Category Archives: Teaching Materials

My thoughts on AI, Big Data, and IS Research

Last update: June 10th, 2021

Recently, I had a chance to share my thoughts on how Big Data Analytics and AI will impact Information Systems (IS) research. Thanks to ever-growing datasets (public and proprietary) and powerful computational resources (cloud API, open-source projects), AI and Big Data will be important in IS research in the foreseeable future. If you are an aspiring IS researcher, I believe that you should be able to embrace this and take advantage of this.

First, AI and Big Data are powerful “tools” for IS research. It could be intimidating to see all the fancy new AI techniques. But they are just tools to analyze your data. You don’t need to reinvent the wheel to use them. There are many open-source projects in Python and R that you can use to analyze your data. Also, many cloud services (e.g., Amazon Rekognition, Google Cloud ML, Microsoft Azure ML) allow you to use pre-trained AI models at a modest cost (that your professors can afford). What you need is some working knowledge in programming languages like Python and R. And a high-level understanding of the idea behind algorithms.

Don’t shy away from hands-on programming. Using AI and Big Data tools may not be a competitive advantage in the long run because of the democratization of AI tools. However, I believe it will be the new baseline. So you need to have it in your research toolbox. Specifically, I believe that IS researchers should have a working knowledge of Python/R programming and Linux environment. I recommend these online courses: Data ScienceMachine LearningLinuxSQL, and NoSQL.

Second, AI and Big Data Analytics are creating a lot of interesting new “phenomenon” in personal lives, firms, and societies. How AI and robots will be adopted in the workplace and how that will affect the labor market? Are we losing our jobs? Or can we improve our productivity with AI tools? How AI will be used in professional services by the experts? What are the unintended consequences (such as biases, security, privacy, misinformation) of AI adoptions in the organization and society? And how can we mitigate such issues? There are so many new and interesting research questions.

In order to conduct relevant research, I think that IS researchers should closely follow the emerging technologies. Again, it could be hard to keep up with all the advances. I try to keep up to date by reading industry reports (from McKinsey and Deloitte) and listening to many podcasts (e.g., Freakonomics Radio, a16 Podcasts by Andreessen Horowitz, Lex Fridman Podcast, Stanford’s Entrepreneurial Thought Leaders, HBR’s Exponential View by Azeem Azhar).

I hope this post may help new IS researchers shape their research strategies. I will try to keep updating this post. Cheers!

 

 

IS / Marketing Papers on Visual Data Analytics (Image, Video)

Last update: July 19, 2021

With the advent of social media and mobile platforms, visual data are becoming the first citizen in big data analytics research. Compared to textual data that require significant cognitive efforts to comprehend, visual data (such as image and video) can easily convey the message from the content creator to the general audience. To conduct large-scale studies on such data types, researchers need to use machine learning and computer vision approaches. In this post, I am trying to organize studies in Information Systems, Marketing, and other management disciplines that leverage large-scale analysis of image and video datasets. The papers are ordered randomly:

