The Effect of R&D Alliance Diversity and Network Position on Firm Innovation Performance: Evidence from the Emerging Biotechnology Industry

Abstract

Despite increasing attention to the role of diverse alliances in emerging market, few studies have explicitly explored the roles of environment and network position on diverse R&D alliances. This study investigates how environment and network position interact with diverse R&D alliances to affect firm innovation performance. Specifically, we examine the moderating effects of technology uncertainty, market uncertainty, network centrality position and competitive intensity. The findings from a survey of 144 biotechnology firms indicate that diverse R&D alliances have a positive effect on firms’ innovation performance, and these effects are moderated by environmental factors and alliance network position. The overall R&D alliance network was also examined to understand a broad variety of collaboration patterns of emerging biotechnology firms.

Introduction

The importance of inter-firm collaborations for the survival and growth of emerging biotechnology firms has been widely acknowledged and demonstrated empirically (Powell, Koput, & Smith-Doerr, 1996; Roijakkers & Hagedoorn, 2006). Inter-firm alliance networks are important vehicles for emerging biotechnology firms to acquire external knowledge (Roijakkers & Hagedoorn, 2006), thus increasing the likelihood of successful innovation efforts (Ahuja, 2000; Uzzi, 1997). Through such inter-firm alliance networks, firms can obtain crucial knowledge and technology to help them improve their overall innovation capabilities, obtain new technologies, discover new markets and pool complementary resources (Du, Lu, & Guo, 2015; Inkpen & Tsang, 2005; Powell, Koput, & Smith-Doerr, 1996). In particular, while emerging biotechnology firms conduct significant amounts of R&D activities, inter-firm alliances provide access to specialised knowledge to supplement their in-house R&D efforts.

For firms in emerging markets, with high environmental uncertainty and intense competition, inter-firm alliances represent an important source of science and technology knowledge for ongoing R&D and consequent innovation. Additionally, biotechnology firms require science- and technology-intensive knowledge and rely on a wide set of disciplines. This variety of knowledge and technology makes biotechnology firms engage in inter-firm collaboration to further develop their technologies into marketable products.

The biotechnology industry is a relatively young high technology industry emerging in Taiwan. New biotechnology firms there greatly depended on external resources to improve their in-house R&D capability. In-house R&D resources may be acquired through inter-firm R&D alliances, and furthermore, these inter-firm R&D alliance strategies may shape a biotechnology firm’s innovative performance (Baum, Calabrese, & Silverman, 2000; Patra & Krishna, 2015). The use of inter-firm alliance networks is especially important for biotechnology firms that operate in emerging and turbulent markets because inter-firm alliances serve as effective mechanisms for resource acquisition, technology development and entry into new markets. Scholars like Jinhyo Yun have further explored the dynamic aspect of open innovation, especially putting it to a larger context to help understand how firms acquire external technology and knowledge. For instance, through exploring the case of the smartphone sector, Yun, Won and Park (2016) brought up an integrated model, which takes into consideration the interaction between open innovation and complex adaptive system (such as national or regional innovation system) and evolutionary change. Yun, Yang and Park (2016) connects the concept of open innovation with business model development. They propose that business model development can be based on customer open innovation, user open innovation, social open innovation and engineer open innovation. It is common that emerging biotechnology firms are simultaneously engaged in multiple and diverse inter-firm alliances with different partners to access resources in the turbulent environments.

Despite the distinct benefits that inter-firm alliances can contribute to firm performance, some questions remain unresolved. To date, no studies have sufficiently explored and tested theories to explain the effects of diverse R&D alliance on innovation performance in the biotechnology industry. Nor has previous research argued or tested which contingency factors are important for firm R&D alliances to lead to superior innovation performance in emerging biotechnology firms. Answers to these questions are relevant in theory and practice, and are the goal of our research. This study addresses the research gap by examining 144 emerging biotechnology firms in Taiwan.

