How does the partner type in R&amp;D alliances impact technological innovation performance? A study on the Korean biotechnology industry

Shin, Kwangsoo; Kim, Sang Ji; Park, Gunno

doi:10.1007/s10490-015-9439-7

Cited by 47 publications

(48 citation statements)

References 99 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Likewise, Zynga et al [50] prove that the existence of distinct routines and organizational structures can explain why some firms implement open innovation successfully. Shin et al [51] studied technological innovation performance and found the moderating effect of absorptive capacity and potential competition by categorizing strategic alliances for R&D activities in the biotechnology industry into vertical-downstream alliances, vertical-upstream alliances, and horizontal alliances. Vertical alliances have a positive impact on technological innovation performance, while horizontal alliances have an inverted U-shaped relationship with technological innovation performance caused by the effect of competition.…”

Section: Open Innovation and Determinants Of Cooperation Formmentioning

confidence: 99%

“…The heterogenous findings of extant research might be the result of different performance measures (innovative performance, financial performance, firm performance, R&D performance, absorptive capacity, joint patents) used in the research, as well as different approaches to investigating these phenomena, mainly in Biopharma and ICT industries. Future studies should verify the effects of strategic alliances on innovation performance from a greater variety of perspectives taking into consideration various measures [51]. It could also be interesting to consider including performance measures to capture the impact of open innovation on social innovation objectives, such as sustainability or climate change [97], rather than merely financial aspects or innovation understood as patent outputs.…”

Section: Dyadic Levelmentioning

confidence: 99%

See 1 more Smart Citation

Determinants of Innovation Cooperation Performance: What Do We Know and What Should We Know?

2018

View full text Add to dashboard Cite

The involvement of firms in innovation cooperation with different partners has become a widespread phenomenon in the contemporary business landscape. Our paper provides a review of extant alliance, innovation, open innovation and inter-firm collaboration literature and organizes it based on a conceptual framework featuring three levels of analysis: (a) the dyadic level, (b) the network level, and (c) the location level. The article identifies roadmaps in each of these areas and also highlights existing gaps in the present understanding of innovation cooperation. Thereby, it outlines a research agenda by identifying key research questions and issues in the areas where further research is needed and encouraged.Sustainability 2018, 10, 4517 3 of 32 changes that create the economic space of a given country [29] In this space, which Freeman called the National Innovation System, there are more or less formalized networks of cooperating companies and institutions. Indeed, as Fomina et al. [30] argue, the stability of economic development is determined by the features of the network structure in a collaborative engagement of enterprises. Therefore, it is legitimate to explore whether and how the location of companies and other institutions influence their cooperation for the development of innovation, and the performance outcomes thereof.Economic geography has developed the concept of proximity to explain the formation of networks and clusters of innovative companies in industrial districts [31,32]. The different dimensions of proximity may explain some of the positive externalities, such as knowledge creation and transfer, as well as innovation, which are generated by companies that are co-located [33]. According to Boschma [34], proximity implies similarity between actors and organizations, including both a geographical or spatial dimension and other non-spatial dimensions. Knoben and Oerlemans [35] note that "the concept of proximity has been used in many different ways in the literature, this including different measures and definitions".Boschma [34] identifies five types of proximity:

show abstract

Section: Open Innovation and Determinants Of Cooperation Formmentioning

confidence: 99%

Section: Dyadic Levelmentioning

confidence: 99%

Determinants of Innovation Cooperation Performance: What Do We Know and What Should We Know?

