Measurement and Moderation: Finding the Boundary Conditions in Logistics and Supply Chain Research

A moderator is any variable that affects the strength of a relationship between a predictor and an outcome variable. While simple in concept, the application of moderation analysis can yield profound implications to research conducted in logistics and supply chain management. Moderation analysis illuminates boundary conditions to purported relationships, providing a deeper perspective on what may, to date, represent generalizable findings and commonly held beliefs in the field. Such findings prove interesting and enrich our theories. Further, moderation relies on precise measurement of theoretical constructs in order to avoid attenuation of statistical tests and detect interaction effects. This thought leadership piece seeks to: (1) assert the value of moderation analysis and encourage a more prominent place in our survey-based research projects, (2) provide best practice approaches for using this type of analysis in pursuit of greater depth and clarity in our research, and (3) provide seeds for potential research projects that could benefit from the use of this type of analysis. Guidance is also provided for reviewers who assess manuscripts featuring moderation.

[1]  Jason W Miller,et al.  Extensions of the Johnson-Neyman Technique to Linear Models With Curvilinear Effects: Derivations and Analytical Tools , 2013, Multivariate behavioral research.

[2]  Ryan T. Wright,et al.  Assessing Common Method Bias: Problems with the ULMC Technique , 2012, MIS Q..

[3]  Dev K. Dalal,et al.  Some Common Myths About Centering Predictor Variables in Moderated Multiple Regression and Polynomial Regression , 2012 .

[4]  Andrew F. Hayes,et al.  Cautions Regarding the Interpretation of Regression Coefficients and Hypothesis Tests in Linear Models with Interactions , 2012 .

[5]  Chad W. Autry,et al.  Toward Greater Validation of Supply Chain Management Theory and Concepts: The Roles of Research Replication and Meta‐Analysis , 2011 .

[6]  S. Fawcett,et al.  Moving the Needle: Making a Contribution When the Easy Questions Have Been Answered , 2011 .

[7]  Rebecca Walker Naylor,et al.  Using Behavioral Experiments to Expand Our Horizons and Deepen Our Understanding of Logistics and Supply Chain Decision Making , 2011 .

[8]  Dieter Zapf,et al.  Advanced Nonlinear Latent Variable Modeling: Distribution Analytic LMS and QML Estimators of Interaction and Quadratic Effects , 2011 .

[9]  Richard P. Bagozzi,et al.  Measurement and Meaning in Information Systems and Organizational Research: Methodological and Philosophical Foundations , 2011, MIS Q..

[10]  Jeff Peterson,et al.  Theory Pruning: Strategies to Reduce Our Dense Theoretical Landscape , 2010 .

[11]  J. Edwards,et al.  The Presence of Something or the Absence of Nothing: Increasing Theoretical Precision in Management Research , 2010 .

[12]  Stephan M. Wagner,et al.  DO PERCEPTIONS BECOME REALITY? THE MODERATING ROLE OF SUPPLY CHAIN RESILIENCY ON DISRUPTION OCCURRENCE , 2010 .

[13]  Carl Marcus Wallenburg,et al.  CUSTOMER LOYALTY IN LOGISTICS OUTSOURCING RELATIONSHIPS: AN EXAMINATION OF THE MODERATING EFFECTS OF CONFLICT FREQUENCY , 2010 .

[14]  Kristopher J Preacher,et al.  Please Scroll down for Article Multivariate Behavioral Research Quantifying and Testing Indirect Effects in Simple Mediation Models When the Constituent Paths Are Nonlinear , 2022 .

[15]  Herman Aguinis,et al.  Best-practice recommendations for estimating interaction effects using moderated multiple regression , 2010 .

[16]  Enno Siemsen,et al.  Common Method Bias in Regression Models With Linear, Quadratic, and Interaction Effects , 2010 .

[17]  Barbara B. Flynn,et al.  The impact of supply chain integration on performance: A contingency and configuration approach , 2010 .

[18]  Andrew F. Hayes,et al.  Computational procedures for probing interactions in OLS and logistic regression: SPSS and SAS implementations , 2009, Behavior research methods.

[19]  Ulrich Trautwein,et al.  Please Scroll down for Article Structural Equation Modeling: a Multidisciplinary Journal Exploratory Structural Equation Modeling, Integrating Cfa and Efa: Application to Students' Evaluations of University Teaching , 2022 .

[20]  Marcia J. Simmering,et al.  A Tale of Three Perspectives , 2009 .

[21]  C. Moorman,et al.  Cross-Sectional versus Longitudinal Survey Research: Concepts, Findings, and Guidelines , 2008 .

[22]  F. Chen.  Sensitivity of Goodness of Fit Indexes to Lack of Measurement Invariance , 2007 .

[23]  J. D. Hess,et al.  Mean-Centering Does Not Alleviate Collinearity Problems in Moderated Multiple Regression Models , 2007 .

[24]  Daniel J. Bauer,et al.  Computational Tools for Probing Interactions in Multiple Linear Regression, Multilevel Modeling, and Latent Curve Analysis , 2006 .

[25]  C. Carter,et al.  Opportunities for Research in Third-Party Logistics , 2006, Transportation Journal.

[26]  Daniel J Bauer,et al.  Probing Interactions in Fixed and Multilevel Regression: Inferential and Graphical Techniques , 2005, Multivariate behavioral research.

[27]  Scott B. MacKenzie,et al.  Common method biases in behavioral research: a critical review of the literature and recommended remedies. , 2003, The Journal of applied psychology.

[28]  Richard Schulz,et al.  Investigating Moderator Hypotheses in Aging Research: Statistical, Methodological, and Conceptual Difficulties with Comparing Separate Regressions , 2003, International journal of aging & human development.

[29]  G. McClelland,et al.  Misleading Heuristics and Moderated Multiple Regression Models , 2001 .

[30]  R. Vandenberg,et al.  A Review and Synthesis of the Measurement Invariance Literature: Suggestions, Practices, and Recommendations for Organizational Research , 2000 .

[31]  William T. Ross,et al.  The Asymmetric Impact of Negative and Positive Attribute-Level Performance on Overall Satisfaction and Repurchase Intentions , 1998 .

[32]  Judith A. Hall,et al.  Testing for moderator variables in meta‐analysis: Issues and methods , 1991 .

[33]  M. Evans.  A Monte Carlo study of the effects of correlated method variance in moderated multiple regression analysis , 1985 .

[34]  L. E. Jones,et al.  Analysis of multiplicative combination rules when the causal variables are measured with error. , 1983 .

[35]  R. Oliver.  A Cognitive Model of the Antecedents and Consequences of Satisfaction Decisions , 1980 .

[36]  Jae-On Kim,et al.  Standardized and Unstandardized Coefficients in Causal Analysis , 1976 .

[37]  Murray S. Davis,et al.  That's Interesting! , 1971 .

[38]  J. Platt.  Strong Inference: Certain systematic methods of scientific thinking may produce much more rapid progress than others. , 1964, Science.