Mobile User Behavior Research Articles

Exploring digital natives' mobile addiction syndrome in Taiwan: psychological issues and beyond

PurposeThis study seeks to explore digital natives' mobile usage behaviors and, in turn, develop an analytic framework that helps articulate the underlying components of mobile addiction syndrome (MAS), its severity levels and mobile usage purposes.Design/methodology/approachThe investigation adopts a survey method and a case study. The results of the former are based on 411 random classroom observations and 205 questionnaire responses, and the insights of the latter are derived from 24 interviews and daily observations.FindingsThe findings validate five distinctive signs that constitute MAS and their significant correlations with each of the Big Five personality traits. Classroom observations confirm the prevalence of addiction tendency among digital natives in the research context. Seven levels of MAS and six different mobile usage purposes further manifest themselves from case analysis. There appears to be a sharp contrast between the addicted and non-addicted groups in their mobile purposes and behavioral patterns. Additionally, family relationships seem influential in shaping non-addictive mobile usage behaviors.Research limitations/implicationsPsychological perspectives on MAS may be important but insufficient. Empirical investigation on a global scale, especially with distinctive cross-cultural comparisons, will be highly encouraged. How MAS evolves over time should also serve as future research interests.Practical implicationsTeaching pedagogy of college education might need certain adjustments to intrigue digital natives' learning interests. Future managers might also need to adopt better performance measurements for digital natives who barely separate work from personal matters in their mobile devices.Social implicationsParents and healthcare institutions may need to develop response mechanism to tackle this global issue at home and in society. The long-term effects of the COVID-19 pandemic on MAS might also deserve global attention.Originality/valueThe analytic framework developed provides an original mechanism that can be valuable in identifying MAS severity and associated behavioral patterns.

Mobile phone user’s attitude toward SMS advertising

Mobile Advertising, a wireless media, acts as an advertising tool for promoting goods and services. The mobile phone is no longer just a communication tool since it has also become a commercial channel of products and services. Many companies are currently using mobile Advertising as a potential marketing tool and have a profound influence on day-to-day business activities. So the present work is to study the attitude and behaviour of mobile phone users toward SMS ads and their effectiveness as an advertising tool for various products and services. The primary data was collected using a questionnaire consisting of demographic factors and other factors like entertainment, credibility, and monetary benefit relevant to this study. The collected data was analyzed using Statistical tools. The results show that most of the respondents favour receiving SMS ads if their preferences are taken into consideration. Their attitude was favourable if advertisements were sent with permission which implies that permission-based advertising is a major mechanism in the field of mobile phone advertising.

Editorial: Special Issue “Unmanned Aerial Vehicle (UAV)-Enabled Wireless Communications and Networking”

In the last decade, the behavior of mobile data users has completely changed [...]

Optimizing Electric Vehicle Charging Schedules Based on Probabilistic Forecast of Individual Mobility

Abstract. The number of electric vehicles (EVs) has been rapidly increasing over the last decade, motivated by the effort to decrease greenhouse gas emissions and the fast development of battery technology. This trend challenges distribution grids since EVs will bring significant stress if the charging of many EVs is not coordinated. Among the many strategies to cope with this challenge, next-day EV energy demand forecasting plays a key role. Existing studies have focused on predicting the next-day energy demand of EVs on the aggregated and individual levels. However, these studies have not yet extensively considered individual user mobility behaviors, which exhibit a high level of predictability. In this study, we consider several mobility features of individual users when forecasting the next-day energy demand of individual EVs. Three types of quantile regression models are used to generate probabilistic forecasts of energy demand, particularly the next-day energy consumption and parking duration. Based on the prediction results, two time-shifting smart charging strategies are designed: unidirectional and bidirectional smart charging. These two strategies are compared with an uncontrolled charging baseline to evaluate their financial benefits and peak-shaving effects. Our results show that human mobility features can partially improve the prediction of next-day individual EV energy demand. Additionally, users and distribution grids can benefit from smart charging strategies both financially and technically.

