Search Results (4)

This study investigates how smartphone app usage diversity (SAUD)—defined as the breadth of applications individuals actively engage with—relates to quality of life, and how these effects are conditioned by age and digital literacy. Drawing on Uses and Gratifications Theory and Cognitive Load Theory, we conceptualize SAUD as having both beneficial and detrimental potential, depending on users’ cognitive and demographic characteristics. Using cross-sectional, self-reported data from the annual nationwide surveys on smartphone overdependence in South Korea, we analyzed a final sample of 20,967 adults (48.4% male, 51.6% female; M_age = 46.0, SD = 13.7; age range 20–69). Results demonstrate that SAUD is positively associated with quality of life among younger and digitally literate users, but negatively associated among older adults and those with lower digital literacy, suggesting the presence of conditional effects. The hypothesized three-way interaction between SAUD, age, and digital literacy was not supported. These findings extend the literature by moving beyond simplistic time-based metrics of smartphone use, offering a more differentiated understanding of mobile technology’s impact on well-being. Practically, the study highlights the need for tailored digital literacy programs and policy interventions that recognize demographic and cognitive diversity in technology engagement. Future research should incorporate longitudinal designs and objective behavioral data to further validate these insights. Full article

Along with the proliferation of the mobile information system, broadcasters depend on multiple channels to distribute the massive amount of Olympic content. Users’ viewing habits for the Olympics gradually tend to be diverse, and undergo changes from the outlets of television to mobile broadcasting on smartphones. Through the mobile application of rights holding broadcasters, the users not only watch high-quality live-streaming content via multiple platforms but also enjoy interviews with athletes after the competition. In this way, it is necessary to investigate the users’ potential attitudes and intentions toward mobile viewing regarding the emerging techniques. In this study, we conduct an online survey to reveal the influencing factors of users’ attitudes and continuance intention of Olympic viewing on a mobile app during the period of Tokyo 2020 Summer and Beijing 2022 Winter Olympics, where a total of 439 valid responses are collected. A conceptual model integrating the technology acceptance model and information system success model is established, which consists of information quality (IQ), system quality (SYQ), subjective norms (SN), innovativeness (INN), perceived ease of use (PEOU), perceived enjoyment (PE), attitude (ATT), and continuance intention (CI). For the measurement, partial least square structural equation modeling is adopted to test the proposed model. The results show that respondents hold positive attitudes and robust continuous intentions towards mobile viewing. We also find that the constructs of IQ, SYQ, SN, INN, PEOU, and PE have a direct impact on attitude and continuance intention, which explained 80.6% and 70.8% of the variance, respectively. Although PEOU, PE, and SN are unexpected to have no direct correlation with CI, all of them can indirectly impact CI via the mediation of ATT. Therein, the moderation effects of average time focusing on Olympic contents and app usage time per session exists between PEOU and CI and SN and CI, accordingly. Through empirical investigation, this study offers a glimpse into individuals’ perception and willingness to mobile Olympic viewing, which aims to provide a reference for relevant Olympic service providers. Full article

A smart home is a residence that provides a variety of automation services based on Internet of Things (IoT) devices equipped with sensors, cameras, and lights. These devices can be remotely controlled through controllers such as smartphones and smart speakers. In a smart home, IoT devices collect and process data related to motion, temperature, lighting control, and other factors and store more diverse and complex user data. This data can be useful in forensic investigations but it is a challenge to extract meaningful data from various smart home devices because they have different data storage methods. Therefore, data collection from different smart home devices and identification and analysis of data that can be used in digital forensics is crucial. This study focuses on how to acquire, classify, and analyze smart home data from Google Nest Hub, Samsung SmartThings, and Kasa cam for forensic purposes. We thus analyzed the smart home data collected using companion apps, Web interfaces, and APIs to identify meaningful data available for the investigation. Moreover, the paper discusses various types of smart home data and their usage as core evidence in some forensic scenarios. Full article

Modelling and simulation of social interaction and networks are of high interest in multiple disciplines and fields of application ranging from fundamental social sciences to smart city management. Future smart city infrastructures and management are characterised by adaptive and self-organising control using real-world sensor data. In this work, humans are considered as sensors. Virtual worlds, e.g., simulations and games, are commonly closed and rely on artificial social behaviour and synthetic sensor information generated by the simulator program or using data collected off-line by surveys. In contrast, real worlds have a higher diversity. Agent-based modelling relies on parameterised models. The selection of suitable parameter sets is crucial to match real-world behaviour. In this work, a framework combining agent-based simulation with crowd sensing and social data mining using mobile agents is introduced. The crowd sensing via chat bots creates augmented virtuality and reality by augmenting the simulated worlds with real-world interaction and vice versa. The simulated world interacts with real-world environments, humans, machines, and other virtual worlds in real-time. Among the mining of physical sensors (e.g., temperature, motion, position, and light) of mobile devices like smartphones, mobile agents can perform crowd sensing by participating in question–answer dialogues via a chat blog (provided by smartphone Apps or integrated into WEB pages and social media). Additionally, mobile agents can act as virtual sensors (offering data exchanged with other agents) and create a bridge between virtual and real worlds. The ubiquitous usage of digital social media has relevant impact on social interaction, mobility, and opinion-making, which has to be considered. Three different use-cases demonstrate the suitability of augmented agent-based simulation for social network analysis using parameterised behavioural models and mobile agent-based crowd sensing. This paper gives a rigorous overview and introduction of the challenges and methodologies used to study and control large-scale and complex socio-technical systems using agent-based methods. Full article