Emerging Trends in Crowd Science

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    The Challenges of Knowledge Combination in ML-based Crowdsourcing – The ODF Killer Shrimp Challenge using ML and Kaggle
    ( 2021-01-05) Bumann, Adrian ; Teigland, Robin
    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.
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    Serial Integration, Real Innovation: Roles of Diverse Knowledge and Communicative Participation in Crowdsourcing
    ( 2021-01-05) Sun, Yao ; Majchrzak , Ann ; Malhotra , Arvind
    Despite a burgeoning public and scholarly interest on open innovation and crowdsourcing, how to enable members of online temporary crowd to maintain knowledge integration and innovation remains underexplored. This study seeks to understand the ways in which online crowd members collectively generate more innovative and serial integrative solutions to crowdsourced open innovation challenges. Analyzing 3,200 unique posts generated by 486 participants of 21 organization-sponsored online crowdsourcing innovation challenges, this research demonstrates that crowd members contribute more innovative solutions when being exposed to explicitly shared diverse knowledge, and that crowd members’ communicative participation acts as a catalyst for the production of both innovation and serial knowledge integration. Findings suggest that managers who seek to generate knowledge integration and innovation should endeavor to implement systems that afford high-level communicative participation, as well as encourage crowd members to make their diverse knowledge explicit while minimizing their cognitive load in knowledge sharing.
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    Integrative Solutions in Online Crowdsourcing Innovation Challenges
    ( 2021-01-05) Zaggl, Michael ; Sun , Yao ; Majchrzak , Ann ; Malhotra , Arvind
    Online crowdsourcing challenges are widely used for problem-solving and innovation. Existing theory has characterized such challenges as tools for tapping distant knowledge. By building on information processing theory we move beyond this characterization and present a perspective that describes innovation challenges as virtual places in which ideas are not simply submitted or commented upon but knowledge is integrated. This perspective shifts the role of crowdsourcing challenges from mere tools for gathering ideas to representing the locus of innovation. Our perspective suggests that three types of knowledge affect the quality of integrative solutions: elementary ideas, facts, and analogical examples. Based on a large dataset, we find that elementary ideas and analogical examples are related to increased solution quality, while facts are related to decreased solution quality. We expand the research on online crowdsourcing innovation challenges to include how crowd participants influence the quality of solutions through the content of their postings.
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    Introduction to the Minitrack on Emerging Trends in Crowd Science
    ( 2021-01-05) Schau, Hope ; Akaka, Melissa ; Heilgenberg, Kerstin