UNIVERSIDADE FEDERAL FLUMINENSE - UFF Instituto de Ciências Humanas e Sociais - ICHS Programa de Pós-Graduação em Administração - PPGA
JOÃO FABRICIO GAVIÃO FRAGOSO JUNIOR
RELATIONSHIP AND INFLUENCE OF PATENTO-SCIENTOMETRIC
INDICATOR AND THE PERFORMANCE OF VENTURE CAPITAL
PROPOSALS
Volta Redonda/RJ 2018
UNIVERSIDADE FEDERAL FLUMINENSE - UFF Instituto de Ciências Humanas e Sociais - ICHS Programa de Pós-Graduação em Administração - PPGA
JOÃO FABRICIO GAVIÃO FRAGOSO JUNIOR
RELATIONSHIP AND INFLUENCE OF PATENTO-SCIENTOMETRIC
INDICATOR AND THE PERFORMANCE OF VENTURE CAPITAL
PROPOSALS
Dissertação de Mestrado apresentada ao Programa de Pós-Graduação em Administração, Instituto de Ciências Humanas e Sociais, Universidade Federal Fluminense, como requisito para a obtenção do título de Mestre em Administração.
Orientador: Prof. Dr. Gustavo da Silva Motta
Volta Redonda/RJ 2018
TERMO DE APROVAÇÃO
JOÃO FABRICIO GAVIÃO FRAGOSO JUNIOR
RELATIONSHIP AND INFLUENCE OF PATENTO-SCIENTOMETRIC
INDICATOR AND THE PERFORMANCE OF VENTURE CAPITAL
PROPOSALS
Dissertação apresentada ao Curso de
Administração da Universidade Federal
Fluminense como requisito para a obtenção do Grau de Mestre em Administração.
BANCA EXAMINADORA
___________________________________________________________________________________________________________________________________________
Prof. Dr. Gustavo da Silva Motta
Universidade Federal Fluminense – UFF - Orientador
_____________________________________________________________________
Prof. Dr. Pauli Adriano de Almada Garcia Universidade Federal Fluminense - UFF
_____________________________________________________________________
Prof. Dr. Luiz Octávio Gavião Escola Superior de Guerra - ESG
Volta Redonda/RJ 2018
AGRADECIMENTOS
Quero primeiramente agradecer a Gustavo Junqueira por prover os dados sobre as companhias investidas pelo fundo Criatec, que permitiu ir além com esse estudo.
Agradeço ao meu orientador Gustavo Motta por sua confiança e orientação precisa nos momentos cruciais do estudo, ao professor Pauli Garcia por seus insights e desafios, ao Programa de Pós-Graduação em Administração - PPGA por toda estrutura o suporte e, claro, ao professores e colegas da turma do Mestrado Profissional em Administração. Deixo meu abraço forte aos parceiros Leonardo Judice e Alessandro Marino.
Registro meu inspirado agradecimento a Raquel Lopes e Fernando Gavião, por dividirem comigo sua paixão pela Língua Inglesa, e ao amigo Felipe Jing Pan por sua ajuda na tradução de Zhao e Shen (2011).
Finalmente, sou profundamente grato aos meus pais Fabricio e Edelquim e a minha futura esposa Mônica, por seu carinho, incentivo e total apoio durante essa jornada.
RESUMO
Título: Relação e influência dos indicadores patento-scientométricos e o desempenho de propostas
de venture capital.
Objetivo do trabalho: Analisar o desempenho da abordagem patento-cientométrica aplicada na
priorização de investimentos de venture capital para entender correlação dos indicadores assim como a influência causal de cada critério no processo de priorização.
Procedimentos/Método para a solução do problema: O estudo exploratório e
predominantemente quantitativa aplica a correlação Spearman Rho para avaliar a força e significância entre os indicadores patento-cientométricos empregados em Motta et al. (2015a) e os dados fornecidos pelo gerente do fundo sobre as 5 companhias investidas. A influência causal é determinada com a construção de um Rede Bayesiana utilizando o software MSBNx e validado com o cálculo da entropia para testar a inferência da contribuição de cada indicador patento-cientométrico, dimensão e critério e, também, para gerar aprendizados a partir da Rede Bayesiana ao identificar os valores dos parâmetros que maximizam a possibilidade de cada resultado estimado para as companhias.
Resultados: O estudo identificou a significância e a força da correção entre os conjuntos de
indicadores analisados. O grau de incerteza de cada indicador, dimensão e critério foi identificado com o cálculo da entropia e a Rede Bayesiana relevou os indicadores que maior influência e aqueles relacionados com o retorno positivo para o fundo.
Implicações práticas: O estudo contribui para redução da assimetria da informação entre
investidores e empreendedores. Permitindo que investidores possam escolher companhias com maior possibilidade de retorno positivo para o fundo, baseado na análise tangível e objetiva de critérios não-financeiros e promovendo o investimento em companhias capazes de prover maiores retornos não só para o fundo como também para sociedade. Empreendedores se beneficiam com o conhecimento dos critérios e áreas em que podem focar para aumentar a possibilidade de terem suas companhias selecionadas por um fundo de venture capital.
Originalidade e Contribuições: O estudo contribui ao avançar em pesquisas acadêmicas
relacionadas a dinâmica entre ciência e tecnologia, analisando produções tecnológicas e científicas para suportar o processo de tomada de decisão e explorando a interdisciplinaridade entre gestão, estatística, fermentas analíticas e tecnologia da informação.
Produção Técnica/Tecnológica: Construção de uma Rede Bayesiana para análise de indicadores
patento-cientométricos e da influência causal de cada critério, dimensão e indicador no retorno para o fundo. (EIXO 1 * 15 Processo/Tecnologia não patenteável)
Palavras-Chave: Indicadores Patento-Cientométricos, Venture Capital, Criatec, Rede Bayesiana.
ABSTRACT
Title: Relationship and influence of patento-scientometric indicator and the performance of
venture capital proposals
Objetive: Analyze the performance of the patento-scientometric approach applied to the venture
capital investment prioritization, in order to understand the correlation of the indicators, as well as the causal influence of each criteria in the prioritization process.
Procedures/Methods to the solution of the problem: This exploratory and predominantly
quantitative nature study applies the Spearman Rho correlation to evaluate the strength and the statistical significance between the patento-scientometric indicators employed in Motta et al. (2015a) and data related to the outcome of the 5 invested companies provided by the fund manager. The causal influence is determined constructing Bayesian Network using the software MSBNx and validated with the calculation of the entropy to test the inference from the contribution of each patento-scientometric indicators, dimension and criteria, and also to learn from the Bayesian Network by finding values of parameters that maximize the likelihood of each estimated company’s outcome.
Results: The study identified the significance and the strength of the correlation between the set
of indicators analyzed. In addition, the degree of uncertainty related to each indicators, dimension and criteria was identified with the entropy and Bayesian Network reveled the most influential variables and those related to a positive return to the fund.
Practical Implications: This study contributes to the mitigation of information asymmetries
between venture capitalists and entrepreneurs. Enabling venture capitalists to choose companies with increased probability of divesting with positive return the fund based on a tangible and objective analysis of non-financial criteria and fostering the investment to companies capable to provide higher returns not only to the fund but to society as well. Entrepreneurs benefit from the knowhow of the criteria and actions they should focus on to increase the possibility of having their company selected by a venture capital fund.
