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Knowledge diffusion paths of blockchain domain: the main path analysis

Author

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  • Dejian Yu

    (Nanjing Audit University)

  • Libo Sheng

    (Nanjing Audit University)

Abstract

Blockchain technology, as a disruptive technology, has received widespread attention in the past few years from all over the world, leading to rapid growth in research outputs. This paper adopts a quantitative method, the main path analysis, to comprehensively and systematically investigate the development trajectories of blockchain. Four different main paths, the global main path, the forward local main path, the backward local main path and the key-route main path are conducted simultaneously. By analyzing these various paths, on the one hand, this paper finds that papers on paths focus on two aspects, cryptocurrencies and blockchain-based applications. On the other hand, this paper discovers several major research areas of blockchain, including internet of things (IoT), healthcare, energy industry, voting, insurance and supply chain management. At the same time, this paper further analyzes the research hotspots, as well as the development trajectories of blockchain in the areas of IoT, healthcare and supply chain management by using the key-route main path analysis. This paper is conductive for both the new and experienced researchers to identify some influential papers and grasp the knowledge diffusion paths in these domains.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:scient:v:125:y:2020:i:1:d:10.1007_s11192-020-03650-y
    DOI: 10.1007/s11192-020-03650-y
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    1. Philippas, Dionisis & Rjiba, Hatem & Guesmi, Khaled & Goutte, Stéphane, 2019. "Media attention and Bitcoin prices," Finance Research Letters, Elsevier, vol. 30(C), pages 37-43.
    2. Zięba, Damian & Kokoszczyński, Ryszard & Śledziewska, Katarzyna, 2019. "Shock transmission in the cryptocurrency market. Is Bitcoin the most influential?," International Review of Financial Analysis, Elsevier, vol. 64(C), pages 102-125.
    3. Katsiampa, Paraskevi, 2017. "Volatility estimation for Bitcoin: A comparison of GARCH models," Economics Letters, Elsevier, vol. 158(C), pages 3-6.
    4. Dyhrberg, Anne Haubo, 2016. "Bitcoin, gold and the dollar – A GARCH volatility analysis," Finance Research Letters, Elsevier, vol. 16(C), pages 85-92.
    5. Sachin Kamble & Angappa Gunasekaran & Himanshu Arha, 2019. "Understanding the Blockchain technology adoption in supply chains-Indian context," International Journal of Production Research, Taylor & Francis Journals, vol. 57(7), pages 2009-2033, April.
    6. Sara Saberi & Mahtab Kouhizadeh & Joseph Sarkis & Lejia Shen, 2019. "Blockchain technology and its relationships to sustainable supply chain management," International Journal of Production Research, Taylor & Francis Journals, vol. 57(7), pages 2117-2135, April.
    7. Vincent C. Ma & John S. Liu, 2016. "Exploring the research fronts and main paths of literature: a case study of shareholder activism research," Scientometrics, Springer;Akadémiai Kiadó, vol. 109(1), pages 33-52, October.
    8. Urquhart, Andrew, 2016. "The inefficiency of Bitcoin," Economics Letters, Elsevier, vol. 148(C), pages 80-82.
    9. Weishu Liu, 2019. "The data source of this study is Web of Science Core Collection? Not enough," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(3), pages 1815-1824, December.
    10. Sikorski, Janusz J. & Haughton, Joy & Kraft, Markus, 2017. "Blockchain technology in the chemical industry: Machine-to-machine electricity market," Applied Energy, Elsevier, vol. 195(C), pages 234-246.
    11. Urquhart, Andrew, 2017. "Price clustering in Bitcoin," Economics Letters, Elsevier, vol. 159(C), pages 145-148.
    12. Rainer Böhme & Nicolas Christin & Benjamin Edelman & Tyler Moore, 2015. "Bitcoin: Economics, Technology, and Governance," Journal of Economic Perspectives, American Economic Association, vol. 29(2), pages 213-238, Spring.
    13. Bariviera, Aurelio F., 2017. "The inefficiency of Bitcoin revisited: A dynamic approach," Economics Letters, Elsevier, vol. 161(C), pages 1-4.
    14. Tiwari, Aviral Kumar & Jana, R.K. & Das, Debojyoti & Roubaud, David, 2018. "Informational efficiency of Bitcoin—An extension," Economics Letters, Elsevier, vol. 163(C), pages 106-109.
    15. Yaya, OlaOluwa S. & Ogbonna, Ahamuefula E. & Olubusoye, Olusanya E., 2019. "How persistent and dynamic inter-dependent are pricing of Bitcoin to other cryptocurrencies before and after 2017/18 crash?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 531(C).
    16. Seonghyeon Gong & Erzhena Tcydenova & Jeonghoon Jo & Younghun Lee & Jong Hyuk Park, 2019. "Blockchain-Based Secure Device Management Framework for an Internet of Things Network in a Smart City," Sustainability, MDPI, vol. 11(14), pages 1-17, July.
    17. Balcilar, Mehmet & Bouri, Elie & Gupta, Rangan & Roubaud, David, 2017. "Can volume predict Bitcoin returns and volatility? A quantiles-based approach," Economic Modelling, Elsevier, vol. 64(C), pages 74-81.
    18. Wang, Yingli & Singgih, Meita & Wang, Jingyao & Rit, Mihaela, 2019. "Making sense of blockchain technology: How will it transform supply chains?," International Journal of Production Economics, Elsevier, vol. 211(C), pages 221-236.
    19. 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.
