A Meta-Level Design Science Process for Integrating Stakeholder
Needs
Demonstrated for Smart City Services
Antti Knutas
1
, Zohreh Pourzolfaghar
2
and Markus Helfert
2
1
School of Business and Management, Lappeenranta University of Technology, Lappeenranta, Finland
2
Department of Computing, Dublin City University, Dublin, Ireland
Keywords: Smart City, Smart City Services, Service Design, Design Science, Grounded Theory.
Abstract: Currently there is an issue in the design process of smart city services, where citizens as the main stakeholders
are not involved enough in requirements engineering. In this paper, we present a meta-level design science
process, based on an extended version of design science research methodology, that can be used to create
requirements engineering frameworks to inform smart city service requirements engineering processes. The
introduced meta-level process is beneficial as it can be used to ensure that design guideline research processes
are rigorous, just as design science process ensures scientific rigor in design research. Additionally, we present
a previous case study and frame it using the new meta-level design science process.
1 INTRODUCTION
Smart cities are innovative cities, which use ICT to
improve quality of life for citizens (Anthopoulos et
al., 2016; Booch, 2010; Kondepudi and others, 2014).
According to Ferguson (2004) services are the
enablers in the digital cities and therefore, responsible
to improve the citizens’ quality of life. In the other
word, the services in the smart cities need to respond
to the needs of the citizens. In this regard,
Pourzolfaghar and Helfert (2017) have defined the
term ‘smart service’ for the services which meet the
smart cities quality factors and respond to the smart
cities stakeholders’ concerns.
Prior to introduction of agile method in early
2000, software developers were using traditional
approaches to develop software. In traditional
development methods, such as the waterfall,
requirements are divided into two different categories
of user and system requirements, or functional and
non-functional requirements. Functional
requirements are statements of software features and
depend on some factors, e.g. the expected users
(Sommerville, 2011). The functional user
requirements define specific facilities to be provided
by the software. According to Sommerville (2011),
imprecision in the requirements specifications is the
cause of many software engineering problems. Smart
cities should ensure quality factors as perceived by
the end users, which are often the citizens, and ensure
that they are included as stakeholders already in the
requirements engineering phase of projects.
A review of the recent literature suggests that
requirements engineering processes, as it exists in
smart city service design today, need guidance. A
requirements framework would enable service
developers to define the user requirements in line
with the citizens’ needs in smart cities. However, how
to ensure that the requirements framework responds
to the needs of the application domain, is valid and is
scientifically rigorous?
To address the issue, we
1. extend Ostrowski’s design science process
for creating meta-level artefacts (2011,
2012), and
2. present how this new design science
process can be applied to create
requirements engineering frameworks.
Ostrowski et al. (2013) presented a method for
creating abstract design knowledge with business
process modelling, which is suitable for situations
where it is possible to capture explicit organizational
knowledge. In this paper, we present an alternative,
grounded theory -based approach, which is more
suitable for complex problems with human factors
that are difficult to address with formal modelling
Knutas A., Pourzolfaghar Z. and Helfert M.
A Meta-Level Design Science Process for Integrating Stakeholder Needs - Demonstrated for Smart City Services.
DOI: 10.5220/0006512500750084
In Proceedings of the International Conference on Computer-Human Interaction Research and Applications (CHIRA 2017), pages 75-84
ISBN: 978-989-758-267-7
Copyright
c
 2017 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved

(Urquhart et al., 2010). The new approach presented
in this approach is more suited for social systems
where the knowledge is tacit instead of formal, which
is often the case in human societies.
To summarize the research problem, we want to
investigate how stakeholder needs can be better
included in the smart city service design process. To
address this issue, we present a design science
research process for meta-level knowledge artefacts
that can be used to design requirements engineering
frameworks.
The rest of the paper is structured as follows. In
section two we review recent literature on smart city
service design and requirements engineering. In
section three we review design science and meta-level
knowledge artefact creation. In section four we
present a new framework for creating abstract design
knowledge and an initial evaluation of the framework
in the context of requirements engineering research.
The paper ends with section five, conclusion.
