Journal of Business Venturing 18 (2003) 727–744

The role of networking alliances in information acquisition

and its implications for new product performance

Pek-Hooi Soh*

NUS Business School, National University of Singapore, 1 Business Link, Singapore 117591, Singapore

Received 1 August 2001; received in revised form 1 October 2002; accepted 1 November 2002

Abstract

The premise of Austrian economics on entrepreneurial discovery suggests that mutual knowledge

about market participants defines who will acquire potential information about opportunities to bring

future products into existence. Building upon this argument, this research investigates the role of

networking alliances in information acquisition and its lagged effect on the new product performance

of the firm. By using a longitudinal analysis, the study shows that a firm improves its new product

performance as it increases the number of repeated partners and its centrality position relative to others

in the technology collaboration network.

D 2003 Elsevier Science Inc. All rights reserved.

Keywords: Entrepreneurship; Strategic alliances; New product development

1. Executive summary

Few studies have examined how opportunities are discovered or identified by the

entrepreneurial firms or individuals (Shane and Venkataraman, 2000). This research aims to

understand the role of networking alliances in acquiring information about entrepreneurial

opportunities and investigate its lagged effect on the new product performance of the firm.

From the Austrian economics perspective of entrepreneurship, a change in existing resources

exploited under previous entrepreneurial activities will create windows of new opportunities

* Tel.: +65-6874-3180; fax: +65-6779-2621.

0883-9026/03/$ – see front matter D 2003 Elsevier Science Inc. All rights reserved.

doi:10.1016/S0883-9026(03)00026-0

E-mail address: bizsohph@nus.edu.sg (P.-H. Soh).

P.-H. Soh / Journal of Business Venturing 18 (2003) 727–744728

(Kirzner, 1997). A firm will obtain the information about these opportunities by mutual

interaction with other market participants (Hayek, 1945). However, such information is

dispersed unevenly among the firms in the market. Owing to the variation in the information

acquisition process, entrepreneurial firms gain differential access to external information.

In this study, I argue that a firm with more efficient access to other firms in the market

would acquire the competitive information about other firms earlier, gaining a greater window

of opportunities to create or to enhance its own products before its competitors. Given that

closeness and strength of ties are two important interorganizational networking mechanisms

by which information and resources are mobilized in the market (Granovetter, 1985), I

propose to use the social network approach to explain how a firm would acquire potential

information about new technological opportunities more efficiently. In addition, since extant

research has shown that strategic alliances contribute significantly to the acquisition of

information about external innovations (Arora and Gambardella, 1990; Hagedoorn, 1993),

the study examines the implications of a firm’s prior networking experience via technology

alliances on new product performance. Two hypotheses are developed to test the argument.

First, as a firm is more centrally connected to other firms in the industry network, its new

product performance is improved. Second, as a firm increases its number of partners with

whom it forms repeated alliances in the industry network, its new product performance is

improved. I demonstrate the support for both hypotheses by using a longitudinal analysis of

48 leading firms in the U.S. computer networking market from 1991 to 1996.

Two main implications can be inferred from the findings. First, among firms that have equal

inclination to form new alliances, the ones that leverage their direct ties by discreet choice of

partners who have better access to others are more likely to enjoy better new product

performance (Dubini and Aldrich, 1991). Under these circumstances, firms obtain information

not only about the competitors and their innovations but also about the misallocated resources

among other participants in the market, discovering opportunities that may subsequently

enhance their own innovations. Second, increasing information access facilitated through

reciprocal relationships with direct partners is likely to enhance the performance. The reason is

that former partners are more willing and able to share rich information about themselves or

their extended partners since they have developed mutual understanding and trust.

The research has extended the entrepreneurship field by focusing on the mechanisms

leading to information acquisition and opportunity discovery at the firm level. It has explored

the concept of information dispersion through social networks and enhanced the alliance

strategy in accessing information about technological opportunities. Future research could be

examining the variation of information acquisition between technology and business

collaboration networks.

2. Introduction

In entrepreneurship, discovery of opportunities is synonymous with finding potential

economic profits that have not been grasped. Under the purview of Austrian economics,

opportunity discovery depends upon the distribution of information in the market and upon

P.-H. Soh / Journal of Business Venturing 18 (2003) 727–744 729

mutual information and interaction among the market participants (Hayek, 1945; Kirzner,

1997). Entrepreneurial firms will identify and exploit these opportunities when they have

information about the accessibility of appropriate resources at appropriate prices (Shane and

Venkataraman, 2000). Accordingly, even if firms explore seemingly identical opportunities,

they may gain differential access to information that leads to dissimilar exploitations (e.g.,

Shane, 2000). To explain the entrepreneurial outcomes of the firm, we must therefore examine

the process by which external information about potential opportunities is acquired (Hayek,

1945). However, most studies in entrepreneurship to date have focused on the exploitation of

opportunities after their discovery (Shane and Venkataraman, 2000). This empirical research is

thus intended to understand the process by which firms acquire potential information about

technological opportunities and its implications on the firms’ exploitation of new products.

