T. Bobbe, Lenard Opeskin, Lisa-Marie Lüneburg, Helge Wanta, Joshwa Pohlmann, J. Krzywinski
Abstract The importance of inter- and transdisciplinary research for addressing today’s complex challenges has been increasingly recognised. This requires new forms of communication and interaction between researchers from different disciplines and nonacademic stakeholders. Demonstrators constitute a crucial communication tool in technology research and development and have the potential to leverage communication between different bodies of knowledge. However, there is little knowledge on how to design demonstrators. This research aims to understand how demonstrators from the fields Internet of Things and Robotics are designed to communicate technology. The goal is to increase the efficiency and effectiveness of demonstrator practice with readily implemented design knowledge and to advance theoretical knowledge in the field of communicating artefacts. We thematically analysed 28 demonstrator design cases, which led to a typology that assists in categorising and understanding 13 key design principles. The typology is built from three perspectives: First, in terms of the overall goal communication, second, in terms of visitor engagement goals (attraction, initial engagement, deep engagement) and third, in terms of resource-related goals (low effort in development and operation). With this typology, we have taken a significant step towards understanding demonstrator design principles for effective technology communication between different stakeholders.
{"title":"Design for communication: how do demonstrators demonstrate technology?","authors":"T. Bobbe, Lenard Opeskin, Lisa-Marie Lüneburg, Helge Wanta, Joshwa Pohlmann, J. Krzywinski","doi":"10.1017/dsj.2023.1","DOIUrl":"https://doi.org/10.1017/dsj.2023.1","url":null,"abstract":"Abstract The importance of inter- and transdisciplinary research for addressing today’s complex challenges has been increasingly recognised. This requires new forms of communication and interaction between researchers from different disciplines and nonacademic stakeholders. Demonstrators constitute a crucial communication tool in technology research and development and have the potential to leverage communication between different bodies of knowledge. However, there is little knowledge on how to design demonstrators. This research aims to understand how demonstrators from the fields Internet of Things and Robotics are designed to communicate technology. The goal is to increase the efficiency and effectiveness of demonstrator practice with readily implemented design knowledge and to advance theoretical knowledge in the field of communicating artefacts. We thematically analysed 28 demonstrator design cases, which led to a typology that assists in categorising and understanding 13 key design principles. The typology is built from three perspectives: First, in terms of the overall goal communication, second, in terms of visitor engagement goals (attraction, initial engagement, deep engagement) and third, in terms of resource-related goals (low effort in development and operation). With this typology, we have taken a significant step towards understanding demonstrator design principles for effective technology communication between different stakeholders.","PeriodicalId":54146,"journal":{"name":"Design Science","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2023-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43487189","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Archimedes, the founder of statics and hydrostatics, in his mathematics and physics studies, created methods related to his inventions of new machines, for example, the method of mechanical theorems based on his lever invention. He also used the principles of decomposition and replication underlying his heat ray invention, and these two principles permeate his work. Analysis of Archimedes’ work shows how he was perhaps the first to use methodically a strategy for solving diverse complex problems. In this article, we use the term Archimedes Code to encompass the way Archimedes approached problems including those two principles. Archimedes was perhaps the first design theorist and the first to think systematically about how to address design challenges. Furthermore, his work demonstrates the fundamental role of engineering practice in advancing science. The new insights regarding the Archimedes Code and its value in design practice may inspire both design researchers and practitioners.
