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范文独享 售后即删 个人专属 避免雷同

直播电商的商业模式创新与用户购买行为研究

摘    要
随着互联网技术的快速发展和消费者购物习惯的转变,直播电商作为一种新兴商业模式迅速崛起,成为推动数字经济创新的重要力量本研究以直播电商为切入点,旨在探讨其商业模式创新特征及其对用户购买行为的影响基于社会临场感理论和技术接受模型,构建了包含感知有用性、感知娱乐性、主播信任和社会互动等关键变量的研究框架,并通过问卷调查法收集数据,运用结构方程模型进行实证分析结果表明,直播电商的商业模式创新主要体现在即时互动性、内容娱乐化和社交属性强化等方面,这些特性显著影响用户的感知价值和购买意愿具体而言,主播信任与社会互动是驱动用户购买行为的核心因素,而感知娱乐性和感知有用性则分别从情感和功能层面促进用户决策。

关键词:直播电商;社会临场感;用户购买行为


ABSTRACT
With the rapid development of Internet technology and changes in consumer shopping habits, live-stream e-commerce has emerged as a new business model, becoming an important force driving innovation in the digital economy. This study focuses on live-stream e-commerce to explore its business model innovation characteristics and their impact on user purchasing behavior. Based on the theory of social presence and the technology acceptance model, a research fr amework was constructed, including key variables such as perceived usefulness, perceived entertainment, broadcaster trust, and social interaction. Data were collected through questionnaire surveys, and structural equation modeling was used for empirical analysis. The results show that the business model innovation of live-stream e-commerce is mainly reflected in real-time interactivity, content entertainment, and enhanced social attributes. These features significantly influence users' perceived value and purchase intentions. Specifically, broadcaster trust and social interaction are core factors driving user purchasing behavior, while perceived entertainment and perceived usefulness promote user decisions from emotional and functional perspectives, respectively.

KEY WORDS: Live Ecommerce;Social Presence;User Purchase Behavior 


目    录

摘    要 I
ABSTRACT II
1  绪论 1
1.1  直播电商研究的背景与意义 1
1.2  国内外研究现状分析 1
1.3  研究方法与技术路线 2
2  直播电商商业模式创新分析 3
2.1  商业模式创新的核心要素 3
2.2  直播电商的盈利模式探索 3
2.3  创新驱动因素与路径分析 4
2.4  案例分析:典型平台的商业模式 5
3  用户购买行为影响因素研究 5
3.1  用户购买行为的理论框架 5
3.2  社交互动对购买决策的影响 6
3.3  信任机制在直播电商中的作用 6
3.4  价格感知与促销策略的效果评估 7
4  商业模式创新与用户行为的关系探讨 8
4.1  商业模式创新对用户行为的影响机制 8
4.2  内容营销与用户粘性的关系分析 8
4.3  数据驱动的个性化推荐效果研究 9
4.4  用户反馈对商业模式优化的作用 9
结论 11
致    谢 12
参考文献 13
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