2019
DOI: 10.14236/ewic/eva2019.3
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Participatory Innovation and Prototyping in the Cultural Sector: A case study

Abstract: This paper explores the growing pervasiveness of forms of participatory innovation in the cultural sector, with a particular focus on the integration of prototyping approaches. Participatory uses of prototyping are underpinning new developments and opportunities for museums and galleries in transforming both internal and public involvement in the co-design of services and engagement. This transformation is illustrated through landscape perspectives and real-world learnings.

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Cited by 12 publications
(8 citation statements)
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“…The travelers can ride the car on their own without any guide & the travel chatbot installed in the car keeps on describing each place. This technology is named as an Audio tour, which is preferred by the travelers who wish to have privacy & travel alone with their families (Boiano et al , 2019).…”
Section: Artificial Intelligence In Tourismmentioning
confidence: 99%
See 1 more Smart Citation
“…The travelers can ride the car on their own without any guide & the travel chatbot installed in the car keeps on describing each place. This technology is named as an Audio tour, which is preferred by the travelers who wish to have privacy & travel alone with their families (Boiano et al , 2019).…”
Section: Artificial Intelligence In Tourismmentioning
confidence: 99%
“…In addition to that, chatbots also provide the information related to Tourist Infrastructure which includes nearby bars, discotheques, clubs, theme parks, entertainment & sports activities, water parks, zoos, casinos, trekking, adventure activities, shopping malls, etc. (Boiano et al , 2019; Gajdošík and Marciš, 2019). Thus, Chatbots provide all the information related to the Destination tourism infrastructure & Tourist Infrastructure.…”
Section: Artificial Intelligence Affecting Travel Tourism and Hospita...mentioning
confidence: 99%
“…Notwithstanding such differences in capacity, museums and cultural organisations share common capabilities in enabling participation at different audience levels. The Participatory Museum (Simon 2010) outlines approaches designed to aid museums in becoming more open to participation, involving users to inform, co-design programs and exhibitions, and innovate projects, as well as providing platforms for users to construct their own meanings (Boiano et al 2019;Mutibwa et al 2020). In the concept of the social museum, Visser recognizes new models of digital socialization between the museum and public (Visser 2013), with ways to learn with respect to broadening digital citizen engagement.…”
Section: Repositioning Digital Citizenship and Museumsmentioning
confidence: 99%
“…In the literature, citizen science projects have been differentiated according to the extent of responsibilities that the public undertake as research activities, such as collecting and analysing data (contributory) and interpreting and disseminating results (collaborative). Projects are typically instigated by professionally trained researchers in which citizen scientists are supporting tasks in a research process (Bonney 2009;Shirk et al 2012); however, community scaled participatory research projects, for example, can be a cooperative activity (Boiano et al 2019). This may lead to co-creation in which researchers and members of the public work together, for example, in defining a research problem or producing new knowledge (Borda, Gray and Fu 2019).…”
Section: Participationmentioning
confidence: 99%
“…A novel advancement in the use of social tagging, the San Francisco Museum of Modern Art (SFMOMA) has developed Send Me SFMOMA (http://www.sfmoma.org/send-me-sfmoma/), an SMS "chatbot" service providing an accessible method of sharing the breadth of SFMOMA's collection with the public, of which only 5% is seen in the Galleries at any one time (Mollica 2017;Gaia et al 2019). Using the service and texting the words "send me" followed by a keyword, a colour, or an emoji, a visitor receives a related artwork image and caption via text message.…”
Section: Machine Learningmentioning
confidence: 99%