2 OVERVIEW OF SMART CITY
SERVICE DEVELOPMENT
AND REQUIREMENTS
FRAMEWORKS
2.1 Smart City Service Development
The general method to develop the services in smart
cities is agile development method. This is due to the
priceless capabilities of agile methods in terms of
quick delivery, simplicity, flexibility, easy risk
management, and less process time compared to
traditional models (Shah, 2016). However, recently
many researchers disclosed some challenges facing
agile methods in terms of defining appropriate goals
and considering stakeholders concerns. For instance,
Kakarontzas et al. (2014) reported some challenges
related to planning the projects, setting achievable
and realistic goals and objectives, and taking into
account the stakeholders’ concerns. Likewise,
Dingsoyr and Moe (2013) presented some challenges
at the time of project planning, the role of
architecture, collaboration between developers and
stakeholders, and constraints in contracts. Shah
(2016) outlined the challenges facing agile methods
as follows: 1) lack of high quality interactions with
stakeholders; 2) overrunning time and cost problems
due to evolving requirements; 3) lack of quality
requirements in initial stages that is essential for
success; 4) unrealistic expectations; and 5) no formal
modelling for the requirements.
2.2 Requirements Engineering
Requirements engineering is one of the crucial stages
in software design and development, as it addresses
the critical problem of designing the right software
for the right user (Aurum and Wohlin, 2005). It is
concerned with identification of goals for a proposed
system, the operation and conversion of these goals
into services and constraints, and the assignments of
responsibilities for development. There are different
levels of requirements, such as functional
requirements that specify what the system will do or
non-functional requirements which guide solution
design.
Stakeholders play an essential part in
requirements engineering, as they represent all the
involved parties and will in one way or another define
the requirements (Aurum and Wohlin, 2005). Typical
stakeholders are product managers, various types of
users and the products’ software developers.
However, requirement gathering is rarely trivial and
requirements elicitation involves seeking, uncovering
and elaborating requirements in a complex process,
which can involve conflicts between stakeholders
(Zowghi and Coulin, 2005). Conflicts between
different stakeholder parties leads to a requirements
priorization process, where importance, risk cost and
other factors are used in deciding what features to
include in requirements (Berander and Andrews,
2005).
3 DESIGN SCIENCE RESEARCH
APPROACH
The overall research approach for this paper is design
science (Hevner et al., 2004), which is commonly
used in the information system sciences to create
artefacts in the form of instantiated systems or new
design knowledge (Ostrowski et al., 2011). Hevner
and Chatterjee (2010, p. 5) define Design Science
Research (DSR) as follows:
“Design science research is a research paradigm
in which a designer answers questions relevant to
human problems via the creation of innovative
artifacts, thereby contributing new knowledge to the
body of scientific evidence. The designed artifacts are
both useful and fundamental in understanding that
problem.”
From the above, Hevner and Chatterjee (2010, p.
5) derive the first principle of DSR: “The
fundamental principle of design science research is
that knowledge and understanding of a design

problem and its solution are required in the building
and application of an artefact.” What essentially
separates the design science research process from
routine design practise is the creation of new
knowledge (Hevner and Chatterjee, 2010). If the
design process is rigorous, it is based on existing
theories and produces new scientific knowledge, then
the process can be considered design science
research.
The concept of an artefact is at the core of the
research science process. In a synthesis of the
Sciences of the Artificial (Simon, 1996) and
Developing a Discipline of the
Design/Science/Research (Cross, 2001) by Hevner et
al. (2010), they broadly define artefacts, which are the
end-goal of any design science research project, as
follows: Construct (vocabulary and symbols), models
(abstractions and representations), methods
(algorithms and practices), instantiations
(implemented and prototype systems), and better
design theories.
The situations where DSR is well applicable are
situations where humans and software systems
intersect (Hevner and Chatterjee, 2010), like
information systems or software engineering
research. What makes information systems research
unique is that it investigates the phenomenon where
technological and social systems intersect (Lee,
2001), which requires a research methodology that
takes both into account.
