The term “creative space” describes a relatively recent phenomenon of innovative workplace design. Such creative workspaces are becoming popular in industry and academia. However, the impact of specific spatial design decisions on creativity and innovation is not yet fully understood. This paper provides an overview of state-of-the-art research on creative work and learning environments. We conducted a systematic literature search within the Scopus database and identified a total of 73 relevant sources discussing creative spaces within academic, practice, and other innovation environments. Among the included sources are 51 academic publications and 22 sources from company research and illustrative coffee-table books. We analyzed the sources using three lenses of interest: (1) the types of theoretical and practical contributions that are provided, (2) the spatial characteristics that are suggested to be beneficial for creativity and innovation, and (3) the discussed potential of new technologies for designing or researching creative spaces. The results provide in-depth insight into the current state of research on the topic of creative spaces. Practitioners, educators, and researchers can use the presented overview to investigate the possible impact of creative workspace design and identify research gaps that can be filled by conducting further research in the field.

This paper sheds light on the new possibilities for user research activities facilitated by access to makerspaces. We present four case studies of user research conducted in two university-based makerspaces as examples of makerspace-driven user research. Further, by comparing the cases to three classical user research activities, namely observation, prototyping, and user journey mapping, we highlight the main aspects of this new context of user research. We find that accessibility to makerspaces enables user researchers to build low-fidelity yet high-functionality prototypes for exploring users’ preferences and motivations in controlled and repeatable ways. These prototypes fall into the category of experience prototypes, but they have greater functionality than the prototypes previously used in this field. Thus, a user researcher can explore a topic more systematically and in a more hypothesis-driven manner. In summary, this study encourages stakeholders in the early stages of product development to consider a makerspace as a resource for user-related requirement elicitation rather than for only specific product iteration.

With the rise of the “Maker Movement” and the entrepreneurial university, academic makerspaces became widespread. These facilities provide tools and machines that enable making and tinkering; and while the offerings, organizational and operational models, and outreach of the academic makerspaces can vary widely across institutions, their common value proposition is enabling innovation, entrepreneurship, and hands-on project-based learning and these studies are largely qualitative and exploratory by nature. Through a case study, this paper presents an in-depth analysis and insights on the users and usage of an academic makerspace. Using the data generated by and collected from the users of an academic makerspace, we evaluate the effects of having access to the makerspace on users' teaching and learning experiences, and their satisfaction with the offerings. Our results show that attracting courses and educators to the facilities played a strong role in growing the user base, courses and teaching activities introduced new teaching and learning activities to adopt the offerings, group and project work is positively impacted, and the users are very satisfied with the facilities and having the access to its offerings. The analysis also showed that the demand for the offerings can be challenging to manage during certain periods, most of the users come from three departments (mechanical, electrical, civil engineering), and the diversity of the users could improve with the introduction of new offerings, such as a wet lab for bio/chemistry experiments and a food lab to tinker with food processing and preparation.

Design effort is a key resource for product design projects. Environments where design effort is scarce, and therefore valuable, include hackathons and other time-limited design challenges. Predicting design effort needs is key to successful project planning; therefore, understanding design effort-influencing factors (objective considerations that are universally accepted to exert influence on a subject, that is, types of phenomena, constraints, characteristics, or stimulus) will aid in planning success, offering an improved organizational understanding of product design, characterizing the design space and providing a perspective to assess project briefs from the outset. This paper presents the Collaborative Factor Identification for Design Effort (CoFIDE) Method based on Hird's (2012) method for developing resource forecasting tools for new product development teams. CoFIDE enables the collection of novel data of, and insight into, the collaborative understanding and perceptions of the most influential factors of design effort levels in design projects and how their behavior changes over the course of design projects. CoFIDE also enables design teams, hackathon teams, and makerspace collaborators to characterize their creative spaces, to quickly enable mutual understanding, without the need for complex software and large bodies of past project data. This insight offers design teams, hackathon teams, and makerspace collaborators opportunities to capitalize on positive influences while minimizing negative influences. This paper demonstrates the use of CoFIDE through a case study with a UK-based product design agency, which enabled the design team to identify and model the behavior of four influential factors.

An academic makerspace, home to tools and people dedicated to facilitating and inspiring a making culture, is characterized by openness, creativity, learning, design, and community. This nontraditional learning environment has found an immense increase in popularity and investment in the last decade. Further, makerspaces have been shown to be highly gendered, privileging men's and masculine understandings of making. The spike in popularity warrants deeper analysis, examining the value of these spaces for women and if learning is occurring in these spaces, specifically at higher education institutions. We implemented a phenomenologically based interviewing process to capture the making experiences of 20 women students, recruited through purposive and snowball sampling. By eliciting the narratives of women students, we captured how making, designing, and creating evolved through gendered experiences in the university makerspace. Each interview was transcribed and resulted in around 868 pages of single-spaced text transcriptions. The data were analyzed through multiple cycles of open and axial coding for common themes and patterns, where makerspaces create a culture of learning, facilitate students’ design journey, and form a laboratory for creativity. These themes forwarded the creation of a learning model that showcases how design and learning interact in the makerspace. This work demonstrates that women students are engaging learning and inspiration; developing confidence and resilience; and learning how to work with others and collaborate.

