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The application of text mining methods in innovation research: current state, evolution patterns, and development priorities
Author(s) -
Antons David,
Grünwald Eduard,
Cichy Patrick,
Salge Torsten Oliver
Publication year - 2020
Publication title -
randd management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.253
H-Index - 102
eISSN - 1467-9310
pISSN - 0033-6807
DOI - 10.1111/radm.12408
Subject(s) - data science , relevance (law) , computer science , field (mathematics) , biomedical text mining , set (abstract data type) , knowledge management , text mining , data mining , information retrieval , political science , mathematics , pure mathematics , law , programming language
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