Search Results (40)

Location-based services (LBSs) and positioning systems have spread worldwide due to the emergence of Internet of Things (IoT) and other application domains that require real-time estimation of the position of a person, tag, or asset in general in order to provide users with services and apps with added value. Whereas Global Navigation Satellite Systems (GNSSs) are well-established solutions outdoors, positioning is still an open challenge indoors, where different sensory technologies may be considered for that purpose, such as radio frequency, infrared, or ultrasounds, among others. With regard to ultrasonic systems, previous works have already developed indoor positioning systems capable of achieving accuracies in the range of centimeters but limited to a few square meters of coverage and severely affected by the Doppler effect coming from moving targets, which significantly degrades the overall positioning performance. Furthermore, the actual bandwidth available in commercial transducers often constrains the ultrasonic transmission, thus reducing the position accuracy as well. In this context, this work proposes a novel excitation and processing method for an ultrasonic positioning system, which significantly improves the transmission capabilities between an emitter and a receiver. The proposal employs a superheterodyne approach, enabling simultaneous transmission and reception of signals across multiple channels. It also adapts the bandwidths and central frequencies of the transmitted signals to the specific bandwidth characteristics of available transducers, thus optimizing the system performance. Binary spread spectrum sequences are utilized within a multicarrier modulation framework to ensure robust signal transmission. The ultrasonic signals received are then processed using filter banks and matched filtering techniques to determine the Time Differences of Arrival (TDoA) for every transmission, which are subsequently used to estimate the target position. The proposal has been modeled and successfully validated using a digital twin. Furthermore, experimental tests on the prototype have also been conducted to evaluate the system’s performance in real scenarios, comparing it against classical approaches in terms of ranging distance, signal-to-noise ratio (SNR), or multipath effects. Experimental validation demonstrates that the proposed narrowband scheme reliably operates at distances up to 40 m, compared to the 34 m limit of conventional wideband approaches. Ranging errors remain below 3 cm at 40 m, whereas the wideband scheme exhibits errors exceeding 8 cm. Furthermore, simulation results show that the narrowband scheme maintains stable operation at SNR as low as $-32$ dB, whereas the wideband one only achieves up to $-17$ dB, highlighting the significant performance advantages of the proposed approach in both experimental and simulated scenarios. Full article

This study is to explore information synthesis on research topics and emerging trends in privacy within the context of technology adoption. A search for the terms privacy and technology adoption in the Web of Science database yielded information on 2910 publications from 2005 to 2025. The analysis was conducted using CiteSpace, incorporating cluster analysis, timeline analysis, and burst detection to identify key patterns and developments. Fifteen sub-areas of privacy related to technology adoption were identified, including health information exchange, blockchain adoption, artificial intelligence, Internet banking, smart home devices, location-based services, mobile commerce, ubiquitous commerce adoption, tracing apps, metaverse adoption, and facial recognition payment. Timeline analysis provided insights into the growth or decline of these research clusters over time. Based on the findings, a framework was developed to illustrate key insights and their interconnections, offering guidance for future research. The study concludes by discussing its implications, limitations, and recommendations for further research. Full article

This entry explains how China’s e-CNY, the retail form of its Central Bank Digital Currency, translates three design choices into improved access, affordability, and reliability: (1) enabling wallet-to-wallet payments on the CBDC ledger with settlement upon payment (SUP); (2) ensuring seamless integration at checkout with existing QR-code systems and popular payment apps; and (3) providing users with practical control through credentials stored on their devices and managed by licensed operators. With payment finality clarified in law and a two-tier structure in place, offline payments can shift to a hybrid architecture. It blends account- and token-based functionality across online and offline settings, incorporates tiered identity verification, and supports low-cost solutions. In essence, e-CNY demonstrates that strategic decisions regarding settlement, interoperability, and user control can expand financial inclusion while maintaining robust regulatory safeguards. Full article

Mobile banking applications play a crucial role in providing users with access to financial services, and the quality of user experience is a key factor for their sustainability. This study investigates the predictability of application quality signals derived from user ratings of five leading mobile banking apps in Türkiye. The main problem addressed is understanding how these user-driven quality indicators evolve over time and identifying effective methods for forecasting them. This research problem is critical for understanding how banks can monitor customer satisfaction and reputational risk in real time, as fluctuations in app ratings directly affect user trust and engagement. For this purpose, daily average rating series collected from the Google Play Store were analyzed using LSTM-based time series models, and the results were benchmarked against the seasonal naïve (SNaive) method. The findings show that LSTM consistently achieved lower error rates across all banks, with particularly reliable forecasts for YapıKredi and Akbank, where MAPE values ranged between 16% and 28%. However, low R² values for some banks suggest limitations in long-term forecasting. The contribution of this study lies in demonstrating that user experience signals in mobile banking can be systematically monitored from a time series perspective, and that LSTM-based approaches provide a more effective method for capturing these quality dynamics. Full article

