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Evaluating Scenario-Specific Loading Processes on Mobile Phones

China Academy of Information and Communications Technology, Beijing 100191, China
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Technologies 2019, 7(1), 27; https://doi.org/10.3390/technologies7010027
Received: 3 January 2019 / Revised: 21 February 2019 / Accepted: 26 February 2019 / Published: 1 March 2019
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Abstract

The manuscript presents a study that evaluates satisfaction with loading processes during human interactions with mobile devices. This is an innovative study to investigate human perception in terms of loading time for critical scenarios using a realistic mobile device. The scenarios were retrieved by internet searching. Consequently, high-fidelity models were reconstructed based on the identified scenarios. The measurements of contemporary commercial mobile devices yielded typical loading time values, which were subsequently applied in these models. Subjects operated these models, which were installed in a mobile terminal, and scored the models in terms of the loading time and processes. The results indicated that a shorter loading time was generally associated with higher scores. However, unsatisfactory scores were given to the shortest loading interval for the social App, which may indicate that users have higher expectations for this scenario. Furthermore, animation improved subjective satisfaction. These experimental protocols, the developed tools and the obtained results benefit not only manufacturers but also application developers. View Full-Text
Keywords: user experience; loading time; subjective evaluation; high-fidelity model; human-machine interaction user experience; loading time; subjective evaluation; high-fidelity model; human-machine interaction
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Zhang, C.; Lv, M.; Zhang, W.; Chen, J.; Yang, L.; Lv, B.; Wu, T. Evaluating Scenario-Specific Loading Processes on Mobile Phones. Technologies 2019, 7, 27.

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