%0 Journal Article
%T 在线知识付费意愿影响因素的实证研究——基于“得到APP”的文本挖掘和定量分析<br>An Empirical Study on the Influencing Factors of Willingness to Pay for Online Knowledge—Text Mining and Quantitative Analysis Based on “Iget APP”
%A 李涵今
%A 徐艾
%A 成绘茜
%J Advances in Applied Mathematics
%P 5301-5310
%@ 2324-8009
%D 2022
%I Hans Publishing
%R 10.12677/AAM.2022.118557
%X 随着在线知识付费行业的迅猛发展，知识付费产业即将进入存量市场。本文对影响消费者知识付费意愿的因素进行实证研究，旨在提高知识付费平台的竞争力，实现可持续发展。本文以“得到APP”各分区课程为例，借助文本挖掘提取课程关键词，从四个维度选取指标，建立购买意愿影响因子的评价体系，利用带惩罚项的泊松回归，得到各变量对用户付费意愿的影响程度，并相应地提出科学可行的建议。课程的试听人数、发布时间和作者粉丝数对购买意愿的影响最显著，建议平台在制定产品营销策略时加大对这几个因素的考量。本文为平台筛选出对购买意愿有较大正向影响的各分区关键词，建议平台围绕这些关键词打造更多课程。本文采用文本挖掘和定量分析研究在线知识付费意愿影响因素。<br />
With the rapid development of online knowledge payment products, this industry is about to enter the stock market. This paper conducts an empirical study about the factors’ influence on purchas-ers’ willingness to pay for online knowledge, in order to improve platforms’ competitiveness and ul-timately achieve their sustainable development. This paper takes courses from each section of “Iget APP” as samples. Then we extract keywords by text mining, select indicators from four dimensions and establish an evaluation system for factors which influence purchase intention. At last, we use Poisson regression with penalty function to explore the influence of each variable and then corre-spondingly propose scientific suggestions. The number of auditioners, releasing time and the scale of author’s fans have the most obvious impact on willingness to pay, hence we suggest platforms fo-cusing on these factors particularly when developing marketing strategies. This paper furthermore selects keywords for different sections of the platform, which have a greater positive influence on purchase intentions. We suggest platforms releasing more related courses concerning these key-words. This paper uses text mining and quantitative analysis to study the factors which influence the purchasers’ willingness to pay for online knowledge.
%K 在线知识付费意愿，“得到APP”，文本挖掘，惩罚函数，泊松回归<br>Purchase Intention of Online Paid-Knowledge Products
%K “Iget APP”
%K Text Mining
%K Penalty Function
%K Poisson Regression
%U http://www.hanspub.org/journal/PaperInformation.aspx?PaperID=54487