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How do acquirers govern the deal-making process? Three essays on U.S. mergers and acquisitions 1994 – 2017

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Content i
HOW DO ACQUIRERS GOVERN THE DEAL-MAKING PROCESS? THREE ESSAYS
ON U.S. MERGERS AND ACQUISITIONS 1994 – 2017

ZHE (ADELE) XING

University of Southern California
Marshall School of Business
Department of Management and Organization
701 Exposition Blvd – Hoffman Hall 431
Los Angeles, CA 90089
zhexing@usc.edu

A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(BUSINESS ADMINISTRATION)

August 2018
ii
ABSTRACT
Despite the growth of acquisition activities in recent years, these transactions do not
always create value. In an attempt to explain when and how acquirers can benefit from such
costly initiatives, scholars in the field of mergers and acquisitions have examined the
antecedents, consequences and the boundary conditions of acquisition performance. This line of
research generally links the antecedents to the consequences and overlooks the deal-making
process in between. This process, however, is vital to the value creation since it provides the
information that acquirers need to reevaluate the transaction. My dissertation aims to decipher
the “black-box” of the deal-making process by studying what firms negotiate about and how
firms solve problems in this phase of an acquisition. I conducted three empirical studies to
uncover how acquirers govern the deal-making process, using data on U.S. mergers and
acquisitions. Specifically, I study how acquirers evaluate information about themselves and the
targets in negotiating the terms of the deal, and how acquirers disclose information to the public
when they need to solve problems discovered in the due diligence process. The findings suggest
that the negotiation of acquisition agreements can be influenced by acquirers’ previous
acquisition experience and their information about the target. When problems emerge during the
negotiation, the return to acquirers is determined by the way acquirers disclose information to the
public. These findings advance our understanding of the governance of deal-making process by
highlighting how acquirers use the information to facilitate decision making and thus boost value
for the firms.
iii
TABLE OF CONTENTS
ABSTRACT ................................................................................................................................... ii
TABLE OF CONTENTS ............................................................................................................ iii
LIST OF TABLES ...................................................................................................................... vii
LIST OF FIGURES ................................................................................................................... viii
ACKNOWLEDGEMENTS ........................................................................................................ ix
CHAPTER 1. INTRODUCTION ................................................................................................ 1
INTRODUCTION ....................................................................................................................... 1
DISSERTATION OUTLINE ...................................................................................................... 4
REFERENCES ............................................................................................................................ 6
CHAPTER 2. LEARNING FROM BREAKING UP: THE EFFECT OF ACQUISITION
EXPERIENCE ON THE USE OF TERMINATION FEE PROVISIONS ............................. 9
ABSTRACT ................................................................................................................................ 9
INTRODUCTION ..................................................................................................................... 10
THEORY AND LITERATURE REVIEW ............................................................................... 13
Learning from acquisition experience ................................................................................... 13
Learning and M&A contract.................................................................................................. 15
Termination fee provisions in M&A contract ....................................................................... 16
HYPOTHESES ......................................................................................................................... 18
Acquisition experience and termination fee provisions (ATF and TTF) .............................. 18
iv
Failure experience and the acquirer termination fee provisions (ATF)................................. 20
Target value and target termination fee provisions (TTF) .................................................... 21
METHODOLOGY .................................................................................................................... 23
Data and Sample .................................................................................................................... 23
Measurements ........................................................................................................................ 23
Dependent Variable ........................................................................................................... 23
Independent Variables ....................................................................................................... 23
Control Variables ............................................................................................................... 24
Estimation Methods ............................................................................................................... 28
RESULTS.................................................................................................................................. 28
Additional Tests ..................................................................................................................... 34
DISCUSSION ........................................................................................................................... 37
REFERENCES .......................................................................................................................... 41
CHAPTER 3. INCENTIVE VS. INSURANCE: RELATEDNESS AND THE ROLE OF
M&A CONTRACTS IN PUBLIC-PRIVATE ACQUISITIONS 1995 – 2017 ...................... 48
ABSTRACT .............................................................................................................................. 48
INTRODUCTION ..................................................................................................................... 49
THEORY AND HYPOTHESES .............................................................................................. 51
The Role of M&A Contracts: Incentive and insurance ......................................................... 51
Relatedness, Information Asymmetry & the M&A contract ................................................. 54
v
METHODOLOGY .................................................................................................................... 58
Industry Context .................................................................................................................... 58
Data and Sample .................................................................................................................... 59
Measurement ......................................................................................................................... 60
Independent variable. ......................................................................................................... 60
Dependent Variables .......................................................................................................... 60
Control Variables ............................................................................................................... 62
RESULTS.................................................................................................................................. 64
Descriptive Analysis .............................................................................................................. 64
Regression Analysis .............................................................................................................. 70
Estimation Method ............................................................................................................. 70
Hypotheses testing. ............................................................................................................ 71
DISCUSSION ........................................................................................................................... 74
REFERENCES .......................................................................................................................... 77
CHAPTER 4. LEFT AT THE ALTAR: HOW DO FIRMS EXPLAIN M&A
TERMINATIONS? ..................................................................................................................... 84
ABSTRACT .............................................................................................................................. 84
INTRODUCTION ..................................................................................................................... 85
THEORY AND HYPOTHESES .............................................................................................. 88
M&A Termination ................................................................................................................. 88
vi
Causal Attribution and Investor Reactions ............................................................................ 90
Attribution and Perceived Responsibility .............................................................................. 94
Market Reaction and Perceived Responsibility ..................................................................... 97
METHODOLOGY .................................................................................................................... 99
Data and Sample .................................................................................................................... 99
Dependent Variables ........................................................................................................ 101
Independent Variables ..................................................................................................... 101
Moderators ....................................................................................................................... 101
Control Variables ............................................................................................................. 104
Econometric Models and Analysis ...................................................................................... 106
RESULTS................................................................................................................................ 107
Endogeneity Concerns ......................................................................................................... 117
Additional Tests ................................................................................................................... 118
DISCUSSION ......................................................................................................................... 119
Limitation and Future Research .......................................................................................... 122
REFERENCES ........................................................................................................................ 125
CHAPTER 5. CONCLUSION ................................................................................................. 134
REFERENCES ........................................................................................................................ 138
vii
LIST OF TABLES
Chapter 2
Table 2. 1: Frequencies of acquirer termination fee provisions and target termination fee
provisions ...................................................................................................................................... 24
Table 2. 2: Summary statistics of key variables ........................................................................... 26
Table 2. 3: Pairwise correlations of key variables ........................................................................ 27
Table 2. 4: Regressions on acquirer and target termination fee provisions .................................. 29
Table 2. 5: Average marginal effects on the use of acquirer and target termination fee provisions
....................................................................................................................................................... 32
Table 2. 6: The effects of ATF and TTF on acquisition premium ................................................ 36
Chapter 3
Table 3. 1: Contractual characteristics in different types of transactions ..................................... 53
Table 3. 2: Summary statistics of all key variables. ..................................................................... 65
Table 3. 3: Correlations of key variables ...................................................................................... 66
Table 3. 4: Cross tabulations of incentive and insurance clauses ................................................. 67
Table 3. 5: Conditional Mixed Process (CMP) Analysis on Contract Clauses ............................ 71
Table 3. 6: Marginal Effects of Relatedness ................................................................................. 73
Chapter 4
Table 4. 1: Summary statistics of key variables ......................................................................... 108
Table 4. 2: Correlation table of key variables ............................................................................. 109
Table 4. 3: Regressions on acquirer’s CAR when deal is terminated ......................................... 112
Table 4. 4: Heckman selection model on Acquirer Termination CAR ....................................... 115
viii
LIST OF FIGURES
Chapter 1
Figure 1. 1 : The M&A Process ...................................................................................................... 2
Chapter 2
Figure 2. 1: The marginal effect of acquirer experience on the use of ATF................................. 30
Figure 2. 2: The marginal effect of acquirer experience on the use of TTF ................................. 31
Figure 2. 3: The marginal effect of failure experience percentage on the use of ATF ................. 33
Figure 2. 4: The marginal effect of target value on the use of TTF .............................................. 33
Chapter 3
Figure 3. 1: The frequency of clauses in related and unrelated acquisitions ................................ 68
Figure 3. 2: The frequency of clauses in small and large acquisitions ......................................... 68
Figure 3. 3: The frequency of clauses in acquisitions fully paid by cash or not ........................... 69
Figure 3. 4: The frequency of clauses in acquisitions fully paid by stock or not stock ................ 69
Figure 3. 5: The marginal effect of relatedness on the use of clauses .......................................... 74
Chapter 4
Figure 4. 1: Description of Deals Withdrawn ............................................................................. 100
Figure 4. 2: Main Reasons of Terminations................................................................................ 102
Figure 4. 3: The interaction effect of the presence and absence of attribution (full sample) ..... 113
Figure 4. 4: The interaction effect of the presence and absence of attribution (split sample) .... 113
Figure 4. 5: The interaction effect of stability (full sample) ....................................................... 116
Figure 4. 6: The interaction effect of responsibility (split sample)............................................. 116
Figure 4. 7: The interaction effect of responsibility (split sample)............................................. 116
ix
ACKNOWLEDGEMENTS
My PhD program is never a marathon to me even though I have spent probably the most
precious six years of my life on it, living in a different country, speaking a foreign language, and
moving towards an unusual career path that I never dreamed about. Despite all those challenges,
I might be the most fortunate PhD student since my past years have been filled with countless
high moments. I cannot imagine life at USC Marshall without the support and encouragement
from my advisor, dissertation committee members, faculty and colleagues, friends and family. I
owe many thanks to all of you.
My advisor and life-long friend, Kyle Mayer, always believes in me even when I doubt
myself. He is like a proud father who teaches his child knowledge and skills to survive, hopes
she follows his steps and listen to his suggestions, but still paves the road for her when she wants
to take a different route. He is the reason for all my success, if there is any, in this profession, for
now and future. I cannot have a more joyful journey without him being my advisor. Thank you,
Kyle!
I feel eternally grateful to all my committee members, who have unconditionally devoted
their precious time and care. Nandini Rajagopalan not only provides me with detailed feedback
on papers and projects but also helps me shrug off occasional pessimistic feelings about life. As
wonderful a mentor and scholar as she is, I can only wish that I could be like her someday. Nan
Jia is like my big sister. I can never forget those days that we sit down in her office and chat
about pretty much anything and everything. She showed me that female scholars, without any
compromises, can be outstanding researchers, dedicated teachers, and loving mothers all at the
same time. I also owe special thanks to Valerie Folkes, who agreed to become my committee
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member at the last minute. She offered extraordinary help in shaping and broadening my
dissertation as well as teaching me completely new theories. Thank you, my committee!
The support from the MOR department has been amazing. In particular, I would like to
thank Peer Fiss, for his strong expectation of nothing but high-quality research from me and his
“stubborn” belief in me being a successful scholar. I also thank Tom Cummings, Paul Adler,
Lori Yue, Florenta Teodoridis, Joe Raffiee, and Shon Hiatt for their insightful guidance, pointing
me to the right direction, and removing the obstacles on my way to complete my dissertation.
Special thanks go to Feng Zhu, who helped recruit me into this program one year before he left
for HBS; to Libby Weber, who walked me through every experience she had while being a
student at USC.
I appreciate the Strategy Research Foundation for providing me the opportunity to join
the community of experts in this field, and provided the financial support to conduct studies in
this dissertation. Without their help, there will not be any numbers or figures in chapter 3 and 4.
I thank my fellow PhD students and colleagues for the stimulating discussions in the office, for
the sleepless nights we worked together before deadlines, and for all the fun we have had in the
past years. Special thanks to Derek Harmon, Alex Wang, Roshni Raveendhran, Jake Grandy,
Kate Wang, Beverly Rich, Brian Chung, and Alison Comings for the shared journey.
Last, I want to thank my family for bearing with me in these past few years. I would like
to thank my mom, Qiaoling Lu, for understanding my choice despite always pushing me towards
another career path; my dad, Guohou Xing, for trying so hard to help me write this dissertation
by referring to his 40-years’ expertise in teaching high school biology. I know I have been
impatient occasionally in the past years, but I can never become who I am now without the
freedom they gave me to move out of their home country, to settle in a place where they do not
xi
understand a single word, and to leave them behind all by themselves. Sorry and thank you, mom
and dad!
My deepest thanks go to Jing, my dear husband, who improved my programming in data
cleaning, discussed my research ideas, proofread the entire dissertation, and accompanied me to
go through my PhD program from the beginning to the end. Half of my PhD degree goes to him.
Having experienced multiple big shifts in his career, he is still able to support me spiritually with
optimism. He gave me the courage to step up, speak out, and be true to myself. I love you, Jing!
Finally, many thanks to my fluffy teddy bears, for sitting next to me and admiring me
working, inspiring me with their wondering eyes, and always politely remaining silent at the end
of my job talk Q&A practice.
1
CHAPTER 1. INTRODUCTION
INTRODUCTION
Recent years have witnessed unprecedented upsurges of global investments in mergers
and acquisitions (M&A). In the first quarter of 2018, the value of global M&A totaled $1.2
trillion, reaching the highest level in the history (per Dealogic
1
). Riding on this strong
momentum, executives and private equity companies anticipate an surge of M&A activities in
2018, both in the number of deals and the size of those deals (Thomson, Dettmar, & Garay,
2018). Shareholders expect enormous value addition to their companies given the massive
investment they make in those deals. However, after completing those acquisitions, they are
often disappointed by the fact that their shares underperform the market and their competitors in
the same industry (Tortoriello, Oyeniyi, Pope, Fruin, & Falk, 2016). In fact, practitioners found
that M&A activity is nothing more than a coin toss—half of the deals failed to generate value for
the shareholders (Sher, 2012).
It has been widely accepted by researchers that, in general, the post-acquisition firm
value of the acquirer does not increase by the acquisition itself, as measured by either stock
market returns (e.g. Langetieg, Haugen, & Wichern, 1980; Pablo, Sitkin, & Jemison, 1996) or
long-term accounting measures (Porrini, 2004; Zollo & Singh, 2004). In an attempt to understand
why firms are still trying to engage in such costly activities, researchers in the M&A field focus
on the antecedents of low acquisition performance by uncovering why firms acquire in the first
place (Villalonga & McGahan, 2005), which targets firms choose to acquire (Capron & Shen,
2007; Kaul & Wu, 2016), and integration strategies (Puranam & Srikanth, 2007; Zollo & Singh,
2004). Further research has identified potential moderators that shape the established

1 http://www.dealogic.com/insight/q1-2018-ma-highlights/
2
relationships between those antecedents and M&A performance (see a review by Haleblian,
Devers, McNamara, Carpenter, & Davison, 2009).
Although antecedents and moderators of M&A performance are well documented in the
literature, most studies link the factors prior to the announcement of the deal to the overall
performance outcome after deal accomplishment (Figure 1.1). The conversation, however,
largely ignores the deal-making process that is embedded with extensive due diligence after
partner selection and before deal completion. This “black-box” deal-making process is vital to
the value creation of M&A since it has a tremendous impact on the acquisition performance
(Jemison & Sitkin, 1986). A couple of papers took the initiative to open this black-box by
studying the role of top managers and the board of directors, such as whether they accept or
decline tender offers from the acquirers (D’Aveni & Kesner, 1993) and whether they stay or
leave the retained company (Cannella & Hambrick, 1993; Hambrick & Cannella, 1993).
However, firm behaviors during the deal-making process remain unclear.

Figure 1. 1 : The M&A Process

My dissertation aims to unveil the deal-making process by looking at the governance
issue including what firms negotiate about and how firms solve problems during the deal-making
3
process to generate value from the acquisition. Specifically, my dissertation asks the question:
how do acquirers govern the deal-making process? The deal-making process starts after
acquirers and targets reaching a consensus about the transaction and finishes when the
acquisition consummates. This process—paralleling the due diligence—includes both the
negotiation of acquisition agreements before the deal announcement and the adjustments of the
initial agreements after the announcement of terms (Bing, 1996; Lajoux, 2000). Research
suggests that the deal-making process is vital for success since the whole process involves
substantial activities of due diligence which helps the acquirers obtain accurate information
about the target (Cullinan, Le, & Weddigen, 2004). Acquirers undertake efforts to gather
information regarding target value and reevaluate the acquisition opportunity by balancing the
risk of persisting in a value-destroying deal and the risk of abandoning a value-enhancing deal
(Puranam, Powell, & Singh, 2006). In fact, successfully managing the deal-making process
facilitates decision making and eventually boosts shareholder value (Aiello & Watkins, 2000).
Successful managers carefully coordinate the different actors—senior managers, lawyers,
investment bankers, and so on—throughout the process. The care and effort devoted to the deal-
making process enables successful acquirers to create the value.
Given the importance of the deal-making process in shareholder value maximization,
previous acquisition studies explicitly or implicitly draw on agency theory (Jensen & Meckling,
1976) to explain the consequences of this process on shareholder and management (e.g.
turnover). For instance, the incentives for top managers to behave opportunistically by having
more information from due diligence could be aligned with those of the shareholders through a
well-designed corporate governance structure such as potential discipline from institutional
investors (Walsh & Seward, 1990). This line of research generally assumes that the conflicts in
4
M&A could be managed through gathering more accurate information. Taking the agency
perspective that views information as a commodity (Eisenhardt, 1989), previous research about
the M&A process implies that acquirers should focus on possessing a large amount of
information that is precise and correct. However, sufficient and accurate information about the
target does not grant acquirers successful acquisitions. Beyond the quantity and quality of
information, the importance of how firms process the generated information has been largely
ignored in the literature.
My dissertation aims to fill this gap by studying both the antecedents and consequences
of the way that acquirers process information and govern the deal-making process by
incorporating perspectives from transaction cost economics, organizational learning, and
attribution theory. I attempt to accomplish two goals. First, I examine how acquirers evaluate
information about themselves and the targets in negotiating the terms of the acquisition. I
demonstrate the importance of information processing that improves acquirers’ decision-making
process. Second, I study how acquirers disclose information to the public when they need to
solve problems discovered in the due diligence. I reveal the importance of information disclosure
approaches that affect shareholder value. In an attempt to fulfill these two objectives, I conducted
three empirical studies using data from U.S. domestic mergers and acquisitions from 1994 to
2017.

DISSERTATION OUTLINE
The first empirical study (Chapter 2) examines the impact of acquirer experience on the
negotiation of termination clauses in the M&A contract. This chapter looks at the agreement
negotiation phase and analyzes how acquirers leverage and process information generated from
5
their past experience to make decisions on whether they should opt out termination fees.
Specifically, incorporating the perspective on organizational learning, this chapter suggests that
acquirers’ experience decreases the propensity of using both acquirer and target termination fee
provisions in the contract. While the inclusion of acquirer termination fee provision is primarily
determined by acquirer experience with announced but incomplete deals, the addition of target
termination fee provision is driven by the value of the target. Therefore, this chapter advances
our understanding of the effects of learning on acquisition performance.
The second empirical study (Chapter 3) widens the scope of contract provisions that are
negotiated by studying the incentive clauses in addition to the insurance clauses (including but
not limiting to the break-up fee provisions in Chapter 2), along with a shift of focus on the
process of information about the acquirer to that about the target. Drawing on the literature on
transaction cost economics and information economics, this chapter finds that compared to
unrelated targets about whom the information is difficult for acquirers to process, deals involving
related targets often include more incentive clauses but fewer insurance clauses.
The last empirical study (Chapter 4) shifts from the agreement negotiation phase before
the announcement of the deal to the post-agreement stage after the announcement. This chapter
explores how acquirers disclose information to solve problems discovered during the due
diligence process. It adopts a behavioral view by studying the causal dimensions of explanations
for deal termination, suggesting that acquirers could use certain types of explanations to
strategically manage audience reactions. This chapter suggests that while the stock returns to
acquirers at deal termination are negatively associated with their stock returns at the initial
announcement, acquirers can increase their stock valuation to a level exceeding baseline by
attributing the termination to internal and controllable factors.
6
REFERENCES
Aiello, R. J., & Watkins, M. D. (2000). The fine art of friendly acquisition. Harvard Business
Review, 78(6), 100–107.
Bing, G. (1996). Due Diligence Techniques and Analysis: Critical Questions for Business
Decisions. Greenwood Publishing Group.
Cannella, A. A., & Hambrick, D. C. (1993). Effects of executive departures on the performance
of acquired firms. Strategic Management Journal, 14(S1), 137–152.
Capron, L., & Shen, J.-C. (2007). Acquisitions of private vs. public firms: Private information,
target selection, and acquirer returns. Strategic Management Journal, 28(9), 891–911.
Cullinan, G., Le, J. R., & Weddigen, R. M. (2004). When to walk away from a deal. Harvard
Business Review, 82(4), 96–104, 141.
D’Aveni, R. A., & Kesner, I. F. (1993). Top Managerial Prestige, Power and Tender Offer
Response: A Study of Elite Social Networks and Target Firm Cooperation during Takeovers.
Organization Science, 4(2), 123–151.
Eisenhardt, K. M. (1989). Agency Theory: An Assessment and Review. Academy of
Management. The Academy of Management Review; Briarcliff Manor, 14(1), 57.
Haleblian, J., Devers, C. E., McNamara, G., Carpenter, M. A., & Davison, R. B. (2009). Taking
Stock of What We Know About Mergers and Acquisitions: A Review and Research Agenda.
Journal of Management, 35(3), 469–502.
Hambrick, D. C., & Cannella, A. A. (1993). Relative Standing: A Framework for Understanding
Departures of Acquired Executives. Academy of Management Journal, 36(4), 733–762.
Jemison, D. B., & Sitkin, S. B. (1986). Corporate Acquisitions: A Process Perspective. Academy
of Management Review, 11(1), 145–163.
7
Jensen, M. C., & Meckling, W. H. (1976). Theory of the firm: Managerial behavior, agency costs
and ownership structure. Journal of Financial Economics, 3(4), 305–360.
Kaul, A., & Wu, B. (2016). A capabilities-based perspective on target selection in acquisitions.
Strategic Management Journal, 37(7), 1220–1239.
Lajoux, A. R. (2000). Art of M and A Due Diligence. McGraw Hill Professional.
Langetieg, T. C., Haugen, R. A., & Wichern, D. W. (1980). Merger and Stockholder Risk.
Pablo, A. L., Sitkin, S. B., & Jemison, D. B. (1996). Acquisition Decision-Making Processes:
The Central Role of Risk. Journal of Management, 22(5), 723–746.
Porrini, P. (2004). Can a Previous Alliance Between an Acquirer and a Target Affect Acquisition
Performance? Journal of Management, 30(4), 545–562.
Puranam, P., Powell, B. C., & Singh, H. (2006). Due diligence failure as a signal detection
problem. Strategic Organization, 4(4), 319–348.
Puranam, P., & Srikanth, K. (2007). What they know vs. what they do: how acquirers leverage
technology acquisitions. Strategic Management Journal, 28(8), 805–825.
Sher, R. (2012). Why Half of All M&A Deals Fail, and What You Can Do About It. Retrieved
June 30, 2017, from https://www.forbes.com/sites/forbesleadershipforum/2012/03/19/why-half-
of-all-ma-deals-fail-and-what-you-can-do-about-it/#9feca587549f
Thomson, R., Dettmar, S., & Garay, M. (2018). M&A trends report 2018 | Deloitte US.
Retrieved May 30, 2018, from https://www2.deloitte.com/us/en/pages/mergers-and-
acquisitions/articles/ma-trends-report.html
Tortoriello, R., Oyeniyi, T., Pope, D., Fruin, P., & Falk, R. (2016). Mergers & Acquisitions: the
Good, the Bad, and the Ugly (and How to Tell Them Apart). Retrieved May 30, 2018, from
http://www.spglobal.com
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Villalonga, B., & McGahan, A. M. (2005). The choice among acquisitions, alliances, and
divestitures. Strategic Management Journal, 26(13), 1183–1208.
Walsh, J. P., & Seward, J. K. (1990). On the Efficiency of Internal and External Corporate
Control Mechanisms. Academy of Management Review, 15(3), 421–458.
Zollo, M., & Singh, H. (2004). Deliberate learning in corporate acquisitions: post-acquisition
strategies and integration capability in U.S. bank mergers. Strategic Management Journal,
25(13), 1233–1256.

