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Scientific paper on research question 3

Start date of project: 01/01/2006 Duration: 42 months

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DUE DATE February 2009

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PICO

THE IMPACT OF AMBIDEXTROUS ALLIANCES ON INNOVATION:

HOW DO RESEARCH BASED SPIN-OFFS AFFECT THEIR

PARTNERS’ INNOVATION?

Liliana Doganova and Philippe Mustar

Mines ParisTech

Centre de Sociologie de l’Innovation

Massimo G. Colombo, Diego D’Adda, Evila Piva,

Politecnico di Milano,

Department of Management, Economics and Industrial Engineering

Abstract

Combining the literature on organizational ambidexterity and on exploration and

exploitation alliances, we build the concept of ambidextrous alliances, which we define as

inter-firm collaborations that involve both exploration and exploitation, and analyze their

impact on innovation in comparison with alliances specialized in either exploration or

exploitation. The combination of exploration and exploitation gives rise to static and dynamic

tensions, but also to synergies. We investigate the performance implications of ambidexterity

through an exploratory analysis that aims at identifying the types of innovation outputs for

which ambidextrous alliances are better suited. Based upon a survey of 178 firms engaged in

alliances with research based-spin-offs, our results suggest that in spite of the alleged tensions

between exploration and exploitation ambidextrous alliances are not less efficient than

alliances specialized in either exploration or exploitation and that they are particularly

relevant for new product development and for broader innovative effects, such as innovation

in technology strategy.

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I. Introduction

The relation between exploration and exploitation, or “the pursuit of knowledge, of

things that might come to be known” and the “use and development of things already known”

(Levinthal and March, 1993: 105), is paradoxical for these two activities are simultaneously

needed and yet often contradictory. In order to survive in a cycle that consists of periods of

incremental change punctuated by technological discontinuities (Anderson and Tushman,

1990), organizations need to exploit and explore as well. Focus on exploration to the

exclusion of exploitation deprives organizations from the possibility of benefiting from the

new ideas generated within or around them, while focus on exploitation to the exclusion of

exploration inhibits the very generation of these new ideas (March, 1991). However,

combining exploration and exploitation is difficult. On the one hand, they require different

organizational structures (Benner and Tushman, 2003), for they operate in contexts

characterized by contrasting time frames (Ancona, Goodman, Lawrence and Tushman, 2001)

and degrees of uncertainty and variance (McGrath, 2001). On the other hand, they tend to

drive out one another, thereby leading organizations to a “failure trap”, through a self-

sustaining cycle of increasing exploration, or in a “success trap”, due to the prevalence of

exploitation (Levinthal and March, 1993: 105-106)

There are several ways in which ambidextrous organizations (Duncan, 1976), which

pursuit both exploration and exploitation, can resolve the tensions inherent to the combination

of these two types of activities. They may accept the paradox of exploration and exploitation,

by compromising or outsourcing; resolve it, by spatial or temporal separation; or solve it, by

balancing exploration and exploitation within or across units (Jansen, 2005). The literature on

ambidexterity has focused on the organizational level. However, moving beyond the

boundaries of single organizations opens up a new avenue for the tricky balance of

exploration and exploitation. Indeed firms can enter alliances in order to “explore for new

opportunities” or “exploit an existing capability” (Koza and Lewin, 1998: 256), thereby

pursuing exploration or exploitation on the inter-organizational level. They resort to

explorative or exploitative alliances according to the requirements of their position in a

development cycle or to their strategy at a given moment. For instance, following their

product development path, technology ventures use explorative alliances to launch the

development of new products and then switch to exploitative alliances, in order to bring these

new products to the market (Rothaermel and Deeds, 2004). Faced with the need for strategic

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renewal, firms can reposition themselves by means of converting their networks into

exploration (Dittrich, Duysters and de Man, 2007). More generally, firms balance their

different types of alliances over time by shifting from exploration to exploitation and vice

versa (Lavie and Rosenkopf, 2006).

By differentiating and temporally separating the types of relationships in which they

engage, firms can succeed in integrating exploration and exploitation on the level of their

portfolios of alliances. Are they then doomed to resolve the paradox of exploration and

exploitation in their alliances by relinquishing each purpose to a specific period and to a

particular alliance? In contrast to the bulk of previous empirical research in exploration and

exploitation in alliances, which has conceived of these two types of alliances as distinct

(Rothaermel, 2001; Park, Cheng and Gallagher, 2002; Rothaermel and Deeds, 2004; Colombo,

Grilli and Piva, 2006), and in line with the original conceptualization of Koza and Lewin

(2000), who considered the possibility of what they called “hybrid” alliances, we contend that

exploration and exploitation, albeit conceptually distinguishable, can coexist in the practice of

alliances. If the literature on alliances has shunned these hybrid configurations, scholars who

examine exploration and exploitation on the organizational level acknowledge the possibility

their combination, the implications of which they examine through the concept of

ambidexterity. Ambidextrous organizations or units succeed in conducting both types of

activities, in spite of the challenges raised by their balance (Levinthal and March, 1993), and

this tends to enhance their performance (Gibson and Birkinshaw, 2004; He and Wong, 2004).

Herein, we adapt the concept of ambidexterity from the organizational to the inter-

organizational level and seek to understand whether ambidextrous alliances tend to achieve

better results than alliances specialized in either exploration or exploitation. Thus we theorize

and investigate the nature and implications of ambidextrous alliances.

We focus on one particular dimension of the performance of alliances, which is their

impact on innovation. After having defined ambidextrous alliances and outlined their

specificities, which stem from the static and dynamic tensions, but also synergies, to which

the combination of exploration and exploitation may lead, we analyze the impact of

ambidextrous alliances on innovation by comparing them to alliances that are specialized in

either exploration or exploitation. Since different types of alliances are likely to vary not only

in their motives and activities, but also in their results, we distinguish between several

innovation outputs (innovation on the strategic vs. on the operational level, tangible vs.

intangible and upstream vs. downstream outputs) and test the relative performance of

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ambidextrous alliances for each of these dimensions of innovation on a sample of 178

alliances involving European research based spin-offs (RBSOs).

II. The nature and specificities of ambidextrous alliances

Transposing the definition of ambidexterity as the ability to perform two different

things at the same time – alignment and adaptability (Gibson and Birkinshaw, 2004),

incremental and discontinuous change (Tushman and O’Reilly, 1996), exploratory and

exploitative innovations (Benner and Tushman, 2003) - we define ambidextrous alliances as

inter-firm relationships that involve both exploration and exploitation. Since ambidextrous

alliances appear as the combination of explorative and exploitative ones, a first step towards

their definition passes through a review of the literature on exploration and exploitation

alliances.

