Mobile Technology Research Articles

Unveiling Shopee App Continuance Use: Insights from Malaysian Shoppers

The popularity and acceptance of mobile shopping apps have surged widely due to mobile technology advancement and the COVID-19 outbreak. Retaining customers would be challenging for all mobile shopping app platform operators, particularly in the post-Covid era. However, research on the continuance of mobile shopping apps must be addressed. Hence, the Shopee mobile shopping app was adopted as a case study to examine antecedent factors of continuance intention leading to its continuance use among consumers. This quantitative study was conducted in Klang Valley, Malaysia, and data was collected via a self-administered Google Form. The study included 386 respondents and was analysed using Partial Least Squares Structural Equation Model (PLS-SEM). Customer loyalty, the success quality factors of the information system, incentives, trust, and satisfaction positively affect Shopee's mobile shopping app's continuance intention. Furthermore, it is found that continuance intention does indeed have a crucial effect on continuance use. However, the study reported that perceived enjoyment did not influence continuance intention. The objective of this research is achieved as it manages to generate valuable knowledge on continuance intention and use. This paves the way for more effective development of sustainable user retention strategies.

The Feasibility and Acceptability of a Mobile Application for Oral Health Education Among Adults in Rwanda.

Mobile technology health application (mobile Apps) can assist people manage their health and wellness, encourage healthy lifestyles, and provide valuable information whenever necessary. This study aimed to evaluate the feasibility and acceptability of a mobile App for oral health education among adults in Rwanda. This was a descriptive cross-sectional study. A total of 111 adult participants evaluated the acceptability and feasibility of the BrushDJ mobile App after using it for three months. At the end of this period, an online survey was conducted using a structured questionnaire consisting of questions on demographics, habitual oral health, assessing acceptability, and assessing the feasibility of the mobile App. Descriptive statistics were performed to analyse demographic characteristics, the feasibility and acceptability of the mobile App. Bivariate analysis using the Chi-square test was performed to detect significant associations amongst the variables. The response rate was 81% (n=90) of 111 participants who took part in the study, with the majority of participants 44.44% (n=40) aged between 18 and 27 years old. Just over half of the participants were male 51.11% (n=46). The mobile App was found to be highly acceptable (87.78%; n=79) and considered highly feasible (84.44%; n=76) by most of the participants. The differences in acceptability and feasibility across education levels were statistically significant (p=0.000 for acceptability and p=0.038 for feasibility). The study revealed that the mobile App for oral health education (BrushDJ) was highly acceptable and feasible among the participants. The mobile App had a user-friendly interface and comprehensive guidance on oral hygiene procedures facilitated by its diverse and well-integrated functions.

Determinants of mobile stock investment application adoption and its impact on intention to recommend the applications in emerging countries: a case study of Indonesia

Abstract Commonly used research models analysing technological adoption, such as the Technology Adoption Model, Theory of Planned Behaviour, and Unified Theory of Acceptance and Use of Technology, mostly emphasise technology-related variables. In the context of mobile stock investment application adoption, this study extends the existing technological adoption models by adding digital financial service-related variables. The purpose of this study is to investigate the main determinants of mobile stock investment application adoption in emerging countries, specifically in Indonesia. The study deployed a quantitative type of research with an online survey questionnaire by recruiting 256 respondents of stock investors who have used mobile applications for a minimum of one year. Data was analysed using partial least squares structural equation modelling (PLS-SEM) with advanced analysis tests. The results confirm the significant influence of performance expectancy, finfluencers, perceived financial risks, perceived financial benefits, perceived technology security, financial literacy, and e-reputation on adoption behaviour. The results also find a significant influence of adoption behaviour on the intention to recommend. Meanwhile, effort expectancy and facilitating conditions were insignificant toward adoption behaviour. These findings signify that the comprehensive research model could contribute to enriching studies on adoption of the mobile technology by extending TPB and UTAUT with specific variables related to stock investment and its impact on the intention to recommend the applications. Finally, the implications of the proposed new model for future research and FinTech practice are discussed.

