Abstract
Purpose: This paper aims to present a framework to guide managerial action for social media (SM) strategies for innovation by exploring its constituent elements – the “what” (SM types), the “who” (stakeholders to be reached), the “for” (innovation types) and the “how” (innovation process stages), as well as the value, benefits and barriers. Design/methodology/approach: A comprehensive and critical review of literature at the intersection of SM and innovation guides the development of a typology of SM types and their use across innovation types and stages. Findings: SM type and use tend to differ across innovation processes. The authors identify four types of SM in use across four stages of innovation, supporting six types of innovation, influenced by five categories of barriers, benefits and stakeholders each. Research limitations/implications: The research provides an integrative set of building blocks to consider for developing further studies of SM and innovation. Practical implications: By highlighting the intertwined aspects of SM and innovation in an open and collaborative environment, the paper calls for development of an SM readiness organisational diagnosis. It empowers managers with a coherent framework of different elements they should take into consideration when defining their SM strategies for innovation. Originality/value: Research on SM adoption and the extent of its usage for innovation purposes is still at its infancy. Given the increasingly open and collaborative innovation settings, the authors draw managerial attention to the need of SM strategies for innovation activities and provide a coherent analytical framework to guide action for organisational diagnosis.

Abstract
Entrepreneurship is a worldwide reality. Since the beginning of times and all around the world people have created businesses. Entrepreneurial orientation, from a macroeconomic perspective, allows income and employment generation, thus boosting growth. At the microeconomic level, it is a competition booster playing a central role in a globalized market. In this entrepreneurial ecosystem in which knowledge-based activity is the core booster of employment, economic growth, and competitiveness, universities and, in particular, entrepreneurial universities play either the role of knowledge production and dissemination. The present work aims to understand the role of education (formal and entrepreneurship) on entrepreneurial activity combined with heterogeneous individual characteristics and different cultures and geographies. Specifically, the study identifies substitution and complementary effects among both types of education according to individual taxonomies. © 2021 by IGI Global. All rights reserved.

Abstract
Portugal has witnessed some of its largest wildfires in the last decade, due to the lack of forestry management and valuation strategies. A cost-effective biomass collection tool/approach can increase the forest valuing, being a tool to reduce fire risk in the forest. However, cost-effective forestry machinery/solutions are needed to harvest this biomass. Most of bigger operations in forests are already highly mechanized, but not the smaller operations. Mobile robotics know-how combined with new virtual reality and remote sensing techniques paved the way for a new robotics perspective regarding work machines in the forest. Navigation is still a challenge in a forest. There is a lot of information, trees consist of obstacles while lower vegetation may hide danger for robot trajectory, and the terrain in our region is mostly steep. The existence of accurate information about the environment is crucial for the navigation process and for biomass inventory. This paper presents a prototype forest robot for biomass collection. Besides, it is provided a dataset of different forest environments, containing data from different sensors such as 3D laser data, thermal camera, inertial units, GNSS, and RGB camera. These datasets are meant to provide information for the study of the forest terrain, allowing further development and research of navigation planning, biomass analysis, task planning, and information that professionals of this field may require.

Abstract
One of the most difficult scenarios for unsupervised segmentation of moving objects is found in nighttime videos where the main challenges are the poor illumination conditions resulting in low-visibility of objects, very strong lights, surface-reflected light, a great variance of light intensity, sudden illumination changes, hard shadows, camouflaged objects, and noise. This paper proposes a novel method, coined COLBMOG (COLlinearity Boosted MOG), devised specifically for the foreground segmentation in nighttime videos, that shows the ability to overcome some of the limitations of state-of-the-art methods and still perform well in daytime scenarios. It is a texture-based classification method, using local texture modeling, complemented by a color-based classification method. The local texture at the pixel neighborhood is modeled as an..-dimensional vector. For a given pixel, the classification is based on the collinearity between this feature in the input frame and the reference background frame. For this purpose, a multimodal temporal model of the collinearity between texture vectors of background pixels is maintained. COLBMOG was objectively evaluated using the ChangeDetection.net (CDnet) 2014, Night Videos category, benchmark. COLBMOG ranks first among all the unsupervised methods. A detailed analysis of the results revealed the superior performance of the proposed method compared to the best performing state-of-the-art methods in this category, particularly evident in the presence of the most complex situations where all the algorithms tend to fail.

Abstract
The adoption of Electric Vehicles (EVs) will revolutionize the storage capacity in the power system and, therefore, will contribute to mitigate the uncertainty of renewable generation. In addition, EVs have fast response capabilities and are suitable for frequency regulation, which is essential for the proliferation of intermittent renewable sources. To this end, EV aggregators will arise as a market representative party on behalf of EVs. Thus, this player will be responsible for supplying the power needed to charge EVs, as well as offering their flexibility to support the system. The main goal of EV aggregators is to manage the potential participation of EVs in the reserve market, accounting for their charging and travel needs. This work follows this trend by conceiving a chance-constrained model able to optimize EVs participation in the reserve market, taking into account the uncertain behavior of EVs and their charging needs. The proposed model, includes penalties in the event of a failure in the provision of upward or downward reserve. Therefore, stochastic and chance-constrained programming are used to handle the uncertainty of a small fleet of EVs and the risk profile of the EV aggregator. Two different relaxation approaches, i.e., Big-M and McCormick, of the chance-constrained model are tested and validated for different number of scenarios and risk levels, based on an actual test case in Denmark with actual driving patterns. As a final remark, the McCormick relaxation presents better performance when the uncertainty budget increases, which is appropriated for large-scale problems.

Abstract
Whether research output significantly impacts on economic growth, and which research areas/fields of science matter the most to improve the economic performance of countries, stand as fundamental endeavors of scientific inquiry. Although the extant literature has analyzed the impact of research output on economic growth both holistically and by field, the impact of academic knowledge as a capital good (hard and social sciences) versus a final good (medical and humanities) has been largely neglected in analyses involving large sets of countries over a broad period of time. Based on a sample of 65 countries over 36 years (1980 to 2016), and employing system GMM dynamic panel data estimations, four main results are worth highlighting: (1) holistic research output positively and significantly impacts on economic growth; (2) both the academic knowledge of scientific areas that most resemble capital goods (physical sciences, engineering and technology, life sciences or social sciences) or final goods (base clinical, pre-clinical and health or arts and humanities) foster economic performance; (3) the global impact of research output is particularly high in the fields of engineering and technology, social sciences, and physics; and (4) the impact of research output on economic growth occurs mainly through structural change processes involving the reallocation of resources towards the industrial sector.