9
CHAPTER 2. LEARNING FROM BREAKING UP: THE EFFECT OF ACQUISITION
EXPERIENCE ON THE USE OF TERMINATION FEE PROVISIONS

ABSTRACT
Research on acquisition experience has long debated whether and when this experience
can improve acquisition performance. I contribute to this line of research by investigating
learning that occurs in the strategic actions that acquirers take within the deal negotiation period.
As an important tool to prevent breaking up with the target, I evaluated how acquirers use
termination fee provisions in response to lessons learned from past success and failure as well as
variations in the value of the target. Coding acquirer termination fee and target termination fee
provisions from 3,561 agreements of U.S. public acquisitions from 1994 to 2017, I found that
acquirers’ experience decreases the propensity to include both provisions in the agreement, but
that this effect is driven by experiences with deals that are complete. However, the likelihood of
using acquirer termination fee provisions is elevated when acquirers experience a high
proportion of deals that are announced but not completed, while the propensity of using target
termination fee provisions increases as the value of the target increases.

10
INTRODUCTION
Organizational learning theory suggests that firm behaviors are guided by their prior
experience (Levitt & March, 1988; Nelson & Winter, 2009) and research on acquisition
experience has found that acquirers can learn from their past acquisitions (see a review by
Barkema & Schijven, 2008). However, the effect of experience on acquisition performance is
mixed. Scholars have explored when acquirers can benefit from their prior experience (e.g.
Hayward, 2002; Porrini, 2004; Zollo & Singh, 2004; Castellaneta & Conti, 2014). Although
similar efforts have been made to reveal learning mechanisms, it is still not clear what specific
lessons acquirers have learned from their past experience, and how these lessons are incorporated
into future acquisition activity.
This learning mechanism is ambiguous, mainly because while experience could facilitate
the refinement of routines, it may also hamper learning by simultaneously creating superstition
(Zollo, 2009). In the field of mergers and acquisitions this ambiguity is salient, as scholars
largely use performance indicators to infer the learning effect, without fully incorporating
intermediate strategic moves acquirers undertake in response to their prior experience. These
intermediate outcomes of learning are vital because they form a complex deal-making process
that influences M&A performance (Jemison & Sitkin, 1986). In this chapter, I study this
important phase of M&A by focusing on the deal structure and looking at how acquirers could
benefit from their prior experience to govern this deal-making process. More specifically, I aim
to analyze whether acquirers learn to better govern the deal-making process, and whether they
learn equally from past failure and success.
One key decision that acquirers and targets have to make is how to maximize the
likelihood of deal completion. Common reasons for deal termination include high management
resistance, low premium, equity-involved payment mechanism, and low performance of acquirer

11
and target (Wong & O’Sullivan, 2001). In order to ensure that deals go through, termination fee
provisions, if included, are discussed and extensively negotiated before announcing a deal, as
they are most salient deal protection weapon in the merger agreement. It has been widely
accepted that the use of a termination fee could boost the probability of deal completion, and
may even be associated with augmented bidder premiums (e.g. Bates & Lemmon, 2003; Jeon &
Ligon, 2011).
There are two types of common termination fees in M&As—acquirer termination fees
(the acquirer pays the target if the deal is not completed) and target termination fees (when the
target pays the acquirer if the deal is not completed). Acquirer termination fee (ATF) provisions
have been viewed as an insurance for the target—the acquirer is less likely to back out if they
must pay a substantial fee for their actions. Target termination fee (TTF) provisions specify the
conditions under which the target has to pay a fee to the acquirer (Afsharipour, 2010; Officer,
2003) and provide similar insurance for the acquirer. The use of these provisions has been
viewed as insurance that can influence the acquisition premium and the likelihood of deal
completion by providing incentives for both parties to try and ensure the deal goes through
(Bates & Lemmon, 2003).
How do firms know when to push for this type of insurance? I propose that acquirers can
learn from prior experience about when to include these termination provisions to prevent
unnecessary breaking up with the target. Coding termination fee provisions of 3,561 US public
acquisition agreements from 1994 to 2017, I found that inexperienced acquirers tended to use
both types of termination fee provisions. This is mainly because inexperienced acquirers lack the
capability of managing the deal-making process efficiently as they cannot disseminate between
good and bad outcomes. The benefits of using these provisions for both parties are thus

12
maximized when inexperienced acquirers need ample insurance to make sure that the deals can
go through.
Moreover, the need for an insurance is exacerbated when the fear of deal failure arises. This fear
is particularly salient when the acquirers often failed in the past. I found that when acquirers’
past experience comprised many unsuccessful acquisitions, acquirer termination fee provisions
would be included to provide insurance to the target. This type of provision can ease targets’
concern about acquirers’ M&A capabilities, as it offers some compensation to the target to deal
with situations that cause the deal to fail.
Acquirers also need insurance from the target to make sure that the target will not simply
walk away with other potential bidders. I found that target termination fee provisions became
popular when the target was more valuable (i.e., the market-to-book value of the target was
high). The value of the targets leads to higher risks of deal termination, as the acquirer’s offer
becomes less attractive to target shareholders while the target becomes more attractive to other
potential bidders. Therefore, insurance provisions are needed to facilitate the transaction.
By studying the role of acquirers’ experience in negotiating termination fee provisions, I
make several contributions to the strategic management literature. First, I can help explain the
learning mechanism by showing what exactly firms have learned from their different
experiences. Treating the negotiation of M&A safeguards as an important intermediate outcome
of learning, I move one step closer to unveiling the causal ambiguity of experience on acquisition
performance. Although scholars have tried to resolve this issue by discovering boundary
conditions shaping this relationship (e.g. Muehifiled et al., 2012; Castellaneta & Conti, 2014),
the learning mechanism is still underexplored. I contribute to this line of research by looking at

13
what decisions acquirers will make to govern the transaction, showing that past experiences
teach them to use an important aspect of deal safeguard to ensure the deal is closed.
Second, I examine the black box of the negotiation phase of mergers and acquisitions.
While prior research has mainly looked at the antecedents of M&A prior to deal announcement
and their impact on different outcomes (e.g. Cannella & Hambrick, 1993; Larsson & Finkelstein,
1999; Shen, Tang, & Chen, 2014; Wright, Kroll, & Elenkov, 2002), the important phase of deal
governance between partner selection and deal completion has attracted less attention. My paper
studies what acquirers and targets negotiate about to protect their gains out of the deal by using
penalty provisions in the merger agreement, furthering our current understanding of the M&A
process.
Third, I contribute to both the research on acquisition experience and termination fee
provisions by studying when these insurances are considered. I found that acquirers’ decisions
could change if their prior experience comprises a large proportion of failed acquisitions; this
effect is also sensitive to whether the target is valuable to the acquirer. While the insurance
hypothesis suggests that termination fee provisions are used to compensate acquirers and targets
in case of termination (Officer, 2003), I contribute to this theory by finding important
antecedents related to firms’ experience with completed versus failed (announced but not
completed) deals and the value of the target.

THEORY AND LITERATURE REVIEW
Learning from acquisition experience
Researchers have been interested in the relationship between acquisition experience and
M&A performance for a long time. However, the findings on this relationship were mixed.
Several papers have shown a significantly negative effect of acquirers’ experience on acquisition

14
performance (e.g. Hayward, 2002; Kusewitt, 1985; Lee & Caves, 1998), while some others have
found a positive relationship (e.g. Fowler & Schmidt, 1989; Markides & Ittner, 1994). Several
other scholars discovered an insignificant relationship between acquisition experience and
performance (e.g. Kroll, Wright, Toombs, & Leavell, 1997; Porrini, 2004; Wright, Kroll, Lado,
& Van Ness, 2002). To reconcile these inconsistent findings, two streams of research have
emerged.
One direction led scholars to discover the boundary conditions underlying the
relationship between acquisition experience and performance. Some researchers found that
different types of experience might affect performance in distinct ways. For example, Muehlfeld,
Rao Sahib and Van Witteloostuijn (2012) found that acquirers’ successful experience improved
performance, while failure hurt the performance in subsequent deals. Porrini (2004) suggested
that target-specific experience is positively associated with acquisition performance, while
overall experience has no effect. In addition to the studies on types of experience, other scholars
also looked at contextual variables such as the target’s financial condition (Bruton, Oviatt, &
White, 1994), similarity between prior experience and current deal (Hayward, 2002; Schijven &
Barkema, 2007), and the level of knowledge codification from learning (Zollo & Singh, 2004).
The other stream of research focused on the learning process by analyzing the effect of
acquisition experience on acquirers’ strategic actions. Many researchers have then studied
whether acquirers with positive experience will tend to subsequently carry out more acquisitions
(e.g. Haunschild, 1993); and the boundary conditions of this effect (e.g. Haleblian et al., 2006).
Beyond the attempt to make acquisitions, other strategic actions also attracted great attention.
These actions include acquisition premium paid (Kim, Haleblian, & Finkelstein, 2011),

15
subsequent acquisition divestiture (Meschi & Métais, 2015), and knowledge codification (Zollo,
2009).

Learning and M&A contract
While researchers have tried to understand the learning mechanism, one remaining
problem is that we have limited knowledge on what exactly acquirers have learned from prior
experiences. Although people can infer from the second stream of literature that acquirers might
have learned to act strategically to improve acquisition performance, we still understand little
about how acquirers apply their knowledge from prior experience. This is mainly because most
studies link acquisition experience to the overall performance outcome, but skip the deal-making
phase in which acquirers execute their prior knowledge to gain greater profits.
Several scholars realize the importance of studying this phase (Jemison & Sitkin, 1986;
Walsh, 1989), and most of them use the perspective of corporate governance in their research.
They look at the role played by top managers and directors and their networks in the negotiation
phase (D’Aveni & Kesner, 1993) as well as the outcome of this negotiation such as governance
structure after the acquisition (Cannella & Hambrick, 1993; Hambrick & Cannella, 1993; Walsh,
1989). Prior research, however, does not pay much attention to the material part of this deal-
making process—the governance issues—i.e., how the acquirer and target negotiate provisions
and clauses in agreements that define each party’s warranties, rights and responsibilities.
While almost all important figures and procedures are reflected in the merger agreement,
the M&A contracts have not attracted much attention from scholars who study contracts in
management. In conventional studies using transaction cost economics, contracts were mainly
tools for safeguarding (Heide & John, 1988; Macneil, 1977) and coordination (Argyres & Mayer,
2007). This is because prior research on strategy only studied contracts in the context of inter-

16
firm relationships, such as a buyer-supplier relationship and alliances. In M&A however
contracts play a different role, due to the absence of opportunistic behavior by either party once
the merger/acquisition is completed. While the transactions complete exactly when the exchange
parties sign the contract in buyer-supplier relationships and alliances, M&A deals usually need
more time to complete after the merger agreement is signed, and the deal can even be withdrawn
by either party after they agree on the contract terms. Therefore, possible violations of M&A
contracts can happen before the deal is accomplished, with hazards also present even after the
contract is signed. Hence, we may not be able to apply the mechanisms suggested in
conventional studies of contracts in inter-firm relationships to explain the design of M&A
contracts.
However, several articles in the finance field studied M&A contracts using the agency
view and efficiency model. They examined some provisions included in merger agreements as a
reflection of agency problems between the target’s CEO and directors (André, Khalil, &
Magnan, 2007; Bates & Lemmon, 2003a; Officer, 2003). By using provisions such as
termination fee provisions (Bates & Lemmon, 2003; Officer, 2003) and lock-up options (Povel
& Singh, 2006), the target CEO can earn a great amount of benefits while preventing the target
board of directors from rejecting the deal. Other studies also show that using lock-up options can
increase target shareholders’ welfare because the target’s bargaining power has increased (Burch,
2001). In the same vein, it is also supported that a toehold before the deal announcement can
facilitate control transfers that enhance value (Betton, Eckbo, & Thorburn, 2009; Choi, 1991).

Termination fee provisions in M&A contract
While the use of these terms reflects the governance of a deal-making process, it is also a
result of negotiations between acquirers and targets. When an acquirer and a target are

17
negotiating a deal, they both make specific investments in evaluating the terms related to the
deal. This deal-specific investment usually includes time and effort spent in due diligence,
negotiations, and post-acquisition integration planning. Therefore, contracting problems for the
focal acquirer occur due to the risks of wasting such transaction costs if the target walks away.
Under these conditions, acquirers can secure the deal by negotiating termination fee provisions
with the target (Wu & Reuer, 2010).
Target termination fees are payable by the target to the acquirer in the event the seller
terminates the agreement prior to closing under certain conditions specified in the contract
(Afsharipour, 2010). For example, LinkedIn agreed to pay Microsoft a termination fee of $725m
if it terminated the deal and Starwood agreed to pay Marriott $450m if it went away with
Anbang. While target termination fee (TTF) provisions are present in most merger agreements,
acquirer termination fee (ATF) provisions are rare. However, the use of ATF provisions has
increased dramatically in the past decade (Afsharipour, 2010). ATFs are payable by the acquirer
to the target if acquirer terminates the deal under certain circumstances. For example, Pfizer and
Allergan mutually agreed to terminate the deal, and Pfizer agreed to pay $400 million acquirer
break-up fee to Allergan. Since acquirers usually spend a great amount of money on negotiating,
due diligence, writing contracts and other necessary investigations for the deal, it remains
unclear why they are also willing to compensate the target by paying the termination fee.
Two research papers in the field of corporate finance proposed an “insurance hypothesis” to
explain the use of such provisions (Bates & Lemmon, 2003; Officer, 2003). They believed that
such fees “are used to guarantee a fraction of a target's gain in deals in which negotiation costs
are high and bid failure is particularly costly” (Bates & Lemmon, 2003). Although we have some
understanding on the use of these provisions in the acquisition context, we know next to nothing

18
about when firms choose to push for these insurance provisions. In other words, while insurance
may well be a motive, there will be differences between when insurance is more important for
the deal, and when the benefits, relative to costs, may be lower.
What are the benefits and costs of using termination provisions? One of the major
benefits of termination provisions is that they provide safeguards against partner opportunism,
since these clauses are included to ensure commitment and full disclosure. Therefore, these
clauses prevent possible negative outcomes in the presence of limited and asymmetric
information about partner ability and motives. By contrast, the cost of using these insurances
involves the risks of jeopardizing the realization of more positive outcomes even though they are
designed to mitigate the risks against negative outcomes. Ghoshal & Moran (1996) point out that
a contract could engender a very negative response if people perceive the presence of distrust
and suspicion based on its content. The focus of preventing negatives has been found to create
frictions in negotiations where the focus shifts from value creation (i.e., how will we be creating
value together?) to value protection (i.e., how will you cheat me?). Firms will need to consider
both the costs and benefits of incorporating these insurance clauses and decide when to push for
them. Drawing on prior discussion on the effect of acquisition experience on acquirers’ strategic
behavior, I aim to study how acquirers’ experience will affect the probability of including
termination fee provisions in merger agreements.

HYPOTHESES
Acquisition experience and termination fee provisions (ATF and TTF)
The benefits of using insurance are greater than the costs when the negative outcomes are
more likely to emerge from information asymmetry between acquirers and the targets. Therefore,
both parties will seek some insurance. This is particularly true when acquirers lack the ability to

19
discern between good and bad outcomes in the deal-making process. Acquirers’ experience
signals their acquisition capability (Laamanen & Keil, 2008). When acquirers have no
acquisition experience, they are less capable of managing the acquisition process, less
knowledgeable about the negotiation phase, and less confident at closing the deal. Therefore,
acquirers with less experience are prone to mistakes and may act less efficiently in the phase
between target selection and deal announcement. Both the acquirer and target will seek insurance
to protect their deal-specific investment by insisting on the use of termination fee provisions.
For the target, the less efficient the acquirer, the more undesirable influence the target firm has to
face during due diligence, and the more likely that the information about this deal will be
revealed. Moreover, the target firm will incur much more overhead costs by collaborating with
inexperienced acquirers and their professional outside helpers. Since acquirer termination fee
provisions are used to compensate the target for their loss of time and effort in this negotiation
phase, inefficient acquirers will be more likely to pay these fees to the target. Thus the less
experience acquirers have, the more likely they are willing to include ATF provisions in the
agreement.
As acquirers gain more experience, they enhance their capabilities of discerning between
good and bad outcomes. The need for insurance attenuates as the benefits relative to costs are
lower. Prior experience will help the acquirer reduce the costs in the deal negotiation phase.
Compared to the targets, the acquirers will gain more benefits from their prior experience since
the acquirer is usually the party that invests more effort, including hiring professional finance
and consulting firms, spending months in the target firm to conduct due diligence, and crafting
terms and provisions in the contract. Lacking such knowledge and expertise, inexperienced
acquirers are thus more in need of the targets’ compensation for their potential loss in this

20
process. Hence, I believe that acquirers’ experience is negatively associated with the need to use
target termination fee provisions.
To sum up, I hypothesize:
H1a: The less experience the acquirer has, the more likely it is that acquirer termination
fee provisions will be included in the acquisition agreement.

H1b: The less experience the acquirer has, the more likely it is that target termination fee
provisions will be included in the acquisition agreement.

Failure experience
2
and the acquirer termination fee provisions (ATF)
While acquirers’ general experience may have a negative effect on the inclusion of both
ATF and TTF provisions, previously announced but failed acquisitions can also provide
acquirers and targets with valuable information. Most acquisition research in strategic
management analyzes completed acquisitions by excluding unconsummated announced deals
from their sample. However, a growing body of literature is starting to look at terminated M&A
deals and suggests that failing to complete an announced deal may result in substantial changes
in organizations’ financial condition and their strategic position. Research in this area shows that
when a deal is terminated, the target firm will suffer from a decrease in share price (Safieddine &
Titman, 1999), negative abnormal returns (Boubakri, Chazi, & Khallaf, 2010), and lose all of its
premiums gained from the acquisition announcement (Fabozzi, Ferri, Fabozzi, & Tucker, 1988).

2
Prior research has used M&A failure, termination, and withdrawal to describe M&A termination. we use
the terminology “M&A failure” and “failure experience” in this paper to refer to announced but not
consummated M&A deals. We do not relate M&A termination to firm failure as these terminations do not
necessarily suggest that firms fail. However, we use “failure” to suggest that M&A deals fail because they
could not consummate.

21
Moreover, targets that failed to close the deal will send signals of management inefficiency and
thus have to make subsequent changes such as CEO turnover and strategy refocus (Chatterjee,
Harrison, & Bergh, 2003). With these possible negative effects, a target will then be very
cautious when negotiating with an acquirer with extensive experience in deal termination. The
acquirer’s experience with termination signals its tendency to terminate the deal after
announcement, meaning that the current deal is less likely to complete. This high probability of
deal incompletion makes the target more likely to request some advance insurance (ceteris
paribus), such as the inclusion of acquirer termination fee provisions.
Moreover, a high proportion of failure experience may also signal that these acquirers do
not have enough capability to govern M&A transactions and ensure that deals will go through. In
dealing with the focal target, the acquirer will have to not only spend more effort to prevent
making the same mistake, but also provide insurance to the target, so that it is compensated if
things do not work out. Therefore, the value of insurance regarding the use of acquirer
termination fee provisions may be even higher when potential targets can see that the acquirer
has a history of walking away from deals. Taken together, I believe that acquirers’ failure
experience is positively associated with the likelihood of using ATF provisions in the agreement.
H2: The greater proportion of failures experienced by the acquirer, the more likely it is
that acquirer termination fee provisions will be included in the acquisition agreement.

Target value and target termination fee provisions (TTF)
Like targets, acquirers also seek insurance from the target to mitigate opportunistic
behaviors of the target. Research shows that a deal is more likely to be terminated when analysts
issue more favorable recommendations about the target than about the acquirer (Becher, Cohn, &
Juergens, 2015). This is because an increase in the valuation of the target makes the acquirer’s

22
offer less attractive to target shareholders, thus lowering the target’s willingness to complete the
merger. The risks of deal rejection from the target board will make the acquirers seek insurance
to protect themselves as the target becomes more attractive to other potential acquirers.
Although hard to verify, the value of the target can be confirmed via the stock market (for
public deals) in addition to the valuation and due diligence activities conducted by the acquirer.
Assuming that capital markets are generally efficient, share prices reflect all available
information (Fama, 1970), and that investors’ joint assessments are informationally efficient
(Malkiel, 1999; Surowiecki, 2004), the target is more valuable and attractive to shareholders and
investors when its market to book value is high. Anecdotes suggest that acquirers often find it
difficult to assess the value of the target since targets usually “cook the books” when receiving
tender offers, and thus the market is a convenient tool to help the acquirers distinguish between
unicorns and struggling companies. Once acquirers find that the target is highly valued by the
market they benefit more from insurance, as there is a high risk of losing the deal. Although
insisting on target termination fee provisions can incur potential costs, as these clauses may give
rise to distrust and friction in negotiations, acquirers can create more value given the fact that the
target can bring greater value to the acquirer once the deal closes. Therefore I believe that the
value of the target will increase the likelihood of the inclusion of TTF provisions. Hence I
propose:
H3: The higher the value of the target (i.e. market to book value), the more likely it is that
target termination fee provisions will be included in the acquisition agreement

23
METHODOLOGY
Data and Sample
I collected data from several different sources. To obtain M&A deal data, I used SDC
Platinum and selected deals including U.S. public acquirers and targets from 1994 to 2017. Stock
level data came from CRSP, and company financial data were collected from CompuSTAT.
Eventus was used to conduct event analysis and calculate stock market returns. In order to
control for prior alliances between acquirers and targets, I also used the Alliance dataset from
SDC Platinum. To understand the use of termination fee provisions, I scraped the SEC Edgar
database to collect 8-K filings in which merger agreements are revealed. As a result, I had a final
population of 3561 deals conducted by 1861 acquirers.

Measurements
Dependent Variable. Acquirer Termination Fee (ATF) provision measured whether an
acquirer termination fee provision is included in the merger agreement. I obtained this variable
from merger agreements disclosed in acquirers’ 8-K filings in SEC Edgar. This dummy variable
has a mean of 0.204, showing that 20% of the deals have used this provision. I measured Target
Termination Fee (ATF) provision in the same way. About 67% of deals in my sample used this
provision. I conducted a two-way frequency analysis and found that 31.9% of the deals did not
include either of these two provisions, while 19% included both (see Table 2.1).
Independent Variables. I captured Acquisition Experience by counting all acquisitions
that acquirers had announced before the focal deal (e.g. Kim, Haleblian, & Finkelstein, 2011). I
took a logarithm of this variable due to left-skewedness of the data.

24
I calculated Failure Experience Percentage by taking the ratio of incomplete acquisitions
to total acquisitions that acquirers announced before the focal deal (e.g. Muehlfeld, Rao Sahib, &
Van Witteloostuijn, 2012). I found that 6.4% of their prior acquisitions had failed.
Table 2. 1: Frequencies of acquirer termination fee provisions and target termination fee
provisions

Target Termination Fee Provisions

0 1 Total
Acquirer
termination fee
provisions
0 1,136 1,700 2,836

31.9% 47.74% 79.64%

1 46 679 725

1.29% 19.07% 20.36%

Total 1,182 2,379 3,561

33.19% 66.81% 100

The Target Value of each deal was measured by taking the logarithm of the ratio of
market price to the book value of the target. This proxy to capture the value of the target has
been used in several studies (e.g. Muehlfeld et al., 2012). It indicates the degree to which the
market over/under-evaluates the target. I took the logarithm of this variable as I was more
interested in the effect of the percentage increase in the value of the deal, and as this variable was
heavily left-skewed.
Control Variables. I controlled for acquirers’ experience with both acquirer termination
fee provisions and target termination fee provisions, i.e., if acquirers used these provisions in the
past, they would be more likely to use them in future deals. I calculated the numbers of ATF and
TTF provisions that acquirers used in past deals. ATF Experience has a mean value of 0.259,
suggesting that on average, each acquirer used ATF provisions 0.259 acquisitions in the past.

