Use of Triangulation in Comparing the Blockchain Knowledge Structure between China and South Korea: Scientometric Network, Topic Modeling, and Prediction Technique
Blockchain, as a new innovative technology, has become a popular topic in many fields in recent years. In this study, triangulation was used to investigate the development of knowledge structures. First, scientometric network analysis was employed to identify the cooperation of knowledge networks. I...
Gespeichert in:
| Veröffentlicht in: | Sustainability 2022-02, Vol.14 (4), p.2326 |
|---|---|
| Hauptverfasser: | , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | |
|---|---|
| container_issue | 4 |
| container_start_page | 2326 |
| container_title | Sustainability |
| container_volume | 14 |
| creator | Zhu, Yu-Peng Park, Han-Woo |
| description | Blockchain, as a new innovative technology, has become a popular topic in many fields in recent years. In this study, triangulation was used to investigate the development of knowledge structures. First, scientometric network analysis was employed to identify the cooperation of knowledge networks. It was found that the structure of blockchain knowledge networks in China is relatively more complex and diverse than in South Korea. Since increased teamwork in blockchain is conducive to the creation of high-quality knowledge products, the Chinese government appears to strongly promote diversified cooperation on blockchain technology through centralized policies. Second, machine-learning topic modeling was used to analyze the content exchanged via a collaborative network. As a result, it was found that both countries lacked the societal and commercial aspects of blockchain technology. Finally, we developed a prediction technique based on the Ernie model to automatically categorize the nature of blockchain research. |
| doi_str_mv | 10.3390/su14042326 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2633180963</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2633180963</sourcerecordid><originalsourceid>FETCH-LOGICAL-c295t-c3fc7f1f7a0d52e518fb93ce495ecbf4ac65f5702711f2fdd7951091bec4943</originalsourceid><addsrcrecordid>eNpNUctOwzAQtBBIVKUXvsASN9SAHedRc4OKl1oeUss5cpx14za1g-Oo4nv4UUxBgr3MrnZmZ7WL0CklF4xxctn1NCFJzOLsAA1iktOIkpQc_suP0ajr1iQEY5TTbIA-3zrAVuGl08Ks-kZ4bQ3WBk_tthVOmxX2NeCbxsqNrEVozIzdNVCtAC-866XvHeAS_A4giGptBBamwgvb-xrPrANxhRdSg_F2C95piZ8D2brNGC9tG8onW0ETfMZ73auDSsv9EkuQtdHvPZygIyWaDka_OESLu9vl9CGav9w_Tq_nkYx56iPJlMwVVbkgVRpDSieq5ExCwlOQpUqEzFKV5iTOKVWxqqqcp5RwWoJMeMKG6OxnautsMO18sba9M8GwiLNwrgnhAYbo_Iclne06B6pond4K91FQUnx_ofj7AvsCjZ18nQ</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2633180963</pqid></control><display><type>article</type><title>Use of Triangulation in Comparing the Blockchain Knowledge Structure between China and South Korea: Scientometric Network, Topic Modeling, and Prediction Technique</title><source>MDPI - Multidisciplinary Digital Publishing Institute</source><source>EZB-FREE-00999 freely available EZB journals</source><creator>Zhu, Yu-Peng ; Park, Han-Woo</creator><creatorcontrib>Zhu, Yu-Peng ; Park, Han-Woo</creatorcontrib><description>Blockchain, as a new innovative technology, has become a popular topic in many fields in recent years. In this study, triangulation was used to investigate the development of knowledge structures. First, scientometric network analysis was employed to identify the cooperation of knowledge networks. It was found that the structure of blockchain knowledge networks in China is relatively more complex and diverse than in South Korea. Since increased teamwork in blockchain is conducive to the creation of high-quality knowledge products, the Chinese government appears to strongly promote diversified cooperation on blockchain technology through centralized policies. Second, machine-learning topic modeling was used to analyze the content exchanged via a collaborative network. As a result, it was found that both countries lacked the societal and commercial aspects of blockchain technology. Finally, we developed a prediction technique based on the Ernie model to automatically categorize the nature of blockchain research.