The original paper on design science by Hevner et
al. (2004) does not present a model or process for
performing design science research. However, a later
paper (Hevner, 2007) refines the concept further and
identifies the existence of three design science cycles
that are present in all design research projects. These
cycles are the Relevance Cycle, which connects the
contextual environment to the research science
project, the Rigor Cycle, which connects the design
activities to the knowledge base of scientific
foundations, and the Design Cycle which iteratively
connects the core activities of building a design
artefact and research.
Hevner’s three cycle view clarified the elements
of design science research, but it still doesn’t still
provide a clear linear view of design science research
process. To provide a process model Peffers et al.
(2007) synthesized the design science research
methodology based on the evolving body of
knowledge on design science. The process contains
six activities, which are summarized as follows:
Problem identification and motivation, defining the
objects for a solution, design and development,
demonstration, evaluation and communication.
3.1 Creating Meta-Level Knowledge
Artefacts
In this section we describe the design knowledge
framework for design science by Ostrowski et al.
(2012), which underlines the division of design
science research into an empirical part (a design
practice) and a theoretical part (meta-design). These
two parts exchange knowledge. The design
knowledge framework presents a process for creating
meta-knowledge artefacts, which consist of abstract
design knowledge. These meta-artefacts in turn can
be used in the creation of situational design
knowledge, such as instantiations or IT systems.
Meta-design artefacts can be used as 1) a
preparatory activity before situational design is
started, 2) a continual activity partially integrated
with the design practice, or 3) a concluding
theoretical activity summarizing, evaluating and
abstracting results directed for target groups outside
the studied design and use practices (Goldkuhl and
Lind, 2010; Ostrowski et al., 2012). Meta-design
artefacts are based on data types as opposed to
specific instances of data (Ostrowski and Helfert,
2012). These types of artefacts are general, or
“unreal” according to Sun et al. (2006). However,
meta-design produces solid and generic background
for design science activities to construct solutions for
a real environments, systems and people (Ostrowski
and Helfert, 2012; Sun and Kantor, 2006).
In Figure 1 we extend Hevner’s three cycle view
(2007) to include the split between abstract and
situational knowledge. The original three cycle view
included only the top half and considered only
situational knowledge. The top level contains the
environment and situational design, where design
science could be applied to create requirements for
one specific smart city service. The lower level
contains the creation of abstract design knowledge,
which informs and guides the design of situational
artefacts. Ostrowski et al. (2011, 2012) have earlier
created a similar extension for the process model by
Peffers et al. (2007), following the ideas by Goldkuhl
and Lind (2010).
In our case, the meta-design context is fitting for
the creation of requirements framework, because we
create meta-level design knowledge (framework) that
guides the creation of the situational design (set of
requirements). Both levels of design, situational and
abstract, produce a method as the artefact. However,
the situational design produces a set of requirements
and the abstract design part produces a requirements
framework to guide the requirements process.

Ostrowski et al. (2011, 2012) further divide the
meta-artefact design process into three steps that
interact with each other: Modelling, literature review
and engagement scholarship. We relate them to the
cycle model as the (theoretically grounding) rigor
cycle and the meta-relevance cycle, as seen in Figure
1.
In the abstract design knowledge phase two levels
of knowledge, literature and design experts,
contribute to create reference models for design
(Ostrowski and Helfert, 2012). Literature review
allows developing an initial scope and reviewing
existing knowledge, and collaboration with
practitioners allows ensuring problem relevancy and
gaining current knowledge. These two information
sources are combined to a reference model, which
allows modelling and evaluation of solutions. This
model is then compared to existing body of
knowledge in theoretical grounding in a rigor cycle,
and to designers for the design practice phrase in a
meta-relevance cycle.
The knowledge exchanges presented in Figure 1
are also form the three-part grounding process:
Theoretical, empirical and internal grounding
(Goldkuhl and Lind, 2010). Theoretical and empirical
grounding between the meta-artefact and the artefact
design cycle, and internal grounding in both artefact
design cycles.
3.2 Evaluating and Grounding
Meta-Level Knowledge
As with all design science research, the validity of the
artefact is judged by its utility (Hevner et al., 2004).
Therefore, the model resulting from meta-artefact
design should be evaluated to establish its validity
before applying it to the artefact design cycle.
