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Jack of All, Master of Some: Information Network and Innovation in Crowdsourcing Communities

Author

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  • Elina H. Hwang

    (Michael G. Foster School of Business, University of Washington, Seattle, Washington 98195;)

  • Param Vir Singh

    (Tepper School of Business, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213;)

  • Linda Argote

    (Tepper School of Business, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213)

Abstract

This study investigates how the information that individuals accumulate through helping others in a customer support crowdsourcing community influences their ability to generate novel, popular, and feasible ideas in an innovation crowdsourcing community. A customer support crowdsourcing community is one in which customers help each other develop solutions to their current problems with a company’s products. An innovation crowdsourcing community is one in which customers propose new product ideas directly to a company. Because a customer support community provides information regarding customers’ current needs and provides opportunities to help individuals activate relevant means information, we expect that an individuals’ experience of helping in a customer support community enhances the individuals’ new product ideation performance. By utilizing a natural language processing technique, we construct each individual’s information network based on his or her helping activities in a customer support community. Building on analogical reasoning theory, we hypothesize that the patterns of individuals’ information networks, in terms of breadth and depth, influence their various new product ideation outcomes in an innovation crowdsourcing community. Our analysis reveals that generalists who have offered help on broad topic domains in the customer support community are more likely to create novel ideas than are nongeneralists. Further, we find that generalists who have accumulated deep knowledge in at least one topic domain (deep generalists) outperform nongeneralists in their ability to generate popular and feasible ideas, whereas generalists who have accumulated only shallow knowledge across diverse domain areas (shallow generalists) do not. The results suggest that the ability of generalists to outperform nongeneralists in creating popular and feasible ideas is contingent on whether they have also accumulated deep knowledge. History: Robert Fichman, Senior Editor; Yulin Fang, Associate Editor.The online appendix is available at https://doi.org/10.1287/isre.2018.0804 .

Suggested Citation

  • Elina H. Hwang & Param Vir Singh & Linda Argote, 2019. "Jack of All, Master of Some: Information Network and Innovation in Crowdsourcing Communities," Information Systems Research, INFORMS, vol. 30(2), pages 389-410, June.
    • Handle: RePEc:inm:orisre:v:30:y:2019:i:2:p:389-410
    DOI: 10.1287/isre.2018.0804
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    Cited by:

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    2. Ren, Jie & Han, Yue & Genc, Yegin & Yeoh, William & Popovič, Aleš, 2021. "The boundary of crowdsourcing in the domain of creativity✰," Technological Forecasting and Social Change, Elsevier, vol. 165(C).
    3. Vipul Aggarwal & Elina H. Hwang & Yong Tan, 2021. "Learning to Be Creative: A Mutually Exciting Spatiotemporal Point Process Model for Idea Generation in Open Innovation," Information Systems Research, INFORMS, vol. 32(4), pages 1214-1235, December.
    4. Yuan Jin & Ho Cheung Brian Lee & Sulin Ba & Jan Stallaert, 2021. "Winning by Learning? Effect of Knowledge Sharing in Crowdsourcing Contests," Information Systems Research, INFORMS, vol. 32(3), pages 836-859, September.
    5. Resch, Christian & Kock, Alexander, 2021. "The influence of information depth and information breadth on brokers’ idea newness in online maker communities," Research Policy, Elsevier, vol. 50(8).
    6. Bahoo, Salman & Cucculelli, Marco & Qamar, Dawood, 2023. "Artificial intelligence and corporate innovation: A review and research agenda," Technological Forecasting and Social Change, Elsevier, vol. 188(C).
    7. Laurence Ales & Soo‐Haeng Cho & Ersin Körpeoğlu, 2021. "Innovation Tournaments with Multiple Contributors," Production and Operations Management, Production and Operations Management Society, vol. 30(6), pages 1772-1784, June.
    8. C. Gizem Korpeoglu & Ersin Körpeoğlu & Sıdıka Tunç, 2021. "Optimal Duration of Innovation Contests," Manufacturing & Service Operations Management, INFORMS, vol. 23(3), pages 657-675, May.
    9. Tat Koon Koh & Muller Y. M. Cheung, 2022. "Seeker Exemplars and Quantitative Ideation Outcomes in Crowdsourcing Contests," Information Systems Research, INFORMS, vol. 33(1), pages 265-284, March.
    10. Mohammad Daradkeh, 2022. "The Relationship Between Persuasion Cues and Idea Adoption in Virtual Crowdsourcing Communities: Evidence From a Business Analytics Community," International Journal of Knowledge Management (IJKM), IGI Global, vol. 18(1), pages 1-34, January.
    11. Liu, Jialing & Wei, Jiang & Liu, Yang & Jin, Duo, 2022. "How to channel knowledge coproduction behavior in an online community: Combining machine learning and narrative analysis," Technological Forecasting and Social Change, Elsevier, vol. 183(C).
    12. Jiao, Yuanyuan & Wu, Yepeng & Lu, Steven, 2021. "The role of crowdsourcing in product design: The moderating effect of user expertise and network connectivity," Technology in Society, Elsevier, vol. 64(C).

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