There is an increasing demand for humanitarian aid around the world. At the same time, the number of makerspaces has been growing exponentially. Recently, the humanitarian sector has become interested in how these new design spaces can help crisis-affected populations. Despite the emergence of humanitarian makerspaces, there is little research to date that documents their outcomes and impacts. A multi-case study approach is taken to analyze three makerspaces that support migrants, refugees, and asylum seekers in Greece. A maturity grid tool is used to show that humanitarian makerspaces are driving impact in six key areas. The study underlines how these makerspaces support different design activities and have different outcomes. It also considers the challenges which are preventing humanitarian makerspaces from achieving their ultimate goals, drawing attention to the need for an enabling ecosystem in both the local and humanitarian context. This research brings clarity to the poorly understood phenomenon of humanitarian makerspaces and highlights the important role of design in humanitarian interventions. It also reveals practical insights for humanitarian organizations who are considering setting up makerspaces in crisis-affected communities.

This paper presents a new VR interaction environment for the evaluation of digital prototypes, specifically in designer–client review sessions, and documents its implementation via experience mapping. Usability of VR controllers and basic manipulation remains a barrier for lay users, and a range of typical implementations are reviewed, highlighting the need for an easily accessible interface for this setting. The resulting interface configuration – the Control Carousel – demonstrates how the appropriate use of familiar mechanisms can increase VR accessibility. Three case studies using the Carousel in commercial design projects are described, and the subsequent interface refinements outlined. Finally, the development of an experience map describing the logistical, interactive, and emotive factors affecting the Carousel's implementation is documented. This provides insights on how experience mapping can be used as part of a human-centred design process to ensure VR environments are attuned to the requirements of users, in this instance delivering improved collaborative reviews.

This study adopted a Kansei engineering methodology to examine users’ Kansei values toward automotive interior designs by conducting experiments under two models: sensory experience and perceived value evaluation models. In the sensory experience model, 60 participants were recruited to experience using 27 vehicle samples and to assign scores to eight Kansei keywords. Hayashi's quantification theory type 1 was used to calculate the weight of 12 interior space items and 40 interior design categories. In the perceived value evaluation model, 60 participants completed a questionnaire on the influence levels of the Kansei keywords on their Kansei values from the perspectives of four consumer perceived value dimensions. The questionnaire scores were then evaluated using factor analysis to extract nine primary factors affecting participants’ perceptions. This study determined that the interesting and dynamic perceptions corresponded to five design items under the Excitement Quality factors, vigorous and recreational perceptions corresponded to four items under the Relaxation and Comfort factor, and technological and luxurious perceptions corresponded to six items under the Luxury and Splendor factor and the Esteemed Image factor. These findings can help designers and researchers in the relevant sectors create automotive interior designs that fulfill the expectations of users.

This paper proposes an intelligent model-based optimization methodology for optimizing the production cost and material removal rate subjected to surface quality constraint in turning operation of hardened AISI D2. Unlike traditional approaches, this paper deals with finding optimum cutting parameters considering the real condition of the cutting tool. Tool flank wear is predicted by the model obtained using genetic programming. On the basis of the predicted flank wear value, the surface roughness of work piece is estimated by neural networks. Applying the particle swarm optimization algorithm, the optimum machining parameters are determined. The simulation and experimental results show that machining with proposed intelligent optimization methodology has higher efficiency than conventional techniques with constant optimized cutting parameters.

The objective of the paper is to develop a new method to model the manufacturing cost–tolerance and to optimize the tolerance values along with its manufacturing cost. A cost–tolerance relation has a complex nonlinear correlation among them. The property of a neural network makes it possible to model the complex correlation, and the genetic algorithm (GA) is integrated with the best neural network model to optimize the tolerance values. The proposed method used three types of neural network models (multilayer perceptron, backpropagation network, and radial basis function). These network models were developed separately for prismatic and rotational parts. For the construction of network models, part size and tolerance values were used as input neurons. The reference manufacturing cost was assigned as the output neuron. The qualitative production data set was gathered in a workshop and partitioned into three files for training, testing, and validation, respectively. The architecture of the network model was identified based on the best regression coefficient and the root-mean-square-error value. The best network model was integrated into the GA, and the role of genetic operators was also studied. Finally, two case studies from the literature were demonstrated in order to validate the proposed method. A new methodology based on the neural network model enables the design and process planning engineers to propose an intelligent decision irrespective of their experience.