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Multimedia content that is accessible through mobile devices has a larger size than other types of content (e.g. text, images). This may lead to higher prices for accessing the content via mobile devices, as mobile operators are capping mobile data billing plans in an effort to increase their revenues and prevent congestion. This poses problems for the users that are not willing/do not afford to pay the required price but still want to use multimedia content through the mobile networks. A price reduction for the user, as well as minimising bandwidth consumption can be obtained as a trade-off in multimedia quality. However, as previous research shows, not all people are willing to trade-off quality for a lower price; therefore, there is no straightforward approach to this problem. In this context, the purpose of this paper is to present a model of user willingness to pay for multimedia content quality as a function of the user risk attitude with the aim to provide personalised content depending on the user willingness to trade-off price for multimedia content quality.

A user model was proposed based on a literature review and an existing data set. A stereotypical approach was used where users are divided in two groups: risk averse and risk seekers. An experimental study involving six scenarios was used to validate the findings.

The results of the evaluation show that for the proposed user risk model, risk seekers preferred to pay for multimedia quality, whereas risk adverse users preferred to switch to a lower multimedia quality when monetary cost is involved. However, when the mobile data billing plan had the bandwidth limited, rather than a higher price to be paid when the bundle quantity was exceeded, the risk averse people’s preference for a lower quality still holds, but it does not show that most of the risk seekers prefer to pay for the multimedia quality.

This paper adds to the state of the art by providing a novel way to model the user preferences for multimedia quality based on their attitude towards risk, age, and gender.

Mobile data users, content providers (application service providers, over-the-top providers), mobile network operators (MNOs) and internet service providers (ISPs) could benefit from the results of this research. For mobile data users, the outcome of this research could be beneficial, as they can obtain personalised content based on their needs. From the content providers’ point of view, providing personalised content can lead to more satisfied users. It could also reduce the bandwidth consumption and the traffic to the server and/or proxy. Reducing the bandwidth consumption could lead to the possibility to acquire more customers and hence increase the revenues.

This is among the first studies to assess how the user preference towards multimedia quality if affected by the user attitude towards risk.

Artificial Intelligence (AI) in education is a rapidly emerging technology that has revolutionised teaching and learning, administrative tasks and research in higher education. The emergence of AI in higher education has impacted the evolving roles of faculty and students, how data is examined and how results are delivered. In this chapter, the different aspects of the role of AI and the transformative power of AI in both academic and administrative spheres are revealed. The case studies presented and future directions reveal AI's capability in transforming education and preparing for an AI-driven workforce. The personalisation of learning experiences, automation of administrative tasks, enhancement of research and impact on instructional design, all made possible through AI, reveal the possibility of tackling long-standing challenges in education, such as accessibility, engagement and efficiency. In the AI-enhanced future that higher education institutions navigate, it is vital to hold on to the principle that AI should be used as a tool for empowerment, innovation and transformation, ensuring that learning goes through the required progress.

The paper aims to look at small to medium‐sized enterprises' (SMEs') approach to learning and innovation.

The first research direction focuses on the internal and external sources for learning used in the innovation process. A second research direction focuses on the main types of innovation SMEs engage in along with some of the obstacles or constraints identified within the innovation process. In order to answer the research questions set, the authors conducted a survey among 161 Romanian SMEs from various economic sectors and covering different geographical areas.

Romanian SMEs display a learning orientation related to innovation, using best practices within the organization and networking with external partners as internal sources for learning, whereas in terms of external support for learning, SMEs consider changes that take place on the market, changes in technology and the input from experts and consultants. Concerning the innovation type, Romanian SMEs are very much oriented towards innovation in their business model, at the same time being faced with various constraints such as lack of funds and high costs involved.

Firstly, in what concerns the organizational learning dimension, the need for SMEs to focus on strengthening the collaboration with universities (higher education area), as the research pointed out that only few SMEs rely on universities and educational institutions in acquiring knowledge. Secondly, to focus on innovation and manage innovation activities as a core competency inside the firm in relation to designing a process of organizational change in SMEs, which should enhance drastically the performance of such initiatives.

So far, few studies have been conducted on innovation and knowledge creation in South‐Eastern European SMEs. The findings presented in the paper offer a holistic view of the innovation approach in Romanian SMEs, emphasizing the predominant type of innovation implemented along with specific constraints applied. The originality of the paper also relates to the fact that few studies conducted in Romania on SMEs focus on the internal and external sources of knowledge in the innovation process.