System Innovation for Sustainability: Using Systems Thinking and Design Thinking

I recently attended a webinar on using systems thinking and design thinking conjointly to address sustainability challenges. The webinar was presented by Peter Coughlan of IDEO and Colleen Ponto of Seattle University. It was great to hear from these forefront thinkers/doers thoughts similar to mine on the potential of using systems thinking and design thinking conjointly. I also derived a lot of learning on how to communicate these ideas using simple language and examples. I am looking forward to seeing this thinking spread to a wider audience and used by policy makers (top-down actors) and innovators (bottom-up) alike, preferably in collaborative projects. Inspired by this webinar, I explain my thoughts on conjoint use of systems thinking and design thinking that I’ve been mulling over for a while. I have five main messages.

1. All design and innovation efforts to achieve sustainability should be based on sustainability science:

Sustainability is a system property. In order to plan for and achieve the required transformations towards becoming sustainable, we need to work with a set of questions which cannot be answered through traditional disciplinary segmentation of knowledge (Figure 1). First we need to understand the systems needing to be transformed and the interrelationships between these systems. This knowledge is acquired and interpreted by basic disciplines such as physics, chemistry, sociology and ecology. Second, we need to understand what we can do to transform these systems. The knowledge to answer this question comes from the applied disciplines such as engineering, agriculture, architecture and business. Third, we need to establish what we want to do and set our priorities towards our destination based on what we know about the systems and what we can do to transform them. The knowledge for this comes from disciplines such as planning, law, politics and design. Finally, we need to establish a values framework which will oversee our work towards sustainability and will inform our actions. The knowledge for this comes from disciplines dealing with human values such as ethics, philosophy and theology.

Figure 1. Transdisciplinary generation of knowledge (Max-Neef 2005)

Sustainability science has complex adaptive systems theory as its main tenet, focuses on the dynamic interactions between nature and society and aims to bridge the natural and social sciences for seeking creative solutions to these complex challenges.  (Clark & Dickson, 2003; Jernek et al., 2010; Kates et al., 2001; Spangenberg, 2004). Sustainability science is a transdiscipline which integrates knowledge from all disciplinary domains to solve socially relevant complex problems. Sustainability scientists, instead of developing disciplinary expertise, focus on understanding specific sustainability problems by tapping into the knowledge generated from all disciplines relevant to the problem. The expertise gained by sustainability scientists can be described as a new generation expertise because of its transdisciplinary nature.

Although there is a lot of discussion on sustainability within the design and innovation field and there are a lot of claims on sustainability of particular products/services/technologies or business operations/models/processes, I do not observe much of this being based on the science of sustainability. Unless designers/innovators acknowledge and use the growing body of knowledge generated by sustainability science, there is not much potential for design/innovation efforts to address the right problems with the right objectives.

2. In order to achieve sustainability, our design and innovation efforts should intervene into systems:

Today we know that reducing unsustainability through efficiency improvement approaches will not produce sustainability; it will only save us miniscule amounts of time before the systems we rely on collapse or become unviable to support human life. Traditionally and still currently, we focus most of our efforts to improve existing products/services or design new products/services with higher efficiency than the earlier ones. Although these approaches have their place in transforming systems, if remain as our sole strategic framework for innovation they become lock-ins and hinder systemic transformations (e.g. Könnölä & Unruh, 2007). Product-centred innovation approaches should leave their places to innovation efforts aiming to meet particular social functions, thus breaking from incrementalist tendencies and generating opportunities for radical systemic transformations. The new generation innovation approaches do not start with a product concept; instead, they start with identifying new ways of meeting human needs which have traditionally been met by particular products or services or left unmet. For example, in the new approaches to innovation, the starting aim is not to develop a more efficient washing machine but generating ideas on how to provide clean clothes to people. By taking a step back and identifying the actual need, innovative concepts are generated and new organisational models can be developed. This approach also enables moving from a fixation of technological development to developing both technological and social interventions conjointly meeting the specified need.

