Professor Emeritus, University of South Florida
Data Mining Main Page | Quantitative Methods Main Page
Provided by James R. Martin, Ph.D., CMA
Professor Emeritus, University of South Florida
Data Mining Main Page
| Quantitative Methods Main Page
Agrawal, A., J. Gans and A. Goldfarb. 2020. How to win with machine learning. Harvard Business Review (September/October): 126-133. (Artificial intelligence in business, data mining and prediction models).
Aldhizer, G. R. III. 2017. Visual and text analytics: The next step in forensic auditing and accounting. The CPA Journal (June): 30-33.
Allen, E., D. E. O'Leary, H. Qu and C. W. Swenson. 2021. Tax specific versus generic accounting-based textual analysis and the relationship with effective tax rates: Building context. Journal of Information Systems (Summer): 115-147.
Alles, M. and G. L. Gray. 2016. Incorporating big data in audits: Identifying inhibitors and a research agenda to address those inhibitors. International Journal of Accounting Information Systems (22): 44-59.
Alles, M., G. Brennan, A. Kogan and M. A. Vasarhelyi. 2006. Continuous monitoring of business process controls: A pilot implementation of a continuous auditing system at Siemens. International Journal of Accounting Information Systems 7(2): 137-161.
Alzamil, Z., D. Appelbaum and R. Nehmer. 2020. An ontological artifact for classifying social media: Text mining analysis for financial data. International Journal of Accounting Information Systems (38): 100469.
Amani, F. A. and A. M. Fadlalla. 2017. Data mining applications in accounting: A review of the literature and organizing framework. International Journal of Accounting Information Systems (24): 32-58.
Amato, R. M. 2022. Getting data literacy right. Strategic Finance (November): 62-63.
Anders, S. B. 2017. Audit data analytics resources. The CPA Journal (June): 72-73.
Andiola, L. M., E. Masters and C. Norman. 2020. Integrating technology and data analytic skills into the accounting curriculum: Accounting department leaders' experiences and insights. Journal of Accounting Education (50): 100655.
Angelo, B., D. Ayres and J. Stanfield. 2018. Power from the ground up: Using data analytics in capital budgeting. Journal of Accounting Education (42): 27-39.
Anthony, C. 2021. When knowledge work and analytical technologies collide: The practices and consequences of black boxing algorithmic technologies. Administrative Science Quarterly 66(4): 1173-1212.
Appelbaum, D. 2016. Securing big data provenance for auditors: The big data provenance black box as reliable evidence. Journal of Emerging Technologies in Accounting 13(1): 17-36.
Appelbaum, D., A. Kogan and M. A. Vasarhelyi. 2017. An introduction to data analysis for auditors and accountants. The CPA Journal (February): 32-37. (Summary).
Appelbaum, D., A. Kogan and M. A. Vasarhelyi. 2017. Big data and analytics in the modern audit engagement: Research needs. Auditing: A Journal of Practice & Theory 36(4): 1-27.
Appelbaum, D., A. Kogan, M. Vasarhelyi and Z. Yan. 2017. Impact of business analytics and enterprise systems on managerial accounting. International Journal of Accounting Information Systems (25): 29-44. (Summary).
Araz, O. M., T. Choi, D. L. Olson and F. S. Salman. 2020. Data analytics for operational risk management. Decision Sciences 51(6): 1316-1319.
Araz, O. M., T. Choi, D. L. Olson and F. S. Salman. 2020. Role of analytics for operational risk management in the era of big data. Decision Sciences 51(6): 132-1346.
Baader, G. and H. Krcmar. 2018. Reducing false positives in fraud detection: Combining the red flag approach with process mining. International Journal of Accounting Information Systems (31): 1-16.
Bakarich, K. M. 2022. Using data visualization to help uncover fraud. The CPA Journal (March/April): 24-49.
Ballou, B., J. H. Grenier and A. Reffett. 2021. Stakeholder perceptions of data and analytics based auditing techniques. Accounting Horizons (September): 47-68.
Barton, D. and D. Court. 2012. Making advanced analytics work for you: A Practical guide to capitalizing on big data. Harvard Business Review (October): 78-83. (Choose the right data, Build models that predict and optimize business outcomes, and Transform your company's capabilities).