Originality and Contributions: This study makes contribution advancing the academic studies
on the dynamic of science and technology, analyzing the use of scientific and technological production to support the decision-making process, exploring the interdisciplinary bonds among management, statistic and data analytics tools and information technology.
Technical/Technologycal Production: Construction of a Bayesian Network to analyzes
patento-scientometric indicators and the causal influence of each criteria, dimension and indication on the
return to the fund. (
EIXO 1 * 15 Processo/Tecnologia não patenteável)
Palavras-Chave: Patento-scientometric Indicators, Venture Capital, Criatec, Bayesian Network
LIST OF FIGURES
Figure 1 - Publication Statistics for Venture Capital Publications in Scopus. ... 17
Figure 2 - Stage x Investment Vehicle Type. ... 20
Figure 3 - Capital Committed with VC and PE in Brazil. ... 23
Figure 4- Input and Output of Scientific and Technological Activity.. ... 26
Figure 5- Patento-scientometric approach. . ... 31
Figure 6 - Research Time-line. . ... Error! Bookmark not defined. Figure 7 - Spearman Rho correlation. ... 41
Figure 8 - Bayesian Network for the Patento-Scientometric Indicators. ... 43
Figure 9: Steps to create a Bayesian Network in MSBNx. . ... 44
Figure 10 - Assessment of the Indicator International Scientific Collaboration. ... 45
Figure 11 - Assessment of the dimension Scientific Publication. ... 45
Figure 12 - Assessment of the criteria Technology. ... 45
Figure 13 - Assessment of the Result Node. ... 46
Figure 14 - Methodological scheme of the research. ... 47
Figure 16 - Investment x Exit Deal. . ... 54
Figure 17- Investment x Exit Deal (by company). . ... 55
Figure 18 - Assessment of the indicators Nm_Papers. ... 55
Figure 19 - Assessment of the Patento-Scientometric Indicators. . ... 56
Figure 20 - Assessment of the Dimension Sci_Public. . ... 57
Figure 21 - Assessment of the non-financial criteria ... 59
Figure 22- Assessment of the Result Node. . ... 60
Figure 23 - Evaluation of Company i-1. . ... 63
Figure 24 - Evaluation of the influence of the indicators on the “inv_pos” state ... 65
Figure 25 - Evaluation of the influence of the indicators on the “inv_neg” state. ... 67
Figure 26 - Evaluation of the influence of the indicators on the “inv_fund” state. ... 68
Figure 27 - Evaluation of the influence of the indicators on the “rej” state. ... 70
Figure 28 - Entropy of the Patento-Scientometric Indicators. . ... 73
Figure 29 - Evaluation of the influence of the dimension on the “inv_pos” state. . ... 75
Figure 30 - Evaluation of the influence of the dimension on the “inv_neg” state. . ... 76
Figure 31 - Evaluation of the influence of the dimension on the “inv_fund” state. ... 77
Figure 32- Evaluation of the influence of the dimension on the “rej” state. ... 77
Figure 33 - Entropy of the Dimensions. ... 79
Figure 34 - Evaluation of the influence of the criteria on the “inv_pos” state. ... 80
Figure 35 - Evaluation of the influence of the criteria on the “inv_neg” state. ... 80
Figure 36 - Evaluation of the influence of the criteria on the “inv_fund” state. ... 81
Figure 37 - Evaluation of the influence of the criteria on the “rej” state. ... 82
Figure 38 - Entropy of the Criteria. ... 83
Figure 39 - Assessment of the Dimension Sci_Public. ... 94
Figure 40 - Assessment of the Dimension Util_Opp. ... 95
Figure 41 - Assessment of the Dimension Pool_Knwldge. ... 95
Figure 42 - Assessment of the Dimension Demand. ... 96
Figure 43 - Assessment of the Dimension Sci_Prod. ... 97
Figure 44- Assessment of the Dimension Tech_Prod. ... 97
Figure 45 - Assessment of the Dimension Sci_Basis. ... 98
LIST OF TABLES
Table 1- Researches on Entrepreneurial Finance ...17
Table 2 - Venture Capital Stages. ... 19
Table 3- Venture Capital Stages. ... 20
Table 4 - Main Metric Indicators. ... 26
Table 5- Scientometric: Themes of Interest and Applications. ... 27
Table 6 - Data from Patent and Articles ... 31
Table 7- Data used to evaluate the nonfinancial criteria. ... 31
Table 8 - Technology: Dimensions and Indicators ... 33
Table 9 - Market: Dimensions and Indicators ... 33
Table 10- Divestment: Dimensions and Indicators ... 34
Table 11- Team: Dimensions and Indicators ... 35
Table 12- Size of the Correlation. ... 36
Table 13- Achievements of the companies analyzed. Data provided by the fund manager. ... 40
Table 14 - Initial Probability Distribution of the Result node. ... 46
Table 15- Companies ranked according to their achievements ... 48
Table 16 - Technology: Company Ranking by Indicator and Dimension ... 49
Table 17- Market: Company Ranking by Indicator and Dimension ... 49
Table 18 - Divestment: Company Ranking by Indicator and Dimension ... 49
Table 19- Team: Company Ranking by Indicator and Dimension ... 50
Table 20- Technology: Correlation Matrix ... 52
Table 21- Market: Correlation Matrix ... 52
Table 22- Divestment: Correlation Matrix ... 53
Table 23 - Team: Correlation Matrix ... 53
Table 24 - Correlation Matrix of the data provided by the fund manager ... 54
Table 25 - Assessment of the companies for the dimension Sci_Public ... 58
Table 26 - Assessment of the companies for the criteria Technology. ... 60
Table 27 - Assessment of the 4 non-financial criteria ... 61
Table 28 - Initial Probabilistic Distribution ... 62
Table 29 - Updated probabilistic distribution with the evidences of the companies. ... 63
Table 30 - Patento-Scientometric Indicators with major influence. ... 72
Table 31 – Average Entropy of the Patento-Scientometric Indicators ... 74
Table 32 - Dimension with major influence. ... 78
Table 33 - Updated Probability Distribution ... 82
Table 34 - Assessment of the companies for the dimension Sci_Public ... 94
Table 35 - Assessment of the companies for the dimension Util_Opp ... 95
Table 36 - Assessment of the companies for the dimension Pool_Knwldge ... 96
Table 37- Assessment of the companies for the dimension Demand ... 96
Table 38 - Assessment of the companies for the dimension Sci_Prod ... 97
Table 39 - Assessment of the companies for the dimension Tech_Prod ... 98
Table 40 - Assessment of the companies for the dimension Sci_Basis ... 98
Table 41- Assessment of the companies for the dimension Tech_Protect ... 99
Table 42- Assessment of the companies for the criterion Technology ... 99
Table 43- Assessment of the companies for the criterion Market ... 100
Table 44- Assessment of the companies for the criterion Divestment ... 100
Table 45- Assessment of the companies for the criterion Team ... 100
SUMÁRIO 1. INTRODUCTION ... 10 1.1. RESEARCH QUESTION ... 11 1.2. GENERAL OBJECTIVE ... 12 1.3. SPECIFIC OBJECTIVES ... 12 1.4. JUSTIFICATION ... 12 1.5. RESEARCH DELIMITATION ... 13 2. LITERATURE REVIEW ... 14 2.1. CRIATEC ... 14 2.2. VENTURE CAPITAL ... 17
2.3. PATENTOMETRIC AND SCIENTOMETRIC INDICATORS ... 25
3. METHODOLOGICAL PROCEEDINGS ... 30
3.1. CLASSIFICATION OF THE RESEARCH ... 30
3.2. DATA COLLECTION ... 40
4. ANALYSIS OF THE DATA ... 48
5. CONCLUSION ... 84
6. REFERENCES ... 88
7. APPENDIX ... 94
8. ANNEX ... 101
1. INTRODUCTION
From 2011 to 2016, US$ 87 billion dollars were invested in private equity (PE) and venture capital (VC) in Brazil (ABVCAP, 2018), providing evidences of the growth of the venture capital industry in Brazil, reflected in the increasing number of transactions and the size of VC market. However, one point needs to be noted, when comparing the ratio of value invested to the Gross Domestic Product - GDP. In Brazil, these investments counted for 0,26% in 2017, while in UK and USA, they counted for 1,28% and 1,65% of the GDP, respectively, which illustrates the opportunities for this industry in the country (ABVCAP, 2018).
