Jensen, Camilla

King, David R.

Zamborsky, Peter

We propose to contribute to two areas of the literature on meta-studies in international business and finance. Our first contribution is methodological, where we replicate and extend the study by Stahl and Voigt (2008) to aggregate additional research and variables to find the drivers of performance in cross-border acquisitions. This is accomplished through content analysis of existing research to aggregate research results by conducting meta-studies and potentially also expand its scientific domain. Additional methodological contributions of such meta-analysis could include: 1) helping to uncover entirely new findings from existing research, 2) examining complex non-linear models, and 3) easing the task of compiling the data to investigate larger and more diverse samples on their topic of interest. Second, we will apply recently developed techniques to compare drivers of cross-border acquisition performance to identify a set of the most influential factors. Using the validated prediction model on a new and expanded dataset from the first step, we will apply advanced analytical techniques to answer: What influences cross-border acquisition performance? A fundamental challenge of cross-border acquisitions involves cultural distance that reflects a combination displaying both a need for knowledge and barriers to obtaining it (Reus &amp; Lamont, 2009; Stahl &amp; Voigt, 2008). Cultural distance is often expected to have an overall negative effect on acquisition performance (e.g., Goerzen &amp; Beamish, 2003; Hutzschenreuter, Voll &amp; Verbeke, 2011; Stahl &amp; Voigt, 2008). While research recognizes additional dimensions to cultural distance, involving language, geographic and institutional distance (Bauer et al., 2018; Coval &amp; Moskowitz, 1999; Kedia &amp; Reddy, 2016), research rarely examines different dimensions of cultural distance and how associated challenges can be mitigated (Risberg, 2001, Rottig, 2011). Using prediction algorithms (Shaikhina et al., 2017), we can compile studies with variables of interest on measures of performance and distance in cross-border acquisition research. Abstract EmneordWe propose to contribute to two areas of the literature on meta-studies in international business and finance. Our first contribution is methodological, where we replicate and extend the study by Stahl and Voigt (2008) to aggregate additional research and variables to find the drivers of performance in cross-border acquisitions. This is accomplished through content analysis of existing research to aggregate research results by conducting meta-studies and potentially also expand its scientific domain. Additional methodological contributions of such meta-analysis could include: 1) helping to uncover entirely new findings from existing research, 2) examining complex non-linear models, and 3) easing the task of compiling the data to investigate larger and more diverse samples on their topic of interest. Second, we will apply recently developed techniques to compare drivers of cross-border acquisition performance to identify a set of the most influential factors. Using the validated prediction model on a new and expanded dataset from the first step, we will apply advanced analytical techniques to answer: What influences cross-border acquisition performance? A fundamental challenge of cross-border acquisitions involves cultural distance that reflects a combination displaying both a need for knowledge and barriers to obtaining it (Reus &amp; Lamont, 2009; Stahl &amp; Voigt, 2008). Cultural distance is often expected to have an overall negative effect on acquisition performance (e.g., Goerzen &amp; Beamish, 2003; Hutzschenreuter, Voll &amp; Verbeke, 2011; Stahl &amp; Voigt, 2008). While research recognizes additional dimensions to cultural distance, involving language, geographic and institutional distance (Bauer et al., 2018; Coval &amp; Moskowitz, 1999; Kedia &amp; Reddy, 2016), research rarely examines different dimensions of cultural distance and how associated challenges can be mitigated (Risberg, 2001, Rottig, 2011). Using prediction algorithms (Shaikhina et al., 2017), we can compile studies with variables of interest on measures of performance and distance in cross-border acquisition research

What influences cross-border acquisition performance?:A meta-analysis

Abstract

Similar works

Full text

Available Versions

Roskilde Universitet