RESUMO
To simultaneously promote health, economic, and environmental sustainability, a number of cities worldwide have established bike-sharing systems (BSS) that complement the conventional public transport systems. As the rapid spread of COVID-19 becoming a global pandemic disrupted urban mobility due to government-imposed lockdowns and the heightened fear of infection in crowded spaces, populations were increasingly less likely to use public transportation and instead shifted toward alternative transport systems, including BSS. In this study, we use probabilistic machine learning in a quasi-experimental research design to identify how the relevance of a comprehensive set of factors to predict the use of Bicing (the BSS in Barcelona) may have changed as COVID-19 unfolded. We unpack the key factors in predicting the use of Bicing, uncovering evidence of increasing bike-related built infrastructure (e.g., tactical urbanism), trip distance, and the income levels of neighborhoods as the most relevant predictors. Moreover, we find that the relevance of the factors in predicting Bicing usage has generally decreased during the global pandemic, suggesting altered societal behavior. Our study enhances the understanding of BSS and societal behavior, which can have important implications for developing resilient programs for cities to adopt sustainable practices through transport policy, infrastructure planning, and urban development.
RESUMO
Prior research on emotional intelligence (EI) has highlighted the use of incremental models that assume EI and general intelligence (or g) make independent contributions to performance. Questioning this assumption, we study EI's moderation power over the relationship between g and individual performance, by designing and testing a task-dependent interaction model. Reconciling divergent findings in previous studies, we propose that whenever social tasks are at stake, g has a greater effect on performance as EI increases. By contrast, in analytic tasks, a compensatory (or negative) interaction is expected, whereby at higher levels of EI, g contributes to performance at a lesser extent. Based on a behavioral approach to EI, using 360-degree assessments of EI competencies, our findings show that EI moderates the effect of g on the classroom performance of 864 MBA business executives. Whilst in analytic tasks g has a stronger effect on performance at lower levels of EI competencies, our data comes short to show a positive interaction of EI and g in affecting performance on social tasks. Contributions and implications to research and practice are discussed.
RESUMO
Amid the swarm of debate about emotional intelligence (EI) among academics are claims that cognitive intelligence, or general mental ability (g), is a stronger predictor of life and work outcomes as well as the counter claims that EI is their strongest predictor. Nested within the tempest in a teapot are scientific questions as to what the relationship is between g and EI. Using a behavioral approach to EI, we examined the relationship of a parametric measure of g as the person's GMAT scores and collected observations from others who live and work with the person as to the frequency of his or her EI behavior, as well as the person's self-assessment. The results show that EI, as seen by others, is slightly related to g, especially for males with assessment from professional relations. Further, we found that cognitive competencies are more strongly related to GMAT than EI competencies. For observations from personal relationships or self-assessment, there is no relationship between EI and GMAT. Observations from professional relations reveal a positive relationship between cognitive competencies and GMAT and EI and GMAT for males, but a negative relationship between EI and GMAT for females.
