Selected Publications

As an increasing number of executives use their company soapboxes to espouse values unrelated to the businesses they run, they must think carefully about how their actions align with the attitudes of their customers. 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. Actions that push brands into vulnerable or dissonant territory are likely to hurt revenue and growth. Staying out of the political fray allows companies to avoid the risk of alienating customers, but may result in only modest financial performance. It takes a long time for companies to build successful brands, and mega brands have both Republican and Democrat customers. Failure to understand how corporate activism may affect their attitudes is a mistake.
In HBR, 2018

Although research has examined the social media–shareholder value link, the role of consumer mindset metrics in this relationship remains unexplored. To this end, drawing on the elaboration likelihood model and accessibility/diagnosticity perspective, the authors hypothesize varying effects of owned and earned social media (OSM and ESM) on brand awareness, purchase intent, and customer satisfaction and link these consumer mindset metrics to shareholder value (abnormal returns and idiosyncratic risk). Analyzing daily data for 45 brands in 21 sectors using vector autoregression models, they find that brand fan following improves all three mindset metrics. ESM engagement volume affects brand awareness and purchase intent but not customer satisfaction, while ESM positive and negative valence have the largest effects on customer satisfaction. OSM increases brand awareness and customer satisfaction but not purchase intent, highlighting a nonlinear effect of OSM. Interestingly, OSM is more likely to increase purchase intent for high involvement utilitarian brands and for brands with higher reputation, implying that running a socially responsible business lends more credibility to OSM. Finally, purchase intent and customer satisfaction positively affect shareholder value.
In JM, 2018

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The MS in Data Analytics (MSDA) program at the University of Texas at San Antonio (UTSA) is all set to welcome the third batch of students this fall. The program attracts many talented applicants globally. However, due to resource constraints, we can admit only a small number of students. The program has two cohorts—daytime and evening. This year we expect to admit anywhere between 80 to 100 students in the two cohorts combined.


This a short tutorial for the incoming students of UTSA’s MS in Data Analytics program. I am going to assume that the reader has no knowledge of R and RStudio, the Integrated Development Environment (IDE), which we use to code. If you are a Mac user and you are comfortable with command line tools using Terminal, I suggest taking more systematic route for preparing your MacOS for R installation using Homebrew.


This post accompanies the article “Rejoinder to ‘Endogeneity bias in marketing research: Problem, causes and remedies’” in Industrial Marketing Management (IMM). We restate some sections of the main text for completeness in this document. Throughout we include R code to run the 2SLS estimations, create graphs, and generate the datasets. Model setting Before we start working on simulated data, let’s understand how much bias we expect if we use each of OLS, ZKHL, and 2SLS.


I often need to create a list consisting of several data frames. A simple example is when you read an Excel file with multiple worksheets. Rather than reading the sheets one at a time and row binding them as you go, it’s often faster to read all the sheets into a list as separate data frames and then row bind them all at once. Another example is when you are storing data frames as they are returned by a website such as Facebook.



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)



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