There is a critical gap in understanding how meso-scale team processes - interactions between individuals in a team - develop in design teams and specifically how they dynamically balance design with managerial effort. We treat this deficit by contrasting two in-depth cases using work sampling data. We identify a number of contributions. First, we describe how design team processes display both goal/action and temporal heterogeneity. Second, we demonstrate how this heterogeneity is underpinned by common principles that consistently shape team processes in design. Specifically: proportional goal/action composition and recurring process patterns over time. Finally, we describe how these principles can be integrated via ‘archetypal process types’. Together, these substantially extend prior theory and point to specific implications for future design research.

The development of immersive virtual reality (IVR) applications for design reviews is a major trend in the design field. While many different applications have been developed, there is little consensus on the functionalities necessary for these applications. This paper proposes a classification scheme for IVR functionalities related to design reviews (DRs), combining conceptual-to-empirical and empirical-to-conceptual strategies. The classification scheme consists of eight class categories (Input, Representation, Navigation, Manipulation, Collaboration, Edit, Creation, and Output), 22 class subcategories, and 55 classes. The classification scheme has been validated by analysing several commercial IVR applications for DRs. As part of the classification scheme development, Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) was utilised to review 70 articles that develop IVR applications for DRs. The results from systematic literature reviews suggest the development of solutions that integrate several class categories, are better connected to current design workflows, include various design information, support a DR planning cycle, and support distributed work. The proposed classification scheme helps to orient the future development of IVR applications for DRs and provides a framework to systematically accumulate evidence on the effect of such applications on DRs.

Emerging digital technologies enable capturing of a product’s digital footprint through continuous monitoring of its performance, usage, and working environment while using data acquired by its embedded sensors. However, it seems that product development (PD) teams, within engineering companies, have not yet embraced the usage of sensor data acquired by smart products (SPs) when conducting PD activities. This study discusses several challenges that hinder a broader utilization of SP's sensor data, within PD. In addition to the literature review, the discussion is supported with empirical data gathered through a qualitative exploratory case study conducted in a large engineering and manufacturing company. As a result, three challenges are outlined. First, it is challenging for the company's management and PD team to gain transparency over the benefits of sensor data utilization for PD. Second, solutions providing accessibility and visualization of gathered data should be tailored to the PD activities and teams, which requires a holistic understanding. Finally, it is suggested that new skills, roles and processes should be introduced, in order to enable SP's sensor data utilization, within PD activities. 

Design understanding and needed level of the accompanying spatial skills that enable it depend on information input provided by a visual representation of a design solution. During product development, designers use models to visually represent a design solution. These visual representations can be mediated by various technologies (for example, an immersive virtual reality (IVR) technology or 2D user interfaces such as a monitor display), providing designers with different types of information. Capabilities of an IVR technology such as stereopsis, eye-height reference, spatial updating, and multimodal interaction, have shown a potential to mitigate the cognitive load and the need for highly developed spatial skills enabling design understanding. Nevertheless, specific design understanding aspects for which IVR technology may be beneficial over conventional 2D user interfaces are yet to be clarified. The conducted experiment aimed to explore differences in designers’ spatial perception of spatial properties and relations (affordances) of a design solution in virtual environments (VEs). The design solution was presented by a 3D CAD model in immersive virtual environment (IVE) and non-immersive virtual environment (nIVE). IVE was mediated using the IVR technology (head-mounted display; HMD), while nIVE using the conventional 2D user interface (a monitor display, a mouse, and a keyboard). Results indicate that engineering students more accurately perceive spatial properties in the IVE than nIVE. Besides, it is suggested that the likelihood of making the correct judgment of the affordance is similar in both VEs.