25
The variable TTF Experience has a mean of 1.25, showing that on average each acquirer
included TTF provisions in 1.25 previous deals.
Acquirers’ prior experience with ATF and TTF provisions may give them some
performance feedback that affects whether they will use these provisions in the future. Thus I
calculated the number of announced deals, including these provisions that were not complete in
the past (Failure Experience with ATF and Failure Experience with TTF).
Several other factors that may affect the use of ATF provisions were also controlled. I
first controlled for target power by measuring whether the target had an Alternative Bidder in
addition to the focal acquirer. Second, I compared the size of the focal target to the average sizes
of the targets that the acquirer took over prior to the focal deal. Target Relative Size equals 1 if
the total assets of the current target are smaller than the average of those of the acquirer’s past
targets. In my sample, 22% of acquirers are buying smaller targets. Target Size Max equals 1 if
the total assets of the current target are by far larger than that of all the acquirer’s past targets.
Prior research also found that previous collaborations, such as alliances between
acquirers and targets, may affect their behavior in M&A (e.g. Porrini, 2004). Therefore, I also
controlled for Prior Alliance, which equals 1 if acquirers and targets had established alliances
prior to the deal announcement. Deal attitude was captured by Friendly, which was coded 1 if the
focal deal is categorized as friendly. Deal Value captured the monetary value of the focal
transaction estimated by SEC. I took the logarithm of this variable to reduce left-skewedness.
Finally I considered Relatedness, a concept used to capture the strategic fit and resource
complementarity between acquirers and targets (e.g. Barney, 1988; Makri, Hitt, & Lane, 2010;
Shen et al., 2014). I used a 3-digit SIC code to capture relatedness between acquirers and targets,
following Shen et al. (2014). Relatedness was coded 1 if the acquirer and target share the same

26
3-digit SIC code. In my sample, 55% of the deals happened to have acquirers and targets in the
same industry as defined by the 3-digit SIC code. Summary statistics and correlations are
presented in Table 2.2 and Table 2.3.
Table 2. 2: Summary statistics of key variables
Variable Observation Mean Std. Dev. Min Max
Acquire Termination Fee (ATF) 3,561 0.204 0.403 0 1
Target Termination Fee (TTF) 3,561 0.668 0.471 0 1
Acquisition Experience 3,561 1.272 0.952 0 4.787
Failure Experience Percentage 3,561 0.064 0.177 0 1
Target Value 3,561 0.677 0.957 -6.908 8.370
ATF Experience 3,561 0.259 0.580 0 6
TTF Experience 3,561 1.247 2.093 0 18
Failure Experience with ATF 3,561 0.019 0.136 0 1
Failure Experience with TTF 3,561 0.045 0.218 0 2
Target Relative Size 3,561 0.277 0.447 0 1
Target Size Max 3,561 0.643 0.479 0 1
Alternative Bidder 3,561 0.062 0.242 0 1
Prior Alliance 3,561 0.043 0.203 0 1
Friendly 3,561 0.942 0.234 0 1
Deal Value 3,561 5.789 1.909 -1.133 12.012
Relatedness 3,561 0.547 0.498 0 1

27
Table 2. 3: Pairwise correlations of key variables

Acquire Termination Fee
Target Termination Fee
Acquisition Experience
Failure Experience
Percentage
Target Value
ATF Experience
TTF Experience
Failure Experience with
ATF
Failure Experience with
TTF
Target Relative Size
Target Size Max
Alternative Bidder
Prior Alliance
Friendly
Deal Value
Acquire Termination Fee 1

Target Termination Fee 0.288 1

Acquisition Experience -0.037 0.068 1

Failure Experience
Percentage
0.004 -0.045 -0.088 1

Target Value 0.060 0.135 0.095 -0.064 1

ATF Experience 0.035 0.088 0.292 -0.026 0.062 1

TTF Experience -0.066 0.134 0.562 -0.063 0.111 0.452 1

Failure Experience with
ATF
0.012 0.049 0.084 0.198 0.038 0.259 0.221 1

Failure Experience with
TTF
0.023 0.050 0.138 0.260 0.037 0.145 0.266 0.502 1

Target Relative Size -0.163 0.008 0.219 0.107 0.062 0.167 0.266 0.085 0.125 1

Target Size Max 0.181 0.011 -0.354 -0.110 -0.052 -0.223 -0.364 -0.113 -0.155 -0.831 1

Alternative Bidder 0.023 -0.050 -0.028 0.025 -0.030 -0.013 -0.019 -0.002 0.005 -0.050 0.064 1

Prior Alliance 0.037 0.017 0.055 -0.020 0.088 0.032 0.090 -0.009 -0.006 -0.010 0.010 0.043 1

Friendly 0.090 0.253 0.029 -0.052 0.038 0.037 0.058 -0.019 -0.009 0.081 -0.075 -0.264 -0.024 1

Deal Value 0.210 0.188 0.227 -0.071 0.353 0.147 0.197 0.068 0.081 -0.181 0.145 0.112 0.167 -0.100 1
Relatedness 0.049 0.011 0.021 -0.047 0.024 0.022 -0.002 -0.019 -0.031 -0.056 0.055 0.027 0.015 -0.009 0.064

28
Estimation Methods
I tested my hypotheses using biprobit models. Since I have two types of termination fee
provisions as my dependent variables, it is possible that the use of one type of provisions can
affect the use of the other. Thus there could be a correlation between the use of acquirer
termination fee and target termination fee provisions. Biprobit models deal with the correlation
of the error terms of each model and can thus generate more robust results. I calculated the
average marginal effects (AME) of each variable using Stata 14.

RESULTS
Table 2.4 summarizes my analyses for all hypotheses. Model (1) to Model (5) tested the
effects of my independent variables on the use of Acquirer Termination Fee (ATF) provisions.
Model (1) included all control variables, and Model (2) added Acquisition Experience to predict
the probability of using ATF provisions. The coefficient of Acquisition Experience is statistically
significant and negative, suggesting that for every one unit increase in acquirers’ acquisition
experience, the probability of using ATF provisions decreases by 10.8%. This significant effect
is consistent with Model (5) in which Target Value is included and with Model (4) in which the
interaction term is included. The average marginal effect of acquirers’ experience on the use of
ATF is -0.027 with a 95% confidence interval of [-0.043 -0.011] (see Table 2.5), suggesting that
all else being equal, on average for one instantaneous increase

in acquirers’ experience, the
probability of using ATF in the M&A agreement decreases by 2.7%. I plotted the marginal
effects of acquirers’ experience ranging from 0 to 5 on the probabilities of using ATF in Figure
2.1. H1a is strongly supported, suggesting that acquirer experience leads to a lower propensity of
using acquirer termination fee provisions.

29
Table 2. 4: Regressions on acquirer and target termination fee provisions
Acquirer Termination Fee Provisions (0/1) Target Termination Fee Provisions (0/1)
Model (1) Model (2) Model
(3)
Model (4) Model (5) Model (6) Model (7) Model (8) Model (9) Model (10)
Acquisition Experience -0.108*** -0.102*** -0.148*** -0.142***
(0.033) (0.033) (0.039) (0.039)
Failure Experience 0.422*** 0.389** 0.194 0.196
(0.159) (0.158) (0.156) (0.155)
Target Value 0.024 0.028 0.159*** 0.160***
(0.032) (0.032) (0.031) (0.031)
ATF Experience 0.081 0.109** 0.089 0.081 0.115**
(0.055) (0.055) (0.055) (0.056) (0.054)
Failure Experience with ATF 0.089 0.080 -0.021 0.084 -0.027
(0.199) (0.198) (0.202) (0.200) (0.202)
TTF Experience 0.047*** 0.074*** 0.048*** 0.044*** 0.072***
(0.016) (0.019) (0.017) (0.017) (0.020)
Failure Experience with TTF 0.080 0.064 0.047 0.107 0.057
(0.129) (0.127) (0.135) (0.129) (0.133)
Target Relative Size -0.082 -0.126 -0.093 -0.089 -0.142 0.310*** 0.261*** 0.307*** 0.263*** 0.213**
(0.123) (0.122) (0.123) (0.123) (0.124) (0.099) (0.097) (0.098) (0.100) (0.098)
Target Size Max 0.549*** 0.449*** 0.556*** 0.548*** 0.460*** 0.415*** 0.305*** 0.420*** 0.409*** 0.310***
(0.117) (0.118) (0.118) (0.117) (0.118) (0.109) (0.103) (0.110) (0.110) (0.105)
Alternative Bidder 0.144 0.133 0.139 0.146 0.133 -0.001 -0.013 -0.004 0.040 0.027
(0.108) (0.109) (0.108) (0.108) (0.109) (0.116) (0.117) (0.116) (0.118) (0.118)
Prior Alliance 0.097 0.103 0.099 0.092 0.100 -0.067 -0.061 -0.066 -0.092 -0.085
(0.120) (0.119) (0.120) (0.119) (0.119) (0.138) (0.138) (0.138) (0.140) (0.140)
Friendly 1.179*** 1.185*** 1.195*** 1.175*** 1.196*** 1.781*** 1.780*** 1.788*** 1.774*** 1.780***
(0.154) (0.154) (0.154) (0.154) (0.154) (0.133) (0.134) (0.133) (0.134) (0.136)
Deal Value 0.144*** 0.155*** 0.146*** 0.139*** 0.151*** 0.157*** 0.169*** 0.157*** 0.127*** 0.140***
(0.015) (0.015) (0.015) (0.017) (0.017) (0.017) (0.017) (0.017) (0.018) (0.018)
Relatedness 0.064 0.065 0.065 0.063 0.065 -0.037 -0.032 -0.035 -0.040 -0.033
(0.053) (0.052) (0.053) (0.053) (0.052) (0.050) (0.050) (0.050) (0.050) (0.050)
Constant -3.765*** -3.617*** -
3.815***
-3.752*** -3.656*** -3.369*** -3.177*** -3.392*** -3.291*** -3.132***
(0.293) (0.294) (0.294) (0.294) (0.296) (0.255) (0.255) (0.257) (0.256) (0.257)
Model (1) & (5) Model (2) & (6) Model (3) & (8) Model (4) & (9) Model (1) & (5)
athrho 0.614*** (0.049) 0.614*** (0.048) 0.612*** (0.049) 0.616*** (0.049) 0.641*** (0.046)
Observations 3,561 3,561 3,561 3,561 3,561 3,561 3,561 3,561 3,561 3,561
Clusters 1861 1861 1861 1861 1861 1861 1861 1861 1861 1861
Chi2 857.6 862.9 858.6 881.4 886.1 857.6 862.9 858.6 881.4 886.1
(1) Robust standard errors clustered at acquirer level in parentheses***; (2) p<0.01, ** p<0.05, * p<0.1; (3) year fixed effects
included.

30
I used same methods to test H1b. Model (6) through Model (10) capture the effects of my
independent variables on the use of target termination fee provisions. Model (7) captures the
main effect of acquirers’ experience on the use of TTF in the merger agreement. I found that
acquirers’ experience is negative and significant, suggesting that the more acquisitions made, the
less likely it is that acquirers include target termination fee provisions in the merger agreement.
The average marginal effect of acquirers’ experience on the use of TTF is -0.043 with a 95%
confidence interval of [-0.065 -0.021], suggesting that the probability of using TTF in the M&A
agreement decreases by 4.3% for one instantaneous increase

in acquirers’ experience. Figure 2.2
shows the marginal effects of acquirers’ experience ranging from 0 to 5 on the probabilities of
using TTF. Model (6) through Model (10) confirm this finding, supporting H1b.

Figure 2. 1: The marginal effect of acquirer experience on the use of ATF

31

Figure 2. 2: The marginal effect of acquirer experience on the use of TTF

H2 analyzed the effect of failure experience on the likelihood of including acquirer
termination provisions in the merger agreement. Model (3) and Model (5) in Table 2.4
summarize the analyses for H2, focusing on the use of acquirer termination fee provisions. I can
see that in Model (3), failure experience percentage has a positive and significant effect on the
likelihood of using ATF provisions. The AME of failure percentage is 0.11 (95% confidence
interval is [0.028 0.184]), suggesting that for every instantaneous increase in the failure
experience percentage, the likelihood of using ATF provisions increases by 11%. This change is
graphed in Figure 2.3 with the x-axis being failure experience percentage ranging from 0 to 1.
The significant coefficient of failure experience percentage in Model (5) confirms the finding
that failure experience is positively associated with the propensity of using acquirer termination
fee provisions, supporting H2.

32
Table 2. 5: Average marginal effects on the use of acquirer and target termination fee
provisions

AME Std. Err. Z Score P>z [95% Confidence Interval]
Average marginal effects (AME) on the use of acquirer termination fee provisions
Acquisition Experience -0.027 0.008 -3.350 0.001 (-0.043 -0.011)
Failure Experience 0.106 0.040 2.660 0.008 (0.028 0.184)
Target Value 0.006 0.008 0.750 0.451 (-0.010 0.022)

Average marginal effects (AME) on the use of target termination fee provisions
Acquisition Experience -0.043 0.011 -3.780 0.000 (-0.065 -0.021)
Failure Experience 0.056 0.045 1.240 0.213 (-0.032 0.145)
Target Value 0.046 0.009 5.210 0.000 (0.028 0.063)
Note: The AMEs on the use of ATF are calculated based on Model (2), (3), and (4) in Table (4)
for each independent variable respectively; the AMEs on the use of TTF are calculated based on
Model (6), (7), and (8) for each independent variable respectively.

Model (9) and Model (10) study the relationship between target value and the use of
target termination fee provisions. In Model (9), the coefficient of target value is 0.159 and
statistically significant. I calculated the average marginal effect of target value on the use of TTF
and found that AME was 0.046 (95% CI) [0.028 0.063]. This suggest that on average the
likelihood of using TTF provisions increases by 4.6% with one instantaneous increase in the log
value of the target’s market price to book ratio. With the target value ranging from -7 to 8, I plot
its marginal effect on the propensity of using TTF provisions in Figure 2.4. Together with the
coefficients in Model (10), my findings are consistent with H3 that target value is positively
associated with the likelihood of using target termination fee provisions.

33

Figure 2. 3: The marginal effect of failure experience percentage on the use of ATF

Figure 2. 4: The marginal effect of target value on the use of TTF

34
Additional Tests
Several issues may affect the robustness of my results. First, I only presented the effect of
acquirers’ overall and failure experience, thereby omitting their successful experience. I did not
present models using successful experience as the main indicator because it is highly correlated
with acquirers’ overall experience. However, to tease out the effect of failure experience
captured by total experience, I tested H1a, H1b again using the number of acquirers’ successful
prior experiences. I found this successful experience is the key driver for less use of both ATF
and TTF provisions.
Second, I only used public deals that involve public acquirers and public targets. This is
mainly because deals involving private targets do not have to announce information regarding
termination fee provisions (particularly for target termination fee provisions). In fact, while
31.57% of the deals in my sample (public deals) do not contain any types of termination fee
provisions, 87.16% of the deals including private targets do not use (or report) any type of these
provisions. Therefore I relied on the public sample to test the use of ATF and TTF provisions.
Third, although I tested all my hypotheses using public samples, all my measures related
to acquirers’ experience involved their prior deals with private targets. That is, while the current
target that the acquirer deals with is a public firm, the acquirers’ previous success and failure
experience does include experience with private targets in addition to experience with public
targets. It has been widely accepted that learnings from context-specific experience differ from
learnings from general experience (e.g. Muehlfeld et al., 2012). Therefore, I also measured
experience-related variables only considering acquirers’ prior transactions with public deals by
excluding those with private targets. I found that the results were stronger when I look at
acquirer experience that involved public targets only.

35
Fourth, the expected probability of deal termination might affect the use of acquirer
termination fee provisions in advance. I might also need to model this estimated termination
probability to test H1 through H3. Thus, I split the sample and captured this effect of predicted
probability. I first estimated this probability by using sample pre-2005, then I included the
estimated risks of termination to test my main hypotheses using sample after-2005. I obtained
similar results, although the effect sizes of acquirers’ experience and their failure experience are
smaller.
My last concern was the measurement of acquirers’ experience. In my full sample, I
excluded acquirers with no previous experience without considering new buyers. I also included
first-time acquirers to conduct more robustness tests. I created a dummy variable indicating
whether the focal acquirer had experience in the past. In this model, I still found that having
experiences in the past will lead to less use of both ATF and TTF provisions, consistent with H1a
and H1b.
In addition to the robustness of my model, I also wanted to find out the possible
influences of termination fee provisions on acquisitions. Specifically, I wondered if acquirers
will trade these provisions for the acquisition premiums they pay. Therefore, I analyzed the joint
effect of acquirers’ experience, target value, and the use of both acquirer and target termination
fee provisions on acquisition premium. I adopted a Conditional Mixed Process (CMP) Model
(Roodman, 2009, 2018) to allow correlations among error terms across all models by sharing a
multidimensional normal distribution (similar to seemingly unrelated regressions but allowing
different forms of models). The results are presented in Table 2.6. The results in Model (13)
suggest that while the use of ATF negatively affected acquisition premium, TTF had a positive
impact.

36
Table 2. 6: The effects of ATF and TTF on acquisition premium
ATF TTF Premium
Model (11) Model (12) Model (13)
Acquirer Termination Fee Provisions (Instrumented) -16.712***
(2.474)
Target Termination Fee Provisions (Instrumented) 7.534**
(2.982)
Acquisition Experience -0.102*** -0.142*** -1.215
(0.031) (0.032) (1.045)
Failure Experience 0.390*** 0.196 2.098
(0.148) (0.137) (5.705)
Target Value 0.027 0.159*** -5.623***
(0.031) (0.030) (1.327)
ATF Experience 0.116**
(0.046)
Failure Experience with ATF -0.013
(0.187)
TTF Experience 0.072***
(0.017)
Failure Experience with TTF 0.057
(0.121)
Target Relative Size -0.144 0.213** 7.439***
(0.115) (0.096) (2.830)
Target Size Max 0.462*** 0.310*** 2.771
(0.109) (0.094) (2.510)
Alternative Bidder 0.135 0.026 12.497***
(0.106) (0.115) (3.433)
Prior Alliance 0.101 -0.085 0.332
(0.121) (0.135) (3.413)
Friendly 1.194*** 1.780*** 0.030
(0.154) (0.133) (3.105)
Deal Value 0.152*** 0.140*** -0.527
(0.016) (0.016) (0.531)
Relatedness 0.063 -0.033 0.681
(0.051) (0.048) (1.758)
Constant -3.661*** -3.131*** 30.332***
(0.294) (0.250) (5.758)

Observations 3,561 3,561 3,561
Atanhrho
Model 11 & 12 0.618*** Model 11 & 13 0.069***
(0.047) (0.017)
Model 12 & 13 0.000 (0.017)
Note: Robust standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.

37
It is possible that the estimated premium would affect the use of these provisions, so that
these provisions were included because acquirers and targets knew the value of the deal. To deal
with this endogeneity problem, I used instrument variables for acquirer and target termination fee
provisions. ATF Experience and Failure Experience with ATF were used as an instrument for the
use of ATF provision (Model (13) in Table 2.6). Acquirers’ past experience with ATF and past
failure with ATF are supposed to be positively correlated with whether the acquirers will use
ATF in the current deal, but they should not necessarily correlate with the premium that
acquirers decide to pay to the target (unless through the use of ATF in the focal deal). I used TTF
Experience and Failure Experience with TTF as instruments for the use of TTF provisions in the
same way; having seen more use of and more complete deals with TTF will positively affect
whether acquirers would like to include TTF in the current deal, but this experience should not
influence focal deal completion. It was not my intent to prove that the effects of termination fee
provisions on acquisition premium are causal, however, I would like to display some correlations
between the use of insurance and the economic consequences of the acquisition.

DISCUSSION
I explored how acquisition experience influences the use of termination fee provisions in
the deal-making process prior to announcement, and how acquirers respond to their past
successes and failures. While I found that success will make acquirers avoid using both acquirer
and target termination fee provisions, failure has the opposite effect. In addition to the effects of
experience, I also analyzed the role of the target value. When the target is more valuable, the
likelihood of using target termination fee provisions is augmented.
To the best of my knowledge, my paper is the first to connect acquirers’ experience and
the use of deal protection safeguards in M&A. I extend prior analysis on acquisition experience

38
and performance by studying the strategies of acquirers in the negotiation phase that seek to
prevent announced deals from falling apart. I found that when firms have completed many
acquisitions, their experience eliminates their need to use termination fee provisions, which may
ultimately affect the acquisition premium. However, firms will need to use acquirer termination
fee provisions when they have often failed in the past. Acquirers can learn from past announced
but failed acquisitions, which motivate them to use deal protection safeguards in the agreement
and ensure that the current deal closes. My findings are consistent with prior research, suggesting
learning might be superstitious and myopic, and learning from success and failure may lead to
different strategic choices (Levinthal & March, 1993; Zollo, 2009).
I thus contribute to the literature on acquisition experience by suggesting firms can
leverage their acquisition experience to better govern the deal-making process. My results show
that experienced failure only affects the use of acquirer termination fee provisions, but not the
target termination fee provisions. For the acquirers, a high proportion of failure can signal both
heterogeneity in acquirers and heterogeneity in contexts. That is, acquirers either did not have
enough capability to close the deal or they failed to separate high-quality targets from low-
quality ones. If the previous failure is mainly due to the quality of the acquirer, the acquirer will
have to provide insurance to the target by offering ATF provisions. However, if they are dealing
in contexts where they cannot separate “lemons” from high-quality targets, they will need TTF
provisions to provide insurance for themselves. My results thus showed some support for the
first mechanisms that a high percentage of failures of previous acquisitions may be a good signal
for acquirers’ acquisition ability.
Although target value affects the use of target termination fee provisions, it does not
seem to affect the use of acquirer termination fee provisions. One may wonder whether valuable

39
targets may have more bargaining power in avoiding target termination fee provisions and
requesting acquirer termination fee provisions. As a consequence, acquirers may intend to send
insurance to targets when the targets are very valuable. However, my results contradict this
explanation. In acquisitions acquirers are still the more powerful party. This position can help
them move away from providing insurance, but enables them to request it from others when
necessary.
In conclusion, my study provides useful insights into understanding the effect of learning
from past success and failure in the context of safeguard negotiation. However, there are several
ways to extend my findings both theoretically and empirically. First, I only used the market to
book ratio to measure target value, while ignoring the potential effect of target power granted by
the value of the target. Emerson (1962) claimed that power comes from two sources—the value
attributed to the outcome, and the availability of this outcome through alternative sources. My
paper may shed some light on the first source of power. Although I also included Alternative
Bidder, which could be a measure for the second source of power, I did not obtain similar
influences of this variable. This is mostly because my database only included public bidders who
announced their interests in the target, while ignoring other bidders who approached the target
during the deal-making process but did not make a public announcement. In my sample, only 5%
of the deals had more than one bidder, indicating an underestimated effect of this variable. One
potential approach to solve this problem is to manually code the number of bidders that some
targets have disclosed to the press. With an accurate measure of the target’s two sources of
power, I can then have a more holistic understanding on the role of power in this setting.
Second, without a clean measure of acquirer and target power in negotiations, I could not
infer both parties’ willingness of using termination fee provisions. Acquirers and targets may

40
respectively volunteer to put in an ATF/TTF provision, rather than asking for insurance against
the other party doing so. While I almost assume that acquirers request TTF and targets request
ATF by viewing these termination fee provisions as insurance, it is still likely that termination
clauses can be spun as positive signal—to both the partner and the larger stakeholder
community—when the acquirers and targets volunteer to put them in. Therefore, future research
could explore the nature of the intentions of acquirers and targets in using termination fee
provisions.
Third, although I focused on the acquirers’ decision-making process by using several
variables capturing acquirer characteristics, I did not directly examine the role played by
decision-makers in the negotiation. Both the CEO and the board of directors can play a role in
this process. CEOs have the right to initiate the use of these provisions, as high completion rates
and better acquisition performance are partly influenced by their incentives (Bodolica &
Spraggon, 2009; Wright, Kroll, & Elenkov, 2002; Wright, Kroll, Lado, et al., 2002). It is likely
that a more efficient board will be able to avoid bad deals by rejecting CEOs’ suggestions
(Kolasinski & Li, 2013; Paul, 2007), as the board of directors has the final say on acquisition
agreements (especially when stock is used as a payment method). Future research could draw
upon corporate governance literature to explain more variance in the propensity to use deal
protection safeguards.
Fourth, although I believe that the decision to include these termination fee provisions
can reflect the effect of learning, I cannot directly measure the process of learning. I thus call for
more research to unpack this process by looking at more aspects of M&A contracts, as these
documents extensively codify information and knowledge from prior deals. This stock of
knowledge could facilitate subsequent acquisitions by providing codified procedures that

41
acquirers undertook in the past. With more understanding on how acquirers and targets negotiate
M&A contract terms, I can expand the scope of my analysis and further our understanding on
what firms learn from their prior experience.

