Abstract
Software development companies have progressively been experiencing the need to migrate their traditional development processes to agile environments. Several frameworks have emerged that support this paradigm, and Scrum is one of the most widely adopted on a global scale. However, Scrum was originally designed to work in small, local team environments. The challenge lies in designing its adoption in large-scale environments with geographically distributed teams. This chapter seeks to explore this challenge by identifying communication and coordination issues that may arise when adopting distributed Scrum. Three case studies are considered to allow comparison of different approaches considering structural and contextual factors associated with each organization.

Abstract
The complex and interconnected infrastructure of smart cities offers several opportunities for attackers to exploit vulnerabilities and carry out cyberattacks that can have serious consequences for the functioning of cities' critical infrastructures. This study aims to address this phenomenon and characterize the dimensions of security risks in smart cities and present mitigation proposals to address these risks. The study adopts a qualitative methodology through the identification of 62 European research projects in the field of cybersecurity in smart cities, which are underway during the period from 2022 to 2027. Compared to previous studies, this work provides a comprehensive view of security risks from the perspective of multiple universities, research centers, and companies participating in European projects. The findings of this study offer relevant scientific contributions by identifying 7 dimensions and 31 sub-dimensions of cybersecurity risks in smart cities and proposing 24 mitigation strategies to face these security challenges. Furthermore, this study explores emerging cybersecurity issues to which smart cities are exposed by the increasing proliferation of new technologies and standards.

Abstract
PurposeThe current student selection process for short-term mobility actions under the Erasmus + program (i.e. intensive programs and blended intensive programs) is based exclusively on the students' order of enrolment and their grades. This study offers an alternative approach using the analytic hierarchy process based on a four-layer model that collects information about the specificities of each project and the profile of the students and also promotes greater inclusion and homogenization of the project teams.Design/methodology/approachA decision support system was built by decomposing it into three stages: the predesign stage, in which the problem is characterized, and the user requirements are identified; the design stage, in which the models, the database and the interfaces are formulated; and the field stage, in which six test scenarios were built to validate the proposed solution.FindingsThe results show that this model can be applied with various selection criteria among students and consider both their hard and soft skills. It can also be applied to help build teams in which the students' knowledge is aligned with the technical skills required by the projects.Originality/valueThe proposed approach is innovative in that it responds to the emerging challenge of short-term European mobility programs that aim to involve students with multidisciplinary competencies. The solution considers both hard and soft skills in the selection of students, which allows changing the student selection paradigm and obtaining potentially more homogeneous multicultural teams with greater learning potential.

Abstract
Purpose Eco-innovation has been identified as a source of gaining a competitive advantage on a global scale. To build upon that, this study aims to deepen the understanding of eco-innovation in the context of small- and medium-sized enterprises (SMEs) and investigates the impact of having a clear eco-innovation strategy on a company's sustainability and performance. Design/methodology/approach A sample of 249 SMEs located in Portugal and the UK participated and structural equation modelling (SEM) was applied to explore the relationship among the constructs. Findings The findings reveal that both internal and external factors influence the design of an eco-innovation strategy. However, the relevance of external factors seemed to be more significant for Portuguese SMEs. This study concludes that product/process eco-innovations and green innovation systems are determinants for sustainable performance in SMEs. In contrast, the environmental technologies and organisational eco-innovation dimensions are not determinants. This is observed both in Portuguese and UK SMEs. Originality/value Most studies in the field tend to explore the role of eco-innovation in large organisations. This study takes a different approach by exploring its impacts on the sustainable business performance of SMEs. Furthermore, it combines data from two countries, which constitutes a strength and gives the opportunity to explore this phenomenon empirically.

Abstract
This study aims to explore the role of small and medium-sized enterprises (SMEs) in developing data-driven solutions to address the direct and indirect challenges posed by COVID-19. A sample of six case studies of SMEs from the UK and Portugal were selected to explore in-depth the experience of these companies in proposing innovative solutions in the pandemic context. The findings reveal that the pandemic caused amplifying effects on the digitalization of organizations and the emergence of data-driven solutions. However, the development of a data-driven approach involves not only technologies but also the digitalization of processes and highly skilled human resources. The pandemic was also a catalyst for the emergence of collaborative initiatives that have enabled the development of solutions involving diverse players from science, business, and civilian society. This study offers innovative contributions by focusing exclusively on companies developing data-driven solutions supported by technologies such as the internet of things (IoT), big data, and artificial intelligence.

Abstract
Purpose The purpose of this study is to explore the potential and growth of big data across several industries between 2016 and 2020. This study aims to analyze the behavior of interest in big data within the community and to identify areas with the greatest potential for future big data adoption. Design/methodology/approach This research uses Google Trends to characterize the community's interest in big data. Community interest is measured on a scale of 0-100 from weekly observations over the past five years. A total of 16 industries were considered to explore the relative interest in big data for each industry. Findings The findings revealed that big data has been of high interest to the community over the past five years, particularly in the manufacturing, computers and electronics industries. However, over the 2020s the interest in the theme decreased by more than 15%, especially in the areas where big data typically had the greatest potential interest. In contrast, areas with less potential interest in big data such as real estate, sport and travel have registered an average growth of less than 10%. Originality/value To the best of the author's knowledge, this study is original in complementing the traditional survey approaches launched among the business communities to discover the potential of big data in specific industries. The knowledge of big data growth potential is relevant for players in the field to identify saturation and emerging opportunities for big data adoption.