Michael K. Brady

Bio: Michael K. Brady is an academic researcher from Florida State University. The author has contributed to research in topics: Service (business) & Service quality. The author has an hindex of 31, co-authored 60 publications receiving 15926 citations. Previous affiliations of Michael K. Brady include Boston College.

Some New Thoughts on Conceptualizing Perceived Service Quality: A Hierarchical Approach:

Abstract: Through qualitative and empirical research, the authors find that the service quality construct conforms to the structure of a third-order factor model that ties service quality perceptions to distinct and actionable dimensions: outcome, interaction, and environmental quality. In turn, each has three subdimensions that define the basis of service quality perceptions. The authors further suggest that for each of these subdimensions to contribute to improved service quality perceptions, the quality received by consumers must be perceived to be reliable, responsive, and empathetic. The authors test and support this conceptualization across four service industries. They consider the research and managerial implications of the study and its limitations.

Discriminant validity testing in marketing: an analysis, causes for concern, and proposed remedies

Abstract: The results of this research suggest a new mandate for discriminant validity testing in marketing. Specifically, the authors demonstrate that the AVE-SV comparison (Fornell and Larcker 1981) and HTMT ratio (Henseler et al. 2015) with 0.85 cutoff provide the best assessment of discriminant validity and should be the standard for publication in marketing. These conclusions are based on a thorough assessment of the literature and the results of a Monte Carlo simulation. First, based on a content analysis of articles published in seven leading marketing journals from 1996 to 2012, the authors demonstrate that three tests—the constrained phi (Joreskog 1971), AVE-SV (Fornell and Larcker 1981), and overlapping confidence intervals (Anderson and Gerbing 1988)—are by far most common. Further review reveals that (1) more than 20% of survey-based and over 80% of non-survey-based marketing studies fail to document tests for discriminant validity, (2) there is wide variance across journals and research streams in terms of whether discriminant validity tests are performed, (3) conclusions have already been drawn about the relative stringency of the three most common methods, and (4) the method that is generally perceived to be most generous is being consistently misapplied in a way that erodes its stringency. Second, a Monte Carlo simulation is conducted to assess the relative rigor of the three most common tests, as well as an emerging technique (HTMT). Results reveal that (1) on average, the four discriminant validity testing methods detect violations approximately 50% of the time, (2) the constrained phi and overlapping confidence interval approaches perform very poorly in detecting violations whereas the AVE-SV test and HTMT (with a ratio cutoff of 0.85) methods perform well, and (3) the HTMT.85 method offers the best balance between high detection and low arbitrary violation (i.e., false positive) rates.

Customer Orientation: Effects on Customer Service Perceptions and Outcome Behaviors

Abstract: This study investigates the effect of being customer oriented on service performance perceptions and outcome behaviors. Specifically, the focus is on identifying the influence that being perceived ...

When to use and how to report the results of PLS-SEM

Abstract: The purpose of this paper is to provide a comprehensive, yet concise, overview of the considerations and metrics required for partial least squares structural equation modeling (PLS-SEM) analysis and result reporting. Preliminary considerations are summarized first, including reasons for choosing PLS-SEM, recommended sample size in selected contexts, distributional assumptions, use of secondary data, statistical power and the need for goodness-of-fit testing. Next, the metrics as well as the rules of thumb that should be applied to assess the PLS-SEM results are covered. Besides presenting established PLS-SEM evaluation criteria, the overview includes the following new guidelines: PLSpredict (i.e., a novel approach for assessing a model’s out-of-sample prediction), metrics for model comparisons, and several complementary methods for checking the results’ robustness.,This paper provides an overview of previously and recently proposed metrics as well as rules of thumb for evaluating the research results based on the application of PLS-SEM.,Most of the previously applied metrics for evaluating PLS-SEM results are still relevant. Nevertheless, scholars need to be knowledgeable about recently proposed metrics (e.g. model comparison criteria) and methods (e.g. endogeneity assessment, latent class analysis and PLSpredict), and when and how to apply them to extend their analyses.,Methodological developments associated with PLS-SEM are rapidly emerging. The metrics reported in this paper are useful for current applications, but must always be up to date with the latest developments in the PLS-SEM method.,In light of more recent research and methodological developments in the PLS-SEM domain, guidelines for the method’s use need to be continuously extended and updated. This paper is the most current and comprehensive summary of the PLS-SEM method and the metrics applied to assess its solutions.

Structural Equation Modeling

Abstract: Structural equation modeling (SEM) is a multivariate statistical framework that is used to model complex relationships between directly observed and indirectly observed (latent) variables. SEM is a general framework that involves simultaneously solving systems of linear equations and encompasses other techniques such as regression, factor analysis, path analysis, and latent growth curve modeling. Recently, SEM has gained popularity in the analysis of complex genetic traits because it can be used to better analyze the relationships between correlated variables (traits), to model genes as latent variables as a function of multiple observed genetic variants, and to assess the association between multiple genetic variants and multiple correlated phenotypes of interest. Though the general SEM framework only allows for the analysis of independent observations, recent work has extended SEM for the analysis of data on general pedigrees. Here, we review the theory of SEM for both unrelated and family data, describe the available software for SEM, and provide examples of SEM analysis.

Using PLS path modeling in new technology research: updated guidelines

Abstract: Purpose – Partial least squares (PLS) path modeling is a variance-based structural equation modeling (SEM) technique that is widely applied in business and social sciences. Its ability to model composites and factors makes it a formidable statistical tool for new technology research. Recent reviews, discussions, and developments have led to substantial changes in the understanding and use of PLS. The paper aims to discuss these issues. Design/methodology/approach – This paper aggregates new insights and offers a fresh look at PLS path modeling. It presents new developments, such as consistent PLS, confirmatory composite analysis, and the heterotrait-monotrait ratio of correlations. Findings – PLS path modeling is the method of choice if a SEM contains both factors and composites. Novel tests of exact fit make a confirmatory use of PLS path modeling possible. Originality/value – This paper provides updated guidelines of how to use PLS and how to report and interpret its results.