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Multi-label classification of member participation in online innovation communities

Author

Listed:
  • Steven Debaere
  • Kristof Coussement

    (LEM - Lille économie management - UMR 9221 - UA - Université d'Artois - UCL - Université catholique de Lille - Université de Lille - CNRS - Centre National de la Recherche Scientifique)

  • Tom de Ruyck

Abstract

Online innovation communities are defined as internet-based platforms for communication and exchange among customers interested in building innovations for a given product or technology. As firms recognize an online innovation community as a valuable resource for integrating external consumer knowledge into innovation processes, they increasingly ignore to build long-term interactions and collaborations. However, in the pursuit of a long-term community, moderators face enormous challenges, especially due to inferior member participation. Inferior member participation, whether in the form of inferior participation quantity, quality and/or emotionality, produces a community with minimal activity, unhelpful content and a nonconstructive atmosphere, respectively. Because members can be associated with multiple labels of inferior participation behavior simultaneously, the paradigm of multi-label (ML) classification methodology naturally emerges, which associates each member of interest with a set of labels instead of a single label as known in traditional classification problems. Using 1407 members of 7 real-life innovation communities, this study explores 10 state-of-the-art ML algorithms in an extensive experimental comparison to explore the benefit of ML classification methodology. We advance literature by demonstrating a novel application for ML classification adoption in the domain of online innovation communities, while comparing ML classifiers in the smallest possible scenario of 3 labels. The results indicate the effectiveness of the ML classification methodology for inferior member participation prediction, gives insights into ML classifiers' performance and discusses paths for future research.

Suggested Citation

  • Steven Debaere & Kristof Coussement & Tom de Ruyck, 2018. "Multi-label classification of member participation in online innovation communities," Post-Print hal-02990807, HAL.
  • Handle: RePEc:hal:journl:hal-02990807
    DOI: 10.1016/j.ejor.2018.03.039
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    Citations

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    Cited by:

    1. Steven Debaere & Floris Devriendt & Johanna Brunneder & Wouter Verbeke & Tom de Ruyck & Kristof Coussement, 2019. "Reducing inferior member community participation using uplift modeling: Evidence from a field experiment," Post-Print hal-02990787, HAL.
    2. Kim, Phillip H. & Kotha, Reddi & Fourné, Sebastian P.L. & Coussement, Kristof, 2019. "Taking leaps of faith: Evaluation criteria and resource commitments for early-stage inventions," Research Policy, Elsevier, vol. 48(6), pages 1429-1444.
    3. Bogaert, Matthias & Lootens, Justine & Van den Poel, Dirk & Ballings, Michel, 2019. "Evaluating multi-label classifiers and recommender systems in the financial service sector," European Journal of Operational Research, Elsevier, vol. 279(2), pages 620-634.
    4. Ni, Ji & Chen, Bowei & Allinson, Nigel M. & Ye, Xujiong, 2020. "A hybrid model for predicting human physical activity status from lifelogging data," European Journal of Operational Research, Elsevier, vol. 281(3), pages 532-542.
    5. Gupta, Mukul & Kumar, Pradeep, 2020. "Recommendation generation using personalized weight of meta-paths in heterogeneous information networks," European Journal of Operational Research, Elsevier, vol. 284(2), pages 660-674.

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