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Purpose-The relations telework-work interference with the family (WIF), telework-family interference with work (FIW), and telework-performance have been widely studied; however, results of different investigations are contradictory. This may be related to third variables that moderate the effect of relations. The purpose of this paper is to analyze the moderating effect of worker responsibilities outside of the work environment on telework-FIW and telework-WIF relations, as well as the moderating effect of control by the supervisor on teleworkers in the telework-performance relation. Design/methodology/approach-A total of 92 teleworkers were interviewed, and 72 non-teleworkers who work in four public institutions. Non-teleworkers work in the same departments as teleworkers, and carry out similar functions. In addition, 33 supervisors were interviewed who evaluated performance of both groups. Hierarchical lineal regression analysis models were used to evaluate the influence of telework on the dependent variables. Findings-The results obtained reveal that where there are low-responsibility levels, teleworkers present a lower FIW than non-teleworkers; however, with high levels of responsibility, teleworkers show higher FIW. Additionally, supervisors' control of teleworkers was found to have a negative effect on their pro-activity and adaptability to tasks. Originality/value-The findings provide new empirical evidence about the effect of moderating variables in the relation between telework-work-family conflict and telework-performance. Besides the results provide practical and useful implications to organizations that implement telework programs.

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PurposeThis paper analyses how contextual factors at universities (entrepreneurship education and program learning) and cognitive variables (perceived behavioral control, implementation intentions, and attitude) influence entrepreneurial intentions among Latin American university students.Design/Methodology/ApproachThe empirical analysis employs a multilevel (hierarchical) linear model with a sample size of 9012 university students taken in 2018 from nine Latin American countries: Argentina, Brazil, Chile, Colombia, Costa Rica, Ecuador, México, Panamá, and Uruguay.FindingsOverall, the university context and cognitive variables contribute to explaining entrepreneurial intentions in university students. Whereas program learning constitutes a variable that directly and indirectly explains entrepreneurial intentions among university students, attending entrepreneurship courses negatively influences their entrepreneurial intentions.Originality/valueA central premise of this study is that the entrepreneurial process in university students is a multilevel phenomenon, given that university context and cognitive variables are key factors in entrepreneurial intentions. The findings support this premise and contribute to the existing literature on entrepreneurship in emerging economies. Nevertheless, the results reveal a more nuanced picture regarding the role of university context on the entrepreneurial intentions of students.

Buildings are currently among the largest consumers of electrical energy with considerable increases in CO2 emissions in recent years. Although there have been notable advances in energy efficiency, buildings still have great untapped savings potential. Within demand-side management, some tools have helped improve electricity consumption, such as energy forecast models. However, because most forecasting models are not focused on updating based on the changing nature of buildings, they do not help exploit the savings potential of buildings. Considering the aforementioned, the objective of this article is to analyze the integration of methods that can help forecasting models to better adapt to the changes that occur in the behavior of buildings, ensuring that these can be used as tools to enhance savings in buildings. For this study, active and passive change detection methods were considered to be integrators in the decision tree and deep learning models. The results show that constant retraining for the decision tree models, integrating change detection methods, helped them to better adapt to changes in the whole building’s electrical consumption. However, for deep learning models, this was not the case, as constant retraining with small volumes of data only worsened their performance. These results may lead to the option of using tree decision models in buildings where electricity consumption is constantly changing.