Provided by James R. Martin, Ph.D., CMA
Professor Emeritus, University of South Florida
Expert
Systems Main Page | Automation Bibliography
| Data Mining Bibliography
Abdolmohammad, M. J. 1991. Factors affecting auditors' perceptions of applicable decision aids for various audit tasks. Contemporary Accounting Research 7(2): 535-548.
Abernethy, M. A., M. Horne, A. M. Lillis, M. A. Malina and F. H. Selto. 2005. A multi-method approach to building causal performance maps from expert knowledge. Management Accounting Research (June): 135-155.
Ackoff, R. L. 1955. Automatic management: A forecast and its educational implications. Management Science (October): 55-60.
Adam, A. 1998. Artificial Knowing: Gender and the Thinking Machine. Routledge.
Agrawal, A., J. S. Gans and A. Goldfarb. 2017. What to expect from artificial intelligence. MIT Sloan Management Review (Spring): 23-26.
Agrawal, A., J. Gans and A. Goldfarb. 2018. Prediction Machines: The Simple Economics of Artificial Intelligence. Harvard Business Review Press.
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).
Agrawal, A., J. Gans and A. Goldfarb. 2022. From prediction to transformation: To realize their potential, AI technologies need new systems that leverage them. Harvard Business Review (November/December): 100-109.
Akerkar, R. and P. Sajja. 2009. Knowledge-Based Systems. Jones & Bartlett Publishers.
Akers, M. D. and G. L. Porter. 1986. Expert systems for management accountants. Management Accounting (March): 30-34.
Allen, M. K. 1987. The Development of an Artificial Intelligence System for Inventory Management. Council of Logistics Management.
Almufadda, G. and N. A. Almezeini. 2022. Artificial intelligence applications in the auditing profession: A literature review. Journal of Emerging Technologies in Accounting 19(2): 29-42.
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).
Arnold, V. 1998. Discussion of factors that influence reliance on decision aids: A model and an experiment. Journal of Information Systems (Fall): 95-97.
Arnold, V., N. Clark, P. A. Collier, S. A. Leech and S. G. Sutton. 2005. An investigation of knowledge-based systems' use to promote judgment consistency in multicultural firm environments. Journal of Emerging Technologies in Accounting (2): 33-59.
Arrington, C. E., W. Hillison and R. E. Jensen. 1984. An application of analytical hierarchy process to model expert judgments on analytical review procedures. Journal of Accounting Research (Spring): 298-312.
Babic, B., D. L. Chen, T. Evgeniou and A.Fayard. 2020. A better way to onboard AI: Understand it as a tool to assist rather than replace people. Harvard Business Review (July/August): 56-65.
Babic, B., I. G. Cohen, T. Evgeniou and S. Gerke. 2021. When machine learning goes off the rails: A guide to managing the risks. Harvard Business Review (January/February): 76-84.
Bagranoff, N. A. 1999. Select your next system with high-tech tools. Strategic Finance (May): 75-79. (Discussion of decision support software available from a variety of vendors including software related internet sites).
Bailey, A. D. Jr., K. Hackenbrack, P. De and J. Dillard. 1987. Artificial intelligence, cognitive science, and computational modeling in auditing research: A research approach. Journal of Information Systems (Spring): 20-40.
Bakarich, K. M. and P. E. O'Brien. 2021. The robots are coming...But aren't here yet: The use of artificial intelligence technologies in the public accounting profession. Journal of Emerging Technologies in Accounting 18(1): 27-43.
Baker, W. M. and P. R. Witmer. 1997. Intelligent agents go to work for management accountants. Management Accounting (April): 32, 34-35.
Balwin-Morgan, A. 1993. The impact of expert system audit tools on auditing firms in the year 2001. A Delphi investigation. Journal of Information Systems (Spring): 16-34.
Banham, R. 2018. Taking stock of artificial intelligence. Journal of Accountancy (June): 64-66.
Barro, S. and T. H. Davenport. 2019. People and machines: Partners in innovation. MIT Sloan Management Review (Summer): 22-28.
Baxter, K. and Y. Schlesinger. 2023. Managing the risks of generative AI. Harvard Business Review (Winter Special Issue): 137-139.