Design review (DR) is a product development (PD) activity used to inspect the technical characteristics of a design solution. Immersive virtual reality (IVR) technology enables the presentation of spatial information and interaction with 3D CAD models inside an immersive virtual environment (IVE). Such capabilities have shown the potential to mitigate the cognitive load needed for the visual perception of spatial information and, consequently, enhance design understanding and DR performance. Thus, an increasing number of studies have explored the effect of IVR technology on DR activities in different domains. However, determining when the implementation of IVR technology rather than a conventional user interface for DRs in mechanical engineering PD projects will be beneficial remains unclear. Hence, a conceptual DR experimental study was conducted to investigate the differences in the ability of engineering students to identify mechanisms and understand their functions when a design solution for a technical system is presented in an IVE by IVR technology and in a non-immersive virtual environment (nIVE) by a conventional user interface (monitor display, keyboard, and mouse). Data were collected by performing DR tasks and having participants complete a prior experience questionnaire, presence questionnaire, and mental rotations test. Findings of the study indicate that IVR does not support an enhanced ability of engineering students to identify mechanisms and understand their functions compared with a conventional user interface.

The concept of emergence has roots in systems complexity and dynamics, with prominent impact in contemporary science and in design, analysis, and governance of complex engineering systems. It is almost impossible to effectively model emergence within the dynamics of systems, due to the obscurity of its nature and imperfection of our information on its relational systemic interactions. Thus, paying attention to basic meta-questions about emergent properties of complex engineering systems is crucially important in understanding both the trajectories of evolution of systems and correspondingly the patterns of system behaviour. From this trajectorial/behavioural perspective, emergence and dynamics are considered as very close concepts: understanding the variables of one can be realised by tracing and modelling the other. This chapter reviews and summarises the topics of emergence and dynamics through their applications in six case examples. Six studies conducted by researchers around the world are selected, representing a portfolio of cases studied with multiple theoretical foundations, levels of scope, application domains of engineering systems design, phenomena of emergence, and modelling methods used that detect and identify emergence through dynamics. The case reviews provide a gateway to comprehending emergence in systems through emphasising the dynamics of interactions.

The purpose of the study is to model the micro-scale process patterns which can be identified during team conceptual design activities. A state-transition model has been developed and used to empirically investigate the patterns of design operations during two types of team conceptual design activities: ideation and concept review. The presented work builds on the perception of design problems as ill-defined and implies that conceptual design activities involve the simultaneous development of problems and solutions using three distinctive design operations—analysis, synthesis, and evaluation. The three design operations have been defined as fine-grain design steps performed by design teams when exploring the content of both the problem and the solution dimensions of the design space. Moreover, design operations have been conceptualised as transitions between states of the explored design space, thus providing a basis for the state-transition model. The model’s ability to map and visualise proportions of design operation sequences emerging during ideation and concept review has facilitated the identification of both the activity-specific patterns and patterns that were likely to appear during both types of empirically investigated activities. The two activities exhibited similar patterns, such as alternation of solution synthesis and analysis, sequences of synthesis, analysis and evaluation within solution space, and the potential co-evolution episodes. Nevertheless, divergent traits have been identified for ideation, and convergent traits for concept review, based on the significant differences in proportions of design operations and their sequences.

The presented research aims at modelling and formalising the process of team design activity as an interplay between the evolution of design problems and solutions. The motivation founds primarily on a presumption that there exist regularities in designing which can be captured and formalised using the appropriate models. The study thus investigates whether the identified design operation proportions and sequence probabilities are consistent throughout the different parts of team conceptual design activities. It does so by exploring the utility of mathematical models built based on the correlations and statistically significant sequences underlying the previously identified designing patterns. The developed mathematical model was tested by replicating moving-average analyses of design operation proportions and sequences, which were originally observed in the protocol analysis study. A close fit was found between the simulated and the observed data, particularly in providing insights regarding operation patterns and proportion trends. The presented models and modelling methodology are potentially an appropriate means for the next steps in describing, and consequently predicting and supporting team design activity dynamics.

The conventional prescriptive and descriptive models of design typically decompose the overall design process into elementary processes, such as analysis, synthesis, and evaluation. This study revisits some of the assumptions established by these models and investigates whether they can also be applied for modelling of problem-solution co-evolution patterns that appear during team conceptual design activities. The first set of assumptions concerns the relationship between performing analysis, synthesis, and evaluation and exploring the problem and solution space. The second set concerns the dominant sequences of analysis, synthesis, and evaluation, whereas the third set concerns the nature of transitions between the problem and solution space. The assumptions were empirically tested as part of a protocol analysis study of team ideation and concept review activities. Besides revealing inconsistencies in how analysis, synthesis, and evaluation are defined and interpreted across the literature, the study demonstrates co-evolution patterns, which cannot be described by the conventional models. It highlights the important role of analysis-synthesis cycles during both divergent and convergent activities, which is co-evolution and refinement, respectively. The findings are summarised in the form of a model of the increase in the number of new problem and solution entities as the conceptual design phase progresses, with implications for both design research and design education.

