ORGANIZING FOR INNOVATION: PURE VERSUS MIXED NEW PRODUCT ALLIANCES

Alok Kumar

Aric Rindfleisch

8 August 2005

Alok Kumar is a Doctoral Student in Marketing, School of Business, University of Wisconsin-Madison (akumar@bus.wisc.edu). Aric Rindfleisch is Visiting Professor of Marketing at Tilburg University and Associate Professor of Marketing, School of Business, University of Wisconsin-Madison (aric@bus.wisc.edu). The authors thank Jan Heide for his helpful comments. This research was partially funded by a grant from the Institute for the Study of Business Markets at Pennsylvania State University and benefited from a grant from the Netherlands Organization for Scientific Research (NWO).

ORGANIZING FOR INNOVATION: PURE VERSUS MIXED NEW PRODUCT ALLIANCES

ABSTRACT

Institutional entities such as universities and governmental agencies commonly participate in new product alliances (NPA). However, the impact of this institutional presence on NPA outcomes remains uncertain. Our research addresses this issue by comparing the new product-related outcomes of NPAs that have institutional participants (i.e., mixed alliances) versus the outcomes of NPAs that consist solely of for-profit firms (i.e., pure alliances). Drawing on perspectives from institution, alliance, organizational learning, transaction cost, and network theories, we argue that mixed vs. pure NPAs represent very different approaches to organizing innovation. Specifically, we predict that participants in mixed alliances will enjoy superior new product-related outcomes compared to firms in pure alliances. We test these predictions using survey data from 106 NPA participants. Our findings provide broad support for our predictions and offer a number of implications for alliance theory and management.

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New product development (NPD) is widely regarded as one of the most effective ways

to achieve organizational profitability and growth (Wind and Mahajan 1997). Historically,

NPD has been an activity that firms conducted in isolation out of fear that their fledgling ideas

would be leaked to the outside. Increasingly, this isolationist perspective is being steadily

replaced by a collaborative perspective, as many firms have realized that they do not have the

knowledge, resources, or capabilities to accomplish their NPD objectives without help from

the outside (Mowery 1998). Collaborative NPD often occurs in new product alliances (NPA),

which are defined as, “formal collaborative arrangements among two or more organizations to

jointly acquire and utilize information and know-how related to the R&D of new product (or

process) innovations” (Rindfleisch and Moorman 2001, p. 1). These alliances regularly take

place among firms in both established industries such as automobiles as well as emergent

industries such as biotech, and have been recognized by the Marketing Science Institute as a

key research priority (MSI 2004).

In recent years, scholars across a broad swath of domains have explored the factors that

influence the success of NPA activities. One especially intriguing (and generally consistent)

finding emerging from this research is that the success of a NPA is highly dependent upon the

manner in which an alliance is structured (e.g., Gulati 1998; Rindfleisch and Moorman 2001;

Sampson 2004; Scott 2003). For example, Rindfleisch and Moorman (2001) find that

channel-centered alliances exhibit higher levels of information exchange compared to

competitor-centered alliances. In this research, we seek to extend and enrich understanding

about the effects of alliance structure by examining an important structural feature that has,

thus far, eluded attention. Specifically, our objective is to examine how the inclusion of an

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institutional participant, such as a university or government agency, influences the outcomes

associated with new product development alliances.1

Institutional entities play a vital role in new product development in general and NPAs

in particular. For instance, most major U.S. universities have one or more research parks or

technology incubators dedicated toward establishing collaborative R&D with for-profit firms.

Likewise, a variety of U.S. government agencies (e.g., National Science Foundation) and

programs (e.g., the National Institute for Standards and Technology’s Advanced Technology

Program) fund, monitor, and engage in research with firms. This type of institutional

influence upon NPD can be seen in nearly every industrialized economy. For example, a 2003

survey of R&D practices among 800 Japanese firms revealed that 40% of them engage in

collaborations with universities (Motohashi 2004). Chinese universities are also pursuing

R&D collaborations with foreign firms and currently participate in approximately 20% of the

recently formed international NPAs in mainland China (Li and Zhong 2003). Although these

public-private collaborations have received some degree of attention from alliance scholars,

the majority of this research focuses on either documenting such partnerships or exploring the

mechanics by which these collaborations can optimize their outputs (e.g., Bonaccorsi and

Piccaluga 1994; Cyert and Goodman 1997; Ham and Mowery 1998).

We seek to extend and enrich understanding of the role that institutions play in NPA

by examining their comparative influence on the outcomes of alliance activity. Drawing from

the institution literature, supplemented with insights form research on transaction cost,

organizational learning and social network theory, we suggest that alliances that involve

institutional participants (i.e., mixed alliances) experience superior outcomes in terms of inter-

1Commons (1931) uses the term institutions to indicate both a set of rules as well as those entities that enforce such rules. Network scholars (e.g., Saxenian, 1994) use the term to indicate formal entities such as universities, government agencies, and trade associations. We employ this latter definition.

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organizational relational and learning outcomes compared to alliances that lack institutional

participants (i.e., pure alliances). We test this thesis via a survey study of 106 NPA

participants. Our results provide broad support for our thesis and suggest that institutions

provide substantial value to members of new product alliances.

