a b s t r a c tEmpirical research in Supply Chain Management is increasingly interested in complex models involving mediation effects. We support these endeavors by directing attention to the practices for the theorizing of, the testing for, and the drawing of conclusions about mediation effects. Our paper synthesizes diverse literature in other disciplines to provide an accessible tutorial as to the mathematical foundation of mediation effects and the various methods available to test for these effects. We also provide guidance to SCM scholars in the form of eight recommendations aimed at improving the theorizing of, the testing for, and the drawing of conclusions about mediation effects. Recommendations pertaining to how mediation effects are hypothesized and stated and how to select among methods to test for mediation effects are novel contributions for and beyond the Supply Chain Management discipline. (M. Rungtusanatham), miller 5350@fisher.osu.edu (J.W. Miller), boyer 9@fisher.osu.edu (K.K. Boyer). 1 Tel.: +1 614 292 0680. 2 Tel.: +1 614 292 4605.respond to disciplinary calls for more and better theories about SCM phenomena (Carter, 2011;Schroeder, 2008), provided that these endeavors are properly executed. This provision, however, may not be perfectly accurate as it relates to SCM research involving mediation processes. In Appendix A, we summarize the design and discuss the results of an exemplary (i.e., not exhaustive) review of 81 SCM articles involving mediation processes that were published, between 2008-2011, in the Journal of Business Logistics, the Journal of Operations Management, and the Journal of Supply Chain Management. Our review highlights three shortcomings with respect to how SCM research has been theorizing, empirically testing, and concluding for mediation effects. One shortcoming is that we rarely hypothesize mediation effects even when our conceptual models, described pictorially or in prose, depict mediation processes. A second shortcoming is that we often draw erroneous conclusions about mediation effects based on statistical results stemming from applying problematic methods or, more critically, on ad hoc interpretations of statistical results. A third shortcoming is that when our conceptual models incorporate multiple (e.g., three) mediation effects, we sometimes draw erroneous conclusions about all three mediation effects by relying on an omnibus test only.These three shortcomings, we believe, reflect an incomplete exposure by SCM scholars to recent developments regarding the theorizing and testing of mediation processes. Many SCM scholars are undoubtedly familiar with what mediation is and how to test for mediation effects via such familiar methods as the Baron and Kenny (1986) Method, the James et al. ) Method, or the Sobel Test (Sobel, 1982. They are, however, likely to be less conversant about other methods (e.g., Bootstrapping, Monte Carlo 0272-6963/$ -see front matter

P ublishing in top journals is difficult. Common challenges undermine authors' attempts to explain and influence their discipline's understanding and practice. We identify and describe these roadblocks to publishing success. We also benchmark best practice in management, marketing, and supply chain journals to provide a trail guide for writing-and publishing-influential conceptual, qualitative, and survey research. Given equifinality in research, our trail guide should not be viewed as the only way to craft excellent, influential research. However, if we agree on the basics, we can (1) increase consistency in the review process, (2) reduce publication cycles, and (3) begin to roll back the length of articles.

The past decade has witnessed renewed interest in the use of the Johnson-Neyman (J-N) technique for calculating the regions of significance for the simple slope of a focal predictor on an outcome variable across the range of a second, continuous independent variable. Although tools have been developed to apply this technique to probe 2- and 3-way interactions in several types of linear models, this method has not been extended to include quadratic terms or more complicated models involving quadratic terms and interactions. Curvilinear relations of this type are incorporated in several theories in the social sciences. This article extends the J-N method to such linear models along with presenting freely available online tools that implement this technique as well as the traditional pick-a-point approach. Algebraic and graphical representations of the proposed J-N extension are provided. An example is presented to illustrate the use of these tools and the interpretation of findings. Issues of reliability as well as "spurious moderator" effects are discussed along with recommendations for future research.

Motor carrier safety impacts the well‐being of the traveling public and the economic well‐being of the shippers who entrust motor carriers with safely transporting freight. Shippers are affected by motor carrier safety due to accidents damaging their cargo and disrupting their customers’ operations. One characteristic frequently theorized to predict motor carrier safety is motor carriers financial performance. However, the literature offers mixed evidence linking motor carrier financial performance to safety. This does not help practitioners or policy makers and necessitates research to resolve these inconsistent findings, which we undertake in this research. We extend previous work by developing a theoretical framework based on strain theory to explain why both absolute (static) financial performance and year‐to‐year change in financial performance should uniquely affect carrier safety. We test our hypotheses by fitting mixed‐effects models to a repeated‐measure, longitudinal database of publically traded motor carrier financial performance and safety measures. Results indicate that financial performance measures uniquely affect carrier safety. These findings attempt to resolve the inconsistencies in the past literature and, carry important implications for researchers studying motor carrier safety, motor carrier managers, shippers, and policy makers.

Intravenous administration of 0.04-0.08 mg/kg morphine sulfate reduced both sensory intensity and unpleasantness visual analogue scale (VAS) responses to graded 5 sec nociceptive temperature stimuli (45-51 degrees C) in a dose-dependent manner. The lower doses of morphine (0.04 and 0.06 mg/kg) resulted in statistically reliable reductions in affective but not sensory intensity VAS responses, possibly reflecting supraspinal effects on brain regions involved in affect and motivation. However, the highest dose of morphine tested (0.08 mg/kg) reduced both sensory and affective VAS responses to graded nociceptive stimuli as well as VAS sensory responses to first and second pain evoked by brief heat pulses. Morphine also had an especially potent inhibitory effect on temporal summation of second pain that is known to occur when intense nociceptive stimuli occur at rates greater than 0.3/sec. The results support current hypotheses about neural mechanisms of narcotic analgesia and further clarify the relative effects of morphine on sensory and affective dimensions of experimental pain. The derived morphine dose-analgesic response functions also provide a reference standard for quantitatively comparing magnitudes of different CNS-mediated forms of analgesia.

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.

Motor carrier safety is an important concern of shippers, carriers, policy makers, consignees, insurance providers, and the motoring public. One aspect of carrier safety that has garnered substantial attention is whether carriers making greater use of owner–operators are more or less safe vis‐à‐vis carriers making greater use of employee drivers. Currently, conflicting theoretical predictions exist regarding the direction of this relationship. In this article, we offer a reconciliation of the alternative theoretical predictions by developing a coherent theory that merges sociological rational choice theory and theory regarding motor carrier safety. We subject our theory to empirical testing by fitting a series of seemingly unrelated regression models to a vector of safety measures tracked as part of the Federal Motor Carrier Safety Administration's Compliance, Safety, and Accountability program. Our results are consistent with our proposed theory of owner—operator safety and provide meaningful theoretical and managerial implications and directions for future research.

Muthén and Asparouhov introduced an approach for conducting Bayesian inference in the context of structural equation models that they termed Bayesian structural equation modeling (BSEM). In this article, we provide an overview of the BSEM technique, illustrate how this technique relates to confirmatory and exploratory factor analysis, and highlight several key problems with using the BSEM approach as it is currently advocated. Utilizing data from a large-scale study of entrepreneurial self-efficacy, we develop a modified approach for applying the BSEM technique in a manner that is more consistent with accepted principles of reflective measurement, factor analysis, and model selection. We devise a series of recommendations to guide future use of the BSEM technique to help ensure that mainstream use of this approach heralds the coming of a new day in measurement development rather than a false dawn.