Relevant Affect Factors of Smartphone Mobile Data Traffic

Smartphones are used to access a wide range of different information and communication services and perform functions based on data transfer. A number of subscription contracts for smartphones is rapidly increasing, and the development of mobile communications network provides higher speed of data transfer. The continuous increase in the average amount of data traffic per one subscriber contract leads to an increase in the total Mobile Data Traffic (MDT), globally. This research represents a summary of factors that affect the amount of smartphone MDT. Previous literature shows only a few of the factors individually that affect the realization of smartphone MDT. The results of the research clarify the ways which influence the amount of MDT generated by a smartphone. This paper increases the awareness of the users of the methods of generating smartphone MDT. The research also allows users to specify parameters that affect the prediction of generated MDT of a smartphone

Examining Technology Perception and User Competence on Two Types of Smartphone Usages

This study intends to explain smartphone usage behaviours in the post-adoption stage of information systems (IS) based on the IS continuance model, the technology acceptance model (TAM), and the competence of the users. In this study, smartphone usage is divided into two types: usage of the smartphone’s device functions and usage of applications. This is the first time this concept has been proposed and empirically tested. The results found strong predictors of user satisfaction (perceived usefulness and perceived ease of use) toward smartphone satisfaction and finally confirmed the influence of smartphone function use on smartphone app use. Finally, several important theoretical and practical implications and directions for future research based on limitations are suggested

An optimized context-aware mobile computing model to filter inappropriate incoming calls in smartphone

Requests for communication via mobile devices can be disruptive to the receiver in certain social situation. For example, unsuitable incoming calls may put the receiver in a dangerous condition, as in the case of receiving calls while driving. Therefore, designers of mobile computing interfaces require plans for minimizing annoying calls. To reduce the frequency of these calls, one promising approach is to provide an intelligent and accurate system, based on context awareness with cues of a callee's context allowing informed decisions of when to answer a call. The processing capabilities and advantages of mobile devices equipped with portable sensors provide the basis for new context-awareness services and applications. However, contextawareness mobile computing systems are needed to manage the difficulty of multiple sources of context that affects the accuracy of the systems, and the challenge of energy hungry GPS sensor that affects the battery consumption of mobile phone. Hence, reducing the cost of GPS sensor and increasing the accuracy of current contextawareness call filtering systems are two main motivations of this study. Therefore, this study proposes a new localization mechanism named Improved Battery Life in Context Awareness System (IBCS) to deal with the energy-hungry GPS sensor and optimize the battery consumption of GPS sensor in smartphone for more than four hours. Finally, this study investigates the context-awareness models in smartphone and develops an alternative intelligent model structure to improve the accuracy rate. Hence, a new optimized context-awareness mobile computing model named Optimized Context Filtering (OCF) is developed to filter unsuitable incoming calls based on context information of call receiver. In this regard, a new extended Naive Bayesian classifier was proposed based on the Naive Bayesian classifier by combining the incremental learning strategy with appropriate weight on the new training data. This new classifier is utilized as an inference engine to the proposed model to increase its accuracy rate. The results indicated that 7% improvement was seen in the accuracy rate of the proposed extended naive Bayesian classifier. On the other hand, the proposed model result showed that the OCF model improved the accuracy rate by 14%. These results indicated that the proposed model is a hopeful approach to provide an intelligent call filtering system based on context information for smartphones

Impact of mobile Internet use on health-seeking behaviors: evidence from China

IntroductionAlthough health-seeking behaviors are crucial to China’s healthcare delivery system, the influence of mobile Internet use in this context remains under-explored. This study aimed to comprehensively explore the influence of mobile Internet use on health-seeking behaviors, and meticulously examined the heterogeneity in health outcomes associated with the intersection between mobile Internet use and health-seeking behaviors.MethodsWe used nationally representative data derived from the China Family Panel Studies. Given that individuals typically make the decision to use mobile Internet autonomously, an instrumental variable regression methodology was adopted to mitigate potential selection biases.ResultsOur findings revealed that mobile Internet use significantly promoted self-medication and adversely affected the use of primary care facilities among Chinese adults. Furthermore, our findings highlighted the heterogeneous effects of mobile Internet use across diverse health demographic groups.ConclusionThese findings underscore the importance of strategic planning and utilizing mobile Internet resources to steer individuals toward more appropriate healthcare-seeking behaviors

Utilising the co-occurrence of user interface interactions as a risk indicator for smartphone addiction

The push to a connected world where people carry an always-online device which has been designed to max- imise instant gratification and prompts users via notifications has lead to a surge of potentially problematic behaviour as a result. This has lead to a rising interest in addressing and understanding the addictiveness of smartphone usage, as well as for particular applications (apps). However, capturing addiction from us- age involves not only assessment of potential addiction risk but also requires understanding of the complex interactions that define user behaviour and how these can be effectively isolated and summarised. In this paper, we examine the correlation of physical user interface (UI) interactions (e.g. taps and scrolls) and smartphone addiction risk using a large dataset of those smartphone events (65,093,343, N=301,024 ses- sions) collected from 64 users over an 8-week period with an accompanying smartphone addiction survey. Our novel method which reports on the probability of a users addiction risk and in a model case we show how it was be used to identify 57 of 64 users correctly. This supports our observations of UI events during sessions of usage being indicative of addiction risk while improving previous approaches which rely on summative data such as screen on time. Within this we also find that users only exhibit addictive behaviour in a subset of all sessions while using their smartphone