2.1. Exploration and exploitation in alliances

The exploration / exploitation dichotomy (March, 1991) was transposed from the

intra-organizational to the inter-organizational level through the distinction between two

different motivations for alliance formation: “explore for new opportunities” or “exploit an

existing capability” (Koza and Lewin, 1998: 256). Subsequent empirical work applying the

constructs of exploration and exploitation to alliances allowed a greater degree of precision in

their understanding and introduced two main types of nuances. On the one hand, distinct

domains of exploration and exploitation in alliances were outlined: the function of the alliance

in the value chain and the relative profiles of the partners with regard to their prior common

experience and to their similarity (Lavie and Rosenkopf, 2006). On the other hand, a not

explicitly stated, albeit clearly identifiable, difference emerged with regard to the unit of

analysis that is considered when explorative and exploitative alliances are defined and sorted

out. Specifying the unit of analysis is indeed crucial not only on the intra-organizational level

(Gupta, Smith and Shalley, 2006) but also on the inter-organizational one. An example may

be provided by the case of licensing. Licensing has usually been considered as an exploitative

relationship as a licensor exploit a technology developed by the licensee (Park et al., 2002;

Rothaermel and Deeds, 2004; Lavie and Rosenkopf, 2006; Colombo et al., 2006), however it

can also be seen as an explorative one when the point of view of the licensee is considered

(Koza and Lewin, 1998). Beyond this example, we can note that some studies take one of the

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partnering firms as their unit of analysis. In the pharmaceutical industry, for example, authors

position alliances along the value chain of a fully integrated biopharmaceutical company

(Rothaermel, 2001) or the product development path of a biotechnology firm (Rothaermel and

Deeds, 2004) and hence define drug discovery alliances as explorative, while

commercialization-related ones (involving aspects such as regulation, marketing and sales) as

exploitative. Hence, when the unit of analysis is the firm, the exploration/exploitation

dichotomy basically sumps up to the distinction between technology- and market-oriented

alliances. In contrast, when the unit of analysis moves to the dyad, the equivalence between

technology-orientation and exploration is broken. For example, Lavie and Rosenkopf (2006:

807) note that they file alliances in which “a focal firm market[s] a solution developed by its

partner without engaging in joint R&D efforts” in the exploitation category . Colombo et al.

(2006) put this nuance at the centre of their analysis and argue that technological alliances are

not necessarily explorative. They distinguish between commercial exploitative alliances (all

commercial alliances being considered as exploitative), technological explorative alliances

(i.e. alliances that involve inter-organizational learning, such as research joint ventures in

untested technological fields), and technological exploitative alliances (i.e. alliances involving

access to complementary technological knowledge, such as research contracts and in-

licensing agreements).

In this paper, we define explorative and exploitative alliances by focusing on the

dyadic level and on the function domain. Thus we consider alliances in which both partners

engage in developing new knowledge, assets and capabilities (e.g. joint R&D agreements) as

explorative, and alliances in which each firm accesses its partner’s knowledge, assets and

capabilities (e.g. research contracts) as exploitative. Such a definition allows us to explicit the

theoretical lenses through which each type of alliance can be viewed and thus to link the work

on inter-organizational exploration and exploitation to the broader literature on alliances

which exhibits two main lines of argument that are relevant to our purpose here.

A major motive for cooperating consists in gaining access to complementary resources.

In his seminal work, Teece (1986) showed that the successful commercialization of an

innovation requires the innovative know-how to be utilized in conjunction with

“complementary assets” (e.g. competitive manufacturing, distribution, service, or

complementary technologies) possessed by other firms. This entails the need for various

cooperative strategies. Thus partnerships allow firms to access resources that cannot be

efficiently developed internally or obtained through market exchanges. The inter-

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organizational learning literature (e.g. Child, 2001; Ingram, 2002) has put forward another

rationale for partnering by emphasizing the learning effects of alliances. First, a firm can learn

with its partner(s) when the allies jointly generate new knowledge and skills. An example of

such learning alliances can be found in industries such as biotechnology in which inter-

organizational relationships become the very « locus of learning » (Powell, Koput and Smith-

Doerr, 1996). In addition to or instead of learning with its partner(s), a firm can also learn

from its partner(s) (Child, 2001; Inkpen, 1991), by not merely gaining access to the other

party’s knowledge and skills – as it accesses other complementary resources – but actually

internalizing them (Hamel, 1991; Grant and Baden-Fuller, 2004). These two effects echo the

distinction between competitive and cooperative learning in alliances (Khanna, Gulati and

Nohria, 1998): partners can earn private benefits (learn from the partner unilaterally and apply

in other areas) and common benefits (which arise from mutual earning and apply to the

alliance’s own operations). An illustration of the difference between learning from and

learning with a partner is provided by the teaching metaphor: in one case there is a teacher and

a student firm (Lane and Lubatkin, 1998), while in the other case both firms are students.

Grant and Baden-Fuller (2004: 64) link alliances “as vehicles of learning” to

exploration and alliances in which “each member firm accesses its partner’s stock of

knowledge in order to exploit complementarities” – to exploitation. The seminal papers on

explorative and exploitative alliances rely on a similar association. Indeed, explorative

alliances deal with “probing or codeveloping new markets, products or technological

opportunities”, while exploitation alliances involve “pooling complementary resources that

neither partner is interested in developing on its own” (Koza and Lewin, 2000: 147-148),

thereby building on the “joint maximization of complementary assets” (Koza and Lewin,

1998: 256). Colombo et al. (2006) provide empirical support for this theoretical association by

showing that the complementary assets model has explanatory power for exploitative

alliances, but not for explorative ones. We should note that if the association between

exploitative alliances and the access to complementary assets is quite straightforward,

learning is not the monopole of explorative alliances. Exploitative alliances can indeed lead to

learning too: a firm can enhance its technical and collaborative competences through learning-

by-doing and also acquire new competences by learning what its partners know. On the level

of the dyad, exploration leads to learning with the partner, while exploitation is limited to

learning from the partner. These considerations lead us to retaining the following definition of

explorative and exploitative alliances: the former deal with the production of new knowledge,

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and hence learning, while the latter aim at the access to assets, among which knowledge and

skills, that do already exist but are not possessed by both partners. We build on this definition

in order to specify ambidextrous alliances.

2.2. Specificities of ambidextrous alliances

Ambidextrous alliances are inter-firm relationships that combine exploration and

exploitation. In such alliances, the partners not only access their complementary assets

(knowledge, technological and physical resources) but also jointly engage in the development

of new ones (knowledge, technologies). Our definition is in line with Koza and Lewin’s

(2000: 149) evocation of “hybrid” alliances in which “the companies seek to simultaneously

maximize opportunities for capturing value from leveraging existing capabilities, assets, and

the like, as well as from the opportunity to create new value through their joint learning

activities”. The specificity of ambidextrous alliances stems from the fact that exploration and

exploitation involve different and sometimes contradictory processes. In order to outline the

characteristics of ambidextrous alliances, we combine insights from the literature on

organizational ambidexterity on the one hand and exploration and exploitation in alliances on

the other hand.

The tension between exploration and exploitation, which stems from the

contradictions inherent in their nature and their dynamics, is present on the intra- and on the

inter-organization levels. On the one hand, the competition for the allocation of resources

between two activities that do not share the same requirements is a source of what we call

static tensions. Already present when single organizations are the unit of analysis, these

tensions are likely to be exacerbated when inter-organizational relationships are considered

because of the more limited scope of the resources that can be devoted to an alliance. Static

tensions result from the difference between the time frames, the degree of uncertainty and the

structures of exploration and exploitation in organizations and alliances.

