This study explores how large language models like ChatGPT comprehend language and assess information. Through two experiments, we compare ChatGPT's performance with humans', addressing two key questions: 1) How does ChatGPT compare with human raters in evaluating judgment-based tasks like speculative technology realization? 2) How well does ChatGPT extract technical knowledge from non-technical content, such as mining speculative technologies from text, compared to humans? Results suggest ChatGPT's promise in knowledge extraction but also reveal a disparity with humans in decision-making.

This paper presents a mapping of theory use in the design discipline based on the corpus of the published ICED and DESIGN conference papers since 2010. We searched the resulting 4,451 papers for occurrences of theories and compared them with an existing ontology of named theories through natural language processing (NLP). The results yielded a variety of analyses, illustrating, for example, the most-used theories and which disciplines these theories stem from. This paper presents a rich overview of the theories relevant to the design discipline and a novel approach to bibliometric analyses.

This paper explores the interplay of feedback principles in design and systems science. From their roots in engineering, biology, and economics, it investigates intersections between design, cybernetics and servomechanisms. The synthesis emphasizes the need for considering feedback in anticipating unintended consequences and proposes an integrative view reconciling fundamental assumptions from the different fields through simulation. This holistic approach underscores the pivotal role of feedback in understanding and addressing complex phenomena, such as rebound effects, in design science.

Workspaces can enhance the creativity of the designers that occupy them. Here, we review experimental studies of creative spaces to identify constructs that mediate (explain) the relationship between physical spaces and creative performance. Through a literature review of 8 journal articles comprising 13 experiments, we identify 14 constructs involving cognitive, affective and physiological components. Knowledge of these mediators can help researchers to formulate hypotheses, select control variables, and develop conceptual models and theories of creative spaces in design.

External sources of inspiration can promote the discovery of new ideas as designers ideate on a design task. Data-driven techniques can increasingly enable the retrieval of inspirational stimuli based on nontext-based representations, beyond semantic features of stimuli. However, there is a lack of fundamental understanding regarding how humans evaluate similarity between non-semantic design stimuli (e.g., visual). Toward this aim, this work examines human-evaluated and computationally derived representations of visual and functional similarities of 3D-model parts. A study was conducted where participants (n=36) assessed triplet ratings of parts and categorized these parts into groups. Similarity is defined by distances within embedding spaces constructed using triplet ratings and deep-learning methods, representing human and computational representations. Distances between stimuli that are grouped together (or not) are determined to understand how various methods and criteria used to define non-text-based similarity align with perceptions of 'near' and 'far'. Distinct boundaries in computed distances separating stimuli that are 'too far' were observed, which include farther stimuli when modeling visual vs. functional attributes.

Product architecture decisions are made early in the product development process and have far-reaching effects. Unless anticipated through experience or intuition, many of these effects may not be apparent until much later in the development process, making changes to the architecture costly in time, effort, and resources. Many researchers through the years have studied various elements of product architecture and their effects. By aggregating observations on the effects of architecture strategies from a selection of the literature on the topic and storing them in a systematic data set, this information can be recalled in a matrix structure which allows for the identification, comparison and evaluation, and then selection of the most desirable product architecture strategies before expending resources along any development path. This paper introduces this matrix, referred to as the Product Architecture Strategy and Effect (PASE) Matrix, how to construct one, and a demonstration of its use.

Generative Design (GD) is a design approach that uses algorithms to generate designs. This paper investigates the role of optimisation algorithms in GD process. We study how Pareto Fronts – a classical optimization algorithm output – help designers to browse the variety associated with a design problem. Thanks to the “splitting condition” from design theory, we show that valuable Pareto Fronts for designers are those that allow the exploration of a variety of design parameters without modifying substantially the performance of the designed solution. We call “Splitting Pareto Front” the Pareto Fronts that display this property and investigate how to generate them. We compare, on an electrical battery design problem, two optimization algorithms – NSGA-II and MAP-Elites – based on the design parameters variety they generate. Our results show that MAP-Elites generates Pareto Fronts that are more splitting than those generated by NSGA-II. We then discuss this result in term of the design process: which algorithm is best suited for which design task? We conclude with the importance for future research on Generative Design Algorithms (GDA) to study jointly the functioning of GDA and their expected contribution to the design process.