Theory and Hypotheses

Contingent Effects of Environmental Uncertainty

Some studies on inter-firm alliances and collaboration have examined the impact of uncertainty, which leads a firm to a preference of establishing network ties with partners (Gulati & Gargiulo, 1999; Ireland, Hitt, & Vaidyanath, 2002; Mitsuhashi & Greve, 2009; Wang & Chien, 2013). Meanwhile, uncertainty may weaken a firm’s capabilities to accomplish inter-firm collaboration and alliance activity (Beckman, Haunschild, & Phillips, 2004; Ireland, Hitt, & Vaidyanath, 2002). New biotechnology firms operating in an emerging market often face high risk and uncertainty. Thus, it is important to investigate the interplay between inter-firm alliances and environmental uncertainty. Research suggests that environmental uncertainty typically arises from technological uncertainty and market turbulence (Atuahene-Gima & Li, 2004).

Technological Uncertainty

Technological uncertainty refers to the extent to which production or processing technologies or know-how cannot be anticipated or accurately predicted (Pfeffer & Salancik, 1978). For example, novel external technology and knowledge may lead to unexpected technological developments and changes that are likely to impact existing R&D efforts. The uncertainty introduced by novel and unanticipated technologies characteristics can lead to losses in some alliances. We reason that high technological uncertainty will increase integration and coordination risks and costs, which may reduce the effectiveness of external alliance networks.

Emerging biotechnology firms rely on external knowledge sources for the development of in-house R&D. Given the characteristics of science and technology intensity of biotechnology firms, inter-firm R&D alliances are highly uncertain (Sampson, 2007). The highly uncertain and non-codifiable nature of the scientific know-how of biotechnologies can result in high transaction costs and systemic failures in integrating diverse and novel knowledge and technologies. Likewise, unanticipated problems may make it difficult for a firm to determine what a partner contributes to an alliance (Sampson, 2007). In the case of inter-firm R&D alliances, information asymmetries and the technologically uncertain and tacit nature of biotechnology may diminish the benefits of inter-firm collaborations and alliances. Thus, as technological uncertainty increases, a negative relationship between R&D alliance diversity and firm innovation performance would be expected to become stronger.

Hypothesis 1: Technological uncertainty negatively moderates the relationship between R&D alliance diversity and firm innovation performance.

Market Turbulence

Market turbulence refers to the degree to which the composition and preferences of customers change over time, which inf luences the need for continual modifications to products and services (Jaworski & Kohli, 1993). Previous research has argued that market turbulence inf luences the relationships between inter-firm collaborations and their performance (Kandemir, Yapruk, & Cavusgil, 2006). Under highly turbulent market environments, firms have a greater need to collaborate with external partners to acquire complementary resources and reduce market uncertainty (Ahuja, 2000; Beckman, Haunschild, & Phillips, 2004; Uzzi, 1997). Inter-firm collaboration in a rapidly changing market environment may provide new opportunities for the firm and the need for innovative strategies to take advantage of collaborative alliances to respond to changes in demand.

Although the arguments above suggest that high market turbulence is likely to increase inter-firm collaboration, the present study also notes that firms may have to bear increasing risks as market turbulence increases, which might reduce the ability of R&D alliances. This is especially true in science- and technology-intensive firms operating in emerging markets. In emerging markets, market turbulence may make it increasingly difficult to forecast market developments where new product and service offerings cannot quickly meet fast-changing market demand. As noted by Beckman, Haunschild and Phillips (2004), market turbulence is caused by rapid changes in customers’ needs and preferences, which cannot be easily managed or reduced by the actions of a single firm alone. Thus, under such conditions, where market uncertainty increases, many inter-firm R&D collaborations and alliance activities are not easy to achieve anticipated goals; some of these collaborations and activities will even fail. This discussion suggests that high market uncertainty may deter the firm from forming diverse R&D alliances. However, previous researchers have also suggested that environmental contingent factors, such as market turbulence serves as a moderator, which can significantly inf luence strategies and the performance of firms (Jaworski & Kohli, 1993). In the light of this contingent view, this article posits that external market turbulence is a moderator that can reduce the innovation performance effects of R&D alliance diversity.

Hypothesis 2: Market turbulence negatively moderates the relationship between R&D alliance diversity and firm innovation performance.

Moderating Effects of Network Position and Competitive Pressure

This study considers both network centrality and competitive pressures to capture alliance network position that impact emerging biotechnology firms in an emerging market. When market competition pressure is intense in an emerging market, firms will need to engage in collaborative efforts with partners to respond to competitive behaviour. Scholars assert that the more central a firm is in the network, the more information it is able to gather, and hence, the more competitive it is likely to be (Sanou, Roy, & Gnyawali, 2016; Wasserman & Faust, 1994). Firms in highly competitive network centrality position focus considerable attention on cooperative linkages between firms. Hence, we argue that both centrality and competitive pressure are theoretically interesting in our context as they inf luence a firm’s ability to access network resources as well as to learn and use rivals’ resources, thereby enhancing innovation activities.

