IDEAS home Printed from https://ideas.repec.org/a/spr/scient/v129y2024i1d10.1007_s11192-023-04867-3.html
   My bibliography  Save this article

Analyzing the impact of companies on AI research based on publications

Author

Listed:
  • Michael Färber

    (Institute AIFB, Karlsruhe Institute of Technology (KIT))

  • Lazaros Tampakis

    (Institute AIFB, Karlsruhe Institute of Technology (KIT))

Abstract

Artificial Intelligence (AI) is one of the most momentous technologies of our time. Thus, it is of major importance to know which stakeholders influence AI research. Besides researchers at universities and colleges, researchers in companies have hardly been considered in this context. In this article, we consider how the influence of companies on AI research can be made measurable on the basis of scientific publishing activities. We compare academic- and company-authored AI publications published in the last decade and use scientometric data from multiple scholarly databases to look for differences across these groups and to disclose the top contributing organizations. While the vast majority of publications is still produced by academia, we find that the citation count an individual publication receives is significantly higher when it is (co–)authored by a company. Furthermore, using a variety of altmetric indicators, we notice that publications with company participation receive considerably more attention online. Finally, we place our analysis results in a broader context and present targeted recommendations to safeguard a harmonious balance between academia and industry in the realm of AI research.

Suggested Citation

  • Michael Färber & Lazaros Tampakis, 2024. "Analyzing the impact of companies on AI research based on publications," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(1), pages 31-63, January.
  • Handle: RePEc:spr:scient:v:129:y:2024:i:1:d:10.1007_s11192-023-04867-3
    DOI: 10.1007/s11192-023-04867-3
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11192-023-04867-3
    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/s11192-023-04867-3?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. Bastian Krieger & Maikel Pellens & Knut Blind & Sonia Gruber & Torben Schubert, 2021. "Are firms withdrawing from basic research? An analysis of firm-level publication behaviour in Germany," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(12), pages 9677-9698, December.
    2. Loet Leydesdorff & Lutz Bornmann & Rüdiger Mutz & Tobias Opthof, 2011. "Turning the tables on citation analysis one more time: Principles for comparing sets of documents," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 62(7), pages 1370-1381, July.
    3. Kathryn Tunyasuvunakool & Jonas Adler & Zachary Wu & Tim Green & Michal Zielinski & Augustin Žídek & Alex Bridgland & Andrew Cowie & Clemens Meyer & Agata Laydon & Sameer Velankar & Gerard J. Kleywegt, 2021. "Highly accurate protein structure prediction for the human proteome," Nature, Nature, vol. 596(7873), pages 590-596, August.
    4. Vincent Larivière & Benoit Macaluso & Philippe Mongeon & Kyle Siler & Cassidy R Sugimoto, 2018. "Vanishing industries and the rising monopoly of universities in published research," PLOS ONE, Public Library of Science, vol. 13(8), pages 1-10, August.
    5. Bornmann, Lutz & Leydesdorff, Loet & Mutz, Rüdiger, 2013. "The use of percentiles and percentile rank classes in the analysis of bibliometric data: Opportunities and limits," Journal of Informetrics, Elsevier, vol. 7(1), pages 158-165.
    6. Dag W. Aksnes & Liv Langfeldt & Paul Wouters, 2019. "Citations, Citation Indicators, and Research Quality: An Overview of Basic Concepts and Theories," SAGE Open, , vol. 9(1), pages 21582440198, February.
    7. John Jumper & Richard Evans & Alexander Pritzel & Tim Green & Michael Figurnov & Olaf Ronneberger & Kathryn Tunyasuvunakool & Russ Bates & Augustin Žídek & Anna Potapenko & Alex Bridgland & Clemens Me, 2021. "Highly accurate protein structure prediction with AlphaFold," Nature, Nature, vol. 596(7873), pages 583-589, August.
    8. Anne-Wil Harzing & Satu Alakangas, 2017. "Microsoft Academic: is the phoenix getting wings?," Scientometrics, Springer;Akadémiai Kiadó, vol. 110(1), pages 371-383, January.
    9. Anne-Wil Harzing & Satu Alakangas, 2017. "Microsoft Academic is one year old: the Phoenix is ready to leave the nest," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(3), pages 1887-1894, September.
    10. Sven E. Hug & Michael Ochsner & Martin P. Brändle, 2017. "Citation analysis with microsoft academic," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(1), pages 371-378, April.
    11. Bernd Fitzenberger & Ute Schulze, 2014. "Up or Out: Research Incentives and Career Prospects of Postdocs in Germany," German Economic Review, Verein für Socialpolitik, vol. 15(2), pages 287-328, May.
    12. Iain M. Cockburn & Rebecca Henderson & Scott Stern, 2018. "The Impact of Artificial Intelligence on Innovation: An Exploratory Analysis," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 115-146, National Bureau of Economic Research, Inc.
    13. Bornmann, Lutz, 2014. "Do altmetrics point to the broader impact of research? An overview of benefits and disadvantages of altmetrics," Journal of Informetrics, Elsevier, vol. 8(4), pages 895-903.
    14. Kate Williams, 2022. "What counts: Making sense of metrics of research value," Science and Public Policy, Oxford University Press, vol. 49(3), pages 518-531.
    15. Hayter, Christopher S. & Parker, Marla A., 2019. "Factors that influence the transition of university postdocs to non-academic scientific careers: An exploratory study," Research Policy, Elsevier, vol. 48(3), pages 556-570.
    16. Tian Yu & Guang Yu & Peng-Yu Li & Liang Wang, 2014. "Citation impact prediction for scientific papers using stepwise regression analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(2), pages 1233-1252, November.
    17. Xi Zhang & Xianhai Wang & Hongke Zhao & Patricia Ordóñez de Pablos & Yongqiang Sun & Hui Xiong, 2019. "An effectiveness analysis of altmetrics indices for different levels of artificial intelligence publications," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(3), pages 1311-1344, June.
    18. Iain M. Cockburn & Rebecca Henderson & Scott Stern, 2018. "The Impact of Artificial Intelligence on Innovation," NBER Working Papers 24449, National Bureau of Economic Research, Inc.
    Full references (including those not matched with items on IDEAS)

