Nan, you have so many different elements of expertise.  I think of them as including non-market strategy, innovations in Chinese businesses, and the AI/human interface.  Can I ask you first about AI?  You have a new article called “When and How Artificial Intelligence Augments Employee Creativity.”  Can you tell us your findings on this?  What and how does AI augment human creativity?

Nan explained that her paper on this topic has to do with allowing AI to take on administrative tasks so as to relieve talented people to do the parts of their jobs that are more interesting, important, and creative.   For example, salespeople can do more persuasion after leaving lead qualification to the technology.   They may even be able to innovate in their approach to sales after then get access to productivity enhancements by virtue of the relief from these kinds of tasks.  The effects of the increase in productivity are greatest for high-performing employees.

Some interesting points about this work:  First, Nan stumbled into AI research and now loves it.   She is excited to look at creativity.  Second, she’s really enjoyed learning about organizational behavior research on creativity, especially because her paper is really about clearing the desk to allow creativity to emerge.   She is excited to begin to look directly at creativity.

AI addresses the more tedious elements of work, which frees up humans to develop more positive emotions, better stories, and more creativity.  Is there anything that we give up as humans to the AI that matters?  What about trust and relationships?

Nan is very interested in this topic.  She has a recent paper in PNAS on how people responded to the emotional support that AI can provide.  The findings:

1.      If people don’t know that AI is providing the emotional support they experience through a technology, then they like it a lot, i.e., more than when the support is provided by a person. 

2.      But as soon as subjects find out that it’s AI that’s providing the support, the effect is washed out.   People don’t like it.

There’s some evidence that this effect may be different across geographies and generations.  In different research conducted in Asia, young people opened up to an AI platform called Deep Seek knowing that it was AI-driven.   There was no algorithm aversion like Nan found in her PNAS paper.  

Could it be that AI aversion is not dominant?   Is there an opportunity for AI to enhance personal relationships?  There is a lot we need to learn about this.

How do you see this as mapping to the corporate political environment?   Do different countries support this kind of creativity better than others?

Nan explained that data is critical to AI development.  In China, much of the available data is from government surveillance.   This has important implications for how the technology may develop.

As a doctoral student in Toronto, Nan was very interested in business-government relationships as a student.   The focus of industrial policy was the mass production of goods.  Now, we are seeing a change in focus toward tech choices that may have profound implications beyond the production of goods and services today.   This may arise around data collection, for example, that powers AI algorithms that ultimately generate a wide range of insights for future product and service development.  Nan is quite interested in understanding this.

We also talked about policies such as the US’s investment protection programs, through which the US screens prospective foreign purchasers of US technology.  This program shapes incentives for US companies to invest in particular ways.   The siloing by country of investments in technologies may have long-term geopolitical implications.

Can you comment on the implications of this for innovation productivity for countries?  Any differences between the U.S. and China that you can remark on?

Many technologies may develop quite differently across countries because of policy choices.  Tech development becomes very high stakes.  Socially constructed differences may become built into tech trajectories.  There’s a great deal to learn by bringing together the literatures on non-market strategy and technology investments.

What motivates Nan to keep working so hard on these topics?

Conducting research in these domains is really fun.  It’s fun to find two hours to work on a paper.  The motivation for her is plain and simple passion for the topics. 

There are some obstacles in our profession, such as the long time between the generation of a paper and its publication.  If you don’t enjoy writing the paper, then it’s hard to stay motivated.  It’s also worthwhile to consider the value for the world that your work delivers.  But in the calculation, it’s passion that matters most for Nan.