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Digital Transformation of the Automotive Industry: An Integrating Framework to Analyse Technological Novelty and Breadth
Surrey Business School, University of Surrey, Guildford, UK; Umeå School of Business, Economics and Statistics, Umeå University, Umeå, Sweden.ORCID iD: 0000-0002-7986-5423
Jönköping University, Jönköping International Business School. Centre for Innovation Research (CIRCLE), Lund University, Lund, Sweden.ORCID iD: 0000-0002-8861-8731
2023 (English)In: Industry and Innovation, ISSN 1366-2716, E-ISSN 1469-8390, Vol. 30, no 1, p. 67-102Article in journal (Refereed) Published
Abstract [en]

Research demonstrates that digital technologies stimulate industrial transformation by enabling new interdependencies with firms outside and across firm and industry boundaries. However, we know little about the degree of novelty and breadth of digital technologies that have the potential to transform industries. Understanding the degree of novelty (spanning from radical to incremental) and breadth (spanning from one sector to multiple sectors) of digital technologies is important for measuring their impact on industrial transformation. Through a topic modelling research approach on autonomous vehicle technology patents from firms operating in Sweden and a confirmatory survey with the inventors of those patents, this paper reveals 26 digital technology topics that are transforming the automotive industry. The digital technology topics are distributed across four ideal-typical technology categories for transformation: augmenting, spanning, transforming, and disrupting. This study illustrates the value of studying digital technologies’ transformative nature using an integrating framework; it reveals that digital technologies in the automotive industry have mainly incremental characteristics but that these characteristics provide necessary preconditions for the few more radical technologies to achieve their potential in transforming the industry.

Place, publisher, year, edition, pages
Taylor & Francis, 2023. Vol. 30, no 1, p. 67-102
Keywords [en]
Artificial intelligence, D83, Digital Technologies, Emerging technologies, Industry digitalization, O14, O32, O33, R4, Topic modelling
National Category
Business Administration
Identifiers
URN: urn:nbn:se:hj:diva-59340DOI: 10.1080/13662716.2022.2151873ISI: 000906320200001Scopus ID: 2-s2.0-85145475061Local ID: HOA;intsam;852233OAI: oai:DiVA.org:hj-59340DiVA, id: diva2:1724866
Funder
Marianne and Marcus Wallenberg Foundation, 2016.0014Available from: 2023-01-09 Created: 2023-01-09 Last updated: 2023-01-19Bibliographically approved

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Lopez-Vega, HenryMoodysson, Jerker

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