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Data Analysis and Knowledge Discovery  2020, Vol. 4 Issue (8): 119-129    DOI: 10.11925/infotech.2096-3467.2020.0271
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The Determinants of Continuance Intention to Pay: Empirical Research from Online Knowledge Payment Users
Wei Wu1,Xie Xingzheng2()
1College of Journalism and Communications, Shih Hsin University, Taipei 116, China
2School of Journalism, Fudan University, Shanghai 200433, China
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Abstract  

[Objective] The current study aims to investigate the relationship among the characteristics of online knowledge payment products, individual needs, and continuance intention to pay, which offers the guideline to the industry. [Methods] Based on the Elaboration Likelihood Model and Uses and Gratifications Theory, the conceptual model of continuance intention to pay is conducted. Both structural equation model (SEM) and fuzzy-set qualitative comparative analysis (fsQCA) are used to analyze the collected data. [Results] According to the results of SEM, the argument quality has positive effect on individual needs, which can further affect users’ continuance intention to pay. The fsQCA findings reveal that three causal recipes of motivations predicting high continuance intention to pay. [Limitations] Most of the samples are audio knowledge content users, which reflects that the sample representativeness is limited. Also, the conceptual model ignores the moderators, namely, usage scenarios. [Conclusions] The current online knowledge payment products do not fully meet the individual needs of knowledge payment users. The knowledge content and the individual needs are the key factors of enhancing their continuance intention to pay.

Key wordsOnline Knowledge Payment      Individual Needs      Continuance Intention to Pay      Elaboration Likelihood Model      Uses and Gratifications Theory     
Received: 31 March 2020      Published: 05 June 2020
ZTFLH:  G206  
Corresponding Authors: Xie Xingzheng     E-mail: 278347285@qq.com

Cite this article:

Wei Wu, Xie Xingzheng. The Determinants of Continuance Intention to Pay: Empirical Research from Online Knowledge Payment Users. Data Analysis and Knowledge Discovery, 2020, 4(8): 119-129.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2020.0271     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2020/V4/I8/119

Research Model
基本信息 题项 频率 占比/%
性別 113 31.3
248 68.7
总计 361 100.0
年龄 20以下 161 44.6
21~30岁 174 48.2
31~40岁 15 4.2
41~50岁 5 1.4
51岁以上 6 1.6
总计 361 100.0
教育程度 高中或中专 18 5.0
大专 13 3.6
本科 282 78.1
硕士 28 7.8
博士 20 5.5
总计 361 100.0
工作年限 5年以下 318 88.1
5~10年 21 5.8
10~15年 8 2.2
15~20年 6 1.7
20年以上 8 2.2
总计 361 100.0
所在城市 一线城市 39 10.8
二线城市 142 39.3
三线城市 104 28.8
其他 76 21.1
总计 361 100.0
职业 行政/事业单位 18 5.0
工薪族 3 0.8
设计师 3 0.8
白领 8 2.2
教师 25 6.9
自由职业者 5 1.4
研究人员 3 0.8
个体户/小业主 1 0.3
学生 284 78.8
其他 11 3.0
总计 361 100.0
月收入 4000元以下 289 80.0
4000~6000元 22 6.1
6000~8000元 13 3.6
8000~10000元 14 3.9
10000元以上 23 6.4
总计 361 100.0
Sample Profile
维度 题项 内容 因子载荷量 组成信度 AVE
