Mobile User Behavior Research Articles

Predicting Individual Mobility Behavior of Ride-Hailing Service Users Considering Heterogeneity of Trip Purposes

Predicting Individual Mobility Behavior of Ride-Hailing Service Users Considering Heterogeneity of Trip Purposes

Measuring Multi-Dimensional Mobile Behavior Effect on Inclusive Finance: Evidence from China

Credit Invisible is one key area that many countries put much effort to solve in decades. According to the 2020 World Bank statistics, for example, there are over 500 million Chinese and 45 million American, classified as credit invisible who don’t have banking and finance history in bank or credit bureau, making them difficult to borrow money from financial institution. Previous studies adopted different non-financial information to evaluate one’s credit worthiness and status to address this issue. However, they provide little information about how real mobile user interactions can be used to solve this issue in inclusive finance. This paper proposes a novel data generative framework to fusion APP data, call detail record data and SMS data with a total of 4,689 attributes derived from a large-scale mobile dataset. We then construct a unique set of mobile behavior-driven credit risk factors based on statistical diversity, intensity, consistency, and regularity of mobile user behavior characterizing user preferences, attitudes, geolocation, and temporal patterns. Empirical analysis demonstrates that the newly discovered mobile behavior factors are useful as new inputs for credit scoring and proves the factors representing new source of positive and negative credit information. Decision tree analysis and Quantile regression are conducted to validate effect of these factors to credit default. It facilitates credit assessment based on non-financial data for the credit invisible people, which promoting inclusive finance to larger community in society. We also analyze implications of mobile user characterization findings in relation to credit default which helps decision makers to optimize credit policy and product design.

Image-Acceleration Multimodal Danger Detection Model on Mobile Phone for Phone Addicts.

With the popularity of smartphones, a large number of "phubbers" have emerged who are engrossed in their phones regardless of the situation. In response to the potential dangers that phubbers face while traveling, this paper proposes a multimodal danger perception network model and early warning system for phubbers, designed for mobile devices. This proposed model consists of surrounding environment feature extraction, user behavior feature extraction, and multimodal feature fusion and recognition modules. The environmental feature module utilizes MobileNet as the backbone network to extract environmental description features from the rear-view image of the mobile phone. The behavior feature module uses acceleration time series as observation data, maps the acceleration observation data to a two-dimensional image space through GADFs (Gramian Angular Difference Fields), and extracts behavior description features through MobileNet, while utilizing statistical feature vectors to enhance the representation capability of behavioral features. Finally, in the recognition module, the environmental and behavioral characteristics are fused to output the type of hazardous state. Experiments indicate that the accuracy of the proposed model surpasses existing methods, and it possesses the advantages of compact model size (28.36 Mb) and fast execution speed (0.08 s), making it more suitable for deployment on mobile devices. Moreover, the developed image-acceleration multimodal phubber hazard recognition network combines the behavior of mobile phone users with surrounding environmental information, effectively identifying potential hazards for phubbers.

Investigating the healthcare-seeking behaviors of mobile phone users to improve cervical cancer screening in rural Uganda.

Cervical cancer is the leading cause of cancer in low- and middle-income countries, despite being preventable. Uganda, which lacks an effective screening program, has one of the highest global cervical cancer incidence rates. Mobile health (mHealth) technology has the potential to improve healthcare-seeking behaviors and access. The present study describes the connection between mobile phone access and healthcare-seeking behaviors in rural Uganda. Women were eligible for this cross-sectional study if they had no prior screening or treatment for cervical cancer in the past 5 years, were aged 30 to 49 years old, and were residents of the South Busoga Forest reserve. Survey data was analyzed using descriptive statistics and chi-square tests. Of the 1434 participants included in the analysis, 91.4% reported having access to a mobile phone. Most respondents were aged 30-40 years, had a partner, had ≤primary education, and were farmers. Participants with mobile phone access were significantly more likely to report attending a healthcare outreach visit (access = 87.3%, no access = 72.6%, P < 0.001) or visiting a health center (access = 96.9%, no access = 93.5%, P < 0.001). Participants in both groups had largely positive attitudes around and good knowledge of cervical cancer screening. While attendance to healthcare services was high amongst participants, those with mobile phone access were more likely to seek healthcare services. Further inquiry into this association between mobile phone access and healthcare-seeking behavior is needed to optimize the improvements to cervical cancer screening when implementing interventions such as mHealth technology.