Beane, M. 2019. Learning to work with intelligent machines. Harvard Business Review (September/October): 140-148.
Benbasat, I. and A. S. Dexter. 1982. Individual differences in the use of decision support aids. Journal of Accounting Research (Spring): 1-11.
Bender, E. A. 1996. Mathematical Methods in Artificial Intelligence (IEEE Computer Society Press). Wiley-IEEE.
Benson, B. and A. Aljabr. 2022. Artificial intelligence and data science: Next wave change. Cost Management (September/October): 18-22.
Berner, E. S. 1998. Clinical Decision Support Systems: Theory and Practice. Springer-Verlag.
Bertomeu, J. 2020. Machine learning improves accounting: Discussion, implementation and research opportunities. Review of Accounting Studies 25(3): 1135-1155.
Bertomeu, J., E. Cheynel, E. Floyd and W. Pan. 2021. Using machine learning to detect misstatements. Review of Accounting Studies 26(2): 468-519.
Biggs, S., M. Selfridge and G. R. Krupka. 1993. A computational model of auditor knowledge and reasoning processes and the going-concern judgment. Auditing: A Journal of Practice and Theory (12 Supplement): 82-99.
Bharadwaj, A., V. Karan, R. K. Mahapatra, U. S. Murthy and A. S.Vinze. 1994. APX: An integrated knowledge based system to support audit planning. International Journal of Intelligent Systems in Accounting, Finance, and Management 3(3): 149-164.
Blackman, R. 2022. Why you need an AI ethics committee: Expert oversight will help you safeguard your data and your brand. Harvard Business Review (July/August): 118-125.
Blyakhman, A. 2021. Technology workbook: The new era of Automl. Strategic Finance (February): 60-61. (Machine learning).
Boer, G. B. and J. Livnat. 1990. Using expert systems to teach complex accounting issues. Issues in Accounting Education (Spring): 108-119.
Bojinov, I. 2023. Keep your AI projects on track: Most go off course. To make sure yours succeed, consider these five steps. Harvard Business Review (November/December): 53-59.
Bonson, E., D. Lavorato, R. Lamboglia and D. Mancini. 2021. Artificial intelligence activities and ethical approaches in leading listed companies in the European Union. International Journal of Accounting Information Systems (43): 100535.
Bothick, A. F. and O. D. West. 1987. Expert systems - A new tool for the professional. Accounting Horizons (March): 9-16.
Bowling, S. and C. Meyer. 2019. How we successfully implemented AI in audit. Journal of Accountancy (June): 26-28.
Brown, C., E. J. Coakley and M. E. Phillips. 1995. Neural networks enter the world of management accounting. Management Accounting (May): 51-57. (Related to artificial intelligence).
Brown, C. E. and D. S. Murphy. 1990. The use of auditing expert systems in public accounting. Journal of Information Systems (Fall): 63-72.
Brown, C. E. and M. E. Phillips. 1990. Expert systems for management accountants. Management Accounting (January): 18-23.
Brown, D. L. and D. R. Jones. 1998. Factors that influence reliance on decision aids: A model and an experiment. Journal of Information Systems (Fall): 75-94.
Bughin, J. 2018. Wait-and-see could be a costly AI strategy. MIT Sloan Management Review (Summer): 1-4.
Bughin, J. 2018. Why AI isn't the death of jobs. MIT Sloan Management Review (Summer): 42-46.
Burden, D. J. H. 2009. Deploying embodied AI into virtual worlds. Knowledge-Based Systems (October): 540-544.
Burney, L., K. Church, M. Akpan and S. Dell. 2023. ChatGPT and AI in accounting education and research. Strategic Finance (August): 61-68.
Butler, S. A. 1985. Application of a decision aid in the judgmental evaluation of substantive test of details samples. Journal of Accounting Research (Autumn): 513-526.
Cai, C. W., M. K. Linnenluecke, M. Marrone and A. K. Singh. 2019. Machine learning and expert judgement: Analyzing emerging topics in accounting and finance research in the Asia-Pacific. Abacus 55(4): 709-733.
Candelon, F., R. C. di Carlo and S. D. Mills. 2021. AI-at-scale hinges on gaining a 'social license'. MIT Sloan Management Review (Fall): 1-4.