The new generation innovation efforts aiming to address interrelated environmental and social issues should be based on sustainability science and innovate not only for developing new technological solutions to sustainability problems but also to generate new organisational models, inspire new social and cultural norms and to eventually alter the institutional context within which socio-technical systems reside. This require both macro and micro-level innovations; in other words we need to optimise our designs for the systems as well as for the individuals using the products/service/technologies of the systems. Leveraging micro- (product/service) and macro-level (system) innovations simultaneously mandates business plans to cover longer periods than they traditionally have and strategic and market-creating approaches to innovation than market-following approaches.

Figure 2. Levels of innovation for sustainability (Brezet, 1997; Gaziulusoy, 2010; 2011)

3. Design thinking is a very appropriate approach to use in innovation for sustainability especially when used in conjunction with systems thinking:

Design in society has been understood with references to its outputs such as fashion design (clothes), urban design (cities), architectural design (buildings), car design (automobiles), product design (products), service design (services) etc. However, design is a fundamental human cognitive ability, it is a particular way of thinking. Design professionals are trained to use design thinking to generate solutions for specified challenges. Design thinkers tap into different types of knowledge available to humanity to reach a normative goal. Design thinking is a process which starts with defining/redefining the problem to be addressed. This is followed by research, creative exploration, evaluation of ideas and implementation and communication of the solution. The output of design thinking can be any of the above mentioned outputs but also through design thinking one can conceive new systems, processes, organisational models, enterprises, policies and even community campaigns. The strength of design thinking in the context of innovation for sustainability lies in its emphasis on the divergent process of generating alternative solutions before acting upon one compared to traditional optimisation approaches which selects the optimum solution among available options. Design thinking process can be applied to almost any problem to transform people, organisations and systems. This has been coined with a new term by UK Design Council: Transformation Design. The new generation innovation approaches explicitly or implicitly use design thinking for transforming the society.

Figure 3. Conjointly using systems thinking and design thinking (Coughlan and Ponto, 2012)

Design thinking can reach its potential to address sustainability challenges only if it is conjointly used with systems thinking as these two approaches complement each other in achieving system innovations. Systems thinking looks at the history and present state of systems to analyse and understand them. Design thinking looks at the present state of a system and asks the normative, future-oriented question of “what can be?” in order to innovate and transform the systems. Systems thinking has qualitative and quantitative tools and methods which help to uncover patterns and structures within a system to explain how the events -the problems we observe- have been created through time. On the other hand, design thinking has several tools and methods to uncover the mental models which created the structures and historical patterns. Systems thinking optimises at system level whereas design thinking optimises at individual level therefore together they can create alignment between the innovation direction of system components and systems consisting of those components.

4. Design and innovation efforts should be collaborative and empowering:

One fundamental systems thinking rule states: When intervening in a system, effort should be put in restoring or enhancing the system’s own ability to solve its problems (Meadows, 2008). Aligned with this, it is really important that in our design and innovation efforts we analyse and address problems with a good contextual understanding and in a way to create opportunities within that context. Two solutions addressing the problem of access to safe drinking water can be used to illustrate this point.

It is common knowledge that currently approximately 800,000 people lack access to an improved water source. There have been many efforts to address this problem which also include developing purification technologies. One product marketed as LifeStraw, designed by a Swiss company, became one of the iconic products addressing this problem in developing countries. This product consists of a plastic tube through which a person can suck water from a water body. The water is filtered by fibres that are in the tube as the person drinks it. A person generally goes through one or two of these products in a year. This product is often referred to as a great example of sustainable design. This product has received criticitism for being too expensive for the intended use contexts and the funding was supplied by health campaigns run by NGOs which probably tapped into foreign aid.

Purifying water is not rocket science; there is no need for sophisticated production technologies, plastic cases and top-secret filter formulae. The problem with access to drinking water does not rise from the lack of appropriate technologies in a context to purify water, it rises because of the lack of incentives to act in ways to enhance and restore those contexts their own ability to purify their own water (because the so-called “innovators” cannot make a business case otherwise). Low cost water purification systems can be easily made using local materials and low-tech manufacturing technologies. A good example is the clay pot filter developed by Australian material scientist and potter Tony Flynn. This filter is made by mixing clay with fine grained organic material fired without the requirement of kilns. This technology is open source so that anyone can make these filters and the knowledge of making them can be transferred to the communities experiencing the problem.

