A toolbox for idea generation and evaluation: Machine learning, data-driven, and contest-driven approaches to support idea generation
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The significance and abundance of data are increasing due to the growing digital data generated from social media, sensors, scholarly literature, patents, different forms of documents published online, databases, product manuals, etc. Various data sources can be used to generate ideas, yet, in addition to bias, the size of the available digital data is a major challenge when it comes to manual analysis. Hence, human-machine interaction is essential for generating valuable ideas where machine learning and data-driven techniques generate patterns from data and serve human sense-making. However, the use of machine learning and data-driven approaches to generate ideas is a relatively new area. Moreover, it is also possible to stimulate innovation using contest-driven idea generation and evaluation. The results and contributions of this thesis can be viewed as a toolbox of idea-generation techniques, including a list of data-driven and machine learning techniques with corresponding data sources and models to support idea generation. In addition, the results include two models, one method and one framework, to better support data-driven and contest- driven idea generation. The beneficiaries of these artefacts are practitioners in data and knowledge engineering, data mining project managers, and innovation agents. Innovation agents include incubators, contest organizers, consultants, innovation accelerators, and industries. Since the proposed artefacts consist of process models augmented with AI techniques, human-centred AI is a promising area of research that can contribute to the artefacts' further development and promote creativity.
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