526782 J.T.H. Medema (2)

23/3/2011
Master Thesis
Alliance experience
and performance: a
contingency study
J.T.H. Medema

Tilburg University March 2011
2
Title page
Title: Alliance experience and performance: a contingency study
Name: J.T.H. Medema
ANR: 526782
Supervisor: Dr. L.M.A. Mulotte
Second reader: Dr. E. Dooms
Number of Words: 13.798
Faculty: Tilburg School of Economics and Management
Educational Program: MSc. Strategic Management
Date of Defense: 23-03-2011

Alliance experience and performance: a contingency study J.T.H. Medema
3
Management Summary
This research has been written as a Master’s Thesis for the MSc. program of Strategic Management at
Tilburg University. The research studies different contingencies surrounding the effect of alliance
experience on alliance performance.
The benefits of alliances seem to be apparent and broadly agreed upon (Anand & Khanna, 2000; Li,
Boulding & Staelin, 2009; Wittmann, Hunt & Arnett, 2009). Nevertheless, some studies show failure
rates of more than 50% (Lambe, Spekman & Hunt, 2002; Kale & Singh, 2007; Pangarkar, 2009).
Therefore this is undoubtedly an important area of research for companies.
In previous research, there seems to be some kind of consensus about alliance experience being one of
the most important determinants of alliance performance (Fiol & Lyles, 1985; Child & Yan, 1999; Kale,
Dyer & Singh, 2002; Hoang & Rothaermel, 2005; Heimeriks & Duysters, 2007). However, there is no
empirical consensus about the impact of experience on performance. This study focuses on the impact
of different types of alliance experience on alliance performance.
In this study we have tried to find out why different studies found different relationships between
alliance experience and alliance performance. We have done this by identifying and testing different
alliance experience contingencies that impact alliance performance. Based on the deliberate learning
mechanisms theory, the study identified four important contingencies that influence alliance
performance: partner specific alliance experience, the timing of alliance experience, similarity of alliance
experience and alliance governance design experience.
We empirically investigated the effect of alliance experience and the contingencies mentioned above on
alliance performance. The sample that was used to conduct this study contained 267 alliances
performed by the 6 largest pharmaceutical companies in the US. Using different regression methods, we

4
analyzed the data and found positive results for the effect of different types of alliance experience on
alliance performance.
We found that heterogeneous alliance experience and equity alliance experience were significant and as
a result were the contingencies that mostly influenced alliance performance in a positive way. In a less
obvious way partner specific experience was found significant at a one tail level and had a moderating
effect on the relationship between non-partner specific experience and performance. Our research
found that firms benefit most from non-similar and therefore heterogeneous experiences. Following the
learning curve and deliberate learning mechanisms theories, we argued that firms benefit most from
heterogeneous experiences because they contain the best learning opportunities.

5
Foreword
This Master’s thesis has been written to complete my Master’s in Strategic Management at Tilburg
University. After months of reading, thinking, writing and researching, it is finally complete. Although it
has taken quite some effort and a lot of time, I look back upon this period with satisfaction. Through
reading, writing, collecting and analyzing data, I have found results and thereby contributed to the
previous literature and to the academic world. Although I had my ups and downs, in the end I think I
have learned a lot with regard to academic writing and researching skills.
Before we will embark on this scientific journey through the alliance experience literature which was the
centre of my life these last months, I would like to thank some people who have helped me along the
way. First of all I would like to thank my supervisor, Dr. Mulotte for his guidance and supervision during
this project. Furthermore I would like to thank my friends and family for their support, needed and
unneeded advice, and their sharp remarks which kept me motivated.
A special thanks goes out to my parents, who have always unconditionally supported me in many ways,
and without whom this would not have been possible for me. A second special thanks goes out to my
girlfriend Lubna, for always being there for me, taking care of me and putting up with me. Thank you.
Jeroen Medema
Tilburg, 19th
of February, 2011.

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Table of Contents
Title page ................................................................................................................................................2
Management Summary...........................................................................................................................3
Foreword ................................................................................................................................................5
Chapter 1 – Introduction .........................................................................................................................9
1.1. Problem Indication...................................................................................................................9
1.2. Problem Statement.....................................................................................................................11
1.3. Research questions.....................................................................................................................11
1.4. Research method........................................................................................................................13
1.5. Structure of the thesis.................................................................................................................14
Chapter 2 – Review of literature............................................................................................................16
2.1. Alliance experience leads to success: theory...............................................................................16
2.1.1. The traditional learning curve perspective............................................................................17
2.1.2. The deliberate learning mechanisms perspective .................................................................18
2.2. Alliance experience leads to success: empirical findings..............................................................20
2.3. Our contribution.........................................................................................................................22
Chapter 3 – Different types of experience .............................................................................................23
3.1. General alliance experience ........................................................................................................23
3.2. Partner specific experience.........................................................................................................26
3.2.1. Theory .................................................................................................................................26

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3.2.2. Empirical support.................................................................................................................27
3.2.3. Hypothesis ...........................................................................................................................28
3.3. The timing of experience.............................................................................................................29
3.3.1. Theory .................................................................................................................................29
3.3.3. Hypothesis ...........................................................................................................................32
3.4. The similarity of experience ........................................................................................................33
3.4.1. Theory .................................................................................................................................33
3.4.3. Hypothesis ...........................................................................................................................37
3.5. Governance design .....................................................................................................................39
3.5.1. Theory .................................................................................................................................39
3.5.3. Hypothesis ...........................................................................................................................41
Chapter 4 – Empirical investigation........................................................................................................42
4.1. Sample and data.........................................................................................................................42
4.2. Dependant variable ....................................................................................................................43
4.3. Independent variables ................................................................................................................44
4.4. Control variables.........................................................................................................................46
4.5. Analysis.......................................................................................................................................47

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4.6. Validity and Reliability.................................................................................................................49
Chapter 5 – Results................................................................................................................................50
Chapter 6 – Discussion ..........................................................................................................................54
Chapter 7 – Managerial recommendations, limitations, future research and conclusion........................57
7.1. Contribution to the existing literature.........................................................................................57
7.2. Managerial recommendations ....................................................................................................59
7.3. Limitations and recommendations for future research................................................................60
7.4. Conclusion..................................................................................................................................62
Reference List........................................................................................................................................63
Appendices ...........................................................................................................................................71
Appendix 1: Graphical representation of the hypotheses...................................................................71
Appendix 2: Descriptive statistics.......................................................................................................73
Appendix 3: Correlations ...................................................................................................................74
Appendix 4: Regression results ..........................................................................................................78
Appendix 5: Normality and Homoscedasticity....................................................................................79
Appendix 6: Graphical representation of the results ..........................................................................80
Appendix 7: Schematic representation of the results.........................................................................82

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Chapter 1 – Introduction
Every big company has at least once, but probably several times engaged in an alliance, in whatever
form it might have been. The benefits of these alliances seem to be apparent and broadly agreed upon
(Anand & Khanna, 2000; Li, Boulding & Staelin, 2009; Wittmann, Hunt & Arnett, 2009). Yet not every
firm can seem to reap the benefits of their alliances. Some studies even show failure rates of more than
50% (Lambe, Spekman & Hunt, 2002; Kale & Singh, 2007; Pangarkar, 2009). Therefore this is undeniably
an important area of research for companies. This study will focus on the impact of different types of
alliance experience on alliance performance. The study is written as a Master’s thesis for the study of
Strategic Management at Tilburg University. The first part of this chapter will elaborate upon the
structure of this thesis by providing the problem indication, followed by the problem statement. Next,
the accompanying research questions will be provided. Finally, the method and the outline of this study
will be presented.
1.1.Problem Indication
As was stated before, alliances are a widely researched topic in the management literature. In previous
research, there seems to be some kind of consensus about alliance experience being one of the most
important determinants of alliance performance (Fiol & Lyles, 1985; Child & Yan, 1999; Kale, Dyer &
Singh, 2002; Hoang & Rothaermel, 2005; Heimeriks & Duysters, 2007). However, there is no empirical
consensus about the impact of experience on performance. Some researchers were convinced that
there is a positive correlation between alliance experience and performance (Gulati, 1998; Anand &
Khanna, 2000; Chang, Chen & Lai, 2008), or positive with diminishing returns (Hoang & Rothaermel,
2005). Others did not find a significant relationship (Simonin, 1997; Merchant & Schendel, 2000), or
found relationships like a U-curve (Nadolska & Barkema, 2007), or even an inverted U-curve (Lavie &
Miller, 2008). Despite a lot of research it still remains unclear why most studies showed different results.

