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Antecedent configurations and performance of business models of intelligent manufacturing enterprises.
- Source :
- Technological Forecasting & Social Change; Aug2023, Vol. 193, pN.PAG-N.PAG, 1p
- Publication Year :
- 2023
-
Abstract
- Tackling climate change crises needs intelligent manufacturing and effective business models. This paper uses the adaptive structuration theory (AST) and configuration perspective to investigate the effects of digital infrastructure, digital orientation, top management team heterogeneity, servitization, government support, and customer demand uncertainty on the business model of intelligent manufacturing enterprises in China. The fuzzy set qualitative comparative analysis (fsQCA) was employed to analyze the data. The study found that there were five configurations of business model formation, which classified business models into five types: executive-led enhanced, digital leadership-enhanced, adaptive, extended, and complex business models. There was an intrinsic relationship between the five models with respect to the dimensions of digitalization and servitization. Further analysis revealed that executive-led, digital leadership-enhanced, and adaptive business models were positively associated with enterprise performance. The paper discusses the potential implications of these findings. • There are five formation pathways of the business model. • An interrelated relationship among the models regarding the digitalization and servitization • Business models influenced by digitization make enterprises more sensitive to climate change. • Enhanced and adaptive business models are associated with enterprise performance. [ABSTRACT FROM AUTHOR]
- Subjects :
- BUSINESS models
MANUFACTURING industries
DIGITAL technology
ARTIFICIAL intelligence
Subjects
Details
- Language :
- English
- ISSN :
- 00401625
- Volume :
- 193
- Database :
- Supplemental Index
- Journal :
- Technological Forecasting & Social Change
- Publication Type :
- Academic Journal
- Accession number :
- 164261469
- Full Text :
- https://doi.org/10.1016/j.techfore.2023.122550