Does current pre-experience matter? A study of customer perceived characteristics and MarTech usage behavior of mobile banking application users in Sri Lanka

Purpose: The integration of Marketing Technology (MarTech) in Mobile Banking (MB) apps gains recognition in marketing automation, previous research lacks a comprehensive framework for understanding customer behavior. This study addresses this gap by proposing a new model within Financial Technology (FinTech), incorporating customer characteristics.&#x0D; Design/methodology/approach: The Integrated MarTech Usage Behavior Model (IMTUBM) triangulates three theories to explore the MarTech landscape in a longitudinal survey of 400 MB app users in Sri Lanka, utilizing Partial Least Squares Structural Equation Modeling (PLS-SEM) with Smart-PLS software.&#x0D; Findings: The resultant Integrated MB App Usage Behavior Model (IMBUBM) provides a foundational understanding of customer characteristics in the MarTech domain. Notably, this study conceptualizes awareness, elucidating that experiential aspects are shaped by both previous and pre-experience.&#x0D; Originality: This study introduces the concept of current pre-experience as a moderator in the MarTech landscape within FinTech, arguing for its deeper exploration compared to previous experience. &#x0D; Implications: These findings not only suggest avenues for future research in MarTech but also provide managerial insights, encouraging refinement of strategies based on heightened customer awareness. Additionally, the study emphasizes the importance of current pre-experience in bridging the gap between customer intention and behavior in MarTech usage.&#x0D; Keywords: Awareness, Customer characteristics, FinTech organization, MarTech domain, MB app, Pre-experience, Sri Lanka

Research On Influencing Factors of Mobile Short Video Users' Health Information Avoidance Behavior

This paper aims to maximize the attention of different users to avoid health information and reduce its negative impact, so as to maintain the balance and stability of the mobile short video health information ecosystem. Based on the S-O-R theory and the theory of information ecology, this paper constructs a model of the influencing factors of health information avoidance behavior of mobile short video users, puts forward a research hypothesis, and analyzes it by combining the structural equation model method. The results show that mobile short video users' health information avoidance behavior involves the interweaving of multiple factors. The emotional and cognitive factors of users play a mediating role in the influence of information elements, environmental factors and technical factors on health information avoidance. There are differences in the impact of external stimuli on the internal state of users with different levels of demand for health information.

Persistent impact of COVID-19 pandemic on mobility behavior of paratransit users, montreal case study

IntroductionCOVID-19 pandemic has had and still has impacts on many aspects of society. This crisis has exacerbated certain injustices among various population groups. Mobility was already an issue for people with disabilities (PWDs) with limited travel options and numerous daily barriers. MethodsThe aim of this study is to identify and measure the impact of the pandemic on the mobility of paratransit users. To do this, trip data from the paratransit service of the Société de transport de Montréal (STM), covering the March 2019 to March 2022 period, are used. Four analyzes are carried out: (1) categorial Analysis to evaluate the link between users’ characteristics and mobility behavior during the pandemic, (2) frequency use during the various pandemic phases, (3) retention rate using survival analysis of paratransit users depending on admission date and (4) evolution of the activity time to travel time ratio. ResultsThe categorical analysis reveals that only the type of disability had a different effect on the modification of mobility behavior of paratransit users. Furthermore, users who began using paratransit service during the pandemic seem more loyal than users who started before COVID-19. Only 22% of users who reduced their travel in the first year of the pandemic resumed their mobility behavior in the second year of the pandemic. Finally, waiting times and travel times for paratransit decreased due to ridership lost caused by the COVID-19. However, activity duration has slightly decreased. ConclusionThese results raise questions about the persistence of the impact of the COVID-19 pandemic. Thus, some perspectives on the stickiness of new mobility behaviors are proposed.

Research on Privacy Disclosure Behavior of Mobile App Users from Perspectives of Online Social Support and Gender Differences

The provision of information and emotions is the core competitive edge for mobile apps to attract users to utilize their services and functionalities. Considering user demands and gender differences, it holds great significance for mobile apps to optimize personalized services by elucidating the formation mechanism of privacy disclosure behavior from the perspective of online social support (information and emotional). We adopt online social support theory and construct a model based on Stimulus-Organism-Response (SOR) framework to collect questionnaire data from 548 respondents for empirical analysis. The results showed that both online information support and online emotional support have direct or indirect positive effects on privacy disclosure behavior, with perceived justice and privacy concern mediating this effect. Perceived justice has a negative impact on privacy concern. Besides, females exhibit higher levels of privacy concern and lower levels of disclosure, and there are differences in the types of demand support between males and females in privacy decision-making.

