| With the rapid development of online audio market and the rapid expansion of domestic audio users,it is urgent for audio platforms to understand the important factors affecting users’ payment decisions in order to improve their profitability.At present,the existing research mainly focuses on the evaluation of users’ willingness to pay after experiencing the product.In order to better understand the users’ paying habits in the process of selecting the product,this study will refer to the actual users’ browsing logic,and discuss the key factors affecting users’ paying behavior from the perspective of work evaluation,producer information and audition audio.Taking Himalaya FM,an online audio platform,as the research object,audio works are distinguished according to popular and ordinary sections,and python crawler is used to obtain the real operation data of the platform.In the factor quantification,in order to study the influence of comment emotion on fee coping behavior,the deep learning Bert model is used to analyze the comment emotion,extract the negative emotion comment quantity index,and construct the short panel fixed effect model together with other indexes.The results show that:(1)from the perspective of work evaluation: the sales volume of ordinary audio works is more likely to be affected by work evaluation and users’ word-of-mouth than that of popular audio works,and negative emotional reviews will have a significant negative impact on users’ payment behavior(2)Producer information perspective: compared with the hot plate audio,the high-level and high-quality producers of ordinary plate audio can get more users to pay(3)From the perspective of audition audio,audition audio of popular plate works will promote users’ payment,on the contrary,audition audio will inhibit the payment behavior of ordinary plate audio users.At the same time,listening to audio also weakens the impact of the number of comments and negative emotional comments on sales.This study makes a supplement to the existing research,which can help the online audio platform more accurately understand and adjust the key factors affecting the audio sales,provide targeted suggestions to the audio platform,effectively help the platform build a community atmosphere,improve user experience,enhance user stickiness,and promote consumption. |
