CONSTRUCT VALIDITY IN PARTIAL LEAST SQUARES PATH MODELING

Partial least squares path modeling (PLS) has seen increased use in the information systems research community. One of the stated key advantages of PLS is that it weights the indicator variables based on the strength of the relationship between the indicators and the underlying constructs, which presumably decreases the effect of measurement error in the analysis results. In this paper we argue that this assumption is not valid. While PLS indeed does weight the indicators to maximize the explained variance, it does this by including error variance in the model thus reducing construct validity. We use a simulation study of a simple PLS model to show that when compared to traditional sum scale approach, PLS estimates are actually often less valid. Although our study has its limitations, it hints that the use of PLS as a theory testing tool should be reevaluated and that more research testing the effectiveness of the PLS approach is in order

Rejoinder to Comments on Recent Developments in PLS

When we were first invited to write an essay on the use of PLS for CAIS, we wanted to focus on recent developments to help applied IS researchers, and the CAIS community of authors, reviewers, and editors make use of the latest research on and methodological advances in PLS. Recognizing that Information Systems is arguably the discipline in which the use of PLS as an alternative to CB-SEM originated and is most widely used, we realized that, pragmatically, our essay must focus on how to use PLS, not whether to use PLS. We received six interesting responses from researchers active in the PLS community. Their thoughts on our presentation of recent developments in PLS show very different perspectives with many points of difference amongst points of agreement in all the responses. In this rejoinder, we briefly respond to the received comments, clarify our position and ideas, and identify points of agreement (and disagreement). We emphasize that none of the responses give us cause to revise or eliminate our recommendations in recent developments. Overall, we believe this discussion on PLS to be valuable in advancing the use of PLS in Information Systems, which is, as we show in this rejoinder, an urgent issue

Sample Size Determination and Statistical Power Analysis in PLS Using R: An Annotated Tutorial

Partial least squares (PLS) is one of the most popular analytical techniques employed in the information systems field. In recent years, researchers have begun to revisit commonly used rules-of-thumb about the minimum sample sizes required to obtain reliable estimates for the parameters of interest in structural research models. Of particular importance in this regard is the a priori assessment of statistical power, which provides valuable information to be used in the design and planning of research studies. Though the importance of conducting such analyses has been recognized for quite some time, a review of the empirical research employing PLS indicates that they are not regularly conducted or reported. One likely reason is the lack of software support for these analyses in popular PLS packages. In this tutorial, we address this issue by providing, in tutorial form, the steps and code necessary to easily conduct such analyses. We also provide guidance on the reporting results

Between-Group Equivalence in Comparisons Using PLS: Results from Three Simulation Studies

The examination of between-group differences in theoretical relationships of interest is central to the conduct of research in the organizational and behavioral sciences, including Information Systems research. One such approach for examining these differences relies on the conduct of multi-group PLS analyses, where each group of interest is modeled separately, and the structural results from each analysis are then compared. Though this approach has been employed in the empirical literature, the ability of separate analyses to obtain models that are equivalent from a measurement perspective – which is a pre-requisite to making any comparisons between them – has not been carefully studied. In this research we perform three simulation studies under varying invariant conditions that highlight the performance of PLS in this regard. Our results indicate that sampling variability plays a major role in whether equivalent results can be obtained, and showcase the conditions in which the technique performs best

USE OF PARTIAL LEAST SQUARES AS A THEORY TESTING TOOL – AN ANALYSIS OF INFORMATION SYSTEMS PAPERS

Motivated by recent critique toward partial least squares path modeling (PLS), we present a research question if the PLS method, as used currently, is at all an appropriate tool for theory testing. We briefly summarize some of the recent critique of the use of PLS in IS as a theory testing tool. Then we analyze the results of 12 PLS analyzes published in leading IS journals testing if these models would have been rejected in the case that the data used for model testing had very little correspondence with the theorized models. Our Monte Carlo simulation shows that PLS will often provide results that support the tested hypotheses even if the model was not appropriate for the data. We conclude that the current practices of PLS studies have likely resulted in publishing research where the results are likely false and suggest that more attention should be paid on the assumptions of the PLS model or that alternative approached like summed scales and regression or structural equation modeling with estimators that have known statistical properties should be used instead

Protection Motivation Theory in Information Security Behavior Research: Reconsidering the Fundamentals

Scholars commonly use protection motivation theory (PMT) by Rogers to examine information systems (IS) security behaviors and behavioral intentions. A recent influential paper by Boss, Galletta, Lowry, Moody, and Polak (2015; hereafter BGLMP) in MIS Quarterly outlines correct and incorrect uses of PMT in Information Security behavior research. In this paper, we review some of BGLMP’s key recommendations, such as the claim that all IS behavior studies that apply PMT should always use the model of the full theory, contain and measure fear, and measure actual behaviors. We defend an interpretation of Rogers (1975, 1983) that differs from the interpretation that BGLMP propose. We present evidence that Rogers’ PMT and the empirical evidence do not adequately support many of BGLMP’s suggestions and that these suggestions contradict good scientific practices (e.g., restricting the use of the method of isolation) that the philosophy of science and the original literature on PMT uphold. As a result, if reviewers and editors continue to embrace these recommendations, they could hinder the progress of IS behavior research by not allowing isolation or the combination of different theoretical components. In contrast to BGLMP’s paper, we argue that further PMT research can focus on isolated PMT components and combine them with other theories. Some of our ideas (e.g., isolation) are not PMT-specific and could be useful for IS research in general. In summary, we contest BGLMP’s recommendations and offer revised recommendations in return

