Methodology workshop - How to study globalization of innovation

Transcription

1 Methodology workshop - How to study globalization of innovation Cristina Chaminade Prof. Innovation Studies Lund University Outline 1. Background from theoretical to empirical challenges 2. Sources of data Foreign direct investment Innovation networks social network analysis 1

2 What we learn this morning Innovation is becoming truly global in Dickens sense This is challenging existing theories in innovation studies, economic geography and international business Conceptual stretching and ambiguity Future: beyond either-or how can the different frameworks be combined to better understand innovation dynamics globally? Develop discrete theories to answer specific questions and link them together (Ponte and Sturgeon, 2014) Understand possibilities and limitations of existing theories & concepts From theoretical to empirical challenges Most of our (innovation) theories are based on empirical evidence from the North, for example. Innovation systems Functions of innovation systems (Edquist and Hommen, 2009) Importance of local interactions and regional innovation systems (Asheim and Gertler, 2005) ASSUMING: well functioning systems and high technological capabilities but what happens when we are not talking about advanced countries, with advanced technological capabilities? (Lundvall et al, 2009) And emerging innovation systems or innovation systems in formation that can hardly support innovation in the firm? (Padilla et al, 2008, 2009, Chaminade and Vang, 2008) 2

3 From theoretical to empirical challenges International business Offshoring of R&D, driven by large MNEs, for exploration (in the North) and exploitation (in the South) Narula and Zanfei (2005) Offshoring of R&D explained by R&D intensity (technology push) (Barbosa et al, 2013) but how can it explain innovation investments by multinationals from emerging economies? (Goldstein, 2006, Ramamurti, 2008, Amighini et al, 2013) how can it explain globalization of innovation by SMEs? (Barnard and Chaminade, 2012, Harirchi and Wiig, forthcoming) From theoretical to empirical challenges Limitations of existing empirical literature Limited to the use of certain indicators for which there is international comparable data like patents (Saliola and Zanfei, 2009, Piscitello and Zanfei, 2007) or FDI investments (Castellani and Castelli, 2013) Are these the right ones? Eg. Global research collaboration Generally not capturing sectoral and regional differences Too aggregated data (Fagerberg and Srholec) or studies in just one industry or region (Cassi et al, 2012 on wine, for example) Not capturing interrelations between macro and other levels of analysis need for multilevel models 3

4 So, how can we study globalization of innovation??? Data bases Own data Foreign Direct Investments FdI Markets ( Proprietary data base - $$$ Information on firm s FDI (Foreign direct investments) Name of firm Host and home country Nature of the investment (Amount of the investment) World coverage Longitudinal data 2003-current Based on announcements (LBIO) 4

5 Foreign Direct Investments FdI Markets ( Foreign Direct Investments FdI Markets ( Articles published in Research Policy, World Development and Economic Geography using this data 5

6 Foreign Direct Investments Our own experience (Spin-off of the Globelics Academy 2015!) Foreign Direct Investments Our own experience (Spin-off of the Globelics Academy 2015!) Chaminade and Gómez (2016) Technology-driven foreign direct investment within the Global South 6

7 Foreign Direct Investments However In our sampe, 50% of the data was wrong! Based on announcements (LBIO) Not real transactions Trade World input output data Source: Timmer et a (2016) An anatomy of the global trade slowdown on the WIOD 2016 release 7

8 Trade World input output data Techniques to estimate the R&D input in each industry (R&D vectors) Inter-industry R&D spillovers Hard to do at World level- in principle the R&D intensity of a sector depends on the country country & sector specific Possible to estimate the knowledge intensity of trade flows and value chains Global innovation networks Patent data The evolution of the global technological collaboration network Source: Prato and Nepelski (2012) based on Patstat 8

9 Global innovation networks Patent data However Patent data quite limited to specific industries (not all industries patent) Biased towards developed countries Does patent capture innovation output? E.g. Services Global innovation networks Scientific publications Limitations: Scientific output is not equal to innovation Limited to a specific sector and handful of countries 9

10 Advantages and limitations Advantages of existing data Availability World coverage Time coverage Limitations of existing data Reliability (FdI Markets) Representativity (Patents; MNEs) Do they really capture INNOVATION networks? 2. Creating your own data Firm based surveys Firm-based surveys Multi-country Multi-region Multi-sector Advantage Identification of commonalities and differences (inter-firm, inter sectoral, inter country) Tailored multiple mechanisms, impact 10

