IDEAS home Printed from https://ideas.repec.org/a/spr/elcore/v22y2022i3d10.1007_s10660-020-09408-1.html
   My bibliography  Save this article

Investigating participants’ attributes for participant estimation in knowledge-intensive crowdsourcing: a fuzzy DEMATEL based approach

Author

Listed:
  • Xuefeng Zhang

    (Anhui Polytechnic University)

  • Bengang Gong

    (Anhui Polytechnic University)

  • Yaqin Cao

    (Anhui Polytechnic University)

  • Yi Ding

    (Anhui Polytechnic University)

  • Jiafu Su

    (Chongqing Technology and Business University)

Abstract

In knowledge-intensive crowdsourcing (KI-C), estimating proper participants is an important way to ensure tasks crowdsourcing outcomes. Participants’ attributes (PAs) act as the main decision factors which are viewed as criteria for evaluating and estimating potential participants. Actually, multiple interdependent PAs have effect on participant estimation. It is an initial and vital work in estimating participants in KI-C to identify those PAs and measure their relationships. Consequently, this study first identifies PAs for participant estimation in KI-C by integrating PAs presented in the related academic studies and some practical KI-C sites. Subsequently, this study develops an integrated 2-tuple linguistic method and decision making trial and evaluation laboratory method to describe and measure causal relationships of the identified PAs. Identification of PAs would offer a common list of criteria for participant estimation in KI-C and aid to enrich studies in this field. Additionally, measurement of the PAs’ relationships through causality and prominence can assist requesters and managers of KI-C sites to understand and deal with those PAs in practical.

