IDEAS home Printed from https://ideas.repec.org/a/spr/scient/v127y2022i11d10.1007_s11192-022-04514-3.html
   My bibliography  Save this article

Unpacking research lock-in through a diachronic analysis of topic cluster trajectories in scholarly publications

Author

Listed:
  • Matteo Lascialfari

    (AGIR, Université de Toulouse, INRAE)

  • Marie-Benoît Magrini

    (AGIR, Université de Toulouse, INRAE)

  • Guillaume Cabanac

    (IRIT, Université de Toulouse, CNRS)

Abstract

Lock-in and path-dependency are well-known concepts in economics dealing with unbalanced development of alternative options. Lock-in was studied in various sectors, considering production or consumption sides. Lock-in in academic research went little addressed. Yet, science develops through knowledge accumulation and cross-fertilisation of research topics, that could lead to similar phenomena when some topics do not sufficiently benefit from accumulation mechanisms, reducing innovation opportunities from the concerned field consequently. We introduce an original method to explore these phenomena by comparing topic trajectories in research fields according to strong or weak accumulative processes over time. We combine the concepts of ‘niche’ and ‘mainstream’ from transition studies with scientometric tools to revisit Callon’s strategic diagram with a diachronic perspective of topic clusters over time. Considering the trajectories of semantic clusters, derived from titles and authors’ keywords extracted from scholarly publications in the Web of Science, we applied our method to two competing research fields in food sciences and technology related to pulses and soya over the last 60 years worldwide. These highly interesting species for the sustainability of agrifood systems experienced unbalanced development and thus is under-debated. Our analysis confirms that food research for soya was more dynamic than for pulses: soya topic clusters revealed a stronger accumulative research path by cumulating mainstream positions while pulses research did not meet the same success. This attempt to unpack research lock-in for evaluating the competition dynamics of scientific fields over time calls for future works, by strengthening the method and testing it on other research fields. Graphical abstract

