News-Based Soft Information as a Corporate Competitive Advantage

Abstract

This study establishes a decision-making conceptual architecture that evaluates decision making units (DMUs) from numerous aspects. The architecture combines financial indicators together with a variety of data envelopment analysis (DEA) specifications to encapsulate more information to give a complete picture of a corporate’s operation. To make outcomes more accessible to non-specialists, multidimensional scaling (MDS) was performed to visualize the data. Most previous studies on forecasting model construction have relied heavily on hard information, with quite a few works taking into consideration soft information, which contains much denser and more diverse messages than hard information. To overcome this challenge, we consider two different types of soft information: supply chain influential indicator (SCI) and sentimental indicator (STI). SCI is computed by joint utilization of text mining (TM) and social network analysis (SNA), with TM identifying the corporate’s SC relationships from news articles and SNA to determining their impact on the network. STI is extracted from an accounting narrative so as to comprehensively illustrate the relationships between pervious and future performances. The analyzed outcomes are then fed into an artificial intelligence (AI)-based technique to construct the forecasting model. The introduced model, examined by real cases, is a promising alternative for performance forecasting.

Applications of Social Network Analysis in Promoting Circular Economy: A Literature Review

“…Radcliffe-Brown (1940), an anthropologist, described the social structure as the "network of social relations", which is regarded as the origin of SNA conceptualization. SNA focuses on networks whose nodes are social entities like nations, organizations, and individuals (Hsu et al, 2020;Feng et al, 2022). Accordingly, the edge of the networks corresponds to a certain connection or relation between social entities.…”

Identifying Key Factors for Adopting Artificial Intelligence-Enabled Auditing Techniques by Joint Utilization of Fuzzy-Rough Set Theory and MRDM Technique

“…High dimensionalities (i.e., too many criteria) will confuse users and lead to improper judgments (Hsu, 2019a;Hsu et al, 2020). To combat this, feature selection (FS) aims at determining the minimal feature subsets from a problem domain while retaining appropriate precision in representing the original data semantics, and thus we consider it in the second stage.…”

Corporate Social Responsibility and Corporate Performance: A Hybrid Text Mining Algorithm

“…We used DEA to handle the aforementioned task, because it has proven remarkably popular for conducting performance measurement tasks in recent decades. In accordance with previous related works [8,15,16], we chose total assets (TA) and total liabilities (TL) as input variables and designated three profitability measures, return on assets (ROA), sales growth rate (SGR), and profit margin (PM), as output variables. To test the representativeness of the chosen variables, the Pearson correlation was performed.…”

“…Data envelopment analysis (DEA) can deal with this obstacle, as it can handle multiple input and multiple output variables without an a priori assumption of profit maximization or cost minimization [13]. It can also be extended into different modifications and provide a synthesized outcome, thus offering adequate flexibility for practical applications [14][15][16]. How to precisely measure a corporation's operating performance and further construct a reliable forecasting model are notable topics for decision makers [17].…”