Network Centrality Position

Networks of inter-firm collaborations play crucial roles in acquiring complementary resources to develop in-house R&D activities. Different network positions may provide different opportunities for a firm to acquire diverse information and resources. Network centrality refers to the degree to which a firm has quick and independent access to all firms in the network through the fewest possible links (Stam & Elfring, 2008). Firms occupying high centrality positions in inter-firm alliance networks are likely to have better access to strategic resources (Powell, Koput, & Smith-Doerr, 1996; Stam & Elfring, 2008; Tsai, 2001). Being in a central position ensures that the firm is located in an important and visible point for information f lowing through the network, thereby having more ties connecting diverse collaborators, which results in firms improving their R&D capabilities. These arguments suggest that emerging biotechnology firms are more likely to acquire R&D resources when they occupy a central position within the particular alliance network.

Diverse technologies and knowledge occupied by central firms may fuel the firms’ R&D efforts, resulting in a higher likelihood of innovation success. Additionally, firms with high network centrality can interact with diverse partners who are also highly central, which makes it easier to integrate and coordinate with the actions of others, thereby further enhancing their access to rich technologies and knowledge. Indeed, a firm that occupies a central position can gain well-connected partners to enhance its profitability by applying partners’ knowledge or practices to respond to emerging market trends (Tsai, 2001). Consequently, a central firm is likely to increase its collaborative opportunities because it can enjoy the benefits of network ties by accessing the technology and knowledge developed by many collaborative partners. Thus, a firm occupying a more central position in its inter-firm alliance network is likely to access diverse R&D resources which lead to better innovation. These arguments suggest the following hypothesis:

Hypothesis 3: Network centrality position positively moderates the relationship between R&D alliance diversity and firm innovation performance.

Market Competition Effect

Competitive pressure is widely recognised as a major contingency factor in determining a firm’s strategic orientation and outcome (Ang, 2008; Jaworski & Kohli, 1993). Competitive intensity refers to the degree to which a firm faces competitive activity within its industry (Jaworski & Kohli, 1993), which is one of the factors determining a firm’s survival chances. Inter-firm collaboration in the high technology and knowledge industries is crucial to capture resources and address competitive pressure (Burgers, Hill, & Kim, 1993), and inter-firm alliance formation is likely to be contingent on the competitive intensity (Ang, 2008). Firms facing a high degree of competitive intensity will have an increased likelihood of inter-firm collaboration because they need to address the competitive pressure. As noted by Wu (2012), in highly competitive emerging markets, cooperating and interacting with other partners provide a focal firm with more opportunities to refine and improve internal routines and processes, which, in turn, increase its capability to discover and absorb new technology. Specifically, biotechnology firms require science and technology knowledge for R&D, which are disparate in a variety of organisations.

Researchers have argued that high market competition pressure generates external inducements for inter-firm collaborations for two reasons. First, high market competition pressure may stimulate firms to engage in strategic alliance or cooperate with partners to gain timely access to new technologies and knowledge to improve performance (Ang, 2008; Doz, 1996). Second, inter-firm collaboration, as a response to highly competitive environment, can enhance a firm’s ability to respond rapidly to technological change and help a firm to quickly benefit from emerging market opportunities (Uzzi, 1997) by helping focal firms tap into advanced technology developments from diverse partners (Ahuja, 2000). This finding is in line with the Schumpeterian effect argument of Aghion, Harris, Howitt and Vickers (2001): a highly competitive market is almost always outweighed by the increased innovation incentives of firms to escape competition. As a result, in a highly competitive emerging market, firms are more likely to collaborate with partners to enable focal firms to adopt joint problem-solving arrangement behaviour and thus greatly improve chances of success in innovation (Uzzi, 1997). Drawing on these arguments, this study, therefore, assumes a positive effect of competitive intensity on R&D alliance and innovation performance.

Hypothesis 4: Competitive intensity positively moderates the relationship between R&D alliance diversity and firm innovation performance.