There are two levels of evaluation in design
science research: artificial and naturalistic (Venable,
2006). Artificial evaluation is contrived or non-real in
some manner and may consist of simulations,
field experiments or lab experiments. Naturalistic
Figure 1: Extended Cyclic View of Design Science Research Process.

evaluation is full evaluation of the situational artefact
in its intended environment, the application domain.
Naturalistic evaluation may consist of methods such
as case studies, survey studies or action research.
Ostrowski et al. (2012) present that for abstract
design knowledge artificial evaluation is more
suitable and for situational design knowledge
naturalistic evaluation is most suitable. They also
present a process model where situational design
knowledge is validated with naturalistic evaluation
and abstract design knowledge is further validated by
that after an empirical grounding process.
4 META-LEVEL DESIGN
PROCESS FOR DESIGN
SCIENCE RESEARCH
In this section, we present a new meta-level design
process that is builds on Ostrowski’s work (2011) and
earlier work on fitting the grounded theory research
methodology in design science research process
(Gregory, 2011). Ostrowski’s framework is business
process modelling -based and suitable for design
science cases where large organizations are central
and the knowledge is explicit. We present an
alternative that is aimed for situations with complex
human factors and individuals as actors, such as
citizens as stakeholders in service design processes,
and the knowledge is tacit.
4.1 A Framework for Creating
Meta-abstract Design Knowledge
Artefacts
Ostrowski’s framework for creating meta-abstract
design knowledge for information systems
recommends three steps for creating models for
information systems: 1) Literature review, 2)
collaboration with practitioners, and 3) then creating
an ontological model using one of the business
modelling languages (Ostrowski and Helfert, 2012).
It is aimed for process-oriented environments, such as
large organizations or situations where business
process modelling is appropriate (Ostrowski and
Helfert, 2012, 2013).
In this section, we present an alternative process
that uses grounded theory (Glaser and Strauss, 1967)
as defined by Urquhart et al. (2012; 2010) for
information systems research to generate a design
theory. In the process design we follow the line of
research that discusses and evaluates using grounded
theory in design theories (Adams and Courtney,
2004; Goldkuhl, 2004; Gregory, 2011; Holmström et
al., 2009). This alternative approach is valuable for
creating meta-level design knowledge for situations
that involve complex human factors that are a
challenge for formal models, or for situations where
is not initially clear who are the actors, their
relationships and the exact nature of the issue is not
clearly defined.
The objective of grounded theory is the discovery
of a theoretically comprehensive explanation about
phenomena, using techniques and analytical
procedures that enable investigators to develop a
theory that is significant, generalizable, reproducible
and rigorous (Adams and Courtney, 2004). The aim
of grounded theory is not only to describe a
phenomenon, but also to provide an explanation of
relevant conditions, how actors respond to the
conditions and consequences of the actors’ actions
(Kinnunen and Simon, 2010; Urquhart et al., 2010).
For data analysis, it has a systematic set of procedures
that support the development of theory that is
inductively derived and continuously tested against
empirical data through constant comparison (Strauss
and Corbin, 1990).
Grounded theory has three levels of theory: 1)
narrow concepts, 2) substantive theories, and 3)
formal theories (Urquhart et al., 2010). Substantive
theories have been generated within a specific areas
of inquiry, The highest level of abstraction is a
“formal theory”, which focuses on conceptual
entities, such as organizational knowledge (Strauss,
1987). Our alternative process uses design science to
first 1) first generate a situational grounded theory
based on relevance cycle interactions, 2) use
theoretical integration (Urquhart, 2007) in the rigor
cycle to compare and extend the grounded theory to
create a substantive theory, and 3) uses the theory to
create a model to assist in situational design phase.
In Table 1 we present the three process details as
synthesized from guidelines by Urquhart et al. (2010;
2012) for information systems research and Gregory
(2011) for design science research methodology, and
how they can be applied to requirements framework
generation. The Table 1 also includes a subset of
Figure 1, and relates the three process steps to the
extended three cycle view of design science.