A major difference resides in the time frames of exploration, which is oriented towards

the future, and exploitation, which deals with the present (Ancona et al., 2001). The matter of

exploration is what “might come to be known”, while the exploitation uses “things already

known” (Levinthal and March, 1993: 105). Similarly, explorative alliances can serve as

“prospecting” strategies (Koza and Lewin, 2000) and are thus forward-looking. Still, we

should note that if explorative alliances look towards the future, they do not necessarily last

longer than exploitative alliances, due to the different cycles (learning cycle vs. industry

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cycle) that they follow (Koza and Lewin, 2000). Operating in different “time zones” (Ancona

et al., 2001), exploration and exploitation vary in their degree of uncertainty and in the

structures they require. First, since exploration implies variation, while exploitation focuses

on choice and selection (March, 1991), the former presents a greater variation in its

performance (McGrath, 2001; He and Wong, 2004). Second, the organizational designs that

allow the discovery, play and flexibility targeted by exploration and those that allow the

efficiency, implementation and execution targeted by exploitation (March, 1991: 71) are

likely to differ in terms of their structure and degree of control. On the organizational level,

the units which carry out exploration should be smaller, more decentralized and have a looser

culture than their exploitation-oriented counterparts (Benner and Tushman, 2003). The former

require an organic structure while the latter – a mechanistic one (Burns and Stalker, 1961).

Teams learn more efficiently when they are granted goal and supervision autonomy for the

management of exploratory projects (McGrath, 2001). A similar difference holds for

exploration and exploitation in alliances. Given the difference in their targets - learning vs.

specific performance objectives - the former require an organization and control system

oriented toward processes and the latter towards outcomes (Koza and Lewin, 1998).

Performance goals are ambiguous and open-ended in explorative alliances, while they are

defined in terms of measurable objectives in exploitative alliances (Koza and Lewin, 2000).

On the other hand, exploration and exploitation exhibit dynamic tensions in the sense

that they are both self-sustaining processes that may lead to the dominance of one at the

expense, or at the exclusion, of the other (Levinthal and March, 1993). Exploration may drive

out exploitation, through a sequence of experimentation, failure and new search. Exploitation

may drive out exploration, because of its more certain and closer results, and lead

organizations in a « success trap » (Levinthal and March, 1993) in which their capabilities

become core rigidities (Leonard-Barton, 1992). Likewise, on the inter-organizational level,

exploration and exploitation are subject to path dependencies that may lead to a focus on

exploitation, through the development of routines, or on exploration, through the

improvement of absorptive capacity (Lavie and Rosenkopf, 2006).

Thus, the combination of exploration and exploitation in alliances is likely to create

both static and dynamic tensions, due to the contradictory time frames, degrees of uncertainty,

and structures that the two types of activities involve and to the their self-reinforcing nature.

Since exploration and exploitation are both needed by organizations (March, 1991), the

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organizational literature has advanced several solutions for their combination or balance in

what scholars have called ambidextrous organizations.

First, organizations can become ambidextrous through structural separation, by

building dual structures (Duncan, 1976) or devoting some units to exploration and others to

exploitation (Tushman and O’Reilly 1996). Such a solution can hardly be applied to inter-

organizational relationships given their more limited scope and resources. Firms can then

balance exploration and exploitation in the frame of their portfolio of alliances, devoting some

alliances to exploration and others to exploitation. This is the underlying assumption of most

studied which consider alliances as either explorative or exploitative (Rothaermel, 2001; Park

et al., 2002; Rothaermel and Deeds, 2004; Colombo et al., 2006). Within the same inter-

organizational relationship, firms can only achieve balance between exploration and

exploitation by compensating the different domains of exploration and exploitation, as

outlined by Lavie and Rosenkopf (2006). For instance, they can explore, by engaging into

upstream activities in the alliance, and yet exploit, by choosing a similar or better known firm

as a partner.

Second, organizations can become ambidextrous through temporal separation, by

switching between exploration and exploitation and the structures that they require depending

on their position in the innovation process (Duncan, 1976). Exploration and exploitation are

thus viewed as the alternating stages of a cycle (Gilsing and Nooteboom, 2006). Holmqvist

(2004) extends the cycle of exploration and exploitation by spanning both the organizational

and inter-organizational level: for example, organizations can explore together and then

exploit internally what they have learnt. Firms can also resort to alliances to leverage their

renewal or repositioning (Dittrich et al., 2007): here the use of explorative and exploitative

alliances is contingent upon the strategy of the focal firm and the position in time. More

generally, firms can balance exploration and exploitation in alliances over time by shifting

from one to the other and thus alternating periods of exploration and exploitation within the

same domain (Lavie and Rosenkopf, 2006). Rothaermel and Deeds (2004) show a similar

balance of exploration and exploitation in alliances over the time frame of product

development, with the latter being a natural continuation of the former, needed to transform

products in development into products on the market. Indeed, firms may use exploration

alliances to discover new ideas, technologies or products ; the success of the exploration

phase leads to the production of intermediary innovative outputs, which in turn calls for

exploitation and may thus entail the establishment of exploitation alliances. Nevertheless, the

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inter-temporal balance of exploration and exploitation in alliances raises two main challenges.

On the one hand, such a solution is only possible within a linear model of innovation that has

been shown not to be adapted to an interactive process of innovation (Kline and Rosenberg,

1986) in which, for example, users - supposed to be passive recipients at the end of the cycle

– actively intervene in the very upstream stages of innovation (Von Hippel, 1986). On the

other hand, since alliances are often established for a specific purpose and may be impacted

by the evolution in the strategies of the partners, they are less stable in time than organizations,

which means that the possibility to balance over time is not guaranteed when the number of

appropriate partners is limited.

A third solution for balancing between exploration and exploitation is what Gibson

and Birkinshaw (2004) call “contextual ambidexterity”. They contend that units are capable of

simultaneously pursuing alignment and adaptation, provided an organizational context that

enables individuals within the same business unit to engage in both exploration and

exploitation. The inter-organizational equivalent of contextual ambidexterity are hybrid, or

ambidextrous, alliances. An example of a hybrid alliance is the “the pre-Novartis Ciba Geigy

alliance with Alza [which] was designed to ensure that the companies would go to market

with lower risk products, but also facilitate Ciba Geigy’s learning of the ADDS (advanced

drug delivery system) technology” (Koza and Lewin, 2000: 149). Empirical evidence for the

existence of ambidextrous alliances, simultaneously combining exploration with exploitation,

or the development of new knowledge with the access to complementary assets, is abundant.

Let us illustrate such alliances with two examples taken from the life sciences industry.

In a press release dated November 2003, the alliance between Servier, a major pharmaceutical

company, and Hybrigenics, a French biotechnology start-up, was presented as follows:

“During its previous collaboration with Servier, Hybrigenics applied its proprietary

technology to map protein-protein interactions in cell-death pathways apoptosis (…).

Hybrigenics selected a number of potential therapeutic targets to help validate lead

compounds as potential Servier cancer therapeutics. Servier will now supply several early

lead anticancer compounds to Hybrigenics, which will evaluate their biological and

pharmacological properties.” In this case, joint exploration is made possible by the mutual

access to complementary assets: the protein interactions mapping technology of one partner

and the compounds of the other partner. Another alliance between a pharmaceutical company

and a biotechnology start-up epitomizes the case of joint exploration through the mutual

access to complementary competences, as shown in the following excerpt from a press release

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dated April 2006: “The French biopharmaceutical company Innate Pharma SA and the

Danishbased global healthcare company Novo Nordisk today announced a major partnership

to develop new drugs targeting natural killer (NK) cells. (…) Having already collaborated on

an initial project for three years, the partners have established competitive and

complementary competences in this area of immunology, which effectively advanced one

project through research and preclinical development. Building on this synergy, the

partnership also takes advantage of the individual strengths of the two companies (…).”

