Loading…
Data-Driven Insights into the Industrial Transformation Literature
This paper addresses the burgeoning challenge of navigating the expansive literature, particularly within industrial transformation and innovation. Given the multidisciplinary nature of this research area, which spans technological, economic, and organizational studies, the volume of relevant public...
Saved in:
Main Authors: | , , , |
---|---|
Format: | Conference Proceeding |
Language: | English |
Subjects: | |
Online Access: | Request full text |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
cited_by | |
---|---|
cites | |
container_end_page | 9 |
container_issue | |
container_start_page | 1 |
container_title | |
container_volume | |
creator | Kling, Nico Kling, Chantal Reuther, Kevin Ungerer, Christina |
description | This paper addresses the burgeoning challenge of navigating the expansive literature, particularly within industrial transformation and innovation. Given the multidisciplinary nature of this research area, which spans technological, economic, and organizational studies, the volume of relevant publications has grown significantly, necessitating efficient literature review methodologies. In response, the authors advocate for a no-code text mining approach that leverages word embedding, cosine distance calculations, complete linkage hierarchical clustering, and rapid automatic keyword extraction. This methodology is applied to a dataset comprising of 2.742 peer-reviewed journal articles from Scopus, focusing on their abstracts, keywords, and titles as the corpus. Through this approach, the paper systematically dissects the prevailing discourse, identifying key thematic clusters that encapsulate the current research landscape's methodological, technological, security-related, and business-oriented dimensions. The authors highlight a significant emphasis on sustainability, underscoring the integral role of digital technologies in fostering environmental stewardship alongside industrial innovation. |
doi_str_mv | 10.1109/ICE/ITMC61926.2024.10794375 |
format | conference_proceeding |
fullrecord | <record><control><sourceid>ieee_CHZPO</sourceid><recordid>TN_cdi_ieee_primary_10794375</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>10794375</ieee_id><sourcerecordid>10794375</sourcerecordid><originalsourceid>FETCH-ieee_primary_107943753</originalsourceid><addsrcrecordid>eNqFjrEKwjAUAKMgWLR_4BBwbn1JmrRZbRULunWXgE-NaCpJFPx7HXR2OrhbjpA5g5wx0Iu2Xi3ablcrprnKOfAiZ1DqQpRyQFJd6kpIEIoXQg5JwpUWWVVJOSZpCBcAEBwK0Dwhy8ZEkzXePtHR1gV7OsdArYs9jWf8mMMjRG_NlXbeuHDs_c1E2zu6tRG9iQ-PUzI6mmvA9MsJma1XXb3JLCLu797ejH_tf3viT34D3Q0_Mw</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>Data-Driven Insights into the Industrial Transformation Literature</title><source>IEEE Xplore All Conference Series</source><creator>Kling, Nico ; Kling, Chantal ; Reuther, Kevin ; Ungerer, Christina</creator><creatorcontrib>Kling, Nico ; Kling, Chantal ; Reuther, Kevin ; Ungerer, Christina</creatorcontrib><description>This paper addresses the burgeoning challenge of navigating the expansive literature, particularly within industrial transformation and innovation. Given the multidisciplinary nature of this research area, which spans technological, economic, and organizational studies, the volume of relevant publications has grown significantly, necessitating efficient literature review methodologies. In response, the authors advocate for a no-code text mining approach that leverages word embedding, cosine distance calculations, complete linkage hierarchical clustering, and rapid automatic keyword extraction. This methodology is applied to a dataset comprising of 2.742 peer-reviewed journal articles from Scopus, focusing on their abstracts, keywords, and titles as the corpus. Through this approach, the paper systematically dissects the prevailing discourse, identifying key thematic clusters that encapsulate the current research landscape's methodological, technological, security-related, and business-oriented dimensions. The authors highlight a significant emphasis on sustainability, underscoring the integral role of digital technologies in fostering environmental stewardship alongside industrial innovation.</description><identifier>EISSN: 2693-8855</identifier><identifier>EISBN: 9798350362435</identifier><identifier>DOI: 10.1109/ICE/ITMC61926.2024.10794375</identifier><language>eng</language><publisher>IEEE</publisher><subject>Bibliographies ; Couplings ; Economics ; Focusing ; Industrial Transformation ; Literature Review Methodology ; Navigation ; No-code ; Reviews ; Sustainable development ; Technological innovation ; Text mining</subject><ispartof>International ICE Conference on Engineering, Technology and Innovation (Online), 2024, p.1-9</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed><orcidid>0000-0003-4987-9645</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/10794375$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,776,780,785,786,27902,54530,54907</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/10794375$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Kling, Nico</creatorcontrib><creatorcontrib>Kling, Chantal</creatorcontrib><creatorcontrib>Reuther, Kevin</creatorcontrib><creatorcontrib>Ungerer, Christina</creatorcontrib><title>Data-Driven Insights into the Industrial Transformation Literature</title><title>International ICE Conference on Engineering, Technology and Innovation (Online)</title><addtitle>ICE/ITMC</addtitle><description>This paper addresses the burgeoning challenge of navigating the expansive literature, particularly within industrial transformation and innovation. Given the multidisciplinary nature of this research area, which spans technological, economic, and organizational studies, the volume of relevant publications has grown significantly, necessitating efficient literature review methodologies. In response, the authors advocate for a no-code text mining approach that leverages word embedding, cosine distance calculations, complete linkage hierarchical clustering, and rapid automatic keyword extraction. This methodology is applied to a dataset comprising of 2.742 peer-reviewed journal articles from Scopus, focusing on their abstracts, keywords, and titles as the corpus. Through this approach, the paper systematically dissects the prevailing discourse, identifying key thematic clusters that encapsulate the current research landscape's methodological, technological, security-related, and business-oriented dimensions. The authors highlight a significant emphasis on sustainability, underscoring the integral role of digital technologies in fostering environmental stewardship alongside industrial innovation.