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.


6

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


7

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


8

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


9

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.


17

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).

Exp

eri

en

ce

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


19

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.




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.



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


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.





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



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.


Industry specific alliance

industry specific alliance


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




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



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



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:

��

�� is the common stock return of firm i on day �, �� is the market return for the equally weighed S&P

500 index, which we found trough DataStream. The α and � are firm specific parameters, which we

found by using the WRDS database for each alliance separately. The term �� 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 �̂�� ,

which are the predicted returns and estimates for this window. We subtracted this from the first model,

so �� = �� − �̂��, 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.

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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)

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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



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.



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




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





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.


Tilburg University

Hypothesis 4a. Hypothesis 4a predicted a bigger influence of similar industry alliance e

than non-similar industry alliance experience. We regressed both variables on CAR in our ninth model

and find a strong significant effect at a 0.05 level.

favoring non-specific industry alliance experience.

Hypothesis 4b. Using the same method

hypothesis 4b. We tested for country specific alliance experience at the partner level and at the alliance

level, but both models 10 and 11 found a highly significant effect on the 0.05 level, but in the opposite

direction. So hypotheses 4a and 4b were both

direction.

Industry specific alliance experience

Non-industry specific alliance experience

Country specific alliance experience

Non-country specific alliance experience

NS

NS

Hypothesis 4a predicted a bigger influence of similar industry alliance e

similar industry alliance experience. We regressed both variables on CAR in our ninth model

and find a strong significant effect at a 0.05 level. However, the effect was in the opposite direction,

liance experience.

same method as we did with hypothesis 4a, we got the same results for

country specific alliance experience at the partner level and at the alliance

11 found a highly significant effect on the 0.05 level, but in the opposite

direction. So hypotheses 4a and 4b were both not supported, but they were significant



industry specific alliance



country specific alliance

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52

Hypothesis 4a predicted a bigger influence of similar industry alliance experience on CAR

similar industry alliance experience. We regressed both variables on CAR in our ninth model

, the effect was in the opposite direction,

we got the same results for

country specific alliance experience at the partner level and at the alliance

11 found a highly significant effect on the 0.05 level, but in the opposite

they were significant in the opposite




Hypothesis 5. Finally, hypothesis 5 predicted that equity al

than non-equity alliance experience. Model 12 tested this by regressing general alliance experience, the

dummy variable of equity and the interaction effect on equity and general alliance experience on CAR.

This did not lead to any significant results. Next we used the same method as with th

experience variable and we split the sample into two. One group was equity alliance experience and the

other group was non-equity experience. When we regressed ge

two models, equity alliance experience proved to be highly significant at a 0.05 level, while the non

equity sample was not. Therefore

Figure 5.1. Overview of the results

Appendix 6 and 7 show a simplified

results. The next chapter will discuss the results into further detail


Non-equity based alliance experience

Hypothesis

H1

H2

H3

H4a

H4b

H5


NS

, hypothesis 5 predicted that equity alliance experience had a greater impact on CAR

equity alliance experience. Model 12 tested this by regressing general alliance experience, the


s did not lead to any significant results. Next we used the same method as with th

e split the sample into two. One group was equity alliance experience and the

equity experience. When we regressed general alliance experience on CAR in these

two models, equity alliance experience proved to be highly significant at a 0.05 level, while the non

Therefore our fifth and last hypothesis was also supported.

simplified overview of the graphical and schematic

will discuss the results into further detail.



equity based alliance

Predicted effect

+

+

+

+

+

+

Found effect

J.T.H. Medema

53

liance experience had a greater impact on CAR

equity alliance experience. Model 12 tested this by regressing general alliance experience, the


s did not lead to any significant results. Next we used the same method as with the timing of

e split the sample into two. One group was equity alliance experience and the

neral alliance experience on CAR in these

two models, equity alliance experience proved to be highly significant at a 0.05 level, while the non-

our fifth and last hypothesis was also supported.

the graphical and schematic representations of the


Found effect

+

ns

ns

-

-

+


54

Chapter 6 – Discussion

This study contributes to the existing literature by shedding more light on the contingencies surrounding

general alliance experience and alliance performance. It advances previous research by investigating

how general alliance experience and the contingencies partner specific alliance experience, the timing of

alliance experience, similarity of alliance experience and alliance experience with governance designs

impact alliance performance. Our findings show that although the relationship between general alliance

experience and alliance performance is positive, different types of experience influence performance in

different ways. The results show us that not all of the different contingencies have an impact on

performance, or not in the direction that we expected.