The empirical evidence supporting the wide-spread use of ambidextrous alliances

contrasts the absence of their scholarly consideration and analysis. In spite of the emphasis on

the tensions between exploration and exploitation that is prevalent in the literature, arguments

supporting the need for (March, 1991) and the possibility of (Gibson and Birkinshaw, 2004)

their combination have also been put forward. While the relationship between exploration and

exploitation includes a dynamics of mutual exclusion (Levinthal and March, 1993), it also

reveals a converse process by which exploration and exploitation sustain each other. First,

joint exploration may call for joint exploitation. On the one hand, as demonstrated in the

examples above, the possibility to perform exploration may be conditioned to the access to the

complementary assets or competences possessed by the two partners. In this case, joint

exploration requires joint exploitation. On the other hand, joint exploration provides

opportunities for joint exploitation. As shown by Rothaermel and Deeds (2004), exploration

produces new knowledge, which is then applied through exploitation. In a non-linear model of

innovation, these two processes are not subsequent but simultaneous. Here, joint exploration

and joint exploitation both progress in an interactive manner. Second, joint exploitation may

lead to joint exploration. On the one hand, the success of joint exploitation facilitates joint

exploration (Koza and Lewin, 2000), for it supports the fit between the partners, while its

failure, when due to reasons independent from the competences and behaviour of the partners,

may pave the way for joint exploration of alternative technologies and solutions. On the other

hand, often joint exploration entails exploration because the application of a piece of

knowledge, technology or competence, to a new organization context or to a new purpose,

implies its adaptation and hence the production of new knowledge: adopting means adapting

(Akrich, Callon and Latour, 2002).

To sum up, the simultaneous combination of exploration and exploitation in

ambidextrous organizations and alliances involves both tensions and synergies. The

reconciliation of these tensions requires the implementation of complex organizational

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designs. Ambidextrous business units need to achieve an organizational context in which

stretch, discipline, support and trust are present as well (Gibson and Birkinshaw, 2004). The

management of ambidextrous alliances requires the combination of different control

mechanisms – both output- and process-oriented – and multiple time horizons (Koza and

Lewin, 2000). Do such tensions and complex designs alleviate the synergies between

exploration and exploitation and reap out the assumed benefits of ambidexterity? Empirical

evidence shows that, for organizational ambidexterity, the answer is no. Tushman and

O’Reilly (1996) provide anecdotic evidence that structurally ambidextrous organizations (i.e.

organizations that achieve ambidexterity through temporal or structural separation of

exploration and exploitation), although rare, are better able to survive and maintain their

positions in periods of radical technological change. Gibson and Birkinshaw (2004) show that

contextually ambidextrous business units (i.e. units that achieve ambidexterity by building a

context that enables and encourages their members to engage in both exploration and

exploitation) exhibit a better performance, as assessed by their members. He and Wong

(2004) demonstrate that ambidextrous firms (which have both explorative and exploitative

innovation strategies) achieve a better performance, in terms of sales growth. They also find

that ambidextrous organizations display a lower variance in performance than those

specialized in exploration.

III. Ambidextrous alliances and innovation

Alliances affect the innovative performance of the partners involved. They foster

innovation by allowing the partner firms to generate (Powell et al., 1996) or gain access to

new knowledge (Grant and Beden-Fuller, 2004) and/or to bring new knowledge to the market

by combining it with the necessary complementary assets (Teece, 1986). Empirical studies

tend to support the idea of a positive relationship between the number of alliances of a focal

firm and its innovation performance (e.g. Baum, Calabrese and Silverman, 2000; Kelley and

Rice, 2002; Rothaermel, 2001), although this relationship may be curvilinear (Deeds and Hill,

1996), i.e. the innovative performance increases with the number of alliances up to a threshold

level but then starts decreasing. In analysing this relation, most of these studies have

considered only one measure of firm innovative performance, be it the number of patents

(Ahuja, 2000; Baum et al., 2000; Stuart, 2000) or the number of new products (Deeds and Hill,

1996; Kelley and Rice, 2002; Rothaermel, 2001) and have put this measure in relation to the

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number of alliances the focal firm. In doing so, they implicitly assume that alliances are

homogeneous and indistinct with regard to the types of innovative outputs that they may

result in. In contrast, the divergence in the objectives and nature of explorative and

exploitative alliances urges to take into account their heterogeneity when examining their

effects on innovation. In other words, different types of alliances have different motivations

and are likely to breed different outcomes (Rothaermel and Deeds, 2004).

We distinguish between several types of innovation outputs. First, alliances may result

in innovation changes on the operational level, by engendering new codified knowledge and

technological artefacts, new products and processes, as well as learning effects, or on the

strategic level, by allowing firms to renew their technology strategy and enter into new

technological fields. An example of innovation on the strategic level through alliances is the

entry of pharmaceutical companies into the field of biotechnology (Rothaermel, 2001).

Second, within the operational level of innovation, the results of alliances differ in terms of

their position in the value chain and their degree of tangibility. On the one hand, innovative

outputs can be situated upstream, as in the case of patents (Stuart, 2000), publications,

feasibility studies or prototypes (Larédo and Mustar, 1996) and learning, or downstream, as in

the case of new products (Deeds and Hill, 1996). On the other hand, they can be tangible, due

to their codification in patents or publications or to their embodiment in new technological

artefacts, products and processes, or intangible because resulting in learning (Mowery, Owley

and Silverman, 1996).

The distinct impacts of explorative and exploitative alliances on the operational level

have been examined in the literature. On the one hand, explorative alliances are likely to

result in more upstream innovative outputs than exploitation ones. Indeed, while explorative

alliances lead to the “the embodiment of new knowledge learned through exploration into a

prototype product”, “the codification of new knowledge through patenting” or new products

in development, while the outputs of exploitative alliances are to be found in the

commercialization endeavours of firms, i.e. in new products on the market (Rothaermel and

Deeds, 2004: 204-205). On the other hand, explorative alliances are more likely to result in

greater intangible innovative outputs as compared to exploitative alliances. First, as noted

above, explorative alliances result in learning. If the exploration is successful, such learning

will be embodied in tangible outputs, such as patents, prototypes, new products. However,

even in the case of failure, exploration may generate new knowledge. Since the results of

exploration are highly variable (Koza and Lewin, 1998), their transformation into tangible

14

innovative outputs becomes more uncertain than in the case of exploitation. This difference

between explorative and exploitative alliances is accentuated by another property of the

results of exploration which consists in their greater distance in time (Koza and Lewin, 1998).

The production of tangible outputs becomes then even more problematic in explorative

alliances because it is not only marked by high uncertainty, but also requires time to unfold,

which obscures the causal relationship between the partnership and its outcomes and poses the

methodological challenge of the right time of observation and measure of the effects. In

addition to the implications of the fact that “the certainty, speed, proximity and clarity of

feedback ties exploitation to its consequences more quickly and more precisely than in the

case with exploration” (March, 1991: 73), the differences in the governance of explorative

and exploitative alliances are likely to reinforce their trends towards intangible vs. tangible

outputs. Indeed, explorative vs. exploitative alliances are organized to produce learning

objectives vs. performance outcomes, rely on behaviour and process controls vs. output

controls (Koza and Lewin, 1998). Expectations are formulated as non-operation learning

outcomes in the case of exploration, and as operational goals, targeting explicit and

measurable outcomes, in the case of exploitation (Lewin, Long and Carroll, 1999). Since the

goals attributed to alliances, and the corresponding governance mechanisms that are

implemented in them, shape the results to which they may lead, these differences further link

exploration alliances with intangible outputs and exploitation alliances with intangible ones.

