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Main path analysis considering citation structure and content: Case studies in different domains

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  • Yu, Dejian
  • Yan, Zhaoping

Abstract

Main path analysis (MPA) is an effective method widely accepted in science and technology for extracting knowledge diffusion paths. Traditional citation analysis assumes that all citations are treated equally. In contrast, this paper proposes a new MPA framework from the perspective of citation structure and content. Three indicators are considered to adjust edge weight: (1) Structural similarity, (2) Topic similarity and (3) Sentiment analysis. This study takes the bullwhip effect and the Internet of Things domain as examples to verify the reliability and feasibility of improved MPA. The results show that the improved main path uncovers the knowledge trajectories appropriately, which has an ability to distinguish citations and detect important papers. This research enriches MPA theory and provides future research directions from perspective of citation structure and content.

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  • Yu, Dejian & Yan, Zhaoping, 2023. "Main path analysis considering citation structure and content: Case studies in different domains," Journal of Informetrics, Elsevier, vol. 17(1).
    • Handle: RePEc:eee:infome:v:17:y:2023:i:1:s1751157723000068
    DOI: 10.1016/j.joi.2023.101381
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    1. Kim, Jeon G. & Chatfield, Dean & Harrison, Terry P. & Hayya, Jack C., 2006. "Quantifying the bullwhip effect in a supply chain with stochastic lead time," European Journal of Operational Research, Elsevier, vol. 173(2), pages 617-636, September.
    2. Liao, Shu-Chun & Chou, Tzu-Chuan & Huang, Chen-Hao, 2022. "Revisiting the development trajectory of the digital divide: A main path analysis approach," Technological Forecasting and Social Change, Elsevier, vol. 179(C).
    3. Saeed-Ul Hassan & Iqra Safder & Anam Akram & Faisal Kamiran, 2018. "A novel machine-learning approach to measuring scientific knowledge flows using citation context analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(2), pages 973-996, August.
    4. Lai, Kuei-Kuei & Bhatt, Priyanka C. & Kumar, Vimal & Chen, Hsueh-Chen & Chang, Yu-Hsin & Su, Fang-Pei, 2021. "Identifying the impact of patent family on the patent trajectory: A case of thin film solar cells technological trajectories," Journal of Informetrics, Elsevier, vol. 15(2).
    5. Boute, Robert N. & Disney, Stephen M. & Lambrecht, Marc R. & Van Houdt, Benny, 2007. "An integrated production and inventory model to dampen upstream demand variability in the supply chain," European Journal of Operational Research, Elsevier, vol. 178(1), pages 121-142, April.
    6. Junmo Kim & Juneseuk Shin, 2018. "Mapping extended technological trajectories: integration of main path, derivative paths, and technology junctures," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(3), pages 1439-1459, September.
    7. Liu, Yunmei & Yang, Liu & Chen, Min, 2021. "A new citation concept: Triangular citation in the literature," Journal of Informetrics, Elsevier, vol. 15(2).
    8. John S. Liu & Louis Y.Y. Lu, 2012. "An integrated approach for main path analysis: Development of the Hirsch index as an example," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 63(3), pages 528-542, March.
    9. Chatfield, Dean C. & Pritchard, Alan M., 2013. "Returns and the bullwhip effect," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 49(1), pages 159-175.
    10. Springer, Mark & Kim, Ilhyung, 2010. "Managing the order pipeline to reduce supply chain volatility," European Journal of Operational Research, Elsevier, vol. 203(2), pages 380-392, June.
    11. Kim, Ilhyung & Springer, Mark, 2008. "Measuring endogenous supply chain volatility: Beyond the bullwhip effect," European Journal of Operational Research, Elsevier, vol. 189(1), pages 172-193, August.
    12. Chen, Liang & Xu, Shuo & Zhu, Lijun & Zhang, Jing & Xu, Haiyun & Yang, Guancan, 2022. "A semantic main path analysis method to identify multiple developmental trajectories," Journal of Informetrics, Elsevier, vol. 16(2).
    13. Junyi Lin & Virginia L.M. Spiegler & M.M. Naim, 2018. "Dynamic analysis and design of a semiconductor supply chain: a control engineering approach," International Journal of Production Research, Taylor & Francis Journals, vol. 56(13), pages 4585-4611, July.
    14. Persson, Olle, 2010. "Identifying research themes with weighted direct citation links," Journal of Informetrics, Elsevier, vol. 4(3), pages 415-422.
    15. ParvezAhamad Kazi & Manasi Patwardhan & Pushkar Joglekar, 2016. "Towards a new perspective on context based citation index of research articles," Scientometrics, Springer;Akadémiai Kiadó, vol. 107(1), pages 103-121, April.
    16. Stephen M. Disney & Ingrid Farasyn & Marc R. Lambrecht & Denis R. Towill & Wim Van De Velde, 2007. "Controlling bullwhip and inventory variability with the golden smoothing rule," European Journal of Industrial Engineering, Inderscience Enterprises Ltd, vol. 1(3), pages 241-265.
    17. Heng Huang & Donghua Zhu & Xuefeng Wang, 2022. "Evaluating scientific impact of publications: combining citation polarity and purpose," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(9), pages 5257-5281, September.
    18. Kuan, Chung-Huei & Lin, Jia-Tian & Chen, Dar-Zen, 2021. "Characterizing Patent Assignees by Their Structural Positions Relative to a Field’s Evolutionary Trajectory," Journal of Informetrics, Elsevier, vol. 15(4).
