[Objective] The paper aims to identify useful message from open innovation community with numerous redundant and low quality information. [Methods] First, we retrieved 23,137 users’ comments on programming bugs from the official Xiaomi MIUI Forum based on the information adoption model. Then, we applied binary logistic regression method to explore factors affecting the usefulness of these comments. [Results] The timeliness of information had positive impact on their usefulness, the integrity of information also affected their usefulness, and the semantics of information had negative effects on their usefulness. The users’ previous experience did not influence the usefulness of information. However, users’ previous contribution had positive effects on the usefulness of information. [Limitations] The research data was collected from small portion of one community, which might yield biased results. [Conclusions] This paper could help us effectively identify usefulness information from open innovation communities.
(Zhan Xiangdong.The Research on Open Innovation Based on User Innovation Community[J]. Forum on Science and Technology, 2013(8): 34-39.)
doi: 10.3969/j.issn.1002-6711.2013.08.007
(Yuan Xinwei, Yang Shaohua.Knowledge Creation of Online User Innovation Communities: A Research Review and Theoretical Analysis Framework[J]. Information Science, 2017, 35(7): 162-168.)
doi: 10.13833/j.cnki.is.2017.07.028
[6]
Martinez-Torres R, Olmedilla M.Identification of Innovation Solvers in Open Innovation Communities Using Swarm Intelligence[J]. Technological Forecasting & Social Change, 2016, 109: 15-24.
doi: 10.1016/j.techfore.2016.05.007
(Liao Xiao, Li Zhihong, Xi Yunjiang.Modeling and Analysis of Users’ Knowledge Network in Innovative Community Based on Clustering[J]. Computer Simulation, 2016, 33(4): 316-319, 351.)
(Qi Guijie, Li Yiying.Research on the Contribution Degrees of Online Users in the Open Innovation Communities for Enterprises[J]. Science & Technology Progress and Policy, 2016, 33(14): 81-87.)
doi: 10.6049/kjjbydc.2016010256
[9]
Gangi P M D, Wasko M M, Hooker R E. Getting Customers’ Ideas to Work for You: Learning from Dell How to Succeed with Online User Innovation Communities[J]. MIS Quarterly Executive, 2010, 9(4): 213-228.
(Li Yiying, Qi Guijie.System Dynamic Analysis on Managing the Open Innovation Communities for Enterprises from the View of Innovation Value Chain[J]. Journal of Business Economics, 2017(6): 60-70.)
doi: 10.14134/j.cnki.cn33-1336/f.2017.06.006
[11]
Petty R E, Cacioppo J T.Communication and Persuasion: Central and Peripheral Routes to Attitude Change[A]// Schlüsselwerke der Medienwirkungsforschung[M]. Springer VS, Wiesbaden, 2016.
[12]
Sussman S W, Siegal W S.Informational Influence in Organizations: An Integrated Approach to Knowledge Adoption[J]. Information Systems Research, 2003, 14(1): 47-65.
doi: 10.1287/isre.14.1.47.14767
[13]
Shu M Y, Scott N.Influence of Social Media on Chinese Students’ Choice of an Overseas Study Destination: An Information Adoption Model Perspective[J]. Journal of Travel & Tourism Marketing, 2014, 31(2): 286-302.
doi: 10.1080/10548408.2014.873318
[14]
Erkan I, Evans C.The Influence of eWOM in Social Media on Consumers’ Purchase Intentions: An Extended Approach to Information Adoption[J]. Computers in Human Behavior, 2016, 61: 47-55.
doi: 10.1016/j.chb.2016.03.003
[15]
Cheung C M K, Lee M K O, Rabjohn N. The Impact of Electronic Word-of-Mouth[J]. Internet Research, 2008, 18(3): 229-247.
doi: 10.1108/10662240810883290
[16]
Salehi-Esfahani S, Ravichandran S, Israeli A, et al.Investigating Information Adoption Tendencies Based on Restaurants’ User-Generated Content Utilizing a Modified Information Adoption Model[J]. Journal of Hospitality Marketing & Management, 2016, 25(8): 925-953.
doi: 10.1080/19368623.2016.1171190
[17]
Wixom B H, Todd P A.A Theoretical Integration of User Satisfaction and Technology Acceptance[J]. Information Systems Research, 2005, 16(1): 85-102.
doi: 10.1287/isre.1050.0042
[18]
Wang R Y, Strong D M.Beyond Accuracy: What Data Quality Means to Data Consumers[J]. Journal of Management Information System, 1996, 12(4): 5-33.
