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An evaluation methodology for crowdsourced design
DOI:10.1016/j.aei.2015.09.005.png)
摘要
En 中文
In recent years, the power of the crowd has been repeatedly demonstrated and various Internet platforms have been used to support applications of collaborative intelligence in tasks ranging from open innovation to image analysis. However, crowdsourcing applications in the fields of design research and creative innovation have been much slower to emerge. So, although there have been reports of systems and researchers using Internet crowdsourcing to carry out generative design, there are still many gaps in knowledge about the capability and limitations of the technology. Indeed the process models developed to support traditional commercial design (e.g. Pugh's Total Design, Agile, Double-Diamond etc.) have yet to be established for Crowdsourced Design (cDesign). As a contribution to the development of such a general model this paper proposes a cDesign framework to support the creation of crowdsourced design activities. Within the cDesign framework the effective evaluation of design quality is identified as a key component that not only enables the leveraging of a large, virtual workforce's creative activities but is also fundamental to almost all iterative optimisation processes. This paper reports an experimental investigation into two different Crowdsourced design evaluation approaches; free evaluation and 'Crowdsourced Design Evaluation Criteria' (cDEC). The results are benchmarked against a 'manual' evaluation carried out by a panel of experienced designers. The results suggest that the cDEC approach produces design rankings that correlate strongly with the judgements of an expert panel. The paper concludes that cDEC assessment methodology demonstrates how Crowdsourcing can be effectively used to evaluate, as well as generate, new design solutions. (C) 2015 Elsevier Ltd. All rights reserved.
Keyword:
Crowdsourcing
Crowdsourced design methodology
Design evaluation
Crowdsourced design evaluation criteria
Collaborative design
Human based genetic algorithm
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