MST-RNN: A Multi-Dimension Spatiotemporal Recurrent Neural Networks for Recommending the Next Point of Interest

With the increasing popularity of location-aware Internet-of-Vehicle services, the next-Point-of-Interest (POI) recommendation has gained significant research interest, predicting where drivers will go next from their sequential movements. Many researchers have focused on this problem and proposed solutions. Machine learning-based methods (matrix factorization, Markov chain, and factorizing personalized Markov chain) focus on a POI sequential transition. However, they do not recommend the user’s position for the next few hours. Neural network-based methods can model user mobility behavior by learning the representations of the sequence data in the high-dimensional space. However, they just consider the influence from the spatiotemporal dimension and ignore many important influences, such as duration time at a POI (Point of Interest) and the semantic tags of the POIs. In this paper, we propose a novel method called multi-dimension spatial–temporal recurrent neural networks (MST-RNN), which extends the ST-RNN and exploits the duration time dimension and semantic tag dimension of POIs in each layer of neural networks. Experiments on real-world vehicle movement data show that the proposed MST-RNN is effective and clearly outperforms the state-of-the-art methods.

Leveraging network representation learning and community detection for analyzing the activity profiles of adolescents

Human mobility analysis plays a crucial role in urban analysis, city planning, epidemic modeling, and even understanding neighborhood effects on individuals’ health. Often, these studies model human mobility in the form of co-location networks. We have recently seen the tremendous success of network representation learning models on several machine learning tasks on graphs. To the best of our knowledge, limited attention has been paid to identifying communities using network representation learning methods specifically for co-location networks. We attempt to address this problem and study user mobility behavior through the communities identified with latent node representations. Specifically, we select several diverse network representation learning models to identify communities from a real-world co-location network. We include both general-purpose representation models that make no assumptions on network modality as well as approaches designed specifically for human mobility analysis. We evaluate these different methods on data collected in the Adolescent Health and Development in Context study. Our experimental analysis reveals that a recently proposed method (LocationTrails) offers a competitive advantage over other methods with respect to its ability to represent and reflect community assignment that is consistent with extant findings regarding neighborhood racial and socio-economic differences in mobility patterns. We also compare the learned activity profiles of individuals by factoring in their residential neighborhoods. Our analysis reveals a significant contrast in the activity profiles of individuals residing in white-dominated versus black-dominated neighborhoods and advantaged versus disadvantaged neighborhoods in a major metropolitan city of United States. We provide a clear rationale for this contrastive pattern through insights from the sociological literature.

Mobile phone usage behavior: A longitudinal study of university students in Oman

AbstractMobile phones have emerged as ubiquitous devices beyond simple means of communication. Students studying at tertiary‐level educational institutions are one of the major target groups for mobile companies. Notwithstanding, the educators and the marketers of mobile phones and services do not know much about their needs and mobile phone usage behavior. This study aims to determine the mobile phone needs and usage behavior of university students in Oman. The study delves upon longitudinal data collected over three different points of time in a decade and analyzes the changes in students' needs and mobile usage behavior. The findings suggest that students' preferences over time for mobile phones have changed not only for brands but also the way they patronize mobile phones and associated services. The study gives a vital lead to educators and the marketers of mobile phones and services. By understanding the mobile phone needs and usage behavior of students, educators can target their content and deliveries and marketers can target their features, apps, and services innovatively to this emerging segment.

Mobile Usage Behaviour Among Agricultural Students During Covid-19

Mobile phones have become an increasingly important part of our lives, especially in recent years due to the COVID-19 pandemic, particularly among students. Since the beginning of the pandemic, we have witnessed a series of lockdowns that prevented us from communicating with the outside world, leaving us with only a phone as the means of communication. In a matter of days, schools and colleges went online, increasing students’ mobile usage. The present study was conducted to understand the mobile usage behaviour among agricultural students of Tamil Nadu Agricultural University, during COVID-19. Empirical data was collected from 60 students using a structured interview schedule. Findings revealed that students used their mobile phones for more than 8 hours a day during the lockdown. Video conferencing apps like Microsoft Teams, Google classroom, etc. were used more frequently, followed by social networking apps. Age showed a significant and positive correlation with mobile usage behaviour at 0.05 level of probability whereas, education, social media addiction, monthly expenditure, and internet usage were found to exhibit a positive and significant correlation with mobile usage behaviour at 0.01 level of probability.

