Towards addressing ontological issues in design cognition research, this paper presents the first generic classification of cognitive processes investigated in protocol studies on conceptual design cognition. The classification is based on a systematic review of 47 studies published over the past 30 years. Three viewpoints on the nature of design cognition are outlined (search, exploration and design activities), highlighting considerable differences in the concepts and terminology applied to describe cognition. To provide a more unified view of the cognitive processes fundamentally under study, we map specific descriptions of cognitive processes provided in protocol studies to more generic, established definitions in the cognitive psychology literature. This reveals a set of 6 categories of cognitive process that appear to be commonly studied and are therefore likely to be prevalent in conceptual design: (1) long-term memory; (2) semantic processing; (3) visual perception; (4) mental imagery processing; (5) creative output production and (6) executive functions. The categories and their constituent processes are formalised in the generic classification. The classification provides the basis for a generic, shared ontology of cognitive processes in design that is conceptually and terminologically consistent with the ontology of cognitive psychology and neuroscience. In addition, the work highlights 6 key avenues for future empirical research: (1) the role of episodic and semantic memory; (2) consistent definitions of semantic processes; (3) the role of sketching from alternative theoretical perspectives on perception and mental imagery; (4) the role of working memory; (5) the meaning and nature of synthesis and (6) unidentified cognitive processes implicated in conceptual design elsewhere in the literature.

As a discipline, design science has traditionally focused on designing products and associated technical processes to improve usability and performance. Although significant progress has been made in these areas, little research has yet examined the role of human behaviour in the design of socio-technical systems (e.g., organizations). Here, we argue that applying organizational psychology as a design science can address this omission and enhance the capability of both disciplines. Specifically, we propose a method to predict malfunctions in socio-technical systems (PreMiSTS), thereby enabling them to be designed out or mitigated. We introduce this method, describe its nine stages, and illustrate its application with reference to two high-profile case studies of such malfunctions: (1) the severe breakdowns in patient care at the UK’s Mid-Staffordshire NHS Foundation Trust hospital in the period 2005–2009, and (2) the fatal Grayrigg rail accident in Cumbria, UK, in 2007. Having first identified the socio-technical and behavioural antecedents of these malfunctions, we then consider how the PreMiSTS method could be used to predict and prevent future malfunctions of this nature. Finally, we evaluate the method, consider its advantages and disadvantages, and suggest where it can be most usefully applied.

‘Practice what you preach’ is a phrase occasionally used to refer to those not acting as they want others to act. There are countless opportunities in professional work and daily life to bring such criticism upon ourselves. While the subject is broad, this study focuses on the application of this idea in research practice, and more specifically in design research. One point of departure is the question: ‘how should we practice research if its results are products just like other products?’ The Principle of Reflexive Practice (PRP) states that considering the outcome of design research or research itself as a product, many design principles, tools, methods or knowledge are applicable to design research.

A corollary of the principle is that in order to succeed in contemporary research environments, design researchers would gain significant benefit such as improving the success rate of their research projects, if they exercise design methods and tools in designing their research. By exercising these methods, researchers would gain quick and rich feedback about the methods they develop; they would become aware of issues that require users’ perspective that could not be possible without their own practical use.

The PRP makes participants in design research aware of the reflexive opportunity in studying design that could be mobilized to advancing their practice and making their research results more effective. Notwithstanding, adopting the PRP is not easy; therefore, it is presented as a challenge to design research. Four examples of using the PRP as a guiding principle in research are presented to demonstrate its importance and benefits.