CONCEPTUAL FRAMEWORK

There is a rich tradition of research on institutions in both economics (e.g., Commons

1931; North 1994), and sociology (e.g., DiMaggio and Powell 1983; Saxenian 1994). A

common theme echoed across both research traditions is that institutions exert two different,

and somewhat paradoxical, forces upon the exchange behavior of economic and social actors.

On one hand, institutions may “enable” exchange by providing actors with resources or

information. For example, trade associations provide member companies with access to

information about new industry developments that would be difficult to acquire by other

means. On the other hand, institutions also “constrain” exchange by establishing and

enforcing standards or rules. For example, trade associations commonly have codes of

conduct that guide how member companies conduct certain activities. Building off this

research tradition, we suggest that institutions play an important role in shaping the outcomes

of NPA by both enabling and constraining the activities of alliance members. Moreover, we

argue that these enabling and constraining functions work in concert to provide firms in

mixed alliances with superior NPA outcomes compared to firms in pure alliances. In order to

succeed in a NPA, a firm must (1) establish good relations with it alliance partners, and (2)

acquire and successfully apply the information that it receives from these partners

(Rindfleisch and Moorman 2001). Thus, our conceptualization focuses on establishing the

role of institutional NPA participants upon both of these two types of outcomes.

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Relational Outcomes in Pure versus Mixed Alliances

Like most forms of interorganizational relationships, NPAs often fail because of a lack

of perceived cooperation due to suspicion about the motives and behaviors of their alliance

partners (Gulati 1995). Indeed, a common complaint among NPA participants is the fear that

fellow alliance members will free-ride off their contributions or may try to opportunistically

appropriate their collective efforts for self-gain (Rindfleisch and Moorman 2001). For

example, Park and Ungson (2001) document how the NPA between of General Electric and

Rolls-Royce to design and manufacture jet engines failed due to a lack of sufficient

information exchange brought about by their inability to establish strong relational ties.

The risk of free-riding or other forms of opportunism creates a need for appropriate

safeguarding mechanisms (Williamson 1996). In principle, firms in both pure and mixed

alliances can deploy a variety of safeguarding strategies, including both formal control

mechanisms such as explicit contracts and informal mechanisms such as interfirm trust in

order to minimize the risks of opportunism (Heide 1994). While often effective, these

mechanisms entail significant transaction costs to establish and enforce. We suggest that

institutional entities provide firms in mixed alliances an alternative, and potentially less

costly, set of safeguarding mechanisms due to their ability to enable and constrain the

activities of fellow alliance members. Consequently, firms in mixed alliances should enjoy

more favorable relational outcomes, in the form of perceived cooperation compared to firms

in pure alliances. These institutional-based safeguarding mechanisms (and relationship

facilitators) are described below.

Our review of the literature suggests that institutions have the potential to enable

cooperation (and constrain opportunism) among NPA members via several routes. First,

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members of a NPA often lack the ability to assess the quality of the technological inputs

offered by their partners, especially when an alliance includes firms with a diverse range of

skills and competencies (Appleyard 1996; Zucker, Darby, and Brewer 1998). Institutions may

assist members in this assessment process because their expertise across a broad range of

technologies and disciplines should provide them with expertise not generally available to

these members. Thus, an institutional presence in a mixed alliance should reduce information

asymmetry and promote higher levels of cooperation among alliance participants (Poyago-

Theotoky, Beath, and Seigel 2002).

In addition to helping firms assess the quality of alliance inputs, institutions may also

enable cooperation by serving as a credible authority about a new or existing partner’s

reputation (Granovetter 1985). For instance, negative word-of-mouth by university

researchers about a firm’s uncooperative behavior may prompt its exclusion from future

product development opportunities with other firms. This should create an incentive for firms

entering a mixed alliance to behave in a cooperative manner. In principle, firms in pure

alliances could also restrain opportunism through this type of reputation mechanism.

However, this mechanism should be more effective in mixed alliances, as an institution is

likely to be perceived as a neutral and disinterested party, and hence its claims about the

quality of an alliance member’s reputation should carry greater credibility.

Beyond assisting NPA alliance members constrain opportunism by serving as an

adjudicator of the quality of a firm’s overall reputation as well as its specific alliance

contributions, institutions may also take on a more direct and active role in alliance

safeguarding by sanctioning opportunism and rewarding cooperation. For example, an

institutional partner may penalize opportunistic alliance participants by withholding critical

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resources (DiMaggio and Powell 1983). This strategy is regularly employed by the U.S.

National Institute of Standards and Technology (NIST), which carefully monitors the

progress of a number of the NPAs which it funds. Alliances that fail to exhibit a high level of

cooperation run the risk of having their funding reduced or eliminated. This resource

withholding strategy helps align the selfish interests of member firms with the collective

interests of the broader alliance. In such situations, cooperation can flourish even though

alliance members may lack mutual trust (Scott 2003).

In sum, institutions appear capable of fostering cooperation among NPA members by

(1) helping firms assess the quality of partner inputs, (2) creating an incentive to act in a

reputable fashion, and (3) directly sanctioning opportunistic behavior. This combination of

mechanisms suggests that firms in mixed alliances should enjoy greater levels of cooperation

compared to firms in pure alliances. Hence:

H1: Firms in mixed alliances will perceive higher levels of alliance cooperation than those in pure alliances.

