Participatory Design – an iterative, flexible design process that closely involves stakeholders, often end users – is growing in use across design disciplines. As more practitioners use Participatory Design (PD), it has become less rigidly defined, with stakeholders engaged to varying degrees through disjointed techniques. This ambiguity can be counterproductive when discussing PD processes. We performed a systematic literature review that builds shared, foundational knowledge of PD processes and techniques while also summarizing the state of PD research in the field, as a first step in supporting richer understandings of how best to equitably engage with stakeholders. We found that a majority of PD literature examined specific case studies of PD, with the design of intangible systems representing the most common design context. Stakeholders most often participated throughout multiple stages of a design process, recruited in a variety of ways, and engaged in several of the 14 specific participatory techniques identified. Our findings also identify leverage points for creators of PD processes and how the leverage points impact design equity, including: (1) emergent versus predetermined processes; (2) direct versus indirect participation; (3) early versus late participation; (4) one time versus iterative participation; and (5) singular versus multiple PD techniques.

The central role of creativity in product engineering is evident in the generation of solutions with high innovative potential. Even in times of artificial intelligence being creative is still a skill in which the human outperforms the machine. Product engineering activities often take place in distributed environments, which elevates the importance of creative tasks due to the unique challenges these settings present. Furthermore, these distributed environments frequently involve intercultural teams. With intercultural team settings come additional benefits but also challenges. To support the creative processes of intercultural, distributed product engineering teams, the cultural synergy spectrum (CSS) method has been developed. The CSS method is designed to assist distributed product engineering teams with being creative while being culturally sensitive. To achieve this goal, mutual understanding is enhanced, and learning within the team is promoted. Using five phases to lead the participants through a creative process, the CSS starts with a warm-up, followed by building a knowledge baseline. The third phase is targeted at cultural learning, after which the creativity phase starts. Here, the actual problem-solving takes place. The final phase is for reflection and feedback. This study seeks to validate the CSS method’s effectiveness through application in a partially distributed team. Two teams, consisting of mechanical engineers in a research group at Shanghai Jiao Tong University, collaborated to address a practical problem using this method. The team is primarily Chinese as a follow-up to previous validation iterations that were done with teams with more diverse backgrounds, but who lived in Germany. To ensure that this bias due to the intercultural experience of living in another country is overcome, this study is performed with researchers in China with little intercultural experience. The CSS was applied successfully, proving that the CSS is suitable for the partially distributed or hybrid setting in which it was applied and for the team that applied it. The participants made use of the option to include additional tools and improvements to the method, like a more comprehensive warm-up.

In this study, professional engineers and designers (n = 30) participated in a 1-hour-long design activity in which they brainstormed a list of ideas for two design problems (a smart grill and a smart laundry machine), created a sketched concept for each design problem, filled out a survey about their perceptions of the market for the concept they developed, participated in a bias mitigation intervention and then repeated the pre-intervention steps. The design problems were intended to trigger availability bias based on the participants’ occupations (engineers and designers at a kitchen appliance company) as well as conflict between the gender of the participants and the gender-stereotyping of the household tasks fulfilled by the smart machines. Based on correlations in the market survey, the participants, who were mostly men, displayed availability bias toward the smart laundry machine design problem. A key marker of availability bias – an association between participants’ personal enjoyment of the product and the belief that the product would be commercially successful – was eliminated after the bias mitigation intervention. Qualitative analysis of participants’ reflections indicated that the intervention primarily assisted designers in making additional considerations for users, such as increasing accessibility and building awareness of excluded user groups.

