1 Sample and Data
The sample used in this analysis contains 160 articles on “mergers and acquisitions” published on top management and finance journals since 2008.
2 Central Themes
2.1 Network of Co-occurrence Keywords
This graph shows the most common themes (top 30 co-occuring keywords) of these papers.
2.2 MDS Mapping of the Conceptual Structure
We can use MDS (Multi-dimentional Scaling) and Dendrogram to map the distance/dissimilarity among the themes.
3 Central Papers
3.1 the “Main Stream” Papers
## [1] "most central papers (top 20) in bibliographic coupling network"
## BARKEMA HG, 2008 KIM JY, 2009 NADOLSKA A, 2014
## 1.0000000 0.8682856 0.8598660
## KIM JY, 2011 HALEBLIAN JJ, 2017 GORANOVA M, 2010
## 0.8555986 0.8210859 0.7714827
## DEVERS CE, 2013 STEINBACH AL, 2017 KROLL M, 2008
## 0.7253757 0.7121932 0.7104844
## MUEHLFELD K, 2012 EL-KHATIB R, 2015 ELLIS KM, 2011
## 0.6669592 0.6612580 0.6551406
## MCDONALD ML, 2008 SHI W, 2017-1 HEIMERIKS KH, 2012
## 0.6185902 0.5899796 0.5799630
## GRAEBNER ME, 2009 ZOLLO M, 2009 ELLIS KM, 2009
## 0.5753946 0.5740604 0.5715228
## YIM S, 2013 ZOLLO M, 2010
## 0.5578773 0.5558791
To identify the “main stream” papers, we can construct a “bibliographic coupling” (BC) network to see which papers co-cited the same prior research with other papers. The top 20 “main stream” (by eigenvector centrality in the BC network) papers.
3.2 the “Classic” Papers
## [1] "most central papers (top 20) in co-citation network"
## MOELLER SB 2004 HALEBLIAN J 1999 JENSEN MC 1986
## 1.0000000 0.9891969 0.9313744
## MORCK R 1990 HAYWARD MLA 2002 HASPESLAGH P. 1991
## 0.8737119 0.8477468 0.7896179
## HAYWARD MLA 1997 KING DR 2004 ROLL R 1986
## 0.7119846 0.7104482 0.6958790
## ZOLLO M 2004 MASULIS RW 2007 MOELLER SB 2005-1
## 0.6931354 0.6809497 0.6665792
## ASQUITH P 1983 ANDRADE G 2001 JENSEN MC 1976
## 0.6269530 0.6209498 0.5782434
## MALMENDIER U 2008 HALEBLIAN JJ 2006 FULLER K 2002
## 0.5702609 0.5595749 0.5314969
## HECKMAN JJ 1979-1 GOMPERS P 2003
## 0.5272799 0.5248858
To identify the “classic” papers, we can construct a “co-citation” (CC) network to see which papers are cited together with other papers by later researchers. The top 20 “classic” (by eigenvector centrality in the CC network) papers.