystem
Execute successful business
processes and transactions
Promoting networking Full control over the data
Readiness for digitization
Data governance
Data protection/security
Usability/Intuitively/self-explanatory
Personalization for different roles
Individualization of the user interface
Identification of Behaviour Variables
The core tasks have been identified as high-risk, eco-
nomically critical activities, ranging from data ex-
change, monitoring to data control. As a result, a high
level of trust and data protection plays on an over-
riding role. The users have a high level of expertise,
combined with a strong analytical capability as well
as information technology and domain-specific skills.
Nevertheless, they attach great importance to
the greatest possible simplicity of the user interface,
which is intuitive to use. The identified behavior vari-
ables are shown in the table 3 and sorted by relevance.
Identification of Personas in IDS
The study revealed a number of 5 personas. These
are: Robert Becker – Data Economy Executive,
Virginia Williams – Engineer, David Holler – De-
partment manager, Dr. Paul Conner – Data Ana-
lyst/Scientists and Mike Chester – Developer. Three
personas are described as examples which show the
diversity of the tasks. The description of personas
consists of the following parts;
• name with additional term in the context of its be-
havioral variable,
• a persona illustration and a typical statement,
• the most representative requirements,
• overview of personal information,
• expectations of the IDS and
• description of a typical daily routine.
In the figure 8 a persona is introduced.
IDS Profile
Usage: Daily use of IDS in logistics.
System knowledge: medium
System use: 3 years
Interaction partners: approx. 12
It is important to him: secure data exchange, promote digitisation
Preferences: adherence to schedules, planning scope
Limitation: limited IT know-how
David Holler – The on-schedule logistics
specialist
is 43 years old and single.
lives in Dortmund.
David expects
Prompt information on the status of deliveries and outgoing deliveries
an easy handling of the system
a fast and efficient completion of tasks
no delays
Notes of daily work
#works daily with about 6 different participants under and overlapping
suppliers #must work in different systems – it is not easy and clear –
causes stress #must follow every process closely # would prefer to work in
one system only #has many years of experience with the IDS #has a lot of
confidence in the system #has worked in a project on the introduction of
the system #wants to have access to all information and tasks at a glance
#sees the IDS as the best way to access all information and tasks #wants
to work on his tasks without media and information breaks #no longer
wants to worry about late status reports of deliveries #needs real-time
data on his dashboard, advises new participants in IDS – very complex
"Especially small and medium sized companies want to use
IDS, but they do not want to think about what the system
actually does."
Figure 8: Persona David Holler The on-schedule logistics
specialist.
Personas and Tasks for International Data Space-based Ecosystems
207
Persona David Holler – Logistics specialist David
Holler is a 43-year-old single department manager of
an automotive supplier from Dortmund. He works
with the Business Ecosystem IDS and likes to save
himself the work with different systems. He is an
on-schedule IT-affine person who tries to complete
his tasks on time. Due to his regular use of the
system, David is very familiar with it, but lacks
in-depth background knowledge. His working day is
facilitated by trust in the system. Every now and then
he has to work with different systems, but he is able
to gain more and more participants who participate in
the digital processes of the company. David expects
timely information about the status of deliveries, easy
handling of the system to get the job done in the best
possible way and to avoid delays.
Persona Julian Massey – Domain and IT con-
sultant Julian (29 years) works as a consultant in a
company with over 5000 employees. In his 10 years
of experience he has developed analytical skills.
Julian also has good IT skills, has a good overview
of the departments due to his education and is also
good with people. Most recently, he participated in a
further training course on the topic of digitalization.
All this enables Julian to deal with processes, actions
and people related to it in his daily work. In his reg-
ular training sessions with his colleagues he imparts
knowledge about data exchange, data switching, data
control and monitoring. With the IDS, Julian sees a
great potential to bring his company to a good level
of maturity in digitization.
Persona Mike Chester – Developer and IT ex-
pert The developer and programmer Mike is 28 years
old and an innovative employee. For about 3 years
he has been working on industrial data and business
ecosystems. Two years ago the CEO decided that his
company should be IDS certified. For this, Mike had
to do a lot of bureaucratic and programming work.
After the certification, he developed interfaces which
established a connection between the machines of his
company and a supplier. Since then, Mike’s tasks
include the implementation of the requirements,
which are taken up by a domain and IT consultant.
For this, Mike develops front- and backend solutions.
