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Purpose – Despite the increasing use of formative measurement models in literature, little is known about potential consequences for substantive theory testing. Against this background, the aims of this chapter are (1) to highlight some problems that may arise when formative instead of reflective measures are used to test even simple theoretical models with covarianced-based methodologies, (2) to illustrate some approaches that might help overcome these problems, (3) to pinpoint potential interpretation difficulties of the results involving re-specified measurement models, and (4) to stimulate discussion on the implications for theory development when models are tested with formative measures.

Methodology/approach – Potential consequences of formative measurement models for theory testing are highlighted using an empirical study on consumer animosity as an illustrative example and applying covarianced-based structural equations modeling procedures for estimation purposes.

Findings – The empirical study shows (a) that some scaling options for the (composite) latent variable result in non-convergence problems, (b) that, assuming convergence, parameter estimates, standard errors, and significance levels vary depending on the scaling method used, and (c) that goodness-of-fit statistics cannot be used as diagnostic measures for the appropriateness of divergent results.

Originality/value of paper – The contribution of this chapter is two-fold: First, it shows that to enable estimation, it is often necessary to modify (i.e., expand) the original theoretical model in a conceptually reasonable manner and to do so before data collection. Second, it demonstrates that alternative scaling options for composite latent variables may result in inconsistent substantive conclusions. Consequently, the impact of formative measurement on theory testing is a critical topic and needs to receive further attention in future literature.

“Garbage in, garbage out” is a common expression that academics and practitioners use to emphasize that empirical analysis is only as good as the basis on which it relies. Although the importance of sound data and valid measures has long been acknowledged, it is nevertheless often problematic to follow required quality standards in concrete research situations. Potential sources of error are usually unknown, methods to ensure data quality are unavailable, and existing methods for scale development, index construction, data collection, and data analysis are insufficient or erroneously applied. This is especially true of international marketing research, which often makes great demands on the data and measures used, as well as on the research methodology applied. Against this background, this volume addresses issues pertaining to measurement and research methodology in an international marketing context. Thanks to the efforts of authors and reviewers, we are pleased to present nine articles that deal with cutting-edge topics such as formative measurement, response-bias in cross-cultural research, marketing efficiency measurement, and segmentation methods.