Analysis of the Impact of Mobile Banking Technology, Fintech, and Digital Transaction Security on Customer Loyalty at BUMN Banks in Indonesia

This study investigates the impact of mobile banking technology, fintech innovations, and digital transaction security on customer loyalty at state-owned banks in Indonesia. Utilizing a quantitative approach, data were collected from 300 respondents through a structured questionnaire with a Likert scale ranging from 1 (Strongly Disagree) to 5 (Strongly Agree). The data were analyzed using SPSS version 25, employing descriptive statistics, reliability analysis, exploratory factor analysis, correlation analysis, and multiple regression analysis. The results revealed that all three independent variables—mobile banking technology, fintech innovations, and digital transaction security—positively and significantly influence customer loyalty. Mobile banking technology had a correlation coefficient of 0.62, fintech innovations 0.57, and digital transaction security 0.68 with customer loyalty. The regression analysis showed that these factors collectively explained 38% of the variance in customer loyalty, with digital transaction security having the strongest impact. The findings underscore the importance of enhancing technological capabilities and security measures to foster customer loyalty in state-owned banks. The study provides valuable insights for banking institutions seeking to improve customer satisfaction and retention through advanced technological solutions and robust security protocols.

Mobile Learning and Resource Sharing Mode of College English Translation Based on 5G Mobile Communication Technology

The application of 5G and other mobile communication technologies to education and teaching, and the construction of College English Translation mobile learning and resource sharing model and related models have important scientific value and significance for the effective organization and management of learning resources and the improvement of the speed and efficiency of college english translation resource retrieval. This research mainly combines the characteristics of complementarity and expansion of mobile learning technology for traditional online learning technology. Based on Moodle online teaching platform, this research develops a mobile learning and resource sharing mode for college english translation based on 5G mobile communication technology, designs and implements a mobile learning model based on Moodle platform, and designs the system architecture, functional modules, learning mode and learning process, Finally, the main functional modules of the system model are implemented and tested. To promote mobile learning in the teaching field to play a helping role in a wide range. The experimental results show that the AKAZE algorithm is used to optimize the model, and the overall accuracy rate reaches 74.7%, and the test probability value P (siq)=0.00, which is significantly less than the significant level of 0.05. Therefore, we believe that the application of mobile learning instructional design mode will help students improve their academic performance.

Privacy concerns in mobile technology: Can interactivity reduce friction?

Many, if not most, mobile applications tend to elicit personal information from users to offer personalized services. However, users may not be comfortable with such intrusiveness and therefore hesitate to download or adopt a new app even when its use could be beneficial to their health and well-being. To overcome this friction and help users make informed decisions, we propose message interactivity as a solution. Guided by privacy calculus, the theory of interactive media effects (TIME), and the elaboration likelihood model (ELM), we conducted a 4-condition, pre-registered online between-subjects experiment (N = 305) to assess the effect of message interactivity on attitudes and behavioral intentions pertaining to information disclosure in mobile health apps. Data indicate a significant positive effect via three serial mediators, including perceived contingency, elaboration, and perceived benefits. Theoretical and practical implications are discussed.

Predictors of substance use during treatment for addiction: A network analysis of ecological momentary assessment data.

Ecological momentary assessment (EMA) studies have previously demonstrated a prospective influence of craving on substance use in the following hours. Conceptualizing substance use as a dynamic system of causal elements could provide valuable insights into the interaction of craving with other symptoms in the process of relapse. The aim of this study was to improve the understanding of these daily life dynamic inter-relationships by applying dynamic networks analyses to EMA data sets. Secondary analyses were conducted on time-series data from two 2-week EMA studies. Data were collected in French outpatient addiction treatment centres. A total of 211 outpatients beginning treatment for alcohol, tobacco, cannabis, stimulants and opiate addiction took part. Using mobile technologies, participants were questioned four times per day relative to substance use, craving, exposure to cues, mood, self-efficacy and pharmacological addiction treatment use. Multi-level vector auto-regression models were used to explore contemporaneous, temporal and between-subjects networks. Among the 8260 daily evaluations, the temporal network model, which depicts the lagged associations of symptoms within participants, demonstrated a unidirectional association between craving intensity at one time (T0) and primary substance use at the next assessment (T1, r = 0.1), after controlling for the effect of all other variables. A greater self-efficacy at T0 was associated with fewer cues (r = -0.04), less craving (r = -0.1) and less substance use at T1 (r = -0.07), and craving presented a negative feedback loop with self-efficacy (r = -0.09). Dynamic network analyses showed that, among outpatients beginning treatment for addiction, high craving, together with low self-efficacy, appear to predict substance use more strongly than low mood or high exposure to cues.

