As the external environment changes rapidly, organizations need management innovation to adapt to and exploit change as an opportunity. To innovate, it is necessary to evaluate management innovation, because if an organization can measure the degree of management innovation, it can also achieve it. Moreover, if management innovation is predictable, profits can be maximized, and costs can be minimized by allocating efficient resources and establishing appropriate strategies. Therefore, this study attempts to predict the management innovation in public research institutions. Basic data mining and ensemble data mining techniques were used for the prediction. This analysis targeted public research institutes in South Korea. The results showed that the predictive power of public research institutions with high innovation was high. This study suggests that management innovation can be predicted in highly innovative public research institutions. Furthermore, this study’s framework can be applied to other industries.
(1) Background: The economic impact of supply and demand in the energy industry can be seen throughout raw material production, processing and distribution. The purpose of this study is to provide academic information to prepare for crisis management through prediction using indices. (2) Method: In order to predict a crisis of the energy industry, the Crisis Index presented by the Korea Institute of Science and Technology Information was utilized instead of the economic indicators that have hitherto been widely used. Specifically, we propose a new forecasting model based on the concept of economic momentum theory used in financial research and the proper weighting of the earning downside risk theory. Then, statistical empirical analysis was performed to verify the new prediction model. (3) Results: As the result of the empirical analysis, the risk index prediction model proposed in this study showed statistically significant results in predicting the future risk index. (4) Conclusions: This study proposed a risk prediction model using an index and thereby provides an academic analysis of the predictability of numerous indicators created for various purposes and, furthermore, provides implications for predicting crises in the Korean energy industry.
This study analyzed the effect of the increase/decrease rate of manpower on the change in the profit margin by using Korea’s manufacturing company-year data from 2010 to 2020. The time lag effect was considered by analyzing how the increase/decrease rate of manpower compared to the previous year affects the change in the future profit margin. As a result of empirical analysis, a statistically significant negative relationship was found between the rate of increase in manpower and the rate of profit on sales. These results indicate that when Korean manufacturing companies increase the number of manpower, the profit margin decreases, and when the number of manpower decreases, the profit margin increases. This study is different from the design of previous studies on restructuring in that it takes into account not only the reduction in manpower but also the aspect of manpower increase by using the rate of increase/decrease in manpower compared to the previous year. In addition, there is a difference in performing long-term analysis of future performance by using the change after 1 year and 3 years as the change in performance after the increase or decrease of the manpower. Therefore, we present empirical evidence that Korean companies need to actively reduce manpower in order to reorganize into an efficient corporate structure for improving future performance.
Industrial crises exert considerable influence on a wide range of industries, national economies, and global economic landscapes. The primary objective of this study is to devise a crisis index specifically tailored for the petroleum sector—a vital component of South Korea’s energy industry. An exhaustive analysis of the existing literature was conducted to extract pertinent elements and indicators, and indicator weights were determined using the analytic network process (ANP). Moreover, a combination of qualitative and quantitative methods was employed to rigorously evaluate the validity of the proposed crisis index. The implications derived from this study offer critical insights for stakeholders into the petroleum industry and demonstrate the potential applicability of a crisis index framework for other industries.
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