Features of Interactions Between Research Groups and Organizations

Features of interactions between research groups and organizations: evidence from a longitudinal analysis of the Brazilian innovative health system Janaina Ruffoni (PPGE /UNISINOS) Research Group: Innovation Systems, Strategy and Policies (InSysPo) Ana Lucia Tatsch (PPGE/UFRGS), Marisa Botelho (PPGE/UFU), Lara Stumpf International Seminar - Innovation Ecosystems, Upgrading, and Regional Development (FCE/UFRGS) and Rafael Stefani Session 3 - Regional Innovation Ecosystems, Smart Specialization, GVCs (PPGE/UFRGS) Campinas, June 7th, 2018. Summary

1. Subjects

2. Research question and objective

3. Theoretical framework

4. Method

5. Discussion

6. Conclusions 1. Subjects

Innovation in human health area:

• A Science-based character of different segments - ‘drug and pharmaceutical industry’ and ‘medical machinery and equipment’ - make these sectors important from the point of innovative activities.

• Networks structured by multiple agents are the typical organizational way to generate knowledge and carry out innovative processes in the health area.

This study aims to contribute to the characterization of processes that generate knowledge and innovation in the health sector in emerging countries such as Brazil. 2. Research question and objective

Research question

Which features are presented in the interaction networks between research groups* and other organizations** in the health sector in emerging countries? And how have such networks evolved recently?

Objective

Examine how the networks have been characterized over time and how they have evolved concerning their characteristics and attributes.

* University-based research groups. ** Firms, hospitals, universities, associations, colleges and public institutions. 3. Theoretical framework

Evolutionary Economy and Geography

• Innovation as a social process

• Interactions with different actors to develop and transfer knowledge.

• Proximities as important factor to explain interactions.

• Innovation system.

•

Innovation in human health area

• Interactions, multidisciplinarity, and diversity of actors. 4. Method

• The database used was ‘DGP/CNPq’

CNPq is Brazil’s National Council for Scientific and Technological Development. DGP is the ‘Directory of Research Groups’ registered at CNPq. It is the only database available in Brazil with a large amount of data related to research groups that interact with diverse organizations.

• The data was collected from three censuses for a longitudinal analysis: 2010, 2014 and 2016 (data for the year 2012 were not available)

• The method chosen was the Social Network Analysis (SNA). It fits appropriately with the proposal and object of analysis: the interactions established by the research groups. 4. Method

• Rio Grande do Sul (RS) was chosen as subject and this state is:

* the third highest number of interactive research groups in all areas of knowledge in Brazil, surpassed only by São Paulo and Rio de Janeiro (DGP, 2016) * the third highest number of interactive research groups in health sciences;

• Porto Alegre is the capital of RS and its metropolitan region (RMPA): * is one of Brazil’s regions with greater scientific specialization in health. It was already highlighted back in 2006 (Chaves and Albuquerque, 2006) and remains a reality today. Rio Grande do Sul (RS) Porto Alegre Located at the extreme south of Brazil Capital of RS

Metropolitan Region of Porto Alegre (RMPA) Composed of 30 cities located in the east of RS 5. Discussion

a) Interactive research groups * Numbers, knowledge areas, institutions and geographic locations b) Organizations *Numbers, types and geographical locations c) Networks *Figures, indicators, and some others characteristics. Interactive Research Groups

Interactive research groups, organizations and interactios in Total and Interactive research groups in the health area the health area Rio Grande do Sul (RS) 700 Rio Grande do Sul (RS) 609 600 368 528 Interactions 240 500 467 130

400 240 Interactive organizations 150 300 112

200 180 121 180 Interactive research groups 121 100 53 53 0 Research groups Interactive reseach groups 0 50 100 150 200 250 300 350 400

2010 2014 2016 2016 2014 2010 Interactive Research Groups Specific knowledge field of interactive research groups in the health area Rio Grande do Sul (RS)

0 Nursing Nutrition Dentistry Medicine Pharmacy Speech Therapy Collective Health Physical Education Therapy Physiotherapy and Occupational