Half of the total greenhouse gas emissions and 90% of biodiversity loss come from resource extraction and processing. (EC 2020). To counter this, we must switch to sustainable, long-lasting products and slow down the use of resources. It is clear that these systems will not be fixed by incremental changes but by a series of disruptions. This article uses the Aristotelian causalities as a vehicle to break down the concept of “why” industrial design and discuss the underlying value propositions of distributed manufacturing. This critical perspective allows designers and engineers to bridge the knowledge-siloes and rewire the way a product is designed, sourced, built and consumed in relation to the four Aristotelian causalities. The paper discusses the limitations and potentialities for each causality in relation to a distributed manufacturing paradigm and argues for a new sustainable design concept: The Local Limited Edition. A site-specific product design, realised by brands to enrich brand value on local markets, improve market fit and increase attachment, ultimately improving the products' longevity and value of the products.

Data-pushed projects are common in companies and consist in the design of a model in order to deliver a desirable output. The design of data science models appears at the intersection of optimisation and creativity logic, with in both cases the presence of anomalies to a various extent but no clear design process.

This paper therefore proposes to study the possible design processes in data-pushed projects, highlighting distinct knowledge exploration logics and the role of anomalies in each. This research introduces a theoretical framework to study data-pushed projects and is based on design theory. Three case studies complete this theoretical work to examine each of the processes and test our hypothesis.

As a result, this paper derives three design processes adapted to data-pushed projects and put forward for each of them: 1) the various knowledge leveraged and generated and 2) the specific role of anomalies.

Prior research has shown the importance of latent user needs for enabling innovation in early product development phases. The success of a product is largely dependent on to what extent the product satisfies customer needs, and latent user needs play a significant role in impacting the way the product or service unexpectedly delights the user. Complications arise because traditional need finding methods are not able to account for the nuances of latent user needs. A user's need is multidimensional while traditional methods are built on deductive reasoning. The traditional method isolates parts of the user's needs, only pointing to what is deducible within its search space. To address this, we introduce abduction as a way to broaden traditional need finding methods. From a logic based argument it is shown that abduction accounts for the dimensionality of user needs by integrating various traditional need finding theories using design knowledge to isolate the latent need. This theoretical development shows that latent need finding must go beyond a deductive focus, to developing methods that are able to conjecture with the deduced facts in order to abduce the latent user need.

Engineering departments design infrastructure by applying rule systems. The latter are an old creation heritage, based on decades of engineering, that makes it possible to design and govern the operation of the physical heritage which is the infrastructure. Replacing the infrastructure is not sustainable in the meaning of grand challenges; renovating it by applying engineering rules is but could appear too expensive. The literature highlights situations where renovation by respecting the state of the art is too costly and so is the validation of new renovation rules. Are there forms of frugal validation that allow for sustainable renovation of existing systems? This paper tries to explore a third way, a renovation of the physical heritage from a renovation of the system of rules, conceiving in the system of rules, new propositions, and their validation. Using the C-K theory, a case analysis was performed within the French national rail network manager (SNCF Network), a company that has a historical engineering heritage and is at the same time implementing a renovation of it. The paper shows that the renovation of engineering departments’ creation heritage can go through frugal validation patterns and constructive proof logics.

Mindset has been identified as an essential aspect of design and innovation, impacting both behaviours and performance. However, the concept of design mindset is elusive. Often design mindset is used indistinguishably from design behaviour, diminishing the complexity of the mechanisms and cognitive processes underlying design behaviour. As the initial step in researching these mechanisms, we operationalise the concept of design mindset and present the design mindset inventory (D-Mindset0) to measure it. The initial inventory centered around 16 agreement-to-value statements related to design practice. To analyse the inventory, we conducted an exploratory factor analysis based on 473 master students from different engineering disciplines participating in a course on innovation in engineering. The analysis revealed a four-factor structure with 11 final items. The four factors align with the concepts of ‘conversation with the situation,’ ‘iteration,’ ‘co-evolution of problem-solution,’ and ‘imagination.’