Figure 1 synthesizes the differences in the nature and innovation outputs of explorative and

exploitative alliances.

Relying on the above arguments we address the following questions. How do

ambidextrous alliances affect innovation, as compared with alliances specialized in either

exploration or exploitation? Are they more or less efficient is fostering the innovation activity

of partner firms? For which types of innovation outputs are ambidextrous alliances more

efficient, given the tensions from which they may suffer and the synergies that they may

engender? When do firms need to invest in building ambidextrous alliances, in spite of the

risks that such alliances bear? In the following sections we investigate this issue through an

exploratory empirical analysis.

IV. Methods

4.1. The dataset

15

In this paper we consider a sample composed of 149 inter-firm collaborations. All

sample alliances involve at least one European research-based spin-off (RBSO), i.e. a new

technology-based firm founded to commercially exploit the research results generated in a

public research organization. The choice to focus on the alliances established by these firms is

particularly appropriate and consistent with the objective of our research. In fact, in this paper

we are interested in the effects of (ambidextrous) alliances on the innovation activities of

partnering companies. RBSOs are more likely to possess leading-edge technological

competences and tend to be more focused on innovation and R&D activities than other firms

(Colombo and Piva, 2008). As a consequence, the alliances established with RBSOs should

be, at least in principle, more likely to affect the innovation activity of partner companies than

alliances established with less innovation-oriented firms. Hence, we collected data on the

effects of these particular alliances on the firms that partnered with RBSOs. It is worth

acknowledging that our sample includes also alliances established by a RBSO with two or

more partner firms. In all these cases we focused on the effects of the alliance on only one of

the partner firms.

Data on sample alliances were collected between May 2007 and November 2008. The

data collection process went through a series of steps. First, we developed a structured

questionnaire addressed to managers of partner firms who had been personally involved in the

activities performed within the collaborations under scrutiny. The questionnaire was

composed of three sections. The first section provides information on the characteristics of the

alliance and on the activities performed within it. The second section comprises questions on

the innovative performance of the partner before alliance formation and the third one on the

changes engendered by the alliance in the innovation activity of the partner. While answering

the questions the interviewed managers were asked to focus on the partner unit, i.e. the entity

involved in the partnership. The unit might be either the whole partner firm or one of its

subsidiaries, divisions or departments. We decided to focus on the unit as we realized that

many alliances affect only the activities of a limited portion of partner firms, especially when

these firms are large incumbents that operate in different countries and have a diversified

product portfolio. Hence, considering the effects on the whole partner firm could lead to

underestimate the impact of these alliances. As all the extant studies on the effects of alliances

have considered partner firms as a whole, the focus on the unit differentiates the present work

from prior research.

16

Second, we identified the alliances which could become our object of analysis. As we

were interested in alliances established by European RBSOs, we first had to identify

European companies that complied with the above definition of RBSO. For the construction

of the target population of European RBSOs, a number of sources were used. These included

lists of academic high-tech start-ups published on the websites of universities/public research

organizations and university incubators, lists provided by national industry associations, lists

of new technology-based firms applying for public support and lists of participants in industry

trades and expositions. Altogether, 922 RBSOs were identified.

Then we collected information on the relationships in which these RBSOs were

involved. As we are interested on the effects on the innovation activities of these firms, we

did not consider the collaborations that started after the end of 2006. The idea is that in order

to detect any effects on the innovation activity of partner units, a sufficiently long time must

have passed since the formation of the alliance. We also excluded collaborations that started

more than fifteen years ago, as we feared that the greater the number of years elapsed since

the beginning of the collaboration, the more relevant the effects of the (alleged) retrospective

bias. In order to build our sample of alliances, we used two sources of information: public

sources (internet, the press) and contacts with RBSOs. We performed a search through public

sources looking for the names of the companies that partnered with these RBSOs and for the

names of the managers involved in these collaborations. More specifically, we visited the

websites of the RBSOs, searched for articles on the alliance activity of these firms through

Lexis Nexis and used the Cordis website to find out information on the EU-funded

collaborative research projects that involved the 922 RBSOs. While performing this search,

we contacted one of the owner-managers of the RBSOs for which the names of partner firms

and/or the names of the managers involved had not been identified through the above

mentioned public sources of information. We presented them the aim of our research and

asked them for the names of the companies they partnered with as well as for contact details.

The search through public sources and the contacts with the RBSOs allowed us to identify

1394 partner firms.

Third we contacted the 1394 partner firms. We sent a request for cooperation to the

managers within the partner companies that had been directly involved in the focal alliance by

email and, if necessary, also contacted them by phone. Interviews based on a questionnaire

were realized with the relevant managers by phone or face-to-face. When we did not have the

necessary contact details, we sent the questionnaire to the CEO of the partner company asking

17

him to forward our request to the manager(s) that have been involved in the alliance. At the

end of the data collection we had obtained answers on 178 alliances established by 106

RBSOs. Table 1 reports some descriptive statistics on these sample alliances. The data

analysis presented in the following sections is based on a sub-sample of 149 alliances due to

missing data.

Note that there is no presumption here to have a random sample. In fact, absent

reliable official statistics, it is very difficult to identify unambiguously the universe of

alliances established by European RBSOs. Therefore, one cannot check ex-post whether the

sample used in this work is representative of the universe or not. However this problem is

common to all prior studies on alliances. Instead, it is worth highlighting that with respect to

the databases used in prior studies our dataset exhibits clear strengths. First, while most

previous studies on alliances analyzed a specific sector (especially biotechnology) and

focused on the USA, we consider here a large and heterogeneous sample of alliances, which

spans over five high-tech sectors (namely, biotech and pharmaceuticals, software, equipment,

and others) and includes companies located in different European countries. Hence, we (at

least partially) solve the problems of generalizability of prior studies without bringing too

much heterogeneity in our sample. Second, most of the extant works rely on secondary

information gathered from trade journals, press releases, business newspapers and magazines,

and other publications. All these sources usually provide only limited information on the

specific activities performed within the relationship. Moreover due to the fact that the

information on sample alliances might be non-homogenous (e.g. diffused for purposes that

differ from alliance to alliance), problems of comparability might arise. Conversely, we can

take advantage of an ad hoc database. Even though our sample is not representative of the

alliances established by European RBSOs, it is suitable for the needs of the analysis because,

as shown in table 1, it displays sufficient variety in terms of the types of alliances that we

consider.

In order to ensure the validity of the data, the preliminary version of the questionnaire

was tested through pilot interviews and accordingly modified. In addition, as far as we could,

we relied on prior published works to develop the questions and items we included in the

questionnaire and the variables we use in the empirical section.

4.2. The methodology of the econometric analysis

18

The econometric analysis is aimed at assessing whether ambidextrous alliances

perform better or worse than other alliances.