In Brazil, the two main sources of financing for new companies are debt capital and venture capital (ENDEVOR, 2018). The 2017 Brazil’s Entrepreneurial City Index shows evidence of the huge concentration of these deals in the Southeast region of the country. An initiative by the Brazilian Government through its development bank BNDES to overcome this concentration and geographically extend the venture capital activity in Brazil is the Criatec, a program created in 2007. The program has had three editions since then, Criatec I, II and III, with focus on emerging innovative companies not only in the Southeast, but also in the South, North and in the Northeast regions in Brazil. Authors highlight the innovative nature of the emerging companies and the uncertain and risky environment in which they operate (RIES, 2011; BLANK, 2012; MEIRA, 2013). Blank (2012) proposes four factors that fosters this increase of emerging innovative companies: 1) Startups can now be built for thousands, rather than millions of dollars; 2) A higher resolution venture finance industry; 3) Entrepreneurship developing its own management science; and 4) Speed of consumer adoption of new technology.
Despite this higher resolution venture finance industry, VC capitalist and entrepreneurs in Brazil have faced many challenges from an unclear regulatory environment - as indicated in the 2018 Doing Business Report from World Bank, in which Brazil occupies the 125th position in the ranking (of 190 economies) - to its history of high interest rates. In addition, according to Carvalho, Ribeiro, and Furtado (2006), the approval rate of projects by VC investors is only 1% in Brazil, which is in line with the finding of Cortes (2010) on his study of the demand for venture capital in Brazil regarding the percentage of projects approved by Criatec I: 2,17%.
Criatec program is also the object studied by Cherobim et al. (2011), in which they explore how the companies selected by de fund prioritize their investments, and Ferraz (2013) in which the author analyses Criatec I to and discuss the lessons learned for the implementation of Criatec II.
Motta et al. (2015a) studied the selection process of Criatec in order to propose an approach to analyze and prioritize venture capital investments with the use of scientometric and patentometric indicators to evaluate the non-financial criteria: Technology, Market, Divestment and Team, aiming at consolidating an alternative to subjective evaluation criteria, through its objectification via the extraction of data from articles and patents. The importance of the study of this data for the future of VC is highlighted by Gomper and Lerner:
While the increase in innovative outputs can be seen through several measures, probably the clearest indication is in the extent of patenting. Patent applications by U.S. inventors, after hovering between forty and eighty thousand annually over the first eighty-five years of this century, have surged over the past decade to over 120 thousand per year. This does not appear to reflect the impact of changes in domestic patent policy, shifts in the success rate of applications, or a variety of alternative explanations. Rather, it appears to reflect a fundamental shift in the innovative fecundity in the domestic economy. The breadth of technology appears wider today than it ever has been before. The greater rate of intellectual innovation provides fertile ground for future. (GOMPER; LERNER, 1999, p.326)
A glimpse of this future is illustrated by the Global Startup Ecosystem Report 2018, showing that:
The foundation for startups in this new era of tech comes in no small part due to global growth in research and development (R&D). Patent applications have grown by an astounding 174% in the past 20 years, with R&D spending as a share of the GDP growing by 13% in the same time period. On a similar upward trend, the number of R&D researchers per capita has grown 18% in the past 10 years. (STARTUP GENOME, 2018, p. 9)
In this context, this study aims at investigating the performance of the patento-scientometric approach applied to the venture capital investment prioritization in Motta et al. (2015a), analyzing the correlation of the indicators, as well as the causal influence of each indicator in the prioritization, not only mitigating information asymmetries but also supporting the decision making process by the fund managers and also deepening the learning on how technology, market, divestment and team influence the results of venture capital invested companies.
1.1. RESEARCH QUESTION
Very few projects submitted to venture capital funds are selected, one of the major hinders is the information asymmetries between investors and investee. In addition, the evaluation of nonfinancial criteria (technology, market, divestment, and team) during the selection of venture capital proposals relies on subjective judgement from the managers. Scientometric and patentometric indicators help in understanding the nonfinancial criteria and contribute to the construction of a method for prioritizing VC investments. It is not known, however, the influence
of each criteria, nor how the indicators are related to the performance to the invested companies. In this sense, this study proposes to answer the questions: 1) What is the relationship between the patento-scientometric indicators and the results achieved by the invested companies? What is the influence of each criteria on the results of the invested companies?
1.2. GENERAL OBJECTIVE
Analyze the performance of the patento-scientometric approach applied to the venture capital investment prioritization, in order to understand the correlation of the indicators, as well as the causal influence of each criteria in the prioritization process.
1.3. SPECIFIC OBJECTIVES
In order to accomplish the main objective, the following specific objectives were defined: Revise the literature regarding venture capital, patento-scientometric indicators and
studies on the Criatec program;
Formulate a methodological scheme to guide the analysis of the correlation and analysis;
Identify the relationship between the patento-scientometric indicators from Motta et al. (2015a) and the indicators related to the achievements of the invested companies; Identify supporting evidence in the literature for the findings related to the correlation
of the patento-scientometric indicators from Motta et al. (2015a) to the achievements of the companies invested by the fund;
Model and test the interrelationship among the criteria technology, market, divestment, and team using the Bayesian network to calculate the causal influence of each patento-scientometric indicator, dimension and criteria on the outcomes of the companies in this study using the software Microsoft Belief Networks – MSBNx, and measuring their entropy to evaluate the degree of uncertainty associated.
1.4. JUSTIFICATION
The justification for the study of the analysis of performance of the patento-scientometric approach applied to the venture capital investment prioritization is found in the purpose of improving the process of selecting venture capital proposal by mitigating information asymmetries between investors and potential investee and supporting the decision-making process. Thus,
fostering the investment to companies capable to provide higher returns not only to the fund but to society as well.
1.5. RESEARCH DELIMITATION
This study addresses the patento-scientometric approach applied to the venture capital investment prioritization based on the findings from Motta et al. (2015a) related to the nonfinancial criteria (technology, market, divestment, team) and on the achievements of these companies after the investment provided by the fund manager in October/2017. These criteria were used to evaluate the companies that submitted investment proposals to the Criatec I fund created by BNDES.