{"title":"The Archimedes Code: a dialogue between science, practice, design theory and systems engineering","authors":"Y. Reich","doi":"10.1017/dsj.2022.27","DOIUrl":"https://doi.org/10.1017/dsj.2022.27","url":null,"abstract":"Abstract Archimedes, the founder of statics and hydrostatics, in his mathematics and physics studies, created methods related to his inventions of new machines, for example, the method of mechanical theorems based on his lever invention. He also used the principles of decomposition and replication underlying his heat ray invention, and these two principles permeate his work. Analysis of Archimedes’ work shows how he was perhaps the first to use methodically a strategy for solving diverse complex problems. In this article, we use the term Archimedes Code to encompass the way Archimedes approached problems including those two principles. Archimedes was perhaps the first design theorist and the first to think systematically about how to address design challenges. Furthermore, his work demonstrates the fundamental role of engineering practice in advancing science. The new insights regarding the Archimedes Code and its value in design practice may inspire both design researchers and practitioners.","PeriodicalId":54146,"journal":{"name":"Design Science","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2023-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46317521","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
F. Wöhr, Ekin Uhri, S. Königs, J. Trauer, M. Stanglmeier, M. Zimmermann
Abstract The continuous integration and verification of components is essential in distributed design processes. Identifying the optimal integration and verification frequency, however, can be challenging due to the complexity of product development. Especially the effect of human decision-making in partially isolated development scenarios is difficult to consider. Thus, we performed an experimental study based on the following three steps: first, an extension of the existing parameter design framework, which is used to conduct experiments under laboratory conditions, in which human subjects solve quantitative surrogate design tasks. Second, a series of experiments in which 32 subjects divided into groups of two solved 229 parameter design tasks with a varying integration and verification frequency. And, third, a statistical analysis of the results with respect to development time, coupling strength and process costs. According to our results, development time can be reduced by up to 71%, if the integration and verification frequency is doubled. If process costs are also considered, the optimal frequency can be subject to a conflict of goals between reducing development time and minimising process cost.
{"title":"Coordination and complexity: an experiment on the effect of integration and verification in distributed design processes","authors":"F. Wöhr, Ekin Uhri, S. Königs, J. Trauer, M. Stanglmeier, M. Zimmermann","doi":"10.1017/dsj.2022.26","DOIUrl":"https://doi.org/10.1017/dsj.2022.26","url":null,"abstract":"Abstract The continuous integration and verification of components is essential in distributed design processes. Identifying the optimal integration and verification frequency, however, can be challenging due to the complexity of product development. Especially the effect of human decision-making in partially isolated development scenarios is difficult to consider. Thus, we performed an experimental study based on the following three steps: first, an extension of the existing parameter design framework, which is used to conduct experiments under laboratory conditions, in which human subjects solve quantitative surrogate design tasks. Second, a series of experiments in which 32 subjects divided into groups of two solved 229 parameter design tasks with a varying integration and verification frequency. And, third, a statistical analysis of the results with respect to development time, coupling strength and process costs. According to our results, development time can be reduced by up to 71%, if the integration and verification frequency is doubled. If process costs are also considered, the optimal frequency can be subject to a conflict of goals between reducing development time and minimising process cost.","PeriodicalId":54146,"journal":{"name":"Design Science","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2023-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46115006","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Data-driven generative design (DDGD) methods utilize deep neural networks to create novel designs based on existing data. The structure-aware DDGD method can handle complex geometries and automate the assembly of separate components into systems, showing promise in facilitating creative designs. However, determining the appropriate vectorized design representation (VDR) to evaluate 3D shapes generated from the structure-aware DDGD model remains largely unexplored. To that end, we conducted a comparative analysis of surrogate models’ performance in predicting the engineering performance of 3D shapes using VDRs from two sources: the trained latent space of structure-aware DDGD models encoding structural and geometric information and an embedding method encoding only geometric information. We conducted two case studies: one involving 3D car models focusing on drag coefficients and the other involving 3D aircraft models considering both drag and lift coefficients. Our results demonstrate that using latent vectors as VDRs can significantly deteriorate surrogate models’ predictions. Moreover, increasing the dimensionality of the VDRs in the embedding method may not necessarily improve the prediction, especially when the VDRs contain more information irrelevant to the engineering performance. Therefore, when selecting VDRs for surrogate modeling, the latent vectors obtained from training structure-aware DDGD models must be used with caution, although they are more accessible once training is complete. The underlying physics associated with the engineering performance should be paid attention. This paper provides empirical evidence for the effectiveness of different types of VDRs of structure-aware DDGD for surrogate modeling, thus facilitating the construction of better surrogate models for AI-generated designs.
