Jin Lee Young

Young Jin Lee

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Young Jin (YJ) Lee is an associate professor in the Department of Business Information and Analytics at the Daniels College of Business. He is also a primary director of the Center for Analytics and Innovation with Data (CAID). His research interests span economic and marketing aspects of online social media, mobile IT markets, piracy and digital rights management, and the adoption and diffusion of IT innovations. His work has appeared in prestigious academic journals and conference proceedings, including Management Science, Information Systems Research, Journal of Management Information Systems, International Conference of Information Systems, Workshop on Information Systems and Economics, and Conference on Information Systems and Technology.

Lee gained professional experience as an information strategist for Samsung Group. He is the recipient of numerous honors, awards, and grants, and served as an associate editor on the editorial board of Communications of the Association for Information Systems.

What do you study?

My research focuses on understanding how people adopt and utilize information technology, particularly in business, community contexts and for the public good. For the past 10 years or so, I’ve looked at online consumers’ behavior and how that behavior can be impacted by social media platforms and public reviews—and then how that behavior can impact product sales, revenue or adoption. Because online reviews usually show the quality measure of a service or product, it’s important information for a consumer.

What are you currently working on?

These days I’m more interested in the adoption of artificial intelligence (AI) among programmers and software developers. Compared to the old-fashioned coding behavior of developers, these days anyone can easily access and create code through AI. So I want to understand the legal risk of creating and using AI-generated code versus using traditional in-house programmers to develop their own code.

Through CAID we’re trying to get some good data of programmers’ behavior so that we can measure their patterns of AI code utilization, as well as their own programming patterns. This is so that we can assess how much AI is influencing coding right now and understand what legal issues may arise because of AI adoption, like copyright infringement. From there, we may be able to better understand what level of AI adoption might be acceptable by companies or governments.

How do you collect your data? And what are some of the biggest challenges?

The project I just mentioned would be in collaboration with a software development consulting company that provides proprietary dashboard solutions to software developers, and they keep track of and compile the scripts and coding that programmers generate. They can identify patterns in coding that differentiate between AI-generated code (which often follows specific patterns) and human-written code. It’s a big challenge to access data like this because private companies are typically pretty sensitive about sharing coding schemes. We can get some macro-level summary data, but it’s not very useful because it doesn’t allow us to actually see what’s going on internally.

Can you talk more about the work you’re doing with CAID?

At CAID, we’re expanding in a way where we can collaborate more with faculty members within Daniels but also throughout DU, and there are international scholars that we want to work with as well. We’re planning to collaborate with South Korean companies and universities to look more into strategic AI adoption. So, for example, how supply chain management or marketing companies are adopting AI, and then we want to quantify the impact of that AI adoption in their value chains.

How do you bring your research into the classroom?

I’m really interested in the big picture of AI adoption in academia. The top 30 business schools all have classes on AI these days, so my goal is to create new classes here at Daniels that are relevant to AI usage in the business world, especially within BIA. Within my current classes, I see that students are capable of using AI in their coding and data mining work. The problem is that they have been struggling to talk about the procedure and interpretations from analytics technologies in actual business cases, because they aren’t doing that first layer of work themselves. So, I think moving forward we really need to focus on how to utilize the technology in a way that they can connect to the business.

How do you want your work to impact the business world?

The traditional technology adoption model (TAM) has been widely used in the tech industry for decades at this point, and AI isn’t too different from that adoption model, except that it can be accomplished very, very quickly. Just take the internet. At the start, everyone wanted to learn HTML, learn how to create great websites, but there was a barrier to entrance at that time: the knowledge of a programmer. These days you can just tell ChatGPT what you want to create, and it’s there. So we need to revisit or revamp the original TAM model so that it can be more relevant to the industry and also in organizations.