Abstract-Ubiquitous technologies are changing our lives. We are becoming more connected and able to conduct our computing tasks anywhere at anytime from any device. Vertical interaction with an individual interactive system is no longer the only way to achieve tasks. Currently, users can interact horizontally with multiple user interfaces to achieve their tasks. This has created a need for measuring usability of multiple interactive systems, concerning users horizontal interaction beside their vertical interaction. In this paper, we surveyed the actual meanings and interpretations of usability and its attributes across several standards and models. We found that the existing usability standards and models do not consider horizontal usability aspects. Therefore, taking into consideration the characteristics of user interaction with multiple interactive systems, a hierarchical model, which is called Cross-Platform Usability Measurement (CPUM), has been developed. This model decomposes cross-platform usability into 12 factors. These factors were further decomposed into measurable criteria, and finally into specific metrics.Index Terms-Cross-platform services, horizontal usability, software quality, user experience.
It is becoming common for several devices to be utilised together to access and manipulate shared information spaces and migrate tasks between devices. Despite the increased worldwide use of cross-platform services, there is limited research into how cross-platform service usability can be assessed. This paper presents a novel cross-platform usability model. The model employs the think-aloud protocol, observations, and questionnaires to reveal cross-platform usability problems. Two Likert scales were developed for measuring overall user satisfaction of cross-platform usability and user satisfaction with the seamlessness of the transition between one device and another. The paper further employs a series of objective measures for the proposed model. The viability and performance of the model were examined in the context of evaluating three cross-platform services across three devices. The results demonstrate that the model is a valuable method for assessing and quantifying cross-platform usability. The findings were thoroughly analysed and discussed, and subsequently used to refine the model. The model was also evaluated by eight user experience experts and seven out of the eight agreed that it is useful.
Evaluating the usability of cross-platform interactive systems has become increasingly important. In this paper, we review the interpretations of current eye-movement metrics across several usability and HCI studies. We found that the existing eye-tracking metrics and their associated interpretations do not consider cross-platform usability (CPU) aspects. Therefore, taking into consideration the characteristics of user interaction with Multiple User Interfaces (MUIs), a usability-engineering model, which is called Eye Tracking Measurement and Analysis model for Cross-Platform Usability (CPU-EMA), has been developed. This model decomposed eyetracking metrics for cross-platform usability into four high-level metrics, namely, cross-platform fixation, saccade, scanpath and gaze. The high-level metrics were further decomposed into low-level metrics with possible interpretations for cross-platform usability. The model also provided procedures for measuring and analysing cross-platform usability using eye-movement data.
Evaluating the cross-platform usability of multiple interactive systems has become increasingly essential. Despite eye tracking being used to supplement traditional usability assessment, there is little research on its use for cross-platform usability evaluation. Our exploratory study seeks relationship between eye-tracking metrics and cross-platform usability problems. We user-tested three cross-platform services and identified a set of usability problems. We separated the identified problems into traditional and cross-platform usability problems. Some of the cross-platform usability problems were associated with users' eye-tracking patterns. We found that consistency on many levels is a major problem cross-platform and we recommend some considerations for evaluators to use as indicators to predict possible cross-platform usability problems.
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