Abstract

This paper describes an ontological attempt in the understanding of co-design activity in the wild within the context of service innovation. The research has an aim to analyse the transformation of ideas during co-design by examining informal data from a workshop that inspired villagers in Turkey to innovate collaboratively. Contrary to the often process-oriented analysis of co-design activity, the workshop facilitates designing by envisioning and enacting participants’ collective imagery in physical forms in an iterative cycle of deconstruction, construction and reconstruction. We report an understanding of the ontology established to describe and analyse the informal data collected from the physical forms of collective imagery. A machine learning approach is used to underpin assumptions made in the understanding of the activity based on the ontology. The analysis suggests the frequency and relevancy of ideas significantly influenced the possibility that an idea will become part of a design solution. An evaluation of the machine learning analysis delivers insights into the understanding of data collected during co-design in the wild.

Keywords:

Co-Design, Design Ontology, Service innovation, Machine Learning

Creative Commons License

Creative Commons Attribution-Noncommercial 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License

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Jun 17th, 12:00 AM

A Creative Ontological Analysis of Collective Imagery during Co - Design for Service Innovation

This paper describes an ontological attempt in the understanding of co-design activity in the wild within the context of service innovation. The research has an aim to analyse the transformation of ideas during co-design by examining informal data from a workshop that inspired villagers in Turkey to innovate collaboratively. Contrary to the often process-oriented analysis of co-design activity, the workshop facilitates designing by envisioning and enacting participants’ collective imagery in physical forms in an iterative cycle of deconstruction, construction and reconstruction. We report an understanding of the ontology established to describe and analyse the informal data collected from the physical forms of collective imagery. A machine learning approach is used to underpin assumptions made in the understanding of the activity based on the ontology. The analysis suggests the frequency and relevancy of ideas significantly influenced the possibility that an idea will become part of a design solution. An evaluation of the machine learning analysis delivers insights into the understanding of data collected during co-design in the wild.

 

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