Disrupted immobilities: giving space and time to the discussion of immobility dynamics in transport shipping

Ships are technologies of maritime mobility. But sometimes ships are immobile—they stop and remain stationary for short or prolonged times. A degree of stasis inside and outside ports is both usual and essential for facilitating the movement of ships in global markets. This paper makes two important points: first, echoing existing literature, frequent stationarity (or waiting) is a normal occurrence in the industry. Second, it is not just mobilities that are disrupted via moments of stasis; immobilities themselves have distinct patterns that too can be disrupted. This nuance is vital: there is a need to understand the disruptions to immobilities rather than understanding immobilities as disruptions to the general condition of mobility, both within—and beyond—the shipping example. We argue that understanding disruptions to immobilities is vital to grasping the dynamics of shipping, alongside other (im)mobilites (car, train, plane), their conditions, and the politics that shape our world on the move. Using a data-driven approach, embracing AIS methods for exploring ship stationarity around US waters during the COVID-19 pandemic, this paper upends the assumptions of ship (im)mobilities through the example of wait times, calling for scholars to give space and time to everyday immobilities and their disruptions too.

A Multi-Center, Open-Label, Single-Arm Study to Investigate the Early Effectiveness of Esketamine Nasal Spray in Patients with Treatment-Resistant Depression Using a Mobile Self-Monitoring Application.

This study assesses the early effectiveness of esketamine nasal spray (ESK) in adults with treatment-resistant depression (TRD) 1 day after the first administration, as monitored through self-assessment via the mobile application, Esketamine Continuing Assessment for Relapse Prevention (EsCARe). In this multi-center, open-label, single-arm study, adults aged 18-65 years diagnosed with TRD after failing at least two antidepressant therapies were enrolled from five tertiary hospitals in South Korea. During the induction period, participants self-administered ESK twice weekly and used the EsCARe app daily to record mood, sleep, and somatic symptoms. Key clinical assessments, the Patient Health Questionnaire-9 (PHQ-9), the Hamilton Depression Rating Scale (HAMD), and the Generalized Anxiety Disorder Scale (GAD-7), were measured at baseline and at weeks 2 and 4. The reliability and validity of EsCARe was assessed. The treatment results indicated significant improvements in depressive and anxiety symptoms, with notable reductions in the PHQ-9 and the GAD-7 by week 2, and the HAMD by week 4. The EsCARe app reliably and validly monitored depressive symptoms and demonstrated a significant reduction in depressive symptoms 1 day after the first administration of ESK. Using ESK, complemented by mobile self-monitoring, effectively reduces the symptoms of TRD early in the treatment course. Integrating mobile health technology into the therapeutic regimen highlights a significant advancement in managing TRD, offering patients and clinicians immediate feedback on treatment efficacy.

Aspects of mobile phone use and behavioural intentions for learning in rural South African schools

This paper focuses on a study conducted in two historically disadvantaged schools (deep-rural and semi-urban) in South Africa where the rising access to mobile technology implies that the long-standing promise of mobile learning is becoming accessible despite the lack of recent research. This research aims to investigate the relationship between the type of school and various aspects of mobile phone use as well as differences in behavioural intention constructs to use mobile phones for learning. After a literature review, several hypotheses were developed. Using non-probability sampling techniques and the survey method, data was obtained from 128 participants, 79 from the deep-rural school and 49 from the semi-urban school. The semi-urban school exhibited slightly higher percentages in all aspects of cell phone use. Outside of mobile phone ownership and Internet access, all associations were significant. Significant differences were reported in favour of the semi-urban school for all constructs regarding the behavioural intention to use mobile phones for learning. However, the future of m-learning appears to be more promising than reported in earlier research by reporting positive behaviour from students in both schools. Critical stakeholders in education should take note and move from the goals of improving broad access to explicit objectives to implement m-learning in rural schools.

