One: Asking – how might we prompt ourselves to think as humans

Creating design science futures, I call for us to be deliberate about asking and crafting (research) questions. Questions create futures. Imagining possibilities through questions generates, re-generates, dares to care. In a time of natural urgencies, technological pace and social transformation, design science futures are not about perfecting answers, but about the courage and clarity of the questions we ask. The question, the process of inquiry, is not merely a precursor to discovery; it is the design itself (Price & Lloyd Reference Price, Lloyd, Maier, Oehmen and Vermaas2022). As human design researchers and human designers (Maier & Cash Reference Maier, Cash, Maier, Oehmen and Vermaas2022), we need to (re-)learn the skill of asking questions.

The discipline’s future lies in embracing a more deliberate culture of imagining and crafting questions, beyond questioning. Too often, we chase questions that are measurable but not meaningful. The future demands more of us: to frame problems that challenge the status quo, disrupt technocentric perspectives and reflect social, environmental and ethical contexts. The next evolution of design science must prioritize the quality and framing of the questions we ask. In that way, design science will (re)evolve into a discipline that remains human.

Tomorrow’s most impactful innovations will not emerge from answers optimized in isolation, but from questions grounded in lived experience, systems thinking and moral imagination. We must cultivate a design research culture that values the crafting of meaningful, generative and disruptive questions. This means interrogating the assumptions behind our definitions of value, innovation and success. It means reflecting on the voice we take and the future we are implicitly or explicitly designing into our systems. The future of design science is shaped by researchers trained not only to build artefacts, but to build theory- and practice-based rigor, imagine multiple futures, solve problems worth solving and ask questions worth asking.

Two: Connecting – how might we embrace researching at intersections as generative spaces

I see the journal actively continuing to embrace and promote interdisciplinarity – across the variety of design research and across the arts and humanities, the social-, the engineering- and the natural sciences. Designing happens in and through a web of connections, and design science thrives where disciplines converge, intersect, collide and reconfigure. Writing for multiple audiences (possibly in a fusion of research cultures) is not easy. It demands humility, curiosity and an acceptance of methodological plurality (possibly seen as messiness) and rigor in that plurality. This is hard. But it is possibly precisely this messiness and rigor in plurality that allows for the most creative and robust solutions to challenges ranging from local to global. In a world increasingly shaped by complex situations from pandemics to planetary sustainability, no single discipline holds all the answers. Design science can serve as the connective tissue that makes interdisciplinary work actionable.

Design science has always lived at the boundaries of disciplines, but its future depends on embracing those boundaries as generative spaces (McMahon & Maier Reference McMahon and Maier2024). Whether it is sustainable energy systems, ethical AI, healthcare innovation, or urban resilience, today’s socio-technical engineering systems are inherently interconnected (Maier, Oehmen, & Vermaas Reference Maier, Oehmen, Vermaas, Maier, Oehmen and Vermaas2022a). They require insights from multiple disciplines, including form anthropology, design engineering, economics, ecology, medicine, physics, psychology and political science, to name a few. The most powerful design interventions are born from interdisciplinary – and methodological cross-pollination. Going forward, design science must therefore remember to be(come) fluent in many “languages”: including the precision of computational modeling, the nuance of qualitative inquiry, the urgency of policy debate, and the creativity of the arts. It must also cultivate the humility to listen, to connect and to nurture the courage that allows it to continue doing what it is well known for: synthesis.

Three: Impacting – how might we recognize and report on design impact beyond academia

Theoretical rigor must go hand in hand with practical relevance and impact beyond academia. Designing is generating societal change. Impact cannot be an afterthought. It is a responsibility. It must be designed into research from day one, a design constraint from the outset. Design science research must be embedded in communities, organizations and policy frameworks, not simply to test ideas but, most importantly, jointly to create ideas. The discipline’s legitimacy will increasingly be judged not by citations but by consequences. The field must report on how it engages more directly with the messy, real-world systems it seeks to influence. I would like to create a paper category in the journal called Design Impact. Offering a model to report on embedding design research in industry and policy contexts, keeping societal value in view. As design scientists, we have already reported and will continue to do so on methods, demonstrators and tools used beyond academia, but we also need to move to reporting on governance models, on behavioral nudges and on perspectives. In this design, science must adopt a broader metric of success than citations: it must acknowledge effects on sustainability, equity, adaptability and inclusion. It must also evolve to report on the variety of its outputs beyond peer-reviewed journal manuscripts, for example, including white papers, digital platforms, living laboratories and open-source toolkits that reach people where they are.

Concluding, I see the future of design science as designing pathways and directions, designing connections and whole systems, calling us to (re)-think how we will measure our success with the purpose of designing better futures. It means training scholars who are not only skilled research methodologists, but also system thinkers, translators and ethical actors. It needs bold, thoughtful design scientists who can ask: What should and what ought to be? And then build toward those answers, not alone, but with others. This is coming back to the future of Simon (Reference Simon1969), compellingly drafting that design is about imagining and creating what ought to be, positioning design as a science of transformation. To keep strengthening the realization of this powerful foresight in researching and designing responsible, sustainable and natural futures, design science futures must grow in three crucial dimensions: by re-centering the human design researcher and human designer on the importance of asking questions, researching at intersections and ensuring impact beyond academia.

The imperative of design science for Africa’s future

Despite considerable academic output, management research in Africa remains predominantly retrospective, focused on explaining what has occurred rather than on shaping what might be. This explanatory orientation has contributed to an increasing disconnect between scholarly inquiry and the lived challenges of communities, organizations and policymakers across the continent (van Aken & Romme Reference van Aken and Romme2009). The time has come to adopt a more actionable paradigm – design science – that enables scholars to actively create and test innovative solutions to Africa’s most pressing societal challenges. Nowhere is this need more urgent than in under-resourced African contexts, where systemic challenges in governance, infrastructure, entrepreneurship and service delivery persist. These environments are not merely sites of scarcity; they are crucibles of innovation. Yet, the prevailing research culture tends to overlook these conditions, treating them as anomalies rather than opportunities for design-led problem solving. Praxistical design science, by contrast, embraces the complex realities of such settings and mobilizes iterative cycles of building, evaluating and refining solutions in collaboration with stakeholders (Gregor & Hevner Reference Gregor and Hevner2013).

Design science, as articulated by Simon (Reference Simon2019), seeks not merely to understand the world but to change it. It draws on the best available knowledge, be it theoretical, empirical, or experiential, to construct “how-to” artefacts: models, processes, systems, or tools that solve real problems. These artefacts are not static; they evolve through rigorous evaluation by designers who promote indigenous African knowledge and co-develop culturally responsive design practices and processes in specific contexts (Eriksson, Motlhanka, & Mosepedi Reference Eriksson, Motlhanka, Mosepedi, Moalosi and Rapitsenyane2023). For African scholars, this is an invitation to move beyond theorizing about change and toward actively engineering it.

The opportunities are vast. In the realm of responsive supply chains, design science can help overcome the unpredictability and fragmentation that characterize many African logistics systems. Iterative design approaches can generate adaptive coordination models that reflect regional constraints and capabilities. Likewise, in collective governance arrangements, among cooperatives, civil society networks, or informal economic clusters, design science can uncover scalable mechanisms for participatory decision-making, trust-building and conflict resolution (Lukka Reference Lukka, Ojala and Hilmola2003).