The premise of Austrian economics implies that information about market resources is

unevenly distributed, and that information gaps present sources of entrepreneurial opportun-

ities waiting to be discovered (Kirzner, 1997). Given that firms face the information

asymmetry problem in the market, how do they acquire more information, and thus discover

new opportunities? The strategic alliance literature has informed us that direct ties contribute

significantly to the exchange of resources and information between partnering firms (e.g.,

Arora and Gambardella, 1990; Barley et al., 1992; Eisenhardt and Schoonhoven, 1996;

Hamel, 1991; Larson, 1992; Shan, 1990), whereas entrepreneurship studies have suggested

that extended networking through indirect ties increases the access to resources and

information (e.g., Burt, 1992; Dubini and Aldrich, 1991). While strategic alliances are a

form of governance structure that enables bilateral exchange for a limited purpose, network-

ing describes the entrepreneurial behavior in building relationships with an expectation to

develop mutual trust and reciprocity in the network of firms (Powell, 1990).

In fact, to build networking relationships, firms can leverage their strategic alliances by

discreet choice of partners who have access to others. Networking is an activity by which

entrepreneurs obtain potential information about untapped opportunities (Dubini and Aldrich,

1991) and new firms discover new resources that are not known to existing organizations

(Wiewel and Hunter, 1985). Through networking, firms come into mutual awareness,

discovering differences of price and resource availability generated by earlier entrepreneurial

activities (Kirzner, 1997). Nevertheless, networking may still result in a time consuming and

costly effort because information is dispersed unevenly among the firms in the market. Owing

to the variation in networking strategy, entrepreneurial firms and individuals may therefore

gain access to external information at different rates.

In this paper, I first develop an understanding of how information about technological

opportunities is created and then acquired through strategic alliances in the organizational

environments. Next, I examine whether a firm’s prior networking experience via alliances

has implications on its new product performance in the subsequent period. The fact that a

firm’s new product performance, hence its exploitation of an identified technological

opportunity, can be attributed to networking with direct and indirect partners suggests that

the firm has obtained certain information benefits in the periods prior to opportunity

discovery. Since it is not possible to quantify the quality of information and the diversity of

opportunities arising from various types of alliances, I limit the analysis to technology

P.-H. Soh / Journal of Business Venturing 18 (2003) 727–744730

alliances wherein the objective is to exchange technical information and cooperate in new

product development. To this end, the hypotheses developed here are qualified since the

study provides no explicit evidence of the technological opportunities discovered through

alliances. Rather, they illustrate the importance of networking in information acquisition so

long as technology alliances are actively utilized as conduits for technical information.

There are four sections in the paper. First, I discuss the central issue of information

acquisition and develop the hypotheses. Then, I describe the method and measures. In the last

two sections, I summarize the findings and discuss their implications for future research.

3. Theoretical development

3.1. Sources of information about opportunities

Studies of entrepreneurship and technological innovation have much in common when

explaining sources of information about entrepreneurial opportunities (Freeman, 1982;

Scherer and Perlman, 1992). Opportunities are created in the process of technological

innovation because of the change in existing resources, whether the change is disruptive or

incremental (Roberts, 1988; Utterback, 1994). If entrepreneurs discover information about

these opportunities, they will capitalize upon the opportunities because the new combination

of assets will result in profits. The account of innovation process described by Rosenberg

(1976) below suggests the importance of external information and the recognition of new uses.

It was impossible to take advantage of higher cutting speeds with machine tools designed

for the older carbon steel cutting tools . . . As a result, the availability of high-speed steel

for the cutting tool quickly generated a complete redesign in machine tool components. . .The final effect of this redesigning . . . was to transform machine tools into much heavier,

faster, and more rigid instruments which, in turn, enlarged considerably the scope of their

practical operations and facilitated their introduction into new uses (pp. 7–8).

Opportunity discovery as described above was triggered by mutual awareness of technical

imbalances arising from existing innovations. And if high-speed steels were not exploited in

machine cutting tools, further uses would not have been explored and created. Opportunity

discovery is thus similar to the identification of misallocated resources among the market

participants in a given technological change (Kirzner, 1997). The innovation process is often

engaged in by parties who do not have complete information about the others until they come

into direct interaction. In accordance with Austrian economics, even if the original intent is to

exploit identified opportunities, a firm may identify a ‘‘surprise’’ or new opportunity to create

and/or enhance its own products by learning more about existing innovations.

3.2. Alliances as conduits of technical information

Strategic alliances contribute significantly to the acquisition of information about external

innovations. Specifically, the studies of learning in alliances have discussed such mechanisms

P.-H. Soh / Journal of Business Venturing 18 (2003) 727–744 731

as reciprocal exchange and intensive interaction to facilitate the transfer of knowledge

between dyadic partners regardless of the alliance intents (e.g., Hamel, 1991; Khanna et al.,

1998; Kogut and Zander, 1992; Lane and Lubatkin, 1998; Mody, 1993). Hamel (1991) found

that much of the interaction requires mutual understanding of the partners’ internal activities

and that the learning rates are unequal between partners. Conversely, other studies have

argued that alliances with the intent to explore new things rather than alliances with strong

relational intensity are more likely to be the sources of information for innovations (Koza and

Lewin, 1998; Rowley et al., 2000). Do motivations of alliances differentiate the quantity or

quality of information about opportunities? From the evolutionary perspective, Koza and

Lewin (1998) propose that alliances coevolve as a result of alliance formation and strategic

actions of each participating firm. Thus, alliances with the objective of exploiting existing

technologies may in fact have the genesis of future exploration and vice versa. Moreover, as

illustrated in the case of the machine tools above, the intent to maximize the utilization of

existing assets should not exclude entrepreneurial firms from discovering and exploiting

opportunities to bring into existence ‘‘future’’ products and services (Venkataraman, 1997).