Suggested Citation

  • Xuefeng Zhang & Bengang Gong & Yaqin Cao & Yi Ding & Jiafu Su, 2022. "Investigating participants’ attributes for participant estimation in knowledge-intensive crowdsourcing: a fuzzy DEMATEL based approach," Electronic Commerce Research, Springer, vol. 22(3), pages 811-842, September.
    • Handle: RePEc:spr:elcore:v:22:y:2022:i:3:d:10.1007_s10660-020-09408-1
    DOI: 10.1007/s10660-020-09408-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10660-020-09408-1
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10660-020-09408-1?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Jasneet Kaur & Ramneet Sidhu & Anjali Awasthi & Satyaveer Chauhan & Suresh Goyal, 2018. "A DEMATEL based approach for investigating barriers in green supply chain management in Canadian manufacturing firms," International Journal of Production Research, Taylor & Francis Journals, vol. 56(1-2), pages 312-332, January.
    2. Nguyen Hoang Thuan & Pedro Antunes & David Johnstone, 2016. "Factors influencing the decision to crowdsource: A systematic literature review," Information Systems Frontiers, Springer, vol. 18(1), pages 47-68, February.
    3. Tracy Xiao Liu & Jiang Yang & Lada A. Adamic & Yan Chen, 2014. "Crowdsourcing with All-Pay Auctions: A Field Experiment on Taskcn," Management Science, INFORMS, vol. 60(8), pages 2020-2037, August.
    4. Sonja Marjanovic & Caroline Fry & Joanna Chataway, 2012. "Crowdsourcing based business models: In search of evidence for innovation 2.0," Science and Public Policy, Oxford University Press, vol. 39(3), pages 318-332, February.
    5. Yuxiang Zhao & Qinghua Zhu, 2014. "Evaluation on crowdsourcing research: Current status and future direction," Information Systems Frontiers, Springer, vol. 16(3), pages 417-434, July.
    6. Yiwei Gong, 2017. "Estimating participants for knowledge-intensive tasks in a network of crowdsourcing marketplaces," Information Systems Frontiers, Springer, vol. 19(2), pages 301-319, April.
    7. Ford, Robert C. & Richard, Brendan & Ciuchta, Michael P., 2015. "Crowdsourcing: A new way of employing non-employees?," Business Horizons, Elsevier, vol. 58(4), pages 377-388.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yiwei Gong, 0. "Estimating participants for knowledge-intensive tasks in a network of crowdsourcing marketplaces," Information Systems Frontiers, Springer, vol. 0, pages 1-19.
    2. Yiwei Gong, 2017. "Estimating participants for knowledge-intensive tasks in a network of crowdsourcing marketplaces," Information Systems Frontiers, Springer, vol. 19(2), pages 301-319, April.
    3. Livio Cricelli & Michele Grimaldi & Silvia Vermicelli, 2022. "Crowdsourcing and open innovation: a systematic literature review, an integrated framework and a research agenda," Review of Managerial Science, Springer, vol. 16(5), pages 1269-1310, July.
    4. Dan Li & Longying Hu, 2017. "Exploring the effects of reward and competition intensity on participation in crowdsourcing contests," Electronic Markets, Springer;IIM University of St. Gallen, vol. 27(3), pages 199-210, August.
    5. Sultana Lubna Alam & John Campbell, 2017. "Temporal Motivations of Volunteers to Participate in Cultural Crowdsourcing Work," Information Systems Research, INFORMS, vol. 28(4), pages 744-759, December.
    6. Olivera Marjanovic & Vijaya Murthy, 2022. "The Emerging Liquid IT Workforce: Theorizing Their Personal Competitive Advantage," Information Systems Frontiers, Springer, vol. 24(6), pages 1775-1793, December.
    7. Evangelos Mourelatos & Manolis Tzagarakis, 2018. "An investigation of factors affecting the visits of online crowdsourcing and labor platforms," Netnomics, Springer, vol. 19(3), pages 95-130, December.
    8. Segev, Ella, 2020. "Crowdsourcing contests," European Journal of Operational Research, Elsevier, vol. 281(2), pages 241-255.
    9. Till Blesik & Markus Bick & Tyge-F. Kummer, 2022. "A Conceptualisation of Crowd Knowledge," Information Systems Frontiers, Springer, vol. 24(5), pages 1647-1665, October.
    10. Ayomikun Idowu & Amany Elbanna, 2022. "Digital Platforms of Work and the Crafting of Career Path: The Crowdworkers’ Perspective," Information Systems Frontiers, Springer, vol. 24(2), pages 441-457, April.
    11. Nguyen Hoang Thuan & Pedro Antunes & David Johnstone, 2016. "Factors influencing the decision to crowdsource: A systematic literature review," Information Systems Frontiers, Springer, vol. 18(1), pages 47-68, February.
    12. Zhanwen Shi & Erbao Cao & Kai Nie, 2023. "Capacity pooling games in crowdsourcing services," Electronic Commerce Research, Springer, vol. 23(2), pages 1007-1047, June.
    13. Ted Y. T. Suen & Simon K. S. Cheung & Fu Lee Wang & John Y. K. Hui, 2022. "Effects of Intrinsic and Extrinsic Motivational Factors on Employee Participation in Internal Crowdsourcing Initiatives in China," Sustainability, MDPI, vol. 14(14), pages 1-23, July.
    14. Nguyen Hoang Thuan & Pedro Antunes & David Johnstone, 2018. "A Decision Tool for Business Process Crowdsourcing: Ontology, Design, and Evaluation," Group Decision and Negotiation, Springer, vol. 27(2), pages 285-312, April.
    15. Schenk, Eric & Guittard, Claude & Pénin, Julien, 2019. "Open or proprietary? Choosing the right crowdsourcing platform for innovation," Technological Forecasting and Social Change, Elsevier, vol. 144(C), pages 303-310.
    16. Xu, Hui & Wu, Yang & Hamari, Juho, 2022. "What determines the successfulness of a crowdsourcing campaign: A study on the relationships between indicators of trustworthiness, popularity, and success," Journal of Business Research, Elsevier, vol. 139(C), pages 484-495.
    17. Carlos Devece & Daniel Palacios-Marqués & D. Enrique Ribeiro-Soriano, 2021. "IT-based strategy, capabilities, and practices: crowdsourcing implementation in market-oriented firms," Review of Managerial Science, Springer, vol. 15(1), pages 15-32, January.
    18. Vincenzo Vignieri, 2021. "Crowdsourcing as a mode of open innovation: Exploring drivers of success of a multisided platform through system dynamics modelling," Systems Research and Behavioral Science, Wiley Blackwell, vol. 38(1), pages 108-124, January.
    19. Konrad Grabiszewski & Alex Horenstein, 2022. "Profiling dynamic decision-makers," PLOS ONE, Public Library of Science, vol. 17(4), pages 1-22, April.
    20. Chiara Belletti & Daniel Erdsiek & Ulrich Laitenberger & Paola Tubaro, 2021. "Crowdworking in France and Germany," Working Papers hal-03468022, HAL.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:elcore:v:22:y:2022:i:3:d:10.1007_s10660-020-09408-1. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.