Basu, A., 2013. Executive edge: Five pillars of prescriptive analytics success. Analytics Magazine. (March/April): 8-12. (Hybrid data, integrated predictions and prescriptions, prescriptions and side effects, adaptive algorithms, and feedback mechanism).
Beaulieu, P. R. 2020. Contract-based cost analytics. Journal of Emerging Technologies in Accounting 17(1): 11-19.
Benson, B. and A. Aljabr. 2022. Artificial intelligence and data science: Next wave change. Cost Management (September/October): 18-22.
Berinato, S. 2014. With big data comes big responsibility. Harvard Business Review (November): 100-104.
Berinato, S. 2019. Data science and the art of persuasion. Harvard Business Review (January/February): 126-137.
Berry, M. J. A. and G. S. Linoff. 2004. Data Mining Techniques: For Marketing, Sales, and Customer Relationship Management. Wiley Computer Publishing.
Blix, L. H., M. A. Edmonds and K. B. Sorensen. 2021. How well do audit textbooks currently integrate data analytics. Journal of Accounting Education (55): 100717.
Blyakhman, A. 2022. Even accountants benefit from experiments. Strategic Finance (September): 12.
Blyakhman, A. 2022. Technology workbook: Selecting data visualization tools. Strategic Finance (June): 62-63.
Bochkay, K., S. V. Brown, A. J. Leone and J. W. Tucker. 2023. Textual analysis in accounting: What's next? Contemporary Accounting Research 40(2): 765-805.
Bogaert, M., M. Ballings, R. Bergmans and D. Van den Poel. 2021. Predicting self-declared movie watching behavior using Facebook data and information-fusion sensitivity analysis. Decision Sciences 52(3): 776-810.
Bojinov, I., G. Saint-Jacques and M. Tingley. 2020. Avoid the pitfalls of A/B testing: Make sure your experiments recognize customers varying needs. Harvard Business Review (March/April): 48-53.
Borthick, A. F. and L. N. Smeal. 2020. Data analytics in tax research: Analyzing worker agreements. and compensation data to distinguish between independent contractors and employees using IRS factors. Issues in Accounting Education (August): 1-23.
Borthick, A. F. and R. R. Pennington. 2017. When data become ubiquitous, what becomes of accounting and assurance? Journal of Information Systems (Fall): 1-4.
Borthick A. F., G. P. Schneider and T. R. Viscelli. 2017. Analyzing data for decision making: Integrating spreadsheet modeling and database querying. Issues in Accounting Education (February): 59-66.
Boumediene, S. and S. Boumediene. 2023. Electronic evidence: A framework for applying digital forensics to data base. Journal of Forensic Accounting Research 8(1): 266-286.
Bouwens, J. 2018. Data science: The contribution of the financial manager. Cost Management (November/December): 44-47.
Bradford, M., E. Taylor and M. Seymore. 2021. The critical first step to data security: Management accountants are equipped to apply business performance measurement skills in identifying KPIs for data security and classification. Strategic Finance (December): 26-33.
Bramer, M. 2007. Principles of Data Mining (Undergraduate Topics in Computer Science). Springer.
Brands, K. 2023. Design thinking for innovation. Strategic Finance (February): 56-57.
Brands, K. M. 2023. Data democratization for value creation. Strategic Finance (August): 104-106.
Brands, K. M. 2023. Digital first: The ESMA data strategy. Strategic Finance (September): 73-75.
Brown-Liburd, H., A. Cheong, M. A. Vasarhelyi and X. Wang. 2019. Measuring with exogenous data (MED), and government economic monitoring (GEM). Journal of Emerging Technologies in Accounting 16(1): 1-19.
Brown-Liburd, H., H. Issa and D. Lombardi. 2015. Behavioral implications of big data's impact on audit judgment and decision making and future research directions. Accounting Horizons (June): 451-468.
Byrnes, P. E. 2019. Automated clustering for data analytics. Journal of Emerging Technologies in Accounting 16(2): 43-58.
Cainas, J. M., W. M. Tietz and T. Miller-Nobles. 2021. KAT insurance: Data analytics cases for introductory accounting using Excel, Power BI, and/or Tableau. Journal of Emerging Technologies in Accounting 18(1): 77-85.
Calderon, T. G. and C. G. Onita. 2017. Big data and the perceived expectations gap in digital authentication processes. Journal of Forensic & Investigative Accounting 9(2): 736-750.