Candelon, F., R. C. di Carlo, M. De Bondt and T. Evgeniou. 2021. AI regulation is coming. Harvard Business Review (September/October): 102-111.
Carmon, Z., R. Schrift, K. Wetenbrock and H. Yang. 2020. Designing AI systems that customers won't hate. MIT Sloan Management Review (Winter): 1-6.
Castelluccio, M. 2017. Technology workbook: AI rising. Strategic Finance (March): 63-65.
Castelluccio, M. 2017. Technology workbook: Artificial intelligence in business. Strategic Finance (April): 55-57.
Castelluccio, M. 2018. Technology workbook: The far limits of AI - Part II. Strategic Finance (January): 55-56.
Castelluccio, M. 2018. Technology workbook: The malicious use of AI. Strategic Finance (April): 55-56. ("The rise of AI could be the worst or the best thing that has happened for humanity." Stephen Hawking).
Castelluccio, M. 2018. Technology workbook: The Malicious use of AI: Part II. Strategic Finance (May): 55-56.
Castelluccio, M. 2020. Technology workbook: A written test for artificial general intelligence. Strategic Finance (November): 53-54.
Causey, A. 1998. The Essence of Artificial Intelligence. Prentice-Hall PTR.
Chabris, C. E. 1987. Artificial Intelligence and Turbo Pascal/Book and Disk. Irwin Professional Publication.
Chamorro-Premuzic, T. 2020. Can surveillance AI make the workplace safe? MIT Sloan Management Review (Fall): 13-15.
Chan, K. H. and B. Dodin. 1986. A decision support system for audit-staff scheduling with precedence constraints and due dates. The Accounting Review (October): 726-734.
Chan, S. H., D. J. Lowe and L. J. Yao. 2008. The legal implications of auditors using a fraud decision aid vs. professional judgment. Journal of Forensic Accounting 9(1): 63-82.
Chaudhuri, S., U. Dayal, and V. Narasayya. 2011. An overview of business intelligence technology. Communications of the ACM 54(8), 88-98.
Cho, S., M. A. Vasarhelyi, T. Sun and C. Zhang. 2020. Learning from machine learning in accounting and assurance. Journal of Emerging Technologies in Accounting 17(1): 1-10.
Chu, P. 1991. A study of the influence of a decision support aid on decision processes: Exploring the blackbox. Journal of Information Systems (Fall): 1-17.
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.
Coleman, B., K. Merkley and J. Pacelli. 2022. Human versus machine: A comparison of robo-analyst and traditional research analyst investment recommendations. The Accounting Review (September): 221-244.
Colvin, G. 2015. Humans Are Underrated: What High Achievers Know that Brilliant Machines Never Will. Portfolio/Penguin. (Contents and Summary).
Commerford, B. P., S. A. Dennis, J. R. Joe and J. W. Ulla. 2022. Man versus machine: Complex estimates and auditor reliance on artificial intelligence. Journal of Accounting Research (March): 171-201.
Comunale, C. L. and T. R. Sexton. 2005. A fuzzy logic approach to assessing materiality. Journal of Emerging Technologies in Accounting (2): 1-16.
Cook, D. and S. Das. 2004. Smart Environments: Technology, Protocols and Applications (Wiley Series on Parallel and Distributed Computing). Wiley-Interscience.
Cormen, T. H., C. E. Leiserson, R. L. Rivest and C. Stein. 2009. Introduction to Algorithms, 3rd Edition. The MIT Press.
Costello, A. M., A. K. Down and M. N. Mehta. 2020. Machine + man: A field experiment on the role of discretion in augmenting AI-based lending models. Journal of Accounting and Economics (November-December): 101360.
Cui, G., M. L. Wong and H. Lui. 2006. Machine learning for direct marketing response models: Bayesian networks with evolutionary programming. Management Science (April): 597-612.
Cusumano, M. A., D. B. Yoffie and A. Gawer. 2020. The future of platforms: Platforms power some of the world's most valuable companies, but it will get harder and harder to capture and monetize their disruptive potential. MIT Sloan Management Review (Spring): 46-54. (Summary).