Recent academic contributions explore the integration of Digital Twins (DTs) within smart Product-Service System (sPSS). This integration aims to innovate business propositions, hardware and services. However, gaps persist in developing DT environments to support early-stage collaborative innovation for sPSS, and limited studies explore how real-time synchronized digital replicas enhance value co-creation in this area. This paper addresses this gap by presenting a framework and practical example of integrating value-driven decision support into early sPSS conceptual design. A case study on the development of the Smart Electric Vehicle (SEV) conducted with a global automotive Original Equipment Manufacturer (OEM) demonstrates the framework’s efficacy. Through qualitative data analyses based on experimental validation in a case company, the DT proves effective in aiding decision makers in selecting value-adding configurations within specific scenarios. Furthermore, the DT serves as a visual decision-making tool, fostering collaboration across diverse teams within the automotive company. This collaboration facilitates value creation across practitioners with varied backgrounds, emphasizing the DT’s role in enhancing early-stage innovation and co-creation processes in the sPSS domain.

Design computing refers to the usage of computer frameworks, models or systems in design-related activities. Design computing research, in turn, refers to the development of these frameworks/models/systems, and so forth. As design practice increasingly relies on computer tools, the demand for research in design computing grows. While this opens innumerable venues for research, the profusion of information in the field poses significant challenges for researchers. Therefore, meta-level surveys of the field are called for. To provide researchers with a useful overview of design computing research, we set out to identify some of the main clusters of activity in the field. By “clusters of activity”, we refer to groups of researchers pursuing similar or identical research questions. Our PRISMA-style review focuses on the identification of such clusters, based on the complete proceedings (N = 404) of a long-standing conference (Design Computing and Cognition, DCC, 2004–2024), which captures the richness and diversity of the field. The primary contribution of this work is a map that organizes the main questions explored and the approaches taken in exploring them, which are informative for researchers and educators alike. This map may also help to execute large-scale surveys via automation, toward obtaining a comprehensive view of the field.

The value-creation opportunities enabled by the ubiquitous availability of data indisputably lead to the necessity of restructuring innovation processes. Moreover, the variety of stakeholders potentially involved in innovation processes and the apparent heterogeneity of scenarios and contexts imply much less established practices and routines and not yet constituted reference frameworks to lead the transition to data-driven product innovation. In this context, the paper attempts, from the analysis of the data-driven innovation processes of 36 Italian companies, to recognise the emerging innovation opportunities offered by the rich network of the resulting data flows. However, these opportunities also imply new tasks, which in turn raise further concerns. Building on data-driven design literature and on industrial practices in the field of innovation management, the authors discuss the role that research achievements in the field of engineering design can play in addressing such concerns.

Life cycle assessment (LCA) reports are commonly used for sustainability documentation, but extracting useful information from them is challenging and requires expert oversight. Designers frequently face technical obstacles and time constraints when interpreting LCA documents. As AI-driven tools become increasingly integrated into design workflows, there is an opportunity to improve access to sustainability data. This study used a mixed-methods approach to develop life cycle design heuristics to help non-LCA experts acquire relevant design knowledge from LCA reports. Developed through in-depth interviews with LCA experts (n = 9), these heuristics revealed five prominent categories of information: (1) scope of analysis, (2) priority components, (3) eco hotspots, (4) key metrics, and (5) design strategies. The utility of these heuristics was tested in a need-finding study with designers (n = 17), who annotated an LCA report using the heuristics. Findings suggest a need for additional support to help designers contextualize quantitative metrics (e.g., carbon footprints) and suggest relevant design strategies. A follow-up reflective interview study with LCA experts gathered feedback on the heuristics. These heuristics offer designers a framework for engaging with sustainability data, supporting product redesign, and a foundation for AI-assisted knowledge extraction to integrate life cycle information into design workflows efficiently.

Unintended technical interactions across system interfaces can lead to costly failures and rework, particularly in the early design stages of complex products. This study examines how structured risk assessment tools influence teams’ ability to identify, evaluate and mitigate risks from such indirect interactions. In a controlled experiment, 14 engineering teams (comprising professionals and graduate students) engaged in simulated design decisions across three system configurations. Tool usage – including models of direct and indirect risk propagation and value-based trade-offs – was continuously logged and linked to outcomes. Teams that engaged earlier and more deliberately with the tools identified risks sooner and selected mitigation actions with more favourable cost–benefit profiles. Results show that strategic, not merely frequent, tool use improves risk management performance, particularly when addressing cascading effects from indirect physical interactions. These findings support the use of structured supports to enhance both the efficiency of early-stage risk evaluation and the efficacy of risk treatment.