Hence, the attempt to examine the relative merits of alliances in information access by

alliance objectives might be fraught with difficulties since the innovation activities are seldom

linear in nature.

Other alliance studies that stress the information exchange process include those that

examine complementarity and firm-specific resources as the prior conditions in collaboration

(Reuer and Koza, 2000; Sinha and Cusumano, 1991). However, most of the above-mentioned

studies assume information asymmetry between dyadic partners as the availability condition

for exchange, whereas the Austrian economics approach considers the existence of informa-

tion asymmetry distributed across firms. Only a few alliance studies investigate the influence

of prior alliance activities and information dissemination in the network about potential

partners (e.g., Ahuja, 2000; Gulati, 1995a; Stuart, 1998). From the Austrian economics

perspective, as increasingly more firms establish new alliances, mutual information among the

firms may become more widespread through multiple indirect partners in the industry. Under

these circumstances, in the web of complex interorganizational relationships, perhaps only

more efficient networking strategy will define who gains faster access to potential information

about new opportunities. Insomuch as information exchange occurs in alliances, the longit-

udinal perspective of alliance formation in a technology collaboration network should provide

some preliminary insights into the dispersion and acquisition of information in the market.

3.3. The implications of networking alliances

Recent studies of entrepreneurship have put more emphasis on the network perspective of

ties among entrepreneurial firms or individuals in information access but few have discussed

the central issue of opportunity discovery (e.g., Aldrich and Zimmer, 1986; Baum et al.,

2000; Burt, 1992; Larson, 1992; Stuart et al., 1999). Aldrich and Zimmer (1986) argue that

the social network structure of aspiring entrepreneurs can influence and constrain the

discovery of new opportunities. Burt’s (1992) study showed that diverse information arising

from nonredundant ties contributes to entrepreneurial opportunities. In studying the video-

P.-H. Soh / Journal of Business Venturing 18 (2003) 727–744732

cassette recorder (VCR) industry, Baba and Imai (1992) described how the Japanese

entrepreneurial firms had discovered new opportunities flowing from repeated interactions

in such arrangements as licensing agreements, management contracts, subcontracting, and

R&D collaboration. The information shared among the former and present partners had

served as a heuristic context for continuous problem solving activities in VCR (Cusumano et

al., 1996). To date, however, few empirical studies have examined the performance effects of

information benefits via networking alliances (e.g., Ahuja, 2000). In this study, I argue that

firms with a more efficient networking strategy will gain access to potential information about

new technological opportunities before others, and thus lead to better new product

performance.

Granovetter’s seminal research on structural and relational embeddedness provides an

important theoretical explanation of the implications of networking through strategic alliances

(Granovetter, 1973, 1985). In structural embeddedness, the social network of firms built upon

past and present relationships defines the extent of information spread within the network. In

particular, diverse information can travel more efficiently in densely connected networks than

in sparsely connected networks. Furthermore, firms that are more centrally located relative to

other firms in the network can expect greater information benefits. In contrast, relational

embeddedness refers to strength of dyadic ties that increases with time, emotional intensity,

intimacy, and reciprocity engaged during the interaction between partners. Strong ties are

governed by trust and reciprocity that facilitate the exchange of ‘‘thick’’ information between

partners (Coleman, 1988; Larson, 1992; Uzzi, 1997).

From both structural and relational perspectives, proximity and strength of alliances will

determine the efficiency and accessibility of information within the network. In contrast, Burt

(1992) argues that only those firms in the structural holes with the most nonoverlapping ties

with direct and indirect partners will gain access to diverse information about entrepreneurial

opportunities. The structural hole argument is nonetheless applicable primarily to studies that

consider market transactions with multiple entities including customers, suppliers, govern-

ment agencies, and others (Walker et al., 1997). Ahuja (2000) further demonstrated in the

longitudinal analysis of direct and indirect ties versus structural holes that structural holes are

negatively correlated with the innovation performance of the firm. He explains that in

technology collaboration networks, dense ties between partners in fact prevent deviant

behaviors and foster the development of trust and shared norms conducive to knowledge

exchange. Since the density of ties has performance implications on the firm, the study of

networking alliances should at least control for this effect.

Based on the proximity of firms, a firm that is more centrally located in the network is

positioned to access external information via shorter paths from all firms other than its

immediate partners. Conversely, a less centrally located firm is likely to receive external

information, which traverses more indirect partners. Why would getting information more

efficiently about third parties necessarily contribute to the product performance of the firm?