CQ CQ1 知识付费内容的论据是令人信服的 0.886 0.962 0.807
CQ2 知识付费内容的论据有着良好的支撑 0.926
CQ3 知识付费内容的论据是强而有力的 0.941
CQ4 知识付费内容的逻辑非常周密 0.937
CQ5 知识付费内容包含了足够的资料 0.867
CQ6 知识付费内容涉及的信息非常广泛 0.826
SoC SoC1 知识内容的提供者是值得信赖的 0.949 0.949 0.861
SoC2 知识内容的提供者是可靠的 0.980
SoC3 知识内容的提供者是知名的 0.850
IR IR1 知识付费的内容可以让我学习到新的事物 0.937 0.977 0.916
IR2 知识付费的内容可以让我获取到有用的信息 0.969
IR3 知识付费的内容有助于我的个人提升 0.960
IR4 知识付费的内容对我而言是有帮助的 0.961
ER ER1 知识付费的内容使我精神愉悦 0.962 0.964 0.871
ER2 知识付费的内容能够给我带来欢乐 0.962
ER3 知识付费的内容能够打发闲暇时光 0.895
ER4 我很享受知识付费的内容 0.912
SR SR1 我希望参与该知识内容的讨论 0.852 0.900 0.693
SR2 我希望通过知识付费平台结交朋友 0.836
SR3 我希望了解他人对于事物的看法 0.815
SR4 我希望能够在知识付费平台中扮演与现实生活中不同的身份 0.827
CPI CPI1 未来,我愿意付更多的钱去购买知识内容 0.909 0.900 0.752
CPI2 在下次购买知识内容时,我将首先考虑曾经购买过的知识平台 0.776
CPI3 未来,我会增加知识付费平台的使用 0.909
Results of Confirmatory Factor Analysis
CPI SR ER IR SoC CQ
CPI 0.867
SR 0.775 0.832
ER 0.765 0.768 0.933
IR 0.737 0.709 0.858 0.957
SoC 0.654 0.605 0.728 0.800 0.928
CQ 0.661 0.629 0.767 0.841 0.922 0.898
Results of Discriminant Validity Testing
假设 路径 β t p 结果
H1 工具性需求→继续付费意向 0.228 3.993 *** 接受
H2 娱乐性需求→继续付费意向 0.288 5.210 *** 接受
H3 社交性需求→继续付费意向 0.427 7.928 *** 接受
H4 内容质量→工具性需求 0.961 9.309 *** 接受
H5 内容质量→娱乐性需求 0.995 8.373 *** 接受
H6 内容质量→社交性需求 0.845 5.640 *** 接受
H7 来源可信度→工具性需求 -0.098 -1.008 0.313 拒绝
H8 来源可信度→娱乐性需求 -0.203 -1.776 0.076 拒绝
H9 来源可信度→社交性需求 -0.186 -1.285 0.199 拒绝
Results of the Hypotheses Testing
构型 继续付费意向
模式一(S1) 模式二(S2) 模式三(S3)
内容质量
来源可信度
工具性需求
娱乐性需求
社交性需求
一致性 0.940 0.867 0.921
覆盖率 0.613 0.599 0.537
净覆盖率 0.141 0.095 0.034
总体一致性 0.847
总体覆盖率 0.789
Configurations Leading to Users' Continuance Intention to Pay
[1] 张利洁, 张艳彬. 从免费惯性到付费变现——数字环境下知识传播模式的变化研究[J]. 编辑之友, 2017(12):50-53.
[1] ( Zhang Lijie, Zhang Yanbin. From Chronic Free-Issue to Conscious Payment: A Study on the Change of Knowledge Dissemination Pattern in Digital Environment[J]. Editorial Friend, 2017(12):50-53.)
[2] 人民网. 2019喜马拉雅123狂欢节内容消费总额超8.28亿,新增付费用户占比25% [EB/OL]. (2019-12-06). [2020-05-14]. http://sh.people.com.cn/n2/2019/1206/c134768-33612140.html.
[2] ( People.cn. 2019 Himalaya 123 Carnival Content Consumption Exceeds 828 Million,and New Paying Users Account for 25% [EB/OL]. (2019-12-06). [2020-05-14]. http://sh.people.com.cn/n2/2019/1206/c134768-33612140.html
[3] 中投顾问. 2020-2024年中国知识付费行业深度调研及投资前景预测报告[R/OL]. (2020-04-10). [2020-05-14]. http://www.ocn.com.cn/reports/17960zhishifufei.shtml.
[3] ( CIConsulting. 2020-2024 Chinese Knowledge Payment Industry In-depth Investigation and Investment Prospect Forecast Report [R/OL]. (2020-04-10). [2020-05-14]. http://www.ocn.com.cn/reports/17960zhishifufei.shtml
[4] 比达咨询. 2019 Q1知识付费报告[R/OL]. (2019-05-21). [2020-05-14]. http://www.bigdata-research.cn/content/201905/959.html.
[4] ( BigData-Research. 2019 Q1 Knowledge Payment Report [R/OL]. (2019-05-21). [2020-05-14]. http://www.bigdata-research.cn/content/201905/959.html
[5] 刘述平, 潘力, 陈常松. 知识付费时代的博弈——从传统广播运行实践所想到的[J]. 中国广播电视学刊, 2018(5):65-69.
[5] ( Liu Shuping, Pan Li, Chen Changsong. Game in the Era of Knowledge Payment: Thinking from the Practice of Traditional Broadcasting Operation[J]. China Radio & TV Academic Journal, 2018(5):65-69.)