Daniels, R. B. and J. Beeler. 2001. An archival investigation of a late 19th century accounting information system: The use of decision aids in the American printing industry. The Accounting Historians Journal 28(1): 3-18.
Daugherty, P. R., H. J. Wilson and P. Michelman. 2019. Revising the jobs artificial intelligence will create. MIT Sloan Management Review (Summer): 1-8.
Daugherty, P. R., H. J. Wilson and R. Chowdhury. 2019. Using artificial intelligence to promote diversity. MIT Sloan Management Review (Winter): 1-6.
Davenport, T. H. 2016. Rise of the strategy machines. MIT Sloan Management Review (Fall): 13-16.
Davenport, T. H. 2019. Can we solve AI's 'trust problem'? MIT Sloan Management Review (Winter): 1-5.
Davenport, T. H. and J. Glaser. 2002. Just-in-time delivery comes to knowledge management. Harvard Business Review (July): 107-111. (Summary).
Davenport, T. H. and J. G. Harris. 2005. Automated decision making comes of age. MIT Sloan Management Review (Summer): 83-89.
Davenport, T. H. and J. Kirby. 2016. Just how smart are smart machines? MIT Sloan Management Review (Spring): 21-25.
Davenport, T. H. and N. Mittal. 2023. Stop tinkering with AI. Harvard Business Review (January/February): 116-127.
Davenport, T. H. and R. Ronanki. 2018. Artificial intelligence for the real world. Harvard Business Review (January/February): 108-116.
Davenport, T. H. and S. Kudyba. 2016. Designing and developing analytics-based data products. MIT Sloan Management Review (Fall): 82-89.
Davenport, T. H. and S. M. Miller. 2022. What machines can't do (yet) in real work settings. MIT Sloan Management Review (Fall): 1-5.
Davenport, T. H., G. Guha and D. Grewal. 2021. How to design an AI marketing strategy: What the technology can do today - and what's next. Harvard Business Review (July/August): 42-47.
Davenport, T. H., I. Barkin and K. Tomak. 2023. We're all programmers now: With generative AI, anyone can code. Here's how to help your enterprise embrace this change. Harvard Business Review (September/October): 98-107.
David, J. S. , C. L. Dunn, W. E. McCarthy and R. S. Poston. 1999. The research pyramid: A framework for accounting information systems research. Journal of Information Systems 13(1): 7-30.
Dickey, G., S. Blanke and L. Seaton. 2019. Machine learning in auditing: Current and future applications. The CPA Journal (June): 16-21.
Dickie, J., B. Groysberg, B. P. Shapiro, and B. Trailer. 2022. Can AI really help you sell? It can, depending on when and how you implement it. Harvard Business Review (November/December): 120-129.
Ding, K., B. Lev, X. Peng, T. Sun and M. A. Vasarhelyi. 2020. Machine learning improves accounting estimates: Evidence from insurance payments. Review of Accounting Studies 25(3): 1098-1134.
Ding, K., X. Peng and Y. Wang. 2019. A machine learning-based peer selection method with financial ratios. Accounting Horizons (September): 75-87.
Dorr, P. M. Eining and J. E. Groff. 1988. Developing an accounting expert system decision aid for classroom use. Issues in Accounting Education (Spring): 27-41.
Dowling, C. 2008. Discussion of “An examination of contextual factors and individual characteristics affecting technology implementation decisions in auditing”. International Journal of Accounting Information Systems 9(2): 122-126.
Dowling, C. and S. Leech. 2007. Audit support systems and decision aids: Current practice and opportunities for future research. International Journal of Accounting Information Systems 8(2): 92-116.
Drew, J. 2017. Real talk about artificial intelligence and blockchain. Journal of Accountancy (July): 22-26, 28.
Drew, J. 2019. What's 'critical' for CPAs to learn in an AI-powered world. Journal of Accountancy (June): 20-24. (Panel).
Drew, J. 2019. Why patience is important with AI. Journal of Accountancy (July): 38-42.
Drew, J. and K. Tysiac. 2019. What to expect in 2020. Journal of Accountancy (December): 22-27.
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.
Durkin, J. 1998. Expert Systems: Design and Development. Macmillan College Division.