Methodology

Sample and Data Sources

The hypotheses were tested using biotechnology firms in Taiwan, which is an emerging industry that contains more than 400 start-up firms. The Taiwanese emerging biotechnology firms were selected as the empirical context for two reasons. First, the firms generally have significant technology intensity and requires diversified knowledge. Second, most of them have engaged in inter-firm collaborations to acquire complementary resources. As noted by Hoang and Rothaermel (2005), biotechnology firms have strong motives for engaging in inter-firm R&D alliances to increase their performance versus other industries. This study, therefore, takes emerging biotechnology firms as its focus.

To identify the sampling frame, we used the 2011 Taiwan Biotechnology Directory, which was compiled and published by the Industrial Development Bureau (IDB), Ministry of Economic Affairs (MOEA) in Taiwan. The dataset provides information on 422 biotechnology firms. These firms engage in technologies such as therapeutic products, regenerative medicine, biomaterial, diagnostics, biochips, etc. This study sent a first set of 422 postal questionnaires in April 2012 and a second set in May 2012; the questionnaires were followed up with reminders via e-mail and telephone calls in June and July 2012. In total, 155 questionnaires were returned. After deleting incomplete questionnaires, 144 were valid for subsequent empirical analysis, representing a response rate of 34.12 per cent. A T-test analysis revealed that there was no statistically significant difference between the non-respondents and respondents.

To capture the R&D alliance network information, this study drew on two data sources. First, a questionnaire was designed asking for information on a wide range regarding inter-firm R&D collaborations. In particular, it asked on the (a) types of collaborators, (b) environmental effects on collaborative activity and (c) alliance network effects on collaborative activity. Second, to obtain detailed information on firm-level innovation performance, the sample was merged with a dataset from the Taiwan Patent Database, which was established by the Intellectual Property Office (IPO) of the MOEA. This dataset provides well-defined firm-level patent information according to the International Patent Classification (IPC).

Consistent with the definition in the previous literature, this study identified R&D alliances as any durable inter-firm R&D collaborative activity that involved the exchange of complementary capabilities and resources between partners (Sampson, 2007). Furthermore, to construct inter-firm R&D alliance networks, this study followed two criteria that were originally proposed by Rowley, Behrens and Krackhardt (2000). First, all focal firms were included in the target population and participated in a set of R&D alliances. Second, firms in the biotechnology industry had at least one R&D alliance with other partners to develop technologies, processes, products and services. According to these criteria, a total of 368 firms were identified in the inter-firm R&D alliance network. Overall inter-firm R&D alliance networks and matrices for the biotechnology industry were created, which were then used to operationalise the network constructs. Further, because the lifespan of inter-firm alliances is usually no more than five years (Kogut, 1988), this study therefore adopted a 5-year moving window to capture the cumulative nature of a firm’s alliance portfolio, which may have effects on continuing activities that started in 2008. Thus, an inter-firm’s network embeddedness was constructed using a symmetric matrix (368 × 368) that was imported into UCINET 6 (Borgatti, Everett, & Freeman, 2002) for constructing the R&D alliance network.

To assess the potential common method bias, this study collected independent and dependent variables from different sources. This study used a questionnaire survey to measure and collect variables from top management at emerging biotechnology firms. Additionally, the dependent variable was collected through a government domain database (IPO of the MOEA). In doing this, the common method bias was unlikely to be a concern.

Measurement

Dependent Variable

As argued earlier, patents are an important innovation performance indicator for science and technology-intensive firms (Griliches, 1990). However, it may not be directly appropriate to adopt R&D alliance and output data in the same year, due to the R&D lag effect. To address this issue, this study accounted for the time lag between R&D alliances and the realisation of its outcomes. Consistent with the definition in the work of Hagedoorn and Cloodt (2003), the average time lag between R&D inputs activity and the years of patents was three in the high-technology industry. Thus, this study measured firm innovative performance via a count of firm patents in a 3-year moving average window.