[6] 张帅, 王文韬, 李晶. 用户在线知识付费行为影响因素研究[J]. 图书情报工作, 2017,61(10):94-100.
[6] ( Zhang Shuai, Wang Wentao, Li Jing. Research on the Influencing Factors of User’s Online Knowledge Payment Behavior[J]. Library and Information Service, 2017,61(10):94-100.)
[7] 赵杨, 袁析妮, 李露琪, 等. 基于社会资本理论的问答平台用户知识付费行为影响因素研究[J]. 图书情报知识, 2018(4):15-23.
[7] ( Zhao Yang, Yuan Xi’ni, Li Luqi, et al. The Impact Factors of Users’ Paying Behavior for Knowledge on Social Q&A Platform Based on Social Capital Theory[J]. Documentation, Information & Knowledge, 2018(4):15-23.)
[8] 赵宇翔, 刘周颖, 宋士杰. 新一代知识问答平台中提问者付费意愿的影响因素探究[J]. 数据分析与知识发现, 2018,2(8):16-30.
[8] ( Zhao Yuxiang, Liu Zhouying, Song Shijie. Exploring the Influential Factors of Askers’ Intention to Pay in Knowledge Q&A Platforms[J]. Data Analysis and Knowledge Discovery, 2018,2(8):16-30.)
[9] Shi X, Zheng X, Yang F. Exploring Payment Behavior for Live Courses in Social Q&A Communities: An Information Foraging Perspective[J]. Information Processing and Management, 2020,57(4):102241.
doi: 10.1016/j.ipm.2020.102241
[10] Zhao Y, Zhao Y, Yuan X N, et al. How Knowledge Contributor Characteristics and Reputation Affect User Payment Decision in Paid Q&A? An Empirical Analysis from the Perspective of Trust Theory[J]. Electronic Commerce Research and Applications, 2018,31:1-11.
doi: 10.1016/j.elerap.2018.07.001
[11] Zhang J, Zhang J L, Zhang M Y. From Free to Paid: Customer Expertise and Customer Satisfaction on Knowledge Payment Platforms[J]. Decision Support Systems, 2019,127:113140.
doi: 10.1016/j.dss.2019.113140
[12] 李武, 艾鹏亚, 许耀心. 在线付费问答平台的用户付费模式及付费意愿研究[J]. 图书情报工作, 2018,62(13):24-29.
[12] ( Li Wu, Ai Pengya, Xu Yaoxin. Research on User Payment Pattern and Purchase Intention Towards Online Q&A Paid Platform[J]. Library and Information Service, 2018,62(13):24-29.)
[13] 陈谦. 精心的可能性模式与广告传播策略[J]. 当代传播, 2013(1):93-95, 98.
[13] ( Chen Qian. Elaboration Likelihood Model and Advertising Communication Strategy[J]. Contemporary Communication, 2013(1):93-95, 98.)
[14] Render B, O’Connor T S. The Influence of Price, Store Name, and Brand Name on Perception of Product Quality[J]. Journal of the Academy of Marketing Science, 1976,4(3):722-730.
doi: 10.1007/BF02729832
[15] 李东进, 安钟石, 周荣海, 等. 基于Fishbein合理行为模型的国家形象对中国消费者购买意向影响研究——以美、德、日、韩四国国家形象为例[J]. 南开管理评论, 2008,11(5):40-49.
[15] ( Li Dongjin, An Zhongshi, Zhou Ronghai, et al. A Study on Impact of Country Image on Purchase Intention of Customer Based on Fishbein’s Model of Reasoned Action: A Case of County Images of American, Germany, Japan and Korea[J]. Nankai Business Review, 2008,11(5):40-49.)
[16] Kimble G A, Perlmuter L C. The Problem of Volition[J]. Psychological Review, 1970,77(5):361-384.
doi: 10.1037/h0029782 pmid: 4319166
[17] Ajzen I, Fishbein M. Factors Influencing Intentions and the Intention-Behavior Relation[J]. Human Relations, 1974,27(1):1-15.
doi: 10.1177/001872677402700101
[18] Katz E, Blumler J G, Gurevitch M. The Uses of Mass Communications: Current Perspectives on Gratifications Research[M]. Thousand Oaks, CA: Sage Publications, 1974: 19-32.
[19] Katz E, Haas H, Gurevitch M. On the Use of the Mass Media for Important Things[J]. American Sociological Review, 1973,38(2):164-181.
doi: 10.2307/2094393
[20] McQuail D, Blumler J G, Brown J R. The Television Audience: A Revised Perspective[A]//McQuail D. Sociology of Mass Communications[M]. Harmondsworth: Penguin, 1972: 135-165.
