An Information-Processing Perspective of IS-Business Integration: A Review of Research and a Conceptual Model

This paper presents a review framework of research related to inter-departmental integration, with a focus toward research into the IS-business relationship. While much of the literature reviewed lies outside the Information Systems realm, it is proposed here that related research in other business disciplines has much to contribute to the current interest in the integration between the Information Systems function, and line management in organizations. The main contribution of this manuscript lies in the identification of an appropriate theoretical base that can be employed in the study of this relationship

The Use of PLS When Analyzing Formative Constructs: Theoretical Analysis and Results From Simulations

Partial Least Squares (PLS) has become an increasingly popular approach to testing research models with multiple proposed causality links. Moreover, recent interest in the specification of constructs in a formative manner has accentuated this tendency, given the purported ability of PLS to handle this methodological development. While a review of the literature reveals an extensive use of PLS in this capacity, there is neither theoretical nor empirical evidence supporting this property of the technique. An examination of the inner workings of PLS shows several limitations of PLS when used in \u27formative\u27 (Mode B) estimation, and compares it to linear regression and covariance-based approaches. Results from Monte Carlo simulations comparing the performance of PLS and covariance-based techniques in estimating models with formatively specified constructs in either exogenous or endogenous positions reveals important biases for PLS, but not for covariance-based SEM. The results are discussed and recommendations for researchers are proposed

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

There All Along? A Preliminary Meta-Analysis of the Moderating Gender Effects in Technology Acceptance Research

Technology acceptance is one of the most extensive streams of research in the information systems literature. Building on previous work, it has recently been proposed that the gender of information system users moderates the relationships between intention to use a technology and its most important determinants, perceived usefulness and perceived ease of use. A better understanding of the magnitude of this effect and its implications seems important in the development of practical applications of the theory, such as those related to training and motivational interventions. This research reports on a preliminary meta-analysis of extant literature in mainstream journals employing the proportion of men and women in the studies as an indicator of the expected relationships. An explanation of this approach is provided, and preliminary results are discussed

A Monte Carlo Investigation of Partial Least Squares, With Implications for Both Structural and Measurement Models

Partial Least Squares (PLS) is a popular technique with extensive adoption within the Information Systems research community. However, the statistical performance of PLS has not been extensively studied, and recent research has questioned some of its purported advantages. The simulation study reported here analyzed the performance of PLS with regards to the recovery and estimation accuracy of both structural and measurement parameters. Somewhat surprisingly, the effects of estimation bias on the latter and their implications for the evaluation of measurement models have not been the focus of past research. Results show the existence of an important degree of bias in both sets of estimates, and the conflicting effect of increased sample size with additional indicators per composite variable

HOW MANY TECHNOLOGY TYPES ARE THERE? PRELIMINARY RESULTS FROM THE TECHNOLOGY ACCEPTANCE LITERATURE

To investigate a generalizable moderating effect of the type of technology tested upon its acceptance, a classification of technologies is needed. This study aims to develop a preliminary framework to describe information technologies based upon 200 randomly selected technology descriptions taken from a comprehensive TAM meta-analysis effort currently in progress. We report on the use of a classification method involving both human judgment and statistical techniques. A manual sorting process is followed by multidimensional scaling (MDS) and cluster analysis to aggregate the individual interpretations of the sorters into hierarchical cluster structures. The results of this method reveal several potential technology grouping solutions, one of which was selected for further discussion. Limitations and future research are also discussed

The Internal-External Efficacy Model: Towards the Integration of Computer Self-Efficacy and Task Technology Fit into a Comprehensive View

This research reviews the task-technology fit literature and draws parallels with the internal-external efficacy model recently developed by Eden (2001). In particular, it argues that the construct of task-technology fit, operationalized with perceptual measures as is commonly done, is equivalent to the concept of means efficacy included in the internal-external efficacy model. As a result, the latter provides a theoretical lens through which existing results in the task-technology fit literature can be interpreted, as well as a number of avenues for further research that have not been conceptualized before. A research model based on these arguments is outlined, as well as the potential contribution of carrying out such study

Statistical Inference with PLSc Using Bootstrap Confidence Intervals

Partial least squares (PLS) is one of the most popular statistical techniques in use in the Information Systems field. When applied to data originating from a common factor model, as is often the case in the discipline, PLS will produce biased estimates. A recent development, consistent PLS (PLSc), has been introduced to correct for this bias. In addition, the common practice in PLS of comparing the ratio of an estimate to its standard error to a t distribution for the purposes of statistical inference has also been challenged. We contribute to the practice of research in the IS discipline by providing evidence of the value of employing bootstrap confidence intervals in conjunction with PLSc, which is a more appropriate alternative than PLS for many of the research scenarios that are of interest to the field. Such evidence is direly needed before a complete approach to the estimation of SEM that relies on both PLSc and bootstrap CIs can be widely adopted. We also provide recommendations for researchers on the use of confidence intervals with PLSc

Revisiting Bias Due to Construct Misspecification: Different Results from Considering Coefficients in Standardized Form

Researchers in a number of disciplines, including Information Systems, have argued that much of past research may have incorrectly specified the relationship between latent variables and indicators as reflective when an understanding of a construct and its measures indicates that a formative specification would have been warranted. Coupled with the posited severe biasing effects of construct misspecification on structural parameters, these two assertions would lead to concluding that an important portion of our literature is largely invalid. While we do not delve into the issue of when one specification should be employed over another, our work here contends that construct misspecification, but with a particular exception, does not lead to severely biased estimates. We argue, and show through extensive simulations, that a lack of attention to the metric in which relationships are expressed is responsible for the current belief in the negative effects of misspecification