The Diginomics team spoke with Prof. Dr. Felix Eggers during his visit to Bremen on December 11, 2024. We discussed his academic path from preference measurement to digital business models, his views on AI in marketing research, and advice for early-career scholars.

**This transcript has been edited for length and clarity.**

Chris Johnson:

Your doctoral thesis focused on preference measurement in marketing and you’ve since moved on to study digital business models, corporate digital responsibility and the impact of digitalisation on branding, among others. How did you move from that narrower marketing focus to a broader scope over time? What sparked your initial interest in marketing, and how did your research develop?

Felix Eggers:
Starting my PhD was actually more or less a coincidence. I had written a master's thesis on preference measurement and found that I really enjoyed the topic. Then my supervisor, Henrik Sattler, mentioned he had a position available and asked if I was interested. So I took it, and that’s how I ended up doing my doctorate.

I didn’t exactly choose the topic in a strategic way, I just started working on it, found it rewarding, and stayed with it. That’s how I developed a methodological focus…I worked for years on preference measurement, got a good overview of the field, and even developed my own methods.

Having a methodological focus can be quite valuable as it makes you a sought-after co-author. Researchers who work on substantive topics often need solid methods, so they reach out to collaborate. That’s a good position to be in, and it helped me build a strong network.

As for broadening out: methodological work always needs a research context. In marketing, that includes branding, pricing, or digital business models. So you're hitting two birds with one stone — doing methodological research while also engaging with a substantive topic. You end up with two projects at once, and over time you stick with the areas that seem most promising or interesting.

Later, when I became an assistant professor in Groningen, my mentors encouraged me to broaden my scope further. They advised me to be known for at least two distinct research areas. That’s how I started looking into privacy, especially privacy in marketing and data collection, which is a big topic in the field.

From there, my interest expanded to algorithmic biases and how data are used by algorithms. That led to my work on corporate digital responsibility, which covers a lot of ground. More recently, I’ve started focusing on algorithms and generative AI and how they can be applied in marketing.

Chris Johnson:
So in your case, it sounds like a mix of natural progression, mentorship and building a reputation through collaboration?

Felix Eggers:
Exactly. As a PhD student, you need to become a specialist and focus narrowly in order to publish high-quality papers. But if you stay in academia long-term, it’s wise to broaden your expertise so you're not a one-trick pony.

Chris Johnson:
What are some emerging trends in your field that younger scholars should pay attention to? Both in terms of topics and also methods or broader shifts?

Felix Eggers:
In the area of preference measurement and consumer data collection, generative AI and large language models are definitely becoming important. Studying how consumers interact with AI is opening up a new research field.

One especially exciting area is whether we can substitute real survey responses with artificial ones. Can we train language models to generate responses that look and behave like real data? The projects I’ve worked on in that area are looking surprisingly good.

In the future, we might run pretests using synthetic data instead of real respondents, just to see if a study design works or to answer certain types of questions early on. These "silicon samples," as some people call them, could change how we conduct survey-based research quite significantly.

Chris Johnson:
That’s fascinating. So to wrap up, what were some key steps you took early in your career that helped get you to where you are now? And what are some early-career pitfalls to avoid?

Felix Eggers:
After finishing my PhD, I actually left academia and worked in market research for a while. But I realized I was more interested in scientific work as it felt less repetitive and gave me more freedom.

I assumed that industry experience would help when applying for academic jobs, but in fact it didn’t. In practice, universities mostly look at your publication record. So if there's a gap where you weren’t doing academic research, it shows up as a lack of publications, which can be a disadvantage.

That was disappointing. But I think things are starting to change as there’s more interest now in research that has societal impact. If you can show how your work affects firms or consumers, then practical experience becomes more valuable. Having exposure to how firms actually work is helpful when trying to implement findings in the real world.

Still, if someone is making that choice today, it’s probably best to go straight from a PhD into a postdoc or assistant professorship because that’s what universities currently expect.

Chris Johnson:
Yes, it's a bit of a shame. You would think industry experience, whether before or after a PhD, would help build contacts and give access to applied research opportunities. That seems like something that should be considered in hiring.

Felix Eggers:
Exactly. And now, as researchers, we’re being asked to reach out to firms, create collaborations, and apply our findings, which is much harder if you've never worked in a firm or don’t have those connections.

Chris Johnson:
Thanks a lot, Felix!

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