Antecedents of consumers' brand switching behavior in mobile service provider

PurposeSwitching behavior is predominantly seen in the consumer buying behavior of the mobile industry. This research aims to identify the factors influencing consumers to switch from their present mobile service provider. The consumer of the mobile industry operates in a dynamic and ever-changing environment that is difficult to predict, so this paper aims to focus on these issues.Design/methodology/approachThe selection of factors was made with the help of qualitative study and quantitative research methods for further findings; with the help of a structured questionnaire, a total of 514 valuable responses were collected to get the results. Exploratory factor analysis (EFA), confirmatory factor analysis (CFA) and structural equation modeling (SEM) were used to analyze the data.FindingsThe finding shows that technology and edge-on-competition (TEC) and pricing have a negative influence on customer switching behavior. The switching cost (SC) is the most significant factor and has a positive impact, while service encounter failure (SEF) also positively impacts switching behavior.Research limitations/implicationsThe findings provide important implications for consumers switching brands if they are finding alternative offers that are cost-effective and SEF from service providersPractical implicationsThe study of one of the largest mobile markets is learning lessons for other markets around the world. This study will be helpful for mobile service provider companies in their branding and marketing strategies. This study will also be helpful to practitioners, educators and researchers in understanding the consumer behavior of mobile users.Social implicationsThe learning of the largest mobile market will be a great learning lesson for other mobile markets around the world. Consumer behavior will help marketers follow ethical practices and make their strategy so a consumer does not switch brands and remain satisfied with the existing brand.Originality/valueThe study provides unique learning for practitioners, educators and researchers to understand the consumer behavior of mobile users. This will help marketers create factors that stop consumers from switching brands and develop strategies to retain customers.

Niat dan Perilaku Penggunaan Mobile Wallet pada Mahasiswa Akuntansi

The Covid-19 pandemic has led to an increase in the use of mobile wallets. The aim of the study was to analyze the intention and behavior of using a mobile wallet using the UTAUT 2 model. The research was conducted on accounting students at the Faculty of Economics and Business, Udayana University. The sample was determined by the slovin formula and 92 people were found as the research sample. The research data was processed using multiple linear regression analysis. The results of the study show that performance expectations, social influence and price values have no effect on the intention to use a mobile wallet. Business expectations and hedonic motivation have a positive effect on the intention to use a mobile wallet. The test results also show that facilitating conditions do not affect the behavior of mobile wallet users. User habits and usage intentions have a positive effect on mobile wallet usage behavior.Keywords: Intention of Use; User Behavior; Mobile Wallets; UTAUT 2

Examining the Determinants of Mobile Banking App Continuance Intention in India: An Extension of the IS Success Model

This research investigates the factors that influence the post-adoption behavior of users of mobile banking applications, i.e., continuance intention. A research framework was proposed by extending the IS success model in the context of post-adoption behavior of mobile banking app users. A sample of 345 current users of mobile banking applications was selected using convenience sampling. Service quality, information quality, system quality, perceived risk, and perceived trust substantially affect customer satisfaction. Satisfaction has mediated the relationship between these indicators and the desire to continue using mobile banking services in India. Moreover, the key drivers of mobile banking continuation intention were service quality, perceived risk, trust, and satisfaction.

A Systematic Literature Review and a Conceptual Framework Proposition for Advanced Persistent Threats (APT) Detection for Mobile Devices Using Artificial Intelligence Techniques

Advanced persistent threat (APT) refers to a specific form of targeted attack used by a well-organized and skilled adversary to remain undetected while systematically and continuously exfiltrating sensitive data. Various APT attack vectors exist, including social engineering techniques such as spear phishing, watering holes, SQL injection, and application repackaging. Various sensors and services are essential for a smartphone to assist in user behavior that involves sensitive information. Resultantly, smartphones have become the main target of APT attacks. Due to the vulnerability of smartphone sensors, several challenges have emerged, including the inadequacy of current methods for detecting APTs. Nevertheless, several existing APT solutions, strategies, and implementations have failed to provide comprehensive solutions. Detecting APT attacks remains challenging due to the lack of attention given to human behavioral factors contributing to APTs, the ambiguity of APT attack trails, and the absence of a clear attack fingerprint. In addition, there is a lack of studies using game theory or fuzzy logic as an artificial intelligence (AI) strategy for detecting APT attacks on smartphone sensors, besides the limited understanding of the attack that may be employed due to the complex nature of APT attacks. Accordingly, this study aimed to deliver a systematic review to report on the extant research concerning APT detection for mobile sensors, applications, and user behavior. The study presents an overview of works performed between 2012 and 2023. In total, 1351 papers were reviewed during the primary search. Subsequently, these papers were processed according to their titles, abstracts, and contents. The resulting papers were selected to address the research questions. A conceptual framework is proposed to incorporate the situational awareness model in line with adopting game theory as an AI technique used to generate APT-based tactics, techniques, and procedures (TTPs) and normal TTPs and cognitive decision making. This framework enhances security awareness and facilitates the detection of APT attacks on smartphone sensors, applications, and user behavior. It supports researchers in exploring the most significant papers on APTs related to mobile sensors, services, applications, and detection techniques using AI.

