Scientometric analysis of COVID-19: A basis for developing a general theory of pandemic from scholarly communications perspective

Authors

DOI:

https://doi.org/10.51660/ridhs12181

Keywords:

COVID-19 Pandemic, Scientometric Analysis, Scholarly Communications

Abstract

This study conducted a scientometric analysis of the COVID-19 pandemic with the aim of providing a foundation for developing a general theory of pandemics from a scholarly communications perspective. To achieve this, the study sought to answer a single question: How do knowledge, innovation, and environment relate to one another during a pandemic? Carayannis and Campbell (2010) posed a similar question from a different perspective, and this study builds on that by attempting to provide a framework in case another pandemic occurs. To understand the publication behavior of scholars over the five-year period from 2019 to 2024, the authors analyzed data extracted from Scopus between August 18 and 28, 2023. The search strategy used was “COVID-19 OR Coronavirus OR Coronaviruses OR SARS-CoV-2 OR 2019-nCoV.” The search yielded 511,920 results, of which 17,487 were used for this study. It was found that many countries around the globe formed six clusters. As a result, researchers from these countries continued to produce significant research outputs, leading to a high number of citations and enhancing their position within scholarly communications. An interesting finding of this research revealed new and relevant topics, prompting the authors to link these findings with the quintuple helix theory. The study recommended using empirical and theoretical models to develop theories that can further define pandemics.

Downloads

Download data is not yet available.

References

Achem, V. O., & Ani, K. J. (2022). Systemic crisis of infodemic in a pandemic: COVID-19, 5G network, society and symbolic interactionism. Journal of African Films and Diaspora Studies, 5(4). https://doi.org/10.31920/2516-2713/2022/5n4a2

Adakawa, M. I., & Harinarayana, N. S. (2022). Insight into intellectual property in patent medicine: An Indian perspective. Unnes Law Journal, 8(2), 377-391. https://doi.org/10.15294/ulj.v8i2.60716

Adakawa, M. I., Balachandran, C., Kumara, P. B., & Harinarayana, N. S. (2023). History of pandemics—A critical pathway to challenge scholarly communication? National Conference on Exploring the Past, Present, and Future of Library and Information Science, May 29 & 30, 2023.

Agarwal, B., Agarwal, A., Harjule, P., & Rahman, A. (2023). Understanding the intent behind sharing misinformation on social media. Journal of Experimental and Theoretical Artificial Intelligence, 35(4), 573-587. https://doi.org/10.1080/0952813X.2021.196063

Ahadzadeh, A. S., Ong, F. S., & Wu, S. L. (2023). Social media skepticism and belief in conspiracy theories about COVID-19: The moderating role of the dark triad. Current Psychology. https://doi.org/10.1007/s12144-021-02198-1

Ahmed, W., Vidal-Alaball, J., Downing, J., & Seguí, F. L. (2020). COVID-19 and the 5G conspiracy theory: Social network analysis of twitter data. Journal of Medical Internet Research. https://doi.org/10.2196/19458

Alassad, M., Hussain, M. N., & Agarwal, N. (2020). How to control coronavirus conspiracy theories in Twitter? A systems thinking and social networks modeling approach. Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020. https://doi.org/10.1109/BigData50022.2020.9378400

Aviv-Reuven, S., & Rosenfeld, A. (2021). Publication patterns’ changes due to the COVID-19 pandemic: A longitudinal and short-term scientometric analysis. Scientometrics, 126, 6761–6784. https://doi.org/10.1007/s11192-021-04059-x

Ay, İ. E., Tazegul, G., & Duranoğlu, Y. (2022). A comparison of scientometric data and publication policies of ophthalmology journals. Indian Journal of Ophthalmology, 70, 1801-1807. https://doi.org/10.4103/ijo.IJO_2720_21

Carayannis, E. G., & Campbell, D. F. J. (2010). Triple helix, quadruple helix and quintuple helix and how do knowledge, innovation and the environment relate to each other? A proposed framework for a trans-disciplinary analysis of sustainable development and social ecology. International Journal of Social Ecology and Sustainable Development, 1(1), 41-69. https://doi.org/10.4018/jsesd.2010010105

COVID-19 Treatment Guidelines Panel. (2024). Coronavirus Disease 2019 (COVID-19) Treatment Guidelines. National Institutes of Health.

