21-AM-04: ST5.5 - the New Silk Road of Innovation: Knowledge Flow, R&D Networks, and Open Innovation
The present track is connected to a call for a special issue on the R&D Management Journal and it is promoted by the Guest editors of the call. The Silk Road connected the East and West for almost 2000 years providing the network of trade routes and the knowledge interactions that diffused great innovations such as paper, printing, and gunpowder. With the decay of East Asian civilization, the Silk Road declines and fades out. The technological knowledge links, between the East and West, become intermittent and unidirectional from the Western to the Oriental countries. The recent rise of East Asia has changed the context starting a reverse knowledge flows, which challenges the existing models. The Chinese Government launched, in 2013, the “One Belt One Road” initiative an ambitious program to re-connect Asia, Africa, Middle East, and Europe economically, politically and socially. Innovation collaborations and transfer are crucial factors so the track aims to explore the possible impacts of this “New Silk Road of Innovation”.
The dynamics of collaboration are classified into, at least, three perspectives. Firstly, the “New Silk Road” generates opportunities to develop new R&D networks because it involves a large number of countries with various research institutions that cooperate. Secondly, it may also have a far-reaching impact on how knowledge diffuses among Asia, Europe, and Africa. Usually, latecomer countries attempt to catch up acquiring production equipment based on which they learn manufacturing know-hows (or tacit knowledge). However, recently, the fast development of Eastern economies provides good “window opportunities” for traditional technology-followers to catch up or even leapfrogging, so their demands on lead-edge science and technology help to form the basis for science-technology based innovation locally, which involves the new ways of cross-border knowledge diffusions within these regions.
Finally, the “New Silk Road” represents an opportunity to explore East-West open innovation practices. Traditionally, iconic open innovation mainly happens in advanced countries (Westerns firms working with Western universities), and then spillover over to Asian firms which would continue to improve the process for cost reduction and manufacturing efficiency so that they can produce in scale, while Asian universities barely play with their own. The growth of national innovation system of Eastern economies provides new enabling factors for Eastern firms augment inbound and outbound knowledge flows with local universities for original innovations. Digital-age technologies (e.g. big data, smart manufacturing, 3D printing, etc.) also bring significant changes on how Eastern firms and universities can innovate together – the alliances, innovation ecosystems, and the triple helix along the “New Silk Road” are being significantly re-framed.
The “New Silk Road” poses new opportunities to studies on R&D networks, knowledge diffusions, and open innovation and in the track, we aim to explore some linked research questions.
Impacts of Strategic Proximities on Knowledge Flow in Inter-Firm Networks: An Empirical Study in 3D Printing Industry
1Beijing University of Posts and Telecommunications, China, People's Republic of; 2Tsinghua University, China, People's Republic of
International knowledge flow has been a driving force for promoting the economic growth. With the rise of new types of knowledge cooperation such as open innovation, especially for emerging industries, the inter-firm knowledge flow has shown new phenomenon, and its effect factors may be different from traditional industries.
With the rise of global manufacturing networks, the inter-firm relationships are no longer a simple linear relationship, but a dynamic and complex network relationship. The inter-organizational network relationship has gradually become the focus of research. Network's heterogeneity and structure such as degree, betweenness and closeness centrality, also social cohesion and network range has been shown to have an impact on knowledge flow(Jiang, Goel, & Zhang, 2017; Leon, Rodríguez-Rodríguez, Gómez-Gasquet, & Mula, 2017).
Many scholars explore the influencing factors of organizational proximity on knowledge flow, mainly from the perspective of geo-economics(Knoben & Oerlemans, 2010) and technological-economics (Marrocu, Paci, & Usai, 2013) As strategy is a roadmap employing firm knowledge to achieve strategic goals in business(Zack, 1999), the exploration from the strategic aspects is of significance.
Most research of the influencing factors and mechanism of knowledge flow is based on bi-lateral relationship, and it lacks of studies from a network perspective.
Previous studies explore the influential elements of knowledge flow from organizational proximities such as geographic proximity and technological proximity, while the strategic proximities are neglected.