11 2. Creating your own data Firm based surveys our own work Latin America Europe Asia 2. Creating your own data Firm based surveys Our work on global innovation networks - The GLOBINN data sets Survey on innovation and internationalization of firms in Pune and Beijing (regional focus) and South Africa (Cape Town and Johannesburg), 3 industries Survey on engagement in global innovation networks of firms in 5 countries in Europe plus BICS (Ingineus survey) Interviews same guide all over the world Outcome: Unique data base with comparable information at firm and system level on: Strategy, Capabilities, Linkages and Globalization 11

12 2. Creating your own data Firm based surveys Disadvantages Time consuming Expensive Generability Challenges Questionnaire that can work across sectors Questionnaire that can work across countries Different methods for data collection (China, India, Sweden) 2. Creating your own data Firm based surveys How to minimize the challenges Clear criteria for sampling Glossary of main definitions ALL terms used in the questionnaire are defined Charts for the questionnaires Training of interviewees Pilot in all countries Non-response sample 12

13 2. Creating your own data The future Big data Source: Hutamaki et al (2013) Analysing the data Social Network Analysis 13

14 Objective Look at how countries/ regions/technologies are connected Two types Static analysis UciNEt, Gephi (Giuliani, Morrisson) Dynamic analysis R (Pierre Alex Ballant) Advantages Multilevel Allows simulations Longitudinal (changes over time) Clear advantage from standard analysis of collaboration patterns Challenges Global data we end up using traditional indicators (co-patenting, co-publication, investment data, trade data) New approaches: internet data (algorithms), research collaboration data 14

15 Traditional approach Based on Innovation surveys data (Oslo Manual) Who do you collaborate with? What are the sources of information for innovation? Firm A User University Firm B Would this be firm A? User Consultant Competitor Is this the same user as firm A?? Traditional approach an example Source: Tsai K-H (2009) Collaborative networks and product innovation performance: Toward a contingency perspective, Research Policy. SLIDE BY ELISA GIULIANI (2014) 15

16 Competitor B Supplier C University A University C Firm A University B Supplier A Competitor C Competitor D Competitor A University D Supplier B Firm B User Consultant Firm A User University Firm C Firms in a cluster Giuliani and Bell (2005) Countries Chaminade and Gomez (2016) 16

17 A new world of research questions Can knowledge of Firm C reach Firm A? How? What happens if the user that connects A and C disappears? Can they connect otherwise? Are there firms (or nodes) that are more important than others? E.g. central nodes, boundary spanners Do we have diversity of knowledge types? We need to understand: How these networks are structured Whether certain network structures are better than others What drives the formation of certain structures How can certain networks evolve Structural analysis Dynamic analysis 17

18 Type of Data Two types of networks: Full networks: You are able to collect relationships existing between a population of actors (e.g. school mates, firms in a specific industrial location, etc.) Example: Using patent data or collecting data from all actors in network Ego-centered networks You are only able to collect data about the relationship that a given focal actor has with other actors, and (possibly) the linkages that these alters maintain among themselves Example: Asking MNE about their networks SNA SLIDES ADAPTED FROM GIULIANI (2014) Whole networks Figure: Knowledge sourcing through collaboration in new media Source: Martin/Moodysson

19 Ego centered networks Source: Liu, Chaminade and Asheim (2013) Full networks Data sourcing options 1. Existing data bases (World Input Output, FdI Markets, Patents, Publications) 2. Direct data collection through relational questionnaires You know all the population- Rooster You do not know the entire population Snowball 3. You infer data Big data 19

20 Full networks If you want to know more Social Network Analysis for Innovation - Giuliani & Pietrobelli Packages UciNET Gephi R very mathematician; used for dynamic analysis. In sum 1. Multiple sources of data to analyze globalization of innovation and the different mechanisms 2. Usually limited to one mechanism (trade, offshoring or collaboration) 3. Capturing multiple mechanisms and its interactions is a MAJOR gap in the literature For example: can mobility of human capital be a predictor of research collaboration? 4. Different methods for analysis: Standard: Econometrics, quite linear Future & dynamics (Big data) 20

21 Closing up Novel and challenging research area New empirical challenges new data is needed, comparable at global scale Input from young researchers is needed!!! How can GLOBELICS help address the challenge of Globalization of Innovation? Evidence from different regions/countries around the world Large scale projects comparative analysis Data collection and sharing! THANKS!!! So, if you have NOT been browsing funny cat videos during the presentation ANY QUESTIONS? 21