dr. (Antonia) Krefeld-Schwalb
Please also see my personal website and the website about an ongoing research project to foster sustainable behavior for more information.
Publications
Article (9)
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Academic (9)
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Krefeld-Schwalb, A., Sugerman, E. R., & Johnson, E. J. (2024). Exposing omitted moderators: Explaining why effect sizes differ in the social sciences. Proceedings of the National Academy of Sciences of the United States of America, 121(12), Article e2306281121. https://doi.org/10.1073/pnas.2306281121
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Krefeld-Schwalb, A., & Scheibehenne, B. (2023). Tighter nets for smaller fishes? Mapping the development of statistical practices in consumer research between 2008 and 2020. Marketing Letters, 34(3), 351-365. https://doi.org/10.1007/s11002-022-09662-3
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Krefeld-Schwalb, A., Wall, D., Johnson, E. J., Toubia, O., Bartels, D. M., & Li, Y. (2022). The More You Ask, the Less You Get: When Additional Questions Hurt External Validity. Journal of Marketing Research, 59
(5), 963-982. Article journals.sagepub.com/doi/pdf/10.1177/00222437211073581.
https://doi.org/10.1177/00222437211073581 -
Krefeld-Schwalb, A., Pachur, T., & Scheibehenne, B. (2021). Structural parameter interdependencies in computational models of cognition. Psychological Review, 129(2), 313-339. https://doi.org/10.1037/rev0000285
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Krefeld-Schwalb, A., & Rosner, A. (2020). A new way to guide consumer's choice: Retro-cueing alters the availability of product information in memory. Journal of Business Research, 111, 135-147. https://doi.org/10.1016/j.jbusres.2019.08.012
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Krefeld-Schwalb, A., Donkin, C., Newell, B. R., & Scheibehenne, B. (2019). Empirical comparison of the adjustable spanner and the adaptive toolbox models of choice. Journal of Experimental Psychology: Learning Memory and Cognition, 45(7), 1151-1165. https://doi.org/10.1037/xlm0000641
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Krefeld-Schwalb, A. (2018). The Retro-Cue Benefit for Verbal Material and Its Influence on the Probability of Intrusions Under Dual-Task Conditions. Experimental Psychology, 65(3), 128-138. https://doi.org/10.1027/1618-3169/a000400
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Krefeld-Schwalb, A., Witte, E. H., & Zenker, F. (2018). Hypothesis-Testing Demands Trustworthy Data-A Simulation Approach to Inferential Statistics Advocating the Research Program Strategy. Frontiers in Psychology, 9, 460. https://doi.org/10.3389/fpsyg.2018.00460, https://doi.org/10.3389/fpsyg.2018.00460
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Krefeld-Schwalb, A., Ellis, A. W., & Oswald, M. E. (2015). Source Memory for Mental Imagery: Influences of the Stimuli's Ease of Imagery. PLoS One (online), 10(11), e0143694. https://doi.org/10.1371/journal.pone.0143694
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Conference proceeding (1)
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Academic (1)
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Krefeld-Schwalb, A., Scheibehenne, B., Rieskamp, J., & Berkowitsch, N. (2017). Dependent Choices in Employee Selection: Modeling Choice Compensation and Consistency. In CogSci 2017 - Proceedings of the 39th Annual Meeting of the Cognitive Science Society: Computational Foundations of Cognition (pp. 2457-2462). The Cognitive Science Society.
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Preprint (1)
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Academic (1)
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Krefeld-Schwalb, A., & Scheibehenne, B. (2020). Tighter nets for smaller fishes: Mapping the development of statistical practices in consumer research between 2011 and 2018. Center for Open Science. https://doi.org/10.31234/osf.io/xjgyk
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Course (1)
- Specialization Module on Consumer Behavior (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.
Address
Burgemeester Oudlaan 50
3062 PA Rotterdam
Postbus 1738
3000 DR Rotterdam
Netherlands