10
Apparently, sometimes experience is more beneficial than other times. Therefore further research on
this relationship is needed. The acquisition literature has also tried to explain the impact of experience
on performance and faces the same kind of problems (Haleblian & Finkelstein, 1999; Hayward, 2002;
Barkema & Schijven, 2008). In order to solve these problems we would like to combine the two streams
of literature as Kale and Singh (2009) did in their research.
One way to try and explain the effects of experience on performance is by investigating different types
of experience or contingencies1
. While some researchers studied only general alliance or acquisition
experience (Gulati, 1998; Anand & Khanna, 2000; Heimeriks, 2010), others have looked beyond. They
studied different types of experience, like partner specific experience (Zollo, Reuer & Singh, 2002;
Goerzen, 2007; Gulati, Lavie & Singh, 2009), or ‘new’ and ‘old’ experience (Cho & Padmanabhan, 2001;
Hayward, 2002; Sampson, 2005). Next, other researchers focused on factors like similarity of experience
(Saxton, 1997; Shaver, Mitchell & Yeung, 1997; Haleblian & Finkelstein, 1999) and finally the experience
with different governance designs (Padmanabhan & Cho, 1999; Anand & Khanna, 2000; Pangarkar,
2009). Although these different types of experience and settings have already been researched and
described individually, until now no empirical research has studied the effect of all these different types
of experience or contingencies on alliance performance in a single study. Therefore, this study will try to
find out what the effects of different contingencies will be on the relationship between general alliance
experience and performance. By conducting this study, we hope to better understand why the
relationship between experience and performance differs in different settings.
1
We will use the terms ‘types of experience’ and ‘contingencies’ interchangeably.

11
1.2. Problem Statement
In order to be able to investigate the gap in the literature as has been identified above, our problem
statement will be the following:
When do firms benefit from alliance experience?
With this question, this thesis tries to find out how the different contingencies mentioned earlier
influence the performance of alliances.
The dependent variable within this research, alliance performance, entails the future performance of
the alliances that the firm will undertake. We expect that the different contingencies will have some
kind of influence on the dependent variable. They will be described further in section 1.3. and chapter 3
of the research. Figure 1.1. at the end of this chapter shows a graphical representation of the research
framework.
1.3. Research questions
As was already mentioned before, alliance experience appears to be one of the most important
determinants of alliance performance (Fiol & Lyles, 1985; Child & Yan, 1999; Kale, Dyer & Singh, 2002;
Hoang & Rothaermel, 2005; Heimeriks & Duysters, 2007). Despite many studies that have been done on
this relationship, different researchers have found different outcomes as with respect to the relationship
between general alliance experience and alliance performance (Simonin, 1997; Gulati, 1998; Anand &
Khanna, 2000; Merchant & Schendel, 2000; Lavie & Miller, 2008). Because this relationship is important
for the purpose of this research, our first research question will be:
- RQ1: What is the impact of general alliance experience on alliance performance?

12
In this study, we would also like to know what contingencies impact this relationship between general
alliance experience and alliance performance. Gulati, Lavie & Singh (2009) found that prior experience
with the same partner would provide the firm with more benefits than just their general alliance
experience. They called this type of experience ‘partner-specific experience’. Zollo, Reuer & Singh (2002)
found a similar outcome and stated that partner-specific experience has a positive impact on alliance
performance. There has been some debate around this matter. Some studies have found a negative
relationship between repeated equity-based partnerships and performance (Hoang & Rothaermel, 2005;
Goerzen, 2007). In order to be able to investigate the impact of partner-specific experience on alliance
performance, our next research question will be:
- RQ2: What is the impact of partner specific alliance experience on alliance performance?
Another type of experience pertains to the ‘timing’ of the experience. Cho & Padmanabhan (2001)
found that there is a difference between the importance of ‘old’ and ‘new’ decision specific experience.
According to their study, it appears that ‘new’ or recent experience is more important for investments
in developed countries. Sampson (2005) found that knowledge depreciates and that only recent
experience has positive effects on returns. Hayward (2002) added to this discussion that experience also
should not be too temporally close. To find out whether and how this type of experience influences
alliance performance, our third research question will be:
- RQ3: What is the impact of the timing of alliance experience on alliance performance?
According to Haleblian & Finkelstein (1999), the more similar the acquisition targets of a firm are with
regard to prior acquisitions, the better they seem to perform. Hence similarity in experience appears to
be good. Barkema & Schijven (2008) found industry- and country-specific experience to foster learning
to a larger degree than general experience does. Reuer, Park & Zollo (2002) found that if prior
international joint venture (IJV) experience was gathered in domains different from the skill and cultural

13
domains of the other international joint ventures, it could harm rather than improve the performance
of the IJV. In order to investigate the effect of the similarity of experience on alliance performance, the
next two research questions will be:
- RQ4: What is the impact of industry specific alliance experience on alliance performance?
- RQ5: What is the impact of country specific alliance experience on alliance performance?
The effects of alliance experience with different governance designs also differ greatly (Anand & Khanna,
2000; Pangarkar, 2009). The alliance governance design is normally divided into equity and non-equity
arrangements (Zollo, Reuer & Singh, 2002). Anand & Khanna (2000) found big learning effects from joint
ventures, but not for contracting alliances. Kale and Singh (2009) even stated that equity structures are
critical to success. Pangarkar (2009) found that companies with equity stakes in alliances have alliances
that last longer than companies that do not have these equity stakes. However when applied to
partner-specific experience, Reuer & Zollo (2005) found that the favorability of termination outcomes
for non-equity alliances is greater than for equity structures. Therefore our last research question,
pertaining to the effect of different governance designs will be:
- RQ6: What is the impact of experience with alliance governance designs on alliance performance?
1.4. Research method
This research, as most academic research in general, consists of a literature review and an empirical
part. The literature review of this thesis will make use of databases like Web of Science and ABI/Inform
in order to find previous literature in this field of interest. The thesis itself will be an exploratory
research, since the aim of the thesis is to explore how different contingencies will impact our dependent
variable. The empirical part of the thesis will be constructed by gathering our information primarily from
the SDC Database. This database will allow us to make use of objective information and as a result not

14
to have to consult alliance managers or other more subjective sources of information. The SDC database
contains most of the data concerning alliances that have taken place in the last 25 to 30 years,
consequently it is a profound database. This study will focus primarily on pharmaceutical companies,
since the pharmaceutical industry has proven to be an industry in which alliances are important (Anand
& Khanna, 2000; Kale, Dyer & Singh, 2002; Reuer & Zollo, 2005). In order to have a large sample, this
thesis will focus on six of the biggest pharmaceutical companies in the U.S. (Abbott Laboratories, Bristol-
Myers Squibb, Eli Lilly, Johnson & Johnson, Merck & Co. and Pfizer). The DataStream database will be
used to provide this study with the precise stock prices of these companies in the past.
1.5. Structure of the thesis
The remaining part of this thesis will be structured like a scientific article. The second and third chapter
will elaborate on the theoretical framework, explain the different variables and finally work towards the
different hypotheses. The hypotheses will be structured in such a manner that they will answer the
research questions mentioned above in a consecutive order. The theoretical chapters will be treating
the different theoretical and empirical insights and they will be focusing on literature pertaining to
alliances and literature pertaining to acquisitions. Chapter 2 will focus on explaining and summarizing
the alliance experience literature in general. Next, chapter 3 will explain the different contingencies,
while referring back to the previous literature. Throughout this chapter the hypotheses will be
developed. After having laid the foundations of this thesis by means of the theoretical framework, the
thesis will continue with the empirical part of this study in chapter 4 and the results of this research in
chapter 5. After having elaborated on the results, the thesis will end with a discussion and conclusions
section in chapter 6 and 7, completed by a limitations section and suggestions for future research.

Alliance experience and performance: a contingency study
Figure 1.1. A graphical representation of the research
General
alliance
experience
Partner
specific
experience
Non-partner
specific
experience
Too recent
alliance
experience
Recent
alliance
experience
Old alliance
experience
Similar
alliance
experience
Non-similar
alliance
experience
Equity
alliance
experience
Non-equity
alliance
experience
d performance: a contingency study
Figure 1.1. A graphical representation of the research
experience
Equity
alliance
experience
equity
alliance
experience
J.T.H. Medema
15
Alliance
performance

16
Chapter 2 – Review of literature
Alliance experience seems to be one of the most important determinants of alliance performance (Kale,
Dyer & Singh, 2002; Hoang & Rothaermel, 2005; Heimeriks & Duysters, 2007). Heimeriks & Duysters
(2007) defined alliance experience as being ‘the lessons learned, as well as the know-how generated
through a firm’s former alliances’, which is in line with previous literature (Gulati, 1995; Kale & Singh,
1999; Kale et al., 2002; Reuer, Zollo & Singh, 2002). Although most authors agreed with this statement,
there is no real consensus on how this prior experience will lead to better alliance performance. As
already mentioned before in the first chapter there are several explanations for the influence of
experience on performance (Simonin, 1997; Gulati, 1998; Anand & Khanna, 2000; Merchant & Schendel,
2000; Lavie & Miller, 2008). The next few sections will elaborate on what these influences are, the
theoretical explanations for them and how the empirics depict these relationships.
2.1. Alliance experience leads to success: theory
Most alliance studies predicted a positive relationship between alliance experience and alliance
performance (Gulati, 1998; Anand & Khanna, 2000; Dyer, Kale & Singh, 2001; Heimeriks & Duysters,
2007; Chang, Chen & Lai, 2008). The general idea behind these theories is that organizations learn from
their past experiences in alliances, and because of this accumulation of knowledge these firms can
perform better in future alliances (Anand & Khanna, 2000; Lambe et al., 2002; Reuer, Park & Zollo, 2002;
Barkema & Schijven, 2008; Pangarkar, 2009). Some studies labeled this an alliance capability, while
others named it a dedicated alliance function or an alliance competence (Dyer, Kale & Singh, 2001; Kale
et al., 2002; Heimeriks & Duysters, 2007; Kale & Singh, 2007; Kale & Singh, 2009). In this research we will
distinguish between the traditional learning curve perspective and the deliberate learning mechanisms
perspective.