In order to investigate the effects of ambidextrous alliances on innovation, the

following econometric model is specified:

iiiii ZiveDExploitatveDExploratiInnoOutput ''' (1)

iInnoOutput

iive

is a measure of the effects of the alliance i on the innovation activities of

the partner firm (see paragraph 4.2.1. for a detailed description); and

are two dummy variables equal to one for alliances specialized in exploration

and in exploitation, respectively (i.e. ambidextrous alliances are the baseline of the estimates),

are controls (see paragraph 4.2.2); and finally,

iveDExplorati

DExploitat

iZ i are i.i.d. disturbance terms.

If the combination of exploration and exploitation engendered synergies and thus more

positive effects on innovation than alliances that involve only exploration or only exploitation,

the coefficients of both and would be negative and significant.

Conversely, if the costs of the combination were greater than its benefits (and thus

ambidextrous alliances performed worse than alliances involving only explorative and/or

exploitative activities), at least one of the two coefficients would be positive and significant.

iveDExplorati iiveDExploitat

Since we have different types of dependent variables, i.e. ordinal categorical, binary

and continuous variables, we make use of different regression models to empirically

understand the effects of ambidextrous alliances. In particular we use multinomial ordered

logit model, linear regression model and logistic regression model.

4.2.1. The dependent variables

In the econometric analysis we consider four measures of outputs.

The operational downstream outputs are measured by the variable ProductInnovation.

ProductInnovation is a categorical variable ranging from 1 to 4 that indicates whether any

product(s) or service(s) resulted from the alliance and reflects the degree of innovativeness of

such product(s)/service(s). More specifically ProductInnovation is equal to 1 for alliances that

had no effects on the product/service portfolio of the partner unit, it equals 2 if the alliance

resulted in the introduction of at least one customized or upgraded product/service, 3 if it led

to the introduction to the market of at least one product/service new to the company and 4 if

this(these) new product(s)/service(s) was also new to the market.

19

The operational upstream outputs of the alliances are measured by

DCodifiedKnowledge, a binary variable that indicates whether any tangible upstream outputs

resulted from the alliance. During the interviews we asked our respondents whether the

alliance led to the application for at least one patent, the publication of at least one scientific

article or the defence of at least one PhD thesis financed by the partner unit. Whenever one of

these outputs had been engendered by the alliance, we assigned 1 to a variable corresponding

to the specific output (and 0 otherwise). CodifiedKnowledge is equal to one when at least one

of these three variables is equal to one; it is equal to zero when all these three variables are

equal to zero.

The operational intangible outputs are the learning effects of the alliance (Learning).

They are calculated as the average values of 9 items measured through a 7-point Likert scale.

These items have been adapted from Zahra, Ireland and Hitt (2000).

The last variable, StrategicInnovation, is a measure of the strategic innovation outputs

of the alliance. It is a categorical variable ranging from 1 to 4 that measures the changes

engendered by the alliance in the strategic positioning of the partner unit in its technological

field. StrategicInnovation is equal to 1 when the alliance had no effects on the positioning of

the partner unit, 2 when it resulted in an improvement of the position of the unit in an existing

field, 3 when it engendered the entry in a new technological field and 4 when it led to the

creation of a new technological field.

4.2.2. The explanatory variables

The explanatory variables are the two dummies DExplorative and DExploitative. The

former equals one when the cooperation involved only explorative activities, while the latter

equals one when the cooperation involved only exploitative activities. As mentioned above,

previous empirical research on explorative and exploitative alliances has used secondary data

and has summed the distinction between the two types of alliances to the one between

technological and commercial alliances, with the presence of R&D activities being the major

criteria on which the categorization of the alliances relied. Given the drawbacks of such

measures, which we discussed above, we developed an operationalization of explorative

alliances by describing the types of activities involved in them. We classified as explorative

activities the joint development of a new technology/product/service or the joint investigation

of a research field new to both partners. We classified as exploitative activities the acquisition

and the commercialization by one partner of an innovative technology/product/service

20

developed by the other partner, or the joint creation or investigation of a new market for an

existing technology/product/service.

Besides the two dummies DExplorative and DExploitative, the estimates also include a

number of control variables. These latter variables can be classified in two groups. The first

group includes six measures of alliance-specific characteristics. TimeToObservation is the

number of years elapsed since the beginning of the collaboration. We included this control

variable because the likelihood to have any innovation outputs is greater the greater the

number of years between the time of alliance formation and the time of data collection.

DEnded is a dummy equal to one for ended alliances. On the one hand, successfully ended

alliances should be more likely to have resulted in innovation outputs than alliance still

underway. On the other hand, ended alliances might have been interrupted by partner firms as

they did not meet their objectives. These latter alliances are obviously less likely to have

engendered any effects on innovation. DPublicSupport is a dummy equal to one for alliances

that benefited from European, national or regional public support. Finally the models include

three sector dummies (DBiopharma, DEquipment, DSoftware; the baseline is other high-tech

sectors) that identify the sector of the alliance, proxied by the sector of the RBSO.

The second group of variables includes six measures of characteristics of partnering

companies. DPriorRelations is a dummy variable equal to one when, prior to the formation

of the alliance under scrutiny, either personnel of the RBSO had informal relationships with

personnel of the partner unit, or the RBSO and the partner unit had already established an

alliance. DCrossBorder is a dummy variable equal to 1 when the RBSO and the partner unit

are not located in the same country. PartnerUnitInnov is a variable proxying the

innovativeness of the partner company when the alliance was established. It equals the

percentage of employees of the partner unit who were engaged in R&D, engineering and

design activities. PartnerUnitSize is the logarithm of the total number of employees in the

partner unit. PartnerUnitAge and RBSOAge are, respectively, the age of the partner unit and

that of the RBSO. DUnitIndep is a dummy variable equal to one when the partner unit

coincides with the partner firm.

V. Results

We used different regression models, i.e. multinomial ordered logit, logit and linear

regression, to test the effect of ambidextrous alliances. First we run the models with the

21

control variables only and then we add the two independent variables, testing the increase in

the explanatory power of the model both with Likelihood Ratio and Wald tests (i.e. we test

the joint significance of DExplorative and DExploitative). We also test for the joint

significance of the two categories of control variables, namely firm-specific and alliance-

specific. The results are reported in the tables 4 and 5, the standard errors in all models are

robust to heteroskedasticity. For all nonlinear models, i.e. multinomial ordered logit and logit,

we report odds ratios instead of coefficients and omit the cut points to save space.

We checked for the potential collinearity between our independent and control

variables looking both at correlations (see Table 2) and Variance Inflation Factors (maximum

VIF is 2.02, below 10 threshold; the average of the VIFs is 1.31, below 6 threshold). We also

looked at the correlation between our dependent variables to confirm that we are measuring

different constructs. The results do not show strong correlations between our construct (see

Table 3). As a further check we computed also Variance Inflation Factors obtaining similar

results (max VIF equal to 1.38, below 10 threshold; average of the VIFs equal to 1.22, below

6 threshold).

One might argue that ambidextrous alliances are more likely to occur if a relationship

last longer, for example due to the fact that it requires time to pass from an exploration to an

exploitation phase in the innovation process (assuming a linear model of innovation). To

ensure that the effects of our ambidextrous alliances are not driven by this fact, we performed

an ANOVA on the length of the relationship against the type of alliance, namely explorative

only, exploitative only or ambidextrous. The result show that there is no empirical evidence of

any relation between ambidexterity and the length of the partnership (F(2 , 173) = 0.98). We

also control for this fact, at least in part, using TimeToObservation and DEnded as a control

inside all models.

