1,720,960 research outputs found
Are Auditors\u27 Reliance on Conclusions from Data Analytics Impacted By Different Data Analytic Inputs?
No full textGlobal stakeholders have expressed interest in increasing the use of data analytics throughout the audit process. While data analytics offer great promise in identifying audit-relevant information, auditors may not uniformly incorporate this information into their decision making. This study examines whether conclusions from two data analytic inputs, the type of data analytical model (anomaly vs. predictive) and type of data analyzed (financial vs. nonfinancial), result in different auditors\u27 decisions. Findings suggest that conclusions from data analytical models and data analyzed jointly impact budgeted audit hours. Specifically, when financial data is analyzed auditors increase budgeted audit hours more when predictive models are used than when anomaly models are used. The opposite occurs when nonfinancial data is analyzed, auditors increase budgeted audit hours more when anomaly models are used compared to predictive models. These findings provide initial evidence that data analytics with different inputs do not uniformly impact auditors\u27 judgments
Three Studies Examining Auditors\u27 Use of Data Analytics
No full textThis dissertation comprises three studies, one qualitative and two experimental, that center on auditor\u27s use of data analytics. Data analytics hold the potential for auditors to reallocate time spent on labor intensive tasks to judgment intensive tasks (Brown-Liburd et al. 2015), ultimately improving audit quality (Raphael 2017). Yet the availability of these tools does not guarantee that auditors will incorporate the data analytics into their judgments (Davis et al. 1989; Venkatesh et al. 2003). The first study investigates implications of using data analytics to structure the audit process for nonprofessionalized auditors. As the public accounting profession continues down a path of de-professionalization (Dirsmith et al. 2015), data analytics may increasingly be used as a control mechanism for guiding nonprofessionalized auditors\u27 work tasks. Results of this study highlight negative ramifications of using nonprofessionalized auditors in a critical audit setting. The second study examines how different types of data analytics impact auditors\u27 judgments. This study demonstrates the joint impact that the type of data analytical model and type of data analyzed have on auditors\u27 judgments. This study contributes to the literature and practice by demonstrating that data analytics do not uniformly impact auditors\u27 judgments. The third study examines how auditors\u27 reliance on data analytics is impacted by the presentation source and level of risk identified. This study provide insights into the effectiveness of public accounting firms\u27 development of data scientist groups to incorporate the data analytic skillset into audit teams. Collectively, these studies contribute to the literature by providing evidence on auditors\u27 use of data analytics. Currently, the literature is limited to demonstrating that auditors are not effective at identifying patterns in data analytics visualizations when viewed before traditional audit evidence (Rose et al. 2017). The three studies in this dissertation highlight that not all data analytics influence judgments equally
Going Beyond Counting First Authors in Author Co-citation Analysis
No full textThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
No full text“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
No full textWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
No full textWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
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