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2.1.1. The traditional learning curve perspective
The traditional learning curve perspective is about firms learning to create more value by gathering
more experience (Penrose, 1959; Yelle, 1979; Dutton & Thomas, 1984; Epple, Argote & Devadas, 1991;
Day, 1995; Anand & Khanna, 2000; Reuer et al., 2002; Hoang & Rothaermel, 2005; Pangarkar, 2009). It
assumes that learning effects are always positive, equates experience with learning and only looks at the
firm learning from its own experience, thereby neglecting learning from others. Graphically the
traditional learning curve perspective would look like figure 2.1. It shows that if the experience of a firm
with alliances increases, the alliance performance will increase.
Figure 2.1. Traditional learning curve perspective
Learning by doing is an important factor in the learning curve theory (Morrison, 2008). Penrose (1959)
wrote about the knowledge base of the firm, and how it will increase with repeated experiences. Prior
experience with for example a particular type of ownership structure, allows a firm to learn from
previous experiences. This learning will become very valuable when dealing with similar ownership
structures (Padmanabhan & Cho, 1999). The literature of the learning curve is built on the assumption
that companies learn from their experiences and improve their performance by a repetition of actions
(Reuer et al., 2002).
Experience
Performance
Learning Curve

18
Nevertheless, as Chang et al. (2006) stated, experience plays an important role in value creation, ‘but is
dependent on the ability of a firm to learn and to subtract knowledge.’ General Electric, for example
have developed routines in their acquisition processes in such a way as that it can integrate the
knowledge of their acquisitions within 100 days (Ashkenas, DeMonaco, & Francis, 1998). This example
shows a second important perspective of learning from experience: developing deliberate learning
mechanisms (Dyer & Singh, 1998; Ireland, Hitt & Vaidyanath, 2002; Rothaermel & Deeds, 2006; Barkema
& Schijven, 2008; Kale & Singh, 2009; Schreiner, Kale & Corsten, 2009).
2.1.2. The deliberate learning mechanisms perspective
Through the development of deliberate learning mechanisms experience can be transferred into
learning and as a result knowledge can be internalized (Heimeriks & Duysters, 2007). Learning and
especially internalizing knowledge can help firms with improving their abilities with respect to ‘selecting
and negotiating with potential partners’ and ‘planning the mechanics of the alliance so that roles and
responsibilities are clear cut’ (Day, 1995). Reuer (1999) suggested that deriving value from alliances ‘. . .
requires companies to select the right partners, develop a suitable alliance design, adapt the
relationship as needed, and manage the end game appropriately.’ Not only does this pertain to the
selection of and negotiation with partners, it also pertains to the development of collaborative know-
how, so learning how to effectively manage alliances (Simonin, 1997).
As Heimeriks & Duysters (2007) put it, ‘by capturing, disseminating and applying alliance management
knowledge, individuals within the firm are more likely to engage in stable and repetitive activity
patterns … A firm’s alliance capability can thus be seen as its ability to internalize alliance management
knowledge’. Kale & Singh (2007) concurred to this notion by stating that firms with greater success in
alliances are presumed to have a greater alliance capability or deliberate learning mechanisms. They
mentioned some previous work of Kale et al. (2002), which stated that one way to gain this alliance

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capability and thus greater alliance success, was ‘to create a dedicated alliance function’. One way to do
this is by articulating, codifying, sharing and internalizing alliance experience (Kale & Singh, 2007). Kale &
Singh (2009) illustrated this whole process in the following way (figure 2.3.). The figure shows how
alliance experience by means of a dedicated alliance function leads to an alliance capability. This alliance
capability in turn leads to greater alliance success.
Figure 2.2. Drivers of firm-level alliance capability
Kale & Singh (2007), in their article refer to Dyer et al. (2001), who applied this notion to practice by
investigating companies that were actually ‘systematically generating more alliance value than others’.
They stated that companies like Hewlett-Packard, Oracle and Eli Lilly & Co were able to generate this
excess alliance value because they have a dedicated alliance function.
Kale et al. (2002) noted that even tacit knowledge, which is an important asset to create a competitive
advantage because it is valuable and imperfectly imitable (Barney, 1991), can be increased with the
proper use of the dedicated alliance function. They stated that it ‘can facilitate the sharing of tacit
knowledge through training programs and by creating internal networks of alliance managers.’

20
The acquisition literature also discussed the deliberate learning mechanisms perspective (Zollo & Singh,
2004; Hébert, Very & Beamish, 2005). Hébert, Very & Beamish (2005) argued that learning in itself is not
good enough to enhance performance, because the lessons learned cannot always be appropriated to
the right situations. They give expatriates the role of deliberate learning mechanism by proposing that
they will transfer the knowledge from the acquired to the acquiring firm. Zollo & Singh (2004) argued
that firms learn directly from their past experiences by codifying and articulating this knowledge. With
the right learning mechanisms in place, firms will learn from past experiences. They stated that a firm
that is absent of these mechanisms will not learn from previous acquisitions.
2.2. Alliance experience leads to success: empirical findings
Anand & Khanna (2000) were one of the first to establish systematic evidence that significant learning
effects in the management of alliances existed. They found very strong evidence that firms learn to
create more value from joint ventures as they gather more experience.
Merchant & Schendel (2000), who tried to identify conditions under which the announcements of
international joint ventures had an impact on shareholder value, identified several conditions under
which this relationship holds. However, previous JV experience did not appear to be one of them, since
they found no significant evidence for the effect of previous experience on shareholder value.
According to Barkema & Schijven (2008), alliance and acquisition experience should be sufficiently
specific to enable learning. They looked at the studies of Barkema, Shenkar, Vermeulen & Bell (1997),
Barkema & Vermeulen (1997), Shaver, Mitchell & Yeung (1997), Merchant & Schendel (2000) and finally
Reuer, Park & Zollo (2002) and concluded that all these studies found positive effects, but only when
experience was specific. For example, Barkema & Vermeulen (1997) found a positive relationship
between experience and alliance longevity, but only if this experience was specific to the host country.
Reuer, Park & Zollo (2002) found alliance experience to increase performance, but only if there were

21
similarities in national culture and skills. Reuer & Zollo (2005) also did not find any significant evidence
for the existence of a positive or negative impact of general alliance experience or alliance accumulation.
In their empirical study, they researched if alliance experience was favorable to research alliances’
termination outcomes. However they could not find evidence for this relationship.
Surprisingly, some studies found partially negative results. Haleblian & Finkelstein (1999) found that in
some cases, learning from acquisitions can actually be negative instead of positive. Hayward (2002),
Nadolska & Barkema (2007) and Lavie & Miller (2008) found similar outcomes. They argued that
inexperienced firms have problems appropriating new lessons rightfully. This is called negative
experience transfer (Barkema & Schijven, 2008). Reuer, Park & Zollo (2002) stated that ‘experience may
be detrimental when transferred to a setting where previous lessons do not apply’. However,
experienced firms will know how to appropriately discriminate between lessons learned. So in the end,
these studies find a U-shaped relationship (Haleblian & Finkelstein, 1999; Hayward, 2002; Nadolska &
Barkema, 2007; Lavie & Miller, 2008).
Figure 2.3. A U-curved relationship between alliance experience and alliance performance
The above figure shows us the U-curved relationship between alliance experience and alliance
performance. As we can see, if a firm is inexperienced, performance will go down due to appropriation
-6
-4
-2
0
2
4
6
Inexperienced firm Moderately experienced
firm
Highly experienced firm
Alliance experience and performance: a U-shape

22
errors. However, the more experienced a firm becomes, the better it will learn to appropriate
knowledge. Therefore, alliance performance goes up, resulting in a U-curve.
2.3. Our contribution
As we have seen in the previous literature, there is still no empirical consensus about the effect of
alliance experience on alliance performance. Although there is an extensive body of research suggesting
positive effects of general alliance experience on alliance performance, the empirical evidence is lacking.
Apparently, further research is necessary. This research will contribute to the alliance experience
literature by empirically studying the alliance experience contingencies that influence alliance
performance. By conducting this research, we hope to be able to better explain why the impact of
alliance experience on alliance performance differs in different situations. The next chapter will
introduce these contingencies and will be used to develop the different hypotheses.

