Human Behavior and Emerging Technologies最新文献

Open banking (OB) refers to financial institutions opening their data and services to external parties via application programming interfaces (APIs), a practice that has been increasingly adopted globally since its 2018 regulatory inception in the United Kingdom. Despite its growth, there is still a lack of academic studies examining its impact on consumer financial behaviors on a global scale. This study addresses this gap by exploring OB’s influence on consumers’ formal saving and digital remittance behaviors worldwide. Using a mixed methods design, we combine bibliometric analysis and geospatial econometric modeling on Scopus OB bibliographic data and consumer financial preferences data from 2021 to 2022 across 139 countries. While the bibliometric results highlight the need for more international collaborations in OB research that reflect the ongoing collaborations in its implementation around the world, the econometric findings reveal significantly positive benefits for consumers globally, increasing the likelihood of formal saving and digital remittance. Specifically, consumers in countries with Revised Payment Services Directive (PSD2)–regulated initiatives, market-driven initiatives, and other non-PSD2 initiatives show higher marginal utilities (MUs) from digital remittance (39.1%–56.7%) compared to those in countries without OB initiatives. Additionally, consumers in PSD2 and market-driven countries exhibit higher MUs from formal saving by 61.8% and 37%, respectively, compared to those without OB initiatives. Overall, in addition to the implications for global open innovation, the paper provides reasonable evidence, supporting OB implementation to achieve several Sustainable Development Goals (SDGs) and the associated benefits to consumers’ worldwide.

This study is aimed at evaluating users’ perceptions of open government data (OGD) in Qatar, addressing disparities between the perceived performance and perceived importance of OGD, and identifying the antecedents and consequences of OGD gaps. Data collected from 1426 respondents through a questionnaire were analyzed using multiple quantitative approaches, including importance-performance analysis (IPA)–based strengths, weaknesses, opportunities, and threats (SWOT) analysis; gap analysis; factor analysis; multiple regression; and path analysis. Among 13 OGD attributes, six were identified as opportunities, five required improvement, and two were considered lower priorities. The OGD gap revealed an 82% surplus. Path analysis demonstrated that the OGD gap, combined with two other latent variables, significantly influenced users’ trust in OGD. This study prioritizes areas for improving OGD attributes, aiming to enhance users’ trust in OGD.

Information attacks have increased worldwide as more information is available in digital form. Image encryption is essential to prevent attackers from unauthorized access to confidential images. In this paper, we introduce a novel image encryption method called the smart codebook, which combines an intelligent codebook technique with the RSA (Rivest–Shamir–Adleman) algorithm. The proposed method is designed to be dynamic as it selects the most effective codebooks in encrypting each image to increase its ambiguity. The smart codebook method divides an image into several segments based on image dimensions, and multiple random codebooks are generated for each segment. Moreover, images go through several processing stages before they are converted into cipher images, including noise, segmentation, and encryption. The best codebooks are selected to create the encrypted image in the next stage. Finally, the RSA algorithm sends the recipient the code books with the cipher image. The evaluation of the proposed smart codebook depends on several factors, including entropy, unified averaged changed intensity (UACI), number of changing pixel rate (NPCR), and correlation coefficient analysis. The assessment of the proposed encryption method demonstrates its resilience against cryptographic attacks, affirming its security and precision. The results show an impressive improvement in securing images compared to previous related efforts.

Industry 4.0, characterized by the integration of advanced technologies across various industrial domains, is now evolving into Industry 5.0, which emphasizes the human perspective, resilience, and sustainability. In this context, the study of human behavior and attitudes towards human–robot interaction (HRI) is crucial for understanding the acceptance of this emerging technology, which, in turn, can drive the development of more well-designed industrial robotic systems. This paper is aimed at translating, adapting, and validating a scale designed to measure acceptance in the context of HRI within industrial settings, with a focus on collaborative robots (cobots). To conduct an exploratory factor analysis (EFA), 140 participants (male = 45%, female = 52%, and nonbinary = 3%) were recruited. The results revealed a four-factor structure for the Frankenstein Syndrome Questionnaire–Industrial Context (FSQ-IC): “general anxiety towards cobots” (α = 0.87), “trustworthiness towards developers of cobots” (α = 0.83), “apprehension towards cobots in the industrial context” (α = 0.73), and “expectation of cobots in social change” (α = 0.69). For further validation and to help ensure the validity and reliability of the adapted scale, a confirmatory factor analysis (CFA) was conducted with a sample of 210 participants (male = 45%, female = 53%, and nonbinary = 2%). The model fit indices, including a χ²/df of 3.14 and root mean square error of approximation (RMSEA) of 0.10, indicated an acceptable fit. The goodness-of-fit index (GFI), comparative fit index (CFI), and normed fit index (NFI) were 0.88, 0.90, and 0.86, respectively, all within acceptable ranges. Convergent and discriminant validities were also analyzed. An analysis of the differences in perceptions of acceptance based on sociodemographic variables (gender, experience with robots, educational level, and age) was conducted. Only gender revealed significant differences. Considering the psychometric qualities of the instrument, the FSQ-IC is valid and reliable for assessing acceptance in HRI.

Rape myth acceptance (RMA) is a crucial predictor of rape proclivity. It has been extensively analyzed for its gender differences to aid in designing clinical interventions and health programs. Although it is well known that males generally exhibit higher levels of RMA than females, the impact of digital devices, the Internet, and dating apps on RMA and how this impact differs between genders remain understudied. This study addresses these gaps by examining a sample of 647 Chinese-speaking college students in Canada. The findings indicate that the use of dating apps is positively associated with higher RMA; male students exhibited greater RMA levels than female students; and gender moderates the impact of dating app usage, with a more elevated effect on RMA observed in male students compared to female students. The study’s limitations are discussed, including the specificity of the sample (Chinese college students in Canada) and caution against generalizing to broader populations, along with the research and policy implications of the study.

