NEWS-BASED SOFT INFORMATION AS A CORPORATE COMPETITIVE ADVANTAGE

• Published in: Technological and economic development of economy Vol. 26; no. 1; pp. 48 - 70
• Main Authors: Hsu, Ming-Fu, Chang, Te-Min, Lin, Sin-Jin
• Format: Journal Article
• Language: English
• Published: Vilnius Vilnius Gediminas Technical University 01.01.2020
• Subjects: Accounting; Architecture; Artificial intelligence; Business competition; Data envelopment analysis; Data mining; Decision making; Forecasting; Mathematical models; Network analysis; Operations research; sentimental analysis; Social network analysis; Social networks; supply chain network; Supply chains
• ISSN: 2029-4913; 2029-4921
• DOI: 10.3846/tede.2019.11328

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.