Identification Analysis and Marketing Strategy of Mobile Game Users' Behavior Characteristics

In recent years, the mobile game market has developed rapidly and became the largest market segment in the game industry in 2016. What matches this is that the discussion on mobile games on the Internet also occupies the center of public opinion. At the same time, with the change of consumption concept, the payment ability of mobile game players has been greatly improved. This paper explores the consumer behavior of mobile game users and analyzes their behavior in order to further improve the mobile game market.

Routine mining on location sequences

In this article, we propose a novel routine pattern extraction architecture to analyze the daily behaviors of mobile users. The key component of the proposed architecture is a dynamic programming-based sequence dissimilarity calculation method, which aims to measure the dissimilarity between trajectories and then extract patterns using the clustering method. The method exploits three different information: (1) spatial-temporal information, (2) information in the continuous same location in the sequence and (3) the probabilities of location occurrences in the data. We conduct experiments on a synthetic dataset and two real-world datasets. The obtained results demonstrate that our proposed method is efficient in extracting hidden routine patterns from users’ trajectory data.

A Bidirectional Trajectory Prediction Model for Users in Mobile Networks

Future mobile networks are envisioned to have critical limitations in terms of latency, energy usage, capacity and network resources since these networks are expected to become extremely dense and complex. The rapid enormous advances in recent technologies such as Internet of Things (IoT) highlights the urgent need for network performance enhancement as well. To this end, self-organizing networks are a promising solution to push the network performance to the next level. These scalable networks can dynamically adapt to possible changes in the network. Smart mobility management, in particular mobility prediction, is a subsection of self-organizing functions which are mainly based on the machine learning techniques. In this paper, we propose to estimate user’s future trajectory using machine learning approaches for a better network management. We propose a novel bidirectional trajectory prediction model called BTPM to model the user mobility behavior. The proposed method exploits the potential benefits of bidirectional gated recurrent unit (GRU) for having an accurate prediction. Moreover, we introduce a data preprocessing phase to obtain better results with significantly lower execution time. The proposed approach takes full advantage of data analysis in both directions (backward and forward) in order to provide a long-term prediction and model user’s mobility even with complex patterns. Experimental results show that the proposed bidirectional approach significantly improves the performance of the mobility predictor in terms of model accuracy, robustness and execution time. It achieves a model error of 0.014 and decreases the execution time up to 97%.

EXPLORING THE RELATIONSHIP BETWEEN MOBILE ADDICTION, INTERPERSONAL RELATIONSHIP AND ACADEMIC BEHAVIOR AMONG YOUNG ADULTS IN TERTIARY INSTITUTIONS

Purpose – Smartphones have become part and parcel of life in the 21st century. However, limited studies have examined the impact of mobile addiction and interpersonal relationship on academic behaviour among adolescents in higher education. Hence, this study was conducted to explore the relationship between these three variables in the Malaysian higher education context. Methodology – A descriptive correlational research design was employed to collect and analyse the data from a total of 150 young adults who responded to an online Google form distributed through a WhatsApp link. The items in the questionnaire were adapted from various doctorate studies. The data were analysed using descriptive and inferential statistics such as mean and standard deviation, correlation and multiple regression. Findings – The study found that adolescents in Higher Education in Malaysia have a moderate level of mobile addiction. The results also established that the three variables, namely mobile addiction, interpersonal relationship, and academic behaviour, were interrelated. The findings suggest that relationship status, interpersonal relationship and gender contributed to the variance in the academic behaviour of adolescents in higher education, with relationship status being the most significant. The study also found that smartphones do not affect the academic behaviour of adolescents. Significance – The findings provide valuable information to help facilitate the monitoring of mobile usage behaviour of adolescents in higher education.

