dr. A.M. (Alina) Ferecatu
My research interests lie at the intersection between behavioral decision making and marketing research. Generally, I study decision heuristics and biases in marketing contexts, and devise individual-specific strategies aimed to overcome these biases. Theoretically, I draw from behavioral decision theory, which describes how individuals incorporate their set of beliefs and values into their decisions. Methodologically, I utilize experimental economics techniques and Bayesian analysis to understand and predict individual level behavior. My work is divided into two streams of research: (1) to investigate decision makers’ learning behavior and related decision heuristics and biases; and (2) to apply Bayesian models of heterogeneity to understand individual-level behavior. For more information, see my resume.
Publications
Article (5)
-
Academic (5)
-
Ferecatu, A., De Bruyn, A., & Mukherjee, P. (Accepted/In press). Silently killing your panelists one email at a time: The true cost of email solicitations. Journal of the Academy of Marketing Science. https://doi.org/10.1007/s11747-023-00992-w
-
Bolton, G. E., Ferecatu, A., & Kusterer, D. J. (2024). Rate This Transaction: Coordinating Mappings in Market Feedback Systems. Management Science, 70(1), 567-588. https://doi.org/10.1287/mnsc.2023.4694
-
Liberali, G., & Ferecatu, A. (2022). Morphing for Consumer Dynamics: Bandits Meet Hidden Markov Models. Marketing Science, 41(4), 341-366. https://doi.org/10.1287/mksc.2021.1346
-
Ferecatu, A., & De Bruyn, A. (2021). Understanding Managers’ Trade-Offs Between Exploration and Exploitation. Marketing Science, 41(1), 139-165. https://doi.org/10.1287/mksc.2021.1304, https://doi.org/10.1287/mksc.2021.1304
-
Ferecatu, A., & Onculer, A. (2016). Heterogeneous Risk and Time Preferences. Journal of Risk and Uncertainty, Article 53(1). https://doi.org/10.1007/s11166-016-9243-x
-
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 (2021/2022)
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.
Address
Office: Mandeville Building T10-18
Burgemeester Oudlaan 50
3062 PA Rotterdam
Postbus 1738
3000 DR Rotterdam
Netherlands
