be no incremental effect of prior year ratings being embedded as defaults in prepopulated workpapers. Such a finding would be consistent with prior research showing few changes to risk ratings from year to year (e.g., Mock and Wright [1993, 1999]), and more generally, the “same as last year” effect in auditing (e.g., Brazel et al. [2004]). 2.2.2 Hypotheses for Responses that Mediate the Effects of Prepopulation on Risk Rating Accuracy While there is strong evidence that people more frequently adopt an option when it is presented as the default, the theory as to why people do so is muddier given a relative dearth of empirical evidence. We focus on the explanation most relevant for auditing: people act as if a default option carries meaning (Brown and Krishna [2004]). One way in which a default option can carry meaning is that decision-makers (consciously) believe a policymaker has selected the default because that policymaker finds value in the default, i.e., either prefers the default option or at least considers it to be acceptable. These beliefs then lead decision-makers to construct preferences and choose in line with those preferences. Studies examining this idea ask decision-makers to their beliefs about the reasons underlying the policymaker’s selection of the default. For example, in settings of organ donation (McKenzie et al. [2006]), retirement plan enrollment (McKenzie et al. [2006]; Tannenbaum and Ditto [2011]), and course deadline options (Tannenbaum and Ditto [2011]), studies have found that people report that policymakers have chosen a default because they think decision-makers should choose that option, and also that policymakers themselves would choose the default. A second way in which a default option can carry meaning is that people (nonconsciously) believe it is appropriate, or has value, simply because it occupies the space in which the final choice will be documented (here, a cell in a spreadsheet). Studies
Fast (Link 4; p = 0.001) on Accuracy for Increasing Risk Factors. Finally, there is a direct, negative effect of Prepopulation on Accuracy for Increasing Risk Factors (p = 0.029). [Insert Figure 2 about here] Consistent with Griffith et al. [2015], we use the bias-corrected bootstrapping method (Preacher and Hayes [2008]) to formally test for mediating effects because simulation tests indicate that it is the most accurate method for sample sizes less than 500 (Hayes and Scharkow [2013]). The findings are consistent with those from the structural equation model. Specifically, the 90% bias-corrected confidence interval for Stick with Last Year is (-2.65, -1.57), showing the negative effect of prepopulation on accuracy through the stick-with-last year response. The confidence interval for Work Fast is (-0.43, -0.04), supporting the negative effect of prepopulation on accuracy through the work fast response. The direct negative effect of prepopulation on accuracy for increasing risks suggests there are other mediators (e.g., Rucker et al. [2011]). For example, prepopulation may lead auditors to be more concerned about the increased substantive testing (i.e., reduced efficiency) ramifications of increasing risk ratings. 4.1.2 for Decreasing Risk Factors Figure 3 illustrates the model for decreasing risk factors. Again, the model fits the data well (χ2(1) = 0.67, p = 0.413; CFI = 1.00; RMSEA = 0.00). As expected, there are positive effects of Prepopulation on Stick with Last Year (Link 1; p < 0.001) and Work Fast (Link 2; p = 0.032). Also as expected, there are negative effects of Stick with Last Year (Link 3; p < 0.001) and Work Fast (Link 4; p = 0.001) on Accuracy for Decreasing
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.
TABLE 1 Panel A – Descriptive Statistics for Dependent Variable (Auditors’ Risk Rating Accuracy, by Risk Factor Type) Prepopulated Workpapers Non-Prepopulated Workpapers Increasing Risks (8 total risk factors) 2.92 (2.49) 5.96 (1.40) Decreasing Risks (6 total risk factors) 1.41 (1.38) 3.46 (1.09) No Change Risks (5 total risk factors) 4.21 (1.24) 2.21 (1.76) Number of Staff Participants 56 61 Panel B – Descriptive Statistics for Mediators (Auditors’ Behavioral Responses to Prepopulation) Prepopulated Workpapers Non-Prepopulated Workpapers Stick with Last Year – Increasing Risks 8.26 (2.74) 3.63 (2.22) Stick with Last Year – Decreasing Risks 9.23 (3.36) 3.91 (2.36) Stick with Last Year – Unchanged Risks 9.07 (4.06) 3.11 (1.83) Work Fast – Across all Risk Factors 16.13 (7.67) 19.33 (10.87)