Studies of design activity have been dominantly reporting on different aspects of the design process, rather than the content of designing. The aim of the presented research has been the development and application of an approach for a fine-grain analysis of the design content communicated between designers during the team conceptual design activities. The proposed approach builds on an engineering design ontology as a foundation for the content categorisation. Two teams have been studied using the protocol analysis method. The coded protocols offered fine-grain descriptions of the content communicated at different points in the design session and enabled comparison of teams’ approaches and deriving some generalisable findings. For example, it has been shown that both teams focused primarily on the use of the developed product and the operands within the technical process, in order to generate new technical solutions and initial component design. Moreover, teams exhibit progress from abstract to concrete solutions as the sessions proceeded and focused on the functional requirements towards the end of the sessions.

The objective of the research presented in this paper is to develop an algorithm for predicting behaviour of complex technical systems in an uncertain working environment. System's dynamic behaviour modelling and simulation should help to develop new and improve existing architectures of complex technical systems by mapping in both directions, from the structural to the behavioural domain and vice versa. The algorithm for predicting behaviour during system's architecture development consists of several operations shortly described in this paper. The proposed algorithm was verified on an example of a complex technical system - an air-handling unit.

Process modularity describes the extent to which processes can be decomposed into modules to be executed in parallel. So far, research has approached process modularity from a static perspective, not accounting for its temporal evolution. As a result, the understanding of process modularity has been limited to inferences drawn from aggregated analyses that disregard process execution. This article introduces and develops the notion of dynamic process modularity considering the evolving activity network structure as executed by people. Drawing on network science, the article quantifies process modularity over time using archival data from an engineering design process of a biomass power plant. This article shows how studying the temporal evolution of process modularity enables a more complete understanding of activity networks, facilitates the comparison of actual process modularity patterns against formal engineering design stages, and provides data-driven decision-support for process planning and interventions. Finally, managerial recommendations for interface management, resource allocation, and process decomposition are proposed, to help practitioners better to understand and manage dynamic processes.

This paper presents the results of computational experiments aimed at studying the effect of experience on design teams’ exploration of problem-solution space. An agent-based model of a design team was developed and its capability to match theoretically-based predictions is tested. Hypotheses that (1) experienced teams need less time to find a solution and that (2) in comparison to the inexperienced teams, experienced teams spend more time exploring the solution-space than the problem-space, were tested. The results provided support for both of the hypotheses, demonstrating the impact of learning and experience on the exploration patterns in problem and solution space, and verifying the system's capability to produce the reliable results. 

When observing a design space expansion during teamwork, several studies found that cumulative solution-related issues' occurrence follows a linear trend. Such findings contradict the hypothesis of solution-related issues being characteristic for the later design stages. This work relies on agent-based simulations to explore the emerging patterns in design solution space expansion during teamwork. The results demonstrate trends that accord with the empirical findings, suggesting that a cognitive effort in solution space expansion remains constant throughout a design session. The collected data on agents' cognitive processes and solution space properties enabled additional insights, which led to the detection of four distinct regimes of design solution space expansion.

This paper furthers the study of creative design by taking a situated view of novelty. A set of computational experiments is performed utilizing an agent-based model of a design team, and resulting data is used to examine the influence of a change in a situation (or a design frame) on the perception of a design’s novelty in terms of its difference from existing or possible designs. The experiments demonstrate that, over the course of designing, solutions which were regarded as novel, can become not novel. They also show that a solution which was not seen as novel in one situation can be assessed as novel when a situation changes. The results, therefore, emphasize the importance of studying novelty as a situated measure.