Digital transformation has fundamentally altered how individuals manage their finances. The expansion of financial technologies and the digitalization of banking services underscore the need for digital financial literacy, defined as the ability to safely use financial applications and make informed decisions within virtual environments. This study examined the perceptions of financial application use across age groups and their corresponding level of digital financial literacy. This exploratory study used a convenience sample of 41 semi-structured interviews conducted in 2025. The data were analyzed using content analysis and descriptive statistics. The findings indicated that most participants prioritized digital apps over traditional channels and expressed confidence in their use, although concerns about data security remained. Participants identified key advantages, including convenience, efficiency, and centralized access, yet few used apps for financial planning. Most respondents demonstrated an intermediate level of digital knowledge, with limited proficiency in executing complex financial tasks. Perceptions revealed both optimism and apprehension: while participants valued the practicality of digital tools, they also recognized risks such as fraud, exclusion of vulnerable groups, and technological dependence. The limited and non-representative sample limits generalization, suggesting the need for broader surveys. Enhanced public policies promoting digital financial education in Brazil are recommended. Full article

In today’s fast-paced release cycles, mobile app user reviews offer a valuable source for tracking the evolution of user needs. At the core of these needs lies a structure of interdependencies—some enhancements are only relevant in specific usage contexts, while others may conflict when implemented together. Identifying these relationships is essential for anticipating feature interactions, resolving contradictions, and enabling context-aware, user-driven planning. The present work introduces an ontology-enhanced AI framework for predicting whether the requirements mentioned in reviews are interdependent. The core component is a Bidirectional Encoder Representations from Transformers (BERT) classifier retrained within a large-language-model-driven active learning loop that focuses on instances with uncertainty. The framework integrates contextual and structural reasoning; contextual analysis captures the semantic intent and functional role of each requirement, enriching the understanding of user expectations. Structural reasoning relies on a domain-specific ontology that serves as both a knowledge base and an inference layer, guiding the grouping of requirements. The model achieved strong performance on annotated banking app reviews, with a validation F1-score of 0.9565 and an area under the ROC curve (AUC) exceeding 0.97. The study results contribute to supporting developers in prioritizing features based on dependencies and delivering more coherent, conflict-free releases. Full article

The introduction of mobile banking is essential in today’s financial sector, where technological innovation plays a critical role. To remain competitive in the current market, businesses must analyze client attitudes and perspectives, as these influence long-term demand and overall profitability. While previous studies have explored general adoption behaviors, limited research has examined how individual factors such as social influence, lifestyle compatibility, financial technology self-efficacy, and perceived usage cost affect mobile banking adoption among specific generational cohorts. This study addresses that gap by offering insights into these variables, contributing to the growing literature on mobile banking adoption, and presenting actionable recommendations for financial institutions targeting younger market segments. Using a structured questionnaire survey, data were collected from both users and non-users of mobile banking among the Gen Z population in the United States. The regression model significantly predicts mobile banking adoption, with an intercept of $-0.548$ ($p < 0.001$). Among the independent variables, perceived cost of usage has the strongest positive effect on adoption ($B=0.857$, $\beta =0.722$, $p < 0.001$), suggesting that adoption increases when mobile banking is perceived as more affordable. Social influence also has a significant positive impact ($B=0.642$, $\beta =0.643$, $p < 0.001$), indicating that peer influence is a central driver of adoption decisions. However, self-efficacy shows a significant negative relationship ($B=-0.343$, $\beta =-0.339$, $p < 0.001$), and lifestyle compatibility was found to be statistically insignificant ($p=0.615$). These findings suggest that reducing perceived costs, through lower fees, data bundling, or clearer communication about affordability, can directly enhance adoption among Gen Z consumers. Furthermore, leveraging peer influence via referral rewards, Partnerships with influencers, and in-app social features can increase user adoption. Since digital self-efficacy presents a barrier for some, banks should prioritize simplifying user interfaces and offering guided assistance, such as tutorials or chat-based support. Future research may employ longitudinal designs or analyze real-life transaction data for a more objective understanding of behavior. Additional variables like trust, perceived risk, and regulatory policies, not included in this study, should be integrated into future models to offer a more comprehensive analysis. Full article

