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学者姓名：徐硕
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Abstract ：
Compared to the science-technology linkages, the linkages among science, technology, and industry are largely under-studied. Therefore, this paper proposes a main path analysis based framework to discover the science-technology-industry linkages, in which scientific publications, patents, and products are viewed as respective proxies of scientific research, technological advance, and industrial development. To validate the feasibility and effectiveness of our framework, after the DrugBank dataset in pharmaceutical industry was downloaded in XML form on 1 November 2019, this dataset is further enriched, drug entity mentions are recognized from scholarly articles and patents, and several citation cycles are eliminated. The scientific publications span from 1871 to 2019, and patents from 1953 to 2019. There are 8,421, 5,590, and 2,136 article, patent, and drug nodes and 41,200 citations in the largest weakly connected component of the constructed heterogeneous citation network. From empirical analysis on the largest weakly connected component, main conclusions can be drawn as follows. (1) The discovered developmental trajectories indeed encode the interactions among science, technology, and industry. Science and technology not only interact with each other, but also jointly promote the development of the industry, and the industry, in turn, influences the advancement of science and technology. (2) The developmental modes in the pharmaceutical industry can be grouped into three categories: pushed by only science, pushed by only technology, and pushed by science and technology simultaneously. (3) The drugs bridge scientific research and technological advance, and thereby help enhance knowledge exchanges between science and technology and shorten the cycle of drug development. This study contributes to discovering the linkages among science, technology, and industry from the perspective of mutual citations among scholarly articles, patents, and products. However, a scientific verification of our framework in other industries apart from pharmaceutical industry still needs to be further investigated.
Keyword ：
Cycle elimination Cycle elimination Linkages among science Linkages among science Pharmaceutical industry Pharmaceutical industry Main path analysis Main path analysis Heterogeneous citation network Heterogeneous citation network industry industry technology technology and and
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| GB/T 7714 | Xu, Shuo , Liu, Zhen , An, Xin et al. Linkages among science, technology, and industry on the basis of main path analysis [J]. | JOURNAL OF INFORMETRICS , 2024 , 19 (1) . |
| MLA | Xu, Shuo et al. "Linkages among science, technology, and industry on the basis of main path analysis" . | JOURNAL OF INFORMETRICS 19 . 1 (2024) . |
| APA | Xu, Shuo , Liu, Zhen , An, Xin , Wang, Hong , Pang, Hongshen . Linkages among science, technology, and industry on the basis of main path analysis . | JOURNAL OF INFORMETRICS , 2024 , 19 (1) . |
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Abstract ：
International entrepreneurship is key to creating business value and advancing sustainable development goals (SDGs) globally at the crossroads of business acumen, innovative thinking, and the ever-growing reach of technology. This study examines how different factors of international entrepreneurship influence the sustainable development of organizational goals, focusing specifically on the role of artificial intelligence (AI) technologies and a global mindset. The young entrepreneurs of China are an important target group for such interventions, as they are technologically savvy and highly interested in sustainable entrepreneurship. A survey will be conducted on 448 young entrepreneurs who started businesses in different sectors. This study uses a questionnaire with structural equation modeling (SEM) to test the hypotheses and assess the proposed relationships between the variables. The findings of this study reveal that factors of international entrepreneurship, such as entrepreneurial orientation, international market capabilities, and entrepreneurial innovation, significantly impact SDGs. Artificial intelligence technologies mediate the relationship between factors of international entrepreneurship and SDGs. Furthermore, the global mindset significantly moderates the relationship between AI technologies and SDGs. This study underscores the importance of cultivating a global mindset and embracing AI technologies as strategic backgrounds of international entrepreneurship, offering valuable insights for organizations aiming to foster sustainable development in an increasingly competitive global landscape.
