Abstract
Higher Education Institutions must define and monitor strategies and policies essential for decision-making in their various areas and levels, in which Business Intelligence plays a leading role. This research addresses the problem of Business Intelligence system adoption in Higher Education Institutions, with a view, in the first instance, to identify and characterise the strategic objectives that underpin decision-making, activities, processes, indicators and information in Higher Education Institutions. After a literature review, it was found that the absence of a roadmap that can serve as a reference to implement a Business Intelligence system in Higher Education Institutions may limit the adoption of this type of solution. Therefore, this research intends to present the methodology of a proposed roadmap for the implementation of Business Intelligence systems in Higher Education Institutions, that allows for increasing its capacity for analysis and evaluation of the data and information available in the various systems and platforms. © 2023 ICSBT International Conference on Smart Business Technologies. All rights reserved.

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Abstract
The human-environment-vehicle triad and how it relates to crashes has long been a topic of discussion, in which the human factor is consistently seen as the leading cause. Recently, more sophisticated approaches to Road Safety have advocated for a road-driver interaction view, in which human characteristics influence road perception and road environment affects driver behavior. This study focuses on road-driver interaction by using a driving simulator. The objective is to investigate how the driver profile influences driving performance and the effects of three countermeasures (peripheral transverse lines before and after the beginning of the curves and roadside poles in the curves). Fifty-six middle-aged male participants drove a non-challenging rural highway simulated scenario based on a real road where many single-vehicle crashes occurred. The drivers' profiles were assessed through their behavioral history measured by a validated version of the Driver Behavior Questionnaire (DBQ) comprising three dimensions: Errors (E), Ordinary Violations (OV), and Aggressive Violations (AV). The relationship between speed and trajectory measures and drivers' profiles was investigated using randomparameter models with heterogeneity in the means. The models' results showed that the DBQ subscale scores in OV explained a considerable part of the heterogeneity found in drivers' performance. Furthermore, the heterogeneity in the means caused by the DBQ subscale scores in OV and E in the presence of peripheral transverse lines indicates a difference in how drivers react to the countermeasures. The peripheral lines were more efficient than roadside poles to moderate speed but did not positively influence all drivers' trajectories. Although the peripheral lines could be seen as an alternative to change driver behavior in a non-challenging or monotonous road environment, the design used in this study should be reviewed.