2. LITERATURE REVIEW
2.1.CRIATEC
The Criatec Program originated from a public bid tendered by BNDES in 2007 and, after that, two more editions have been launched: Criatec II in 2013 and Criatec III in 2014. Regarding Criatec I, it is classified as “seed capital” on its definition. However, Cortes (2010) clarifies that the fund has invested in emerging companies from the three stages in the range covered by the venture capital modality (Seed, Startup e Early Stage). Thus, that is the reason why Criatec I fits the venture capital category in his study. In Criatec II and Criatec III the “seed capital”
determination do not occur1.
The first edition started its operation in November, 2007 with R$ 100 million of committed capital, R$ 80 million invested by BNDESPar and R$ 20 million by Banco do Nordeste do Brasil (BNB). (CGEE, 2008; CORTES, 2010; MOTTA et al. 2015a). The fund was managed by a consortium between Antera Gestão de Recursos S.A. and Inseed Investimentos Ltda. and focused on biotechnology, chemistry, machines and equipment, microelectronics, nanotechnology, new materials, nuclear medicine, robotics software and systems (H+S). Regional managers were located in Florianópolis; Campinas; Rio de Janeiro; Belo Horizonte; Fortaleza; Belém and Recife. The investment policy of Criatec I stated that the fund would invest in companies with net revenue of up R$ 6 million in the year before the fund capitalization, considering that at least 25% of the fund asset must be invested in companies with revenue of up R$ 1,5 million; at most 25% of the fund asset must be invested in companies with revenue between R$ 4,5 million and R$ 6 million; and
the investment by company may reach 5 million.The investment period of the fund was closed in
2011 and the portfolio includes 36 companies from different the areas and regions mentioned above.
The second edition Criatec II started in September, 2013 with R$ 186 million of committed capital, R$ 123,7 million invested by BNDES, R$ 30 million by BNB, R$ 10 million by Banco de Desenvolvimento de Minas Gerais S/A - BDMG, R$ 10 million by Banco de Brasília S/A - BRB, R$ 10 million by BADESUL Desenvolvimento S/A and R$ 2,3 million by the National Manager of Criatec II, Bozano Investimentos, in accordance with the fund governance. Regional managers were located in Porto Alegre, São Paulo, Rio de Janeiro, Belo Horizonte and Fortaleza. The
1 Disponível em:
investment policy of Criatec II states that the national fund manager shall invest in innovative companies with net revenue up to R$10 million in the year before the approval of the investment by fund, in order to promote its capitalization and accelerated growth. Investment in each company may reach at most R$ 6 million, meaning R$ 2,5 million in the first round and up to R$ 3,5 million in the following investment rounds. In addition, Criatec II provides the implementation of good practices and corporate governance to the invested companies. The fund portfolio contains 28 invested companies from different sectors (information technology and communication - ITC, agribusiness, nanotechnology, biotechnology and new materials) and was still open for new
submissions on June 1st, 2018.
The third edition Criatec III was created in 2014 based on CVM Instruction 391 with a total of R$ 200 million of committed capital. The fund is managed by Inseed Investimentos Ltda and its shares were underwritten by BNDESPAR, Agência de Fomento do Estado do Amazonas S/A – AFEAM, BADESUL, Banco de Desenvolvimento do Espirito Santo S/A – BANDES, BDMG, Banco Regional de Desenvolvimento do Extremo Sul S/A - BRDE; Fundação de Amparo à Pesquisa do Estado de Minas Gerais - FAPEMIG; Agência de Fomento do Estado do Paraná - FOMENTO PR; and VALID S/A. The investment policy of Criatec III states that the national fund manager shall invest in innovative companies with net operating revenue up to R$12 million in the year before the approval of the investment by fund, in order to promote its capitalization and accelerated growth. The location of the Regional managers has not been defined. However, it was defined that (i) one center must be settled in the State of Amazonas or Pará, (ii) one center in the State of Pernambuco or Paraiba, (iii) one center in the State of Santa Catarina or Paraná, (iv) three centers will be settled in the Southeast region, one must be in the State of Minas Gerais. In addition, Criatec III shall provide the implementation of good practices and corporate governance to the invested companies. Applications to the fund are accepted from 2016 to mid-2019 and target companies in agribusiness, biotechnology, media, nanotechnology, new materials and information technology.
Cortes (2010) studies aims at identifying the characteristics of the demand for the venture capital in Brazil and highlighting the elements of the business plan that maximize the chances of receiving investments. The author analyzed the 1061 business plans from the Criatec I database, considering the criteria for the selection defined by the program: (i) the entrepreneur and his team profiles, (ii) technological and/or competitive advantages, (iii) market potential; (iv) projected
financial results, (v) capital needed, and (vi) divestment (exit) possibilities. Each of these criteria received score from 1 (worse) to 5 (best). After that, there was a final evaluation by the managers with one of the following recommendations: (a) discard, (b) follow-up and further decision, or (c) approved for investment. His findings can be seen below and the author explains that despite being low (2,17%) these number is the double compared to the percentage of investments in the Brazilian industry verified by Carvalho et al. (2006) which is 1%:
23 business plans were approved by the Investment Committee; 2 business plans were waiting the definition from the Committee; 3 business plans were rejected;
69 business plans haven’t been submitted to the Committee, though their score was 3 or 4; 964 business plans have been discarded, as their score was 1 or 2.
Cortes (2010) confirms that there is a significant correlation between the selection criteria and the final evaluation of the investment opportunities and highlights that the most significant criteria are: technological advantage, projected financial results and market, while the capital needed is the list significant criteria. In order to maximize the chances for investments, the author explains which factors must be considered: (a) the fund selection criteria and it influence on the investment decision; (b) the existing tangible assets of the company; (c) the existing intangible assets and the company capabilities; and (d) the format of the business plan.
Cherobim et al. (2011) investigated the role of the Criatec I in financing innovative technology-based companies. The authors found a concentration of invested companies in the Southeast region, some in the Northeast and in South regions, but no invested company from the Central-West region. Regarding the area of the invested companies: most of them belong to biotechnology and information technology. The study highlighted that the priorities for the companies analyzed were to invest in the acquisition of machinery and equipment, R&D and hiring new employees.
Ferraz (2013) conducts a case study of the Criatec Program in order to identify key lessons of role of the public sector in direct support to venture capital and the development of good practices. The author discusses the improvements implemented in Criatec II from lessons learned with Criatec I and highlights the participation of universities and research centers in the program as well as the decentralization that expand the coverage of the public instrument, taking the venture capital in different regions of Brazil.
2.2.VENTURE CAPITAL
The beginning of the 21st Century witness the sharp growth of venture capital researches,
reflecting the increasing interest on this field. Drover et al. (2017) and Wallmeroth et al. (2018) provide a complete and update state of venture capital research in their studies on entrepreneurial finance and both share similar objectives:
Table 1- Researches on Entrepreneurial Finance Drover et al. (2017)
Organize and review the equity venture financing literature, placing particular emphasis on articles from 2004 forward.