Firstly, the indirect ties are akin to weak ties, which are particularly useful for such

information as who knows what or who can help with what problem. The development of

new products does depend on the identification of appropriate resources at appropriate prices

and where useful capabilities reside. Secondly, in rapid technological change, knowing the

P.-H. Soh / Journal of Business Venturing 18 (2003) 727–744 733

competitive information about other products and getting one’s own products out before

others are the entrepreneurial strategies that create tremendous competitive advantage

(Deeds and Hill, 1996). Thirdly, potential information may create the awareness about

misallocated resources, presenting an opportunity to develop new or enhanced products.

Above all, a more efficient information acquisition process will enable the focal firm to

identify and to exploit the entrepreneurial opportunities earlier than others. Under these

circumstances, a firm that is centrally connected to all others in the technology collaboration

network is more likely to improve its new product performance. The above argument leads

to the first hypothesis.

Hypothesis 1: As a firm is more centrally connected to all other firms in the industry

network, its new product performance is improved.

While a central firm may gain access to information about opportunities more efficiently, it

may not exploit all identified opportunities. The innovation process is an information-

intensive activity that involves the absorption, processing, and integration of information so

as to bring into existence new products and services. The process also requires specific know-

how that is costly to transfer (von Hippel, 1994). Cohen and Levinthal (1990, p. 134) argue,

‘‘to the extent that an organization develops a broad and active network of internal and

external relationships, individuals’ awareness of others’ capabilities and knowledge will be

strengthened.’’ Therefore, in the face of rapid technological changes, such active relationships

require the focal firm to selectively invest its time and efforts with certain partners. As

uncertainty increases, the firm is more likely to collaborate with partners of similar status and

with partners with whom it has previous relationships (Podolny, 1994).

From the relational embeddedness perspective, firms that engage in reciprocal, preferen-

tial, and mutually supportive relations with formers partners are more likely to exploit

specific opportunities (Powell, 1990). Two arguments support this line of reasoning. First, as

more exchanges take place, all partners are assumed to have developed some mutual

understanding that becomes the primary basis for communication and coordination; thus,

reducing the potential costs of governance and interaction (e.g., Gulati, 1995b). Second,

empirical evidence has shown that trust and reciprocity do enhance the flows of knowledge

between partners (e.g., Bouty, 2000; Coleman, 1988; Larson, 1992; Podolny, 1994; Uzzi,

1997). Even in a densely connected network, reciprocal exchange still provides superior

access to specific information and know-how for exploitation purposes (Saxenian, 1991).

Hence, a greater number of partners with repeated alliances imply the likelihood of more

efficient channels of rich information being identified. The willingness of these partners to

disclose information about themselves or third parties increases with the degree of trust

established from prior relationships. The result of this could lead to more effective

exploitation of opportunities and possibly opportunity discovery, enhancing the new product

performance of the firm. The above argument leads to the second hypothesis.

Hypothesis 2: As a firm increases its number of partners with whom it forms repeated

alliances in the industry network, its new product performance is improved.

P.-H. Soh / Journal of Business Venturing 18 (2003) 727–744734

4. Methodology

4.1. Data and sample

Existing studies have used several criteria to define the boundary of industry network,

namely, SIC code, revenue, and even availability of alliance and firm data (e.g., Ahuja, 2000;

Gulati, 1995a; Hagedoorn and Schakenraad, 1994; Rowley et al., 2000; Stuart, 1998). I

obtained a vendor list from a special issue of Network World, an annual trade publication,

which ranked 240 leading vendors in the telecommunications, computer, wireless commu-

nications, LANs (local area networks), and WANs (wide area networks) markets by revenues

in 1994 and 1995.1 I selected all 39 leading vendors that produced at least two of the four

main product lines in the computer networking market, i.e., LAN/WAN switches, routers,

remote access, and network management, which are used primarily in the Internet and

extranet connections today. I also included 10 other leading firms that spanned multiple

markets because they had had important influences in setting the initial standards for the

overall data communications industry. They were ADC, Compaq, DEC, HP, IBM, Intel,

Lucent Technologies, Microsoft, NorTel, and Novell. Specifically, DEC, HP, IBM, Microsoft,

and Novell were influential in the development of LANs and network management

technologies like Ethernet, 100VG-AnyLAN, Token Ring, OS/2 LAN, and Netware in the

80s. These technologies have since been enhanced and become part of the Internet

infrastructure today.

I selected the computer networking market for three reasons. First, my review of the trade

publications indicated the importance of alliances in new product development. A firm could

shorten the development cycle of new products by obtaining competitive information and

resources from other firms via strategic alliances. Second, timely information about com-

petitors’ products or available resources would present significant entrepreneurial opportun-

ities to the firm. Third, the firms in this market frequently collaborated among themselves to

ensure the compatibility of vendor solutions. To check that the sample firms were

representative of the computer networking market, I consulted the CorpTech Directory,

which publishes company profiles in high-tech industries annually, and found that the firms

had been operating the same product lines since the late 80s.2 I also consulted two industry

analysts and was told that most firms continued in the same market throughout the 90s, but 21

of them were acquired after 1996.