This means that he needs to know the user needs and
he must have knowledge of interface programming.
Of course, there are always hurdles in development
and unfortunately there are not many forums where
he can share his knowledge and quickly absorb
new knowledge. Mike’s development work would
be made much easier by design templates, so he
wouldn’t have to start from scratch again and again.
Nevertheless, Mike is convinced that his CEO’s
decision was the right one and that data exchange via
business ecosystems is a forward-looking approach.
Identification of Tasks in IDS
Core activities were identified which are configurable,
manageable as well as processable, traceable and
traceable as information objects:
0 A
pp
Store/Mark
etp
lace 16 Lan
g
ua
g
e mana
g
ement
1 Participants 17 Development environment
2 Dashboard 18 IT Asset
3 R
ig
hts and role administration 19 Research and
development
4 Contracts 20 Mediate
5 Data securit
y
21 Connector
6 Data/data streams 22 Mana
g
ing
unit
7 Official documents 23 Digital twins (Machines)
8 L
eg
al rules 24 Communication
9 Business Rules 25 Collaboration
10 T
ransp
orts 26 Document
11 Verification 27 A
cquiring
application
12 Projekt Management 28 Operate machines
13 Mark
eting
29 Finance
14 Events 30 Environmental Mana
g
ement
15 Tasks 31
Q
ua
lity as
surance s
y
stem
Figure 9: Identified core tasks.
The table 9 shows an consolidation example of the
major task Manage contracts (Plan 4: 1-2-3-4-end)
for the management of the contracts and its subtasks
(1. Create a new contract, 2. Edit existing contract, 3.
Show all contracts).
0. Manage contracts
1. Create a new
contract
2. Edit existing
contract
3. Show all contracts
4.3.2 Show all
expired contracts
4.2.1 Show
contract partner
4.2.2 Edit
contract partner
4.1.1 Show contract
partner
4.1.2 Choose
contract partner
4.1.3 Show template
contracts
(Plan 4: 1-2-3-4-end)
(Plan 4.1:1.1-1.2-1.3-end)
4.2.3 Delete
contract partner
(Plan 4.2:2.1-2.2-2.3-end)
4.3.1 Show all
current contracts
(Plan 4.3:3.1-3.2-end)
Figure 10: Hierarchical task analysis – Manage contracts.
4 DISCUSSION AND
CONCLUSION
The proposed combination of methods presents in
its entirety an approach for identifying personas and
tasks of a new domain. The basic variables, per-
sonas and tasks related to the IDS are to be confirmed
in this empirical study, so far an exemplary set of
five personas and 31 core tasks could be identified.
The application of the methods promotes the intro-
duction of a human-centric design in the domain of
business ecosystems. Personas intensify the differ-
entiated consideration of the widespread target group
and the translation of user needs into concrete tasks.
CHIRA 2020 - 4th International Conference on Computer-Human Interaction Research and Applications
208
In this highly specialized field of knowledge, hardly
any knowledge about the users is known, so that cur-
rently information technicians or scientists are in-
volved. The persona method, however, is mostly used
to identify familiar people in everyday life, so it is
difficult to imagine in the context of the IDS. The
IDS is subject to constant development. The knowl-
edge gained about the users is also subject to constant
further development due to this fact, but represents a
first human-centred approach. The personas can also
be represented in different business areas and compa-
nies in various forms. Going forward, the identified
tasks have a generic approach. Both can be modified
in the future. However, the tasks and processes have
to be reviewed again and again, as they are subject to
constant change. Taking these points into account, it
should be noted that the personas and tasks confirmed
in this study are not exhaustive and are being further
developed and modified in the scientific debate, they
represent a first approach. The described personas and
tasks are a framework for the overall design of util-
ity and usability in all phases of development. Inter-
action patterns can be derived from the tasks, which
can be tailored exactly to the needs of the personas.
For further developments they also have the function
to provide a framework of orientation within which
the requirements for interaction with the International
Data Space can be detailed.
REFERENCES
Benyon, D. (2013). Designing Interactive Systems: A Com-
prehensive Guide to Hci, Ux & Interaction Design.
Pearson Education Limited, Harlow, London, New
York, a. o., 3 edition. pp.32-251.
Bohnsack, R., Meuser, M., and Geimer, A. (2018). Haupt-
begriffe Qualitativer Sozialforschung. UTB GmbH.
