Bo Xiang

Nanjing University Nanjing University

I am Bo Xiang, I am pursuing my PhD degree in the Intelligent Intelligence Laboratory of Nanjing University and working under the supervision of Prof. Jin Shi. Research on AI in healthcare, patent and business information mining, generative artificial intelligence.

Google Scholar GitHub ORCID
Education
  • Nanjing University

    Nanjing University

    Ph.D in Information Resource Management (Supervisor is Jin Shi) Sep. 2024 - now

  • Nanjing Audit University

    Nanjing Audit University

    M.S. in Business Adiministration (Supervisor is Dejian Yu) Sep. 2021 - Jul. 2024

  • East China University of Technology

    East China University of Technology

    B.S. in Mathematics and Applied Mathematics (Supervisor is Zhihui Yang) Sep. 2017 - Jul. 2021

Honors & Awards
  • China National Scholarship 2024
  • China National Encouragement Scholarship (Twice) 2020
  • Special Grade Scholarship (Top 1%) 2020
  • China Undergraduate Mathematical Contest in Modeling - Provincial Second Prize 2019
  • The Interdisciplinary Contest in Modeling - Meritorious Winner 2019
  • Teddy Cup National Data Mining Challenge - National Second Price 2019
Experience
  • DiDi Digital

    DiDi Digital

    Data Analyst

  • Qinglan Education

    Qinglan Education

    Math Teacher

  • Geminno

    Geminno

    Front-end Engineer

Service
News
2025
🎉 Here is my web! Lab Link
Apr 23
Selected Publications (view all )
Detecting technology opportunities appropriate for enterprise R&D: The synthesis analysis of industrial technical windows and enterprise competition relations
Detecting technology opportunities appropriate for enterprise R&D: The synthesis analysis of industrial technical windows and enterprise competition relations

Bo Xiang, Zhuoya Pan, Dejian Yu†, Wenjin Zuo(† corresponding author)

Technology in Society 2025 Journal

Technological opportunities (TOs) are the potential and set of possibilities for technology advances in a given industry. When enterprises are able to catch and adapt to them in a timely manner, they can grab market share from competitors who have failed to adapt to these challenges. However, when there exist large gaps between enterprises and their competitors, it should be carefully evaluated whether enterprise-specific TOs are worth exploring. Moreover, faced with diversified competitive relations, enterprises also need to formulate differentiated research and development (R&D) strategies for different TOs. To address these research gaps, this paper argues for the theoretical concepts of technical windows (TWs), emerging technologies (ETs), and TOs, and proposes a three-stage framework to detect enterprise-specific TOs.

[Paper]

Detecting technology opportunities appropriate for enterprise R&D: The synthesis analysis of industrial technical windows and enterprise competition relations
Detecting technology opportunities appropriate for enterprise R&D: The synthesis analysis of industrial technical windows and enterprise competition relations

Bo Xiang, Zhuoya Pan, Dejian Yu†, Wenjin Zuo(† corresponding author)

Technology in Society 2025 Journal

Technological opportunities (TOs) are the potential and set of possibilities for technology advances in a given industry. When enterprises are able to catch and adapt to them in a timely manner, they can grab market share from competitors who have failed to adapt to these challenges. However, when there exist large gaps between enterprises and their competitors, it should be carefully evaluated whether enterprise-specific TOs are worth exploring. Moreover, faced with diversified competitive relations, enterprises also need to formulate differentiated research and development (R&D) strategies for different TOs. To address these research gaps, this paper argues for the theoretical concepts of technical windows (TWs), emerging technologies (ETs), and TOs, and proposes a three-stage framework to detect enterprise-specific TOs.

[Paper]

Customized integrated decision model for CBEC enterprise credit evaluation: The fusion of multi-source features and machine learning
Customized integrated decision model for CBEC enterprise credit evaluation: The fusion of multi-source features and machine learning

Dejian Yu, Bo Xiang†(† corresponding author)

Electronic Markets 2025 Journal

The cross-border e-commerce (CBEC) industry plays a crucial role in the transformation of foreign trade and the upgrading of innovative development, driven by information technology and international trade policies. However, the distinctive operational pattern of CBEC enterprises necessitates the customization of the corporate credit evaluation framework to their specific features, which is absent in the existing studies. This paper proposes an integrated decision framework that incorporates multi-source features and machine learning algorithms to achieve customized credit evaluation for CBEC enterprises.

