Research
Published and Accepted
Attribute Sentiment Scoring With Online Text Reviews: Accounting for Language Structure and Missing Attributes
with Minkyung Kim and K. Sudhir
Published in Journal of Marketing Research (2022)
https://journals.sagepub.com/doi/pdf/10.1177/00222437211052500
Winner, Donald R. Lehmann Award, 2023
Honorable Mention John A. Howard ( American Marketing Association) Doctoral Dissertation , Winner, American Marketing Society (Mary Kay) Doctoral Dissertation Award , 1st Runner Up, EMAC Doctoral Dissertation Award, 2022
AI and AI-Human Based Screening and Selection for Salesperson Hiring Using Interview Videos
with Khai Chiong, Howard Dover and K. Sudhir
Accepted, Marketing Science (2024)
SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4137872
Artificial Intelligence Applications to Customer Feedback Research: A Review
with Shrabastee Banerjee and Peter Lee
Review of Marketing Research, Special Issue AI in Marketing (2022)
Revise and Resubmit
Labor unions, perceptions of service quality, and customer satisfaction: Evidence from Starbucks
with Minkyung Kim, Soohyun Kim (Doctoral Student, USC), and Isamar Troncoso
Major Revision, Marketing Science (2024)
Hybrid Marketing Research: Large Language Models as an Assistant
with Neeraj Arora and Yohei Nishimura (Doctoral Student, UW Madison)
Revision Requested, Journal of Marketing (2024)
Neither a Picasso nor a Da'Vinci: A Multi-modal Model of Artwork Pricing
with Sharmistha Sikdar and Nika Dogonadze (Sr. Data Engineer, Takeup.ai)
Revision Requested, Journal of Marketing (2024)
Manuscripts Under Review
When do consumers talk?
What’s in a Response? Uncovering Management Response Strategies and Their Impact on Future Ratings and Sales
with Hulya Karaman and Shrabastee Banerjee
Under Review
Patents
https://patents.justia.com/patent/11574266
Patent number: 11574266
Abstract: A human interaction replacement evaluation system analyzes actions taken by a user with an application on a client device that provides features to replace human interaction services with computer-based services. The results of the action provide an indication of the success of a particular action supported by the application (e.g., whether the action has a positive or negative effect on a key performance indicator) or an indication of how likely the user is to be ready to adopt a particular computer-based service. Recommendations are then provided to the user of the application or a manager of the application indicating actions to use, actions that have negative or positive effects on a key performance indicator, and so forth.
Type: Grant
Filed: August 6, 2020
Date of Patent: February 7, 2023
Assignee: Adobe Inc.
Inventors: Atanu R. Sinha, Ishita Sunity Kumar Chakraborty
Works in Progress
FEWD: A Fused Explainable Model using Wide and Deep Networks for Synthesizing Multi-Modal Content
with John Lalor and Vamsi Kanuri