About this Research Topic
Processing large-scale data, recognizing uncertain and complex relationships, forecasting potential trends, investigating hidden mechanisms, and developing models with robustness and adaptability are all challenges that have risen due to the creation of increasingly complicated environments relevant to decision making in the real world. Bibliometrics, as an effective analytic tool, has provided systematic solutions for understanding the activities of science technology and innovation (ST&I). It has, thus, been supporting research policy and strategic management. The rapid development of information technologies—in particular, artificial intelligence techniques—has been bringing evolutionary changes to traditional bibliometrics as well as its applications. For example, natural language processing approaches, together with deep learning techniques, effectively encode semantic structures and identify sentimental elements from ST&I data sources. Similarly, large-scale network analytics leverage understandings on the topological structure of bibliometric networks to enable new angles for evaluating the dynamics of ST&I and to support science policy and entrepreneurship.
This Research Topic aims to explore the ways of fully facilitating the power of advanced analytics in order to enhance decision support for research policy and strategic management in scalable, uncertain, and complicated environments in the real world. Targeting broad issues in science, technology, and innovation policy, it emphasizes on the development and applications of advanced analytic approaches that provide transparent, explainable, and valuable evidence for decision making. This Research Topic spearheads a cross-disciplinary direction by covering information science, computer science, and business so as to present fundamental theories, conceptual methodologies, and practical toolkits for studies and practices in research policy and strategic management.
Driven by discipline-specific issues in research policy and strategic management, topics of interest include, but are not limited to, the following:
• advanced bibliometric indicators and approaches
• advanced network analytics for science maps
• scholarly knowledge discovery and management
• evidenced-based policy making in science, technology, and innovation policy
• organizational methods and protocols in decision making for strategic management.
This Research Topic aims to explore the ways of fully facilitating the power of advanced analytics in order to enhance decision support for research policy and strategic management in scalable, uncertain, and complicated environments in the real world. Targeting broad issues in science, technology, and innovation policy, it emphasizes on the development and applications of advanced analytic approaches that provide transparent, explainable, and valuable evidence for decision making. This Research Topic spearheads a cross-disciplinary direction by covering information science, computer science, and business so as to present fundamental theories, conceptual methodologies, and practical toolkits for studies and practices in research policy and strategic management.
Driven by discipline-specific issues in research policy and strategic management, topics of interest include, but are not limited to, the following:
• advanced bibliometric indicators and approaches
• advanced network analytics for science maps
• scholarly knowledge discovery and management
• evidenced-based policy making in science, technology, and innovation policy
• organizational methods and protocols in decision making for strategic management.
Keywords: Bibliometrics, Data analytics, Decision making, Research policy, Strategic management
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