Provide a platform from which to pursue new inquiry and future research that facilitates theoretically grounded work in the face of the dynamic entrepreneurial financing landscape.
Wallmeroth et al. (2018)
Provide theoretical and empirical information on these financial mechanisms research (Venture Capital, Business Angels, Crowdfunding) and the way they interact during each stage of the investment process.
Develop key research topics.
Source: Developed by the author from Drover et al. (2017) and Wallmeroth et al. (2018)
Other evidence of the increasing interest can be noted on venture capital research, Drover et al. (2017) point the strong increase after 2000, from 675 in the 1990´s to 2606 in the 2000’s and 2749 from 2010 to 2016. In line with Cumming and Zhang (2016) who note a significant increase in VC literature for emerging markets in recent years.
Figure 1 - Publication Statistics for Venture Capital Publications in Scopus Database by Decade. Source: Data from Drover et al. (2017)
Gompers and Lerner (1998) offer one reasoning behind venture capital financing:
Many new firms require substantial capital. A company's founder may not have sufficient funds to finance company projects and might therefore seek outside financing. Entrepreneurial firms that are characterized by significant intangible assets, expect years of negative earnings, and have uncertain prospects are unlikely to receive bank loans or other debt financing. For many of these young companies, the tremendous uncertainty and asymmetric information may make venture capital the only potential source of financing. Venture capital organizations finance these high-risk, potentially high-reward projects, purchasing equity stakes while the firms are still privately held. (GOMPERS; LERNER, 1998, p.151)
From this reasoning, it is possible to highlight some characteristics involving VC which were identified in Motta et al. (2015a): (a) the presence of two agents (investor and investee); (b) investment in enterprises perceived to have a potential for high return, due to the development of innovative technology; and (c) high risks.
MacMillan et al. (1985) investigate the criteria used by venture capitalists to evaluate new venture proposals. Through interviews, the authors identified 27 characteristics that were grouped in six major groups: (i) The entrepreneur’s personality; (ii) The entrepreneur’s experience; (iii) Characteristics of the product or service; (iv) Characteristics of the market; (v) Financial considerations; and (vi) The venture team. The authors highlight the importance the entrepreneurs’ characteristic in the selection of venture capital proposals:
The business plan should also show as clearly as possible that the ‘jockey is fit to ride”-namely, indicate by whatever feasible and credible means possible that the entrepreneur has staying power, has a track record, can react to risk well, and has familiarity with the target market. Failing this, he or she needs to be able to pull together a team that has such characteristics and show that he or she is capable of leading that team. (MACMILLAN et al, 1985, p.128)
Other important aspect for the selection of the venture capital proposal is the divestment options, that is, process of selling an asset for either financial, social or political goals. In this sense, Cumming (2008) studies the relation between venture capital contracts and exit, considering variables in five categories: (i) VC control rights; (ii) investor characteristics; (iii) investee characteristics; (iv) market conditions, and (v) legal and institutional factors. Their findings indicate “a statistically and economically significant positive association between acquisitions and the use of VC veto and control rights, particularly for the right to replace the founding entrepreneur as CEO” (CUMMING, 2008, p. 19).
Taking into account the finding from MacMillan et al. (1985) and Cumming (2005) as well as the criteria used by Criatec fund (CORTES, 2010), Motta et al. (2012, 2015a, 2015b) focus on the non-financial criteria involved in the selection of venture capital proposal and apply the
patento-scientometric approach to evaluate the criteria technology, market, divestment and team in the selection of technology-based enterprises.
In order to analyze the venture capital financing in detail, authors have identified distinct phases in venture capital financing. Tyebee and Bruno (1984) describe their model containing five sequential stages. Pavani (2003) and Cortes (2010) specify Post-Investment Activities, dividing this stage in (v) Monitoring and Acceleration, and (vi) Divestment.
Table 2 - Venture Capital Stages.
Venture Capital Stage Milestones of each Stage
Deal Origination Identification of a deals as investment prospect.
Deal Screening Definition of key policy variables to limit investment prospects to
a manageable few for in-depth evaluation.
Deal Evaluation Assessment of perceived risk and expected return weighing
characteristic of the prospected venture. Decision to invest or not depending perceived risk and expected return.
Deal Structing Negotiation of the deal price, equity to the investor and covenant
that mitigate the risk to the investor.
Post-Investment Activities Support to the venture in recruiting key executives, strategic planning, locating expansion financing and orchestrating a M&A or IPO.
Source: Data from Tyebee and Bruno (1984)
Gomper and Lerner (1999) analyze the venture capital cycle describing the steps of the venture capital, fundraising, investing and exit. In line with this structure, in ABDI (2011), the authors address the venture capital cycle in Brazil, identifying the characteristics, determinants for the success challenges and obstacles for each of these steps through the eyes of fund manager firms in Brazil.
Kaplan and Srömberg (2001) compare their empirical findings with the theory on venture capitalists describing actions venture capitalists can add value and mitigate conflicts in three different stages of the venture capital financing: contracting, screening and monitoring.
Table 3- Venture Capital Stages.
Venture
Capital Stage Description.
Fund Raising Fund managers contact long term investors in order to obtain resources to form the fund. The author highlights that, in general, the fund only starts operating when achieve a minimum pre-defined amount.
Screening Fund managers contact firms and entrepreneurs, advertise the fund and prospect a number of investment opportunities. Then, they carry on a preliminary analysis, in order to discard the opportunities that do not match with the objectives of the fund.
Evaluation In depth evaluation of business viability, growth potential, risk of failure, market dynamic is conducted. Few opportunities are approved and follow to the next stage.
Negotiation and
Investment Equity price, share of the fund and the form of the participation are negotiated and the investment occurs through a formal agreement among investor and investee. Monitoring and
Acceleration
The fund team monitors the invested firm providing expertise in (i) commercial, finance and legal areas, (ii) management support, (iii) strategy definition, (iv) building management team. The fund makes use of its network to add value to the business and accelerate its growth. Divestment The fund targets liquidity and financial return for its investment. Ideally, the exit takes place via
IPO. In fact, few companies invested by venture capital reaches IPO, alternatively other exits options may occur: (a) Redeemable Shares or Debentures issue, (b) buyback, (c) leveraged buy-out, (d) trade sales or secondary sale; (e) write-off
Source: Based on Pavani (2003) and Cortes (2010).
Scholar has not reached a consensus in the application of the terms Venture Capital and Private Equity. According to Sharp (2002), one accepted distinction between the use of both terms is related to the development stage of the company. Some authors claim that venture capital occur in emerging companies through: (a) seed capital, (b) start-up, and (c) early stage. While private equity investment occurs on more mature companies through: (c) management buyout/in; (d) bridge finance, (e) turnaround, (f) mezzanine investments, and (g) private investment in public equity. (Cortes, 2010; GVCEPE, 2008; Carvalho et al. 2006)
Regarding the origin of the venture capital term, the scholarly literature presents different perspectives. Ante (2008) highlights the New England Council in formed by a group of politicians, businessmen, and educators in 1925, whose purpose was the development of the economic scenario in New England – USA as well as the creation of a New Products Committee in 1939 and its subcommittee “Development Procedures and Venture Capital”. The actions of the committee resulted in the New England Industrial Foundation in 1942, fulfilling the need of an organization and technique capable of appraising opportunities for scientific enterprises, expecting that existing New England businesses would provide money and advice for those enterprises.
