Initial validation of the trust of automated systems test (TOAST)

ABSTRACT

Trust is a key determinant of whether people rely on automated systems in the military and the public. However, there is currently no standard for measuring trust in automated systems. In the present studies, we propose a scale to measure trust in automated systems that is grounded in current research and theory on trust formation, which we refer to as the Trust in Automated Systems Test (TOAST). We evaluated both the reliability of the scale structure and criterion validity using independent, military-affiliated and civilian samples. In both studies we found that the TOAST exhibited a two-factor structure, measuring system understanding and performance (respectively), and that factor scores significantly predicted scores on theoretically related constructs demonstrating clear criterion validity. We discuss the implications of our findings for advancing the empirical literature and in improving interface design.     

A meta-analysis of the criterion-related validity of prehire work experience

Organizations frequently screen or select job applicants based on their work experience. Despite this, surprisingly little is known about the criterion-related validity of prehire experience, which reflects the amount, duration, or type of experience workers have acquired before they enter a new organization. To address this critical gap in the literature, we used meta-analysis to synthesize data from 81 independent samples that reported relations between prehire experience and performance or turnover. Results revealed overall corrected correlations of .06 for job performance (k = 44, n = 11,785), .11 for training performance (k = 21, n = 8,176), and .00 for turnover (k = 32, n = 11,676). Measures that capture prehire experience with tasks, jobs, or occupations relevant to workers’ current position also are only weakly related to the outcomes (e.g.,  = .07 for job performance). Two exceptions to our main findings are that (a) prehire experience is somewhat more predictive of job performance when workers first start a new job and (b) measures of task-level experience predict training performance, although these results are based on small subsets of primary studies. Overall, the present findings suggest that the types of prehire experience measures organizations currently use to screen job applicants generally are poor predictors of future performance and turnover. We therefore caution organizations from selecting employees based on such measures unless more positive evidence emerges.     

Attentional bias predicts outcome in smoking cessation.

Most attempts to quit smoking end in failure, with many quitters relapsing in the first few days. Responses to smoking-related cues may precipitate relapse. A modified emotional Stroop task-which measures the extent to which smoking-related words disrupt performance on a reaction time (RT) task-was used to index the distracting effects of smoking-related cues. Smokers (N = 158) randomized to a high-dose nicotine patch (35 mg) or placebo patch completed the Stroop task on the 1st day of a quit attempt. Smokers using an active patch exhibited less attentional bias, making fewer errors on smoking-related words. Smokers who showed greater attentional bias (slowed RT on the first block of smoking words) were significantly more likely to lapse in the short-term, even when controlling for self-reported urges at the test session. Attentional bias measures may tap an important component of dependence.