The challenges of knowledge combination in ML-based crowdsourcing - The ODF Killer shrimp challenge using ML and kaggle
Paper i proceeding, 2021

Organizations are increasingly using digital technologies, such as crowdsourcing platforms and machine learning, to tackle innovation challenges. These technologies often require the combination of heterogeneous technical and domain-specific knowledge from diverse actors to achieve the organization's innovation goals. While research has focused on knowledge combination for relatively simple tasks on crowdsourcing platforms and within ML-based innovation, we know little about how knowledge is combined in emerging innovation approaches incorporating ML and crowdsourcing to solve domain-specific innovation challenges. Thus, this paper investigates the following: What are the challenges to knowledge combination in domain-specific ML-based crowdsourcing? We conducted a case study of an environmental challenge - how to use ML to predict the spread of a marine invasive species, led by the Swedish consortium Ocean Data Factory Sweden using the crowdsourcing platform Kaggle. After discussing our results, we end the paper with recommendations on how to integrate crowdsourcing into domain-specific digital innovation processes.

Författare

Adrian Bumann

Chalmers, Teknikens ekonomi och organisation, Entrepreneurship and Strategy, Entrepreneurship and strategy

Robin Teigland

Chalmers, Teknikens ekonomi och organisation, Entrepreneurship and Strategy

Proceedings of the Annual Hawaii International Conference on System Sciences

15301605 (ISSN)

Vol. 2020-January 4930-4939

54th Annual Hawaii International Conference on System Sciences, HICSS 2021
Virtual, Online, ,

Ämneskategorier

Tvärvetenskapliga studier

Företagsekonomi

Systemvetenskap, informationssystem och informatik med samhällsvetenskaplig inriktning

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Senast uppdaterat

2021-07-01