23
Chapter 3 – Different types of experience
This chapter will review the contingency variables that could explain why the relationship between
alliance experience and performance differs in the previous literature. Based on previous literature from
both the fields of alliances and acquisitions we have chosen the variables partner specific alliance
experience (Zollo et al., 2002; Pangarkar, 2003; Hoang & Rothaermel, 2005; Goerzen, 2007; Gulati et al.,
2009), the timing of alliance experience (Cho & Padmanabhan, 2001; Hayward, 2002; Sampson, 2005),
the similarity of alliance experience (Saxton, 1997; Shaver, Mitchell & Yeung, 1997; Haleblian &
Finkelstein, 1999) and alliance experience with governance designs (Padmanabhan & Cho, 1999; Anand
& Khanna, 2000; Pangarkar, 2009). First we will discuss the relationship between alliance experience and
performance and after that the different contingencies and their impact on alliance performance will be
discussed. These discussions will lead to the development of the accompanying hypotheses, which will
be presented one by one throughout this chapter.
3.1. General alliance experience
The previous chapter thoroughly reviewed the previous literature on the relationship between general
alliance experience and alliance performance. It shows that until now, empirical consensus has not been
reached. However, theoretical consensus does appear to be reached. Although empirically the different
studies found different results, theoretically most of the studies that investigated this relationship
expected a positive effect of general alliance experience on alliance performance (Gulati, 1998; Anand &
Khanna, 2000; Dyer, Kale & Singh, 2001; Zollo & Singh, 2004; Heimeriks & Duysters, 2007; Barkema &
Schijven, 2008; Chang, Chen & Lai, 2008).

24
Nevertheless, this study disagrees with the traditional learning curve perspective (Penrose, 1959; Yelle,
1979; Dutton & Thomas, 1984; Epple, Argote & Devadas, 1991; Anand & Khanna, 2000; Reuer et al.,
2002; Hoang & Rothaermel, 2005; Barkema & Schijven, 2008; Pangarkar, 2009), since this perspective
equates experience with learning, and does not take learning mechanisms into account. Although we
agree that experience plays an important part in learning, it is not experience alone that helps
companies to learn.
Another stream of literature that we did not include in the previous chapter pertains to learning from
others. Beckman & Haunschild (2002) proposed a model wherein firms learn by tapping into the
heterogeneous experience of their network partners. Gulati (1999) found, when comparing American,
European and Japanese companies in multiple industries, that firms that have a larger network of
alliances are more prone to enter into new alliances. This could mean that the network itself is a good
source of information about new alliance opportunities (Barkema & Schijven, 2008). Sarkar, Echambadi
& Ford (2003) also investigated the idea of learning from others in their network and found that internal
mechanisms encourage vicarious learning. Nonetheless, these studies all take a network perspective on
alliances and therefore focused on learning from others. However our study is not focusing on networks,
but rather on individual alliances and the effect of experience of these alliances on performance.
Therefore, our study focuses on learning from a firms own experience rather than on learning from
others or from experience from others. Consequently we disagree with this stream of literature.
We will employ a learning perspective using the deliberate learning mechanisms theory (Dyer & Singh,
1998; Dyer et al., 2001; Ireland et al., 2002; Kale et al., 2002; Lambe et al., 2002; Zollo & Singh, 2004;
Chang et al. 2006; Rothaermel & Deeds, 2006; Heimeriks & Duysters, 2007; Kale & Singh, 2007; Kale &
Singh, 2009; Schreiner et al., 2009) and believe that experience can only be converted into learning by
deliberate learning mechanisms.

Because our sample only includes firms that already have experience with different alliances, we assume
that all of the firms in this sample posses
these firms have already learned from their previous experiences.
firms and therefore, the U-curved relationship between alliance experience and performance can be
ruled out. The lessons learned by these firms
To conclude we hypothesize the following:
Hypothesis 1: The relationship between general alliance experience and alliance performance will be
positive.
General alliance experience
our sample only includes firms that already have experience with different alliances, we assume
all of the firms in this sample posses some kind of deliberate learning mechanism
ned from their previous experiences. Hence, these firms are experienced
curved relationship between alliance experience and performance can be
by these firms can be used in future alliances to yield
o conclude we hypothesize the following:
J.T.H. Medema
25
our sample only includes firms that already have experience with different alliances, we assume
deliberate learning mechanism. We assume that
Hence, these firms are experienced
curved relationship between alliance experience and performance can be
in future alliances to yield better performance.
Alliance
performance

26
3.2. Partner specific experience
Gulati et al. (2009) explained the difference between partner specific experience and general alliance or
partnering experience by terming general partnering experience to be the accumulated experience that
the company has gained from any previous alliances. Partner specific experience on the other hand,
refers to the specific experience a firm has accumulated by having had multiple alliances with the same
partner. This facilitates mutual understanding and collaboration (Zollo et al., 2002; Pangarkar, 2003;
Gulati et al., 2009).
3.2.1. Theory
Gulati et al. (2009) reasoned that benefits that accumulate for general partnering experience also
accumulate for partner specific experience, but to an even greater extend. They stated that partner
specific experience has a more efficient learning process and thereby lower transaction costs than
general partnering experience. They also proposed that partner specific experience offers more benefits
than general partnering experience.
Zollo, Reuer & Singh (2002), introduced a concept that they named interorganizational routines. They
defined it as being ‘stable patterns of interaction among two firms developed and refined in the course
of repeated collaboration’. The researchers stated that ‘by engaging in multiple alliances with each
other over time, partners might tacitly develop a set of routines which undergird the way they interact
among themselves’. By working together on more than one occasion, firms get a better understanding
of for example each others’ cultures, capabilities, management systems and weaknesses. Since these
firms get to know each other better and better, iterative learning and adjustment cycles will be easier to
attain, because their improved understanding of each other helps to alleviate problems of coordination,
conflict resolution and information gathering (Doz, 1996).

27
Hoang & Rothaermel (2005) agreed with this line of thought. They argued that alliance performance will
augment when firms ally with the same partner firm as before. According to them, performance will
augment because partner specific alliance experience reduces transaction costs, makes way for
interorganizational routines, and facilitates conflict resolution and partner-specific decision making.
Pangarkar (2003) hypothesized that interorganizational routines and reduced transaction costs due to
repetitive partnerships positively influence performance.
3.2.2. Empirical support
Gulati et al. (2009) researched stock market returns for joint venture announcements, and found
support for the value of partnering experience. They found that although it was dependent upon firm-
and relational-specific factors, accumulated partnering experience increased the gains from alliances.
They empirically tested different types of alliances and finally found that the contingent value of
partnering experience existed.
In earlier research Zollo et al. (2002) also found a positive effect of partner specific experience on
performance. They proposed that amongst others, partner-specific experience accumulation at the
partnering-firm level enables firms to achieve their strategic objectives and influences to what extent
alliances will result in knowledge accumulation and to what extent they will create new growth
opportunities. Only partner-specific experience appeared to have a positive effect on alliance
performance. As was already mentioned before, they argued that this positive effect can be explained
by the development of interorganizational routines, which is facilitated by partner specific experience.
Unexpectedly, some studies found a negative effect of partner specific experience on performance
(Pangarkar, 2003; Hoang & Rothaermel, 2005; Goerzen, 2007). However, they did not make a strong
case against partner specific experience and it’s positive effect on alliance performance.

Tilburg University
3.2.3. Hypothesis
In conclusion, most studies fou
performance. There have been some
specific experience and alliance
results from Hoang & Rothaermel (2005)
(2003) were affected by the lack of data.
we have seen earlier, our study focuses on learning from own experience and not on learning from
others. In contrast, the studies of Gulati et al. (2009) and Zollo et al. (2002)
empirical evidence for the existence of a positive link between partner
effect of general alliance experience on
More efficient learning effects,
interorganizational routines and a better understanding of each ot
Rothaermel, 2005) provide us with sufficient reasons to expect a positive relationship between partner
specific experience and performance.
focal firm had previous experience with the partner firm
appropriate the knowledge learned from these alliances.
Hypothesis 2: Partner specific experience has a
partner specific alliance experience
The next section will discuss how
Non-partner specific experience
Partner specific experience
most studies found a positive effect of partner specific experience on alliance
here have been some empirical studies that found the relationship between partner
alliance performance to be negative, but the evidence is not overwhelming. The
results from Hoang & Rothaermel (2005) were only marginally significant, and the results of Pangarkar
affected by the lack of data. The study of Goerzen (2007) took a network perspective and as
our study focuses on learning from own experience and not on learning from
In contrast, the studies of Gulati et al. (2009) and Zollo et al. (2002)
evidence for the existence of a positive link between partner-specific experience and
effect of general alliance experience on performance.
More efficient learning effects, lower transaction costs, trust and collaboration,
interorganizational routines and a better understanding of each other (Zollo et al., 2002
) provide us with sufficient reasons to expect a positive relationship between partner
specific experience and performance. The learning curve of the firm would become steeper
s experience with the partner firm, thereby making it easier for them to
appropriate the knowledge learned from these alliances. Therefore, our second hypothesis will be:
specific experience has a more positive effect on alliance pe
alliance experience.
discuss how the timing of experience impacts alliance performance.
partner specific experience
March 2011
28
nd a positive effect of partner specific experience on alliance
the relationship between partner-
evidence is not overwhelming. The
only marginally significant, and the results of Pangarkar
a network perspective and as
our study focuses on learning from own experience and not on learning from
In contrast, the studies of Gulati et al. (2009) and Zollo et al. (2002) provide us with ample
specific experience and the
, trust and collaboration, (Gulati et al., 2009),
her (Zollo et al., 2002; Hoang &
) provide us with sufficient reasons to expect a positive relationship between partner-
The learning curve of the firm would become steeper when the
, thereby making it easier for them to
Therefore, our second hypothesis will be:
positive effect on alliance performance than non-
alliance performance.
Alliance
performance