{"title":"Design representation for performance evaluation of 3D shapes in structure-aware generative design","authors":"Xingang Li, Charles Xie, Zhenghui Sha","doi":"10.1017/dsj.2023.25","DOIUrl":"https://doi.org/10.1017/dsj.2023.25","url":null,"abstract":"Abstract Data-driven generative design (DDGD) methods utilize deep neural networks to create novel designs based on existing data. The structure-aware DDGD method can handle complex geometries and automate the assembly of separate components into systems, showing promise in facilitating creative designs. However, determining the appropriate vectorized design representation (VDR) to evaluate 3D shapes generated from the structure-aware DDGD model remains largely unexplored. To that end, we conducted a comparative analysis of surrogate models’ performance in predicting the engineering performance of 3D shapes using VDRs from two sources: the trained latent space of structure-aware DDGD models encoding structural and geometric information and an embedding method encoding only geometric information. We conducted two case studies: one involving 3D car models focusing on drag coefficients and the other involving 3D aircraft models considering both drag and lift coefficients. Our results demonstrate that using latent vectors as VDRs can significantly deteriorate surrogate models’ predictions. Moreover, increasing the dimensionality of the VDRs in the embedding method may not necessarily improve the prediction, especially when the VDRs contain more information irrelevant to the engineering performance. Therefore, when selecting VDRs for surrogate modeling, the latent vectors obtained from training structure-aware DDGD models must be used with caution, although they are more accessible once training is complete. The underlying physics associated with the engineering performance should be paid attention. This paper provides empirical evidence for the effectiveness of different types of VDRs of structure-aware DDGD for surrogate modeling, thus facilitating the construction of better surrogate models for AI-generated designs.","PeriodicalId":54146,"journal":{"name":"Design Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135551076","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mark Goudswaard, Ric Real, Chris Snider, Luis Ernesto Muñoz Camargo, Nicolas Salgado Zamora, Ben Hicks
Abstract Prototyping is a knowledge generation activity facilitating improved understanding of problem and solution spaces. This knowledge can be generated across a range of dimensions, termed knowledge dimensions (KDs) , via a range of methods and media, each with their own inherent properties. This article investigates and characterises the relationships between prototypes and knowledge generated from prototyping activities during the design process, by establishing how different methods and media contribute across KDs. In so doing, it provides insights into prototyping activity, as well as affording a means by which prototyping knowledge generation may be studied in detail. The investigation considers sets of prototypes from eight parallel 16-week design projects, with subsequent investigation of the knowledge contributions that each prototype provides and at what stage of the design process. Results showed statistical significance supporting three inferences: i) teams undertaking the same design brief create similar knowledge profiles; ii) prototyping fidelity impacts KD contribution and iii) KDs align with the different phases of the project. This article demonstrates a means to describe and potentially prescribe knowledge generation activities through prototyping. Correspondingly, the article contends that consideration of KDs offers potential to improve aspects of the design process through better prototyping method selection and sequencing.
{"title":"Knowledge dimensions in prototyping: investigating the what, when and how of knowledge generation during product development","authors":"Mark Goudswaard, Ric Real, Chris Snider, Luis Ernesto Muñoz Camargo, Nicolas Salgado Zamora, Ben Hicks","doi":"10.1017/dsj.2023.24","DOIUrl":"https://doi.org/10.1017/dsj.2023.24","url":null,"abstract":"Abstract Prototyping is a knowledge generation activity facilitating improved understanding of problem and solution spaces. This knowledge can be generated across a range of dimensions, termed knowledge dimensions (KDs) , via a range of methods and media, each with their own inherent properties. This article investigates and characterises the relationships between prototypes and knowledge generated from prototyping activities during the design process, by establishing how different methods and media contribute across KDs. In so doing, it provides insights into prototyping activity, as well as affording a means by which prototyping knowledge generation may be studied in detail. The investigation considers sets of prototypes from eight parallel 16-week design projects, with subsequent investigation of the knowledge contributions that each prototype provides and at what stage of the design process. Results showed statistical significance supporting three inferences: i) teams undertaking the same design brief create similar knowledge profiles; ii) prototyping fidelity impacts KD contribution and iii) KDs align with the different phases of the project. This article demonstrates a means to describe and potentially prescribe knowledge generation activities through prototyping. Correspondingly, the article contends that consideration of KDs offers potential to improve aspects of the design process through better prototyping method selection and sequencing.","PeriodicalId":54146,"journal":{"name":"Design Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135551107","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jin Woo Lee, Shanna R. Daly, Varghese Vadakumcherry, Gabriella Rodriguez
Abstract Developing effective design solutions requires successful idea generation, development and selection. Studies have demonstrated that engineering students face challenges in these idea phases and may struggle to implement recommended practices, hindering the potential for an innovative outcome. The first part of the study investigated student practices in idea generation, development and selection through think-aloud experimental sessions and post-session interviews. Data analysis from mechanical engineering students’ sessions, with think-aloud and interview data, revealed that students focused on existing ideas, assumed requirements that constrained their divergence, limited their development of ideas and did not engage much in idea selection. Then, in the second phase of the study, we implemented a learning intervention that leveraged research-based education practices to examine student adoption of recommended practices. After engaging with the learning blocks, students generated unconventional ideas, abstained from requirement assumptions early in ideation, generated a larger quantity of ideas, developed ideas intentionally and used more rigorous idea selection methods. These outcomes demonstrated that a relatively short and targeted intervention can support students in leveraging recommended approaches to idea generation, development and selection.
