M&A literature viewed through structural topic modeling: An insightful perspective?

79 Pages Posted: 26 Mar 2020 Last revised: 27 Jun 2021

Date Written: December 12, 2019

Abstract

Literature overviews help to summarize and structure extant knowledge. As the body of academic texts increasingly exceeds human processing capabilities, one might turn to automated data-driven methods for help. As a potential blueprint example for high-level literature overviews in the economic sciences, this study investigates whether one of the latest topic modeling methods, namely structural topic modeling (STM), can provide an insightful perspective to the merger and acquisition (M&A) literature. First, I ask whether STM provides insights into the M&A literature that may help scholars who are unfamiliar with the overall body of M&A research. Second, I explore whether this new perspective can shed light on the degree of M&A literature fragmentation, thereby guiding future research. Considering 961 M&A papers published in leading journals from 1988 to 2018, I find that this sample of the M&A literature can be appropriately structured into 30 topics, which may help scholars to gain a better organized access to the extant M&A research. Furthermore, I provide a tabular overview of the 25 most important papers per topic, which may guide scholars’ initial reading. In addition, I demonstrate the evolution of M&A research interest over time. Regarding the literature fragmentation, I find that some topics are well connected across topic clusters, and across research fields. However, significant fragmentation for several topics is uncovered, and gaps for more integrative future research are identified. In conclusion, STM allows for a new perspective on the M&A literature that can be considered insightful for both novice and seasoned scholars.

Keywords: Mergers, Acquisitions, Literature Review, Topic Modeling, Structural topic modeling

JEL Classification: G34

Suggested Citation

Schmitz, Alexander, M&A literature viewed through structural topic modeling: An insightful perspective? (December 12, 2019). Available at SSRN: https://ssrn.com/abstract=3546958 or http://dx.doi.org/10.2139/ssrn.3546958

Alexander Schmitz (Contact Author)

Paderborn University ( email )

Warburger Str. 100
Paderborn, 33098
Germany

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