Design Science for
Socio-technical System Design
Leixlip, 6 December 2012
Joan E van Aken
content of presentation (1)
 Design science (DS): valid generic knowledge on how
and what to design, produced by rigorous research
 Information systems are socio-technical systems,
both technical and social components are important
 Developing DS for the social components is fundamentally
different from developing DS for the technical ones,
so paradigmatic issues are important
Industrial Engineering and Innovation Sciences
6 Dec 2012
PAGE 1
content of presentation (2)
 Background: two research paradigm issues
- design versus explanation (March & Smith, 1995)
- design paradigm for the technical components of
information systems, behavioural paradigm for the
social ones
(Hevner et al., 2004)
 Conclusion: behavioural science does not produce valid
design knowledge for the social components of IT-systems
Industrial Engineering and Innovation Sciences
6 Dec 2012
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question
 Question to the audience
(please vote by raising your hand)
 ‘It is not possible to develop design science
for the social components of information systems’
A. yes, not possible
B. no, possible but not in a rigorous way
C. yes, should be possible, but don’t know how
D. yes, is possible and I know how to do it
E. other
Industrial Engineering and Innovation Sciences
6 Dec 2012
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content of presentation (3)
1. Introduction
2. Design science
3. Research paradigms
design science versus explanatory science
4. Design science for social system design
the issue of human agency and the basic research strategy
5. Design science research projects in management
6. Possible contributions conversations in information
system research
7. Concluding remarks
Industrial Engineering and Innovation Sciences
6 Dec 2012
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design science
Science can be defined as a type of knowledge (scientia: Latin
for knowledge) and as an academic discipline
 Design science on or for designing:
descriptive and explanatory or supporting designing
 Design science for designing: valid knowledge on how and
what to design, produced by rigorous research
(both methodological and substantive knowledge; validity used here
as a container concept)
 Rigorous: following the ‘rules of the game’, so adapted to
the nature of research object and of research question
Industrial Engineering and Innovation Sciences
6 Dec 2012
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social and technical system components
 Information system performance is critically dependent on
the quality and coordination of both the technical and the
social components of the system,
so we need design science for both types of components
 The fundamental difference between both types:
human agency (both a asset and a liability for the designer)
Industrial Engineering and Innovation Sciences
6 Dec 2012
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objective of presentation
 The objective of this presentation:
- to discuss the nature of design science research for
social system design
(in the field of organization and management)
- to show how one can do so
- to link this to IT-conversations
Industrial Engineering and Innovation Sciences
6 Dec 2012
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research paradigms (1)
 Research paradigm, following Lakatos(1991):
combination of research questions asked,
research methodologies allowed (including what is
accepted as evidence) and research products pursued
 March & Smith (1995) contrast the design and the
‘natural sciences’ paradigms
Hevner et al. (2004) contrast the design and the
behavioural science paradigms
Industrial Engineering and Innovation Sciences
6 Dec 2012
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research paradigms (2)
 One can distinguish (van Aken, 2004, 2005)
- the explanatory research paradigm
(explanatory sciences like physics and sociology)
- the design science research paradigm
(design sciences, like medicine and engineering)
 Explanatory research:
aims at understanding the world that is
 Design science research:
aims at developing knowledge to create the
world that can be
Industrial Engineering and Innovation Sciences
6 Dec 2012
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research paradigms (3)
 Explanatory research (as used in e.g. in physics)
- driven by pure knowledge problems; observer perspective
(knowledge as an end)
- mission: to understand, a quest for truth
- students are trained to become researchers
by researchers
- iconic research product: the causal model
 Design science research (as in medicine and engineering)
- driven by field problems, actor perspective
(knowledge as a means)
- mission: to improve the human condition
- students are trained to become professionals
largely by professionals
- iconic research product: generic solution and
the design proposition
Industrial Engineering and Innovation Sciences
6 Dec 2012
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DSR research products (1)
 DSR produces many kinds of knowledge, but the iconic
ones are is alternative generic solutions/interventions for
types of field problems and design propositions
 The design proposition puts the generic solution into
its context
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6 Dec 2012
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generic alternative solutions
 Examples of field problems for which generic alternative
solutions have been developed in the field of
management (by PhD-DSR-studies):
- how to create an effective virtual team
- how to find reliable overseas partners for cooperative
arrangements (for SMEs)
- how to involve end users in product innovation
- how to promote intrapreneuring
- how to deal with setbacks in radical innovation
Industrial Engineering and Innovation Sciences
10 October
2012
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DSR research products (2)
The design proposition gives the basic pragmatic logic
“if you want to solve this type of problem-in-context,
you may use this generic solution/intervention
(which will produce the desired outcome through this mechanism)”
 A design proposition is not a prescription
(in management research the term prescriptive or normative
is a legacy from the time of the one best way of organizing)
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6 Dec 2012
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the design proposition (CIMO-logic)
 Prison problem C – locked door, bars, guards I –
no escapes O – physical constraining of movements M
 Theft in car park C – introduction CCTV I – less theft O –
deterrence, allocation of personnel, less careless
behaviour of parkers M
 Distributed team C – FtF kick-off meeting I – effective
team O – collective insight and collective commitment M
 Promotion of intrapreneurship C – gaming I –
insight and commitment O – experiential learning M
