基于详尽可能性模型的用户创新社区创意采纳影响因素研究

doi:10.16381/j.cnki.issn1003-207x.2020.03.022

摘要/Abstract

摘要： 本文基于详尽可能性模型构建了用户创新社区四维度创意采纳模型，分析各维度中不同因素对创意采纳的影响作用。基于小米MIUI社区提交的16.3万余条创意的实证研究表明：用户社区地位对创意采纳有积极影响；用户主动贡献行为对创意采纳有积极影响，而反应贡献行为对创意采纳无显著影响；社区认同度对创意采纳有显著的积极影响；创意质量包括创意长度及创意论据数量均与创意采纳成倒U型曲线关系；社区吸收能力正向调节创意长度与创意采纳之间的曲线关系。研究结果可以为解决企业在创意采纳过程中的认知负荷问题提供有益的指导和借鉴。

关键词: 创意采纳, 详尽可能性模型, 认知负荷, 影响因素

Abstract: With the continuous operation of online innovation community, the conflict between the increasing user’s ideas and the limited cognitive resources of the community is more and more prominent. However, prior research related to online innovation community mainly focus on how to motivate users to contribute ideas, only a small number of scholars have studied the phenomenon of idea adoption, not clearly revealing the phenomenon of user idea adoption and still having great limitations. Firstly, most previous studies have implicitly assumed that the ideas submitted by users can be fully evaluated, which is inconsistent with the reality. Secondly, most prior studies considered the impact of individual factors on idea adoption and lack a systematic analytical framework of influencing factors for idea adoption. Further, there not be consensus on the impact of various factors on idea adoption.It is urgent to introduce a new system perspective holistically analyzing the antecedents of users’ idea adoption to fill in above gaps in prior researches.
Through the review of literatures on idea adoption and Elaboration Likelihood Model(ELM), a four-dimensional idea adoption model is constructed based on ELM, including user community status, user contribution behavior, community recognition and creative quality. The model is tested by Stata14.0 software based on more than 163,000 ideas submitted from June 2011 to February 2018 by users in New Function Recommendations section of Xiaomi MIUI Community. The results show that the user community status has a positive impact on idea adoption; user initiative contribution has a positive impact on creative adoption and response contribution behavior has no significant impact on idea adoption; the community recognition of ideas has a significant positive impact on idea adoption; creative quality includes both the idea Length and Argument have an inverted U-curve relationship with idea adoption; the community absorptive capacity positively adjusts the curvilinear relationship between idea length and idea adoption.
Based on Elaboration Likelihood Model, a four-dimensional model of community creative adoption which reveals the phenomenon of community idea adoption is constructed. The research expands the research perspective of idea adoption, broadens the application areas of detailed models and deepens the understanding of various factors on community idea adoption. Some implications are also provided for guiding users to better publish ideas and solving the cognitive load problem existing in community idea adoption process.

Key words: idea adoption, elaboration likelihood model, cognitive load, influencing factors

中图分类号:

F124.3

王楠, 陈详详, 祁运丽, 王莉雅. 基于详尽可能性模型的用户创新社区创意采纳影响因素研究[J]. 中国管理科学, 2020, 28(3): 213-222.

WANG Nan, CHEN Xiang-xiang, QI Yun-li, WANG Li-ya. The Research on Influence Factors of User Innovation Community Idea Adoption Based on Elaboration Likelihood Model[J]. Chinese Journal of Management Science, 2020, 28(3): 213-222.