GaitKeeper: An AI-Enabled Mobile Technology to Standardize and Measure Gait Speed.

Gait speed is increasingly recognized as an important health indicator. However, gait analysis in clinical settings often encounters inconsistencies due to methodological variability and resource constraints. To address these challenges, GaitKeeper uses artificial intelligence (AI) and augmented reality (AR) to standardize gait speed assessments. In laboratory conditions, GaitKeeper demonstrates close alignment with the Vicon system and, in clinical environments, it strongly correlates with the Gaitrite system. The integration of a cloud-based processing platform and robust data security positions GaitKeeper as an accurate, cost-effective, and user-friendly tool for gait assessment in diverse clinical settings.

Digital-Signature Oriented Steganography Approach against Man-in-the-Middle Attack

Nowadays, man-in-the-middle (MITM) attacks have become a large problem due to the advancement of computational power and interactive mobile technologies. Message security is a crucial concern that ought to be managed in order to help protect vital data from unauthorized people, such as MITM. Steganography is the technique of hiding secret data within an ordinary, non-secret file or message in order to avoid detection when communicating through an unsecured network. Steganography applications play a vital role in various fields that involve classified data transfer, such as healthcare, multimedia, and the military. Hence, the application of steganography in those fields attracts MITM attacks. Thus, in an attempt to address the challenge. This study proposes a hybrid approach that integrates an image steganography technique, the Advanced Encryption Standard (AES) for message encryption, and EdDSA (Edward-Curve Digital Signature Algorithm) for signature verification to enhance steganography against MITM attacks. The proposed hybrid approach was tested and measured using image metrics (MSE, PSNR, and SSIM) and histogram visualization and verified through experimentation. The results have proven that the proposed hybrid approach is an enhanced security approach with low execution time, more payload size for hiding messages, and a high invisibility embedded message to MITM compared with other existing approaches. This study has potential limitations. It does not explore tamper resistance or algorithm robustness, and it was not tested on a public image dataset.

Mobile Web Application for Durian Orchard Management and Geospatial Data Visualization Using Deep Learning

Durian, a globally popular fruit, is primarily exported by Thailand, making it the foremost contributor to the world market. Nevertheless, there remains a notable absence of a comprehensive platform or application catering to both consumer tourists and businesses seeking domestic purchases. Prior research has highlighted several shortcomings, notably the inability of existing applications to provide location-based search functionality or automatically identify durian plantation plots from digital photographs. Consequently, this study proposes the development of a mobile web application aimed at managing, processing, and visualizing geospatial data pertaining to orchards and durian plantations. Through the integration of mobile technology, geospatial technology, and machine learning, the research endeavours to address these deficiencies. The findings indicate promising results, particularly in the accurate classification of durian plantations using four machine learning algorithms: convolutional neural network (CNN), support vector machine (SVM), random forest, and k-nearest neighbor (KNN). Among these algorithms, CNN exhibited the highest accuracy, achieving a value of 95%, with precision, recall, and f-measure values of 95.55%, 94.44%, and 94.97%, respectively.

Evaluating the efficacy of machine learning in calibrating low-cost sensors

Ambient air quality monitoring requires low-cost environmental sensor devices that are affordable and feasible for large-scale implementation. However, issues such as sensor drift, environmental sensitivity, and inter-sensor variability affect data accuracy and cannot be adequately addressed by traditional calibration methods. This paper summarizes the use of machine learning techniques for calibrating low-cost sensors. The literature review shows that machine learning models like Random Forest, Support Vector Regression, and Neural Networks significantly improve sensor accuracy and reliability. For instance, Random Forest models reduced the root mean squared error by 30% for PM2.5 measurements, while Neural Networks achieved an R value of 0.997 for methane sensors. Integrating machine learning with IoT and mobile technologies enhances real-time monitoring and spatial resolution. Identified gaps include the quality of training datasets, managing environmental variability, and improving model transferability across different contexts. Addressing these gaps through advanced models and real-time calibration methodologies will further enhance sensor performance, ensuring more precise and reliable environmental data.