[Paper]

Customized integrated decision model for CBEC enterprise credit evaluation: The fusion of multi-source features and machine learning
Customized integrated decision model for CBEC enterprise credit evaluation: The fusion of multi-source features and machine learning

Dejian Yu, Bo Xiang†(† corresponding author)

Electronic Markets 2025 Journal

The cross-border e-commerce (CBEC) industry plays a crucial role in the transformation of foreign trade and the upgrading of innovative development, driven by information technology and international trade policies. However, the distinctive operational pattern of CBEC enterprises necessitates the customization of the corporate credit evaluation framework to their specific features, which is absent in the existing studies. This paper proposes an integrated decision framework that incorporates multi-source features and machine learning algorithms to achieve customized credit evaluation for CBEC enterprises.

[Paper]

An ESTs detection research based on paper entity mapping: Combining scientific text modeling and neural prophet
An ESTs detection research based on paper entity mapping: Combining scientific text modeling and neural prophet

Dejian Yu, Bo Xiang†(† corresponding author)

Journal of Informetrics 2024 Journal

Existing studies on the detection of emerging scientific topics (ESTs) overemphasize the newness and neglect content innovation of knowledge. Moreover, they also ignore the lag existing in knowledge diffusion. In this paper, we propose a four-stage detection framework for ESTs that maps emerging attributes from paper entities to scientific topics.

[Paper]

An ESTs detection research based on paper entity mapping: Combining scientific text modeling and neural prophet
An ESTs detection research based on paper entity mapping: Combining scientific text modeling and neural prophet

Dejian Yu, Bo Xiang†(† corresponding author)

Journal of Informetrics 2024 Journal

Existing studies on the detection of emerging scientific topics (ESTs) overemphasize the newness and neglect content innovation of knowledge. Moreover, they also ignore the lag existing in knowledge diffusion. In this paper, we propose a four-stage detection framework for ESTs that maps emerging attributes from paper entities to scientific topics.

[Paper]

Discovering topics and trends in the field of Artificial Intelligence Using LDA topic modeling
Discovering topics and trends in the field of Artificial Intelligence Using LDA topic modeling

Dejian Yu, Bo Xiang†(† corresponding author)

Expert Systems with Applications 2023 Journal

Artificial Intelligence (AI) has affected all aspects of social life in recent years. This study reviews 177,204 documents published in 25 journals and 16 conferences in the AI research from 1990 to 2021, and applies the Latent Dirichlet allocation (LDA) model to extract the 40 topics from the abstracts. This study aggregates the results of the LDA model from the perspectives of year, publication source, and country/region. The aggregated result is the topic distribution from different perspectives. Analysis of the aggregated results reveals the research characteristics of different publication sources (countries/regions) in the AI research, and which publication sources (countries/regions) have similar research content. These results provide help for scholars and research institutions to choose research directions, and new entrants to understand the dynamics of the field.

[Paper]

Discovering topics and trends in the field of Artificial Intelligence Using LDA topic modeling
Discovering topics and trends in the field of Artificial Intelligence Using LDA topic modeling

Dejian Yu, Bo Xiang†(† corresponding author)

Expert Systems with Applications 2023 Journal

Artificial Intelligence (AI) has affected all aspects of social life in recent years. This study reviews 177,204 documents published in 25 journals and 16 conferences in the AI research from 1990 to 2021, and applies the Latent Dirichlet allocation (LDA) model to extract the 40 topics from the abstracts. This study aggregates the results of the LDA model from the perspectives of year, publication source, and country/region. The aggregated result is the topic distribution from different perspectives. Analysis of the aggregated results reveals the research characteristics of different publication sources (countries/regions) in the AI research, and which publication sources (countries/regions) have similar research content. These results provide help for scholars and research institutions to choose research directions, and new entrants to understand the dynamics of the field.

[Paper]

All publications