This paper aims to examine the impact mechanism of strategic proximities on inter-firm knowledge flow in an emerging industry from a network and dynamic perspective.
This paper puts forward a conceptual model of impact mechanism of strategic proximities on inter-firm knowledge flow, and conducts a longitudinal empirical study. Quadratic assignment procedure (QAP) method is applied for analysis. This paper uses QAP correlation analysis and QAP multiple regression analysis to testify the hypotheses. In addition, we replace some indicators and conduct a robustness test.
This paper conducts an empirical study in 3D printing industry. 12-year Longitudinal data (2005-2016) on 150 firms in 3D printing industry from all over the world is collected from multiple databases. This study conducts quantitative research by combining patent data, financial data and basic information of listed companies. We retrieved worldwide patent data from the databases of Derwent World Patent Index (DWPI) and Derwent Patents Citation Index (DPCI). Financial data and basic information from the global listed company database OSIRIS.
The specific process of data collection and cleaning is as follows. First, up to 2016, 11640 pieces of 3D printing related patent family data are retrieved in the DWPI. Second, after data cleaning, the top 300 companies with the highest number of patent citations in DPCI are selected. Third, match these 300 companies in OSIRIS, and get 150 companies with relatively complete data.
The results provide strong support for the arguments that strategic proximities in inter-firm networks facilitate knowledge flow. Two of the strategic factors— proximity of internationalization strategy and proximity of value-chain positioning strategy—have significant effects on knowledge flow. These findings highlight the critical roles of strategic planning in the knowledge flow.
Also, the results verify the moderating role of firm-size proximity in knowledge flow in inter-firm networks. Specifically, firm-size proximity positively moderates the relationship between the proximity of internationalization strategy and knowledge flow, and it also moderates the relationship between the proximity of value-chain positioning strategy and knowledge flow.
Contribution to Scholarship
This research analyzes the promotion or inhibition effect of strategic proximity on inter-firm knowledge flow from a network perspective. It develops a conceptual model for explaining the relationships among strategic factors and knowledge flow, and amplifies the research of the effects of organizational proximities on knowledge diffusion.
In addition, this study uses the multi-source heterogeneous data for 12 years, and use the QAP analysis method to perform the regression of network to network. Since earlier research method mostly is case study and the questionnaire approach, this research applies new data and new methods to the knowledge diffusion studies.
Contribution to Practice
The results indicates that firms with similar internationalization strategy and value-chain positioning strategy are more likely to have inter-firm knowledge flow. This has implications for how knowledge diffuses over time and for technology policy. In order to promote knowledge transfer, we should also take account of the specific strategies of firms.
This research fits the conference topic “Adoption and diffusion of deep-tech (e.g. Autonomous Vehicles, Additive Manufacturing/3D printing, Blockchain, IoT, Machine Learning, Biotech, Nanotech)”. It's also quite relevant to Theme 5: Emerging Markets (Track 5.5 The New Silk Road of Innovation: Knowledge flow, R&D networks, and open innovation).
Jiang, J., Goel, R. K., & Zhang, X. 2017. Knowledge flows from business method software patents: influence of firms’ global social networks. Journal of Technology Transfer: 1-27.
Knoben, J., & Oerlemans, L. A. G. 2010. Proximity and inter-organizational collaboration: A literature review. International Journal of Management Reviews, 8(2): 71-89.
Leon, R. D., Rodríguez-Rodríguez, R., Gómez-Gasquet, P., & Mula, J. 2017. Social network analysis: A tool for evaluating and predicting future knowledge flows from an insurance organization. Technological Forecasting & Social Change, 6(6): 114: 103–118
Marrocu, E. , Paci, R. , & Usai, S. 2013. Proximity, networking and knowledge production in europe: what lessons for innovation policy? Technological Forecasting and Social Change, 80(8), 1484-1498.
Zack, M. H. 1999. Developing a Knowledge Strategy. California Management Review, 41(3): 125-145.
