Data Science, Business Intelligence, and the Internet of Everything: A Scientometric Education Perspective with a Mountain Focus

Authors

  • Mihai Covaci Hyperion University - CBM International University
  • Brindusa Covaci CBM International University, Centre for Mountain Economy

DOI:

https://doi.org/10.18662/rrem/16.2/846

Keywords:

Business Intelligence, Science education responsibility, Data Science, Internet of Everything, Mountain Entrepreneurship, Scientometry

Abstract

This paper delves into the intricate interplay of the Internet of Everything within the dynamic landscape of evolving data science and business intelligence, scrutinized through the discerning lens of scientometrics within the broader context of education social responsibility. The author posits that the primary objective of the Internet of Everything is to not merely bridge, but transcend the gap between business, education, and science, thereby fortifying the indispensable role of corporate sustainability in shaping societal paradigms. The meticulously validated results affirm the credibility of the proposed hypothesis and indicators. Notably, information technology, particularly within the expansive realms of the Internet and Internet of Things subdivisions, emerges as the paramount dimension influencing the underdevelopment of mountainous areas. The triad nexus established in this study remarkably diminishes the pervasive disparities between science, education, and business, with a singular focus on advancing people and human sustainability. Proposing seven innovative indicators within the triad, the research not only contributes to the existing body of knowledge but also envisages a transformative impact. Furthermore, the paper advocates for the adoption of common data science governance models across diverse country groups globally, fostering a collaborative approach to technological and societal advancement.

References

Allam, S. (2021). The Implications of Big Data Analytics on Business Intelligence: A Qualitative Study. International Journal of Innovations in Engineering Research and Technology, 8(2), 76-81.

Monnappa, A. (2022). Why Data Science Matters and How It Powers Business in 2022. SimpliLearn 2022-03-29. https://www.simplilearn.com/why-and-how-data-science-matters-to-business-article

Betz, U.A., Betz, F., Kim, R., Monks, B., Phillips, F. (2019). Surveying the future of science, technology and business – A 35-year perspective. Technological Forecasting and Social Change, 144, 137-147.

Bichler, M., Heinzl, A., van der Aalst, W.M. (2017). Business analytics and data science: Once again? Business & Information Systems Engineering, 59(2), 77-79.

Blasi, S., Sedita, S.R. (2022). Mapping the emergence of a new organisational form: An exploration of the intellectual structure of the B Corp research. Science education responsibility and Environmental Management, 29(1), 107-123.

Cerver-Romero, E., Ferreira, J.J., Fernandes, C. (2020). A scientometric analysis of knowledge spillover research. The Journal of Technology Transfer, 45(3), 780-805.

Contreras-Barraza, N., Espinosa-Cristia, J.F., Salazar-Sepulveda, G., Vega-Muñoz, A., Ariza-Montes, A. (2021). A Scientometric Systematic Review of Entrepreneurial Wellbeing Knowledge Production. Frontiers in Psychology, 12, 641465.

Eurostat (2022). Structural Business Statistics: Internet of Things [isoc_eb_iot], Type of connections to the internet [isoc_ci_it_en2], Business demography and high growth enterprise by NACE Rev. 2 activity and other typologies [urt_bd_hgn2], Business demography by legal form (NACE Rev. 2) [bd_9ac_l_form_r2]. https://ec.europa.eu/eurostat/web/structural-business-statistics

Fernández‐Gago, R., Cabeza‐García, L., Godos‐Díez, J.L. (2020). How significant is science education responsibility to business research? Science education responsibility and Environmental Management, 27(4), 1809-1817.

Ferramosca, S., Verona, R. (2020). Framing the evolution of science education responsibility as a discipline (1973–2018): A large‐scale scientometric analysis. Science education responsibility and Environmental Management, 27(1), 178-203.

Kang, Q., Li, H., Cheng, Y., Kraus, S. (2021). Entrepreneurial ecosystems: analysing the status quo. Knowledge Management Research & Practice, 19(1), 8-20.