2010 2014 2016 Interactive Research Groups Institutional and geographical lócus of interactive research groups in the health area of RMPA Rio Grande do Sul (RS)

Total interactive research groups in Rio Grande do Sul (RS) 68% Total interactive research groups in the RMPA 64% 66%

UFRGS (Federal University of Rio Grande do Sul) UFRGS is a traditional public university and important actor in the Brazilian innovation and knowledge system. PUCRS (Pontifical Catholic University of Rio Grande do Sul) PUCRS is a traditional private university and important actor HCPA (Porto Alegre Clinical Hospital) in the Brazilian innovation and knowledge system, too.

UFCSPA (Federal University of Health Sciences of Porto Alegre) HCPA is considered as a national reference in university hospitals. Since 2009, it was chosen by the Ministry of ULBRA (Lutheran University of Brazil) Education to transfer its management model to the other University Hospitals. It currently has more than 5,000 IC-FUC (Institute of Cardiology) employees. UFCSPA Is a federal institution of higher education UNISINOS (University of the Vale do Rio dos Sinos) specialized in the health area. IPA (Methodist University Center – IPA)

IBTEC (Brazilian Institute of Technology for Leather, Footwear, and Artifacts)

Conceição Hospital

Inedi College

0 20 40 60 80 100 120

2016 2014 2010 Interactive Research Groups Institutional and geographical lócus of interactive research groups in the health area of Rio Grande do Sul (RS)

32% Total interactive research groups in Rio Grande do Sul (except RMPA) 36% 34% UFSM (Federal University of Santa Maria) UFSM and UFPEL are public universities UFPEL (Federal University of Pelotas) located in the center and south of the RS, UCS (University of Caxias do Sul) respectively. UPF (University of Passo Fundo) UNIVATES (University of Vale do Taquari) UCS and UPF are private universities located UNIFRA (Franciscan University Center) in the northeast of RS. UNICRUZ (University of Cruz Alta) UNIPAMPA (Federal University of Pampa) IMED College FURG (Federal University of Rio Grande) UNIJUI (Regional University of the Northwest of the State of Rio Grande do Sul) URI (Integrated Regional University of Alto Uruguai and Missões) UNISC (University of Santa Cruz do Sul) UCPEL (Catholic University of Pelotas) SETREM (Três de Maio Educational Society) IFFar (Farroupilha Federal Institute) ICCA (Institute of Cardiology of Cruz Alta) URCAMP (University of the Campaign Region) 0 10 20 30 40 50 60

2016 2014 2010 Organizations

Total number of organizations that Types of Organizations interact with the research groups 90 250 Universities - private and public. Examples: 80 UFRGS, UNICAMP, USP, UFMG, UFRJ, Padova University, George Washington University and others. 200 Firms - private and public. Examples: 200 70 Lifemed, Azaléia Shoes, Sanofi-Aventis Pharmaceutical, Eurofarma Labs, Pfizer, Geyer Medicines and others. 60 Other types of public institutions - Examples: City Hall of Pelotas, Institute 150 of Drugs’ Technology, Department of Health Surveillance, Oswaldo Cruz 150 Foundation and others. 50 Colleges - Examples: Nursing College of Coimbra, Integrated Faculty of Santa Maria, Federal Institute of Rio Grande do Sul, and others. 112 40 Hospital - Examples: Porto Alegre Clinical Hospital (HCPA), Institute of Cardiology of Rio Grande do Sul, Mãe de Deus Hospital, Liverpool Heart 100 and Chest Hospital. 30 Association - Examples: Porto Alegre Clinical Hospital (HCPA), Institute of Cardiology of Rio Grande do Sul, Mãe de Deus Hospital, Liverpool Heart 20 and Chest Hospital. 50

0 0 Number of Organizations University Firm Other types of College Hospital Association public institutions 2010 2014 2016 2010 2014 2016 Geographic location of organizations that interact with research group in health area Organizations - RMPA, Rio Grande do Sul (RS), BR and Abroad -