Tie strength or structural configurations? The network effect of innovation diffusion of emerging technologies from network evolution perspective
Tsinghua University, China, People's Republic of China
Emerging industries with the characteristic of intensive knowledge technology, which has provides an important opportunity for the catch-up countries. Diffusion networks play more and more important role in emerging technology diffusion, especially in providing the resources and building relationships.
How do networks effect of innovation diffusion of emerging technologies? Substantial research on inter-organizational networks conducted across a variety of empirical setting and theoretical orientations sustains two broad views (Mizruchi & Marquis, 2005). The first view is based on the concept of strength of contact between organizations and their network partners. Network ties between organizations act as conduits for information, knowledge, organizational practices and material resources. As a consequence the presence of strong network ties is likely to make partners more homogeneous in terms of structures, behavioral orientation and, arguably, performance (Ahuja, 2000). The second view is based on the notion of structural equivalence, or similarity in networks positions that organizations come to occupy by virtue of similarity in their patterns of relation with third parties. Earlier comparative studies treated tie strength and structural equivalence as alternative mechanisms underlying social influence, and as competing explanations of behavioral similarity (Mizruchi, 1993).
Some researchers have simply using attribute data to examine the characteristics to identify the knowledge flow and transfer ability (Park et al., 2018), but still lacks the inquiry about network factors like tie strength and structural equivalence in the innovation diffusion of emerging technologies and its inherent network mechanism.
What is the specific impact mechanism of tie strength and structural equivalence among firms in the process of innovation diffusion of emerging technologies and to what extent does it affect the innovation diffusion of emerging technologies?
An exponential random graph models are used for us to analysis the impact of structure and attributes on the direct networks. ERGMs treat the whole network as a single observation, thus freeing it from any independence assumptions. The purpose of ERGM is to explain how and why the connections in the network occur, so the explanatory variables are the probability of the emergence of a network. The ERGM estimate is to analyze the special structure that affects the probability of network implementation, and to find out a more important process of building the network local relationship.
Dataset: patent data, OSIRIS data and expert interview data
Patent data source: the worldwide patent data from the well recognised Derwent World Patents Index (DWPI) and Derwent Patents Citation Index (DPCI) databases through the Thomson Innovation (TI) search engine.
Case: the natural language processing in artificial intelligence industry.
IPC code: IC=(G06N000300 OR G06N000302 OR G06N000304 OR G06N000306 OR G06N0003063 OR G06N0003067 OR G06N000308 OR G06N000310 OR G06N000312 OR G06N000500 OR G06N000502 OR G06N000504 OR G06N000700 OR G06N000702 OR G06N000704 OR G06N000706 OR G06N000708 OR G06N009900).
Methods: Exponential random graph model of the longitudinal network with 93 firms’ network panel data
Time range: 2001-2015
From the view of tie strength, both the knowledge absorptive and spillovers homogeneity have negative effect, but in the 2011-2015, the knowledge spillovers homogeneity become gradually not significant. The geographic homogeneity has positive effect in 2001-2010, and no effect in the 2011-2015,which demonstrate the geographic has not been an important factors in the network diffusion.For the structural equivalence, reciprocity, the transitive triplets, and popularity have positive effect on the diffusion network. While the activity (2011-2015) and outdegree have negative effect.
Contribution to Scholarship
This study construct the network diffusion of emerging technologies (AI) based on the concept of endogenous attributes and exogenous configurations factors for the emerging industry. Through the ERGM of longitudinal network which helps identify the network evolution for diffusion.It clarifies the different impact mechanisms and helps to identify international opportunities and challenges that firms faced in the process of innovation diffusion.At the same time, it is of great significance to strengthen the diffusion transfer and secondary innovation, to foster regional innovation system and to promote the transformation of China's innovation model.
Contribution to Practice
Taking the natural language processing technology in the AI industry as example, this paper opens the “black box” of innovation diffusion of emerging technology, using tie strength and structural equivalence factors to examine the functions and evolution mechanisms of network by using the ERGM. The diffusion network provides an effective means for disseminating emerging technologies, which is a critical way to foster the competitiveness of emerging industries and promote national innovation capacity. Thus, we can put forward recommendations for improving the diffusion of emerging technologies, and improves our understanding on the dynamics of emerging technology diffusion network.