In previous literature alliance experience impacted alliance performance in different ways. There was no

real consensus about what the direction of this relationship should be. We found that general alliance

experience, ceteris paribus, has a positive influence on alliance performance. Based on the existing

literature, this is what we had expected. Our findings become more interesting when we look at the

contingencies studied in this thesis.

Following Zollo et al. (2002) and Gulati et al. (2009) we expected that partner specific experience would

influence alliance performance more positively than non-partner specific experience. Nevertheless, we

could not find any significant support for this hypothesis. This could be explained by the fact that the

firms in this sample did not have much partner specific experience. In a sample containing firms with

more partner specific experience, the outcome could have been different. What we did find is that

partner specific experience actually moderated the relationship between non-partner specific

experience and alliance performance. Apparently, partner specific experience improves the relationship

between non-partner specific experience and alliance performance.


55

In accordance with Cho & Padmanabhan (2001), we found that both old and recent alliance experience

influence alliance performance. Nevertheless, recent alliance experience did have a slightly bigger

impact on alliance performance than old experience. Accordingly, from these results we can conclude

that recent knowledge is slightly more useful for firms. This makes sense according to Cho &

Padmanabhan (2001), because of the rapidly changing of the environment. However both types of

experience were found insignificant. We also did not find any evidence for the existence of an inverted

U-shape, based on the premises that too recent experience will not influence alliance performance.

One of the more interesting and unexpected outcomes of this thesis pertains to the effect that the

similarity of experience has on alliance performance. We hypothesized that similarity in alliance

experience would have a bigger impact on alliance performance than non-similarity of experience. As

we discussed before, this is in line with most of the alliance experience literature. Most of the studies

found positive results pertaining to similar alliance experiences and attributed this to appropriability

issues and a steeper learning curve in these alliances (Barkema, Shenkar, Vermeulen, & Bell, 1997;

Shaver, Mitchell & Yeung, 1997; Reuer, Park & Zollo, 2002) or to better cooperation and more

understanding (Saxton, 1997; Merchant & Schendel, 2000). However, our results showed a very

different result. This is what makes this outcome unexpected and therefore interesting. Non-similarity

appeared to be significantly better than similarity of experience for as well country specific experience

as for industry specific experience, which is in line with the findings of Hayward (2002). In his study of

acquisitions he argued that previous acquisition experiences should not be too similar or too dissimilar

from the focal acquisition. He argues that diverse acquisition experience yields more understanding of

acquisition performance. Accordingly, our hypothesis was not supported, but was found significant and

positive in the opposite direction. Apparently, following the logic of Haleblian & Finkelstein (1999), these

companies know very well how to appropriate the knowledge gained from dissimilar alliances. Most

researchers hypothesized that similar alliances perform better and thereby advertised homogeneity


56

amongst alliance experience. Our study however, would promote dissimilar and thus heterogeneous

experiences, because these seem to have the largest learning effects due to their diverse nature. This is

quite a different outcome than most studies reviewed In this research would expect, and therefore one

of the more interesting outcomes of this research.

Finally this thesis found that equity alliance experience indeed had a bigger impact on alliance

performance than non-equity alliance experience. Although we already expected this outcome based on

earlier research (Gulati, 1995; Anand & Khanna, 2000; Kale, Singh & Perlmutter, 2000; Pangarkar, 2003;

Kale & Singh, 2009; Pangarkar, 2009), it could also be due to the small amount of non-equity alliance

experience that the firms in this sample had.

In conclusion, we are now able to answer our problem statement:

When do firms benefit from alliance experience?

According to this research, firms will always benefit from alliance experience, since we found a positive

and significant outcome on this relationship. However, some contingencies appeared to be more

beneficial and influential than others. In order for a firm to benefit most from its previous alliance

experience, a firm should try to increase its general alliance experience base by experiences that are

non-similar rather than similar and based on an equity structure rather than on a non-equity structure.

