Analysis of smartphone user behavior pdf

Qin ding, phd chair of the department of computer science. Forensic analysis and incident response for526 memory forensics indepth for585 advanced smartphone forensics gasf. A week in the life analysis of smartphone users pew. A smartphone not only fulfill the task of calling and receiving calls but also serve various need of users like internet and social connectivity, multimedia, selfie, health traits measurement, video calling etc. User behavior analysis using smartphones by seyedfaraz yasrobi july, 2017 director of thesis. Edges capture similarity distance between clickstreams of users, and clusters represent user groups with similar behavior. Performance analysis of smartphonesensor behavior for human.

Results suggest that smartphone usage collected for a minimum of 5 days will reflect typical weekly usage in hours, but habitual checking behaviors uses lasting correction. The data for all countries, with all the granularity, is available far in advance of other agencies, giving our customers a significant advantage in analyzing their businesses. Mar 15, 2017 on the basis of the analysis of the feedback collected during the interviews the collected data is available in, we show that smartphone users avoidance of security and privacy threats is influenced by their threat perceptions, awareness, and knowledge. Behaviors in different smartphone usages as aforementioned, the understanding of the consumers attitude and behavior toward various types of smartphone usages are important. Consumer purchase intentions read report, word apr 15, 2020. According to market research, 65% of us smartphone user check their phones within 15 minutes of rising. Realtime recognition of smartphone user behavior based on. For 2017, the number of smartphone users in vietnam is estimated to reach 28.

A growing number of analyses in recent years have sought to. User behavior analysis report novosad hayes associates. Cellular smartphone traffic and user behavior analysis. Cyberpsychology, behavior, and social networking vol. Mobile smartphone user behavior, activities, and content. The versatility of modern smartphones presents an interesting alternative waste management strategy. The hierarchical multiple regression analysis finds the associations among motives of smartphone use, social relations, perceived social support, and variables of psychological wellbeing.

Factors influencing consumer behavior of smartphone users. The growing trend of using smartphones as personal computing platforms to access and store private information has stressed the demand for secure and usable authentication mechanisms. Below, additional findings from a portrait of todays smartphone user, by the opa in collaboration with frank n. For instance, the relative application popularity for can be modeled using an exponential distribution, with di. User behavior analysis using smartphones by seyedfaraz yasrobi approved by. Wedemonstratethevalueof adapting to user behavior in the context of a mechanism to predict future energy drain. Identifying smartphone users based on how they interact. The data was collected from 1814 respondents across major cities in malaysia.

Holistic, objective and precise data on mobile user behavior and experience are needed in todays product development and marketing activities. The experimental results on the unimib shar public dataset show that the user only needs to do 2 cycles of specified actions to realize the prediction of the next time series. Although they are widely used authentication mechanisms but choosing the right password is not an easy task. In a second analysis, we consider whether a selfreport measure of problematic smartphone use is associated with realtime patterns of use. Therefore this research is primarily focused to find out whether smartphone that people buy is because they really. Currently, indonesia is the fourthlargest smartphone market worldwide after china, india and the. Performance analysis of smartphonesensor behavior for. Introduction we present a largescale longitudinal analysis that seeks to quantify smartphone application use habits. The number of smartphone users in indonesia was estimated to reach 81. All these things created an arousal in the authors mind to study about the consumer behavior regarding smartphone. Travel behavior analysis with social media data and smartphone gps data 5. Analysis of smartphone user behavior ieee conference.

Analysis of behavioral characteristics of smartphone addiction using data mining article pdf available in applied sciences 87. It delivers highly detailed quarterly data and forecasts for all mobile phone categories. The correlation analysis shows that the motives of smartphone use were positively related to bonding relations but negatively related to bridging relations. Smartphones news, research and analysis the conversation.

The expansion of mobile communication technology e. Consumer behaviors toward usage of smartphone in malaysia. Pdf analysis of behavioral characteristics of smartphone. Revisitation, smartphone use, habits, user behaviour acm classification keywords h.

