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We investigate how the design of audit processes influences employees’ reporting decisions. We focus specifically on detective employee audits for which several employees are randomly selected after a defined period to audit their ex-post behavior. We investigate two design features of the audit process, namely, employee anonymity and process transparency, and analyze their impact on misreporting. Overall, we find that both components influence the extent of individuals’ misreporting. A nonanonymous audit decreases performance misreporting more than an audit in which the employee remains anonymous. Furthermore, the high incidence of performance misreporting in the case of anonymous audits can be decreased when the process transparency is low. Thus, our study informs accountants about how the two design features of employee anonymity and transparency of the audit process can be used to constrain performance misreporting to increase the efficiency of audits
The business problem of having inefficient processes, imprecise process analyses, and simulations as well as non-transparent artificial neuronal network models can be overcome by an easy-to-use modeling concept. With the aim of developing a flexible and efficient approach to modeling, simulating, and optimizing processes, this paper proposes a flexible Concept of Neuronal Modeling (CoNM). The modeling concept, which is described by the modeling language designed and its mathematical formulation and is connected to a technical substantiation, is based on a collection of novel sub-artifacts. As these have been implemented as a computational model, the set of CoNM tools carries out novel kinds of Neuronal Process Modeling (NPM), Neuronal Process Simulations (NPS), and Neuronal Process Optimizations (NPO). The efficacy of the designed artifacts was demonstrated rigorously by means of six experiments and a simulator of real industrial production processes.