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target company top management turnover. Strategic Management Journal, 10(4), 307–322.
Williamson, O. E. (1995). Hierarchies, Markets and Power in the Economy : An Economic
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Wright, P., Kroll, M., & Elenkov, D. (2002). Acquisition Returns, Increase in Firm Size, and
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25(13), 1233–1256.

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CHAPTER 3. INCENTIVE VS. INSURANCE: RELATEDNESS AND THE ROLE OF
M&A CONTRACTS IN PUBLIC-PRIVATE ACQUISITIONS 1995 – 2017

ABSTRACT
Previous research has mainly examined the antecedents of M&A prior to the
announcement of the deal and their impact on different outcomes. The important phase between
partner selection and the deal completion has attracted less attention. This chapter studies how
acquirers and targets negotiate in order to protect their gains from the deal by designing the
incentive and insurance clauses in merger agreements, furthering our current understanding of
the M&A process. Using a manually collected dataset of M&A agreements between public
acquirers from the software industry and their private targets from 1995 to 2017, I explore how
information asymmetry between acquirers and targets influences the use of risk-allocation
clauses in the contracts. I found that, compared with unrelated acquisitions in which information
asymmetry is high, contracts of related acquisitions involve more incentive clauses and fewer
insurance clauses.

49
INTRODUCTION
Research on mergers and acquisitions (M&A) has been largely focused on the drivers of
acquisition (e.g., Villalonga & McGahan, 2005), the performance outcomes (Porrini, 2004), and
post-merger integration (e.g., Puranam & Srikanth, 2007; Zollo & Singh, 2004). While this
research has mainly examined how these antecedents of M&A prior to the deal announcement
had an impact on different outcomes (Cannella & Hambrick, 1993; Larsson & Finkelstein, 1999;
Shen, Tang, & Chen, 2014; Wright, Kroll, & Elenkov, 2002), the important phase between
partner selection and deal completion has attracted less attention. This negotiation process is vital
to a deal because it represents a complex decision-making process which itself influences M&A
performance (Jemison & Sitkin, 1986). In this chapter I study this important phase of M&A by
examining how acquirers and targets design their merger agreements.
Taking the perspective of Transaction Cost Economics (TCE), or other economic models,
contracts have been viewed mainly as safeguards for preventing ex-post opportunistic behavior
(Williamson, 1991a, 1991b, 1995). Previous research has viewed the contract as a hybrid
mechanism that governs interfirm collaboration, thus ignoring the role of the contract in
governing the formation of hierarchies. Studying contracts for mergers and acquisitions is a
useful tool for understanding the negotiation process because they not only reflect the
consequences of such negotiation by offering all the financial details and guidelines for deal
completion, but also comprise the matter being negotiated. Moreover, M&A contracts convey
important information about the deal to the stakeholders. Therefore the design of merger
agreements reveals the deliberate decision-making process that involves both acquirers and
targets.
I intend to open the ‘black box’ of the negotiation phase of mergers and acquisitions by
studying how specific provisions and clauses are negotiated. More specifically, I compare M&A

50
contracts in related and unrelated acquisitions. Under the assumption that the acquirers possess
better information about their targets from the same industry in related acquisitions than those in
unrelated acquisitions, I study how information asymmetry between acquirers and targets affect
M&A contract design.
Specifically, I look at clauses that provide incentives to the target management team and
employees, as well as clauses that specify insurance to acquirers and targets to deal with
potential disputes. From a proprietary database, I manually coded a sample of 240 merger
agreements that involve public acquirers from the software industry and their private targets. I
found that, compared to unrelated acquisitions, M&A contracts of related acquisitions include
more incentive clauses but fewer insurance clauses. Acquirers have only limited information
about the targets in unrelated acquisitions and thus need insurance clauses to solve the problems
arising from moral hazard and adverse selection (Reuer & Ragozzino, 2008). However, when
they possess more knowledge from the targets in related acquisitions, they then need incentive
clauses in order to reduce the agency cost between acquirers and the target management and
employees.
My paper thus contributes to the M&A literature by opening the black box of the
negotiation outcome of mergers and acquisitions. While prior research has mainly examined the
antecedents of M&A prior to the deal announcement and their impact on different outcomes
(Cannella & Hambrick, 1993; Larsson & Finkelstein, 1999; Shen, Tang, & Chen, 2014; Wright
et al., 2002), the important phase between partner selection and deal completion has attracted
less attention. By examining insurance provisions that protect the gains from the deal, as well as
incentive clauses which coordinate post-merger integrations, this study can enhance our current
understanding of the M&A process.

51
The paper follows with a review on the role of M&A contract before the key theoretical
development for hypotheses. I next present my data and sample and show some of the patterns of
the clauses in M&A contracts. After showing analyses that test our hypotheses, I conclude with
limitations and suggestions for future research.

THEORY AND HYPOTHESES
The Role of M&A Contracts: Incentive and insurance
Research on merger and acquisition suggests that, in general, the firm value of the
acquirer does not increase by the acquisition itself, as measured by either stock market returns or
long-term accounting performance (Porrini, 2004; Zollo & Singh, 2004). In light of this finding,
scholars have studied the antecedents of M&A performance by uncovering why firms acquire in
the first place (see a review by Villalonga & McGahan, 2005). Apart from the governance choice
issues, further research has focused on identifying both potential moderators of acquisition
performance (e.g. environmental factors) and other acquisition-related outcomes (see a review by
Haleblian, Devers, McNamara, Carpenter, & Davison, 2009).
Although antecedents and moderators of M&A performance are well documented in the
literature, studies also examine those variables chronologically by grouping them into each phase
of M&A. However, most studies linked the factors prior to the announcement of the deal to the
overall performance outcome, but their framework seems to jump over the negotiation phase
before deal completion. Specifically, less attention has been paid to examining what parties
negotiate – how do the acquirer and target negotiate provisions and clauses in agreements that
define each party’s warranties, rights, and responsibilities.
While almost all the important figures and procedures are reflected in the merger
agreement, M&A contracts have not attracted much attention from scholars who study contracts

52
in management. In conventional studies using transaction cost economics, contracts are viewed
as a hybrid governance mechanism that has nothing to do with hierarchies. Contracts were
mainly tools for safeguarding (Heide & John, 1988; Macneil, 1977) and coordination (Argyres &
Mayer, 2007). This is because prior research in strategy has only studied contracts in the context
of inter-firm relationships such as a buyer-supplier relationship and alliances.
I first compare the differences of contractual characteristics between M&A, buyer-
supplier contract, and alliances (Table 3.1). Two major differences can be summarized
comparing M&A and the other types of transactions. First, as the target asset is no longer
specific to the seller, asset specificity is no longer a problem in M&A when the deal is
completed. Therefore, ex-post opportunistic behaviors for both acquirer and target are absent,
and hold up is not an issue for M&A. Second, two levels of incentive misalignment are present
in M&A. The first level is between buyer and seller. Since the targets’ resource and knowledge-
based assets are oftentimes difficult to quantify and value, acquirers are usually trying to avoid
overpayment while the targets want to sell at a high price (Coff, 1999). Another level of
incentive misalignment occurs at individual level between CEO and directors and it is unique to
the M&A setting. Researchers largely suggests that CEO hubris is a key driver to deal initiation
while boards of directors are gatekeepers to prevent CEO from overpaying (e.g., (Hayward &
Hambrick, 1997; Wright et al., 2002). Efficient boards have been found to help avoid bad deals
by rejecting CEO’s suggestions (Kolasinski & Li, 2013; Paul, 2007). Therefore, I believe that the
contracting problems mergers and acquisitions face are less of hold up but more of adverse
selection and moral hazards, compared to buyer-supplier contracts and alliances.
Compared to alliances, these two issues in M&A require specific functions of M&A
contracts. First, the target asset is no longer specific to the seller, as such asset specificity is no

53
longer a problem in M&A when the deal is completed. Therefore, ex-post opportunistic
behaviors for both acquirer and target are absent, and thus contracting problems for M&A are
more concerned with moral hazards and adverse selection rather than hold-up. Recent studies
suggest that over 30% of announced deals fail to reach completion, and thus material adverse
clauses (MAC) are lengthy in the contract due to potential moral hazard problems (hidden
action) of both acquirers and targets (Gilson & Schwartz, 2005). The adverse selection problem
(hidden information), on the other hand, often leads to overpayment by acquirers (Reuer &
Ragozzino, 2008). While both alliance and M&A contracts state the responsibilities and roles of
each party in order to coordinate the transaction, alliance contracts often do so in a way that
enhances trust and facilitates communication. In M&A contracts, however, constraints such as
non-solicitation clauses are more salient, since problems regarding adverse selection and moral
hazard are highly significant given the fact that the valuation of the target is challenging (Capron
& Shen, 2007; Coff, 1999), uncertainty regarding the realization of synergies is high (King,
Dalton, Daily, & Covin, 2004), and the true intention of the acquirer difficult to assess
(Graebner, 2009). Therefore, the first function of the M&A contract is to provide insurance
against the moral hazard and adverse selection problems.
Table 3. 1: Contractual characteristics in different types of transactions
Buyer-supplier
relationship
Alliance/Joint
Venture
M&A
Behavior Assumption
Ex-ante opportunism Y Y Y
Ex-post opportunism Y Y Limited
Bounded rationality Y Y Y
Risk Aversion N N Y
Principal-Agent Conflict N N Y

Exchange Hazards
Adverse Selection (hidden information) + + ++
Moral Hazard (hidden action) + + ++
Hold up ++ ++ 0

54

The second difference between alliance and M&A contracts comes from the principal
agent problems. Specifically, the M&A contract outlines the price
3
for the target to give up part
or all of its ownership. Thus the M&A contract is designed for targets to sign away their rights.
The new principal-agent relationship emerges between acquirers and the retained target
management, since the target management has different incentives than the parent firm. While
the target management often requires a high financial incentive to stay, its behavior can
significantly deviate from that desired by the acquirer firm. It has been found that acquirers
usually try to avoid overpayment, while the targets want to sell at a high price, particularly when
the targets’ resource and knowledge-based assets are difficult to value (Coff, 1999). Therefore,
the M&A contract needs to align the incentives between these two parties by providing
incentives for the target firm.

Relatedness, Information Asymmetry & the M&A contract
When do firms need to consider the incentive and insurance problems of the M&A? The
problems arising from moral hazard, adverse selection and the incentive misalignment between
acquirers and target management largely drives the heterogeneity of M&A contracts. These
problems, however, can be mitigated by the knowledge that acquirers possess about the target
firm. When acquirers buy targets operating in a similar industry, they increase their market
power by obtaining the consumer base, products, and distribution channels from the targets
(Scherer & Ross, 1990). They also generate high strategic synergy by purchasing other firms
from complementary markets, since this market complementarity can create value by allowing

3 The term price is a general term, including but not limited to the payment by the acquirers.

55
merging firms to exploit differences in their relative markets, thus providing a possible win-win
situation (Kim & Finkelstein, 2009; Zollo & Singh, 2004). Makri, Hitt and Lane (2010)
demonstrated that knowledge relatedness and technology complementarity are key drivers of
innovation in high technology acquisitions.
Compared to unrelated acquisitions, acquirers conducting related acquisitions benefit
from a lower degree of information asymmetry with the target (Capron & Shen, 2007).
Information economics has long been applied to research on M&A. This line of research focuses
on three sets of dependent variables, namely target selection, the acquirer’s cost and benefit and
deal structure. Acquirers may be more likely to select a target that is geographically distant when
they know more information about the target from signals (Ragozzino & Reuer, 2011). These
signals may come from the reputation of the VC that backs the newly public target (Ragozzino &
Reuer, 2011) and prior alliances between the acquirer and the target (Reuer & Ragozzino, 2008).
Moreover, acquirers may end up overpaying when they do not have enough information (Reuer,
Tong, & Wu, 2012). By contrast, acquirers can earn positive returns from the acquisition if they
have ample information about the target as well as acquisition capabilities acquired from
previous experiences (Cuypers, Cuypers, & Martin, 2017). Many studies on cross-border
acquisitions have examined the deal structure (such as the percentage of the target’s ownership
taken by the acquirer and the contingency payment structure) as a result of the level of
information asymmetry between the acquirer and the target. These studies reveal that acquirers
will take more equity of the target when the level of information asymmetry is low (Dow,
Cuypers, & Ertug, 2016).
Although prior research has explored the effect of information asymmetry on the
outcome variables of the deal (i.e., who the acquirer selects, how much the acquirer pays for and

56
gets from the deal, and how much equity the acquirer owns), I suggest that information
asymmetry also affects the process of deal negotiation, as evidenced by the use of risk allocation
clauses in the contract. The M&A contract acts as a deal protection weapon for reducing
information asymmetry between acquirers and targets. Thus the level of this information
asymmetry ex ante will affect the contract design. When information asymmetry is high,
contingencies in the contracts should be used to provide guidance for unexpected circumstances.
Acquirers and targets can use their knowledge of each other, which helps to reduce information
asymmetry during the negotiation phase. Both parties can gain information from contemplating
some forms of business agreements and/or shared ties. For example, studies have shown that
information about each other gained from previous alliances and interlocking directors increases
acquisition likelihood, affects the premium paid, and improves performance (Haunschild, 1993,
1994; Porrini, 2004).
I believe that M&A contracts should focus on providing insurance to both acquirers and
targets in unrelated acquisitions in which information asymmetry is high. First, information
asymmetry gives rise to the issue of moral hazards, as individual behaviors are hard to observe
(Hölmstrom, 1979). With asymmetric information the acquirer may worry that the target may
walk away from the deal before the deal is closed, hide key assets and talents after the deal is
closed, and/or breach the contract by materially changing their businesses. Insurance clauses are
needed in this case to prevent the target from taking these actions. They also serve an
information-forcing function because the negotiation of insurance clauses typically involves the
buyer proposing tight restrictions and the target having to ask and explain the reasons for
exceptions (Coates & John, 2015). By revealing more information about why they qualify for

57
these exceptions, targets’ potential actions are subject to a high degree of observability by the
acquirer.
Second, acquirers with limited information often go to great lengths in order to avoid
‘buying a lemon’ and prevent the target from hiding information (Dierickx & Koza, 1991).
Insurance clauses, including indemnifications, are thus important in such a scenario to define the
solutions when disputes arise. Therefore, insurance clauses are negotiated and likely to be
excluded when the acquirer can clearly anticipate and observe the actions of the target, and when
the information asymmetry between the acquirer and the target is low in related acquisitions.
However, more incentive clauses are needed in related acquisitions. Acquirers buying
resources from the target need to provide incentives for the target management and the
employees in order to better combine and integrate the resource. Hence it is also critical for
acquirers to retain the human capital from acquired targets, and this need is often an important
topic in negotiating the merger agreement (Ranft & Lord, 2002). Agency theory (Alchian &
Demsetz, 1972; Jensen & Meckling, 1976) suggests that performance-based incentives can lower
agency cost by aligning the incentives between principal and agent. Acquirers and targets can
retain key knowledgeable employees by increasing their benefits. Ranft and Lord (2002) have
identified three types of incentives often used: stay-put bonuses, long-term contracts, and the
stock option. By specifying incentives clearly in the contract, both the management and
employees of the target firm can gather credible information that helps to mitigate uncertainty
and suspicion (Schweiger & Denisi, 1991), so as to improve their understanding of the new
organizational identity (Maguire & Phillips, 2008).
Acquirers with industry-specific knowledge about the target in related acquisitions also
know how to specify these incentive clauses. They may rely on their knowledge base and their

58
expertise in the target industry to evaluate the value of the resources and the potential in growth
of the target (Levitt & March, 1988; Singh & Montgomery, 1987). By contrast, outsiders may
run the risk of overpaying and failure in integration. Therefore, acquirers buying targets from the
same industry not only need incentive clauses to retain target management and employees to
reduce agency costs, but they also need to have better knowledge about what specific incentives
to offer and how these clauses work in the contract.
Hypothesis 1a: Compared to unrelated acquisitions, the M&A contracts in related
acquisitions will involve more incentive clauses.
Hypothesis 1b: Compared to unrelated acquisitions, the M&A contracts in related
acquisitions will involve fewer insurance clauses.

METHODOLOGY
Industry Context
I focus on the acquisitions initiated by firms from the prepackaged software industry
(SIC: 7372). Firms classified in this industry primarily engage in the design, development and
production of prepackaged computer software
4
. Leading companies in this industry include
Microsoft and Hewlett-Packard. Mergers conducted between public acquirers from the software
industry and private targets are well-suited for testing the proposed hypotheses for three reasons.
First, these firms are often serial acquirers, leading to a significant level of M&A activity within
the industry. Second, mergers within this industry tend to involve more information asymmetry
in their M&A contracts, because it is difficult for the public acquirer and private target to agree
on the value of an early-stage technology (Kohers & Ang, 2000). Third, many target firms are

4 Source: https://www.osha.gov/pls/imis/sic_manual.display?id=149&tab=description

59
private and entrepreneurial (Reuer, Shenkar, & Ragozzino, 2004) and acquiring firms usually
view target human assets as key to acquisition success (Ranft & Lord, 2002). Therefore, both
incentives and insurance clauses are particularly important to the targets and their employees in
this context. Thus, examining a sample of mergers within the IT industry is particularly useful
for investigating how acquirers and targets design contracts to deal with the information
asymmetry problem.

Data and Sample
Since my focus is on public-private deals, I cannot rely on public information to gather
the M&A contracts. In general, public acquirers are not required to disclose the full M&A
contracts and report them to the SEC (U.S. Securities and Exchange Commission) regarding
deals with private targets, particularly when the deals do not involve stocks. In fact, the
commonly used data source for M&A – the SDC Platinum – only records 94 deals from 1995 to
2017. Given the above problems, I relied on M&A agreements that have been constrained to be
viewed by lawyers in order to conduct my analyses.
Specifically, I used the Bloomberg Law Database to obtain my M&A contracts. I refined
my search by selecting public acquirers from the prepackaged software industry and their private
targets from 1995 to 2017. This search returned 292 M&A agreements; among them 240 possess
information about the deal value. Each of the deals has been connected to a public press release
regarding the details of the acquisition. Since I could not match the majority of the deals with
SDC Platinum, I relied on the general information about each deal that was disclosed in this
database. Each deal is associated with a webpage that records general information about the
acquirer, the target and the deal itself. Therefore, I scraped these pages and obtained a full
dataset consisting of all available information.

60
I then downloaded all the M&A contracts and had one RA to help me code these
agreements. We first read the first 20 agreements and developed a coding scheme. We also
updated and refined our coding scheme after coding the first 50 agreements. We primarily relied
on several articles in law journals to develop and refine our coding scheme. I also consulted with
lawyers specializing in M&A whenever we experienced confusion over the value of any of the
variables while coding these agreements. The inter-rater agreeability is 0.89.

Measurement
Independent variable. I considered Relatedness, a concept used to capture the strategic fit
and resource complementarity between acquirers and targets. I used a 4-digit SIC code to capture
relatedness between acquirers and targets. Relatedness may not fully reflect the information
asymmetry between an acquirer and a target. Scholars have used three variables to capture
information asymmetry: the target's product-market scope, whether the deal is friendly (Cuypers,
Cuypers, & Martin, 2017), as well as whether the acquirer and the target have prior alliances
(Reuer & Ragozzino, 2008). In my data, however, all deals are friendly and only rarely had any
acquirers and targets formed previous alliances (3 deals out of 307). The target’s product-market
scope is measured by the number of industries (4-digit SIC code) that the target operates
(Barkema & Schijven, 2008), but my sample only contains information about the primary
industry that the target operates in. Given this data limitation, I measured relatedness between
acquirers and targets and believe that this variable can affect the degree of information
asymmetry. In my sample 35% of targets also operate in the prepackaged software industry.
Dependent Variables. I primarily focus on two types of clauses – incentive versus
insurance. Clauses and provisions provide incentives and potential extra gain to the targets
include equity incentive plan and earnouts. Equity incentive plan offers the target certain firm

61
considerations about existing stock options and grants by converting them into cash, the acquirer
firm’s equity or stock options. Typically in public-private deals, the target plan holders can gain
high compensation the acquisition of their company. By defining considerations concerning
these plans in the M&A agreement, the board of directors, managers, and employees of the target
understand how much they gain from the merger. Earnouts have been used widely in the small
business acquisition (Field, 2005), and are particularly popular in the high-tech industry (Datar,
Frankel, & Wolfson, 2001; Kohers & Ang, 2000). While earnouts are designed to offer potential
extra gains to the target in addition to upfront payment for the acquisition, they are also found to
be associated with the information asymmetry between acquirers and targets (Ragozzino &
Reuer, 2009). Cain, Denis and Denis (2011) reported that the size (but not the sensitivity) of
earnouts was correlated with measures of risk or uncertainty.
While an equity incentive plan and earnouts define potential gains, indemnification
clauses and escrow funds have been used to define how disputes should be solved post-merger.
Indemnification clauses offer ways for acquirers and targets to seek compensation if the other
party breaches their warranties and representations. These clauses are not bilateral as both parties
do not always see clear provisions in the M&A contract defining their right to indemnify the
other party. In fact, these clauses often do not even exist in deals involving public targets, but are
quite common (87%) in private deals (Coates, 2012). I coded indemnification as 1 if both parties
are able to seek for indemnification after the deal closes and as 0 otherwise. Escrow funds are
used extensively as mechanisms to ‘support’ the enforceability of payment, repayment or
indemnifications post-closure (Coates & John, 2015). Both parties reserve a certain amount of
money with a trusted third party in order to solve potential disputes in price adjustments and

62
indemnifications. I coded Escrow funds as 1 if the M&A contract specifies the amount of the
fund and the use of this fund and as 0 otherwise.
Control Variables. I first controlled for the use of a set of covenants in M&A contracts.
These covenants either define how each party should operate during the transaction, or their right
to terminate the transaction and any subsequent considerations if the deal is terminated. The
negative covenants are often discovered in the ‘Covenants’ section in an M&A contract and are
identified by whether the clauses restrict the target’s operations and actions before the deal is
closed. It equals 1 if such restrictions exist in the M&A contract. No-shops are used to prevent
the target from shopping around for other potential bidders for a certain amount of time. It has
been shown that no-shops yield fewer searches by the targets but lower returns to target
shareholders compared to go-shops in private equity-led buyouts (Subramanian, 2007). No-shops
take the value of 1 if they are specified in the M&A contracts. AcqTermCond measures the
number of conditions under which acquirers can terminate the ongoing acquisition, whereas
TarTermCond counts the number of conditions for targets to terminate the deal.
Second, I controlled for employee-specific clauses that define the potential benefits and
restrictions for employees of the target firm. Specifically, I measured whether non-competition
clauses exist. Research has found that the existence of non-competition clauses can trigger
acquisitions, as they lower the expected employee mobility post-merger (Younge, Tong, &
Fleming, n.d.). Although the use of these clauses is mainly restricted by state laws, I still looked
them up in M&A contracts and coded them as 1 if they are clearly specified in the deal. In
addition to non-competition clauses, I also measured whether special agreements are offered to
key employees. Since human capital is extremely important for acquisitions in high-tech (Ranft
& Lord, 2002), acquirers often offer retention compensations for key employees as well as

63
incentives for the management of the target firm. For example, in the public deal between
Allergan and Actavis in 2015, Allergan prepared a $20M pool for cash bonuses to retain
employees as well as $15M for CEOs and $5M for other management from Actavis (per Capital
IQ). Although I failed to observe the details in these key employee agreements due to the nature
of public-private acquisitions, I was able to measure whether these key employee agreements
exist in M&A contracts.
Third, I controlled for deal-related variables. Deal value refers to the transaction value of
the deal. They are calculated based on the amount of cash and/or stocks offered by the acquirer
to the target at the announcement of the deal. Due to left-skewness of the distribution of the deal
value, I took the logarithm of this variable. Even though the mean value of the deal value is
$165M, the median value is only $37M. This implies that the majority of acquisitions in this
context are relatively small. In addition, I also controlled for the type of payment. Acquirers pay
by cash or stock, or a combination of both. Occasionally they also use debt to leverage the
acquisition. I coded cash as 1 if the deal is fully paid by cash or as 0 otherwise. I also coded
stock as 1 if it is a full stock acquisition.
Finally, I controlled for two legal considerations. Jury Waiver equals 1 if the M&A
contracts specify that the rights to jury trials are waived. In a recent study on a sample of 500
public deals from 2001 to 2011, Palia & Scott (2015) discovered that in general 60% had in their
sample jury waiver clauses, although they become more popular when large buyers acquire
relatively small targets. The choice of forum was also controlled in this paper. Delaware equals 1
if Delaware is designated as the choice of law clauses.