Although clearly important to the success of a NPA, interfirm cooperation is only one

indicant of successful relational outcomes. Strong relationships should also exhibit a high

level of mutual communication and relationship satisfaction (Ganesan 1994). Communication

provides the means by which firms resolve disputes and develop strong relational ties, and is

often viewed as a key antecedent to relationship success (Heide 1994). Conversely,

relationship satisfaction is viewed as consequence of successful partnerships and a key

indicator of the likelihood of future relations among participating firms (Heide 1994). Hence,

as corollaries of H1, we predict that firms in mixed alliances will experience greater levels of

alliance (1) communication, and (2) satisfaction compared to firms in pure alliances. Thus:

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H2: Firms in mixed alliances will perceive higher levels of alliance communication than those in pure alliances.

H3: Firms in mixed alliances will perceive higher levels of alliance satisfaction than those in pure alliances.

Learning Outcomes in Pure Versus Mixed Alliances

Prior research suggests that the principle reason firms enter new product alliances is to

learn from their alliance partners (Mowery 1998; Powell, Koput, and Smith-Doerr 1996). This

learning involves both the acquisition of information from fellow alliance members as well as

the utilization of this information to develop successful new projects. Following prior

research (e.g., Im and Workman 2004; Rindfleisch and Moorman 2001), we focus on two

specific types of information: (1) process information (e.g., manufacturing techniques and

systems), as well as product information (e.g., product features and specifications). Based on

our review of the literature, we suggest that an institutional presence should enhance both

information acquisition and utilization among NPA member firms.

Prior research strongly indicates that the flow of novel information among economic

and social actors is positively related to the amount of diversity in their networks of contacts

(Burt 1992; Granovetter 1973). In essence, actors with overlapping networks have little new

information that they can share with one another, as they are likely to be exposed to the same

sources of knowledge inputs. The importance of network diversity in a NPA context is

illustrated by Rindfleisch and Moorman (2001), who find that knowledge redundancy

hampers information acquisition among alliance members.

In principle, NPAs could include firms from several industries and span a diverse set

of network contacts. In practice, however, most NPAs appear to be comprised of firms from a

single industry (Scott 2003), and many of these alliance members have a shared history of

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prior relations (Gulati 1998). Thus, these cooperating firms are likely to have considerable

overlap in their network connections, as industry members (including customers, suppliers,

and competitors) belong to common population (Miner and Haunschild 1994) and often

follow a shared “industry recipe” (Spender 1989). In contrast, by virtue of their status as an

entity external to an industry, a university or government agency is likely to possess a set of

more diverse and non-redundant network linkages. These linkages should provide a bridge by

which novel ideas can flow into an alliance, and thus, increase the amount of new information

available for consumption among members in mixed alliances compared to members of pure

alliances. This supposition is supported by Powell et al. (1996), who provide evidence that

biotech firms intentionally seek relationships with research universities in order to increase

their exposure to a diverse range of technologies. Similarly, Saxenian (1994) documents the

important role that institutions served in nurturing the fledgling semiconductor industry.

In addition to benefiting from a diverse network, information exchange among NPA

alliance participants may also be facilitated by the establishment of incentives that promote

the flow of new ideas (Rindfleisch and Moorman 2003). We believe that these incentives can

be greatly enhanced by the participation of an institutional entity in a mixed alliance. By

definition, pure alliances are comprised exclusively of for-profit firms. In order to justify their

alliance investments, these firms typically seek to maintain proprietary rights over the new

products and processes that emerge from their alliance activity (Hitt, Keats, and DeMarie

1998). Hence, firms in such alliances may be hesitant to share valuable product and process

information with their partners (Rindfleisch and Moorman 2001). In contrast, institutions are

likely to encourage open information sharing due to their interest in collective rather than

individual outcomes. Thus, institutional participants may help alter the incentive climate by

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encouraging firms to seek collective gains, which may deter their information-hoarding

tendencies.

In sum, institutions appear capable of promoting information exchange among NPA

members by (1) providing a diverse network of contacts, and (2) building a culture that values

an open exchange of ideas. This suggests that firms in mixed alliances should enjoy greater

levels of information acquisition compared to firms in pure alliances. Hence:

H4: Firms in mixed alliances will acquire higher levels of (a) product and (b) process information than firms in pure alliances.

As noted above, institutional entities should provide firms with greater access to novel

information. The presence of this novel information should enhance a firm’s ability to engage

in knowledge exploration (March 1991), which has been shown to be a critical element in a

firm’s ability to utilize their stock of information to develop innovative new products (Chandy

and Tellis 1998; Madhavan and Grover 1998; Powell and Brantley 1992).2 This supposition is

supported by recent alliance research, which indicates that NPA that contain a university

participant typically generate a higher level of patent filings compared to those without a

university member (Darby, Zucker, and Wang 2004; George, Zahra, and Wood 2002).

In order for information to be utilized, it must not only be acquired, but it must also be

trusted (McEvily and Zaheer 1999; Moorman, Zaltman, and Deshpande 1992). Due to their

aforementioned ability to constrain opportunism and enable a culture of collective norms,

institutions should increase the confidence that alliance members place upon the information

they receive from their NPA partners. Consequently, firms in mixed alliances should be more

2 On the other hand, some research also suggests that knowledge redundancy may also facilitate a firm’s ability to integrate and utilize information in a creative manner (Cohen and Levinthal 1990; Rindfleisch and Moorman 2001).

