Abstract
This introductory chapter provides a synopsis of the underlying factors that enable the existence of the mobility as a service (MaaS) concept and its role regarding the improvement of the performance of transportation systems. Therefore, several considerations are made to contextualize MaaS in modern societies explaining not only its main functions and advantages but also the challenges facing its widespread implementation.

Abstract
The environmental concerns together with social inclusion issues and the need to promote economic equity in the society have profound implications regarding the sustainable mobility concept. This allied to a technological (r)evolution leads to the path of the internet of mobility (IoM). On the other hand, we are witnessing the prosperity of mobility associated with services, mobility as a service (MaaS), which also aims at the integration of different transport modes. Linking together IoM and MaaS, the internet of mobility as a service (IoMaaS) concept is introduced, which can learn from the end user experiences and behaviors, enabling the reduction of ease of use and sustainable mobility, while supporting a much-needed cultural shift regarding mobility habits.

Abstract

Abstract
The current literature focuses on the cocreation of brands in dynamic contexts, but the impact of the relationships among brands on branding is poorly documented. To address this gap a concept is proposed concerning the relationships between brands and a model is developed, showing the influence of the latter on the identity and reputation of brands. Therefore, the goal of this study is to develop a brand relationships concept and to build a framework relating it with corporate brand identity and reputation, in a higher consumer involvement context like higher education. Structural equation modelling (SEM) was used for this purpose. In line with this, interviews, cooperatively developed by higher education lecturers and brand managers, were carried out with focus groups of higher education students, and questionnaires conducted, with 216 complete surveys obtained. Data are analyzed using confirmatory factor analysis and structural equation modelling. Results demonstrate that the concept of brand relationships comprises three dimensions: trust, commitment, and motivation. The structural model reveals robustness regarding the selected fit indicators, demonstrating that the relationships between brands influence brand identity and reputation. This suggests that managers must choose and promote brand relationships that gel with the identity and reputation of the primary brand they manage, to develop an integrated balanced product range.

Abstract
This paper investigated the impact of employee quality on corporate social responsibility (CSR). Based on data from China A-share-listed companies for the years 2012-2016 and using ordinary least squares, our empirical results show that the educational level of the workforce, as a proxy for employee quality, is positively associated with CSR, which suggests that higher education can promote CSR implementation. Additional analyses found that this positive relationship is more pronounced in non-state-owned enterprises, enterprises in regions with lower marketisation processes, and firms with lower proportions of independent directors. This study extends the literature on human capital at the level of firms' entire workforce and CSR by elaborating the positive effect of employee quality on CSR in the context of an emerging economy (China). The results suggest that it is necessary to consider the educational level of employees when analysing CSR, which is of strategic significance for corporate sustainable development.

Abstract
Machine Learning is a field in which significant steps forward have been taken in the last years, resulting in a wide variety of available algorithms, for many different problems. Nonetheless, most of these algorithms focus on the training of static models, in the sense that the model stops evolving after the training phase. This is increasingly becoming a limitation, especially in an era in which datasets are increasingly larger and may even arrive as sequential streams of data. Frequently retraining a model, in these scenarios, is not realistic. In this paper we propose evoRF: a combination of a Random Forest with an evolutionary approach. Its key innovative aspect is the evolution of the weights of the Random Forest over time, as new data arrives, thus making the forest's voting scheme adapt to the new data. Older trees can also be replaced by newly trained ones, according to their accuracy, ensuring that the ensemble remains up to date without requiring a whole retraining.