In this way, firms can get the most out of their alliance experience for future alliances. .


57

Chapter 7 – Managerial recommendations, limitations, future research and conclusion

7.1. Contribution to the existing literature

As we have already stated many times in this thesis, alliance experience is a very important antecedent

of alliance performance. Despite a lot of research on this subject this is still no easy to explain

relationship. Therefore further exploration of this specific relationship was warranted. This is where our

research made a contribution to the existing literature.

For academics, the main contribution of this thesis is that it sheds more light on the contingencies

explaining differences in the effect of alliance experience on alliance performance. We investigated

several contingencies that could influence the relationship between general alliance experience and

performance and we found that some types of experience indeed performed better than others. We

found that experience could best be partner specific, recent, heterogeneous and equity structured to

influence performance in the best possible way. Nevertheless both partner specific experience and

recent experience were found not to be significant.

What we have seen in the literature is that a lot of arguments appear to be based on the learning curve

and the deliberate learning mechanisms theory. Apparently the type of experience with the steepest

learning curve benefit a firm most. According to our research, experience matters most within

heterogeneous and equity structured alliances. Therefore we can state that learning effects will be

greater in situations of greater complexity. Consequently, experience from these kinds of alliances will

be more valuable to a firm. Like Anand & Khanna (2000) stated, firms can interpret unforeseen events

easier when they have a broad repertoire of experiences. Although they do not distinguish between

homogeneous or heterogeneous experiences and argue that any experience provides a firm with a

broader repertoire of experiences, this study finds that this repertoire become most broad and


58

therefore most valuable when experiences are heterogeneous. More simply put; a firm learns most in

situations where there is the most to learn. Hayward (2002) also argued that diverse experience yields a

richer understanding of situations. Therefore heterogeneous and ambiguous experiences benefit a firm

most, because they provide the best learning opportunities. In the end, although homogeneous

experiences might make it easier for firms to learn, it is not equitable with bigger learning effects.

Heterogeneous experiences provide a firm with the steepest learning curve.

Like Cho & Padmanabhan (1999) already stated, knowledge depreciates because of the rapidly changing

(business) environment. That is why firms cannot keep relying on success stories of the past and

consequentially need to focus on learning from heterogeneous and complex experiences. As Sampson

(2005) discussed, firms cannot fall into a competency trap. Benefits of experience can depreciate rapidly

and best practices only stay best practices for a very short amount of time. That is why companies need

to keep on learning and keep on developing. Like Hayward (2002) stated in his article about acquisitions:

‘because these acquirors fail to explore new markets and capabilities, they cannot attain new

knowledge bases. Therefore, they are vulnerable to competitors whose acquisitions co- evolve with

market’. Knowledge needs to co-evolve to be most beneficial to firms.

In this light we could argue that the reason why we did not find a significant positive impact of partner

specific experience on experience, is because this does not encourage a firm to learn. The situation

becomes known and less complex because the company is already familiar with its partner. Of course,

when doing business with this same partner, this might help to build trust and therefore will improve

performance, but maybe because of the decreased learning effects, in the end partner specific

experience could have a smaller impact on performance than non-partner specific experience does.

This research provides academics with a base to further investigate the contingencies surrounding

alliance experience and performance. As we have argued, learning effects are very important for these


59

contingencies and learning effects appear to be greater in diverse situations characterized by

uncertainty, complexity and ambiguity. Therefore experiences need to be heterogeneous, non-similar

and equity based to best stimulate learning effects. This provides future studies with some food for

thought and some extra insights in what matters most when it comes to alliance experience.

7.2. Managerial recommendations

Although this study has more to offer to academics, by its contribution to the alliance experience

literature, it also offers implications for managers. The importance of this kind of research is apparent.

For managers it is very important to know what part of their previous experience with alliances they

would have to use to have better alliance performance in the future. This thesis sheds more light on the

contingencies explaining why some types of alliance experience are better than others, and therefore

also what kind of experience managers should cherish.