doi: 10.1080/07421222.1996.11518099
[19]
Wang R Y, Reddy M P, Kon H B.Toward Quality Data: An Attribute-based Approach[J]. Decision Support System,1995, 13(3-4): 349-372.
doi: 10.1016/0167-9236(93)E0050-N
[20]
Xiang Z, Du Q, Ma Y, et al.A Comparative Analysis of Major Online Review Platforms: Implications for Social Media Analytics in Hospitality and Tourism[J]. Tourism Management, 2017, 58: 51-65.
doi: 10.1016/j.tourman.2016.10.001
(Wu Jiang, Liu Wanwan.Identifying Reviews with More Positive Votes——Case Study of Amazon.cn[J]. Data Analysis and Knowledge Discovery, 2017, 1(9): 16-26.)
[22]
Lohan G, Conboy K, Lang M.Examining Customer Focus in IT Project Management: Findings from Irish and Norwegian Case Studies[J]. Scandinavian Journal of Information Systems, 2011, 23(2): 29-58.
[23]
Gretzel U, Yoo K H, Purifoy M.Online Travel Review Study: Role&Impact of Online Travel Reviews[R]. Texas A&M University: Laboratory for Intelligent Systems in Tourism, 2007.
[24]
Mcauley J J, Leskovec J.From Amateurs to Connoisseurs: Modeling the Evolution of User Expertise Through Online Reviews[C]// Proceedings of the 22nd International Conference on World Wide Web, 2013.
(Yuan Xinwei, Yang Shaohua, Wang Chaochao, et al.Identifying Lead Players of User Innovation Communities Based on Feature Extraction and Random Forest Classification[J]. Data Analysis and Knowledge Discovery, 2017, 1(11): 62-74.)
[26]
Von Hippel E.Lead Users: A Source of Novel Product Concepts[J]. Management Science, 1986, 32(7): 791-805.
doi: 10.1287/mnsc.32.7.791
[27]
Mahr D, Lievens A.Virtual Lead User Communities: Drivers of Knowledge Creation for Innovation[J]. Research Policy, 2012, 41(1): 167-177.
doi: 10.1016/j.respol.2011.08.006
(Fang Xiaofei, Huang Xiaoxi, Wang Rongbo, et al.Identifying Hot Topics from Mobile Complaint Texts[J]. Data Analysis and Knowledge Discovery, 2017, 1(2):19-27.)
[29]
Blei D M, Lafferty J D.Dynamic Topic Models[C]// Proceedings of the 23rd International Conference on Machine Learning, Pittsburgh, USA. 2006:113-120.
(Tang Xiaobo, Xiang Kun.Hotspot Mining Based on LDA Model and Microblog Heat[J]. Library and Information Service, 2014, 58(5): 58-63.)
doi: 10.13266/j.issn.0252-3116.2014.05.010
(Jin Biyi, Xu Xin.Research on Theme Features in Online Health Community[J]. Library and Information Service, 2015, 59(12): 100-105.)
doi: 10.13266/j.issn.0252-3116.2015.012.015
(Xu Jiajun, Yang Yang, Yao Tianfang, et al.LDA Based Hot Topic Detection and Tracking for the Forum[J]. Journal of Chinese Information Processing, 2016, 30(1): 43-49.)
(Li Yongzhong, Cai Jia.Theme Evolvement and Visual Analysis of Domestic E-government Research Based on LDA[J]. Journal of Modern Information, 2017, 37(4): 158-164.)
doi: 10.3969/j.issn.1008-0821.2017.04.025
[34]
Blei D M, Ng A Y, Jordan M I.Latent Dirichlet Allocation[J]. Journal of Machine Learning Research, 2003, 3: 993-1022.
(Wang Wei, Zhou Yongmei, Yang Aimin, et al.Method of Sentiment Analysis for Comment Texts Based on LDA[J]. Journal of Data Acquisition and Processing, 2017, 32(3): 629-635.)
doi: 10.16337/j.1004-9037.2017.03.023
[36]
Wei X, Croft W B.LDA-based Document Models for Ad-Hoc Retrieval[C]// Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 2006: 178-185.
[37]
Cao J, Xia T, Li J, et al.A Density-based Method for Adaptive LDA Model Selection[J]. Neurocomputing, 2009, 72(7-9): 1775-1781.
doi: 10.1016/j.neucom.2008.06.011