Studies revealed that, while collaborating, humans tend to synchronise on multiple levels (e.g., neurocognitive or physiological). Inter-brain synchrony has been linked to improved problem-solving, decision-making, and creativity. Nevertheless, studies on synchrony in design teams started to emerge only recently. This study contributes to this stream of research by utilising a computational model of a design team to explore the relationships between team cohesion, synchrony, and team performance. The experiments revealed a positive link between team cohesion level and the emergence of (cognitive) synchrony. Furthermore, cohesive teams were found to be more efficient, converging quicker and producing solutions at a higher rate. In addition, the diversity of the solutions generated by highly cohesive teams tends to increase over time. Teams in medium- and low-cohesive settings initially generate highly diverse solutions, but such diversity decreases as the simulation progresses. Finally, highly-cohesive teams were found to be prone to premature convergence.

The success of product development highly depends on the quality of cooperation among members of a team involved in the process. Thus, a tool capable of simulating product development team may be beneficial for researchers interested in teamwork, as well as useful for managers struggling with team formation during process planning phase. This work aims at providing a detailed overview of agent-based simulators of product development teams. Specifically, the scientific databases Web of Science, Scopus, ACM DL, and IEEE were searched to extract relevant agent-based models of teamwork in mechanical engineering and aerospace context and obtained models were reviewed to identify their key advantages and limitations. 

Traditional product teardown exercises, while educationally valuable, are resource-intensive and environmentally unsustainable. By leveraging affordable 3D scanning and CAD modeling tools, an approach is proposed for the creation of virtual product replicas that can be used to reduce costs, environmental impact,and logistical constraints of product teardowns. Two case studies—a leaf blower and a hand mixer—demonstrate the effectiveness of the methodology. The case studies also demonstrate that 3D scanning is particularly advantageous for capturing complex geometries, while CAD modeling is more efficient for simpler components with straightforward geometries. The integration of both techniques ensures that the strengths of each approach are maximized, leading to substantial time savings in creating assembly models. The study confirms that high-quality 3D models for virtual product teardowns can be created using a combination of affordable reverse engineering approaches, making it a viable solution for educational settings.

Developing new technologies is one of the most important goals of today’s scientific and industrial research. Understanding how technology evolves, as well as its current state, is invaluable in an ecosystem where technology is evolving at an increasingly rapid pace. In this paper, patent data is used to determine a technology’s life cycle. Two patent maps are created, one based on patent citations and one based on keywords. The citation patent map visualizes how patents cite each other, while the keyword patent maps visualize keywords used to describe patents and their relations. Both of these patent maps are dynamic, meaning they change over time thus giving insight into an examined technology’s evolution. A growth analysis of both networks is conducted as well as a degree distribution analysis. Both of these analyses are used to help determine the technology’s lifecycle phase as well as its patterns of growth. This insight is invaluable to stakeholders tasked to make strategic decisions related to technology development.

Purpose

This paper aims to present a methodology by which future knowledge flow can be predicted by predicting co-citations of patents within a technology domain using a link prediction algorithm applied to a co-citation network.

Design/methodology/approach

Several methods and approaches are used: a dynamic analysis of a patent citation network to identify technology life cycle phases, patent co-citation network mapping from the patent citation network and the application of link prediction algorithms to the patent co-citation network.

Findings

The results of the presented study indicate that future knowledge flow within a technology domain can be predicted by predicting patent co-citations using the preferential attachment link prediction algorithm. Furthermore, they indicate that the patent – co-citations occurring between the end of the growth life cycle phase and the start of the maturation life cycle phase contribute the most to the precision of the knowledge flow prediction. Finally, it is demonstrated that most of the predicted knowledge flow occurs in a time period closely following the application of the link – prediction algorithm.

Practical implications

By having insight into future potential co-citations of patents, a firm can leverage its existing patent portfolio or asses the acquisition value of patents or the companies owning them.

Originality/value

It is demonstrated that the flow of knowledge in patent co-citation networks follows a rich get richer intuition. Moreover, it is show that the knowledge contained in younger patents has a greater chance of being cited again. Finally, it is demonstrated that these co-citations can be predicted in the short term when the preferential attachment algorithm is applied to a patent co-citation network.

