Nicholas Patrick Danks

Nicholas Patrick Danks
Trinity College Dublin | TCD · School of Business

PhD, MBA, B. Acc.

About

25
Publications
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249
Citations

Publications

Publications (25)
Preprint
Construct-based models have become a mainstay of management and information systems research. However, these models are likely overfit to the data samples they are estimated on, which makes them risky to use in explanatory, prescriptive, or predictive ways outside a given sample. Empirical researchers currently lack tools to analyze why and how the...
Article
Full-text available
Purpose The research aims to addresses the limitations of previous literature regarding choosing the appropriate conceptualization of trust (i.e. interpersonal trust or system trust) and the role of design aesthetics in generating system trust and intention to adopt mobile banking. Design/methodology/approach The research conducts two studies. Stu...
Chapter
Computational statistics is now an increasingly popular method of analysis for researchers that combines a vast array of algorithms, statistical methods, and the power of functional coding. The R programming language, in particular, has benefitted from this development alongside of traditional graphical user interface (GUI) software. Today, it has...
Chapter
Full-text available
Mediation occurs when a third variable, referred to as a mediator construct, intervenes between two other directly related constructs. More precisely, a change in the exogenous construct results in a change of the mediator construct, which in turn changes the endogenous construct. The mediator analysis evaluates the factors related to the cause–eff...
Chapter
Full-text available
Structural equation modeling is a multivariate data analysis method for analyzing complex relationships among constructs and indicators. To estimate structural equation models, researchers generally draw on two methods: covariance-based SEM (CB-SEM) and partial least squares SEM (PLS-SEM). Whereas CB-SEM is primarily used to confirm theories, PLS r...
Chapter
Full-text available
When moderation is present, the strength and even the direction of a relationship between two constructs depend on a third construct (i.e., the moderator). In other words, the strength of the relationship between two constructs changes as the levels of the moderator construct change. As an example, the relationship between customer satisfaction and...
Chapter
Full-text available
The goal of reflective measurement model assessment is to ensure the reliability and validity of the construct measures and therefore provides support for the suitability of their inclusion in the path model. This chapter introduces the key criteria that are relevant in reflective measurement model assessment: indicator reliability, internal consis...
Chapter
Structural model assessment in PLS-SEM focuses on evaluating the significance and relevance of path coefficients, followed by the model’s explanatory and predictive power. In this chapter, we discuss the key metrics relevant to structural model assessment in PLS-SEM. We also discuss model comparisons and introduce key criteria for assessing and sel...
Chapter
SEMinR is a software package developed for the R statistical environment (R Core Team, 2021). The package includes a user-friendly syntax for creating and estimating structural equation models using estimators such as partial least squares. In this chapter, we introduce the syntax to create, estimate, and report structural equation models using SEM...
Chapter
PLS-SEM is the preferred approach when formatively specified constructs are included in the PLS path model. In this chapter, we discuss the key steps for evaluating formative measurement models. These include the assessment of (1) convergent validity, (2) indicator collinearity, and (3) statistical significance and relevance of the indicator weight...
Book
Full-text available
Partial least squares structural equation modeling (PLS-SEM) has become a standard approach for analyzing complex inter-relationships between observed and latent variables. Researchers appreciate the many advantages of PLS-SEM such as the possibility to estimate very complex models and the method’s flexibility in terms of data requirements and meas...
Article
Full-text available
Partial least squares structural equation modeling (PLS-SEM) is increasingly popular as a joint explanatory predictive approach to modeling complex causal mechanisms. Researchers are becoming cognizant of the value of conducting predictive analysis using PLS-SEM for both the evaluation of overfit and to illustrate the practical value of their model...
Article
Full-text available
There are broadly two dimensions on which researchers can evaluate their statistical models: explanatory power and predictive power. Using data on job satisfaction in aging workforces, we empirically highlight the importance of clearly distinguishing between these two dimensions by showing that a model with a certain degree of explanatory power can...
Code
SEMinR brings a friendly syntax to creating and estimating structural equation models (SEM). The syntax allows applied practitioners of SEM to use terminology that is very close to their familiar modeling terms (e.g., reflective, composite, interactions) instead of specifying underlying matrices and covariances. SEM models can be estimated either u...
Article
Evaluations of convergent and discriminant validity are generally conducted by analyzing constructs in isolation or by comparing pairs of latent variables. These approaches ignore the broader nomological network that is intrinsic to a measure’s construct validity, and fail to test the implications of either perfect correlations (convergence) or imp...
Article
Comparing alternative explanations for behavioral phenomena is central to the process of scientific inquiry. Recent research has emphasized the efficacy of Information Theoretic model selection criteria in partial least squares structural equation modeling (PLS-SEM), which has gained massive dissemination in a variety of fields. However, selecting...
Conference Paper
Methodological research in Partial Least Squares Path Modeling (PLS-PM), a construct-based modeling technique, has seen a flurry of efforts to introduce predictive analytic methods. However, there is still confusion about how prediction can be applied to refine theory and integrate with this traditionally inferential technique. We feel that predict...
Chapter
Apart from the theoretic explanations offered by our empirical models, practitioners are also interested in the practical implications that they can apply to future cases. Being able to provide predictive diagnoses is an increasingly important issue linking theory and pracice, and empirical researchers in hospitality and tourism should heed the cal...
Article
Partial least squares path modeling (PLS-PM) has become popular in various disciplines to model structural relationships among latent variables measured by manifest variables. To fully benefit from the predictive capabilities of PLS-PM, researchers must understand the efficacy of predictive metrics used. In this research, we compare the performance...
Conference Paper
Generating predictions from PLS models is a recent and novel addition to the research and practice of structural equation modeling. Shmueli et al. (2016) gave us an explicit understanding of what prediction should entail in the context of PLS. That study also demonstrated how to generate predictions using the measurement items and structure of the...
Conference Paper
Open-source modules are software libraries that allow other developers to freely download and use them, and even to collaboratively contribute back with bugfixes or enhancements. For example, consider a Ruby programming language developer who creates a module that converts any data serialized in XML format into a native data structure for the Ruby...
Conference Paper
Despite the growing interest in predictive analytics using PLS models, there are no practical studies that demonstrate the application of predictive PLS modeling. This study reexamines an established empirical model and reanalyzes it through the lens of predictive analytics. In implementing predictive PLS procedures in recent literature, we uncover...

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