Therefore, both in identifying and addressing problems design and innovation efforts should use human-centred approaches to generate solutions which are empowering for the intended users. This of course also requires shifting from profit-centred economic models of doing business to people-centred models, which essentially can be conceived as a design problem.

5. Design and innovation efforts should be based on a personal vision aligned with the future we would like to see in the world

Unfortunately, vision as a term has been narrowed down to mean a single-sentence, “measurable” statement by the mainstream management literature and practice of 1990s. Recently, the power and importance of visions and the proper practice of visioning is being rediscovered by people who are working in the field of sustainability, from scientists to grassroots activists to policymakers. Futurists define visions as “futures for the heart”. On the contrary to single-sentence visions of 1990s, the more detailed and the more collaboratively developed the visions for sustainable futures are the better. Our design and innovation efforts can lead us towards achieving system innovations for sustainability only if our day to day actions are informed by a personal vision which takes into consideration the spatial and temporal influence we have as individuals on our workmates, company vision, fellow citizens, policy, and future generations.

Temporal and spatial influences of personal action and vision

References I used in this post:

Brezet, H. (1997). Dynamics in ecodesign practice. Industry and Environment, 20(1-2), 21-24.

Clark, W. C., & Dickson, N. M. (2003). Sustainability science: The emerging research program. Proceedings of the National Academy of Sciences of the United States of America, 100(14), 8059-8061.

Coughlan, P., & Ponto, C. (2012). Systems Thinking + Design Thinking: Moving from What Was and What Is to What Could Be [Webinar]. USA

Gaziulusoy, A. I. (2010). System Innovation for Sustainability: A Scenario Method and a Workshop Process for Product Development Teams (Ph.D. thesis). University of Auckland, Auckland.

Gaziulusoy, A. I. (2011). System Innovation for Sustainability at Product Development Level: A Conceptual Framework. Proceedings of the Tao of Sustainability: An International Conference on Sustainable Design Strategies in a Globalization Context, October 27-29, 2011, Beijing, China.

Jerneck, A., Olsson, L., Ness, B., Anderberg, S., Baier, M., Clark, E., … Persson, J. (2010). Structuring sustainability science. Sustainability Science, 1-14.

Kates, R. W., Clark, W. C., Corell, R., Hall, J. M., Jaeger, C. C., Lowe, I., … Svedin, U. (2001). Environment and development: Sustainability science. Science, 292(5517), 641-642.

Könnölä, T., & Unruh, G. C. (2007). Really changing the course: the limitations of environmental management systems for innovation. Business Strategy & the Environment 16(8), 525-537.

Max-Neef, M. A. (2005). Foundations of transdisciplinarity. Ecological Economics, 53(1), 5-16.

Meadows, D. H. (2008). Thinking in systems: a primer. White River Junction, Vt.: Chelsea Green Publishing.

Spangenberg, J. H. (2004, August 27-28, 2004). Sustainability Science: Which Science and Technology for Sustainable Development? Presented at the meeting of the IRDF Forum on Sustainable Development, Johannesburg. Available from

UNICEF/WHO. (2012). Progress on Drinking Water and Sanitation. Available from 

Complexity and co-evolution

Socio-technical systems are complex adaptive systems. Therefore, in order to attempt initiating and steering system innovations, we must understand what complex adaptive systems are and how they do behave.

Defining complex systems is not an easy task. As a starting point, complex systems are what simple systems are not. The major distinguishing characteristics of simple systems are predictable behaviour, small number of components with few interactions among them, centralised decision-making and decomposability (Casti, 1986). Therefore, through negation of these characteristics, the major characteristics of complex systems are identified as unpredictable behaviour, large number of components with many interactions among them, decentralised decision-making and limited or no decomposability. A distinction between complicated and complex systems is also useful here. Cilliers (1998) argues that if a system has a very large amount of components but yet can still be fully analysed, the system is complicated rather than complex. A complex system, on the contrary to a complicated one, has intricate sets of non-linear feed-back loops so that it can only be partially analysed at a time. In this sense a machine of any kind with large quantity of parts is complicated whereas a human being or an ecosystem is complex. 

