Monthly Archives: August 2019

Strategic Competitive Positioning: An Unsupervised Structural Hole-based Firm-specific Measure

Lee, Myunghwan, Gene Moo Lee, Hasan Cavusoglu, Marc-David L. Seidel. “Strategic Competitive Positioning: Unsupervised Operationalization of a Structural Hole-based Firm-specific Construct”, [Latest version: Aug 15, 2023]

  • 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

In this paper, we build on the network structural hole concept of organizational theory to theorize an individual firm-specific strategic competitive positioning (SCP) construct. We use unsupervised document embedding approaches to operationalize the SCP construct by capturing each firm’s relative competitive and strategic positioning in a strategic similarity matrix of all existing U.S. publicly traded firms’ annual corporate filings. This approach enables us to construct a theoretically driven firm-level SCP measure with minimal human expert intervention. Our construct dynamically captures competitive positioning across different firms and years without using artificially bounded and often outdated industry classification systems. We illustrate how the dynamic measure captures industry-level and cross-industry strategic changes. Then, we demonstrate the effectiveness of our construct with an empirical analysis showing the imprinting and dynamic effects of SCP on firm performance. The results show that our dynamic SCP measure outperforms existing competition measures and successfully predicts post-IPO performance. This paper makes significant contributions to the information systems and organizations literatures by proposing an organizational theory-based unsupervised approach to dynamically conceptualize and measure firm-level strategic competitive positioning. The construct can be easily applied to firm-specific, industry-level, and cross-industry research questions in many contexts across many disciplines.

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