Suchergebnisse

Extant theory suggests that candidates with an unfocused identity—those spanning different categories—suffer from a valuation penalty because evaluators are confused by their profile and concerned they lack the required skills. We argue that unfocused candidates may be penalized for another reason; they threaten established social boundaries. This happens in contexts where evaluators act as gatekeepers for social entities, such as professions. We test how the penalty applied to unfocused candidates varies in an academic accreditation process, a setting where evaluators decide on admitting candidates to an academic discipline and where candidates' prior performance is observable. We find using data on the 2012 national scientific qualification in Italian academia that the valuation penalty applied to unfocused (multidisciplinary) candidates was most pronounced for the most high-performing candidates. High-performing yet ill-fitting candidates threaten the distinctiveness and knowledge domain of the discipline and are hence penalized by evaluators. High-performing multidisciplinary candidates suffered the greatest penalty in small and distinctive academic disciplines and when accreditors were highly typical members of their discipline. Our theory and findings suggest that the categorical imperative may be driven not only by cognitive or capability considerations as typically argued in the literature but also, by attempts to maintain social boundaries.Supplemental Material: The e-companion is available at https://doi.org/10.1287/orsc.2022.1610 .

Sustainability Transitions (ST) is a complex phenomenon, encompassing environmental, societal and economic aspects. Its study requires a proper investigation, with the identification of a robust indicator and the definition of a suitable method of analysis. To identify the most informative geographical boundaries for analysing ST pathways, we consider the Carbon Emission Intensity (CEI) and estimate a four-level growth model to study its pattern over time for all the EU regions. We apply this model to a novel longitudinal dataset that covers CEI data of European regions at four different geographical scales (state, areas, regions, and provinces) over a nine-year timespan. This approach aims at supporting the decision-makers in developing more effective sustainability transitions policies across Europe, especially focusing on regions and overcoming the well-known "one-size fits all" approach. • The unconditional growth model has been applied to a multi-level structure considering four levels, defined by three geographical scales and time. • The ideal structure of the model would have required five levels, but the sample size of the dataset made the application computationally unfeasible; • The application of the model allowed to identify patterns of stability and change over time of the variable amongst different geographical units.