64
RESULTS
Descriptive Analysis
I present the summary statistics in Table 3.2 and the correlations in Table 3.3. I can see
that none of the four clauses are prevalent across all deals, and this is particularly true for
earnouts. Covenants, by contrast, are very popular. The deal value and payment methods also
vary greatly across all deals. To better understand how these four clauses are negotiated in the
contracts, I first conducted a cross tabulation analysis presented in Table 3.4. I first summarized
how many insurance clauses are used. 0 means that neither indemnification clauses nor escrow
funds are included; 1 means that either one of them is included; 2 means that both of them are
present. I summarized the distribution of incentive clauses in the same way. Altogether I found
that only 30% of deals did not use any types of these incentive and insurance clauses, but rarely
did any deals contain all four of them (1 out of 229). Most of the deals involved at least one of
these clauses; 17% of them included both incentive and insurance clauses.

65
Table 3. 2: Summary statistics of all key variables.
Variable Observation Mean Std. Dev. Min Max
Equity Incentive Plan 232 0.397 0.490 0 1
Earnouts 235 0.136 0.344 0 1
Indemnification 234 0.261 0.440 0 1
Escrow Fund 240 0.304 0.461 0 1
Relatedness 240 0.354 0.479 0 1
Covenants

Negative Covenants 236 0.928 0.259 0 1
No Shops 236 0.873 0.334 0 1
AcqTermCond 236 2.898 1.624 0 6
TarTermCond 236 2.364 1.344 0 6
Employee Specific

Non-Competition 236 0.568 0.496 0 1
Key Employee
Agreement
236 0.288 0.454 0 1
Deal Specific

Deal Value 240 3.657 1.568 0 9.116
Cash 240 0.333 0.472 0 1
Stock 240 0.296 0.457 0 1
Legal Consideration

Jury Waiver 236 0.508 0.501 0 1
Delaware 240 0.483 0.501 0 1

66
Table 3. 3: Correlations of key variables

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
1 Equity Incentive
Plan
1

2 Earnouts 0.070 1

3 Indemnification 0.044 -0.004 1

4 Escrow Fund 0.081 0.015 0.014 1

5 Relatedness 0.130 0.153 -0.160 -0.067 1

6 Negative Covenants 0.013 0.058 -0.070 0.108 -0.007 1

7 No Shops -0.040 0.112 -0.072 0.053 0.041 0.720 1

8 AcqTermCond -0.005 0.067 -0.186 0.167 0.188 0.241 0.331 1

9 TarTermCond -0.012 0.046 -0.139 0.018 0.214 0.235 0.290 0.843 1

10 Non-Competition -0.048 0.036 -0.021 0.177 0.053 0.280 0.198 0.056 0.052 1

11 Key Employee
Agreement
-0.016 0.034 -0.155 0.129 -0.031 0.097 0.152 0.115 0.054 0.237 1

12 Deal Value 0.126 -0.053 -0.020 0.201 -0.071 0.084 0.059 0.203 0.134 0.060 0.054 1

13 Cash 0.140 0.070 -0.109 0.330 0.002 0.014 0.077 0.143 0.068 -0.032 0.126 0.112 1

14 Stock -0.208 -0.030 0.053 -0.323 0.034 -0.012 -0.073 -0.040 -0.002 -0.078 -0.128 -0.092 -0.458 1

15 Jury Waiver 0.355 0.059 -0.067 0.295 0.017 0.072 0.018 0.179 0.134 0.073 0.079 0.216 0.296 -0.325 1
16 Delaware 0.041 -0.023 -0.116 0.045 -0.034 -0.114 -0.054 0.090 0.058 -0.043 0.015 0.211 0.185 -0.100 0.197

67
Table 3. 4: Cross tabulations of incentive and insurance clauses
Number of Incentive Clauses (Equity
Incentive Plan + Earnout)

0 1 2 Total
Number of
Insurance
Clauses
(Indemnification
+ Escrow Fund)
0
67 43 8 118

29.26% 18.78% 3.49% 51.53%

1
48 38 6 92

20.96% 16.59% 2.62% 40.17%

2
7 11 1 19

3.06% 4.8% 0.44% 8.3%
Total
122 92 15 229

53.28% 40.17% 6.55% 100%

Moreover, I analyzed how these clauses are used under different scenarios. I first studied
their distribution under related and unrelated acquisitions. I split the sample by relatedness and
graphed the usage of each of the four clauses in Figure 3.1. The y-axis denotes the number of
deals involving a specific clause. The graph shows that while the number of deals including
insurance clauses (indemnification and escrow funds) are much smaller in related acquisitions,
the differences in the use of incentive clauses are subtle. I also divided the sample by the median
number of the deal value. Figure 3.2 suggests that all four clauses are far more popular in
substantial deals. I looked further into the distribution of these clauses under different types of
payments. Figures 3.3 and 3.4 both suggest that acquisitions paid entirely in cash or stock
involve fewer than these four insurance and incentive clauses. Deals made with other types of
payments (e.g., cash plus stock, debt, debt plus stock) may require clauses that provide both
incentive and insurance.

68

Figure 3. 1: The frequency of clauses in related and unrelated acquisitions

Figure 3. 2: The frequency of clauses in small and large acquisitions

69

Figure 3. 3: The frequency of clauses in acquisitions fully paid by cash or not

Figure 3. 4: The frequency of clauses in acquisitions fully paid by stock or not stock

70
Regression Analysis
Estimation Method. The greatest problem in estimating my models is that each clause is
not negotiated independently. The decision to include certain clauses is jointly determined by
whether other clauses are also included. Therefore, given the fact that I have four binary
dependent variables that may correlate with each other, I cannot estimate them separately
because unobserved factors can simultaneously affect all four models. However, traditional
biprobit analysis cannot solve my problem because this can only estimate two probit models at
the same time. Therefore, I adopted the Conditional Mixed Process (CMP) model (Roodman,
2009, 2018) in order to allow correlations among error terms across all models by simulating a
multidimensional normal distribution. CMP builds on the idea of ‘seemingly unrelated’
regression (SUR) equations while allowing each model to vary in different forms. CMP has two
benefits: (1) it is not limited to two equations for the estimations on limited dependent variables
such as the biprobit model; (2) it works well with a relatively small sample because it
implements the Geweke, Hajivassiliou, and Keane (GHK) algorithm to simulate the
multidimensional normal distribution, and then computes the maximum likelihood value. I
estimated four probit models simultaneously: Equity Incentive Plan (𝑌 1
), Earnout (𝑌 2
),
Indemnification (𝑌 3
), and Escrow Fund (𝑌 4
). I assumed that the error terms fell in a 4-dimension
normal distribution. I also calculated and graphed the Average Marginal Effects (AME) using
Stata 14.
𝑌 1
= 𝐼 ( 𝑌 1
∗
= 𝛽 1
𝑋 + 𝜀 1
> 0)
𝑌 2
= 𝐼 ( 𝑌 2
∗
= 𝛽 2
𝑋 + 𝜀 2
> 0)
𝑌 3
= 𝐼 ( 𝑌 3
∗
= 𝛽 3
𝑋 + 𝜀 3
> 0)
𝑌 4
= 𝐼 ( 𝑌 4
∗
= 𝛽 4
𝑋 + 𝜀 4
> 0)

(
𝜀 1
𝜀 2
𝜀 3
𝜀 4
) ~ 𝑁 4
( 0, 𝑉 )

71
Hypotheses testing. Table 3.5 summarizes the regression results using the CMP model.
At the bottom of the table I report the atanhrho values which calculate the arc-hyperbolic tangent
of rhos across two models. These values exhibit whether and how unobserved factors affect two
of the equations simultaneously. A negative and significant atanhrho value between Equation (2)
and (4) suggests that the error terms of these two equations are likely to correlate negatively.
Indeed, it shows that unobserved factors that increase the likelihood of using earnout clauses
may decrease the propensity of using escrow funds. The CMP model estimates my equations by
correcting such correlations among error terms.
Table 3. 5: Conditional Mixed Process (CMP) Analysis on Contract Clauses
Incentive Clauses Insurance Clauses
Equation (1)
Equity Incentive
Plan
Equation
(2)
Earnout
Equation (3)
Indemnification
Equation (4)
Escrow
Fund
Relatedness 0.548*** 0.761*** -0.622*** -0.383*
(0.204) (0.272) (0.230) (0.239)
Negative Covenants 0.546 -5.211*** -0.508 1.400**
(0.557) (0.688) (0.623) (0.678)
No Shops -0.101 5.921*** 0.412 -0.615
(0.421) (0.577) (0.482) (0.470)
AcqTermCond -0.104 -0.052 -0.260** 0.436***
(0.114) (0.126) (0.112) (0.133)
TarTermCond -0.031 -0.023 0.126 -0.440***
(0.132) (0.157) (0.136) (0.161)
Non-Competition -0.298 0.020 0.068 0.610**
(0.212) (0.280) (0.219) (0.254)
Key Employee
Agreement
-0.101 -0.024 -0.487* 0.157
(0.235) (0.352) (0.259) (0.248)
Deal Value 0.128* 0.009 0.016 0.169**
(0.071) (0.101) (0.064) (0.080)
Cash -0.003 0.170 -0.328 0.728***
(0.241) (0.298) (0.246) (0.250)
Stock -0.207 1.220*** 0.070 -0.506
(0.239) (0.368) (0.278) (0.364)
Jury Waiver 0.732*** -0.577** -0.511** 0.118
(0.235) (0.292) (0.244) (0.276)
Delaware -0.111 -0.511 -0.369* -0.818***
(0.213) (0.320) (0.220) (0.247)

72
Constant 3.369*** -5.202*** 6.930*** 2.486***
(0.625) (0.796) (0.698) (0.759)
atanhrho_12 0.213 atanhrho_23 -0.053
(0.195) (0.186)
atanhrho_13 0.172 atanhrho_24 -0.475**
(0.137) (0.202)
atanhrho_14 -0.217 atanhrho_34 0.116
(0.141) (0.164)
Observations 234 234 234 234
Note: Robust standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1; year fixed effects
included.

Equations 1 to 4 show the effect of relatedness on the choice of clauses. Equation 1
shows that the effect of relatedness on the use of an equity incentive plan is significantly positive
with a coefficient of 0.548. I calculated the value of the margins of relatedness as 0 (unrelated)
and 1 (related), as well as an average marginal effect of relatedness (AME), shown in Table 3.6
and plotted this in Figure 3.5. The AME of relatedness is 0.153, which is significantly different
from 0 (p = 0.006), and with a confidence interval [0.044 – 0.263]. This result suggests that when
comparing unrelated acquisitions, the likelihood of specifying the equity incentive plan in the
contract increases by 15.3% in related acquisitions. Equation 2 shows the analysis for the use of
earnout clauses. The positive and significant coefficient of relatedness (𝛽 = 0.761) indicates a
positive relationship between relatedness and the probability of using earnouts. The AME of
relatedness on earnout is 0.107 with a p value of 0.005 (95% CI [0.032 – 0.182]), suggesting that
related acquisitions are 10.7% more likely to involve earnouts than unrelated acquisitions.
Equations 1 and 2 jointly demonstrate that incentive clauses are more likely to be used in related
acquisitions than unrelated acquisitions, supporting H1(a).
Equations 3 and 4 in Table 3.5 display the results for H1(b). The negative and significant
coefficient of relatedness in Equation 3 indicates that the effect of relatedness on indemnification
clauses are negative. In fact, Table 3.6 shows that the AME of relatedness is -0.157 (p = 0.005)

73
with a 95% confidence interval of [-0.266 – (-0. 048)], suggesting that unrelated acquisitions are
15.7% more likely than related acquisitions to include indemnification clauses that specify the
right of both parties to seek compensations from the other party. Equation 4 estimates the effect
of relatedness on the use of escrow funds. The negative coefficient is only marginally significant.
I also calculated the AME and found that it is -0.079 with a p value of 0.105. The 95%
confidence interval of this coefficient includes 0. Therefore, this negative effect is marginally
significant in that relatedness may marginally decrease the likelihood of defining escrow funds in
M&A contracts. Taken together, my results suggest that insurance clauses are more likely to be
included in unrelated acquisitions. H1(b) is thus strongly supported for indemnification clauses
and marginally supported for escrow fund.
Table 3. 6: Marginal Effects of Relatedness

Equity Incentive Earnout Indemnification Escrow Fund

Unrelated Related Unrelated Related Unrelated Related Unrelated Related
Margin 0.364 0.520 0.097 0.211 0.350 0.200 0.527 0.449
Std. Err. 0.033 0.046 0.019 0.036 0.035 0.038 0.030 0.037
Z value 11.130 11.260 5.140 5.850 10.010 5.210 17.720 11.970
P>z 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
95% CI lower 0.300 0.430 0.060 0.140 0.281 0.125 0.468 0.375
95% CI upper 0.429 0.611 0.133 0.282 0.418 0.275 0.585 0.522
Average Marginal Effect: Related vs. Unrelate
Differences 0.153 0.107 -0.157 -0.079
Std. Err. 0.056 0.038 0.056 0.049
Z value 2.750 2.790 -2.810 -1.620
P>z 0.006 0.005 0.005 0.105
95% CI lower 0.044 0.032 -0.266 -0.174
95% CI upper 0.263 0.182 -0.048 0.016

74

Figure 3. 5: The marginal effect of relatedness on the use of clauses

DISCUSSION
While studies on mergers and acquisitions have examined both the antecedents and
consequences of M&A extensively, we still know little about how acquirers and targets negotiate
the deal. Specifically, research on the negotiation and design of merger agreements has received
limited attention. This is largely due to the fact that prior research treats the contract as a hybrid
mechanism, while ignoring the fact that it also plays a role in governing the formation of
hierarchies. Although several analyses have been done primarily in the finance and law
literatures on several aspects of the merger agreement (e.g., lockup clauses, non-compete
clauses, termination provisions, etc.), we still need a fine-grained study to help understand how
acquirers and targets govern the deal-making process.
Adopting the theoretical approach from information economics, I assumed that problems
arising from asymmetric information are more severe in unrelated acquisitions than in related

75
acquisitions. I found that while incentive clauses are less prevalent in unrelated acquisitions,
insurance clauses are more popular. Despite the fact that I study four types of clauses at the same
time, these results deviate from previous research on incentive clauses, particularly earnouts. For
example, Ragozzino and Reuer (2009) found that the knowledge gap between acquirers and
targets increases the likelihood of earnouts because acquirers can use these contingent payments
to reduce the costs of buying targets that are ‘lemons’. However, instead of risk-sharing tools, I
reviewed earnouts as incentive clauses that provide targets with extra pay when the target
performance meets the acquirer’s expectation. In a working paper by Weber and Xing, earnouts
can be viewed as either promoting good performance or preventing bad results as a result of the
choice of framing. Future studies can thus further our understanding of earnouts by analyzing the
effects of knowledge and resource relatedness.
While previous studies focused on resource and knowledge relatedness as well as
complementarity, my study only presents a very simple idea about business relatedness measured
by a 4-digit SIC code. Future studies can measure knowledge distance following Coff (1999) and
Farjoun (1994) using SIC codes, or develop more sophisticated ways to measure resource
similarity and complementarity, thus providing more fine-grained analyses on the relationship
between relatedness and M&A contracts.
In addition to the incentive and insurance clauses, I also coded other M&A clauses in the
sample. For instance, employee related clauses include non-competition clauses and key
employee agreements that are crucial for acquirers if they seek for the acquirer talents from the
target (Ranft & Lord, 2002). Covenant clauses also impose constrains to both parties and are
crucial in deal negotiation. These clauses may be used in different ways in related and unrelated
acquisitions.

76
I also encourage future research to examine the boundary conditions that may shift the
positive relationship between information asymmetry and incentive/insurance clauses. These
boundary conditions could be the acquirers’ acquisition capability that may be embedded in their
knowledge from prior deals (Schijven & Barkema, 2007) and that arise from their efficient
boards of directors (Kolasinski & Li, 2013; Paul, 2007), as well as their power in negotiating
these clauses (Argyres & Bercovitz, 2015).
The M&A contract contains many more aspects than those mentioned in this paper. I
believe that a good knowledge of the contract formation and execution will help us move closer
to the complicated process of mergers and acquisitions. I hope to open the black box of M&A
negotiation (including due diligence) by proposing a different research angle from the contract
side.

77
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CHAPTER 4. LEFT AT THE ALTAR: HOW DO FIRMS EXPLAIN M&A
TERMINATIONS?

ABSTRACT
Although a growing body of literature has begun to examine the drivers and
consequences of M&A terminations, we still know little about how firms strategically manage to
influence stock market reaction to these terminations. Aiming to fill this gap, this paper draws
insights from attribution theory to examine whether investors react differently to firms’
heterogeneous attributions on deal terminations. Results suggest that while stock market
reactions to deal terminations are negatively associated with its reactions to the initial
announcements of the deals, these reactions may be softened by acquirers when they attribute the
termination to external and uncontrollable factors. This paper thus contributes to the literature on
M&A by suggesting that firms could use various types of attributions to strategically manage
audience reactions.

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INTRODUCTION
M&A activity has an overall success rate of about 50%—basically a coin toss.
Forbes, 2012
As the year’s tally of broken mergers and dropped takeover bids grows, dozens of executives
who spent months preaching the merits of a proposed deal are now scrambling to re-draft
their strategies and explain why their companies will be just fine on their own.
Julie Macintosh (2008) in the Financial Times
Firms engage in thousands of acquisitions and spend trillions of U.S. dollars every year
(Strumpf & Jarzemsky, 2014). However, research has shown that around 20% of all announced
mergers and acquisitions (M&A) are not consummated (Dong, Hirshleifer, Richardson, & Teoh,
2006; Shen, Tang, & Chen, 2014). About 100 deals worth almost $200bn U.S. dollars have been
withdrawn each year for the past five years, generating an annual loss in revenue amounting to
more than one billion dollars (Zeth, 2016). Yet despite the prevalence of M&A terminations,
questions still remain not only about why terminations take place (Becher, Cohn, & Juergens,
2015; Kau, Linck, & Rubin, 2008; Liu & McConnell, 2015) but also about how investors react to
termination announcements (Tang, 2015). Drawing on emerging research in the field of firm
attributions (Bettman & Weitz, 1983; Billett & Qian, 2008; Clapham & Schwenk, 1991; Salancik
& Meindl, 1984; Vaara, Junni, Sarala, Ehrnrooth, & Koveshnikov, 2014) this paper addresses
questions about M&A terminations by examining the explanations and attributions firms give to
investors when they terminate announced acquisitions.
Firms frame their activities by legitimizing their behavior and winning support from their
investors in order to obtain financial benefits from the stock market. While investors generally
receive crude and fractured information from the market, they face problems when they attempt
to verify this information in order to reduce uncertainty (Schijven & Hitt, 2012; Zhang &

86
Wiersema, 2009). In highly uncertain situations such as failed M&A attempts, investors and
stockholders are particularly interested in understanding what causes the terminations. It is
therefore important for firms to make causal attributions about these termination decisions in
order to overcome information asymmetry between firms and the market. In this paper, I draw on
insights gained from theories of attribution to analyze two research questions: Do acquirers
make attributions of failed M&A attempts? And do different types of attributions influence stock
market reactions to terminations?
Attribution theory suggests that people are motivated to explain the causes of events
(Kelly, 1955). Prior research has applied this theory to the study of how firms make attributions
on firm performance. These studies have mostly concluded that firms generally make self-
serving attributions by attributing bad performance to others while taking credit for positive
performance (Clapham & Schwenk, 1991; Salancik & Meindl, 1984; Staw, Mckechnie, &
Puffer, 1983). Despite this conventional differentiation in the locus of attribution (self vs.
others), I adopt Weiner’s (1985, 2008, 2010) multi-dimensional framework in this paper to study
not only the locus of attribution, but also controllability (controllable vs. uncontrollable factors)
of firms’ attributions.
Specifically, I identified 559 terminated acquisitions that occurred between U.S. public
firms from 1996 to 2015. I coded whether acquirers and targets made attributions in their press
releases and also the types of attributions they made. I found when the initial market assessment
to the announcement of an acquisition is more negative, the market will react more positively
when the deal is terminated. Attributions could help the acquirer in that the market will react
even more positively when the acquirer attributes deal termination to external (i.e. the target or
the environment) and uncontrollable factors, as the acquirer is perceived as less responsible for

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the termination. However, when the initial market assessment to the announcement of an
acquisition is more positive, the market will react more negatively when the deal is terminated
and this effect is not affected by the acquirers’ attributions.
The present study contributes to the literature on mergers and acquisitions in several
ways. First, it extends a small but growing body of scholarship on acquisition terminations
(Boubakri, Chazi, & Khallaf, 2010; Tang, 2015) by suggesting that firms proactively manage
their stakeholders’ reactions to deal terminations. Second, following a recent call to apply
attribution theory to the M&A setting (Vaara et al., 2014), this research adopts two important
factors in the attribution literature (i.e. locus and controllability) to study an important but
underexplored phenomenon: M&A termination. The results of this approach suggest that
investor reactions may be influenced by the various types of attributions that firms make. Third, I
identify a boundary condition and suggests that whether the market is affected by the acquirers’
attributions depends on the market initial assessments on the deal. Finally, by adopting a more
nuanced approach and studying the controllability of attributions, this paper extends existing
scholarship that only studies the locus of firm attributions (Billett & Qian, 2008; Clapham &
Schwenk, 1991). The paper begins with a review of the literature on M&A termination and
attribution theories and provides hypotheses accordingly. It then describes the sample and
methods for estimation before turning to a discussion of the results.