    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. Dunaiski, Marcel & Geldenhuys, Jaco & Visser, Willem, 2019. "On the interplay between normalisation, bias, and performance of paper impact metrics," Journal of Informetrics, Elsevier, vol. 13(1), pages 270-290.
    2. Zhentao Liang & Jin Mao & Kun Lu & Gang Li, 2021. "Finding citations for PubMed: a large-scale comparison between five freely available bibliographic data sources," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(12), pages 9519-9542, December.
    3. Thelwall, Mike, 2018. "Microsoft Academic automatic document searches: Accuracy for journal articles and suitability for citation analysis," Journal of Informetrics, Elsevier, vol. 12(1), pages 1-9.
    4. Bornmann, Lutz & Leydesdorff, Loet, 2017. "Skewness of citation impact data and covariates of citation distributions: A large-scale empirical analysis based on Web of Science data," Journal of Informetrics, Elsevier, vol. 11(1), pages 164-175.
    5. Liu, Meijun & Jaiswal, Ajay & Bu, Yi & Min, Chao & Yang, Sijie & Liu, Zhibo & Acuña, Daniel & Ding, Ying, 2022. "Team formation and team impact: The balance between team freshness and repeat collaboration," Journal of Informetrics, Elsevier, vol. 16(4).
    6. Kousha, Kayvan & Thelwall, Mike, 2018. "Can Microsoft Academic help to assess the citation impact of academic books?," Journal of Informetrics, Elsevier, vol. 12(3), pages 972-984.
    7. Xiancheng Li & Wenge Rong & Haoran Shi & Jie Tang & Zhang Xiong, 2018. "The impact of conference ranking systems in computer science: a comparative regression analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(2), pages 879-907, August.
    8. Sang Yoon Kim & Won Kyung Lee & Su Jung Jee & So Young Sohn, 2025. "Discovering AI adoption patterns from big academic graph data," Scientometrics, Springer;Akadémiai Kiadó, vol. 130(2), pages 809-831, February.
    9. Anne-Wil Harzing, 2019. "Two new kids on the block: How do Crossref and Dimensions compare with Google Scholar, Microsoft Academic, Scopus and the Web of Science?," Scientometrics, Springer;Akadémiai Kiadó, vol. 120(1), pages 341-349, July.
    10. Mingers, John & Leydesdorff, Loet, 2015. "A review of theory and practice in scientometrics," European Journal of Operational Research, Elsevier, vol. 246(1), pages 1-19.
    11. Kousha, Kayvan & Thelwall, Mike & Abdoli, Mahshid, 2018. "Can Microsoft Academic assess the early citation impact of in-press articles? A multi-discipline exploratory analysis," Journal of Informetrics, Elsevier, vol. 12(1), pages 287-298.
    12. Dunaiski, Marcel & Geldenhuys, Jaco & Visser, Willem, 2019. "Globalised vs averaged: Bias and ranking performance on the author level," Journal of Informetrics, Elsevier, vol. 13(1), pages 299-313.
