Skip to main content
SpringerLink
Log in

Evolution analysis of cross-domain collaborative research topic: a case study of cognitive-based product conceptual design

  • Published:
Scientometrics Aims and scope Submit manuscript

Abstract

Knowledge absorption and integration between research domains can generate new concepts and ideas, and cross-domain research cooperation has become an effective way to promote innovation. Observing and discovering the patterns and trends in the development of cross-domain collaborative research topics is an important area of research for promoting the innovative development of disciplines. In this paper, we propose an evolution analysis method for cross-domain collaborative research topics, which first employs LDA and Word2vec models to extract topics from the domain corpus, and proposes a cross-domain topic evolution model (CDTEM) based on cross-time and cross-domain topic associations. Based on CDTEM, combined with the evolution analysis strategy of forward extrapolation and backward tracking, the method realizes the evolution analysis of cross-domain topics (CDTs) and generates a synergistic evolution vein of CDTs. Finally, we combine the integration and evolution of research topics in conceptual design (CD) and design cognition (DC) to perform validation analysis. The methodology of this paper provides a new perspective for studying interdisciplinary topic convergence trends based on collaborative goal-oriented research, which can help scholars capture the convergence and development trends of cross-domain collaborative research topics over the years and explore dynamic CDTs to effectively support interdisciplinary scientific exchange. At the same time, the case study part of this paper provides scholars conducting research in cognition-based product design with a scientific analysis of the integration and development of research topics.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

References

  • Blei, D. M., Ng, A., & Jordan, M. I. (2003). Latent dirichlet allocation. Journal of Machine Learning Research, 3, 993–1022.

    MATH  Google Scholar 

  • Blei, D. M., & Lafferty, J. (2006). Dyamic topic models. In Proceedings of the 23rd International Conference on Machine Learning (pp. 113–120).

  • Bu, Y., Ding, Y., Liang, X., & Murray, D. S. (2018). Understanding persistent scientific collaboration. Journal of the Association for Information Science and Technology, 69(3), 438–448.

    Article  Google Scholar 

  • Chakraborty, T. (2018). Role of interdisciplinarity in computer sciences: Quantification, impact and life trajectory. Scientometrics, 114(3), 1011–1029.

    Article  Google Scholar 

  • Chen, S., Arsenault, C., Gingras, Y., et al. (2015a). Exploring the interdisciplinary evolution of a discipline: The case of Biochemistry and Molecular Biology. Scientometrics, 102, 1307–1323.

    Article  Google Scholar 

  • Chen, S., Arsenault, C., & Larivière, V. (2015b). Are top-cited papers more interdisciplinary? Journal of Informetrics, 9(4), 1034–1046.

    Article  Google Scholar 

  • Chen, B., Tsutsui, S., Ding, Y., & Ma, F. (2017). Understanding the topic evolution in a scientific domain: An exploratory study for the field of information retrieval. Journal of Informetrics, 11, 1175–1189.

    Article  Google Scholar 

  • Da Silva, R. H., Kaminski, P. C., & Armellini, F. (2020). Improving new product development innovation effectiveness by using problem solving tools during the conceptual development phase: Integrating Design Thinking and TRIZ. Creativity and Innovation Management, 29(4), 685–700.

    Article  Google Scholar 

  • Dong, K., Xu, H. Y., Luo, R., et al. (2018). An integrated method for interdisciplinary topic identification and prediction: A case study on information science and library science. Scientometrics, 115(2), 849–868.

    Article  Google Scholar 

  • Emami, A., Packard, M. D., & Welsh, D. H. B. (2021). On the cognitive microfoundations of effectual design: The Situated Function-Behavior-Structure framework. Management Decision, 59(5), 953–972.

    Article  Google Scholar 

  • Fjaellegaard, C. B., Beukel, K., & Alkaersig, L. (2019). Designers as the determinants of design innovations. Creativity and Innovation Management, 28(2), 144–156.

    Article  Google Scholar 

  • Kim, J., & Geum, Y. (2021). How to develop data-driven technology roadmaps: The integration of topic modeling and link prediction. Technological Forecasting and Social Change, 171, 120972.

