• Energy Research

Governments around the world are planning to ban sales of vehicles running on petroleum-based fuels as an effort to reduce greenhouse gas emissions, and electric vehicles surfaced as a solution to decrease pollutants produced by the transportation sector. As a result, wireless power transfer technology has recently gained much attention as a convenient and practical method for charging electric vehicles. In this paper, patent analysis is conducted to identify emerging and vacant technology areas of wireless power transfer. Topics are first extracted from patents by text mining, and the topics with similar semantics are grouped together to form clusters. Then, the process of identifying emerging and vacant technology areas is improved by applying a time series analysis and innovation cycle of technology to the clustering result. Lastly, the results of clustering, time series, and innovation cycle are compared to minimize the possibility of misidentifying emerging and vacant technology areas, thus improving the accuracy of the identification process and the validity of the identified technology areas. The analysis results revealed that one emerging technology area and two vacant technology areas exist in wireless power transfer. The emerging technology area identified is circuitries consisting of transmitter coils and receiver coils for wireless power transfer, and the two vacant technology areas identified are wireless charging methods based on resonant inductive coupling and wireless power transfer condition monitoring methods or devices.

This study aims to determine the influential factors on the market share of electric vehicles through panel data analysis based on time series data from 2011 to 2015 in 31 countries. We selected five significant independent variables that are expected to affect electric vehicle adoption based on literature review. The econometric model in this study suggests that the relative price of electric vehicle compared to internal combustion engine vehicle, driving range, and number of models available in markets are correlated to the market share of electric vehicles. On the other hand, relationship between recharging infrastructure—an important factor for electric vehicle adoption in many studies—and market share of electric vehicles turned out to be insignificant in this study. From a political point of view, we argue that policy makers need to allocate more resources to research and development in order to extend driving range at the early stage of electric vehicle deployment in the markets.

An electric motor is a device that changes electrical energy into mechanical energy for railway vehicles. When developing the electric motor, it used to be developed simply for structures or control methods of the motor itself without considering convergence with other devices or technologies. However, as the railway vehicles become more advanced, technology development through convergence with other devices or technologies is spreading. Therefore, based on patent data related to the electric motors applied to the railway vehicles, this research aims to carry out technical forecasting for establishing research and development (R and D) direction for new technologies by predicting vacant technologies from the point of view of technology convergence. In other words, we studied how to find the vacant technologies in a field of convergence technology for the electric motor of the railway vehicles by analyzing the patent data. More specifically, we search the patents data associated with the electric motor of the railway vehicle that contain multiple IPC codes, and use multiple IPC codes to determine the field of convergence technology. In addition, we extract keywords from the patents data related to each of the determined convergence technologies and define the vacant technologies by interpreting the field of convergence technology and the extracted keywords.

Recently, last mile delivery has emerged as an essential process that greatly affects the opportunity of obtaining delivery service market share due to the rapid increase in the business-to-consumer (B2C) service market. Express delivery companies are investing to expand the capacity of hub terminals to handle increasing delivery volume. As for securing massive delivery quantity by investment, companies must examine the profitability between increasing delivery quantity and price. This study proposes two strategies for a company’s decision making regarding the adjustment of market density and price by developing a pricing and collaboration model based on the delivery time of the last mile process. A last mile delivery time function of market density is first derived from genetic algorithm (GA)-based simulation results of traveling salesman problem regarding the market density. The pricing model develops a procedure to determine the optimal price, maximizing the profit based on last mile delivery time function. In addition, a collaboration model, where a multi-objective integer programming problem is developed, is proposed to sustain long-term survival for small and medium-sized companies. In this paper, sensitivity analysis demonstrates the effect of delivery environment on the optimal price and profit. Also, a numerical example presents four different scenarios of the collaboration model to determine the applicability and efficiency of the model. These two proposed models present managerial insights for express delivery companies.

Dual channels have become popular strategies for manufacturers due to the development of innovative selling platforms. Examples in practice also show that the lack of relationship management, such as cooperation and sharing, may cause an unsustainable supply chain performance. However, previous studies on coordination of dual-channel supply chains always focus on the contribution to profits and neglect the sustainability of relationship development between channel members. In this paper, we study the coordination of a dual-channel supply chain including a direct channel and a traditional channel. Under the fact that sustainable economy, instead of profit maximization, is the more appropriate objective in channel members’ decision making, we consider the retailer’s risk exposure and assume the risk degree is also a factor that impacts decision making. We assume the manufacturer is risk-neutral and the retailer is risk-averse, and measure the risk attitude with Conditional Value-at-Risk (CVaR) approach. Two traditional contracts widely used in single-channel supply chains, i.e., revenue-sharing contract and buy-back contract, are analyzed first. Although some researchers have discussed that traditional contracts cannot coordinate the dual-channel supply chain, our results show that traditional contracts can still come into play with restrictions on the risk-averse degree. Then we propose a risk-sharing contract which could distribute profits between two channel members and coordinate the system under varied risk-averse degrees with a fixed risk-sharing degree. Finally, we analyze the sensitivity of different parameters to illustrate the stable coordinating outcomes of this contract, and prove its generalization with more powerful channel members. The results provide important managerial insights.

In this study, we suggest methodologies for identifying promising and vacant technologies on smart farms by analyzing patent information. Additionally, a technology roadmap for smart farms is suggested using network analysis. The database of patents related to smart farms was extracted from the United States Patent and Trademark Office (USPTO) by keyword search, and valid patents data was selected and clustered using the Latent Dirichlet Allocation (LDA) algorithm. We also conducted the technical importance analysis and trend analysis to identify promising technology topics. By developing a patent map based on a self-organizing map (SOM), we were able to identify vacant technologies among smart farm technology groups. In order to develop vacant technologies, we presented a stepwise technology roadmap by analyzing the relationship between technology elements using network analysis. The proposed procedure and analysis method provides useful insights in establishing research and development (R&D) strategies for the development of smart farm technology roadmaps.

The modern semiconductor industry is going through rapid changes as new markets and technologies appear. In this paper, such technology-intensive firms’ relationship between technological capability and financial performance is analyzed with regression analysis. Revenue and market capitalization are used as dependent variables. For the independent variables, the technological intensity, technological diversity, technological asset, and technological efficiency are used. The analysis results revealed different effects of technological capability on financial performance. Also, regression analysis is conducted by dividing firms into high and low groups based on technological asset and technological efficiency, and the analysis result revealed different effects of technological intensity and technological diversity on financial performance. For technological asset, the financial performance in the high group is affected more by technological intensity, and the financial performance in the low group is affected more by technological diversity. For technological efficiency, only the financial performance in the high group is affected by technological intensity. Although both groups’ financial performance is somewhat affected by technological diversity, there was no statistically significant differences between the groups. By separating the effect of technological capability on financial performance, this research can provide more detailed analysis results compared to previous literature and the methods of managing technological capability for semiconductor firms.