Looking at ProductInnovation, results show that our independent variables are jointly

significant and hence they increase the explanatory power of the model. The odds ratios show

that the fact that an alliance has only explorative or only exploitative content reduces the

degree of product innovativeness, in other words ambidextrous alliances are associated with

higher performances for this innovation measure. We do not find any differences in terms of

performances between the two non-ambidextrous strategies. We find also that

TimeToObservation, a measure of the time passed since the beginning of the collaboration, is

positively associated with this innovation type, conversely Ended has a negative effect.

22

The finding that ambidextrous alliances lead to better results in terms of product

innovation, as compared with alliances specialized in either exploration or exploitation, is in

line with the literature. The mutually beneficial effects of exploration and exploitation have

been demonstrated on the organizational level (Katila and Ahuja, 2002). The explanation put

forward is based upon the notions of absorptive capacity and uniqueness. On the one hand,

when firms can rely in their existing knowledge, or exploit, they are better positioned to

develop new knowledge, or explore. On the other hand, firms are more likely to find unique

combinations of knowledge, leading to product innovation, when they apply existing

understandings (exploitation) on new solutions (exploration). Such interactive effects

correspond to the mutually feeding process of exploration and exploitation in ambidextrous

alliances, which we described above. Moreover, the better performance of ambidextrous

alliances, as seized by our measure of product innovation, can be explained by the

combination of the lesser uncertainty of the results of exploitation (March, 1991), which

increases the probability of product innovation, with the greater innovativeness of the results

of exploration (Benner and Tushman, 2003), which increases the degree of innovativeness of

the products resulting from the alliance.

For what concerns StrategicInnovation, we obtain results similar to ProductInnovation

on our focal variables, indicating increasing performances in terms of technological strategic

positioning with ambidextrous collaborations, and suggesting once more potential synergies

stemming from the combination of these activities. Surprisingly, PriorRelations exhibits a

negative effect in term of technological positioning, contrary to the PublicSupport variable

that has a positive one.

This finding is in line with the literature too. As shown by Tushman and O’Reilly

(1996), organizations need to be ambidextrous in order to face technological change, by

entering new technological fields, while maintaining their positions in the fields in which they

were already present. Alliances are one means for diversification (Roberts and Berry, 1985).

However, such alliances result in higher performance when the value of the complementary

assets of incumbents is not destroyed by technological change (Rothaermel, 2001). Therefore,

they need to combine learning and pursuing new opportunities, which are the characteristics

of exploration, with leveraging complementary assets, which is the basis for exploitation.

Despite the tensions that they involve, ambidextrous alliances appear as the only viable

solution for the innovation in technology strategy that is triggered by discontinuous change.

23

The presence of operational upstream outputs, i.e. DCodifiedKnowledge, is positively

related to the fact that the two partners are not located in the same country, and also that they

benefited from public support. Also TimeToObservation has a positive effect on the likely to

observe these types of outputs. Since only DExploitative shows a significant negative effect

we could say that only the presence of some exploration activities increases the likelihood to

generate new codified knowledge, however the non-significant test on the difference between

DExploitative and DExplorative weakens this argument.

The beta coefficients of the regression model on Learning, indicate that our measure

of operational intangible output is negatively related to the presence of only exploitative

activities inside the collaboration. The test on the difference between DExploitative and

DExplorative confirms that the level of intangible innovative outputs is primarily associated

with the presence of explorative activities.

The findings that explorative alliances are better suited for the production of codified

knowledge and firm-level learning are in line with the arguments on the distinct results of

explorative and exploitative alliances that we developed above. Indeed, explorative alliances

are more likely to lead to upstream tangible outputs and to intangible outputs than exploitative

alliances. Adding exploitation to exploration within the same inter-organizational relationship

is not likely to provide any additional drive towards producing new codified knowledge or

learning, while it may divert resources from exploration and thus lead to the prevalence of the

static tensions that we described above.

To sum up we do not observe any significant negative effects related to ambidextrous

alliances, which would have pointed to hazards in combining exploration and exploitation

within the same inter-organizational relationship.

Conclusion

In this paper we have conducted an exploratory investigation of the effects of a

particular type of inter-firm relationships, ambidextrous alliances, on the innovation

performances of firms that partnered with high-tech start-ups. In particular, we have analyzed

whether alliances that involve both exploration and exploitation are more or less efficient in

fostering the innovation activity of partner firms than alliances specialized in either

exploration or exploitation. For this purpose, we have used an original dataset including

information on 178 alliances established by European RBSOs and we have examined data

through OLS and ordinal logit estimates.

24

This paper has provided some interesting new insights. In spite of the alleged tensions

between exploration and exploitation, ambidextrous alliances have never resulted to be less

efficient than alliances that involve only one type of activities. However, the relative effects

of ambidextrous alliances and other collaborations depend on the innovation output

considered. More specifically, ambidextrous alliances are more efficient on the strategic level

and engender more positive effects in terms of downstream outputs compared with alliances

specialized in either exploration or exploitation. Conversely, they lead to greater learning and

upstream outputs with respect to alliances involving only exploitative activities, but they are

not more efficient than purely explorative alliances.

This study contributes to the literature on alliances in two directions. First, it

introduces the new construct of ambidextrous alliances, transposing the definition of

ambidexterity from the intra-organizational to the inter-organizational level. In doing so, the

tensions and synergies resulting from the combination of exploration and exploitation

activities are discussed in detail. Second, it provides a comprehensive analysis of the effects

of different types of alliances on firms’ innovation performance. Most of the extant studies

that examined the impact of alliances on partners’ innovation activity have not considered the

heterogeneity as to the indicators of innovation performance (for an exception see Rothaermel

and Deeds, 2004). Conversely, here we have distinguished between several types of

innovation outputs and we have shown that the same alliance might have different effects on

different outputs.

In spite of the interest of these results, we are aware that this study has some

limitations, which open up further opportunities for future research. First, we could not build a

sample representative of the (unknown) population of the alliances established by high-tech

start-ups. This raises the issue of whether our results are generalizable. Second, we measured

the effects of alliances by directly asking to managers involved in the alliance activities which

innovation outputs and changes in the innovation activities were engendered by the

collaborations. The availability of such data clearly is a strength of this paper with respect to

prior studies that relied on public data only. However, we could not triangulate this subjective

information with more objective data, as very limited public information is available on our

sample alliances due to the youngness and the small size of the firms under scrutiny.

Besides its weaknesses, this study has also significant strengths compared to the extant

literature on alliances. First, we have complemented prior studies on the alliances established

by high-tech start-ups. In fact, while the extant studies mainly examined the effects on the

25

performances of the start-ups, here we have considered the effects on their partner firms.

Second, the majority of the articles on the performances of alliances focused on a high-tech

sector only (De Man and Duysters, 2005: 1382), while here we have considered four sectors.

Third, we have recognized that as some alliances involve only a department, a division or a

subsidiary of a company, the effects of such alliances cannot be evaluated at the firm level;

conversely they are to be measured at the level of the unit involved in the collaborative

activities.

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Figure1: rationale and innovative outputs of exploration and exploitation alliances.