Keyword ：
PLS-SEM PLS-SEM Entrepreneurial innovation Entrepreneurial innovation Entrepreneurial orientation Entrepreneurial orientation Artificial intelligence technologies Artificial intelligence technologies Sustainable development goals Sustainable development goals
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| GB/T 7714 | Anser, Muhammad Khalid , Shahzad, Muhammad Farrukh , Xu, Shuo . Exploring the nexuses between international entrepreneurship and sustainable development of organizational goals: mediating role of artificial intelligence technologies [J]. | ENVIRONMENT DEVELOPMENT AND SUSTAINABILITY , 2024 . |
| MLA | Anser, Muhammad Khalid et al. "Exploring the nexuses between international entrepreneurship and sustainable development of organizational goals: mediating role of artificial intelligence technologies" . | ENVIRONMENT DEVELOPMENT AND SUSTAINABILITY (2024) . |
| APA | Anser, Muhammad Khalid , Shahzad, Muhammad Farrukh , Xu, Shuo . Exploring the nexuses between international entrepreneurship and sustainable development of organizational goals: mediating role of artificial intelligence technologies . | ENVIRONMENT DEVELOPMENT AND SUSTAINABILITY , 2024 . |
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Abstract ：
The adoption of generative artificial intelligence (GAI) tools, such as ChatGPT, in higher education presents numerous opportunities and challenges. The use of GAI technologies in various fields, including education, has accelerated as technology develops. The widely used language model ChatGPT, developed by OpenAI, has become progressively more important, especially in the field of education. This study employs the technology acceptance model to investigate the factors influencing the employment of ChatGPT within the higher education sector of Pakistan. This study employed the PLS-SEM method for probing data collected from 368 Pakistani university students. The findings indicate that ChatGPT trust positively mediates the affiliation between ChatGPT self-efficacy, ChatGPT actual use, ChatGPT use for information and ChatGPT use for interaction. Further, ChatGPT usefulness and ChatGPT ease of use significantly moderate the association between ChatGPT self-efficacy and ChatGPT trust. Educators must encourage students to use ChatGPT safely to preserve their critical thinking, problem-solving abilities and creativity during assessments. This study contributes to understanding generative AI tools such as ChatGPT that are used in educational settings and provides insights for administrators and policymakers aiming to implement these technologies effectively.
Keyword ：
ChatGPT ease of use ChatGPT ease of use ChatGPT self-efficacy ChatGPT self-efficacy ChatGPT actual use ChatGPT actual use generative artificial intelligence generative artificial intelligence ChatGPT trust ChatGPT trust ChatGPT usefulness ChatGPT usefulness
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| GB/T 7714 | Shahzad, Muhammad Farrukh , Xu, Shuo , Asif, Muhammad . Factors affecting generative artificial intelligence, such as ChatGPT, use in higher education: An application of technology acceptance model [J]. | BRITISH EDUCATIONAL RESEARCH JOURNAL , 2024 . |
| MLA | Shahzad, Muhammad Farrukh et al. "Factors affecting generative artificial intelligence, such as ChatGPT, use in higher education: An application of technology acceptance model" . | BRITISH EDUCATIONAL RESEARCH JOURNAL (2024) . |
| APA | Shahzad, Muhammad Farrukh , Xu, Shuo , Asif, Muhammad . Factors affecting generative artificial intelligence, such as ChatGPT, use in higher education: An application of technology acceptance model . | BRITISH EDUCATIONAL RESEARCH JOURNAL , 2024 . |
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Abstract ：
Artificial Intelligence (AI) technologies have rapidly transformed the education sector and affect student learning performance, particularly in China, a burgeoning educational landscape. The development of generative artificial intelligence (AI) based technologies, such as chatbots and large language models (LLMs) like ChatGPT, has completely changed the educational environment by providing individualized and engaging programs. This study brings forward a model and hypothesis based on social cognitive theory and appropriate research. This investigation centers on how generative AI-based technologies influence students' learning performance in higher education (HE) institutions and the function of self-efficacy, fairness & ethics, creativity, and trust in promoting these connections. Data is collected from 362 students at Chinese universities using purposive sampling. The proposed structural model was evaluated using partial least squares-structural equation modeling (PLS-SEM). The findings reveal that generative AI technologies such as LLM models exemplified by ChatGPT and chatbots significantly influence students' learning performance through self-efficacy, fairness & ethics, and creativity. Furthermore, trust significantly moderates the relationship between fairness & ethics, creativity, and learning performance but negatively moderates the relationship between self-efficacy and learning performance. This study supports the new explanatory potential of social cognitive theory in technological practices. Additionally, this research suggests using generative AI technologies to enhance students' digital learning and boost academic achievement.
