2022
DOI: 10.1080/03080188.2022.2131086
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From researching to making futures: a design mindset for transdisciplinary collaboration

Abstract: Addressing complex future challenges requires transdisciplinary practices. However, existing approaches for transdisciplinary collaboration tend to be limited to science-expert directions. Successful collaboration across disciplines and diverse contexts requires community agency, blurring disciplinary boundaries, and combining sciences and arts. We argue that traditional and emergent design practices provide a powerful mindset to support productive transdisciplinary collaborations for addressing complex societ… Show more

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Cited by 9 publications
(3 citation statements)
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“…The work of Simon was foundational on developing scientific approaches to design research such as the problem-solution co-evolution model [8] and protocol analyses [12], and additionally addresses the notion of 'cognitive comparative advantage', in which early ideas of machine and artificial intelligence are set on imagining human-computer collaborative futures [17]. Here, we see XRH design as an instrumental space for future-making in transdisciplinary collaboration [21], continuously negotiating both reflective (focused on past and lived experiences) and projective (focused on speculative future visions and imaginaries) actions. Although this temporal distinction has been hardly explored within healthcare and design literature (see for instance the work of Burke and Veliz Reyes on the codesign of care home facilities for dementia patients [5]), it gains relevance in the development of XRH in the context of communities often deprived of the means of envisioning futures amid pressing socioeconomic and accessibility challenges.…”
Section: Codesign and Transdisciplinary Boundariesmentioning
confidence: 99%
“…The work of Simon was foundational on developing scientific approaches to design research such as the problem-solution co-evolution model [8] and protocol analyses [12], and additionally addresses the notion of 'cognitive comparative advantage', in which early ideas of machine and artificial intelligence are set on imagining human-computer collaborative futures [17]. Here, we see XRH design as an instrumental space for future-making in transdisciplinary collaboration [21], continuously negotiating both reflective (focused on past and lived experiences) and projective (focused on speculative future visions and imaginaries) actions. Although this temporal distinction has been hardly explored within healthcare and design literature (see for instance the work of Burke and Veliz Reyes on the codesign of care home facilities for dementia patients [5]), it gains relevance in the development of XRH in the context of communities often deprived of the means of envisioning futures amid pressing socioeconomic and accessibility challenges.…”
Section: Codesign and Transdisciplinary Boundariesmentioning
confidence: 99%
“…To pursue academic excellence and bolster cutting-edge research, one must determine how to cultivate the research and innovative (R&I) capability of university teachers effectively (van Driel, 2021). Many studies begin in academic institutions, so it seems reasonable to keep educators at the forefront of cutting-edge methodologies, for, through cultivating a research culture, universities can create an environment conducive to innovation and interdisciplinary collaboration (Bayuo, Chaminade & Göransson, 2020;Mejía, Henriksen, Xie, García-Topete, Malina & Jung, 2023). By harnessing the academic potential of the teaching staff, universities can make their research efforts more effective, establish close partnerships with various industries, and boost socioeconomic growth in the region (Asbari, Purwanto, Ong, Mustikasiwi, Maesaroh, Mustofa et al, 2020).…”
Section: Introductionmentioning
confidence: 99%
“…By its very nature, transdisciplinary thinking and approaches often require a high level of collaboration, cooperation, and co‐creation of ideas and knowledge by stakeholders throughout the entire research or problem‐solving process. (Camkin & Neto, 2013; Ghodsvali et al, 2019; Icyimpaye et al, 2022; Mejía et al, 2023; Pohl & Hirsch Hadorn, 2007; Studer & Pohl, 2023).…”
Section: Introductionmentioning
confidence: 99%