An emerging online business decision making architecture in a dynamic economic environment

Abstract

It is widely acknowledged that a corporate's profitability that decreases dramatically not only threatens both potential and current investors, but also can freeze stock market transactions as well as deteriorate the flow of economic resources. However, far too little attention has been paid to this issue, which also has been deemed as a main trigger for a financial crisis. To confront this problem, a decision support system can be built up to evaluate a corporate's operating performance. Thus, this study introduces a novel architecture for forecasting the online operating performance of a firm when entering data at different time intervals. The introduced architecture is grounded on multiple data envelopment analysis specifications, dynamic fuzzy c-means (DFCM), and extreme support vector machine (ESVM). Because obtaining a comprehensible architecture is essential for achieving high accuracy in today's knowledge-based economy, this study advances the opaque nature of the introduced architecture and extracts the inherent knowledge to represent it in a transparent, human-readable format. The decision logics can be judged or examined by users, which will increase the acceptance rate of the architecture as well as enhance its practical application. Our built-up architecture, tested by real cases, is a promising alternative for financial performance forecasting.

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

“…First, we worked on the target sample of Taiwan's electronic industry, which suggests that the ability to generalize the results is limited. Second, future research can enhance the model's forecasting quality by integrating much more sophisticated structures, such as ensemble learning and stacking [80,81]. Finally, recent related works suggest that the debate concerning corporate operating performance and CSR status should be taken further by including additional intermediate variables or the variables derived from theoretical constructs that can enhance our realization of the procedures through which CSR influences corporate operating performance [72,73,76,82].…”

Drilling down artificial intelligence in entrepreneurial management: A bibliometric perspective

“…Akgun et al (2010) use a comparative rough set data analysis method to identify the key factors that determine the level of infiltration of peasant entrepreneurs. Lin et al (2019) introduce a decision support system that includes multiple data envelopment analysis specifications, dynamic fuzzy c-means (DFCM) and extreme support vector machine (ESVM) methods to evaluate business performance in real time.…”

An Empirical Analysis of Corporate Financial Management Risk Prediction Based on Associative Memory Neural Network

“…Then examples will be given to illustrate the design and the weight solving process of recurrent neural network-based heteroassociative memory applications. Traditional character recognition algorithms include template matching and OCR [ 19 ]. For unbalanced sample data, you can start with downsampling and upsampling methods.…”