Keyword ：
Creativity Creativity Self-efficacy Self-efficacy Generative AI-based technologies Generative AI-based technologies Trust Trust Fairness & ethics Fairness & ethics LLM models LLM models Learning performance Learning performance Higher education Higher education
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| GB/T 7714 | Shahzad, Muhammad Farrukh , Xu, Shuo , Zahid, Hira . Exploring the impact of generative AI-based technologies on learning performance through self-efficacy, fairness & ethics, creativity, and trust in higher education [J]. | EDUCATION AND INFORMATION TECHNOLOGIES , 2024 . |
| MLA | Shahzad, Muhammad Farrukh et al. "Exploring the impact of generative AI-based technologies on learning performance through self-efficacy, fairness & ethics, creativity, and trust in higher education" . | EDUCATION AND INFORMATION TECHNOLOGIES (2024) . |
| APA | Shahzad, Muhammad Farrukh , Xu, Shuo , Zahid, Hira . Exploring the impact of generative AI-based technologies on learning performance through self-efficacy, fairness & ethics, creativity, and trust in higher education . | EDUCATION AND INFORMATION TECHNOLOGIES , 2024 . |
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Abstract ：
In the rapidly evolving landscape of industrial and societal progress, technology convergence plays a pivotal role. This dynamic process is usually characterized by the emergence of new nodes and new links. With the long-term and recent interests in predicting technology convergence, link prediction has become the primary approach on the basis of large-scale patent data. Though, the problem of node dynamics is still not addressed in the literature. For this purpose, this paper presents a technology convergence prediction framework with three core modules as follows. (1) A candidate node set is introduced during the network construction phase, mimicking the generation of newly-emerging nodes. (2) An inductive graph representation learning approach is deployed to generate feature vectors for newly-emerging nodes as well as existing ones. (3) The evaluation criteria are revised to shift from the predictable range to the actual predicted range, which can provide a more realistic assessment of predictive performance. Finally, experimental results on the domain of cancer drug development validate the feasibility and effectiveness of our framework in capturing the dynamics of technology convergence, especially concerning the relationships of newly emerged nodes and links. This study provides valuable insights into technology convergence dynamics and points to future research and applications.
Keyword ：
Link prediction Link prediction Technology convergence Technology convergence Inductive graph representation learning Inductive graph representation learning Node dynamics Node dynamics
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| GB/T 7714 | Yang, Guancan , Xing, Jiaxin , Xu, Shuo et al. A framework armed with node dynamics for predicting technology convergence [J]. | JOURNAL OF INFORMETRICS , 2024 , 18 (4) . |
| MLA | Yang, Guancan et al. "A framework armed with node dynamics for predicting technology convergence" . | JOURNAL OF INFORMETRICS 18 . 4 (2024) . |
| APA | Yang, Guancan , Xing, Jiaxin , Xu, Shuo , Zhao, Yuntian . A framework armed with node dynamics for predicting technology convergence . | JOURNAL OF INFORMETRICS , 2024 , 18 (4) . |
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Abstract ：
As technology continues to advance, the integration of generative artificial intelligence tools in various sectors, including education, has gained momentum. ChatGPT, an extensively recognized language model created by OpenAI, has gained significant importance, particularly in education. This study investigates the awareness, acceptance, and adoption of ChatGPT, a state-of-the-art language model developed by OpenAI, in higher education institutions across China. This study applies the partial least squares structural equation modeling (PLS-SEM) method for examining data collected from 320 Chinese university students. The study's conceptual framework integrates key determinants from the Technology Acceptance Model (TAM) and extends it by incorporating perceived intelligence as a critical factor in the adoption process. The study findings reveal that ChatGPT awareness significantly influences the intention to adopt ChatGPT. Perceived ease of use, usefulness, and intelligence significantly mediate the association between ChatGPT awareness and adoption intention of ChatGPT. Additionally, perceived trust significantly moderates the relationship between ChatGPT awareness and perceived ease of use, usefulness, and intelligence. Moving forward, in order to maintain students' critical thinking skills and inventiveness in their assessment writing, assessments must promote the safe use of ChatGPT. Therefore, educators will be crucial in ensuring that artificial intelligence tools are used in assessments ethically and suitably by providing clear guidelines and instructions.