Independent Variables

R&D Alliance Diversity: This study used the Jacquemin–Berry entropy (the so-called entropy-based diversity index) to measure biotechnology firms R&D alliance diversity (Jacquemin & Berry, 1979). The entropy-based diversity index is widely used in prior studies and is considered the most common measure of diversity (Østrgaard, Timmermans, & Kristinsson, 2011). An entropy-based diversity index is defined as

H = - \sum_{i = 1}^{n} P_{i} (\ln P_{i})

where i is a particular R&D alliance partner, n is the total number of possible R&D alliance partners and P_i is the proportion of the potential R&D alliance partners within the group. The range of the entropy-based diversity index is in the interval [0,3], r, and is a real number. A minimum value of 0 indicates that focal firms only collaborate with their partners in one field. In contrast, a maximum value of the entropy index indicates that focal firms collaborate with a large number of partners from different fields who all engage in R&D alliance diversity.

Technology Uncertainty: Technology uncertainty was measured with Jaworski and Kohli’s indicators (1993) using 5-point Likert scales (1 = strongly disagree, 5 = strongly agree). For the ref lective scales, this study used a confirmatory factor analysis (CFA) to assess the validity of technological uncertainty, market uncertainty and competitive intensity. After running CFA and deleting two items for technology uncertainty, the value of Cronbach’s α was 0.731. The measurement items and results of the validity analyses appear in the Appendix.

Market Uncertainty: According to Jaworski and Kohli’s (1993) work, this study developed a scale of market uncertainty to capture the characteristics of the emerging market environment using 5-point Likert scales. The six items for market uncertainty measured the extent to which the market in biotechnology is changing. After running CFA and deleting two items for market uncertainty, the value of Cronbach’s α was 0.610.

Network Centrality Position: To capture network centrality positions of firms, this study calculated centrality positions using the metrics of degree of centrality. The degree of centrality is defined as the total number of ties that are direct connections with other partners in the R&D alliance network (Wasserman & Faust, 1994). Firms with a greater number of ties can continuously update existing information and accumulate higher quality information (Coleman, 1988). To calculate the network centrality position for each firm in the R&D alliance network, we constructed a symmetric matrix for R&D alliance network, using UCINET 6 (Borgatti, Everett & Freeman, 2002).

A firm with a high degree centrality can communicate directly with many other actors, acting as an important conduit for information, and therefore enjoys high visibility and prominence (Wasserman & Faust, 1994). Because degree centrality determines a firm’s ability to share, integrate and utilise complementary, heterogeneous resources such as information, technology and knowledge (Borgatti, 2005), such centrality will fuel a firm’s innovation activities by accessing plentiful and diverse external resources. The betweenness centrality is referred to the degree to which a firm falls on a shortest path between other members (Freeman, 1977) and may have a potential for control of communication. Betweenness centrality measures the ability of the firm occupying a critical gatekeeping position to act as an intermediary (often refer to as a broker) (Valente, 2012). The eigen value centrality is based on the idea that the importance of a firm depends both on number and quality of connection (Bonacich, 1987). However, smaller number of well connections may contribute to the relative importance over medium or large amount connections, especially for ranking based on importance (Abbasi et al., 2011; Borgatti, Everett, & Freeman, 2002). In such conditions, both the betweenness centrality and eigen value centrality may not well capture the strength of a dominant position in the overall R&D network and may even produce poor results for application such as measurements. As a result, we decide not use the Freeman betweenness or eigen value for centrality measure in R&D network. Instead, we used the degree centrality as our proxy measurement of network positions.

Competitive Intensity: This study adapted Jaworski and Kohli’s (1993) work to measure the competitive intensity via six items using 5-point Likert scales. After implementing CFA and deleting three items for market uncertainty, the value of Cronbach’s α was 0.794.

Controls: This study controlled several variables that could have inf luenced firm innovation performance. First, firm size was measured by the total number of employees. Second, firm age was the number of years since the firm was established. Third, the total number of alliances of a firm was measured for the focal firm in the alliance network with whom the firm has direct collaboration ties because firms with a greater number of alliance partners may benefit more from their alliances (Zahra, Ireland, & Hitt, 2000).

Methods

As mentioned above, firm-innovative performance was measured via firm patenting. The empirical model has to accommodate the nature of these counts as positive integers, starting from zero. However, two issues also arise: zero values and the small integer values of many emerging biotechnology firm patent counts. To address these issues, this study used a zero-inf lated Poisson regression model (Lambert, 1992).

