Monthly Archives: August 2019

Strategic Competitive Positioning: A Structural Hole-based Firm-level Opportunity Construct for Information Systems Research

Lee, Myunghwan, Gene Moo Lee, Hasan Cavusoglu, Marc-David L. Seidel. “Strategic Competitive Positioning: A Structural Hole-based Firm-level Opportunity Construct for Information Systems Research”, [Latest version: Nov 27, 2024]

  • 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

We build on Burt’s structural hole concept to theorize a firm-specific strategic competitive positioning (SCP) construct for information systems (IS) research. Using unsupervised document embeddings, we operationalize the SCP construct to capture a firm’s relative competitive and strategic positioning in a similarity matrix of U.S. public firms based on their annual reports. Our construct dynamically captures competitive positioning across firms and years, relying on neither artificially bounded industry classification systems nor significant expert intervention to construct the measure, ensuring a more efficient and adaptable approach. We demonstrate the effectiveness of this construct through a series of empirical analyses investigating the effects of SCP on firm value and survival. The results show that our measure outperforms existing measures in successfully predicting post-IPO performance. This paper makes significant contributions to the IS literature by proposing an organizational theory-based unsupervised approach to dynamically conceptualize and measure firm-level strategic competitive positioning from unstructured corporate disclosure documents.

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