    20. Dele Raheem & Maxim Shishaev & Vladimir Dikovitsky, 2019. "Food System Digitalization as a Means to Promote Food and Nutrition Security in the Barents Region," Agriculture, MDPI, vol. 9(8), pages 1-19, August.
    21. Fry, John & Cheah, Eng-Tuck, 2016. "Negative bubbles and shocks in cryptocurrency markets," International Review of Financial Analysis, Elsevier, vol. 47(C), pages 343-352.
    22. 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.
    23. Yu, Dejian & He, Xiaorong, 2020. "A bibliometric study for DEA applied to energy efficiency: Trends and future challenges," Applied Energy, Elsevier, vol. 268(C).
    24. Merediz-Solà, Ignasi & Bariviera, Aurelio F., 2019. "A bibliometric analysis of bitcoin scientific production," Research in International Business and Finance, Elsevier, vol. 50(C), pages 294-305.
    25. Esther Salmerón-Manzano & Francisco Manzano-Agugliaro, 2019. "The Role of Smart Contracts in Sustainability: Worldwide Research Trends," Sustainability, MDPI, vol. 11(11), pages 1-16, May.
    26. Koutmos, Dimitrios, 2018. "Return and volatility spillovers among cryptocurrencies," Economics Letters, Elsevier, vol. 173(C), pages 122-127.
    27. 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.
    28. Ji, Qiang & Bouri, Elie & Roubaud, David & Kristoufek, Ladislav, 2019. "Information interdependence among energy, cryptocurrency and major commodity markets," Energy Economics, Elsevier, vol. 81(C), pages 1042-1055.
    29. Hackius, Niels & Petersen, Moritz, 2017. "Blockchain in logistics and supply chain: Trick or treat?," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Kersten, Wolfgang & Blecker, Thorsten & Ringle, Christian M. (ed.), Digitalization in Supply Chain Management and Logistics: Smart and Digital Solutions for an Industry 4.0 Environment. Proceedings of the Hamburg Inter, volume 23, pages 3-18, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
    30. Ji, Qiang & Bouri, Elie & Lau, Chi Keung Marco & Roubaud, David, 2019. "Dynamic connectedness and integration in cryptocurrency markets," International Review of Financial Analysis, Elsevier, vol. 63(C), pages 257-272.
    31. 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.
    32. 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.
    33. Koutmos, Dimitrios, 2018. "Liquidity uncertainty and Bitcoin’s market microstructure," Economics Letters, Elsevier, vol. 172(C), pages 97-101.
    34. Choi, Tsan-Ming & Luo, Suyuan, 2019. "Data quality challenges for sustainable fashion supply chain operations in emerging markets: Roles of blockchain, government sponsors and environment taxes," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 131(C), pages 139-152.
    35. Bouri, Elie & Molnár, Peter & Azzi, Georges & Roubaud, David & Hagfors, Lars Ivar, 2017. "On the hedge and safe haven properties of Bitcoin: Is it really more than a diversifier?," Finance Research Letters, Elsevier, vol. 20(C), pages 192-198.
    36. Khuntia, Sashikanta & Pattanayak, J.K., 2018. "Adaptive market hypothesis and evolving predictability of bitcoin," Economics Letters, Elsevier, vol. 167(C), pages 26-28.
    37. Junwen Zhu & Weishu Liu, 2020. "A tale of two databases: the use of Web of Science and Scopus in academic papers," Scientometrics, Springer;Akadémiai Kiadó, vol. 123(1), pages 321-335, April.
    38. Choi, Tsan-Ming & Wen, Xin & Sun, Xuting & Chung, Sai-Ho, 2019. "The mean-variance approach for global supply chain risk analysis with air logistics in the blockchain technology era," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 127(C), pages 178-191.
    39. Handika, Rangga & Soepriyanto, Gatot & Havidz, Shinta Amalina Hazrati, 2019. "Are cryptocurrencies contagious to Asian financial markets?," Research in International Business and Finance, Elsevier, vol. 50(C), pages 416-429.
    40. Corbet, Shaen & Meegan, Andrew & Larkin, Charles & Lucey, Brian & Yarovaya, Larisa, 2018. "Exploring the dynamic relationships between cryptocurrencies and other financial assets," Economics Letters, Elsevier, vol. 165(C), pages 28-34.
    41. Symitsi, Efthymia & Chalvatzis, Konstantinos J., 2018. "Return, volatility and shock spillovers of Bitcoin with energy and technology companies," Economics Letters, Elsevier, vol. 170(C), pages 127-130.
    42. Mei Hsiu-Ching Ho & John S. Liu & Kerr C.-T. Chang, 2017. "To include or not: the role of review papers in citation-based analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 110(1), pages 65-76, January.
    43. Marten Risius & Kai Spohrer, 2017. "A Blockchain Research Framework," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 59(6), pages 385-409, December.
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    4. Abderahman Rejeb & Karim Rejeb & Suhaiza Zailani & Yasanur Kayikci & John G. Keogh, 2023. "Examining Knowledge Diffusion in the Circular Economy Domain: a Main Path Analysis," Circular Economy and Sustainability, Springer, vol. 3(1), pages 125-166, March.
    5. Dejian Yu & Zhaoping Yan, 2022. "Combining machine learning and main path analysis to identify research front: from the perspective of science-technology linkage," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(7), pages 4251-4274, July.
    6. Siddique, Maryam, 2023. "Does the Adaptive Market Hypothesis Exist in Equity Market? Evidence from Pakistan Stock Exchange," OSF Preprints 9b5dx, Center for Open Science.
    7. 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).
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