The application of algorithm recommender systems introduces several potential risks, including privacy infringement, discriminatory outcomes, and opacity. Governments worldwide have introduced regulatory policy instruments with varying degrees of coerciveness. However, few studies have examined the impact of regulatory policy instruments on public acceptance. This study investigates the nuanced ways in which regulatory policy instruments spanning a spectrum of coerciveness shape public perceptions of privacy risks and acceptance of algorithm recommender systems. Through a survey experiment involving a sample size of 2015 participants, we created three distinct categories of regulatory policy instruments to serve as experimental treatments and one control group with no intervention. Our empirical findings illustrate the substantial treatment effects stemming from regulatory policy instruments. Furthermore, these regulatory policy instruments, infused with varying degrees of coerciveness, assume a dynamic moderating role, initially mitigating and subsequently intensifying the adverse influence of perceived privacy risks on public acceptance. By synthesizing two streams of literature on technology acceptance and regulatory policy, our research underscores the fact that regulatory policy instruments have the potential to reshape the public's perceived risk associated with technology use, exerting a considerable influence on public technology acceptance. These findings have implications for governments seeking effective, fine-grained algorithmic governance based on individual behavior. Policymakers should consider public risk perceptions and technology acceptance when designing regulatory policies for enterprises.

Guo Y, Li SR, Zhou L, Sun Y (corresponding author). Exploring the influence of technology regulatory policy instruments on public acceptance of algorithm recommender systems[J]. Government Information Quarterly, 2024, 41(3): 101940.