drs. X. Yu (Xi)

I finished my bachelor's and master's degree at the University of Twente in the field of Business Administration. Currently, I am doing my PhD under the supervision of Prof. dr. ir. Jörg Henseler at the Chair of Product-Market Relations (PMR), the Faculty of Engineering Technology (ET). My PhD research is "Modeling Aggregate Constructs with Structural Equation Modeling". I am interested in statistics and brand-related research topics.

Engineering & Materials Science

# Composite Materials
# Information Systems
# Latent Class Analysis

Business & Economics

# Information Systems Research
# Latent Variables
# Modeling
# Proceeds
# Structural Equation Modeling

Faculty of Engineering Technology (ET), Product–Market Relations (PMR)

Yu, X. , Schuberth, F. , & Henseler, J. (Accepted/In press). Specifying composites in growth curve analysis. Abstract from Advanced Techniques for Longitudinal Data Analysis in Social Science, ATLASS 2023 , Bielefeld, North Rhine-Westphalia, Germany.

Yu, X., Fassott, G. , Schuberth, F. , & Henseler, J. (2022). A new approach for modeling aggregate constructs. Abstract from Structural Equation Modeling, Tilburg, Netherlands.

Yu, X. , Schuberth, F. , & Henseler, J. (2022). Specifying composites in structural equation modeling: A refinement of the Henseler-Ogasawara specification. Statistical Analysis and Data Mining. https://doi.org/10.1002/sam.11608

Yu, X. , Constantinides, E. , Herrando, C. , & de Vries, S. A. (2021). The impact of artificial intelligence application on customer engagement. Paper presented at 20th International Marketing Trends Conference 2021, Venice, Italy.

Yu, X. , Kristal, S. , Schuberth, F. , & Henseler, J. (2021). Customers as advocates in times of brand crises: Why, when, and how?. Paper presented at 50th EMAC Annual Conference, EMAC 2021.

Yu, X., Zaza, I. , Schuberth, F. , & Henseler, J. (2021). Counterpoint: Representing Forged Concepts as Emergent Variables Using Composite-based Structural Equation Modeling. Data Base for Advances in Information Systems, 52(SI), 114-130. https://doi.org/10.1145/3505639.3505647

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Quantitative research methods