29
3.3. The timing of experience
Experience and the knowledge that flows from this experience do not only accumulate. This is because
when one learns, one also forgets (Darr, Argote & Epple, 1995; Hoang & Rothaermel, 2005). Therefore,
old and recent experience may have different effects on performance.
3.3.1. Theory
Morrison (2008) stated in his research that there is something that he would like to call a ‘forgetting
loop’. The following figure graphically explains this:
Figure 3.1. The learning curve with learning by doing and a forgetting loop (Morrison, 2008)
The figure shows that learning precedes cumulated experience. While learning by doing, firms learn new
things and these new things results in learning by doing etc. Something that jumps out from this figure is
the forgetting loop. It means that when one learns, one also forgets. Experience or knowledge
depreciates over time (Darr, Argote & Epple, 1995; Hoang & Rothaermel, 2005). Intuitively this makes
sense, since it will be quite hard for a company to store all their lessons learned in a long time interval
without information becoming unavailable, inaccessible or inapplicable (Argote, Beckman & Epple, 1990;
Ginsberg & Baum, 1998).

30
Levitt & March (1988) noted that learning not only resides in routines, but also in the people that know
these routines. When these people leave the firm, they take their knowledge and experience with them.
Sampson (2005) argued that firms may become stuck within their current routines. This could cause
inertia and could make a firm unable to adopt newer and more productive ways to conduct business.
She hypothesized that returns will marginally decrease because firms keep with suboptimal routines
which are based on and caused by older experience.
Although these arguments make a good statement about the depreciation of distant knowledge or
experience, it appears to be quite difficult to learn from very recent experiences as well (Haunschild,
Davis-Blake & Fichman, 1994; Kaplan, Mitchell & Wruck, 1996; Hayward, 2002). The main reasoning that
was used is that managers after an alliance are too preoccupied with doing the next deal, and thereby
forego the opportunity to learn from their previous deal.
Hayward (2002) agreed in this respect with the alliance literature. He summarized this idea by saying
that ‘very long intervals between a focal acquisition and the one before it magnify the inaccessibility of
learning. Very short intervals between such acquisitions prevent intervals from taking root and being
applied in a timely fashion’. Figure 3.2. shows this graphically.
Figure 3.2. The effect of the timing of alliance experience on alliance performance.
0
1
2
3
4
5
Old experience Recent experience Too recent experience
Timing of alliance experience and alliance performance

31
Sampson (2005) found evidence for the depreciation of knowledge over time. Although she found that
collaborative benefits are improved by previous experience with alliances, she also found that from a
certain point onwards additional experience does not lead to more benefits. She attributed this lack of
benefits from more experience to the depreciation of experience over time. The researcher stated that
‘the lack of impact of additional experience on outcomes may well be attributable to the age of such
experience; the benefits of prior alliance experience depreciate rapidly over time’. Sampson (2005)
claimed that the optimal techniques for the managing of alliances will change rapidly over time. In this
way, firms can only learn from the most recent experiences, since experience from further in the past
will be outdated. She referred to this as the competency trap. While firms try to exploit their ‘best
practices’ from previous experience, a new best practice has already arrived. This means that while firms
may learn from previous alliance experience, this experience will only be productive for a very short
time.
Although evidence from the literature that we have described above appears to suggest that only recent
experience should have an effect on decisions, Cho & Padmanabhan (2001) investigated decision
specific experience, and actually prove that firms tend to rely on both old and new or recent decisions
specific experience. However, in line with our previously mentioned studies, new decision specific
experience also appeared to be marginally more important than old decision specific experience. The
researchers used the same reasoning as has been done by the other researchers, by stating that new
decision specific experience is more valuable than old decision specific experience, because of the
rapidly changing of the environment.
Hayward (2002) found support for his statement that there should not be too much time between
acquisitions, nor too little. His findings are in line with earlier research which found that a very long, or a

Tilburg University
very short interval between different projects, has a negative effect on project development (
1994; Brown & Eisenhardt, 1997).
3.3.3. Hypothesis
As Morrison (2008) already stated, experience does not always stay within the company. While he
it a forgetting loop, others call
considered in this chapter tend to agree on the fact that
important, be it by forgetting (Morrison, 2008) or by just becoming outdated (
2001; Hayward, 2002). Therefore
the more impact the experience will ha
hand, experiences should not be too
Davis-Blake & Fichman, 1994; Kaplan, Mitchell & Wruck, 1996;
not have time to evaluate the lessons
have time to appropriate it in a rightful manner.
Hypothesis 3: The relationship between the timing o
inverted U-shaped.
The following section will discuss
relationship between general alliance experience and alliance performance.
Recent alliance experience
Too recent alliance experience
Old alliance experience
NS
very short interval between different projects, has a negative effect on project development (
Eisenhardt, 1997).
ted, experience does not always stay within the company. While he
it a forgetting loop, others called it depreciation of experience (Sampson, 2005).
considered in this chapter tend to agree on the fact that experience does not tend
important, be it by forgetting (Morrison, 2008) or by just becoming outdated (
Therefore the shorter the interval between the focal alliance and the one before,
the more impact the experience will have on the performance of the upcoming alliance
experiences should not be too recent, because it is quite difficult to learn from them
Fichman, 1994; Kaplan, Mitchell & Wruck, 1996; Hayward, 2002
evaluate the lessons learned from the previous experience, and therefore they
have time to appropriate it in a rightful manner. Consequently, our third hypothesis will be:
The relationship between the timing of experience and alliance performance
discuss the similarity of experience and the effect of this variable on the
relationship between general alliance experience and alliance performance.
Alliance
performance
March 2011
32
very short interval between different projects, has a negative effect on project development (Gersick,
ted, experience does not always stay within the company. While he called
it depreciation of experience (Sampson, 2005). Most researchers
experience does not tend to stay equally
important, be it by forgetting (Morrison, 2008) or by just becoming outdated (Cho & Padmanabhan,
the shorter the interval between the focal alliance and the one before,
on the performance of the upcoming alliance. On the other
quite difficult to learn from them (Haunschild,
Hayward, 2002). Managers or firms do
and therefore they do not
, our third hypothesis will be:
alliance performance will be
the similarity of experience and the effect of this variable on the
Alliance
performance

33
3.4. The similarity of experience
Another contingency that we will study in this thesis is the similarity of alliance experience. The
diversification theory literature (Chandler, 1962; Rumelt, 1974; Porter, 1987) stated that an unrelated
business expansion will not be as successful as a related business expansion. A company has to
understand the business that it tries to run. Following this reasoning we expect that similarity of
experience will be an important contingency.
3.4.1. Theory
Saxton (1997) studied the impact of partner and relational characteristics and alliance behavior. His
study made a link between the alliance literature and the diversification theory literature (Chandler,
1962; Rumelt, 1974; Porter, 1987). He stated that related or similar businesses will have a positive
influence on alliance outcomes because similarity advances understanding. Saxton (1997) combined this
with a link to the organizational learning theory which proposed that ‘similarities between partners
affect alliance performance because they facilitate the appropriability of tacit and articulated
knowledge’. A firm that has a common frame of reference learns easier from these alliances with similar
partners because they can easier understand and therefore appropriate the lessons learned from these
alliances.
Merchant & Schendel (2000) researched a specific part of similarity between partners and suggested
that cultural similarity between partners positively influences JV execution, because it is easier to
harmonize the approach of the partners. They discussed that cultural relatedness eliminates the need of
a firm to sustain the institutional incentives of the partnering firm, and thereby it reduces the costs of
such an alliance. Moreover, similarity in culture facilitates better cooperation and coordination.
Therefore, alliances that are based on a similar culture will be less likely to fail and as a result learning
effects will be easier to achieve.

34
Barkema & Schijven (2008) conducted a careful literature review about the acquisition and alliance
literature that had been written until then in their research. They summed up the alliance research by
stating that ‘in sum, the review above suggests that industry- or country-specific experience fosters
learning to a greater extent than more general experience and that it seems to be particularly beneficial
if it is both industry and country specific’. Apparently, similarity is a good thing, and especially when
measured by industry- or country-specific experience.
Similarity in general
Saxton (1997) studied the outcome of the impact of partner and relational characteristics and alliance
behavior. One of the things that he found in this study was that strategic similarities between partners
had a positive influence on the benefits of partnering.
On the other hand, Barkema, Shenkar, Vermeulen, & Bell (1997) did not find a relationship between the
international alliance experience of Dutch firms and the longevity of their alliances. They did find
positive learning effects, however only when the international alliance experience was preceded by
other international experience or domestic alliance experience. Thus although they did not find a
relationship between international alliance experience and the longevity of alliances, the research did
prove that similarity of experience was valuable.
Pangarkar & Choo (2001) found that firms actually tend to choose symmetric alliance partners. They
explained that experience with for example joint ventures could help firms to be better prepared for a
new and similar experience. They stated that an experienced firm would be better able to recognize and
overcome pitfalls involved when doing alliances. According to them, when the experience of both firms
is similar, firms would make the best use of this advantage.

