Management response and user idea generation: evidence from an online open innovation community

被引:0
|
作者
Dongmei Han
Zhiliang Pang
Lifeng He
Xiaohang Zhou
Shaohua Zhang
机构
[1] Shanghai University of Finance and Economics,School of Information Management and Engineering
[2] Shanghai Business School,Faculty of Business Information
[3] Shanghai University of Finance and Economics,Shanghai Key Laboratory of Financial Information Technology
[4] Shanghai Business School,undefined
来源
关键词
Management response; Re-innovation; Open innovation community; Idea generation; Text mining;
D O I
暂无
中图分类号
学科分类号
摘要
In recent years, open innovation communities (OICs), in which users are allowed to post ideas, have become a momentous source of product innovation and development for enterprises. Continuous generation of ideas is critical to the success of an OIC. When users post an idea more than once, they are considered to be conducting re-innovation behavior (RIB). Many enterprises have implemented management response systems in their OICs to motivate users’ RIB and promote the prosperity of the OIC. However, researchers have not yet examined whether management response is an effective management tool for motivating users’ RIB in OICs. This research shows that receiving management response has a positive effect on the volume, quality and novelty of user’s RIB. Furthermore, we found that the enterprise’s official adoption willingness expressed through management response may reduce the volume, quality, and novelty of ideas in user’s RIB, which may have an impact on the enterprise’s insights. Additionally, we conducted heterogeneity analyses to explore the effects of different lengths and sentiments of management response in this mechanism. This research has implications for managers and enterprises and contributes to the literature on management response, idea generation behavior, OICs, and, more broadly, co-innovation between enterprises and consumers.
引用
收藏
页码:381 / 400
页数:19
相关论文
共 50 条
  • [1] Management response and user idea generation: evidence from an online open innovation community
    Han, Dongmei
    Pang, Zhiliang
    He, Lifeng
    Zhou, Xiaohang
    Zhang, Shaohua
    INFORMATION TECHNOLOGY & MANAGEMENT, 2023, 24 (04): : 381 - 400
  • [2] Empirical Evidence of Idea Generation in Open Innovation Community
    Yang Z.
    Liu Q.
    Zhao X.
    Zhao Y.
    International Journal of Crowd Science, 2023, 7 (01) : 40 - 45
  • [3] User idea implementation in open innovation communities: Evidence from a new product development crowdsourcing community
    Liu, Qian
    Du, Qianzhou
    Hong, Yili
    Fan, Weiguo
    Wu, Shuang
    INFORMATION SYSTEMS JOURNAL, 2020, 30 (05) : 899 - 927
  • [4] User innovation evaluation: Empirical evidence from an online game community
    Ma, Jifeng
    Lu, Yaobin
    Gupta, Sumeet
    DECISION SUPPORT SYSTEMS, 2019, 117 : 113 - 123
  • [5] Exploring the Usefulness of User-Generated Content for Business Intelligence in Innovation: Empirical Evidence From an Online Open Innovation Community
    Daradkeh, Mohammad Kamel
    INTERNATIONAL JOURNAL OF ENTERPRISE INFORMATION SYSTEMS, 2021, 17 (02) : 44 - 70
  • [6] Idea generation performance in open innovation communities: The role of user interaction
    Wang, Tianmei
    Qi, Tuotuo
    Zhou, Xinxue
    Xin, Xiaping
    INFORMATION & MANAGEMENT, 2024, 61 (03)
  • [7] OPEN INNOVATION AND IDEA GENERATION IN SMES
    Aschehoug, Silje Helene
    Ringen, Geir
    DESIGN FOR HARMONIES, VOL 1: DESIGN PROCESSES, 2013,
  • [8] Users' subsequent innovation after organizational adoption: evidence from an online game user innovation community
    Li, Weimo
    Lu, Yaobin
    Ma, Jifeng
    Wang, Bin
    INTERNET RESEARCH, 2023, 33 (04) : 1446 - 1472
  • [9] Diversity in online self-organizing teams: longitudinal evidence from an open innovation community
    Ma, Jifeng
    Lu, Yaobin
    Gong, Yeming
    Li, Ran
    MANAGEMENT DECISION, 2024, 62 (01) : 219 - 239
  • [10] Stimulating innovation by user feedback on social media: The case of an online user innovation community
    Ogink, Timko
    Dong, John Qi
    TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE, 2019, 144 : 295 - 302