Calderon, T. G., J. J. Cheh and I. Kim. 2003. How large corporations use data mining to create value. Management Accounting Quarterly (Winter): 1-11.
Canace, T. G., A. Jaffer and P. Juras. 2019. The CFO as asset manager. Management accountants have the opportunity to use data analytics to align asset management with their company's strategic direction. Strategic Finance (December): 24-31.
Cao, M., R. Chychyla and T. Stewart. 2015. Big data analytics in financial statement audits. Accounting Horizons (June): 423-429.
Cao, T., R. Duh, H. Tan and T. Xu. 2022. Enhancing auditors' reliance on data analytics under inspection risk using fixed ad growth mindsets. The Accounting Review (May): 131-153.
Castillo, A. 2021. Natural language processing: Machine learning methods in forensic accounting. The CPA Journal (June/July): 16-19. (Case study).
Cataldi, B., B. Callahan, J. F. Sander and A. S. Kelly. 2017. Cutting through the numbers: How data mining was used to uncover multiple frauds at a hospital system medical center. Journal of Forensic & Investigative Accounting 9(3): 936-940.
Cefaratti, M. A. and H. Lin. 2018. Exploring data center migration: A case study. Journal of Information Systems (Spring): 1-17.
Chae, B. and D. L. Olson. 2013. Business analytics for supply chain: A dynamic-capabilities framework. International Journal of Information Technology & Decision Making 12 (01): 9-26.
Chae, B. K., C. Yang, D. Olson, and C. Sheu. 2014. The impact of advanced analytics and data accuracy on operational performance: A contingent resource based theory (RBT) perspective. Decision Support Systems (59): 119–126.
Chai, S. and W. Shih. 2017. Why big data isn't enough. MIT Sloan Management Review (Winter): 57-61.
Chan, C. and S. P. Landry. 2019. Financial statements too good to be true? An instructional case assessing that question using analytical procedures and Beneish's M-Score. Journal of Forensic & Investigative Accounting 11(2): 380-394.
Chaudhuri, S., U. Dayal, and V. Narasayya. 2011. An overview of business intelligence technology. Communications of the ACM 54(8): 88-98.
Chen, C. X., R. Hudgins and W. F. Wright. 2022. The effect of advice valence on the perceived credibility of data analytics. Journal of Management Accounting Research 34(2): 97-116.
Chen, X., Y. H. Cho, Y. Dou and B. Lev. 2022. Predicting future earnings changes using machine learning and detailed financial data. Journal of Accounting Research (May): 467-515.
Cheng, C. and A. Varadharajan. 2021. Using data analytics to evaluate policy implications of migration patterns: Application for analytics, AIS, and tax classes. Issues in Accounting Education (May): 111-128.
Cheng, C. and C. Lee. 2023. A case study using data analytics to detect hail damage insurance claim fraud. Journal of Forensic Accounting Research 8(1): 287-306.
Cheng, C., S. Rhoades-Catanach and L. Watson. 2023. Data analytics for undergraduate tax students: A Alteryx case study for MACRS depreciation. Issues in Accounting Education (November): 145-164.
Cheong, A., K. Yoon, S. Cho and W. G. No. 2021. Classifying the contents of cybersecurity risk disclosure through textual analysis and factor analysis. Journal of Information Systems (Summer): 179-194.
Chiu, T., H. Brown-Liburd and M. A. Vasarhelyi. 2019. Performing test of internal controls using process mining: What could go wrong? The CPA Journal (June): 54-57.
Chiu, T., V. Chiu, T. Wang and Y. Wang. 2022. Using textual analysis to detect initial coin offerings frauds. Journal of Forensic Accounting Research 7(1): 165-183.
Chiu, T., Y. Wang and M. A. Vasarhelvi. 2020. The automation of financial statement fraud detection: A framework using process mining. Journal of Forensic & Investigative Accounting 12(1): 86-108.
Cho, S., M. A. Vasarhelyi and C. Zhang. 2019. Editorial: The forthcoming data ecosystem for business measurement and assurance. Journal of Emerging Technologies in Accounting 16(2): 1-21.
Chugh, R. and S. Grandhi. 2013. Why business intelligence? Significance of business intelligence tools and integrating BI governance with corporate governance. International Journal of E-Entrepreneurship and Innovation (IJEEI) 4 (2), 1-14.