Designers often rely on their self-evaluations – either independently or using design tools – to make concept selection decisions. When evaluating designs for sustainability, novice designers, given their lack of experience, could demonstrate psychological distance from sustainability-related issues, leading to faulty concept evaluations. We aim to investigate the accuracy of novice designers’ self-evaluations of the sustainability of their solutions and the moderating role of their (1) trait empathy and (2) their beliefs, attitudes and intentions toward sustainability on this accuracy. We conducted an experiment with first-year engineering students comprising a sustainable design activity. In the activity, participants evaluated the sustainability of their own designs, and these self-evaluations were compared against expert evaluations. We see that participants’ self-evaluations were consistent with the expert evaluations on the following sustainable design heuristics: (1) longevity and (2) finding wholesome alternatives. Second, trait empathy moderated the accuracy of self-evaluations, with lower levels of fantasy and perspective-taking relating to more accurate self-evaluations. Finally, beliefs, attitudes and intentions toward sustainability also moderated the accuracy of self-evaluations, and these effects vary based on the sustainable design heuristic. Taken together, these findings suggest that novice designers’ individual differences (e.g., trait empathy) could moderate the accuracy of the evaluation of their designs in the context of sustainability.

Due to the ever-increasing complexity of technical products, the quantity of system requirements, which are typically expressed in natural language, is inevitably rising. Model-based formalization through the application of Model-based Systems Engineering is a common solution to cope with rising complexity. Thereby, grouping requirements to use cases forms the first step towards model-based requirements and allows to improve the understanding of the system. To support this manual and subjective task, automation by artificial intelligence and methods of natural language processing are needed. This contribution proposes a novel pipeline to derive use cases from natural language requirements by considering incomplete manual mappings and the possibility that one requirement contributes to multiple use cases. The approach utilizes semi-supervised requirements graph generation with subsequent overlapping graph clustering. Each identified use case is described by keyphrases to increase accessibility for the user. Industrial requirement sets from the automotive industry are used to evaluate the pipeline in two application scenarios. The proposed pipeline overcomes limitations of prior work in the practical application, which is emphasized by critical discussions with experts from the industry. The proposed pipeline automatically generates proposals for use cases defined in the requirement set, forming the basis for use case diagrams.

Implementing changes to digital health systems in real-life contexts poses many challenges. Design as a field has the potential to tackle some of these. This article illustrates how design knowledge, through published literature, is currently referenced in relation to the implementation of digital health. To map design literature’s contribution to this field, we conducted a scoping review on digital health implementation publications and their use of references from nine prominent design journals. The search in Scopus and Web of Science yielded 382 digital health implementation publications, of which 70 were included for analysis. From those, we extracted data on publication characteristics and how they cited the design literature. The 70 publications cited 58 design articles, whose characteristics were also extracted. The results show that design is mainly cited to provide information about specific design methods and approaches, guidelines for using them and evidence of their benefits. Examples of referenced methods and approaches were co-design, prototyping, human-centered design, service design, understanding user needs and design thinking. The results thus show that design knowledge primarily contributed to digital health implementation with insights into methods and approaches. In addition, our method showcases a new way for understanding how design literature influences other fields.

Limited research has explored the delivery of sustainable design in higher education globally. Therefore, the aim of this paper is to investigate educational practices on the topic. Through an online survey, we investigated numerous aspects of units of study exposing topics related to sustainable design with a focus on contents, teaching methods and educational objectives. The survey was accessed by almost 400 educators in the field of sustainable design. The data show that a variety of teaching methods are used, with a critical role played by project-based learning in addition to traditional lectures. Most respondents rated all investigated intended learning outcomes as relevant or very relevant. In terms of contents and methods treated by the respondents, product eco-design and design for X are the most frequently taught methods. Educational approaches and teaching objectives are poorly affected by the discipline of the degree in which units of study are taught. In terms of contents, design degrees include approaches to sustainable design at the spatio-social level more frequently than engineering degrees do.

