Cutting Edge or Crystal Clear? How Does the Supervisibility of Algorithm Throughput Influence the Coping Behaviors of Targeted Advertising Audiences?

The increasing complexity of artificial intelligence systems and algorithms can negatively affect individual attitudes and prompt them to adopt coping behaviors. Based on the technology threat avoidance theory, this study explores the mechanisms through which the supervisibility of recommendation algorithm throughput influences the coping behaviors of targeted advertising audiences using scenario-based online experiments conducted between August 2022 and January 2023. In particular, we examine the mediating effect of perceived threat and the moderating effects of privacy concerns and self-efficacy. The results reveal that perceived threat arising from the characteristics of recommendation algorithm throughput determines the possible coping behaviors of targeted advertising audiences. Moreover, perceived threat mediates the impact of the supervisibility of algorithm throughput on audiences’ coping behaviors. Furthermore, users’ privacy concerns and self-efficacy positively moderate the influence of the supervisibility of algorithm throughput on perceived threat and the relationship between perceived threat and audiences’ coping behaviors, respectively. The results help to explain the black box of the relationship between the characteristics of recommendation algorithms and audience responses from an algorithm throughput perspective. The findings offer implications for improving the effectiveness of personalized recommendations and provide a new perspective for studying the transparency of algorithmic recommendations in consumer behavior.