Eapen, T. T., D. J. Finkenstadt, J. Folk and L. Venkataswamy. 2023. How generative AI can augment human creativity: Use it to promote divergent thinking. Harvard Business Review (July/August): 56-64. (Cover story).
Edelman, D. C. and M. Abraham. 2022. Customer experience in the age of AI. Harvard Business Review (March/April): 116-125.
Ege, G. and W. G. Sullivan. 1990. Expert systems update. Management Accounting (January): 21. (Summary).
Eining, M. M. 1995. Discussion of the impact of elaboration-based expert system interfaces on de-skilling: An epistemological issue. Journal of Information Systems (Spring): 19-22.
Eining, M. M. and P. B. Dorr. 1991. The impact of expert system usage on experiential learning in an auditing setting. Journal of Information Systems (Spring): 1-16.
El-Wakeel, F. and M. Nandy. 2023. AI from cradle to SDG achievement. Strategic Finance (May): 62-63.
Elbashir, M. Z., P. A. Collier and M. J. Davern. 2008. Measuring the effects of business intelligence systems: The relationship between business process and organizational performance. International Journal of Accounting Information Systems 9(3): 135-153.
Elliot, V. H., M. Paananen and M. Staron. 2020. Artificial intelligence for decision-makers. Journal of Emerging Technologies in Accounting 17(1): 51-55.
Fedyk, A., J. Hodson, N. Khimich and T. Fedyk. 2022. Is artificial intelligence improving the audit process? Review of Accounting Studies 27(3): 938-985.
Fisher, M., S. Gallino and J. Li. 2023. A step-by-step guide to real-time pricing: An advanced AI model considers much more than what competitors are charging. Harvard Business Review (November/December): 92-101.
Fountaine, T., B. McCarthy and T. Saleh. 2019. Building the AI-powered organization. Harvard Business Review (July/August): 62-73.
Fountaine, T., B. McCarthy and T. Saleh. 2021. Getting AI to scale: Don't try to change everything at once, but do begin with something important. Harvard Business Review (May/June): 116-123.
Friedland, J. 2019. AI can help us live more deliberately. MIT Sloan Management Review (Summer): 45-51.
Gal, G. and P. Steinbart. 1987. Artificial intelligence and research in accounting information systems: Opportunities and issues. Journal of Information Systems (Fall): 54-62.
Gandhi, S. and E. Gervet. 2016. Now that your products can talk, what will they tell you? MIT Sloan Management Review (Spring): 49-50.
Gerbert, P. and M. Spira. 2019. Learning to love the AI bubble. MIT Sloan Management Review (Summer): 1-3.
Giarratano, J. C. and G. D. Riley. 1998. Expert Systems: Principles and Programming. Course Technology.
Gomaa, M. I., J. E. Hunton, E. H. J. Vaassen and M. A. Carree. 2011. Decision aid reliance: Modeling the effects of decision aid reliability and pressures to perform on reliance behavior. International Journal of Accounting Information Systems 12(3): 206-224.
Gray, G. L., V. Chiu, Q. Liu and P. Li. 2014. The expert systems life cycle in AIS research: What does it mean for future AIS research? International Journal of Accounting Information Systems 15(4): 423-451.
Greer, W. R. Jr. and H. Rockness. 1987. Management decision support systems for medical group practice. Journal of Information Systems (Spring): 65-79.
Grenfell, N. 2021. Artificial intelligence and cost and profitability management. Cost Management (November/December): 43-48.
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.
Hagel, J. 2013. Why accountants should own big data. Journal of Accountancy (November): 20-21. (Business intelligence).
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.
Harvard Business Review. 2017. AI's early conquests. Harvard Business Review (July/August): 30.
Harvard Business Review. 2017. Augmented reality in the real world. Harvard Business Review (November/December): 59.
Harvard Business Review. 2017. How does augmented reality work? Harvard Business Review (November/December): 58.
Harvard Business Review. 2019. A new leader in AI research? Harvard Business Review (July/August): 27.
Harvard Business Review. 2019. The industries in which artificial intelligence start-ups are being funded. Harvard Business Review (May/June): 30.