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THEORY AND HYPOTHESES
M&A Termination
5

Most acquisition research in strategic management has analyzed completed acquisitions
by excluding unconsummated announced deals from their samples. However, a growing body of
literature has started to look at terminated M&A deals, and suggests that failure to complete an
announced deal may result in substantial changes in the financial conditions and strategic
positions of an organization. This research is particularly salient, as more than 20% of announced
M&As are terminated prior to completion. Studies in this area have shown that when a deal is
terminated, the target firm will suffer from a decrease in share prices (Safieddine & Titman,
1999) and negative abnormal returns (Boubakri et al., 2010), and will also lose all of the
acquisition premium gained from the acquisition announcement (Fabozzi et al., 1988).
Moreover, targets that fail to close the deal send signals of management inefficiency, and thus
force subsequent changes such as CEO turnover and strategy refocusing (Chatterjee, Harrison, &
Bergh, 2003). These changes take place partly because the targets’ management teams become
more risk-seeking since the target firms are usually more financially leveraged when a deal is
withdrawn (Jandik & Makhija, 2005). For the acquirer, however, the stock market reaction to a
termination announcement is generally positive—especially for public firms (Tang, 2014)—
although long-term performance remains negative (Savor & Lu, 2009). Acquirers who failed to
complete an announced deal may become more cautious about subsequent acquisition
opportunities (Limbach, Schwetzler, & Reusche, 2014) and make better decisions in the future,
particularly if they have been rewarded by the market for the termination (Jacobsen, 2014).

5 Prior research has used the terms failure, termination, and withdrawal to describe M&A termination. I
use the term “M&A termination” in this project to refer to deals that have been announced but not yet
consummated.

89
Despite different consequences for acquirers and targets when terminating an announced
deal, the market reaction to a termination announcement is primarily driven by the market
assessment of the deal when it was first announced. Although firms are sometimes forced to
terminate deals for exogenous reasons that they cannot control (Savor & Lu, 2009; Tang, 2014),
most of the time, firms take the initiative and choose to abandon certain deals. Research has
shown that announced deals are more likely to be withdrawn when they receive more negative
reactions from the media (Liu & McConnell, 2013), obtain negative stock market returns when
announced (Kau et al., 2008), or when they witness more favorable recommendations (from
analysts) from the target but less favorable recommendations from the acquirer (Becher et al.,
2015).
It is apparent that firms make decisions about whether they should abandon an announced
deal based on the initial market evaluation. But how will the market react when a firm chooses to
terminate a deal? Some general assumptions here are that capital markets are generally efficient,
that share prices reflect all available information (Fama, 1970), and that investors’ joint
assessments are informationally efficient (Malkiel, 1999; Surowiecki, 2004). Therefore, stock
market returns at the announcement of a deal should indicate how investors evaluate the deal. For
instance, Hietala, Kaplan and Robinson (2003) suggest that the stock price at the time of the
announcement of a deal contains investor assessments on the potential synergies from the
combination, the bidder’s stand-alone value, and possible bidder overpayment.
If a deal receives a negative market reaction when announced, the market is very likely to
appreciate the decision to terminate the deal. This is partly because CEOs abandon unfavorable
deals in attempts to recover the financial and reputational value lost at the time of the initial
announcement (Liu & McConnell, 2015). The market generally appreciates CEOs’ decisions

90
following investor assessments. This finding is also consistent with the expectancy conformation
theory, which suggests that individuals are more satisfied when they perceive an outcome or
performance that matches their expectations (Darley & Fazio, 1980; Coombs & Holladay, 2006).
In contrast, the withdrawal of a deal favored by the market could provoke a negative
market reaction. According to the expectancy violation theory (Burgoon & Jones, 1976),
perceivers form expectations about the behavior of others, and violations of these expectations
may cause anxiety and uncertainty (Griffin, 2012). Investors will perceive firms’ decisions to
terminate deals that they liked as a violation of their expectations, and will thus be dissatisfied
with the decisions. Moreover, it is likely that investors will be suspicious of whether the
management team is discharging its fiduciary duty in making the right decisions on behalf of the
shareholders (Boubakri et al., 2010). In this case, the higher the initial reaction of the stock
market to an acquisition when announced, the lower the market’s evaluation of a firm’s decision
to terminate the deal. Therefore, I propose a baseline hypothesis
6
:
Hypothesis 1: The acquirer’s stock market return on an M&A termination is negatively
related to the stock market reaction to the announcement of the M&A.

Causal Attribution and Investor Reactions
Prior literature suggests that firms know when they should make termination
announcements and that they may also anticipate how the market will react to such events.
However, there has been less research on how firms strategically manage market reactions to the
announcement of deal terminations. In order to study how firms proactively manage market

6 Although this hypothesis reflects a straightforward relationship, to our knowledge, it has not been tested
in the literature. This paper does not intend to claim a strong theoretical contribution around this
hypothesis. Rather, it is used as a baseline relationship that sets the foundation for the moderating effect
of attribution.

91
expectations of deal terminations, I adopt theories of attribution to analyze how firms explain
their decisions to terminate announced acquisitions. I also examine whether investors react
differently to the attributions that firms make.
Individuals make attributions when they need to make sense of an outcome or an event
(Kelly, 1955). Heider (1958) was the first to analyze attributions in his work. The study of
attributions was then further developed by Kelley (1971) and Weiner (1985), who dominated the
theory in social psychology for decades. Their theories have been applied to studies of a wide
range of phenomena in management including trust repair (Tomlinson & Mayer, 2009), work
exhaustion (Moore, 2000), and leadership building (Martinko, Harvey, & Douglas, 2007).
Applying the attribution theory to organizational studies, researchers have found that
organizations tend to make attributions to deal with negative and unfavorable events. For
instance, managers who have experienced low acquisition performance may take responsibility
and project a sense of control to the public rather than blame cultural differences (Vaara et al.,
2014). Failed entrepreneurs also use different narrative attributions for both the cognitive and
emotional processing of failure (Mantere, Aula, Schildt, & Vaara, 2013). As results, attributions
provide firms’ explanations to investors to manage their reactions.
In the context of failed M&A attempts, investors and stockholders are particularly
interested in understanding what causes such terminations, since they often receive fractured
information from the market that is generally crude and hard to verify (Schijven & Hitt, 2012;
Zhang & Wiersema, 2009). Investors are thus assumed to have imperfect information and must
rely on managers’ opinions toward deals to form their own opinions (Svedsater, Karlsson, &
Garling, 2009). This information asymmetry is far from trivial. Management obtains rich but
tacit knowledge through first-hand data collection when conducting interdependent activities

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such as due diligence, valuation, financing, and integration (Hitt, Harrison, & Ireland, 2001).
However, this information remains available only to firms and there is little possibility of
spillover to investors (Schijven & Hitt, 2012).
One way for investors to obtain this better information about terminations is through the
explanations provided by acquirers and targets in their announcements of deal terminations.
Attributions provided by firms reflect the unobservable perceptions of managers (Zhang &
Wiersema, 2009), and could therefore aid investors in interpreting decisions made by the
management by identifying causes that investors should consider (Baginski, Hassell, &
Kimbrough, 2004).
Investor assessments of deal terminations are generally contingent on the initial
assessments made when the deal was announced. Making attributions thus helps to break this
link both by mitigating information asymmetry between firms and their stakeholders and by
giving audiences more information that they can use to reevaluate their initial assessments. If no
attributions are made, acquirers risk analysts making up stories about the terminations; they thus
lose control of their investors’ and stockholders’ reactions to the terminations. It has been widely
shown that analysts’ suggestions can influence both investors’ purchase behaviors and stock
prices (Beunza & Garud, 2007; Rao & Sivakumar, 1999; Womack, 1996).
It is therefore important for firms to decide what to announce about the termination. On
the one hand, if the market showed negative reactions when deals were announced, it is
important for firms to justify why they choose to take the actions that met market expectations.
These justifications would make market reactions to firms’ termination decisions even more
positive—they would reveal that firms’ rational decisions to make their own choices confirm
investors’ expectations. These results are consistent with the conclusions of the expectancy

93
confirmation theory (Darley & Fazio, 1980; Maheswaran, Mackie, & Chaiken, 1992; Coombs &
Holladay, 2006). On the other hand, when firms terminate deals that were initially favored by the
market, they will receive fewer negative reactions by explaining why they decided to abandon
the deal. When firms make attributions on the termination, stockholders and investors are able to
obtain more information. They will thus be less disappointed by the termination announcement,
as the information and explanations provided by the firms could offset investor impressions that
the firms violated audience expectations (Graffin, Haleblian, & Kiley, 2016).
7
If firms do not
make attributions but instead leave the analysts to interpret their decisions, it is very likely that
analysts will take actions (such as dropping coverage) that will reduce stock prices. In these
cases, firm decisions and strategies depart from the expectations of the market (Litov, Moreton,
& Zenger, 2012). Moreover, providing reasons for termination decisions can send proactive
signals to investors and stockholders. Explaining the terminations demonstrates that firms
understand that investors may desire more information about the termination, and indicates that
firms do care about their investors and seek to provide accurate information to their audiences.
Thus, I propose:
Hypothesis 2: The negative relationship between the acquirer’s stock market return on an
M&A termination and the stock market return on the announcement of the M&A is
weakened (i.e. less negative) when the acquirer makes attributions of the termination (i.e.
any explanation is better than none).

7 It is likely that more information is not always seen as beneficial in the context of violation of
expectations, as more information could justify that people were right to believe that their expectations
were violated. However, in the context of M&A terminations where the termination decision is a clear
signal that the expectations were violated if the market liked the deal initially, information given by the
firms could help investors to make sense of the reason behind the event.

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Attribution and Perceived Responsibility
Do any types of attributions help investors to understand firms’ decisions equally? Prior
research suggest that firms usually choose to make self-serving attributions, namely that firms
will attribute unfavorable outcomes to the external environment, while they take credit for
favorable outcomes (Bettman & Weitz, 1983). The effect of making self-serving attributions is
mixed. Several early studies found that this self-serving attribution seemed to be convincing to
the public, as it is associated with increases in stock price (Staw et al., 1983), enhanced profit
margins, sales, and earnings per share (Salancik & Meindl, 1984). However, other papers also
found that such attribution strategy may negatively affect firm performance (Clapham &
Schwenk, 1991), increase the tendency to reorient firms’ strategy (Lant, Milliken, & Batra,
1992), and lead to CEO overconfidence (Billett & Qian, 2008).
These previous studies looked at one common factor: the locus of attribution.
Attributions could be external or internal. It is argued that internal attributions are generally
associated with individual characteristics, whereas external attributions are related to the
situation, the task or other people. Internal attributions for negative outcomes are found to be
associated with guilt and shame; whereas external attributions are associated with anger and
frustration. Internal attributions for positive outcomes promote the feeling of pride, whereas
positive emotions such as gratitude are present when external attributions are made (Weiner,
1985).
In the workplace setting, the locus of attribution could influence the employees’ behavior
and performance assessed by their supervisors. For instance, employees making internal
attributions for positive outcomes will receive high performance ratings (Stajkovic & Luthans,
1998) as these attributions to their own capabilities and experience can enhance self-confidence
and efficacy (Silver, Mitchell, & Gist, 1995). However, it is detrimental for employees to make

95
external attributions when facing negative outcomes, particularly when these attributions are
questioned by their supervisors. These attributions could prevent employees from accurately
evaluating and improving their performance (Harvey & Martinko, 2009).
The locus of attribution has been widely used in studies on corporate governance. It is
generally agreed that CEOs taking the credit for good performance are associated with negative
stock market responses (Kim, 2013; Malmendier & Tate, 2008). Implausible performance
explanations, such as blaming environmental factors for poor firm performance, also lead to
negative stock value and a lack of support from external stakeholders (Barton & Mercer, 2005),
and firms are less likely to make changes when poor performance is attributed to external factors
(Haleblian & Rajagopalan, 2006; Lant et al., 1992).
If firms always attribute negative performance to external factors but take credit for
positive performance, why does this attribution tendency generate both positive and negative
consequences? Prior research primarily looked at one dimension of attribution—the locus of
attribution; that is whether the cause is internal of external to the firm. Beyond this dimension,
researchers have long applied the attribution theory, from social psychology to organizational
behavior research, and used more nuanced aspects of this theory (Harvey, Madison, Martinko,
Crook, & Crook, 2014). They found that the locus of attribution only explained a very small part
of how people make sense of an event. These studies have found that it is not the attribution by
itself but rather the perception of the responsibility inferred from attributions that impact
people’s emotions and reactions towards the event (Smith, Haynes, Lazarus, & Pope, 1993). For
example, it has been proposed that the causal locus will be held more or less responsible as a
function of additional attributions such as controllability (Weiner, Amirkhan, Folkes, & Verette,
1987). Therefore, rather than simply categorizing attributions to be internal versus external, I add

96
another important aspect of attributions—controllability (i.e. whether factors causing the current
event are perceived as out of firms’ volition (Weiner, 1985) and suggest that it is the degree of
perceived responsibility, a combination of the causal locus and the controllability of the causes,
that affects how the perceivers react to the attributions given by the actor (e.g. firm).
Controllability is not orthogonal to the locus of attribution, as external factors such as
task difficulty are usually perceived as uncontrollable. However, internal factors could be either
controllable (e.g. ability and effort) or uncontrollable (e.g. luck). Attributing negative outcomes
to controllable causes is found to be associated with less negative emotions and attitudes
compared to uncontrollable causes. This is because controllable causes are perceived as easy to
remedy or even avoid in the future (Aquino, Douglas, & Martinko, 2004), whereas
uncontrollable causes for unfavorable outcomes could be present in similar situations and are
difficult to manipulate. The actors’ responsibility for certain consequences is thus perceived high
when the factors are internal and controllable and low when the factors are external and
uncontrollable (Weiner et al., 1987)
In the context of M&A terminations, acquirers can point to internal causes, such as their
own board’s disapproval, modifications of financial support, shifts in business strategy, and/or
changes in performance or external factors, such as the target’s board disapproval, regulatory
issues (usually antitrust concerns), industry disturbances, or the market’s misevaluation. Wheras
external causes are often uncontrollable
8
, internal causes could be both controllable (such as the
board’s rejection, changes in business strategy) and uncontrollable (such as the CEO passed
away). Acquirers are perceived as more responsible for the termination if they attribute to

8 One possible exception is that one party may lobby the government to call off an acquisition. Although
government’s decision is an external factor, this decision is still controllable by the firm.

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internal and controllable factors whereas they are perceived as less responsible for the
termination if they attribute to external and uncontrollable factors.

Market Reaction and Perceived Responsibility
The more the market favors the deal initially, the more negative assessment the acquirers
will receive from the market when the deal is terminated, and this assessment could be even
more negative if the acquirer is perceived as responsible for the termination. Making internal and
controllable attributions could make the investors question the firm’s capability of closing a deal
and conjecture that they intended to violate the market’s expectations. Investors and stockholders
will be more concerned about a termination when they view the reasons behind the termination
as more controllable, because they worry that firms intend to violate their expectations as it is the
firm’s own decision to terminate the deal. Moreover, these internal and controllable factors also
signal firms’ strong belief that they have the right and volition to dispute the market’s and violate
the investors’ expectations.
By contrast, attributing the termination decision to external and uncontrollable factors
would help offset the impressions of the investors that the firms intentionally violated their
expectations by providing alternative information that is external to the firm. These impression
offsetting tactics (i.e. making external attributions) inhibit expectancy violation and reduce a
negative market reaction (Graffin et al., 2016), thus making the market generate less unfavorable
assessments on the termination decision. Moreover, if external factors are expressed as the key
drivers of the termination, their uncontrollability will make investors and stockholders less
skeptical of firms’ behavior and conclude that the deal termination is fully unintended and
unexpected, despite the firms’ effort to conform with the market’s expectations. Although
investors will be upset about the firms’ decision to terminate a deal that they like, they will be

98
more willing to forgive this behavior when the decision is outside of the firms’ control
(Struthers, Eaton, Mendoza, Santelli, & Shirvani, 2010). In addition, the stock market’s reaction
to a deal termination is less sensitive to external and uncontrollable attributions. Tang (2015)
found that the acquirer’s stock market return when a deal is terminated is not significantly
different from zero if external and uncontrollable factors drive the termination. Therefore, the
more positive the market’s initial evaluation on the deal, the more negative its assessment of the
termination decision will be, but it will be less negative when the acquirer is perceived as less
responsible by making external and uncontrollable attributions. Putting together, I propose:
H3a: When the initial stock market reaction to the announcement of the deal is positive,
the stock market reaction to termination will be less negative if the acquirer is perceived
as less responsible for the termination.

When the market strongly devalues the deal initially, it will favor the termination
decision. In this situation, the termination decision confirms with the market’s expectation.
Compared to external and uncontrollable attributions, internal and controllable attributions
reinforce firms’ capability and experience of making the right decisions, and make firms more
confident (Silver et al., 1995), thus generating less negative feedback from the evaluator (i.e. the
market) (Stajkovic & Luthans, 1998). These factors also signal to investors that they do have any
influence in changing firms’ decisions to terminate a deal that they dislike, as firms could partly
rely on the news analysts and stock prices on deal announcements to conjecture stakeholders’
reaction to the disclosure of deal withdrawal.
Moreover, if a firm’s M&A was cancelled due to controllable factors, investors would be
less disappointed and worry less about whether the firm could have capabilities of handling such

99
factors because controllable causes are often perceived as easy to remedy and avoid in the future
(Aquino et al., 2004). This is particular important, as investors do not only worry about
uncertainty to assess the current event, they also evaluate firm’s standalone value by making
predictions on future events (Grinblatt & Titman, 2002; Tang, 2015). Therefore, when the
market’s initial reaction to deal termination is negative, acquirers could expect positive market
returns for their termination decisions, and these returns could be more positive when the
acquirer is perceived as more responsible for the termination by making internal and controllable
attributions. Therefore, I propose:
H3b: When the initial stock market reaction to the announcement of the deal is negative,
the stock market reaction to termination will be more positive if the acquirer is perceived
as more responsible for the termination.

METHODOLOGY
Data and Sample
I collected data from several different sources. To obtain M&A deal data, I used SDC
Platinum and selected terminated deals including U.S. public acquirers and targets from 1996 to
2015. Although deals before 1996 are also obtainable, I specifically chose this timeframe as SEC
systematically collected 8-K filings, which public firms should use to file for these termination
announcements since 1996.
9
Public deals allowed me to obtain ample information from both
acquirers and targets in order to control several aspects of both parties. I also excluded deals in
the finance sector, management buyout deals, and minority stock purchase deals. A final sample
of 559 M&A withdrawals was collected.

9 SEC started to collect and publish this information at 1993, but only very few deals were reported in the
first three years, thus I chose 1996 as a cutoff point.

100
The information about firms’ attribution tendency was hand coded, using press releases
published when acquirers and targets announced deal terminations. I obtained most of the
announcements from companies’ 8-k filings and 425 filings. I also used PR Newswire, Business
Wire and LexisNexis to triangulate the search for press releases containing termination
announcement information. Analysts’ recommendations were adopted from I/E/B/S databases.
Stock level data were generated from CRSP and company financial data were collected from
CompuSTAT. Eventus was used to conduct the event analysis and generate stock market returns.
In this sample, acquirers and targets may jointly announce the termination and make
consistent attributions, but they may also make separate announcements on the same day, or even
subsequent press releases if one party unilaterally terminates the deal. I found that about 60% of
the announcements at least included some explanation of the deal failure.
Figure 4.1 describes the trends of deals complete (orange bar), deals withdrawn (blue
bar), and average transaction values of deals withdrawn each year (red line).

Figure 4. 1: Description of Deals Withdrawn

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Measurement
Dependent Variables. I used Cumulative Abnormal Return (CAR) to measure stock
market reaction. CAR is widely used to capture the part of return on a stock that is unexpected
by an economic model during a specified window, thus capturing the influence of an event that is
specific during that time period. I used a value-weighted market portfolio to capture the normal
return and thus calculated the cumulative abnormal return using the formula:
CA𝑅 𝑡 ( 𝑇 1
, 𝑇 2
)= ∑ {𝑅 𝑖𝑡
− ( 𝛼 𝑖 + 𝛽 𝑖 𝑅 𝑚𝑡
) }
𝑇 2
𝑡 =𝑇 1
,
where 𝑅 𝑖𝑡
denotes the return on a stock i for day t, 𝑅 𝑚𝑡
is the expected market return on the
value-weighted market portfolio for day t; 𝛼 𝑖 and 𝛽 𝑖 are the coefficients estimated using a
window that falls between 295 and 45 days before the acquisition termination announcement. 𝑇 1

and 𝑇 2
are the lower and upper bound of the event window. I used a short window (0, 1) to
measure stock market reaction and also other short windows within a three-day timeframe for
robustness checks. I denoted Acq_Termiantion_CAR for the stock market return to an acquirer
when it terminates an announced acquisition. In addition to this measurement, I also used price
change to capture stock market reaction. I specifically calculated the difference of the closing
stock prices on the day before deal termination and on the day of the announcement.
Independent Variables. I used the same event analysis method to capture the stock
market’s reaction to acquirers (Acq_Ann_CAR) when the deal was first announced.
Moderators. The first independent variable is the attribution dummy—whether firms
made attributions for M&A termination or whether they did not provide explanations to the
public. If acquirers and/or targets announced deal termination but did not include any
explanations in their announcement, I denoted it 0 and 1 otherwise. The variable Acq_Attribution
has a mean value of 0.48, indicating 48% of acquirers chose to make some attribution. One

102
hundred-sixty deals in my sample include attributions from both the acquirer and the target,
representing 28.7% of the sample.
I adopted the attribution style questionnaires (Peterson et al., 1982) and the application of
this questionnaire to organizations (Kent & Martinko, 1995) to guide the coding for three
dimensions of attributions. The majority deals were withdrawn either because the target took
another offer, the target board changed its mind, or regulators intervened in the deal. Figure 4.2
plots the major reasons for terminations and the size of each bubble represents the relative
prevalence of the reason (38% of the reasons is not captured in this figure).

