An information delivery strategy for multi criteria reporting: a case study on bankruptcy prediction analysis

Q4 Business, Management and Accounting
Aruldoss Martin, T. Lakshmi, V. Venkatesan
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引用次数: 0

Abstract

Reporting is very important in the enterprise information processes and it is very much essential for decision making. Finding the user's current requirements and preference and then providing information accordingly could be a challenging task. An efficient information delivery strategy should provide the suitable information to suitable user according to various criteria. The information which is customised based on the criteria should be delivered at the right time for decision making. Bankruptcy prediction analysis (BPA) consists of different number of users with requirement to different levels of information. The proposed information delivery strategy customises the information, resolves inconsistency and uncertainty issues and finds the suitable user to receive the information with respect to criteria using fuzzy analytic hierarchy process (fuzzy AHP). The different level user preference (most suitable user to least suitable user) has been identified according to the criteria using this strategy to deliver the multi criteria reporting.
多准则报告的信息传递策略:以破产预测分析为例
报告是企业信息处理的重要组成部分,是企业决策的基础。找到用户当前的需求和偏好,然后提供相应的信息可能是一项具有挑战性的任务。有效的信息传递策略应该根据各种标准向合适的用户提供合适的信息。根据标准定制的信息应在正确的时间交付决策。破产预测分析(BPA)由不同数量的用户和对不同信息层次的需求组成。提出的信息传递策略利用模糊层次分析法(fuzzy AHP)对信息进行定制,解决不一致和不确定性问题,并根据标准找到合适的用户接收信息。不同级别的用户偏好(最合适的用户到最不合适的用户)已经根据使用此策略交付多标准报告的标准确定。
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来源期刊
International Journal of Multicriteria Decision Making
International Journal of Multicriteria Decision Making Business, Management and Accounting-Strategy and Management
CiteScore
0.70
自引率
0.00%
发文量
9
期刊介绍: IJMCDM is a scholarly journal that publishes high quality research contributing to the theory and practice of decision making in ill-structured problems involving multiple criteria, goals and objectives. The journal publishes papers concerning all aspects of multicriteria decision making (MCDM), including theoretical studies, empirical investigations, comparisons and real-world applications. Papers exploring the connections with other disciplines in operations research and management science are particularly welcome. Topics covered include: -Artificial intelligence, evolutionary computation, soft computing in MCDM -Conjoint/performance measurement -Decision making under uncertainty -Disaggregation analysis, preference learning/elicitation -Group decision making, multicriteria games -Multi-attribute utility/value theory -Multi-criteria decision support systems and knowledge-based systems -Multi-objective mathematical programming -Outranking relations theory -Preference modelling -Problem structuring with multiple criteria -Risk analysis/modelling, sensitivity/robustness analysis -Social choice models -Theoretical foundations of MCDM, rough set theory -Innovative applied research in relevant fields
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