Funtowicz and Ravetz (1994) classify complex systems as ordinary and emergent. They argue that ordinary complex systems tend to remain in a dynamic stability until the system in overwhelmed by perturbations such as direct assaults like fire or invaders. Conversely, in emerging complex systems there is continuous novelty and these systems cannot be fully explained mechanistically or functionally since some of their elements possess individuality, intention, purpose, foresight and values. Any system involving society is thus an emergent complex system.

Hjorth and Bagheri (2006) state that complex systems cannot be fragmented without losing their identities and purposefulness. Similarly, Linstone (1999) refers to the general illusion or misassumption that we can break complex systems into parts and study these parts in isolation. He calls this as ‘a crucial assumption of reductionism (p.15)’ and points to the fact that such implied linearity is not a characteristic of complex systems. Indeed, in complex systems, the complexity is not determined by the characteristics of the components of the system but rather the relationships and the interaction between the components (Manson, 2001). The interaction between the components is not necessarily physical but can be in the form of information exchange as well (Cilliers, 1998). Mant (1997) gives an illustrative example of irreducibility of complex systems in his frog and bike analogy. One can dismantle a bicycle, carry out maintenance and reassemble it. The bicycle is still a bicycle and works perfectly. Nevertheless, if you separate a part of frog for any reason and keep on breaking it apart, the frog will perform unpredictable adjustments to survive until a time comes and the system (i.e. frog) tips over into collapse. Therefore, it is not possible to study complex systems meaningfully by breaking them into their components. At times when there is a need to define system boundaries, this should be done acknowledging how the part under study relates to the rest of the system.    

In addition to irreducibility and emergent behaviour, the other characteristics of complex systems are self-organisation, continuous change, sensitivity to initial conditions, learning, irreducible uncertainty, and contextuality (Cilliers, 1998; Gallopín, Funtowicz, O’Connor & Ravetz, 2001; Manson, 2001; Cooke-Davies, Cicmil, Crawford & Richardson, 2007). Complex systems in general are hierarchic or have multiple-levels and each element is a subsystem and each system is part of a bigger system (Casti, 1986; Gallopín et al. 2001; Holling, 2001; Gallopín, 2004). Hierarchical structures have adaptive significance (Simon, 1974). This adaptive significance is not due to a top-down authoritative control but rather due to the formation of semi-autonomous levels which interact with each other and pass on material and/or information to the higher and slower levels (Holling, 2001).

It is impossible for an analyst to understand a complex system totally and correctly. However, some requirements can be extracted with references to characteristics counted above. First, emergent behaviour, sensitivity to initial conditions and learning which takes place by system components imply time-dependency of complex systems. This time-dependency is two-fold; both history of the system and the particular moment the analysis is undertaken will affect the outcome. Since context is important to understand adaptive systems, and there are multiple-levels in a system, an analysis should include more than one level as well as the different perspectives present in the system (Gallopín et al. 2001; Gallopín, 2004). For an effective analysis, the analyst needs to oversee the (sub)system being analysed from a vantage point. This vantage point should be at a higher or preferably meta-level to identify a context specific perspective while still acknowledging the interconnections between the (subsystem) being analysed and the rest (Espinosa, Harnden & Walker, 2008).

The three major subsystems of the meta-system (i.e. ecology, economy, society) and most of the sub-systems of these components (e.g. evolutionary processes, market operations, individual animals, companies, etc.) are classified under a special category of complex systems terminologically known as complex adaptive systems (CAS). The distinguishing feature of CAS is that ‘they interact with their environment and change in response to a change (Clayton & Radcliffe, 1996, p.23)’. They are resilient; therefore, they ‘can tolerate certain levels of stress or degradation (p. 31)’. As a result, sustainability of a CAS can be achieved if the adaptive capacity of it is not destroyed.

The sustainability of a single entity is dependent on and determined by sustainability of the other components with which that single entity has interactions. Together all these components form a system, and therefore, sustainability can only be achieved using non-reductionist, dynamic systems thinking. The subsystems of a system should be adaptable to changes which occur both in the other subsystems, and as a result, in the entire system. The subsystems must co-evolve to render sustainability possible.