Figure 4. 2: Main Reasons of Terminations
Note: the size of each bubble refers to % of terminations were caused by the reason

The locus of attribution is assessed based on the question “to what extent is this cause
something about you or something about other people or circumstances?” (Kent & Martinko,
1995: 71). Firms made internal attributions if they claimed that deal terminations happened
because of factors within the firm. In contrast, firms’ attribution tendency will be coded
“external” if they claimed that deals were terminated because of other parties that are external to

103
them. For example, the announced deal between AirTran and Midwest was terminated because
“Midwest’s shareholders are concerned that the acquisition of Midwest by a private equity firm,
along with Northwest Airlines, will block competition, raise fares, reduce employment levels and
reduce service”, as revealed by a joint press release. In this case, AirTran, the acquirer, will be
coded as “external attribution” because the shareholders of Midwest changed their mind;
whereas Midwest, the target, will be coded as “internal attribution”. It is also possible that the
firms do not blame the other party but attribute the deal termination to the external environment;
this was also coded as “external attribution”. For instance, two Nasdaq-listed companies in the
software development industry terminated the deal because of economic downturns. Ariba, the
acquirer, maintained that "we are disappointed that adverse economic and market conditions
prevent the merger with Agile from proceeding as planned". It is often the case that companies
terminate the deals due to antitrust concerns or regulatory issues, and these factors are not only
external to the firm but are also are external to the deal. Acq_Locus was denoted 1 if external
attributions were made and 0 if internal attributions were made. For acquirers who chose to
provide explanations, 65% of them tended to blame the target (44%) or the environment (21%).
Controllability is coded by assessing whether this cause is under the control of the firm
(Kent & Martinko, 1995: 71). External causes are, in general, out of control, while internal
causes could be either under the firm’s control or not. Controllable reasons are those under the
firm’s volition. For example, in a deal between Las Vegas Entertainment Network and Jackpot
Enterprise, the acquirer “said it is withdrawing its tender offer, which may be refiled at a later
date, for 100% of the shares of Jackpot Enterprises Inc., in order to re-evaluate the price per
share”. Internal factors could also be uncontrollable, and this is very common when firms have
large institutional shareholders who may exercise their right to vote against the deal that the

104
board of directors approved. It is also likely that firms point to uncontrollable factors, such as the
stock price change. For instance, Applied Magnetic Group said that “although we continue to
believe the strategic rationale for this combination is compelling, we recognize that based on the
current value of Applied Magnetics' common stock the transaction is not feasible” to the acquirer
Read-Rite Group. Acq_Control was denoted 1 if the acquirer pointed to a controllable cause and
0 if the cause was out of control. 73% of acquirers believed that they have control over the
causes that led to terminations.
Therefore, Perceived_Responsibility is coded 1 if the factor is coded “internal” and
“controllable” (Acq_Locus equals 0 and Acq_Control equals 1), 2 if the factor is coded “internal”
and “uncontrollable” (Acq_Locus equals 1 and Acq_Control equals 0), and the rest is coded 3.
This is because no causes were coded as both external and controllable in the sample.
Perceived_Responsibility is treated as a categorical variable and contains three levels:
Most_Responsible (equals 1), Mod_Responsible (equals 2), and Least_Responsible (equals 3).
Control Variables. I controlled for several factors that may confound the effect of
attribution on stock market returns and firms’ subsequent acquisition behavior. I first controlled
for factors that are revealed together with the attributions in the announcement. Tar_Term_CAR
measures the stock market return to an target when it terminates an announced acquisition.
Who_Withdraw is a categorical variable and represents who initiated the termination decision. It
is denoted 1 if the acquirer terminated the deal, 2 if the target initiated it, and 3 if both the
acquirer and the target mutually decide to abandon the deal. Out of all deals 54% were
terminated by the acquirer, 16% by the target and 30% by both firms. Acq_Term_Fee and
Tar_Term_Fee are dummy variables capturing whether acquirer and/or target termination fees
were announced payable upon termination. Termination fees are widely used in merger

105
agreements and affect the likelihood of deal completion (Bates & Lemmon, 2003; Officer, 2003).
The market may perceive the termination as less detrimental to firms, if told that firms could
receive some monetary compensation. Although the target could only receive this compensation
from the acquirer in 5% of the terminated deals, the chances that the acquirer received
termination fees from the target doubled.
Acq_Noise captured whether the acquirer and/or the target announced news that was
unrelated to the termination decision around the day that they made the decision public. It has
been shown that this strategic noise could be used as a tool to offset the market’s impressions of
critical events (Graffin, Carpenter, & Boivie, 2011), and this noise may inhibit negative
evaluations and increase stock market returns of an acquisition (Graffin et al., 2016). In my
sample, acquirers often do not announce other information (26%).
I also controlled for whether firms explained the impact of this termination decision on
their strategies and future developments. In a working paper by Rhee and Harmon, it is
concluded that how firms explain this impact could affect the stock market’s reaction to firms’
decision to terminate an announced deal. Acq_Impact has a mean value around 0.55, indicating
that over half of the acquirers would bother to explain how this termination decision affects their
businesses.
Second, I controlled for the impact of analysts. It has been widely shown that analysts’
suggestions could influence the investors’ purchase behavior and the stock price (Beunza &
Garud, 2007; Rao & Sivakumar, 1999; Womack, 1996). I controlled for Acq_Buy as whether the
majority of analysts recommended “buy” for acquirers between when deal was announced and
when deal was terminated. These two variables were denoted 1 if more than 50% of the analysts

106
recommend “buy”. I also controlled for the number of analysts covering the acquirer and took a
logarithm of this variable (Acq_Analysts).
Third, I took into account acquirers’ acquisition capability. I captured Acq_Exp by
counting all acquisitions that acquirers had announced before the focal deal (e.g. Kim, Haleblian,
& Finkelstein, 2011). I took a logarithm of this variable due to left-skewedness of the data.
Fourth, I controlled for several deal level variables that may affect the stock market
reaction when the deal is terminated. Duration measures number of days between when the deal
was first announced and when the deal was terminated. Friendly was coded 1 if the focal deal is
categorized as friendly. Deal_Value captured the monetary value of the focal transaction
estimated by SEC. Relatedness was coded 1 if the acquirer and target share the same 2-digit SIC
code. This is a concept used to capture the strategic fit and resource complementarity between
acquirers and targets (e.g. Barney, 1988; Makri, Hitt, & Lane, 2010; Shen et al., 2014). Cash
indicated whether cash is the primary source of payment, as the stock market reactions to M&A
terminations have found to be different between deals using cash and stock (Tang, 2015). It is
also likely that the market will be less concerned about a target’s decision to terminate a deal if it
also had other possible bidders. Therefore, I controlled for this situation by measuring whether
the target had an Alternative_Bidder in addition to the focal acquirer. Lastly, I took into account
whether a Lawsuit between the acquirer and the target exists, particularly when the acquirer and
the target blame each other for breaching the agreement. Year fixed effects are also included.

Econometric Models and Analysis
I used linear regressions (OLS) to test whether the market reaction to deal termination is
affected by its initial assessment (H1) and whether firms make attributions (H2). I only looked at
those who chose to give attributions in order to measure what types of attributions they made

107
(H3, H4, and H5), but it is likely that firms who chose to provide explanations are different from
those who did not. Therefore, simply using OLS may generate biased estimates.
I used the Heckman selection model to test the effects of attribution on firms’ stock
market reaction to termination announcements. I used a probit model in the first stage to predict
the probability of firms making attributions of deal terminations, and then calculated the inverse
Mills ratio and inserted it in the second stage that uses OLS estimation on CAR. I used whether
the deal was terminated after the presence of Sarbanes-Oxley Act (2002) as the instruments for
the likelihood of making attributions. This variable may affect whether firms choose to publicly
make an attribution about why the deal is terminated (i.e. public firms might be more likely to
publish explanations when they face the pressure to disclose information), but may not directly
affect how the stock market would react to the termination announcement.

RESULTS
Table 4.1 provides summary statistics of all the variables that I use in the analysis, and
Table 4.2 displays the correlation coefficients between these variables. Acq_Attribution is not
included in the correlation table, as all variables about the types of attributions only exist when
acquirers and targets choose to make an attribution. The correlation between these two variables
and other control variables are available upon request.

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Table 4. 1: Summary statistics of key variables
Variable N Mean Std. Dev. Min Max
Acq_Termiantion_CAR 444 0.00 0.11 -0.78 0.89
Acq_Ann_CAR 456 -0.02 0.10 -0.62 0.78
Acq_Attribution 557 0.48 0.50 0 1
Mod_Responsible 266 0.08 0.28 0 1
Least_Responsible 266 0.65 0.48 0 1
Acq_Locus 266 0.65 0.48 0 1
Acq_Control 266 0.27 0.44 0 1
Tar_Termination_CAR 409 -0.05 0.17 -1.25 1.20
Acq_Buy 557 0.64 0.48 0 1
Acq_Analyst 557 0.72 0.94 0 3.71
Duration 557 4.37 1.13 0 7.62
Acq_Exp 557 1.49 1.03 0 4.90
Deal Value 557 5.26 2.42 -1.14 11.89
Tar_Withdraw 374 0.16 0.36 0 1
Both_Withdraw 374 0.30 0.46 0 1
Lawsuit 557 0.05 0.21 0 1
Acq_Impact 294 0.55 0.50 0 1
Alternative_Bidder 421 0.40 0.49 0 1
Acq_Noise 431 0.26 0.44 0 1
Hostile 557 0.11 0.31 0 1
Acq_Term_Fee 557 0.05 0.21 0 1
Tar_Term_Fee 557 0.08 0.27 0 1
Cash 557 0.27 0.45 0 1
Relatedness 557 0.61 0.49 0 1

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Table 4. 2: Correlation table of key variables

Acq_Termiantion_
CAR
Acq_Ann_CAR
Acq_Responsibility
Acq_Locus
Acq_Control
Tar_Termination_
CAR
Acq_Buy
Acq_Analyst
Duration
Acq_Exp
Deal Value
Who_Withdraw
Lawsuit
Acq_Impact
Alternative_Bidder
Acq_Noise
Hostile
Acq_Term_Fee
Tar_Term_Fee
Cash
Acq_Termiantion_CAR 1.00

Acq_Ann_CAR -0.32 1.00

Acq_Responsibility -0.02 0.12 1.00

Acq_Locus 0.00 0.15 0.96 1.00

Acq_Control 0.04 -0.08 -0.95 -0.83 1.00

Tar_Termination_CAR 0.07 0.01 0.01 0.04 0.02 1.00

Acq_Buy -0.01 -0.01 -0.03 -0.06 -0.01 -0.02 1.00

Acq_Analyst 0.02 0.05 0.12 0.15 -0.08 -0.07 -0.72 1.00

Duration 0.04 -0.01 -0.02 -0.01 0.03 -0.03 -0.32 0.54 1.00

Acq_Exp 0.08 0.09 0.16 0.19 -0.10 -0.02 -0.17 0.26 0.14 1.00

Deal Value 0.08 -0.05 0.03 0.06 0.01 -0.05 -0.28 0.53 0.25 0.28 1.00

Who_Withdraw -0.08 -0.08 0.02 0.02 -0.02 -0.03 -0.08 0.23 0.21 -0.07 0.07 1.00

Lawsuit -0.01 -0.03 0.06 0.03 -0.08 -0.03 -0.16 0.21 0.23 0.16 0.16 -0.03 1.00

Acq_Impact -0.10 -0.13 0.09 0.10 -0.07 0.04 -0.08 0.13 0.05 0.08 0.14 0.04 0.07 1.00

Alternative_Bidder 0.09 0.00 -0.10 -0.05 0.15 0.35 -0.07 -0.06 -0.06 -0.02 0.15 -0.26 0.03 -0.02 1.00

Acq_Noise 0.08 -0.01 0.14 0.15 -0.12 -0.06 -0.05 0.07 0.01 0.04 0.07 -0.05 -0.05 0.12 -0.08 1.00

Hostile 0.01 0.06 -0.11 -0.10 0.11 0.13 0.04 -0.06 0.09 0.09 0.18 -0.31 -0.06 -0.02 0.17 0.01 1.00

Acq_Term_Fee 0.05 -0.17 0.03 0.00 -0.06 -0.08 -0.03 0.13 0.15 -0.01 0.17 0.21 0.10 0.18 -0.07 0.03 -0.11 1.00

Tar_Term_Fee 0.11 -0.03 -0.12 -0.08 0.16 0.22 -0.14 0.06 -0.04 0.09 0.09 0.01 -0.12 0.14 0.32 -0.10 -0.07 0.03 1.00

Cash -0.01 0.18 0.02 0.03 0.00 0.04 -0.05 -0.09 -0.12 0.11 -0.12 -0.38 0.05 -0.13 0.04 -0.16 0.21 -0.09 0.02 1.00
Relatedness 0.09 -0.02 -0.08 -0.07 0.08 -0.01 -0.25 0.17 0.08 0.00 0.18 -0.12 0.06 -0.15 0.10 0.00 0.15 0.13 0.04 0.06
Note: (1) Acq_Attribution is not included as they do not correlate with attribution style. (2) There is high correlation between locus and controllability. Please refer to the section of
additional test for more information. (3) Acq_Responsibility and Who_Withdraw are treated as continuous variables in this table.

110
I use OLS to test the first two hypotheses. Table 4.3 includes regression analyses for both
H1, that is the acquirer’s stock market return on a deal termination is negatively associated with
the stock market’s reaction to the announcement of this M&A, and H2, that is the negative
relationship established in H1 is weakened (less negative) when acquirers make an attribution.
Model (1) in Table (3) includes control variables only, and Model (2) adds the main effect of
acquirer’s stock market return to the announcement of this M&A. The variable Acq_Ann_CAR
has a coefficient of -0.29 and p-value of 0.07, suggesting a statistically significant relationship
that for one unit increase in the acquirer’s CAR on the announcement of this deal, its CAR on the
termination will drop by 0.29. I calculated the standardized coefficient to better interpret the
result and found that for one standard deviation increase in the CAR to the announcement of the
deal, the acquirer’s cumulative abnormal return on the termination will decrease by 0.30 standard
deviations. This result is consistent with H1.
To test the moderating effect of making attributions, I included whether the acquirer
choose to make an attribution (Acq_Attribution) from Model (3) to Model (5). Model (3) and
Model (4) suggest that there is no main effect of attributions on the acquirer’s CAR when a deal
is terminated. However, in Model (5), the interaction term of the acquirer’s CAR to the
announcement of the acquisition and Acq_Attribution is negative (p-value=0.046), contradicting
H2. I plotted this interaction in Figure 4.3(a) and found that the confidence intervals for the
presence of attribution and the absence of attribution overlapped at 95% level. I calculated the
differences between these two choices and found that in Figure 4.3(b) these differences are not
statistically scientifically different from zero, concluding that the negative relationship between
the acquirer’s CAR on the announcement of the deal and its CAR on the termination is no

111
different, whether the acquirer chose to make any attributions or not. Therefore, H2 is not
supported.
I further split the sample by whether the initial market reaction to the announcement of
the deal is positive (Model (7)) or negative (Model (6)). While all the effects go away when the
initial market reaction is positive, they are consistent with the full model (5). I plot this
interaction effect of Acq_Ann_CAR and Acq_Attribution of Model (6) in Figure 4.4. Figure
4.4(a) shows that when the acquirer made attributions about the termination, the relationship
between its CAR on deal announcement and on deal termination becomes negative, whereas this
relationship is positive when the acquirer did not offer any attributions. The differences between
these two situations are significant with 95% confidence interval when the acquirer’s initial
market reaction to the announcement of the deal is negative as plotted in Figure 4.4(b).
Do attributions of responsibility also matter? I analyzed the moderating effect of
perceived responsibility for the acquirer in Table 4.4. This table summarizes the regression
analyses for H3a and H3b. I used the Heckman twostep model to conduct the analysis; Table 4.4
displays the results for the second step.
10
Model (1) to Model (3) in Table 4 demonstrate the
main effects of the moderate and low level of perceived responsibility as compared to high level
of perceived responsibility. These main effects are not significant. I inserted the interaction effect
of the perceived responsibility and the acquirer’s CAR on announcement in Model (4). These
interaction effects suggest that the negative relationship in H1 is not changed when the acquirer
is perceived as more or less responsible for the termination. I plotted this interaction effect in
Figure 4.5.

10
The first step is a probit analysis predicting the probability of acquirers choosing to make attributions, using
control variables and an instrument variable measuring whether the deal was terminated with the presence of the
Sarbanes-Oxley Act. The instrument is statistically positively associated with the probability of making attributions.
The details of this first step analysis are available upon request.

112
Table 4. 3: Regressions on acquirer’s CAR when deal is terminated
Full Sample Split Sample
Model (1) Model (2) Model (3) Model (4) Model (5) Model (6) Model (7)
Acq_Ann_CAR -0.29*** -0.29*** 0.19 0.74 -0.01
(0.07) (0.07) (0.25) (0.50) (0.72)
Acq_Attribution 0.01 0.01 0.01 -0.05 -0.01
(0.03) (0.02) (0.02) (0.04) (0.07)
Acq_Ann_CAR 
Acq_Attribution
-0.52** -1.24** -0.14
(0.26) (0.51) (0.75)
Tar_Term_CAR 0.04 0.04 0.04 0.04 0.04 0.08 0.02
(0.04) (0.04) (0.04) (0.04) (0.04) (0.06) (0.08)
Acq_Buy 0.02 0.02 0.02 0.02 0.02 0.00 0.05
(0.02) (0.02) (0.02) (0.02) (0.02) (0.03) (0.06)
Acq_Analyst -0.00 0.00 -0.00 0.00 0.00 -0.01 0.01
(0.02) (0.01) (0.02) (0.01) (0.01) (0.02) (0.03)
Duration 0.00 0.01 0.00 0.01 0.00 0.02* -0.01
(0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.02)
Acq_Exp 0.00 0.01 0.00 0.01 0.01 0.01 0.03
(0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.02)
Deal Value 0.00 0.00 0.00 0.00 0.00 0.00 0.00
(0.00) (0.00) (0.00) (0.00) (0.00) (0.01) (0.01)
Tar_Withdraw -0.05* -0.05 -0.05* -0.04 -0.04 0.01 -0.10
(0.03) (0.03) (0.03) (0.03) (0.03) (0.03) (0.07)
Both_Withdraw -0.03 -0.02 -0.03 -0.02 -0.01 -0.01 0.01
(0.02) (0.02) (0.02) (0.02) (0.02) (0.02) (0.05)
Lawsuit -0.01 -0.01 -0.01 -0.01 -0.00 -0.01 0.05
(0.03) (0.03) (0.03) (0.03) (0.03) (0.03) (0.07)
Acq_Impact -0.03 -0.04** -0.03 -0.04** -0.04** -0.04** -0.02
(0.02) (0.02) (0.02) (0.02) (0.02) (0.02) (0.04)
Alternative_Bidder 0.01 0.01 0.01 0.01 0.01 -0.01 0.06
(0.02) (0.02) (0.02) (0.02) (0.02) (0.02) (0.06)
Acq_Noise 0.00 0.02 0.00 0.02 0.02 0.02 0.03
(0.02) (0.02) (0.02) (0.02) (0.02) (0.02) (0.04)
Hostile -0.01 -0.01 -0.01 -0.01 -0.01 -0.02 -0.00
(0.02) (0.02) (0.03) (0.02) (0.02) (0.03) (0.06)
Acq_Term_Fee 0.04 0.02 0.04 0.02 0.02 0.02 omit
(0.04) (0.03) (0.04) (0.03) (0.03) (0.03) omit
Tar_Term_Fee 0.04 0.04 0.04 0.04 0.04 0.02 0.07
(0.03) (0.02) (0.03) (0.02) (0.02) (0.03) (0.06)
Cash -0.01 0.01 -0.00 0.01 0.01 0.01 0.03
(0.02) (0.02) (0.02) (0.02) (0.02) (0.02) (0.04)
Relatedness -0.00 0.00 -0.00 0.00 0.00 -0.00 0.03
(0.02) (0.02) (0.02) (0.02) (0.02) (0.02) (0.05)
Constant -0.03 -0.04 -0.04 -0.05 -0.05 -0.03 -0.09
(0.05) (0.05) (0.06) (0.06) (0.05) (0.07) (0.14)

Observations 200 195 200 195 195 133 62
R
2
0.07 0.16 0.07 0.16 0.18 0.19 0.28
F 0.804 1.837 0.771 1.750 1.887 1.343 0.858
Note: Standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 for two tailed tests.

113

Figure 3(a) Figure 3(b)
Note: Figure 3(a) plots the interaction effect of the presence and absence of attribution using Model (5) in Table 3.
The x-axis is the acquirer’s CAR when an acquisition is announced, and the y-axis represents the acquirer’s CAR
when this acquisition is terminated. The red line displays that the acquirer makes some attribution while the blue line
denotes the situations that the acquirer does not make any attribution. Figure 3(b) plots the differences of the red line
and the blue line in Figure 3(a). The bars denote confidence intervals at 95% level.

Figure 4. 3: The interaction effect of the presence and absence of attribution (full sample)

Figure 4 (a) Figure 4(b)
Note: Figure 4(a) plots the interaction effect of the presence and absence of attribution using Model (6) in Table 3.
The x-axis is the acquirer’s CAR when an acquisition is announced, and the y-axis represents the acquirer’s CAR
when this acquisition is terminated. The red line displays that the acquirer makes some attribution while the blue line
denotes the situations that the acquirer does not make any attribution. Figure 4(b) plots the differences of the red line
and the blue line in Figure 4(a). The bars denote confidence intervals at 95% level.

Figure 4. 4: The interaction effect of the presence and absence of attribution (split sample)

I split the sample and directly test H3a and H3b in Model (5) and Model (6) in Table 4.4.
Model (5) shows the relationships when the initial market reaction to the announcement of the
deal is negative. In this model, the interaction term between acquirers’ CAR on deal

114
announcement and low levels of perceived responsibility is positively significant, suggesting that
the negative relationship between the acquirer’s CAR on deal termination and on deal
announcement is less negative when acquirers are perceived as not responsible. I plotted this
interaction effect in Figure 4.5. When the initial market reaction to deal announcement is
negative, the acquirer will receive more positive CAR when its attribution is perceived as more
responsible, supporting H3b.
However, when the initial market reaction to the deal announcement is positive, the
perceived responsibility does not seem to affect the stock market return to deal termination as
shown in Model (6). I plotted this interaction effect in Figure 4.7. When the initial market
reaction to the deal announcement is positive, the high level of perceived responsibility and low
level of perceived responsibility do not differ in changing the relationship between the acquirer’s
CAR on deal termination and on deal announcement. This is inconsistent with H3a.

115
Table 4. 4: Heckman selection model on Acquirer Termination CAR
Full Sample Split Sample
Model (1) Model (2) Model (3) Model (4) Model (5) Model (6)
Acq_Ann_CAR -0.29*** -0.34*** -0.45*** -1.07*** 0.08
(0.07) (0.08) (0.11) (0.21) (0.23)
Mod_Responsible -0.02 -0.04 -0.04 -0.03 0.01
(0.04) (0.03) (0.04) (0.06) (0.17)
Least_Responsible -0.00 -0.00 0.00 0.07** 0.11
(0.02) (0.02) (0.02) (0.03) (0.07)
Acq_Ann_CAR  Mod_Responsible 0.08 0.45 2.47
(0.36) (0.46) (4.30)
Acq_Ann_CAR  Least_Responsible 0.24 1.02*** -0.33
(0.16) (0.27) (0.49)
Tar_Term_CAR 0.04 0.02 0.03 0.04 0.03 0.02
(0.04) (0.05) (0.04) (0.04) (0.05) (0.11)
Acq_Buy 0.03 0.03 0.03 0.03 0.02 0.09
(0.02) (0.03) (0.03) (0.03) (0.03) (0.07)
Acq_Analyst 0.00 0.00 0.01 0.01 0.01 0.02
(0.01) (0.02) (0.02) (0.02) (0.02) (0.05)
Duration 0.01 0.01 0.01 0.01 0.01 -0.00
(0.01) (0.01) (0.01) (0.01) (0.01) (0.03)
Acq_Exp 0.02 0.02* 0.02* 0.02* 0.03* 0.05
(0.01) (0.01) (0.01) (0.01) (0.01) (0.04)
Deal Value -0.00 -0.00 -0.00 -0.00 -0.01 -0.01
(0.01) (0.01) (0.01) (0.01) (0.01) (0.02)
Tar_Withdraw 0.03 0.05 0.04 0.03 0.12* -0.09
(0.06) (0.07) (0.07) (0.07) (0.07) (0.24)
Both_Withdraw -0.02 -0.04 -0.03 -0.03 -0.02 -0.01
(0.02) (0.02) (0.02) (0.02) (0.02) (0.07)
Lawsuit -0.02 -0.04 -0.03 -0.02 -0.02 0.04
(0.03) (0.04) (0.04) (0.04) (0.04) (0.09)
Acq_Impact -0.04** -0.03 -0.04** -0.04** -0.05** -0.02
(0.02) (0.02) (0.02) (0.02) (0.02) (0.05)
Alternative_Bidder -0.00 -0.00 -0.01 -0.01 -0.03 0.10
(0.02) (0.02) (0.02) (0.02) (0.02) (0.08)
Acq_Noise 0.01 -0.01 0.01 0.01 0.01 0.03
(0.02) (0.02) (0.02) (0.02) (0.02) (0.06)
Hostile -0.02 -0.03 -0.02 -0.02 0.01 -0.06
(0.02) (0.03) (0.03) (0.03) (0.03) (0.09)
Acq_Term_Fee 0.05 0.08 0.04 0.05 0.07* omit
(0.04) (0.05) (0.04) (0.04) (0.04) omit
Tar_Term_Fee 0.05* 0.05 0.04 0.04 0.05* 0.10
(0.03) (0.03) (0.03) (0.03) (0.03) (0.08)
Cash 0.01 -0.00 0.01 0.01 -0.01 0.03
(0.02) (0.03) (0.02) (0.02) (0.03) (0.06)
Relatedness 0.00 0.01 0.01 0.01 -0.01 0.07
(0.02) (0.02) (0.02) (0.02) (0.02) (0.06)
Lambda -0.14 -0.21* -0.16 -0.16 -0.20* -0.04
(0.10) (0.12) (0.11) (0.12) (0.11) (0.36)
Constant 0.00 0.04 0.01 0.02 0.00 -0.28
(0.06) (0.08) (0.07) (0.07) (0.08) (0.19)

Observations 195 176 171 171 119 52
R-squared 0.17 0.09 0.21 0.22 0.34 0.43
F 1.860 0.806 1.897 1.835 2.121 0.994
Note: Standard errors in parentheses. First step estimates are not displayed due to page limits and are available upon
request. *** p<0.01, ** p<0.05, * p<0.1 for two tailed tests.

