TY - JOUR
T1 - Applications of Data Analytics
T2 - Cluster Analysis of Not-forProfit Data
AU - Alzamil, Zamil S.
AU - Appelbaum, Deniz
AU - Glasgall, William
AU - Vasarhelyi, Miklos A.
N1 - Funding Information:
We thank the participants and reviewers of our paper at the 2020 JISC Conference, and our paper’s anonymous reviewers for all of their help and support. Zamil S. Alzamil also thanks the Deanship of Scientific Research at Majmaah University for supporting him at the 2020 JISC Conference under Project No. R-2021-151.
Publisher Copyright:
© 2022, American Accounting Association. All rights reserved.
PY - 2022/9/1
Y1 - 2022/9/1
N2 - Since data analytics enables data exploration and the uncovering of hidden relationships, in this study, we use cluster analysis to gain more insight into governmental data and financial reports. This research initiative is performed using the Design Science Research (DSR) methodology, where we develop and apply an appropriate artifact. We apply our artifact to two different datasets: the first uses the U.S. states’ financial statements, and the second utilizes survey results from the Volcker Alliance about states’ budgeting performance. In both applications, we demonstrate how clustering may be used on governmental data to gain new insights about financial statements and budgeting. This study contributes to the literature in two ways: First, the two applications bring advanced data mining techniques into the not-for-profit domain; and second, the results provide guidance for auditors, academics, regulators, and practitioners to use clustering to gain more insights.
AB - Since data analytics enables data exploration and the uncovering of hidden relationships, in this study, we use cluster analysis to gain more insight into governmental data and financial reports. This research initiative is performed using the Design Science Research (DSR) methodology, where we develop and apply an appropriate artifact. We apply our artifact to two different datasets: the first uses the U.S. states’ financial statements, and the second utilizes survey results from the Volcker Alliance about states’ budgeting performance. In both applications, we demonstrate how clustering may be used on governmental data to gain new insights about financial statements and budgeting. This study contributes to the literature in two ways: First, the two applications bring advanced data mining techniques into the not-for-profit domain; and second, the results provide guidance for auditors, academics, regulators, and practitioners to use clustering to gain more insights.
KW - audit analytics
KW - data analytics
KW - governmental data
KW - hierarchical clustering
KW - k-means clustering
KW - not-for-profit data
UR - http://www.scopus.com/inward/record.url?scp=85127677157&partnerID=8YFLogxK
U2 - 10.2308/ISYS-2020-025
DO - 10.2308/ISYS-2020-025
M3 - Article
AN - SCOPUS:85127677157
SN - 0888-7985
VL - 35
SP - 199
EP - 221
JO - Journal of Information Systems
JF - Journal of Information Systems
IS - 3
ER -