The term co-evolution was first coined by Ehrlich and Raven in 1964 to explain the mutual evolutionary processes of plants and butterflies (Ehrlich & Raven, 1964).  Even though the term first emerged in the area of evolutionary biology, it spread in other, especially interdisciplinary, domains studying interactions between natural and human-made systems (Norgaard, 1984, 1995; Winder, McIntosh, & Jeffrey, 2005; Rammel, Stagl, & Wilfing, 2007). Some of the other domains which use the co-evolutionary approach to explain, analyse and manage interacting natural and social systems include technology studies, organisational science, environmental and resource management, ecological economics and policy studies (Rammel et al., 2007; Kallis, 2007a).

It is important here to note that, despite many similarities between biological evolution and social, cultural, technological and economic change, there are differences as well (Rammel & Van Den Bergh, 2003; Kallis, 2007b). In the wider context of sustainable development, co-evolutionary change does not necessarily happen on a reactionary basis as generally happens in ecosystems. Rather, in socio-economic or socio-technical levels, it can also be deliberately aimed at both the individual and collective levels by system components in accordance with changing system conditions (Holling 2001; Cairns Jr, 2007; Kemp, Loorbach, & Rotmans, 2007). Co-evolution is reflexive and refers to the mutual change of all system components. During this mutual change, one component may or may not dictate a change over other(s).

References used in this post:

Cairns Jr, J. (2007). Sustainable co-evolution. International Journal of Sustainable Development and World Ecology, 14(1), 103-108.

Casti, J. L. (1986). On system complexity: identification, measurement and management. In J. L. Casti & A. Karlquist (Eds.), Complexity, Language and Life: Mathematical Approaches (pp. 146-173). Berlin: Springer-Verlag.

Cilliers, P. (1998). Complexity and postmodernism: understanding complex systems. London; New York: Routledge.

Clayton, A. M. H., & Radcliffe, N. J. (1996). Sustainability: a systems approach. London: Earthscan.

Cooke-Davies, T., Cicmil, S., Crawford, L., & Richardson, K. (2007). We’re not in Kansas Anymore, Toto: Mapping the Strange Landscape of Complexity Theory, and Its Relationship to Project Management. Project Management Journal, 38(2), 50-61.

Ehrlich, P. R., & Raven, P. H. (1964). Butterflies and Plants: A Study in Coevolution. Evolution, 18(4), 586-608.

Espinosa, A., Harnden, R., & Walker, J. (2008). A complexity approach to sustainability – Stafford Beer revisited. European Journal of Operational Research, 187(2), 636-651.

Funtowicz, S., & Ravetz, J. R. (1994). Emergent complex systems. Futures, 26(6), 568-582.

Gallopín, G. C., Funtowicz, S., O’Connor, M., & Ravetz, J. (2001). Science for the twenty-first century: From social contract to the scientific core. International Social Science Journal, 53(168), 219-229.

Gallopín, G. (2004). Sustainable Development: Epistemological Challenges to Science and Technology. presented at the meeting of the Workshop on Sustainable Development: Epistemological Challenges to Science and Technology, Santiago, Chile.

Hjorth, P., & Bagheri, A. (2006). Navigating towards sustainable development: A system dynamics approach. Futures, 38(1), 74-92.

Holling, C. S. (2001). Understanding the complexity of economic, ecological, and social systems. Ecosystems, 4(5), 390-405.

Kallis, G. (2007a). Socio-environmental co-evolution: some ideas for an analytical approach. International Journal of Sustainable Development and World Ecology, 14, 4-13. 

Kallis, G. (2007b). When is it coevolution? Ecological Economics, 62(1), 1-6.

Kemp, R., Loorbach, D., & Rotmans, J. (2007). Transition management as a model for managing processes of co-evolution towards sustainable development. International Journal of Sustainable Development and World Ecology, 14(1), 78-91.

Linstone, H. A. (1999). Decision Making for Technology Executives : Using Multiple Perspectives to Improved Performance. Norwood, Mass.: Artech House.