Within the broad context of design research, joint attention within co-creation represents a critical component, linking cognitive actors through dynamic interactions. This study introduces a novel approach employing deep learning algorithms to objectively quantify joint attention, offering a significant advancement over traditional subjective methods. We developed an optimized deep learning algorithm, YOLO-TP, to identify participants’ engagement in design workshops accurately. Our research methodology involved video recording of design workshops and subsequent analysis using the YOLO-TP algorithm to track and measure joint attention instances. Key findings demonstrate that the algorithm effectively quantifies joint attention with high reliability and correlates well with known measures of intersubjectivity and co-creation effectiveness. This approach not only provides a more objective measure of joint attention but also allows for the real-time analysis of collaborative interactions. The implications of this study are profound, suggesting that the integration of automated human activity recognition in co-creation can significantly enhance the understanding and facilitation of collaborative design processes, potentially leading to more effective design outcomes.

VR sketching tools have matured to a practical level, enabling use across various 3D design disciplines. Studies into VR sketching in design report beneficial affordances but are based on brief testing of tools in simulated tasks. Consequently, there is a knowledge deficit in understanding how to effectively integrate VR sketching into design projects. We address this gap with a case study on the sustained use of VR sketching in 10 automotive concept design projects over 10 months. In analysing designers’ logbooks, which captured design development, and post-study reflections, we show how the affordances of VR sketching outlined in literature manifest in practice. Specifically, we show how and when designers can exploit the precedence of 3D geometry embodied in VR sketches to advance the design process in terms of several dimensions of design fidelity. We highlight where process advantages are realised through (1) increased spatial fidelity, reducing the time required to iterate 2D sketches, (2) operational fidelity supporting dynamic testing of concept functionality via animation and (3) environmental fidelity supporting contextualising components and storytelling. As such, our findings highlight how and when practitioners can realise the comparative benefits of VR sketching alongside traditional sketching and 3d modelling during the concept design process.

This paper introduces Setting-Driven Design (SDD) and supporting tool – the Behaviour Setting Canvas (BSC) – which together address a critical gap in behavioural design by shifting the focus from individual behaviour to the broader context in which behaviour occurs. Rooted in behaviour setting theory, SDD is a powerful approach to behavioural design that offers an end-to-end structure for understanding and intervening in a behavioural design challenge. The process comprises three iterative phases: scoping the behavioural challenge, understanding the setting and intervention development. The process structure revolves around the BSC, a tool for mapping key contextual elements such as roles, motives, norms and routines. While SDD is particularly effective for behaviour change interventions, its utility extends to other design challenges, including introducing new products, shifting social norms and enhancing existing systems where behaviour remains constant. The approach integrates a theory of change to guide intervention development, prototyping and evaluation, ensuring alignment with behavioural objectives and contextual realities. A case study on handwashing in low-income Tanzanian households illustrates the method’s utility, culminating in the creation of Tab Soap, a single-use, biodegradable soap designed to improve hygiene behaviours. The study demonstrates how SDD facilitates insight generation and iterative refinement and complements user-centred design. SDD advances behavioural design by combining theoretical rigour with practical application, offering a scalable and adaptable framework for addressing complex design challenges across diverse fields.

Changing environmental, societal, and business conditions shift the priority given to the ‘most valuable’ design solutions to be developed, which risks causing rework and mistakes during the development process. To maintain consistency among changes in what ‘value’ means in evolving business contexts, this paper presents a method – value-driven model-based systems engineering (VD-MBSE) – implemented in a software tool (named Club Design). The method is demonstrated through a case study related to aerospace electrification, highlighting its ability to maintain consistency during the iterations between business development and the design of technical solutions.