116

Note: Figure 5 plots the interaction effect of
responsibility using Model (4) in Table 4. The x-axis is
the acquirer’s CAR when an acquisition is announced,
and the y-axis represents the acquirer’s CAR when this
acquisition is terminated. The blue line displays that the
acquirer is perceived as highly responsible for the
termination, while the red line displays moderate level
of perceived responsibility and the green line displays
low level of perceived responsibility. The differences
between these lines are not significant.

Figure 4. 5: The interaction effect of responsibility (full sample)

Note: Figure 6 plots the interaction effect of
responsibility using Model (5) in Table 4. The x-axis is
the acquirer’s CAR when an acquisition is announced
(when the initial market reaction to the announcement
of the deal was negative), and the y-axis represents the
acquirer’s CAR when this acquisition is terminated.
The blue line displays that the acquirer is perceived as
highly responsible for the termination, while the red
line displays moderate level of perceived responsibility
and the green line displays low level of perceived
responsibility. The difference between the blue line and
the green line is statistically significant.

Figure 4. 6: The interaction effect of responsibility (split sample)

Note: Figure 7 plots the interaction effect of
responsibility using Model (5) in Table 4. The x-axis
is the acquirer’s CAR when an acquisition is
announced (when the initial market reaction to the
announcement of the deal was positive), and the y-axis
represents the acquirer’s CAR when this acquisition is
terminated. The blue line displays that the acquirer is
perceived as highly responsible for the termination,
while the red line displays moderate level of perceived
responsibility and the green line displays low level of
perceived responsibility. The differences between any
two lines are not statistically significant.

Figure 4. 7: The interaction effect of responsibility (split sample)

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Endogeneity Concerns
It is likely that unobserved factors may affect both how responsible acquirers are
perceived for the termination and how the market evaluates the termination. Therefore, I
considered using instruments for acquirers’ perceived responsibility. These instruments should
be directly affect acquirers’ attributions but do not affect the stock market reaction to deal
termination. I used two instruments: S&P 500 monthly index when the deal was terminated and
the real GDP number for that year. These two instruments reflect the market condition. When the
market is generally undesirable, acquirers are easier to attribute to external and uncontrollable
environmental factors and thus perceived as less responsible. Therefore, I treated
Perceived_Responsibility as a continuous variable and conducted a two-stage least square
regression (2SLS)
11
. These two instruments are negatively associated with perceived
responsibility (reverse coded), suggesting that the more adverse the economic environment is,
the less responsible the acquirer will be perceived for the termination. In the second stage, the
interaction term between perceived responsibility and the market return on deal announcement is
positive and significant (p = 0.05), consistent with H3b.
In addition, I also used Coarsened Exact Matching (CEM) to balance the attributions that
are perceived as least responsible and that are not. I matched on acquirer characteristics to
determine a treatment sample (least responsible) and a control sample (moderated and most
responsible). I then compare the market reaction to these two samples and found that the
relationship between the stock market return on deal termination and on deal announcement is
less negative when the acquirers’ attributions are perceived as less responsible.

11
The instruments are valid: Durbin score (1.12) and Sargan Score (1.77) are not significant.

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Additional Tests
I conducted several additional tests to make sure that the results are robust and to better
understand the null findings. First, I used different windows to calculate CAR for acquirers. I
used relatively longer windows (-5,5) and found that the results are stronger. I also used other
windows such as (-2,2), (-1,1), and (-1,0). The results are consistent.
Second, it is likely that some of the control variables at deal level variables (such as deal
value) may correlate with the market reaction to the initial announcement. Although the variance
inflation factor (VIF) does not indicate a strong multicollinearity issue, excluding these variables
enhances the model fit. Therefore, I used these factors to predict the announcement CAR and
used the model residual as a proxy for announcement CAR and incorporated this proxy instead.
The results are robust with the primary findings.
Third, it is possible that the acquirer’s market return may be influenced by the target’s
behavior and vice versa. One potential reason is that the acquirer’s attribution could affect the
target’s CAR on termination; the target’s attribution could also affect the acquirer’s CAR on
termination. I controlled for the other party’s attribution tendency (locus and controllability) in
the analysis of the focal party and reran the regressions. Although the sample size shrunk, the
level of significance increased, suggesting that the main findings are strong. Another plausible
reason is that the acquirer’s CAR on termination is also sensitive to the target’s CAR. Therefore,
I used a seemingly unrelated regression to align the model for Acq_Termination_CAR and a
similar model for Target’s CAR on termination. The results confirm my primary findings.
Fourth, the measure of locus of attribution may raise some concerns as firms could make
attributions to themselves, the other party, or the environment. I categorized external attribution
as firms attributing the termination to the other party or the environment in the primary analysis,
but it might be possible that making attributions to the other party is different from attributing the

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environment. Therefore, I coded external as blaming the environment while internal as blaming
the parties negotiating the deal (oneself and the other party). In this situation, I found that
blaming an external party will still mitigate the negative main effect between firms’ stock market
reaction to the termination and the stock market return on the announcement.
Fifth, although locus and controllability are two independent dimensions in the theory,
they do correlate empirically as external factors are usually uncontrollable. Therefore, it is likely
that my findings on controllability are present not because of the mechanisms I proposed, but
because “controllability” simply relates to the locus of attribution and it is the locus of attribution
driving the results. To rule out this alternative explanation, I chose the sample that firms only
make internal attributions (either controllable or uncontrollable) and found that H3 is still
supported.
Last, the results might be sensitive to whether the acquirer and the target blame each
other. I measured “Attribution_Consistency” referring to whether the acquirer and the target
attribute the deal termination to the same factors. I found that the acquirer’s stock market return
on the termination does not play a significant role.

DISCUSSION
This paper looks at how firms make attributions on their decision to terminate an
announced M&A deal. I found that when the initial market assessment to the announcement of
an acquisition is more negative, the market will react more positively when the deal is
terminated. Attributions could help the acquirer in the way that the market will react even more
positively when the acquirer attributes deal termination to internal (i.e. the acquirer itself) and
controllable factors as it is perceived as more responsible for the termination. However, when the
initial market assessment to the announcement of an acquisition is more positive, the market will

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react more negatively when the deal is terminated and this effect is not affected by the acquirers’
attributions.
Although a growing body of literature has examined the consequences of M&A
termination on acquirers’ and targets’ performance (Boubakri et al., 2010; Fabozzi et al., 1988;
Tang, 2015), these studies generally assumed that acquirers and targets passively accept the
stock market’s evaluation. Rather than taking a reactive view, I suggest that firms proactively
manage their stakeholders’ reactions to deal terminations. Incorporating insights from the
attribution theory, I found that firms strategically manage the perception of stockholders and
investors by attributing different causes to the termination of an M&A.
To the best of my knowledge, this paper is the first to theorize and empirically test the
relationship between the acquisition announcement day CARs and acquisition day termination
CARs. I found that for both the acquirer and the target, the market’s reaction to termination is
negatively associated with the market’s reaction to the announcement of the acquisition. The
market will appreciate firms’ behavior that confirms its expectations, but punish firms that
violate those expectations. While researchers have become increasingly interested in the
determinants of the time to acquisition completion and the determinants of acquisition
terminations (Becher et al., 2015; Kau et al., 2008; Liu & McConnell, 2013), it is important to
establish and empirically test the relationship between the market’s initial reaction to a deal
announcement and its reaction when the deal is terminated. I thus contribute to the M&A
termination literature, which recently focuses on the determinants of the time to acquisition
completion and the determinants of acquisition termination, by testing another plausible
relationship and illustrating the performance consequences of deal termination.

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In addition to this relationship, I also found that whether firms choose to make
attributions and the types of attributions play a role in shaping the investors’ impression. While
the acquirer could not simply impress the market by giving some attributions, it needs more
effort to convince the market by making certain types of attributions: the market always wants
the acquirer to blame external and uncontrollable factors when the acquirer terminates a deal that
the market favors, but attribute to internal and controllable factors when the acquirer terminates a
deal that the market dislikes. Although attribution theories are widely used in research on
impression management, which generally assumes that firms make self-serving attributions. This
research stream discovered that this attribution tendency (i.e. self-serving attribution) could lead
to both positive (e.g. Staw et al., 1983) and negative results (e.g. Clapham & Schwenk, 1991). To
solve this puzzle, I adopt a more nuanced approach by studying controllability of attributions in
addition to the locus of attribution, which extend existing work that assumes firms make self-
serving attributions. Therefore, this study could also contribute to the literature that firms could
use different attributions to strategically manage audience reactions rather than simply making
self-serving attributions.
Several other findings from the control variables are also worth noting. The acquirer will
receive more positive CAR when the acquirer initiated the termination decision and/or when it
will receive a termination fee from the target, but surprisingly it will also receive positive CAR
when the acquirer does not talk about the impact of the termination, the deal has a high value,
and most analysts recommended “buy” before the deal was terminated. For the target, its CAR
when the deal is terminated will be higher when another buyer exists, the withdrawing deal is
hostile, or the acquirer and the target are not in the same industry. However, the market reaction
to the target is also higher, even if the target has to pay a termination fee.

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Limitation and Future Research
In conclusion, our study provides useful insights into understanding the effect of
attributions in the context of terminated mergers and acquisitions. However, there are several
ways to extend our findings both theoretically and empirically.
First, the current study only looks at the stock market return as a reflection of the market
response to the termination event. Although I do not intend to make any implications on firm
performance, it will be interesting to look at other mid or long-term measures. For instance, it is
plausible to use analysts’ forecasts and recommendations after the termination announcement
and see whether different types of attributions could also change the analysts’ evaluations. While
the acquirers and targets may be evaluated differently depending on their attribution tendencies,
one could also examine how attributions affect firms’ behavior in the long term and more
specifically, how acquirers’ and targets’ subsequent acquisition behavior changes following their
attributions for deal termination.
Second, although I considered the cases when the acquirer and the target blame each
other for the termination, I did not conduct more nuanced analyses as the sample size is
relatively small. It will be worthwhile to look at whether the acquirer and the target attribute deal
termination to the same factors. Moreover, I only coded analysts’ recommendations as a proxy
for their thinking, but I did not have direct measures on whether the analysts agree or disagree
over the attributions made by the acquirers and targets. Future research could compare the
attributions made by the acquirer, the target, and the analysts and find out who the market listens
to when an announced acquisition is terminated.
Third, I only looked at acquisitions between public acquirers and public targets, as it is
more convenient to obtain press releases for public firms. Studies have found that the acquirer’s
CAR to the termination differs between public and private targets (Tang, 2015). Future studies

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could extend this finding by surveying private targets and discover whether the market reactions
could be shaped by types of attributions for public-private acquisitions.
Fourth, although I coded the reasons that the acquirers and the targets gave as underlying
the terminations, I could not get the “true” reasons behind these events. The investors only obtain
this information from the firm and the analysts, not knowing what is going on behind the closed
doors. It has been suggested that individuals tend to be more objective when forming attributions
for positive outcomes (Huff & Schwenk, 1990), but firms may hide the true reason when they
violate the market’s expectations. Therefore, future research could look at those deals where the
reasons behind termination are more objective and discover whether firms’ strategy to impress
the investors may still work.
Moreover, I only considered two dimensions of attribution. Later work by (Abramson,
Seligman, & Teasdale, 1978) also proposed three other dimensions—stability, intentionality and
globality—which were later recognized by many scholars. Stability refers to whether a cause is
perceived as variable or permanent in similar situations. This dimension is important, as
perceptions of causal stability affect people’s expectations of future outcomes (Weiner, 1985).
Intentionality refers to whether people intend to make a certain decision or cause a certain
outcome to happen. Intentionality captures the differences between effort and strategy when
people make attributions on negative outcomes (e.g. low performance): one may intend to not
exert sufficient effort but may not intend to use a wrong strategy to cause a certain outcome
(Weiner, 1985). Globality refers to the fact that factors affecting certain types of situations may
also affect other situations. Future research could also look at these two dimensions and apply
them in the organizational setting.

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Scholars have increasingly become interested in a situation where people do not just
blame each other or the environment, moreover, they could also blame the relationship (e.g.
Eberly, Holley, Johnson, & Mitchell, 2011). I found that 13% of the acquirers and targets jointly
blamed the relationship for terminating an acquisition, by suggesting that things did not work out
in the negotiation. Future research could look at this type of attribution and extend the literature
on relational attribution.

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CHAPTER 5. CONCLUSION
M&A is a costly activity and about half of the deals failed to meet financial expectations.
In my dissertation, I am analyzing how acquirers govern the deal-making process in reducing
costs and boosting post-merger performance. My dissertation aims to decipher the “black-box”
of the deal-making process by studying the governance issue including what firms negotiate
about and how firms solve problems in this phase of an acquisition. Specifically, I study how
acquirers evaluate information from themselves and the targets in negotiating the terms and how
acquirers disclose information to the public when they need to solve problems discovered in the
due diligence. I aim to show that well-governed deal-making process can help acquirers make
the right decisions and thus improve M&A performance.
This dissertation seeks to make several theoretical and empirical contributions to our
understanding of firms’ strategies in response to the M&A terminations. First, I contribute to the
M&A literature by uncovering the deal-making process and demonstrating the importance of
governance in this process. While prior research has mainly looked at the antecedents of M&A
prior to deal announcement and their impact on different outcomes , the important phase between
partner selection and deal completion has attracted less attention. My dissertation studies how
acquirers and targets negotiate to protect their gains out of the deal by using penalty provisions in
the merger agreement, enhancing our current knowledge of the M&A process. This dissertation
thus helps move forward our understanding of the ambiguous relationship between acquisition
experience and acquisition performance (e.g. Hayward, 2002; Porrini, 2004; Zollo & Singh,
2004; Castellaneta & Conti, 2014) by suggesting that firms also learn from prior experience that
is incomplete.

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Second, this dissertation also contributes to the M&A literature by introducing the roles
of acquisition agreements in governing the formation of hierarchies. Previous research in
strategy only studied contracts in the context of inter-firm relationships such as buyer-supplier
relationships and alliances. In the M&A context, however, contracts not only govern the average
one-year period of negotiation before deal accomplishment, but they also set the tone for the
management of post-merger integration. Studying contracts for mergers and acquisitions is
useful to understand the negotiation process because they not only reflect the consequences of
such negotiation (by offering all the financials and guidelines for deal completion) but also are
essentially the matter being negotiated. Moreover, M&A contracts convey important information
about the deal to the stakeholders. Therefore, the design of merger agreement reveals the
conscious decision-making process that involves both acquirers and targets.
Third, rather than assuming that the acquirers and targets reduce information asymmetry
by gathering and possessing adequate and accurate information, I analyze the way they process
information. Adopting behavioral perspectives such as organizational learning perspective and
attribution theories, I suggest that acquirers can profit from leveraging the information of
themselves and the targets, as well as cautiously disclosing information to the public.
Fourth, this dissertation is also among the first to apply the attribution theory to the
context of M&A termination and the first to theorize and test the relationship between market
returns to the announcement of an acquisition and market returns to the termination of that deal.
Although a growing body of literature has examined the consequences of M&A termination on
acquirers’ and targets’ performance (Boubakri, Chazi, & Khallaf, 2010; Fabozzi, Ferri, Fabozzi,
& Tucker, 1988; Tang, 2015), these studies generally assumed that acquirers and targets
passively accept the stock market’s evaluation. Rather than taking a reactive view, I suggest that

136
firms proactively manage their stakeholders’ reactions to deal terminations. Incorporating
insights from the attribution theory, I found that firms strategically manage the perception of
stockholders and investors by attributing different causes to the termination of an M&A.
Fifth, attribution theories are mainly used in research on impression management, which
generally assumes that firms make self-serving attributions. This research stream discovered that
this attribution tendency (i.e. self-serving attribution) could lead to both positive (e.g. Staw et al.,
1983) and negative results (e.g. Clapham & Schwenk, 1991). To solve this puzzle, I adopt a
more nuanced approach by studying several dimensions of attributions, which extend existing
work that assumes firms make self-serving attributions. Therefore, this dissertation could also
contribute to the impression management literature that firms could use different attributions to
strategically manage audience reactions rather than simply making self-serving attributions.
Sixth, I also contribute to the behavioral theory of the firm, by suggesting that firms make
attributions to manage the investors’ expectations and then take actions to justify their
attributions in my last essay. The behavioral theory of the firm proposes that firms conduct
“search” according to their social aspirations (Cyert & March, 1963; Kim, Haleblian, &
Finkelstein, 2011). Firms could use the market assessment on their strategy (e.g. announcing an
acquisition) as the reference point and aspiration level, and create expectations about future
outcomes if they move away from the aspiration level (e.g. terminating an acquisition). Firms
could categorize an M&A termination as positive or negative according to the market’s
expectation, and adjust their behaviors not only through local or distant search, but also through
influencing the market aspiration level by making different attributions of their strategic choices,
as well as changing their future behavior (e.g. making a subsequent acquisition).

137
In summary, I believe the introduction of behavioral strategy and contract negotiation as
a novel way to explain how firms govern the deal-making process has the potential to generate
widespread interest. Scholars in finance (Bates & Lemmon, 2003; Boone & Mulherin, 2007;
Officer, 2003; Tang, 2015), organizational behavior (Barry & Crant, 2000; Eberly, Holley,
Johnson, & Mitchell, 2011; Harvey et al., 2014; Lord & Smith, 1983), and law (Afsharipour,
2010; Bainbridge, 1990; Gilson & Schwartz, 2005) in addition to management (Graffin et al.,
2016; Muehlfeld et al., 2012; Schijven & Hitt, 2012) may find these ideas helpful to study the
phenomenon of acquisition in general.

138
REFERENCES
Afsharipour, A. (2010). Transforming the allocation of deal risk through reverse termination
fees. Vanderbilt Law Review, 63(5), 1163.
Bainbridge, S. M. (1990). Exclusive Merger Agreements and Lock-Ups in Negotiated Corporate
Acquisitions. Minnesota Law Review, 75, 239.
Barry, B., & Crant, J. M. (2000). Dyadic Communication Relationships in Organizations: An
Attribution/ Expectancy Approach. Organization Science, 11(6), 648–664.
Bates, T. W., & Lemmon, M. L. (2003). Breaking up is hard to do? An analysis of termination
fee provisions and merger outcomes. Journal of Financial Economics, 69(3), 469–504.
Boone, A. L., & Mulherin, J. H. (2007). Do Termination Provisions Truncate the Takeover
Bidding Process? Review of Financial Studies, 20(2), 461–489.
Boubakri, N., Chazi, A., & Khallaf, A. (2010). Targets Performance in Terminated Bids: An
Empirical Examination. Quarterly Journal of Finance and Accounting, 49(3/4), 87–111.
Cannella, A. A., & Hambrick, D. C. (1993). Effects of executive departures on the performance
of acquired firms. Strategic Management Journal, 14(S1), 137–152.
Castellaneta, F., & Conti, R. (2014). Learning to do What? How Acquisition Experience Affects
Learning to Select and Add Value. Academy of Management Proceedings, 2014(1), 16961.
Clapham, S. E., & Schwenk, C. R. (1991). Self-Serving Attributions, Managerial Cognition, and
Company Performance. Strategic Management Journal, 12(3), 219–229.
Cyert, R. M., & March, J. G. (1963). A BEHAVIORAL THEORY OF THE FIRM. Retrieved
from http://trid.trb.org/view.aspx?id=545261
Eberly, M. B., Holley, E. C., Johnson, M. D., & Mitchell, T. R. (2011). Beyond Internal and
External: A Dyadic Theory of Relational Attributions. Academy of Management Review, 36(4),
731–753.

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Fabozzi, F. J., Ferri, M. G., Fabozzi, T. D., & Tucker, J. (1988). A Note on Unsuccessful Tender
Offers and Stockholder Returns. The Journal of Finance, 43(5), 1275–1283.
Gilson, R. J., & Schwartz, A. (2005). Understanding MACs: Moral Hazard in Acquisitions.
Journal of Law, Economics, and Organization, 21(2), 330–358.
Graffin, S. D., Haleblian, J. (John), & Kiley, J. T. (2016). Ready, AIM, Acquire: Impression
Offsetting and Acquisitions. Academy of Management Journal, 59(1), 232–252.
Harvey, P., Madison, K., Martinko, M., Crook, T. R., & Crook, T. A. (2014). Attribution Theory
in the Organizational Sciences: The Road Traveled and the Path Ahead. Academy of
Management Perspectives, 28(2), 128–146.
Hayward, M. L. A. (2002). When do firms learn from their acquisition experience? Evidence
from 1990 to 1995. Strategic Management Journal, 23(1), 21–39.
Kim, J.-Y. J., Haleblian, J. J., & Finkelstein, S. (2011). When firms are desperate to grow via
acquisition: The effect of growth patterns and acquisition experience on acquisition premiums.
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Larsson, R., & Finkelstein, S. (1999). Integrating strategic, organizational, and human resource
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Affecting Attribution Theory Models of Organizational Behavior. Academy of Management
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Officer, M. S. (2003). Termination fees in mergers and acquisitions. Journal of Financial
Economics, 69(3), 431–467.
Porrini, P. (2004). Can a Previous Alliance Between an Acquirer and a Target Affect Acquisition
Performance? Journal of Management, 30(4), 545–562.
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Abstract (if available)

Abstract Despite the growth of acquisition activities in recent years, these transactions do not always create value. In an attempt to explain when and how acquirers can benefit from such costly initiatives, scholars in the field of mergers and acquisitions have examined the antecedents, consequences and the boundary conditions of acquisition performance. This line of research generally links the antecedents to the consequences and overlooks the deal-making process in between. This process, however, is vital to the value creation since it provides the information that acquirers need to reevaluate the transaction. My dissertation aims to decipher the “black-box” of the deal-making process by studying what firms negotiate about and how firms solve problems in this phase of an acquisition. I conducted three empirical studies to uncover how acquirers govern the deal-making process, using data on U.S. mergers and acquisitions. Specifically, I study how acquirers evaluate information about themselves and the targets in negotiating the terms of the deal, and how acquirers disclose information to the public when they need to solve problems discovered in the due diligence process. The findings suggest that the negotiation of acquisition agreements can be influenced by acquirers’ previous acquisition experience and their information about the target. When problems emerge during the negotiation, the return to acquirers is determined by the way acquirers disclose information to the public. These findings advance our understanding of the governance of deal-making process by highlighting how acquirers use the information to facilitate decision making and thus boost value for the firms.

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Asset Metadata

Creator Xing, Zhe (Adele) (author)

Core Title How do acquirers govern the deal-making process? Three essays on U.S. mergers and acquisitions 1994 – 2017

School Marshall School of Business

Degree Doctor of Philosophy

Degree Program Business Administration

Publication Date 08/03/2020

Defense Date 06/12/2018

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag m,mergers and acquisitions,OAI-PMH Harvest

Format application/pdf (imt)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Mayer, Kyle (committee chair), Folkes, Valerie (committee member), Jia, Nan (committee member), Rajagopalan, Nandini (committee member)

Creator Email adelexing@gmail.com,zhexing@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c89-55542

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Rights Xing, Zhe (Adele)

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