How Do Product Attributes and Reviews Moderate the Impact of Recommender Systems through Purchase Stages?

Published Research

We investigate the impact of several different recommender algorithms (e.g., Amazon.com’s “Consumers who bought this item also bought”), commonly used in ecommerce and online services, on sales volume and diversity, using field experiment data on movie sales from a top retailer in North America. Read More

A Meta-Analytic Investigation of p-hacking in E-commerce Experimentation

Working Papers

Randomized controlled trials—often called A/B tests in industrial settings—are an increasingly important element in the management many organizations. Such experiments are meant to bring the benefits of scientific rigor and statistical measurement to the domain of managerial decision making. Read More

Do Ratings Cut Both Ways? Impact of Bilateral Ratings on Platforms

Working Papers

Traditional online platforms (e.g., Amazon Marketplace) use Unilateral Rating System (URS), where customers rate sellers. However, sharing economy platforms (e.g., Uber, Airbnb) have adopted Bilateral Rating System (BRS) that also allows service providers to rate customers, and even selects customers based on their ratings.Read More

Adoption of Predictive Analytics: Impact of Model Interpretability

Funded Research Proposal

One of the most important trends in business in recent years has been the growth of Big Data and predictive analytics. The trend started with traditional analytics and the emergence of decision support systems. With advances in machine learning (ML), systems can now take in large amounts of data, learn how human decision-makers have made decisions in the past, and make decisions autonomously (achieving human-level or superhuman performance in many activities). Read More

Strategies for Dynamic Experimentation

Funded Research Proposal

In this project, we seek to update the statistical paradigm for conducting A/B tests in the 21st century. In particular, we plan to develop a statistical framework that takes the guesswork out of deciding when to stop an experiment and to calibrate a firm’s decision thresholds based on their own historical data.Read More

Bias-aware AI for Human Capital Management: An Innovative Approach for Algorithmic Job Screening

Funded Research Proposal

A well-known maxim in management is that “your people are your greatest asset”. Recruitment strategies in particular have been linked to firms’ innovative capacity, emphasizing the importance of maintaining competitive advantages in HR as a key goal of effective innovation management. Read More

Network Overlap and Content Sharing on Social Media Platforms

Published Research

We study the impact of network overlap — the overlap in network connections between two users — on content sharing in directed social media platforms. We propose a hazards model that flexibly captures the impact of three different measures of network overlap (i.e., common followees, common followers and common mutual followers) on content sharing.Read More

Advertising Content and Consumer Engagement on Social Media: Evidence from Facebook

Published Research

We describe the effect of social media advertising content on customer engagement using data from Facebook. We content-code 106,316 Facebook messages across 782 companies, using a combination of Amazon Mechanical Turk and Natural Language Processing algorithms.Read More

Bilateral Rating Systems in Online Marketplaces: Design and Impact

Funded Research Proposal

In recent years, digital innovations have fundamentally changed how many services and resources are put together and deployed. These include marketplaces like Uber and Airbnb that help match decentralized supply with demand. Read More

Do Organic Results Help or Hurt Sponsored Search Performance?

Published Research

We study the impact of changes in the position of competing listings in organic search results on the performance of sponsored search advertisements. Using data for several keywords from an online retailer’s ad campaign, we measure the impact of organic competition on both click-through rate and conversion rate of sponsored search ads for these keywords.Read More

Will the Global Village Fracture into Tribes: Recommender Systems and Their Effects on Consumer Fragmentation

Published Research

Personalization is becoming ubiquitous on the World Wide Web. Such systems use statistical techniques to infer a customer’s preferences and recommend content best suited to him (e.g., “Customers who liked this also liked…”). A debate has emerged as to whether personalization has drawbacks.Read More