Data quality

Any analysis can only be as good as the data on which it is based. Following this principle, the research field of data quality deals with fundamental questions about the measurement of phenomena that are not directly observable. These include consumer attitudes towards advertising and brands, purchase intentions or customer satisfaction. Current research projects aim to transfer metrological approaches from the natural sciences to the behavioral sciences. This should promote a higher replicability of behavioral science studies. Further research is addressing the question of how data quality can be ensured in machine learning methods.

Recent publications
Angelelli, M., Ciavollino, E., Ringle, C. M., Sarstedt, M., & Aria, M. (2025). Conceptual Structure and Thematic Evolution in Partial Least Squares Structural Equation Modeling Research. Quality & Quantity, Advance online publication.
Imschloss, M., Sarstedt, M., Adler, S. J., & Cheah, J.-H. (2025). Using LLMs in Sensory Service Research: Initial Insights and Perspectives. The Service Industries Journal, Advance online publication. doi: 10.1080/02642069.2025.2479723
Viglia, G., Adler, S. J., Miltgen, C. L., & Sarstedt, M. (2024). The Use of Synthetic Data in Tourism. Annals of Tourism Research, 108, 103819.
Sarstedt, M., Adler, S. J., Rau, L, Schmitt, B. (2024). Using large language models to generate silicon samples in consumer and marketing research: Challenges, opportunities, and guidelines. Psychology & Marketing, https://doi.org/10.1002/mar.21982
Rigdon, E. E., & Sarstedt, M. (2022). Accounting for uncertainty in the measurement of unobservable marketing phenomena. In: H. Baumgartner and B. Weijters (Eds.), Review of Marketing Research, Volume 19 (pp. 53-73). Bingley, UK: Emerald.
Hair, J. F., & Sarstedt, M. (2021). Data, measurement, and causal inferences in machine learning: Opportunities and challenges for marketing. Journal of Marketing Theory & Practice, 29(1), 65-77.