35
Haleblian & Finkelstein (1999) found that the more similar an acquisition target of a firm would be to
prior acquisition targets, the better that this new acquisition would perform. Where inexperienced firms
tend to make inappropriate generalizations, firms that already have a lot of experience with similar
acquisitions know how to go about such an acquisition. That is why acquisition targets that are similar to
prior acquisition targets actually perform better. The researchers described this relation between prior
acquisition experience and performance as a U-shaped one, following this same logic. Therefore
symmetry in alliance experience appears to be a good thing.
In conclusion, similarity of experience positively influences performance, since firms can appropriate
knowledge more easily and therefore can learn better from these experiences. Thereby their learning
curve becomes steeper, making these experiences more valuable than non-similar experiences. In the
previous literature and in line with Barkema & Schijven (2008), we identified two important types of
similarity: industry specific experience and country specific experience.
Industry and country specific experience
Shaver, Mitchell and Yeung (1997) studied the joint effect of industry- as well as country-specific
experience. They investigated survival rates of FDI’s in the United States, and found that this rate is
enhanced by previous U.S. experience, and even more so if this previous U.S. experience was within the
target industry.
Reuer, Park & Zollo (2002) studied international alliances that involved U.S. firms and found that alliance
experience did increase performance, but only if there were similarities in the national culture and skills
that these firms had. In addition, Barkema & Vermeulen (1997) found a positive effect of alliance
experience on longevity only if the experience was similar to the host country.

36
In line with the appropriation of knowledge concept that we have discussed earlier, Lavie & Miller (2008)
found that cross-national differences are very important for alliance performance. When learning
involves different nationalities, firms tend to misappropriate the experience or lessons learned from
other countries. Similarly, Kale, Singh & Perlmutter (2000) found that if the alliance partners are of
different nationalities, problems involving cultural differences, opinions, beliefs and attitudes will be
even bigger because of this difference. Michael (2004) concurred by naming cultural differences as one
of the biggest factors that explain the failure of alliances.
On the other hand, there are also some studies that did not find evidence for the positive effect of
similarity of culture on returns or longevity (Merchant & Schendel, 2000; Hayward, 2002; Pangarkar,
2003). Merchant & Schendel (2000) studied amongst others the impact of the similarity of cultures on
joint venture returns, but did not find a significant impact. They attribute this lack of significance to the
deficiency of their own theoretical framework, so to their own research. Hayward (2002) found that
experiences should not be too similar, because diverse experience yields a richer understanding of
situations. However, his study focused only on acquisitions, which could have lead to these different
outcomes. Pangarkar (2003) studied the impact of dissimilarity of culture/nationality of partners on the
longevity of the focal alliance, but also found that it did not impact longevity in a negative way. However
he does not give a clear reason for this.

37
3.4.3. Hypothesis
To summarize, most studies found a positive relationship between the similarities of experience and
alliance or acquisition performance (Barkema & Vermeulen, 1997; Shaver, Mitchell & Yeung, 1997;
Haleblian & Finkelstein, 1999; Kale, Singh & Perlmutter, 2000; Reuer, Park & Zollo, 2002; Barkema &
Schijven, 2008; Lavie & Miller, 2008).
Surprisingly some studies did not find a significant relationship between the similarity of previous
alliances and the focal alliance on alliance performance or longevity (Merchant & Schendel, 2000;
Hayward, 2002; Pangarkar, 2003). However they either only focused on JV’s and have deficient
frameworks, studied the longevity of alliances or focused on acquisitions. These perspectives are all
different from our study.
As has been mentioned before, where inexperienced firms make appropriation errors, firms that are
experienced with similar types of alliances do not. In terms of diversification, unrelated business
expansions are less successful than related expansions. The more related experiences are the better
firms can learn from these alliances. This makes their learning curve steeper and thereby improves their
effect on performance.
While most studies only found positive effects of similarity in general, we can distinguish two specific
types of experience to measure this similarity. One of them is industry specific experience (Shaver,
Mitchell & Yeung, 1997; Reuer, Park & Zollo, 2002; Barkema & Schijven, 2008) and the second type of
experience pertains to country or culture specific experience (Shaver, Mitchell & Yeung, 1997; Kale,
Singh & Perlmutter, 2000; Reuer, Park & Zollo, 2002; Lavie & Miller, 2008). As discussed, especially
experience with similar cultures appears to influence performance in a positive way. However, because
culture is commonly bound to the boundaries of a country, we will combine it with the arguments
supporting country specific experience.

Tilburg University
In conclusion, similarity in experience appears to be
learning effects. In order to be able to investigate th
different types of similarity: similarity in industry and similarity in country.
two hypotheses:
Hypothesis 4a: Industry specific
industry specific experience.
Hypothesis 4b: Country specific experience has a
country specific experience.
The final section of this chapter will be about different types of collaborations and the effect of this
variable on alliance performance
Industry specific alliance
experience
Non-industry specific alliance
experience
Country specific alliance
experience
Non-country specific alliance
experience
In conclusion, similarity in experience appears to be beneficial to firm performance,
o be able to investigate the similarity of experience we will
different types of similarity: similarity in industry and similarity in country. This leads
experience has a more positive effect on alliance performance
specific experience has a more positive effect on alliance performance
section of this chapter will be about different types of collaborations and the effect of this
variable on alliance performance.
Alliance
performance
Industry specific alliance
industry specific alliance
Alliance
performance
Country specific alliance
country specific alliance
March 2011
38
beneficial to firm performance, because it enhances
e similarity of experience we will focus on two
This leads us to the following
section of this chapter will be about different types of collaborations and the effect of this
Alliance
performance
Alliance
performance

39
3.5. Governance design
In the alliance literature, a lot of research has been done on the different governance designs and the
effect of these designs on alliance performance (Anand & Khanna, 2000; Kale, Singh & Perlmutter, 2000;
Reuer & Zollo, 2005; Kale & Singh, 2009; Pangarkar, 2009). It appears that some kind of a consensus has
been reached about which design seems to be the best. Most studies found equity structures to be the
best alliance governance design (Gulati, 1995; Anand & Khanna, 2000; Kale, Singh & Perlmutter, 2000;
Pangarkar, 2003; Sampson, 2005; Kale & Singh, 2009; Pangarkar, 2009).
3.5.1. Theory
Anand & Khanna (2000) viewed alliances as incomplete contracts and therefore they appear to be
difficult to manage. One of the complexities revolves around difficulties with interfirm knowledge.
Within alliances, there is always a tension between competition and cooperation. The researchers
stated that therefore it is important for firms to learn how to learn in alliances. The importance of
learning increases when it becomes more difficult for firms to specify the processes or knowledge in
question and more uncertainty is involved, because knowledge is difficult to protect in these situations.
They argued that learning opportunities are greatest for situations with great ambiguity or complexity,
because in these situations a lot of uncertainty is involved. They argue that uncertainty makes it more
important for firms to have flexible designs, because flexible designs allow for greater learning effects.
For licensing contracts, it is easy to protect knowledge since there is not much ambiguity or uncertainty.
Therefore, the learning opportunities in licensing are minimal. For JV’s, on the other hand, ambiguity
and complexity are high, so learning effects will be greater.

40
Kale et al. (2000) argued that equity arrangements promote greater interfirm transfers of knowledge
and therefore greater learning opportunities than do non-equity arrangements. They reasoned that
equity structures result in much higher degrees of interaction. This occurs most in equity structures
because interests need to be aligned and partner behavior needs to be monitored to prevent moral
hazard problems. Because these firms interact more, these structures would facilitate learning and
knowledge sharing, even of tacit knowledge. Consequently, experience with equity structures will be
much more valuable for a firm than experiences from non-equity structures.
Anand & Khanna (2000) found positive results for large learning effects in managing JV’s. However, no
such learning effects were found in managing licensing contracts. This implies that the JV, or in our
research an equity based alliance design, is more valuable than a non-equity design, because it has
larger learning effects. These learning effects exist to a much higher degree in situations with higher
complexity or uncertainty (Anand & Khanna, 2000), making equity structured experience more valuable
than non-equity experience with respect to learning.
Kale & Singh (2009) even found that if uncertainty is involved in a situation, equity structures are critical
for alliances to be successful. Kale, Singh & Perlmutter (2000) found that learning is best achieved when
alliance partners have intensive and continuous contact with each other. They argue that this kind of
contact is most likely to be found in equity structures, because interests need to be aligned and partner
behavior needs to be monitored. Therefore it is expected that these structures result in higher learning
achievements.

