This paper investigates the puzzle of mergers and acquisitions’ (M&A) long-term performance through the strategic management perspective. The authors measure the M&A capability construct and test its relationship with the long-term performance of the firms.
The study employs a natural language processing (NLP) methodology to quantify unstructured data from 564 annual reports and 2,602 M&A synopses from January 01, 2013 to December 31, 2016. The authors combine qualitative document analysis with a quantitative method using a multiple regression analysis model.
Among serial acquirers, M&A capability positively relates to long-term firm performance measured by both return on equity and price-to-book value. The authors also find that the size of the company and the number of acquisitions influence the M&A capability, confirming previous results in the literature.
Detailed M&A management plans are usually confidential and not fully reported. Future studies could employ enhanced artificial intelligence tools to measure the M&A capability construct beyond filing reports, encompassing interviews, social media posts, press releases and other unstructured data sources.
Firms can improve their M&A capability by understanding the underlying foundation of the construct provided in the research. Additionally, researchers can build on the methodology employed using advanced NLP tools to measure M&A capability.
Improving their M&A capability would allow firms to better choose their targets and conduct a superior integration process, which could prevent distressing mergers, unnecessary negative social impacts and culture disruption. As a result, the ensuing organization would be stronger, and the long-term performance would improve.
This study addresses gaps in the literature on M&A performance and provides a new empirical method to measure the M&A capability.
