Selected Publications

The purpose of this paper is to investigate how social media affects US retailers’ customer-based brand equity (CBBE) which is an important indicator of brand success. The authors find strong impacts of owned and earned social media on CBBE across the board. However, they find that owned social media harms CBBE of retailers dealing in hedonic and high involvement products. Whereas owned social media helps general retailers in building CBBE, it reduces CBBE of specialty retailers.
In Admin Sci., 2018

Companies that engage in corporate activism risk taking stances that reflect the values of their management but alienate key segments of a politically divided customer base. Our data suggests that company executives should think carefully about customers’ political affiliations and likelihood to engage in positive and negative word-of-mouth.
In HBR, 2018

Analyzing daily data for 45 brands in 21 sectors using vector autoregression models, we find that social media brand fan following improves brand awareness, purchase intent, and customer satisfaction. Earned social media engagement volume affects brand awareness and purchase intent but not customer satisfaction, while positive and negative valence have the largest effects on customer satisfaction. Owned social media increases brand awareness and customer satisfaction but not purchase intent, highlighting a nonlinear effect of brand-controlled social media.
In JM, 2018

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Jun 1, 2030 1:00 PM

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This week’s articles: A 60-Minutes Course on Fairness in Machine Learning Summary: The course focuses on the bias in machine learning because of humans! I think this is an important area of work. Model-Based Machine Learning Book Summary: This is actually not an article but an entire book. I have read a few pages of the book but I am not at a point where I can summarize anything!


The map below shows the year-on-year (YoY) percentage change in the total number of houses on sale in different zip codes in San Antonio.


Whenever I get time, I am going to post articles on machine learning that I read during a week. I thought today is a good day to start doing it. Using machine learning to index text from billions of images Summary: The article describes how Dropbox built a system to index images based on the text in those images. Dropbox used TensorFlow. Rosetta: Understanding text in images and videos with machine learning Summary: From the article - “[Rosetta] extracts text from more than a billion public Facebook and Instagram images and video frames (in a wide variety of languages), daily and in real time, and inputs it into a text recognition model that has been trained on classifiers to understand the context of the text and the image together.


The colors are from WSJ article I will use the colors extracted from the solid bar at the bottom of the image. How did I do that? I used ColorZilla addon for Firefox: Here are the hex codes for 7 colors I extracted: # Create a color palette from WSJ article wsjPal <- c('#65C1E8', '#D85B63', '#D680AD', '#5C5C5C', '#C0BA80', '#FDC47D', '#EA3B46') Try out a graph using the new color palette


Submission guideline You will submit: All code (not applicable if you used only Tableau) Datasets Slides in Powerpoint, Keynote, or HTML (if used) Tableau workbook (if used) All the above files should be zipped and submitted on Blackboard before 10:30 pm on 10/13 (i.e., the deadline is 2 days after the last class). The Zip file will have to be named as follows if you are from the day cohort:



I am scheduled to teach the following courses in fall 2018:

  • DA 6233 Section 1B: Data Analytics Visualization and Communication (MS in Data Analytics)
  • DA 6233 Section 2B: Data Analytics Visualization and Communication (MS in Data Analytics)
  • MKT 7043 Section 2: Seminar in Marketing Strategy (Marketing PhD)