Unsupervised Learning for Human Mobility Behaviors

Learning human mobility behaviors from location-sensing data are crucial to mobility data mining because of its potential to address a range of analytical purposes in mobile context reasoning, including exploration, inference, and prediction. However, existing approaches suffer from two practical problems: temporal and spatial sparsity. To address these shortcomings, we present two unsupervised learning methods to model the mobility behaviors of multiple users (i.e., a population), considering efficiency and accuracy. These methods intelligently overcome the sparsity in individual data by seeking temporal commonality among users’ heterogeneous location behaviors. The advantages of our models are highlighted through experiments on several real-world mobility data sets, which also show how our methods can realize the three analytical purposes in a unified manner.

Impact of Outsourcing on Effectiveness of Call Center Services

Call center is the frontline of communicating with customers. This paper reports the findings of a survey on service effectiveness of call center operations of mobile communication service providers in Turkey that either use in-house or outsourcing method. The primary objective of this paper is to explore how the customers of mobile communications service provider companies perceive the inbound call center processes for speed, easiness of use, first call resolution, effective inquiry and in doing that what qualities they display. For this purpose a survey was conducted with the customers who use call centers either to make an inquiry or to complain about the services. The aims were: to specify the typical behavior of mobile communications service users; how the firms handle inquiries or complaints; if they solve the problems for good and in what time or how long it takes to get an answer practically useful; to reveal the speed and timeliness of the processes. The survey was conducted with the mobile phone users in a highly populated area of Istanbul.

The Use of Mobile Applications for Travel Booking: Impacts of Application Quality and Brand Trust

The increasing use of mobile applications by travellers and the high adaption of tourism companies into this new contact and sales platform, made it necessary to comprehensively investigate the mobile application users’ behaviours. This research combines the Stimulus-Organism-Response (S-O-R) framework and Technology Acceptance Model (TAM) to develop a theoretical background in examining travel booking behaviour of mobile application users. The conceptual model suggests that mobile application quality (MAQ) directly affects perceived ease of use (PEOU) and perceived usefulness (PU) which influence the intention to use (IU) mobile applications. Moreover, the offline brand trust (BT) has been hypothesised as a moderator between PEOU and PU's impacts on IU mobile applications. Analyses results indicated that system and service quality dimensions of MAQ significantly affect IU mobile application via PEOU and PU. Moreover, offline BT had both direct and moderator influences on the formation of IU mobile application. The study findings contributed to the theory in understanding mobile application users’ behaviours and suggested valuable managerial strategies in the m-commerce context.

An approach to pedestrian walking behaviour classification in wireless communication and network failure contexts

Despite the wide range of research on pedestrian safety, previous studies have failed to analyse the real-time data of pedestrian walking misbehaviour on the basis of either pedestrian behaviour distraction or movements during specific activities to realise pedestrian safety for positive (normal) or aggressive pedestrians. Practically, pedestrian walking behaviour should be recognised, and aggressive pedestrians should be differentiated from normal pedestrians. This type of pedestrian behaviour recognition can be converted into a classification problem, which is the main challenge for pedestrian safety systems. In addressing the classification challenge, three issues should be considered: identification of factors, collection of data and exchange of data in the contexts of wireless communication and network failure. Thus, this work proposes a novel approach to pedestrian walking behaviour classification in the aforementioned contexts. Three useful phases are proposed for the methodology of this study. In the first phase involving factor identification, several factors of the irregular walking behaviour of mobile phone users are established by constructing a questionnaire that can determine users’ options (attitudes/opinions) about mobile usage whilst walking on the street. In the second phase involving data collection, four different testing scenarios are developed to acquire the real-time data of pedestrian walking behaviour by using gyroscope sensors. In the third phase involving data exchange, the proposed approach is presented on the basis of two modules. The first module for pedestrian behaviour classification uses random forest and decision tree classifiers part of machine learning techniques via wireless communication when a server becomes available. The developed module is then trained and evaluated using five category sets to obtain the best classification of pedestrian walking behaviour. The second module is based on four standard vectors for classifying pedestrian walking behaviour when a server is unavailable. Fault-tolerant pedestrian walking behaviour is identified and is initiated when failures occur in a network. Two sets of real-time data are presented in this work. The first dataset is related to the questionnaire data from 262 sampled respondents, and the second dataset comprises data on 263 sampled participants with pedestrian walking signals. Experimental results confirm the efficacy of the proposed approach relative to previous ones.

