隐私视角下社交媒体推荐对用户在线交互意向的影响机理研究  

The Influence Mechanism of Social Media Recommendation on Users'Online Interaction Intention from The Perspective of Privacy

作　　者：张雄涛 甘明鑫[1] Zhang Xiongtao;Gan Mingxin(School of Economics and Management,University of Science and Technology Beijing,Beijing 100083,China)

机构地区：[1]北京科技大学经济管理学院,北京100083

出　　处：《现代情报》2021年第5期33-43,103,共12页Journal of Modern Information

基　　金：国家自然科学基金“移动场景中用户出行意图预测与地点推荐的智能方法研究”(项目编号:71871019);国家自然科学基金“大数据环境下融合多源信息的推荐系统关键问题研究”(项目编号:71471016);国家自然科学基金“多终端网络环境下的用户服务理论与方法研究”(项目编号:71729001)。

摘　　要：[目的/意义]在隐私视角下探究社交媒体推荐对用户在线交互意向的影响机理,有助于指导社交媒体平台提供合理的个性化推荐服务、保证用户在线交互的活跃性。[方法/过程]结合S-O-R理论模型、隐私计算理论和信息边界理论,在隐私视角下构建社交媒体推荐对用户在线交互意向的影响模型。以短视频社交平台为调查情景,通过网络问卷收集数据,运用结构方程模型进行实证检验。[结果/结论]社交媒体推荐对感知收益、感知隐私风险和平台信任具有显著的正向影响;感知收益对感知信息控制和平台信任有显著的正向影响;感知隐私风险对感知信息控制和平台信任有显著的负向影响;感知信息控制和平台信任对用户在线交互意向具有显著的正向影响。[Purpose/Significance]Exploring the influence mechanism of social media recommendation on users'online interaction intentions from the perspective of privacy is helpful to guide social media platforms in providing reasonable personalized recommendation services and ensuring the activity of users'online interaction.[Method/Process]Combined with S-O-R model,privacy computing theory and information boundary theory,the influence model of social media recommendation on users'online interaction intention was constructed from the perspective of privacy.The short video social platform was taken as the investigation scene,and the data were collected through network questionnaire,and the structural equation model was used for empirical test.[Result/Conclusion]The empirical results showed that social media recommendation had a significant positive effect on users'perceived benefit,perceived privacy risk and platform trust.Perceived benefit had a significant positive effect on perceived information control and platform trust.Perceived privacy risk had a significant negative effect on perceived information control and platform trust.Perceived information control and platform trust had significant positive effects on users'online interaction intention.

关 键 词：社交媒体推荐 在线交互意向 S-O-R模型 隐私计算理论 信息边界理论 

分 类 号：G252.0[文化科学—图书馆学]