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Combining machine learning and main path analysis to identify research front: from the perspective of science-technology linkage

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Abstract

With the development of the era of big data, research data are accumulating, and various research directions emerge endlessly. It is difficult for researchers to grasp the hotspots and development trends of the discipline. Therefore, exploring methods to quickly and accurately identify research fronts is of great significance to scientific and technological innovation. This paper proposes a research front identification method integrating machine learning and main path analysis in conjunction with papers and patents based on the existing research. The innovation of this method is the combination of citation analysis and semantic analysis to identify research front from the perspective of science-technology linkage. This article takes the Internet of Things in supply chain as an example to verify the feasibility and effectiveness of the method. It is of great significance to identify important scientific and technological research fronts in a specific domain by intuitively revealing knowledge diffusion and text mining. The proposed method enriches the application of MPA and helps scholars grasp the latest information, mainstreams and future directions.

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References

  • Abad, E., Palacio, F., Nuin, M., De Zarate, A. G., Juarros, A., Gómez, J. M., & Marco, S. (2009). RFID smart tag for traceability and cold chain monitoring of foods: Demonstration in an intercontinental fresh fish logistic chain. Journal of Food Engineering, 93(4), 394–399.

    Article  Google Scholar 

  • Abad, E., Zampolli, S., Marco, S., Scorzoni, A., Mazzolai, B., Juarros, A., Gómez, D., Elmi, I., Cardinali, G. C., Gómez, J. M., & Palacio, F. (2007). Flexible tag microlab development: Gas sensors integration in RFID flexible tags for food logistic. Sensors and Actuators b: Chemical, 127(1), 2–7.

    Article  Google Scholar 

  • Alfian, G., Rhee, J., Ahn, H., Lee, J., Farooq, U., Ijaz, M. F., & Syaekhoni, M. A. (2017). Integration of RFID, wireless sensor networks, and data mining in an e-pedigree food traceability system. Journal of Food Engineering, 212, 65–75.

    Article  Google Scholar 

  • Ba, Z., & Liang, Z. (2021). A novel approach to measuring science-technology linkage: From the perspective of knowledge network coupling. Journal of Informetrics, 15(3), 101167.

    Article  Google Scholar 

  • Balconi, M., Breschi, S., & Lissoni, F. (2004). Networks of inventors and the role of academia: An exploration of Italian patent data. Research Policy, 33(1), 127–145.

    Article  Google Scholar 

  • Barge, P., Gay, P., Merlino, V., & Tortia, C. (2014). Item-level radio-frequency identification for the traceability of food products: Application on a dairy product. Journal of Food Engineering, 125, 119–130.

    Article  Google Scholar 

  • Barratt, M., & Choi, T. (2007). Mandated RFID and institutional responses: Cases of decentralized business units. Production and Operations Management, 16(5), 569–585.

    Article  Google Scholar 

  • Batagelj, V. (2003). Efficient algorithms for citation network analysis (Vol. 41, p. 897). University of Ljubljana, Institute of Mathematics, Physics and Mechanics Department of Theoretical Computer Science.

    Google Scholar 

  • Ben-Daya, M., Hassini, E., & Bahroun, Z. (2019). Internet of Things and supply chain management: A literature review. International Journal of Production Research, 57(15–16), 4719–4742.

    Article  Google Scholar 

  • Bi, Z., Da Xu, L., & Wang, C. (2014). Internet of Things for enterprise systems of modern manufacturing. IEEE Transactions on Industrial Informatics, 10(2), 1537–1546.

    Article  Google Scholar 

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

    MATH  Google Scholar 

  • Cannella, S., Dominguez, R., & Framinan, J. M. (2017). Inventory record inaccuracy—The impact of structural complexity and lead time variability. Omega, 68, 123–138.

    Article  Google Scholar 

  • Cannon, A. R., Reyes, P. M., Frazier, G. V., & Prater, E. L. (2008). RFID in the contemporary supply chain: Multiple perspectives on its benefits and risks. International Journal of Operations & Production Management, 28(5), 433–454.

