The Diginomics team caught up with Stefan during his recent visit to Bremen where he presented his work at our Brownbag seminar. We quizzed him on the route he took to find his intellectual home including the key milestones and challenges he has faced along the way.

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

Christopher Johnson (CJ): You completed your doctoral thesis in 2017 on the topic of product design and visual aesthetics. What sparked your initial interest in product design and visual aesthetics as a younger scholar?

Stefan Mayer (SM): My background is a bit unconventional for a marketing scholar. I originally earned a diploma in psychology and also studied computer science. I then interned at Daimler, in Stuttgart. During my time there, I worked on designing interfaces for various help systems in vehicles. This was the first time I applied what I had learned in my psychology studies to practical scenarios, and I found it incredibly fascinating to think about how to design something so that a human can intuitively understand what to do. For example, we worked on a warning system for truck drivers. When they take a right turn, they have several blind spots. We had to figure out how to design a warning system that drivers could easily distinguish from regular information. This experience made me realize that a purely theoretical career in psychology might not be as interesting to me because I enjoyed the practical application side much more.

By chance, I came across a PhD position in Frankfurt with a professor who had also studied psychology and was working in a similar field. It was a stroke of luck that I ended up there. When I started, I had to choose a research topic, and I spent the first six months exploring literature. I was particularly intrigued by what people find beautiful and how that influences their decisions in daily life. Given my background in computer science and my interest in numbers, I decided to combine these fields and try to quantify aspects of beauty and aesthetics.

CJ: When you have a research interest as broad as “aesthetics”, how does one take account of distant yet still relevant literatures from the wider social sciences but still remain focused on the research question?

SM: It's not easy to manage that because it implies reading papers outside of your field, which can be hard due to the different terminologies. For example, I attended design conferences with people from completely different fields. This exposure helped me understand their languages better over time. However, I didn't incorporate every field and paper fully because if you include too many external sources, it might not be recognized as a marketing paper. There's a trade-off between knowing what's happening outside your field and focusing on relevant literature within your own field. I recommend attending conferences that aren't just purely marketing-focused but are relevant to your research area. It's also highly useful to talk to as many people as possible from different backgrounds, which you find more in specialized content.

CJ: That's helpful advice, especially for the Diginomics students who don't have an established intellectual home yet. It's a challenge for those working on inter or multi-disciplinary research.

SM: Yes, using the language of your field while incorporating other perspectives is crucial. At one conference, the track chair decided to have a panel discussion at the end instead of traditional talks. During this panel, someone asked about aesthetics and referenced philosophers I hadn't studied. This experience made me realize that many other fields might have already explored my research topics. It highlighted the importance of looking outside the traditional scope of what you personally consider relevant.

CJ: What are some emerging trends in your field that younger researchers should pay attention to? In terms of topics but also more generally, such as novel research methods, or any higher-level trends across the discipline?

SM: It's challenging for me to say definitively, especially compared to several years ago, when I had more time to explore research as a PhD student. One notable change is how machine learning has evolved. During my PhD, incorporating machine learning into your work was a significant contribution on its own. Nowadays, it's just another tool in the researcher's toolkit, an additional method that can be applied to various problems.

I think it's increasingly important to demonstrate causality. Ideally, you should have a secondary data set showing economic consequences, combined with experiments to show causal relationships. This multi-perspective approach—combining models, experiments, and data—has become crucial. It's not sufficient to rely on a single method; you need to integrate different approaches to provide a comprehensive view. Another trend is addressing societal problems. For example, during the COVID-19 pandemic, the question was how to convince people to adopt different behaviors. Similarly, environmental concerns remain a significant topic. These issues require answers that cut across traditional disciplinary boundaries. From a methodological standpoint, it's essential to move beyond traditional approaches and combine different methods. This might involve collaborating with people who have expertise in specific methods. For instance, in our group, we have a postdoc with a PhD in computer science, and we've had to learn each other's language to communicate effectively.

CJ: What are some key steps you took early in your career that got you to the position you are in now? What are some early career pitfalls one should watch out for?

SM: The steps that were really useful for me included focusing my efforts as a PhD student. For instance, I learned a second programming language and became deeply invested in a specific method, doing more than the average PhD student. This was beneficial both academically and personally. Another crucial step was spending time outside of Germany as a visiting scholar at UCSD. This experience was valuable for my growth, as I had the opportunity to interact with different researchers from various fields. I was also fortunate to be part of a large group of PhD students where many researchers would visit to discuss their work. Growing your network is essential, even if it sounds mundane. It’s important that people get to know you and understand what you are researching. This familiarity can be helpful when positions open up, as people will have you in mind.

CJ: That sounds like excellent advice. Are there any pitfalls you think early-career researchers should be aware of?

SM: One pitfall is not networking enough. Building a strong network can open many doors. Also, not taking the time to learn new skills or methods can limit your potential. And finally, not stepping out of your comfort zone to gain international experience or to engage with researchers from different fields can be a missed opportunity for growth.

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We thank Stefan for sharing these valuable career insights!