Deep learning has led to the creation of facial recognition technologies using convolutional neural networks (CNNs). This preliminary study explores the application of CNN architectures in face recognition to gain a deeper understanding of the challenges and methodologies in the field. The study systematically reviewed relevant literature using the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) framework. Out of 3622 eligible papers, 266 were included in the review, with 47% proposing new techniques and 1% focusing on method implementation and comparison. Most studies used images rather than video as training or testing data, with 78% using clean data and only 7% utilizing occluded and clean data. It was observed that traditional CNN architectures were predominantly employed. The study identified a lack of research on the implementation and definition of CNN architectures, the development of facial recognition models using both clean and occluded images and videos, and the exploration of nontraditional CNN architectures. The challenges affecting facial recognition included occlusion, distance from the camera, camera angle, and lighting conditions. This preliminary assessment provides an insight into the use of CNN in face recognition and suggests that nontraditional CNN architectures could be further explored in future research.

The advancements in urban commuting have enabled ease of travel for commuters. However, in the underdeveloped world, commuting has become a challenge for the mental health of commuters. A commuter who travels through public transport or their vehicle can develop depression and anxiety due to traffic congestion and unwanted delays. Symptoms of depression and anxiety can be mitigated through psychotherapeutic music. However, this music requires quiet rooms where a patient could listen to them. This can be overcome by playing music available on online streaming services via the commuters’ smart devices. The data from the sensors embedded in a commuter’s smart device is gathered and is termed the current context. The context includes both the data from the sensors and deduced data that is acquired through sensor services. The current context is then processed to determine the context of the commuter. The context is a label that is the outcome of a machine learning algorithm as part of context processing. The authors have utilized Bayesian probability to classify the current context of the commuter. Based on the classification outcome, which is termed context, a suitable playlist is generated and played on the commuters’ smart devices. A feedback loop enables improvement in classification as well as playlist generation. This proposed mechanism would improve the mental health of commuters including students, workers, and passengers, traveling to work and back frequently.

Innovation in the Information and Communication Technology (ICT) sector is critical for organizational growth and societal sustainability. However, the innovation capabilities of developing countries, particularly in the ICT sector, remain underexplored. This study is aimed at assessing the perceptions of senior leadership within Pakistan’s ICT sector to identify the factors contributing to the limited scope of innovation. Through qualitative interviews with senior leaders from various ICT organizations, this research explores their views on the definition, types, and driving factors of innovation. Most interview participants reported that the major type of innovation practiced at the ICT sector of Pakistan is “product,” the level is “partial replication,” and the major driving source is “factors external to the organization.” Based on these insights, we developed an innovation readiness framework (IRF) to help organizations enhance their innovation capacity. IRF is expected to provide actionable strategies for organizations to assess their current innovation practices, identify gaps, and adopt a structured approach to enhance both product development and operational processes. By addressing sector-specific challenges such as limited resources and regulatory barriers, the IRF is aimed at empowering organizations to move from replication to more original, impactful innovations. Hence, implementing this framework is expected to improve the sector’s competitiveness, drive sustainable growth, and contribute meaningfully to societal advancement by aligning with broader development goals.

This study explores intellectual capital’s impact on social media analytics adoption in the banking industry. It also examines the impact of social media analytics on competitive intelligence and banking entrepreneurship. Furthermore, this study explores the mediating role of competitive intelligence in the impact of social media analytics on banking entrepreneurship. Data were collected from 320 bank managers and specialists and analyzed using Smart PLS, Version 4. The findings indicated that human, structural, and relational capital significantly impact the adoption of social media analytics. This study also revealed that social media analytics significantly impacts competitive intelligence and banking entrepreneurship. Furthermore, the results showed a significant mediating role of competitive intelligence in the impact of social media analytics on banking entrepreneurship. This study provides invaluable contributions for both academic discourse and industry professionals. It thoroughly investigates the interrelationships among intellectual capital, social media analytics, competitive intelligence, and banking entrepreneurship. This study advances our understanding of how these capabilities operate within the ever-changing banking realm. Moreover, this study provides novel practical insights for bank managers and policymakers, emphasizing the importance of intellectual capital as well as the contributions of social media analytics and competitive intelligence in driving banking entrepreneurship.

Electronic health records (EHRs) represent an innovative approach to constructing a distributed data analysis framework for managing health data within its original location. The integration of blockchain technology into EHR systems offers substantial improvements in security, privacy, and transparency, thus enhancing overall management efficiency. Our research spanned from January 2017 to December 2022, conducting a meticulous systematic literature review across reputable databases such as Scopus, IEEE Xplore, Springer, PubMed Central, and ScienceDirect. This comprehensive search, finalized in December 2022, utilized stringent inclusion and exclusion criteria to ensure the selection of high-quality articles, thereby guaranteeing transparency and unbiased results. Through this systematic review, our primary objective was to explore, assess, and analyze the diverse architectures, proposed models, limitations, and future trajectories of blockchain-enabled EHR systems. Our emphasis was on highlighting blockchain’s adaptability and robustness in healthcare contexts while identifying potential challenges and areas for further investigation. Adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines, we scrutinized more than 600 scientific studies, culminating in the selection of 31 articles that met our rigorous inclusion standards. Our technical and architectural evaluations delved into critical aspects such as privacy, security, authentication, availability, data control, storage, and resource consumption. Our research outcomes underscore the effective resolution of security, privacy, and availability concerns in EHRs through blockchain integration. However, we observed potential trade-offs, including impacts on performance, resource utilization, and regulatory compliance. To address these complexities, we introduce an integrated framework designed to mitigate key challenges and deliver substantial value within this domain.