Etzkowitz (2002) emphasizes the role of Karl Compton, president of Massachusetts Institute of Technology (MIT), in the creation of ARD – American Research and Development in 1946, and the points that “the invention of the venture capital firm filled a gap in the process of translating academic research into firms with new products and jobs”. Gomper (1994) explains that ARD objective was to finance commercial applications of technologies that were developed during World War II and he quotes Lample when the states that:
the first modern venture capital firm was formed in 1946, when MIT president Karl Compton, Massachusetts Investors Trust chairman, Merrill Griswold, Federal Reserve Bank of Boston president Ralph Flanders, and Harvard Business School professor General Georges F. Doriot started American Research and Development (ARD). (LAMPLE, 1989 apud GOMPER, 1994, p.5)
The first action by the American Government regarding the development of the venture capital industry takes place with the creation of the Small Business Administration – SBA under the Small Business Act approved by the Congress in 1953. In 1958, the Investment Company Act establishes the Small Business Investment Company (SBIC) which allowed the SBA to license, regulate and help provide funds for privately owned and operated venture capital investment funds.
The growth of venture capital industry in the USA is noted after the Revenue Act in 1978 which reduced the tax of capital gain and the Employee Retirement Income Security Act (ERISA) in 1979 under which pension funds were allowed to invest in venture funds. Until then, these funds had previously come from wealthy families, corporations, and financial institutions (WALLMEROTH et al., 2018).
In Brazil, venture capital initiatives started in 1960’s through Adela Investment Company S.A and IFC - International Finance Corporation investing in iron & steel, pulp and paper production and chemicals and petrochemicals businesses. (ABDI, 2011; IFC,1981)
(Brazilian National Bank for Economic and Social Development), Financiadora de Estudos e Projetos S.A – FINEP (Brazilian Innovation Agency) e Brasilpar, a capital company founded in 1976 with domestic and international partners, contributed to the development of the venture capital industry in Brazil. Gorgulho (1997) explains that venture capital organizations were institutionalized in Brazil under the Decree-Law No 2.287 of 1986, regulated by Resolutions No 1184 of 1986 and the Decree-Law No 1346 of 1987. However, they presented design fails that hindered the development of this organizations. In ABDI (2011), the authors point the action taken by the Securities and Exchange Commission of Brazil (Comissão de Valores Mobiliários – CVM) in 1994 when CVM Instruction No 209 is approved regulating investments on Small and Medium Enterprises – SMEs by the creation of the Investment Mutual Funds in Emerging Companies (Fundo de Investimentos em Empresas Emergentes - FMIEE) as well as the approval of CVM Instruction No 391 ruling PE/VC investments in Brazil and allowed a greater share of pension funds to take part in venture capital. These actions enabled the organization of the venture capital industry in Brazil. In 2004, the first divestments take place through IPOs.
In addition, in ABDI (2011), the authors emphasize the role of the Brazilian government in the development of the PE/VC industry, underlining the amount allocated to BNDES, through its subsidiary BNDESPar and programs such as Criatec and Inovar.
The Criatec program originated from a public bid tendered by BNDES in 2007, ruled by CVM Instruction No 209, aiming at capitalizing innovative micro and small enterprises in early stage and with high-growth potential and focused in the following areas: biotechnology, chemistry, machines and equipment, microelectronics, nanotechnology, new materials, nuclear medicine, robotics software and systems (H+S). (CGEE, 2008; CORTES, 2010).
Leamon and Lerner (2012) explore the creation of a venture capital ecosystem in Brazil addressing the actions taken by FINEP in 2000 with the purpose of assessing the obstacles and challenges faced by technology-based SMEs. This assessment resulted in the creation and execution of the programs INOVAR I between 2001 and 2006, and INOVAR II, between 2007 and 2012.
Ourique (2017) analyses the impact of CMV Instruction No 578/16 that replaced CVM Instruction 391/03, bringing changes and innovations to the PE/VC industry. Furtado and Belluzzo (2017) show the growth of the total capital committed in Brazil for venture capital and private equity, shown in Figure 4. The authors discuss the relation between risk and return involved in
investing in innovation and explain that CVM Instructions 578 and 579 implemented the requirement for fund managers to report their results and equity position at fair value, improving the economic foundation and the public database for PE/VC founds. According to the Brazilian Private Equity and Venture Capital Association (Associação Brasileira de Private Equity e Venture Capital – ABVCAP) (2017), the new CVM Instructions innovate the PE/VC operations by consolidating and creating different categories of Fund of Equity Investments (Fundos de Investimento em Participações – FIPs), implementing improved governance mechanism on the invested firms, as well as making important changes in the design and disclosure of accounting statements in line with international standards.
Figure 3 - Capital Committed with VC and PE in Brazil. Source: Furtado and Belluzzo (2017)
Information asymmetry, investments in emerging markets, cross-borders investment are common concerns present on current venture capital research regarding investment selection. Bengtsson (2013) studies the relationship between the venture capital firm and venture founders. The author found that a repeated relationship is more likely when the relational VC firm has acquired more private information about the founder, thus mitigating information asymmetry, one of the key issues in venture capital financing.
Devigne and Manigart (2013) analyze VC investments in young technology-based companies aiming at studying the different investment strategies cross-border VC firms use to reduce liabilities of foreignness (LOF), focusing on characteristics of the investment targets, of syndicate partners and structural strategies. The authors confirm that cross-border VC firms invest in companies with lower information asymmetries. In Devigne et al. (2016), the cross-border
investment theme is revisited, and the authors find that as cross-border investors have a lower social and emotional involvement with the project, lower embeddedness in the local economic and social environment and also, they are affected to a lower extent by normative pressures, these traces may safeguard them from escalating their commitment to a failing course of action, compared to domestic investors.
Nahata et al. (2015) analyze the impact of institutional and cultural differences on success in global venture capital investing. Cultural differences, especially related to trust, impact VC sorting activity, local investor participation for screening and monitoring of portfolio companies, while institutional differences, regarding law and capital markets, impact sorting and harvesting of investments. Therefore, their findings show that VC performance is significantly enhanced by superior legal rights and enforcement and also by better-developed stock markets.
Groh and Wallmeroth (2016) study the determinants of venture capital investments in emerging markets. Their findings suggest that M&A activity, legal rights and investor protection, innovation, IP protection, corruption and also corporate taxes and unemployment have impact of venture capital investments in emerging markets. The authors noted that determinants of venture capital activity will vary in developed and developing countries. Moreover, they observe that determinants either play a stronger role in emerging markets or only a role in emerging markets. As a future research, they suggest to test if additional determinants specific to the emerging markets exist.
Cumming and Zhang (2016) provide a review of the central topics addressed on the conference on alternative investments in emerging markets held by Shanghai University of International Business and Economics and discuss new trends in the field. The central themes highlight (i) the pronounced importance of information asymmetries between investors and investees in alternative investments in emerging markets, (2) the importance of due diligence and governance in dealing with information asymmetries in alternative investments in emerging markets, (3) the diversification benefit of alternative investments in emerging markets, (4) the pronounced role of country conditions (culture, law, and political issues), as well as (5) lack of data representativeness and comparability across studies.