Entrepreneurial ecosystems, particularly in underserved urban and rural areas, also benefit from design interventions. Traditional models of incubation and support often falter when transposed into African contexts without adaptation. Through design science, scholars can co-develop entrepreneurial platforms that are grounded in local realities and responsive to evolving needs (Baskerville et al. Reference Baskerville, Baiyere, Gregor, Hevner and Rossi2018).

Importantly, design science is not a call to abandon rigor; it is a call to redefine it. When relevance and rigor are brought into productive tension, research can produce cumulative design knowledge that improves both theory and practice. For African scholars, in particular, this shift holds transformative potential. It allows us not only to explain the past but to design the future – responsively, collaboratively and contextually.

Design science: quo vadis

We are surrounded and affected by designs intended to address an identified or perceived desire, need, or problem: these include physical and virtual artifacts, intangibles such as actions and procedures, and systems such as transportation or education. Designs are meant to effect change: changing the way people think, act, or experience, or changing context, course, or boundaries. Deep understanding of current and future desires, needs and problems of the target users, stakeholders, society and the environment, as well as intended use and context, increases the chance of a successful change, but only if the knowledge required to create a feasible design is available, and the possible impact – intended and unintended, positive and negative, short-term and long-term – on users, stakeholders, society and environment are taken into account. Design causes Change; Change causes Impact.

Designing requires a wide range of knowledge (factual, conceptual, procedural, or strategic) ideally obtained through scientific inquiry, that is, a systematic search for knowledge based on the best available evidence and brought together in a structured body of knowledge to “support proper actions, judgments, and evaluations” (Eekels & Roozenburg Reference Eekels and Roozenburg1991).

For a long time, design science was considered unnecessary or impossible: design could fully rely on knowledge created by other sciences. Design, however, also has its own substance, which requires scientific inquiry in order to build the foundation necessary for design to make a positive and lasting impact on individuals, society and the environment.

Design science aims not only at systematically developing and validating knowledge about the phenomenon of design (in the sense of process and object) but also aims at the development and validation of design support founded on this knowledge (Blessing & Chakrabarti Reference Blessing and Chakrabarti2009).

Design science is a relatively young field, dealing with a very complex phenomenon that combines natural law, human purpose and continuous change. The scientific efforts in the last decades – empirical, theoretical and methodological – have substantially improved our understanding of design, the quality of our research and our position as a scientific discipline, but further effort is required. Our knowledge production is messy, affecting the visibility, strength and coherence of our outcomes and, hence, their impact: agreed upon definitions of basic concepts are lacking; research results are sprinkled throughout literature rather than integrated in a shared body of knowledge; theories and models are not widely used; validation is weak; young researchers often are confused; methodologies and methods from other disciplines are used without proper assessment of the underlying principles and theories and the impact on practice remains sporadic. We still do not “share a common view of what are appropriate research methodologies, what are the difficult unanswered questions and what constitutes high quality research” typical of a mature field (Finger & Dixon Reference Finger and Dixon1989).

Concerted efforts are needed to establish a common paradigm (or paradigms), to develop a structured body of knowledge and to create a sound methodological foundation, not only to distinguish ourselves from other scientific communities but, more importantly, to guide the research activity and ensure high-quality, impactful research.

Design science for a sustainable future …

Over the past 30 years, we have witnessed a profound transformation in engineering practices and organizational models. Post-World War II paradigms – characterized by top-down, hierarchical, company-centric approaches – have gradually given way to more fluid, user-centered and adaptive frameworks. Tackling complexity seems to be one of the major challenges modern societies must face today, and, as part of society, companies, organizations of any kind. In this context, organizations, institutions and companies can no longer afford to operate under traditional assumptions. But what methods should help? What theoretical foundations and philosophical principles should guide them?

Design as a discipline is rooted in some invariant principles. Frameworks such as C-K theory, the Double Diamond model and various cognitive approaches have captured some of these constants. Over the last 50 years, the design research community has accumulated a vast and valuable body of knowledge. This is an incommensurable asset, but only known by a few academics or educated engineers, or designers…. One of our critical challenges is to democratize this knowledge. Revealing the designer within each individual could empower broader societal participation in shaping a better future.

Today’s rapidly evolving world is testing the limits of our current models. Social transformations have altered the ways people interact with technology. Smartphones, IoT devices and a web of interconnected technologies now permeate our everyday lives. The design of these artifacts cannot be separated from their social context. No longer is it possible to disconnect the creation process from societal changes and people. Increasingly, people are no longer limited to the position of users; they are considered as co-creators and active participants. Design has moved beyond the realm of experts to reach a wider audience while at the same time remaining a very technical activity that requires specific and complex skills.

Facing climate change is also a deep and urgent concern that design science should address. Under the pressure of younger generations, academic programs are progressively integrating sustainability, frugality and even degrowth into engineering and design curricula. Concepts such as planetary boundaries, low-tech innovation and strong sustainability are no longer fringe ideas – they are at the heart of emerging research agendas (Bihouix Reference Bihouix2020; Gaultier, Masclet, & Boujut Reference Gaultier, Masclet and Boujut2024). These shifts compel us to revisit and challenge our theoretical foundations, including the mental models that underpin them.

Moreover, our field must expand beyond its traditional focus on capitalist enterprises. Nonprofit organizations, grassroots initiatives (local production), territorial projects (urban factories) and open-source collectives are also important spaces for design. Movements like Open-Source Ecology or France’s L’Atelier Paysan offer alternative visions of production – centered on autonomy, collaboration and environmental stewardship (Bonvoisin, Mies, & Boujut Reference Bonvoisin, Mies and Boujut2021). These groups are pioneering new modes of design and manufacturing that reflect deeply held societal values.

Why should researchers in design science pay attention to these developments? Because they signal a transformation in how design is practiced, understood and valued. Classical models – whether for decision-making, product development or business – are being questioned and reshaped. This presents an exciting opportunity for our community.

As scholars and practitioners, we must actively contribute to this shift. Design is central to navigating today’s challenges. Our role is to generate knowledge, develop tools and create frameworks that support inclusive, democratic and collaborative design practices – helping to shape a more sustainable and equitable future.

Design Science: moving from impact to Impact

When Design Science was launched 10 years ago, one of the ideas was to develop a platform for multidisciplinary advances in the academic and industrial understanding of design, such as to support a scientifically grounded approach to the practice of design. By analogy, the idea was to bring to design what the concept of “Management Science” had done to the profession of management. Have we reached this objective and what is still missing?

If one browses the papers published in the journal, I would answer “quite so!.” It’s hard to find another journal in which the topic of design is covered with a similar shared philosophy. Most papers are multidisciplinary, but without being superficially generalist. The authors focus on the root phenomenon of design per se, also breaking loose of their own backgrounds within the many branches of design (i.e., “engineering,” “industrial,” etc.). They take a broad view of design, but without indulging in pure conceptual speculation. The attention paid to the scientific method is strong, but without losing a view on industrial implications.

So, what is still missing?

One obvious answer is popularity, readership and impact, which I think is significantly less than the journal deserves. This is probably due to the ongoing inflationary phenomena in academic publishing, where quantity and bibliometric indicators tend to overwhelm substantial quality and insight. A stronger effort by the community to nurture the popularity of the journal is not only due but also likely to be advantageous.

