基于社会化媒体全新变现模式的内容消费对内容创造者的内容选择策略行为的影响研究——双边市场动态决策建模

With the booming development of the Internet content industry, tipping and advertising revenue sharing are regarded as new monetization business models, which closely links the two major economies of social media platforms: content contributors and content consumers. The interaction between content consumers and content contributors plays a key role in promoting the healthy development of the entire ecosystem and the realization of the value chain. The existing research on user-generated content (UGC) in the field of marketing and information systems mainly studies the content generation behavior of Internet users from the three aspects: psychological cognition, social relationship and system perception. Few studies focus on the commercialization of user-generated content (content monetization). The research group will start from examining the impact of different monetization mechanisms and constructing the expectation model of content consumers' tipping behavior and product click-on behavior, and then apply the dynamic learning theory to analyze the content contributor's content supply decision on whether to generate advertising content or non-commercial content. The results provide decision support for content contributors’ advertising scheduling planning, and also make suggestion of traffic distribution strategies to social media platform and e-commerce platform.

随着互联网内容产业的蓬勃发展，内容打赏和广告收入分成作为全新的价值变现商业模式, 将社交媒体平台两大经济体——内容创造者和内容消费者紧密联系在一起，两者的互动行为对于整个生态系统的健康发展和价值链的实现具有关键性推动作用。现有市场营销和信息系统领域文献中关于用户生成内容（user-generated content或UGC）的研究主要从心理认知、社会关系、系统感知三个层面的动机要素对互联网用户的内容生成行为进行理论剖析，很少有文献对用户生成内容的商业化现象（价值变现）进行研究。本课题组将从不同变现模式的影响机制研究出发，构建内容消费者打赏行为和产品点击行为的期望模型，然后利用动态学习理论框架分析内容创造者的内容选择行为决策过程，并依据实证模型结果对微观个体层面的广告调度规划以及平台总体层面的流量分配策略提供决策支持和建议。