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An innovative approach to identify the knowledge diffusion path: the case of resource-based theory

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  • Louis Y. Y. Lu

    (Yuan Ze University)

  • John S. Liu

    (National Taiwan University of Science and Technology)

Abstract

This study presents an innovative approach for identifying the knowledge diffusion path of a target research field. We take the resource-based theory (RBT) as an example to demonstrate the usefulness of this methodology. Several survey studies have provided valuable summarization and commentaries to the RBT from different perspectives. These analyses are useful and pertinent for understanding the development of RBT. However, limited by the methodologies they used, previous scholars can only select part of the RBT literature to conduct the survey work. To eliminate the limitation, this study develops an innovative approach which can handle thousands of articles. This study analyzes a dataset including 2,105 theoretical developments, empirical studies, and review papers to explore the knowledge diffusion path of the RBT. Citation data are used to build the citation network. Main paths are then probed and visualized via social network analysis methodology. To figure out the total picture of the knowledge diffusion path, this study integrates various main path analyses to supplement the traditional approach. The traditional main path analysis investigates the knowledge diffusion from a local view. The global analysis provides a main path from a macro view. The key-route analysis helps explore and clarify a complete picture of the convergence-divergence phenomena. We believe that through this novel tool, new researchers can easily identify the papers that have made major contributions to RBT knowledge diffusion and uncover the interrelationships among them.

Suggested Citation

  • Louis Y. Y. Lu & John S. Liu, 2013. "An innovative approach to identify the knowledge diffusion path: the case of resource-based theory," Scientometrics, Springer;Akadémiai Kiadó, vol. 94(1), pages 225-246, January.
    • Handle: RePEc:spr:scient:v:94:y:2013:i:1:d:10.1007_s11192-012-0744-3
    DOI: 10.1007/s11192-012-0744-3
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    2. Chuang, Thomas C. & Liu, John S. & Lu, Louis Y.Y. & Lee, Yachi, 2014. "The main paths of medical tourism: From transplantation to beautification," Tourism Management, Elsevier, vol. 45(C), pages 49-58.
    3. Lu, Louis Y.Y. & Liu, John S., 2016. "A novel approach to identify the major research themes and development trajectory: The case of patenting research," Technological Forecasting and Social Change, Elsevier, vol. 103(C), pages 71-82.
    4. Liu, John S. & Lu, Louis Y.Y. & Lu, Wen-Min, 2016. "Research fronts in data envelopment analysis," Omega, Elsevier, vol. 58(C), pages 33-45.
    5. John S. Liu & Louis Y. Y. Lu & Mei Hsiu-Ching Ho, 2019. "A few notes on main path analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(1), pages 379-391, April.
    6. Zhu, Hengmin & Yin, Xicheng & Ma, Jing & Hu, Wei, 2016. "Identifying the main paths of information diffusion in online social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 452(C), pages 320-328.
    7. Yue, Zenghui & Xu, Haiyun & Yuan, Guoting & Pang, Hongshen, 2019. "Modeling study of knowledge diffusion in scientific collaboration networks based on differential dynamics: A case study in graphene field," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 524(C), pages 375-391.
    8. Bruno Miranda Henrique & Vinicius Amorim Sobreiro & Herbert Kimura, 2018. "Building direct citation networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 115(2), pages 817-832, May.
    9. Abderahman Rejeb & Alireza Abdollahi & Karim Rejeb & Mohamed M. Mostafa, 2023. "Tracing knowledge evolution flows in scholarly restaurant research: a main path analysis," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(3), pages 2183-2209, June.
    10. 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).
    11. Lu, Louis Y.Y. & Hsieh, Chih-Hung & Liu, John S., 2016. "Development trajectory and research themes of foresight," Technological Forecasting and Social Change, Elsevier, vol. 112(C), pages 347-356.
    12. Dejian Yu & Libo Sheng, 2020. "Knowledge diffusion paths of blockchain domain: the main path analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(1), pages 471-497, October.
    13. Hansin Bilgili & Jonathan L. Johnson & Tsvetomira V. Bilgili & Alan E. Ellstrand, 2022. "Research on social relationships and processes governing the behaviors of members of the corporate elite: a review and bibliometric analysis," Review of Managerial Science, Springer, vol. 16(8), pages 2285-2339, November.
    14. Yu, Qi & Ding, Ying & Song, Min & Song, Sungjeon & Liu, Jianhua & Zhang, Bin, 2015. "Tracing database usage: Detecting main paths in database link networks," Journal of Informetrics, Elsevier, vol. 9(1), pages 1-15.

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