    13. Robin Haunschild & Sven E. Hug & Martin P. Brändle & Lutz Bornmann, 2018. "The number of linked references of publications in Microsoft Academic in comparison with the Web of Science," Scientometrics, Springer;Akadémiai Kiadó, vol. 114(1), pages 367-370, January.
    14. Pierre Azoulay & Joshua Krieger & Abhishek Nagaraj, 2024. "Old Moats for New Models: Openness, Control, and Competition in Generative Artificial Intelligence," NBER Chapters, in: Entrepreneurship and Innovation Policy and the Economy, volume 4, pages 7-46, National Bureau of Economic Research, Inc.
    15. Jun-Yu Si & Yuan-Mei Chen & Ye-Hui Sun & Meng-Xue Gu & Mei-Ling Huang & Lu-Lu Shi & Xiao Yu & Xiao Yang & Qing Xiong & Cheng-Bao Ma & Peng Liu & Zheng-Li Shi & Huan Yan, 2024. "Sarbecovirus RBD indels and specific residues dictating multi-species ACE2 adaptiveness," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    16. Zhou, Yixiao & Tyers, Rod, 2019. "Automation and inequality in China," China Economic Review, Elsevier, vol. 58(C).
    17. Deyun Qiu & Jinxin V. Pei & James E. O. Rosling & Vandana Thathy & Dongdi Li & Yi Xue & John D. Tanner & Jocelyn Sietsma Penington & Yi Tong Vincent Aw & Jessica Yi Han Aw & Guoyue Xu & Abhai K. Tripa, 2022. "A G358S mutation in the Plasmodium falciparum Na+ pump PfATP4 confers clinically-relevant resistance to cipargamin," Nature Communications, Nature, vol. 13(1), pages 1-18, December.
    18. Shuo-Shuo Liu & Tian-Xia Jiang & Fan Bu & Ji-Lan Zhao & Guang-Fei Wang & Guo-Heng Yang & Jie-Yan Kong & Yun-Fan Qie & Pei Wen & Li-Bin Fan & Ning-Ning Li & Ning Gao & Xiao-Bo Qiu, 2024. "Molecular mechanisms underlying the BIRC6-mediated regulation of apoptosis and autophagy," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    19. Zhao-Shan Chen & Hsiang-Chi Huang & Xiangkun Wang & Karin Schön & Yane Jia & Michael Lebens & Danica F. Besavilla & Janarthan R. Murti & Yanhong Ji & Aishe A. Sarshad & Guohua Deng & Qiyun Zhu & David, 2025. "Influenza A Virus H7 nanobody recognizes a conserved immunodominant epitope on hemagglutinin head and confers heterosubtypic protection," Nature Communications, Nature, vol. 16(1), pages 1-17, December.
    20. Sourav Nayak & Thomas J. Peto & Michal Kucharski & Rupam Tripura & James J. Callery & Duong Tien Quang Huy & Mathieu Gendrot & Dysoley Lek & Ho Dang Trung Nghia & Rob W. Pluijm & Nguyen Dong & Le Than, 2024. "Population genomics and transcriptomics of Plasmodium falciparum in Cambodia and Vietnam uncover key components of the artemisinin resistance genetic background," Nature Communications, Nature, vol. 15(1), pages 1-17, 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:129:y:2024:i:1:d:10.1007_s11192-023-04867-3. 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.