Cunningham, E., Smyth, B., & Greene, D. (2021). Collaboration in the time of COVID: A scientometric analysis of multidisciplinary SARS-CoV-2 research. Humanities & Social Sciences Communications, 8(240), 1-8. https://doi.org/10.1057/s41599-021-00922-7

Desai, A. N., Kraemer, M. U. G., Bhatia, S., Cori, A., Nouvellet, P., Herringer, M., Cohn, E. L., Carrion, M., Brownstein, J. S., Madoff, L. C., & Lassmann, B. (2019). Spatial/temporal analysis in infectious disease outbreaks—Real-time epidemic forecasting: Challenges and opportunities. Health Security, 17(4), 1-8. https://doi.org/10.1089/hs.2019.0022

Douglas, K. M. (2021). COVID-19 conspiracy theories. Group Processes and Intergroup Relations. https://doi.org/10.1177/1368430220982068

Galli, M., Migliano, F., Fasano, V., Silvani, A., Passarella, D., & Citarella, A. (2024). Nirmatrelvir: From discovery to modern and alternative synthetic approaches. Processes, 12(1242), 1-33. https://doi.org/10.3390/pr12061242

Gannuscio, V. (2022). From holocough to great reset. Antisemitic conspiracy theories around the coronavirus. Muttersprache. https://doi.org/10.53371/60415

Groicher, M. J., Grattagliano, I., Loconsole, P., & Maglie, R. (2022). A review of the psychosocial and criminological factors underlying COVID-19 conspiracy theories. Rassegna Italiana di Criminologia. https://doi.org/10.7347/RIC-032022-p189

Gurnani, B., Kaur, K., & Nath, M. (2022). Publication addiction during COVID-19 pandemic - A rising boon or a bane. Indian Journal of Ophthalmology, 70, 1402-1403. https://doi.org/10.4103/ijo.IJO_386_22

He, X., Chen, H., Zhu, X., & Gao, W. (2023). Real-world effectiveness of non-pharmaceutical interventions in containing COVID-19 pandemic after the roll-out of coronavirus vaccines: A systematic review. medRxiv preprint. https://doi.org/10.1101/2023.11.07.23297704

He, J., Liu, X., Lu, X., Zhong, M., Jia, C., Lucero-Prisno, D. E. III., Ma, Z. F., & Li, H. (2023). The impact of COVID-19 on global health journals: An analysis of impact factor and publication trends. BMJ Global Health, 8, 1-12. https://doi.org/10.1136/bmjgh-2022-011514

Jones, M., & Karsten, H. (2003). Review: Structuration theory and information systems research. Research Papers in Management Studies: University of Cambridge, Judge Institute of Management.

Kaur, K., & Gurnani, B. (2021). Intricate scientometric analysis and citation trend of COVID-19-related publications in Indian Journal of Ophthalmology during COVID-19 pandemic. Indian Journal of Ophthalmology, 69, 2202-2210.

Lewis, D. (2021). The COVID pandemic has harmed researcher productivity – and mental health. https://www.nature.com/articles/d41586-021-03045-w

Mahdavi, A., Atlasi, R., & Naemi, R. (2022). Teledentistry during COVID-19 pandemic: Scientometric and content analysis approach. BMC Health Services Research, 22, 1-17. https://doi.org/10.1186/s12913-022-08488-z

Malik, A. A., Butt, N. S., Bashir, M. A., & Gilani, S. A. (2020). A scientometric analysis on coronaviruses research (1900–2020): Time for a continuous, cooperative and global approach. Journal of Infection and Public Health, 14, 310-319. https://doi.org/10.1016/j.jiph.2020.12.008