</description><subject>Bibliographies</subject><subject>Couplings</subject><subject>Economics</subject><subject>Focusing</subject><subject>Industrial Transformation</subject><subject>Literature Review Methodology</subject><subject>Navigation</subject><subject>No-code</subject><subject>Reviews</subject><subject>Sustainable development</subject><subject>Technological innovation</subject><subject>Text mining</subject><issn>2693-8855</issn><isbn>9798350362435</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2024</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNqFjrEKwjAUAKMgWLR_4BBwbn1JmrRZbRULunWXgE-NaCpJFPx7HXR2OrhbjpA5g5wx0Iu2Xi3ablcrprnKOfAiZ1DqQpRyQFJd6kpIEIoXQg5JwpUWWVVJOSZpCBcAEBwK0Dwhy8ZEkzXePtHR1gV7OsdArYs9jWf8mMMjRG_NlXbeuHDs_c1E2zu6tRG9iQ-PUzI6mmvA9MsJma1XXb3JLCLu797ejH_tf3viT34D3Q0_Mw</recordid><startdate>20240624</startdate><enddate>20240624</enddate><creator>Kling, Nico</creator><creator>Kling, Chantal</creator><creator>Reuther, Kevin</creator><creator>Ungerer, Christina</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope><orcidid>https://orcid.org/0000-0003-4987-9645</orcidid></search><sort><creationdate>20240624</creationdate><title>Data-Driven Insights into the Industrial Transformation Literature</title><author>Kling, Nico ; Kling, Chantal ; Reuther, Kevin ; Ungerer, Christina</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-ieee_primary_107943753</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Bibliographies</topic><topic>Couplings</topic><topic>Economics</topic><topic>Focusing</topic><topic>Industrial Transformation</topic><topic>Literature Review Methodology</topic><topic>Navigation</topic><topic>No-code</topic><topic>Reviews</topic><topic>Sustainable development</topic><topic>Technological innovation</topic><topic>Text mining</topic><toplevel>online_resources</toplevel><creatorcontrib>Kling, Nico</creatorcontrib><creatorcontrib>Kling, Chantal</creatorcontrib><creatorcontrib>Reuther, Kevin</creatorcontrib><creatorcontrib>Ungerer, Christina</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE/IET Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Kling, Nico</au><au>Kling, Chantal</au><au>Reuther, Kevin</au><au>Ungerer, Christina</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Data-Driven Insights into the Industrial Transformation Literature</atitle><btitle>International ICE Conference on Engineering, Technology and Innovation (Online)</btitle><stitle>ICE/ITMC</stitle><date>2024-06-24</date><risdate>2024</risdate><spage>1</spage><epage>9</epage><pages>1-9</pages><eissn>2693-8855</eissn><eisbn>9798350362435</eisbn><abstract>This paper addresses the burgeoning challenge of navigating the expansive literature, particularly within industrial transformation and innovation. Given the multidisciplinary nature of this research area, which spans technological, economic, and organizational studies, the volume of relevant publications has grown significantly, necessitating efficient literature review methodologies. In response, the authors advocate for a no-code text mining approach that leverages word embedding, cosine distance calculations, complete linkage hierarchical clustering, and rapid automatic keyword extraction. This methodology is applied to a dataset comprising of 2.742 peer-reviewed journal articles from Scopus, focusing on their abstracts, keywords, and titles as the corpus. Through this approach, the paper systematically dissects the prevailing discourse, identifying key thematic clusters that encapsulate the current research landscape's methodological, technological, security-related, and business-oriented dimensions. The authors highlight a significant emphasis on sustainability, underscoring the integral role of digital technologies in fostering environmental stewardship alongside industrial innovation.</abstract><pub>IEEE</pub><doi>10.1109/ICE/ITMC61926.2024.10794375</doi><orcidid>https://orcid.org/0000-0003-4987-9645</orcidid></addata></record> |
fulltext | fulltext_linktorsrc |
identifier | EISSN: 2693-8855 |
ispartof | International ICE Conference on Engineering, Technology and Innovation (Online), 2024, p.1-9 |
issn | 2693-8855 |
language | eng |
recordid | cdi_ieee_primary_10794375 |
source | IEEE Xplore All Conference Series |
subjects | Bibliographies Couplings Economics Focusing Industrial Transformation Literature Review Methodology Navigation No-code Reviews Sustainable development Technological innovation Text mining |
title | Data-Driven Insights into the Industrial Transformation Literature |
url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-31T08%3A32%3A47IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-ieee_CHZPO&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=Data-Driven%20Insights%20into%20the%20Industrial%20Transformation%20Literature&rft.btitle=International%20ICE%20Conference%20on%20Engineering,%20Technology%20and%20Innovation%20(Online)&rft.au=Kling,%20Nico&rft.date=2024-06-24&rft.spage=1&rft.epage=9&rft.pages=1-9&rft.eissn=2693-8855&rft_id=info:doi/10.1109/ICE/ITMC61926.2024.10794375&rft.eisbn=9798350362435&rft_dat=%3Cieee_CHZPO%3E10794375%3C/ieee_CHZPO%3E%3Cgrp_id%3Ecdi_FETCH-ieee_primary_107943753%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=10794375&rfr_iscdi=true |