Most products are developed in generations. This needs to be considered with regard to development methods and processes to make existing knowledge available to achieve increased efficiency. To realize this, the approach of PGE – product generation engineering – is formulated. Product generation engineering is understood as the development of products based on reference products (precursor or competitor products). The subsystems are either adapted to the new product generation by means of carryover or they are newly developed based on shape variation or principle variation. Validation is considered as the central activity in the product engineering process and is a major challenge, especially for complex mechatronic systems. Therefore, it is important to understand validation as an ongoing activity during product development. The pull principle of validation describes the definition and development of validation activities, including models and validation environments based on specific validation objectives. In order to have effectiveness within validation of subsystems, it is necessary to map the interactions with the overall system, namely the super-system. The relevant subsystems can be connected under consideration of functional and energetic aspects by means of virtual, physical or mixed virtual–physical modeling applied by the holistic IPEK-X-in-the-Loop approach within the integrated Product engineering Model (iPeM).

Models of products and design processes are key to interacting with engineering designs and managing the processes by which they are developed. In practice, companies maintain networks of many interrelated models which need to be synthesised in the minds of their users when considering issues that cut across them. This article considers how information from product and design process models can be integrated with a view to help manage these complex interrelationships. A framework highlighting key issues surrounding model integration is introduced and terminology for describing these issues is developed. To illustrate the framework and terminology, selected modelling approaches that integrate product and process information are discussed and organised according to their levels and forms of integration. Opportunities for further work to advance integrated modelling in engineering design research and practice are discussed.

The design process often requires work by teams, rather than individuals. During team based design it is likely that situations will arise in which individual members of the team have different opinions, yet a group decision must still be made. Unfortunately, Arrow’s impossibility theorem indicates that there is no method for aggregating group preferences that will always satisfy a small number of ‘fair’ conditions. This work seeks to identify methods of combining individual preferences that can come close to satisfying Arrow’s conditions, enabling decisions that are fairer in practice. First, experiential conjoint analysis was used to obtain individual empirical utility functions for drinking mug designs. Each empirical utility function represented individual members who were part of a design team. Then, a number of functions for constructing group preference were analysed using both randomly generated preferences and empirical preferences derived from the experiential conjoint survey. The analysis involved checking each of Arrow’s conditions, as well as assessing the potential impact of strategic voting. Based on the results, methods that should be used to aggregate group preference within a design team in practice were identified and recommended.

In many engineering design contexts models are indispensable. They offer decision support and help tackle complex and interconnected design projects, capturing the underlying structure of development processes or resulting products. Because managers and engineers base many decisions on models, it is crucial to understand their properties and how these might influence their behaviour. The level of detail, or granularity, of a model is a key attribute that results from how reality is abstracted in the modelling process. Despite the direct impact granularity has on the use of a model, the general topic has so far only received limited attention and is therefore not well understood or documented. This article provides background on model theory, explores relevant terminology from a range of fields and discusses the implications for engineering design. Based on this, a classification framework is synthesised, which outlines the main manifestations of model granularity. This research contributes to theory by scrutinising the nature of model granularity. It also illustrates how this may manifest in engineering design models, using Design Structure Matrices as an example, and discusses associated challenges to provide a resource for modellers navigating decisions regarding granularity.

Sketches are an essential tool for designers. They allow the externalizing of ideas and are therefore economic cognitively. Sketches also provide the designer with new insights, which play an important role in the emergence of ideas. However, some studies tend to show that sketching does not systematically have a positive effect on idea generation. Our research thus aims to analyze the generative effects of sketches by studying the way sketches support the design strategy of designers. We especially focus on the role of knowledge in comparison with concepts. Three sequences of sketches are analyzed employing C–K design theory; we show that drawings refer to both concepts and knowledge, but mostly to knowledge. In particular, sketching helps the architect mobilize knowledge distant from the initial topic. Moreover, the designer carries out through sketching an important work of knowledge structuration that we call ‘knowledge preordering’; by carefully selecting, testing and, if necessary, removing knowledge, the designer organizes a strategically built knowledge space. In particular, all elements involving modularity or determinism in the knowledge basis are abandoned. Such knowledge preordering thus allows the building of a splitting knowledge structure, which offers new rules for concept generation and enhances the production of original and disruptive ideas.

