Characterizing and predicting mobile application usage

Lim, Keun Woo; Secci, Stefano; Tabourier, Lionel; Tebbani, Badis

doi:10.1016/j.comcom.2016.04.026

Cited by 21 publications

(13 citation statements)

References 23 publications

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“…Currently, 95% of the US population own a cell phone and 77% of those phones are smartphones (Duggan et al, 2017;eMarketer, 2017;Sanakulov and Karjaluoto, 2015). The commonality of mobile technologies is even changing how some people access the internet often forgoing more traditional broadband access at home and instead relying on their smartphone (Lim et al, 2016;Yoon, 2016).…”

Section: Introductionmentioning

confidence: 99%

User's perceptions of perceived usefulness, satisfaction, and intentions of mobile application

Eveleth

Stone

2020

IJMC

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Mobile applications (apps) have become ubiquitous in our daily lives. This research is focused on users' perceptions of mobile apps' usefulness and the influence they have on user satisfaction and intentions toward these mobile apps. The data was collected via a questionnaire, with 121 responses, by students enrolled in business courses at a medium-sized university in the USA. Using structural equation modelling, self-efficacy of mobile apps and two of the three perceived usefulness measures significantly influenced satisfaction. Mobile app use also facilitated information seeking and social relationships to influence perception of satisfaction with these apps. A discussion of these results and conclusions are also provided.

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Section: Introductionmentioning

confidence: 99%

User's perceptions of perceived usefulness, satisfaction, and intentions of mobile application

Eveleth

Stone

2020

IJMC

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“…Outros estudos exploram não somente as redes sociais para identificar perfis de usuários, mas também outros aspectos como o tráfego HTTP [Yang et al 2015], a troca de mensagens SMS [de Almeida Oliveira et al 2015], o padrão de tráfego [Wang et al 2015, Lim et al 2016, o comportamento do usuário [Chittaranjan et al 2013], padrões de mobilidade [Leo et al 2016, Pavan et al 2015, Hong et al 2015] e ligações feitas nos arredores de um estádio de futebol [Xavier et al 2012]. Apesar desses trabalhos realizarem Tabela 1.…”

Section: Trabalhos Relacionadosunclassified

“…Apesar desses trabalhos realizarem Tabela 1. Características dos principais trabalhos relacionados Trabalho Local da coleta Critério de análise Escala Duração [Yang et al 2015] Provedor Tráfego de dados ≈ 4.500.000 1 semana [de Almeida Oliveira et al 2015] Provedor Padrões de uso de SMS ≈ 20.000 1 semana [Wang et al 2015] Provedor Tráfego de dados ≈ 150.000 1 mês [Malmi e Weber 2016] Provedor Uso de aplicações ≈ 3.500 1 mês [Xavier et al 2012] Provedor Padrões de mobilidade ≈ 30.000 3 dias [Leo et al 2016] Provedor Padrões de mobilidade ≈ 7.000.000 1 ano [Lim et al 2016] WiFi AP Padrões Uso ≈ 3.500 4 meses [Pavan et al 2015] GPS Padrões de mobilidade 13 4 dias [Hong et al 2015] GPS Padrões de mobilidade ≈ 1.000.000 30 dias [Xu et al 2015] Smartphone Uso de aplicações ≈ 25 semanas [Chittaranjan et al 2013] Smartphone Características pessoais ≈ 120 17 meses [Li et al 2015] Smartphone Tráfego de dados ≈ 2.000.000 1 mês [Do et al 2011] Smartphone Uso de aplicativos 77 8 meses Trabalho Atual Smartphone Uso detalhado 5.342 1 ano de aplicativos a caracterização de perfis de usuários, os dados usados no presente estudo permitem uma elaboração mais precisa de perfis sob diferentes perspectivas, com informações detalhadas de uso coletadas diretamente do smartphone de um grande número de usuários por um longo período.…”

Section: Trabalhos Relacionadosunclassified

Perfil de Uso de Aplicativos Móveis: Caracterização e Aplicações

Domingues¹,

Silva²,

Silva³

et al. 2018

Anais Do XXXVI Simpósio Brasileiro De Redes De Computadores E Sistemas Distribuídos (SBRC 2018)

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O grande volume de dados gerados pelos avanços das tecnologias móveis tem feito com que os provedores de serviço se interessem cada vez mais pela sua coleta e análise. Neste trabalho, um conjunto de dados real e de larga escala relacionado ao uso detalhado de aplicativos móveis é investigado pela primeira vez com o objetivo de se identiﬁcar padrões de acesso, de tráfego de dados, de tempo de uso e de transição entre serviços. Além da caracterização, este trabalho contribui com a comunidade cientíﬁca em outras duas frentes. Primeiramente, foi proposto e validado um modelo para geração de dados sintéticos, que poderá ser adotado por outros pesquisadores. Além disso, foi possível avaliar as métricas relevantes para a predição do próximo aplicativo a ser utilizado, o que permite a prévia preparação de algum aplicativo para reduzir o tempo de espera dos usuários, aumentando a sua satisfação.

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“…As well as being an important communication need today, they have also been transformed into a dynamic device that helps people play games, listen to music, and provide them with navigation applications [1]. With this transformation, the data consumption of people and the variety of the data have been increased in recent years [2]. The data types are presented to people through "application markets" included in these phones.…”

Section: Introductionmentioning

confidence: 99%

What Is Being Played in the World? Mobile eSport Applications

Atalay¹,

Topuz²

2018

ujer

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In this study, the aim is to examine the most popular eSport applications at a global scale. In this context, the App Store and Google Play Store application platforms which have the highest number of users at a global scale were focused on. For this reason, the eSport applications included in these two platforms constituted the sampling of the present study. A data collection form was developed by the researcher of the study in order to collect the data in the study. This form included the number of the countries, the popularity ratings of the application, the name of the application, the type of it, the age limit, the rating of the likes, the company that developed it, the version and the first appearance date. The study was conducted with the Qualitative Research Method, and the Case Study design was made use of in this process; and the Descriptive Analysis Method was used to analyze the data. As a result of the study, it was determined that the most popular eSport applications at a global scale were football, which ranked the first, basketball, billiards, badminton, skateboarding, golf and dart. It was also determined that the popularity of the mobile eSport applications changed according to countries and according to being free or paid. It was determined that the popularity of these applications differed according to the individuals using the App Store and Google Play Store application markets. As a result, it is possible to claim that mobile eSport applications have a wide usage area at a global scale and are accepted widely. In addition, it was observed that the interest in eSport applications was similar to that in traditional sports. However, in the present study, a certain date was set, and the interest in mobile eSport applications was analyzed according to this specific date. In future studies, different dates and different fields like educational sciences may be set to analyze the interest in mobile eSport applications. In this way, findings may be obtained on the change of the interest in mobile eSport applications according to time. The findings of the present study and similar studies may have the quality of guiding researchers and system/software developers in terms of showing the present status of the topic and revealing the relevant needs.

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Characterizing and predicting mobile application usage

Cited by 21 publications

References 23 publications

User's perceptions of perceived usefulness, satisfaction, and intentions of mobile application

User's perceptions of perceived usefulness, satisfaction, and intentions of mobile application

Perfil de Uso de Aplicativos Móveis: Caracterização e Aplicações

What Is Being Played in the World? Mobile eSport Applications

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