Monthly Archives: August 2019

A Structural Hole Theory-Guided Computational Framework for Opportunity Measurement: A Case of IPO Success

Lee, Myunghwan, Gene Moo Lee, Hasan Cavusoglu, Marc-David L. Seidel. “A Structural Hole Theory-Guided Computational Framework for Opportunity Measurement: A Case of IPO Success”, [Latest version: March 2026]

  • Previous title: Strategic Competitive Positioning: Unsupervised Operationalization of a Structural Hole-based Firm-specific Construct
  • doc2vec model of 10-K reports: Link
  • Presented at UBC MIS Seminar 2018, CIST 2019 (Seattle, WA), KrAIS 2019 (Munich, Germany), DS 2021 (online), KrAIS 2021 (Austin, TX), UT Dallas 2022, KAIST 2022, Korea Univ 2022, INFORMS 2022 (Indianapolis, IN)
  • 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.

Books on Analytics Methodologies

  1. Data Science and Analysis
    1. Provost and Fawcett (2013) Data Science for Business
    2. Grus (2015) Data Science from Scratch: First Principles with Python
    3. Python for Data Analysis
    4. Jupyter notebooks
  2. How to collect the right data?
    1. Savoia (2019) The Right It: Why so many ideas fail and how to make sure yours succeed
      1. How to collect data in the early-stage product ideation

Recommended Books on “How technology is changing the industry and society?”

Book Review Assignment:
  1. Read one of the following books during the course.
  2. Write a book review with the following questions:
    1. Why did you select this book?
    2. Write a brief summary of the book.
    3. What did you learn from this book? Did you get a new idea from this?
Recommended books on “How technology is changing the industry and society”
  1. Andrew McAfee and Erik Brynjolfsson (2017) Machine, Platform, Crowd: Harnessing Our Digital Future. Link: Norton
  2. Kartik Hosanagar (2019). A Human’s Guide to Machine Intelligence: How algorithms are shaping our lives and how we can stay in control. Link: Penguin Random House
  3. Cathy O’Neil (2016). Weapons of Math Destruction: How Big Data increases inequality and threatens democracy. Link: Penguin Random House
  4. Michael D. Smith and Rahul Telang (2016) Streaming, Sharing, Stealing: Big Data and the Future of Entertainment. Link: MIT Press.
  5. Ajay Agrawal, Joshua Gans, and Avi Goldfarb (2018) Prediction Machines: The Simple Economics of Artificial Intelligence. Link: Book website
  6. Anindya Ghose (2017). Tap: Unlocking the Mobile Economy. Link: MIT Press.
  7. Arun Sundararajan (2016) The Sharing Economy: The end of employment and the rise of crowd-based capitalism. Link: MIT Press
  8. Eric Topol (2019) Deep Medicine: How AI can make healthcare human again. Link: Basic Books.

Discussion: Your Tech/Analytics Story

Your Tech/Analytics Story

  • Objective: To understand student’s prior experience and expectation of the course
  • Ask students to describe their experiences on technology and analytics
    • What prior work/school project experience have you had that required data analysis?
    • Programming experiences?
      • R, Stata, Excel, Tableau, SQL, Python, SPSS/SAS, Matlab)
      • Scale: 0 (none), 1 (some familiarity), 2 (used in the project), 3 (strong)
    • What do you want to learn about tech/analytics in this course?
    • What is the most interesting thing you heard about tech/analytics in the past one year?
  • Debrief
    • Collect text data
    • Show word cloud, sentiment analysis, LDA topics