hero image
Panagiotis (Panos) Adamopoulos - Emory University, Goizueta Business School. Atlanta, GA, US

Panagiotis (Panos) Adamopoulos Panagiotis (Panos) Adamopoulos

Assistant Professor of Information Systems & Operations Management | Emory University, Goizueta Business School





Panagiotis (Panos) Adamopoulos is an Assistant Professor of Information Systems at the Goizueta Business School of Emory University.

Panos' research program studies how information systems and technological artifacts affect the user behavior and transform business and society. His research focuses on personalization, mobile and social commerce, and online education. Some of the main research questions his recent papers address include how to alleviate the over-specialization and concentration bias problems of personalization techniques (e.g., "filter bubbles"); what is the effectiveness of the different types of mobile recommendations; what is the business value of Internet-of-Things (IoT) in retail for different types of products; whether specific personality characteristics can accentuate or attenuate the effectiveness of word-of-mouth (WOM) in social media. Much of this research is grounded in big data employing data science and machine-learning techniques to leverage the abundance of unstructured data in social media, while combining these approaches with more conventional econometric and other quantitative methods as well as experimental research designs.

His research has appeared in peer-reviewed academic journals and conferences, including Information Systems Research (ISR), MIS Quarterly (MISQ), ACM Transactions on Intelligent Systems and Technology (ACM TIST), ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), and ACM Conference on Recommender Systems (RecSys).

Prior to joining the faculty at Emory, Adamopoulos was an Assistant Professor at the University of Minnesota. Before joining academia, Panos Adamopoulos worked as a senior Business Intelligence Engineer and Consultant with Relational S.A. and in Toyota as an Information Technology Business Analyst. Panos received his PhD from the Department of Information, Operations & Management Sciences (IOMS) of the Stern School of Business of New York University and his BSc from the Department of Management Science and Technology of the Athens University of Economics and Business, where he achieved the 2nd ranking in the history of the department.

Areas of Expertise (10)

Social Media

Information Systems

Recommender Systems

Social Commerce

Word of Mouth

Internet of Things


Machine Learning

Big Data

Data Science

Education (3)

New York University, Leonard N. Stern School of Business: PhD in Information Systems

Department of Information, Operations and Management Sciences

New York University, Leonard N. Stern School of Business: MPhil in Information Systems

Department of Information, Operations and Management Sciences

Athens University of Economics and Business, Greece: BSc in Information Systems and E-Business

Department of Management Science and Technology

In the News (2)

Estimating the Impact of User Personality Traits on Electronic Word-of-Mouth: Text-mining Social Media Platforms

Marketing Science Institute  

As users share opinions, choices, and decisions on social media, nurturing positive online word of mouth is a critical aim of marketing activity. In this report, Panos Adamopoulos, Anindya Ghose, and Vilma Todri examine whether personality traits of social media users attenuate or accentuate the effectiveness of WOM. Specifically, using recent advancements in big data and machine-learning techniques to extract information from unstructured textual content, they examine whether and how latent personality characteristics of a user affect purchases of actual products.

view more

How Will the Internet of Things Affect Retailing?

Marketing Science Institute  

To determine how an IoT channel might affect sales and what types of products will benefit most, Panagiotis Adamopoulos, Vilma Todri and Anindya Ghose analyze data from an online retailer that added an IoT channel for a wide range of different products in different geographic markets and at different times over a two-year period. The price of the products was the same across all the available selling channels and there was no additional cost to consumers for utilizing the IoT channel. Taking advantage of the variability in this “natural quasi-experiment” they compare sales for products available through the IoT channel (treatment) versus other similar or substitute products (controls).

view more