{"title":"Idea generation, development and selection: a study of mechanical engineering students’ natural approaches and the impact of hybrid learning blocks","authors":"Jin Woo Lee, Shanna R. Daly, Varghese Vadakumcherry, Gabriella Rodriguez","doi":"10.1017/dsj.2023.26","DOIUrl":"https://doi.org/10.1017/dsj.2023.26","url":null,"abstract":"Abstract Developing effective design solutions requires successful idea generation, development and selection. Studies have demonstrated that engineering students face challenges in these idea phases and may struggle to implement recommended practices, hindering the potential for an innovative outcome. The first part of the study investigated student practices in idea generation, development and selection through think-aloud experimental sessions and post-session interviews. Data analysis from mechanical engineering students’ sessions, with think-aloud and interview data, revealed that students focused on existing ideas, assumed requirements that constrained their divergence, limited their development of ideas and did not engage much in idea selection. Then, in the second phase of the study, we implemented a learning intervention that leveraged research-based education practices to examine student adoption of recommended practices. After engaging with the learning blocks, students generated unconventional ideas, abstained from requirement assumptions early in ideation, generated a larger quantity of ideas, developed ideas intentionally and used more rigorous idea selection methods. These outcomes demonstrated that a relatively short and targeted intervention can support students in leveraging recommended approaches to idea generation, development and selection.","PeriodicalId":54146,"journal":{"name":"Design Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135701667","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Noémie Chaniaud, Sylvain Fleury, Benjamin Poussard, Olivier Christmann, Thibaut Guitter, Simon Richir
Abstract Virtual reality (VR) sketching has many advantages for product design and tends to be more and more used among designers and non-designers (end-users). Nevertheless, few studies have focused on the skills needed to use VR sketching for non-designers especially VR novices in VR software. This study focuses on identifying the cognitive impact of VR sketching compared to traditional sketching on VR expert and VR novice in an experimental setting. Thirty-one participants composed of VR experts ( N = 15) and VR novices ( N = 16) completed a mental rotation test and then performed one traditional paper and pencil sketching task and two VR sketching tasks. We also measured the participants’ movements when using the VR sketching. Results show that VR experts perform better than VR novices in VR sketching because training is an essential element for the quality of traditional and VR sketching. Nevertheless, VR novices with previous training in traditional drawing and/or high mental rotation skills will be able to produce good-quality sketches. In addition, the results show that users moving more in the immersive environment performed better quality sketches if the drawing requires more complex shapes. Our results suggest that VR sketching can be complex to use for a part of the population that may be end-users, especially for those with little experience in traditional and VR sketching and with poor visuospatial abilities. We, therefore, advise to check the non-designers’ prior skills, otherwise, it will be necessary to train these users in VR sketching.