Manca, D. (2022). Different approaches to epidemic modeling – The COVID-19 case study. Proceedings of the 32nd European Symposium on Computer Aided Process Engineering (ESCAPE32), June 12-15, 2022, Toulouse, France. https://doi.org/10.1016/B978-0-323-95879-0.50274-5

Mesfin, Y. M., Blais, J. E., Kibret, K. T., Tegegne, T. K., Cowling, B. J., & Wu, P. (2024). Effectiveness of nirmatrelvir/ritonavir and molnupiravir in non-hospitalized adults with COVID-19: Systematic review and meta-analysis of observational studies. J Antimicrob Chemother, 1-13. https://doi.org/10.1093/jac/dkae163

Nikolopoulos, K., Punia, S., Schäfers, A., Tsinopoulos, C., & Vasilakis, C. (2020). Forecasting and planning during a pandemic: COVID-19 growth rates, supply chain disruptions, and governmental decisions. European Journal of Operational Research, 290, 99-115. https://doi.org/10.1016/j.ejor.2020.08.001

Okolie, C. C., & Ogundeji, A. A. (2022). Effect of COVID-19 on agricultural production and food security: A scientometric analysis. Humanities & Social Sciences Communications, 9(64), 1-13. https://doi.org/10.1057/s41599-022-01080-0

Reis, S., Metzendorf, M.-I., Kuehn, R., Popp, M., Gagyor, I., Kranke, P., Meybohm, P., Skoetz, N., & Weibel, S. (2023). Nirmatrelvir combined with ritonavir for preventing and treating COVID-19. Cochrane Database of Systematic Reviews, 11(CD015395). https://doi.org/10.1002/14651858.CD015395.pub3

Rodríguez-Rodríguez, I., Rodríguez, J.-V., Shirvanizadeh, N., Ortiz, A., & Pardo-Quiles, D.-J. (2022). Applications of artificial intelligence, machine learning, big data and the internet of things to the COVID-19 pandemic: A scientometric review using text mining. International Journal of Environmental Research and Public Health, 18(8578), 1-26. https://doi.org/10.3390/ijerph181685

Romer, D., & Jamieson, K. H. (2020). Conspiracy theories as barriers to controlling the spread of COVID-19 in the U.S. Social Science and Medicine. https://doi.org/10.1016/j.socscimed.2020.113356

Santos, B. S., Silva, I., Lima, L., Endo, P. T., Alves, G., & Ribeiro-Dantas, M. C. (2022). Discovering temporal scientometric knowledge in COVID-19 scholarly production. Scientometrics, 127, 1609–1642. https://doi.org/10.1007/s11192-021-04260-y

Spink, A., & Cole, C. (2007). Information behavior: A socio-cognitive ability. Evolutionary Psychology, 5(2), 257-274. https://doi.org/10.1177/147470490700500201

Sternisko, A., Cichocka, A., Cislak, A., & Van Bavel, J. J. (2023). National narcissism predicts the belief in and the dissemination of conspiracy theories during the COVID-19 pandemic: Evidence from 56 countries. Personality and Social Psychology Bulletin. https://doi.org/10.1177/01461672211054947

Suleyman, M., & Bhaskar, M. (2023). The coming wave: Technology, power, and the 21st century's greatest dilemma. New York: Crown Publishing Group.

Sun, J., Chen, X., Zhang, Z., Lai, S., Zhao, B., Liu, H., Wang, S., Huan, W., Zhao, R., Ng, M. T. A., & Zheng, Y. (2020). Forecasting the long-term trend of COVID-19 epidemic using a dynamic model. Scientific Reports, 10(21122), 1-10. https://doi.org/10.1038/s41598-020-78084-w

Whittington, R. (2015). Giddens, structuration theory, and strategy as practice. https://doi.org/10.1017/CCO9781139681032.009

Downloads

Published

2024-09-01

How to Cite

Scientometric analysis of COVID-19: A basis for developing a general theory of pandemic from scholarly communications perspective. (2024). International Journal of Human Development and Sustainability, 1(2), 87-113. https://doi.org/10.51660/ridhs12181