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The more, the better? The effect of feedback and user's past successes on idea implementation in open innovation communities

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  • Qian Liu
  • Zhengfa Yang
  • Xiaofang Cai
  • Qianzhou Du
  • Weiguo Fan

Abstract

Establishing open innovation communities has evolved as an important product innovation and development strategy for companies. Yet, the success of such communities relies on the successful implementation of many user‐submitted ideas. Although extant literature has examined the impact of user experience and idea characteristics on idea implementation, little is known from the information input perspective, for example, feedback. Based on the information overload theory and knowledge content framework, we propose that the amount and types of feedback content have different effects on the likelihood of subsequent idea implementation, and such effects depend on the level of users' success experience. We tested the research model using a panel logistic model with the data of MIUI Forum. The study results revealed that the amount of feedback has an inverted U‐shaped effect on idea implementation, and such effect is moderated by a user's past success. Moreover, the type of feedback content (cost and benefit‐related feedback and functionality‐related feedback) positively affects idea implementation, and a user's past success positively moderated the above effects. Finally, we discuss the theoretical and practical implications, limitations of our research, and suggestions for future research.

Suggested Citation

  • Qian Liu & Zhengfa Yang & Xiaofang Cai & Qianzhou Du & Weiguo Fan, 2022. "The more, the better? The effect of feedback and user's past successes on idea implementation in open innovation communities," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 73(3), pages 376-392, March.
    • Handle: RePEc:bla:jinfst:v:73:y:2022:i:3:p:376-392
    DOI: 10.1002/asi.24555
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    References listed on IDEAS

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    1. Ashraf Labib & Salem Chakhar & Lorraine Hope & John Shimell & Mark Malinowski, 2022. "Analysis of noise and bias errors in intelligence information systems," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 73(12), pages 1755-1775, December.

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