Abstract
Cross-border acquisitions (CBA) are a common tool of multinational firms, but what drives CBA performance remains inconclusive. We aggregate research on CBA performance to identify impacts of cultural distance and home and host country effects using a relatively new approach, predictive modeling, to classify research to identify patterns with machine learning. Predictive modeling enables considering effects not traditionally captured by meta-analysis and it offers different interpretations. We find an overall positive impact of cultural distance on CBA performance. However, we also find important host and home country effects, and the home country setting of acquirers may be as or more important than host and distance effects. As a result, there are questions surrounding the usefulness of cultural distance, and CBA research needs to consider home country effects.
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