IDEAS home Printed from https://ideas.repec.org/a/spr/scient/v127y2022i1d10.1007_s11192-021-04208-2.html
   My bibliography  Save this article

The other side of the coin: The declining of Chinese social science

Author

Listed:
  • Kun Chen

    (Xinjiang University
    Xinjiang University
    Development Research Center of the People’s Government of Hunan Province)

  • Xian-tong Ren

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

  • Guo-liang Yang

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

  • Hai-bo Qin

    (Xinjiang University
    Xinjiang University)

Abstract

This article aims to study the other side of the coin under the background of skyrocketing of Chinese natural science papers: the anomaly phenomenon in which Chinese social science papers are getting fewer and fewer. After analysis through the cross-efficiency DEA (Data envelopment analysis) and Malmquist index, we find that: (1) Although there are fewer and fewer published papers on social science in China, the number of social science full-time teachers and PhD students in Chinese universities, the number of fund grants and grant funding from National Science Foundation of China and National Social Science Foundation of China are in increasing trends. (2) The number of gross natural science papers per capita increased while the number of gross social science papers per capita decreased. However, the number of Chinese papers per capita published in domestic journals of CSCD (Chinese science citation database)/CSSCI (Chinese social science citation index) decreased from 2009 to 2018 both in natural science and social science. (3) From 2009 to 2018, the efficiency of the publication of Chinese social science papers in every subject declined, with an average decrease of 13.5%, which is mainly caused by the regression of the production frontier. Sport Science gained the largest decrease by 45.5% while Economics gained the smallest decrease by 11.2%. After an investigation of almost all CSSCI journals, we conclude that the reason for the decline in the publication of Chinese social science papers may be that China's unique journal system could not handle the problem that the average article length increased due to the improvement of scientific research, where the increase of 19.89% in average CSSCI journal pages is much smaller than the increase of 55.10% in average paper length during 2009–2018, causing the decrease in the publication and publication efficiency of Chinese social science papers. Therefore, the academic journal system of a country may have an important impact on its scientific research outputs, and China should improve the academic journal system to promote the development of social science. Our analysis method and results can also make sence for policy makers in other countries.

