Dr. Nicholas Danks

• Sharma, P., Sarstedt, M., Shmueli, G., Danks, N.P., Ray, S., Prediction-oriented model selection in partial least squares path modeling, Decision Sciences, 2018Journal Article, 2018, TARA - Full Text
• Danks, N.P., Sharma, P.N., Sarstedt, M., Model selection uncertainty and multimodel inference in partial least squares structural equation modeling (PLS-SEM), Journal of Business Research, 113, 2020, p13 - 24Journal Article, 2020
• Predictions from Partial Least Squares Models in, editor(s)Ali, G., Rasoolimanesh, S.M., Cobanoglu, C. , Applying partial least squares in tourism and hospitality research, Emerald Publishing Limited, 2018, pp35 - 52, [Danks, N.P., Ray, S.]Book Chapter, 2018, TARA - Full Text
• Nicholas Danks, Soumya Ray, Validity and Reproducibility Of Computational Research: A Teaching Agenda, SIG-DSA, Hyderabad, India, 12/12/2020, 2020Conference Paper, 2020
• George Franke, Marko Sarstedt, Nicholas P. Danks, Assessing measure congruence in nomological networks, Journal of Business research, 130, (June), 2021, p318 - 334Journal Article, 2021, TARA - Full Text
• Marko Sarstedt, Nicholas P. Danks, Prediction in HRM research-A gap between rhetoric and reality, Human Resource Management Journal (UK), 2021Journal Article, 2021, URL , TARA - Full Text
• Danks, N.P., The Piggy in the Middle: The Role of Mediators in PLS-SEM-based Prediction, Data Base for Advances in Information Systems, 52, (SI), 2021, p24 - 42Journal Article, 2021, URL , TARA - Full Text
• Heyam Abdulrahman Al Moosa, Mohamed Mousa, Walid Chaouali, Samiha Mjahed Hammami, Harrison McKnight, Nicholas Patrick Danks, Using humanness and design aesthetics to choose the "best" type of trust: a study of mobile banking in France, International Journal of Retail & Distribution Management, 2021Journal Article, 2021, TARA - Full Text
• Ray, S., Danks, N.P., and Calero Valdez, A., 'SEMinR: Domain-specific language for building, estimating, and visualizing structural equation models in R', V2.3.1, CRAN, The Comprehensive R Archive Network, 2021, -Software, 2021, URL , TARA - Full Text
• Chaouali, W., Danks, N., Hey chatbot, why do you treat me like other people? The role of uniqueness neglect in human-chatbot interactions, Journal of Strategic Marketing, 2023Journal Article, 2023, DOI
• Danks, N.P., Ray, S., Shmueli, G., The Composite Overfit Analysis Framework: Assessing the Out-of-sample Generalizability of Construct-based Models Using Predictive Deviance, Deviance Trees, and Unstable Paths, Management Science, 2023Journal Article, 2023
• Valdez, A., Kojan, L., Danks, N.P., and Ray, S., Structural Equation Modeling in HCI Research using SEMinR, CHI EA '23: Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems, CHI Conference on Human Factors in Computing Systems 2023, Hamburg, April 2023, edited by ACM , (553), 2023, pp1 - 3Conference Paper, 2023, DOI
• Miloš Fišar, Ben Greiner, Christoph Huber, Elena Katok, Ali I Ozkes, Management Science Reproducibility Collaboration, Reproducibility in Management Science, Management Science, 2024Journal Article, 2024

• Hair, J.F., Hult, G.T.M., Ringle, C.M., Sarstedt, M., Danks, N.P., Ray, S., Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R: A Workbook, Germany, Springer , 2021Book, URL , TARA - Full Text

Structural Equation Modeling (SEM) is now a dominant methodology in business and management, life sciences, and social science research - both in academic and industrial contexts. My primary research focus is on the rigorous and statistically correct use of SEM for scientific research. I am particularly interested in quantitative research methods and machine learning for explanatory models. To this end my work introduces predictive methodology to traditionally explanatory methods and finds opportunities for the intersection of these to supplement the scientific conclusions that can be drawn from research. To this end I generate new statistical methods, and refine existing methods in SEM (and in particular Partial Least Squares - PLS). My research therefore lies at the intersection of methodology and practice. In addition, I also research the use of computational statistics as a field and how these modern computational methods can be applied to solve imminent business and social challenges. This research is focused on ensuring that the progress in computational statistics is supported by the appropriate frameworks to ensure computational validity, reproducibility, and open access. I am a co-author and the primary maintainer of SEMinR, an open-source package for the R Statistical Environment for the estimation and evaluation of PLS path models. I publish in journals such as Managment Science (ABS 4*, FT50), Human Resource Management Journal (ABS4*), Decision Science (ABS3), Journal of Business Research (ABS3), and The Database for ACM (ABS2).

