Stephanie M. Merritt

Learn More
OBJECTIVE We provide an empirical demonstration of the importance of attending to human user individual differences in examinations of trust and automation use. BACKGROUND Past research has generally supported the notions that machine reliability predicts trust in automation, and trust in turn predicts automation use. However, links between user(More)
To determine whether profiles of predictor variables provide incremental prediction of college student outcomes, the authors 1st applied an empirical clustering method to profiles based on the scores of 2,771 entering college students on a battery of biographical data and situational judgment measures, along with SAT and American College Test scores and(More)
OBJECTIVE This study is the first to examine the influence of implicit attitudes toward automation on users' trust in automation. BACKGROUND Past empirical work has examined explicit (conscious) influences on user level of trust in automation but has not yet measured implicit influences. We examine concurrent effects of explicit propensity to trust(More)
OBJECTIVE We present alternative operationalizations of trust calibration and examine their associations with predictors and outcomes. BACKGROUND It is thought that trust calibration (correspondence between aid reliability and user trust in the aid) is a key to effective human-automation performance. We propose that calibration can be operationalized in(More)
Many students during their college careers consider withdrawing from their respective college or university. Understanding why some students decide to withdraw yet others persist has implications for both the well being of students as well as for institutes of higher education. The present study develops a model of the decision to withdraw drawing on(More)
OBJECTIVE A self-report measure of the perfect automation schema (PAS) is developed and tested. BACKGROUND Researchers have hypothesized that the extent to which users possess a PAS is associated with greater decreases in trust after users encounter automation errors. However, no measure of the PAS currently exists. We developed a self-report measure(More)
  • 1