Church, K. S., J. Riley and P. J. Schmidt. 2022. Has Excel become a "golden hammer": The paradox of data analytics in SME clusters. Journal of Emerging Technologies in Accounting 19(2): 211-234.
Churyk, N. T., A. Dzuranin and P. J. Schmidt. 2019. Special issue on preparing accounting students for careers using big data. Journal of Accounting Education (48): 48-49.
Churyk, N. T., D. Janvrin and M. W. Watson. 2017. Special issue on Big Data. Journal of Accounting Education (38): 1-2.
Cokins, G. 2013. Top 7 trends in management accounting. Strategic Finance (December): 20-29.
Collins, J. C. 2017. Data mining your general ledger with Excel. Journal of Accountancy (January): 27-32.
Collins, V. and J. Lanz. 2019. Managing data as an asset. The CPA Journal (June): 22-27.
Comunale, C. L., D. C. Hayes and J. H. Irving. 2022. Keeping an investigative eye on the financial pulse of a company using data analytics. Journal of Forensic & Investigative Accounting 14(3): 514-528.
Corban, T. 2021. Technology workbook: Data as a strategic asset. Strategic Finance (April): 60-61.
Cosic, R., G. Shanks and S. Maynard. 2012. Towards a business analytics capability maturity model. Location, location, location. Proceedings of the 23rd Australasian Conference on Information Systems: 1-11.
Daniel, S. J., Y. Xiao and T. Yeh. 2022. Improving business resilience: Part of a management accountant's job is to help guide the company through challenging economic times. Data analytics can help. Strategic Finance (October): 36-43.
Davenport, T., 2014. Big Data at Work: Dispelling the myths, Uncovering the Opportunities. Harvard Business Review Press.
Davenport, T. H. 2006. Competing on analytics. Harvard Business Review (January): 98-107. ("Some companies have built their very businesses on their ability to collect, analyze, and act on data. Every company can learn from what these firms do." Some applications include: 1) Simulating and optimizing supply chain flows, reducing inventory and stock-outs, 2) Identifying customers with the greatest profit potential, 3) Identifying the price that will maximize yield or profit, 4) Selecting the best employees for tasks or jobs, 5) Detecting and minimizing quality problems, 6) Proving a better understanding of the drivers of financial performance including nonfinancial factors, 7) Improving quality, efficacy and safety of products and services).
Davenport, T. H. 2013. Analytics 3.0. Harvard Business Review (December): 64-72.
Davenport, T. H. 2014. What businesses can learn from sports analytics. MIT Sloan Management Review (Summer): 10-13.
Davenport, T. H. and J. G. Harris. 2007. Competing on Analytics: The New Science of Winning. Harvard Business School Press.
Davenport, T. H., J. G. Harris and R. Morison. 2010. Analytics at Work: Smarter Decisions, Better Results. Harvard Business Press.
Davenport, T. H. and S. Kudyba. 2016. Designing and developing analytics-based data products. MIT Sloan Management Review (Fall): 82-89.
Davenport, T. H., J. G. Harris and Robert Morison. 2010. Analytics at Work: Smarter Decisions, Better Results. Harvard Business Press.
Davenport, T. H., P. Barth and R. Bean. 2012. How 'big data' is different. MIT Sloan Management Review (Fall): 43-46.
Debreceny, R. and G. L. Gray. 2004. Grab your picks and shovels! There's gold in your data. Strategic Finance (January): 24-28.
Decision Sciences. 2021. Special issue on data mining & decision analytics. Decision Sciences 52(3): 542.
Desai, V., T. Fountaine and K. Rowshankish. 2022. A better way to put your data to work: Package it the way you would a product. Harvard Business Review (July/August): 100-107. (What is a data product and how are they used?)
Dichev, I. D. and J. Qian. 2022. The benefits of transaction-level data: The case of NielsenIQ scanner data. Journal of Accounting and Economics (August): 101495.
Dilla, W., D. J. Janvrin and R. Raschke. 2010. Interactive data visualization: New directions for accounting information systems research. Journal of Information Systems (Fall): 1-37.
Dilla, W. N. and R. L. Raschke. 2015. Data visualization for fraud detection: Practice implications and a call for future research. International Journal of Accounting Information Systems (16): 1-22.
Dimes, R., C. De Villiers and L. Chen. 2023. How integrated thinking can be detected in management disclosures in annual reports: Insights from a large-scale text-analysis approach. Journal of Management Accounting Research 35(3): 75-99.