Identifying SME customers from click feedback on mobile banking apps: Supervised and semi-supervised approaches

Nowadays, the banking industry has moved from traditional branch services into mobile banking applications or apps. Using customer segmentation, banks can obtain more insights and better understand their customers' lifestyle and their behavior. In this work, we described a method to classify mobile app user click behavior into two groups, i.e. SME and Non-SME users. This task enabled the bank to identify anonymous users and offer them the right services and products. We extracted hand-crafted features from click log data and evaluated them with the Extreme Gradient Boosting algorithm (XGBoost). We also converted these logs into images, which captured temporal information. These image representations reduced the need for feature engineering, were easier to visualize and trained with a Convolutional Neural Network (CNN). We used ResNet-18 with the image dataset and achieved 71.69% accuracy on average, which outperformed XGBoost, which only achieved 61.70% accuracy. We also evaluated a semi-supervised learning model with our converted image data. Our semi-supervised method achieved 73.12% accuracy, using just half of the labeled images, combined with unlabeled images. Our method showed that these converted images were able to train with a semi-supervised algorithm that performed better than CNN with fewer labeled images. Our work also led to a better understanding of mobile banking user behavior and a novel way of developing a customer segmentation classifier.

Effect of Attitude on Mobile Banking Acceptance Using Extended UTAUT Model

This research aims to evaluate the effect of attitude on mobile banking acceptance using the extended UTAUT Model. Specifically, surveying the Medan city area covered 392 mobile banking users from several banks. By using Structural Equation Modeling (SEM) and SmartPLS software. This research’s man contribution is introducing attitude variables in the combination of UTAUT models, task technology fit, and trust, which have a significant effect on behavior intention. Based on this study results, it shows that social influence and attitude are a significant effect on behavior intention,facilitating condition, and behavior intention is a significant effect use behavior of mobile banking users from several banks. While performance expectancy, effort expectancy, task technology fit, and trust didn’t significantly effect on the behavior intention of mobile banking users from several banks. This Research implies two important policymakers’ findings. First, Banking Management needs to ensure that it always makes necessary improvements in simplifying technology to understand it easily. Second, need to ensure that we regularly evaluate the performance of mobile banking users from several banks to mae benefits of using M-Banking technology to support financial transactions needs.

Design of Library Mobile User Behavior Analysis model for Personalized Information Service

This paper summarizes the research status of Library mobile user behavior at home and abroad, and provides a reference for the design of Library mobile user behavior analysis model for personalized information service. Using the method of theoretical research and model analysis, the relationship between user behavior sequence and user behavior characteristics is studied from three aspects of data collection, data processing and data application. This paper defines and represents the invisible feedback behavior of Library mobile users on IOS app, android app and web page and product UI by combining the front-end operation behavior with the back-end business database, collects the data from three aspects: basic information field, user behavior related field and configuration flume of mobile users’ client, and introduces the time dimension, user dimension, Department dimension, resource dimension, resource dimension The data warehouse level modeling is carried out from six dimensions of behavior dimension and entrance channel, and the data application is realized from two steps of user behavior sequence prediction and user interest guidance.

Modeling mobile apps user behavior using Bayesian networks

Modeling mobile apps user behavior using Bayesian networks