[21] Greenberg B S. Gratifications of Television Viewing and Their Correlates for British Children[A]// Blumler J G, Katz E. The Uses of Mass Communications: Current Perspectives on Gratifications Research[M]. Beverly Hills: Sage, 1974: 19-32.
[22] Levy M R. Television News Uses: A Cross-national Comparison[J]. Journalism Quarterly, 1978,55(2):334-337.
doi: 10.1177/107769907805500217
[23] Palmgreen P, Rayburn J D. Uses and Gratifications and Exposure to Public Television: A Discrepancy Approach[J]. Communication Research, 1979,6(2):155-179.
doi: 10.1177/009365027900600203
[24] Blumler J G. The Role of Theory in Uses and Gratifications Studies[J]. Communication Research, 1979,6(1):9-36.
doi: 10.1177/009365027900600102
[25] December J. Units of Analysis for Internet Communication[J]. Journal of Communication, 1996,46(1):14-38.
doi: 10.1111/j.1460-2466.1996.tb01459.x
[26] Kaye B K. Uses and Gratifications of the World Wide Web: From Couch Potato to Web Potato[J]. Atlantic Journal of Communication, 1998,6(1):21-40.
[27] Papacharissi Z, Rubin A M. Predictors of Internet Use[J]. Journal of Broadcasting & Electronic Media, 2000,44(2):175-196.
[28] 王平. 数字化传播时代电子阅读用户需求分析[J]. 科技与出版, 2014,33(7):80-82.
[28] ( Wang Ping. Demand Analysis of Electronic Reading Users in the Era of Digital Communication [J]. Science-Technology & Publication, 2014,33(7):80-82.)
[29] 卢恒, 张向先, 张莉曼. 语音问答社区用户知识付费意愿影响因素研究——基于现状偏差的视角[J]. 情报科学, 2019,37(6):119-125, 162.
[29] ( Lu Heng, Zhang Xiangxian, Zhang Liman. Influence Factors of Users’ Willingness to Pay for Knowledge in Voice Question and Answer Community: In the Perspective of Status Quo Bias[J]. Information Science, 2019,37(6):119-125, 162.)
[30] Perse E M, Rubin A M. Chronic Loneliness and Television Use[J]. Journal of Broadcasting & Electronic Media, 1990,34(1):37-53.
[31] 郭晓晨, 牛盼强. 知识付费新解:知识获取资讯化[J]. 青年记者, 2018(36):79-80.
[31] ( Guo Xiaochen, Niu Panqiang. New Solution of Knowledge Payment: Informationalized Knowledge Acquisition[J]. Youth Journalist, 2018(36):79-80.)
[32] 鲍静, 裘杰. 内容、平台、社交、服务[J]. 出版科学, 2019,27(2):65-70.
[32] ( Bao Jing, Qiu Jie, Content, Platform, Social Communication and Service, Four Directions for the Sustainable Development of Online Knowledge Payment[J]. Publishing Journal, 2019,27(2):65-70.)
[33] Petty R E, Cacioppo J T. Attitudes and Persuasion: Classic and Contemporary Approach[M]. Dubuque, IA: Westview Press, 1981: 130-139.
[34] Li C Y. Persuasive Messages on Information System Acceptance: A Theoretical Extension of Elaboration Likelihood Model and Social Influence Theory[J]. Computers in Human Behavior, 2013,29(1):264-275.
doi: 10.1016/j.chb.2012.09.003
[35] Cacioppo J T, Petty R E. The Elaboration Likelihood Model of Persuasion[J]. Advances in Consumer Research, 1984,19(4):123-205.
[36] 王军, 丁丹丹. 商品在线评论动态变化评价指标体系的研究[J]. 图书情报工作, 2015,59(12):106-112.
[36] ( Wang Jun, Ding Dandan. Research on Dynamic Evaluation Index System of Goods Online Reviews[J]. Library and Information Service, 2015,59(12):106-112.)
[37] Bhattacherjee A, Sanford C C. Influence Processes for Information Technology Acceptance: An Elaboration Likelihood Model[J]. MIS Quarterly, 2006,30(4):805-825.
doi: 10.2307/25148755
[38] 张梦雅, 王秀红. 精细加工可能性模型研究现状及应用领域分析[J]. 图书情报研究, 2018,11(4):73-79, 85.
