Purpose: The purpose of this paper is to explore the type of personalisation services satisfying the needs of cancer websites’ target users, and the influence of their emotional states on website usage intentions. Design/methodology/approach: Three data collection methods were employed. Survey questionnaires were distributed to online health users. Interviews with representatives of the cancer-affected population further explored emotions as stimuli for online cancer-related activities. Finally, availability of personalisation features was evaluated on existing health websites in Bosnia and Herzegovina and the UK. Findings: A clear preference emerged for personalisation on cancer-related websites. There are specific personalisation features the cancer-affected population desires. Interestingly, certain emotions were found to stimulate visits to health websites. Research limitations/implications: Fighting cancer implies constant support, including from cancer-related websites. It is thus vital to understand the required personalisation, stemming from target users’ actual needs, including the neglected user characteristics, as are emotions for cancer-affected people. This supports emotion-based personalisation. Originality/value: The paper focusses on the cancer-affected population, and developing a comprehensive understanding of their personalisation needs in online health services. It further shows which emotions influence intentions to use cancer websites. The three concepts combined have not yet been studied, to the best of the authors’ knowledge.

The art of personalized e-advertising relies in attracting a user attention to the recommended product, as it relates to their taste, interest and data. Whilst in practice, companies attempt various forms of personalization, research of e-advertising is rare, and seldom routed in solid theory. Adaptive hypermedia (AH) techniques have contributed to the development of personalized tools for adaptive content delivery, mostly in educational domain. This study explores the use of these theories and techniques in a specific field -- adaptive e-advertisements, via implementing and evaluating an adaptive e-advertisement system called "MyAds". The novelty of this approach relies in a systematic design, implementation and evaluation based on an adaptive hypermedia taxonomy-. In particular, this paper reports on a case study, which investigated users' perceptions' on the proposed system MyAds and the reflection of the acceptance level of the users. The results from both implicit and explicit feedback indicated that users found MyAds acceptable and agreed that the implemented AH features within the system contributed to an enhanced experience through the exposure to different personalization methods. Appealing ideas of the system were ease of use, personalization and diversity in a non-intrusive way.

Personalized e-advertising is the art of attracting users' attention by correlating to their tastes and interests. The evolving research of adaptive hypermedia with its techniques and theories contributed to the personalization field. This paper reports the case study of "My Ads", an adaptive e-advertisement delivery system that is based on a new theoretical framework, to evaluate users' acceptance of personalized ads. The results indicated that the implemented features within the system contributed to an enhanced experience through the exposure to different personalization methods and the appealing ideas of the system.

Evidence points to the fact that the integration of Social Networking Sites (SNS) features, into e-learning environments has been highly accepted by students, because of its benefits of improving the learning experience. Yet, not enough attention has been paid to what role learners' profiles play in the use of social e-learning environments, which does not match the importance of profiles in SNS. This paper presents how profiles are implemented in the second version of Topolor, a social personalised adaptive e-learning environment (SPAEE), and learners' perceived acceptance of the design and the implementation. To complement the findings, a case study is conducted to analyze the profile-related features in Topolor, which illustrates a generally high level of learner acceptance of these features. The analysis is finally concluded to suggest future research directions, in order to further analyze and improve these features.

In current e-learning studies, one of the main challenges is to keep learners motivated in performing desirable learning behaviours and achieving learning goals. Towards tackling this challenge, social e-learning contributes favourably, but it requires solutions that can reduce side effects, such as abusing social interaction tools for ‘chitchat’, and further enhance learner motivation. In this paper, we propose a set of contextual gamification strategies, which apply flow and self-determination theory for increasing intrinsic motivation in social e-learning environments. This paper also presents a social e-learning environment that applies these strategies, followed by a user case study, which indicates increased learners’ perceived intrinsic motivation.

Open social learner modelling (OSLM) approaches are promoted in order to assist learners in self-directed and self-determined learning in a social context. Still, most approaches only focus on visualising learners’ performance, or providing complex tools for social navigation. Our proposal, additionally, emphasises the importance of visualising both learners’ performance and their contribution to a learning community. We seek also to seamlessly integrate OSLM with learning contents, in order for the multifaceted OSLM’s prospect for ubiquity and context-awareness to enrich the adaptive potential of social e-learning systems. This paper thus presents the design of multifaceted OSLM by introducing novel, personalised social interaction features into Topolor, a social personalised adaptive e-learning environment. The umbrella target is to create and study aspects of open social learner models. An experimental study is conducted to analyse the impact of the newly introduced features. The results are finally concluded to suggest future research and further improvements.