The R&D Alliance Network of Emerging Biotechnology Firms

In this section, the actual R&D alliance network structure of the emerging biotechnology sectors was plotted in Figure 1. The R&D alliance is captured by network visualisation and analysis (NVA) to plot diverse R&D alliance relationships and reveal the complexities of the alliance networks. The nodes in the network represent R&D actors, and the lines connecting the actors represent inter-firm R&D alliance ties. The size of actors indicates the degree of centrality, where firms with high values (hence high degree of centrality) have relatively large visibility and inf luence in the R&D alliance network. In addition to providing each emerging biotechnology firm’s R&D collaborative position in the diverse R&D alliance network, Figure 1 also helps to identify their centrality position and alliance inf luence in the overall network. Actors occupying central positions often achieve substantial social inf luence because they are considered technically competent and convincing (Rogers, 1995). Specifically, those actors situated in central positions play a facilitating role in supporting R&D collaborations and can create valuable opportunities for themselves and others by acting as crucial resources holders. Additionally, the present R&D collaborative network pattern of connections indicates the presence of a core–periphery network structure (Borgatti, Everett, & Freeman, 2002) in which core actors may have far greater visibility than peripheral actors. The pattern also explains how one core actor can inf luence and support collaborators in the R&D alliance network.

Figure 1

Source: Authors’ own.

Actors holding central roles in the R&D alliance networks include AgriAqua, Food Industry Research and Development Institute (FIR&D), National Taiwan University (NTU), National Yang-Ming University (NYMU), Asia Nova, Taiwan United Biomedical, Inc. (TUBI), Industrial Technology Research Institute (ITRI) and Development Center for Biotechnology (DCB) (Figure 1). These key actors hold the highest number of R&D alliance ties to map technologies and information f low as well as relations among strategically important groups to improve knowledge creation and sharing. This phenomenon is further accentuated by the involvement of research institutions, government and educational, and funding agencies that together create synergy in the R&D community (Leydesdorff & Ivanova, 2016). This discovery was consistent with Powell, Koput and Smith-Doerr (1996), who argued that large groups of collaborators and centrally located actors play crucial roles in supporting diverse technologies and knowledge, and thus in diffusing innovation in a science and technology-based area.

Furthermore, detailed analysis showed that the R&D alliance networks are characterised by various economic actors with complementary technologies and competencies. In particular, the R&D collaborative ties are characterised by complementarities and diversity in the emerging biotechnology sector. The differences among the various actors are the main driving forces of the collaborations. This finding implies that inter-firm R&D alliances in the Taiwanese biotechnology sector are usually for the purpose of obtaining complementary resources. We found a total of 369 R&D collaborations among 144 partners, and that the R&D collaboration alliance is gradually developing into a very dense, interrelated and large network. As a result, emerging biotechnology firms increasingly engage in inter-firm R&D alliances to conduct R&D across a broad range of new technologies and to discover potentially commercialisable opportunities.

Results

Table 1 shows the descriptive statistics of the key variables used in this study. Table 2 shows the results of the hierarchical zero-inf lected Poisson regression analysis. Model 1 is the baseline model, which uses only the control variables, Model 2 adds all of the main effect variables and Model 3 shows the moderating effects on the relationships between R&D alliance diversity and innovation performance. Additionally, all models use robust standard deviations to test the robustness of the explanatory variables of the models.

For the hypothesis testing, a hierarchical regression model was designed to test the moderated effects. Following a hierarchical estimation strategy, Model 2 includes only the control and main effect variables for firm innovation performance. Hypotheses 1 and 2 predict the moderating effect of environmental uncertainty (technology uncertainty and market uncertainty) on the relationship between R&D alliance diversity and firm innovation performance. The empirical results show that both the interaction effects of technology uncertainty (β = –0.950, p < 0.05) and market uncertainty (β = –2.519, p < 0.05) were negative and significant, thus, supporting Hypotheses 1 and 2.

Hypotheses 3 and 4 examine the moderating role of network position and competitive intensity. The empirical results show that both the interaction effects of network centrality position (β = 2.44, p < 0.05) and competitive intensity (β = 2.075, p < 0.001) between diverse R&D alliances and innovation performance were positive and significant, respectively, supporting Hypotheses 3 and 4. These hypotheses predict that firms with high degrees of centrality can enhance their innovation performance. Additionally, biotechnology firms facing high market competitive pressure may also have higher motivation to collaborate with diverse partners to access resources to improve their innovation performance.