On the Impact of Recharging Behavior on Mobility

We consider the behavior of mobile users that, upon depletion of their device's energy, can detour from their regular path, in order to reach a location where the device can be recharged. We develop abstractions of this recharging behavior and analytically derive a charging-aware mobility model. The device is viewed as a mobile node. Based on the Palm inversion formula, we derive the (integral-form) stationary probability density function of the node's location, subject to attraction exerted by one charger in a bounded convex area. The analytical and numerical results demonstrate the distortion effect the charger has on the spatial node distribution. We observe, and explain, a counter-intuitive effect whereby the node density exhibits peaks that are asymmetric, coupled with a relative decrease of the density around the charger area. In addition, we provide a method to approximate the probability density function when multiple chargers are deployed. We examine the accuracy of this approximation and comment on its limitations.

Analysis of Factors Affecting Use Behavior towards Mobile Payment Apps: A SEM Approach

The main aim of this research study is to examine the impact of five independent variables viz. performance expectancy, effort expectancy, social influence, facilitating conditions, and hedonic motivation on behavioral intention to use mobile payment apps. This study is further aimed at investigating the influence of behavioral intention on the use behavior of mobile payment app users. This study strives to investigate the use behavior of people who have already used mobile payment apps like Google Pay, PhonePe, and PayTM previously for making payments for the products and services they have purchased from various sellers. The researchers used the UTAUT2 theory to examine the relationship between the independent and dependent variables mentioned above. The data was collected from 618 mobile payment app users from Vidarbha, M.S., India. Availability sampling and purposive sampling techniques were adopted for the final selection of the respondents. A structured questionnaire was designed by the researchers for collecting the required primary data. The six proposed hypotheses were tested by using SMART-PLS 3.3.5 software. The results indicated support for all six proposed hypotheses. The proposed model explained a substantial amount of variance in behavioral intention ( R 2 = 71.8 % and R 2 = 76.5 %) in use behavior towards mobile payment apps explained by independent variables. Facilitating conditions exhibited the strongest effect on behavioral intention.

Research on the Influence of Mobile Short Video Platform Recommendation Information Characteristics on User Stickiness

The outbreak of the scientific and technological revolution has brought about the vigorous development of the digital economy. Many short video APPs have gradually come into people’s sight and become important tools for leisure and entertainment. At this time, improving user stickiness has become the key to their competitiveness and long-term development. As a major feature of short video platform, information recommendation plays an important role in the sticky behavior of users. Therefore, targeted research on the internal mechanism of platform recommendation information features for user stickiness will help to improve the platform user stickiness. Based on the stimulus organism response theory, this paper constructs a theoretical model of the impact of the characteristics of recommendation information on the user’s sticky behavior during the use of mobile short video, and investigates 336 mobile short video platform users through online questionnaires. The research shows that the accuracy, benefit, interest and surprise of mobile short video recommendation information have a positive impact on users’ flow experience and sticky behavior; The flow experience has a positive impact on the sticky behavior of mobile short video users. This paper puts forward management suggestions for the management and operators of mobile short videos to help them optimize the design of their products, and recommends more accurate, profitable, interesting and surprising information based on user characteristics to stimulate users’ flow experience, thus improving users’ stickiness and helping enterprises achieve long-term development.

Exploring Factors Shaping Mobile Application Usage in Rural Uttarakhand: A Study in Selected Villages of Chamoli, Pauri, Rudraprayag, Tehri, and Uttarkashi Districts

This research aimed to investigate the impact of diverse factors on the usage behavior of mobile applications among users in the rural regions of Uttarakhand specifically in villages situated in Chamoli, Pauri Garhwal, Rudraprayag, Tehri Garhwal, and Uttarkashi districts. The primary objective was to ascertain whether factors such as the Ease of Use of applications, Privacy &amp; Security considerations, Satisfaction levels, Digital Literacy levels, and Internet Accessibility collectively influence the usage behavior of mobile application users. To conduct the study, a descriptive research design was employed, and primary data was collected from rural areas in Uttarakhand. The influence of various factors was analyzed using the ANOVA test. The findings of the study indicate that all the aforementioned factors significantly impact the usage behavior of mobile applications in these rural areas. Additionally, the study suggests a widespread increase in the future adoption of mobile applications in these regions.

