The application of text mining methods in innovation research: current state, evolution patterns, and development priorities.
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| Title: | The application of text mining methods in innovation research: current state, evolution patterns, and development priorities. |
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| Authors: | Antons, David1 (AUTHOR) antons@time.rwth-aachen.de, Grünwald, Eduard1 (AUTHOR) gruenwald@time.rwth-aachen.de, Cichy, Patrick1 (AUTHOR) cichy@time.rwth-aachen.de, Salge, Torsten Oliver1 (AUTHOR) salge@time.rwth-aachen.de |
| Source: | R&D Management. Jun2020, Vol. 50 Issue 3, p329-351. 23p. 1 Diagram, 4 Charts, 7 Graphs. |
| Subjects: | Knowledge management, Innovation management, Technological innovations, Biological evolution |
| Abstract: | Unstructured data in the form of digitized text is rapidly increasing in volume, accessibility, and relevance for research on innovation and beyond. While traditional attempts to analyze text (i.e., qualitative analysis) are limited in processing large amounts of data, text mining presents a set of approaches that allow researchers to explore large‐scale collections of texts in an efficient manner. Given the potential of text mining as a method of inquiry, the primary purpose of this manuscript is to enable both novice and more experienced innovation researchers to select, specify, document, and interpret text mining techniques in a way that generates valid and reliable knowledge for the innovation management community. This involved taking stock of text mining applications in the field of innovation research to date by means of a systematic review of 124 journal articles employing text mining techniques and are published in a basket of the 10 premier innovation management and 8 top general management journals. The results of the systematic manual and computational analysis of these articles do not only illustrate the state and evolution of text mining applications in our field, but also allow for evidence‐based recommendations regarding their future use. Here, our paper presents methodological, conceptual, and contextual development priorities that will contribute to establishing higher methodological standards in text mining and enhance the methodological richness in our field [ABSTRACT FROM AUTHOR] |
| Copyright of R&D Management is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 143218606 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: The application of text mining methods in innovation research: current state, evolution patterns, and development priorities. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Antons%2C+David%22">Antons, David</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> antons@time.rwth-aachen.de</i><br /><searchLink fieldCode="AR" term="%22Grünwald%2C+Eduard%22">Grünwald, Eduard</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> gruenwald@time.rwth-aachen.de</i><br /><searchLink fieldCode="AR" term="%22Cichy%2C+Patrick%22">Cichy, Patrick</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> cichy@time.rwth-aachen.de</i><br /><searchLink fieldCode="AR" term="%22Salge%2C+Torsten+Oliver%22">Salge, Torsten Oliver</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> salge@time.rwth-aachen.de</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22R%26D+Management%22">R&D Management</searchLink>. Jun2020, Vol. 50 Issue 3, p329-351. 23p. 1 Diagram, 4 Charts, 7 Graphs. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Knowledge+management%22">Knowledge management</searchLink><br /><searchLink fieldCode="DE" term="%22Innovation+management%22">Innovation management</searchLink><br /><searchLink fieldCode="DE" term="%22Technological+innovations%22">Technological innovations</searchLink><br /><searchLink fieldCode="DE" term="%22Biological+evolution%22">Biological evolution</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Unstructured data in the form of digitized text is rapidly increasing in volume, accessibility, and relevance for research on innovation and beyond. While traditional attempts to analyze text (i.e., qualitative analysis) are limited in processing large amounts of data, text mining presents a set of approaches that allow researchers to explore large‐scale collections of texts in an efficient manner. Given the potential of text mining as a method of inquiry, the primary purpose of this manuscript is to enable both novice and more experienced innovation researchers to select, specify, document, and interpret text mining techniques in a way that generates valid and reliable knowledge for the innovation management community. This involved taking stock of text mining applications in the field of innovation research to date by means of a systematic review of 124 journal articles employing text mining techniques and are published in a basket of the 10 premier innovation management and 8 top general management journals. The results of the systematic manual and computational analysis of these articles do not only illustrate the state and evolution of text mining applications in our field, but also allow for evidence‐based recommendations regarding their future use. Here, our paper presents methodological, conceptual, and contextual development priorities that will contribute to establishing higher methodological standards in text mining and enhance the methodological richness in our field [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of R&D Management is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=143218606 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1111/radm.12408 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 23 StartPage: 329 Subjects: – SubjectFull: Knowledge management Type: general – SubjectFull: Innovation management Type: general – SubjectFull: Technological innovations Type: general – SubjectFull: Biological evolution Type: general Titles: – TitleFull: The application of text mining methods in innovation research: current state, evolution patterns, and development priorities. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Antons, David – PersonEntity: Name: NameFull: Grünwald, Eduard – PersonEntity: Name: NameFull: Cichy, Patrick – PersonEntity: Name: NameFull: Salge, Torsten Oliver IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2020 Type: published Y: 2020 Identifiers: – Type: issn-print Value: 00336807 Numbering: – Type: volume Value: 50 – Type: issue Value: 3 Titles: – TitleFull: R&D Management Type: main |
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