Fourth International Conference on Science & Technology Metrics (STMet 2023): Proceedings BITS Pilani, Dubai, UAE. November 16-17, 2023
 

 

Metrics To Mastery: A Roadmap For Innovative Technologies And Scientific Productivity
P. Ravichandran, Ranjini Syam
Professor & Protocolofficer Annamalai University Chidambaram., Library Specialist, Higher Colleges of Technology-CERT, Abu Dhabi, UAE
Abstract: In today’s fast-paced world of technological advancements and scientific breakthroughs, the pursuit of innovation and scientific productivity is both an aspiration and a necessity. As we traverse this transformative landscape, it becomes evident that “Metrics to Mastery” equips organizations to unlock the full potential of their research and development endeavors, foster innovation, and establish themselves as leaders in the dynamic technology and scientific productivity domain. This paper encapsulates the essence of this transformative journey, emphasizing the indispensable role of strategic metrics. “Metrics to Mastery” provides a structured approach to measure progress, evaluate performance, and make informed decisions rooted in data-driven insights. It empowers organizations to cultivate a culture of continuous improvement, where every action is guided by the compass of strategic metrics. In the present study, 13125 records were collected from the Web of Science database, and the period for the present study is from 1990 to 2021.
Keywords: Technological Innovation, Technology Roadmap, Scientific Productivity, Scientometrics, Relative Growth Rate, Bradford Law, Innovation Culture Metrics To Mastery: A Roadmap For Innovative Technologies And Scientific Productivity
DOI:https://doi.org/10.6025/stm/2023/4/42-54
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References:

[1] Zhou, L., Zhang, L., Zhao, Y., Zheng, R., Song, K. (2020). A scientometric review of blockchain research. Information Systems and eBusiness Management, 1-31.
[2] Koondhar, M. A., Shahbaz, M., Memon, K. A., Ozturk, I., Kong, R. (2021). A visualization review analysis of the last two decades for environmental Kuznets curve “EKC” based on cocitation analysis theory and pathfinder network scaling algorithms. Environmental Science and Pollution Research, 28(13), 16690-16706.
[3] Shao, H., Yu, Q., Bo, X., Duan, Z. (2013). Analysis of oncology research from 2001 to 2010: a scientometric perspective. Oncology reports, 29(4), 1441-1452.
[4] Chang, Y. H., Chang, C. Y., Tseng, Y. H. (2010). Trends of science education research: An automatic content analysis. Journal of Science Education and Technology, 19(4), 315-331.
[5] Chen, T., Zhu, J., Zhao, Y., Li, H., Li, P., Fan, J., Wei, X. (2021). The global state of research in pain management of osteoarthritis (2000–2019): A 20-year visualized analysis. Medicine, 100(2).
[6] Du, J., Li, P., Guo, Q., Tang, X. (2019). Measuring the knowledge translation and convergence in pharmaceutical innovation by funding-science-technology-innovation linkages analysis. Journal of informetrics, 13(1), 132-148.
[7] Glänzel, W., Zhang, L. (2018). Scientometric research assessment in the developing world: A tribute to Michael J. Moravcsik from the perspective of the twenty-first century. Scientometrics, 115(3), 1517-1532.
[8] Kademani, B. S., Anil, S., Bhanumurthy, K. (2017). Research and impact of materials science publications in India: 1999- 2008. Malaysian Journal of Library & Information Science, 16(2), 63-82.
[9] Karpagam, R. (2014). Global research output of nanobiotechnology research: A scientometrics study. Current Science, 1490-1499.
[10] https://www.intechopen.com/chapters/61502
[11] https://www.lucidchart.com/blog


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