This study addresses a critical gap in understanding how mobile banking applications contribute to sustainable development by introducing a novel text mining framework to analyze sustainability dimensions through user-generated content. We analyzed 120,000 reviews from six major Turkish mobile banking applications using an ownership-sensitive analytical approach that integrates structural topic modeling with four sustainability dimensions (environmental, social, governance, and economic). Our analysis reveals significant institutional differences in sustainability approaches: government-owned banks demonstrate substantially stronger overall sustainability orientation (23.43% vs. 11.83% coverage) with pronounced emphasis on social sustainability (+181.7% growth) and economic development (+104.2% growth), while private banks prioritize innovation-focused sustainability. The temporal analysis (2022–2025) shows accelerating sustainability emphasis across all institutions, with distinct evolution patterns by ownership type. Institution-specific sustainability profiles emerge clearly, with each government bank demonstrating distinctive focus areas aligned with historical missions: cultural heritage preservation, agricultural sector support, and small business development. Mapping to Sustainable Development Goals reveals that government banks prioritize development-focused goals (SDGs 1, 8, and 10), while private banks emphasize innovation-focused goals (SDGs 9 and 17). This research makes three key contributions: demonstrating user-generated content as an effective lens for authentic sustainability assessment, establishing ownership-sensitive evaluation frameworks for digital banking sustainability, and providing empirical evidence for contextualized rather than universal sustainability strategies. The findings offer strategic implications for financial institutions, policymakers, and app developers seeking to enhance sustainable digital banking transformation. Full article

Background/Objectives: Breast milk donation is increasingly recognized as a vital public health resource, providing optimal nutrition for infants who cannot be breastfed by their biological mothers. Human milk banks play a crucial role in supporting infant health, particularly for preterm and medically vulnerable infants. Despite its recognized benefits, the practice of breast milk donation and utilization remains influenced by a complex interplay of individual, social, and cultural factors. Methods: This study systematically examines how demographic and sociocultural variables are associated with attitudes and behaviors related to breast milk donation. Data were collected from 1223 Israeli mothers via questionnaires distributed through WhatsApp groups between April and May 2025. By analyzing the questionnaire results, the research identified key predictors of willingness to donate and use donated milk, as well as patterns of awareness and perceived support within different population groups. Results: Higher education (χ² = 12.87, p = 0.0016) and settlement type (χ² = 83.096, p = 0.000) were significantly associated with greater willingness to donate breast milk. Income level had no effect on donation behavior, though higher-income participants were more open to its use (χ² = 86.838, p = 0.000). Lifestyle also influenced perceptions of social support (F(2, 1220) = 259.4036, p < 0.001) and cultural support (F(2, 1220) = 601.2383, p < 0.001) of milk donation and use. Significant correlation was found between perceived cultural and social support (t = 5.140, p = 0.000), emphasizing their interrelated influence on milk donation attitudes. Conclusions: The findings assist in guiding the development of public health policies, as well as targeted awareness campaigns, to promote equitable access to donor milk services across varied communities. From the public health perspective, the findings of this study can orientate campaigns that encourage both donation and use of breast milk focusing on segments of the population in which the level of awareness is relatively low. Full article

The surge in digital transformation within financial technology has catalyzed the development of super-apps—comprehensive mobile applications designed to serve a multitude of a customer’s daily needs on a single platform. Despite their widespread use, there is a dearth of research regarding customer adoption in the banking industry. Employing the integrated Information Systems (IS) success model, this study delves into how the characteristics of mobile banking super-apps influence user adoption intentions, taking into account various levels of service integration, in South Korea. The results reveal that factors such as interactivity, service diversity, process completeness, and technological service innovation positively affect the perceived ease of use. However, only service diversity and process completeness significantly influence perceived usefulness. Furthermore, distinct relationships between constructs are observed among different user groups based on their preferred service integration levels. This research can help banks formulate app management strategies and identify the optimal levels of service integration for their mobile banking super-apps. Full article

This paper expands the extended unified theory of acceptance and use of technology (UTAUT2) to include four additional constructs (security, risk, institutional trust, and technology trust), providing a more comprehensive understanding of mobile banking applications (m-banking apps) adoption. It also highlights the significant role of demographic factors in moderating the impact of these constructs, offering practical insights for promoting the use of mobile devices to access and manage banking services. Data were collected using an online survey from 315 mobile banking users and analysed using covariance-based structural equation modelling (CB-SEM). Most constructs of the baseline UTAUT2 were validated in the m-banking context, with the additional constructs confirmed to affect user intention to adopt m-banking apps, except perceived risk. The model explained 79% of the variance in behavioural intention (BI), and 54.7% in use behaviour (UB), achieving higher fit than the baseline UTAUT2. Age, gender, experience, income, and education moderated the impact of perceived security and institutional trust on BI; age, education, and experience moderated technology trust on BI; and age, gender, and experience moderated perceived security on UB. The guarantee of enhanced security, advanced privacy mechanisms, and trust should be considered paramount in future strategies aimed at promoting m-banking app adoption and use. Overall, the paper advances scientific knowledge by providing a more nuanced and comprehensive framework for understanding m-banking app adoption, validating new constructs, and offering practical recommendations for promoting m-banking usage. Full article

