Perceived Personalized Recommendation and Content Creation Strategies in Content Creation Platforms
Yuhan Zuo, Yang Yu, Xiji Zhu, Junjie Wei · Academy of Management Proceedings · 2024
This study explores the evolving landscape of content creation platforms such as YouTube, Instagram, and Flickr, and the new mode of creative production on these platforms. We examine how content creators interpret and react to platform recommendation algorithms and how this affects their creative performance. In order to answer this research question, we first developed and validate measurement tool for perceived personalized recommendation. We then combined survey data and platform unobtrusive data from video bloggers (N = 195) on a Chinese content creation platform to test our hypotheses. Based on signaling theory, we propose and empirically demonstrate that content creators’ perceived personalized recommendation promotes their content novelty and further boost their creative performance; moreover, their platform embeddedness strengthens these effects. Our research diverges from existing user-centric studies on the influences of platform algorithms, providing preliminary empirical evidence on content creators' responses to platform recommendation algorithms and their impact on creative performance. It also contributes to understanding the social consequences of algorithms in cultural production and extends creativity studies into the arena of human-algorithmic interaction.