Abstract
As the ongoing evolution in the higher education sector changes the roles of universities, entrepreneurial practices become more prominent in their agendas. The literature on academic entrepreneurship focuses predominantly on the commercialization of research and less on other intrapreneurial activities-namely those performed by non-academic employees. To fill this gap, this study aims to provide a comprehensive understanding of the factors that influence universities' faculty members and non-academic staff to engage in intrapreneurial activities. The article analyzes Latvian university employees' perceptions of 13 organizational, individual, and environmental factors and how they influence intrapreneurial behavior. Regarding the organizational factors, the results show that higher trust in managers, more available resources for innovative ideas, less formal rules and procedures, and greater freedom in decision-making can lead to higher levels of intrapreneurial behavior. With regard to individual factors, intrapreneurial behavior is associated with an employee's initiative, but is not correlated with risk-taking and personal initiative. As to external factors, while environmental munificence is positively correlated with innovativeness, dynamism and unfavorable change influence employees' engagement in intrapreneurial activities.

Abstract
Literature on analysis of inter-organizational networks mentions the benefits that collaboration networks can provide to firms, in terms of managerial decision-making, although rarely analysed in terms of their overall performance. This paper aims to identify the existence of common factors of evolutionary patterns in the networks that determine its performance and evolution through a Multiple Factor Analysis (MFA). Subsequently, a hierarchical clustering procedure was performed on the factors that determine these networks, trying to find similarities in the evolutionary behavior. Data were collected on twelve real collaboration networks, characterized by four variables: Operational Result, Stock of Knowledge, Operational Costs and Technological Distance. The hierarchical clustering allowed the identification and distinction of the networks with the worst and best performances, as well as the variables that characterize them, allowing to recognize poorly defined strategies in the constitution of some networks. © Springer Nature Switzerland AG 2020.

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[No abstract available]

Abstract
The COVID-19 outbreak has proven to be a challenge for most communities, requiring them to adapt to a newfound reality. Cities need now to accommodate the circulation of their populations in a safe manner, dealing with economic repercussions, and avoiding to oversaturate the countries' healthcare facilities. So far, the latter has happened with dramatic consequences in terms of loss of human lives. In this context, we propose a social simulation meta-model suitable to represent the complex socio-technical system of a campaign hospital, created to support existing healthcare facilities as a response to the demands created by the coronavirus pandemic. With this model we intend to support the analysis of social coordination policies towards the improvement of a given set of characteristics of the system. By considering both technical and social dimensions, we expect to gain insights into how certain aspects such as the collaborativeness of patients or the nature of staff might affect the healing speed of patients and, similarly, the efficiency of the campaign hospital. Ultimately, all emergent behaviour should provide useful insights allowing for the identification of key social practices influencing its performance. © 2020 IEEE.

Abstract
Personalization is increasingly being perceived as an important factor for the effectiveness of Recommender Systems (RS). This is especially true in the tourism domain, where travelling comprises emotionally charged experiences, and therefore, the more about the tourist is known, better recommendations can be made. The inclusion of psychological aspects to generate recommendations, such as personality, is a growing trend in RS and they are being studied to provide more personalized approaches. However, although many studies on the psychology of tourism exist, studies on the prediction of tourist preferences based on their personality are limited. Therefore, we undertook a large-scale study in order to determine how the Big Five personality dimensions influence tourists' preferences for tourist attractions, gathering data from an online questionnaire, sent to Portuguese individuals from the academic sector and their respective relatives/friends (n=508). Using Exploratory and Confirmatory Factor Analysis, we extracted 11 main categories of tourist attractions and analyzed which personality dimensions were predictors (or not) of preferences for those tourist attractions. As a result, we propose the first model that relates the five personality dimensions with preferences for tourist attractions, which intends to offer a base for researchers of RS for tourism to automatically model tourist preferences based on their personality. © 2020 ACM.

Abstract