Suggested Citation

  • Matteo Lascialfari & Marie-Benoît Magrini & Guillaume Cabanac, 2022. "Unpacking research lock-in through a diachronic analysis of topic cluster trajectories in scholarly publications," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(11), pages 6165-6189, November.
    • Handle: RePEc:spr:scient:v:127:y:2022:i:11:d:10.1007_s11192-022-04514-3
    DOI: 10.1007/s11192-022-04514-3
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11192-022-04514-3
    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/s11192-022-04514-3?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. Ying Yang & Mingzhi Wu & Lei Cui, 2012. "Integration of three visualization methods based on co-word analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 90(2), pages 659-673, February.
    2. Johannes Stegmann & Guenter Grohmann, 2003. "Hypothesis generation guided by co-word clustering," Scientometrics, Springer;Akadémiai Kiadó, vol. 56(1), pages 111-135, January.
    3. Epicoco, Marianna & Oltra, Vanessa & Maïder Saint, Jean, 2014. "Knowledge dynamics and sources of eco-innovation: Mapping the Green Chemistry community," Technological Forecasting and Social Change, Elsevier, vol. 81(C), pages 388-402.
    4. Rezaeian, M. & Montazeri, H. & Loonen, R.C.G.M., 2017. "Science foresight using life-cycle analysis, text mining and clustering: A case study on natural ventilation," Technological Forecasting and Social Change, Elsevier, vol. 118(C), pages 270-280.
    5. David Chavalarias & Jean-Philippe Cointet, 2013. "Phylomemetic Patterns in Science Evolution—The Rise and Fall of Scientific Fields," PLOS ONE, Public Library of Science, vol. 8(2), pages 1-11, February.
    6. Sorenson, Olav & Fleming, Lee, 2004. "Science and the diffusion of knowledge," Research Policy, Elsevier, vol. 33(10), pages 1615-1634, December.
    7. Rafols, Ismael & Hopkins, Michael M. & Hoekman, Jarno & Siepel, Josh & O'Hare, Alice & Perianes-Rodríguez, Antonio & Nightingale, Paul, 2014. "Big Pharma, little science?," Technological Forecasting and Social Change, Elsevier, vol. 81(C), pages 22-38.
    8. Cobo, M.J. & López-Herrera, A.G. & Herrera-Viedma, E. & Herrera, F., 2011. "An approach for detecting, quantifying, and visualizing the evolution of a research field: A practical application to the Fuzzy Sets Theory field," Journal of Informetrics, Elsevier, vol. 5(1), pages 146-166.
    9. Nees Jan Eck & Ludo Waltman & Ed C. M. Noyons & Reindert K. Buter, 2010. "Automatic term identification for bibliometric mapping," Scientometrics, Springer;Akadémiai Kiadó, vol. 82(3), pages 581-596, March.
    10. Gaston Heimeriks & Ron Boschma, 2014. "The path- and place-dependent nature of scientific knowledge production in biotech 1986–2008," Journal of Economic Geography, Oxford University Press, vol. 14(2), pages 339-364.
    11. Marvuglia, Antonino & Havinga, Lisanne & Heidrich, Oliver & Fonseca, Jimeno & Gaitani, Niki & Reckien, Diana, 2020. "Advances and challenges in assessing urban sustainability: an advanced bibliometric review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 124(C).
    12. Neal Coulter & Ira Monarch & Suresh Konda, 1998. "Software engineering as seen through its research literature: A study in co‐word analysis," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 49(13), pages 1206-1223.
    13. Rafael Bailón‐Moreno & Encarnación Jurado‐Alameda & Rosario Ruiz‐Baños, 2006. "The scientific network of surfactants: Structural analysis," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 57(7), pages 949-960, May.
    14. David Chavalarias & Jean-Philippe Cointet, 2008. "Bottom-up scientific field detection for dynamical and hierarchical science mapping, methodology and case study," Scientometrics, Springer;Akadémiai Kiadó, vol. 75(1), pages 37-50, April.
    15. Magrini, Marie-Benoit & Anton, Marc & Cholez, Célia & Corre-Hellou, Guenaelle & Duc, Gérard & Jeuffroy, Marie-Hélène & Meynard, Jean-Marc & Pelzer, Elise & Voisin, Anne-Sophie & Walrand, Stéphane, 2016. "Why are grain-legumes rarely present in cropping systems despite their environmental and nutritional benefits? Analyzing lock-in in the French agrifood system," Ecological Economics, Elsevier, vol. 126(C), pages 152-162.
    16. Thara Prabhakaran & Hiran H. Lathabai & Susan George & Manoj Changat, 2018. "Towards prediction of paradigm shifts from scientific literature," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(3), pages 1611-1644, December.
    17. Xu, Haiyun & Winnink, Jos & Yue, Zenghui & Liu, Ziqiang & Yuan, Guoting, 2020. "Topic-linked innovation paths in science and technology," Journal of Informetrics, Elsevier, vol. 14(2).
    18. Geels, Frank W., 2004. "From sectoral systems of innovation to socio-technical systems: Insights about dynamics and change from sociology and institutional theory," Research Policy, Elsevier, vol. 33(6-7), pages 897-920, September.
    19. Tomas Cahlik, 2000. "Comparison of the Maps of Science," Scientometrics, Springer;Akadémiai Kiadó, vol. 49(3), pages 373-387, November.
    20. Matteo Lascialfari & Marie-Benoît Magrini & Pierre Triboulet, 2019. "The drivers of product innovations in pulse-based foods: insights from case studies in France, Italy and USA," Journal of Innovation Economics, De Boeck Université, vol. 0(1), pages 111-143.
    21. Hu, Xiaojun & Rousseau, Ronald, 2018. "A new approach to explore the knowledge transition path in the evolution of science & technology: From the biology of restriction enzymes to their application in biotechnology," Journal of Informetrics, Elsevier, vol. 12(3), pages 842-857.
    22. Leydesdorff, Loet & Welbers, Kasper, 2011. "The semantic mapping of words and co-words in contexts," Journal of Informetrics, Elsevier, vol. 5(3), pages 469-475.
