Mobile Services

Niida, Sumaru; Uemura, Shunsuke; Nakamura, Hajime

doi:10.1109/mvt.2010.937850

Cited by 28 publications

(7 citation statements)

References 8 publications

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“…From the results presented in Figure 8, we can see that a logarithmic fitting of QoE iLT can be used for both satellite imagery and simple map content. This logarithmic relationship corresponds with a well-known WQL hypothesis which shows iLT holds the expected relationship with QoE [24], [48].…”

Section: Establishing the Relationship Between Ilt And Perceived supporting

confidence: 67%

“…This data was collected in several experiments and used to explore the relationship between users' QoE and waiting time for web page, voice, email and file-download web tasks. The dashed lines show results previously presented in [48]. The solid lines show the map and satellite mean iLT results from this work overlaid for comparison.…”

Section: Comparing Ilt To State Of the Art Non-interactive Metricmentioning

confidence: 83%

“…The first category involves e-mail transmission with attached files and content downloads. The second category involves the time required to display the home page when accessing a web site (PLT), the time required to connect a voice call, and the time required to transmit a plain-text e-mail [48]. Figure 9 presents user waiting time (seconds) plotted against perceived quality (MOS) for a variety of web applications.…”

Section: Comparing Ilt To State Of the Art Non-interactive Metricmentioning

confidence: 99%

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Beyond First Impressions: Estimating Quality of Experience for Interactive Web Applications

2020

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The number of web applications for both personal and business use will continue to increase. The popularity of web applications has grown, increasing the need to estimate Quality of Experience for web applications (Web QoE). Web QoE helps providers to understand how their end-users perceive quality and point towards areas to improve. Waiting time has been proven to have a significant influence on user satisfaction. Most studies in the field of Web QoE have focused on modelling Web QoE for the user's first interaction with the application, e.g., the waiting time for the first page load to complete. This does not include a user's subsequent interactions with the application. Users keep interacting with the application beyond the first page load resulting in an experience that consists of a series of waiting times. In this study, we have chosen web maps as a use case to investigate how to measure waiting time for a user's interactions across a web browsing session, and to measure the correlation between waiting time and user-reported perceived quality. We provide a short survey of existing Web QoE estimation metrics and models. We then propose two new measures: interactive Load Time (iLT) and Total Completed interactive Load (TCiL) to establish the waiting time associated with a web application user's interactions. A subjective study confirms a logarithmic relationship for interactive web application sessions between iLT and perceived quality. We compare the correlation between QoE for iLT and the state of the art, non-interactive equivalent, Page Load Time (PLT)/Waiting Time. We demonstrate how the iLT/QoE fitting curve deviates from PLT/QoE. The number of clicks in completing tasks and TCiL are explored to explain the connections between user's interactions behaviour and the perceived quality. INDEX TERMS Web QoE, interactive QoE, quality measurement, quality metrics, time metrics, waiting time, iLT, TCiL.

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Section: Establishing the Relationship Between Ilt And Perceived supporting

confidence: 67%

Section: Comparing Ilt To State Of the Art Non-interactive Metricmentioning

confidence: 83%

Section: Comparing Ilt To State Of the Art Non-interactive Metricmentioning

confidence: 99%

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Beyond First Impressions: Estimating Quality of Experience for Interactive Web Applications

2020

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“…Mobile device quality, mobile software quality, and overall mobile product quality were contextualized from Zamfiroiu (2014), Ranganathan and Ganapathy (2002), and Chau and Lai (2003). Measures for network quality and overall mobile provider service quality were adapted from both Ickin et al (2012) and Niida et al (2010) as well as Loiacono et al (2002). Customer intention to switch was contextualized from previous research conducted by Malhotra and Malhotra (2013) and Lam et al (2004).…”

Section: The Survey Instrumentmentioning

confidence: 99%

Product quality vs service quality in the mobile industry: Is there a dominant driver of customer intention to switch providers?

et al. 2014

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As service-dominant logic is being proposed and tested in several industries, this study extends the investigation of this theory and provides insights for both researchers and practitioners under the context of the mobile industry. This study advances the understanding of customer switching intention by assessing the relative strength of product quality and service quality in terms of mobile provider switching behavior. This study develops the proposed research model that integrates previous validated constructs contextualized to the mobile industry and employs partial least square -structural equation modeling (PLS-SEM) to analyze the corresponding survey data and test the model fit. The results strongly support the hypotheses related to quality and its antecedents. The analysis results also suggest that overall mobile provider service quality and customer intention to switch mobile products significantly influence customer intention to switch providers. However, the overall product quality does not significantly influence customer intention to switch mobile products. This research contributes to the debate of goods-dominant and service-dominant logic by empirically supporting the dominance of service quality within the context of one industry, the mobile industry. In addition, this paper discusses the implications of the findings and presents future research directions.

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“…We parametrize x according to the Mean Opinion Score (MOS) for different waiting times from [20], where the authors studied the satisfaction of users when they have to wait for different applications. We map a MOS 4 of satisfied users to U(t exp ) and a MOS 2 of dissatisfied users to U(t infl ).…”

Section: Utility Functionsmentioning

confidence: 99%

Context-aware resource allocation for cellular wireless networks

Proebster

Kaschub

Werthmann

et al. 2012

J Wireless Com Network

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Current cellular networks are often overloaded by Smartphone traffic, while the users' Quality of Service (QoS) demands are not met. To cope with this problem, we demonstrate a new radio resource management approach. With Context-Aware Resource Allocation, the base station's scheduler (i) observes Context Information (CI) from the user's environment and (ii) utilizes this knowledge for an efficient throughput-delay tradeoff. After introducing our framework for accessing CI from the handheld's applications and operating system, we use time-utility functions to develop a practical scheduling algorithm. Studying this heuristic under various traffic assumptions shows that our context-aware scheduler can support three times the load of proportional fair scheduling, at equal capacity and utility. Thus, even a small degree of CI increases the wireless links' efficiency without sacrificing the users' QoS.

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Mobile Services

Cited by 28 publications

References 8 publications

Beyond First Impressions: Estimating Quality of Experience for Interactive Web Applications

Beyond First Impressions: Estimating Quality of Experience for Interactive Web Applications

Product quality vs service quality in the mobile industry: Is there a dominant driver of customer intention to switch providers?

Context-aware resource allocation for cellular wireless networks

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