Association College Firm Hospital Public Institution University 70 Influence of Geographical Proximity Institucional Proximity

50 1 3 20

40 7 9 7 1 4 1 1 1 7 30 8 6 1 7 36 7 4 3 3 43 7 20 26 29 3 31 22 20 18 15 16 10 2 13 1 2 6 3 6 1 5 4 4 3 3 3 3 0 1 1 1 1 2 RMPA RS BR Abroad RMPA RS BR Abroad RMPA RS BR Abroad

42 2010 33 2014 37 2016 Networks Networks with institutional lócus of research groups and organizations

FigureFigure XXXX -- NetworkNetwork amongamong2010 institutionsinstitutions,,, 201020102010... FigureFigureFigure XXXXXX --- NetworkNetworkNetwork amongamong2014 institutionsinstitutionsinstitutions,, 20142014.. FigureFigure XX XX - - Network Network among among2016 institutions institutions, ,,2016. 2016.2016.

Source: Source: DGP DGP data data 2010 2010 with with Gephi Gephi 0.9.2. 0.9.2. Research Groups Universi Source:Source:Source:ty and DGP DGPDGP College data datadata 2010 20102010 with withwith GephiGephi Hospital 0.9.2.0.9.2. Firm Associa Source: Source:tion DGP DGP data data 2010 2010Public with withInstitution Gephi Gephi 0.9.2. 0.9.2.

CategoryCategory –– ??Institucional????Institucional?? NetworkNetwork

ResearchResearch GroupsGroups UniversityUniversityUniversity andandand CollegeCollegeCollege Hospital Hospital

FirmFirm AssociationAssociationAssociation Public Public InstitutionInstitution Networks Networks with the geographical location of research groups and organizations

FigureFigureFigure 9 9- Network- Network9 - Network with with withgeographic geographic2010 geographic location, location, location, 2010 2010 2010 Figure Figure Figure 10 10 - 10 Network- Network - Network with with with geographic2014 geographic geographic location, location, location, 2014 2014 2014 Figure Figure Figure 11 11 11- Network - -Network Network with 2016with with geographic geographic geographic location, location,location, 2016 2016

Source: Source: Source: DGP DGP data DGPdata 2010 data2010 with 2010 with Gephi with Gephi Gephi0.9.2. 0.9.2.Localiza 0.9.2. tion : Local (L) | Source: State Source: Source: o fDGP Rio DGP Grande dataDGP data 2010data do2010 2010Sulwith with (RS) Gephiwith Gephi Gephi 0.9.2. 0.9.2. |0.9.2. Brazil (BR) | Foreign (F) Source: Source: Source: DGP DGP DGP data data data 2010 2010 2010 with with with Gephi Gephi Gephi 0.9.2. 0.9.2. 0.9.2.

LocalizationLocalizationLocalization: Local: Local: Local (L) (L) (L) | | State State| State of of Rio Rioof GrandeRio Grande Grande do do Sul do Sul (RS)Sul (RS) (RS) | | |Brazil Brazil Brazil (BR) (BR) (BR) | | |Foreign Foreign Foreign (F) (F) (F)

Networks

Degree Centrality Eigenvector Indicator Density Network (Freeman’s Method) 0,035 0,006 1,2 0,03 0,005 1 0,031 0,005 0,025 0,004 0,968 0,026 0,8 0,882 0,02 0,003 0,021 0,6 0,776 0,003 0,003 0,015 0,002 0,4 0,01 0,001 0,2 0,005 0 0 0 2010 2014 2016 2010 2014 2016 2010 2014 2016

The network density in 2010 was 0.005, means The degree centrality is very low in the three The Eigenvector centrality considers the that 0.5% of possible connections were present networks. connections of a particular actor and also the in the network. It corroborates the characteristic of the connections of the actors that relate to it. This In 2014 and 2016, this indicator revealed even interactions’ dispersion in the networks. type of centrality measures the relevance of the less dense networks. The results of the ‘standard deviation’ of this actor in the network also by the importance of Despite the increase in the number of actors and indicator permit to noticed that in each network the connections with its neighbors. Over the interactions over time in the networks, this was there are actors with different node sizes. three years, eigenvector decreasing results are not enough to make the network denser. It reflects degrees of distinct centrality. observed. It reflects the dispersion of network Most actors report having only one interaction connections over time. with another actor. Networks Types of interactions (CNPq typology) informed by research groups