To summarize the process, in phase 1a the
situation is investigated, and phenomena and actors
around the current situations are identified. This
involves gathering source material from the actors,
often interviews, and using the grounded theory
methodology to code the results. In phase 1b the
meta-application domain is engaged, with the
researcher interacting with domain and design

experts. This results in a situational theory of the issue
and initial ideas for a solution. This situational theory
is then scaled up in phase 2 by engaging current
academic knowledge and using theoretical
integration to scale up the situational theory. Finally,
in phase 3 the researcher should create a meta-level
Table 1: Meta-level artefact design, as guided by grounded theory, connected to the extended three cycle model.
Desi
g
n science
activity
1a. Relevance
phase
1b. Meta-relevance
phase
2. (Meta-level)
rigor phase
3. Meta-artefact
design phase;
empirical
g
roundin
g
Grounded
theory activity
Open and selective
coding; initial
theoretical coding
Advanced
theoretical coding;
theoretical
grounding.
Theoretical
grounding and
scaling up.
Relating the
emergent theory
to literature.
Constant comparison.
Grounding the theory
back to original data.
Outcomes Identifying core
phenomena,
relationships and
initial explanation.
Concepts defined.
Grounding the
emergent theory in
existing expert
views. Initially
grounded situational
theory.
Rigorous
situational
theory.
A prescriptive meta-
level artefact that can
guide artefact design.
Design informed by
the descriptive
situational grounded
theor
y
.
As applied in
requirements
framework
design
Discovering key
concepts and issues
in smart city
service design and
how the
stakeholders in the
application domain
perceive it.
Grounding the
initial solution
concept in
requirements
engineering expert
opinions. Having a
concept for solution
that is supported by
practitioners.
An initial
concept of a
requirements
framework.
Supported by
existing
literature.
Requirements
engineering
framework that has
been created to
address the issues in
requirements design
as explained in the
situational theory.
Supported by the
(empirically and
theoretically)
grounded theory.

artefact based on the scaled-up theory that can be used
to inform situational design processes. In the example
presented in Table 1 this meta-artefact would be a
requirements engineering framework that addresses
issues discovered in phases 1 and 2.
4.2 Evaluating the Meta-abstract
Design Knowledge Framework
with a Sample Case
In this section, we evaluate the utility of our meta-
abstract design knowledge framework by presenting
how it can guide and inform an ongoing design
science meta-artefact design process. This is an initial
form of artificial evaluation (Venable, 2006), which
should establish a preliminary utility of the
framework (Ostrowski et al., 2012; Pries-Heje et al.,
2008), and thus its validity (Hevner et al., 2004).
The evaluation consists of framing an existing
series of case studies by Pourzofarghar et al., where
abstract design knowledge is created by using the
meta-abstract design knowledge framework. In this
series of case studies Pourzofarghar et al. have
discovered that currently citizens are not involved
enough as stakeholders in current smart city design
processes (Pourzolfaghar et al., 2016; Pourzolfaghar
and Helfert, 2017), even though they are most often
the end users. This is a clear issue in software system
design, because requirements elicitation from all
stakeholders is a critical part of requirements
engineering (Zowghi and Coulin, 2005).
This far the research group has identified the issue
and established a problem definition (Pourzolfaghar
et al., 2016), and have created a taxonomy based on a
literature review to inform smart city service
developers (Pourzolfaghar and Helfert, 2017). The
next step is to create a requirements engineering
framework that would enable smart city requirements
engineering processes to better consider citizens as
stakeholders.
When one frames the entire process in the context
of a design science process as presented in Figure 1
and Table 1, there are two levels. On the situational
level, the application environment is 1) smart city
service developers using a requirements design
framework to create services, and 2) the service users
as stakeholders. The knowledge base is the scientific
body of knowledge on the topic. The meta level on
the other hand consists of Pourzolfaghar’s research
group, who are creating a requirement engineering
framework to inform individual service requirements
engineering processes. What we are presenting in this
paper is a framework to describe and formalize meta-
level design.
The research group is creating several meta-
artefacts, of which the smart city service taxonomy
was the first one (Pourzolfaghar and Helfert, 2017).
The taxonomy is a meta-artefact, because it is not the
result of smart city service design processes, but
instead has been created to inform the design process.
