This article presents and discusses empirical evidence on external technology absorption facilitated by activities performed inside firms. Indicators of internal and external learning are developed and applied in statistical causality models, to distinguish possible ways in which technology can be absorbed. Industrial activity sectors at the three-digit level of the National Classification of Economic Activities (nace) are the basic reference units of the research, which uses the information generated by Brazil's Survey of Technological Innovation (pintec). The results show that in-house research and development (r&d) is the main source of technology absorption, followed by the knowledge generated from the "learning by doing" and "training practices".

ResumoO objetivo deste artigo é identificar padrões setoriais de aprendizagem na indústria brasileira.A literatura evolucionária/neoschumpeteriana é utilizada como referencial teórico, ao passo que a metodologia contempla o uso de técnicas de análise multivariada, aplicadas a indicadores de aprendizagem construídos para 93 setores da atividade industrial brasileira, a partir de dados da Pesquisa Industrial de Inovação Tecnológica (Pintec), do IBGE. Os resultados revelaram quatro padrões setoriais de aprendizagem que podem ser utilizados como referências para outras análises setoriais relacionadas ao tema.PalavRas-chave | Processo de Aprendizagem; Padrões Setoriais.Código JEL | O33.

Purpose The purpose of this paper is to investigate the academic side of university–firm linkages, reporting the results of research (called the “BR Survey”, a primary database) conducted in Brazil with leaders of research groups that interacted with firms. The authors analysed the answers from 662 research groups (from both universities and research institutes) to investigate whether the intensity of private funds affects the results of the interactions. The main intent is to answer the following question: Is there a difference between funding sources and the type of results achieved by research groups when interacting with firms? Design/methodology/approach To verify the impact of some variables on the perception of the main results of university–firm interactions, highlighting the impact of funding sources, the authors present a Logit Model defined with binary dependent variables. The null value is categorized as a “scientific result” (new scientific discoveries and research projects; publications, theses and dissertations; human resources’ and students’ education) and the value 1 is classified as an “innovative/technological result” (new products, artefacts and processes; improvement of industrial products and processes; patents, software, design and spin-off firms). Findings The authors found that the modes of interaction (relationship types) and some knowledge transfer channels, besides the number of interactions with firms, have statistically significant coefficients, so their values present different impacts on the results of the interaction. The results suggest that the Brazilian innovation policy towards a more active and entrepreneurial role of universities is fostering innovative/technological results from university–firm interactions. Originality/value The originality of the study lies on the results found that given the fact that private funding sources do not affect the conventional mission of Brazilian universities – teaching and research – university research groups should be even more incentivized to search for private funds to carry out their research. This may be a solution to the public fund scarcity and may help in reducing the historical distance between universities and firms in Brazil.

RESUMO A complexidade do processo inovativo exige que as firmas colaborem com um conjunto de instituições públicas e privadas, assim como outras firmas, para construir e utilizar conhecimentos especializados, inerentes ao processo de inovação. Utilizando um modelo probit ordenado e microdados de 3.691 firmas argentinas, este trabalho tem como objetivo investigar e analisar a relação entre características chave das firmas do setor industrial de um país em desenvolvimento e a rede de fornecedores de conhecimentos especializados com a qual as firmas estabelecem suas redes. Os resultados apontam que o nível de abertura da empresa, sua capacidade de absorção, o engajamento no desenvolvimento de inovações de alto impacto, além da forma como as subsidiárias de multinacionais interagem com as matrizes e a atuação em mercados de exportação, são determinantes das redes formadas pelas firmas argentinas em processos de inovação.

The growing U.S. R&D internationalization has historically been concentrated in developed countries. However, in the past few decades, the internationalization has moved toward less‐developed countries (LDCs), particularly Brazil, China, and India. What location factors are making some LDCs more “inviting” for U.S. R&D offshore? To answer this first question, we constructed a panel data using secondary data from the U.S. Bureau of Economic Analysis regarding the R&D investment made by the majority‐owned foreign affiliates of U.S. parent companies in 71 countries. We then applied a Heckman two‐step correction for selection bias test. The results highlight some important differences between developed countries’ and LDCs’ attractiveness. Based on these initial results, we conducted a detailed analysis of the determinants of U.S. R&D investments in Brazil, China, and India, which revealed that China’s determinants mostly match those found in more developed countries.

Through an investigation of data available from the Brazilian Innovation Survey (Pesquisa de Inovação) of the Brazilian Institute of Geography and Statistics (Instituto Brasileiro de Geografia e Estatística) and from a national survey on university–firm interactions (the BR Survey), the authors show that Brazilian industrial firms lack qualified individuals for two key activities: the promotion of innovation and the establishment of a dialogue between universities and firms. Brazilian firms’ weaknesses and the inadequacy of the workforce supply generate scarce (or inadequate) ‘know-why’. Furthermore, a restricted perspective in Brazilian universities, focused on creating a more educated society rather than augmenting the stock of technological knowledge, constrains the generation of ‘know-what’ and ‘know-who’.

The motivation for this article comes from the proposition in the literature that Latin American universities are detached from the research needs of the productive sector and that they limit their role to the human resources and training missions. The authors investigated the Brazilian scenario, using data from a survey conducted in 2008–2009 with universities and firms that interacted with each other. They found that some university–firm interactions in Brazil were diversified and embraced sophisticated knowledge generation and exchange. They also found that interactions with firms reinforced universities' missions of human resources and training provision and generated new knowledge for universities. Brazilian firms themselves have also been changing. They have been increasingly involved in innovative activities and also in more sophisticated interactions with universities.

As plataformas de aplicativos fazem parte de ecossistemas de inovação, onde as interações entre usuários finais e desenvolvedores autorregulam o crescimento do próprio ecossistema. Uma das informações mais importantes para esse processo são as avaliações dos usuários. Com o intuito de analisar o impacto dessas informações, este artigo utiliza uma regressão por mínimos quadrados ordinários (MQO) para o ano de 2016, com base em 19 variáveis para 60 países. É mensurado o grau de exigência do mercado consumidor, utilizando um novo indicador baseado nas avaliações dos usuários finais para verificar a consistência de relações entre qualidade da demanda (mensurada pelo novo indicador) e o desempenho inovador de diferentes países nesse segmento. Os resultados evidenciam a robustez e as novas possibilidades de pesquisa que surgem, dadas as características positivas apresentadas pelo novo indicador construído através da utilização de ferramentas de big data e data analytics. Esse indicador mostra que a qualidade da demanda apoia inovações no segmento produtivo, o que levou à concussão de que obter feedback da demanda mais sofisticada representa um estímulo potencial poderoso ao avanço no desenvolvimento de aplicativos.