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willing to utilize the disparate and novel information acquired from fellow alliance

participants in their efforts to develop innovative new products. Thus:

H5: Firms in mixed alliances will experience higher levels of (a) new product creativity, and (b) new process creativity than firms in pure alliances.

METHOD

Sample and Procedures

The sample for this study is the NPA database developed by Aric Rindfleisch and

Christine Moorman. This database was created from a survey of 106 firms (44% response

rate) that began NPAs between 1989 and 1995. The sampling frame for this survey was

drawn from a listing of NPAs provided in the Federal Register and compiled by the US

Government. Survey respondents (mainly VPs of R&D or new product development)

exhibited a high level of knowledge about their firm’s NPA activities and were carefully pre-

qualified. This dataset shows no evidence of non-response bias (see Rindfleisch and

Moorman 2001 for more details about the sample and the procedures used to acquire it).

Measures

All of the measures employed in this database were carefully pretested and examined

for internal consistency, unidimensionality, and content validity. Appendix A lists the specific

items used in each measure and their key statistics are provided in Table 1. These measures

are briefly described below.

Alliance Type. Our conceptualization argues that the presence of an institutional actor

such as a government agency or university influences the nature of relations and outcomes

among alliance members due to their ability to constrain and enable members’ actions. In

effect, an institution is presumed to serve as a change agent that fundamentally alters alliance

dynamics. Thus, our theory suggests that there is a sharp discontinuity between alliances that

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possess an institutional member versus those without such an entity. In accordance with this

conceptualization, respondents were asked to report (yes or no) if their alliance included any

“third parties, such as a governmental agency or university.” An affirmative response to this

question was coded as indicating that the respondents’ firm was participating in a mixed

alliance, while a negative response was coded as a pure alliance. The 106 firms in this database

were almost equally divided into these two types of alliances, with 49% in pure alliances and 51% in

mixed alliances. These two types of alliances exhibit now statistical differences (p > .05) in terms of

their means or variances for any of our key measures. For statistical analysis, alliance type is

coded as “1” for mixed and “0” for pure.

Relational Outcomes. We examine three separate indicants of the relational outcomes

associated with NPA activity: (1) alliance cooperation, (2) alliance satisfaction, and (3)

alliance communication. Although related, these three indicants represent distinct aspects of

relationship quality (Heide 1994). Cooperation taps the degree to which a firm has a close and

reciprocal working relationship with its fellow alliance participants using a four item (seven-

point) Likert scale. Satisfaction measures a firm’s degree of contentment with the new

product-related outcomes (e.g., product design, profitability) associated with alliance activity

and is assessed using a five item Likert scale. Communication measures the frequency of

information exchange between a firm and its alliance partners through a variety of various

mechanisms (e.g., phone, email) and is assessed using an eight item (six-point) frequency

scale, ranging from “none” to “daily.” All three scales display good reliability (cooperation: α

= .80; satisfaction: α = .92; communication: α = .93).

Learning Outcomes. Our learning outcomes focus on both the amount of information

acquired from fellow NPA participants as well as the creativity of the new products and

processes that emerge from alliance activity. Following prior research (e.g., Rindfleisch and

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Moorman 2001), we assess both the acquisition of new product information (e.g., key product

specifications, end-user requirements) as well as new process information (e.g., new

manufacturing processes, new ways to approach product development). Both types of

information were measured using four-item (7-point) Likert scales and exhibit solid reliability

(product information: α = .85; process information: α = .88). Similarly, we assessed both new

product and new process creativity. Both measures were obtained using four item (7-point)

semantic-differential scales that ask respondents to assess the degree to which the new

products/processes that resulted from their alliance exhibit novel and innovative qualities

(compared to existing industry standards). These two creativity measures displayed good

reliability (product creativity: α = .92; process creativity: α = .90).

Control Variables. This dataset also includes a number of variables that are designed

to control for factors that might serve as potential confounds or alternative explanations. For

example, cooperation should be higher among organizations that have a shared history with

other alliance participants as well as alliances that have a small number of participants

(Parkhe 1993). Thus, we include a (single-item) measure that assesses each firm’s level of

relationship history (1 = low, 7 = high) with fellow alliance members, as well as a measure

that counts the number of alliance participants (drawn from each alliance’s Federal Register

filing). In addition, because the outcomes of alliances among channel members may differ

from that of alliances among competitors (Rindfleisch and Moorman 2001), we assess

alliance directionality by coding each alliance as either vertical (i.e., channel-centered) or

horizontal (i.e., competitor-centered). Following Rindfleisch and Moorman (2003), alliances

in which 50% or more of the participants were reported to be competitors to the focal firm

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were categorized as horizontal (coded as a “0”), while the remaining alliances were

categorized as vertical (coded as a “1”).

New product alliances may be formed to accomplish a wide variety of objectives,

including both a short-term desire to exploit existing competencies (e.g., quickly developing

new products) as well as a longer-term desire to explore new skills and technologies (e.g.,

keeping abreast of changing technologies). These objectives may influence alliance relational

and learning outcomes. For example, firms with an exploitative objective may be hesitant to

share information (Park and Russo 1996). Thus, we account for both types of alliance

objectives by using a three-item (seven-point Likert) scale to assess exploitative objectives (α

= .77), and a two-item (seven-point Likert) scale to assess explorative objectives (r = .65).