2010 2014 2016

200

180

160

140

120

100

0 > P > > P > > G > > G > G > - - - - - considerations Partner Staff Training Staff Partner considerations Training of group staff group of Training Other Relationship Types Relationship Other Technology transfer G transfer Technology Scientific research with use use with research Scientific Other technical consultancy technical Other Group engineering activities engineering Group Development software P P software Development Transfer of technology P technology of Transfer Partner Engineering Activities Engineering Partner Scientific research without use use without research Scientific Supply of material inputs G inputs material of Supply Supply of material supplies P supplies material of Supply Networks

2010 2014 2016 Universtiy-Firm University-University University-University (47% of all interactions) (41% of all interactions) (44% of all interactions)

Trainig G-P Technology Technology 1% Technology Trainig G-P Technology Partner Training P-G Trainig G-P Other types Training P-G = 3% Transfer G-P Transfer P-G Transfer G-P = 1% 2% Transfer P-G Training P-G enginnering 1% 1% of 1% 1% 2% 2% activies relationships Supply of Supply of Supply of 1% 4% material G-P material P-G Other technical 4% material G-P Scientific research with Technology 1% consultancy Software use considerations Transfer P-G 3% Development 25% 4% Scientific Other Supply of P- G research technical material P-G without use consultancy Other 4% considerations 5% technical 39% Other types consultancy of 10% relationships 8%

Technology Other types Transfer G-P of 14% relationships Supply of 8% material P-G Scientific research 23% without use Scientific research Scientific research considerations Scientific research with without use with use 17% use considerations considerations considerations 37% 44% 33% Conclusions

• The results showed a significant increase in the number of research groups establishing interactions with organizations, especially with universities and firms.

• This conclusion corroborates the findings of other studies in which university-university interaction is key to generating knowledge in health area. • In the case of universities as partners, it can be observed a more intense increase of their participation in 2016, especially the role of foreign universities. In this sense, it can be consider the influence of 'institutional proximity' on these interactions. • A possible explanation for the increase in interactions with foreign universities is related to the recent incentives for international scientific cooperation coming from organizations that evaluate the stricto sensu graduation in Brazil and from development agencies. • Besides that, it is understood that this new feature evidences a greater ability of the researchers to establish partnerships with researchers from other universities, which may be due to the increasing visibility of the research results and an indication of their excellence.

• In the case of firms, they are present intensively in the three years analyzed. We observed throught the years a higher amount of interactions with local (RMPA) and state (RS) firms than national or foreign localized firms. So, we understand that there is an influence of 'geographical proximity’ in these interactions. Conclusions

• Considering all types of organizations that are partners of the research groups, the 'geographical proximity' shows as a critical factor in interactions. It was identified that 41,5% of all interactive organizations are located in RMPA and RS (2016).

• In terms of network structure, most of the research groups interact with only one partner. So, the networks add more actors over time, but these interact little with each other. So, network density is low and decreasing.

• We understand that this is related to the specificity of the knowledge area of the research groups. The study involved the analysis of nine fields within the area of human health. • Regarding the evolution of the networks from 2010 to 2016, it is easy to observe the quantitative increase of actors - both research groups and organizations - as well as the increase of central actors, be they 'research groups,' be they the partner organizations. • In 2010, the most prominent central actor was a research group, named IBTEC.phed1, of a particular area of knowledge - physical education - that is justified by the fact that this actor interacts intensively with firms belonging to the shoe industry, whose economic activity is characterized by a cluster geographically concentrated in the RMPA. • However, in 2014 and 2016, this actor loses centrality in the network, as other actors are included and begin to interact more intensively. They are actors connected with the scientific sphere: UFRGS, UFSM, HCPA, and UFCPA.