Beyond connectivity: Artificial intelligence and the internationalisation of digital firms
Year: 2024
Type: article
Abstract: With the rapid development of artificial intelligence/machine learning technologies (AI/ML) it is now opportune to consider the potential for these technologies to create new strategies for internationalisation. In this paper we examine the implications of deploying AI technologies on the internationalisation process of firms. We show how firms can create digital interactions from which information about individual's revealed preferences can be imputed. The use of this information by a proprietary AI model, rather than using FDI designed to learn about the characteristics of country markets as proxies to predict the general behaviour of consumers located there, provides a new, country-agnostic way to enter foreign markets and adapt to differences among consumers based on segments of one. AI can, therefore, enable born-digital firms, and potentially other businesses where digitalisation can play a role, to build new kinds of competitive advantages based on data network effects. We illustrate these theoretical propositions with reference to value-creation activities of the short-video streaming born-digital TikTok. • By creating a digital space where users interact a firm can impute each user's revealed preferences. • Then by deploying AI/ML tools to analyse this data the firm to create additional value through personalisation when it enters a new market. • Foreign direct investment to learn experientially about country differences becomes unnecessary, enabling new, country-agnostic internationalisation strategies. • Country proxies for user preferences are replaced by personalisation based on user-centric segments-of-one. • This enables new sources of advantage based on AI and associated data network economies.
Source: Information and Organization
Authors: José F.P. dos Santos, Peter J. Williamson
Institutions: INSEAD, University of Cambridge
Language: English
Subfield: Strategy and Management
Domain: Social Sciences
Open Access status: hybrid
Estimated APC paid: $3,820
Funder: University of Cambridge
OpenAlex ID: W4403052069