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Discussion 1:

Big data and cloud computing, which can help to implement innovation-driven development strategy and promote industrial transformation and upgrading, is a new and emerging industrial field. Educated, productive and healthy workforces are necessary factor to develop big data and cloud computing industry, especially top talents are essential. Therefore, a three-step method named 3-F has been introduced to help describing the distribution of top talents globally and making decision whether they are needed. The 3-F method relies on calculating the brain gain index to analysis the top talent introduction demand of a country. Firstly, focus on the high-frequency keywords of a specific field by retrieving the highly cited papers. Secondly, using those keywords to Find out the top talents of this specific field in the Web of Science. Finally, figure out the brain gain index to estimate whether a country need to introduce top talents of a specific field abroad.

The result showed that the brain gains index value of big data and cloud computing field which means it needs to introduce top talents abroad. The international flow of top talent has become convenient and frequent. Facing the world’s top talent shortage, China and the world’s major countries have developed overseas top talent introduction programs. Until 2007, almost all European countries had introduced some skill selective migration policies to attract the top talents. To make the overseas top talent introduction programs more effective and targeted is helpful for occupying the strategic high ground in the global top talent competition. This has improved the traditional talent evaluation function of bibliometric method, and presented the 3-F analysis method, which was applied to analyze the demand of top talents. The 3F method could help the government official to make decision whether need to introduce top talents to develop a new industry field and lock these top talents geographic location (Alnoman, 2020).

References

Alnoman, R. (2020). Binary Liquid Crystal Mixtures Based on Schiff Base Derivatives with Oriented Lateral Substituents. Crystals (Basel), 10(4), 319–. https://doi.org/10.3390/cryst10040319

Discussion 2:

The 3-F method sims to calculate the brain gain index to analyze the top talent introduction demand of a country. In this method, the first part is figuring out the high-frequency keywords of a specific field by referring to the highly cited papers, the second part is using those keywords to find the top talents of the specific field in the Web of Science, third and final step is to find the brain gain index to estimate whether the country needs to introduce top talents of a specific field. According to research done by Linjia et.al, the brain gain index is calculated by the formula Iik = (Twk / Tik) / (Pw / Pi). Where Iik is the brain gain index value of country (i) in the field (k), Twk is the number of the world’s top talents in the field (k), Tik is the number of country’s (i) top talents in the field (k), Pw is the world population, and Pi represents the country’s (i) population (L. Zhao et al., 2017). The research further did a comparative analysis of the brain gain index among several countries and the brain gain index of the United States in the field of Big Data and cloud computing was found to be 0.11 (L. Zhao et al., 2017). A brain gain index of less than one indicates a higher presence of talent in the specific field and that greater than one indicates the need to introduce top talent in the field. This indicates that compared to the rest of the world, United States ranks the top in terms of the presence of top talent in the field of big data and cloud computing and does not have a pressing need to introduce top talents in the field of big data and cloud computing.

References

L. Zhao, Y. Huang, Y. Wang, & J. Liu. (2017). Analysis on the Demand of Top Talent Introduction in Big Data and Cloud Computing Field in China Based on 3-F Method. 2017 Portland International Conference on Management of Engineering and Technology (PICMET), 1–3. https://doi.org/10.23919/PICMET.2017.8125463

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