Lee, Myunghwan, Gene Moo Lee, Hasan Cavusoglu, Marc-David L. Seidel (2026) “Computational Framework for Measuring Strategic Opportunities Based on Structural Hole Theory“, Journal of Management Information Systems, Forthcoming.
- Dataset: https://github.com/MyunghwanLee88/Hole-Opportunity-Position-HOP
- Part of Myunghwan Lee‘s Dissertation
- doc2vec model of 10-K reports: Link
- Presented at UBC MIS 2018, CIST 2019, KrAIS 2019, DS 2021, KrAIS 2021, UT Dallas 2022, KAIST 2022, Korea Univ 2022, INFORMS 2022
- Funded by Sauder Exploratory Grant 2019
- Research assistants: Raymond Situ, Sahil Jain
Although opportunities play a central role in 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. To address this gap, we propose a structural hole theory-guided computational design framework that enables fine-grained strategic opportunity measures: hole-opening, hole-entering, and non-hole positions. We demonstrate the effectiveness of this framework through a systematic analysis of IPO outcomes using panel data on U.S. public firms. We find that hole-opening positions are associated with higher post-IPO valuations, but a lower likelihood of 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 highlight distinct opportunity roles embedded in firms’ structural positions. We conclude the paper by discussing the broad applicability of the theory-guided computational framework for opportunity measurement in various IS research contexts.