A Study on the Effect of Government Support on Innovation Efficiency of Logistics Firms
Hee Jay Kang, Yeongjun Kim, Changhee Kim · 한국 SCM 학회지 · 2019
With the development of various technologies for the 4th industrial revolution, such as IoT, Big Data, CPS, and AI, innovation is taking place in many areas of the logistics industry and its importance is emphasized even more. However, since the efforts made to innovate do not directly lead to outcomes, the analysis should be made from the perspective of innovation efficiency which takes into account a variety of inputs and outputs. In this study, Data Envelopment Analysis is applied to 98 logistics firms(input factors: costs of innovation, number of innovators / output factors: total sales) to derive innovation efficiency, and the impact of government support policies (in 7 areas: taxation, funding, finance, human resources, technology, certification, purchase) is verified by Tobit regression and Kruskal-Wallis one-way ANOVA. The results show that the effects of the current policy for innovation in the logistics industry are concentrated on financial support, such as taxation and finance, and that the efficiency of firms with significantly high or low dependency is low.