Table 1

Descriptive Statistics and Correlations

Variables	1	2	3	4	5	6	7	8	9
1. Firm size
2. Firm age	0.433**
3. Total number of alliances	0.266**	0.250**
4. R&D alliance diversity	0.169**	0.111	0.590**
5. Technological uncertainty	0.080	–0.088	0.159	0.099
6. Market uncertainty	0.050	0.023	–0.033	–0.054	0.116
7. Network centrality	0.261**	0.239**	0.980**	0.572**	0.156	–0.054
8. Competitive intensity	0.086	0.092	0.124	0.010	0.177*	0.351**	0.102
9. Firm performance	0.144	0.172*	–0.022	–0.098	0.092	0.064	–0.019	–0.089
Mean	195.23	21.77	3.83	0.51	4.07	3.84	1.05	3.62	21.52
S.D.	446.35	17.69	2.90	0.50	0.62	0.49	0.79	0.72	122.38

Source: Authors’ own.

Notes: N = 144; *p < 0.1; **p < 0.05; ***p < 0.001.

Table 2

Results of the Hierarchical Zero-inf lated Poisson Regression on Innovation Performance

Models	Model 1	Model 2	Model 3
Firm age	0.027**(0.011)	0.033**(0.013)	0.045***(0.014)
Firm size	0.001(0.000)	0.001**(0.000)	0.000(0.000)
Total number of alliances	–0.180(0.255)	0.107(0.399)	0.006(0.341)
R&D alliance diversity		2.770*(0.940)	0.997(3.296)
Technological uncertainty		1.249*(0.668)	1.323**(0.475)
Market uncertainty		0.483(0.742)	0.556(0.744)
Network centrality		0.037(1.701)	–0.833(1.306)
Competitive intensity		–0.837**(0.397)	–1.518***(0.364)
H1: R&D alliance diversity × Technological uncertainty			–0.950**(0.481)
H2: R&D alliance diversity × Market uncertainty			–2.519**(1.199)
H3: R&D alliance diversity × Network centrality			2.440**(0.784)
H4: R&D alliance diversity × Competitive intensity			2.075***(0.647)
Log likelihoodDegree of freedom (df)Wald (χ²df)Prob. >0.000	–5070.083317.540.000	–3015.93817.730.002	–2067.1321256.800.000

Source: Authors’ own.

Notes: N = 144; *p < 0.1; **p < 0.05; ***p < 0.001. Robust standard deviations are indicated in parentheses.

Discussion

Our results provide evidence that the benefit of diverse R&D alliance on firm innovation is contingent upon environmental factors and competitive network position. One of the main incentives for biotechnology firms in R&D alliance networks is to access complimentary network resources in an emerging market. We also found that the combination of high network centrality and competitive pressure strengthened the relationship between R&D alliance diversity and innovative performance. This study’s main contribution is thus to highlight several contingencies that underlie the relationship between alliance network and firms’ innovation performance.

First, biotechnology firms that form more diverse R&D alliances tend to have more contributions to innovation performance. This finding suggests that diverse R&D alliances may bring plentiful resources to the focal firms, which is consistent with prior studies (Sampson, 2007).

Second, the role of diverse R&D alliances in firm innovation performance may be weakened by environmental uncertainty and be enhanced through network position. This study shows that the role of diverse R&D alliances in achieving successful innovation may be mitigated due to the high level of environment uncertainty (that is, technology uncertainty and market uncertainty). The main effect of network position with competitive intensity in our analysis in some way confirmed arguments in prior studies (Ang, 2008) and in other ways offered some interesting findings. The successful implementation of innovation endeavours is not only dependent on diverse R&D alliances as a main vehicle for accessing resources but also greatly dependent on external environment factors (that is, technology and market uncertainty).

Third, by empirically examining how R&D alliances networks among competitors affect firm innovation, this study advances our understanding of co-opetition. The findings highlight the importance of inter-firm collaborations and alliances in highly competitive environments. Firms are stimulated to engage in collaborative alliance efforts that enable them to acquire advanced technologies and outrun a competitor’s pace of innovation (Wu, 2012). Highlighting this feature is fundamental to explain the effects that network position and competitive pressure impacts have on R&D and innovation performance in an emerging market. To counteract market competitive pressure, a firm may have high incentives to collaborate with external partners to acquire an innovative base (Ang, 2008; Sampson, 2007). Paralleling this perspective, the current study shows that in an intensely competitive emerging market, the abundant technology and knowledge residing in the external partners can facilitate the exploitation of collaborative opportunities, promoting innovation initiatives.

