Preparedness for Data-Driven Business Model Innovation: A Knowledge Framework for Incumbent Manufacturers
Source abstract
Abstract
This study investigates data-driven business model innovation (DDBMI) for incumbent manufacturers, underscoring its importance in various strategic and managerial contexts. Employing topic modeling, the study identifies nine key topics of DDBMI. Through qualitative thematic synthesis, these topics are further refined, interpreted, and categorized into three levels: Enablers, value creators, and outcomes. This categorization aims to assess incumbent manufacturers’ preparedness for DDBMI. Additionally, a knowledge framework is developed based on the identified nine key topics of DDBMI to aid incumbent manufacturers in enhancing their understanding of DDBMI, thereby facilitating the practical application and interpretation of data-driven approaches to business model innovation.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Big Data and Business Intelligence · Service and Product Innovation · Digital Transformation in Industry
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Josef Ressel Centre for Data-Driven Business Model Innovation, University of Applied Sciences Upper Austria, Wehrgrabengasse 1–3, 4400 Steyr, Austria
Faculty of Behavioural, Management and Social Sciences, Entrepreneurship and Technology Management, University of Twente, Drienerlolaan 5, 7522 NB Enschede, The Netherlands
CRET-LOG—Centre de Recherche sur le Transport et la Logistique, Aix Marseille Université, Avenue Gaston Berger 413, F-13625 Aix-en-Provence, France
Funding
Austrian Federal Ministry for Digital and Economic Affairs · National Foundation for Research, Technology and Development · Christian Doppler Research Association
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