Apparently heterogeneous experience and equity experience are the types of experience that

contribute most to better alliance performance. Therefore managers should promote these kinds of

experience. The most important and interesting outcome for managers as well as for academics is the

fact that heterogeneous experience is better for future alliance performance than homogeneous

experience. In the end, firms should go for a wide scale of alliance experiences and employ alliances in

different countries and different industries. Managers should go for learning experiences, to keep up

with their changing environment. As already was stated before, a firm cannot keep relying on their best

practices, but should continue to learn and to incorporate new knowledge.

To conclude, managers could use the outcomes of this thesis as a guideline for future alliances, in order

to be able to determine from what kind of alliances and what kind of alliance experience they can

benefit the most. Next we will discuss the limitations of this study and we will provide recommendations

for future research.


60

7.3. Limitations and recommendations for future research

Our study hinges heavily on a few assumptions that might not be true in practice. Firstly, we assumed

that every firm in our sample has some kind of learning ability to actually turn experience into learning.

However, in practice this might not be true. It may be that some firms have a dedicated alliance function

of some kind, however it may also be that they do not. Future research could combine this empirical

work with interviews, to check if this assumption actually holds.

A second assumption we made is that firms have already learned from previous misappropriations of

knowledge learned from experience. As we have seen in the previous literature (Barkema & Schijven,

2008; Lavie & Miller, 2008; Heimeriks, 2010) firms with little experience tend to make errors of

appropriation. Therefore there would be a U-curved relationship between general alliance experience

and alliance performance. In our research however, we did not test for this relationship because we

assumed that the firms in our sample already passed this stage. However, this might not be the case,

and misappropriation issues may still exist. With a new kind of experience, new inferences need to be

drawn. Therefore misappropriation can still happen even though a firm already has a lot of experience

with alliances.

In general, our sample was not very extensive. Although the sample contained 267 alliances, we started

our data sample in 2000 and with only 6 firms. In future research the sample could be extended to

contain more alliances and as a result lead to better and more significant results. For example, this study

did not find any significant results for a direct effect of partner specific experience on alliance

performance, since not many firms in our sample had a lot of partner specific experience.

In this study we used an ex-ante type of dependent variable for performance. Cumulative abnormal

stock returns have been proven to be a good ex-ante measure of performance in the past, but it rules

out any positive influences of the alliance afterwards. Qualitative studies are better to assess ex-post


61

performance, and therefore it might be good to repeat this study using a qualitative study, or at least a

study with an ex-post measure of performance.

Next, future research could focus on other industries and other countries, to overcome generalisability

problems. Because this research only looked at six U.S. firms in the pharmaceutical industry, the results

of this study are not very generalisable. Therefore, future research should focus on other industries and

countries in order to solve these issues.

Future research could also identify the extent to which experiences should be heterogeneous to still

influence alliance performance positively. Just like with over diversified firms, alliance experience could

also become over diversified and too heterogeneous (Hayward, 2002). It is important to know how

heterogeneous firm alliance experience should be in order to avoid performance deterioration. Next

maybe not every kind of heterogeneity is good for the firm. Maybe in some situations, homogeneous

experiences might actually be outperform heterogeneous experiences. Therefore this is also a concept

that should be investigated further.

Furthermore, future research could further explore the benefits from combining the acquisition and the

alliance literature. This might not only pertain to issues of experience, but also to other fields of research.

As we have seen in this study, both fields share some important insights and can be used to support

arguments for both fields of research.

Finally, more research needs to be done on the contingencies surrounding alliance experience and

performance. This study only identified some contingencies, but there will be other factors influencing

alliance performance as well.


62

7.4. Conclusion

This study tried to find out which contingencies influence the relationship between alliance experience

and alliance performance. We found that heterogeneous alliance experience and equity alliance

experience were the contingencies that mostly influenced alliance performance in a positive way.

Furthermore, in a less obvious way partner specific experience had a moderating effect on the

relationship between non-partner specific experience and performance and recent experience was

slightly more important than old experience. Based on the traditional learning curve theory and the

deliberate learning mechanisms theory we argue that learning opportunities will be highest in situations

that are non-similar to previous ones. In the end, these heterogeneous situations provide a firm with the

broadest repertoire of experiences, allowing them to draw inferences from different types of experience

more easily. In terms of alliance experience we find that heterogeneous alliance experience and equity

alliance experience provide a firm with the most benefits and they have the highest impact on alliance

performance.