The open-source structure and ease of development in the Android platform are exploited by attackers to develop malicious programs, greatly increasing malicious Android apps aimed at committing financial fraud. This study proposes a machine learning (ML) model based on static analysis to detect malware. We validated the significance of private datasets collected from Bank A, comprising 183,938,730 and 11,986 samples of benign and malicious apps, respectively. Undersampling was performed to adjust the proportion of benign applications in the training data because the data on benign and malicious apps were unbalanced. Moreover, 92 datasets were compiled through daily training to evaluate the proposed approach, with benign app data updated over 70 days (D-70 to D-1) and malware app data cumulatively aggregated to address the imbalance. Five ML algorithms were used to evaluate the proposed approach, and the optimal hyperparameter values for each algorithm were obtained using a grid search method. We then evaluated the models using common evaluation metrics, such as accuracy, precision, recall, F1-Score, etc. The LightGBM model was selected for its superior performance, achieving high accuracy and effectiveness. The optimal decision threshold for determining whether an application was malicious was 0.5. Following re-evaluation, the LightGBM model obtained accuracy and F1-Score values of 99.99% and 97.04%, respectively, highlighting the potential of using the proposed model for real-world financial fraud detection. Full article

The rise in internet-based services has raised risks of data exposure. The manipulation and exploitation of sensitive data significantly impact individuals’ resilience—the ability to protect and prepare against cyber incidents. Emerging technologies seek to enhance cybersecurity resilience by developing various security tools. This study aims to explore the adoption of security tools using a qualitative research approach. Twenty-two semi-structured interviews were conducted with users of mobile banking apps from Pakistan. Data were analyzed using thematic analysis, which revealed that biometric authentication and SMS alerts are commonly used. Limited use of multifactor authentication has been observed, mainly due to a lack of awareness or implementation knowledge. Passwords are still regarded as a trusted and secure mechanism. The findings indicate that the adoption of security tools is based on perceptions of usefulness, perceived trust, and perceived ease of use, while knowledge and awareness play a moderating role. This study also proposes a framework by extending TAM to include multiple security tools and introducing knowledge and awareness as a moderator influencing users’ perceptions. The findings inform practical implications for financial institutions, application developers, and policymakers to ensure standardized policy to include security tools in online financial platforms, thereby enhancing overall cybersecurity resilience. Full article

In this article, we propose a novel solution to address the current challenges related to charging an Electric Vehicle (EV). The Power on the Go: Single-drop and Double-drop problems allow an EV to be charged at a convenient location, where a service vehicle (drone), which is launched from a nearby charging station, can bring a compatible power bank at the request of the driver through an app. The objective is to reduce the cumulative wait time between service requests and the start of charging. We present mathematical formulations and two order-first split-second-based heuristic approaches for both problems. While the mathematical formulations can generate optimal solutions for small instances in a reasonable amount of time, the heuristics are fast and perform very well, with gaps < 5% for up to 20 node instances. Moreover, the formulations highlight the mean savings in wait time (29.37%) when the power bank can charge two EVs consecutively before a replacement is needed. The implications of the proposed model can be extensive, as we seek to overcome the availability and technological challenges of EV charging while advocating its adoption. Full article

The concept of open banking has emerged only recently within the fintech sector, and it is rapidly becoming popular in many regions across the world. Currently, there are very few studies on the relationship between customer experience and intention to use fintech apps, none of which focus on open banking. This relationship is significant for a number of reasons, one of which is the emerging importance of the connection between fintech and an environmentally sustainable economy. This paper seeks to add to our understanding of the factors that shape the customer experience and that determine loyalty levels toward open banking brands and apps. We propose a model in which a number (six) of affective and cognitive factors influence customer experience, which ultimately determines loyalty intention. The model is tested using data collected via a quantitative (survey) methodology involving open banking users in Saudi Arabia. The results show that customer experience is affected by all of the proposed factors (ease of use, perceived value, quality of support, reliability, perceived risk and ability to innovate). These factors, in turn, actively influence the level of customer loyalty. The study contributes to the current literature by identifying the various cognitive and affective determinants of customer experience, which therefore influences loyalty intention in open banking, and provides valuable insights into how both new and established brands should integrate customer experience into promotional and development strategies. Full article