Abstract
Until now, the works regarding the relationships between corporate operating performance and corporate social responsibility (CSR) could not reach a conclusive result (positive, natural, and negative). This circumstance can be attributed to two main reasons: (1) inadequate performance measurement and (2) ignoring the multi-dimensional nature of CSR. To combat this, we provided a hybrid decision framework that consisted of two main procedures: (1) performance measurement via linear programming algorithm and (2) CSR’s multi-dimensional nature extraction via text mining. By joint utilization of a linear programming algorithm and text mining, we could gain more insights from the outcome. The proposed decision framework, tested by real cases, is a promising alternative method for performance prediction. Managers can take this model as a roadmap and allocate resources to suitable places, as well as reach the goal of sustainable development.
Highlights
Market crises and accounting scandals like Black October 1987, the 1997 Asia financial turmoil, and the subprime mortgage debacle in the U.S have received tremendous attention from academia and practitioners, as is evident from current financial literature [1]
Stock transactions in the electronics sector make up over 70% of Taiwan’s stock market turnover. This specific industry was taken as our research target, with the data gathered from public websites and databases, such as the Taiwan Stock Exchange (TWSE), Taiwan Economic Journal (TEJ) databank, and Taipei Exchange (TE) from the period 2016–2018
Prior related works have yielded non-conclusive results regarding the relationships between corporate operating performance and corporate social responsibility
Summary
Market crises and accounting scandals like Black October 1987, the 1997 Asia financial turmoil, and the subprime mortgage debacle in the U.S have received tremendous attention from academia and practitioners, as is evident from current financial literature [1]. These extraordinary and unusual events have had fatal side effects or caused damage to the respective financial markets, and to societies as a whole. In contrast to well-examined issues such as credit rating forecasting and financial crisis prediction, research on corporate operating performance forecasting is rather scant. Kamei [10] stated that 99% of financial crises are due to bad operating performance—that is, the decline in operating performance can be viewed as a prior stage before a financial crisis bursts forth
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