Suggested Citation

  • Kun Chen & Xian-tong Ren & Guo-liang Yang & Hai-bo Qin, 2022. "The other side of the coin: The declining of Chinese social science," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(1), pages 127-143, January.
    • Handle: RePEc:spr:scient:v:127:y:2022:i:1:d:10.1007_s11192-021-04208-2
    DOI: 10.1007/s11192-021-04208-2
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11192-021-04208-2
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11192-021-04208-2?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Yang, Guo-liang & Rousseau, Ronald & Yang, Li-ying & Liu, Wen-bin, 2014. "A study on directional returns to scale," Journal of Informetrics, Elsevier, vol. 8(3), pages 628-641.
    2. Norman Baker & James Freeland, 1975. "Recent Advances in R&D Benefit Measurement and Project Selection Methods," Management Science, INFORMS, vol. 21(10), pages 1164-1175, June.
    3. Guan, Jiancheng & Chen, Kaihua, 2012. "Modeling the relative efficiency of national innovation systems," Research Policy, Elsevier, vol. 41(1), pages 102-115.
    4. Lee, Hyoungsuk & Choi, Yongrok & Seo, Hyungjun, 2020. "Comparative analysis of the R&D investment performance of Korean local governments," Technological Forecasting and Social Change, Elsevier, vol. 157(C).
    5. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    6. Chen, Kaihua & Kou, Mingting & Fu, Xiaolan, 2018. "Evaluation of multi-period regional R&D efficiency: An application of dynamic DEA to China's regional R&D systems," Omega, Elsevier, vol. 74(C), pages 103-114.
    7. Yang, Guo-liang & Fukuyama, Hirofumi & Song, Yao-yao, 2018. "Measuring the inefficiency of Chinese research universities based on a two-stage network DEA model," Journal of Informetrics, Elsevier, vol. 12(1), pages 10-30.
    8. Mulyanto,, 2014. "Performance of Indonesian R&D institutions: Influence of type of institutions and their funding source on R&D productivity," Technology in Society, Elsevier, vol. 38(C), pages 148-160.
    9. Kairui Zuo & Jiancheng Guan, 2017. "Measuring the R&D efficiency of regions by a parallel DEA game model," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(1), pages 175-194, July.
    10. Jaehun Park & Joonyoung Kim & Si-Il Sung, 2017. "Performance Evaluation of Research and Business Development: A Case Study of Korean Public Organizations," Sustainability, MDPI, vol. 9(12), pages 1-16, December.
    11. Lee, Hakyeon & Park, Yongtae & Choi, Hoogon, 2009. "Comparative evaluation of performance of national R&D programs with heterogeneous objectives: A DEA approach," European Journal of Operational Research, Elsevier, vol. 196(3), pages 847-855, August.
    12. Adetutu, Morakinyo O. & Ajayi, Victor, 2020. "The impact of domestic and foreign R&D on agricultural productivity in sub-Saharan Africa," World Development, Elsevier, vol. 125(C).
    13. Chun, Dongphil & Hong, Sungjun & Chung, Yanghon & Woo, Chungwon & Seo, Hangyeol, 2016. "Influencing factors on hydrogen energy R&D projects: An ex-post performance evaluation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 1252-1258.
    14. Wang, Eric C. & Huang, Weichiao, 2007. "Relative efficiency of R&D activities: A cross-country study accounting for environmental factors in the DEA approach," Research Policy, Elsevier, vol. 36(2), pages 260-273, March.
    15. Xiong, Xi & Yang, Guo-liang & Guan, Zhong-cheng, 2018. "Assessing R&D efficiency using a two-stage dynamic DEA model: A case study of research institutes in the Chinese Academy of Sciences," Journal of Informetrics, Elsevier, vol. 12(3), pages 784-805.
    16. Zhong, Wei & Yuan, Wei & Li, Susan X. & Huang, Zhimin, 2011. "The performance evaluation of regional R&D investments in China: An application of DEA based on the first official China economic census data," Omega, Elsevier, vol. 39(4), pages 447-455, August.
    17. Yang, Guoliang & Ahlgren, Per & Yang, Liying & Rousseau, Ronald & Ding, Jielan, 2016. "Using multi-level frontiers in DEA models to grade countries/territories," Journal of Informetrics, Elsevier, vol. 10(1), pages 238-253.
    18. Lee, Seonghee & Lee, Hakyeon, 2015. "Measuring and comparing the R&D performance of government research institutes: A bottom-up data envelopment analysis approach," Journal of Informetrics, Elsevier, vol. 9(4), pages 942-953.
    19. Eilat, Harel & Golany, Boaz & Shtub, Avraham, 2008. "R&D project evaluation: An integrated DEA and balanced scorecard approach," Omega, Elsevier, vol. 36(5), pages 895-912, October.
    20. R. D. Banker & A. Charnes & W. W. Cooper, 1984. "Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis," Management Science, INFORMS, vol. 30(9), pages 1078-1092, September.
    21. Fare, Rolf & Grosskopf, Shawna & Norris, Mary, 1997. "Productivity Growth, Technical Progress, and Efficiency Change in Industrialized Countries: Reply," American Economic Review, American Economic Association, vol. 87(5), pages 1040-1043, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Kaile Gong, 2023. "The influence of discipline consistency between papers and published journals on citations: an analysis of Chinese papers in three social science disciplines," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(5), pages 3129-3146, May.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Junhee Bae & Yanghon Chung & Hyesoo Ko, 2021. "Analysis of efficiency in public research activities in terms of knowledge spillover: focusing on earthquake R&D accomplishments," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 108(2), pages 2249-2264, September.
