FIGURE 3 Results of Process Model for Effects of Prepopulation on Accuracy for Decreasing Risk Factors is our manipulated variable auditors are provided with either prepopulated or non-prepopulated workpapers. Prepopulation Accuracy for Decreasing Risk – measured as the count of decreasing risk factors for which auditors correctly moved down from last year’s risk rating, is the dependent variable. We Factors, expect auditors to show two behavioral responses to prepopulation of workpapers: sticking with last year’s risk rating (Stick and working fast on with Last Year) the risk assessment (Work We measure by the total number of times auditors stick to the prior year rating for increasing and no change Fast). Stick with Last Year risk factors; sticking should reduce accuracy for decreasing risk factors. We measure by the time spent on the risk assessment task; spending less time Work Fast should reduce accuracy for decreasing risk factors. We adjust the time spent by participants in the prepopulated workpaper condition to make it comparable to the non-prepopulated workpaper condition by subtracting their actual time spent and adding an estimate of the average typing time of participants in the workpaper condition. We test the model using a structural equations modeling approach. The model fits the data well, as demonstrated by the test (χ2(1) = 0.67, = 0.413), and other standard measures of fit (CFI = 1.00 and RMSEA = 0.00). Process mediators are accompanied by the 90% p bootstrapped confidence intervals (Preacher and Hayes [2008]); we use this approach to formally test the mediation of the paths. All are one-tailed for p-values directional predictions.
FIGURE 3 Results of Process Model for Effects of Prepopulation on Accuracy for Decreasing Risk Factors Prepopulation is our manipulated variable – auditors are provided with either prepopulated or non-prepopulated workpapers. Accuracy for Decreasing Risk Factors, measured as the count of decreasing risk factors for which auditors correctly moved down from last year’s risk rating, is the dependent variable. We expect auditors to show two behavioral responses to prepopulation of workpapers: sticking with last year’s risk rating (Stick with Last Year) and working fast on the risk assessment (Work Fast). We measure Stick with Last Year by the total number of times auditors stick to the prior year rating for increasing and no change risk factors; sticking should reduce accuracy for decreasing risk factors. We measure Work Fast by the time spent on the risk assessment task; spending less time should reduce accuracy for decreasing risk factors. We adjust the time spent by participants in the prepopulated workpaper condition to make it comparable to the non-prepopulated workpaper condition by subtracting their actual time spent and adding an estimate of the average typing time of participants in the workpaper condition. We test the model using a structural equations modeling approach. The model fits the data well, as demonstrated by the test (χ2(1) = 0.67, p = 0.413), and other standard measures of fit (CFI = 1.00 and RMSEA = 0.00). Process mediators are accompanied by the 90% bootstrapped confidence intervals (Preacher and Hayes [2008]); we use this approach to formally test the mediation of the paths. All p-values are one-tailed for directional predictions.
BMR_JARforPublication_FINAL-1.docx