Is it Possible to Integrate Affective and Instrumental Ties into the same Concept?Managers' perspective on commitment to the organization Abstract T he aim of this study is to understand the vision that managers have about worker commitment with the organization exploring the relevance of treating affective and instrumental dimensions in the same construct, as proposed Meyer and Allen (1991). A description was made of the elements that make up the schematic concept of employee commitment and the burden of affective and instrumental elements that constitute it. The schematic elements were identifi ed by assigning a verbal label to two different profi les of committed worker, the spontaneous response to questioning about what commitment is and its main characteristics and the choice of features available in the literature on the issue of commitment. Eight managers working for two companies in a medium to large metropolitan region of Salvador-BA participated in the study (four from each organization). The data underwent content analysis and the results were reported with the use of cognitive mapping techniques. It was concluded that the managers surveyed associate the concept of affective commitment to their affective base and consider tinstrumental commitment as the antithesis of commitment. Extra effort and additional contribution are considered important for the worker to be regarded as committed.

ResumoAmpliar a compreensão dos motivos que levam as organizações a implementarem diferentes práticas de gestão constituiu o objetivo central da pesquisa conduzida. Mais precisamente, buscou-se identificar, em empresas com distintos perfis de inovação, diferenças entre estruturas cognitivas -esquemas -de atores que podem explicar a intensidade de uso de novas práticas de gestão. Selecionaram-se quatro empresas classificadas em dois padrões de inovação: empresas consideradas muito inovadoras e pouco inovadoras. Participaram do estudo 12 atores organizacionais que responderam a oito questões abertas que envolveram a investigação dos significados associados à mudança organizacional, as explicações subjacentes à adoção das práticas de gestão e a percepção dos seus impactos. As respostas às questões foram sistematizadas por meio da análise de conteúdo gerando categorias e subcategorias em torno das quais os conteúdos foram classificados. Representações gráficas foram utilizadas para mostrar os esquemas cognitivos associados aos diferentes padrões de inovação organizacional que evidenciaram tanto conteúdos compartilhados entre empresas de um mesmo padrão assim como elementos que singularizam a visão dos atores.

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Abstract. PRODES and DETER project together turned 33 years-old with an undeniably contribution to the state-of-art in mapping and monitoring tropical deforestation in Brazil. Monitoring systems all over the world have taken advantage of big data repositories of remote sensing data as they are becoming freely available together with artificial intelligence. Thus, considering the advent of new generation remote sensing data hubs, online platforms of big data that can fill in spatial and temporal resolutions gaps in current deforestation mapping, this work aims to present recent innovations at INPE´s deforestation monitoring systems in Brazil and how they are gauging new realms of technological levels. Recent innovations at INPE´s monitoring systems are: 1) the development of TerraBrasilis platform of data access and analysis; 2) the adoption of new sensors and cloud detection strategies; 3) the complementary use of multi-sensor images; 4) the complementary adoption of SAR C-band images using cloud data to sample and process Sentinel-1. Future innovations are: 1) development of a Brazilian data cube to be used in deep learning techniques of image classification; 2) Routine uncertainty analysis of PRODES data. Automatization might fasten mapping process, but the real challenge is to succeed in automatization maintaining data quality and historical series. The hyper-availability of remote sensing data, the initiative of a Brazilian Data Cube and promising machine learning techniques applied to land cover change detection, allowed INPE to reinforce its central role in tropical forest monitoring.