    Article  Google Scholar 

  • Carpenter, M. P., & Narin, F. (1983). Validation study: Patent citations as indicators of science and foreign dependence. World Patent Information, 5(3), 180–185.

    Article  Google Scholar 

  • Condea, C., Thiesse, F., & Fleisch, E. (2012). RFID-enabled shelf replenishment with backroom monitoring in retail stores. Decision Support Systems, 52(4), 839–849.

    Article  Google Scholar 

  • Costa, C., Antonucci, F., Pallottino, F., Aguzzi, J., Sarriá, D., & Menesatti, P. (2013). A review on agri-food supply chain traceability by means of RFID technology. Food and Bioprocess Technology, 6(2), 353–366.

    Article  Google Scholar 

  • Dantu, R., Dissanayake, I., & Nerur, S. (2021). Exploratory analysis of Internet of Things (IoT) in healthcare: A topic modelling & co-citation approaches. Information Systems Management, 38(1), 62–78.

    Article  Google Scholar 

  • Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. Preprint retrieved from http://arXiv.org/1810.04805

  • Fan, T., Tao, F., Deng, S., & Li, S. (2015). Impact of RFID technology on supply chain decisions with inventory inaccuracies. International Journal of Production Economics, 159, 117–125.

    Article  Google Scholar 

  • Frank, A. G., Dalenogare, L. S., & Ayala, N. F. (2019). Industry 4.0 technologies: Implementation patterns in manufacturing companies. International Journal of Production Economics, 210, 15–26.

    Article  Google Scholar 

  • Girvan, M., & Newman, M. E. (2002). Community structure in social and biological networks. Proceedings of the National Academy of Sciences, 99(12), 7821–7826.

    Article  MathSciNet  MATH  Google Scholar 

  • Glänzel, W., & Meyer, M. (2003). Patents cited in the scientific literature: An exploratory study of “reverse” citation relations. Scientometrics, 58(2), 415–428.

    Article  Google Scholar 

  • Heese, H. S. (2007). Inventory record inaccuracy, double marginalization, and RFID adoption. Production and Operations Management, 16(5), 542–553.

    Article  Google Scholar 

  • Huang, M. H., & Chang, C. P. (2014). Detecting research fronts in OLED field using bibliographic coupling with sliding window. Scientometrics, 98(3), 1721–1744.

    Article  Google Scholar 

  • Huang, M. H., Yang, H. W., & Chen, D. Z. (2015). Increasing science and technology linkage in fuel cells: A cross citation analysis of papers and patents. Journal of Informetrics, 9(2), 237–249.

    Article  Google Scholar 

  • Huang, Y., Zhu, D., Qian, Y., Zhang, Y., Porter, A. L., Liu, Y., & Guo, Y. (2017). A hybrid method to trace technology evolution pathways: A case study of 3D printing. Scientometrics, 111(1), 185–204.

    Article  Google Scholar 

  • Hummon, N. P., & Dereian, P. (1989). Connectivity in a citation network: The development of DNA theory. Social Networks, 11(1), 39–63.

    Article  Google Scholar 

  • Hwang, S., & Shin, J. (2019). Extending technological trajectories to latest technological changes by overcoming time lags. Technological Forecasting and Social Change, 143, 142–153.

    Article  Google Scholar 

  • Ji, J., Barnett, G. A., & Chu, J. (2019). Global networks of genetically modified crops technology: A patent citation network analysis. Scientometrics, 118(3), 737–762.

    Article  Google Scholar 

  • Jiang, L., Chen, J., Bao, Y., & Zou, F. (2021). Exploring the patterns of international technology diffusion in AI from the perspective of patent citations. Scientometrics. https://doi.org/10.1007/s11192-021-04134-3

    Article  Google Scholar 

  • Jiang, W. (2021). Applications of deep learning in stock market prediction: Recent progress. Expert Systems with Applications, 184, 115537.