The Growth of Mobile Learning Applications in Language Education: A Bibliometric Overview

The rapid advancement of mobile technology has transformed education, with mobile learning applications (m-learning) becoming integral to language education. This study provides a bibliometric analysis of research on mobile learning applications in language education, examining trends, types of research, and key themes from 2010 to 2023. Data were collected from major academic databases, including Scopus, Web of Science, SINTA, and Google Scholar, using keywords related to mobile learning and language education. The findings reveal a significant increase in publications over the past five years, reflecting the growing academic interest in m-learning. Qualitative research methods have been predominantly used, focusing on user experiences and the contextual nature of mobile learning. Critical thinking skills have emerged as a key area of focus, indicating a shift towards using mobile learning for deeper cognitive processes. The study also highlights the central role of students as research subjects, while noting a gap in research on other stakeholders, such as teachers and administrators. Data collection has primarily relied on tests to measure cognitive outcomes, but the study suggests incorporating a variety of methods to capture a more holistic view of the learning experience. The conclusion emphasizes the need for ongoing research to optimize the use of mobile learning applications in language education, advocating for a broader inclusion of stakeholders and diverse research methodologies. This bibliometric overview contributes to the understanding of mobile learning's impact on language education and offers insights for future research directions.

EHerbarium: A Global Platform for Exploring and Sharing Herbarium and Live Plant Images to Enhance Biodiversity Preservation

Historically, herbaria have been instrumental in preserving and studying plant biodiversity. The widespread use of smartphones has led to a surge in the digital documentation of plants. Over the last decade, herbarium digitizing projects are also underway. Many of the digitized herbarium images are available online through different portals. The examples of these portals include Kew Gardens and New York Botanic Garden Herbarium. Combining the latest mobile, cloud and AI technologies as well as plant taxonomy and herbarium artifacts, the mission of eHerbarium*1 is to provide an accessible digital platform for plant enthusiasts, including botanists, researchers, students, as well as the general public around the world, to collect, identify and share live plant and herbarium images. It promotes data collection and data sharing around the globe for biodiversity preservation. It further provides educational benefits about plants and the plant biodiversity around us and the world. eHerbarium offers access to an extensive collection of herbarium images from Kew’s Herbarium at the Royal Botanic Gardens, Kew. The platform is also extensible, allowing it to host images from other herbaria. In addition, this platform provides high-quality botanical visuals, enabling users to capture and share live plant photos or herbarium images, or upload from existing collections. Customizable licensing options facilitate community use and enhance collaborative botanical research. The platform’s AI-powered tools streamline plant identification, offering autocomplete suggestions for taxonomy details such as scientific name, family name or common names. Our proprietary technology ensures seamless and accurate botanical data entry by automatically retrieving corresponding scientific or common names. Additionally, eHerbarium enriches botanical records by automatically associating image locations with Biodiversity Information Standards (TDWG) Level 2 regions. TDWG Level 2 regions are specific geographical areas defined by TDWG to standardize the recording and reporting of biodiversity data. These regions, which typically correspond to large countries or significant subnational areas, help ensure consistent and accurate geographical referencing in botanical studies and herbarium records, while also respecting image owners' location privacy. eHerbarium has more than 40,000 worldwide downloads since its debut less than six months ago on March 18, 2024. We encourage further collaborations to expand our repositories for herbarium and live plant images. We also welcome partnerships to tailor the application to specific needs for natural history and biodiversity data collection.