The process of innovation takes significant resources, and therefore it is of great importance for companies to recognize the ideas with high innovation capacity as early as possible, and in a transparent manner, with the least necessary amount of expert knowledge. Current research indicates that companies often carry out the selection of ideas ad hoc or intuitively, and that only a small number of companies have defined the methods for ideas assessment and evaluation. In doing so, such problems as imprecise definition of the variables used in the evaluation process of the innovation capacities of ideas, undefined metrics and interaction variables will arise. In order to determine the practical points of view in this area, a study was conducted in the form of a survey on a representative sample of Croatian companies which have product innovation in their production program. The survey is aimed at determining what motivates companies to innovate, and the ways in which companies carry out the assessment and selection of ideas. Through a thorough study of the literature, a set of variables that are commonly used in the idea capacities assessment for product development have been defined, and the survey tried to establish the practical significance of individual variables for the participants of the process. This paper presents the results of the study.

The successful collaboration of team members plays a crucial role in product development. Research of different teamwork factors may help understand the process, and computer simulation can be a suitable method of achieving this goal. This work aims to give an overview of agent-based modelling of product development teams and survey existing models that tackle this problem. Some tools and frameworks for agent-based modelling will be described with an emphasis on parallel and distributed architectures which are used to improve performance and enable very big and complex simulations.

Recently, the design and digitalisation approaches have become increasingly utilised in the legal context, typically under the names of legal design and legal tech. One of their goals is to help legal practitioners be more efficient and to provide better quality and more comprehensive legal services. Also, given that both movements rely heavily on participatory and co-design, they will require increased support not only from design practitioners but also from design researchers and educators. Therefore, this paper investigates, from a design research viewpoint, the opportunities and challenges of developing and implementing legal tech, with a particular focus on legal practitioners. It reports on four cases of designing legal tech solutions and their implementation in a law firm. The main insights are related to the importance of value perception through participatory and co-design, the need for efficient and effective testing methodologies, and the opportunity to test a wide range of design methods and tools in the legal context. The paper also complements the legal design and legal tech literature with additional arguments on why designing in the legal context is challenging compared to designing in other domains.

Understanding team diversity has become essential for modern-day organisations. This study explores the impact of knowledge diversity in design teams through computational simulations. By analysing design space characteristics, we study how diverse teams perform compared to less diverse counterparts. Results reveal that highly diverse teams exhibit increased efficiency, quicker convergence, and larger but sparser design spaces. This work contributes to understanding the impact of knowledge diversity in design teams and sets the stage for future systematic studies of diversity.

Recent developments in engineering design management point to the need for more dynamic, fine-grain measurement approaches able to deal with multi-dimensional, cross-level process performance in product design. Thus, this paper proposes a new approach to the measurement and management of individual and teamwork performance in engineering design projects. This integrates multiple, previously disparate, aspects of design management and performance measurement theory in a single framework. Further, a fully realised performance measurement approach is developed, which complements existing management strategies. This framework is synthesised from an extensive review and illustrated via an in-depth case study. As such, this work contributes to performance measurement theory in engineering design and has significant implications for both engineering design research and industry.

For effective management of development projects, it is necessary to take into consideration its socio-technical perspective – working processes, teamwork and features of the working environment. Data gathering about socio-technical aspects of the product development activities is often hampered by constraints of the actual working environment in R&D organizations. For that reason, to support practical data collection, the work sampling application for mobile phones has been introduced. The work sampling application consists of sequence of input screens with predefined input menus enabling data gathering about different aspects of the product development activity (such as work type, activity type, context, participants, execution manner, information transaction and motivation). For validation of the proposed application, the empirical study was carried out in the company whose research and development activities are focused on the systems for the generation, distribution and transformation of electrical energy.

By embracing insights from project management and intellectual capital measurement research fields, basis can be established for development of new performance indicators for monitoring intangible project aspects of individual and team work within the product development context. Focusing on individual and team level of product development projects, data gathering is hampered by constraints of the real organizational environment. Therefore, in this research paper, development of work sampling self-report application is presented which allows data capturing for real-time measurement of intellectual capital elements in a practical and straight-forward way. Preliminary work sampling study was executed in R&D company whose main preocupation is development of the electro-mechanical devices for distribution and transformation of the electrical energy within the energy infrastructure or mass transportation systems. Information about potential trends of particular underperforming values related to the communication and information sharing, innovativeness/ideation and motivation/satisfaction on individual or team level, could provide added value for the project managers and decision makers.