Domino, M. A., D. Schrag, M. Webinger and C. Troy. 2021. Linking data analytics to real-world business issues: The power of the pivot table. Journal of Accounting Education (57): 100744.
Dow, K. E., N. Jacknis and M. W. Watson. 2021. A framework and resources to create data analytics-infused accounting curriculum. Issues in Accounting Education (November): 183-205.
Drew, J. 2018. Merging accounting with 'big data' science. Journal of Accountancy (July): 48-52.
Drew, J. 2018. Paving the way for a new digital world. Journal of Accountancy (June): 18-22.
Druică, E., B. Oancea and C. Vâlsan. 2018. Benford's law and the limits of digit analysis. International Journal of Accounting Information Systems (31): 75-82.
Du, N., T. Wang and O. R. Whittington. 2021. Accounting data analytics exercise for intermediate accounting: Warranty expense and product liability. Journal of Emerging Technologies in Accounting 18(2): 201-208.
Duman, E. and M. H. Ozcelik. 2011. Detecting credit card fraud by genetic algorithm and scatter search. Expert Systems with Applications 38 (10): 13057-13063.
Dunn, C. L., G. J. Gerard and S. V. Grabski. 2017. The combined effects of user schemas and degree of cognitive fit on data retrieval performance. International Journal of Accounting Information Systems (26): 46-67.
Dyckman, T. R. 2010. Interpreting Economic and Social Data: A Foundation of Descriptive Statistics by Othmar W. Winkler. The Accounting Review (September): 1820-1822.
Dzuranin, A. C. 2021. Statement on Management Accounting: Data Visualization. Institute of Management Accounting.
Dzuranin, A. C. 2022. Explanatory data visualizations: Data visualizations can be used to effectively communicate the results of analyses and guide decision making. Strategic Finance (January): 42-49.
Dzuranin, A. C., J. R. Jones and R. M. Olvera. 2018. Infusing data analytics into the accounting curriculum: A framework and insights from faculty. Journal of Accounting Education (43): 24-39.
Eilifsen, A., F. Kinserdal, W. F. Messier, Jr. and T. E. McKee. 2020. An exploratory study into the use of audit data analytics on audit engagements. Accounting Horizons (December): 75-103.
El-Wakeel, F. 2019. Technology workbook: Agile project management in analytics: Agile project management is an iterative adaptive approach that helps ensure the project delivers what the customer truly needs. Strategic Finance (May): 66-67. (Summary).
El-Wakeel, F. 2020. Technology workbook: Considerations in data analytics problem structuring. Strategic Finance (October): 60-61.
El-Wakeel, F. 2022. Change management in disguise: When looking at scaling data analytics within your organization, remember that factoring in change management is essential. Strategic Finance (January): 62-63.
El-Wakeel, F. 2022. Storytelling in data strategy. Strategic Finance (September): 62-63.
El-Wakeel, F., L. Jiles and R. Lawson. 2020. Storytelling with data visualization: Leverage data visualization tools and techniques to tell the story behind the data and deliver greater strategic value. Strategic Finance (December): 34-39.
Eliman, E. E. 2023. Leading with data analytics. Strategic Finance (July): 28-30.
Enget, K., G. D. Soucedo and N. S. Wright. 2017. Mystery, Inc.: A Big Data case. Journal of Accounting Education (38): 9-22.
Fantini, F. and D. Narayandas. 2023. Analytics for marketers: When to rely on algorithms and when to trust your gut. Harvard Business Review (May/June): 82-91.
Fass, N. 2018. CFOs are making data and analytics top priorities. Strategic Finance (October): 9.
Fass, N. 2019. Mature analytics improve profit margins. Strategic Finance (October): 9.
Fay, R. and E. M. Negangard. 2017. Manual journal entry testing: Data analytics and the risk of fraud. Journal of Accounting Education (38): 37-49.
Ferguson, C. M. 2021. The IRS's big plans for big data. The CPA Journal (August/September): 73-74.
Fischetti, T. 2015. Data Analysis with R. Packt Publishing.
Fisher, I. E., M. R. Garnsey, S. Goel and K. Tam. 2010. The role of text analytics and information retrieval in the accounting domain. Journal of Emerging Technologies in Accounting (7): 1-24.
Fitzgerald, M. 2015. General Mills builds up big data to answer big questions. MIT Sloan Management Review (Summer): 34.
