How does serif vs sans serif typeface impact the usability of e-commerce websites?

This study tries to find evidence that points towards the best typeface to use in e-commerce websites to maximize usability, trust, loyalty, appearance and overall user satisfaction. We tested the difference between serif and sans serif inside the same font family. A total of 246 volunteers participating in the experiment were asked to complete a set of tasks and a questionnaire on an e-commerce website prototype. We measured task completion time, reading speed and reading comprehension. From the results, using multiple linear regression, we deduced that only gender determines user preferences. Females tend to prefer the serif version of the typeface under study. Although most e-commerce websites use sans serif typefaces, we could not find evidence supporting this decision. The serif and sans serif characteristic inside the same font family does not affect usability on a website, as it was found that it has no impact on reading speed and user preference.

Hacia una herramienta de cribado temprano del trastorno del espectro del autismo (TEA) / Towards an Early Screening Tool for AutismSpectrum Disorder (ASD)

En los últimos años el empleo de la metodología del eye tracking ha sido objeto de estudio de diferentes investigaciones en torno a esta técnica como posible medida objetiva para la detección temprana de indicadores de Trastorno del Espectro Autista (TEA). En el presente estudio se ha elaborado un total de 15 vídeos estímulo divididos en cuatro bloques con el objeto de diseñar una prueba objetiva de sencilla y rápida aplicación, mediante un dispositivo eye tracker, que permita realizar una detección objetiva y muy temprana de indicadores de riesgo de desarrollar TEA; para ello se cuenta con una muestra formada por 148 sujetos, 74 sujetos con indicadores clínicos de TEA y 74 sujetos con desarrollo típico. Los resultados confirman que es posible configurar un software que, tras analizar los registros de mirada de un bebé obtenidos con un dispositivo eye tracker, nos proporcione automáticamente su probabilidad de presentar un “patrón de mirada de máxima atención al objeto (riego de TEA) o un patrón de mirada de recogida de información social (no TEA)”. Con las tareas aquí presentadas, tras analizar la sensibilidad de cada vídeo y cada variable, se han obtenido resultados estadísticamente significativos en cada uno de los vídeos estímulo, por lo que se concluye que la metodología del eye tracking con el empleo de los estímulos aquí diseñados puede operar como método de detección objetivo y eficaz de indicadores de alarma de TEA. (AU)

In recent years, eye-tracking methodology technique has been the objectof study used in different investigations as a potential objective measure of the diagnosis ofASD. In the present study, a total of 15 stimulus videos have been prepared. These are divided into four blocks to design an objective test with simple and rapid application, by using an eye-tracker device, which allows an early diagnosis of ASD. To do so, we measured a sample made up of 148 subjects, 74 subjects with ASD and 74 with typical development. The results confirm that it is possible to configure a software that, after analysing the gaze records of a baby obtained with an eye-tracker device, automatically provides us with its probability of presenting an “ASD or no ASD Gaze Pattern”. With the tasks presented here, after analysing the sensitivity of each video and each variable, statistically significant results have been obtained in each of the stimulus videos. Based on that output, it can be concluded that the eye-tracking methodology with the use of the stimuli designed here can be used as an objective and effective diagnostic method for ASD. (AU)

Implicit detection of user handedness in touchscreen devices through interaction analysis.

Mobile devices now rival desktop computers as the most popular devices for web surfing and E-commerce. As screen sizes of mobile devices continue to get larger, operating smartphones with a single-hand becomes increasingly difficult. Automatic operating hand detection would enable E-commerce applications to adapt their interfaces to better suit their user's handedness interaction requirements. This paper addresses the problem of identifying the operative hand by avoiding the use of mobile sensors that may pose a problem in terms of battery consumption or distortion due to different calibrations, improving the accuracy of user categorization through an evaluation of different classification strategies. A supervised classifier based on machine learning was constructed to label the operating hand as left or right. The classifier uses features extracted from touch traces such as scrolls and button clicks on a data-set of 174 users. The approach proposed by this paper is not platform-specific and does not rely on access to gyroscopes or accelerometers, widening its applicability to any device with a touchscreen.