Continuance Intention of Mobile Banking Applications in Indonesia: Integrated TAM-Delone and Mclean Model

This study attempts to explain the continuance intention of mobile banking applications using the integrated technology acceptance model (TAM) and Delone and Mclean's information systems success model. A total of 258 responses were collected from mobile banking users in Indonesia. PLS-SEM was used to analyze the data and test the proposed hypotheses. The results showed that perceived ease of use has a significant influence on perceived usefulness but not satisfaction. Perceived usefulness, system quality, and service quality positively influence satisfaction, but perceived security and information quality are not significant determinants of satisfaction. This study confirmed the positive effects of perceived usefulness and satisfaction toward the continued intention to use mobile banking apps. This study is one of the first to integrate two popular information system theories, specifically TAM and Delone's and Mclean's IS success models, to explain the post-adoption behavior of mobile banking users, which is still relatively under explored.

Human Behavior-Based Mobility Models in Mobile Wireless Networks: a Literature Survey

Recent advancement in processing power, mobile wireless networks enabled technology are leading to more researches and studies, investigating mobility prediction in wireless network environments. User mobility behavior knowledge is a key element to understand the network traffic behavior in today mobile applications, including context-aware advertising and city wide sensing applications. We extend our previous work that was dedicated to mobility prediction schemes, in order to cover a correlated research topic: study of the user mobility behavior.

Factors Influencing Use of Fitness Apps by Adults under Influence of COVID-19.

During the coronavirus disease 2019 (COVID-19) pandemic, many countries imposed restrictions and quarantines on the population, which led to a decrease in people's physical activity (PA) and severely damaged their mental health. As a result, people engaged in fitness activities with the help of fitness apps, which improved their resistance to the virus and reduced the occurrence of psychological problems, such as anxiety and depression. However, the churn rate of fitness apps is high. As such, our purpose in this study was to analyze the factors that influence the use of fitness apps by adults aged 18-65 years in the context of COVID-19, with the aim of contributing to the analysis of mobile fitness user behavior and related product design practices. We constructed a decision target program model using the analytic hierarchy process (AHP), and we analyzed and inductively screened 11 evaluation indicators, which we combined with an indicator design questionnaire. We distributed 420 questionnaires; of the respondents, 347 knew about or used fitness apps. Among these 347, we recovered 310 valid questionnaires after removing invalid questionnaires with a short completion time, for an effective questionnaire recovery rate of 89.33%. We used the AHP and entropy method to calculate and evaluate the weight coefficient of each influencing factor and to determine an influencing factor index. Our conclusions were as follows: first, the effect of perceived usefulness on the use of fitness apps by the study groups was the most notable. Second, personal motivation and perceived ease of use considerably influenced the adult group's willingness to use fitness apps. Finally, the perceived cost had relatively little effect on the use of fitness apps by adults, and the study group was much more concerned with the privacy cost than the expense cost.

POI recommendation with queuing time and user interest awareness

Point-of-interest (POI) recommendation is a challenging problem due to different contextual information and a wide variety of human mobility patterns. Prior studies focus on recommendation that considers user travel spatiotemporal and sequential patterns behaviours. These studies do not pay attention to user personal interests, which is a significant factor for POI recommendation. Besides user interests, queuing time also plays a significant role in affecting user mobility behaviour, e.g., having to queue a long time to enter a POI might reduce visitor’s enjoyment. Recently, attention-based recurrent neural networks-based approaches show promising performance in the next POI recommendation task. However, they are limited to single head attention, which can have difficulty in finding the appropriate user mobility behaviours considering complex relationships among POI spatial distances, POI check-in time, user interests and POI queuing times. In this research work, we are the first to consider queuing time and user interest awareness factors for next POI recommendation. We demonstrate how it is non-trivial to recommend a next POI and simultaneously predict its queuing time. To solve this problem, we propose a multi-task, multi-head attention transformer model called TLR-M_UI. The model recommends the next POIs to the target users and predicts queuing time to access the POIs simultaneously by considering user mobility behaviours. The proposed model utilises POIs description-based user personal interest that can also solve the new categorical POI cold start problem. Extensive experiments on six real-world datasets show that the proposed models outperform the state-of-the-art baseline approaches in terms of precision, recall, and F1-score evaluation metrics. The model also predicts and minimizes the queuing time. For the reproducibility of the proposed model, we have publicly shared our implementation code at GitHub (https://github.com/sajalhalder/TLR-M_UI).