Fitzgerald, M. 2016. Better data brings a renewal at the Bank of England. MIT Sloan Management Review (Summer): 3-13.
Fitzgerald, M. 2016. Building a better car company with analytics. MIT Sloan Management Review (Summer): 40-44.
Fitzgerald, M. 2016. Data-driven city management. MIT Sloan Management Review (Summer): 3-10.
Fitzgerald, M. 2016. General Motors relies on IoT to keep its customers safe and secure. MIT Sloan Management Review (Summer): 86-91.
Fogarity, D. and P. C. Bell. 2014. Should you outsource analytics? MIT Sloan Management Review (Winter): 41-45.
Foreman, J. W. 2013. Data Smart: Using Data Science to Transform Information into Insight. Wiley.
Gantman, S. and L. Metzger. 2022. Vendor master data cleaning - A project for accounting class. Journal of Emerging Technologies in Accounting 19(1): 165-171.
Geerts, G. 2021. Drive business success with data analytics: Data comes from many sources and in many types. Your insights from this data are what will lead to better business performance. Journal of Accountancy (June): 37, 39, 41, 43, 45, 47, 49, 51.
Godin, S. 2019. Leaders don't hide behind data. MIT Sloan Management Review (Fall): 1-3.
Govindarajan, V. and N. V. Venkatraman. 2022. The next great digital advantage: Smart businesses are using datagraphs to reveal unique solutions to customer problems. Harvard Business Review (May/June): 56-63.
Gray, D. 2021. What makes successful frameworks rise above the rest: Business leaders can better assess and strengthen analytical frameworks using seven evaluation criteria. MIT Sloan Management Review (Summer): 1-6.
Gray, G. L. and R. S. Debreceny. 2014. A taxonomy to guide research on the application of data mining to fraud detection in financial statement audits. International Journal of Accounting Information Systems 15(4): 357-380.
Gregg, A. 2017. Start-ups embrace cryptocurrency to raise needed capital: 'Initial coin offerings' let companies raise money without ceding control. The Washington Post (December 4): A13. (Note).
Griffin, P. A. and A. M. Wright. 2015. Commentaries on big data's importance for accounting and auditing. Accounting Horizons (June): 377-379.
Grobart, J. 2020. Preparing for whistleblower complaints. AI and analytics can be useful tools when investigating whistleblower claims and rooting out fraud and other illegal behaviors. Strategic Finance (September): 34-39.
Grus, J. 2015. Data Science from Scratch: First Principles with Python. O'Reily Media.
Guan, J., A. S. Levitan and S. Goyal. 2018. Text mining using Latent Semantic Analysis: An illustration through examination of 30 years of research at JIS. Journal of Information Systems (Spring): 67-86.
Guerriero, E., R. L. Engebretson and C. W. Parker. 2019. Leveraging data analytics: Uncovering hidden opportunities, generating revenue, and serving clients. The CPA Journal (December): 70-75.
Hagel, J. 2013. Why accountants should own big data. Journal of Accountancy (November): 20-21. (Business intelligence).
Hagiu, A. and J. Wright. 2020. When data creates competitive advantage... and when it doesn't. Harvard Business Review (January/February): 94-101. ("To determine to what degree a competitive advantage provided by data-enabled learning is sustainable, companies should answer seven questions).
Han, J. and M. Kamber. 2006. Data Mining Concepts and Techniques. Morgan Kaufmann Publishers.
Haq, I., M. Abatemarco and J. Hoops. 2020. The development of machine learning and its implications for public accounting. The CPA Journal (June): 6-9.
Harmon, W. K., S. Mutlu and Z. Ye. 2023. Book review: Richardson, V. J., R. A. Teeter and K. L. Terrell. 2003. Data Analytics for Accounting (3rd edition). McGraw Hill. Accounting Horizons (December): 207-211.
Harper, C. and C. Dunn. 2018. Building better accounting curricula: Data management and data analytics, as well as the new technologies associated with them, are driving modern accounting. Higher learning needs to catch up. Strategic Finance (August): 46-53.
Harvard Business Review. 2017. How companies really use big data. Harvard Business Review (September/October): 26.
Harvard Business Review. 2017. How data science is disrupting the job market. Harvard Business Review (September/October): 24.
Harvard Business Review. 2019. People don't need as much data as they think. Harvard Business Review (May/June): 28.
