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.

What is a realistic and positive outcome for US-China relations in the realm of global finance over the next ten years? While some policymakers, especially in the US, have feared that China holds ambitions for the renminbi (RMB) to replace the dollar as a global reserve currency, we argue that this is far from the case.

As consumers increasingly interact with AI applications specialized for social relationships, what is the nature and depth of these relationships among actual users, and can company actions influence these dynamics? We find that active users of the US-based AI companion, Replika, feel closer to their AI companion than even their best human friend, and anticipate mourning the loss of their AI companion more than any other technology.

Research involving multiple experiments found that consumers have biased views of their driving abilities relative to those of other drivers and automated vehicles.

From Apple to Merck to Wikipedia, more and more organizations are turning to crowds for help in solving their most vexing innovation and research questions, but managers remain understandably cautious.

We present the results of a field experiment conducted at Harvard Medical School to understand the extent to which search costs affect matching among scientific collaborators.

As predictive models seep into several real-world applications, it has become critical to ensure that individuals who are negatively impacted by the outcomes of these models are provided with a means for recourse.

Can accelerators pick the most promising startup ideas no matter their provenance? Using unique data from a global accelerator where judges are randomly assigned to evaluate startups headquartered across the globe, we show that judges are less likely to recommend startups headquartered outside their home region by 4 percentage points.

The chief principle of antitrust law and theory is that reducing market concentration—having more, smaller firms instead of fewer, bigger ones—reduces anticompetitive behavior.

We respond to calls in the strategy and international business literature for elucidating how multinational subsidiaries develop contextual intelligence in host countries and how they use the local context as a source of valuable opportunities for learning.

In this article, I review the theoretical and empirical literature on the effects of public and private equity markets on firm behavior, emphasizing the consequences that emerge from disclosure requirements, ownership concentration, and degree of firm standardization.

Fewer than 40% of companies that invest in AI see gains from it, usually because of one or more of these errors: (1) They don’t ask the right question, and end up directing AI to solve the wrong problem.

As machine learning black boxes are increasingly being deployed in critical domains such as healthcare and criminal justice, there has been a growing emphasis on developing techniques for explaining these black boxes in a post hoc manner.

What is the optimal form of firm organization during “bad times”? We present a model of delegation within the firm to show that the effect is ambiguous.