参考文献

[1] 刘倩,孙宝文. COI社区在线交互对用户创意质量的影响——专业成功经验的调节效应[J]. 南开管理评论, 2018,21(2):16-27.
[2] Huang Y, Singh P V, Srinivasan K. Crowdsourcing new product ideas under consumer learning[J]. Management Science, 2014, 60(9):2138-2159.
[3] Hanjun Lee Y S. Who creates value in a user innovation community? A case study of MyStarbucksIdea.com[J].Online Information Review, 2016, 40(2):170-186.
[4] Schemmann B, Herrmann A M, Chappin M M H, et al. Crowdsourcing ideas:Involving ordinary users in the ideation phase of new product development[J]. Research Policy, 2016, 45(6):1145-1154.
[5] Haas M R, Criscuolo P, George G. Which problems to solve? Online knowledge sharing and attention allocation in organizations[J]. Academy of Management Journal, 2015, 58(3):680-711.
[6] 王楠,李瑶.网络大数据在在线用户创新研究中的应用:现状与展望[J]. 中国管理科学,2019,(4):206-210.
[7] Wisdom J P, Chor K H B, Hoagwood K E, et al. Innovation adoption:A review of theories and constructs[J]. Administration and Policy in Mental Health and Mental Health Services Research, 2014, 41(4):480-502.
[8] Yang Xue, Li Guoxin. Factors influencing the popularity of customer-generated content in a company-hosted online co-creation community:A social capital perspective[J]. Computers in Human Behavior, 2016, 64:760-768.
[9] Chen Wei, Wei Xiahua, Zhu Xiaoguo. Engaging voluntary contributions in online communities:A hidden markov model[J]. MIS Quarterly, 2018, 42(1):83-100.
[10] Hossain M I K M Z. Ideation through online open innovation platform:Dell IdeaStorm[J]. Journal of the Knowledge Economy, 2015, 3(6):611-624.
[11] Bayus B L. Crowdsourcing new product ideas over time:An analysis of the Dell IdeaStorm community[J]. Social Science Electronic Publishing, 2013, 59(1):226-244.
[12] Ho S Y, Bodoff D. The effects of web personalization on user attitude and behavior:An integration of the elaboration likelihood model and consumer search theory[J].MIS Quarterly, 2014,38(2):497-520.
[13] 杨爽. 信息质量和社区地位对用户创造产品评论的感知有用性影响机制——基于Tobit模型回归[J]. 管理评论, 2013,25(5):136-143.
[14] Lampel J, Bhalla A. The role of status seeking in online communities:Giving the gift of experience[J]. Journal of Computer-Mediated Communication, 2007, 2(12):434-455.
[15] Fuller S. The new sociological imagination[M].Sociological Inquiry,2006.
[16] Mahr D, Lievens A. Virtual lead user communities:Drivers of knowledge creation for innovation[J]. Research Policy, 2012, 41(1):167-177.
[17] Nambisan S, Baron R A. Virtual customer environments:Testing a model of voluntary participation in value co-creation activities[J]. Journal of Product Innovation Management, 2009, 26(4):388-406.
[18] Tsai K, Chou C, Kuo J. The curvilinear relationships between responsive and proactive market orientations and new product performance:A contingent link[J]. Industrial Marketing Management, 2008, 37(8):884-894.
[19] Chang H H, Chuang S. Social capital and individual motivations on knowledge sharing:Participant involvement as a moderator[J]. Information & Management, 2011, 48(1):9-18.
[20] 杨光,汪立. 思维定势如何影响创意质量——基于"众包"平台的实证研究[J]. 管理世界, 2017,(12):109-124.
[21] Di Gangi P M, Wasko M. Steal my idea! Organizational adoption of user innovations from a user innovation community:A case study of Dell IdeaStorm[J]. Decision Support Systems, 2009, 48(1):303-312.
[22] Clevinger A. Test performance:The influence of cognitive load on reading comprehension[D]. Georgia State University, 2014.
[23] Hamilton M A M P J. A framework for understanding equivocation[J]. Journal of Language & Social Psychology, 1988, 17(1):3-35.
[24] Huang Y, Kou G. A kernel entropy manifold learning approach for financial data analysis[J]. Decision Support Systems, 2014, 64:31-42.
[25] Miller G A. The magical number seven plus or minus two:Some limits on our capacity for processing information.[J]. Psychological Review, 1994, 101(2):343-352.