The results of this study also have important managerial implications. First, the findings highlight the inf luence of environmental uncertainty and alliance network position on diverse R&D alliances in the biotechnology sector. Biotechnology firms must not only be aware of environmental factors, but also pay attention to the impact that moderate the effects of diverse R&D alliances on innovation performance. In dynamic emerging market environments, the effects of technology and market must be considered because their effects on innovation performance may significantly weaken diverse R&D alliance capabilities. Second, firms should be cautious about network positioning and market competitive pressures because their effects can also greatly impact innovation performance. Third, biotechnology firms facing high levels of competitive pressure have greater innovation performance as a result of diverse collaborative alliances and can be more aggressive in their search for diverse R&D collaborations. Opportunities to collaborate are also enhanced by higher levels of visibility in a collaborative alliance network. Firms with high centrality positions are likely to form diverse alliances for R&D innovation, developing these alliances with highly connected partners in such an environment to seek opportunities in the emerging market. Thus, the ability of firms to maintain diverse connections and identify market competitive resources is a critical enabler to capture higher levels of innovation performance. Fourth, managers must not only be concerned about how to build collaborations to access diverse and useful resources in a competitive market but also recognise how to increase their visibility and connections with potential partners to maximise their innovation performance.

Conclusions

In a highly competitive emerging market, inter-firm R&D alliances in the emerging biotechnology sector have become attractive vehicles for technology and resource acquisition for successful innovation. However, to use such alliances successfully, it is important to identify environmental uncertainty and alliance network position. By introducing the moderating effects, this study can provide a more accurate picture of the relationships between diverse R&D alliance and innovation performance. Additionally, the negative inf luences of technology uncertainty and market uncertainty may be as harsh for emerging biotechnology firms as they are for diverse R&D alliances and innovation performance in an emerging market. Furthermore, the positive effects of a central network position and market competitive pressures on a firm’s innovation performance are conspicuous because the emerging market competitive pressure is high. These findings help clarify the role of environmental factors and alliance network position in determining the potential risks and benefits associated with diverse R&D alliances. Overall, this study provides strong evidence that environmental factors and alliance position, both separately and simultaneously, shape the performance of diverse R&D alliance on innovation in an emerging market.

Footnotes

Appendix

Measurement and Validity Assessment

Construct and Sources	Description	Factor Loading
R&D alliance diversity Formative scale	Our company has engaged in multilateral collaborative efforts with different partners in cooperative research, development, joint technologies development, new products or service development. Collaborative partners include competitors, suppliers, research institutions, labs, universities and government entities.
Technological uncertaintyCronbach alpha = 0.731CR = 0.852	1. The technology in our industry is changing rapidly.2. Technological changes provide large opportunities in our industry.3. A large number of new product ideas have been made possible through technological breakthroughs in our industry.	0.8320.8160.786
Market uncertaintyCronbach alpha = 0.610CR = 0.779	1. In our type of business, customer product preferences change quite a bit over time.2. Our customers tend to look for new products all the time.3. Sometimes our customers are very price-sensitive, but on other occasions, price is relatively unimportant.4. New customers tend to have product-related needs that are different from those of our existing customers.	0.6050.7280.7360.664
Competitive intensityCronbach alpha = 0.794CR = 0.879	1. There are many promotion wars in our industry.2. Price competition is a hallmark of our industry.3. One hears of a new competitive move almost every day.	0.8800.8290.816
Network centralityFormative scale	In capturing the network centrality position of a firm, the metric of degrees of centrality was used to estimate the total number of ties that are direct connections to others partners in the R&D alliance network.
Firm innovation performanceFormative scale	To capture firm-level innovation performance, patents are an appropriate index for measuring innovation performance.

Source: Authors’ own.

Note: Items are measured with 5-point Likert scales (1 = ‘strongly disagree’, 5 = ‘strongly agree’). CR = composite reliability.

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