Brost, G., Huber, M., Weiß, M., Protsenko, M., Sch
¨
utte, J.,
and Wessel, S. (2018). An ecosystem and iot device
architecture for building trust in the industrial data
space. Cyber-Physical System Security Workshop.
Cooper, A., Reimann, R., Cronin, D., and Noessel, C.
(2015). About Face: The Essentials of Interaction De-
sign. John Wiley & Sons, Inc., Indianapolis, 4 edition.
p. 61.
Diaper, D. and Stanton, N. (2003). The Handbook of
Task Analysis for Human-Computer Interaction. CRC
Press.
European Commission, Directorate-General of Com-
munications Networks, C. . T. (2013). Ten-
der specifications-european data market-smart
2013/0063. Retrieved on May 23, 2019.
Gl
¨
aser, J. and Laudel, G. (2010). Experteninterviews und
Qualitative Inhaltsanalyse. Lehrbuch - VS Verlag
f
¨
ur Sozialwissenschaften. VS Verlag f
¨
ur Sozialwis-
senschaften.
Graube, M. (2018). Linked enterprise data as semantic and
integrated information space for industrial data. Mas-
ter’s thesis, Technische Universit
¨
at Dresden.
Jung, C. G. (2011). Psychologische Typen. Patmos Verlag,
3 edition.
Mathes, R. (1992). Hermeneutisch-klassifikatorische
inhaltsanalyse von leitfadengespr
¨
achen:
¨
uber das
verh
¨
altnis von quantitativen und qualitativen ver-
fahren der textanalyse und die m
¨
oglichkeit ihrer kom-
bination. In Hoffmeyer-Zlotnik, J. H. P., editor, Anal-
yse verbaler Daten :
¨
uber den Umgang mit qual-
itativen Daten, ZUMA-Publikationen. Westdt. Verl.,
Opladen. Retrieved on May 6, 2020, pp. 402-424.
Mayas, C., H
¨
orold, S., and Kr
¨
omker, H. (2012). Meet-
ing the challenges of individual passenger information
with personas. Retrieved on June 3, 2020.
Mayring, P. (2010). Qualitative Inhaltsanalyse: Grundla-
gen und Techniken. Beltz P
¨
adagogik. Beltz, Weinheim
and Basel, 12 edition. p. 70.
Mulder, S. (2006). User Is Always Right, The: A Practical
Guide to Creating and Using Personas for the Web.
New Riders.
Otto, B., Auer, S., Cirullies, J., J
¨
urjens, J., Menz,
N., Schon, J., and Wenzel, S. (2016). Indus-
trial Data Space - Digitale Souver
¨
anit
¨
at
¨
uber Daten.
Fraunhofer-Gesellschaft zur F
¨
orderung der ange-
wandten Forschung e.V., Industrial Data Space e.V,
M
¨
unchen. Retrieved on March 15, 2017, pp. 4-35.
Otto, B., Steinbuß, S., Teuscher, A., and Lohmann, S.
(2019). Reference Architecture Model. International
Data Spaces Association, Berlin, 3 edition. Retrieved
on April 08, 2019, p. 61.
Pruitt, J. S. and Adlin, T. (2005). The Persona Lifecycle: A
Field Guide for Interaction Designers. Keeping Peo-
ple in Mind Throughout Product Design.
Pruitt, J. S. and Adlin, T. (2006). The Persona Lifecycle-
Keeping People in Mind Throughout Product Design.
Morgan Kaufmann Publishers, 500 Sansome Street,
Suite 400, San Francisco, CA 94111.
Schorn, A. (2000). Das themenzentrierte interview. ein ver-
fahren zur entschl
¨
usselung manifester und latenter as-
pekte subjektiver wirklichkeit. Forum: Qualitative
Sozialforschung, 1(2). Retrieved on May 15, 2020.
Sears, A. and Jacko, J. (2009). Human-Computer Interac-
tion: Development Process. Human Factors and Er-
gonomics. CRC Press.
Sharp, H., Preece, J., and Rogers, Y. (2019). Interaction
Design: Beyond Human-Computer Interaction. Wi-
ley.
Teuscher, A. (2018). Industrial Data Space Association -
Souver
¨
anit
¨
at
¨
uber Daten, volume 3. Fraunhofer Insti-
tut. Retrieved on May 17, 2019.
Witzel, A. (2000). The problem-centered interview. Forum
Qualitative Sozialforschung, 1(1). Retrieved on May
15, 2020.
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209