This research examines whether a machine, specifically artificial intelligence (AI), can be creative by comparing design solutions for a practical competition – a light fixture for a pediatric waiting room – among AI, collaboration efforts and a human designer. Amazon Mechanical Turk and Prolific workers observed the design solutions throughout the design process, from sketches ($ S $) to three-dimensional renderings ($ 3D $) to fully developed models in virtual waiting rooms ($ VR $). Using the well-established Creative Product Semantic Scale (CPSS), the workers rated each design solution in three distinctive stages – $ S $, $ 3D $ and $ VR $ – on three criteria – novelty (freshness or newness), resolution (relevance and logic) and style (craftsmanship and desirability). Despite some demographic discrepancies, the workers expressed general senses of happiness and calmness, resonating with the competition’s requirements. Statistical results of CPSS ratings revealed that while AI excelled in style for $ 3D $, the human designer outperformed in novelty for both $ S $ and $ VR $. Collaboration efforts surprisingly finished last. Such findings challenge current assumptions of AI’s creative ability in design research and highlight the need to be agile in the age of disruptive technologies. This research also offers guidance for product and interior designers and educators on thoughtfully integrating AI into the design process.

Speculative design is widely used in research contexts across multiple disciplines, emphasising problem-finding over problem-solving, and involves methods for exploring possibilities that challenge ingrained assumptions. This systematic literature review analyses speculative design methods used in 52 studies within disciplines such as human–computer interaction, fashion, urban planning, and healthcare, among other fields. It presents results about the common phases and methods of speculative design that are utilised in these studies. It identifies and characterises four core phases that appear to be common within speculative design processes, namely select, explore, transform, and provoke. It shares examples of how these phases are used to achieve the goals of speculative design. The discussion section considers the process of speculative design, leading to the synthesis of a framework that visually and conceptually organises these findings to facilitate their comprehension and application. This paper contributes to the understanding of speculative design by providing a clear process that addresses gaps in its theoretical and methodological foundations.

Excellent products often contain profound cultural connotations. To improve the quality of cultural products, it is important to study how typical cultural carriers can be more promptly and efficiently identified and incorporated into products through a detailed and easy-to-use design process. In this article, we propose an approach from three different levels to assist designers in incorporating cultural features into products, including: (1) the integrated framework of the composition and division of cultural carriers, (2) the extraction and translation model from cultural carriers, cultural elements to cultural features and (3) the cultural product design process. The proposed approach was applied in a large and complex cultural product case, that is, inter-city train design. The evaluation of the recognition of culture features indicated that the approach contributed to conferring culture on products through thoughtful design and could ensure that the product schemes reflect cultural features as well as interesting cultural connotations.

Complete exploration of design spaces is often computationally prohibitive. Classical search methods offer a solution but are limited by challenges like local optima and an inability to traverse dislocated design spaces. Quantum computing (QC) offers a potential solution by leveraging quantum phenomena to achieve computational speed-ups. However, the practical capability of current QC platforms to deliver these advantages remains unclear. To investigate this, we apply and compare two quantum approaches – the Gate-Based Grover’s algorithm and quantum annealing (QA) – to a generic tile placement problem. We benchmark their performance on real quantum hardware (IBM and D-Wave, respectively) against a classical brute-force search. QA on D-Wave’s hardware successfully produced usable results, significantly outperforming a classical brute-force approach (0.137 s vs 14.8 s) at the largest scale tested. Conversely, Grover’s algorithm on IBM’s gate-based hardware was dominated by noise and failed to yield solutions. While successful, the QA results exhibited a hardware-induced bias, where equally optimal solutions were not returned with the same probability (coefficient of variation: 0.248–0.463). These findings suggest that for near-term engineering applications, QA shows more immediate promise than current gate-based systems. This study’s contribution is a direct comparison of two physically implemented quantum approaches, offering practical insights, reformulation examples and clear recommendations on the utilisation of QC in engineering design.