This section covered the development of the venture capture industry and current researches on the field. In addition, it highlights this emerging industry in Brazil and address some actions taken by the Brazilian government, such as the Criatec program.
2.3.PATENTOMETRIC AND SCIENTOMETRIC INDICATORS
In a context in which Science and Technology play a major role in the economic and social development, measuring and understanding their implications and mitigating information asymmetries have become a growing concern. It is the Scientometric that embrace the physical, natural and social sciences aiming at understanding their structure, evolution and connection, allowing the relationship between the sciences and the technological, economic and social development (GREGOLIN, 2005). Verbeek et al. (2002) explain that understanding and interpreting the Science & Technology (S&T) interaction requires an articulated insight into both the scientific and technological system, thus, patents and publications, as representatives of technology and science, are so-called proxy measures.
Mingers and Leydesdorff (2015) conduct an in-depth review of theory and practice in Scientometrics. The authors highlight that some of the main themes include ways of measuring research quality and impact, understanding the processes of citations, mapping scientific fields and the use of indicators in research policy and management. By conducting studies on patent bibliometric indicators, that is Patentometric, Guzmán Sanchez (1999) highlight that their application may enrich the understanding of the scientific and technology dynamic. Díaz-Pérez and Moya-Anegón (2008) add by stating that the application of metric analyses on the information extracted from patent documents have become one of the may techniques to model technological scenarios for government, business and industry and research institutes and projects.
In Spinak (1996), it is possible to find the following definition for science indicators: “It is a measure that provide information about the results of the scientific activity in an institution, country or region in the world.”. In addition, the author complements that:
The indicators, as all measures, can be collected, tabulated and allow comparisons. These activities are characteristics of monitoring procedures. The collection of indicators cannot be confused with evaluation or conclusions, since they correspond to the determination of scientific policies. (SPINAK, 1996, p.114)
According to Guzmán Sanchez (1999) indicators are measure that illustrate a particular aspect of a complex question with many facets. the author provides a list of different scientific indicators of the scientific and technological activity. classified as input or output based on the way the they are collected, shown in Figure 2. Motta et al. (2012) highlight that articles and patents are classified as output, since they represent the product of the S&T development process.
INPUT
Human Resources:
Quantity of Scientists; Quantity of Scientists x area;
Quantity of Scientists x category (Dr., PhD, MSc); Quantity of auxiliary staff involved in R&D;
Quantity of staff dedicated to scientific management.
Material Resources:
Value of real state;
Number of institutions dedicated to R&D; Value of fixed assets;
Value of raw and other materials;
Financial Resources:
Value and percentage of R&D spending; Salaries
Communication (telephone, fax, e-mail)
Scientific and Technological Activity OUTPUT Scientific Articles; Patents;
Awards and Scientific recognition; Work presented at events; Participation at events and conferences; Quantity of products; Balance of payments; Etc.
Figure 4- Input and Output of Scientific and Technological Activity. Source: Based on Gúzman Sánches (1999).
Macias-Chapula (1998) discuss the role of informetric and scientometric in a national and international perspective and provides a list of indicators most regarded nationally and internationally:
Table 4 - Main Metric Indicators.
Indicator Description
Number of Studies
Reflects the products of science, measured by counting the studies and by the type of document (books, articles, scientific publications, reports, etc.) The research dynamic in a country may be monitored and its trends charted over time.
Number of citations Reflect the impact of articles and topics cited.
Co-authorship Reflects the degree of collaboration in science at national and international level. The growth or decline in cooperative research may be measured.
Number of patents
Reflects the trends in technical changes over time and assess the outcomes of resources invested in R&D. These indicators determine the approximate degree of technological innovation in a country.
Number of citations of
patents. Measures the impact of the technology Map of scientific
fields and of countries. Assist in detecting the relative ranking of different countries in relation to global scientific cooperation. Source: Based on data from Macia-Chapula (1998, p.137)
In Spinak (1998), the author presents the major themes of interest in scientometric and feasible application of scientometric techniques:
Table 5- Scientometric: Themes of Interest and Applications.
Themes of Interest in Scientometric Application of Scientometric Techniques
The quantitative growth of science; The development of the field and
sub-fields of knowledge;
The relationship between science and technology;
Obsolescence of scientific paradigms;
Communication networks among scientists;
Relationship between scientific development and economic growth
Identifying trends and increasing knowledge in different areas; Investigating scientific journal coverage from one nucleus and from
secondary sources;
Identifying actors from the different areas;
Studying the usefulness of service for the selective spreading of information;
Predicting trends in publication;
Studying the dispersion and obsolescence of scientific literature;
Assisting in the processes of indexation, classification and automatic generation of abstracts;
Analyzing the productivity of editors, individual authors, organizations and countries.
Source: Based on Spinak (1998)
Maricato (2010) observe that many indicators, documents and variables can be employed to analyze the Scientific and Technological production. Considering their target, focus and application, they can be grouped in three main categories: (i) Indicators aimed at measuring scientific and technological productivity (e.g. number of scientific publications and registered patents), (ii) Indicators aimed at making estimations regarding the use and quality of published documents, mainly based on the study of citations; (iii) Indicators of collaboration aimed at analyzing, above all, collaborative social networks established among researchers, institutions, countries, etc. (e.g. using techniques to analyze co-authorship {for articles} and co-invention and co-ownership {for patents}).
Huang et al. (2013) explores the relationships between science and technology in biotechnology field the temporal gap between them by identifying clusters of high-impact documents (publications and patents) that belong to either the scientific front or the technical front and their trajectories. The authors highlight that technology preceding science, synchronous development, and science preceding technology show connections between S&T in disease treatment and the gene analysis platform. In Huang et al. (2015), the authors explore the relationship between science and technology in fuel cells, using as indicators: (i) Science linkage – SL, (ii) Technology linkage – TL, (iii) Technology cycle time (TCT) of papers and patents, and (iv) Science cycle time (SCT) of papers and patents. Their finds point to a growing number of cross references between patents and papers.
The advance of information technology and data science offer new perspectives and trends in Scientometrics. Zhang et al. (2016) developed an entropy-based indicator system for measuring
the potential of patents in technological innovation considering technological, legal and economic perspectives. While Kyebambea et al. (2017) developed and tested an algorithm to forecast emerging technologies from patent citation techniques using supervised machine learning approach, Chen et al. (2017) employ data mining techniques in patent to build a topic-based technological forecast.
The application of scientometric provide meaningful insights for different domains. I’ Anson (2016) applies scientometric analysis of the emerging technology emerging technologies and opportunities that may have implications for UK Defense. Lee and Su (2016) conduct a review of patent indicators accepted in scientific literature to build up a framework to optimize the selection of the indicator according to the management implications. Jürgens and Herrero-Solana (2017) make use patent bibliometrics in competitive intelligence to monitor, evaluate technology activities of nanotechnology in Spain. Krätzig and Sick (2017) aim at developing of a patent-based approach to identify particular strategic reactions in a setting of emerging battery technologies in order to provide insights to support technology managers to better assess investment options in different technologies and policy makers to create enhanced incentives applied to this context.