[38] ( Zhang Mengya, Wang Xiuhong. The Status of the Research on the Elaboration Likelihood Model (ELM) and Its Application Fields[J]. Library and Information Studies, 2018,11(4):73-79, 85.)
[39] Shi W W. An Empirical Research on Users’ Acceptance of Smart Phone Online Application Software[C]// Proceedings of the 2009 International Conference on Electronic Commerce and Business Intelligence. 2009: 106-110.
[40] Petty R E, Cacioppo J T, Goldman R. Personal Involvement as a Determinant of Argument Based Persuasion[J]. Journal of Personality and Social Psychology, 1981,41(5):847-855.
doi: 10.1037/0022-3514.41.5.847
[41] Cheung M, Luo C, Sia C. Credibility of Electronic Word-of-Mouth: Informational and Normative Determinants of On-Line Consumer Recommendations[J]. International Journal of Electronic Commerce, 2009,13(4):9-38.
[42] Lederman R, Fan H, Smith S, et al. Who Can You Trust? Credibility Assessment in Online Health Forums[J]. Health Policy and Technology, 2014,3(1):13-25.
doi: 10.1016/j.hlpt.2013.11.003
[43] Davis F. Perceived Usefulness, Perceived Ease of Use and User Acceptance of Information Technology[J]. MIS Quarterly, 1989(3):319-340.
[44] 王嵩音. 网路使用之态度,动机与影响[J]. 信息社会研究, 2007(12):57-85.
[44] ( Wang Songyin. Attitudes, Motivations and Consequences of Internet Use[J]. Journal of Information Society, 2007(12):57-85.)
[45] Bhattacherjee A. Understanding Information Systems Continuance: An Expectation Confirmation Model[J]. MIS Quarterly, 2001(3):351-370.
[46] Kim B. Understanding Antecedents of Continuance Intention in Social-Networking Services[J]. Cyber Psychology, Behavior and Social Networking, 2011(4):199-205.
[47] Fornell C, Larcker D F. Evaluating Structural Equation Models with Unobservable Variables and Measurement Error[J]. Journal of Marketing Research, 1981,18(1):39-50.
doi: 10.1177/002224378101800104
[48] Ordanini A, Parasuraman A, Rubera G. When the Recipe is More Important Than the Ingredients: A Qualitative Comparative Analysis (QCA) of Service Innovation Configurations[J]. Journal of Service Research, 2014,17(2):134-149.
doi: 10.1177/1094670513513337
[49] Ragin C C. Set Relations in Social Research: Evaluating Their Consistency and Coverage[J]. Political Analysis, 2006,14(3):291-310.
doi: 10.1093/pan/mpj019
[50] 李念武, 岳蓉. 网络口碑可信度及其对购买行为之影响的实证研究[J]. 图书情报工作, 2009,53(22):133-137.
[50] ( Li Nianwu, Yue Rong. An Empirical Study on Credibility of Online Word of Mouth and Its Effects on Consumers Purchase Behavior[J]. Library and Information Service, 2009,53(22):133-137.)
[51] Smith D, Menon S, Sivakumar K. Online Peer and Editorial Recommendations, Trust, and Choice in Virtual Markets[J]. Journal of Interactive Marketing, 2005,19(3):15-37.
doi: 10.1002/dir.20041
[52] 杜智涛, 徐敬宏. 从需求到体验:用户在线知识付费行为的影响因素[J]. 新闻与传播研究, 2018,25(10):18-39, 126.
[52] ( Du Zhitao, Xu Jinghong. From Demand to Experience: Impact Factors of Users’ Online Knowledge Payment Behavior[J]. Journalism & Communication, 2018,25(10):18-39, 126.)
[53] 黄勇, 蔡政. 知识付费模式的困境与重构[J]. 新闻战线, 2018(12):63-64.
[53] ( Huang Yong, Cai Zheng. The Dilemma and Reconstruction of the Knowledge Payment Model[J]. The Press, 2018(12):63-64.)
[54] 杜智涛. 网络知识社区中用户“知识化”行为影响因素——基于知识贡献与知识获取两个视角[J]. 图书情报知识, 2017(2):105-119.
[54] ( Du Zhitao. Discussion the Influence Factors on User’s ‘Knowledge-based’ Behavior in Network Knowledge Community: Based on the Perspectives of Knowledge Contribution and Acquisition[J]. Documentation, Information & Knowledge, 2017(2):105-119.)