Finally, because alliance outcomes may be influenced by the stage of a project’s development

cycle when the respondents’ firm joined the alliance, we assess stage of product development

using a five-item (seven-point Likert) scale developed by Garud (1994). This scale displayed

good reliability (α = .77).

ANALYSIS AND RESULTS

We tested our hypotheses using OLS regression in SPSS. Because all our hypotheses

are directional, we employed one-tailed tests. We conducted six separate regressions (one for

each of our predicted outcomes). Each regression included alliance type as our key predictor

variable, and relationship history, alliance directionality, number of alliance participants,

stage of product development, and degree of exploitative and explorative objectives as control

variables. Variance Inflation Factor (VIF) tests conducted on each regression indicate that

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multicollinearity is unlikely (all VIFs < 3). The results of these regressions are provided in

Table 2.

These regression results reveal that alliance type (i.e., mixed vs. pure) has a positive

and significant influence on alliance cooperation (b = .55, p < .01), alliance satisfaction (b

= .76, p < .01), and alliance communication (b = .85, p < .01). Thus, H1, H2, and H3 are

supported, as firms in mixed alliances exhibit superior relational outcomes compared to firms

in pure alliances. Similarly, our results also indicate that alliance type (i.e., mixed vs. pure)

has a positive and significant influence on both product (b = .50, p < .05) and process (b

= .65, p < .05), information acquisition as well as new product (b = .46, p < .10) and new

process creativity (b = .91, p < .01). Thus, H4 and H5 are supported, as firms in mixed

alliances exhibit superior learning outcomes compared to firms in pure alliances.

DISCUSSION

Institutions clearly serve a vital role in both economic and social life. The importance

of institutions was recognized by early researchers (e.g., Commons 1931) and has enjoyed

revived interest among a growing number of current thinkers (e.g., Williamson 1996).

Whether classic or contemporary, institutional scholars argue that in order to fully understand

the behavior of economic and social organizations, it is important to identify the institutional

entities that enable and constrain their activities. In accordance with this perspective, our

research has sought to identify the role that institutional actors such as universities or

government agencies play in shaping the outcomes of new product alliances. Our findings

provide evidence that NPAs that include an institutional participant exhibit superior

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performance than alliances that lack an institutional influence. In this final section, we discuss

the implications of these findings for both new product development theory and practice.

In order develop innovative new products firms are increasingly relying upon external

entities to help them acquire valuable technical expertise. Thus far, the emerging literature on

collaborative new product development has largely focused on the expertise that firms acquire

from other firms (e.g., Rindfleisch and Moorman 2001; Sividas and Dwyer 2000). Although

these prior studies have provided some interesting findings, this focus on firm-to-firm

relations ignores the important role played by non-firm entities, such as institutions. Our

results suggest that institutions enhance the outcomes of collaborative new product

development by providing a set of resources (both informational and governance) that are not

generally available from profit-seeking firms. Thus, institutions appear to serve as a unique

mechanism capable of both expanding alliance member’s learning capabilities and reducing

their transaction costs. This finding provides a new perspective on how to enhance alliance

outcomes by suggesting that these outcomes depend not only on the relationships and skills

that are embodied in member firms, but also upon the manner in which these relationships and

skills are shaped by participating institutions.

From a broader perspective, new product alliances can be viewed as a specific

example of the growing phenomenon of cooperative interfirm relationships (e.g., Heide

1994). Over the past two decades, these relationships have received a substantial amount of

attention from business scholars, who have sought to understand how these relationships can

be effectively governed. One important recent development in this body of research is the

notion that the governance of any given interfirm relationship is strongly influenced by the

broader portfolio of relationships in which it is structurally embedded (Wathne and Heide

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2004; Wuyts, Dutta, and Stremersch 2004). Our research enriches and extends this notion by

suggesting that institutions play an important structural influence in terms of governing the

relationships among firms involved in new product alliances. Future research is needed in

order to examine the degree to which institutional entities such as government agencies and

universities help govern other types of interfirm relationships.

Our findings also have implications for research on relational embeddedness in

interorganizational networks (e.g., Dyer and Singh 1998; Granovetter 1985; Powell 1990;

Uzzi 1996). According to this literature, the outcomes of networks of independent firms are

facilitated by a high degree of relationship embeddedness (i.e., strong ties) because these

relational ties serve as important bridges for the flow of information exchange. However, this

embeddedness may also result in complacency and reduce the infusion of new ideas. Thus,

strong ties appear to have both a positive as well as dark side in terms of network outcomes.

We believe that this dilemma of relational embeddedness may, at least in part, be alleviated

by the inclusion of an institutional entity into a network. Institutions such as universities and

government agencies appear to posses both the advantages of strong ties in terms of

enhancing information flow, as well as the advantages of weak ties in terms of providing a

source of novel ideas. The implications of institutions for the dynamics of relational

embeddedness in specific and interorganizational networks in general is an area ripe for future

research.