{"title":"Is virtual reality so user-friendly for non-designers in early design activities? Comparing skills needed to traditional sketching versus virtual reality sketching","authors":"Noémie Chaniaud, Sylvain Fleury, Benjamin Poussard, Olivier Christmann, Thibaut Guitter, Simon Richir","doi":"10.1017/dsj.2023.27","DOIUrl":"https://doi.org/10.1017/dsj.2023.27","url":null,"abstract":"Abstract Virtual reality (VR) sketching has many advantages for product design and tends to be more and more used among designers and non-designers (end-users). Nevertheless, few studies have focused on the skills needed to use VR sketching for non-designers especially VR novices in VR software. This study focuses on identifying the cognitive impact of VR sketching compared to traditional sketching on VR expert and VR novice in an experimental setting. Thirty-one participants composed of VR experts ( N = 15) and VR novices ( N = 16) completed a mental rotation test and then performed one traditional paper and pencil sketching task and two VR sketching tasks. We also measured the participants’ movements when using the VR sketching. Results show that VR experts perform better than VR novices in VR sketching because training is an essential element for the quality of traditional and VR sketching. Nevertheless, VR novices with previous training in traditional drawing and/or high mental rotation skills will be able to produce good-quality sketches. In addition, the results show that users moving more in the immersive environment performed better quality sketches if the drawing requires more complex shapes. Our results suggest that VR sketching can be complex to use for a part of the population that may be end-users, especially for those with little experience in traditional and VR sketching and with poor visuospatial abilities. We, therefore, advise to check the non-designers’ prior skills, otherwise, it will be necessary to train these users in VR sketching.","PeriodicalId":54146,"journal":{"name":"Design Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135700127","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Outside of community-led design projects, most participatory design processes initiated by a company or organisation maintain or even strengthen power imbalances between the design organisation and the community on whose purported behalf they are designing, further increasing the absencing experience. Radical participatory design (RPD) is a radically relational answer to the coloniality inherent in participatory design where the community members’ disappointment is greater due to the greater expectations and presencing potential of a ‘participatory design’ process. We introduce the term RPD to show how research and design processes can be truly participatory to the root or core. Instead of treating participatory design as a method, a way of conducting a method, or a methodology, we introduce RPD as a meta-methodology, a way of doing any methodology. We explicitly describe what participation means and compare and contrast design processes based on the amount of participation, creating a typology of participation. We introduce ‘designer as community member’, ‘community member as designer,’ and ‘community member as facilitator’ models and provide characteristics for the meta-methodology of RPD.
{"title":"An introduction to radical participatory design: decolonising participatory design processes","authors":"V. Udoewa","doi":"10.1017/dsj.2022.24","DOIUrl":"https://doi.org/10.1017/dsj.2022.24","url":null,"abstract":"Abstract Outside of community-led design projects, most participatory design processes initiated by a company or organisation maintain or even strengthen power imbalances between the design organisation and the community on whose purported behalf they are designing, further increasing the absencing experience. Radical participatory design (RPD) is a radically relational answer to the coloniality inherent in participatory design where the community members’ disappointment is greater due to the greater expectations and presencing potential of a ‘participatory design’ process. We introduce the term RPD to show how research and design processes can be truly participatory to the root or core. Instead of treating participatory design as a method, a way of conducting a method, or a methodology, we introduce RPD as a meta-methodology, a way of doing any methodology. We explicitly describe what participation means and compare and contrast design processes based on the amount of participation, creating a typology of participation. We introduce ‘designer as community member’, ‘community member as designer,’ and ‘community member as facilitator’ models and provide characteristics for the meta-methodology of RPD.","PeriodicalId":54146,"journal":{"name":"Design Science","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2022-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48698722","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract This paper critically reviews literature about design framing to clarify an understanding of what is meant by the construct of a design frame. It describes the origins of the term design frame and characterises three distinct definitions that can be found within the literature. It reviews empirical studies of design framing to highlight definitional confusion between studies. It discusses the significance of Dorst’s propositional model of design frames and juxtaposes design frames with other related constructs. It clarifies ways that the resolution of nomenclature for describing design framing might lead to a more coherent body of empirical research into this topic. It suggests that there is value in developing a better cognitive model of design framing and outlines potential steps towards such a model.
