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Professional Skepticism: Practitioners’ Perceptions And Training Practices

While regulators criticize auditors for lacking appropriate professional skepticism (SEC, 2010, 2013; PCAOB, 2012), auditing standards lack a clear, consistent definition (Nelson, 2009; Hurtt, Brown-Liburd, Earley, & Krishnamoorthy, 2013), leaving application of professional skepticism “open to interpretation” (Glover & Prawitt, 2013, p. 2). If individual auditors view professional skepticism as open to interpretation (i.e., subjective), auditors may believe they are appropriately applying standards on professional skepticism based on their individual interpretations. However, if regulators apply a different definition of professional skepticism when reviewing auditors’ work, this may help explain ongoing criticisms from regulators stating auditors lack appropriate professional skepticism. The author reports insights of 66 auditors’ perceptions and finds the majority believe professional skepticism has a subjective (as opposed to uniform) definition. This finding is consistent across auditor rank and firm size, suggesting the potential for variations in application of professional skepticism in practice. Supplemental analyses indicate tax practitioners are more likely than auditors to view professional skepticism as subjective, particularly at the partner rank. The author presents professional skepticism training practices for 25 firms that suggest most firms recognize the importance of professional skepticism training and its regular reinforcement. However, there are concerns surrounding the fact that mentoring is listed as the most common training method, which lacks benefits of more formal training activities. Overall, this study provides relevant insights from practitioners and strengthens recent calls for developing a “common definition and shared understanding” of professional skepticism and a framework for evaluating application of professional skepticism (Glover & Prawitt, 2014, p. 5-6).

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Integrating Data Cleansing With Popular Culture: A Novel SQL Character Data Tutorial

Big data and data science have experienced unprecedented growth in recent years. The big data market continues to exhibit strong momentum as countless businesses transform into data-driven companies. From salary surges to incredible growth in the number of positions, data science is one of the hottest areas in the job market. Significant demand and limited supply of professionals with data competencies has greatly affected the hiring market and this demand/supply imbalance will likely continue in the future. A major key in supplying the market with qualified big data professionals, is bridging the gap from traditional Information Systems (IS) learning outcomes to those outcomes requisite in this emerging field. The purpose of this paper is to share an SQL Character Data Tutorial. Utilizing the 5E Instructional Model, this tutorial helps students (a) become familiar with SQL code, (b) learn when and how to use SQL string functions, (c) understand and apply the concept of data cleansing, (d) gain problem solving skills in the context of typical string manipulations, and (e) gain an understanding of typical needs related to string queries. The tutorial utilizes common, recognizable quotes from popular culture to engage students in the learning process and enhance understanding. This tutorial should prove helpful to educators who seek to provide a rigorous, practical, and relevant big data experience in their courses.

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