Patent-Enhancing Strategies by Industry in Korea Using a Data Envelopment Analysis

Supporting Better Decision-Making: A Combined Grey Model and Data Envelopment Analysis for Efficiency Evaluation in E-Commerce Marketplaces

E-commerce has become an integral part of businesses for decades in the modern world, and this has been exceptionally speeded up during the coronavirus era. To help businesses understand their current and future performance, which can help them survive and thrive in the world of e-commerce, this paper proposes a hybrid approach that conducts performance prediction and evaluation of the e-commerce industry by combining the Grey model, i.e., GM (1, 1) and data envelopment analysis, i.e., the Malmquist-I-C model. For each e-commerce company, GM (1, 1) is applied to predict future values for the period 2020–2022 and Malmquist-I-C is applied to calculate the efficiency score based on output variables such as revenue and gross profit and input variables such as assets, liabilities, and equity. The top 10 e-commerce companies in the US market are used to demonstrate model effectiveness. For the entire research period of 2016–2022, the most productive e-commerce marketplace on average was eBay, followed by Best Buy and Lowe’s; meanwhile, Groupon was the worst-performing e-commerce business during the studied period. Moreover, as most e-commerce companies have progressed in technological development, the results show that the determinants for productivity growth are the technical efficiency change indexes. That means, although focusing on technology development is the key to e-commerce success, companies should make better efforts to maximize their resources such as labor, material and equipment supplies, and capital. This paper offers decision-makers significant material for evaluating and improving their business performance.

Sustainable Technology Analysis Using Data Envelopment Analysis and State Space Models

To find sustainable technology in various areas, we propose an analytical methodology based on data envelopment analysis (DEA) and the state space model (SSM). DEA is an analytical method used to compare the efficiencies and performances of several items. In DEA, for sustainable technology analysis, the items of DEA can be the technological keywords or international patent classification (IPC) codes in patent documents. In this paper, the proposed method is used to find the relative performance of different patent keywords using comparison and evaluation. We apply this methodology to compare the technological efficiencies between patent keywords for sustainable technology analysis. We apply the additive model and directional distance function of DEA to develop the proposed methodology for building the technological structure of target technology. In addition, we forecast the future trend of target technology using the SSM and find the area of sustainable technology by its result. The SSM is well suited for time series forecasting on technology analysis. We extract the IPC codes from patent documents for the SSM. In our research, we combine the results of DEA and the SSM to find the area of technological sustainability. To illustrate the validity and performance of our research, we conduct a case study using the patent documents used and registered by Apple.