Dr. A. M. (Ana) Martinovici
Dr. Martinovici is an Assistant Professor of Marketing at the Rotterdam School of Management, Erasmus University (The Netherlands). She holds a PhD degree in Marketing and two master’s degrees (Econometrics and Mathematical Economics, and Marketing), from Tilburg University (The Netherlands). Dr. Martinovici’s research focuses on the role of attention in consumer choice processes. She develops dynamic Bayesian models calibrated on eye-tracking data to gain access to otherwise unobservable utility accumulation processes that take place during choice. For more information, please visit www.anamartinovici.com
Publications
Article (3)
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Academic (3)
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Loenneker, H. D., Buchanan, E. M., Martinovici, A., Primbs, M. A., Elsherif, M. M., Baker, B. J., Dudda, L. A., Đurđević, D. F., Mišić, K., Peetz, H. K., Röer, J. P., Schulze, L., Wagner, L., Wolska, J. K., Kührt, C., & Pronizius, E. (2024). We don't know what you did last summer. On the importance of transparent reporting of reaction time data pre-processing. Cortex, 172, 14-37. https://doi.org/10.1016/j.cortex.2023.11.012
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Martinovici, A., Pieters, R., & Erdem, T. (2023). Attention Trajectories Capture Utility Accumulation and Predict Brand Choice. Journal of Marketing Research, 60(4), 625-645. https://doi.org/10.1177/00222437221141052
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Speer, S., Martinovici, A., Smidts, A., & Boksem, M. (2023). The acute effects of stress on dishonesty are moderated by individual differences in moral default. Scientific Reports, 13(1), Article 3984. https://doi.org/10.1038/s41598-023-31056-2
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PhD Tracks (2)
- Role: Co-promotor
- PhD Candidate: Marina Lenkovskaya
- Time frame: 2021 -
- Role: Co-promotor
- PhD Candidate: Ting-Yi Lin
- Time frame: 2021 -
Course (1)
- Current Topics in Marketing Research (2023/2024)
PhD Vacancy (1)
The Marketing group at Rotterdam School of Management, Erasmus University seeks a highly motivated PhD student with strong quantitative skills to study the problem of algorithmic biases in marketing.
As machines are trained to analyse complex problems, many tasks that previously required humans are now guided by Artificial Intelligence. Marketing is no exception in this domain. Increasingly, companies use algorithms to design targeted marketing campaigns. Causal Machine Learning is an emerging research field that can learn the causal effect of an intervention and how it varies within a population based on a large set of potential moderating variables. Its use in marketing has been rapidly growing over the last years (Lemmens and Gupta 2020; Esterzon, Lemmens, Van den Bergh 2023).
Unfortunately, algorithms can be discriminatory. The number of cases reporting biases in algorithms has exploded. Algorithms reproduce and amplify biases present in human decisions. They may even inadvertently create new discriminatory outcomes.
This PhD project ambitions to tackle this crucial managerial and societal challenge. The goal will be to better understand the problem of algorithmic biases in the context of targeting marketing campaigns and to develop a novel methodological framework to design effective and fair personalized policies. The project will include large-scale field experiments in collaboration with company partners.
Strong applicants typically have backgrounds in computer science, statistics or econometrics but should have an intrinsic interest for marketing problems. The PhD will be supervised by Prof. Dr. Aurélie Lemmens and funded by a VICI NWO grant.
Events (39)
Address
Office: Mandeville Building T10-23
Burgemeester Oudlaan 50
3062 PA Rotterdam
Postbus 1738
3000 DR Rotterdam
Netherlands