  1. Zhou, M., Chen, G. H., Ferreira, P., Smith, M. D. (2021) “Consumer Behavior in the Online Classroom: Using Video Analytics and Machine Learning to Understand the Consumption of Video Courseware,” Journal of Marketing Research, forthcoming.
  2. Shin, D., He, S., Lee, G. M., Whinston, A. B., Cetintas, S., Lee, K.-C. (2020) “Enhancing Social Media Analysis with Visual Data Analytics: A Deep Learning Approach,” MIS Quarterly 44(4): 1459-1492[Details]
  3. Li, Y., Xie, Y. (2020) “Is a Picture Worth a Thousand Words? An Empirical Study of Image Content and Social Media Engagement,” Journal of Marketing Research 57(2): 1-19.
  4. Zhang, Q., Wang, W., Chen, Y. (2020) “Frontiers: In-Consumption Social Listening with Moment-to-Moment Unstructured Data: The Case of Movie Appreciation and Live comments,” Marketing Science 39(2).
  5. Liu, L., Dzyabura, D., Mizik, N. (2020) “Visual Listening In: Extracting Brand Image Portrayed on Social Media,Marketing Science 39(4): 669-686.
  6. Peng, L., Cui, G., Chung, Y., Zheng, W. (2020) “The Faces of Success: Beauty and Ugliness Premiums in E-Commerce Platforms,” Journal of Marketing 84(4): 67-85.
  7. Liu, X., Zhang, B., Susarla, A., Padman, R. (2020) “Go to YouTube and Call Me in the Morning: Use of Social Media for Chronic Conditions,” MIS Quarterly 44(1b): 257-283.
  8. Zhao, K., Hu, Y., Hong, Y., Westland, J. C. (2020) “Understanding Characteristics of Popular Streamers in Live Streaming Platforms: Evidence from Twitch.tv,” Journal of the Association for Information Systems, Forthcoming.
  9. Ordenes, F. V., Zhang, S. (2019) “From words to pixels: Text and image mining methods for service research,” Journal of Service Management 30(5): 593-620.
  10. Wang, Q., Li, B., Singh, P. V. (2018) “Copycats vs. Original Mobile Apps: A Machine Learning Copycat-Detection Method and Empirical Analysis,” Information Systems Research 29(2): 273-291.
  11. Lu, S., Xiao, L., Ding, M. (2016) “A Video-Based Automated Recommender (VAR) System for Garments,” Marketing Science 35(3): 484-510.
  12. Xiao, L., Ding, M. (2014) “Just the Faces: Exploring the Effects of Facial Features in Print Advertising,” Marketing Science 33(3), 315-461.
  13. Lysyhakov M, Viswanathan S (2021) “Threatened by AI: Analyzing users’ responses to the introduction of AI in a crowd-sourcing,” Working Paper.
  14. Park, S., Lee, G. M., Shin, D., Han, S.-P. (2020) “Targeting Pre-Roll Ads using Video Analytics,” Working Paper.
  15. Zhang, S., Lee, D., Singh, P. V., Srinivasan, K. (2018) “How Much is an Image Worth? Airbnb Property Demand Estimation Leveraging Large Scale Image Analytics,” CMU Working Paper.
  16. Choi, A., Ramaprasad, J., So, H. (2021) Does Authenticity of Influencers Matter? Examining the Impact on Purchase Decisions, Working Paper.
  17. Park, J., Kim, J., Cho, D., Lee, B. Pitching in Character: The Role of Video Pitch’s Personality Style in Online Crowdsourcing, Working Paper.
  18. Yang, J., Zhang, J., Zhang Y. (2021) First Law of Motion: Influencer Video Advertising on TikTok, Working Paper.
  19. Davila, A., Guasch (2021) Manager’s Body Expansiveness, Investor Perceptions, and Firm Forecast Errors and Valuation, Working Paper.
  20. Peng, L., Teoh, S. H., Wang, U., Yan, J. (2021) Face Value: Trait Inference, Performance Characteristics, and Market Outcomes for Financial Analysts, Working Paper.
  21. Zhang, S., Friedman, E., Zhang, X., Srinivasan, K., Dhar, R. (2020) Serving with a Smile on Airbnb: Analyzing the Economic Returns and Behavioral Underpinnings of the Host’s Smile,” Working Paper.
  22. Park, K., Lee, S., Tan, Y. (2020) “What Makes Online Review Videos Helpful? Evidence from Product Review Videos on YouTube,” UW Working Paper.
  23. Doosti, S., Lee, S., Tan, Y. (2020) “Social Media Sponsorship: Metrics for Finding the Right Content Creator-Sponsor Matches,” UW Working Paper.
  24. Koh, B., Cui, F. (2020) “Give a Gist: The Impact of Thumbnails on the View-Through of Videos,” KU Working Paper.
  25. Gunarathne, P., Rui, H., Seidman, A. (2019) “Racial Discrimination in Social Media Customer Service: Evidence from a Popular Microblogging Platform,” Rochester Working Paper.
  26. Hou J.R., Zhang J., Zhang K. (2018) Can title images predict the emotions and the performance of crowdfunding projects? Workshop on e-Business.

Observations and Strategies of Online Teaching

Last update: April 21, 2021

All of a sudden, instructors are in the situation to teach online. I am taking this opportunity to develop a hybrid model for effective teaching. In this post, I will summarize my observations, experiences, and possible solutions. A caveat is that I teach “technical” courses in business analytics, so some of the issues I discussed here may not be directly applicable to “qualitative” courses. Also, note that this post is a work-in-progress and may be updated in the future.

