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Impacts of inter-institutional mobility on scientific performance from research capital and social capital perspectives

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Abstract

Industries have gradually relied on basic scientific research and discovery in academia to produce innovative products and enhance innovation capabilities. This division of labor has promoted researchers’ mobility from academic research institutions to enterprises (aca.ind mobility), especially in the artificial intelligence domain. However, limited research has been conducted to explore the impact of aca.ind mobility on the researchers’ performance in this domain. The findings elucidate the motivation and necessary conditions of researchers’ aca.ind mobility in this domain based on bibliometric methods, propensity score matching (PSM), and regression analysis methods. The results also demonstrate the impact of this type of mobility on researchers’ performance compared to mobility between academic research institutions (aca.aca mobility) from a research capital perspective and social capital perspective. The results of this paper indicate that researchers in this domain need to accumulate more research publications and establish collaborative relationships with corporate researchers and high-impact researchers within a short academic age timeline to maximize the opportunities for aca.ind mobility. Furthermore, compared with the aca.aca mobility, which allows researchers to accumulate more research capital, aca.ind mobility seems to be more conductive to the accumulation of social capital. The aca.ind mobility not only helps researchers expand the scale of their scientific collaborative networks, but also establish collaborative relationships with more high-impact researchers. This research can help researchers plan their own career development. The results also provide suggestions for policy makers to formulate policies on talent recruitment, structure optimization, and evaluation of knowledge and innovation systems.

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Notes

  1. We extracted relevant studies from 4 top international academic journals and 7 top international academic conferences in the MAG database. The 4 academic journals are Artificial Intelligence (AI), IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), International Journal of Computer Vision (IJCV), Journal of Machine Learning Research (JMLR), and the 7 academic conferences are AAAI Conference on Artificial Intelligence (AAAI), Annual Conference on Neural Information Processing Systems (NeuriPs), Ammual Meeting of the Association for Computational Linguistics (ACL), IEEE Conference on Computer Vision and Pattern Recognition (CVPR), International Conference on Computer Vision (ICCV), International Conference on Machine Learning (ICML), and International Joint Conference on Artificial Intelligence (IJCAI).

  2. We adopt the default calculation method of “cor function” in R language to calculate the correlation coefficient between variables, which is Pearson correlation.

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Acknowledgements

This work was supported by the National Science Foundation of China (NSFC No. 71874077).

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Authors and Affiliations

  1. School of Information Management, Nanjing University, Nanjing, China

    Yitong Chen, Keye Wu, Yue Li & Jianjun Sun

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  1. Yitong Chen
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  2. Keye Wu
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  3. Yue Li
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Contributions

The study conception and design were conducted by JS. The first draft of the study was written by YC. Material preparation, data collection and analysis were performance by KW, YL, YC and JS. All authors read and approved the final manuscript.

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Correspondence to Jianjun Sun.

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Appendix

Appendix


Part 1:

The results presented in Table 6 correspond to Part 1 of the Appendix. It can be seen from Table 6 that a small number of “Asian name” have reached the bset accuracy after the first stage of disambiguation, while most of them achieve best effect after the second stage of disambiguation. It means that our work can solve the problem of ambiguity of the author name to some degree.

Table 6 Results on the accuracy of name disambiguation for mobile researchers
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Part 2:

Table 7 Part of results of identification of institutions’ type
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Part 3:

The complete process of matching procedure are presented in Fig. 8. The variables chosen for matching in this paper are mainly publication and collaboration indicators of researchers before mobility. As these indicators relate to the time of mobility, they cannot be calculated for non-mobile researchers. In this regard, we first screen out non-mobile researchers who have similar characteristics to the two types of mobile researchers separately to determine a virtual year of mobility for them.

The conditions used for screening include: 1. the first publication of the non-mobile researchers is published at the same time as the mobile researchers; 2. The non-mobile researchers belong to the same type of institution as the mobile researchers before their nobility; 3. The non-mobile researchers belong to the same discipline as the mobile researchers (the discipline of the researchers is judged based on the department of the institution to which the researchers belong). The aca.aca and aca.ind mobile researchers are embedded in the above conditions to find the non-mobile researchers who meet the conditions, respectively. There are 22994 non-mobile researchers meeting the above conditions. We then assign virtual mobility years to the screened non-mobile researchers based on the year of mobility of mobile researchers. We calculate the metrics used for matching based on their virtual mobility year.

Based on the PSM, we obtain two additional sets of matching results. In the second match, the aca.aca mobile researchers represent the treatment group, while the non-mobile researchers represent the control group. 15640 observations are matched successfully in the second matching, including 4758 observations in the treatment group and 10882 in the control group. In the third match, the aca.ind mobile researchers represent the treatment group, while the non-mobile researchers also represent the control group. 4530 observations are matched successfully in the third matching, including 805 observations in the treatment group and 1366 in the control group.

Fig. 8
figure 8

Complete process of matching procedure

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Table 8 presents the balance check for the second matching results. From Table 8, the mean difference between the treatment and control group becomes 0 after matching. The means of most of variables also become smaller after matching. It demonstrates that the distribution of the variables in the treatment and control group is more balanced after matching. Similar conclusions are also presented in Table 9.

Table 8 Balance of variables before and after matching
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Table 9 Balance of variables before and after matching
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After passing the balance check, the matched data is used in the regression analysis. Table 10 presents the impact of aca.aca mobility on researchers’ performance compared to non-mobile researchers. In the first two columns of Table 10, the results are presented from the research capital respective, while the last two columns are presented the results from the perspective of social capital. As can be seen from the Table 10, aca.aca mobility of researchers seems to have a significance positive impact on the improvement and accumulation of their research and social capital compared to non-mobile researchers.

Table 10 Impact of aca.aca pattern on the performance of research and social capital
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Table 11 presents the impact of aca.ind mobility on researchers’ performance compared to non-mobile researchers. Table 11 presents similar findings to Table 10. The above regression results indicate that the performance of mobile researchers is better than that of non-mobile researchers when other variables remain consistent under control. It means that both aca.aca and aca.ind mobility have a positive impact on researchers’ performance from the perspectives of research and social capital. As can be observed from the magnitude of the regression coefficients alone, aca.ind mobility has a weaker impact on the improvement of research capital of researchers than aca.aca mobility, while its impact on the improvement and accumulation of researchers’ social capital is greater than that of aca.aca mobility. To further explore the differences in researchers’ performance resulting from these two mobility patterns, we further conduct the regression analysis based on them.

Table 11 Impact of aca.ind pattern on the performance of research and social capital
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Chen, Y., Wu, K., Li, Y. et al. Impacts of inter-institutional mobility on scientific performance from research capital and social capital perspectives. Scientometrics 128, 3473–3506 (2023). https://doi.org/10.1007/s11192-023-04690-w

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