Häubl, G. and K. B. Murray. 2006. Double agents. MIT Sloan Management Review (Spring): 8-12.
Hisey, L. 2020. Leveraging lean six sigma with artificial intelligence. Cost Management (July/August): 20-23.
Hodge, F. D., K. I. Mendoza and R. K. Sinha. 2021. The effect of humanizing robo-advisors on investor judgments. Contemporary Accounting Research 38(1): 770-792.
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.
Homes, A. F. and A. Douglass. 2022. Artificial intelligence: Reshaping the accounting profession and the disruption to accounting education. Journal of Emerging Technologies in Accounting 19(1): 53-68.
Hopper, G. 2021. Deep Finance: Corporate Finance in the Information Age. Leaders Press.
Hornik, S. and B. M. Ruf. 1997. Expert systems usage and knowledge acquisition: An empirical assessment of analogical reasoning in the evaluation of internal controls. Journal of Information Systems (Fall): 57-74.
Howard, A. 2019. The regulation of AI - Should organizations be worried? MIT Sloan Management Review (Summer): 1-3.
Huerta, E., T. Glandon and Y. Petrides. 2012. Framing, decision-aid systems, and culture: Exploring influences on fraud investigations. International Journal of Accounting Information Systems 13(4): 316-333.
Hurson, J. 2018. Creating a brighter future with artificial intelligence. Strategic Finance (April): 12-13.
Issa, H., T. Sun and M. A. Vasarhelyi. 2016. Research ideas for artificial intelligence in auditing: The formalization of audit an workforce supplementation. Journal of Emerging Technologies in Accounting 13(2): 1-20.
Jackson, P. 1998. Introduction to Expert Systems (3rd Edition). Addison Wesley.
Jeacle, E. and C. Carter. 2011. In TripAdvisor we trust: Rankings, calculative regimes and abstract systems. Accounting, Organizations and Society 36(4-5): 293-309.
Johnson, B. G., F. Phillips and L. G. Chase. 2009. An intelligent tutoring system for the accounting cycle: Enhancing textbook homework with artificial intelligence. Journal of Accounting Education 27(1): 30-39.
Jones, D. R., D. Brown and P. Wheeler. 2001. The effect of justification type on agreement with a decision aid and judgment performance. Advances in Accounting Behavioral Research (4): 187-206.
Jones, M. T. 2008. Artificial Intelligence: A Systems Approach. Jones & Bartlett Publishers.
Juma'h, A. H. and L. Arumugam. 2023. Reaping AI benefits on a budget. Strategic Finance (October): 29-31.
Kapanowski, G. 2023. The rise of AI and ChatGPT is here: Now what? Cost Management (July/August): 43-47.
Kaufmann, M., P. Manolios and J. S. Moore. 2000. Computer-Aided Reasoning: An Approach (Advances in Formal Methods). Kluwer Academic Publishers.
Kavadias, S., K. Ladas and C. Loch. 2016. The transformative business model: How to tell if you have one. Harvard Business Review (October): 90-98. (Summary).
Kellogg, K. C., M. Sendak and S. Balu. 2022. AI on the front lines: AI progress can stall when end users resist adoption. Developers must think beyond a projects business benefits and ensure that end users' workflow concerns are addressed. MIT Sloan Management Review (Summer): 44-50.
Khalifeh, T. 2023. Accounting in the age of generative AI: Accounting and finance professionals can benefit from ChatGPT, but they must take steps to mitigate risks. Strategic Finance (October): 55-64.
King, M. and L. McAulay. 1991. A standard costing knowledge base: Building and using an expert system in management accounting education. Issues in Accounting Education (Spring): 97-111.
Kiron, D. 2022. AI can change how you measure - and how you manage. MIT Sloan Management Review (Spring): 24-28.
Kiron, D. and G. Unruh. 2019. Even if AI can cure loneliness - Should it? MIT Sloan Management Review (Winter): 1-4.
Kiron, D. and M. Schrage. 2019. Strategy for and with AI. MIT Sloan Management Review (Summer): 30-35.
Klotz, F. 2016. Are you ready for robot colleagues? MIT Sloan Management Review (Fall): 60-65.
