Ying Liu

Ying Liu


Research Focus & Metrics

Academic Career Length

15Years (2012-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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16 May 2017 in Annals of Data Science
Verified
Journal Article
Closed access
PDF file
FLfeng liu
feng liu
YSYong Shi
Yong Shi
YLYing Liu
Ying Liu

15 March 2023 in Sustainability
Verified
Journal Article
Open access
PDF file
MDMa Diandian
Ma Diandian
BLBenfu Lv
Benfu Lv
YLYing Liu
Ying Liu
SLShuqin Liu
Shuqin Liu
Xiuting LiXiuting Li
Xiuting Li
Could the corporate carbon information disclosure strategy influence a firm’s brand value, and how does corporate carbon information affect it? Previous research mainly examines the impact of ESG information disclosure on firm value and other financial indicators, but little research has focused on the effect of carbon information on brand value. This paper focuses on the influence of corporate carbon information disclosure on brand value, and we find that it positively impacts corporate brand value. In addition, when a company chooses to adopt a more quantitative and diverse carbon information strategy, it increases its brand value. We also examine the potential mechanisms involved in how corporate carbon information disclosure influences brand value. We focus on three types of factor: analyst rating, customer attitude, and corporate financial performance, and find that higher analyst forecasts and positive customer attitudes have a positive impact on the association between the carbon information strategy and corporate brand value. In contrast, corporate financial performance provides only weak evidence. These results are consistent with demands by users for more precise guidelines from regulators and standard-setters for measuring and disclosing carbon-related information.


4 October 2017 in arXiv
Verified
Preprint
Open access
PDF file
FLfeng liu
feng liu
YSYong Shi
Yong Shi
YLYing Liu
Ying Liu
Although artificial intelligence is currently one of the most interesting areas in scientific research, the potential threats posed by emerging AI systems remain a source of persistent controversy. To address the issue of AI threat, this study proposes a standard intelligence model that unifies AI and human characteristics in terms of four aspects of knowledge, i.e., input, output, mastery, and creation. Using this model, we observe three challenges, namely, expanding of the von Neumann architecture; testing and ranking the intelligence quotient of naturally and artificially intelligent systems, including humans, Google, Bing, Baidu, and Siri; and finally, the dividing of artificially intelligent systems into seven grades from robots to Google Brain. Based on this, we conclude that AlphaGo belongs to the third grade.