Secondly, I think that the relevance of Design Science to the world of 2025 is even higher than in 2015. The world today is ever more turbulent, and a significant part of this is associated with the dynamics of technological innovation and its connections and ramifications with broader dimensions, such as economic, geopolitical and social. Now, at the heart of technological innovation is the design process (Isaksson et al. Reference Isaksson, Eckert, Borgue, Hallstedt, Hein, Gericke, Panarotto, Reich and Öhrwall Rönnbäck2019; Montagna & Cantamessa Reference Montagna and Cantamessa2019), that is, the cognitive phenomenon by which individuals, groups and organizations bring together diverse forms of knowledge and create new systems and artifacts. This link is crucial and should probably become a stronger issue both within the community and when disseminating the results of research to broader audiences. In fact, many scholars and practitioners who deal with innovation from a managerial and economic standpoint, but without understanding the “inner circuitry” of design, risk ending up with superficial studies and decisions. At the same time, focusing on design without understanding the bigger picture within which it occurs leads to the risk of limiting the relevance of the work being done.

In sum, this is a journal that still has limited impact (bibliometric, with a small “i”) but with the potential of delivering a significant Impact (to the world, with a big “I”).

However, this transition will not occur without some further commitment by all.

What

Science refers to systematic studies for developing knowledge about some system of interest and/or its behavior; the knowledge so developed is:

• • “purposeful”: it serves a purpose that is either “descriptive,” that is knowledge about the system or its behavior “as is,” or “prescriptive,” that is knowledge about the system or its behavior “as should be” (Blessing & Chakrabarti Reference Blessing and Chakrabarti2009).

• • “generic”: the knowledge produced has some degree of generality, that is, it applies to a variety of specific systems and behaviors; the more generic the knowledge is, the better it is considered to be, for its greater reuse value

• • “valid”: the knowledge produced is “true” or “correct,” something that is possible to be demonstrated and

• • at the time of its development, “new” that is, it has at least some elements that distinguish it from all existing pieces of knowledge or context in which it is applied.

We take design science (Chakrabarti Reference Chakrabarti2015) as having two broad foci: understanding and supporting (a) design and/or designing and (b) its relationships with its “facets,” where “facets” are the baskets of factors, such as people, product, process, etc., that influence and/or get influenced by designs and designing.

Why

According to Chakrabarti (Reference Chakrabarti2015), Simon (Reference Simon1969), “a design is a plan for intervention which, when implemented, is intended to change an undesirable situation into a (less un-) desirable one. Designing (that is to design) is the process of identifying these situations as well as developing designs to support the transition.” However, designing does not automatically guarantee that the designs, when implemented, will work or work well. Hence, the need for understanding designs and designing, and their relationships with the facets, and based on these, develop prescriptive knowledge for improving the act of designing, better addressing or taking into account the facets in play, or developing improved designs.

How

For the sciences is general, philosophy of science is the area that addresses this question; the outcome is called a research methodology: the accepted framework and associated methods for carrying out science in an area, and the kinds of outcomes considered to be scientifically valid. In design science, these are termed design research methodology. Various such methodologies have been proposed, including DRM (Blessing & Chakrabarti Reference Blessing and Chakrabarti2009). According to DRM, each piece of design research should undergo one or more of the following four stages: (a) Research Clarification: identification of research goals called “success criteria”; (b) Descriptive Study I: development of descriptive knowledge of what (currently) “influences” success criteria, how well and where the gaps are; (c) development of prescriptive knowledge, termed “support,” for improving the influences on success criteria and (d) evaluation of how well the support is likely to influence success. The overall process is iterative across these stages.

Creating an ecosystem of design methods and tools

For decades, one of the raisons d’êtres of design research has been the creation of tools and methods for industry (Blessing & Chakrabarti Reference Blessing and Chakrabarti2009). There have been notable successes over the years. As a community, we have had a collective impact either directly through our research with industry or indirectly through our students. However, we find it difficult to evidence our impact beyond the positive and encouraging comments of our friends and contacts in industry (Mallalieu et al. Reference Mallalieu, Isaksson Hallstedt, Isaksson, Watz and Almefelt2024). It is often difficult to trace our tools and methods in industry practice, and it is a frequent lament that industry does not make use of our collective efforts.

Even if tools and methods are used in organizations, the impact of engineering design research is notoriously hard to evidence and measure, as much is aimed at avoiding mistakes. For example, our research on engineering change prediction (Clarkson, Simons, & Eckert Reference Clarkson, Simons and Eckert2004) aims to alert designers to the potentially catastrophic consequences of changes spreading through complex systems and direct them to less risky alternative solutions. Maybe we have saved engineering projects from great failures. Who knows?

There are many reasons why the introduction of tools and methods is challenging (Wallace Reference Wallace and Birkhofer2011). Underlying them is often the same root cause: we think of the methods and tools in isolation. A problem is often identified in collaboration with industry experts and users, and a method is proposed to address this specific problem. This is then published or handed over, and the researchers move on. Methods fundamentally help designers to convert input into outputs through a set of steps. However, where the inputs come from, where the outputs go is often not explained, and how tricky steps are carried out is rarely explained in detail. This leads to users of the methods getting stuck and abandoning the methods.

We need to help the users of our research outputs by putting them into the context of the other methods, tools and steps that the organization is already using. Any new method needs to be seen in the context of an ecosystem of methods and tools that can be used in conjunction with what we are using (Gericke et al. Reference Gericke, Eckert, Campean, Clarkson, Flening, Isaksson, Kipouros, Kokkolaras, Köhler, Panarotto and Wilmsen2020). As in a biological ecosystem, multiple methods and tools can operate in similar application areas and to some extent compete, but without suitable connections to and understanding of other methods, they either disappear without a trace or they damage or hinder other methods.

Looking at methods and tools in the context of an ecosystem also points to the sociotechnical nature of tools and method development in engineering design. To develop successful tools and methods, we need to understand not only the technical problems we are addressing, but also the cognitive, social and commercial context in which they are operating. This requires an interdisciplinary and holistic approach to engineering design research. The ability to bridge the social and technical is the greatest asset of this community, but also its greatest scientific vulnerability.

Design science in 2025 and beyond: a project for humanity

People often ask exactly what Design Science is, and to me, nothing has captured it better than Papalambros’ foundational maxim decades ago: “Traditional science studies the world as we found it; Design Science studies the world as we make it” (Frischknecht et al. Reference Frischknecht, Gonzalez, Papalambros and Reid2009). Nature – often touted as the first and most ingenious designer – bequeathed humanity a planet filled with promise, wonder and danger, a primordial environment where we are component, not benefactor. Design Science aims at purposefully remaking that world so that humans can not only survive, but create, fulfill, plan and thrive (Buchanan Reference Buchanan1992; Simon Reference Simon1996).

My home field of marketing tells students to “find a need and fill it,” which sounds easy on paper: just commune with those around us, and intuit what might make their lives easier and happier. But this leaves out the “Science” part of “Design Science”: What exactly should we produce, and how many types? Can we provide something not only adequate, but durable, masterful, inspiring? And how does this all affect the local community and the environment at large in the future? (Michalek et al. Reference Michalek, Ebbes, Adigüzel, Feinberg and Papalambros2011).

It has become a cliché to suggest we are at the dawn of a new era, but “Generative AI” has sprung forth to allow us to explore design landscapes of vast dimension in a world becoming “flatter” and facing accelerating global and environmental challenges. The question is no longer whether design matters, but how we engage its complexity responsibly. That is, Design Science is not just a methodological orientation or research program, but a civic and epistemic necessity.