Although nanoparticles have been shown to have clear technological advantages, their use in some consumer products remains controversial, particularly where these products come in direct contact with our bodies. There has been much discussion about using metal oxide nanoparticles in sunscreens, and numerous technology assessments aimed at predicting the type, size and concentration of nanoparticles and surface treatments that will be best for consumers. Yet, the optimal configuration is ultimately the one that people actually want and are willing to pay for, but until now consumer preferences have not been included in model predictions. We describe and discuss a proof of concept study in which we design and implement a hypothetical sunscreen product configurator to predict how people tradeoff sun protection factor (SPF), product transparency and potential toxicity from reactive oxygen species (ROS) in configuring their most preferred sunscreen. We also show that preferred nanoparticle sizes and concentrations vary across demographic groups. Our results suggest that while consumers choose to reduce or eliminate potential toxicity when possible, they do not automatically sacrifice high SPF and product transparency to avoid the possibility of toxicity from ROS. We discuss some advantages of using product configurators to study potential product designs and suggest some future research possibilities.

Motivated by overcoming the existing utility-based choice modeling approaches, we present a novel conceptual framework of multidimensional network analysis (MNA) for modeling customer preferences in supporting design decisions. In the proposed multidimensional customer–product network (MCPN), customer–product interactions are viewed as a socio-technical system where separate entities of ‘customers’ and ‘products’ are simultaneously modeled as two layers of a network, and multiple types of relations, such as consideration and purchase, product associations, and customer social interactions, are considered. We first introduce a unidimensional network where aggregated customer preferences and product similarities are analyzed to inform designers about the implied product competitions and market segments. We then extend the network to a multidimensional structure where customer social interactions are introduced for evaluating social influence on heterogeneous product preferences. Beyond the traditional descriptive analysis used in network analysis, we employ the exponential random graph model (ERGM) as a unified statistical inference framework to interpret complex preference decisions. Our approach broadens the traditional utility-based logit models by considering dependency among complex customer–product relations, including the similarity of associated products, ‘irrationality’ of customers induced by social influence, nested multichoice decisions, and correlated attributes of customers and products.

Inspiration is a widely recognized phenomenon in everyday life. However, researchers still know very little about what the process of inspiration entails. This paper investigates designers’ approaches when selecting inspirational stimuli during the initial phases of a design process. We conducted a think-aloud protocol study and interviews with 31 design Masters students while generating ideas for a design problem. The results indicate that searching for and selecting stimuli require different levels of cognitive effort, depending on whether there is unlimited or limited access to stimuli. Furthermore, three important stages of the inspiration process were identified: keyword definition, stimuli search and stimuli selection. For each of these stages, we elaborate on how designers define keywords, which search approaches they use and what drives their selection of stimuli. This paper contributes to an understanding of how designers can be supported in their inspiration process in a more detailed manner.

Designers faced with the task of developing a new product model of a brand must balance several considerations. The design must be novel and express attributes important to the customers, while also recognizable as a representative of the brand. This balancing is left to the intuition of the designers, who must anticipate how customers will perceive the new design. Oftentimes, the design freedom used to meet a product attribute can compromise the recognition of the product as a member of the brand. In this paper, an experiment is conducted for measuring changes in ten styling attributes common to both design freedom and brand recognition for automotive designs from four brands, Audi, BMW, Cadillac, and Lexus, using customer responses to two- and three-dimensional vehicle designs created and presented interactively through a crowdsourced web application. Results show that while brand recognition is highly dependent on the manufacturer, two brands have strong negative relationship between design freedom and brand recognition, suggesting that these two manufacturers face a significant challenge when evolving their respective brand styling. This study is a first effort toward quantifying and predicting tradeoffs between design freedom and brand recognition, contributing to existing efforts that augment human intuition during strategic design decisions.