2018

View full text Add to dashboard Cite

show abstract

“…Accordingly, ICT companies need to improve the performance of products and services and continuously pioneer new markets in order to survive [1][2][3][4]. Numerous past studies showed that innovation increases sales and further significantly impacts firm survival [5][6][7]. Innovation has become a crucial goal for ICT companies.…”

Section: Introductionmentioning

confidence: 99%

Sustaining Innovative Success: A Case Study on Consumer-Centric Innovation in the ICT Industry

Choy

Park

2016

Sustainability

Self Cite

View full text Add to dashboard Cite

Abstract:The phenomenon of innovation growing rapidly and having a shorter lifespan is a structural change due to the development of ICT (Information and Communications Technology), diverse investment methods, and reduced pattern of innovation adoption. For ICT companies to survive and maintain their success in this ever-changing environment, they need to succeed in fulfilling both productivity and accuracy of innovation. To sustain their innovative success, ICT companies should consistently maintain the direction of innovation towards consumers. The present study analyzes various cases of ICT companies which succeeded or failed to maintain their prior innovative success, and suggests consumer-centric innovation as a solution. To create consumer-centric innovations, companies have to (1) predict the dynamically evolving demand of consumers and continuously transform; (2) proactively employ observation method and big data analysis to discover hidden demands; and (3) identify hassles such as wastes, inconveniences, and anxieties, and put effort in solving these hassles.

show abstract

“…In the same way, these different activities may be carried out with a variety of partners and it is also likely that different partners produce dissimilar effects (Shin et al, 2016). Consequently, Table 5 displays the ATT for the different exclusive cooperation variables by type of non-R&D innovation activity and by type of partner.…”

Section: Resultsmentioning

confidence: 99%

“…Lastly, as the effects of cooperation may differ depending on the type of partner with which the firms cooperate (Shin et al, 2016), we also need to take into account that the cooperative agreements of firms in developing countries not only differ from those of the developed world in terms of the type of activities, but also in the type of partners with which these agreements are established. For instance, in developing countries only a small proportion of the firms have developed cooperative agreements with science and technology institutions and the most common links are established with customers and suppliers and to a lesser extent with competitors (Anlló and Suárez, 2008).…”

Section: Literature Reviewmentioning

confidence: 99%

Cooperation for innovation in developing countries and its effects: evidence from Ecuador

Sastre

Vera

2017

Journal of Technology Management & Innovation

View full text Add to dashboard Cite

This paper evaluates the impact of the most common innovation linkages by type of innovation activity on firms' innovation inputs and outputs. In order to estimate the casual effects, we employ Inverse Probability Weighting, while focusing on exclusive cooperative agreements to prevent our results from being affected by the presence of additional relationships in other activities or with other types of partners. Results indicate that cooperation in non-R&D innovation activities positively affects the introduction of new-to-the-firm product, marketing and organizational innovations, while cooperation in both R&D and non-R&D activities also affects R&D intensity and new-to-the-market product innovation. Furthermore, firms that cooperate exclusively in obtaining information, which is mainly carried out with suppliers and customers, are more likely to introduce new-to-the-firm product and organizational innovations.Keywords: non-R&D cooperation; technological capabilities; impact assessment.Abstract: This paper evaluates the impact of the most common innovation linkages by type of innovation activity on firms' innovation inputs and outputs. In order to estimate the casual effects, we employ Inverse Probability Weighting, while focusing on exclusive cooperative agreements to prevent our results from being affected by the presence of additional relationships in other activities or with other types of partners. Results indicate that cooperation in non-R&D innovation activities positively affects the introduction of new-to-the-firm product, marketing and organizational innovations, while cooperation in both R&D and non-R&D activities also affects R&D intensity and new-to-the-market product innovation. Furthermore, firms that cooperate exclusively in obtaining information, which is mainly carried out with suppliers and customers, are more likely to introduce new-to-the-firm product and organizational innovations.

show abstract

How does the partner type in R&D alliances impact technological innovation performance? A study on the Korean biotechnology industry

Cited by 47 publications

References 99 publications

Determinants of Innovation Cooperation Performance: What Do We Know and What Should We Know?

Determinants of Innovation Cooperation Performance: What Do We Know and What Should We Know?

Sustaining Innovative Success: A Case Study on Consumer-Centric Innovation in the ICT Industry

Cooperation for innovation in developing countries and its effects: evidence from Ecuador

Contact Info

Product

Resources

About