This page has only limited features, please log in for full access.

Unclaimed
Jia Liu
National Economics Research Center, Guangdong University of Finance & Economics, Guangzhou, China

Basic Info

Basic Info is private.

Honors and Awards

The user has no records in this section


Career Timeline

The user has no records in this section.


Short Biography

The user biography is not available.
Following
Followers
Co Authors
The list of users this user is following is empty.
Following: 0 users

Feed

Article
Published: 04 July 2021 in Scientometrics
Reads 0
Downloads 0

This paper develops a macro examination framework for simultaneously testing the incentive effect and uncertainty effect under R&D-based growth theory. A stochastic frontier innovation model with heterogeneity has been established and estimated, in which the exogenous cites’ demand changes measured by market potential increases induced by China’s high-speed rails are introduced into both inefficiency mean equation and inefficiency variance equation. The empirical results show that market potential has significantly negative correlation with inefficiency mean and inefficiency variance, which are robust to various market potential measurement, as well as robust to DID setting and IV regressions. The study provides the first macro evidence for supporting both Schmookler hypothesis and Myers-Marquis hypothesis, and the examination framework has obvious advantages over the previous FG framework.

ACS Style

Jun Chen; Jia Liu. Incentive and uncertainty: the simultaneous effects of demand on innovation. Scientometrics 2021, 126, 7743 -7757.

AMA Style

Jun Chen, Jia Liu. Incentive and uncertainty: the simultaneous effects of demand on innovation. Scientometrics. 2021; 126 (9):7743-7757.

Chicago/Turabian Style

Jun Chen; Jia Liu. 2021. "Incentive and uncertainty: the simultaneous effects of demand on innovation." Scientometrics 126, no. 9: 7743-7757.

Journal article
Published: 07 June 2020 in Sustainability
Reads 0
Downloads 0

The logistics industry around the world has proliferated over recent years as a large number of business organizations have come to recognize the importance of logistics. Cost control used to be emphasized to remain competitive, but recently green logistics has gained attention with the awareness of the integration of economy and society as a whole. Nowadays, green logistics is a useful concept to improve the sustainability of logistics operations, and its related policies and theoretical research have been investigated and explored. However, the practical applications of green logistics are impeded by real-time data sharing, which is common in the logistics industry. Blockchain technology is adopted to address this challenge and enable data sharing among related stakeholders. This paper presents a reference framework for green logistics based on blockchain to reach the sustainable operations of logistics, with the integration of the Internet of Things and big data. Finally, potential benefits and limitations are analyzed when implementing this framework.

ACS Style

Bing Qing Tan; Fangfang Wang; Jia Liu; Kai Kang; Federica Costa. A Blockchain-Based Framework for Green Logistics in Supply Chains. Sustainability 2020, 12, 4656 .

AMA Style

Bing Qing Tan, Fangfang Wang, Jia Liu, Kai Kang, Federica Costa. A Blockchain-Based Framework for Green Logistics in Supply Chains. Sustainability. 2020; 12 (11):4656.

Chicago/Turabian Style

Bing Qing Tan; Fangfang Wang; Jia Liu; Kai Kang; Federica Costa. 2020. "A Blockchain-Based Framework for Green Logistics in Supply Chains." Sustainability 12, no. 11: 4656.