Given the complexity of addressing sustainability issues and technological innovation simultaneously, methods which focus on just one dimension seem insufficient. For this reason, Geneva Macro Labs united a community of professionals from many different sectors, all with the shared belief in the importance of the long-term goals of sustainable development. They applied a systems-oriented approach, which incorporated the knowledge and perspectives of a wide range of stakeholders, in order to gain a better understanding of how different components of an innovation methodology could be combined.
Systems theory provides the theoretical foundation for designing an innovation methodology as it takes into account the very characteristics of open innovation communities (Tani et al. 2018). These characteristics include the interdependence of its members, the presence of a feedback system to regulate them as systems, and the necessity to adapt to external stimuli.
As an analytical tool in the realm of innovation, system theory refers to the distinction between a system and its environment. There are many diverse branches of systems theory, which historically cover the three areas of mechanical, biological, and social. Within these three areas, different and often conflicting schools of thought have been developed (Saake and Nassehi 2007, Carayannis et al. 2016). The common central thread in systems theory is that a system consists of individual elements, their interdependence and interaction.
In a sociological context, communities may be described as “systems” and the interpersonal behaviour or “functions” of each individual within them can be seen as co-forming the structure. By changing elements of the system, for example, the communication between the individuals, the system can be reshaped. By contrast, the branch of structural functionalism, which explores how system structures determine the behaviour of individuals in a society, argues that functions of a society are extremely stable and can only be changed by external factors. Such definitions can help us to conceptualize how innovation (systems) can respond to today’s challenges, as innovation can be seen as a “knowledge-concept” (Carayannis et al. 2016, p. 9) that brings the political, economic, education, and the research and development (R&D) systems together.
The application of systems theory, otherwise known as systems thinking, helps to handle complex situations and problems by identifying patterns and rules within them (Mulgan 2021). As a project management principle, systems thinking encourages innovation project managers to define early on, what levels of control and formalization of communication the project needs in order to succeed (Kapsali 2011).
Systems thinking can assist to take a big picture view of complex environments, for example, innovation ecosystems or large organizations. In other words, systems thinking can help innovators appreciate operational flexibility of their ecosystem by identifying and using boundary management activities to understand and potentially re-shape their system; on a practical level this could help them adjust to the economic factors of their environment and find ways overcome difficulties of limited resources.
Systems thinking can be seen as referring to people’s ability and willingness to think beyond the borders of an existing system, recognize the interrelatedness between individuals within the system, and start to ‘play’ with the borders and rules of several systems. This is possible when experts with different specializations and backgrounds come together and create a common room of systems thinking. Collaboration of this nature can spark spontaneous and creative idea generation that can energize the participants and both broaden and enrich the output of the innovative process. Where potential gaps in perspectives arise, facilitators who apply systemic methods help bridge them by asking hypothetical questions (Daimler, Sparrer, and Varga von Kibéd 2016).
From the outset, systems thinking underpinned the Geneva impACTs initiative; instead of aiming to control the outcome of the innovation cycle, the team focused their efforts on adapting to the behavior and needs of the innovation teams themselves and turned to methods known from social innovation and software engineering.
Evidence suggests that conventional management practices, such as detailed planning, formalized communication and restrictive managerial action to handle change are not conducive to the success of innovation projects (Kapsali 2011). In other words, innovation processes which include measures for adjusting to changing demands of their environment are more likely to contribute to the success of a project. In light of these findings, Geneva impACTs used elements from agile development to support their open innovation process.
The evolution of software development might be able to convey important learnings for society. Technically speaking, software engineering is completed once the software requirements have been defined in a formal and complete way and can be interpreted by a computer. While architects need construction workers to build what they have designed, software is automatically executing plans. The relative simplicity of software engineering compared, for example, with building engineering allows software to tackle more complex problems. While developing highly complex software, software engineers realized that project management needed to be revolutionized to reduce the risk of failure for software development projects.
Software development has long followed the waterfall model (Balaji 2012), which is rigidly structured in sequential phases; the next phase is only commenced once the prior phase has been finished. As such, the detailed design is completed before any lines of code are written. Obergfell at the SCRUM-Institute claims that the waterfall model is to blame for many software project failures since in large software projects the majority of requirements tend to change (Obergfell 2011). Although in other areas society is fighting failures by increasing documentation requirements, control and strict compliance to the project plan, this does not work for software development. Software development did not follow this line and was revolutionized when the Agile Manifesto was born (Beck et al. 2001).
The Agile Manifesto is based on the idea of facilitating change rather than trying to prevent deviation from a plan (Fowler and Highsmith 2001); the ability to react to unpredictable events is considered more important than trying to plan for disasters. Getting early feedback is as important as constantly adjusting priorities. While in the waterfall model, originally planned functionality is prioritized over change requests, agile development constantly adjusts the plan according to new priorities. The development of a software project is a learning exercise; during the course of the project, knowledge about the requirements and the knowledge about the ability of software to model them continue to increase. Early prototypes, minimal viable products and close contact to the customer support that learning curve. Discovering design flaws early reduces lost effort compared to fixing flaws later. Things that can be decided later, should be decided later, because deciding later means deciding while being wiser. Timeboxing which gives priority to available resources rather than to a predefined plan enables resources to be used more efficiently.
