Mobile app adoption in different life stages: An empirical analysis

Frey, Remo Manuel; Xu, Ran; Ilic, Alexander

doi:10.1016/j.pmcj.2017.01.006

Cited by 34 publications

(23 citation statements)

References 50 publications

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“…An individual life course can be divided into sequences-life stages-that mark periods with different living circumstances influencing behavior, attitudes and preferences Van Acker 2017). There is no one standard classification of life stages (Frey et al 2017), though commonly distinguished stages include childhood, young adulthood, living with a partner, early career, marriage, parenthood, and retirement (Gilly and Enis 1982). Recent trends towards more complex, non-linear life courses (e.g.…”

Section: Life Stage Influences Travel Behaviormentioning

confidence: 99%

Examining the effect of life course events on modality type and the moderating influence of life stage

2020

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Previous research has demonstrated the relevance of life events to explain changes in travel behavior. Less clear is the moderating role played by life stages on the relationship between life events and travel behavior. Our goal in this study is to explore how the influence of life events on travel behavior differs by life stage. We use data from a travel survey of faculty, staff and students at a US university. We define four life stages: millennials living in shared apartments or alone, millennials living with partners, parents (of any generation) living with their children, and non-millennial adults living without children. Four modality types were defined: active travelers, car users, transit users, and multimodal travelers. We use a Manifest Markov Model to estimate probabilities of switching modality types between two waves of the survey. Life stage does not significantly moderate effects of life events on change in travel behavior but does affect modality type: the prevalence of active travel modality types, particularly the share of women who are active travelers, decreases throughout the life stages. Millennials living with their partners and parents living with their children relocate to another town more often than those in other life stages, a life event associated with a higher probability of switching to car use only. Our results identify "windows of opportunity", such as residential relocation, that planners can use to promote sustainable travel behavior.

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Section: Life Stage Influences Travel Behaviormentioning

confidence: 99%

Examining the effect of life course events on modality type and the moderating influence of life stage

2020

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“…There have been some studies using smartphone apps to infer user personal information. For example, demographic attributes (e.g., gender, region and marital status), interests, personality traits and life stages have been learned from app lists installed on smartphones, app installation behaviors (installation, updating and uninstallation) and app usage behaviors (Chittaranjan et al 2011(Chittaranjan et al , 2013Frey et al 2015Frey et al , 2017Jesdabodi and Maalej 2015;Malmi and Weber 2016;Qin et al 2016;Rivron et al 2016;Seneviratne et al 2015;Tu et al 2019;Wang et al 2015Wang et al , 2018Xu et al 2011Xu et al , 2016bZhao et al 2016Zhao et al , 2017aZhao et al , b, c, 2018Zhao et al , 2019bLi et al 2015a;Mo et al 2012;Brdar et al 2012;Ying et al 2012;Andone et al 2016;Peltonen et al 2018;Zou et al 2013;Yu et al 2018;Ouyang et al 2018;Wang et al 2019;Böhmer et al 2011;Liu et al 2018). In this section, we will review the related work in three aspects: inferring demographics, explaining personality, and discovering life patterns.…”

Section: Related Workmentioning

confidence: 99%

“…Among the three types of data, app usage records are a better reflection of what activity users perform, what they truly needs or what they look like. There is a major limitation of installed app lists is that, whether one user has installed an app may be a weak indicator of whether he/she actually needs the app (Frey et al 2017;Xu et al 2016b, a;Zhao et al 2017a). He/she may simply want to try the app out, and may never use it again or may have uninstalled it.…”

Section: Introductionmentioning

confidence: 99%

Investigating smartphone user differences in their application usage behaviors: an empirical study

Zhao

et al. 2019

CCF Trans. Pervasive Comp. Interact.

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Smartphone applications (Abbr. apps) have become an indispensable part in our everyday lives. Users determine what apps to use depending on their personal needs and interests. Users with different attributes may have different needs, making it natural for their app usage behaviors to be different. The differences in app usage behaviors among users make it possible to infer their attributes. Knowing such differences could help improve mobile user experiences by enhancing smart services and devices. In this paper, we present an empirical study of investigating smartphone user differences on a large-scale dataset of recently used app lists from 106,672 Android users from China. We first investigate the user differences in app usage behaviors with respect to their attributes (gender, age, and income level). We find significant differences in app usage frequency, app usage with time context and functions. We then extract corresponding features from app usage records to infer the attributes of each user, and investigate the predictive ability of individual features and combinations of different individual features. We achieve the accuracy of 83.29% for gender, 69.94% for age (four age ranges) and 71.43% for income level (three income levels) with the best set of features, respectively. Finally, we discuss the implications of our findings and the limitations of this work.

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“…The popularity and social impact of IM applications, especially Mobile IM (MIM) applications has been very significant [2] [3]. For example, MIM use has been associated with job satisfaction and better job performance [3].…”

Section: Introductionmentioning

confidence: 99%

“…For example, MIM use has been associated with job satisfaction and better job performance [3]. However, it is also known that mobile application selection is influenced by a person's age group and social setting [2]. IM applications tend to provide a fixed set of features and capabilities per application.…”

Section: Introductionmentioning

confidence: 99%

A Modular Instant Messaging System

Raad¹

2017

IJERA

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ABSTRACT:Instant Messaging (IM) Android applications are a trend nowadays. These applications are categorized according to their features: usability, flexibility, privacy and security. However, IM applications tend to be inflexible in terms of functionality offered. The "Dble-U" system was developed as a solution to this inflexibility, with a focus on privacy as an example use case. "Dble-U" is a configurable modular system consisting of an Android chatting application, a privacy controller application, along with a tracking server application. This modularity allows for groups of individuals to build custom features into their IM application.

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Mobile app adoption in different life stages: An empirical analysis

Cited by 34 publications

References 50 publications

Examining the effect of life course events on modality type and the moderating influence of life stage

Examining the effect of life course events on modality type and the moderating influence of life stage

Investigating smartphone user differences in their application usage behaviors: an empirical study

A Modular Instant Messaging System

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