    Article  Google Scholar 

  • Huang, L., Chen, X., Zhang, Y., Wang, C., Cao, X.-H., & Liu, J. (2022). Identification of topic evolution: Network analytics with piecewise linear representation and word embedding. Scientometrics, 127, 5353–5383.

    Article  Google Scholar 

  • Jebari, C., Herrera-Viedma, E., & Jesus, C. M. (2021). The use of citation context to detect the evolution of research topics: A large-scale analysis. Scientometrics, 126(4), 2971–2989.

    Article  Google Scholar 

  • Karunan, K., Lathabai, H. H., & Prabhakaran, T. (2017). Discovering interdisciplinary interactions between two research fields using citation networks. Scientometrics, 113(1), 335–367.

    Article  Google Scholar 

  • Lee, H., & Kang, P. (2018). Identifying core topics in technology and innovation management studies: A topic model approach. Journal of Technology Transfer, 43(5), 1291–1317.

    Article  Google Scholar 

  • Li, X., Su, J., Zhang, Z., et al. (2021a). Product innovation concept generation based on deep learning and Kansei engineering. Journal of Engineering Design, 32(10), 559–589.

    Article  Google Scholar 

  • Li, Y., Chen, Y., & Wang, Q. (2021b). Evolution and diffusion of information literacy topics. Scientometrics, 126(5), 4195–4224.

    Article  Google Scholar 

  • Liikkanen, L. A., & Perttula, M. (2011). Inspiring design idea generation: Insights from a memory-search perspective. Journal of Engineering Design, 21(5), 545–560.

    Article  Google Scholar 

  • Liu, A., & Lu, S. C. Y. (2016). A crowdsourcing design framework for concept generation. Cirp Annals-Manufacturing Technology, 65(1), 177–180.

    Article  MathSciNet  Google Scholar 

  • Liu, J., Wei, J., & Liu, Y. (2021). Technology forecasting based on topic analysis and social network analysis: A case study focusing on gene editing patents. Journal of Scientific & Industrial Research, 80(5), 428–437.

    Google Scholar 

  • Lozano, S., Calzada-infante, L., Adenso-DÍaz, B., et al. (2019). Complex network analysis of keywords co-occurrence in the recent efficiency analysis literature. Scientometrics, 120, 609–629.

    Article  Google Scholar 

  • Ma, J., Wang, L., Zhang, Y.-R., et al. (2023). An integrated latent Dirichlet allocation and Word2vec method for generating the topic evolution of mental models from global to local. Expert Systems with Applications, 212, 118695.

    Article  Google Scholar 

  • Micheli, P., Wilner, S. J. S., Bhatti, S. H., et al. (2019). Doing design thinking: Conceptual review, synthesis, and research agenda. Journal of Product Innovation Management, 36(2), 124–148.

    Article  Google Scholar 

  • Mikolov T, Chen K, Corrado G , et al. (2013). Efficient Estimation of Word Representations in Vector Space. Computer Science.

  • Mikolov, T., Sutskever, I., Kai, C., et al. (2013). Distributed representations of words and phrases and their compositionality. In Proceedings of the 26th International Conference on Neural Information Processing Systems (Vol. 2, pp. 3111–3119).

  • Miller, T., Pedell, S., Lopez-Lorca, A. A., et al. (2015). Emotion-led modelling for people-oriented requirements engineering: The case study of emergency systems. Journal of Systems and Software, 105, 54–71.

    Article  Google Scholar 

  • Nerur, S. P., Rasheed, A. A., & Natarajan, V. (2008). The intellectual structure of the strategic management field: An author co-citation analysis. Strategic Management Journal, 29, 319–336.

    Article  Google Scholar 

  • Newman, M. E. J. (2004). Coauthorship networks and patterns of scientific collaboration. Proceedings of the National Academy of Sciences of the United States of America, 101(1), 5200–5205.

