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- Ayele, Workneh Yilma,1978-Stockholms universitet,Institutionen för data- och systemvetenskap (author)
A toolbox for idea generation and evaluation : Machine learning, data-driven, and contest-driven approaches to support idea generation
- BookEnglish2022
Publisher, publication year, extent ...
- Stockholm :Department of Computer and Systems Sciences, Stockholm University,2022
- 154 s.
- electronicrdacarrier
Numbers
- LIBRIS-ID:oai:DiVA.org:su-201963
- ISBN:9789179117900
- ISBN:9789179117917
- https://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-201963URI
Supplementary language notes
- Language:English
- Summary in:English
Part of subdatabase
- SwePubSwePub
Classification
Series
- Report Series / Department of Computer & Systems Sciences,1101-8526 ;22-001
Notes
- Ideas are sources of creativity and innovation, and there is an increasing demand for innovation. For example, the start-up ecosystem has grown in both number and global spread. As a result, established companies need to monitor more start-ups than before and therefore need to find new ways to identify, screen, and collaborate with start-ups.The significance and abundance of data are also 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. However, the measurement of contest-driven idea generation processes needs to be supported to manage the process better. In addition, post-contest challenges hinder the development of viable ideas. A mixed-method research methodology is applied to address these challenges.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.Future projects could develop a technical platform to explore and exploit unstructured data using machine learning, visual analytics, network analysis, and bibliometric for supporting idea generation and evaluation activities. It is possible to adapt and integrate methods included in the proposed toolbox in developer platforms to serve as part of an embedded idea management system. Future research could also adapt the framework to barriers that constrain the development required to elicit post-contest digital service. In addition, 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.
Subject headings and genre
- NATURVETENSKAP Data- och informationsvetenskap Datavetenskap hsv//swe
- NATURAL SCIENCES Computer and Information Sciences Computer Sciences hsv//eng
- NATURVETENSKAP Data- och informationsvetenskap Systemvetenskap, informationssystem och informatik hsv//swe
- NATURAL SCIENCES Computer and Information Sciences Information Systems hsv//eng
- Idea generation
- idea mining
- data-driven idea generation
- data-driven idea evaluation
- toolbox for idea generation
- toolbox for idea evaluation
- contest-driven idea generation
- machine learning for idea generation
- text mining for idea generation
- analytics for idea generation
- human-centred AI for creativity
- data- och systemvetenskap
- Computer and Systems Sciences
Added entries (persons, corporate bodies, meetings, titles ...)
- Juell-Skielse, Gustaf,Associate ProfessorStockholms universitet,Institutionen för data- och systemvetenskap(Swepub:su)gjuel (thesis advisor)
- Johannesson, Paul,ProfessorStockholms universitet,Institutionen för data- och systemvetenskap(Swepub:su)pajo (thesis advisor)
- Elragal, Ahmed,ProfessorInstitutionen för system- och rymdteknik, Luleå tekniska universitet (opponent)
- Stockholms universitetInstitutionen för data- och systemvetenskap (creator_code:org_t)
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