In Table 2 we frame the research group’s design
process as a meta-artefact design process with the
new meta-abstract design knowledge framework and
present a proposed plan how they would proceed. The
benefits of the framework in this case is ensuring that
their framework is strongly grounded in actual citizen
needs while enabling scaling up the theory to a more
general level. Having a framework to support the
meta-level design science research process also
ensures that the relevance, rigour and design
grounding are all considered in the process.
After creation of the research plan (as summarized
in Table 2) the members of the research group were
interviewed first individually and then as a group. The
research group agreed that the plan is beneficial and
could inform their meta-artefact design process.
While not full proof of the framework’s validity, it
can be considered a promising initial evaluation and
suggests that the framework evaluation should
proceed with further, empirical testing. The
interview-based evaluation found the following
benefits from the proposed approach.
• The main goal for the process is designing
effective services that can improve citizen’s
quality of life, and so grounding the design
theory back to stakeholders is beneficial.
• The design process involves complex, human
problems, as service design is a complex,
human-centered issue. In this case the new,
proposed framework would be suitable.
• The research topic exists at intersection of
human computer interaction studies with users
and smart city services, business process
modelling and software development processes.
Flexible model creation process allows
addressing all of these issues.
5 CONCLUSIONS
In this paper, we presented an issue in smart city
service design stakeholder involvement and a new
method that can be used to inform the design of meta-
artefacts, such as requirements frameworks. This has
the potential to improve the quality of services and
design processes, not by directly addressing the issue,
but by presenting a method for creating abstract
design knowledge for design science research.

Table 2: Framing an existing case with the design science-based meta-artefact framework.
Desi
g
n
science
activity
1a. Relevance phase 1b. Meta-relevance
phase
2. (Meta-level)
rigor phase
3. Meta-artefact
design phase;
empirical
g
roundin
g
Grounded
theory
activity
Open and selective
coding; initial
theoretical coding
Advanced theoretical
coding; theoretical
grounding.
Theoretical
grounding and
scaling up.
Relating the
emergent theory
to literature.
Constant
comparison.
Grounding the
theory back to
original data.
Case
activities
and
outcomes
- Discovering what
needs exist in regard to
smart city services in
the local context with
interviews and the
grounded theory
coding-based analysis
process. Interviewing
both smart city service
designers and
stakeholders.
- Creating a simple,
situational grounded
theory model to
describe what needs
exist, how
requirements
engineering processes
respond to current
needs and what is
missing.
- Grounding the local,
situational model of
smart city service
requirements design in
smart city service
designer opinions.
Creating a model of
requirements
engineering processes
as part of theoretical
coding.
- Having a model of
user needs in the
environment that
allows weighing the
taxonomy.
- Scaling up the
taxonomy and
model of user
needs by
rigorously
comparing it to
existing scientific
literature.
- Seeing if the
local situational
theory matches
existing scientific
knowledge and
identifying the
novel
contribution.
- Scaling up the
requirements
engineering
model
- A formal
taxonomy that has
been generated from
local observations
and then scaled up
by literature review.
- A situational
model of user needs
in smart city
services that can be
used to weigh the
smart city service
taxonomy and to
inform smart city
service design
processes.
- Validation:
Grounding the
scaled-up model by
applying it in the
original context.
We also extend the current state of the art in meta-
artefact creation processes (Iivari, 2015; Ostrowski et
al., 2011; Ostrowski and Helfert, 2013) with a
grounded theory -based approach and present a novel
process description for meta-abstract artefact
creation. In this approach, the grounded theory
research method can be used in conjunction with a
design science meta-artefact creation process to
create abstract design knowledge and situational
design theories, providing an example of how to
apply the combination of grounded theory and design
science, as originally proposed by Gregory (2011).
The framework presented in this paper warrants
future investigation and evaluation in order to
establish its utility and thus the validity. As future
work, the researchers will proceed by creating a
requirements framework, informed by the meta-
artefact creation process presented in this paper.
ACKNOWLEDGEMENTS
The first author graciously acknowledges the funding
by Ulla Tuominen Foundation. This work was
supported, in part, by Science Foundation Ireland
grant 13/RC/2094 and co-funded under the European
Regional Development Fund through the Southern &
Eastern Regional Operational Programme to Lero -
the Irish Software Research Centre (www.lero.ie).
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