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Accounting method selection using neural networks and multi-criteria decision making

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dc.contributor.author Duan, Yang
dc.contributor.author Yeh, Chung-Hsing
dc.contributor.author Dowe, David L.
dc.date.accessioned 2020-12-24T19:17:56Z
dc.date.available 2020-12-24T19:17:56Z
dc.date.issued 2021-01-05
dc.identifier.isbn 978-0-9981331-4-0
dc.identifier.uri http://hdl.handle.net/10125/70800
dc.description.abstract The selection of accounting methods has significant impacts on companies’ accounting results and strategic goals. However, this selection problem has not been effectively addressed by existing studies. To fill this important gap, we propose a novel approach for evaluating two accounting method alternatives, namely Full Cost (FC) and Successful Effort (SE) with an empirical case of an oil and gas company. Neural networks (NNs), fuzzy multi-criteria decision making (MCDM) with optimal weighting are applied to evaluate the consequent effects of FC and SE on strategic goals of the case company. The empirical study conducted demonstrates the effectiveness of the proposed approach. Methodologically, this paper provides a structured approach for evaluating accounting method alternatives in a rational and informed manner. Empirically, the evidence obtained from applying the proposed approach can be used to support the case company’s decision on accounting method selection.
dc.format.extent 10 pages
dc.language.iso English
dc.relation.ispartof Proceedings of the 54th Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Machine Learning and Predictive Analytics in Accounting, Finance, and Management
dc.subject accounting method
dc.subject accounting results
dc.subject company strategic goals
dc.subject multi-critiera decision making
dc.subject neural networks
dc.title Accounting method selection using neural networks and multi-criteria decision making
dc.identifier.doi 10.24251/HICSS.2021.188
prism.startingpage 1550
Appears in Collections: Machine Learning and Predictive Analytics in Accounting, Finance, and Management


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