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What Feedback Matters? The Role of Experience in Motivating Crowdsourcing Innovation

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  • Kimmy Wa Chan
  • Stella Yiyan Li
  • Jian Ni
  • John JianJun Zhu

Abstract

Recent open innovation literature indicates increasing concern about the quality of crowdsourced ideas. Building on a framework of creativity capability, rooted in behavioral literature, and intrinsic (vs. extrinsic) motivation, derived from personnel economics and social psychology literature, this study predicts the influence of feedback on ideation performance. Specifically, the effectiveness of feedback on ideation performance in firm‐sponsored, non–financially incentivized, idea‐crowdsourcing communities may depend on its valence (positive vs. negative), source (peers vs. firm), and ideators’ ideation experience. Field data, obtained using text‐mining techniques from an idea‐crowdsourcing community, reveal that the effects of positive (negative) peer feedback for increasing (decreasing) subsequent idea quality strengthen (weaken) as ideators gain experience. However, the effects of positive (negative) firm feedback for increasing (decreasing) subsequent idea quality weaken with ideation experience. Experienced ideators are more motivated (less demotivated) to improve subsequent ideation performance when they receive positive peer (negative firm) feedback; inexperienced ideators are less motivated by negative peer feedback but more motivated by positive firm feedback. The results are robust to tests with alternative field data and model specifications, as validated by a controlled laboratory experiment. They also suggest feedback strategies that managers can use to boost customer ideation performance in crowdsourcing contexts.

Suggested Citation

  • Kimmy Wa Chan & Stella Yiyan Li & Jian Ni & John JianJun Zhu, 2021. "What Feedback Matters? The Role of Experience in Motivating Crowdsourcing Innovation," Production and Operations Management, Production and Operations Management Society, vol. 30(1), pages 103-126, January.
    • Handle: RePEc:bla:popmgt:v:30:y:2021:i:1:p:103-126
    DOI: 10.1111/poms.13259
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    2. Shi, Xiaoxiao & Evans, Richard & Shan, Wei, 2022. "Solver engagement in online crowdsourcing communities: The roles of perceived interactivity, relationship quality and psychological ownership," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
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    5. Cui, Xiling & Zhu, Zhongshan & Liu, Libo & Zhou, Qiang & Liu, Qiang, 2024. "Anomaly detection in consumer review analytics for idea generation in product innovation: Comparing machine learning and deep learning techniques," Technovation, Elsevier, vol. 134(C).
    6. Michael Rivera & Cheng Jiang & Subodha Kumar, 2024. "Seek and Ye Shall Find: An Empirical Examination of the Effects of Seeking Real-Time Feedback on Employee Performance Evaluations," Information Systems Research, INFORMS, vol. 35(2), pages 783-806, June.
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