Reference to the age of the smartphone users appeared in the study by van deursen et al. Mobile devices and social media have led to a profound revolution of modern society, obliging many companies to reorient their sales systems towards more successful commercial formats mobile commerce and social commerce. Traditional smartphone authentication or user identification methods are based on passwords and pins for protecting the smartphone users privacy. Omar prestwichunsplash november 6, 2019 the ethical. Smartphone usage proceedings of the seventeenth americas conference on information systems, detroit, michigan august 4 th 7 th 2011 3 16.

Analysis of crossgenerational differences in smartphone addictive behavior most of the aforementioned studies focus on young users and college students from generation y. Dec 24, 20 the user can set the app to remind him or her at appropriate times to go to sleep, take a study break or quit after just one game of beer pong, for example. In this paper, we present a feasibility and applicability study of using motionsensor behavior for user authentication on smartphones. This study has looked into the familiarity of users towards smartphones. Segmentation, target segment, latent class analysis. We therefore have developed a tool, inputscope, that automatically detects both the execution context of user input validation and also the content. Computer science users activities produce an enormous amount of data when using popular devices such as smartphones. According to salesforces mobile behavior survey report 2014, 53% of smartphone users allow location sharing while using an app and 76% of the people who use location sharing say that the feature helps them to receive meaningful and relevant content. Crossgenerational analysis of predictive factors of.

In this analysis, we found smartphone apps to be superior to controls with a pooled effect of g 0. The rationale behind our work is that touchinput actions from different users would generate different levels of posture and motion change of smartphones. Understanding the traffic characteristics and user behaviors in cellular data networks becomes critical in the rapidly evolving market. Standalone smartphone apps for mental healtha systematic. These data can be used to develop behavioral models in several areas including. We protect user privacy by removing all user identi. The proliferation of smartphones has significantly facilitated peoples daily life, and diverse and powerful embedded sensors make smartphone a ubiquitous platform to acquire and analyze data, which may also provide great potential for efficient human.

We then systematically evaluate the ability to drive behavioral change via messag. Final report travel behavior analysis with social media. Automatic uncovering of hidden behaviors from input. Performance analysis of motionsensor behavior for user. To avoid the limitations associated with passwords, tokens and. Pdf a study of the trend of smartphone and its usage. Similarly, they are weak and vulnerable to guessing attacks 18, 19. Permission to make digital or hard copies of all or part of this work for personal or. Among the smartphone owners surveyed, 48% say they access social networks, 46% listen to music, and 43% play games. This paper explores the use of market segmentation on the perspective of. We construct a revisitation curve for a certain application by considering the duration between revisits to that application by users figure 1. This study presents a thematic analysis of three focus group discussions around attitudes and experiences of owning and using smartphones.

Themes that emerged included a bifurcation in attitudes to smartphones as simultaneously materialistic objects, and ones which users express anthropomorphic and sentimental views about. In this paper, we establish a baseline for user security behavior from a population of over one hundred fifty smart phone. A smartphone not only fulfill the task of calling and receiving calls but also serve various need of users like internet and social connectivity, multimedia, selfie. This explorative paper develops customer segmentation on relevant metrics from the perspective of network operators, handset manufacturers, and application developers. Recently, more and more researchers pay attention to understanding the user behavior of smartphone users, and the following topics have been focused on 10 12, including mobile audience. Aug 23, 2012 moreover, 68% of surveyed smartphone owners say they cannot live without their smartphones. Global smartphone revenues and asp forecasts by 88 countries.

Waste management for end of life eol smartphones is a growing problem due to their high turnover rate and concentration of toxic chemicals. Highrisk user behavior risky downloads, browsing or linkclicking spyware. Numerous oral health promotion programs are directed at reducing the prevalence of early childhood caries. Additionally, for runtime behavior analysis of an application, we monitor the io system calls generated the by application under monitoring to the underlying linux kernel. Why does user behavior analysis help because siem and uba approach security from different directions, they give different insight into an organizations security environment.