Current thinking in the NPA literature in particular and the interfirm relationship

literature in general suggests that managers can enhance the outcomes of collaboration by

either developing their internal skills or improving their external relations with their partners

(Morgan and Hunt 1994; Rindfleisch and Moorman 2001). This focus on internal skills and

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external relations represent a process-focused perspective which suggests that a firm can, at

least partly, control its outcomes by engaging in a particular set of activities. While this

process focus has clear and substantial merit, our research suggests that its prescriptions may

be incomplete. By focusing on trying to fulfill a recommended set of guidelines (e.g.,

developing a stock of difficult-to-imitate consequences or building trust among its exchange

partners), managers may lose sight of the broader structure in which these activities are

embedded. Our research suggests that the structure of a NPA plays an important role in

shaping its outcomes and that managers should be cautious of entering alliances that do not

contain an institutional entity that can help them enable and constrain alliance activities.

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APPENDIX

Key Measures

Alliance Type (Yes/No)

Are there any third parties, such as a governmental agency or university that play an active role in the monitoring and enforcement of participant activities?

Alliance Cooperation (7-point Likert Scale)

Please rate the degree to which the following statements describe your firm’s overall relationship with other organizations participating in this venture:

1. Our relationship with our alliance partners can be described as “mutually gratifying”2. We expect that we will be working with our collaborators far into the future3. Our engineers share close social relations with the engineers from collaborating organizations in this venture4. Our firm has a well-established set of expectations regarding the behavior of other organizations in this venture

Alliance Satisfaction (7-point Likert scale)

Thinking about the new products which have been developed or are being developed from this relationship, how satisfied are you with the following aspects:

1. The quality of the new products compared other products developed by your firm2. The product design3. The time it will take to reach the break-even point after introduction 4. The degree to which sales objectives will be reached5. The degree to which profit objectives will be reached

Alliance Communication (6-point frequency scale, ranging from “none” to “daily”)

Please provide an estimate of how often your firm engaged in various forms of communication with other participants in this venture over the last three months:

1. Written Memos2. Written Reports 3. Faxes4. Email Messages5. Formal Meetings 6. Informal Meetings7. Phone Conversations8. Teleconferences

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New Product Information Acquisition (7-point Likert scale)

Please rate the amount of the following types of information that your firm has acquired from other participants in this venture:

1. Research findings related to the development of new product2. Information about key product specifications3. Information about end-user requirements4. Information about competitor’s technology

New Process Information Acquisition (7-point Likert scale)

Please rate the amount of the following types of information that your firm has acquired from other participants in this venture:

1. Information about new manufacturing processes2. Insights into new ways to approach product development 3. Insights about keys involved in the production process4. Insights into new ways to streamline existing manufacturing processes

New Product Creativity (7-point SD scale)

In regard to new product creativity, please rate the degree to which new products generated by your firm’s participation in this venture are or are expected to be: 1. Very ordinary for our industry - Very novel for our industry2. Not challenging to existing ideas in our industry- Challenging to existing ideas in our industry3. Not offering new ideas to industry - Offering new ideas to industry4. Not promoting fresh thinking - Promoting fresh thinking

New Process Creativity (7-point SD scale)

In regard to new process creativity, please rate the degree to which new processes generated by your firm’s participation in this venture are or are expected to be: 1. Very ordinary for our industry - Very novel for our industry2. Not challenging to existing ideas in our industry- Challenging to existing ideas in our industry3. Not offering new ideas to industry- Offering new ideas to industry4. Not promoting fresh thinking- Promoting fresh thinking

Stage of Product Development (7-point Likert scale)

Please circle the degree to which each of the following items provides an accurate description of your firm’s level of product development at the time of venture formation:

1. The projects were in an early stage of development2. The projects were derived from state-of-the-art technologies.3. The projects were going through a substantial number of design changes4. The projects were highly unique5. The projects were experimental in nature

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Alliance Objectives (7-point Likert scale)Please rate each of the following objectives in terms of your firm’s initial participation in this venture:

Exploitative Objectives:1. Developing new products more quickly2. Reducing the costs associated with new product development3. Reducing the risks associated with new product development

Explorative Objectives:1. Acquiring information from other participants2. Keeping abreast of changing technologies3. Setting common industry standards4. Eliminating the duplication of research and development

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TABLE 1Key Measure Statistics

Measures MeanStd Dev a b c d e f g h i j k l m n

a. Alliance Type (M vs. P) .49 .50 n.a.                          

b. Alliance Cooperation 4.10 1.30 -.04 .80  

c. Alliance Satisfaction 3.99 1.28 .09 .47 .92  

d. Alliance Communication 1.28 1.07 .02 .33 .19 .93  

e. Product Info. Acquisition 3.48 1.44 -.07 .41 .35 .40 .85  

f. Process Info. Acquisition 3.03 1.43 .06 .34 .34 .35 .68 .88  

g. New Product Creativity 5.25 1.34 .07 .19 .26 .35 .34 .26 .92  

h. New Process Creativity 4.80 1.39 .25 .11 .21 .19 .28 .32 .64 .90  

i. Relationship History 2.67 1.50 -.08 .41 .25 .28 .29 .16 .11 .05 n.a.  

j. Number of Firms 6.42 2.68 .15 .03 -.05 -.21 -.28 -.10 -.35 -.03 -.21 n.a.  

k. Stage of Product Develop. 4.83 1.30 .07 -.04 .01 .14 .12 -.01 .33 .31 .17 -.26 .77  

l. Alliance Directionality .37 .41 -.25 .38 .10 .40 .45 .30 .34 .06 .30 -.28 .10 n.a.  

m. Exploitative Objectives 5.30 1.39 .01 .15 .19 .19 .15 .09 .36 .21 .09 -.11 .27 .18 .77  

n. Explorative Objectives 4.83 1.15 .05 .15 .16 .11 .29 .27 .09 .18 .05 .10 .14 .02 .31 n.a.