    2. Liu, Hui-hui & Yang, Guo-liang & Liu, Xiao-xiao & Song, Yao-yao, 2020. "R&D performance assessment of industrial enterprises in China: A two-stage DEA approach," Socio-Economic Planning Sciences, Elsevier, vol. 71(C).
    3. Chun, Dongphil & Hong, Sungjun & Chung, Yanghon & Woo, Chungwon & Seo, Hangyeol, 2016. "Influencing factors on hydrogen energy R&D projects: An ex-post performance evaluation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 1252-1258.
    4. Yang, Guoliang & Ahlgren, Per & Yang, Liying & Rousseau, Ronald & Ding, Jielan, 2016. "Using multi-level frontiers in DEA models to grade countries/territories," Journal of Informetrics, Elsevier, vol. 10(1), pages 238-253.
    5. Yongqi Feng & Haolin Zhang & Yung-ho Chiu & Tzu-Han Chang, 2021. "Innovation efficiency and the impact of the institutional quality: a cross-country analysis using the two-stage meta-frontier dynamic network DEA model," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(4), pages 3091-3129, April.
    6. Xiangyu Guo & Canhui Deng & Dan Wang & Xu Du & Jiali Li & Bowen Wan, 2021. "International Comparison of the Efficiency of Agricultural Science, Technology, and Innovation: A Case Study of G20 Countries," Sustainability, MDPI, vol. 13(5), pages 1-16, March.
    7. Ibrahim Alnafrah, 2021. "Efficiency evaluation of BRICS’s national innovation systems based on bias-corrected network data envelopment analysis," Journal of Innovation and Entrepreneurship, Springer, vol. 10(1), pages 1-28, December.
    8. Bresciani, Stefano & Puertas, Rosa & Ferraris, Alberto & Santoro, Gabriele, 2021. "Innovation, environmental sustainability and economic development: DEA-Bootstrap and multilevel analysis to compare two regions," Technological Forecasting and Social Change, Elsevier, vol. 172(C).
    9. Shiping Mao & Marios Dominikos Kremantzis & Leonidas Sotirios Kyrgiakos & George Vlontzos, 2022. "R&D Performance Evaluation in the Chinese Food Manufacturing Industry Based on Dynamic DEA in the COVID-19 Era," Agriculture, MDPI, vol. 12(11), pages 1-19, November.
    10. Jung Ho Park & Kwangsoo Shin, 2018. "Efficiency of Government-Sponsored R&D Projects: A Metafrontier DEA Approach," Sustainability, MDPI, vol. 10(7), pages 1-17, July.
    11. Wang, Qunwei & Hang, Ye & Sun, Licheng & Zhao, Zengyao, 2016. "Two-stage innovation efficiency of new energy enterprises in China: A non-radial DEA approach," Technological Forecasting and Social Change, Elsevier, vol. 112(C), pages 254-261.
    12. Khoshnevis, Pegah & Teirlinck, Peter, 2018. "Performance evaluation of R&D active firms," Socio-Economic Planning Sciences, Elsevier, vol. 61(C), pages 16-28.
    13. Jiawei Yang & Lei Fang, 2022. "Average lexicographic efficiency decomposition in two-stage data envelopment analysis: an application to China’s regional high-tech innovation systems," Annals of Operations Research, Springer, vol. 312(2), pages 1051-1093, May.
    14. Yukun Shi & Duchun Wang & Zimeng Zhang, 2022. "Categorical Evaluation of Scientific Research Efficiency in Chinese Universities: Basic and Applied Research," Sustainability, MDPI, vol. 14(8), pages 1-16, April.
    15. Lee, Seonghee & Lee, Hakyeon, 2015. "Measuring and comparing the R&D performance of government research institutes: A bottom-up data envelopment analysis approach," Journal of Informetrics, Elsevier, vol. 9(4), pages 942-953.
    16. Xiong, Xi & Yang, Guo-liang & Guan, Zhong-cheng, 2020. "A parallel DEA-based method for evaluating parallel independent subunits with heterogeneous outputs," Journal of Informetrics, Elsevier, vol. 14(3).
    17. Kangjuan Lv & Yu Cheng & Yousen Wang, 2021. "Does regional innovation system efficiency facilitate energy-related carbon dioxide intensity reduction in China?," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(1), pages 789-813, January.
    18. Lin, Tzu-Yu & Chiu, Sheng-Hsiung & Yang, Hai-Lan, 2022. "Performance evaluation for regional innovation systems development in China based on the two-stage SBM-DNDEA model," Socio-Economic Planning Sciences, Elsevier, vol. 80(C).
    19. Avilés-Sacoto, Sonia Valeria & Cook, Wade D. & Güemes-Castorena, David & Zhu, Joe, 2020. "Modelling Efficiency in Regional Innovation Systems: A Two-Stage Data Envelopment Analysis Problem with Shared Outputs within Groups of Decision-Making Units," European Journal of Operational Research, Elsevier, vol. 287(2), pages 572-582.
    20. Kai Xu & Bart Bossink & Qiang Chen, 2019. "Efficiency Evaluation of Regional Sustainable Innovation in China: A Slack-Based Measure (SBM) Model with Undesirable Outputs," Sustainability, MDPI, vol. 12(1), pages 1-21, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:scient:v:127:y:2022:i:1:d:10.1007_s11192-021-04208-2. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.