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Table 1: Descriptive statistics on sample alliances

Number of alliances Ambidextrous alliances Explorative only alliances Exploitative only alliances

59 34 56

Ended alliances 32 Cross-border alliances 50 Bilateral alliances 90 Alliances that benefited from public support 49 Percentage of alliances in the sector… … software … equipment …biotech and pharmaceuticals

44% 30% 21%

Exploration in alliances

Joint development of new knowledge

Access to complementary assets

Intangible and upstream innovative outputs

Tangible and downstream outputs

Exploitation in alliances

Table 2 – Descriptive statistics and correlation table of explanatory and control variables.

Me a .D. in axn S M M

DExplorative_only 0.22 0.42 0.00 1.00 1.000

DExploitative_only 0.38 0.49 0.00 1.00 -0.422 1.000

TimeToObservation 1.37 0.48 0.00 2.56 0.111 -0.052 1.000

DEnded 0.21 0.41 0.00 1.00 0.226 -0.240 0.146 1.000

DPriorRelations 0.55 0.50 0.00 1.00 -0.120 -0.083 0.067 -0.022 1.000

DCrossBorder 0.35 0.48 0.00 1.00 0.170 0.022 -0.176 0.096 -0.230 1.000

DPublicSupport 0.32 0.47 0.00 1.00 0.305 -0.455 -0.006 0.403 -0.087 0.027 1.000

PartnerUnitAge 2.65 0.88 1.10 5.14 0.072 -0.070 0.227 0.020 -0.186 0.127 0.104 1.000

PartnerUnitSize 3.30 2.22 -0.29 10.93 0.102 -0.151 0.056 0.085 -0.183 0.102 0.180 0.431 1.000

PartnerUnitInnov 0.41 0.37 0.00 1.00 -0.069 -0.125 0.052 -0.003 -0.033 -0.046 0.088 -0.101 -0.131 1.000

DUnitIndep 0.34 0.48 0.00 1.00 -0.021 -0.053 0.031 -0.066 -0.076 -0.120 0.066 0.130 0.131 0.158 1.000

RBSOAge 2.16 0.33 1.39 3.22 0.039 -0.076 0.179 0.104 0.113 -0.021 0.053 0.076 0.101 -0.143 -0.061 1.000

Table 3 - Descriptive statistics and correlation table of dependent variables.

Mean S.D. Min Max

ProductInnovation 2.92 1.15 1.00 4.00 1.000

StrategicInnovation 2.54 0.99 1.00 4.00 0.178 1.000

Learning 3.04 1.34 1.00 6.67 0.297 0.392 1.000

DCodifiedKnowledge 0.47 0.50 0.00 1.00 0.091 0.264 0.357 1.000

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Table 4 – The effects of ambidextrous alliances on ProductInnovation and StrategicInnovation

ProductInnovation

StrategicInnovation

Multinomial ordered logit

Multinomial ordered logit

(1) (2) (3) (4) TimeToObservation 3.500 *** 3.981 *** 1.208 1.340 (1.520) (1.751) (0.429) (0.473) DEnded 0.373 * 0.363 ** 0.519 0.486 + (0.221) (0.186) (0.238) (0.216) DPriorRelations 1.496 1.247 0.653 0.486 ** (0.580) (0.475) (0.216) (0.171) DCrossBorder 0.862 0.937 1.170 1.411 (0.322) (0.350) (0.387) (0.463) DPublicSupport 0.815 0.551 2.350 ** 2.050 * (0.397) (0.271) (0.934) (0.829) PartnerUnitAge 0.887 0.907 0.823 0.839 (0.200) (0.221) (0.155) (0.164) PartnerUnitSize 0.869 0.836 * 1.019 0.982 (0.096) (0.087) (0.091) (0.098) PartnerUnitInnov 0.585 0.416 * 0.816 0.613 (0.270) (0.202) (0.353) (0.283) DUnitIndep 1.222 1.153 0.967 0.909 (0.454) (0.423) (0.342) (0.313) RBSOAge 1.170 0.962 1.771 1.862 (0.603) (0.521) (0.767) (0.802) DExplorationOnly 0.409 * 0.344 ** (0.219) (0.174) DExploitationOnly 0.266 *** 0.329 *** (0.112) (0.125) Number of Obs. 149 149 149 149 Pseudo-R2 0.090 0.118 0.026 0.052 LR test (df) 10.710 (2) 10.209 (2) LR p-value 0.005 0.006 Wald test (df): DExplorativeOnly = DExploitativeOnly = 0

9.951 (2) 9.354 (2)

Wald p-value 0.007 0.009 Wald (df): DExplorativeOnly = DExploitativeOnly

0.690 (1) 0.008 (1)

Wald p-value 0.406 0.927 Wald test (df): all alliance specific variables jointly = 0

20.160 (5) 9.653 (5)

Wald p-value 0.001 0.086 Wald test (df): all firm specific variables jointly = 0

18.228 (8) 4.217 (8)

Wald p-value 0.020 0.837

Legend: + p<0.125, * p<0.10, ** p<0.05, *** p<0.01. Reporting odds ratios and robust standard errors in round brackets for all models.

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Table 5 – The effects of ambidextrous alliances on DCodifiedKnowledge and Learning

DCodifiedKnowledge

Learning

Logit Linear regression (5) (6) (7) (8)

TimeToObservation 2.933 * 3.255 ** 0.299 0.322 (1.659) (1.850) (0.251) (0.241) DEnded 2.275 + 2.224 -0.282 -0.341 (1.185) (1.198) (0.368) (0.363) DPriorRelations 1.031 0.845 0.163 0.017 (0.436) (0.379) (0.230) (0.230) DCrossBorder 2.607 ** 2.999 ** 0.239 0.286 (1.117) (1.373) (0.255) (0.256) DPublicSupport 6.000 *** 5.059 *** 0.559 * 0.259 (2.823) (2.526) (0.326) (0.351) PartnerUnitAge 1.116 1.119 -0.025 -0.027 (0.313) (0.337) (0.160) (0.158) PartnerUnitSize 0.896 0.867 0.007 -0.016 (0.112) (0.117) (0.061) (0.059) PartnerUnitInnov 0.753 0.604 0.182 0.029 (0.433) (0.371) (0.300) (0.298) DUnitIndep 1.293 1.259 -0.116 -0.145 (0.553) (0.547) (0.241) (0.236) RBSOAge 0.570 0.535 0.350 0.304 (0.363) (0.361) (0.343) (0.321) DExplorationOnly 0.470 -0.268 (0.233) (0.296) DExploitationOnly 0.392 * -0.885 *** (0.210) (0.306) Constant 1.644 0.641 1.696 * 2.582 *** (2.745) (1.205) (0.923) (0.945) Number of Obs. 149 149 148 148 Pseudo-R2 0.207 0.227 LR test (df) 4.150 (2) 11.077 (2) LR p-value 0.126 0.004 Wald test (df): DExplorativeOnly = DExploitativeOnly = 0

3.761 (2) 4.253 (2)

Wald p-value 0.153 0.016 Wald (df): DExplorativeOnly = DExploitativeOnly

0.109 (1) 3.626 (1)

Wald p-value 0.741 0.059 Wald test (df): all alliance specific variables jointly = 0

25.966 (5) 0.600 (5)

Wald p-value 0.000 0.700 Wald test (df): all firm specific variables jointly = 0

6.004 (8) 0.332 (8)

Wald p-value 0.647 0.952

Legend + p<0.125, * p<0.10, ** p<0.05, *** p<0.01. Reporting odds ratios for models (5) and (6) and coefficients in models (7) and (8). Robust standard errors in round brackets.