LAUSR

Privacy concerns can effectively predict behavioral intention between users and short-form video platforms, but existing studies lack of multidimensional scales to measure privacy concerns towards short-form video platforms. To this end, this study took privacy concerns theory as the theoretical foundation to develop and validate a multidimensional privacy concerns scale in short-form video platforms by referring to the development of Smith, Milberg and Burke' multidimensional scale of concerns for information privacy (CFIP), Sheehan and Hoy's multidimensional scale of privacy concerns, Malhotra, Kim and Agarwal's Internet users' information privacy concerns (IUIPC) scale, and Hong and Thong's Internet privacy concerns (IPC) multidimensional scale. In this research, three representative short-form video platforms, TikTok, Kuaishou and Xigua, were selected as research samples. The multidimensional privacy concerns scale was refined by qualitative interviews and open-ended questionnaires et al. and tested by item analysis, exploratory factor analysis, confirmatory factor analysis, and discriminant validity et al. The results show that the privacy concerns scale towards short-form video platforms consists of three dimensions: collection concerns, awareness concerns, and usage concerns. And the multidimensional scale developed in this study has good reliability, convergent validity, and content validity, which can help guide short-form video platforms to take targeted measures to manage privacy concerns in business practices and provide a basis for future empirical studies on privacy concerns.