Nicholas P. Danks

9.7k total citations · 1 hit paper
18 papers, 4.5k citations indexed

About

Nicholas P. Danks is a scholar working on Information Systems and Management, Artificial Intelligence and Marketing. According to data from OpenAlex, Nicholas P. Danks has authored 18 papers receiving a total of 4.5k indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Information Systems and Management, 4 papers in Artificial Intelligence and 3 papers in Marketing. Recurrent topics in Nicholas P. Danks's work include Technology Adoption and User Behaviour (4 papers), Behavioral Health and Interventions (3 papers) and Consumer Behavior in Brand Consumption and Identification (2 papers). Nicholas P. Danks is often cited by papers focused on Technology Adoption and User Behaviour (4 papers), Behavioral Health and Interventions (3 papers) and Consumer Behavior in Brand Consumption and Identification (2 papers). Nicholas P. Danks collaborates with scholars based in Ireland, Taiwan and Germany. Nicholas P. Danks's co-authors include Marko Sarstedt, Soumya Ray, Joseph F. Hair, Christian M. Ringle, G. Tomas M. Hult, Pratyush Nidhi Sharma, Galit Shmueli, Walid Chaouali, George R. Franke and Orapin Chienthavorn and has published in prestigious journals such as Management Science, Journal of Business Research and Journal of Chromatography A.

In The Last Decade

Nicholas P. Danks

17 papers receiving 4.3k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R

2021 4.0k citations Joseph F. Hair, G. Tomas M. Hult et al. Trinity's Access to Research Output (TARA) (Trinity College Dublin) profile →

Peers

Nicholas P. Danks

SPS

MARKE

ISM

OBHRM

Ryan Matthews United States

Lucy M. Matthews United States

Santha Vaithilingam Malaysia

Tat‐Huei Cham Malaysia

James Gaskin United States

Huiwen Wang China

Kai Oliver Thiele Germany

Dan‐Cristian Dabija Romania

Jose Benitez France

Mumtaz Ali Memon Pakistan

Ryan Matthews United States

Nicholas P. Danks

1.1k ×1.0

SPS

915 ×0.9

MARKE

597 ×0.7

ISM

696 ×0.9

733 ×1.1

OBHRM

Citations per year, relative to Nicholas P. Danks Nicholas P. Danks (= 1×) peers Ryan Matthews

Countries citing papers authored by Nicholas P. Danks

Since Specialization

Citations

This map shows the geographic impact of Nicholas P. Danks's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Nicholas P. Danks with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nicholas P. Danks more than expected).

Fields of papers citing papers by Nicholas P. Danks

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Nicholas P. Danks. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Nicholas P. Danks. The network helps show where Nicholas P. Danks may publish in the future.

Co-authorship network of co-authors of Nicholas P. Danks

This figure shows the co-authorship network connecting the top 25 collaborators of Nicholas P. Danks. A scholar is included among the top collaborators of Nicholas P. Danks based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Nicholas P. Danks. Nicholas P. Danks is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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18 of 18 papers shown

Chaouali, Walid, et al.. (2024). Not all communication styles are created equal: the differing effects of communicating the family business brand’s roots and virtues. Journal of Business and Industrial Marketing. 39(12). 2673–2683.

Danks, Nicholas P., et al.. (2024). An application of the COA Framework: Building a sound foundation for organizational resilience. Journal of Business Research. 179. 114702–114702. 10 indexed citations

Valdez, André Calero, et al.. (2023). Structural Equation Modeling in HCI Research using SEMinR. 1–3. 2 indexed citations

Chaouali, Walid, et al.. (2023). Hey chatbot, why do you treat me like other people? The role of uniqueness neglect in human-chatbot interactions. Journal of Strategic Marketing. 32(2). 170–186. 17 indexed citations

Danks, Nicholas P., Soumya Ray, & Galit Shmueli. (2023). 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. 70(1). 647–669. 4 indexed citations

Danks, Nicholas P.. (2021). The Piggy in the Middle. ACM SIGMIS Database the DATABASE for Advances in Information Systems. 52(SI). 24–42. 25 indexed citations

Hair, Joseph F., G. Tomas M. Hult, Christian M. Ringle, et al.. (2021). Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R. Trinity's Access to Research Output (TARA) (Trinity College Dublin). 4026 indexed citations breakdown →

Ray, Soumya, et al.. (2021). SEMinR: Domain-Specific Language for Building, Estimating, and Visualizing Structural Equation Models in R. SSRN Electronic Journal. 40 indexed citations

Sarstedt, Marko & Nicholas P. Danks. (2021). Prediction in HRM research–A gap between rhetoric and reality. Human Resource Management Journal. 32(2). 485–513. 72 indexed citations

10.

Ray, Soumya, Nicholas P. Danks, & André Calero Valdez. (2021). Building and Estimating Structural Equation Models [R package seminr version 2.2.1]. 1 indexed citations

11.

Franke, George R., Marko Sarstedt, & Nicholas P. Danks. (2021). Assessing measure congruence in nomological networks. Journal of Business Research. 130. 318–334. 11 indexed citations

12.

Mousa, Mohamed, et al.. (2021). 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. 50(2). 251–275. 5 indexed citations

13.

Ray, Soumya, et al.. (2020). Domain-Specific Language for Building and Estimating Structural Equation Models [R package seminr version 1.1.0]. 2 indexed citations

14.

Danks, Nicholas P., Pratyush Nidhi Sharma, & Marko Sarstedt. (2020). Model selection uncertainty and multimodel inference in partial least squares structural equation modeling (PLS-SEM). Journal of Business Research. 113. 13–24. 90 indexed citations

15.

Danks, Nicholas P., Soumya Ray, & Galit Shmueli. (2019). Predictive Analysis and Out-of-Sample Generalizability of Construct-based Models. Journal of the Association for Information Systems. 2 indexed citations

16.

Sharma, Pratyush Nidhi, Galit Shmueli, Marko Sarstedt, Nicholas P. Danks, & Soumya Ray. (2018). Prediction‐Oriented Model Selection in Partial Least Squares Path Modeling. Decision Sciences. 52(3). 567–607. 191 indexed citations

17.

Danks, Nicholas P., Soumya Ray, & Galit Shmueli. (2017). Evaluating the Predictive Performance of Constructs in PLS Path Modeling. SSRN Electronic Journal. 9 indexed citations

18.

Smith, Roger M., Orapin Chienthavorn, Nicholas P. Danks, & Ian D. Wilson. (1998). Fluorescence detection in packed-column supercritical fluid chromatographic separations. Journal of Chromatography A. 798(1-2). 203–206. 11 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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