Agile project management is increasingly used in contexts outside software development. It has been proven to be superior to traditional project management when projects involve many unknowns. In other situations, a hybrid approach might be favorable (Ciric et al. 2018). Increased regulation can be a challenge to the use of agile development methods (Mehrfard and Hamou-Lhadj 2011). The EU is trying to foster and shape digital innovation in its Digital Agenda with a flood of regulation. It started with the General Data Protection regulation, GDPR (Regulation (EU) 2016/679) and is now being followed by the Artificial Intelligence Act, Digital Markets Act, Data Act and the Digital Services Act. Crypto assets will be regulated by the coming MICA regulation and the revised eIDAS regulation will serve as a legal basis for digital identities (European Commission. Directorate General for Communications Networks, Content and Technology 2020). Regulatory compliance imposes processes that often conflict with agile innovation. Although this regulatory approach is intended to increase trust, ethics, and sustainability of innovation, it runs the risk of becoming a barrier to innovation in general, including innovation for sustainability.
Innovation is dependent on finding creative solutions. Design thinking is one way to define problems and to find solutions in an iterative and human-centered way. It focuses on trying to better understand the end user, challenging assumptions, and redefining problems so as to find creative new solutions that might not be immediately apparent to our initial level of understanding (Johansson-Sköldberg et al., 2013; Brown and Katz 2009; Liedtka 2015).
The development and testing of multiple and rapid iterations during the process is a way to clarify and define the real problem and empathize with the end user’s needs and experience of the output. This process is particularly useful in addressing complex problems that might be ill-defined or misunderstood. The main goal is to identify and define real problems and any underlying issues in order to create real solutions and real innovations, as opposed to creating products and services that nobody will use or needs.
There are five phases in design thinking, which—in practice—do not need to follow any particular order. They do not need to run sequentially and can even occur simultaneously and repeat iteratively.
Empathize with the people the product or service is being designed for. What are their pain points and challenges? What do they care about? There are several methods to deepen understanding of the end user, such as conducting interviews and using personas, which represent a specific group of people with particular characteristics.
Define the problem and the user’s needs. This is done by analyzing the information obtained in the first phase (or other phases of the process) in terms of what the people described and getting to the root of their real needs from their perspective, instead of basing the problem definition on our own assumptions. At the end of the second step, a problem statement can be formulated.
Ideate through brainstorming exercises to come up with many ideas that could potentially solve the problem formulated in phase two. In this phase, it can be very useful to already gather feedback from the end users on some ideas, for example by sketching and showing them.
Prototype something simple that can be tested. The focus is set on a particular idea from step three; however, it is not yet the final product but a very basic version of it that allows for change.
Test the prototype with real people and get feedback in real-time while avoiding explaining how it works or defending the idea. The feedback should be as authentic as possible in order to understand what works and what needs improving. From there, go back to step three and four and apply the feedback and learnings. This process is repeated until the prototype solves the real problem.
In practice, this process is usually applied in “a system of spaces” rather than in this predefined order (Brown 2008). It means that the search for a solution is motivated by the process and particularly by looking beyond the surface and finding inspiration from the users who encounter the problems instead of our own mindset.
Co-creating innovation for sustainability requires incremental strategies that facilitate coordination and policy influencing and allow for shared sustainability objectives. Hence, in order to cater to societal needs while acknowledging boundaries of the world’s ecosystem, innovation trajectories and sustainability pathways need to be aligned. This approach accentuates collective intelligence as a prerequisite for innovation that supports sustainable development.
Collective intelligence is frequently used as synonym for swarm intelligence, wisdom of crowds, and crowd science, as well as with methods such as open innovation and crowdsourcing (Noveck 2021, p. 173). While the Greek philosopher Aristoteles already looked at achieving better results through engaging more people in decision-making in Athenian polis as a “middle way” between independent-guess aggregation and deliberation (Ober 2013), today’s scholars look at collective intelligence as a means of dealing with uncertainty and creating the “best” solution for challenging problems. Collective intelligence unites people with different backgrounds and terminology. This diverse crowd could turn into a tower of Babel. Collective intelligence involves the challenge to turn this Babel tower into a lighthouse.
Research of (Hong and Page 2004) suggests that under specific circumstances, a random group of intelligent problem solvers will outperform a group of the best problem solvers (see ibid, p. 16389). In other words, in a problem-solving context, diversity within a group prevents its members from becoming too similar and positively affects their ability to perform well. Diversity helps to counter the trade-offs of group thinking, including how easy possible counterarguments are addressed (Surowiecki 2004). Other conditions that seem to support good group intelligence are independence and private judgement. Recent evidence also suggests that “combining independent decisions substantially increased performance relative to average individual performance” (Kämmer et al. 2017). In addition, under certain conditions, negotiated group judgments can even outperform averaged individual judgments (Bonner and Baumann 2012). Different techniques such as encouraging critical thinking (Postmes, Spears, and Cihangir 2001) or allowing for smooth communication may improve collective intelligence further. Woolley and Aggarwal (2020) documented that “groups that communicated more were more collectively intelligent, but groups in which one or two people dominated the discussion and activity were less collectively intelligent” (ibid., p. 5). Such insights help to develop collective intelligence driven do tanks where subject matter expertise meets entrepreneurial spirit and sustainable finance—three ingredients that can be effectively combined by open innovation.