63

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71

ns

Appendices

Appendix 1: Graphical representation of the hypotheses

H1:

H2:

H3:


(IV)


(DV)


(IV)


(DV)


(IV)


(IV)


(IV)


(DV)


(IV)


72

H4a:

H4b:

H5:


experience (IV)

Non-industry specific alliance

experience (IV)


(DV)


experience (IV)

Non-country specific alliance

experience (IV)


(DV)


(IV)

Non-equity based alliance

experience (IV)


(DV)


73

Appendix 2: Descriptive statistics

Descriptive Statistics

N Minimum Maximum Mean Std. Deviation

GAE 267 41,00 164,00 119,4944 26,00323

TIMDAY 267 ,00 353,00 130,0974 141,10237

PSE 267 ,00 3,00 ,2060 ,54036

EQUITY 267 ,00 1,00 ,9476 ,22332

SIMIND 267 ,00 54,00 19,0112 12,77553

NSIMIND 267 41,00 162,00 100,4831 22,51655

SIMCTRPT 267 ,00 106,00 47,9026 38,13202

NSIMCTRPT 267 16,00 163,00 71,5918 41,08182

SIMCTRALL 267 ,00 118,00 52,2959 39,88610

NSIMCTRALL 267 12,00 163,00 67,1985 42,96932

ASR 267 -2,80 3,76 -,0217 ,55800

CVSIZE 267 1731,00 125848,00 43553,3828 32802,26928

DERATIO 267 6,62 60,84 30,3665 11,84854

Valid N (listwise) 267

∗ In our dataset we used the term ASR for cumulated abnormal returns (CAR)


74

Appendix 3: Correlations

H1: Correlations

GAE ASR CVSIZE DERATIO

GAE Pearson Correlation 1 ,065 ,134* ,328

**

Sig. (2-tailed) ,287 ,029 ,000

N 267 267 267 267

ASR Pearson Correlation ,065 1 -,095 ,101

Sig. (2-tailed) ,287 ,123 ,099

N 267 267 267 267

CVSIZE Pearson Correlation ,134* -,095 1 -,385

**

Sig. (2-tailed) ,029 ,123 ,000

N 267 267 267 267

DERATIO Pearson Correlation ,328** ,101 -,385

** 1

Sig. (2-tailed) ,000 ,099 ,000

N 267 267 267 267

*. Correlation is significant at the 0.05 level (2-tailed).

