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Prior knowledge, industry 4.0 and digital servitization. An inductive framework.
- Source :
- Technological Forecasting & Social Change; Oct2021, Vol. 171, pN.PAG-N.PAG, 1p
- Publication Year :
- 2021
-
Abstract
- • Digital servitization impacts on the renewal of corporate business models. • Digital servitization is affected by the prior knowledge of the firm. • Our findings outline four ideal-types of business models using prior knowledge. Over the last few years digital servitization has become a very popular topic in the industrial marketing and technology management literature. The present article contributes to the extant literature on business models for digital servitization by investigating the roles and effects of prior technological knowledge. To date, this rich and growing body of literature has underestimated a crucial corporate asset for value creation, and that is firms' past experience and knowledge. Such a corporate heritage may have relevant implications for a firm's approach and decisions regarding digital servitization, however, especially if it is related to one (or more) of the I4.0 technologies. The research question posed in the present article is thus: how does a company's prior knowledge affect its digital servitization strategies? To answer this question, we conducted a multiple case study, collecting and analyzing primary and secondary data about Italian medium- to large-sized enterprises that had recently implemented digital servitization. The findings illustrate the different effects of the technological solutions adopted on the companies' business models, and delineate an inductive matrix with four different ideal-typical business models: expert industrializer; explorative solutioner; explorative industrializer; and expert solutioner. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00401625
- Volume :
- 171
- Database :
- Supplemental Index
- Journal :
- Technological Forecasting & Social Change
- Publication Type :
- Academic Journal
- Accession number :
- 151560881
- Full Text :
- https://doi.org/10.1016/j.techfore.2021.120963