acquisition on Ray Yang, Ph.D.

Tracking the Change of Industry Positioning Caused by an Acquisition

Sun, 19 Apr 2020 00:00:00 +0000

1. Motivation

Due to enduring resource heterogeneity, firms tend to hold persistent positionings within an established industry (like commercial banking). However, transformative corporate actions may alter a firm’s asset structure and create a new industry for the acquirer. Thus, we can map out the industry positions and track the position change caused by an acquisition.

2. Mapping the Positioning and Tracking the Movement

2.1 the Biplot

Consider commercial banks as an example. Different banks may hold differently structured financial asset portfolios and occupy different positions. We can assess a bank’s position based on the proportion of cash, commercial loans, mortgage loans, etc., as a percentage in the total asset (i.e., multiple dimensions). A biplot can provide us a quick intuition about how those dimensions relate to one another. However, the relative positions shown on biplot would not be accurate due to the loss of information in dimension reduction.

2.2 the 3-D and 2-D Scatterplots based on MDS

In an alternative approach, we can build distance-based features by exploring the inter-firm distance using multi-dimensional scaling (MDS). On Feb. 12th, 2018, Pacific Premier Bancorp launched an acquisition of Grandpoint Capital. Using this acquisition event as an example, we can first calculate the interfirm distance using MDS and then track how the acquisition shifts a bank’s position (in both a 3-D and 2-D scatter plots).

Citation Analysis of Recent Acquisition Research

Mon, 15 Apr 2019 00:00:00 +0000

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.

A.I./Blockchain Adoption and Technology Substitution

Tue, 02 Apr 2019 00:00:00 +0000

Artificial intelligence (A.I.) and blockchain are the hottest new technologies right now. It would be fun to think about the future of them (see the patterns, draw the inference, and make predictions). Would A.I. and blockchain be the next “internet,” which has disrupted many traditional business models and creates many new giants across the globe? Or, since businesses have learned a lesson from the internet disruption, A.I. and blockchain will be merely incorporated into the existing technology terrain as an expansion of opportunities by the incumbents? Do A.I. and blockchain have the same fate?

I’ve been leaving an eye on the adoption/acquisition of new technology in the banking sector (since one of my dissertation chapters was on banking acquisitions). Here is some latest news.

The banking sector has a bright prospect incorporating the A.I. in its six core functions.

JP Morgan has adopted cryptocurrency in the payment businesses.

Wells Fargo and Mastercard CEOs express doubt about blockchain.

PayPal made a major investment on the blockchain.

Could there be a unified explanation for what is happening right now? Well, one may associate the topic of technology development with Clayton Christensen’s ‘innovator’s dilemma’ assertion, which predicts emerging new technology will quickly take over declining old ones (often depicted as the double-S curves). It’s the good management–trying best to hear about customer needs, play with competition carefully, and align resource allocation with calculated risk/benefit profiles–who prevent the incumbents from adopting new technologies. Then shouldn’t managers try their best? According to Clayton Christensen, it is difficult for good management not to do good–it is difficult for the incumbents to utilize new technologies even they tend to spot them earlier than startups. At the initiation stage of new technology, incumbents can’t risk their existing market share and customer relationships with unproven models, whereas startups have little to lose. The prediction is: startups disrupt incumbents.

However, things are different right now. The boundary between new tech. and old tech. are melting down; they are intertwined ecosystems rather than isolated communities. Incumbents right now have taken deliberate effort and allocate resource for emerging technology. They are agiler and more determined to disrupt themselves before being disrupted by others. If important pre-existing conditions are different, it might be necessary to modify the predictions from Clayton Christensen’s double-S curves .

First, the compatibility between incumbents’ existing technology and emerging technology should lower the likelihood and rate of disruption. Let’s consider two cases here. One is about different incumbent technologies: as compared to traditional banking, the higher level of digitization of PayPal allows a higher compatibility potential with blockchain (consider internet and blockchain don’t rely on a central authority/agency as traditional banking does). The other is about different emerging technologies: as compared to the blockchain, A.I. allows a higher level of compatibility with traditional banking (both banking and A.I. are aimed at decision/prediction accuracy and efficiency.)

Second, the intensity of the incumbent technology expansion should also lower the likelihood of disruption. This factor is not only related to incumbents’ existing resource base but also has something to do with management. A recent paper in SMJ ¹ has updated the “double-S” framework by incorporating the “incumbent technology expansion” in predicting the rate of substitution and found empirical support for the model’s prediction efficacy.

Conclusion: since A.I. tends to have a higher level of compatibility with, and also face a stronger expansion threat from, the incumbent technology than blockchain, it’s predicted to have a lower likelihood/rate of disruption. However, contingencies should also exist due to the significant variance in incumbents’ technology, resource, and management conditions.

Adner, R., & Kapoor, R. (2016). Innovation ecosystems and the pace of substitution: Re‐examining technology S‐curves. Strategic management journal, 37(4), 625-648.↩