Scientific and business environment literature shows that green, sustainable innovation or eco-innovation has proven to be a source of competitive advantage today. The industrial clusters, their dynamism, and the synergies created within them attract a lot of attention from the scientific community. However, to date, the joint study of these two concepts and, more specifically, the factors that drive eco-innovation specifically in a cluster, have not been studied in depth. This article models eco-innovation in industrial clusters, thus addressing this gap and proposing a model based on information gathered from the literature and a detailed analysis of behavior in relation to eco-innovation in different sectors. Results suggest that including eco-innovation variables and measures may have positive implications such as improvements at the strategic level and the reduction of costs and use of resources. An eco-innovation model for clusters is proposed. It considers eight key factors that seek to raise its competitive level by promoting eco-innovation within clusters. The model includes five internal factors that analyze business capabilities and three external factors that study the effect of launching eco-innovative activities. This model could help the companies’ managers and those responsible for clusters in creating more successful strategies to increase competitiveness by enhancing eco-innovation. It could also serve as a guide for government entities in the performance of eco-innovative activities in economic sectors.

The change brought by Big Data about the way to analyze the data is revolutionary. The technology related to Big Data supposes a before and after in the form of obtaining valuable information for the companies since it allows to manage a large volume of data, practically in real time and obtain a great volume of information that gives companies great competitive advantages. The objective of this work is evaluating the factors that affect the acceptance of this new technology by small and medium enterprises. To that end, the technology acceptance model called Unified Theory of Technology Adoption and Use of Technology (UTAUT) was adapted to the Big Data context to which an inhibitor was added: resistance to the use of new technologies. The structural model was assessed using Partial Least Squares (PLS) with an adequate global adjustment. Among the results, it stands out that a good infrastructure is more relevant for the use of Big Data than the difficulty of its use, accepting that it is necessary to make an effort in its implementation.

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