</description><identifier>ISSN: 2071-1050</identifier><identifier>EISSN: 2071-1050</identifier><identifier>DOI: 10.3390/su14042326</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Artificial intelligence ; Automatic text analysis ; Blockchain ; Collaboration ; Cooperation ; Data mining ; Digital currencies ; Knowledge ; Learning algorithms ; Machine learning ; Modelling ; Network analysis ; R&D ; Research & development ; Research methodology ; Scientometrics ; Sustainability ; Technology</subject><ispartof>Sustainability, 2022-02, Vol.14 (4), p.2326</ispartof><rights>2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c295t-c3fc7f1f7a0d52e518fb93ce495ecbf4ac65f5702711f2fdd7951091bec4943</citedby><cites>FETCH-LOGICAL-c295t-c3fc7f1f7a0d52e518fb93ce495ecbf4ac65f5702711f2fdd7951091bec4943</cites><orcidid>0000-0002-1378-2473 ; 0000-0003-0544-3911</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids></links><search><creatorcontrib>Zhu, Yu-Peng</creatorcontrib><creatorcontrib>Park, Han-Woo</creatorcontrib><title>Use of Triangulation in Comparing the Blockchain Knowledge Structure between China and South Korea: Scientometric Network, Topic Modeling, and Prediction Technique</title><title>Sustainability</title><description>Blockchain, as a new innovative technology, has become a popular topic in many fields in recent years. In this study, triangulation was used to investigate the development of knowledge structures. First, scientometric network analysis was employed to identify the cooperation of knowledge networks. It was found that the structure of blockchain knowledge networks in China is relatively more complex and diverse than in South Korea. Since increased teamwork in blockchain is conducive to the creation of high-quality knowledge products, the Chinese government appears to strongly promote diversified cooperation on blockchain technology through centralized policies. Second, machine-learning topic modeling was used to analyze the content exchanged via a collaborative network. As a result, it was found that both countries lacked the societal and commercial aspects of blockchain technology. Finally, we developed a prediction technique based on the Ernie model to automatically categorize the nature of blockchain research.</description><subject>Artificial intelligence</subject><subject>Automatic text analysis</subject><subject>Blockchain</subject><subject>Collaboration</subject><subject>Cooperation</subject><subject>Data mining</subject><subject>Digital currencies</subject><subject>Knowledge</subject><subject>Learning algorithms</subject><subject>Machine learning</subject><subject>Modelling</subject><subject>Network analysis</subject><subject>R&D</subject><subject>Research & development</subject><subject>Research methodology</subject><subject>Scientometrics</subject><subject>Sustainability</subject><subject>Technology</subject><issn>2071-1050</issn><issn>2071-1050</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNpNUctOwzAQtBBIVKUXvsASN9SAHedRc4OKl1oeUss5cpx14za1g-Oo4nv4UUxBgr3MrnZmZ7WL0CklF4xxctn1NCFJzOLsAA1iktOIkpQc_suP0ajr1iQEY5TTbIA-3zrAVuGl08Ks-kZ4bQ3WBk_tthVOmxX2NeCbxsqNrEVozIzdNVCtAC-866XvHeAS_A4giGptBBamwgvb-xrPrANxhRdSg_F2C95piZ8D2brNGC9tG8onW0ETfMZ73auDSsv9EkuQtdHvPZygIyWaDka_OESLu9vl9CGav9w_Tq_nkYx56iPJlMwVVbkgVRpDSieq5ExCwlOQpUqEzFKV5iTOKVWxqqqcp5RwWoJMeMKG6OxnautsMO18sba9M8GwiLNwrgnhAYbo_Iclne06B6pond4K91FQUnx_ofj7AvsCjZ18nQ</recordid><startdate>20220201</startdate><enddate>20220201</enddate><creator>Zhu, Yu-Peng</creator><creator>Park, Han-Woo</creator><general>MDPI AG</general><scope>AAYXX</scope><scope>CITATION</scope><scope>4U-</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><orcidid>https://orcid.org/0000-0002-1378-2473</orcidid><orcidid>https://orcid.org/0000-0003-0544-3911</orcidid></search><sort><creationdate>20220201</creationdate><title>Use of Triangulation in Comparing the Blockchain Knowledge Structure between China and South Korea: Scientometric Network, Topic Modeling, and Prediction Technique</title><author>Zhu, Yu-Peng ; Park, Han-Woo</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c295t-c3fc7f1f7a0d52e518fb93ce495ecbf4ac65f5702711f2fdd7951091bec4943</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Artificial