Faculty & Research

• HBS Book

The world has changed dramatically in just the past few years—and so has the game of negotiation. COVID-19, Zoom, political polarization, the online economy, increasing economic globalization, and greater workplace diversity—all have transformed the who, what, where, and how of negotiation. Today, traditional negotiating tactics, while still effective, need to be tailored to vastly different situations and circumstances. In Negotiation: The Game Has Changed, legendary Harvard Business School professor Max Bazerman, a pioneer in the field of negotiation, shows you how to negotiate successfully today by adapting proven negotiation principles and strategies to the challenging new contexts you face—from negotiating across cultural and political differences to trying to reach an agreement over Zoom or during a supply chain crisis.

• Management Science 71, no. 2 (February 2025): 1335-1355.

Through a large-scale field experiment with 393,036 customers considering opening a credit card account with a nationwide retail bank, we investigate how providing transparency into an offering’s tradeoffs affects subsequent rates of customer acquisition and long-run engagement. Although we find tradeoff transparency to have an insignificant effect on acquisition rates, customers who were shown each offering’s tradeoffs selected different products than those who were not. Moreover, prospective customers who experienced transparency and subsequently chose to open an account went on to exhibit higher quality service relationships over time. Monthly spending was 9.9% higher and cancellation rates were 20.5% lower among those who experienced transparency into each offering’s tradeoffs. Increased product usage and retention accrued disproportionately to customers with prior category experience: more- experienced customers who were provided transparency spent 19.2% more on a monthly basis and were 33.7% less likely to defect after nine months.

• D^3 Institute

The literature on communities of practice demonstrates that a proven way for senior professionals to upskill themselves in the use of new technologies that undermine existing expertise is to learn from junior professionals. It notes that juniors may be better able than seniors to engage in real-time experimentation close to the work itself, and may be more willing to learn innovative methods that conflict with traditional identities and norms. [...] In our study conducted with Boston Consulting Group, we interviewed 78 such junior consultants in July-August 2023 who had recently participated in a field experiment that gave them access to generative AI (GPT-4) for a business problem solving task. Drawing from junior professionals’ in situ reflections soon after the experiment, we argue that such juniors may fail to be a source of expertise in the use of emerging technologies for more senior professionals.

• Featured Case

Intuition Robotics, a startup that makes an AI companion robot to alleviate older adults’ loneliness, debates whether to pursue a B2C model or B2G route. If it opts for the government vertical, it must determine how to negotiate a favorable deal. Two weeks after launching ElliQ, Intuition Robotics leadership had to make a critical decision. Since its founding, the team believed that the most efficient path to widespread adoption of ElliQ was the business-to-consumer model, targeting older adults and their family members. However, soon after entering the U.S. consumer market, Intuition Robotics received significant interest from the New York State Office of the Aging. Company management considered whether a pivot to a limited business-to-government contract, fraught with complexities, would open the door for future contracts with government entities.

• Featured Case

This case describes two innovators in the Olympic sport of speed skating: the U.S. Men’s team, which devised a new approach to the team pursuit event following their disappointing performance in the 2018 Winter Olympics; and Nils van der Poel, a Swedish skater who responded to his own disappointing showing in 2018 with a new training plan that defied conventional wisdom. Both van der Poel and the U.S. Men’s Team saw promising initial results from their innovations. But they faced a decision: whether to reveal their new techniques. The U.S. Team’s strategy was easily imitated if competitors witnessed it in a race, but it was a risk not to test it in competition before the Olympics. If van der Poel shared his training plan, other athletes might use it to surpass him, but it also could enrich the sport as a whole. Should they share their techniques, and if so, when?

• HBS Working Paper

Generative AI is poised to reshape the labor market, affecting cognitive and white-collar occupations in ways distinct from past technological revolutions. This study examines whether generative AI displaces workers or augments their jobs by analyzing labor demand and skill requirements across occupations. Our findings reveal a heterogeneous effect: generative AI-driven automation reduces labor demand and skill requirements in structured cognitive-task jobs, while increasing both demand and skill complexity in positions that involve human-AI collaboration. These results highlight the importance of understanding generative AI's nuanced impact on the labor market and designing targeted policies to mitigate job displacement while supporting skills development for human-AI collaboration.

• Working Paper

We estimate the value employees place on remote work using revealed preferences in a high-stakes, real-world context, focusing on U.S. tech workers. On average, employees are willing to accept a 25% pay cut for partly or fully remote roles. Our estimates are three to five times that of previous studies. We attribute this discrepancy partly to methodological differences, suggesting that existing methods may understate preferences for remote work. Because of the strong preference for remote work, we expected to find a compensating wage differential, with remote positions offering lower compensation than otherwise identical in-person positions. However, using novel data on salaries for tech jobs, we reject that hypothesis. We propose potential explanations for this puzzle, including optimization frictions and worker sorting.