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数据分析与知识发现  2020, Vol. 4 Issue (8): 119-129     https://doi.org/10.11925/infotech.2096-3467.2020.0271
     研究论文 本期目录 | 过刊浏览 | 高级检索 |
线上知识付费用户继续付费意向影响因素研究*
魏武1,谢兴政2()
1台湾世新大学新闻传播学院 台北 116
2复旦大学新闻学院 上海 200433
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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摘要 

【目的】探究线上知识付费产品特征、用户需求和继续付费意向之间的影响关系,为行业发展提供理论依据和意见参考。【方法】基于精细加工可能性模型和使用与满足理论,本研究构建了用户继续付费意向影响因素模型。运用结构方程模型和模糊集定性比较分析对所收集的样本进行分析。【结果】根据结构方程模型,产品内容质量对付费用户需求的满足具有显著的正向影响;付费用户的社交性需求显著地正向影响其继续付费意向。模糊集定性比较分析则发现了三种能够触发继续付费意向的前因构型。【局限】研究样本类型有限,主要为音频型知识内容用户,且未纳入使用场景等因素的调节作用。【结论】当前的线上知识付费产品并未完全契合知识付费用户的需求,对知识内容的把控和用户社交性需求的重视,是提升其继续付费意向的关键所在。

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魏武
谢兴政
关键词 线上知识付费用户需求继续付费意向精细加工可能性模型使用与满足理论    
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
收稿日期: 2020-03-31      出版日期: 2020-06-05
ZTFLH:  G206  
通讯作者: 谢兴政     E-mail: 278347285@qq.com
引用本文:   
魏武, 谢兴政. 线上知识付费用户继续付费意向影响因素研究*[J]. 数据分析与知识发现, 2020, 4(8): 119-129.
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.
链接本文:  
http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2020.0271      或      http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/Y2020/V4/I8/119
Fig.1  研究模型
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
Table 1  样本信息
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
Table 2  验证性因子分析结果
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
Table 3  维度间区别效度分析结果
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
Table 4  结构方程模型路径分析结果
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
Table 5  继续付费意向前因条件构型
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