Notes: The coefficient alpha for each measure is on the diagonal , and the intercorrelations among the measures are on the off-diagonal. Correlations ≥ .19 in absolute value are significant at p < .05, while correlations ≥ .26 in absolute value are significant at p < .01.

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TABLE 2Regression Analyses

 

AllianceCooperation

AllianceSatisfaction

AllianceCommunication

New Product Information Acquisition

New Process Information Acquisition

New Product Creativity

New Process Creativity

B t-score B t-score B t-score B t-score B t-score B t-score B t-score

Alliance Type (M vs. P) .55 1.95** .76

2.19** .85

2.52*** .50 1.70** .65 2.13** .46 1.47* .91

2.63***

Relationship History .33 3.36*** .24 1.94** .55 2.06** .10 .95 .02 .16 -.01 -.11 -.05 -.38

Number of Firms .04 .76 -.11 -1.56* -.10 -.31 -.13 -2.27*** -.03 -.57 -.11 -1.87** .02 .30

Stage of Product Development -.23 -2.12** -.20 -1.48* -.69 -1.08 -.15 -1.31* -.18 -1.51* .20 1.61** .28 2.08**

Alliance Directionality 1.30 3.61*** .45 1.04 1.053.29*** 1.29

3.45*** 1.10 2.81*** .87 2.19** .29 .66

Exploitative Objectives .02 .22 .02 .15 .20 1.97** .01 .11 .00 .03 .24 2.08** .12 .92

Explorative Objectives .17 1.48* .17 1.16 .15 .22 .41 3.33*** .33 2.61*** .05 .36 .17 1.16

R2 (Adjusted) .28 .10 .25 .29 .14 .23 .16

Notes: ***p < .01, **p < .05, ***p < .10, all tests are one-tailed. All regressions are significant at p < .05.

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REFERENCES

Appleyard, M. (1996), “How Does Knowledge Flow? Interfirm Patterns in the Semicondcutor Industry,” Strategic Management Journal, 17 (Special Issue), 127-154.

Bonaccorsi A. and A. Piccaluga (1994), “A Theoretical Framework for Evaluation of University-Industry Relationships,” R&D Management, 24 (3), 229-247.

Burt, Ronald S. (1992), Structural Holes: The Social Structure of Competition. Cambridge, MA: Harvard University Press.

Chandy, Rajesh K. and Gerard R. Tellis (1998), “Organizing for Radical Product Innovation: The Overlooked Role of Willingness to Cannibalize,” Journal of Marketing Research, 35 (4), 474-487.

Commons John R. (1931), “Institutional Economics,” American Economic Review, 21, 648-657.

Cyert, R. M. and P. S. Goodman (1997), “Creating Effective University-Industry Alliances: An Organizational Learning Perspective,” Organizational Dynamics, 25 (4), 45-57.

DiMaggio, Paul J. and Walter W. Powell (1983), “The Iron Cage Revisited: Institutional Isomorphism and Collective Rationality in Organizational Behavior,” American Sociological Review, 48 (April), 147-160.

Dyer, Jeffrey H. and Harbir Singh (1998), “The Relational View: Co-Operative Strategy and Sources of Interorganizational Competitive Advantage,” Academy of Management Review, 23 (4), 660-679.

Ganesan, Shankar (1994), “Determinants of Long-Term Orientation in Buyer-Seller Relationships,” Journal of Marketing, 58 (April), 1-19.

Garud, Raghu (1994), “Cooperative and Competitive Behaviors During the Process of Creative Destruction,” Research Policy, 23 (July), 385-394.

Granovetter, Mark (1973), “The Strength of Weak-Ties,” American Journal of Sociology, 78, 1360-1380

Granovetter, Mark (1985), “Economic Action and Social Structure: The Problem of Embeddedness,” American Journal of Sociology, 91 (November), 481-500.

Gulati, Ranjay (1995), “Does Familiarity Breed Trust? The Implications of Repeated Ties,” Academy of Management Journal, 38 (1), 85-112.

24

Gulati, Ranjay (1998), “Alliances and Networks,” Strategic Management Journal, 19 (April), 293-317.

Ham, R. M. and D. C. Mowery (1998), “Improving the Effectiveness of Public-Private R&D: Case Studies at a US Weapons Laboratory,” Research Policy, 23, 673-695.

Heide, Jan B., (1994), “Inter-Organizational Governance in Marketing Channels,” Journal of Marketing, 58, 71-85.

Hitt, M. A., B. W. Keats, and S. M. DeMarie (1998), “Navigating in the New Competitive Landscape: Building Strategic Flexibility and Competitive Advantage in the 21st Century,” Academy of Management Executive, 12, 22-42.

Li, Jiatao and Jing Zhong (2003), “Global R&D alliances in China: Collaborations with Universities and Research Institutes,” working paper, Hong Kong University of Science and Technology.

