This paper applies real options theory to explain how a firm’s AI specialization shapes its acquisition choices and the kinds of technology targets it successfully integrates. The authors show that deep internal AI expertise creates a portfolio of strategic options that make AI-target acquisitions more actionable, and that target attributes (R&D intensity, self-fluidity, and product-market overlap) change the attractiveness and exercisability of those options.
Authors & affiliations:
- Chi Hon Li (Tilburg University)
- Steven Boivie (Texas A&M University)
- Gerry McNamara (Michigan State University)
- Pok Man Tang (Hong Kong Shue Yan University)