In marketing, design used to play at best a supporting role, an aesthetic garnish on a functional offering that consumers will purchase at above production cost. Today, design is the offering. Experiences, platforms, algorithms and services are no longer just shaped post hoc by designers; they constitute the very value propositions firms offer. The aesthetic is now ethic, the interface the infrastructure. From this vantage point, Design Science studies how purposeful artifacts – material or conceptual – are ideated, developed and evaluated. But it is increasingly about how these artifacts structure interaction, shape markets and transform behavior. Consider the rise of AI-infused personalization systems, whose architecture requires engineering precision, artistic sensibility and consumer-centric psychological insight. These systems design consumers even as they are designed for them, a recursive dynamic is where Design Science is uniquely vital (Norman Reference Norman2013).

What has changed in the past decade is the velocity and visibility of this recursion. As design decisions increasingly shape not only individual choice but collective outcomes – from (mis)information flows to sustainability trade-offs – the personal and global stakes have risen. Design Science today is less about solving known problems and more about unearthing unknown ones, exploring their vast space of possibilities: framing, not just fixing. As our daily lives are increasingly shaped by algorithmically-mediated, designed environments that are dynamic, opaque and adaptive, Design Science creates the tools to make the invisible visible, to render consequential the seams between disciplines (Krippendorff Reference Krippendorff2006).

Ultimately, Design Science is not just an academic endeavor but a commitment to shaping collective futures worth inhabiting. In an age where every click, swipe and purchase embeds a design choice, the call is not just to design better, but to design wisely. That is a task no single discipline can claim alone, and Design Science will ideally provide a firm yet flexible agora for collectively productive exploration.

What

Design science is the systematic study of design, where the word design encompasses the people doing the design, the act or process of designing and the artifact being designed. As an example, when focusing on people doing the design, it could be a study of how a designer constructs user understanding or empathy. When focusing on the act or process, it could be about a particular approach, such as comparing extreme lead users versus regular lead users as a source for potential latent innovative user needs. And, the artifact focus could be on, for example, the level of inclusiveness of the product.

Why

Design as a discipline has grown independently in many other disciplines but shares similarities across, making it a unique entity worth studying on its own. For example, user/customer/people-focused research has been present in many areas but often under a different name, such as human factors engineering, human–computer interaction, ergonomics, or requirements engineering. All have a goal of understanding the user to create a better solution, and thus it is important to develop an understanding of what all impacts constructing user understanding and how to best do it in different contexts.

In order to solve the so-called wicked problems, we need design. Design is problem-focused, and problems do not care what discipline you come from. We need an understanding of design itself. Design science is needed to accumulate knowledge and to build a proper theoretical understanding of design. This knowledge can then be packaged into specific design methods (Gericke, Eckert, & Stacey Reference Gericke, Eckert and Stacey2022) that form the practical path for knowledge transfer into practice.

How

By using proper scientific methods, the same as in any scientific research. Design research can be theoretical, experimental, computational and so forth, just like any other field of research. Some of the weaknesses at the moment in design science include weak conceptualization and, consequently, operationalization of those concepts (Surma-aho & Hölttä-Otto Reference Surma-aho and Hölttä-Otto2022). For example, there is no general agreement on what empathy is and how it should be measured. Similarly, there is no general consensus on what creativity is, modularity, or many other concepts in design, and how they should be measured. This is even though each of these areas has decades of research. Instead, researchers tend to develop their own metrics to measure something they are interested in (Eisenmann et al. Reference Eisenmann, Grauberger, Üreten, Krause and Matthiesen2021). This prevents comparison of approaches, building on past knowledge and thus the accumulation of knowledge and advancement of the field. This design science needs to take advantage of the full spectrum of scientific methods, starting from clear definitions of concepts.

Reflections in the context of engineering design

Why: Design science is essential as it offers a structured, systematic approach to understanding and refining design processes (Hatchuel et al. Reference Hatchuel, Le Masson, Reich and Subrahmanian2018). Within a journal context, it involves the rigorous investigation of design principles, methodologies and their applications to generate academically sound knowledge (Papalambros Reference Papalambros2015). This highlights its emphasis on design research rather than solely design practice or domain-specific implementation. Unlike purely artistic or intuitive approaches, design science focuses on creating knowledge that enhances efficiency, fosters innovation and advances problem-solving across disciplines.

Generality is a fundamental trait of design research, ensuring that findings extend beyond specific cases to contribute to broader knowledge. A well-conducted design study allows its principles to be applied across similar domains. In this regard, design science acts as a bridge between theory and practice – uncovering the underlying rationale behind best practices to transform creative concepts into structured, repeatable and evidence-based methodologies. Through systematic analysis, design science enhances problem-solving and refines the development of products, systems and experiences. It plays a critical role in optimizing complex systems and fostering innovation, empowering researchers and practitioners to explore new methodologies, streamline workflows, drive technological advancement and improve design efficiency.

What: Given the inherently contextual nature of design, it is essential to examine the “What” dimension of design science in relation to its working subject – specifically, the problem context. Like any scientific research, hypothesis testing is a fundamental aspect of design research, where establishing a meaningful problem context is paramount. Compared to the traditional focus on mechanical design, contemporary engineering design goes beyond isolated product development, encompassing product industrial realization within an extended enterprise (Jiao et al. Reference Jiao, Luo, Malmqvist and Summers2022). Engineering design encompasses the entire spectrum of product realization within an extended enterprise, integrating engineering, manufacturing, supply chains and value networks to ensure seamless and holistic coordination across disciplines.

Engineering design has evolved into a complex and multidisciplinary discipline, shifting the product creation horizon from a physical artifact-centered approach to a holistic life-cycle experience, enacted through cyber-physical-human and sociotechnical systems. Product design is no longer limited to hardware; rather, it emerges through the co-design of an interconnected product ecosystem. This ecosystem coordinates product fulfillment, services, experiences and human satisfaction at both individual and community levels. The artefact aspect of design extends beyond physical products to include service and digital products. Its scope spans the entire product realization lifecycle, encompassing areas such as supply chain design and the development of product-service systems.

How: Scientific research is grounded in several fundamental traits that ensure its rigor and reliability. A crucial aspect of design science that warrants emphasis is verification and validation (V&V). While definitions may vary, Jiao et al. (Reference Jiao, Commuri, Panchal, Milisavljevic-Syed, Allen, Mistree and Schaefer2021) assert that “validation is to make sure that our research is doing the right thing, whereas verification is to justify to what extent our research is doing things right.” Design problem-solving inherently involves a what-to-how mapping process (Suh Reference Suh2001), where validation pertains to the what aspect (i.e., problem formulation), while verification addresses the how (i.e., methods or solutions).

Similar to computational studies and algorithm development, benchmarking and performance comparisons are commonly used for design verification. For instance, evaluating objective functions using the same dataset or numerical example helps assess the extent to which a proposed design optimization model outperforms alternative methods in solving similar problems.

Ensuring validity – particularly in terms of the motivation and significance of the problem context – is a challenging aspect of design research. Formulating a meaningful research problem that closely reflects real-world conditions is essential for plausible validation. Field studies, rather than relying solely on hypothetical or numerical examples, help anchor research in practical contexts, ensuring its relevance to industrial applications.

For design science, explicit industry relevance and concrete engineering contexts are vital for conducting meaningful and rigorous sound design research.