    23. Balázs Borsi & András Schubert, 2011. "Agrifood research in Europe: a global perspective," Scientometrics, Springer;Akadémiai Kiadó, vol. 86(1), pages 133-154, January.
    24. Qian, Yue & Liu, Yu & Sheng, Quan Z., 2020. "Understanding hierarchical structural evolution in a scientific discipline: A case study of artificial intelligence," Journal of Informetrics, Elsevier, vol. 14(3).
    25. Epicoco, Marianna & Oltra, Vanessa & Maïder Saint, Jean, 2014. "Knowledge dynamics and sources of eco-innovation: Mapping the Green Chemistry community," Technological Forecasting and Social Change, Elsevier, vol. 81(C), pages 388-402.
    26. Balconi, Margherita & Breschi, Stefano & Lissoni, Francesco, 2004. "Networks of inventors and the role of academia: an exploration of Italian patent data," Research Policy, Elsevier, vol. 33(1), pages 127-145, January.
    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. Babak Amiri & Ramin Karimianghadim & Navid Yazdanjue & Liaquat Hossain, 2021. "Research topics and trends of the hashtag recommendation domain," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(4), pages 2689-2735, April.
    2. Bar-Ilan, Judit, 2008. "Informetrics at the beginning of the 21st century—A review," Journal of Informetrics, Elsevier, vol. 2(1), pages 1-52.
    3. Belfiore, Alessandra & Cuccurullo, Corrado & Aria, Massimo, 2022. "IoT in healthcare: A scientometric analysis," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
    4. Ruben Heradio & David Fernandez-Amoros & Cristina Cerrada & Manuel J. Cobo, 2020. "Group Decision-Making Based on Artificial Intelligence: A Bibliometric Analysis," Mathematics, MDPI, vol. 8(9), pages 1-20, September.
    5. Xu, Haiyun & Yue, Zenghui & Pang, Hongshen & Elahi, Ehsan & Li, Jing & Wang, Lu, 2022. "Integrative model for discovering linked topics in science and technology," Journal of Informetrics, Elsevier, vol. 16(2).
    6. E. M. Murgado-Armenteros & M. Gutiérrez-Salcedo & F. J. Torres-Ruiz & M. J. Cobo, 2015. "Analysing the conceptual evolution of qualitative marketing research through science mapping analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(1), pages 519-557, January.
    7. Lu Huang & Xiang Chen & Yi Zhang & Changtian Wang & Xiaoli Cao & Jiarun Liu, 2022. "Identification of topic evolution: network analytics with piecewise linear representation and word embedding," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(9), pages 5353-5383, September.
    8. Xiaoguang Wang & Qikai Cheng & Wei Lu, 2014. "Analyzing evolution of research topics with NEViewer: a new method based on dynamic co-word networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(2), pages 1253-1271, November.
    9. Qian-Jin Zong & Hong-Zhou Shen & Qin-Jian Yuan & Xiao-Wei Hu & Zhi-Ping Hou & Shun-Guo Deng, 2013. "Doctoral dissertations of Library and Information Science in China: A co-word analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 94(2), pages 781-799, February.
    10. Santana, Monica & Cobo, Manuel J., 2020. "What is the future of work? A science mapping analysis," European Management Journal, Elsevier, vol. 38(6), pages 846-862.
    11. Hanen Khaldi & Vicente Prado-Gascó, 2021. "Bibliometric maps and co-word analysis of the literature on international cooperation on migration," Quality & Quantity: International Journal of Methodology, Springer, vol. 55(5), pages 1845-1869, October.
    12. Epicoco, Marianna, 2016. "Patterns of innovation and organizational demography in emerging sustainable fields: An analysis of the chemical sector," Research Policy, Elsevier, vol. 45(2), pages 427-441.
    13. Sung Kim & Derek Hansen & Richard Helps, 2018. "Computing research in the academy: insights from theses and dissertations," Scientometrics, Springer;Akadémiai Kiadó, vol. 114(1), pages 135-158, January.
    14. Mónica Santana & Rafael Morales-Sánchez & Susana Pasamar, 2020. "Mapping the Link between Corporate Social Responsibility (CSR) and Human Resource Management (HRM): How Is This Relationship Measured?," Sustainability, MDPI, vol. 12(4), pages 1-28, February.
    15. Massimo Aria & Michelangelo Misuraca & Maria Spano, 2020. "Mapping the Evolution of Social Research and Data Science on 30 Years of Social Indicators Research," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 149(3), pages 803-831, June.
    16. Huichen Gao & Shijuan Wang, 2022. "The Intellectual Structure of Research on Rural-to-Urban Migrants: A Bibliometric Analysis," IJERPH, MDPI, vol. 19(15), pages 1-19, August.
    17. Eva-María Mora-Valentín & Marta Ortiz-de-Urbina-Criado & Juan-José Nájera-Sánchez, 2018. "Mapping the conceptual structure of science and technology parks," The Journal of Technology Transfer, Springer, vol. 43(5), pages 1410-1435, October.
    18. Subtil Lacerda, Juliana & van den Bergh, Jeroen C.J.M., 2020. "Effectiveness of an ‘open innovation’ approach in renewable energy: Empirical evidence from a survey on solar and wind power," Renewable and Sustainable Energy Reviews, Elsevier, vol. 118(C).
    19. Alkemade, Floortje & Heimeriks, Gaston & Schoen, Antoine & Villard, Lionel & Laurens, Patricia, 2015. "Tracking the internationalization of multinational corporate inventive activity: national and sectoral characteristics," Research Policy, Elsevier, vol. 44(9), pages 1763-1772.
    20. Alejandro Bengoa & Amaia Maseda & Txomin Iturralde & Gloria Aparicio, 2021. "A bibliometric review of the technology transfer literature," The Journal of Technology Transfer, Springer, vol. 46(5), pages 1514-1550, October.

    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:11:d:10.1007_s11192-022-04514-3. 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.