Análisis e interpretación de resultados

My design reflects the key features of a typical audit, in which auditors gain client- specific experience and face pressure to increase audit efficiency in a 2 x 2 between-subject design. Where I randomly assign between participants to experience (NO-PATTERN – PATTERN or PATTERN – NO-PATTERN) or cue cost (HIGH or LOW) conditions. In each period, participants are able to select 0, 1, or 2 costly (LOW vs. HIGH) risk cues, which they use to generate a prediction (risk assessment) as to whether or not an error will occur in the current period. This prediction automatically generates experimental effort cost in the period and affects the penalty in those periods in which there is an error. The system then provides feedback to the participants regarding the accuracy of their risk-of-error prediction at each period end.

Participants repeat this task in each period of the session, thus mimicking the general flow of an audit while focusing on the ability of individuals to identify and assess current-period risk cues after a long series of no-error results. This is shown graphically in Figure 4.

FIGURE 4

Experimental Design Overview

This figure graphically represents the general flow of a typical audit (top row). In the second row, I include the primary attributes of my experimental design. This provides a visual illustration of how my experiment generally follows the audit flow. This is discussed in more detail in the text.

2.3 Data Sets

To examine whether individuals make different decisions when facing differing experience with a client, I build two data sets with differing patterns of error/no-error

(experience) series. Cue-processing literature generally provides a set of informational cues and asks participants to make decisions based on these cues. For example, Barron et al. (2008)

H yp o th es is o_n e D_V H :2_a

evaluate the impact of a risk warning that follows a series of client-specific experiences. In each of 100 periods, participants choose between two buttons where each button contains a different payoff option (a different risk profile). The authors then manipulate a descriptive warning by varying the timing of when participants receive this descriptive warning to either before the first period, or after the fiftieth period. The experimenters then measure the change in participant choices.

In a typical audit setting, auditors do not receive a single descriptive warning after some n number of periods with the client. Rather, they generate risk assessments each period and

incorporate currently identified and known information into the current year’s risk assessment and resulting audit, as discussed in more detail in Section 1.1. To operationalize experience with descriptive risk cues utilizing a more realistic audit setting, I generate a series of data containing financial reporting errors (“error”) or no-errors (“no-error”) in each period. I generate the NO- PATTERN and PATTERN data series in the following manner:

1. I randomly draw a series of values between zero and one in Excel. I assign numbers below 0.70 a value of “no-error,” and those above (or equal to) 0.70 an “error” value to create the period results.30

2. For the NO-PATTERN condition, I rerun the random values until obtaining a series of periods that visually does not contain a long series of no-error or error periods.

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While I realize that it would be unlikely an auditor would face an error rate of this magnitude, I choose these (and cue) target values to provide some variability for the participants to learn, while still maintaining a high level of no- error periods.

3. For the PATTERN condition, I rerun the random values until I obtain one long no-error series of periods.

a. To be consistent with prior literature, I draw the results until reaching a series of at least 20 periods that have no errors (Hogarth and Einhorn 1992; Pinsker 2007). In fact, I end up with a total no-error series of 25 periods.

4. For each PATTERN/NO-PATTERN experience data set, I then generate risk cues. I

randomly generate two risk-cue values for each period. I code values below 0.80 to match the period error/no-error results, and those above (or equal to) 0.80 I code to not match (be the opposite of) the error/no-error results for the period.

a. For example, if the period contains an error and cue one has a random value less than 0.80, then cue one would provide information that the period contains an error. Alternately, if cue two has a random value greater than 0.80, the cue would provide (incorrect) information that the period contains no error.

i. As I generate the risk-cue values to match or not match the error/no-error results in each period, risk cues may incorrectly identify the current period error/no-error in either direction.

5. I rerun the random values for the cues until there is a dispersion matching or not-matching the error/no-error risk cues across all periods. In this case, I do not target any specific series of risk-cue informativeness.

The instructions outline that the objective is to correctly predict whether there is a high or low risk of a financial-reporting error in the current period. In addition, the instructions inform the participants that they have no control over whether an error occurs in any given period. Rather, they have the opportunity to obtain risk cues that provide some indication as to whether the current period contains an error/no-error.31

See Figure 5 for visual representation of the distribution/cue patterns.

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FIGURE 5

Distribution/Cue Patterns

Periods

NO-PATTERN Data Series

PATTERN Data Series

Practice periods Practice periods contain the same cue information and error/no-error results for all participants—included as the first 15 periods of the first session.

First 7 periods of