scholarly journals Are auditors' reliance on conclusions from data analytics impacted by different data analytic inputs?

2021 ◽  
Author(s):  
Jared Koreff
Keyword(s):  
Predictive Models ◽  
Data Analytics ◽  
Relevant Information ◽  
Analytical Models ◽  
Great Promise ◽  
Audit Process ◽  
Use Of Data ◽  
Anomaly Models ◽  
Data Analytic ◽  
Global Stakeholders

Global 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' 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' judgments.

Valuing Social Responsibility in the Era of Data Analytics

2020 ◽  
pp. 143-160
Author(s):  
Melissa R. Irvin
Keyword(s):  
Social Responsibility ◽  
Social Mobility ◽  
Students Of Color ◽  
Predictive Models ◽  
Data Analytics ◽  
Process Model ◽  
Use Of Data ◽  
Potential Benefits ◽  
Student Information ◽  
Corporate Social

Higher education is increasingly interested in utilizing data analytics to support all aspects of university operations, including enrollment management and learning outcomes. Despite potential benefits to improve results and resource efficiency, the use of student information and the creation of predictive models is a potential minefield which could undermine larger higher educational missions tied to civic responsibility and social mobility. Questions remain as to the impacts of predictive modeling on underrepresented communities like students of color and differently abled students. Emerging research on similar fields of analytics, including predictive policing, provides a window into the ethical considerations that must be made to use data analytics responsibly. This chapter uses the construct of social responsibility to propose a process model for the responsible use of data analytics in colleges and universities derived from Carroll's Pyramid of Corporate Social Responsibility.

scholarly journals Mathematical Issues in Data Science and Applications for Health care

2019 ◽  
Author(s):  
Sitti Zuhaerah Thalhah ◽  
Mohammad Tohir ◽  
Phong Thanh Nguyen ◽  
K. Shankar ◽  
Robbi Rahim
Keyword(s):  
Data Analytics ◽  
Data Science ◽  
Predictive Analytics ◽  
Big Data Analytics ◽  
Decision Models ◽  
Computational Techniques ◽  
Healthcare Applications ◽  
Use Of Data ◽  
Source Data ◽  
Military Applications

For development in military applications, industrial and government the predictive analytics and decision models have long been cornerstones. In modern healthcare system technologies and big data analytics and modeling of multi-source data system play an increasingly important role. Into mathematical models in these domains various problems arising that can be formulated, by using computational techniques, sophisticated optimization and decision analysis it can be analyzed. This paper studies the use of data science in healthcare applications and the mathematical issues in data science.

A Framework and Resources to Create a Data Analytics-Infused Accounting Curriculum

2021 ◽  
Author(s):  
Kevin E Dow ◽  
Norman Jacknis ◽  
Marcia Weidenmier Watson
Keyword(s):  
Critical Thinking ◽  
Diffusion Of Innovation ◽  
Data Analytics ◽  
Thinking Skills ◽  
Critical Thinking Skills ◽  
Accounting Students ◽  
Diffusion Of Innovation Theory ◽  
Fully Integrated ◽  
Accounting Curriculum ◽  
Data Analytic

The technology and Data Analytics developments affecting the accounting profession in turn have a profound effect on accounting curriculum. Accounting programs need fully-integrated accounting curriculum to develop students with strong analytic and critical thinking skills that complement their accounting knowledge. This will meet the profession's expectation that accounting students have expert level skills in both technical accounting knowledge and Data Analytics. This paper provides a framework and the resources for creating a Data Analytics-infused accounting curriculum. Specifically, using the Diffusion of Innovation Theory, we apply the theory's five stages to the infusion of Data Analytics into the accounting curriculum: Knowledge, Persuasion, Decision, Implementation, and Confirmation. We formulate an Analytics Value Cube to guide the use of different analytic techniques as accounting programs integrate. We recommend free tools, questions, and cases for use across the curriculum. While our focus is on accounting programs new to Data Analytics, these resources are also useful to accounting programs and practitioners that wish to expand their data analytic offerings.

Review of data analytic teaching cases, have we covered enough?

2021 ◽  
Author(s):  
Robyn L Raschke ◽  
Kimberly F. Charron
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Accounting Education ◽  
Data Preparation ◽  
Business Partner ◽  
Education Literature ◽  
Analytic Process ◽  
Principal Tool ◽  
Teaching Cases ◽  
Data Analytic

Big Data and data analytics are changing the accounting profession requiring different skills. To prepare students, faculty are tasked with teaching data analytics and the tools used to perform data analysis. This study examines data analytic teaching cases across the curriculum from peer-reviewed accounting education literature using the lens of the data analytic process. We include the breadth of tools students employ to complete the cases that will give students the experience needed to be a valued business partner. We find that the majority of teaching cases focus on analysis and communication, while few cover necessary steps prior to analysis such as extraction and data preparation. We also find that Excel is the principal tool used, while other much needed tools are rarely employed in teaching cases. Our findings provide an opportunity for academics to create new teaching cases to provide students with comprehensive experiences throughout the data analytics cycle.

From Metasearching to Discovery

2012 ◽  
pp. 22-43 ◽  
Author(s):  
LeiLani Freund ◽  
Christian Poehlmann ◽  
Colleen Seale
Keyword(s):  
Decision Making ◽  
Search Engine ◽  
Usability Testing ◽  
Relevant Information ◽  
Great Promise ◽  
Federated Search ◽  
University Of Florida ◽  
Library Users ◽  
Future Evaluation ◽  
The University

Many academic libraries implemented a metasearch or federated search platform as a way to expand the amount of relevant information available to library users. While the metasearch concept seemed to hold great promise, it failed to live up to expectations and users failed to embrace the technology. Nevertheless, the single search box proved to be popular with search engine users, and metasearch would prove to be a forerunner to more evolved discovery solutions. In this chapter, the authors describe experiences with a metasearch product, usability testing, and how that experience shaped decision-making for the chosen discovery solution platform. The available discovery services are explored, and the process for selection at the University of Florida Libraries is described along with the plans for future evaluation of the implemented service.

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