**. Correlation is significant at the 0.01 level (2-tailed).

H2: Correlations

GAE PSE ASR CVSIZE DERATIO

GAE Pearson Correlation 1 ,104 ,065 ,134* ,328

**

Sig. (2-tailed) ,090 ,287 ,029 ,000

N 267 267 267 267 267

PSE Pearson Correlation ,104 1 ,048 -,035 ,073

Sig. (2-tailed) ,090 ,432 ,572 ,234

N 267 267 267 267 267

ASR Pearson Correlation ,065 ,048 1 -,095 ,101

Sig. (2-tailed) ,287 ,432 ,123 ,099

N 267 267 267 267 267

CVSIZE Pearson Correlation ,134* -,035 -,095 1 -,385

**

Sig. (2-tailed) ,029 ,572 ,123 ,000

N 267 267 267 267 267

DERATIO Pearson Correlation ,328** ,073 ,101 -,385

** 1

Sig. (2-tailed) ,000 ,234 ,099 ,000

N 267 267 267 267 267



75

H3: Correlations

GAE ASR CVSIZE DERATIO TIMDAY

GAE Pearson Correlation 1 ,065 ,134* ,328

** ,024

Sig. (2-tailed) ,287 ,029 ,000 ,691

N 267 267 267 267 267

ASR Pearson Correlation ,065 1 -,095 ,101 -,004

Sig. (2-tailed) ,287 ,123 ,099 ,945

N 267 267 267 267 267

CVSIZE Pearson Correlation ,134* -,095 1 -,385

** ,166

**

Sig. (2-tailed) ,029 ,123 ,000 ,007

N 267 267 267 267 267

DERATIO Pearson Correlation ,328** ,101 -,385

** 1 -,141

*

Sig. (2-tailed) ,000 ,099 ,000 ,022

N 267 267 267 267 267

TIMDAY Pearson Correlation ,024 -,004 ,166** -,141

* 1

Sig. (2-tailed) ,691 ,945 ,007 ,022

N 267 267 267 267 267



H4-1: Correlations

ASR DERATIO CVSIZE SIMIND NSIMIND

ASR Pearson Correlation 1 ,101 -,095 -,026 ,090

Sig. (2-tailed) ,099 ,123 ,673 ,141

N 267 267 267 267 267

DERATIO Pearson Correlation ,101 1 -,385** ,036 ,358

**

Sig. (2-tailed) ,099 ,000 ,555 ,000

N 267 267 267 267 267

CVSIZE Pearson Correlation -,095 -,385** 1 ,111 ,091

Sig. (2-tailed) ,123 ,000 ,069 ,136

N 267 267 267 267 267

SIMIND Pearson Correlation -,026 ,036 ,111 1 ,010

Sig. (2-tailed) ,673 ,555 ,069 ,866

N 267 267 267 267 267

NSIMIND Pearson Correlation ,090 ,358** ,091 ,010 1

Sig. (2-tailed) ,141 ,000 ,136 ,866

N 267 267 267 267 267



76

H4-2: Correlations

ASR CVSIZE DERATIO SIMCTRPT NSIMCTRPT

ASR Pearson Correlation 1 -,095 ,101 ,074 -,027

Sig. (2-tailed) ,123 ,099 ,227 ,655

N 267 267 267 267 267

CVSIZE Pearson Correlation -,095 1 -,385** -,063 ,143

*

Sig. (2-tailed) ,123 ,000 ,303 ,019

N 267 267 267 267 267

DERATIO Pearson Correlation ,101 -,385** 1 ,103 ,112

Sig. (2-tailed) ,099 ,000 ,094 ,067

N 267 267 267 267 267

SIMCTRPT Pearson Correlation ,074 -,063 ,103 1 -,787**

Sig. (2-tailed) ,227 ,303 ,094 ,000

N 267 267 267 267 267

NSIMCTRPT Pearson Correlation -,027 ,143* ,112 -,787

** 1

Sig. (2-tailed) ,655 ,019 ,067 ,000

N 267 267 267 267 267



H4-3: Correlations

ASR DERATIO CVSIZE SIMCTRALL NSIMCTRALL

ASR Pearson Correlation 1 ,101 -,095 ,079 -,034

Sig. (2-tailed) ,099 ,123 ,199 ,583

N 267 267 267 267 267

DERATIO Pearson Correlation ,101 1 -,385** ,181

** ,030

Sig. (2-tailed) ,099 ,000 ,003 ,620

N 267 267 267 267 267

CVSIZE Pearson Correlation -,095 -,385** 1 -,030 ,109

Sig. (2-tailed) ,123 ,000 ,622 ,075

N 267 267 267 267 267

SIMCTRALL Pearson Correlation ,079 ,181** -,030 1 -,806

**

Sig. (2-tailed) ,199 ,003 ,622 ,000

N 267 267 267 267 267

NSIMCTRALL Pearson Correlation -,034 ,030 ,109 -,806** 1

Sig. (2-tailed) ,583 ,620 ,075 ,000

N 267 267 267 267 267



77

H5: Correlations

ASR DERATIO CVSIZE EQUITY GAE

ASR Pearson Correlation 1 ,101 -,095 -,027 ,065

Sig. (2-tailed) ,099 ,123 ,665 ,287

N 267 267 267 267 267

DERATIO Pearson Correlation ,101 1 -,385** ,092 ,328

**

Sig. (2-tailed) ,099 ,000 ,132 ,000

N 267 267 267 267 267

CVSIZE Pearson Correlation -,095 -,385** 1 -,079 ,134

*

Sig. (2-tailed) ,123 ,000 ,198 ,029

