Additional information:Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. AbstractThis article investigates the influence of informal and formal institutions on the university students' decision of becoming employer entrepreneurs in the context of Catalonia. A sample of 1,207 students from the Universitat Autònoma de Barcelona (UAB) and the Universitat Oberta de Catalunya were surveyed for the period of 2012-2015, and probit regressions over pooled data were used. The main findings suggest that formal factors (university's lack of incentives to create a new business, entrepreneurial knowledge, training and skills, and entrepreneurship education) are higher correlated with the student employer entrepreneurs than informal institutions (role models, entrepreneur's social image and fear of failure). Despite that the entrepreneur's social image does not seem to have an influence on the entrepreneurial decision of university students, the other variables analysed are statistically significant, correlated with entrepreneurship as a choice. Specifically, entrepreneurship education is the most relevant variable in explaining the decision of university students becoming employer entrepreneurs. The paper contributes with policy discussions in order to extend the current debate about the role of the universities in the entrepreneurial process and also the importance of entrepreneurial universities to the society.2

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Knowledge sharing (KS) behavior is one of the main drivers to generate social sustainability. It predicts high organizational performance and innovation capabilities, and creates enjoyment and happiness in helping others. Even if incentives to enhance KS behaviors exist, employees would still be reluctant to share knowledge. For this reason, we test a comprehensive model of sustainable human resource management with the inclusion of KS to explain how to enhance collaborative practices in terms of voluntary knowledge sharing. In a comprehensive model, we incorporate organizational justice, employee perceived organizational support, job satisfaction and affective organizational commitment, and how they relate in order to generate knowledge sharing behavior. Using a sample of 1350 employees working for multinational firms operating in Spain, the present research obtains two main results. First, organizational justice, employee perceived organizational support and affective organizational commitment are positively related with KS. Second, employee perceived organizational support, job satisfaction and affective organizational commitment play a mediating role between organizational justice and KS, which reinforces the positive relationship between both constructs. Consequently, employees would be more willing to cooperate and share in fair organizational contexts, especially when they are satisfied and affectively committed, and when their contributions are valued and recognized. Finally, we discuss human resource management’s (HRM) practical interventions and recommendations for future research on sustainable organizations.

These results have revealed the need for a dynamic approach to the design of telemedicine use, especially when it targets a variety of end-users. Hence, the importance of conducting studies before using telemedicine, and attempting to identify which of the above-mentioned predictors exert an influence and how.

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BackgroundMore advanced methods and models are needed to evaluate the participation of patients and citizens in the shared health care model that eHealth proposes.ObjectiveThe goal of our study was to design and evaluate a predictive multidimensional model of eHealth usage.MethodsWe used 2011 survey data from a sample of 13,000 European citizens aged 16–74 years who had used the Internet in the previous 3 months. We proposed and tested an eHealth usage composite indicator through 2-stage structural equation modelling with latent variables and measurement errors. Logistic regression (odds ratios, ORs) to model the predictors of eHealth usage was calculated using health status and sociodemographic independent variables.ResultsThe dimensions with more explanatory power of eHealth usage were health Internet attitudes, information health Internet usage, empowerment of health Internet users, and the usefulness of health Internet usage. Some 52.39% (6811/13,000) of European Internet users’ eHealth usage was more intensive (greater than the mean). Users with long-term health problems or illnesses (OR 1.20, 95% CI 1.12–1.29) or receiving long-term treatment (OR 1.11, 95% CI 1.03–1.20), having family members with long-term health problems or illnesses (OR 1.44, 95% CI 1.34–1.55), or undertaking care activities for other people (OR 1.58, 95% CI 1.40–1.77) had a high propensity toward intensive eHealth usage. Sociodemographic predictors showed that Internet users who were female (OR 1.23, 95% CI 1.14–1.31), aged 25–54 years (OR 1.12, 95% CI 1.05–1.21), living in larger households (3 members: OR 1.25, 95% CI 1.15–1.36; 5 members: OR 1.13, 95% CI 0.97–1.28; ≥6 members: OR 1.31, 95% CI 1.10–1.57), had more children <16 years of age (1 child: OR 1.29, 95% CI 1.18–1.14; 2 children: OR 1.05, 95% CI 0.94–1.17; 4 children: OR 1.35, 95% CI 0.88–2.08), and had more family members >65 years of age (1 member: OR 1.33, 95% CI 1.18–1.50; ≥4 members: OR 1.82, 95% CI 0.54–6.03) had a greater propensity toward intensive eHealth usage. Likewise, users residing in densely populated areas, such as cities and large towns (OR 1.17, 95% CI 1.09–1.25), also had a greater propensity toward intensive eHealth usage. Educational levels presented an inverted U shape in relation to intensive eHealth usage, with greater propensities among those with a secondary education (OR 1.08, 95% CI 1.01–1.16). Finally, occupational categories and net monthly income data suggest a higher propensity among the employed or self-employed (OR 1.07, 95% CI 0.99–1.15) and among the minimum wage stratum, earning ≤€1000 per month (OR 1.66, 95% CI 1.48–1.87).ConclusionsWe provide new evidence of inequalities that explain intensive eHealth usage. The results highlight the need to develop more specific eHealth practices to address different realities.

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