To construct the variables for central network position and number of partners with repeated

alliances, I gathered the alliance data from the Lexis/Nexis and Dow Jones databases for 49

firms from 1989 to 1996 and found three types of alliances. First, there were business alliances

that dealt mainly with marketing, distribution, and value-added reselling activities. Second,

technology alliances were intended for joint product development and exchange of technical

knowledge. Third, standards-setting forums or megaalliances were aimed at formalizing the

specifications of standards. Interviews with some company executives in the sample firms

2 The CorpTech Directory, Corporate Technology Information Services, Woburn, MA.

1 Network World, December 30, 1996.

P.-H. Soh / Journal of Business Venturing 18 (2003) 727–744 735

suggested that technology alliances had the most direct impact on new product performance.

Conversely, the diversity of business alliances with multiple parties might have vastly different

levels of information exchange that were not comparable across alliances. Finally, a few pointed

out that professional conferences and social activities were also instrumental in information

exchange, but the relationships that emerged from these settings were harder to capture

consistently. For the above reasons, I decided to focus on technology alliances only. Some

studies have similarly put more emphasis on a single type of relationship and found that better

innovation can be attributed to greater information benefits arising from either technology

partners or customers, depending upon the context (e.g., Ahuja, 2000; Yli-Renko et al., 2001).

I checked the full-text alliance agreements to identify the technology alliances and to

ensure that no identical alliances were recorded more than once. The earliest date for each

new announcement was coded as the year in which the alliance was active. I found a total of

161 agreements established among the sample firms and 178 agreements with 112 out-of-

sample partners. About 50% of alliances were formed after 1993. Generally, technology

alliances should indicate a joint development process in which the partners would each

contribute some engineering efforts. An example is the collaboration between 3Com and

IBM. In 1989, they codeveloped a network interface specification, which enabled IBM’s OS/

2 platform to operate 3Com’s Ethernet technology. In 1990, they jointly developed an

Ethernet card for another platform. Less than 6 months later, they began the development of

network-management specifications for mixed-media local area networks. Although many

announcements described the technology alliances as a joint development effort, I found

cases that had little technical collaboration. To countercheck the technology alliances, I

engaged two students to code the documents by activities related to technology exchange and

joint problem solving. For the discrepancies, I verified the data by examining additional

reports describing the same alliances. Out of 339 alliances, 21 were discarded after further

verification. With 161 firms and 318 alliances, I constructed five NxN nondirectional

adjacency matrices from 1991 to 1995 where off-diagonal cells represent the number of

alliances established between two firms. These matrices were used to create the two

independent variables, which I will describe below.

4.2. Dependent variable

4.2.1. New product performance

I used the number of new product awards obtained by each firm yearly to measure new

product performance. In the computer industry, trade publishers frequently screen and test

new products released within the last 9–12 months. Products that obtain the highest scores

across multiple performance measures within a product category will be awarded. I collected

the award data from eight reputable trade publications from 1991 to 1996 to cover a broad

range of product categories.3 In entrepreneurship research, new product performance has been

used as a measure of venture success (Deeds and Hill, 1996; MacMillan et al., 1987).

3 Communications Week,Data Communication, Internetwork, LANMagazine, LAN Times,Network Computing,

Network VAR, and PC Magazine.

P.-H. Soh / Journal of Business Venturing 18 (2003) 727–744736

MacMillan et al. (1987) showed that the degree of market acceptance of new products is a

consistent predictor of new venture success. New product awards indicate the extent of

market acceptance after the first year launch. An alternative indicator is new product sales.

Unfortunately, new product sales were either aggregated into gross sales or not reported by

the firms. Given the availability of new product award data from several credible trade

publications, I selected this unique measure instead of sales revenue.

4.3. Independent variables

In this study, past and present alliances constitute an important theoretical construct for

embedded relations. Therefore, both central network position and number of repeated

partners were measured by using the cumulated number of alliances from 1989 up to the

year of observation. The base year 1989 was chosen for two reasons: (1) before 1989,

alliances were scarce and not well reported by news publishers; and (2) from 1989, an

increasing number of technology standards had led to a greater demand for compatibility of

new products.

4.3.1. Closeness centrality

Hypothesis 1 states that as a firm is more centrally connected to all other firms in the

industry network, its new product performance is improved. I used closeness centrality to

measure a firm’s location in the network. Closeness centrality is widely used in social

networks studies to represent the proximity of actors or firms in the network (Wasserman and

Faust, 1994). Gulati (1999) and Powell et al. (1996) have used this to identify a firm’s

network position. Closeness centrality measures how central a firm is relative to other firms,

including both direct and indirect partners. It is similar to finding the firm’s reachability to

every other firm on the shortest path. The shortest path (or geodesic distance) between two

firms requires the fewest number of intermediate partners through which both firms are

linked.

Closeness centrality can be generated in UCINET IV, a software for social network

analysis (Borgatti et al., 1992). The input was a symmetric binary matrix based on the

cumulated alliance patterns of 49 firms with 112 nonsample partners, representing the

absence or presence of a nondirectional tie between two firms. Since the number of partners

varied each year, UCINET produces normalized centrality values ranging from 0% to 100%

so as to allow comparisons across networks. Zero percent means the firm is not reachable and

100% means the firm is maximally close to all other firms. The details of closeness centrality

are provided by Wasserman and Faust (1994) and in the UCINET IV manual.

