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PurposeThe environment in high-tech industries is highly dynamic, and after COVID-19, it has become even more unpredictable. Hence, it has become critical for firms to develop strategies to cope with a highly dynamic environment. This paper aims to analyze how the impact of the scientific collaboration networks with URIs (universities and research institutes) on firm innovation performance is contingent on technological and market dynamics.Design/methodology/approachUsing a sample of 174 Chinese firms in the new-energy vehicle industry during 2004–2015, the authors applied a random-effects negative binomial modeling approach to model these relationships.FindingsA broad and strong scientific collaboration network promotes firm innovation network effects are contingent on technological and market dynamics. While technological dynamics strengthen the effect market dynamics weaken it due to the different purposes of collaboration for firms and URIs.Practical implicationsFirms should adjust the structure of scientific collaboration networks with URIs when facing different environments. The government should encourage firms to jointly research with diverse URIs and play an active role in stabilizing market environments.Originality/valueThis study contributes to the academic debate on university-industry scientific collaborations. Applying the temporary competitive advantage (TCA) framework, we provide nuances to the literature that studies the factors that condition the effects of networks. This study also adds to the research on firm scientific collaboration networks by measuring networks based on the coauthorship between firms and URIs.

Drawing on social network theory and technological innovation literature, this study investigates how firm innovation mediates the relationship between tie strength of alliance network and the prob...

Research and development (R&D) collaboration plays a vital role in the development of new products. As both external knowledge sourcing and internal knowledge base contribute to new product develop...

Social ties can help firms gain and secure resources. However, it is unclear whether social ties facilitate or inhibit firms’ decision-making on dominant design generation. Our study distinguishes the variant roles of business ties and political ties and examines how contextual factors moderate the effects of these ties. Based on archival data of 443 Chinese automotive firms embedded in standard alliances during the period 2005–2009, we find that weak business ties enhance firms’ influence on dominant design, and the positive effect of the number of business ties is even stronger when firms operate in a context with higher IRP enforcement. Moreover, the empirical results indicate that when either the IPR enforcement or the technological turbulence is high, establishing political ties is detrimental to a firm’s influence on dominant design. Our findings add new insights to the research on social capital, dominant design, and open innovation in the automotive industry. The findings also provide significant implications for managers by showing how they can utilize social ties to influence the emergence of dominant designs in an industry.

Diverse R&D alliance partnerships not only provide access to broad external knowledge but also bring considerable expenses caused by transaction costs and absorption problems. The effective utilization of external knowledge in developing new products is closely related to the internal knowledge base. This study examines how firms' knowledge base properties influence the relationship between diverse R&D alliance partnerships and NPD performance. We empirically find that increases in firms' knowledge complementarity reinforce the positive impact of diverse alliance partners on NPD performance, but increases in firms’ knowledge substitutability attenuate. These findings offer valuable managerial implications that firms should commit more seriously to their internal knowledge base so that they can achieve higher NPD performance when establishing R&D alliances with external actors.

Innovation is a process of knowledge recombination [Fleming, L. 2001. “Recombinant Uncertainty in Technological Search.” Management Science 47: 119 p]. Extant literature highlights the importance of a firm’s knowledge base for innovation, while little is known about the structure of a firm’s knowledge base and how it affects the firm’s explorative innovation. Based upon the perspective of network analysis, we portray a firm’s knowledge base as an intra-organisational knowledge network and examine the effects of two structural features of the network – density and centralisation – on the firms’ exploratory innovation. Using a manual collected dataset of 738 Chinese automobile manufacturers, we find that a firm explores fewer new knowledge elements when the firm holds a dense knowledge network or a centralised knowledge network. More importantly, with the increase of a firm’s R&D collaborations with external actors, the negative effect of density is mitigated while the negative effect of centralisat...

In order to investigate the role of asymmetric spillovers in the stability of R&D cooperation, this paper distinguishes two different types of cooperative partners, and uses a game theory approach to reveal the relationship between asymmetric spillovers and R&D investment in the horizontally and vertically related R&D cooperation. In the horizontal R&D cooperation, higher incoming spillovers and lower outgoing spillovers induce firms to invest on R&D efforts as agreed. However, it is the contradiction between horizontal firms' attitudes towards asymmetric spillovers that leads to the inherent instability of the cooperation. In the vertical R&D cooperation, our results question the usually held opinion about the effects of asymmetric spillovers on the decision of R&D investment. The incoming spillovers are less important in the innovation process for vertically related R&D cooperation. A firm tends to under-invest on the arranged level of R&D efforts when its incoming spillovers increase. Our results also show that efficient mechanisms to restrain firms' non-cooperative behavior are essential to improve the stability of horizontal and vertical R&D cooperation.

The new product development (NPD) is crucial to firms’ survival and success. Tough decisions must be made between the binary NPD strategy (i.e. incremental NPD strategy and radical NPD strategy) to ensure that scarce resources are allocated efficiently. The inappropriate NPD strategy that does not meet the internal and external conditions may lead to resources waste and performance decline. The binary NPD strategy forecasting is helpful to guide the firms when to improve existing products and when to develop ‘really new’ products. Therefore, the primary purposes of this study are to construct an evaluating indicator system and to find the appropriate method for the binary NPD strategy forecasting. Here we obtain 1088 valid sample datasets from Chinese automotive industry, covering the period 2001–2014. The empirical results indicate that RS-MultiBoosting as a kind of hybrid ensemble machine learning (HEML) method demonstrate an outstanding forecasting performance in dealing with the small datasets by comparison with the other four ensemble machine learning (EML) methods and three individual machine learning (IML) methods. The findings can help firms to make the right business decision between incremental and radical NPD strategies so that they can avoid resources waste and improve the overall NPD performance.