Keyword ：
Perceived usefulness Perceived usefulness Perceived trust Perceived trust ChatGPT awareness ChatGPT awareness Perceived intelligence Perceived intelligence Perceived ease of use Perceived ease of use ChatGPT adoption intention ChatGPT adoption intention
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| GB/T 7714 | Shahzad, Muhammad Farrukh , Xu, Shuo , Javed, Iqra . ChatGPT awareness, acceptance, and adoption in higher education: the role of trust as a cornerstone [J]. | INTERNATIONAL JOURNAL OF EDUCATIONAL TECHNOLOGY IN HIGHER EDUCATION , 2024 , 21 (1) . |
| MLA | Shahzad, Muhammad Farrukh et al. "ChatGPT awareness, acceptance, and adoption in higher education: the role of trust as a cornerstone" . | INTERNATIONAL JOURNAL OF EDUCATIONAL TECHNOLOGY IN HIGHER EDUCATION 21 . 1 (2024) . |
| APA | Shahzad, Muhammad Farrukh , Xu, Shuo , Javed, Iqra . ChatGPT awareness, acceptance, and adoption in higher education: the role of trust as a cornerstone . | INTERNATIONAL JOURNAL OF EDUCATIONAL TECHNOLOGY IN HIGHER EDUCATION , 2024 , 21 (1) . |
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Abstract ：
Academic inventors bridge science and technology, and have attracted increasing attention. However, little is known about whether they have more diverse research interests than researchers with a single role, and whether their important position for science-technology interactions correlates with their diverse interests. For this purpose, we describe a rule-based approach for matching and identifying academic inventors, and an author interest discovery model with credit allocation schemes is utilized to measure the diversity of each researcher's interests. Finally, extensive empirical results on the DrugBank dataset provide several valuable insights. Contrary to our intuitive expectation, the research interests of academic inventors are the least diverse, while those of authors are the most. In addition, the important position of the researchers has a certain relation with the diversity of research interests. More specifically, the degree of centrality has a significant positive correlation with the diversity of interests, and the constraint presents a significant negative correlation. A significant weaker negative correlation can also be observed between the diversity of research interests of academic inventors and their closeness centrality. The normalized betweenness centrality seems be independent from interest diversity. These conclusions help understand the mechanisms of the important position of academic inventors for science-technology interactions, from the perspective of research interests.
Keyword ：
Science-technology linkage Science-technology linkage Author interest discovery Author interest discovery Interest diversity Interest diversity Academic inventors Academic inventors
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| GB/T 7714 | Xu, Shuo , Li, Ling , An, Xin . Do academic inventors have diverse interests? [J]. | SCIENTOMETRICS , 2023 , 128 (2) : 1023-1053 . |
| MLA | Xu, Shuo et al. "Do academic inventors have diverse interests?" . | SCIENTOMETRICS 128 . 2 (2023) : 1023-1053 . |
| APA | Xu, Shuo , Li, Ling , An, Xin . Do academic inventors have diverse interests? . | SCIENTOMETRICS , 2023 , 128 (2) , 1023-1053 . |
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Abstract ：
Purpose The purpose of this study is to solve the problems caused by the growing volumes of pre-annotated literature and variety-oriented annotations, including teamwork, quality control and time effort. Design/methodology/approach An annotation collaboration workbench is developed, which is named as Bureau for Rapid Annotation Tool (Brat). Main functionalities include an enhanced semantic constraint system, Vim-like shortcut keys, an annotation filter and a graph-visualizing annotation browser. With these functionalities, the annotators are encouraged to question their initial mindset, inspect conflicts and gain agreement from their peers. Findings The collaborative patterns can indeed be leveraged to structure properly every annotator's behaviors. The Brat workbench can actually be seen as an experienced-based annotation tool by harnessing collective intelligence. Compared to previous counterparts, about one-third of time can be saved on Xinhuanet military news and patent corpora with the workbench. Originality/value The various annotations are very popular in real-world annotation tasks with multiple annotators. Though, it is still under-discussed on variety-oriented annotations. The findings of this study provide the practitioners valuable insight into how to govern annotation projects. In addition, the Brat workbench takes the first step for future research on annotating large-scale text resources.
