知识付费用户的转移意愿及其影响因素 ——基于推拉锚定模型

Behavioral Transfer Intention and Influencing Factors of Knowledge Payers: Based on Push-Pull- Mooring Model

摘要:

文章旨在加强了解用户知识付费行为，特别是用户为何从免费知识检索行为到知识付费行为的转移意愿。选择推拉锚定模型为研究框架，引入感知信息质量、信任、惯性（包括情感、行为和认知惯性）、信息-任务匹配、信息过载、转移意愿和付费行为7个研究变量，利用结构方程模型研究影响用户从知识免费检索向付费行为转移意愿的因素和各因素之间的关系，以及转移意愿与实际付费行为之间的关系。研究结果显示，对于用户的知识付费行为转移意愿，拉力因素（感知信息质量、信任）和推力因素（信息-任务匹配、信息过载）具有显著正向影响，而惯性具有显著负向影响。基于研究结论，为知识提供者和付费平台提高知识服务水平和改善发展策略提出相关建议如下：重视知识内容输出并确保有不断产出新内容，增强用户的感知信息质量；充分考虑惯性因素，努力打造特色知识服务和差异化的精准服务；尽可能提供高信息-任务匹配使用体验并过滤无效信息。

Abstract:

This paper is trying to strengthen the understanding of users' knowledge payment behavior, especially why the users’ switching intention that from free knowledge searching to knowledge payment behavior. With push–pull–mooring framework as the research framework, seven research variables including perceived information quality, trust, inertia (including affective based inertia, behavioral based inertia and cognitive based inertia), information fit-to-task, information overload, switching intention and knowledge payment behavior are introduced. The structural equation model is used to study the factors influencing users' switching intention and the relationship between these factors, as well as the relationship between switching intention and actual payment behavior. The results show that the pulling factor (perceived information quality, trust) and the pushing factor (information-task matching, information overload) have significant positive influence on their switching intention. Inertia has a significant negative effect on the switching intention. According to these research results, this paper could give some advices to knowledge suppliers and knowledge payment platforms about how to improve the knowledge service level and development strategy as following: highlight the content of knowledge output, ensure the output of new content is continuous, and enhance the quality of users’ perceived information quality; fully consider the inertia, strive to make distinctive knowledge services and differentiated precise services; provide high level information-task fit using experiences, and filter invalid information.