    Article  Google Scholar 

  • Jiang, X., & Zhuge, H. (2019). Forward search path count as an alternative indirect citation impact indicator. Journal of Informetrics, 13(4), 100977.

    Article  Google Scholar 

  • Joulin, A., Grave, E., Bojanowski, P., & Mikolov, T. (2016). Bag of tricks for efficient text classification. Preprint retrieved from http://arXiv.org/1607.01759

  • Jung, H., & Lee, B. G. (2020). Research trends in text mining: Semantic network and main path analysis of selected journals. Expert Systems with Applications, 162, 113851.

    Article  Google Scholar 

  • Kalpana, S., Priyadarshini, S. R., Leena, M. M., Moses, J. A., & Anandharamakrishnan, C. (2019). Intelligent packaging: Trends and applications in food systems. Trends in Food Science & Technology, 93, 145–157.

    Article  Google Scholar 

  • Kim, J., & Shin, J. (2018). Mapping extended technological trajectories: Integration of main path, derivative paths, and technology junctures. Scientometrics, 116(3), 1439–1459.

    Article  MathSciNet  Google Scholar 

  • Kim, Y. (2014). Convolutional neural networks for sentence classification. Preprint retrieved from http://arXiv.org/1408.5882

  • Lai, S., Xu, L., Liu, K., & Zhao, J. (2015). Recurrent convolutional neural networks for text classification. In Twenty-ninth AAAI Conference on Artificial Intelligence.

  • Lee, H., & Özer, Ö. (2007). Unlocking the value of RFID. Production and Operations Management, 16(1), 40–64.

    Article  Google Scholar 

  • Leng, K., Jin, L., Shi, W., & Van Nieuwenhuyse, I. (2019). Research on agricultural products supply chain inspection system based on Internet of Things. Cluster Computing, 22(4), 8919–8927.

    Article  Google Scholar 

  • Li, L. (2013). Technology designed to combat fakes in the global supply chain. Business Horizons, 56(2), 167–177.

    Article  Google Scholar 

  • Li, M., & Chu, Y. (2017). Explore the research front of a specific research theme based on a novel technique of enhanced co-word analysis. Journal of Information Science, 43(6), 725–741.

    Article  Google Scholar 

  • Li, S., Wang, X., & Zhang, D. (2008). Node localisation in wireless sensor network based on self-organising isometric embedding. Enterprise Information Systems, 2(3), 259–273.

    Article  Google Scholar 

  • Li, X., Fan, M., Zhou, Y., Fu, J., Yuan, F., & Huang, L. (2020). Monitoring and forecasting the development trends of nanogenerator technology using citation analysis and text mining. Nano Energy, 71, 104636.

    Article  Google Scholar 

  • Liaw, Y. C., Chan, T. Y., Fan, C. Y., & Chiang, C. H. (2014). Can the technological impact of academic journals be evaluated? The practice of non-patent reference (NPR) analysis. Scientometrics, 101(1), 17–37.

    Article  Google Scholar 

  • Liu, J. S., & Lu, L. Y. (2012). An integrated approach for main path analysis: Development of the Hirsch index as an example. Journal of the American Society for Information Science and Technology, 63(3), 528–542.

    Article  Google Scholar 

  • Liu, J. S., Lu, L. Y., & Lu, W. M. (2016). Research fronts in data envelopment analysis. Omega, 58, 33–45.

    Article  Google Scholar 

  • Ma, V. C., & Liu, J. S. (2016). Exploring the research fronts and main paths of literature: A case study of shareholder activism research. Scientometrics, 109(1), 33–52.

    Article  Google Scholar 

  • Marrone, M. (2020). Application of entity linking to identify research fronts and trends. Scientometrics, 122(1), 357–379.

    Article  Google Scholar 

  • Meyer, M. (2002). Tracing knowledge flows in innovation systems. Scientometrics, 54(2), 193–212.

    Article  Google Scholar 

  • Newman, M. E. (2006). Modularity and community structure in networks. Proceedings of the National Academy of Sciences, 103(23), 8577–8582.

