Despite decades of initiatives to address healthcare inequities in the U.S., disparities across race, gender, geography, and income remain stubbornly persistent.

Workplace stress, on the rise for decades, has been treated by many organizations as a personal issue instead of a business-critical risk that merits executive oversight.

How companies market their AI systems affects the repercussions they face when their products fail. Marketers must promote their AI products with potential failure in mind.

We study how media firms can use LLMs to generate news content that aligns with multiple objectives—making content more engaging while maintaining a preferred level of polarization/slant consistent with the firm’s editorial policy.

The use of large language models (LLMs) in consumer research is rapidly evolving, with applications including synthetic data generation, data analysis, and more.

Chatbots are now able to engage in sophisticated conversations with consumers in the domain of relationships, providing a potential coping solution to widescale societal loneliness.

Training investments are essential for improving worker and firm productivity, yet their implementation is often hindered by low participation rates and insufficient worker engagement.

This paper uses novel, firm-level communication measures derived from communications metadata several months before and after a CEO transition for 102 firms to study whether and how this organizational event is reflected in employees’ communication flows.

We examine how artificial intelligence transforms the core pillars of collaboration— performance, expertise sharing, and social engagement—through a pre-registered field experiment with 776 professionals at Procter & Gamble, a global consumer packaged goods company.

We show that when firms compete via supply functions, transferring high-cost capacity to the largest, most efficient firm—thereby diversifying its production technologies while increasing concentration—can lower prices by prompting the leader to expand output and competitors to aggressively defend market shares.

This study explores public preferences for algorithmic and human decision-makers (DMs) in high-stakes contexts, how these preferences are shaped by performance metrics, and whether public evaluations of performance differ depending on the type of DM.

For as long as there has been work, there has been a “future of work,” through humans’ ingenuity and drive to get things done easier, faster, and better.

AI companion applications—designed to serve as synthetic interaction partners—have recently become capable enough to reduce loneliness, a growing public health concern.