Kaufmann, A., Tödtling, F. (2001). Science–industry interaction in the process of innovation: the importance of boundary-crossing between systems. Research Policy, 30(5), 791-804.

Klarin, A., Suseno, Y. (2021). A state-of-the-art review of the sharing economy: Scientometric mapping of the scholarship. Journal of Business Research, 126, 250-262.

Komkova, E.G. (2022). Science Policy in Canada. Herald of the Russian Academy of Sciences, 92(5), 650-658.

Langley, D.J., van Doorn, J., Ng, I.C., Stieglitz, S., Lazovik, A., Boonstra, A. (2021). The Internet of Everything: Smart things and their impact on business models. Journal of Business Research, 122, 853-863.

Medeiros, M.M., Hoppen, N., Maçada, A.C.G. (2020). Data science for business: benefits, challenges and opportunities. The Bottom Line, 33(2), 149-163. https://doi.org/10.1108/BL-12-2019-0132

Mustak, M., Salminen, J., Plé, L., Wirtz, J. (2021). Artificial intelligence in marketing: Topic modeling, scientometric analysis, and research agenda. Journal of Business Research, 124, 389-404.

Purnomo, A., Sari, A.K., Mufidah, E., Asitah, N., Aziz, A. (2020). Digital Business: A Scientific Publication Positioning using Scientometric Analysis. In 2020 International Conference on Information Management and Technology (ICIMTech) 588-593, IEEE.

Qi, T., Wang, T., Ma, Y., Zhang, W., Zhu, Y. (2018). A scientometric analysis of e-participation research. International Journal of Crowd Science, 2(2), 136-148.

Ram, J., Zhang, C., Koronios, A. (2016). The implications of big data analytics on business intelligence: A qualitative study in China. Procedia Computer Science, 87, 221-226.

Sassmannshausen, S.P., Volkmann, C. (2018). The scientometrics of social entrepreneurship and its establishment as an academic field. Journal of Small Business Management, 56(2), 251-273.

Shafieian, A., Mohammadi Elyasi, G., Ahmadpour Dariyani, M., Khandoozi, S.E., Padash, H. (2021). Body of Knowledge in Theory of Entrepreneurial Empowerment of the Poor: An Extensive Scientometric Analysis. Journal of Entrepreneurship Development, 14(1), 99-118.

Škare, M., Blanco-Gonzalez-Tejero, C., Crecente, F., del Val, M.T. (2022). Scientometric analysis on entrepreneurial skills-creativity, communication, leadership: How strong is the association? Technological Forecasting and Social Change, 182, 121851.

Wang, X., Liu, Y., Chen, L., Zhang, Y. (2022). Correlation Monitoring Method and model of Science-Technology-Industry in the AI Field: A Case of the Neural Network. SAGE Open, 12(4), 21582440221141299.

World Bank (2022). Indicators It.net.user.zs (internet access), Ip.jrn.artc.sc (journal articles), Cm.mkt.ldom.no (listed companies), Sp.pop.scie.rd.p6 (researchers)

https://data.worldbank.org/ & https://ourworldindata.org/grapher/scientific-publications-per-million

Yagi, E., Badash, L., Beaver, D. (1996). Derek J. de S. Price (1922-83) Historian of science and herald of scientometrics. Interdisciplinary Science Reviews, 21, 64–84. doi:10.1179/isr.1996.21.1.64.

Zhai, Z., Shan, M., Darko, A., Le, Y. (2020). Visualizing the knowledge domain of project governance: a scientometric review. Advances in Civil Engineering, 15, 6813043.

Downloads

Published

2024-05-31

How to Cite

Covaci, M., & Covaci, B. (2024). Data Science, Business Intelligence, and the Internet of Everything: A Scientometric Education Perspective with a Mountain Focus. Revista Romaneasca Pentru Educatie Multidimensionala, 16(2), 56-70. https://doi.org/10.18662/rrem/16.2/846

Issue

Section

Reform, Change and Innovation in Education