Journal
TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE
Volume 171, Issue -, Pages -Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.techfore.2021.120963
Keywords
Digital Servitization; Industry 4; 0; Prior Knowledge; Disruptive Technologies; Italy
Categories
Ask authors/readers for more resources
This article investigates the roles and effects of prior technological knowledge on business models for digital servitization. Findings from multiple Italian enterprise case studies show that firms' past experience and knowledge significantly affect their digital servitization strategies, leading to the identification of four distinct ideal-typical business models.
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available