Abstract-In this study, we attempt to evaluate the user pref-5 erences for web design attributes (i.e., typography, color, content 6 quality, interactivity, and navigation) to determine the trust, sat-

PurposeThis study examines the effect of design quality (i.e. appearance, navigation, information and interactivity) on cognitive and affective involvement leading to continued intention to use the online learning application.Design/methodology/approachWe assume that design quality potentially contributes to enhance the individual's involvement and excitement. An experimental prototype is developed for collecting data used to verify and validate the proposed research model and hypotheses. A partial-least-squares approach is used to analyze the data collected from the participants (n = 662).FindingsCommunication, aesthetic and information quality revealed to be strong determinants of both cognitive and affective involvement. However, font quality and user control positively influence cognitive involvement, while navigation quality and responsiveness were observed as significant indicators of affective involvement. Lastly, cognitive and affective involvement equally contribute to determining the continued intention to use.Research limitations/implicationsThis study will draw the attention of designers and practitioners towards the perception of users for providing appropriate and engaging learning resources.Originality/valuePrevalent research in the online context is focused primarily on cognitive and utilization behavior. However, these works overlook the implication of design quality on cognitive and affective involvement.

The first stage in the design of a user interface is the quest for its 'typical user', an abstract generalization of each user of the application. However, in web systems and other scenarios where the application can be used by dozens of different kinds of users, the identification of this 'typical user' is quite difficult, if not impossible. Our proposal is to avoid the construction of interactive dialogs during the design stage, building them dynamically once the specific cognitive, perceptual and motor requirements of the current user are known: that is, during the execution stage. This is the approach used by GADEA, an intelligent user interface management system (UIMS) able to separate the functionality of an application from its interface in real time. The system adapts the components of the interface depending on the information stored in a user model which is continuously updated by a small army of data-gathering agents.

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Abstract-The Internet of Things (IoT) envisions billions of devices seamlessly connected to information systems, thus providing a sensing platform for applications. The availability of such a huge number of smart things will entail a multiplicity of devices collecting overlapping data and/or providing similar functionalities. In this scenario, efficient discovery and appropriate selection of things through proper context acquisition and management will represent a critical requirement and a challenge for future IoT platforms. In this work we present a practical approach to model and manage context, and how this information can be exploited to implement QoS-aware thing service selection. In particular, it is shown how context can be used to infer knowledge on the equivalence of thing services through semantic reasoning, and how such information can be exploited to allocate thing services to applications while meeting QoS requirements even in case of failures. The proposed approach is demonstrated through a simple yet illustrative experiment in a smart home scenario.