From the Editors: Explaining interaction effects within and across levels of analysis
Ulf Andersson1, Alvaro
Bo Bernhard Nielsen3 1Area Editor; 2Reviewing Editor 3Consulting Editor
A Cuervo-Cazurra, D’Amore-McKim School of Business, Northeastern University, 313 Hayden Hall, 360 Huntington Avenue,
Boston, MA 02115, USA.
Tel: 00 1 617 373 6568;
Fax: 00 1 617 373 8628; email: email@example.com
Many manuscripts submitted to the Journal of International Business Studies propose an interaction effect in their models in an effort to explain the complexity and contingency of relationships across borders. In this article, we provide guidance on how best to explain the interaction effects theoretically within and across levels of analysis. First, in the case of interactions within the same level of analysis, we suggest that authors provide an explanation of the mechanisms that link the main independent variable to the dependent variable, and then explain how the interaction variable modifies thesemechanisms. Moreover, to ensure that the arguments are theoretically complete, we suggest that authors theoretically rule out the potential reverse interaction effect between the main variable and moderating variable. Second, in the case of interactions across levels of analysis, we suggest that authors identify the cross-level nature of the moderating relationships, specify the level of analysis of the main relationship and the nested nature of the cross-level influences, and theoretically explain these cross-level influences.
Additionally, we suggest that authors pay particular attention to nesting in order to theoretically rule out reverse interactions.
Journal of International Business Studies (2014) 45, 1063–1071. doi:10.1057/jibs.2014.50
Keywords: interaction effects; moderation effects; cross-level interaction; international business; theory development
As editors, we are increasingly seeing papers with interaction effects – also known as multiplicative effects, product terms or moderation effects – that benefit from the powerful statistical analyses now available to scholars. Such research strategy has the potential to yield new theoretical insights that may advance the international business (IB) field. However, incorporating interaction effects is challenging because it identifies new and complex relationships that needs to be adequately explained. To help authors, in this editorial we provide suggestions for how best to explain the theoretical mechanisms1 behind proposed interaction effects in order to clarify the theoretical contribution of their studies. We go beyond statistical explanations of interaction effects and their detection (see, e.g.,
Aguinis & Gottfredson, 2010; Jaccard & Turrisi, 2003; Shieh, 2009), which, depending on how the variables are measured and the type of statistical method used, can be quite challenging.
We discuss two types of interaction effects: within and across levels of analysis. First, for interactions within levels of analysis, we
Journal of International Business Studies (2014) 45, 1063–1071 © 2014 Academy of International Business All rights reserved 0047-2506 www.jibs.net suggest that authors first provide an explanation of the theoretical mechanisms that link the main independent variable to the dependent variable, and then explain how and why the interaction variable modifies these theoretical mechanisms. Additionally, we suggest that authors theoretically rule out the existence of a reverse interaction effect in which the independent variable is actually affecting the relationship between the moderator and dependent variable. Second, for interactions across levels of analysis, we suggest that authors first identify the level of analysis of the main relationship, then specify the cross-level nature of the moderating relationships, before clarifying the hierarchy and nature of theoretical nesting. In addition, we propose that authors theoretically explain the multilevel influences, separating the justification of the cross-level interaction effect from the explanation of the cross-level direct influences.
EXPLAINING INTERACTION EFFECTS
Generally, interaction is said to occur when the effect of an independent variable (X) on a dependent variable (Y) varies across levels of a moderating variable (Z). Identifying and specifying relevant and important interaction effects pertaining to relations between independent and dependent variables is at the heart of theory in social science (Cohen, Cohen,
West, & Aiken, 2003) and indicates the maturity and sophistication of a field of inquiry (Aguinis, Boik, &
Pierce, 2001). Interactions provide researchers with the ability to enrich our understanding of economic and social relationships by establishing the conditions under which such relationships apply, or are stronger or weaker. As such, interactions enable the extension of well-known relationships to contexts that the original research did not consider, and they also help provide more detailed predictions about the relationships, going beyond the simplistic argument “it depends”. However, merely detecting a statistically significant effect of the interaction between independent and moderating variables on the dependent variable is not sufficient to be considered a contribution to the literature. The interaction effect has to be explained, and there must be theoretical arguments for why including this interaction results in better theory.
Research questions involving moderators typically address “when” or “under what conditions” an independent variable most strongly influences an outcome variable. More specifically, a moderator is a variable that alters the nature or strength of the relationship between an independent and an outcome variable (Baron & Kenny, 1986). The distinction between circumstances where the nature of the relationship ofX on Y varies as a function of Z (differential prediction) vs the strength of the relationship ofX on Y varies as a function of Z (differential validity) is important for several reasons. First, only differential prediction is appropriately tested with moderated multiple regression, which is the statistical test typically employed inmoderation studies (Carte & Russell, 2003). Differential validity is typically tested via subgroupmoderation: the sample is split into two ormore groups based on the level of the moderator variable, and t-tests of the correlation coefficients and χ2 tests are performed to assess the strength of themoderation effect and differences among groups. Second, the language and argumentation employed inmoderation hypotheses is often inaccurate in relation to the actual tests performed. For instance, if a researcher asserts that “the strength of the multinationality–performance relationship depends on the level of product diversification”, then he/she must report differences in strength of the multinationality–performance relationship (i.e., rmultinationality–performance) across levels of product diversification rather than the often-reported differences in the slope (nature) of the multinationality–performance relationship across levels of product diversification. Scholars must specify the role of the moderation a priori and make sure that the language, theoretical argumentation, and ensuing empirical tests match.