  1. Reading the class
    1. One challenge in online lectures is that it is hard to “read the class”.
    2. We can ask students to turn on their videos so that instructors can see their facial expressions and catch non-verbal cues.
    3. We can use the chat/poll features to get instant, short feedbacks (even shy students feel comfortable sharing their thoughts in this textual mode).
    4. Now that all class activities are online, instructors have access to detailed analytics data that can be used to read the class throughout the course (not necessarily an individual class meeting).
  2. Effectively delivering materials
    1. In an online situation, the attention span is really short. Thus we need to chunk lectures into 20-30 min pieces with 10-15 min lecture + 10-15 min individual/group exercise.
    2. The breakout group feature works really well. Students can clarify issues with each other during the breakout group time. TAs can help in this process as well.
    3. Sometimes, students may ask some “out-of-scope” questions. In online sessions, we can let TAs find the relevant information and post it in an online Q&A forum (I use Piazza).
    4. Just like in offline teaching, TAs and instructors can hold virtual office hours. Sharing screen works really well.
  3. Building high-touch community
    1. One of the downsides of having online classes is that students don’t have opportunities to build a personal connection with the professor and with each other.
    2. We can create “introduction videos” to build relationships.
    3. For ice-breaking purposes, when starting the online lecture session, instructors can enter the session 5-10 min before the lecture starts (just like we do in offline lectures).
    4. Online forums (e.g., Piazza) can facilitate peer interactions.
    5. Finally, online environments allow us to invite virtually any guest speakers from all around the world. We can easily invite high-profile speakers and alumni to online class sessions. Universities can create a lot of value by leveraging the alumni network.
  4. Course participation
    1. One challenge I faced was the objective measure of the course participation events. I recorded all the chat history and asked TAs to count how many times each student verbally asked questions or made comments. As I used Piazza as the Q&A forum, I also incorporated the question/comment/endorsement counts from its analytics data.
    2. Some students questioned if we can use in-class chats or virtual office hour visits are counted. Whichever option an instructor chose, it has to be clearly stated in the course outline to avoid any confusion.
  5. Exam
    1. I used an open book/note exam given the nature of the subject.
    2. To avoid the possibility of collusion, I created multiple question banks for each subject and difficulty level. In Canvas, the exam is dynamically generated by picking random questions from the question banks. To implement this, I had to create 3x exam questions than a paper-based exam. In Canvas, the order of multiple choice answers can be randomized as well.
    3. One challenge is to inform students of any clarification issues in the exam. In case one student found an issue with the exam, it is hard to share this information with the whole class. So I decided not to handle any content issues during the exam time.

IS Papers on Big Data, Analytics, and AI

Last update: Sept 30, 2021

My research involves Big Data Analytics and AI in Information Systems literature. This post tries to keep track of the editorial and seminal articles on the topic of Big Data, Data Science, Analytics, and AI in the Information Systems and Management literature. The papers are listed in chronological order:

