Design Science for Socio-technical System Design

Design Science forSocio-technical System Design

Leixlip, 6 December 2012

Joan E van Aken

6 Dec 2012

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 PAGE 2

6 Dec 2012


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


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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


6 Dec 2012


1. Introduction

2. Design science

3. Research paradigmsdesign science versus explanatory science

4. Design science for social system designthe 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


6 Dec 2012

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


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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)


6 Dec 2012

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


6 Dec 2012

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


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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


6 Dec 2012


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


6 Dec 2012

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


Industrial Engineering and Innovation Sciences PAGE 1310 October 2012

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

6 Dec 2012

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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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


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

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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

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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


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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)


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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.


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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


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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’


6 Dec 2012

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 PAGE 242 Nov 2012

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 PAGE 252 Nov 2012

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

6 Dec 2012

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


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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


6 Dec 2012

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


6 Dec 2012

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


Documents

Design Science for Socio-technical System Design