    19. Hau L. Lee & V. Padmanabhan & Seungjin Whang, 1997. "Information Distortion in a Supply Chain: The Bullwhip Effect," Management Science, INFORMS, vol. 43(4), pages 546-558, April.
    20. Xiaodan Zhu & Peter Turney & Daniel Lemire & André Vellino, 2015. "Measuring academic influence: Not all citations are equal," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 66(2), pages 408-427, February.
    21. John S. Liu & Louis Y.Y. Lu, 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, Association for Information Science & Technology, vol. 63(3), pages 528-542, March.
    22. Hosoda, Takamichi & Disney, Stephen M., 2006. "On variance amplification in a three-echelon supply chain with minimum mean square error forecasting," Omega, Elsevier, vol. 34(4), pages 344-358, August.
    23. Dejian Yu & Zhaoping Yan, 2021. "Knowledge diffusion of supply chain bullwhip effect: main path analysis and science mapping analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(10), pages 8491-8515, October.
    24. Kim, Erin H.J. & Jeong, Yoo Kyung & Kim, YongHwan & Song, Min, 2022. "Exploring scientific trajectories of a large-scale dataset using topic-integrated path extraction," Journal of Informetrics, Elsevier, vol. 16(1).
    25. Joaquim Jorge Vicente & Susana Relvas & Ana Paula Barbosa-Póvoa, 2018. "Effective bullwhip metrics for multi-echelon distribution systems under order batching policies with cyclic demand," International Journal of Production Research, Taylor & Francis Journals, vol. 56(4), pages 1593-1619, February.
    26. Jiang, Xiaorui & Zhuge, Hai, 2019. "Forward search path count as an alternative indirect citation impact indicator," Journal of Informetrics, Elsevier, vol. 13(4).
    27. Yu, Dejian & Sheng, Libo, 2021. "Influence difference main path analysis: Evidence from DNA and blockchain domain citation networks," Journal of Informetrics, Elsevier, vol. 15(4).
    28. Kevin W. Boyack & Katy Börner, 2003. "Indicator‐assisted evaluation and funding of research: Visualizing the influence of grants on the number and citation counts of research papers," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 54(5), pages 447-461, March.
    29. Huang, Chen-Hao & Liu, John S. & Ho, Mei Hsiu-Ching & Chou, Tzu-Chuan, 2022. "Towards more convergent main paths: A relevance-based approach," Journal of Informetrics, Elsevier, vol. 16(3).
    30. Disney, S. M. & Towill, D. R., 2003. "On the bullwhip and inventory variance produced by an ordering policy," Omega, Elsevier, vol. 31(3), pages 157-167, June.
    31. Faiza Qayyum & Muhammad Tanvir Afzal, 2019. "Identification of important citations by exploiting research articles’ metadata and cue-terms from content," Scientometrics, Springer;Akadémiai Kiadó, vol. 118(1), pages 21-43, January.
    32. Philipp Korom, 2019. "A bibliometric visualization of the economics and sociology of wealth inequality: a world apart?," Scientometrics, Springer;Akadémiai Kiadó, vol. 118(3), pages 849-868, March.
    33. Ciancimino, Elena & Cannella, Salvatore & Bruccoleri, Manfredi & Framinan, Jose M., 2012. "On the Bullwhip Avoidance Phase: The Synchronised Supply Chain," European Journal of Operational Research, Elsevier, vol. 221(1), pages 49-63.
    34. Dangzhi Zhao & Andreas Strotmann, 2016. "Dimensions and uncertainties of author citation rankings: Lessons learned from frequency-weighted in-text citation counting," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 67(3), pages 671-682, March.
    35. Nassiri, Isar & Masoudi-Nejad, Ali & Jalili, Mahdi & Moeini, Ali, 2013. "Normalized Similarity Index: An adjusted index to prioritize article citations," Journal of Informetrics, Elsevier, vol. 7(1), pages 91-98.
    36. Bekkers, Rudi & Martinelli, Arianna, 2012. "Knowledge positions in high-tech markets: Trajectories, standards, strategies and true innovators," Technological Forecasting and Social Change, Elsevier, vol. 79(7), pages 1192-1216.
    37. Dejonckheere, J. & Disney, S. M. & Lambrecht, M. R. & Towill, D. R., 2003. "Measuring and avoiding the bullwhip effect: A control theoretic approach," European Journal of Operational Research, Elsevier, vol. 147(3), pages 567-590, June.
    38. Xiao, Yu & Lu, Louis Y.Y. & Liu, John S. & Zhou, Zhili, 2014. "Knowledge diffusion path analysis of data quality literature: A main path analysis," Journal of Informetrics, Elsevier, vol. 8(3), pages 594-605.
    39. Frank Chen & Zvi Drezner & Jennifer K. Ryan & David Simchi-Levi, 2000. "Quantifying the Bullwhip Effect in a Simple Supply Chain: The Impact of Forecasting, Lead Times, and Information," Management Science, INFORMS, vol. 46(3), pages 436-443, March.
    40. Paolo Priore & Borja Ponte & Rafael Rosillo & David de la Fuente, 2019. "Applying machine learning to the dynamic selection of replenishment policies in fast-changing supply chain environments," International Journal of Production Research, Taylor & Francis Journals, vol. 57(11), pages 3663-3677, June.
    41. Cantor, David E. & Katok, Elena, 2012. "Production smoothing in a serial supply chain: A laboratory investigation," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 48(4), pages 781-794.
    42. John S. Liu & Chung-Huei Kuan, 2016. "A new approach for main path analysis: Decay in knowledge diffusion," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 67(2), pages 465-476, February.
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