AbstractThis article is the second of two parts intended to serve as a primer for structural equations models for the behavioral researcher. The first article introduced the basics: the measurement model, the structural model, and the combined, full structural equations model. In this second article, advanced issues are addressed, including fit indices and sample size, moderators, longitudinal data, mediation, and so forth.

In this paper, we suggest ways to improve mediation analysis practice among consumer behavior researchers. We review the current methodology and demonstrate the superiority of structural equations modeling, both for assessing the classic mediation questions and for enabling researchers to extend beyond these basic inquiries. A series of simulations are presented to support the claim that the approach is superior. In addition to statistical demonstrations, logical arguments are presented, particularly regarding the introduction of a fourth construct into the mediation system. We close the paper with new prescriptive instructions for mediation analyses.

This research investigates how movie ratings from professional critics, amateur communities, and viewers themselves influence key movie performance measures (i.e., movie revenues and new movie ratings). Using movie-level data, the authors find that high early movie revenues enhance subsequent movie ratings. They also find that high advertising spending on movies supported by high ratings maximizes the movie's revenues. Furthermore, they empirically show that sequel movies tend to reap more revenues but receive lower ratings than originals. Using individual viewer–level data, this research highlights how viewers’ own viewing and rating histories and movie communities’ collective opinions explain viewer satisfaction. The authors find that various aspects of these ratings explain viewers’ new movie ratings as a measure of viewer satisfaction, after controlling for movie characteristics. Furthermore, they find that viewers’ movie experiences can cause them to become more critical in ratings over time. Finally, they find a U-shaped relationship between viewers’ genre preferences and genre-specific movie ratings for heavy viewers.

Research has not verified the theoretical or practical value of the brand attachment construct in relation to alternative constructs, particularly brand attitude strength. The authors make conceptual, measurement, and managerial contributions to this research issue. Conceptually, they define brand attachment, articulate its defining properties, and differentiate it from brand attitude strength. From a measurement perspective, they develop and validate a parsimonious measure of brand attachment, test the assumptions that underlie it, and demonstrate that it indicates the concept of attachment. They also demonstrate the convergent and discriminant validity of this measure in relation to brand attitude strength. Managerially, they demonstrate that brand attachment offers value over brand attitude strength in predicting (1) consumers’ intentions to perform difficult behaviors (those they regard as using consumer resources), (2) actual purchase behaviors, (3) brand purchase share (the share of a brand among directly competing brands), and (4) need share (the extent to which consumers rely on a brand to address relevant needs, including those brands in substitutable product categories).

AbstractIn this rebuttal, we discuss the comments of Rucker, McShane, and Preacher (2015) and McClelland, Lynch, Irwin, Spiller, and Fitzsimons (2015). Both commentaries raise interesting points, and although both teams clearly put a lot of work into their papers, the bottom line is this: our research sets the record straight that median splits are perfectly acceptable to use when independent variables are uncorrelated. The commentaries do a good job of furthering the discussion to help readers better develop their own preferences, which was the purpose of our paper. In the final analysis, neither of the commentaries pose any threat to our findings of the statistical robustness and valid use of median splits, and accordingly we can reassure researchers (and reviewers and journal editors) that they can be confident that when independent variables are uncorrelated, it is totally acceptable to conduct median split analyses.