Estimating Formative Measurement Models in IS Research – Analysis of the Past and Recommendations for the Future

While debates on the appropriateness of formative measurement within structural equation models continue, such models are frequently found in IS research. IS researchers faced with such a model must identify the best method to estimate the model parameters, and have at their disposal covariance-based structural equation modeling (CBSEM), Partial Least Squares path modeling (PLS), and regression with summed scales, among other techniques.While all these methods can estimate models with formatively-specified latent variables, IS researchers frequently cite the presence of formative measurement as the reason for choosing PLS for model estimation over alternatives. Intuitively, a composite-based method such as PLS would appear to have an advantage in this particular scenario. In fact, some PLS researchers argue that PLS should only be used for such models. However, there is a dearth of empirical studies showing whether such an advantage does indeed exist.In this research, we discuss the statistical problems posed by models that include formatively-specified latent variables, and present a large-scale simulation study to investigate the relative performance of different estimation methods when faced with formative measurement, using models from studies published in MIS Quarterly. Based on our simulation results, we present recommendations for IS researchers interested in the estimation of models that include formatively-specified latent variables

Improvements to PLSc: Remaining problems and simple solutions

The recent article by Dijkstra and Henseler (2015b) presents a consistent partial least squares (PLSc) estimator that corrects for measurement error attenuation and provides evidence showing that, generally, PLSc performs comparably to a wide variety of more conventional estimators for structural equation models (SEM) with latent variables. However, PLSc does not adjust for other limitations of conventional PLS, namely: (1) bias in estimates of regression coefficients due to capitalization on chance; and (2) overestimation of composite reliability due to the proportionality relation between factor loadings and indicator weights. In this article, we illustrate these problems and then propose a simple solution: the use of unit-weighted composites, rather than those constructed from PLS results, combined with errors-in-variables regression (EIV) by using reliabilities obtained from factor analysis. Our simulations show that these two improvements perform as well as or better than PLSc. We also provide examples of how our proposed estimator can be easily implemented in various proprietary and open source software packages

Outsourcing of Disaggregated Services in Cloud-Based Enterprise Information Systems

In this paper, we examine outsourcing of disaggregated information-intensive services. Drawing on the theoretical streams of Global Information-Intensive Service Disaggregation and Transaction-Cost Economics, we formulate our research model to understand the phenomenon of disaggregation in the context of cloud computing. The model encompasses frequency, asset specificity, uncertainty, information intensity, and need for customer contact as characteristics of tasks that we use to explain the outsourcing of disaggregated services. We are especially interested in understanding the interplay between these characteristics and the increasingly popular cloud-based systems. Our results partially support previous research on the effects of characteristics on outsourcing. The main finding of the paper is impact of the cloud-based information systems on the interaction of these characteristics.Peer reviewe

A commentary on Yuan and Fang (2023)

Schuberth, F., Schamberger, T., Rönkkö, M., Liu, Y., & Henseler, J. (2023). Premature conclusions about the signal‐to‐noise ratio in structural equation modeling research: A commentary on Yuan and Fang (2023). British Journal of Mathematical and Statistical Psychology, 1-13. https://doi.org/10.1111/bmsp.12304 --- Funding Information: Jörg Henseler served as a reviewer for Yuan and Fang's ( 2023 ) manuscript. He gratefully acknowledges financial support from FCT Fundação para a Ciência e a Tecnologia (Portugal), national funding through a research grant from the Information Management Research Center – MagIC/NOVA IMS (UIDB/04152/2020). We thank Hao Wu, Associate Editor of the , for giving us the opportunity to write this commentary. Moreover, we thank Alexandra Elbakyan for her efforts in making science accessible. Finally, we thank Yves Rosseel for his support in replicating Yuan and Fang's results in lavaan. British Journal of Mathematical and Statistical PsychologyIn a recent article published in this journal, Yuan and Fang (British Journal of Mathematical and Statistical Psychology, 2023) suggest comparing structural equation modeling (SEM), also known as covariance-based SEM (CB-SEM), estimated by normal-distribution-based maximum likelihood (NML), to regression analysis with (weighted) composites estimated by least squares (LS) in terms of their signal-to-noise ratio (SNR). They summarize their findings in the statement that “[c]ontrary to the common belief that CB-SEM is the preferred method for the analysis of observational data, this article shows that regression analysis via weighted composites yields parameter estimates with much smaller standard errors, and thus corresponds to greater values of the [SNR].” In our commentary, we show that Yuan and Fang have made several incorrect assumptions and claims. Consequently, we recommend that empirical researchers not base their methodological choice regarding CB-SEM and regression analysis with composites on the findings of Yuan and Fang as these findings are premature and require further research.publishersversionepub_ahead_of_prin