Services innovation and economic performance: An analysis at the firm

In this paper we estimate a model linking innovation effort and economic performance, along the lines of the Mairesse and Mohnen (2003) model. We examine this relationship in the context of services sectors instead of Research and Development intensive manufacturing sectors. Much effort has already been made to explore the innovation-performance relationship for manufacturing sectors but it is still much understudied for services, particularly for Portugal. In this paper we aim to take a step in fulfilling this gap. We use new firm level data for ten services sectors from the Second Community Innovation Survey of Portugal, to estimate the model.

Open innovation and social network analysis

The present article is based on the report for the Doctoral Conference of the PhD programme in Technology Assessment, held at FCT-UNL Campus, Monte de Caparica, July 9th, 2012. The PhD thesis has the supervision of Prof. Cristina Sousa (ISCTE-IUL), and co-supervision of Prof. José Cardoso e Cunha (FCT-UNL).

Towards an integrated technology management across the rail supply chain extended to society: building the case study on high-speed trains

The present paper was prepared for the course “Project III”, with the supervision of Prof. António Moniz, reporting on the author speaking notes at the Winter School on Technology Assessment, 6-7 December 2010, as part of the Doctoral Programme on Technology Assessment at FCT-UNL.

Do social factors matter for innovation, and do they influence innovation in Aeronautics Industry?

Based on the report for the course on “Social Factors of Innovation” of the PhD Program on Technology Assessment, supervised by Prof. António Brandão Moniz, Monte de Caparica, University NOVA Lisbon, Faculty of Sciences and Technology, July 2013

Decisions of technology innovation: the role of indicators

This work presents research conducted to understand the role of indicators in decisions of technology innovation. A gap was detected in the literature of innovation and technology assessment about the use and influence of indicators in this type of decision. It was important to address this gap because indicators are often frequent elements of innovation and technology assessment studies. The research was designed to determine the extent of the use and influence of indicators in decisions of technology innovation, to characterize the role of indicators in these decisions, and to understand how indicators are used in these decisions. The latter involved the test of four possible explanatory factors: the type and phase of decision, and the context and process of construction of evidence. Furthermore, it focused on three Portuguese innovation groups: public researchers, business R&D&I leaders and policymakers. The research used a combination of methods to collect quantitative and qualitative information, such as surveys, case studies and social network analysis. This research concluded that the use of indicators is different from their influence in decisions of technology innovation. In fact, there is a high use of indicators in these decisions, but lower and differentiated differences in their influence in each innovation group. This suggests that political-behavioural methods are also involved in the decisions to different degrees. The main social influences in the decisions came mostly from hierarchies, knowledge-based contacts and users. Furthermore, the research established that indicators played mostly symbolic roles in decisions of policymakers and business R&D&I leaders, although their role with researchers was more differentiated. Indicators were also described as helpful instruments to conduct a reasonable interpretation of data and to balance options in innovation and technology assessments studies, in particular when contextualised, described in detail and with discussion upon the options made. Results suggest that there are four main explanatory factors for the role of indicators in these decisions: First, the type of decision appears to be a factor to consider when explaining the role of indicators. In fact, each type of decision had different influences on the way indicators are used, and each type of decision used different types of indicators. Results for policy-making were particularly different from decisions of acquisition and development of products/technology. Second, the phase of the decision can help to understand the role indicators play in these decisions. Results distinguished between two phases detected in all decisions – before and after the decision – as well as two other phases that can be used to complement the decision process and where indicators can be involved. Third, the context of decision is an important factor to consider when explaining the way indicators are taken into consideration in policy decisions. In fact, the role of indicators can be influenced by the particular context of the decision maker, in which all types of evidence can be selected or downplayed. More importantly, the use of persuasive analytical evidence appears to be related with the dispute existent in the policy context. Fourth and last, the process of construction of evidence is a factor to consider when explaining the way indicators are involved in these decisions. In fact, indicators and other evidence were brought to the decision processes according to their availability and capacity to support the different arguments and interests of the actors and stakeholders. In one case, an indicator lost much persuasion strength with the controversies that it went through during the decision process. Therefore, it can be argued that the use of indicators is high but not very influential; their role is mostly symbolic to policymakers and business decisions, but varies among researchers. The role of indicators in these decisions depends on the type and phase of the decision and the context and process of construction of evidence. The latter two are related to the particular context of each decision maker, the existence of elements of dispute and controversies that influence the way indicators are introduced in the decision-making process.

Effects of team task structure on team climate for innovation and team outcomes

In this cross-sectional study we analyzed, whether team climate for innovation mediates the relationship between team task structure and innovative behavior, job satisfaction, affective organizational commitment, and work stress. 310 employees in 20 work teams of an automotive company participated in this study. 10 teams had been changed from a restrictive to a more self-regulating team model by providing task variety, autonomy, team-specific goals, and feedback in order to increase team effectiveness. Data support the supposed causal chain, although only with respect to team innovative behavior all required effects were statistically significant. Longitudinal designs and larger samples are needed to prove the assumed causal relationships, but results indicate that implementing self-regulating teams might be an effective strategy for improving innovative behavior and thus team and company effectiveness.

Cultural innovation and transmission of tool use in wild chimpanzees:evidence from field experiments

Animal Cognition, V.6, pp. 213-223

Assessing the balance between biodiversity conservation and coastal and estuarine management of the Sado Estuary Natural Reserve, Portugal, using BIO-SAFE

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para obtenção do grau de Mestre em Engenharia do Ambiente, perfil de Engenharia Ecológica

Integration of crowdsourced information with traditional crises and disaster management information using linked data

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.

Innovation and environmental policy: Clean vs. dirty technical change

We study a two sector endogenous growth model with environmental quality with two goods and two factors of production, one clean and one dirty. Technological change creates clean or dirty innovations. We compare the laissez-faire equilibrium and the social optimum and study first- and second-best policies. Optimal policy encourages research toward clean technologies. In a second-best world, we claim that a portfolio that includes a tax on the polluting good combined with optimal innovation subsidy policies is less costly than increasing the price of the polluting good alone. Moreover, a discriminating innovation subsidy policy is preferable to a non-discriminating one.