Journal of Management Information Systems Vol. 43 No. 3 2026
Computational Framework for Measuring Strategic Opportunities Based on Structural Hole Theory
Abstract
Although opportunities are central to firm innovation and performance, prior research lacks a scalable, theory-grounded approach to measuring them. Existing measures are either context-specific or detached from explicit relational mechanisms, limiting their generalizability and interpretability. Leveraging computational methods including text analytics, unsupervised machine learning, and network analysis of large-scale digitized data, we propose a computational design framework guided by structural hole theory that enables fine-grained strategic opportunity measures: hole-opening, hole-entering, and non-hole positions. We validate this framework through systematic analysis of initial public offering (IPO) outcomes using U.S. public firm panel data. The results show that hole-opening positions are associated with higher post-IPO valuations, but with a lower likelihood of mergers and acquisitions (M&A) exits, whereas hole-entering and non-hole positions are linked to lower IPO valuations but higher probabilities of M&A outcomes. These patterns reveal distinct opportunity roles based on firms’ relative structural positions. This computational framework contributes to IS research by offering a replicable, theory-driven foundation for opportunity measurement.
- DOI
- 10.1080/07421222.2026.2692278
- Volume
- 43
- Issue
- 3
- Pages
- 923-957
- Language
- en
- Sources
- openalex crossref