• Title

  Computational Validity
  Summary

  A framework for reproducible, reliable, and valid computational analysis in business analytics and research.
• Title

  DAGifying SEM
  Summary

  Structural Equation Models (SEM)​ are commonly used in MIS, marketing, management and behavioral sciences as a graphical way to represent a causal model between constructs, and as a statistical tool to test causal hypotheses, especially when multiple items are used to measure constructs. SEMs are becoming more complex, as knowledge of mechanisms advances and as more nuanced data can be collected. SEMs used in IS, marketing and behavioral sciences now contain complex relationships between independent and dependent variables (IVs and DVs), including multiple mediators, moderators, and control variables. We find models with moderated mediation, mediated moderation, and even moderated moderation. Researchers use SEM models instead of separate regression models because they aim at capturing the global relationship1 rather than a collection of single direct effects. Methods such as CB-SEM and PLS-SEM estimate the entire system simultaneously, yielding estimates for each of the direct causal effects (arrows in a SEM diagram). However, in such complex diagrams, it is not straightforward to identify which causal effects can be estimated, and moreover, which parts must be conditioned upon (and how) in order to estimate those effects. Moreover, in complex models contradicting theories sometimes posit different causal sequences, and selecting the best sequence among several competing alternatives can be challenging.
• Title

  Composite Overfit Analysis Framework
  Summary

  Construct-based models have become a mainstay of management and information systems research. However, we believe these models are very likely overfit to the data samples they are estimated on, which makes them risky to use in prescriptive or predictive ways. We propose a novel methodological framework for these models that can highlight risks to out-of-sample generalizability in theoretically useful ways using a mixed-methods, explanatory-predictive approach. The proposed Composite Overfit Analysis framework: (1) gauges predictive performance of focal constructs, (2) identifies individual cases that exacerbate overfit, (3) identifies structural relationships between constructs that may not generalize well out-of-sample, and (4) guides qualitative analysis to explore the deeper reasons for such conflicts. Along the way, we seek to distinguish conflated terms in predictive and inferential literatures, and resolve methodological issues that prevent straightforward integration of predictive and inferential mechanics. We demonstrate the practical utility of our analytical framework on a technology adoption model in a new context.

Computer science - software engineering, Computer science - Theory & Methods, Algorithms, Computer science - Information Systems, Information & Communication Technology,

• Best Methodological Paper Award at the 2022 International Conference on Partial Least Squares Structural Equation Modeling Sep 6 - 9, 2022
• Trinity Business School Teaching Excellence Award 2022
• William R. Darden Award for Best Methodological Paper at the Academy of Marketing Science Annual Conference, Vancouver, Canada (2019) May 29 - 31, 2019
• Trinity Business School Teaching Excellence Award 2023
• Trinity Business School Research Excellence Award 2022

• Reproducibility Team Member for the Informs Journal of Data Science
• Association for Information Systems (AIS)
• Academy of Marketing (UK)
• Member of Analytics Institute of Ireland
• Scientific Advisory Board Member for the 2022 International Conference on Partial Least Squares Structural Equation Modeling

• Track Chair at the 2022 International Conference on Service Science and Innovation (ICSSI 2022) at National Sun Yat-sen University, Kaohsiung, Taiwan. November 17 - 19, 2022
• Member of Scientific Advisory Board to the 2022 International Conference on Partial Least Squares Structural Equation Modeling
• Track Chair and Scientific Advisory Board member at 2022 International Conference on Partial Least Squares Structural Equation Modeling at Babe"-Bolyai University, Faculty of Economics and Business Administration, Cluj-Napoca, Romania September 6 - 9, 2022
• Education Track Chair at the Special Interest Group on Decision Sciences and Analytics Pre-ICIS workshop