Functional technical performance usually follows an exponential dependence on time but the rate of change (the exponent) varies greatly among technological domains. This paper presents a simple model that provides an explanatory foundation for these phenomena based upon the inventive design process. The model assumes that invention – novel and useful design – arises through probabilistic analogical transfers that combine existing knowledge by combining existing individual operational ideas to arrive at new individual operating ideas. The continuing production of individual operating ideas relies upon injection of new basic individual operating ideas that occurs through coupling of science and technology simulations. The individual operational ideas that result from this process are then modeled as being assimilated in components of artifacts characteristic of a technological domain. According to the model, two effects (differences in interactions among components for different domains and differences in scaling laws for different domains) account for the differences found in improvement rates among domains whereas the analogical transfer process is the source of the exponential behavior. The model is supported by a number of known empirical facts: further empirical research is suggested to independently assess further predictions made by the model.

This paper gives a general and logical analysis of the expert position in design research by which methods for innovative design can be derived from expert design practices. It first gives a framework for characterising accounts of design by the way in which they define and relate general, descriptive and prescribed types of design practices. Second, it analyses with this framework the expert position’s conservatism of prescribing existing expert design practices to non-expert designers. Third, it argues that the expert status of expert designers does not provide sufficient justification for prescribing expert design practices to non-expert designers; it is shown that this justification needs support by empirical testing. Fourth, it discusses validation of design methods for presenting an approach to this testing. One consequence of the need to empirically test the expert position is that its prescription has to be formulated in more detail. Another consequence is that it undermines the expert position since expert design practices are not anymore certain sources for deriving design methods with. Yet it also opens the expert position to other sources for developing design methods for innovation, such as the practices of contemporary designers and the insights of design researchers.

Governments encourage use of electric vehicles (EV) via regulation and investment to minimize greenhouse gas (GHG) emissions. Manufacturers produce vehicles to maximize profit, given available public infrastructure and government incentives. EV public adoption depends not only on price and vehicle attributes, but also on EV market size and infrastructure available for refueling, such as charging station proximity and recharging length and cost. Earlier studies have shown that government investment can create EV market growth, and that manufacturers and charging station operators must cooperate to achieve overall profitability. This article describes a framework that connects decisions by the three stakeholders (government, EV manufacturer, charging station operator) with preferences of the driving public. The goal is to develop a framework that allows the effect of government investment on the EV market to be quantified. This is illustrated in three scenarios in which we compare optimal public investment for a city in USA (Ann Arbor, Michigan) and one in China (Beijing) to minimize emissions, accounting for customer preferences elicited from surveys conducted in the two countries. Under the modeling assumptions of the framework, we find that high customer sensitivity to prices, combined with manufacturer and charging station operator profit maximization strategies, can render government investment in EV subsidies ineffective, while a collaboration among stakeholders can achieve both emission reduction and profitability. When EV and station designs improve beyond a certain threshold, government investment influence on EV adoption is attenuated apparently due to diminishing customer willingness to buy. Furthermore, our analysis suggests that a diversified government investment portfolio could be especially effective for the Chinese market, with charging costs and price cuts on license plate fees being as important as EV subsidies.

Design grammars enable the formal representation of a vocabulary and rules that describe how designs can be synthesized just as the grammar rules of a spoken language define how to formulate valid, i.e., grammatically correct, sentences. Design grammars have been successfully applied in numerous engineering disciplines and enable the automated synthesis of designs within a defined design language. Design grammar development, however, is challenging and lacks methodological support. In this paper, a novel method is presented that supports the development and application of design grammars using transition graphs. In these, nodes represent generated designs and edges represent grammar rules that transform one design into another. Rather than using a tree structure to represent the possible application of rules, transition graphs are automatically generated and used to help designers better understand the developed grammar. The grammar designer is given feedback on (a) the rules, and (b) rule application sequences. This feedback can be used to (a) improve the grammar, and (b) apply it more efficiently. Two case studies, a gearbox synthesis task and a sliding tile puzzle, demonstrate the method. The results show the feasibility of the method to support design grammar development and application.