Motta et al. (2015a) formulated scientometric and patentometric indicators to support venture capital investment prioritization. Their findings show that the indicators help in understanding the issues addressed in this study related to nonfinancial criteria (technology, market, divestment, and team) as well as contribute to the construction of a method for prioritizing VC investments.
At a country-level, Shelton et al. (2015) analyze casual connections between scientometric indicators and High-Technology (HT) Manufacturing Outputs. The authors identified that business expenditures on R&D, scientific indicators and patents are correlated with HT manufacturing output. Their finds also show that in several countries (29 of the 37 countries analyzed) patents
could be said to have Granger caused2 HT manufacturing.
At industry level, Belenzon and Patacconi (2014) analyze how does firm size moderate firms’ ability to benefit from invention. The authors collect patent, scientific and financial information in order to identify the relationships between firm size and returns to patenting and publishing. Their finds show that while the relationship between performance and patents is stronger for small firms than for large firms, the relationship between performance and scientific
publications is stronger for large firms than for small firms. In addition, their finds suggest that publications may complement large firms’ marketing and sale efforts. Agostini et al. (2015) questions if patenting influence SME sales performance. They analyze 196 Italian SMEs operating in the North-Eastern Italian mechanical area using cross-sectional time series regression to investigate the relationship between patents and SME sales performance. Their findings show that the count of patents does not have any effect on sales performance, while the number of jurisdictions where the protection is extended produces a positive and significant result. Wagner and Wakeman (2016) make use of multivariate regressions to relate patent-based indicators that capture either an invention’s value or the uncertainty surrounding the patenting process to the outcomes of the product development process in the pharmaceutical industry. I Park et al. (2016) explore use of a patent analysis to develop a methodology that can suggest promising technology in the information and communication technology (ICT) sector. The Promising Index is built up from: (i) the Growth Index (using the count of patent applications and their growth rates), (ii) the Impact Index (using patent citations, and (iii) Marketability Index (number of patent families).
This section provided an overview of the literature on the Patentometric and Scientometric Indicators, exploring seminal works such as Spinak (1998) and Guzmán Sanchez (1999) and addressed current Patentometric and Scientometric Indicators applications and advances in the field.
3. METHODOLOGICAL PROCEEDINGS
3.1.CLASSIFICATION OF THE RESEARCH
Conforming to the purpose of investigating the performance of the patento-scientometric approach applied to the venture capital investment prioritization as well as the causal influence of each indicator in the prioritization, the method of research chosen was of an exploratory and predominantly quantitative nature. Gil (2002) highlights that exploratory studies aims at providing greater familiarity with the problem, so that it can become more explicit and hypotheses can be made. The author adds that the main objective of this kind of theory is the improvement of ideas or the discovery of insights. Regarding quantitative studies, Creswell (2007) explains that in this kind of study, the theory is employed deductively and aiming at testing or verifying this theory, the researcher presents it, collect the data to test it and reflect over the confirmation (or non-confirmation) of the theory through its results.
Motta et al. (2012, 2015b) assess the potential of employing scientometric and patentometric indicators in the selection of projects by an investment fund. In order to conduct the assessment, the authors identified and classified the technology involved in the project and defined a search topic to extract articles from The Web of Science (WoS) and the patents from the Derwent Innovation Index (DII). The authors explain that:
These databases were chosen since they both belong to Thomson Reuters, thus facilitating the systemization of data relating to different documents (articles and patents) and because they are the databases with the greatest volume of data that are available free of charge at the Brazilian Public Universities through the CAPES post-graduate research agency. (MOTTA et al., 2012, p.176)
After the extraction, the data were cleaned and systematized using the software VantagePoint v7 in order to eliminate inconsistencies and redundancies. The data used to carry on the study can be seen on Table 6. The analysis of the patent documents and articles examined countries, organizations and its subcategory (Academies, Enterprises and Government) considering: i) Scientific and technological production (quantity of articles and patents produced); ii) Topics of interest identified in the articles and patents; and iii) Activities of scientific collaboration and technological collaboration. The authors justify the use of this data considering the non-financial elements analyzed during the selection process of Criatec (CORTES, 2010), and in Table 7 can be seen the criteria considered by Motta et al (2012, 2015a, 2015b) supported by MacMillan et al. (1985) and Cumming (2005) for the evaluation of VC investment proposals.
Table 6 - Data from Patent and Articles
Data Source
Organizations In articles: Author Affiliations field; In patents: Patent Assignee field. Countries In articles: Country field;
In patents: Designated States National field. Date In articles: Publication year;
In patents: Year of Application. Topics of
Interest International Patent Classification (IPC) and the WoS Subject Category fields were used. The integration and classification were conduct through the technological fields proposed by the WIPO (World Intellectual Property Organization).
Collaboration Techniques of occurrence and co-occurrence of countries and organizations were used to analyze the dynamics of networks of scientific and technological collaboration with the software UCINet v.6 and NETDRAW v.2.
Source: Developed by the author based on Motta et al. (2012, 2015a)
Table 7- Data used to evaluate the nonfinancial criteria.
Non-Financial
Criteria Data uses to build each criterion
Technology Characteristics of general production, of topics of interest, and of scientific collaboration. Market Data on countries (location of author’s affiliated organization; country or countries where the
patent was protected).
Divestment Organizational production, such as interorganizational collaboration and patent registration, pointing to potential organizations interested in the technology.
Team Comparative analysis of the general data related to the enterprise’s technology, general production, topics of interest, and collaboration of team members, on the global stage.
Source: Developed by the author based on Motta et al. (2012, 2015a)
Figure 6 depicts the process describe above with the flow of the Patento-scientometric approach used to assess the selection of projects by an investment fund in Motta et al. (2012, 2015b).
In Motta et al. (2015a), the authors employ the patento-scientometric approach described above not only to assess venture capital investments but also to prioritize them. Ten companies that submitted their proposals to the fund were analyzed in the study:
five of them were invested by the fund, they were identified as i-1, i-2, i-3, i-4 and i-5; five of them were rejected by the fund, they were identified as r-1, r-2, r-3, r-4 and r-5. The authors described the procedures taken to collected the data, detailed in Motta et al. (2012, 2015a), and the construction of the scientometric and patentometric indicators used to analyze each company according to the non-financial criteria: Technology, Market, Divestment and Team.
The indicators for the criterion Technology are detailed in Table 8. In order to analyses this criterion two dimensions were defined:
Scientific Publication: This dimension indicates the level of scientific publication related to the technologies evaluated, providing evidence of a scientific basis for the technological development;
Utilization of Opportunity: This dimension seeks for the evidence of the use of the knowledge produced by exploring its transformation into technology, through the process of patenting.
The indicators for the criterion Market are detailed in Table 9. In order to analyses this criterion two dimensions were defined:
Pool of knowledge: In this dimension, the ability to supply knowledge to the production of technology is evaluated;
Demand: This dimension considers the number of countries covered by the patents as an indicator of demand for the technologies evaluated.
The indicators for the criterion Divestment are detailed in Table 10. In order to analyses this criterion two dimensions were defined:
Scientific production: This dimension identifies potential organizations interested in the technologies of the companies evaluated considering business organizations that published articles related to the areas/companies evaluated;
Technological production: This dimension identifies potential organizations interested in the technologies of the companies evaluated considering business organizations with patents related to the areas/companies evaluated.
