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Dr. Nicholas Danks

Assistant Professor in Business Analytics
Room 310




Nicholas holds a Bachelors degree in Accounting Science from the University of South Africa, and a Masters degree in Business Administration, and a PhD in Service Science from National Tsing Hua University. Nicholas is a member of the prestigious Phi Tau Phi Scholastic Honor Society for having achieved the grade of summa cum laude for both his Masters and PhD.

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Nicholas has diverse research interests that focus on research methodology and business analytics. In particular, Nicholas’s research focuses on Structural Equation Modeling and Partial Least Squares Path Modeling and leveraging these traditionally explanatory methodologies by applying predictive methods.

In addition to methodological research, Nicholas has strong applied experience in analytical software development and is a coauthor and the primary maintainer of SEMinR, an open-source package for the R Statistical Environment for the estimation and evaluation of PLS path models.

  • Business Analytics
  • Structural Equation Modeling
  • Research Validity
  • Ethics of Business Analytics


1. Model selection uncertainty and multimodel inference in partial least squares structural equation modeling (PLS-SEM)
Danks, N. P., Sharma, P. N., & Sarstedt, M. (2020). Journal of Business Research, 113, 13-24.

2. Prediction‐oriented model selection in partial least squares path modeling.
Sharma, P. N., Shmueli, G., Sarstedt, M., Danks, N., & Ray, S. (2019). Decision Sciences.

1. Predictions from partial least squares models
Danks, N. P., & Ray, S. (2018). In Applying partial least squares in tourism and hospitality research. Emerald Publishing Limited.

  • BU7706 – Operational Analytics
  • BUU11550 - Information Systems & Data Management