An exploration of the antecedent to increase society’s use of predictive models: Trust in science or predictive models

Abstract

Research has resulted in profound societal changes, impacting lives and health worldwide. As a result, the public esteems researchers and funds further research. This critical societal process is obstructed if society chooses not to implement research results. Much of this research is the result of predictive models. This study will characterize the antecedents and impact of framing on trust in predictive modeling. Prior research focused on trust in science. There is evidence that there is no commonly understood definition of science. Trust in predictive modeling is a more appropriate area of study. This study contextualized the antecedents from trust in science research to trust in predictive modeling. A new construct was developed,and three trust-in-science constructs contextualized to fit the trust in predictive models were useful. This research surveyed 207 students. The framing questions used were divided into two categories, one that many people are familiar with (GPS automobile routing) and one that they were less familiar with (COVID-19 infection prediction models), where COVID modeling is embroiled in controversy. The political influence construct did not impact questions framed by a familiar noncontroversial example, but the COVID-framed questions were impacted. Through the use of predictive models, this research provides a novel approach to the field of study. It discovers four antecedents to trust in predictive models (conspiracy ideation, intellectual curiosity, self-efficacy, and political influence) and provides a new construct for framing questions (political influence).