The Metaverse as a Behavioral Observatory

marziehalaei

12 hours ago

6 min read

Shijie Lu on why the Metaverse is a behavioral observatory for studying consumers.

Written by Marzieh Alaei, PhD candidate at BI Norwegian Business School

Imagine a study participant who turns up in both your treatment group and your control group. A shopper who can rewind a purchase and live it again. A market that sits nowhere on a map. In the physical world, these are a researcher’s fantasies, or logical impossibilities. For Shijie Lu, Professor of Marketing at the University of Notre Dame, they are not anomalies but unprecedented scientific opportunities: chances to observe consumers behave under conditions no laboratory could create.

Shijie’s featured IJRM paper, coauthored with eleven colleagues, changes the usual questions about virtual worlds. The Metaverse, he argues, matters less as a new marketplace and more as a new place to observe people.

“Our paper argues that the Metaverse is interesting not just as another place where consumers shop, socialize, or interact with brands, but as a kind of behavioral observatory.”

— Shijie Lu

The constraints we forget we live in

In the physical world, we live with rules so familiar that we stop noticing them. We usually have one identity. Time moves forward. Geography limits where we can go, and money limits what we can buy. The Metaverse can loosen all of these limits; the paper builds around four of them: identity, time, space, and financial access. The authors’ interest lies less in the virtual world itself than in what it can reveal about people once these everyday limits are relaxed. The greater value of the Metaverse, Shijie suggests, may be that it gives us a new way to understand consumer behavior more generally.

When the basics break down

Because this is a conceptual paper, Shijie talks about a surprising idea rather than a surprising result. What struck him and his co-authors was how quickly some of the most basic assumptions in marketing research disappear once you treat the Metaverse as a place to do serious research. Take the assumption that one person is one participant. In the Metaverse, a single person can run several avatars. In an experiment, that person could be counted twice: once in the treatment group and once in the control group. And because people may give each avatar a different personality, the same person could react differently depending on which avatar they use. So, who is the participant: the person, or the avatar? We assume time runs forward, but virtual experiences can be replayed. We use location to define markets, but location means little when someone can move between spaces instantly. Thus, the Metaverse not only opens up new research opportunities, it also invites us to reexamine the assumptions behind methods we already use.

Enter the Metaverse

What can a conceptual paper about virtual worlds really deliver? The answer is a new kind of question. Relax a constraint, and something that was impossible to observe comes into view. Let one person run several avatars, and researchers can watch identity being built, tried on, and discarded, rather than inferring it from a survey. Let people replay an experience, and we can see how the same choice shifts the second or third time around. Take away distance, and we can ask what location really does once it is separated from everything it usually entails. Lower the financial barrier, and we can see how people value things they could never normally afford, and what aspirational consumption looks like when the price of entry falls away.

Each loosened rule becomes, in effect, a natural experiment on the rule itself. Consider fashion. If shoppers dress their avatars in designer labels they would never buy for themselves, the gap between their virtual and physical wardrobes shows how much of their everyday choice is driven by budget rather than taste. And what we learn does not stay in the virtual world. It travels back to the physical one, where those rules still bind, and tells us how much of everyday consumer behavior they were quietly shaping all along.

It started without a paper

The project began at the 12th Choice Symposium at INSEAD in the summer of 2023. The organization of the symposium is distinctive: instead of presenting advanced work as at an academic conference, small groups spend days working through a broad question together. Shijie’s group was given consumer choice in the Metaverse, and they started with no paper in mind. They were simply trying to figure out what was novel about it. The idea they kept returning to was that these spaces let people get around the limits that usually shape their behavior.

Turning all their ideas into one paper was a challenge. Twelve co-authors, split into three working teams, produced a lot of ideas quickly, but it also required the group to blend many good but differing views into a single argument. Most of the work after the symposium was not writing more, but cutting: deciding what was central, what was not, and what could tie everything together. In the end, settling on the four constraints of identity, time, space, and financial access was what let them turn wide-ranging discussion into one coherent story.

The question comes first

One theme runs through the way Shijie works: start with a question. He keeps three projects going at different stages, ideally one in revision, one under review, and one still being developed, so there is always something to move forward when another slows down. But he is careful about what makes it into that set. He asks two things of any idea: whether it is a question he really wants the answer to, and, if it is answered well, who else will care.

“I try not to become too attached to a particular dataset, method, or empirical result. The question and potential contribution have to come first.”

— Shijie Lu

The same thinking shapes his advice to younger researchers. Projects cost a lot of time, he says, and early on it is tempting to start too many because every idea feels promising. He would rather spend three to six months deciding whether a question is worth doing than several years on one that never really works out. Starting fewer projects, and choosing them more carefully, can end up making you more productive.

It is also why Shijie is not worried about AI changing the field. The bigger shift, he thinks, is that the lines between behavioral, quantitative, and computational research are fading, as text, voice, images, and video become easier to analyze at scale. As those tools spread, the scarce resource changes.

“As these tools become more accessible, I think the scarce resource will increasingly be good research questions rather than sophisticated methods.”

— Shijie Lu

That view carries into a piece of common wisdom he thinks is worth questioning: the idea that more detailed data always means better insight. Richer data can create a false sense of confidence, he warns. It can improve prediction without improving our understanding of what causes people to behave the way they do. He has run into this in his own work on advertising, where even detailed individual-level data does not resolve the underlying process questions.

Read the paper

Interested in the full story? Read the article here.

Want to cite the paper?

Lu, Shijie, Xin (Shane) Wang, Alixandra Barasch, June Cotte, Yu Ding, Siham El Kihal, Rajdeep Grewal, Praveen K. Kopalle, Xiao Liu, Sridhar Moorthy, Jun Hyun (Joseph) Ryoo, and Isamar Troncoso (2026), “Dissolving Boundaries in the Metaverse: Implications of Loosened Constraints on Identity, Time, Space, and Financial Access for Marketing Research”, International Journal of Research in Marketing, 43(2), 619-635.

Meet Shijie Lu

What drives you to do the research that you do?

One question I have always been fascinated by is whether we can infer an individual’s or a firm’s underlying objective from observed behavior. In marketing, we usually start by assuming an objective function, consumers maximize utility, firms maximize profit and then model the decisions that follow. I am interested in turning that logic around: can we use observed decisions to infer the objective itself? I think this could fundamentally change how we model and interpret consumer and firm behavior.

If you were not in academia, what would you be?

Perhaps either a high school math teacher or a detective.

Which researcher, from any field, would you love to have lunch with, and what would you say to them?

Évariste Galois. He was a brilliant French mathematician whose work laid the foundation for modern group theory, but he died in a duel at only 20. I would ask what other mathematical ideas he had been thinking about before his life was cut short. But given the benefit of hindsight, the more useful purpose of the lunch would probably be to talk him out of the duel or at least persuade him to delegate that particular task to someone else.

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