Innovation speed is as the time that elapses between the development of an innovation and patent acquisition or commercialization (Mansfield, 1988; Clark & Fujimoto, 1991; Murmann, 1994). Thus the concept of innovation speed is the acceleration of activities from first spark to final product, including activities that occur throughout the R&D process (Kessler & Chakrabarti, 1996).
To biopharmaceutical firms, innovation speed has acquired greater importance because of increasing R&D cost, patent competition and barriers of examination prior to approval by the Food and Drug Administration (FDA). Innovation speed is important for R&D alliances made by biopharmaceutical firms, because creating first innovation and applying for co-patent are the main objectives for most biopharmaceutical R&D alliances. Once they have reached their first milestone, they can occupy an exclusive position of new technology development, gain visibility and legitimacy, attract investment, and increase the likelihood of survival and high market share (Heirman & Clarysse, 2007).
The factors that affect innovation speed are complex (Crawford, 1992; Blau, 1994). Allocca and Kessler (2006) explored the relationship between firm’s size and the innovation speed. They found a negative relationship between speed and steady product specification, because less rigid specifications allow managers to think more creatively, and to react favorably in uncertain and turbulent technology contexts (Iansiti & Mac Cormack, 1997). They also found that departure from familiar technology had a positive effect on innovation speed. Heirman and Clarysse (2007) investigated whether tangible and intangible assets matter for innovation speed in start-ups, and found that having a prototype, or beta version, matters for firms in medical, telecom, or other nonsoftware technologies; however, it does not increase innovation speed for software firms.
Zhong and Ozdemir (2010) analyzed the effects of network structure on innovation speed.
They argued that the more potentially connected the structure in which actors interact is, the faster
the actors are able to innovate collectively. Besides, the potential connection structure in which actors interact has a curvilinear effect on the speed of collective innovation. Although the initial increases in potential connectivity rapidly increase the speed, additional increases in potential connectivity are less effective. Furthermore, several articles have studied the benefits of innovation speed, and there is a consensus that innovation speed contributes firms’ development and performance (Teece, 1986; Zehir & Özşahin, 2008; Carbonell & Escudero, 2010).
Generally speaking, the technology owned by biotech firms (ex. biotechnology) is different from that owned by pharmaceutical firms (ex. synthetic technology). Biopharmaceutical R&D alliance consists of multiple types of partners, including biotechnology firms, two pharmaceutical firms, even universities and government laboratories, and alliances made by divergent types of partners might generate technological heterogeneity. In contrast to alliances between two biotech firms, alliances between one biotech firm and one pharmaceutical firm might have greater technological heterogeneity.
According to the social exchange perspective, exchange is created and maintained by the scarcity of resources, prompting actors to engage with one another to obtain valuable inputs (Das &
Teng, 2002). Reciprocal resource commitments and relational influence between partners will ensure collaboration and alliance success (Das & Teng, 1998; Steeusma & Lyies, 2000; Subramani
& Venkatraman, 2003; Muthusamy et al., 2007). Because reciprocity and mutual influence between partners are tangible norms and manifest as mutual control and power sharing or joint decision making, they can supplement trust in collaboration (Provan & Gassenheimer, 1994; Steensma &
Lyies, 2000; Dekker, 2004). Partners are willing to exchange their technology (knowledge) once they predict that they can benefit from it. R&D alliances made by firms with high technological heterogeneity might help them to have divergent technology (knowledge) pooling and to establish the essential conditions of technology (knowledge) exchange. Technological heterogeneity
contributes to technology (knowledge) exchange because partners would like to access and integrate technology (knowledge) that their rivals possess and they themselves do not. The interaction of technology (knowledge) is the most important ingredient of knowledge creation and innovation, and the higher level of technological heterogeneity is an incentive, which would trigger the technology (knowledge) exchange (Lin, T.C. & Huang, 2010; Bertsch et al., 2011). For instance, when a pharmaceutical firm with synthetic technology forms a R&D alliance with a biotech firm with biotechnology, the pharmaceutical company may contribute to the synthetic technology about screening the molecular structure of drug; conversely, the biotech firm may use its biotechnology about gene transfer and duplication to develop new products.
Organizational learning theory helps us to understand the difference between homogeneous
and heterogeneous technology. Through various types of technology searches, an organization could choose the right technology to access and learn from R&D alliances, and then enhance its innovative performance. A technology search could be local search or distant (Stuart & Podolny, 1996; Rosenkof & Nerkar, 2001). A local search focuses on similar (homogeneous) technology, creates incremental innovations, and becomes more expert in its domain (Rosenkof & Nerkar, 2001). In distant research, firms focus on other kinds of (heterogeneous) technology. Previous studies have indicated that firms could easily accumulate expertise and acquire competitive advantages from local searches. Other empirical studies have found a linearly positive relationship between local search and the frequency of exploratory innovation (Methé, Swaninathan, & Mitchell, 1996). At the same time, a distant search leads to recombination inefficiency, because technological heterogeneity increases knowledge integration costs and time (Katila & Ahuja, 2002). The more divergent the knowledge to be integrated, the more complex the problems of creating and managing integration (Grant, 1996). In terms of the innovation speed of R&D alliance, cooperation with partners that have homogeneous technology can enable firms to adopt local searches and create
incremental innovations through the development of routines, and to generate innovation more quickly than distant search.
Actually, both too much and too little technological heterogeneity may be detrimental to innovation. As mentioned above, local search helps firms to access homogeneous technology, to increase technology capability and development of routines, and to speed up innovation. However, the competence made by local search might lead firms to develop core rigidities or fall into competency traps (Levitt & March, 1988; March, 1991; Leonard-Barton, 1995; Rosenkof & Nerkar, 2001), because those organizations exploit only the value of existing knowledge (Cohen &
Levinthal, 1990), and organizations with homogeneous resources have limited opportunities for development. However, even though technological heterogeneity triggers the exchange of technology, and the acquisition of heterogeneous technology by distant search also contributes to the probability of successful R&D, leaning heterogeneous technology from a partner is not easier than learning homogeneous technology, because more time and money must be invested in learning it. In addition, heterogeneous technology is not readily integrated, because every technology has its limits, especially for biopharmaceutical high-technology. Recent developments in research on absorptive capacity contradicts this point of view, for which there is an enhanced role for absorptive capacity as a facilitator for more distant search, thus enabling more explorative learning (Lavie & Rosenkopf, 2006). Furthermore, the majority of conflicts of alliances happened when partners had different technology and objectives.
We argue that technology heterogeneity between partners will be both beneficial and harmful to innovative performance. When a biotechnology company allies with a pharmaceutical company, they might have a better innovative performance, since they have heterogeneous technology;
however, extremely different technology comes at a high cost and difficulty of integration and cooperation. We therefore predict that the relation between heterogeneous technology and
innovative performance is a non-linear curve rather than a linear straight line.
H1: An inverse U–shaped relationship is predicted between technological heterogeneity and innovation speed: the relationship between technological heterogeneity and innovation speed will be nonlinear with innovation speed increasing up to an optimal level beyond which higher levels of technological heterogeneity transfer lead to a decline in innovation speed.
H2: An inverse U–shaped relationship is predicted between technological heterogeneity and innovation quantity: the relationship between technological heterogeneity and innovation quantity will be nonlinear with innovation quantity increasing up to an optimal level beyond which higher levels of technological heterogeneity transfer lead to a decline in innovation quantity.