[55] 王铮, 刘彦芝. 在线知识付费平台的市场机制探究——兼论对知识平台市场机制缺陷的应对与干预[J]. 图书情报知识, 2018(4):24-31.
[55] ( ( Wang Zheng, Liu Yanzhi. Market Mechanism of Online Knowledge Paying Platform: Counter Measure and Intervention to the Failure of Knowledge Platform Market Mechanism[J]. Documentation, Information & Knowledge, 2018(4):24-31.)
[56] 周涛, 檀齐, Bayan T, 等. 社会交互对用户知识付费意愿的作用机理研究[J]. 图书情报工作, 2019,63(4):94-100.
[56] ( Zhou Tao, Tan Qi, Bayan T, et al. The Effect of Social Interaction on Users’ Intention of Knowledge Payment[J]. Library and Information Service, 2019,63(4):94-100.)
[57] 方爱华, 陆朦朦, 刘坤锋. 虚拟社区用户知识付费意愿实证研究[J]. 图书情报工作, 2018,62(6):105-115.
[57] ( Fang Aihua, Lu Mengmeng, Liu Kunfeng. Empirical Study on Users’ Knowledge Purchase Intention for Virtual Community[J]. Library and Information Service, 2018,62(6):105-115.)
[58] 彭兰. 假象、算法囚徒与权利让渡:数据与算法时代的新风险[J]. 西北师大学报(社会科学版), 2018,55(5):20-29.
[58] ( Peng Lan. Illusion, Prisoner of Algorithm, and Transfer of Rights: The New Risks in the Age of Data and Algorithm[J]. Journal of Northwest Normal University (Social Sciences), 2018,55(5):20-29.)
[59] 苏涛, 彭兰. 反思与展望:赛博格时代的传播图景——2018年新媒体研究综述[J]. 国际新闻界, 2019,41(1):41-57.
[59] ( Su Tao, Peng Lan. Reflection and Prospect: The Communication Picture of the Cyber Age: A Review of New Media Studies in 2018[J]. Chinese Journal of Journalism & Communication, 2019,41(1):41-57.)
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[3] Ye Jiaxin,Xiong Huixiang,Tong Zhaoli,Meng Qiuqing. Collaborative Tagging for Doctors in Online Medical Community[J]. 数据分析与知识发现, 2020, 4(6): 118-128.
[4] Fen Chen,Xiaohuan Gao,Yue Peng,Yuan He,Chunxiang Xue. Identifying Weibo Opinion Leaders with Text Sentiment Analysis[J]. 数据分析与知识发现, 2019, 3(11): 120-128.
[5] Chen Fen,Fu Xi,He Yuan,Xue Chunxiang. Identifying Weibo Opinion Leaders with Social Network Analysis and Influence Diffusion Model[J]. 数据分析与知识发现, 2018, 2(12): 60-67.
[6] Zong Hong,Xue Chunxiang,Chen Fen. Growth Pattern of Online News Comments[J]. 数据分析与知识发现, 2018, 2(9): 50-58.
[7] He Yue,Feng Yue,Zhao Shupeng,Ma Yufeng. Recommending Contents Based on Zhihu Q&A Community: Case Study of Logistics Topics[J]. 数据分析与知识发现, 2018, 2(9): 42-49.
[8] Wang Feifei,Zhang Shengtai. Analyzing Information Behaviors of Mobile Social Network Users[J]. 数据分析与知识发现, 2018, 2(4): 99-109.
[9] Zhu Hou. Co-evolution of Social Networks and Public Opinion Considering the Effect of Trust and Authority[J]. 现代图书情报技术, 2015, 31(10): 50-57.
[10] Qiang Shaohua, Wu Peng. The Research of Crowd Simulation in the Evolution Process of Web Public Opinion of Unexpected Event[J]. 现代图书情报技术, 2014, 30(6): 71-78.
[11] He Yumei, Qi Jiayin, Liu Huili. The Study of Local-world Network Evolution Model Based on Microblog[J]. 现代图书情报技术, 2014, 30(5): 66-73.
[12] Xi Yunjiang, Du Diedie, Liao Xiao, Zhang Xuehong. Research and Feature Analysis of Enterprise Microblog User Clustering Based on Supernetwork [J]. 数据分析与知识发现, 0, (): 1-.
[13] Shao Qi, Mu Dongme, Wang Ping, Jin Chunyan. Semantic-based Subject Discovery of Public Health Emergencies Network Public Opinion [J]. 数据分析与知识发现, 0, (): 1-.
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