Hastie, T., R. Tibshirani and J. Friedman. 2009. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, second edition. Springer.
Hawkins, S. R., J. Pickerd, S. L. Summers and D. A. Wood. 2023. The development of the process mining event log generator (PMELG) tool. Accounting Horizons (December): 89-95.
Hayashi, A. M. 2014. Thriving in a big data world. MIT Sloan Management Review (Winter): 35-39.
Hayes, L. and J. E. Boritz. 2021. Classifying restatements: An application of machine learning and textual analytics. Journal of Information Systems (Fall): 107-131.
Heichler, E. 2018. Why the data marketplaces of the future will sell insights, not data. MIT Sloan Management Review (Fall): 1-4.
Heister, S., M. Kaufman and K. Yuthas. 2021. Blockchain and the future of business data analytics. Journal of Emerging Technologies in Accounting 18(1): 87-98.
Hermanson, D. R., J. G. Lawson and D. S. Street. 2022. Detecting and resolving 'dirty' data: Ten steps to better business insights. The CPA Journal (July/August): 36-41. (Dirty data refers to invalid, incomplete, or inaccurate data).
Hernandez, M., R. Raveendhran, E. Weingarten and M. Barnett. 2019. How algorithms can diversify the startup pool: Data-driven approaches can help venture capital firms limit gender bias and make better, fairer investment decisions. MIT Sloan Management Review (Fall): 71-78.
Hey, T. 2010. The next scientific revolution. Harvard Business Review (November): 56-63.
Hines, C. S. and G. P. Tapis. 2022. Accounting-specific data analytics: A framework for addressing AACSB Standard A5 and industry demand. Journal of Emerging Technologies in Accounting 19(1): 173-180.
Hoelscher, J. and A. Mortimer. 2018. Using Tableau to visualize data and drive decision-making. Journal of Accounting Education (44): 49-59.
Hoelscher, J. and T. Shonhiwa. 2023. J&S Publisher problems: A diagnostic analytics case exploring employee expense reimbursement. Journal of Emerging Technologies in Accounting 20(1): 213-221.
Hoelscher, J. L. and T. Shonhiwa. 2021. Not so fuzzy auditing analytics. Journal of Emerging Technologies in Accounting 18(1): 99-112.
Hoffman, R. 2016. Using artificial intelligence to set information free. MIT Sloan Management Review (Fall): 1-15.
Hogarth, R. M. and E. Soyer. 2015. Using simulated experience to make sense of big data. MIT Sloan Management Review (Winter): 49-54.
Holsapple, C., A. Lee-Post and R. Pakath. 2014. A unified foundation for business analytics. Decision Support. Systems. 64, 130-141.
Holt, M. and B. Lang. 2021. GADGET: An accounting data generator. Journal of Emerging Technologies in Accounting 18(1): 113-129.
Holt, M., B. Lang and S. G. Sutton. 2017. Potential employees' ethical perceptions of active monitoring: The dark side of data analytics. Journal of Information Systems (Summer): 107-124.
Holt, T. P. and T. M. Loraas. 2021. A potential unintended consequence of big data: Does information structure lead to suboptimal auditor judgment and decision-making? Accounting Horizons (September): 161-186.
Holton, C., 2009. Identifying disgruntled employee systems fraud risk through text mining: A simple solution for a multi-billion dollar problem. Decision Support Systems 46(4): 853-864.
Hopper, G. 2021. Deep Finance: Corporate Finance in the Information Age. Leaders Press.
Hua, Z., Y. Wang, X. Xu, B. Zhang and L. Liang. 2007. Predicting corporate financial distress based on integration of support vector machine and logistic regression. Expert Systems with Applications 33(2): 434-440.
Huang, A. H., H. Wang and Y. Yang. 2023. FinBERT: A large language model for extracting information from financial text. Contemporary Accounting Research 40(2): 806-841.
Huerta, E. and S. Jensen. 2017. An accounting information systems perspective on data analytics and big data. Journal of Information Systems (Fall): 101-114.
Hume, E. and A. West. 2020. Becoming a data-driven decision making organization. The CPA Journal (April): 32-35.
Islam, M. S., N. Farah and T. Wang. 2023. Accounting data analytics in R: A case study using Tidyverse. Journal of Emerging Technologies in Accounting 20(2): 243-250.
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