By Barbara Crow, Benjamin Poppinga and Kim Sawchuk
The articles included in this issue of wi are the outcome of our participation in this stimulating conference: the First International Workshop on Observing the Mobile User Experience which was held in in Rekyjavik, Iceland (Fall, 2010).
By Liisa Kuparinen
Examples of situations where the use of an eye-tracking system would be useful are when there is a need to get information about the most important objects used in navigation or to identify which objects in traffic a driver of a car notices or misses.
By Benjamin Poppinga, Martin Pielot, Niels Henze, and Susanne Boll
To observe the mobile user experience various observation techniques exist. For field studies ethnographic observation techniques, like shadowing, are often used. In shadowing, an experimenter follows a participant and takes notes on the observed behaviour. Shadowing is known to be highly situated [3, 5]. However, this technique does not scale very well. Additionally, because of its obtrusiveness, it could change the observed participant’s behaviour.
To overcome the disadvantages of low scalability and high obtrusiveness, new observation methods are being developed. In theory, passive automated logging through sensors seems to reach the same “situatedness”, while being scalable and unobtrusive [3, 5]. In practice logging has rarely been applied to mobile observation during the last years. One reason for this might be that suitable data sources, e.g. sensors, were not available on a common mobile device. However, the extension of smart phones through external sensors showed that sensors are able to infer users’ everyday situations [2].
By By Jakob Eg Larsen, Michael Kai Petersen, Nima Zandi, and Rasmus Handler
Mobile phones have become ubiquitous and an integrated part of our everyday life. In the last couple of years smart phones have received increased attention as application stores (e.g. Apple App Store, Android Market, and Nokia Ovi Store) have enabled easy distribution of mobile applications. New smart phone features and sensors have enabled a wide range of novel mobile applications, especially within games and media consumption domains. In the area of music applications a number of different mobile applications have been created. One example is mobile applications for the Internet radio Last.fm, which enable recommendation of music similar to the user’s favorites based on social networking features. MoodAgent is an example of an application that enables the user to navigate a music collection in terms of mood, rhythm, and style of the music.
By Katarzyna Wac and Anind K. Dey
The growing availability of diverse interactive mobile applications, envisaged to assist us in different domains of our daily life, make their perceived quality of experience (QoE) increasingly critical to their acceptance. Comments such as, “If it’s slow, I won’t give my credit card number” indicates the QoE expectations of a typical mobile commerce application user [1]. These expectations can be different given the user’s previous experiences with an application or an application’s criticality to the user’s task at hand. To date, the evaluation of QoE has been mainly conducted with qualitative methods that focus on an applications’ usability [2]. These studies are typically conducted within a limited time span in controlled laboratory environments, conditions that do not resemble users’ natural daily environments. The results of such evaluations may help to discover the mobile application’s serious and immediate usability issues in a design, but they may not help to uncover issues that are relevant to real life situations in the world outside the lab.
By Wen-Chia Wang, Mark Young, Steve Love, and Ian Coxon
User experience is one of the most important elements of mobile phone design and in recent decades has received increased attention in the HCI community. The user experience should include considerations of the usefulness and usability of a product (Alben, 1996; Shedroff, n.d.), the ‘user’s internal state, the context, and the user’s perceptions of the product’ (Väänänen-Vainio-Mattila, Roto, & Hassenzahl, 2008, p.1).
By Dr. Mireia Fernández-Ardèvol
Age plays a role in the adoption and uses of mobile telephony. This evidence has been discussed since the first stages of popularization of this technology (see for instance, Ling, 2002; Castells, Fernández-Ardèvol, Qiu, & Sey, 2006). Furthermore, as we have argued elsewhere, there is a general trend “toward the general diffusion of mobile communication within the whole population, with age continuing to specify the type of use rather than the use itself” (Castells et al., 2006, p. 41).