4.3.2. Repeated partners

Hypothesis 2 states that as a firm increases its number of partners with whom it forms

repeated alliances in the industry network, its new product performance is improved. I

counted the number of different partners with repeated alliances based on cumulated number

of alliances from 1989. Since closeness centrality already captured all direct partners, both

measures could be highly correlated. Nevertheless, the measures are still conceptually

P.-H. Soh / Journal of Business Venturing 18 (2003) 727–744 737

different. Even if two firms have similar centrality values and/or number of direct partners,

the one that has repeated transactions with more partners will gain better information access.

4.4. Control variables

Based on past studies, I identified network density, R&D intensity, firm age, and firm size

as important control variables. Rowley et al. (2000) discuss how the performance impacts of

network density differ between exploration and exploitation of knowledge. They argue that

the density of ties among a firm’s direct partners is positively related to its performance in

environments that demand relatively high investments of exploitation. Ahuja (2000) explains

that dense ties between partners prevent deviant behaviors and foster the development of trust

that is conducive to knowledge exchange. In addition, dense ties are likely to create the

crowding effect that leads to higher inclination of partners to collaborate among themselves

(Stuart, 1998). Hence, we expect a positive effect of tie density on firm performance.

Following the approach in Rowley et al. (2000), I included ego network density as a control

variable. Ego network density is computed as the number of actual ties among a firm’s

partners divided by the total number of potential ties among the partners.

Next, I measured R&D intensity by the ratio of R&D expenditure to sales revenue. Many

studies have found a positive effect of R&D inputs on innovation performance. First, greater

R&D input means firms are able to spread the fixed costs of R&D and exploit complemen-

tarities in wide ranging applications at little additional costs (Henderson and Cockburn,

1994). Second, higher R&D input leads to greater contribution to a firm’s absorptive capacity,

enhancing the firm’s ability to assimilate external knowledge (Cohen and Levinthal, 1990).

Some studies have also argued that variation in opportunity recognition is contingent upon

the absorptive capacity of the firm and its partners (e.g., Lane and Lubatkin, 1998). Third,

increasing R&D input geared towards learning inevitably creates specialization of skills and

knowledge. The resultant knowledge asset is immobile and difficult to replicate, leading to

heterogeneous firm performance (Barney, 1991; Wernerfelt, 1984).

Firm age and firm size may also influence a firm’s new product performance. Younger

firms are generally seen as more innovative because of their flexibility to adapt to rapidly

changing environments, whereas established firms are slow to adapt. Evidence is also found

to support the correlation between firm size and R&D efforts and outcomes (Kamien and

Schwartz, 1982). In this analysis, firm size is measured by sales revenue and firm age is

measured by the number of years a firm had operated its networking business up to the year

of observation. I obtained the founding year of either the firm or its networking business

division from the CorpTech Directory. R&D expenditure and sales revenue were gathered

from Compustat and company reports.

4.5. Model specification

A panel data of 49 firms from 1991 to 1996 were set up for the analysis. For any firm-year

observation, the value of new product performance is zero when the firm did not obtain an

award in that year; otherwise, the number of awards is recorded. A dependent variable with

P.-H. Soh / Journal of Business Venturing 18 (2003) 727–744738

such property is known as a limited dependent variable. In this situation, the residuals of OLS

regression will not satisfy the condition that the error term has a zero mean, which is required

for unbiased estimates. Instead, Tobit models or censored regressions can be applied to obtain

consistent and unbiased estimates (Greene, 1997; Maddala, 1987). The specification of Tobit

model with random effects for the panel data for this study is given below.

yitþ1 ¼

b0xit þ eit if yitþ1* > 0; for a firm which has obtained awards in year t þ 1

0 if yitþ1* � 0; for a firm which has not obtained any award nor

participated in the benchmarking test in year t þ 1

8>>><>>>:

where i=1st, . . ., nth unit; eit=ai+uit such that ai�IN (0,sa2), uit�IN (0, su

2)

The Tobit model with random effects is applicable as long as the observations on the latent

variable y* are missing (or censored) or if y* is below (or above) a certain threshold level. The

normal density function for positive values of y follows a standard normal distribution, whereas

the cumulative distribution function is for zero observations of y. Tobit analysis is then given by

the log-likelihood function of the joint probability for the entire sample. The standard procedure

for obtaining the nonlinear estimates of b is to maximize the likelihood with respect to b and s.The STATA statistical software, which I used to perform the analysis, applies the alternative

Gauss–Hermite quadrature technique.4 This technique is, however, limited to only small panel

sizes, which is the case in this study. In the final analysis, I transformed the y values using a log

function. In the Tobit model, the error termai for cross-sectional data is assumed to be randomly

distributed with the restriction that ai and xit are uncorrelated. Since I will draw inferences from

the findings about other firms in the population, the use of random effects will have an

advantage because it saves many degrees of freedom (Kennedy, 1993). However, if there is

evidence of omitted variables that are potentially correlated with the predictors, fixed-effects

modelsmight bemore appropriate. Unfortunately, a conditional fixed-effects analysis cannot be

applied because sufficient statistics do not exist for such estimation. Given the limitation of

Tobit analysis, we must draw the inferences with caution.