Manson, S. M. (2001). Simplifying complexity: A review of complexity theory. Geoforum, 32(3), 405-414.

Mant, A. (1997). Intelligent leadership. St. Leonards, N.S.W.: Allen & Unwin.

Norgaard, R. B. (1984). Coevolutionary Development Potential. Land Economics, 60(2), 160-173.

Norgaard, R. B. (1995). Development Betrayed: The End of Progress and a Coevolutionary Revisioning of the Future. London; New York: Routledge.

Rammel, C., Stagl, S., & Wilfing, H. (2007). Managing complex adaptive systems — A co-evolutionary perspective on natural resource management. Ecological Economics, 63(1), 9-21.

Rammel, C., & Van Den Bergh, J. C. J. M. (2003). Evolutionary policies for sustainable development: Adaptive flexibility and risk minimising. Ecological Economics, 47(2-3), 121-133.

 Simon, H. A. (1974). The organization of complex systems. In Pattee, H. H. (Ed.), Hierarchy theory: the challenge of complex systems. New York: Braziller. p. 3-27.

Winder, N., McIntosh, B. S., & Jeffrey, P. (2005). The origin, diagnostic attributes and practical application of co-evolutionary theory. Ecological Economics, 54(4), 347-361.



What is system innovation for sustainability?

System innovation is defined as “a transition from one socio-technical system to another (Geels, 2005, p.2)”. Some historical examples of system innovation are the transition from sailing ships to steam ships, the transition from horse-and-carriage to automobiles, and the transition from piston engine aircrafts to jetliners in American aviation (Geels, 2002a, 2002b, 2005). Much more profound examples of system innovation are agricultural revolution and industrial revolution, both of which fundamentally changed how the society operates. The society is currently experiencing another profound system innovation determined by the rapid development and diffusion of information and communication technologies. Since system innovation is a transformation which takes place at the wider societal context, it covers not only product and process innovations but also changes in user practices, markets, policy, regulations, culture, infrastructure, lifestyle, and management of firms (see, for example, Berkhout, 2002; Geels, 2006; Kemp and Rotmans, 2005; Sartorius, 2006).  In other words, system innovation occurs when the societal system functions differently and thus there is a requirement for fundamental structural change (Frantzeskaki and De Haan, 2009).

Historical examples of system innovation differ from system innovation for sustainability simply by not having a predefined and desired output. On the contrary to historical examples, endeavours to achieve system innovation for sustainability has a desired outcome: sustainable socio-technical systems. This raises questions about what sustainability means, how sustainability of a system can be achieved, what characteristics socio-technical systems have and how can we change socio-technical systems. Answers to these will be investigated in my upcoming musings. But next, I’ll write about the history of system innovation, how it all started and where it is now.

References used in this post:

Berkhout, F., 2002. Technological regimes, path dependency and the environment. Global Environmental Change, 12(1), 1-4.

Frantzeskaki, N., De Haan, H., 2009. Transitions: Two steps from theory to policy. Futures, 41(9), 593-606.

Geels, F. W. 2002a. Technological transitions as evolutionary reconfiguration processes: a multi-level perspective and a case-study. Research Policy, 31(8-9), 1257-1274. Retrieved May 20, 2007 from ScienceDirect.

Geels, F. 2002b. Understanding the Dynamics of Technological Transitions: a co-evolutionary and socio-technical analysis. Unpublished Ph.D., University of Twente, Twente.

Geels, F. W., 2005. Technological transitions and system innovations: a co-evolutionary and socio-technical analysis. Cheltenham, UK; Northampton, Mass.: Edward Elgar Pub.

Geels, F. W., 2006. System innovations and transitions to sustainability: challenges for innovation theory. Paper presented at the SPRU 40th Anniversary Conference,11-13 September 2006.

Kemp, R., Rotmans, J., 2005. The Management of the Co-evolution of Technical, Environmental and Social Systems, in: Weber, M., Hemmelskamp, J. (Eds.), Towards environmental innovation systems. Berlin, New York: Springer, pp. 33-55.

Sartorius, C., 2006. Second-order sustainability–conditions for the development of sustainable innovations in a dynamic environment. Ecological Economics, 58(2), 268-286.