This viewpoint paper uses two established classifications – March's distinction between exploration and exploitation and Andreasen's Domain Theory – to develop a classification matrix to map industrial challenges and design research topics. Exploration/exploitation and the organ, part and activity domains of Domain Theory are respectively the two dimensions of this classification matrix. Examples are given of the fit of design research and industrial innovation topics into each cell of this matrix, and the need for change in industrial emphasis towards exploration across the range of domains to meet current challenges is discussed. We show how design research covers all categories in the matrix, and argue that using a simple classification may assist the research community in explaining its activities to a wider world, and in organising and directing these activities. We conclude by making suggestions for topics for debate in the design research community.

Today research has come far in explaining distinct aspects of design expertise at different skill levels. However, with the increasing number of studies, we argue there is a need to assimilate present knowledge. In this paper we advance the field of design expertise by conducting a structured literature review. Through a systematic search of papers, from 1970 to today, we identify 110 papers concerning design expertise. We then analyze the accumulated research, and map how design expertise research has evolved, in terms of what we know and what remains unexplored. Through the review, we contribute with a distinction of skills acquisition at increasing levels of design expertise.

There are innumerable design methods that exist across a wide spectrum of disciplines, ranging from engineering, to marketing, to psychology. However, the organic, multidisciplinary nature of methodological development in design leads to challenges in comparing or combining methods. Disciplinary perspectives can create conceptual 'boundaries' that may not align with the fluidity of the problems that designers may need to address. It is challenging to work between the boundaries of these design methods due to the unclear delimitation of exactly where and how methods may be integrated. Nomenclature is unstandardized and different terminologies may describe similar phenomena. To address this, a boundary object—the Actor-Abstraction matrix—is developed to recontextualize each of these divergent methods onto a common scale so they may be better understood in reference to their peers. A meta-analysis of four established design methods is performed to demonstrate the flexibility of this conceptual device. With this tool, existing design methods may be more easily examined to identify points of compatibility and gaps in their coverage, and could also serve as a powerful platform for the creation of new design methods in the future.

Design is widely understood as a domain-independent notion, comprising any activity concerned with creating artefacts. This paper shows that models can be viewed as artefacts, and that the design of models resembles the design of artefacts in other domains. The function-behaviour-structure (FBS) ontology of design is applied to models, mapping generic characteristics of models derived from literature on modelling onto basic, design-ontological categories. An example of model design, namely the CRISP-DM model for designing data mining models, is analysed and compared with models of designing in other domains (systems engineering, mechanical engineering, software engineering, and service design). The results show that there are fundamental commonalities but also differences, revealing the need for further research in developing a theory of model design.

Government regulation shapes many aspects of the design of a product. This paper addresses the effect of the complexity of a regulation on product architecture through the structure of the regulation itself. The structure of a regulation derives from dependencies among requirements and parameters in the regulation that are ipso facto design elements. Since design elements such as requirements and parameters have no formal definition in regulation, it is difficult to identify them accurately and consistently. We apply two approaches to defining and coding requirements and parameters in the context of washing machine regulation. The two coding approaches generate networks of design elements that are analyzed to measure the complexity of the regulation and by extension the product. We find significant differences in the complexity of the regulation when coded in different ways and note deficiencies and strengths of each approach. These findings will support future research to measure the impact of regulatory complexity on product architecture.

Design process descriptions in the literature in general and those using C-K theory in particular lack some useful cognitive information that may affect the credibility of the process. Notions from abduction research are presented and proposed for enhancing such descriptions. Specifically, it is important to distinguish between design activities that are intuitive and those that result from deliberation; a topic that has long been discussed by philosophers of science and design scholars. The focus of the paper is on the ubiquitous design moves of proposing an idea and selecting among ideas, and on their execution by expert and novice designers.

Evidence-based practices play an essential role in the development of eHealth systems. Prior research has investigated the challenge of shared understanding between professionals from the fields of health sciences and design and has highlighted the need for effective alignment of development and research practices in eHealth. However, there is a limited understanding of epistemological differences between these fields and how professionals conceptualise evidence. In this paper, we investigate how healthcare and design professionals think about evidence and how they implement evidence practices in their work. We interviewed eight professionals and used reflexive thematic analysis to identify the challenges and strategies associated with their evidence practices. Our results identify five shared evidence practices between healthcare and design professionals: stakeholder-driven, process-driven, problem-driven, effect-driven, and solution-driven. These five evidence practices indicate opportunities for closer alignment of development and research practices among healthcare and design professionals and offer a basis to create a shared understanding of evidence between both fields.