Klotz, F. 2018. Building a robotic colleague with personality. MIT Sloan Management Review (Summer): 1-5.
Klotz, F. 2018. How AI can amplify human competencies. MIT Sloan Management Review (Fall): 14-15.
Klotz, F. 2018. The quest to create utterly normal virtual reality experiences. MIT Sloan Management Review (Spring): 1-5.
Klotz, F. 2019. The perils of applying AI prediction to complex decisions. MIT Sloan Management Review (Summer): 1-4.
Koch, R. 2017. Technology workbook: Will artificial intelligence eliminate my job? Strategic Finance (September): 62-63.
Kohavi, R., L. Mason, R. Parekh and Z. Zheng. 2004. Lessons and challenges from mining retail e-commerce data. Machine Learning 57 (1-2): 83-113.
Kokina, J. and T. H. Davenport. 2017. The emergence of artificial intelligence: How automation is changing auditing. Journal of Emerging Technologies in Accounting 14(1): 115-122.
Kozik, E. 1966. Computer augmentation of managerial reasoning. Management Accounting (December): 35-43.
Krolak, P. D. 1971. Portfolio evaluation & re-evaluation: An experiment in subjective probability, man-machine learning and decision making. Decision Sciences 2(2): 225-238.
Latham, S. and B. Humberd. 2018. Four ways jobs will respond to automation. MIT Sloan Management Review (Fall): 11-14.
Lee, M. T. and S. K. Widener. 2016. The performance effects of using business intelligence systems for exploitation and exploration learning. Journal of Information Systems (Fall): 1-31.
Leonardi, P. 2023. Helping employees succeed with generative AI: How to manage performance when new technology brings constant and unpredictable change. Harvard Business Review (November/December): 49-53.
Levesque, H. J. and G. Lakemeyer. 2001. The Logic of Knowledge Bases. The MIT Press.
Lewis, B., M. D. Shields and S. M. Young. 1983. Evaluating human judgments and decision aids. Journal of Accounting Research (Spring): 271-285.
Li, F. 2010. The information content of forward-looking statements in corporate filings - A naive Bayesian machine learning approach. Journal of Accounting Research (December): 1049-1102.
Liang, T. 1988. Expert systems as decision aids: Issues and strategies. Journal of Information Systems (Spring): 41-50.
Lin, P. 2018. Adapting to the new business environment: The rise of software robots in the workplace. The CPA Journal (December): 60-63.
Lin, P. and T. Hazelbaker. 2019. Meeting the challenge of artificial intelligence: What CPAs need to know. The CPA Journal (June): 48-52.
Lowe, D. J., P. M. J. Reckers and S. M. Whitecotton. 2002. The effects of decision-aid use and reliability on jurors' evaluations of auditor liability. The Accounting Review (January): 185-202.
Lucini, F. 2021. The real deal about synthetic data: It's often difficult to access the real-world data needed to train AI models or gain insights, but new techniques for generating look-alike data sets can help. MIT Sloan Management Review (Fall): 1-4.
Maguire, K. 2017. Technology: Machine learning, human fraud. Strategic Finance (March): 70-71.
Malaescu, I. and S. G. Sutton. 2015. The effects of decision aid structural restrictiveness on cognitive load, perceived usefulness, and reuse intentions. International Journal of Accounting Information Systems (17): 16-36.
Malcolm, R. E. 1966. Decision tables in accounting. The Accounting Review (July): 551-555. (An example using the cost-or-market rule).
Malone, T. W. 2018. How human-computer 'superminds' are redefining the future of work. MIT Sloan Management Review (Summer): 34-41.
Martens, B. J., K. P. Scheibe and P. K. Bergey. 2012. Supply chains in sub-Saharan Africa: A decision support system for small-scale seed entrepreneurs. Decision Sciences 43(5): 737-759.
Mascha, M. F. 2001. The effect of task complexity and expert system type on the acquisition of procedural knowledge: Some new evidence. International Journal of Accounting Information Systems 2(2): 103-129.
Mascha, M. F. and G. Smedley. 2007. Can computerized decision aids do “damage”? A case for tailoring feedback and task complexity based on task experience. International Journal of Accounting Information Systems 8(2): 73-91.
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