This paper involves the innovation diffusion, Knowledge flow, network and open innovation, which is highly agree with the R&D Management, special issue of "The New Silk Road of Innovation".
1.Mizruchi, M. S., & Marquis, C. (2006). Egocentric, sociocentric, or dyadic?: Identifying the appropriate level of analysis in the study of organizational networks. Social Networks, 28(3), 187-208.
2.Ahuja, G. (2000). Collaboration networks, structural holes, and innovation: A longitudinal study. Administrative science quarterly, 45(3), 425-455.
3.Mizruchi, M. S., & Galaskiewicz, J. (1993). Networks of interorganizational relations. Sociological Methods & Research, 22(1), 46-70.
Knowledge Spillovers and Innovation in Colombian Manufacturing Firms
School of Business, Pontificia Universidade Catolica do Rio de Janeiro,Rio de Janeiro, Brazil, Brazil
Innovation drives economic growth, a fundamental challenge facing emerging economies. To this end, firms must invest to develop and acquire knowledge. An alternative channel available to foster innovation activities comprises knowledge spillovers, which offer firms an opportunity to improve performance and narrow the technology gap.
Despite efforts to improve its innovation system, Colombia continues to lag behind OECD and other key Latin America economies in terms of R&D spending, patents and scientific publications. An innovation survey revealed that more than half of Colombian firms had not innovated and that enterprises accounted for a small proportion of the country’s R&D spending (Verga-Jurado et al., 2015). Several factors influence firms’ innovation and performance, but since the advent of the open innovation model (Chesbrough, 2003), the use of external resources and knowledge and collaboration with multiple partners has become more common and necessary. The diversity and number of partners influences participants’ performance (Faems et al., 2010). Firms can additionally benefit from knowledge spillovers that involuntarily occur whenever other firms innovate (Cassiman and Veugelers, 2002). However, the absorptive capacity of the recipient firm (Cohen and Levinthal, 1990) influences the level of benefits extracted from any external knowledge sources.
Although the effects of knowledge spillovers on firms’ innovative performance have received some academic attention in developed economies, the same is not true of less innovative countries, including Colombia. However, such countries are the ones that would benefit most from access to this knowledge, emphasising the importance of further research.
Aiming to fill the literature gap, this study empirically analyses the relationship between knowledge spillovers and firms’ innovative performance, with a view to answering the following research question: To what extent do industry level knowledge spillovers affect firms’ innovative performance and sales growth?
The sample was collected from EDIT, an innovation survey conducted by the Colombian National Department of Statistics (DANE), which follows the Oslo Manual guidelines. The proposed model comprises five constructs that were operationalised using proxies from the theory. Data and variables were extracted from three different surveys covering the period from 2011 to 2016 in order to account for a time lag between proxies pertaining to innovation activities and performance. After testing the validity and reliability of the measurement model using confirmatory factor analysis, the hypotheses will be tested using structural equation modelling and multi-group analyses with Bayesian estimation.
The EDIT surveys were accessed through the DANE website. The fourth edition of the survey covered the period 2011-2012 and was responded by 9,137 firms. Subsequent surveys (for 2013-2014 and 2015-2016) received 8,835 and 7,947 respondents, respectively. We selected only manufacturing firms that had developed at least one product or process innovation, or that claimed to have an innovation activity in progress, abandoned or suspended in the period 2012-2013. Furthermore, firms that were absent from the three editions or that had missing or inconsistent values were excluded, resulting in a final sample of 1,562 firms.