A large number of variables affect the buying decision. Feb 26, 2020 the number of smartphone users in indonesia was estimated to reach 81. This statistic shows the number of smartphone users in vietnam from 2015 to 2022. The mobile payment, for instance, as an emerging and supplementary service to these new commercial formats, is now undergoing the adoption process. Advanced smartphone forensics mo st relevant evidence per gigabyte. The key component of our analysis is the revisitation curve 1 representing the number of times that an app is revisited within a predefined time interval. Performing organization name and address university at buffalo, the state university of new york 3 bell hall, buffalo, ny, 14260, usa 10. Smartphone reports predictive analysis intelligent analysis. Apr 29, 2020 as smartphone uptake and connectivity grows in africa, so does the often unhealthy trend of young people betting on sports using their phones. By leveraging advances in technology, ride service companies such as uber, lyft, and their competitorsalso known as transportation network companies tncs or. Currently, indonesia is the fourthlargest smartphone market worldwide after china, india and. Comparative life cycle assessment of smartphone reuse. As a case study, we present two android malware samples.

This paper presents the result of a survey on the trend of smartphone from the perspective of end consumers. Our approach allows us to provide a quantitative analysis of reallife unlocking behavior. Compared with other behaviorbased authentication methods e. For each sample of the passcode, sensory data from motion sensors are. Unsupervised clickstream clustering for user behavior analysis. A global approach to the analysis of user behavior in mobile.

Assessment of users information security behavior in. While customer segmentation for mobile services is typically based on demographics and reported use, smartphone measurement software enables to add directly observed user behavior. Jun 25, 2015 we have researched a number of interesting statistics regarding mobile phone usage frequency. Therefore, this paper examines about factors influencing purchasing intention of smartphone among university students. Canalys smartphone analysis is the most comprehensive service of its type.

Our analysis provides an comprehensive understanding of user behavior in. Structural equation modeling and semantic network analysis of smartphone separation anxiety seunghee han. New delhi, delhi 110096 abstract the cell phones have all qualities and features that qualify them to be called as a mini computer. This paper investigates the environmental impact of smartphone repurposing as compared to traditional refurbishing using life cycle. Advanced network mac and ios mo st relevant evidence. Modifying smartphone user locking behavior proceedings. Tncs, ridesharing, intercept survey, travel behavior introduction the recent emergence of appbased, ondemand ride services has sparked debate over their role in urban transport. Smartphone applications app may be beneficial in oral health promotion. Factors influencing purchasing intention of smartphone. The main contribution of this paper is that we propose a new idea for smartphone user authentication. This study assessed the factors that affect users security behavior on smartphone networks. This article presents a framework for mobile audience measurements, for collecting data at the point of convergence devices. The purpose of this study was to evaluate the effect of a smartphone app, based on the theory of planned behavior tpb, on the oral health behaviors of the parents of preschoolers. Performance analysis of smartphonesensor behavior for human activity recognition abstract.

Smartphone market the worldwide smartphone market grew 25. Apr 01, 2015 home usage is ubiquitous among smartphone owners both young and old, and smartphone owners ages 1829 and those 50 and older are similarly prone to using their phones while in a car or on public transit 85% of younger users and 79% of older users did so, as well as in a community place like a park or coffee shop 49% of both younger and older. This paper investigates the feasibility and applicability of using motionsensor behavior data for user authentication on smartphones. Average smartphone replacement cycle worldwide 2017 penetration of smartphones in spain 202014, by age group number of times individuals look at the smartphone per day in spain in 2014. At a high level, our system uses similarity metrics between clickstreams to build similarity graphs that capture behavioral patterns between users. Smartphone user behavior in the morning spain 2014 statista. Recent emergence of smartphone applications have led to explosive traffic growth in cellular networks. Moreover, the characteristics of the buyer itself also affect the buying behavior. Influence of a smartphone application on the oral health. Performance analysis of smartphone sensor behavior for human activity recognition abstract. Given the large growth of research in this field, there are now enough studies using the gold standard of experimental designthe randomized controlled trial designand employing objective measurements of physical activity, to support a metaanalysis of.