5. Results

To test the lagged effects of independent variables on new product performance in the

subsequent year, a total of 245 firm-year observations for 49 firms from 1991 to 1995 were

used. However, with missing R&D and sales data, 44 firm-year observations including one

firm were dropped from the analysis, leaving a total of 201 observations for 48 firms. Table 1

presents the descriptive statistics and correlation matrix. The correlation matrix shows that all

variables except firm age are significantly correlated with new product performance.

Furthermore, closeness centrality, repeated partners, and ego network density are highly

correlated among themselves. Firm size is positively correlated with all the variables except

4 StataCorp., 1999. Stata Statistical Software: Release 6.0. Stata, College Station, TX.

Table 1

Descriptive statistics and correlation matrix for panel data (1991–1996)

Variable Mean S.D. Min Max 1 2 3 4 5 6 7

1. New product

performance

(1992–1996) (log)

0.54 0.79 0 2.94 1

2. Closeness centrality 24.9 14.43 0 45.4 0.461* 1

3. Repeated partners 0.43 1.08 0 8 0.539* 0.403* 1

4. Ego network

density (log)

1.72 0.90 1.09 4.46 0.458* 0.553* 0.370* 1

5. R&D intensity

(R&D/Sales)

0.12 0.07 0.01 0.79 �0.216* �0.065 �0.106 �0.059 1

6. Firm size

(log sales revenues

in millions)

5.62 2.34 0.86 11.28 0.463* 0.483* 0.442* 0.498* �0.395* 1

7. Firm age

(No. of years since

founding)

13.86 6.52 4 29 �0.102 �0.086 0.005 �0.025 �0.207* 0.199* 1

No. of observations=202.

* P<.001.

P.-H. Soh / Journal of Business Venturing 18 (2003) 727–744 739

R&D intensity, which in turn is negatively correlated with new product performance. These

moderately high correlations may pose a multicollinearity problem that produces inefficient

coefficients, but our analysis, as shown in Table 2, indicates no significant change in the

standard errors across models.

Table 2

The effects of networking alliances on new product performance, Tobit analysis with radom effects, 1991–1996

New product

performance

log(award)it+1

1 2 3 4

Interceptit �0.803 (0.537) �0.641* (0.279) �0.271 (0.288) �0.266 (0.260)

Closeness centralityit 0.010* (0.004) 0.009* (0.004)

Repeated partnersit 0.231*** (0.040) 0.225*** (0.040)

Ego network

density logit

0.213*** (0.062) 0.169** (0.059) 0.162** (0.056) 0.121* (0.057)

R&D intensityit �0.053 (0.718) �0.327 (0.665) �0.583 (0.638) �0.687 (0.621)

Firm size log (sales)it 0.171** (0.065) 0.129*** (0.039) 0.111** (0.041) 0.088* (0.041)

Firm ageit 0.002 (0.016) �0.002 (0.011) �0.007 (0.010) �0.007 (0.010)

No. of firms 48 48 48 48

No. of firm-year

observations

201 201 201 201

Log-likelihood �169.89 �166.83 �154.291 �151.46

S.E. in parentheses.

* P<.05.

** P<.01.

*** P<.001.

P.-H. Soh / Journal of Business Venturing 18 (2003) 727–744740

Table 2 presents the results of Tobit analysis. Model 4 is the full model which tests both

Hypotheses 1 and 2, controlling for ego network density, R&D intensity, firm size, and firm age.

Hypothesis 1 predicted that as a firm is more centrally connected to all other firms in the

industry network, its new product performance is improved. This hypothesis is supported since

the coefficient of closeness centrality is positive and statistically significant at the 5% level.

Hypothesis 2 predicted that as a firm increases its number of partners with repeated alliances in

the industry network, its new product performance is improved. This hypothesis is also

supported since the coefficient of repeated partners is statistically significant at the 1% level.

It is interesting to note that ego network density is statistically significant in all the models.

The omission of such an influential variable would have produced biased estimates in the

random-effects models. Firm size is also significantly and positively correlated with new

product performance. Finally, R&D intensity is not statistically significant across the four

models and its standard errors were also insensitive to the exclusion of firm size and firm age

in the analysis.

6. Discussion and conclusions

Building upon the Austrian economics of entrepreneurial discovery, this research posits

that firms are not aware of what opportunities exist until they acquire more information about

other market participants and their resources (Kirzner, 1997). The entrepreneurial process is a

function of mutual discovery but the uneven distribution of information in the market affects

the outcome of information acquisition (Hayek, 1945). Since closeness and strength of ties

are two important networking mechanisms by which information and resources are mobilized

between firms (Granovetter, 1985), I have proposed to use the social network approach to

explain how a firm would acquire potential information about new technological opportun-

ities via technology alliances, and thus gain new product improvements.