    Article  Google Scholar 

  • Noyons, E. C. M., Van Raan, A. F. J., Grupp, H., & Schmoch, U. (1994). Exploring the science and technology interface: Inventor-author relations in laser medicine research. Research Policy, 23(4), 443–457.

    Article  Google Scholar 

  • Ogasawara, A., & Yamasaki, K. (2006). A temperature-managed traceability system using RFID tags with embedded temperature sensors. NEC Technical Journal, 1(2), 82–86.

    Google Scholar 

  • Persson, O. (1994). The intellectual base and research fronts of JASIS 1986–1990. Journal of the American Society for Information Science, 45(1), 31–38.

    Article  Google Scholar 

  • Piñeiro-Chousa, J., López-Cabarcos, M. Á., Romero-Castro, N. M., & Pérez-Pico, A. M. (2020). Innovation, entrepreneurship and knowledge in the business scientific field: Mapping the research front. Journal of Business Research, 115, 475–485.

    Article  Google Scholar 

  • Prabhaa, S. S., Bindu, N., Manoj, P., & Kumar, K. S. (2020). Citation network analysis of plastic electronics: Tracing the evolution and emerging research fronts. Materials Today: Proceedings, 33, 1345–1350.

    Google Scholar 

  • Price, D. J. D. S. (1965). Networks of scientific papers. Science, 149(3683), 510–515.

    Article  Google Scholar 

  • Qu, T., Lei, S. P., Wang, Z. Z., Nie, D. X., Chen, X., & Huang, G. Q. (2016). IoT-based real-time production logistics synchronization system under smart cloud manufacturing. The International Journal of Advanced Manufacturing Technology, 84(1–4), 147–164.

    Article  Google Scholar 

  • Rejeb, A., Rejeb, K., Zailani, S., Treiblmaier, H., & Hand, K. J. (2021). Integrating the Internet of Things in the halal food supply chain: A systematic literature review and research agenda. Internet of Things, 13, 100361.

    Article  Google Scholar 

  • Sohail, M., Sun, D. W., & Zhu, Z. (2018). Recent developments in intelligent packaging for enhancing food quality and safety. Critical Reviews in Food Science and Nutrition, 58(15), 2650–2662.

    Article  Google Scholar 

  • Sun, L., Zhao, Y., Sun, W., & Liu, Z. (2020). Study on supply chain strategy based on cost income model and multi-access edge computing under the background of the Internet of Things. Neural Computing and Applications, 32(19), 15357–15368.

    Article  Google Scholar 

  • Tang, C. S., & Veelenturf, L. P. (2019). The strategic role of logistics in the industry 4.0 era. Transportation Research Part e: Logistics and Transportation Review, 129, 1–11.

    Article  Google Scholar 

  • Tao, F., Cheng, Y., Da Xu, L., Zhang, L., & Li, B. H. (2014). CCIoT-CMfg: Cloud computing and Internet of Things-based cloud manufacturing service system. IEEE Transactions on Industrial Informatics, 10(2), 1435–1442.

    Article  Google Scholar 

  • Visich, J. K., Li, S., Khumawala, B. M., & Reyes, P. M. (2009). Empirical evidence of RFID impacts on supply chain performance. International Journal of Operations & Production Management, 29(11–12), 1290–1315.

    Article  Google Scholar 

  • Wang, G., & Guan, J. (2011). Measuring science–technology interactions using patent citations and author-inventor links: An exploration analysis from Chinese nanotechnology. Journal of Nanoparticle Research, 13(12), 6245–6262.

    Article  Google Scholar 

  • Wang, J., Lim, M. K., Zhan, Y., & Wang, X. (2020). An intelligent logistics service system for enhancing dispatching operations in an IoT environment. Transportation Research Part e: Logistics and Transportation Review, 135, 101886.

    Article  Google Scholar 

  • Wang, L., Da Xu, L., Bi, Z., & Xu, Y. (2013). Data cleaning for RFID and WSN integration. IEEE Transactions on Industrial Informatics, 10(1), 408–418.

