A BSC-based network DEA model equipped with computational linguistics for performance assessment and improvement

A Fusion Decision-Making Architecture for COVID-19 Crisis Analysis and Management

The COVID-19 outbreak has had considerably harsh impacts on the global economy, such as shutting down and paralyzing industrial production capacity and increasing the unemployment rate. For enterprises, relying on past experiences and strategies to respond to such an unforeseen financial crisis is not appropriate or sufficient. Thus, there is an urgent requirement to reexamine and revise an enterprise’s inherent crisis management architecture so as to help it recover sooner after having encountered extremely negative economic effects. To fulfill this need, the present paper introduces a fusion architecture that integrates artificial intelligence and multiple criteria decision making to exploit essential risk factors and identify the intertwined relations between dimensions/criteria for managers to prioritize improvement plans and deploy resources to key areas without any waste. The result indicated the accurate improvement priorities, which ran in the order of financial sustainability (A), customer and stakeholders (B), enablers’ learning and growth (D), and internal business process (C) based on the measurement of the impact. The method herein will help to effectively and efficiently support crisis management for an organization confronting COVID-19. Among all the criteria, maintaining fixed reserves was the most successful factor regarding crisis management.

Incorporating soft information from financial news media for management decisions in dynamic business environments

Because the nature of numerical information is intuitive and comprehensible, it has been widely used to form a basis for decision making, yet numerical information based on historical principle does not reflect messages about future corporate performance. To confront this issue, one may consider textual information that can transmit future corporate potential without any hysteresis. The key point is how to digest an extensive amount of textual information and identify those topics most likely to precede changes in operation status. Topic modeling can categorize these textual disclosures based on their underlying content and help examine which topics have a strong relevance to corporate operations. To extract decisive words from textual information, we set up a statistical-based approach with objectivity as opposed to frequently used heuristics (i.e., dictionary-based approaches with human involvement). Joint utilization of topic modelling and a statistical-based approach can compress an excessive amount of textual information into a manageable size in a timely manner and further realize a discrepancy among various topics in terms of relevance and influence on corporate operations. Our results benefit managers and current and future investors in how to structure regulatory filings and how word choices are decisive to them in their decision judgments.

An Advanced Decision Making Framework via Joint Utilization of Context-Dependent Data Envelopment Analysis and Sentimental Messages

Compared to widely examined topics in the related literature, such as financial crises/difficulties in accurate prediction, studies on corporate performance forecasting are quite scarce. To fill the research gap, this study introduces an advanced decision making framework that incorporates context-dependent data envelopment analysis (CD-DEA), fuzzy robust principal component analysis (FRPCA), latent Dirichlet allocation (LDA), and stochastic gradient twin support vector machine (SGTSVM) for corporate performance forecasting. Ratio analysis with the merits of easy-to-use and intuitiveness plays an essential role in performance analysis, but it typically has one input variable and one output variable, which is unable to appropriately depict the inherent status of a corporate’s operations. To combat this, we consider CD-DEA as it can handle multiple input and multiple output variables simultaneously and yields an attainable target to analyze decision making units (DMUs) when the data present great variations. To strengthen the discriminant ability of CD-DEA, we also conduct FRPCA, and because numerical messages based on historical principles normally cannot transmit future corporate messages, we execute LDA to decompose the accounting narratives into many topics and preserve those topics that are relevant to corporate operations. Sequentially, the process matches the preserved topics with a sentimental dictionary to exploit the hidden sentiments in each topic. The analyzed data are then fed into SGTSVM to construct the forecasting model. The result herein reveals that the introduced decision making framework is a promising alternative for performance forecasting.