3.5.3. Hypothesis
Most literature has been quite positive about equity
& Perlmutter, 2000; Anand & Khanna, 2000;
that is used to prefer equity structures to non
learning from alliances, because of the high levels of interaction and the higher need to learn
Khanna, 2000; Kale, Singh & Perlmutter, 2000). Because firms learn more from equity alliances, we
expect that the experience based on this collaboration stru
non-equity based structures. Because we believe that a firm can better learn from its previous
experiences when they were equity based, we hypothesize:
Hypothesis 5: Equity based alliance
non-equity based alliance experience
After having carefully examined the previous literature and developed our hypotheses, the next chapter
will deal with the empirical part of this thesis. In this part we
the pharmaceutical industry. A
can be found in Appendix 1.
Equity based alliance experience
Non-equity based alliance
experience
quite positive about equity-based collaborations (Williamson, 1985;
Anand & Khanna, 2000; Pangarkar, 2003 Kale & Singh, 2009
prefer equity structures to non-equity structures is that equity structures facilitate
, because of the high levels of interaction and the higher need to learn
expect that the experience based on this collaboration structure is more valuable than experience from
Because we believe that a firm can better learn from its previous
experiences when they were equity based, we hypothesize:
alliance experience has a more positive effect on alliance performance
experience.
will deal with the empirical part of this thesis. In this part we will test whether our hypotheses hold in
simplified overview of the graphical representation
Alliance
performance
Equity based alliance experience
equity based alliance
J.T.H. Medema
41
Williamson, 1985; Kale, Singh
Kale & Singh, 2009). The main argument
equity structures is that equity structures facilitate
, because of the high levels of interaction and the higher need to learn (Anand &
cture is more valuable than experience from
Because we believe that a firm can better learn from its previous
alliance performance than
will test whether our hypotheses hold in
graphical representations of the hypotheses
Alliance
performance

42
Chapter 4 – Empirical investigation
4.1. Sample and data
In this study we will research the alliances from the six largest companies in the United States’
pharmaceutical industry, which have been conducted in the period 2000-2009. We have chosen the
pharmaceutical industry because this is an industry with a lot of alliance activity and many studies in the
past used this industry as their industry of analysis (Anand & Khanna, 2000; Kale, Dyer & Singh, 2002;
Reuer & Zollo, 2005). We have used the SDC database in order to identify all of the alliances concerning
these companies that took place between the first day of 2000 and the last day of 2009. In total, there
have been 267 alliances within this time period. After having collected the information from these
alliances, we cross-referenced it with the Lexis-Nexis database in order to get the right announcement
dates. Because we will look at cumulative abnormal stock returns following these announcements, these
dates had to be quite precise. Since the Lexis Nexis database contains all the information sources that
are used to bring out the news to the world, we considered this database to be leading above the SDC
database. Consequently when the dates were not similar, we chose the dates in the Lexis Nexis
database, or the first date on which the public could know the information. If for some reason we still
doubted about the alliance announcement date, we checked the annual reports of the companies
concerned. In only one case we were not able to determine the date, and therefore we excluded it from
the sample. The data for the dependent variable were subtracted from the DataStream database and
the WRDS database. These databases provided us with detailed information concerning stock prices,
alphas, beta’s and stock returns. Our independent and control variables were mainly measured by data
from the SDC database, and only a few from the DataStream database and annual reports.

43
4.2. Dependant variable
Cumulative Abnormal Returns (CAR). We measure the performance of alliances by using cumulative
abnormal stock market returns. CAR is a frequently used measure in alliance studies (Anand & Khanna,
2000; Merchant & Schendel, 2000; Kale, Dyers & Singh, 2002; Gulati, Lavie & Singh, 2009). Although CAR
is an ex-ante measure of performance for alliances, prior research shows that CAR has a high correlation
of about 40 percent with qualitative measures of alliance performance (Anand & Khanna, 2000; Gulati,
Lavie & Singh, 2009). That is why we believe that CAR will be a good measure for our quantitative
research about alliance performance. Information about these alliances was readily available for
investors, through newspapers etc. All the six firms are listed in the S&P 500, and so prices could be
influenced by such events quickly.
When calculating the CAR we used the residual analysis of the market model, also known as the Fama-
French model (Fama, Fisher, Jensen & Roll, 1969; Anand & Khanna, 2000; Kale, Dyer & Singh, 2002;
Gulati, Lavie & Singh, 2009). The equation for this model looks as follows:
‫ݎ‬௜ఛ ൌ ߙ௜ ൅ ߚ௜‫ݎ‬ఓఛ ൅ ߝ௜ఛ
‫ݎ‬௜ఛ is the common stock return of firm i on day ߬, ‫ݎ‬ఓఛ is the market return for the equally weighed S&P
500 index, which we found trough DataStream. The α and ߚ are firm specific parameters, which we
found by using the WRDS database for each alliance separately. The term ߝ௜ఛ is the error term. We set
the date of the alliance announcement at ߬=0. Following Gulati et al. (2009), we estimated the market
model for the period ߬= (-250, -10). The estimation that we got from that model was used to predict the
daily returns for the different firms using a two-day window (-1,0). That is how we got ‫̂ݎ‬௜ఛ ൌ ߙො௜ ൅ ߚመ௜‫ݎ‬ఓఛ ,
which are the predicted returns and estimates for this window. We subtracted this from the first model,
so ߝ௜ఛ = ‫ݎ‬௜ఛ − ‫̂ݎ‬௜ఛ, and then we added up the subsequent data to get the cumulated abnormal returns. By
choosing a somewhat narrow two-day window, we followed the studies of Merchant & Schendel (2000)

44
and Gulati et al. (2009). According to them, a two-day window excludes events prior to or following the
announcement of the alliance. We agreed with Gulati et al. (2009) on the fact that ‘researchers should
capture the change in the stock price immediately following the alliance announcement.’ They stated
that announcements are not made on the focal day, but one day earlier. Therefore the optimal window
is (-1,0). They also stated that in previous literature the two-day window has proven to be more
effective in predicting market responses than longer windows. Anand & Khanna (2000) even found that
market responses of joint ventures were only significant on the day of the announcement. Therefore we
chose the two-day window as our window to conduct this research.
4.3. Independent variables
General alliance experience is measured by simply calculating all the accumulated alliances that the focal
firm had, from the first day we could find in the SDC database in 1988, until the day of the focal alliance
(Beckman & Haunschild, 2002; Hoang & Rothaermel, 2005; Gulati et al. 2009).
Partner specific experience is measured by using a continuous variable to measure the total amount of
partner specific experience that the focal firm had with the focal partnering firm until then. This is in line
with the previous literature (Zollo et al., 2002; Hoang & Rothaermel, 2005; Goerzen, 2007). We used the
SDC Database in order to find the numbers pertaining to this variable.
Timing of experience will be measured by a continuous variable. We want to see how much time
(measured in days) there is between the different alliances of one firm and how this impacts the
relationship between alliance experience and alliance performance. This is in line with previous research
(Hayward, 2002), where time is also being measured as a continuous variable. The data from this
variable were gathered from the SDC Database.

45
The similarity of experience measures how similar different alliances are to each other. We want to
measure it by using the continuous variables similarity of industry and similarity of country alliance and -
partner. In previous alliance literature concerning similarity, the industry and country variables have
proven to be a big influence on performance (Saxton, 1997; Shaver, Mitchell & Yeung, 1997; Haleblian &
Finkelstein, 1999). We also got these data out of the SDC Database.
Governance design will be measured by a dummy variable, identifying if it is either an equity or a non-
equity alliance. Kale & Singh (2009) showed the division between the two governance designs in the
following figure:
Figure 4.1. Scope of interfirm relationships
Everything inside the box is termed to be equity arranged and everything left of the box is non-equity
arranged. This study will employ the same distinction between equity and non-equity based alliances.
We gathered the data for this variable using the SDC Database.