Social network theory suggests that high closeness centrality is efficient in information

dissemination, whereas reciprocal exchange facilitates the transfer of know-how. Using a

longitudinal analysis, the study demonstrates that in the technology collaboration network, as

a firm increases its closeness centrality and its number of repeated partners, its new product

performance in the subsequent period is enhanced. The first implication is that if two firms

have a similar number of direct partners and all others being equal, the different innovation

outcomes can be partly attributed to the asymmetric information acquired through extended

networking with indirect partners. The second implication is that even if both competing

firms are able to obtain potential information from a set of common indirect partners, the one

with more efficient channels to other firms would acquire the information earlier, gaining a

greater window of opportunities to create or to enhance its own products before the other

competitor. The inferences above are conceivable provided that technology alliances are

frequently engaged as information channels.

The findings further suggest that repeated partnership is significantly more important than

proximity of firms in the network. Perhaps this can be explained by the willingness of partners

to exchange strategic information. Bouty (2000, p. 61) argues that in a virtuous cycle, as more

P.-H. Soh / Journal of Business Venturing 18 (2003) 727–744 741

trust is created between partners, more equitable exchanges involving strategic information and

resources will take place. Since mutual trust exists in reciprocal exchange, a firm with a greater

number of repeated partners is more likely to enjoy significant information benefits. In other

words, if two firms occupy similar network positions, the one with more trusted direct partners

is likely to acquire more strategic information about their indirect partners.

Given the positive effects of prior networking experience on subsequent new product

performance, why do firms not form more alliances? An explanation may be that firms face

unequal opportunity and cost in forming new alliances. In fact, alliance formation is

influenced and constrained by the density of ties among the firm’s partners (Ahuja, 2000;

Stuart, 1998). Dense ties not only create a greater potential for collaboration but also foster

the development of trust among the partners. Those firms that are located in lower density

will thus require a greater time and effort to build up the extended network. This study shows

that tie density is significantly and positively associated with new product performance. The

main implication is that among firms with equal inclination to form new alliances, the ones

which leverage their direct ties by discreet choice of partners who have more access to others

are likely to enjoy better new product performance (Dubini and Aldrich, 1991). However,

with the limited data constructed for this study, it is not possible to determine if the return to

performance diminishes as density increases around the focal firm.

The study highlights about the importance of competitive collaboration in unstable

technological environments to both entrepreneurs and managers. While technology alliances

create competitive advantages by shortening the time to market, the choice of whom to form

alliances with can have significant impact on a firm’s access to technological opportunities.

The evidence is consistent with Mitchell and Singh’s (1996) findings that large and growing

businesses often form new alliances, which result from past alliances and contribute to future

success. A similar role for networking alliances may be observed in related industries like

telecommunications and IT. In these industries, both emerging and established players

constantly respond to threats and opportunities by leveraging their networks of partners

and associated resources to gain access to competitive information. However, the latest

development of the Internet has led to increasing acquisitions among firms, offering two

interesting propositions for future research on networking strategy. First, the integration of

critical resources in the markets may signify the decline of information benefits arising from

embedded networks. Second, the intense relocation of critical resources may generate a new

network of organizations that renders past networking experience obsolete. From 1997 to

2000, out of 49 sample firms 20 were acquired by established firms (10 of which are also in

the sample) and one by a 4-year-old start-up. A study to extend the alliance data beyond 1996

would certainly help investigate the limits of networking alliances in information access and

the relative impacts of alliances versus acquisitions on firm performance.

The main limitation of the study is that potential information about technological

opportunities is assumed to be in existence and its quality uniform across the alliance sample.

Nonetheless, the evidence of referral information through alliances has been investigated and

supported in other studies (Gulati, 1995a; Larson, 1992; Saxenian, 1991; Uzzi, 1997). Another

limitation is that only technology alliances and technology partners were considered in the

analysis. Business alliances with multiple entities such as government agencies, venture

P.-H. Soh / Journal of Business Venturing 18 (2003) 727–744742

capitalists, and customers might have been the additional catalysts in the discovery and

exploitation of entrepreneurial opportunities. The role of business alliances may have

important implications for resource mobilization; thus, affecting the information acquisition

process of opportunity-seeking firms. Perhaps by including both technology and business

alliances, we can investigate if other networking mechanisms such as the structural holes are

important and assess the relative performance effects of technical versus business information

channels. Future research on this would provide practical insights into the choice of partners

depending on whether the focal firm is seeking technological opportunities per se or gaining

access to diverse opportunities arising from multiple institutions.

In this research I have linked entrepreneurship to innovation and social network theories.

An attempt has been made to extend the field of entrepreneurship by focusing on the

mechanisms leading to information acquisition and opportunity discovery at the firm level.

The study has also broadened our understanding of the alliance strategy in accessing potential

information about opportunities. It is evident that an interdisciplinary approach will better

address the central issues of entrepreneurship in future research.

Acknowledgements

I would like to thank Kulwant Singh, Akira Takeishi, Maw-Der Foo, Kevyn Yong, and

three anonymous reviewers for their comments and critics of the early drafts of this paper.

Many thanks also to the participants at the Nanyang Business School’s IMARC workshop

2000, the Innovation Forum at the Hitotsubashi University’s Institute of Innovation Research,

and the Conference on Technological Entrepreneurship in the Emerging Regions 2001.

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