A Meta‐Analytic Integration of Acquisition Performance Prediction

David R. King, Gang Wang, Mehdi Samimi and Andres Felipe Cortes

Journal of Management Studies, 2021, vol. 58, issue 5, 1198-1236

Abstract: Different areas of focus in merger and acquisition (M&A) research have led to research fragmentation in theories and variables used to predict different measures of acquisition performance. We address fragmentation through broad meta‐analyses to identify relevant theories and predictor variables. Specifically, we find 16 constructs (method of payment (cash); method of payment (stock); acquirer debt; acquisition premium; relatedness; acquisition experience; alliance experience; acquirer firm size; target firm size; acquirer prior performance; target prior performance; acquirer R&D; national cultural distance; geographic distance; relative size; integration depth) that are significant predictors of different measures of acquisition performance. Our results support signalling theory that identifies the importance of deal characteristics, as well as contingency theory and the importance of context. With the exception of method of payment (stock), the impact of a predictor variable often varies across different measures of acquisition performance driving the need to assess theoretical explanations for underlying relationships. Overall, our results show there is value in integrating different theories to inform our understanding of acquisition performance.

Date: 2021
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