  1. Bapna, Goes, Gopal, Marsden (2006) Moving from Data-Constrained to Data-Enabled Research: Experiences and Challenges in Collecting, Validating and Analyzing Large-Scale e-Commerce Data, Statistical Science 21(2): 116-130.
  2. Shmueli and Koppius (2011) Predictive Analytics in Information Systems Research, MIS Quarterly 35(3): 553-572
  3. Chen, Chiang, Storey, (2012) Business Intelligence and Analytics: From Big Data to Big Impact, MIS Quarterly 36(4): 1164-1188
  4. Lin, Lucas Jr., Shmueli (2013) Research Commentary: Too Big to Fail: Large Samples and the p-Value Problem, Information Systems Research 24(4): 906-917.
  5. Agarwal, Dhar (2014) Editorial – Big Data, Data Science, and Analytics: The Opportunity and Challenge for IS Research, Information Systems Research 25(3): 443-448
  6. Varian (2014) Big Data: New Tricks for Econometrics, Journal of Economic Perspectives 28(2): 3-28
  7. Goes (2014) Editor’s Comments: Big Data and IS Research, MIS Quarterly 38(3): iii-viii
  8. Saar-Tsechansky (2015) Editors’ Comments: The Business of Business Data Science in IS Journals, MIS Quarterly 39(4): iii-vi
  9. AMJ Editors (2016) From the Editors: Big Data and Data Science Methods for Management Research, Academy of Management Journal 59(5): 1493-1507
  10. Abbasi, Sarker, Chiang (2016) Big Data Research in Information Systems: Toward an Inclusive Research Agenda, Journal of the Association for Information Systems 17(2): i-xxxii
  11. Rai (2016) Editor’s Comments: Synergies Between Big Data and Theory, MIS Quarterly 40(2): iii-ix
  12. Baesens, Bapna, Marsden, Vanthienen, Zhao (2016) Transformational Issues of Big Data and Analytics in Networked Business, MIS Quarterly 40(4): 807-818
  13. Athey (2017) Beyond Prediction: Using Big Data for Policy Problems, Science 355(6324): 483-485
  14. Chiang, Grover, Liang, Zhang (2018) Special Issue: Strategic Value of Big Data and Business Analytics, Journal of Management Information Systems 35(2): 383-387
  15. Delen, Ram (2018) Research challenges and opportunities in business analytics, Journal of Business Analytics 1(1): 2-12.
  16. Maass, Parsons, Puraro, Storey, Woo (2018) Data-Driven Meets Theory-Driven Research in the Era of Big Data: Opportunities and Challenges for Information Systems Research, Journal of the Association for Information Systems 19(12): 1253-1273
  17. Yang, Adomavicius, Burtch, Ren (2018) Mind the Gap: Accounting for Measurement Error and Misclassification in Variables Generated via Data Mining, Information Systems Research 29(1): 4-24.
  18. Berente, Seidel, Safadi (2019) Research Commentary: Data-Driven Computationally Intensive Theory Development, Information Systems Research 30(1), 50-64.
  19. Johnson, Gray, Sarker (2019) Revisiting IS Research Practice in the Era of Big Data, Information and Organization 29(1): 41-56
  20. Grover, Lindberg, Benbasat, Lyytinen (2020) The Perils and Promises of Big Data Research in Information Systems, Journal of the Association for Information Systems 21(2): 268-291.
  21. Shmueli (2021) INFORMS Journal of Data Science (IJDS) Editorial #1: What is an IJDS paper?, INFORMS Journal of Data Science.
  22. Burton-Jones, Boh, Oborn, Padmanabhan (2021) Editor’s Comments: Advancing Research Transparency at MIS Quarterly: A Pluralistic Approach, MIS Quarterly 45(2): iii-xviii.
  23. Berente, Gu, Recker, Santhanam (2021) Special Issue Editor’s Comments: Managing Artificial Intelligence, MIS Quarterly 45(3): 1433-1450.
  24. Jain, Padmanabhan, Pavlou, Raghu (2021) Editorial for the Special Section on Humans, Algorithms, and Augmented Intelligence: The Future of Work, Organizations, and Society, Information Systems Research 32(3): 675-687.

 

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

Public data sources

Note for prospective students and postdoc researchers

  • For current UBC students (Ph.D., Master, Undergraduate): For exceptional cases, there could be research opportunities. But students should have a working knowledge of Python programming and Linux environment. I recommend interested students to take these online courses: Data Science, Machine Learning, Linux, SQL, and NoSQL. In case you want to contact me, please read my papers, then describe the specific research topics you want to pursue.
  • For non-UBC students (Ph.D. and Master’s program applicants): Thank you for your interest in working with me. Unfortunately, I don’t have the capacity to work with non-UBC students. For possible collaboration, you first need to go through the formal admission process for UBC and Sauder School of Business before I can consider you as a potential collaborator. You do not need to have a commitment from an advisor prior to admission. Please check the related information on the Ph.D. program and the M.Sc. program. Also, please note that admission decisions are collectively made by Sauder school, not by individual faculty members.
  • Regarding postdoc positions or visiting students: Thank you for your interest in working with me as a postdoc or a visiting student. Unfortunately, I don’t have the capacity to host more researchers at the moment. You can find UBC faculty who are taking visiting scholars at https://www.grad.ubc.ca/virs/hosts.
  • Regarding the Visiting Associate Program at Centre for Korean Research, Institute of Asian Research: https://ckr.iar.ubc.ca/ckr-visiting-scholar-program/