Uncovering the logics of generativity with design theory, a contribution of design science to the community of scientific disciplines

Design science for the broader scientific community

A scientific discipline contributes most to the wider scientific community when its results are as universally relevant as possible. Just as physics is not only for physicists, design science is not only for designers.

This is particularly true for design science, as design is a fundamentally universal activity. It is practiced by a wide range of professionals – engineers, architects, industrial designers, artists, scientists, managers, policymakers – and is present across numerous disciplines, including engineering, management, agronomy, life sciences, mathematics, physics and philosophy. At the same time, design is both expected and critiqued by societies grappling with major contemporary challenges. This gives rise to a critical and ambitious goal: for design science to produce universal results that enhance the design capabilities of all.

This ambition was central to Herbert Simon’s program in The Sciences of the Artificial, which sought to establish design science as a rigorous and general discipline, comparable to decision science. His vision helped launch a collective effort that includes many initiatives, among them the founding of the Design Science journal ten years ago.

Design theory: capturing the logic of generativity

It is well acknowledged that Simon’s program was “unfinished” in terms of design theory (Hatchuel Reference Hatchuel2001). One major line of work has been to renew design theory to better account for generativity – the capacity to produce new, valuable propositions from available knowledge. This has led to the foundation of a new paradigm for design science and engineering (Hatchuel et al. Reference Hatchuel, Le Masson, Reich and Subrahmanian2018). Advanced design theory, such as C-K theory, has demonstrated “unique features” (Sharif Ullah, Rashid, & Tamaki Reference Sharif Ullah, Rashid and Tamaki2012), allowing scholars to move beyond commonly misleading analogies (such as abduction, decision-making, or creativity) and to more accurately model strong generativity.

Design theory is increasingly featured in Design Science publications, helping to construct hypotheses, refine analytical frameworks and produce robust research outcomes. Even more noteworthy, it has had a tangible impact across various disciplines and professions. One among many examples is its application to teaching biomimicry (Nagel et al. Reference Nagel, Pittman, Pidaparti, Rose and Beverly2016), illustrating how design science can foster breakthroughs in other disciplines.

Today, design theory is well-positioned to help analyze, interpret and guide the development of critical emerging technologies like generative AI (Chen et al. Reference Chen, Xia, Jiang, Tan, Sun and Zhang2025). It also provides essential tools for tackling the challenges of designing for sustainable and societal transitions (McMahon, Subrahmanian, & Reich Reference McMahon, Subrahmanian and Reich2022).

The future of design theory: an integrative language for transitions?

Recent advances in design theory open exciting opportunities to further develop design science for transitions – toward creative preservation (i.e., non-Schumpeterian innovation), enhanced generativity with data and large language models and the co-design of socio-bio-ecosystems. Design theory offers sharper tools for analysis, observation, modeling and experimentation – meeting the high standards of the Design Science journal.

Conversely, design theory must continue to evolve to fully address contemporary challenges of generativity. To become an increasingly effective integrative language for both disciplines and practitioners, it may require the use of abstract, formal and mathematically grounded constructs – subject to rigorous evaluation by the Design Science journal community.

We must also recognize the tension between the abstract mathematical formulations now needed for advanced design theory and the imperative to make contemporary design methods accessible to a broader audience beyond design science experts. Other fields, such as theoretical physics and, to a lesser degree, decision theory, have faced similar tensions in their development. Their histories teach us that successful disciplinary growth depends on organizing high-level, transdisciplinary knowledge transfer – and even knowledge creation. We hope that the Design Science journal will continue to serve as a crucible for this kind of constructive, transdisciplinary dialogue.

Design science: shaping a human-led future

Design is the deliberate creation of novel, useful artifacts – materials, structures, systems – that transform our world and advance economic and social well-being. Unlike the slow, undirected process of evolution, design is rapid, purposeful and cost-conscious, harnessing knowledge, creativity and tools (Dennett Reference Dennett2015). Design science, the scientific study of design, investigates how artifacts are created, how designers think and act and how these interact with natural and societal environments. While design itself need not be scientific (Cross Reference Cross2001), design science provides a rigorous framework to understand and enhance it.

Humanity’s mastery of design has driven four industrial revolutions: mechanization, electrification, digitalization and now intelligentization. The Fourth Industrial Revolution (4IR) marks a transformative era where intelligent technologies we design – AI, autonomous systems, genome editing – blur boundaries between cyber, physical and biological realms. These design innovations pose complex sociotechnical challenges, such as ethical dilemmas in autonomous decision-making, risks in genetic engineering and AI-driven misinformation (Schwab Reference Schwab2018). Traditional heuristic design methods are inadequate for addressing the 4IR’s complexity.

Designers must adopt holistic, sociotechnical approaches that embed core human values (such as equity, justice, privacy) and “ilities” (such as reliability, sustainability, evolvability) from the outset. This requires navigating intricate system-of-systems ecosystems where stakeholders collaborate and compete. Designers need high-order, high-dimensional empathy and creativity to tackle sophisticated problems, embracing unpredictability through “evolutionary ecosystem design thinking” (Luo Reference Luo2023). AI, with its acquired knowledge and advanced reasoning, augments human empathy and creativity to tackle 4IR challenges, fostering innovation among diverse actors to ensure solutions are adaptive and inclusive. Yet, human leadership remains essential to steer AI as a tool in the design process, ensuring alignment with human values and preventing unintended consequences.

As design, AI and AI for design grow more complex, design science must evolve as a transdisciplinary meta-science, bridging theory and practice across academic disciplines and practical domains. It embeds ethical and sustainable principles to tackle 4IR challenges. Design science breaks ambitious goals, such as circular economies, into manageable systems. It leverages AI to understand human needs, generate and optimize solutions and prioritize social equity. Fostering stakeholder engagement ensures solutions are scientifically robust and socially accepted.

Design science stands as the cornerstone of a human-led future in the 4IR. Through systemic thinking, transdisciplinary collaboration and accountable innovation, it harmonizes 4IR’s emerging technologies with humanity’s needs. By embedding empathy, creativity and rigorous methodologies, design science ensures AI-driven design aligns with sustainable development goals, shaping an equitable, resilient future where technology enhances well-being, sustainability and justice – guided by humans, not algorithms.

Do not miss the real life and nature of design creativity

Warm regards to the editors and congratulations to everyone involved with Design Science on their tenth anniversary. It was a big challenge to develop the field of design based on discussions and establish our position in the history of science. This field is still progressing, and we have a long way to go.

Personally, my biggest challenge was to identify the power of design by investigating human creativity (Nagai Reference Nagai, Cash, Stanković and Štorga2016). Several questions were raised, such as “What is the nature of design creativity?” “Why do we want to create designs?” and “How can we foster design creativity?” These questions are critical in enhancing design creativity and creating a sustainable society.

Surely, no one can design alone. Co-creation is an essential aspect of design creativity; however, the concept involves plural synthesis and combinations of the self and others. For example, “we” are “you” and “combinations of you” at the same time, and “we” can also be “she,” “him” and “them.” Though it may seem like it, this is not a word game. In other words, no one can capture our world. To understand the concept of design, I proposed questions on individual creativity that draw on the meaning of people’s lives. However, I still have not been able to find an answer.

Designing is a fun and enjoyable activity, yet one must approach it with care and consideration. The OECD Learning Compass 2030 is a learning framework that shows that the Anticipation-Action-Reflection (AAR) model is central to learning (OECD, n.d.). People are represented as agencies, and discussions should be fostered among communities. When I saw it, I understood that their competencies represented design creativity.