Tilburg University
4.4. Control variables
Firm size is measured to control for differences in the size of firms. Since this could impact the
performance of a firm, we chose to include it into our control va
Padmanabhan (2001), Beckman & Haunschild (2002)
the total assets of a firm, gathered from annual reports.
for firm specific effects, and therefore
control for industry effects by using dummy variables to measure the industry that the alliance was in
(Hayward, 2002; Kale, Dyer & Singh, 2002; Gulati et. al, 2007).
variable for the countries where the alliance took place in, we are able to control for institutional and
cultural differences in different
Furthermore, we investigated the debt
capital structure. We used the DataStream database to get these data.
in the macroeconomic environment by using the year of formation as
Finkelstein, 1999; Li, Boulding & Staelin, 2009;
variables used in this research. A (C) stands for a continuous variable and a (D) for a dummy variable.
Table 4.1. Overview of different variables
Dependent variable (DV)
• Cumulative Abnormal
Return (CAR) (C)
ize is measured to control for differences in the size of firms. Since this could impact the
performance of a firm, we chose to include it into our control variables.
, Beckman & Haunschild (2002) and Gulati et al. (2007) we will measure it by u
the total assets of a firm, gathered from annual reports. According to Gulati et al. (2009), CAR contro
ects, and therefore we do not need to include any other firm level controls.
(Hayward, 2002; Kale, Dyer & Singh, 2002; Gulati et. al, 2007). By employing a
cultural differences in different alliances (Li, Boulding & Staelin, 2009; Rothaermel & Deeds
the debt-to-equity ratio (Goerzen, 2007) to control for
We used the DataStream database to get these data. Finally, we controlled for changes
in the macroeconomic environment by using the year of formation as a dummy varia
Li, Boulding & Staelin, 2009; Pangarkar, 2009). Table 4.1. gives an overview of the
A (C) stands for a continuous variable and a (D) for a dummy variable.
ew of different variables
Independent variables
(IV's)
• General alliance
experience (C)
• Partner specific
experience (C)
• Timing in days (C)
• Similarity in industry (C)
• Similarity in country
alliance and -partner (C)
• Governance design (D)
Control variables (CV's)
• Firm size (C)
• Industry effects (D)
• Country effects (D)
• Debt
• Year of formation (D)
March 2011
46
ize is measured to control for differences in the size of firms. Since this could impact the
riables. In line with Cho &
we will measure it by using
According to Gulati et al. (2009), CAR controls
we do not need to include any other firm level controls. Next, we
employing a dummy based control
, 2009; Rothaermel & Deeds, 2006).
equity ratio (Goerzen, 2007) to control for differences in
Finally, we controlled for changes
a dummy variable (Haleblian &
Table 4.1. gives an overview of the
A (C) stands for a continuous variable and a (D) for a dummy variable.
Control variables (CV's)
Firm size (C)
Industry effects (D)
Country effects (D)
Debt-to-equity ratio (C)
Year of formation (D)

47
4.5. Analysis
Because our model could not be tested in one single model, we made several models to test our
hypotheses. By making separate models for each hypothesis, we could avoid errors due to
multicolinearity (Gulati et al., 2009). Before we tested the different models, we looked at their mean
values and standard deviations (Appendix 2) and tested for correlation errors in SPSS (see Appendix 3).
As we can see, there were no real correlation errors. The variables similarity and non-similarity of
country partner and –alliance did have some correlation issues, but not with the dependent variable. In
appendix 4, we show the regression results on CAR for the different models.
Hypothesis 1. For this first hypothesis we made one model (model 1) which simply regressed general
alliance experience on CAR (or ASR like it was called in the dataset).
Hypothesis 2. For the second hypothesis, we made two models. The second model added partner
specific experience to our first model. In this way we could check which of the two variables would be
(more) significant. Our third model added an interaction effect between partner specific experience and
general alliance experience as a robustness check for the second model. Especially and only for these
two models, we adapted the general alliance experience variable to contain general alliance experience
minus partner specific experience to effectively yield non-partner specific experience.
Hypothesis 3. To see whether there is an inverted U-curved relationship between timing of experience
and CAR, our fourth model regressed the time in days variable combined with the general alliance
experience on CAR and included the squared term of the time in days variable. In this way we could
check if too recent experience performed worse than less recent experience.
We approached our fifth and sixth model slightly differently by splitting the sample into old and recent
experience, in order to test whether recent experience would be better than old experience. We drew

48
the line halfway, so at the mean, which was 130. Consequently everything above 130 was termed as old
alliance experience, while everything below 130 was termed to be new alliance experience. Next we
regressed the two different models on CAR to see which was (more) significant and had a higher beta,
and therefore a bigger impact on CAR.
In the seventh and eight model we added the time in days variable as a control variable since different
timing also results in different effects.
Hypothesis 4a & 4b. Our ninth, tenth and eleventh models regressed similarity and non-similarity
variables on CAR to see whether similarity was actually better for alliance experience. The ninth model
did this for hypothesis 4a and regressed similar and non-similar industry experience on CAR. The tenth
and eleventh model tested hypothesis 4b in the same way for the similarity in the country of the alliance
partner as well as for similarity in the country of the alliance.
Hypothesis 5. Finally we regressed the dummy variable equity and an interaction effect of the dummy
variable equity and general alliance experience, combined with general alliance experience on CAR to
see if equity experience has a bigger impact on CAR than non-equity experience.
Our twelfth and thirteenth model tried to measure the same, but by using the same kind of method as
we did with the timing of experience variable. We split the sample again into equity experience and non-
equity experience and then regressed general alliance experience on CAR for both of the samples, in
order to compare betas afterwards.
In line with Pallant (2001) we also checked our data for normality and homoscedasticity (see appendix 5).
As we can see from the appendix, the data was reasonably normally distributed and there were only a
few outliers. We tested this for all of the different models, but outcomes were all more or less
distributed in an equal manor. The results from this analysis will be discussed in the next chapter.

49
4.6. Validity and Reliability
Saunders, Lewis & Thornhill (2007) define validity to be the extent to which a research measures what
was intended to be measured by the data collection method. We increased the validity of our research
by using different control variables to account for errors in our sample data that might come from other
factors than the ones our research tried to explain.
External validity is termed to be the generalisability of the data (Saunders et al., 2007). Because our data
pertains to a very specific set of companies, namely 6 companies in the pharmaceutical industry based
in the United States, the generalisability of our data will not be very high. This makes the external
validity not very high, but validity in general higher since we are less prone to make appropriation errors
due to too many variables that we otherwise had to control for.
According to Saunders et al. (2007), reliability is termed to be the extent to which the data collection
technique will provide us with consistent findings. Our data was based on research from some high
quality journals, with high impact factors. The different variables were measured by using high quality
and reliable databases like the SDC database, DataStream, WRDS, annual reports and the S&P 500 index.
Therefore the data used in this research is highly reliable.

Tilburg University
Since we have tested our hypotheses by using different models, we will
separately to discuss our results.
Hypothesis 1. We started out by investigating the relationship between general alliance expe
cumulated abnormal stock returns. As we can see from appendix 4, this relationship appeared to be
highly positive and significant at a 0.05 level. So our first hypothesis was supported.
Hypothesis 2. Our second hypothesis predicted a positive
alliance experience and cumulative abnormal stock returns. In our second model, which regressed
partner specific experience and non
had a higher impact, we did not find any significant results. To check for robustness, we test
relationship with an interaction effect between
experience and regressed that on CAR.
Therefore our second hypothesis has to be rejected,
interaction effect was significant at a one tailed test level, PSE ha
moderating the relationship between
Non-partner specific experience
NS
Chapter 5 – Results
Since we have tested our hypotheses by using different models, we will discuss
separately to discuss our results.
We started out by investigating the relationship between general alliance expe
Our second hypothesis predicted a positive direct relationship between partner specific
and non-partner specific experience on abnormal stock returns to see which
r impact, we did not find any significant results. To check for robustness, we test
relationship with an interaction effect between non-partner specific experience
experience and regressed that on CAR. This appeared to be significant using a one tailed test (model 3).
our second hypothesis has to be rejected, however is not entirely false. Because the
interaction effect was significant at a one tailed test level, PSE has a marginally positive effect on CAR by
the relationship between non-partner specific experience and CAR.
Alliance
performance
partner specific experience
March 2011
50
discuss each hypothesis
We started out by investigating the relationship between general alliance experience and
direct relationship between partner specific
on abnormal stock returns to see which
r impact, we did not find any significant results. To check for robustness, we tested the
partner specific experience and partner specific
cant using a one tailed test (model 3).
is not entirely false. Because the
positive effect on CAR by
Alliance
performance
Alliance
performance

Hypothesis 3. The third hypothesis of this thesis predicted a U
experience and CAR. We tested this by
variable with the general alliance experience variable on CAR.
group being old experience (more than 130 days between subsequent alliances) and one group being
new experience (less than 130 da
find a significant effect to support this hypothesis.
CAR in our fifth and sixth model, the betas appeared to be the same for both mod
included the time in days variable as an extra control variable, new alliance experience appeared to be
slightly more important than old alliance experience. Because the beta of new alliance experience was
now 0.05 and the beta of old e
existence of a more positive effect of new experience
NS
NS
NS
The third hypothesis of this thesis predicted a U-curved relationship between the timing of
experience and CAR. We tested this by regressing the squared time in days varia
variable with the general alliance experience variable on CAR. Next we split the sample into two, one
new experience (less than 130 days) to see if there was proof for hypothesis 3. Unfortunately we did not
find a significant effect to support this hypothesis. When we regressed general alliance experience on
model, the betas appeared to be the same for both mod
the time in days variable as an extra control variable, new alliance experience appeared to be
now 0.05 and the beta of old experience was still 0.04, we have found some weak support for
existence of a more positive effect of new experience. Nevertheless it was not significant
Alliance
performance
J.T.H. Medema
51
curved relationship between the timing of
regressing the squared time in days variable and the time in days
split the sample into two, one
Unfortunately we did not
When we regressed general alliance experience on
model, the betas appeared to be the same for both models. Though when we
the time in days variable as an extra control variable, new alliance experience appeared to be
found some weak support for the
was not significant.
Alliance
performance

526782 J.T.H. Medema (2)

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526782 J.T.H. Medema (2)