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the methodological problem of DSR (1)
 The basic scientific claim of a design proposition is that
the proposed solution/intervention will indeed produce
in the given context the desired outcome
 So the question is how to establish this claim:
one has to predict the outcomes of interventions
 The answer to this question produces the research
strategy of DSR
Industrial Engineering and Innovation Sciences
6 Dec 2012
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the methodological problem of DSR (2)
 In the material world this demand for prediction does
not pose specific methodological problems,
 because in this world there are
universal, invariant, individual behaviour
determining mechanisms
 A machine, developed, produced and tested in Helsinki
will also work next year in Barcelona
Industrial Engineering and Innovation Sciences
6 Dec 2012
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the methodological problem of DSR (3)
 Because of human agency no universal, invariant
and individual behaviour determining mechanisms
in the social world
 This makes the prediction of the outcome of
interventions in the social world difficult
 However, there are patterns and regularities in
human behaviour
Industrial Engineering and Innovation Sciences
6 Dec 2012
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research as experiential learning (1)
 Prediction of the behaviour of others is an almost
universal human competence
(without this competence intentional social behaviour would
be almost impossible, as can be seen with autism)
 This competence is developed by personal
experiential social learning
 The basic research strategy for DSR is experiential
learning; more precisely
objective and systematic experiential social learning
through series of case-studies (developing and testing
in context)
Industrial Engineering and Innovation Sciences
6 Dec 2012
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research as experiential learning (2)
 Experiential learning is the basis for crafts and
trade schools, including the business school of
the past; this is not what we want
 Rigorous research as objective and systematic
experiential learning can produce valid, objective
generic knowledge
 It involves rigorous case studies, using methods
like controlled observations, triangulation, rich
descriptions, careful cross-case analyses,
member checks, beta testing, etc.
Industrial Engineering and Innovation Sciences
6 Dec 2012
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research as experiential learning (3)
 The basic process of designing consists of
synthesis-evaluation iterations
 For the technical components of information systems
the evaluation methods are quite similar to those of
explanatory natural science
 Because of human agency this is not feasible for the
social components; hence the strategy of objective
and systematic experiential learning
 Question to the audience:
do you feel that this strategy is an acceptable way
to develop design science?
Yes / no / don’t know yet
Industrial Engineering and Innovation Sciences
6 Dec 2012
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DSR-projects (1)
 Donald Schön (1983) makes a distinction between
the swamp of practice and the high ground of theory
 The challenge of DSR is to provide some firm ground
in the swamp
 In DSR one works alternately in the ‘practice stream’
and the ‘knowledge stream’
Industrial Engineering and Innovation Sciences
6 Dec 2012
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DSR-projects (2)
 In the practice stream (the swamp) one develops specific
solutions for (series of comparable) specific field
problems, interacting with stakeholders and other
practitioners (e.g. collaborative research)
 In the knowledge stream (the high ground) one develops
generic solution oriented knowledge by generalizing
across cases, interacting with other researchers
(experiential learning: developing generic knowledge by using
the experiences of working in the practice stream )
Industrial Engineering and Innovation Sciences
6 Dec 2012
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DSR-projects (3)
 This can be regarded as a type of Action Research
(for which there is ample methodological literature)
or Action Design Research (Sein et al.,2011)
 In DSR there is an emphasis on
- developing generic knowledge through multiple cases
- on the basis of a clear and generic problem statement
 DSR-projects typically have an explanatory/diagnostic
phase, followed by a design science one
Industrial Engineering and Innovation Sciences
2 Nov 2012
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DSR-projects (4)
 Solutions/interventions, developed by DSR, are to
improve organizational performance, but effects on
‘the bottom line’ are always difficult to prove
 Rigorous design research typically develops a
solution/intervention/system to realize a desired
direct outcome
 Where available, explanatory research outcomes
can link this direct outcome with the desired
final outcome
Industrial Engineering and Innovation Sciences
2 Nov 2012
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DSR-projects (5)
 Further rigour is obtained by testing in the intended
field of application
 Alpha-testing by the designers, beta testing by
third parties
 Further support can be obtained by peer reviews
and user reviews
 The key issue remains: does the system (on average!)
produce the outcomes that are claimed
Industrial Engineering and Innovation Sciences
6 Dec 2012
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linking to IS-research (1)
 A well-known information system research process:
build evaluate theorize justify (March and Smith, 1995)
 Under certain conditions this may be regarded as
objective and systematic experiential social learning
 These conditions include
- not one case but multiple comparable cases
- ‘theorizing’ means generalizing across cases
- ‘justification’ means rigorous field-testing
Industrial Engineering and Innovation Sciences
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linking to IS-research (2)
 A well-known set of IS- research products
constructs models methods instantiations
(March and Smith, 1995)
 Just ‘instantiations’ may be useful for purely
technical systems (to the extent that one may abstract from
the context-dependency of performance)
 Research products for socio-technical design research
rather ‘type of systems’ and corresponding design
propositions, tested across various contexts
Industrial Engineering and Innovation Sciences
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conclusions
 The social components of information systems differ
fundamentally from the technical ones
 The main reason: human agency
 A research strategy based on
objective and systematic experiential social learning
can produce valid design science for these components
Industrial Engineering and Innovation Sciences
6 Dec 2012
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