We can talk to others about our hopes for the future and understand each other well. This makes it easy to collaborate and co-create something. Experiencing the meaning of design through co-creation brings joy. However, we cannot approach the essence of design without ignoring the fact that the world – that is, real human society – is divided. Humanity has caused this divide, based on the needs of each human society. Though such a strict perspective is not required in individual research in design science, after 10 years, I think it will be good for researchers to not only reflect on their achievements but also consider the issues that are beyond their reach, which will positively impact the next 10 years’ research. Thus, it is necessary to keep Design Science up-to-date and encourage high-quality discussions. It is equally important for us to have the courage to face criticism and difficulties.

“Innovation Design,” which means social creativity, is another research theme of DSJ. While innovation is a popular term in all domains of science, the ways of designing innovation are still unclear. I am currently one of the 105 members selected as part of the Science Council of Japan (SCJ), a representative organization of the Japanese scientist community that encompasses all fields of science, including the humanities, social sciences, life sciences, natural sciences and engineering. As a member, I have contributed enthusiastically to the council. Through my work in SCJ, I realized that no one knows the real power of design or the meaning of creativity among them (scientists of traditional domains). In fact, SCJ is being negatively affected by political power. I recommend that every scientist read Design Science to reframe science and the scientific community.

I wish Design Science the best of luck and look forward to its continued development and achievements.

Design science as the pathway to absolute sustainability

Design science has a fundamental role in shaping how we understand and address sustainability challenges. As humanity faces unprecedented ecological crises, there is a growing need to design products, services and systems that remain within the Earth’s ecological boundaries. This editorial article explores the why, what and how of design science in the context of sustainability, focusing on the critical shift from relative to absolute sustainability.

Why

The primary reason for the urgency of design science in sustainability is the pressing global challenges we face. While life cycle thinking revolutionized sustainable design in the 1990s (Hauschild Reference Hauschild2015), the implementation of this approach has yet to deliver the expected results, with many planetary boundaries already surpassed (Richardson et al. Reference Richardson, Steffen, Lucht, Bendtsen, Cornell, Donges, Drüke, Fetzer, Bala, von Bloh, Feulner, Fiedler, Gerten, Gleeson, Hofmann, Huiskamp, Kummu, Mohan, Nogués-Bravo, Petri, Porkka, Rahmstorf, Schaphoff, Thonicke, Tobian, Virkki, Wang-Erlandsson, Weber and Rockström2023). The impact of traditional environmental design strategies, focusing on efficiency-effectiveness-sufficiency, has been insufficient due to rebound effects (Pigosso Reference Pigosso2024). In response, we need to shift toward design strategies that foster absolute sustainability, a paradigm that aims to design systems and products that do not exceed the Earth’s carrying capacity (Kara, Hauschild, & Herrmann Reference Kara, Hauschild and Herrmann2018).

What

Design science offers a methodological framework for creating innovative products, services and systems that enhance the environmental, social and economic impacts of human activities. Through its interdisciplinary approach, design science is essential in preventing the negative consequences of interventions, particularly those arising from behavioral and systemic changes, ultimately driving meaningful, global transformation.

To achieve this, design science must adopt systemic thinking and a holistic perspective, incorporating socio-technical systems, policy and human behavior. It should move beyond the objective of merely developing “less harmful” solutions, aiming instead to design systems that are not only effective but also “good enough,” remaining well within the planetary boundaries.

How

To address the sustainability challenges, design science must evolve to incorporate principles of absolute sustainability. This includes focusing on the design of systems that not only improve efficiency and effectiveness but also address sufficiency and prevent rebound effects (Pigosso Reference Pigosso2024). The how of conducting design science involves:

• • Systems Thinking: emphasizing the interconnectedness of economic, environmental and social factors, while considering the broader systemic impacts of design interventions.

• • Reboundless Design: Developing methods to prevent the rebound effects that arise when improvements in one area lead to negative consequences elsewhere, due to behavioral and systemic changes.

• • Fostering Absolute Sustainability: Moving beyond relative improvements to ensure that products and systems stay within the Earth’s carrying capacity.

Design science in sustainability also requires an ongoing dialogue between researchers, policymakers and industry practitioners. Such collaboration helps ensure that design interventions are not only scientifically sound but also socially and economically viable.

Conclusion

Design science is pivotal in transitioning to absolute sustainability, capable of addressing the global challenges within the planetary boundaries. By incorporating systems thinking, preventing rebound effects and fostering absolute sustainability, design science offers the tools and methodologies needed to create lasting, impactful solutions. As the world continues to face growing ecological pressures, the development of design science for sustainability will be essential for achieving global absolute sustainability goals.

Design science as bridge and beacon

Design Science offers a unique approach to framing and solving problems. At its core, it involves the “creation of artifacts and their embedding in our physical, psychological, economic, social, and virtual environments.” While much of the discourse focuses on how design science impacts design itself, its foundation supports problem-solving far beyond the realm of product or service design.

As the first graduate of the University of Michigan’s Design Science program – where I combined methods from psychology and mechanical engineering – I have seen how this foundation, paired with personal curiosity, has shaped much of my career. When I started my research lab, my goal was to truly address design problems, particularly by bridging customer evaluations and design decisions. In graduate school, my work relied mainly on self-report data. As a postdoctoral fellow, I added eye-tracking. When I launched my lab, I expanded further to include psychophysiological measures such as galvanic skin response and electroencephalography. These tools allowed me to triangulate data and gain a deeper understanding of human responses during product evaluation tasks. The process of answering research questions and mentoring students to be interdisciplinary scholars in a traditional mechanical engineering program influenced how I saw problems and sought to solve them. Mentoring students to become interdisciplinary scholars within a traditional mechanical engineering program changed how I approached problems and sought solutions. So, when classically trained engineers invited me into the field of cyber-physical human systems, I was ready.

Over the past decade, my research has spanned many topics – from integrating human-centric data into algorithms that predict trust in autonomous systems (Akash et al. Reference Akash, Hu, Jain and Reid2018) to studying brain activation patterns in drone operators learning new tasks, with and without distractions. Each step of the journey reflects the spirit of design science – iterative, interdisciplinary and impact-driven. This journey, both personal and professional, has continually reaffirmed for me the power of design science to transform how we think, collaborate and ultimately shape the world around us.

Beyond my own work, design science has created change in diverse settings. One study helped a large North American government agency apply design research methods to improve operations (McGowan, Bakula, & Castner Reference McGowan, Bakula and Castner2017) and uncover key stakeholder challenges in digital design (McGowan & Simpson Reference McGowan and Simpson2025). A third demonstrated how improvements in technology versus preferences are the core driver for new electric vehicle consumers (Forsythe et al. Reference Forsythe, Gillingham, Michalek and Whitefoot2023). Finally, one scholar addressed inequities in Black maternal health by examining how pregnant women in this demographic accessed and engaged with health information – and by identifying ways to improve that process (Burleson, Naseem, & Toyama Reference Burleson, Naseem and Toyama2020).

These examples illuminate how design science transcends disciplinary boundaries by addressing practical problems through rigorously theorized interventions. While rooted in diverse domains, what unifies them is a commitment to generating knowledge that not only explains the world but actively shapes it. This dual orientation – toward both understanding and impact – defines the unique value of design science and underscores its growing relevance in today’s complex socio-technical landscape.