The Colombian manufactory industry is characterised by a significant number of SMEs (75%) with fewer than 200 employees, and national firms predominate (90%) in terms of capital ownership. The main segments are: food products; chemicals; rubber and plastic products; clothing; and non-metallic mineral products, together accounting for 54% of the sample. Compared to their larger counterparts, SMEs cooperate with a smaller diversity of partners (36%) involved in fewer forms of scientific, technological and innovation activity (31%). SMEs also demonstrated less product innovation (by 44%) and process innovation (by 61%) in the period 2013-2014. Given this industry profile, we anticipate that knowledge spillovers will positively influence the innovation and performance of Colombian firms, providing valuable inputs to help overcome resource constraints commonly found in emerging countries. We also expect this contribution to be contingent on firms’ absorptive capacity and the appropriability regime of the industrial segment. By including firms’ cooperation with partners in the model as an alternative source of knowledge, we intend to assess the relative importance of spillovers. Furthermore, we will use multi-group analysis to compare the results of larger and smaller firms in order to provide further insights.
Contribution to Scholarship
We expect this study to add to discussions concerning the complementary role of knowledge spillovers in supporting firms’ innovation activities. Although other studies have been undertaken in developed countries with similar objectives, we aim to improve understandings of the magnitude and significance of these contributions in the case of an emerging economy. Through the introduction of a competing source of knowledge provided by external cooperation, we seek to add value to the analysis of spillover effects.
Contribution to Practice
Knowledge represents an instrumental resource for innovation. Leveraging public sources made available via industry spillovers can support managers in negotiating challenges of increasing firms’ performance and overcoming constraints such as a lack of resources. To benefit from these information channels, managers should focus on enhancing firms’ cognitive capabilities. Training and diversified experiences are examples that help increase firms’ ability to assimilate, make new associations and transform new knowledge. In terms of public policies besides financial support and improving the regulatory framework, it is important to foster firms’ investments in education and professional training to facilitate innovation.
This article will enrich the R&D Management conference by shedding light on discussions concerning knowledge spillovers and will bring a different perspective regarding the benefits and implications of this phenomenon for firms in emerging economies.
Cassiman, B. and Veugelers, R. (2002), “R&D cooperation and spillovers: some empirical evidence from Belgium”, American Economic Review, Vol. 92 No. 4, pp. 1169-1184.
Chesbrough, H.H.W. (2003), “Open Innovation: The New Imperative for Creating and Profiting from Technology”, Harvard Business Press, Boston, MA.
Cohen, W.M. and Levinthal, D.A. (1990), “Absorptive capacity: a new perspective on learning and innovation”, Administrative Science Quarterly, Vol. 35 No.1 , pp.128–152.
Faems, D., Van Looy, B. and Debackere, K. (2005), “Interorganizational collaboration and innovation: toward a portfolio approach”, Journal of Product Innovation Management, Vol. 22 No. 3, pp. 238-250.
Vega-Jurado, J., Juliao-Esparragoza, D., Paternina-Arboleda, C.D., Velez, M.C. (2015), “Integrating Technology, Management and Marketing Innovation through Open Innovation Models”, Journal of Technology Management & Innovation, Vol.10 No. 4, pp. 85-90.
The role of innovation intermediaries in the development of innovation ecosystems
Federal University of Rio de Janeiro, Brazil
Emerging countries continuously struggle to develop context-based strategies to improve their innovation performance. Although those countries may present the relevant actors to pursue innovation, not necessarily they interact in order to create value (Watkins et al., 2015). This flaw may indicate the necessity of innovation intermediaries that foster such interactions.
Both in theory and practice, the concepts of innovation and entrepreneurship have been studied as symbiotic reinforcing the idea that organizations do not act and exist alone, rather are embedded in ecosystems where they influence each other, as well as the whole system (Moore, 1993). Especially in life sciences, this embeddedness may be the root for the health of the ecosystem (Clarysse et al. 2014; Powell, 2010). At the firm level, to be conscious of and active in the surrounding ecosystem is considered a major competitive advantage (Teece, 2007). However, the understanding, active construction and maintenance of interactions inside an ecosystem require both resources and knowledge from the involved organizations (Arikka-Stenroos & Ritala, 2017). Thus, innovation intermediaries may be a valuable source of both knowledge and resources, whereas they may also strengthen the ties of loose ecosystems, in order to promote interactive learning among the actors.
Although coopetitive interaction is the core of an innovation ecosystem, few studies explore how those interactions are created (e.g. Hienerth et al., 2014). We claim that not only innovation intermediaries may foster valuable interactions among actors, but also that they are needed in an emerging country context.