The influence of concurrent mobile notifications on individual responses

Notifications on mobile devices punctuate our daily lives to provide information and prompt for further engagement. Investigations into the cognitive processes involved in consuming notifications are common across the literature, however most research to date investigates notifications in isolation of one another. In reality, notifications often coexist together, forming a “stack”, however the behavioural implications of this on the response towards individual notifications has received limited attention. Through an in-the-wild study of 1,889 Android devices, we observe user behaviour in a stream of 30 million notifications from over 6,000 applications. We find distinct strategies for user management of the notification stack within usage sessions, beyond the behaviour patterns observable from responses to individual notifications. From the analysis, we make recommendations for collecting and reporting data from mobile applications to improve validity through timely responses, and capture potential confounding features

Flawless devices, faulty users: Finnish young adults’ representations of smartphone usage

Finnish smartphone users lead the global statistics of data usage. This makes them an ideal consumer group to research technology consumption practices. It has been estimated that consumers use their smartphones as much as one third of the time that they are awake. The device has become essential in everyday life as consumers have it always with them and it is always on. Smartphone usage has been researched for example in terms of technology adaptation and desired functionalities, but the research on consumers’ emotions towards technology is limited. The focus of this study is especially in the contradictions and paradoxes that Finnish young adults express in their narratives of theirsmartphones and smartphone usage. Past research on technology paradoxes, information technology development, postmodern consumption culture and social constructivism on technology serve as theoretical background for the study. This study has been done by using qualitative research methods. The data consists of ten interviews and projective techniques including sentence compilations and autodriving. Young Finnish adults who live in big cities and have high education were selected for the interviews, as statistically they are heavy users of smartphones, thus making them interesting subject of technology paradox research. The findings of this study outline the major mismatch in consumers’ narratives: they perceive their smartphones as useful and capable devices but consider their own smartphone consumption as incapable and counterproductive, which results into feelings of distress, anxiety and guilt. This misusage appears in multiple forms, interpreted in four themes of guilt: using smartphones to procrastinate, damaging meaningful social relations with smartphone usage, misusing or overdosing the massive amount of content and not meeting the expectations to be available. The narrative of flawless device and faulty user has implications both for consumer research and for management. The main contribution of this study is to widen the focus of academic legacy from the paradoxes of technology to the paradoxes of technology consumption. The study portrays the shift from consumers’ perceptions of their smartphones as devices to perceptions of themselves as smartphone users. This offers a fruitful basis for further research on technology consumption, which is an inseparable part of postmodern life

“It’s Like Being Gone For A Second”: Using Subjective Evidence-Based Ethnography to Understand Locked Smartphone Use Among Young Adults

Smartphone use usually refers to what happens after users unlock their devices. But a large number of smartphone interactions actually take place on the lock screen of the phone. This paper presents evidence from a mixed-methods study using a situated video-ethnography technique (SEBE) and a dataset of over 200h of first-person and interview recordings with 221 unique lock screen checks (n=41). We find eight categories contextual antecedents to locked smartphone use that influence the nature and the content of the subsequent smartphone interaction. Overall, locked smartphone use emerges as a means to structure the flow of daily activities and to balance between not getting too distracted and not experiencing fomo (the fear of missing out). It also appears as highly habitualised, which can cause over-use and disruption. Based on this analysis, we provide recommendations on how intervention and design approaches can leverage differences in context and purpose of locked smartphone use to improve user experience

Diversity and End User Context in Smartphone Usage Sessions

Julkaisun kokoteksti on luettavissa vain Aalto-tunnuksilla.Please note that access to the fulltext is limited to Aalto staff and students.Mobile end user context has gained increasingattention in the mobile services industry. Context information isseen as an important component in developing new, morepersonalized, mobile services and applications. This paper studiesthe effect of end user context on smartphone usage sessions.Smartphone usage sessions are used to depict user behavior andusage habits of smartphone users on a high level. We havedetected end user contexts, and extracted smartphone usagesession information from handset-based data of 140 smartphoneusers. We first examine and describe usage sessions as such, andthen in different end user contexts. According to our usagesession analysis, smartphone usage is highly diversified acrossusers. For example, the average number of sessions per dayranges from 3 to 46. Characteristics of smartphone usage sessionsdiffer in different end user contexts. For example, an averagesession is 37 % longer in the Home-context than in the Office-context,but Office has 56 % more sessions per time unit thanHome. The results imply that mobile services and applicationsneed to adapt to user behavior in order to be personalizedenough, and that context awareness is indeed a worthwhile steptowards this.Peer reviewe