Keyword ：
Annotation teamwork Annotation teamwork Knowledge engineering Knowledge engineering Variety-oriented annotation Variety-oriented annotation Annotation workbench Annotation workbench Quality control Quality control
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| GB/T 7714 | Wang, Zheng , Xu, Shuo , Wang, Yibo et al. Bureau for Rapid Annotation Tool: collaboration can do more among variance annotations [J]. | ASLIB JOURNAL OF INFORMATION MANAGEMENT , 2022 , 75 (3) : 523-534 . |
| MLA | Wang, Zheng et al. "Bureau for Rapid Annotation Tool: collaboration can do more among variance annotations" . | ASLIB JOURNAL OF INFORMATION MANAGEMENT 75 . 3 (2022) : 523-534 . |
| APA | Wang, Zheng , Xu, Shuo , Wang, Yibo , Chai, Xiaojiao , Chen, Liang . Bureau for Rapid Annotation Tool: collaboration can do more among variance annotations . | ASLIB JOURNAL OF INFORMATION MANAGEMENT , 2022 , 75 (3) , 523-534 . |
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Abstract ：
Given that citations are not equally important, various techniques have been presented to identify important citations on the basis of supervised machine learning models. However, only a small volume of instances have been annotated manually with the labels. To make full use of unlabeled instances and promote the identification performance, the semi-supervised self-training technique is utilized here to identify important citations in this work. After six groups of features are engineered, the SVM and RF models are chosen as the base classifiers for self-training strategy. Then two experiments based on two different types of datasets are conducted. The experiment on the expert-labeled dataset from one single discipline shows that the semi-supervised versions of SVM and RF models significantly improve the performance of the conventional supervised versions when unannotated samples under 75% and 95% confidence level are rejoined to the training set, respectively. The AUC-PR and AUC-ROC of SVM model are 0.8102 and 0.9622, and those of RF model reach 0.9248 and 0.9841, which outperform their counterparts and the benchmark methods in the literature. This demonstrates the effectiveness of our semi-supervised self-training strategy for important citation identification. Another experiment on the author-labeled dataset from multiple disciplines, semi-supervised learning models can perform better than their supervised learning counterparts in term of AUC-PR when the ratio of labeled instances is less than 20%. Compared to our first experiment, insufficient amount of instances from each discipline in our second experiment enables the performance of the models to be unsatisfactory.
Keyword ：
Semi-supervised learning Semi-supervised learning Self-training Self-training Important citation Important citation Author-labeled dataset Author-labeled dataset Expert-labeled dataset Expert-labeled dataset
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| GB/T 7714 | An, Xin , Sun, Xin , Xu, Shuo . Important citations identification with semi-supervised classification model [J]. | SCIENTOMETRICS , 2022 , 127 (11) : 6533-6555 . |
| MLA | An, Xin et al. "Important citations identification with semi-supervised classification model" . | SCIENTOMETRICS 127 . 11 (2022) : 6533-6555 . |
| APA | An, Xin , Sun, Xin , Xu, Shuo . Important citations identification with semi-supervised classification model . | SCIENTOMETRICS , 2022 , 127 (11) , 6533-6555 . |
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Abstract ：
The deep learning has become an important technique for semantic relation classification in patent texts. Previous studies just borrowed the relevant models from generic texts to patent texts while keeping structure of the models unchanged. Due to significant distinctions between patent texts and generic ones, this enables the performance of these models in the patent texts to be reduced dramatically. To highlight these distinct characteristics in patent texts, seven anno-tated corpora from different fields are comprehensively compared in terms of several indicators for linguistic characteristics. Then, a deep learning based method is proposed to benefit from these characteristics. Our method exploits the information from other similar entity pairs as well as that from the sentences mentioning a focal entity pair. The latter stems from the conventional practices, and the former from our meaningful observation: the stronger the connection between two entity pairs is, the more likely they belong to the same relation type. To measure quantita-tively the connection between two entity pairs, a similarity indicator on the basis of association rules is raised. Extensive experiments on the corpora of TFH-2020 and ChemProt demonstrate that our method for semantic relation classification is capable of benefiting from characteristic of patent texts.
Keyword ：
Linguistic characteristics Linguistic characteristics Deep learning Deep learning Semantic relation classification Semantic relation classification Patent analysis Patent analysis Similarity measure Similarity measure
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| GB/T 7714 | Chen, Liang , Xu, Shuo , Zhu, Lijun et al. A deep learning based method benefiting from characteristics of patents for semantic relation classification [J]. | JOURNAL OF INFORMETRICS , 2022 , 16 (3) . |
| MLA | Chen, Liang et al. "A deep learning based method benefiting from characteristics of patents for semantic relation classification" . | JOURNAL OF INFORMETRICS 16 . 3 (2022) . |
| APA | Chen, Liang , Xu, Shuo , Zhu, Lijun , Zhang, Jing , Yang, Guancan , Xu, Haiyun . A deep learning based method benefiting from characteristics of patents for semantic relation classification . | JOURNAL OF INFORMETRICS , 2022 , 16 (3) . |
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