    Article  Google Scholar 

  • Nie, Y., Tong, S., Li, J., et al. (2022). Time identification of design knowledge push based on cognitive load measurement. Advanced Engineering Informatics, 54, 101783.

    Article  Google Scholar 

  • Nissani, M. (1997). Ten cheers for interdisciplinarity: The case for interdisciplinary knowledge and research. The Social Science Journal, 34(2), 201–216.

    Article  Google Scholar 

  • Oman, S. K., Tumer, I. Y., Wood, K., et al. (2013). A comparison of creativity and innovation metrics and sample validation through in-class design projects. Research in Engineering Design, 24(1), 65–92.

    Article  Google Scholar 

  • Osnabruegge, M., Ash, E., & Morelli, M. (2023). Cross-domain topic classification for political texts. Political Analysis, 31(1), 59–80.

    Article  Google Scholar 

  • Ou, R., Huang, Y., Pan, F., et al. (2019). Research on information retrieval model under scarcity theory and user cognition. Computers & Electrical Engineering, 76, 353–363.

    Article  Google Scholar 

  • Park, I., & Yoon, B. (2018). Technological opportunity discovery for technological convergence based on the prediction of technology knowledge flow in a citation network. Journal of Informetrics, 12(4), 1199–1222.

    Article  Google Scholar 

  • Pi, Z., Fang, W., Perera, S. C., & Zhang, B. (2022). Enhancing the online buyer perception of consumer experience products in a dual-channel supply chain: A new role of free-riding. International Journal of Production Economics, 253, 108600.

    Article  Google Scholar 

  • Qi, Y., Zhu, N., Zhai, Y., et al. (2018). The mutually beneficial relationship of patents and scientific literature: Topic evolution in nanoscience. Scientometrics, 115(2), 893–911.

    Article  Google Scholar 

  • Siedlok, F., & Hibbert, P. (2014). The organization of interdisciplinary research: Modes, drivers and barriers. International Journal of Management Reviews, 16, 194–210.

    Article  Google Scholar 

  • Stopar, K., Drobne, D., Eler, K., et al. (2016). Citation analysis and mapping of nanoscience and nanotechnology: Identifying the scope and interdisciplinarity of research. Scientometrics, 106, 563–581.

    Article  Google Scholar 

  • Su, H. N., & Maoniba, I. (2017). Investigating the dynamics of interdisciplinary evolution in technology developments. Academy of Management Annual Meeting Proceedings., 2017(1), 11158.

    Google Scholar 

  • Sun, L., Yu, C., Zhang, H., et al. (2018). Understanding the formation of interdisciplinary research from the perspective of keyword evolution. Scientometrics, 117, 973–995.

    Article  Google Scholar 

  • Szell, M., Ma, Y., & Sinatra, R. (2018). A nobel opportunity for interdisciplinarity. Nature Physics, 14(11), 1075–1078.

    Article  Google Scholar 

  • Tong, S., & Nie, Y. (2023). Measuring Designers’ Cognitive Load for Timely Knowledge Push via Eye Tracking. International Journal of Human-Computer Interaction, 39(6), 1230–1243.

    Article  Google Scholar 

  • Tu, Y. N., & Seng, J. L. (2009). Research intelligence involving information retrieval—An example of conferences and journals. Expert Systems with Applications, 36(10), 12151–12166.

    Article  Google Scholar 

  • Van Noorden, R. (2015). Interdisciplinary research by the numbers. Nature, 525(7569), 306–307.

    Article  Google Scholar 

  • Vatamanescu, E.-M., Cegarra-Navarro, J.-G., Andrei, A. G., et al. (2020). SMEs strategic networks and innovative performance: A relational design and methodology for knowledge sharing. Journal of Knowledge Management, 24(6), 1369–1392.

    Article  Google Scholar 

  • Wang, S., Mao, J., Lu, K., et al. (2021). Understanding interdisciplinary knowledge integration through citance analysis: A case study on eHealth. Journal of Informetrics, 15(4), 101214.

    Article  Google Scholar 

  • Wang, X., He, J., Huang, H., et al. (2022). MatrixSim: A new method for detecting the evolution paths of research topics. Journal of Informetrics, 16(4), 101343.