DESIGN SCIENCE: Why we need it, what it is and how we conduct it

I can still recall with awe the design process displayed by Allen Samuels, a professional industrial designer and professor of Art & Design (Figure 2). Over the course of a year-long project, he generated hundreds of ideas for a universal-access home bathroom. Working on paper scrolls, he sketched and labeled his flow of ideas in sequence within each design session. His created designs were imaginative, flexible and innovative in considering human needs. Where did these incredible ideas come from?

Figure 2.

Credit: Allen Samuels, Industrial Designer.

As a cognitive scientist, I study how people think, how computers might think and how to help learners think about new ideas. But just a few moments with Allen opened an entirely new perspective on the capabilities of the human mind. Cross (Reference Cross1982) described Designerly ways of knowing as a method of thinking. At the time, approaches to Artificial Intelligence and rational thought defined only closed problems leading to a single, correct solution (Simon Reference Simon1969). Yet even without a clearly defined search space, designers create solutions that are surprising, novel and delightful. How?

Design science fills this void in our understanding through scientific (i.e., systematic, reliable) investigation (Cross Reference Cross2001). How design thinking can generate unexpected, previously unknown solutions. The human mind automatically brings past experience and knowledge to bear on a new problem, but this leads to fixation, or perseverance within one viewpoint (Jansson & Smith Reference Jansson and Smith1991; Daly et al. Reference Daly, Yilmaz, Christian, Seifert and Gonzalez2012; Leahy et al. Reference Leahy, Daly, McKilligan and Seifert2020). How do designers introduce intentional variation to produce multiple, diverse solutions to open-ended problems?

To pursue the origin of design ideas, my colleagues and I conducted many empirical studies of designers in the act of design creation. We collected observations of design sessions, including over 3500 industrial and engineering design ideas for varied problems in the laboratory and in the field. We included students, practitioners and award-winning designers in these studies. Our results documented systematic patterns of intentional variation evident across designs, called Design Heuristics (Yilmaz et al. Reference Yilmaz, Daly, Seifert and Gonzalez2016).

For example, within his design scrolls, Samuels at times created a new idea that repeated the same object form, producing a novel design where sink, shower and toilet shared the same curved arc form. Exactly which form is highlighted can be different with each use of the repeat heuristic. With training and experience, designers readily learn to apply design heuristics to add variation to their set of candidate design ideas. A total of 77 distinct Design Heuristics were identified through empirical studies of designers at work (Daly et al. Reference Daly, Yilmaz, Christian, Seifert and Gonzalez2012).

Many design scientists continue to contribute important evidence illuminating the magic behind “designerly ways of knowing.” Through scientific studies, the field of Design Science provides a glimpse into how design thinking moves beyond what exists in the world.

Design Science and its body of knowledge

Science of design, as advanced by Design Science over a decade, has customers, which suggests further development: capturing the body of knowledge.

Design science has its goals. Internal ones include bringing together the knowledge gathered and making it available to designers and researchers. Designers can use that to improve their practices and understanding. Design researchers can learn about the state of the art and explore how to extend the field. Students can be introduced and become the researchers propagating and developing design science in the next decades. External goals include enabling non-design experts to understand what designing is, to give them tools for applying designing in their fields, and possibly also showing them that design science exists and has its place in the academic firmament.

Design science thus has its customers, and one can wonder if it serves these customers well. Design Science is for sure serving design researchers, and the same can be said about other journals in the field. How about the other customers? Designers and students need textbooks and visionary monographs, by which they can learn about the knowledge gathered so far, become inspired and choose their topics for future research. Experts in other fields need brief introductions, survey papers and handbooks to scan what is known about designing and making their choices about how to make use of it. Here, I see opportunities for many more contributions by design science.

One of my last contributions to design science was editing a handbook that brings together the body of knowledge gathered about the designing of engineering systems and making it accessible for future design researchers and experts in other fields (Maier, Oehmen, & Vermaas Reference Maier, Oehmen and Vermaas2022b). My experience has been that much design knowledge is available and that the editing effort is rewarding and appreciated. But design researchers have, on average, difficulty with surveying and summarizing the knowledge about their topic, and feel more comfortable with presenting new empirical research, claiming novel insights and emphasizing their own approaches. Yet, handbook texts are typically summarizing existing work and integrating it relatively independently of the authors’ surveying.

Through my current research, I have become a customer of design science. I work on the development of quantum technologies, and this development has led to prototypes of new types of computers and communication channels. Design techniques and methods are quite interesting tools for exploring the future uses and possible societal impact of these technologies. And for making my case, I can use a simple introduction to designing to convince my new colleagues.

Design science is thriving, and I hope that the next decade leads to a series of massive handbooks and handy introductions for making available its body of knowledge.

Why virtual product development is more important today than ever before

Design sciences form the backbone of technical product development – and thus a central pillar for a sustainable, innovative society. In light of profound ecological, technological and social upheavals, engineering research is currently experiencing a new, far-reaching relevance. It not only contributes significantly to increasing efficiency and ensuring quality but also provides systematic approaches to tackling pressing challenges such as climate change, resource scarcity and digital transformation.

The complexity of global problems requires a radical rethink in product development. Climate change, the increasing scarcity of natural resources and the transition to a circular economy make it clear that new, expanded design methods are needed that take into account the entire life cycle of a product – from raw material extraction to recycling. Research and development must therefore increasingly focus on principles such as resource conservation, reuse, modularization and reparability.

At the same time, technological developments are opening up new horizons. In particular, the relatively young field of virtual product development has become a key lever for meeting these requirements in recent years. Based on complete, three-dimensional CAD models – as established since around 1990 – it is now possible to assign semantic information to individual component features during the design phase, define tolerances in a targeted manner and integrate structural mechanical analyses directly.

An important milestone in this regard is the concept of Design for X (DfX), which aims to systematically take into account requirements from downstream areas such as manufacturing, assembly, maintenance and recycling early on in the product development phase. Since around 2000, these requirements can be checked automatically using rule-based systems and production rules (see Wartzack, Reference Wartzack2001). With the advent of powerful machine learning methods around 2010, it became possible to dynamically extend these originally static rules with real data from simulation, testing and production, thus enabling CAD-based, AI-supported analyses (cf. Sauer et al. Reference Sauer, Breitsprecher, Küstner, Schleich and Wartzack2021).

This marked the logical progression from virtual product development to digital product development. This development was further strengthened by the introduction of the digital twin – a concept that allows real product instances to be digitally mapped and their properties analyzed in real time throughout their entire life cycle. This means that DfX-related tasks such as predictive maintenance or service life forecasts can now be reliably implemented (add reference).

At around the same time, model-based systems engineering (MBSE) established itself as another key approach. Especially in complex large-scale projects, MBSE enables systematic, model-based validation of requirements and creates transparency regarding their degree of fulfillment, any deviations, or even forgotten requirements.

But one thing is clear: development is far from complete. On the contrary, the further development of virtual and digital product development is rapidly gaining momentum. Despite numerous available methods and algorithms, we are still in the early stages of integrating and using artificial intelligence (AI). The potential is enormous, as the following example shows:

Modern, cloud-based CAD systems no longer store their models locally, but centrally in the cloud infrastructures of the providers. This creates a database of millions of CAD models – often carefully designed and compliant with regulations – that are ideal for training AI-based systems. We know what enormous progress has been made in the field of generative AI thanks to large text and image datasets. So why should we not assume that large “CAD models” will soon be able to generate realistic, functional and innovative product designs based on given requirements?