intelligence</topic><topic>Automatic text analysis</topic><topic>Blockchain</topic><topic>Collaboration</topic><topic>Cooperation</topic><topic>Data mining</topic><topic>Digital currencies</topic><topic>Knowledge</topic><topic>Learning algorithms</topic><topic>Machine learning</topic><topic>Modelling</topic><topic>Network analysis</topic><topic>R&D</topic><topic>Research & development</topic><topic>Research methodology</topic><topic>Scientometrics</topic><topic>Sustainability</topic><topic>Technology</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhu, Yu-Peng</creatorcontrib><creatorcontrib>Park, Han-Woo</creatorcontrib><collection>CrossRef</collection><collection>University Readers</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Access via ProQuest (Open Access)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><jtitle>Sustainability</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zhu, Yu-Peng</au><au>Park, Han-Woo</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Use of Triangulation in Comparing the Blockchain Knowledge Structure between China and South Korea: Scientometric Network, Topic Modeling, and Prediction Technique</atitle><jtitle>Sustainability</jtitle><date>2022-02-01</date><risdate>2022</risdate><volume>14</volume><issue>4</issue><spage>2326</spage><pages>2326-</pages><issn>2071-1050</issn><eissn>2071-1050</eissn><abstract>Blockchain, as a new innovative technology, has become a popular topic in many fields in recent years. In this study, triangulation was used to investigate the development of knowledge structures. First, scientometric network analysis was employed to identify the cooperation of knowledge networks. It was found that the structure of blockchain knowledge networks in China is relatively more complex and diverse than in South Korea. Since increased teamwork in blockchain is conducive to the creation of high-quality knowledge products, the Chinese government appears to strongly promote diversified cooperation on blockchain technology through centralized policies. Second, machine-learning topic modeling was used to analyze the content exchanged via a collaborative network. As a result, it was found that both countries lacked the societal and commercial aspects of blockchain technology. Finally, we developed a prediction technique based on the Ernie model to automatically categorize the nature of blockchain research.</abstract><cop>Basel</cop><pub>MDPI AG</pub><doi>10.3390/su14042326</doi><orcidid>https://orcid.org/0000-0002-1378-2473</orcidid><orcidid>https://orcid.org/0000-0003-0544-3911</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 2071-1050 |
| ispartof | Sustainability, 2022-02, Vol.14 (4), p.2326 |
| issn | 2071-1050 2071-1050 |
| language | eng |
| recordid | cdi_proquest_journals_2633180963 |
| source | MDPI - Multidisciplinary Digital Publishing Institute; EZB-FREE-00999 freely available EZB journals |
| subjects | Artificial intelligence Automatic text analysis Blockchain Collaboration Cooperation Data mining Digital currencies Knowledge Learning algorithms Machine learning Modelling Network analysis R&D Research & development Research methodology Scientometrics Sustainability Technology |
| title | Use of Triangulation in Comparing the Blockchain Knowledge Structure between China and South Korea: Scientometric Network, Topic Modeling, and Prediction Technique |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-28T12%3A13%3A36IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Use%20of%20Triangulation%20in%20Comparing%20the%20Blockchain%20Knowledge%20Structure%20between%20China%20and%20South%20Korea:%20Scientometric%20Network,%20Topic%20Modeling,%20and%20Prediction%20Technique&rft.jtitle=Sustainability&rft.au=Zhu,%20Yu-Peng&rft.date=2022-02-01&rft.volume=14&rft.issue=4&rft.spage=2326&rft.pages=2326-&rft.issn=2071-1050&rft.eissn=2071-1050&rft_id=info:doi/10.3390/su14042326&rft_dat=%3Cproquest_cross%3E2633180963%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2633180963&rft_id=info:pmid/&rfr_iscdi=true |