Research through co-design

IF 1.8 Q3 ENGINEERING, MANUFACTURING
Design Science Pub Date : 2024-01-15 DOI:10.1017/dsj.2023.35
Daniele Busciantella-Ricci, Sofia Scataglini
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引用次数: 0

Abstract

Abstract Research Through Design (RTD) needs to reconsider the meaning of “designing” in the research process of “through design.” We propose Research Through Co-design (RTC) as a new application of Control System Theory (CST) that includes a research problem assigned to a co-design process in RTD. It embeds the participatory paradigm through collaborative design practice and makes the research a collaborative process for learning from all the participants. To sustain the RTC theory, we present a cognitive model of RTC. It is a “model for” – rather than a “model of” – describing how the co-design, as a neural network process, works through its nodes’ collaboration to find co-designed solutions and the research answer. Diversity increases as non-experts and non-designers with different backgrounds participate. This is valuable for the RTC learning system. The discussions highlight the possibility of considering (i) the RTC model as useful for describing a robust RTD process through CST; (ii) RTC as a cognitive model for explaining the value of co-design in research processes; and (iii) RTC as a strategy for applying the participative paradigm in formal research. Finally, new insights and implications are highlighted, including using RTC as a predictive tool through artificial intelligence.
通过共同设计开展研究
摘要 通过设计进行研究(RTD)需要重新考虑 "通过设计 "的研究过程中 "设计 "的含义。我们提出 "通过协同设计进行研究"(Research Through Co-design,RTC)是控制系统理论(Control System Theory,CST)的一种新应用,它将一个研究问题分配到 RTD 的协同设计过程中。它通过合作设计实践嵌入了参与式范式,使研究成为向所有参与者学习的合作过程。为了支持 RTC 理论,我们提出了一个 RTC 认知模型。这是一个 "模型",而不是 "模型的",它描述了共同设计作为一个神经网络过程,如何通过其节点的协作,找到共同设计的解决方案和研究答案。随着具有不同背景的非专家和非设计人员的参与,多样性也在增加。这对 RTC 学习系统很有价值。讨论强调了以下可能性:(i) 将 RTC 模型视为通过 CST 描述强大 RTD 过程的有用工具;(ii) 将 RTC 视为解释研究过程中共同设计价值的认知模型;(iii) 将 RTC 视为在正式研究中应用参与式范式的策略。最后,强调了新的见解和影响,包括通过人工智能将 RTC 用作预测工具。
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来源期刊
Design Science
Design Science ENGINEERING, MANUFACTURING-
CiteScore
4.80
自引率
12.50%
发文量
19
审稿时长
22 weeks
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