It is often assumed in both education and industry that collaboration encourages creativity. This assumption is explored by investigating the influence of designers’ interactions on creativity-relevant thinking processes by extending creative cognition to the group design context. It is proposed that sharing design entities and questions stimulates creativity-relevant thinking processes through four types of collaborative stimulation. Specific patterns are hypothesized to exist between each type of collaborative stimulation and thinking processes. A case study was conducted to determine whether the hypothesized types and patterns of collaborative stimulation exist. The results were analyzed using a directed coding approach and collaborative retrospective protocol analysis, which enable capturing both internal thoughts and external interactions with minimal interference to collaboration. The results indicate that the identified types of collaborative stimulation are observable and that they have recognizable patterns with stimulated thinking processes. Stimulation occurring through design entity questioning had the strongest relationship with generative thinking processes. Although creativity-relevant generative processes are stimulated by collaborative activity, this does not necessarily mean that collaboration results in a more creative product. However, these patterns can be used in future work to develop methods and interventions for promoting group idea generation and improving group creativity.

There is a well-developed view of artifacts according to which their nature depends on the intentions of their authors or creators. However, in the modern world of artifact design and creation, typically not one but many agents are involved in the process of making an artifact. In this paper, I show how the intentional view can be maintained even for ‘collective’ artifacts having multiple authors. My approach is to combine some basic concepts that have been proposed in the study of collective intentionality with a suitable model of artifact creation that takes account of the multiple agents and processes that arise in design, engineering and manufacturing a new or existing product. In this way, we can explain how an artifactual kind can be understood via a form of collective intentionality. For the design sciences, notions such as we-intentionality and group agency can help to model different types of cooperation and, in particular, to reconcile individualism with strong forms of collectivity at a group level.

Traditional aerospace design methods offer quick and efficient ways to generate new designs, but such that often resemble previous ones. For truly innovative design, however, a different approach is needed. This paper suggests that a general conceptual design method called ‘parameter analysis’ (PA) may be used for teaching and practicing innovative aerospace design. To support this proposition, we investigate four diverse, innovative and unique case studies, all carried out by very experienced aerospace designers: the ‘dam busting’ bouncing bomb of World War II, the Gossamer Condor human-powered plane of the 1970s, the asymmetric Boomerang twin-engine plane of the 1990s and the SpaceShipOne suborbital spacecraft of the early 2000s. The paper elaborates on how the methodology of case-study research has been adapted and applied to provide the evidence supporting the research hypothesis, and presents the results of analyzing the case studies. It shows that the expert aerospace designers followed a thought process similar to PA, even if unknowingly, where the similarity was measured by counting the number of PA characteristics that could be shown to exist in the case studies. Advantages and limitations of the research methodology are also discussed.

Open Innovation (OI) supports companies in systematically collaborating with external partners, offering various advantages. However, companies still face several challenges when applying OI, e.g., identifying relevant OI partners, collaboration methods, and project risks. Often, insufficient planning is the reason for subsequent deficits in OI projects. The analysis of relevant context factors (‘situation’) is important, which affect and constrain OI. To date, a general approach for analyzing (open) innovation situations or guidelines for developing one do not exist. Usually researchers develop their own situation analysis, including extensive literature reviews and experiencing similar challenges. This publication sets the basis for successfully planning OI projects. It focuses on developing an analysis approach for OI situations and supports other researchers in developing their own analysis approaches. The resultant objectives of the publication are to: (1) provide a list of potential situation analysis criteria; (2) provide a guideline for developing a situation analysis; (3) provide initial indications of relevant OI-specific situation criteria. The criteria were derived from the literature and qualitatively evaluated by three industry partners to assess their usability. Although this work is exploratory, and the results are not automatically generalizable, it is an important contribution for ensuring the success of OI, and for analyzing enablers and barriers to knowledge transfer from academia to industry.