Development of Geometry Transformation Learning Media by Utilizing Mobile Technology

This study is dedicated to the creation of geometry transformation learning media through the utilization of Mobile-Based App Inventor and a subsequent comprehensive evaluation of its influence on student academic performance. The research adopts a meticulously structured Research and Development (R&D) methodology, encompassing critical stages such as analysis, design, development, implementation, and evaluation. The culmination of this research journey unveils compelling evidence that underscores the transformative potential of the developed learning media. Through quantitative and qualitative assessments, this study unearths a significant enhancement in students' grasp of the intricate concepts inherent to geometry transformation. The outcomes of this research illuminate the pivotal role of technology, particularly the Mobile-Based App Inventor, in shaping contemporary pedagogical landscapes. The learning media serves as a testament to the symbiotic relationship between innovation and education, offering an engaging and effective resource to empower students in their quest for mathematical proficiency and analytical prowess.

Effectiveness of Mobile Applications in Arabic Language Learning for Elementary School Students

This research explores the role of mobile technology in Arabic language education at the elementary school level. With the increasing use of technology in education, this research highlights how mobile applications can be used as effective learning tools. The purpose of this research is to measure the effectiveness of using mobile applications in Arabic language learning for elementary school students. This research employs a mixed-methods approach that combines quantitative and qualitative methods. Quantitative data was obtained through Arabic language proficiency tests conducted before and after the use of the application, while qualitative data was collected through interviews with students and teachers to gain in-depth insights into their experiences. The results of the study indicate that the use of the mobile application significantly improved students' Arabic language skills, particularly in the areas of vocabulary, reading, and grammar. In addition, this research also reveals that the majority of students and teachers have a positive perception of the use of mobile applications in learning, although there are challenges such as limited access to technology and the need for adequate technical support and training. The recommendations provided include the enhancement of technological infrastructure, support and technical training, collaboration between application developers and educators, as well as parental involvement. This research provides valuable insights into the potential and challenges of using mobile technology in Arabic language education, as well as laying the groundwork for better development and implementation in the future.

Secure workflow scheduling algorithm utilizing hybrid optimization in mobile edge computing environments

The rapid advancement of mobile communication technology and devices has greatly improved our way of life. It also presents a new possibility that data sources can be used to accomplish computing tasks at nearby locations. Mobile Edge Computing (MEC) is a computing model that provides computer resources specifically designed to handle mobile tasks. Nevertheless, there are certain obstacles that must be carefully tackled, specifically regarding the security and quality of services in the workflow scheduling over MEC. This research proposes a new method called Feedback Artificial Remora Optimization (FARO)-based workflow scheduling method to address the issues of scheduling processes with improved security in MEC. In this context, the fitness functions that are taken into account include multi-objective, such as CPU utilization, memory utilization, encryption cost, and execution time. These functions are used to enhance the scheduling of workflow tasks based on security considerations. The FARO algorithm is a combination of the Feedback Artificial Tree (FAT) and the Remora Optimization Algorithm (ROA). The experimental findings have demonstrated that the developed approach surpassed current methods by a large margin in terms of CPU use, memory consumption, encryption cost, and execution time, with values of 0.012, 0.010, 0.017, and 0.036, respectively.

Mobile Health (mHealth) Applications for Stroke Rehabilitation: A mini review.

Mobile Health (mHealth) applications are transforming stroke rehabilitation, through personalized care and datadriven insights. These applications employ AI-driven algorithms, tele-rehabilitation, wearable technologies, and gamification to enhance recovery process. The objective of this mini review is to explore the transformative role of Mobile Health (mHealth) applications in stroke rehabilitation, highlighting its capacity to transcend geographical barriers and establish extensive support networks connecting stroke survivors, caregivers, and healthcare professionals. Particularly in developing countries like Pakistan, where healthcare resources may be limited, mHealth offers a viable solution to bridge the gap in stroke care. By facilitating access to rehabilitation services, mHealth can significantly improve outcomes for stroke survivors in these regions. This integration of mobile technology with stroke rehabilitation not only promises personalized and more effective rehabilitation but also presents a unique blend of technology and compassion. This evolution in healthcare holds the potential to redefine stroke recovery, marking a significant milestone in the journey towards more inclusive, efficient, and compassionate care solutions.