My personal prediction: In the not-too-distant future, we will be designing with the support of AI in combination with such CAD model foundation models – in real time, interactively and automatically.

And one final appeal: let us not leave the shaping of this future solely to CAD system manufacturers. It is the task of us researchers – especially within the Design Society – to actively help shape this future. Only in this way can we ensure that technological developments also meet our scientific and social requirements.

What is design science?

Design has been a central human endeavor for millennia, whether driven by necessity or by the natural human impulse to explore and create. Design Science can be thought of as the formal study of the processes and tools used in design, the artifacts that emerge from designing and the range of humans who hold a stake in the design, such as end users and designers (Cross Reference Cross1999). As implied by the name, it is a field that seeks to uncover design theories and principles and develop models for design.

Why do we need design science?

Design science generates fundamental understanding of the act of designing that can enable new and better products and systems. This is particularly important in the face of the growing sociotechnical complexity of the world we live in today. First, design science can help us understand how to more effectively frame and re-frame problems in ways that can give rise to new perspectives and ways of understanding the true drivers and needs that should be addressed. Second, design science can help unlock the generative aspects of design. Design is inherently synthetic and focused on creativity, idea generation and “making things real” rather than only on analysis (which is, of course, an essential tool). A design undergraduate I know decided to get a graduate degree in a field far different than design. She relayed to me how the detailed analysis and dissection of ideas in this particular field were thrilling, but she missed the generative process of designing. In fact, she told me that her graduate student classmates would always want to be on project teams with her because of her ability to reframe problems and come up with new ideas that were not obvious.

How we conduct design science

Design does not fit neatly in one field but rather cuts across them. Design lives in many disciplines, from architecture, engineering design, product design, interaction design and so forth, which means that design science similarly draws on multiple, interdisciplinary research approaches. One assumption is that how we design is linked to the outcome of design – that is, better design process or more effective design tools correlate with better design outcomes. In my own work, I think about understanding how designers design so we can help them design better. This extends to the tools, methodologies and representations that they use.

And at this moment in time, as in many other fields, Artificial Intelligence (AI) is radically upending the way design can happen. AI is rapidly growing in its capability for generating design concepts with breathtaking speed and with increasing representational fidelity (Regenwetter, Nobari, & Ahmed Reference Regenwetter, Nobari and Ahmed2022). In response, human designers are changing the way they frame design problems and constraints when working with computational design tools (Saadi & Yang Reference Saadi and Yang2023). AI raises new classes of questions about what the process of designing and the role of designers will be like in the not-so-distant future, and will likely open the door to wholly new approaches for conducting design science.

Why should we evolve usage-driven innovation methodologies in companies?

The innovation strategy of “Need Seekers” (as opposed to that of “Technology Drivers” and “Market Readers” in Jaruzelski, Staack, & Goehle (Reference Jaruzelski, Staack and Goehle2014), which is based on a deep understanding of end-users, although difficult to implement, offers the greatest potential for long-term performance. The Design Thinking approach fails to implement this innovation strategy. It does not sufficiently explore the needs and activities of end-users to provide targeted user categories with useful value propositions – comprising products, services and organizations – that are superior to existing competitor solutions, while ensuring that their development and launch are in synergy with the company’s strategic directions. More commercial and business considerations are needed (Cantamessa & Montagna Reference Cantamessa and Montagna2016). In the data age, data analysis should also be used to properly frame the problem. Indicators should be available to geolocate value hotspots to be developed by cross-referencing users, needs and relevant activity episodes with regard to the inefficiencies or strengths of existing solutions in the market. Decision-making tools should help design teams make informed choices in order to, at last, enable traceability of innovation processes, which would strengthen confidence in the final solution and allow the project’s progress to be capitalized on.

What and how with the RID methodology?

The author developed the Radical Innovation Design (RID) methodology for managing usage-driven innovation projects (Yannou & Cluzel Reference Yannou and Cluzel2024). An in-depth definition of the problem is carried out using ethnographic approaches in order to parameterize and fill in a “cognitive model” of the activity to be revisited. This cognitive model cross-references user categories, usage situations and problems/expectations in order to calculate the quantities of pain that can be reduced or eliminated by using existing solutions. This model is questioned in order to first reduce the sub-market of interest and then highlight the value buckets that are useful to address for users and relevant to the company’s commercial orientations. Seven innovative methods are then proposed, adapted to the model-based and data-driven approach of the RID design platform: RID-observe, RID-learn, RID-comparator, RID-compass, RID-creativity, UNPC-monitor, BMC-RID. At the end of the RID process, a final evaluation of the innovative solution is carried out in relation to existing solutions to ensure that there are sufficient margins of differentiation. Ultimately, the RID process (Figure 3) consists of optimizing the pair (problem, solution) in relation to two performance criteria: usefulness for users and profitability for the company.

Figure 3.

The Radical Innovation Design process is a model-based, systematic and informed process where a clear question is asked and answered at each stage. Some original RID constructs are noted in bold.

My second focus was on the absolute necessity of designing artifacts that guarantee the sustainability of systems and organizations.

Why do we need to develop LCA-based AESA methods?

Conventional LCA methods are relative and comparative, informing improvements in eco-efficiency between options (e.g., diesel versus electric vehicles), but they fail to indicate whether any solution is sustainable in absolute terms. As global environmental degradation continues despite product-level improvements, the need to respect Earth’s biophysical limits – outlined by the nine Planetary Boundaries (PBs) – becomes urgent. Current LCA practice does not ensure that total impacts stay within safe thresholds. Hence, we must develop LCA-based Absolute Environmental Sustainability Assessment (AESA) methods that integrate carrying capacities, ensuring that anthropogenic systems align with ecological limits and do not contribute to system-level transgressions.

What do these methods consist in?

LCA-based AESA methods combine life cycle assessment with absolute sustainability criteria (Bjorn et al. Reference Bjorn, Chandrakumar, Boulay, Doka, Fang, Gondran, Hauschild, Kerkhof, King, Margni, McLaren, Mueller, Owsianiak, Peters, Roos, Sala, Sandin, Sim, Vargas-Gonzalez and Ryberg2020). They quantify the environmental impact of a system across categories (e.g., climate change, freshwater use), then compare it to a share of global or regional carrying capacity – that is, the “safe operating space.” Estimating these capacities may rely on planetary boundary science or empirical thresholds. Assigning a system its share of capacity involves normative “sharing principles,” such as equal per capita, capability to reduce, or economic value added. This comparison yields an Absolute Sustainability Ratio (ASR), indicating how far a system is from its sustainability target.

How? Implementation challenges, political issues and the designers’ responsibility

Though theoretically sound, operationalizing AESA is complex. Difficulties include aligning impact categories between PBs and LCA (Kara & Hauschild Reference Kara and Hauschild2024), choosing fair sharing principles, handling uncertainties in thresholds and resolving inconsistencies in spatial scales. Politically, there is resistance due to potential constraints on growth and trade-offs with competitiveness. Moreover, commitments to absolute targets often lack sincerity, particularly where AESA results suggest deep structural changes or reductions in consumption. As such, AESA methods expose not only environmental limits but also the societal and institutional reluctance to fully engage with those limits. Designers who will need to master these approaches will also have to raise these organizational and stakeholder engagement issues and will also have to take responsibility.