• The Journal of Asian Finance, Economics and Business
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• v.8 no.1
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• pp.217-224
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• 2021

This research seeks to determine what intellectual capital represented by indicators of conceptual skills, human skills, and technical skills plays a role in the development of financial technology. The consideration of fintech is more practical and economical. The concept of fintech is related to the rapid development of global technology by creating various new technologies, especially computer technology. This research uses secondary data; the population of this study is the top management companies in Indonesia during the new normal era. The sample in this research used a purposive sampling method, and the quantitative method. The results of this research indicate that the intellectual capital variable represented by conceptual skills has a significant positive role in the development of financial technology in the new normal era. This research posits that intellectual capital also has a role in the development of financial technology in the new normal. This is because the new normal period represents currently a new challenge in responding to the economic crisis that is resulting from Covid-19 pandemic around the world. Therefore, new concepts, new humanity, and new techniques are needed to develop financial technology, so that they can exist and encourage economic growth in this Covid-19 pandemic era.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.36 no.4
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• pp.9-17
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• 2013

The purpose of this study is to figure out the impacts of technological innovation and cost management on productivity in small shipping industries to come up with developmental implications. To achieve this, a survey was carried out to 150 workers in small shipbuilding industries through April 2 to April 20, 2012. As for findings stated above, technological innovation and cost management in the Korean small shipbuilding industries were key factors that elevate financial and non-financial productivity. In the light of low technological prowess and cost structure of small shipbuilding industries, their productivity can be improved when intensive cost management with production factor technology as know-how is realized through quality management, which product development technology is the top priority as an independent niche strategy. Consequently, the combination of infrastructures in small shipbuilding industries with continuous efforts for cost reduction by the link to the systematized structure can't only secure their independent competitiveness, but raise their productivity.

• East Asian Economic Review
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• v.28 no.3
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• pp.359-388
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• 2024

With the escalation of global trade, the Chinese commodity futures market has ascended to a pivotal role within the international shipping landscape. The Shanghai Containerized Freight Index (SCFI), a leading indicator of the shipping industry's health, is particularly sensitive to the vicissitudes of the Chinese commodity futures sector. Nevertheless, a significant research gap exists regarding the application of Chinese commodity futures prices as predictive tools for the SCFI. To address this gap, the present study employs a comprehensive dataset spanning daily observations from March 24, 2017, to May 27, 2022, encompassing a total of 29,308 data points. We have crafted an innovative deep learning model that synergistically combines Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) architectures. The outcomes show that the CNN-LSTM model does a great job of finding the nonlinear dynamics in the SCFI dataset and accurately capturing its long-term temporal dependencies. The model can handle changes in random sample selection, data frequency, and structural shifts within the dataset. It achieved an impressive R² of 96.6% and did better than the LSTM and CNN models that were used alone. This research underscores the predictive prowess of the Chinese futures market in influencing the Shipping Cost Index, deepening our understanding of the intricate relationship between the shipping industry and the financial sphere. Furthermore, it broadens the scope of machine learning applications in maritime transportation management, paving the way for SCFI forecasting research. The study's findings offer potent decision-support tools and risk management solutions for logistics enterprises, shipping corporations, and governmental entities.