Im, Subim and John Workman (2004), “The Impact of Creativity on New Product Success,” Journal of Marketing, 68 (2), 114-132.

Madhavan, R. and R. Grover (1998), “From Embedded Knowledge to Embodied Knowledge: New Product Development as Knowledge Management,” Journal of Marketing, 62 (4), 1-12.

March, James G. (1991), “Exploration and Exploitation in Organizational Learning,” Organization Science, 2, 71-87.

Marketing Science Institute (2004), Research Priorities. Boston: Marketing Science Institute.

McEvily, Bill and Akbar Zaheer (1999), “Bridging Ties: A Source of Firm Heterogeneity in Competitive Capabilities,” Strategic Management Journal, 20, 1133-1156.

Miner, Anne S. and Pamela R. Haunschild (1994), “Population Level Learning,” Research in Organizational Behavior, 17, 115-166.

Moorman, Christine, Gerald Zaltman, and Rohit Deshpande (1992), “Relationships Between Providers and Users of Market Research: The Dynamics of Trust Within and Between Organizations,” Journal of Marketing Research, 29 (August), 314-328.

Morgan, Robert M. and Shelby D. Hunt (1994), “The Commitment-Trust Theory of Relationship Marketing,” Journal of Marketing, 58 (July), 20-38.

Motohashi, Kazuyuki (2004), “Economic Analysis of University-Industry Collaborations: The Role of New Technology Based Firms in Japanese National Innovation Reform,” RIETI Discussion Paper 04-E-001, Tokyo, Japan.

25

Mowery, David C. (1998), “Collaborative R&D: How Effective is It?” Issues in Science and Technology, 15 (Fall), 37-44.

North, Douglass C. (1994), “Economic Performance Through Time,” American Economic Review, 84 (3), 359-368.

Park, Seung Ho and Michael V. Russo (1996), “When Competition Eclipses Cooperation: An Event History Analysis of Joint Venture Failure,” Management Science, 42 (June), 875-890.

Park, Seung Ho and Gerardo Ungson (2001), “To Compete or Collaborate: A Conceptual Model of Alliance Failure,” Organizational Studies, 12 (1), 37-53.

Poyago-Theotoky, Joana, John Beath, and Donald S. Seigel (2002), “Universities and Fundamental Research: Reflections on the Growth of University-Industry Partnerships,” Oxford Review of Economic Policy, 18 (1), 10-21.

Powell, Walter W., and Peter Brantley, (1992), “Competitive Cooperation in Biotechnology: Learning through Networks?” in Networks and Organizations, Nitin Nohria and Robert G. Eccles, eds. Boston: Harvard Business School Press, 366-394.

Powell, Walter W., Kenneth W. Koput, and Laurel Smith-Doerr (1996), “Interorganizational Collaboration and the Locus of Innovation: Networks of Learning in Biotechnology,” Administrative Science Quarterly, 41, 116-145.

Rindfleisch, Aric and Christine Moorman (2001), “The Acquisition and Utilization of Information in New Product Alliances: A Strength-of-Ties Perspective,” Journal of Marketing, 65 (April), 1-18.

Rindfleisch, Aric and Christine Moorman (2003), “Interfirm Cooperation and Customer Orientation,” Journal of Marketing Research, 40 (November), 421-436.

Sampson, Rachelle C. (2004), “Organizational Choice in R&D Alliances: Knowledge Based and Transaction Cost Perspectives,” Managerial and Decision Economics, 25, 421-436.

Saxenian, Annalee (1994), Regional Advantage: Culture and Competition in Silicon Valley and Route 128. Cambridge, MA: Harvard University Press.

Scott, John T. (2003), “Absorptive Capacity and the Efficiency of Research Partnerships,” Technology Analysis & Strategic Management, 15 (June), 247-253.

Sividas, Eugene and F. Robert Dwyer (2000), “An Examination of Organizational Factors Influencing New Product Success in Internal and Alliance-Based Processes,” Journal of Marketing, 64 (January), 31-49.

26

Spender, J. C. (1989), Industry Recipes: An Enquiry into the Nature and Sources of Managerial Judgment. Oxford: Basil Blackwell.

Uzzi, Brian (1996), “The Sources and Consequences of Embeddedness for the Economic Performance of Organizations: The Network Effect,” American Sociological Review, 61 (August), 674-698.

Wathne, Kenneth H. and Jan B. Heide (2004), “Relationship Governance in a Supply Chain Network,” Journal of Marketing, 68 (January), 73-89.

Williamson, Oliver E. (1996), The Mechanisms of Governance. New York: Free Press.

Wind, Jerry and Vijay Mahajan (1997), “Issues and Opportunities in New Product Development: An Introduction to the Special Issue,” Journal of Marketing Research, 34 (February), 1-12.

Wuyts, Stefan, Shantanu Dutta, and Stefan Stremersch (2004), “Portfolios of Interfirm Agreements in Technology-Intensive Markets: Consequences for Innovation and Profitability,” Journal of Marketing, 68 (April), 88-100.

Zucker Lynne G., Michael R. Darby, and Marilynn R. Brewer (1998), “Intellectual Human Capital and the Birth of US Biotechnology Enterprises,” American Economic Review, 88 (1), 290–305.

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