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A Meta‐Analytic Integration of Acquisition Performance Prediction
Author(s) -
King David R.,
Wang Gang,
Samimi Mehdi,
Cortes Andres Felipe
Publication year - 2021
Publication title -
journal of management studies
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 4.398
H-Index - 184
eISSN - 1467-6486
pISSN - 0022-2380
DOI - 10.1111/joms.12636
Subject(s) - payment , cash , stock (firearms) , mergers and acquisitions , econometrics , fragmentation (computing) , context (archaeology) , business , actuarial science , marketing , computer science , economics , finance , engineering , biology , operating system , mechanical engineering , paleontology
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.