{"title":"Reviewing the concept of design frames towards a cognitive model","authors":"Nick Kelly, J. Gero","doi":"10.1017/dsj.2022.25","DOIUrl":"https://doi.org/10.1017/dsj.2022.25","url":null,"abstract":"Abstract This paper critically reviews literature about design framing to clarify an understanding of what is meant by the construct of a design frame. It describes the origins of the term design frame and characterises three distinct definitions that can be found within the literature. It reviews empirical studies of design framing to highlight definitional confusion between studies. It discusses the significance of Dorst’s propositional model of design frames and juxtaposes design frames with other related constructs. It clarifies ways that the resolution of nomenclature for describing design framing might lead to a more coherent body of empirical research into this topic. It suggests that there is value in developing a better cognitive model of design framing and outlines potential steps towards such a model.","PeriodicalId":54146,"journal":{"name":"Design Science","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2022-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46465649","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Method development is at the heart of design research as methods are a formalised way to express knowledge about how aspects of design could or should be done. However, assuring that methods are in fact used in industry has remained a challenge. Industry will only use methods that they can understand and that they feel will give them benefit reliably. To understand the challenges involved in adopting a method, the method needs to be seen in context: it does not exist in isolation but forms a part of an ecosystem of methods for tackling related design problems. A method depends on the knowledge and skills of the practitioners using it: while a description of a method is an artefact that is a formalisation of engineering knowledge, a method in use constitutes a socio-technical system depending on the interaction of human participants with each other as well as with the description of the method, representations of design information and, often, tools for carrying out the method’s tasks. This paper argues that crucial factors in the adoption of methods include how well they are described and how convincingly they are evaluated. The description of a method should cover its core idea, the representations in which design information is described, the procedure to be followed, its intended use, and the tools it uses. The account of a method’s intended use should cover its purpose, the situations or product types within its scope, its coverage of kinds of problems within its scope, its expected benefit and conditions for its use. The different elements need to be evaluated separately as well as the method as an integrated whole. While verification and validation are important for some elements of methods, it is rarely possible to prove the validity of a method. Rather the developers of methods need to gather sufficient evidence that a method will work within a clearly articulated scope. Most design methods do not have binary success criteria, and their usefulness in practice depends as much on simplicity and usability as on the outcomes they produce. Evaluation should focus on how well they work, and how they can be customised and improved.
{"title":"Elements of a design method – a basis for describing and evaluating design methods","authors":"K. Gericke, C. Eckert, M. Stacey","doi":"10.1017/dsj.2022.23","DOIUrl":"https://doi.org/10.1017/dsj.2022.23","url":null,"abstract":"Abstract Method development is at the heart of design research as methods are a formalised way to express knowledge about how aspects of design could or should be done. However, assuring that methods are in fact used in industry has remained a challenge. Industry will only use methods that they can understand and that they feel will give them benefit reliably. To understand the challenges involved in adopting a method, the method needs to be seen in context: it does not exist in isolation but forms a part of an ecosystem of methods for tackling related design problems. A method depends on the knowledge and skills of the practitioners using it: while a description of a method is an artefact that is a formalisation of engineering knowledge, a method in use constitutes a socio-technical system depending on the interaction of human participants with each other as well as with the description of the method, representations of design information and, often, tools for carrying out the method’s tasks. This paper argues that crucial factors in the adoption of methods include how well they are described and how convincingly they are evaluated. The description of a method should cover its core idea, the representations in which design information is described, the procedure to be followed, its intended use, and the tools it uses. The account of a method’s intended use should cover its purpose, the situations or product types within its scope, its coverage of kinds of problems within its scope, its expected benefit and conditions for its use. The different elements need to be evaluated separately as well as the method as an integrated whole. While verification and validation are important for some elements of methods, it is rarely possible to prove the validity of a method. Rather the developers of methods need to gather sufficient evidence that a method will work within a clearly articulated scope. Most design methods do not have binary success criteria, and their usefulness in practice depends as much on simplicity and usability as on the outcomes they produce. Evaluation should focus on how well they work, and how they can be customised and improved.","PeriodicalId":54146,"journal":{"name":"Design Science","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2022-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44162602","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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