Early termination of R&D projects

Early project termination is one of the most difficult decisions to be made by Research and Development managers. While there is the risk of terminating good projects, there is also the opposite risk of not terminating bad projects and overspend resources in unproductive research. Criteria used for identifying these projects are common subject of research in Business Administration. In addition, companies might take important lessons from its interrupted projects that could improve their overall portfolio technical and commercial success. Finally, the set and weight of criteria, as well as the procedures companies use for achieve learning from cancelled projects may vary depending on the project type. This research intends to contribute to the understanding of policies applied to projects that were once considered attractive, but by some reason is not appreciated anymore. The research addressed the question: How companies deal with projects that become unattractive? More specifically, this research tried to answer the following questions: (1) Are projects killed or (otherwise) they die naturally by lack of resources? (2) What criteria are used to terminate projects during development? (3) How companies learn from the terminated projects to improve the overall portfolio performance? (4) Are the criteria and learning procedures different for different types of projects? In order to answer these questions, we performed a multiple case study with four companies that are reference in business administration and innovation: (1) Oxiteno, considered the base case, (2) Natura, the literal replication, (3) Mahle and (4) AES, the theoretical replications. The case studies were performed using a semi-structured protocol for interviews, which were recorded and analyzed for comparison. We found that the criteria companies use for selecting projects for termination are very similar to those anticipated by the literature, except for a criteria related to compliance. We have evidences to confirm that the set of criteria is not altered when dealing with different project types, however the weight they are applied indeed varies. We also found that learning with cancelled projects is yet very incipient, with very few structured formal procedures being described for capturing learning with early-terminated projects. However, we could observe that these procedures are more common when dealing with projects labeled as innovative, risky, big and costly, while those smaller and cheaper derivative projects aren\'t subject of a complete investigation on the learning they brought to the company. For these, the most common learning route is the informal, where the project team learns and passes the knowledge though interpersonal information exchange. We explain that as a matter of cost versus benefit of spending time to deeply investigate projects with little potential to bring new knowledge to the project team and the organization

Impact of artifact correction methods on R-R interbeat signals to quantifying heart rate variability (HRV) according to linear and nonlinear methods.

In the analysis of heart rate variability (HRV) are used temporal series that contains the distances between successive heartbeats in order to assess autonomic regulation of the cardiovascular system. These series are obtained from the electrocardiogram (ECG) signal analysis, which can be affected by different types of artifacts leading to incorrect interpretations in the analysis of the HRV signals. Classic approach to deal with these artifacts implies the use of correction methods, some of them based on interpolation, substitution or statistical techniques. However, there are few studies that shows the accuracy and performance of these correction methods on real HRV signals. This study aims to determine the performance of some linear and non-linear correction methods on HRV signals with induced artefacts by quantification of its linear and nonlinear HRV parameters. As part of the methodology, ECG signals of rats measured using the technique of telemetry were used to generate real heart rate variability signals without any error. In these series were simulated missing points (beats) in different quantities in order to emulate a real experimental situation as accurately as possible. In order to compare recovering efficiency, deletion (DEL), linear interpolation (LI), cubic spline interpolation (CI), moving average window (MAW) and nonlinear predictive interpolation (NPI) were used as correction methods for the series with induced artifacts. The accuracy of each correction method was known through the results obtained after the measurement of the mean value of the series (AVNN), standard deviation (SDNN), root mean square error of the differences between successive heartbeats (RMSSD), Lomb\'s periodogram (LSP), Detrended Fluctuation Analysis (DFA), multiscale entropy (MSE) and symbolic dynamics (SD) on each HRV signal with and without artifacts. The results show that, at low levels of missing points the performance of all correction techniques are very similar with very close values for each HRV parameter. However, at higher levels of losses only the NPI method allows to obtain HRV parameters with low error values and low quantity of significant differences in comparison to the values calculated for the same signals without the presence of missing points.

Efetividade dos instrumentos de políticas públicas nos gastos privados de P&D no Brasil

Esse trabalho teve como objetivo contribuir para o debate sobre a importância das políticas de incentivo à inovação no Brasil. Os resultados esperados do uso que as empresas fizeram dos diferentes tipos de instrumentos sobre os gastos em pesquisa e desenvolvimento (P&D) foram avaliados pelo método de diferenças em diferenças. O método permitiu obter as diferenças de gastos entre empresas beneficiárias de instrumentos e as não-beneficiárias em três períodos consecutivos: 2005 em relação à 2003; 2008 em relação à 2005 e de 2011 em relação à 2008. Ao fazer isso, foi possível identificar se tais diferenças foram positivas e significativas, podendo ser atribuídas às influências dos instrumentos. Os instrumentos utilizados foram: incentivos fiscais, Lei de Informática, financiamentos em parcerias, financiamentos sem parcerias e subvenção. E a utilização dos mesmos pelas empresas teve maior relevância no âmbito de diversos programas de apoio à inovação vigentes no país a partir da retomada das políticas industriais e tecnológicas, nos anos 2000. O estudo concluiu que os efeitos positivos e significativos são limitados à determinados grupos tecnológicos e à poucos instrumentos, em geral, de caráter fiscal. Além disso, esses efeitos positivos surgem em apenas um período, sendo que para cada grupo tecnológico foram efetuadas estimativas para três períodos. Também não houve evidências de que os instrumentos financeiros exerçam efeitos significativos sobre as decisões de gastos em pesquisa e desenvolvimento, apesar da maior ênfase dada aos mesmos no período estudado. Os resultados sugerem fraca influência dos mecanismos de apoio à P&D no Brasil sobre o aumento dos gastos privados, apesar dos avanços recentes.