Preparedness for Data-Driven Business Model Innovation: A Knowledge Framework for Incumbent Manufacturers

Shailesh Tripathi; Nadine Bachmann; Manuel Brunner; Herbert Jodlbauer

doi:10.3390/app14083454

Preparedness for Data-Driven Business Model Innovation: A Knowledge Framework for Incumbent Manufacturers

Shailesh Tripathi, Nadine Bachmann, Manuel Brunner, Herbert Jodlbauer

Research Center Steyr

Research output: Contribution to journal › Article › peer-review

1 Citation (Scopus)

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.

Original language	English
Article number	3454
Journal	Applied Sciences (Switzerland)
Volume	14
Issue number	8
DOIs	https://doi.org/10.3390/app14083454
Publication status	Published - Apr 2024

Keywords

business model innovation
data-driven technology
incumbent manufacturer
knowledge framework
thematic synthesis
topic modeling

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title = "Preparedness for Data-Driven Business Model Innovation: A Knowledge Framework for Incumbent Manufacturers",

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{\textquoteright} 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.",

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AB - 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.

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Preparedness for Data-Driven Business Model Innovation: A Knowledge Framework for Incumbent Manufacturers

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