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

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

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

An Empirical Meta-analysis of E-commerce A/B Testing Practices

Working Papers

Firms often have many competing innovations that are possible for them to implement, but limited information about which one is likely to yield the highest return. An emerging strategy for evaluating various innovations is A/B testing, which gives firms a data-driven strategy for making strategic innovation decisions. 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

How Do Recommender Systems Affect Sales Diversity? A Cross-Category Investigation via Randomized Field Experiment

Working Papers

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

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

Media Exposure through the Funnel: A Model of Multi-Stage Attribution

Working Papers

In this paper, we address the problem of advertising attribution by developing a Hidden Markov Model (HMM) of an individual consumer’s behavior based on the concept of a conversion funnel. We apply the model to a unique dataset from the online campaign for the launch of a car.Read More

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

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