Variance decomposition analysis: What is it and how to perform it – A complete guide for B2B researchers

Abstract

Variance decomposition analysis allows partitioning the total variance in an outcome variable, e.g., firm performance, into several components. Such partitioning allows identifying groups of factors (e.g., firm-, industry-, and country-specific) that explain a significant portion of the variation in firm performance, thus helping researchers, managers, and policymakers better understand the sources of competitive advantage. The present study aims to inform scholars, particularly those in business-to-business (B2B) marketing, about the benefits of utilizing variance decomposition analysis and draw scholarly attention to the relevant statistical techniques needed to produce accurate estimates. We specifically point to multilevel modeling techniques due to their significant advantages over other approaches to decompose the variance in a given outcome variable. We provide a detailed step-by-step guide as well as the related Stata codes on conducting variance decomposition analysis with multilevel modeling techniques. Using a 10-year (2009–2018) dataset comprising 7281 distinct European B2B firms operating in 348 industries and 29 countries, we empirically examine the relative importance of firm, industry, country, year, and residual effects in driving firm performance for B2B firms. Our analysis shows that firm-specific factors have the highest relative importance for B2B firms' performance, followed by home country and industry effects.