    Article  Google Scholar 

  • Wang, L., Wu, Z., & Cao, C. (2019). Technologies and fabrication of intelligent packaging for perishable products. Applied Sciences, 9(22), 4858.

    Article  Google Scholar 

  • Wang, X., Zhang, S., & Liu, Y. (2021a). ITGInsight—Discovering and visualizing research fronts in the scientific literature. Scientometrics. https://doi.org/10.1007/s11192-021-04190-9

    Article  Google Scholar 

  • Wang, X., Zhang, S., Liu, Y., Du, J., & Huang, H. (2021b). How pharmaceutical innovation evolves: The path from science to technological development to marketable drugs. Technological Forecasting and Social Change, 167, 120698.

    Article  Google Scholar 

  • Whitaker, J., Mithas, S., & Krishnan, M. S. (2007). A field study of RFID deployment and return expectations. Production and Operations Management, 16(5), 599–612.

    Article  Google Scholar 

  • Xu, H., Winnink, J., Yue, Z., Liu, Z., & Yuan, G. (2020). Topic-linked innovation paths in science and technology. Journal of Informetrics, 14(2), 101014.

    Article  Google Scholar 

  • Xu, H., Yue, Z., Pang, H., Elahi, E., Li, J., & Wang, L. (2022). Integrative model for discovering linked topics in science and technology. Journal of Informetrics, 16(2), 101265.

    Article  Google Scholar 

  • Xu, S., Zhai, D., Wang, F., An, X., Pang, H., & Sun, Y. (2019). A novel method for topic linkages between scientific publications and patents. Journal of the Association for Information Science and Technology, 70(9), 1026–1042.

    Article  Google Scholar 

  • Yang, H., & Chen, W. (2020). Game modes and investment cost locations in radio-frequency identification (RFID) adoption. European Journal of Operational Research, 286(3), 883–896.

    Article  MathSciNet  MATH  Google Scholar 

  • Yu, D., & Sheng, L. (2020). Knowledge diffusion paths of blockchain domain: The main path analysis. Scientometrics, 125(1), 471–497.

  • Yu, D., & Pan, T. (2021). Tracing knowledge diffusion of TOPSIS: A historical perspective from citation network. Expert Systems with Applications, 168, 114238.

  • Yu, D., & Yan, Z. (2021). Knowledge diffusion of supply chain bullwhip effect: Main path analysis and science mapping analysis. Scientometrics, 126(10), 8491–8515.

  • Zhang, L. H., Li, T., & Fan, T. J. (2018). Radio-frequency identification (RFID) adoption with inventory misplacement under retail competition. European Journal of Operational Research, 270(3), 1028–1043.

    Article  MathSciNet  MATH  Google Scholar 

  • Zhang, L. H., & Yang, H. (2019). Incentives for RFID adoption with imperfect read rates: Wholesale price premium versus cost sharing. Journal of the Operational Research Society, 70(9), 1440–1456.

    Article  Google Scholar 

  • Zhang, T., Wu, F., Katiyar, A., Weinberger, K. Q., & Artzi, Y. (2020). Revisiting few-sample BERT fine-tuning. Preprint retrieved from http://arXiv.org/2006.05987.

  • Zhong, R. Y., Xu, X., Klotz, E., & Newman, S. T. (2017). Intelligent manufacturing in the context of industry 4.0: A review. Engineering, 3(5), 616–630.

    Article  Google Scholar 

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Funding

The funding was provided by the Ministry of Education of Humanities and Social Science Project (19YJC630208), and also by Qinglan Project of Jiangsu Province of China (2019).

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  1. Business School, Nanjing Audit University, Nanjing, 211815, Jiangsu, China

    Dejian Yu & Zhaoping Yan

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  1. Dejian Yu
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Correspondence to Zhaoping Yan.

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Yu, D., Yan, Z. Combining machine learning and main path analysis to identify research front: from the perspective of science-technology linkage. Scientometrics 127, 4251–4274 (2022). https://doi.org/10.1007/s11192-022-04443-1

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