不良研究所

础耻迟丑辞谤蝉:听Gregory Weitzner

Publication: Review of Finance
Volume 27, Issue 6, November 2023, Pages 2125鈥�2161.

Authors: Recep Yusuf Bekci, Mehmet Gumus and Sentao Miao.

Publication: Operations Research, Forthcoming.
Article in advance, published online: August 2, 2023.

When a Los Angeles law firm began adopting new artificial intelligence tools to automate contract reviews, it looked like junior lawyers and paralegals might be out of a job. Yet, even as these tools conducted increasingly complex tasks, the firm鈥檚 workforce didn鈥檛 suffer. However, this shift to AI taking on some tasks previously performed by humans forced the firm to reconfigure its organizational structure.

Congratulations to the 不良研究所 Desautels professors who received 2023 SSHRC Insight Grants.

The Social Sciences and Humanities Research Council of Canada (SSHRC) is the federal research funding agency that promotes and supports research and training in the humanities and social sciences.

Taha Havakhor (Principal Investigator) and Hyunji So (Co-Applicant) for 鈥淪ocial Media Use and Workforce Disparities: A Study of Gender Pay and Promotion Gaps鈥�

Authors: Sudha Mani, Vivek Astvansh and Kersi D. Antia

Publication:听听Journal of Marketing, Forthcoming, EXPRESS: First published online July 27, 2023

Abstract:

Authors: Warut Khern-am-nuai, Hyunji So, Maxime C. Cohen and Yossiri Adulyasak

Publication: Manufacturing & Service Operations Management, Forthcoming

Abstract:

Restaurant review platforms, such as Yelp and Tripadvisor, routinely receive large numbers of photos in their review submissions. These photos provide significant value for users who seek to compare restaurants. In this context, the choice of cover images (i.e., representative photos of the restaurants) can greatly influence the level of user engagement on the platform. Unfortunately, selecting these images can be time consuming and often requires human intervention. At the same time, it is challenging to develop a systematic approach to assess the effectiveness of the selected images. In this paper, we collaborate with a large review platform in Asia to investigate this problem. We discuss two image selection approaches, namely crowd-based and AI-based systems. The AI-based system we use learn complex latent image features, which is further enhanced by transfer learning to overcome the scarcity of labeled data. We collaborate with the platform to deploy our AI-based system through a randomized field experiment to carefully compare both systems. We find that the AI-based system outperforms the crowd-based counterpart and boosts user engagement by 12.43%-16.05% on average. We then conduct empirical analyses on observational data to identify the underlying mechanisms that drive the superior performance of the AI-based system. Finally, we infer from our findings that the AI-based system outperforms the crowd-based system for restaurants with a (i) longer tenure on the platform, (ii) limited number of user-generated photos, (iii) lower star rating, and (iv) lower user engagement during the crowd-based system.