    Article  Google Scholar 

  • Wang, X., & McCallum, A. (2006). Topics over time: A non-Markov continuous-time model of topical trends. In Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 424–433).

  • Wang, Z.-S., Tian, L., Wu, Y.-H., et al. (2016). Personalized knowledge push system based on design intent and user interest. Proceedings of the Institution of Mechanical Engineers Part C-Journal of Mechanical Engineering Science, 230(11), 1757–1772.

    Article  Google Scholar 

  • Wu, Z., He, L., Wang, Y., et al. (2020). Knowledge recommendation for product development using integrated rough set-information entropy correction. Journal of Intelligent Manufacturing, 31(6), 1559–1578.

    Article  Google Scholar 

  • Wu, Z., He, Q., Chen, Q., et al. (2021). A topical network based analysis and visualization of global research trends on green building from 1990 to 2020. Journal of Cleaner Production, 320, 128818.

    Article  Google Scholar 

  • Xie, Z., Li, M., Li, J., et al. (2018). Feature analysis of multidisciplinary scientific collaboration patterns based on PNAS. Epj Data Science, 7, 1–17.

    Article  Google Scholar 

  • Xu, H., Guo, T., Yue, Z., et al. (2016). Interdisciplinary topics of information science: A study based on the terms interdisciplinarity index series. Scientometrics, 106(2), 583–601.

    Article  Google Scholar 

  • Xu, J., Ding, Y., Yi, Bu., Deng, S., Chen, Yu., Zou, Y., & Madden, A. (2019). Interdisciplinary scholarly communication: An exploratory study for the field of joint attention. Scientometrics, 119, 1597–1619.

    Article  Google Scholar 

  • Yu, D., & Hong, X. (2022). A theme evolution and knowledge trajectory study in AHP using science mapping and main path analysis. Expert Systems with Applications, 205, 117675.

    Article  Google Scholar 

  • Yu, F., Fu, J., Guo, J., Tan, R., & Yang, B. (2023). An approach for radical innovative design based on cross-domain technology mining in patents. International Journal of Production Research, 61(21), 7502–7523.

    Article  Google Scholar 

  • Zhao, W., Chen, J. J., Perkins, R., Liu, Z., Ge, W., Ding, Y., & Zou, W. (2015, December). A heuristic approach to determine an appropriate number of topics in topic modeling. In BMC bioinformatics (Vol. 16, pp. 1–10). BioMed Central

  • Zhang, C., Bu, Y., Ding, Y., & Xu, J. (2018). Understanding scientific collaboration: Homophily, transitivity, and preferential attachment. Journal of the Association for Information Science and Technology, 69(1), 72–86.

    Article  Google Scholar 

  • Zhang, P., & Soergel, D. (2016). Process patterns and conceptual changes in knowledge representations during information seeking and sensemaking: A qualitative user study. Journal of Information Science, 42(1), 59–78.

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the TIANJIN RESEARCH INNOVATION PROJECT FOR POSTGRADUATE STUDENTS (2022BKYZ041).

Author information

Authors and Affiliations

  1. Department of Mechanical Engineering, Tianjin University, No.135 Yaguan Road, Haihe Education Park, Tianjin, 300350, People’s Republic of China

    Yuanrong Zhang, Wei Guo, Jian Ma, Zhonglin Fu, Zhixing Chang & Lei Wang

  2. Tianjin Ren’ai College, Bo Xue Yuan, Tuanbo New City, Jinghai District, Tianjin, 301636, People’s Republic of China

    Wei Guo

Authors
  1. Yuanrong Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wei Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jian Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zhonglin Fu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Zhixing Chang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Lei Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Lei Wang.

Ethics declarations

Conflict of interest

All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, Y., Guo, W., Ma, J. et al. Evolution analysis of cross-domain collaborative research topic: a case study of cognitive-based product conceptual design. Scientometrics 128, 6695–6718 (2023). https://doi.org/10.1007/s11192-023-04865-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11192-023-04865-5

Keywords

Search

Navigation