N 267 267 267 267 267

EQUITY Pearson Correlation -,027 ,092 -,079 1 ,124*

Sig. (2-tailed) ,665 ,132 ,198 ,043

N 267 267 267 267 267

GAE Pearson Correlation ,065 ,328** ,134

* ,124

* 1

Sig. (2-tailed) ,287 ,000 ,029 ,043

N 267 267 267 267 267




78

Appendix 4: Regression results

1 2 3 4 5 6 7 8 9 10 11 12 13 14

ASR ASR ASR ASR ASR ASR ASR ASR ASR ASR ASR ASR ASR ASR

General Alliance experience 0.004** 0.004** 0.004** 0.004** 0,004 0,004 0,004 0,005 0,005 0.005** 0,008

0,002 0,002 0,002 0,002 0,003 0,003 0,003 0,003 0,004 0,002 0,002

Partner specific experience continuous 0,027 -0,218

0,075 0,193

genexp_pse 0,003

0,002

Time in days 0,000 0,001 0,005

0,002 0,002 0,003

timeindays2 0,000

0,000

Non-similar industry continuous 0.005**

0,002

Similarity in industry continuous 0,000

0,005

Non-similar country partner continuous 0.005**

0,002

Similarity in country partner continuous 0.004**

0,002

Similarity in country alliance continuous 0.004*

0,002

Non-similar country alliance continuous 0.004**

0,002

Equity 1=eq 0=noneq -0,142

0,499

genexp_eq -0,001

0,004

CV Size -0.000* -0.000* -0.000* -0.000* 0,000 -0.000** 0,000 -0.000** -0.000** -0.000* -0.000* -0.000** -0.000** 0,000

0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000 0,000

CV D E Ratio in pct -0,004 -0,004 -0,004 -0,003 -0,004 -0,004 -0,004 -0,005 -0,004 -0,004 -0,004 -0,004 -0,004 -0,004

0,004 0,004 0,004 0,004 0,007 0,006 0,007 0,006 0,004 0,004 0,004 0,004 0,005 0,009

Industry effects included Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

Country effects included Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

Year effects included Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

Constant -0,118 -0,218 0,420 -0,204 -0,571 -0,754 -0,071 -0,867 -0,089 -0,099 -0,093 -0,133 -0,470 -0,637

0,804 0,851 0,954 0,784 1,108 1,112 1,202 1,108 0,804 0,807 0,816 0,855 0,886 0,504

Observations 267 267 267 267 109 158 109 158 267 267 267 267 253 14

R-squared 0,12 0,12 0,13 0,12 0,12 0,2 0,12 0,22 0,13 0,12 0,12 0,13 0,13 0,99

Hypotheses H1 H2 H2 H3 H3 H3 H3 H3 H4a H4b H4b H5 H5 H5

Standard errors in parentheses

* significant at 10%; ** significant at 5%; *** significant at 1%


79

Appendix 5: Normality and Homoscedasticity


80

ns

ns

ns

ns

ns

Appendix 6: Graphical representation of the results

Results for H1:

Results for H2:

Results for H3:

Results for H4a:


(IV)


(DV)


(IV)


(DV)


(IV)


(IV)


(IV)


(DV)


experience (IV)

Non-industry specific alliance

experience (IV)


(DV)


(IV)


81

ns

ns

Results for H4b:

Results for H5:


(IV)

Non-equity based alliance

experience (IV)


(DV)


experience (IV)

Non-country specific alliance

experience (IV)


(DV)


82

Appendix 7: Schematic representation of the results

Hypothesis Predicted effect Found effect

H1 The relationship between general alliance experience and alliance

performance will be positive.

+ +

H2 Partner specific experience has a more positive effect on alliance

performance than non-partner specific alliance experience.

+ NS

H3 The relationship between the timing of experience and alliance

performance will be inverted U-shaped.

+ NS

H4a Industry specific experience has a more positive effect on alliance

performance than non-industry specific experience.

+ -

H4b Country specific experience has a more positive effect on alliance

performance than non-country specific experience.

+ -

H5 Equity based experience has a more positive effect alliance

performance than non-equity based experience.

+ +

Documents

526782 J.T.H. Medema (2)