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The Effect of Robot Adoption on Profit Margins

Accepted version
Peer-reviewed

Type

Article

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Abstract

This study examines the relationship between the degree of robot adoption and profit margins at country–industry level for 25 European Union (EU) countries during the 1995–2017 period. We find evidence of a negative and U-shaped relationship – robot adoption is negatively associated with profit margins, up to a point, before displaying a positive relationship upon a further increase in robot adoption. We suggest that this is caused by robots affecting the industry’s product life cycle on the one hand, and how firms select competitive strategy, on the other. At low levels of robot adoption, firms use robots to reduce costs via process innovation; at high levels of robot adoption, the technology is applied to increase revenue via product innovation. These mirror cost leadership and market differentiation, respectively, in Porter’s competitive strategy theory. We conducted markup-based analysis to provide further support for our explanation of the observed U-shaped relationship, as well as corroborating evidence from interviewing a major medical equipment manufacturer in the United States. By viewing robots as an emerging advanced manufacturing technology (AMT) in the process of displacing more traditional manufacturing methods, we bring together the theories of technological transition, the product life-cycle model of dominant design, and Porter’s competitive strategy in the context of robot adoption, with implications for both theory and practice.

Description

Keywords

Competitive strategy, product life cycle, profit margins, robotics, technological transition

Journal Title

IEEE Transactions on Engineering Management

Conference Name

Journal ISSN

0018-9391
1558-0040

Volume Title

Publisher

Institute of Electrical and Electronics Engineers (IEEE)
Sponsorship
Engineering and Physical Sciences Research Council (EP/R024367/1)
EPSRC (via University of Nottingham) (EP/T024429/1)
Department for Business, Energy and Industrial Strategy (EP/V062123/1)
ESRC (via University of Manchester) (R125208)
This work was supported by funding from the Engineering and Physical Sciences Research Council (EP/R024367/1, EP/K039598/1 and EP/V062123/1) and the Economic and Social Research Council – The Productivity Institute (ES/V002740/1).