Having said that, we propose the following questions: how do innovation intermediaries may promote valuable interactions among actors in an ecosystem? This question may be extended to which types of interactions are valued, which are the necessary channels to create them and what is necessary to foster those interactions.
To answer the research questions, we performed qualitative research through a case study, which, according to Yin (2001) is applicable when the researcher has little control over the events and when the focus is upon contemporary phenomena, inserted in real life contexts. The object of the case study was the biotechnology internationalization program (Prointer Bio) of SEBRAE (Brazilian Micro and Small Business Support Service). Agreeing with Siggelkow (2007) on the purpose of case studies, we believe that we can provide both an illustration of theory application and an inspiration of how we may improve innovation ecosystems in emerging countries.
The PROINTER programs originated in the actions that SEBRAE / RJ has been implementing since 2007 with the objective of promoting the competitive, sustainable and independent insertion of micro and small Brazilian companies in the international market. The programs have three axes: market intelligence, qualification in international business, cooperation and commercial promotion.
The first edition of PROINTER has been drawn to the O&G sector (2007-2011). With several results achieved, bringing benefits to the participants, another edition was modified to fit the Biotech sector (2015-ongoing).
This research focuses on the biotech edition of Prointer, which encompasses two batches of companies. We provide here a longitudinal case study which includes: (1) primary data from semi-structured interviews (18 interviews conducted with a mean duration of 60 minutes) both with the participating companies and with the program management / consulting team; (2) secondary data from reports provided by SEBRAE; and (3) direct observation from the researcher involved in the case, in several opportunities such as events, meetings and group discussions.
According to the interviews, Prointer Bio was the first initiative to promote the integration of the biotech (or life sciences) sector, whereas supporting the companies in Rio de Janeiro. Therefore, we may introduce results in two different streams: first, the offering of training, specialized knowledge and coaching during the entrepreneurial journey helped entrepreneurs (senior and beginners) in several ways, such as: to better structure the companies, to be more market-oriented and alert for opportunity recognition, and to prepare for internationalization, which was the main aim of the program. This first stream relates to the competences the firm has to have to create and capture value from the ecosystem. The second stream encompasses the recognition and performance of Sebrae (more specifically, ProInter’s team) as a hub, that may strength loose connections. Types of interactions that ProInter could provide to the companies include customers, anchor tenants, senior executives that shared knowledge, R&D organizations, investors, and other small & medium size companies, belonging to the same sector, but unknown to the entrepreneurs. Both streams of work leverage the possibility of coopetitive interactions among the actors of the regional ecosystem, whereas allowed companies to enter other innovation ecosystems in the international market.
Contribution to Scholarship
This research contributes by shedding some light on the missing role of innovation intermediaries on fostering coopetitive interactions among actors in an ecosystem. In emerging countries, where actors may be present but inactive in relating to each other, innovation intermediaries may be an interesting trigger to both prepare and connect actors. Although we agree that ecosystems are too complex to be perfectly designed, sometimes companies need guidance to understand to which ecosystem they should belong, how to enter those ecosystems or what are the codes to be followed. Whereas the literature normally focuses on global companies or technological inter-dependency, this study provides a real-case situation with small innovative companies that struggle to maintain their innovative purpose because of the lack of embeddedness in an innovation ecosystem.
Contribution to Practice
Our practical contribution is two-fold: for policymakers and innovation enthusiasts, not only we provide an example of how interactions might be fostered, but we reinforce the need of an intermediary to sew up those interactions. This means that, in some contexts, the support for innovation should not only rely on “supplies” such as financial support or training, but rather consist of an intensive and close work with entrepreneurs and other stakeholders. For entrepreneurs, we convey the message that to be connected and active in valuable ecosystems provide more opportunities to innovate, learn and therefore secure positioning in the market.
After a major economic crisis and a period of wasted opportunities, Brazil is struggling to show the world its value as a serious emerging country. This case study highlights strengths and weaknesses of innovation ecosystems in Brazil, involving science-based companies from Rio de Janeiro.
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