PurposeThe purpose of this study is to analyze the relationships between Entrepreneurial Orientation, Organizational Learning Capability, Service Innovation and Organizational Performance. To this end, it was sought to analyze the mediating role of organizational learning capability and service innovation within entrepreneurial orientation and organizational performance relationship in knowledge-intensive organizations.Design/methodology/approachThe sample consisted of 159 architecture and urbanism companies from Santa Catarina, Brazil. The study opted to use managers as key informants since they are the ones that have general information about the organization and are a valuable source for assessing the different variables of the organization. For data analysis, the PLS-PM algorithm (Partial Least Squares Path Modeling) was used.FindingsResults showed that entrepreneurial orientation is a strong driver of service innovation and organizational performance. Organizational learning capability acts as a facilitator of innovation and has a positive influence on organizational performance. Another theoretical contribution of this study to organizational learning capability is the confirmation of its mediation in service innovation and organizational performance. Management needs to make its organization more proactive and creative, continually promoting new ideas. Architecture and urbanism organizations should pay more attention to maintaining and promoting entrepreneurial orientation permanently. The trend toward both proactivity and risk-taking can be an inherent advantage of these knowledge-intensive business services.Originality/valueFew studies have explored the mediating role of organizational learning capability and service innovations in organizational performance. In particular, the combined effects of entrepreneurial orientation and organizational learning capability have been neglected by the knowledge-intensive organizations literature. The study is justified by providing a more complete view of the relationship between entrepreneurial orientation and the performance of knowledge-intensive organizations, highlighting the role of organizational learning capability and performance in service innovation.

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This work was supported by Junta De Castilla y León-Consejería De Economía Y Empleo: System for simulation and training in advanced techniques for the occupational risk prevention through the design of hybrid-reality environments with ref J118.

Exoskeletons are wearable mobile robots that combine various technologies to enable limb movement with greater strength and endurance, being used in several application areas, such as industry and medicine. In this context, this paper presents the development of a hybrid control method for exoskeletons, combining admission and impedance control based on electromyographic input signals. A proof of concept of a robotic arm with two degrees of freedom, mimicking the functions of a human’s upper limb, was built to evaluate the proposed control system. Through tests that measured the discrepancy between the angles of the human joint and the joint of the exoskeleton, it was possible to determine that the system remained within an acceptable error range. The average error is lower than 4.3%, and the robotic arm manages to mimic the movements of the upper limbs of a human in real-time.

The long short-term memory (LSTM) is a high-efficiency model for forecasting time series, for being able to deal with a large volume of data from a time series with nonlinearities. As a case study, the stacked LSTM will be used to forecast the growth of the pandemic of COVID-19, based on the increase in the number of contaminated and deaths in the State of Santa Catarina, Brazil. COVID-19 has been spreading very quickly, causing great concern in relation to the ability to care for critically ill patients. Control measures are being imposed by governments with the aim of reducing the contamination and the spreading of viruses. The forecast of the number of contaminated and deaths caused by COVID-19 can help decision making regarding the adopted restrictions, making them more or less rigid depending on the pandemic’s control capacity. The use of LSTM stacking shows an R2 of 0.9625 for confirmed cases and 0.9656 for confirmed deaths caused by COVID-19, being superior to the combinations among other evaluated models.