Human Behavior and Emerging Technologies最新文献

The rise of the metaverse has ignited a surge of interest among researchers and decision-makers, seeking to develop effective virtual commerce (v-commerce) applications that cater to business demands and customer preferences. v-commerce, an emerging concept, redefines the future of shopping experiences and customer-product interactions. While businesses are actively exploring the potential of immersive technologies to deliver captivating and engaging shopping experiences, there remains a lack of consensus on what constitutes an ideal v-commerce experience and how to identify optimal v-commerce stores effectively. Considering this, benchmarking v-commerce applications for the metaverse is crucial for its development. This endeavor falls within the realm of multiple-criteria decision-making, given various critical issues such as the multitude of design attributes, uncertainty regarding their relative importance, and data variability. This study proposes an innovative approach that extends the fuzzy-weighted zero-inconsistency (FWZIC) method with spherical linear Diophantine fuzzy sets (FSs) (SLDFSs) to determine the weights of v-commerce attributes. The obtained weights are integrated with the ranking alternatives by trace median index (RATMI) method to select the optimal v-commerce application for the metaverse. Criterion weighting results reveal that “ease of navigation” and “recommendation agents” are the most significant criteria in assessing v-commerce solutions. Based on these results, 24 v-commerce solutions were evaluated. Additionally, sensitivity analysis and comparative evaluation were used to assess the robustness and validity of the proposed framework. This research provides essential insights for decision-makers and practitioners to facilitate business growth, consumer satisfaction, and further research in this domain.

The rapid expansion of smartphone-based diet applications has been driven by the growing prevalence of mobile technology among consumers and their diverse lifestyle preferences. This study seeks to analyze the factors influencing the use of these applications. The conceptual framework combines three models: the unified theory of acceptance and use of technology (UTAUT2), the DeLone and McLean information system (IS) (D&M IS) success model, and the protection motivation model. Conducted in Saudi Arabia, the research employed a quantitative methodology with a survey questionnaire administered to 572 customers. The study, validated through partial least squares (PLS) structural equation modeling (PLS-SEM), identified several significant predictors for the intention to use smartphone diet applications. These include performance expectancy (PE), effort expectancy (EE), social influence (SI), price value (PV), IS, and service quality (SEQ), as well as perceived vulnerability and perceived severity. Contrary to expectations, factors like facilitating conditions (FCs) and hedonic motivation (HM) did not significantly impact behavioral intention. This pioneering study sheds light on the adoption of smartphone diet applications in an emerging economy, offering valuable insights for scholars, developers, and health professionals.

Instagram Stories advertising has the advantages of precise targeting, diverse advertising formats, and a wide range of users; it has, thus, become an important medium for mobile game marketing. To find the most effective ad designs for mobile games on Instagram Stories, this study applied the stimulus-organism-response (S-O-R) theory to investigate how game involvement and ad designs affect users’ visual attention. Visual attention is an important physiological indicator of advertising effectiveness, and eye-tracking technology can accurately assess consumers’ visual attention. This study applied eye-tracking technology to 80 participants, grouped as follows: high-involvement gamers, low-involvement gamers, and nongamers. This study used the eye movement indicators for first fixation duration (FFD) and total fixation duration (TFD) to, respectively, evaluate the attentional salience and hold of mobile game ads. The ads were presented in three formats (5 s video, 15 s video, and 5 s image) with two types of content (gameplay and game characters). Results showed that the mobile game ads on Stories exerted the highest attentional hold on high-involvement gamers. In terms of ad format, video ads offered a higher attentional hold and attentional salience. In terms of ad content, ads that introduced game characters resulted in better attentional salience; however, ads that introduced gameplay exerted better attentional hold for nongamers. This study examined both individual differences in media users and ad design to provide recommendations for the personalization of mobile game ads for social media. For example, ads designed for a high-involvement gamer should incorporate more diverse and complex information. In addition, we found that when the marketing goal is the promotion of game characters, image ads are the most appropriate format.

The need for knowledgeable programmers has increased, highlighting the importance of strong programming foundations in engineering education. Limited access to high-quality learning materials and educational opportunities presents challenges unrelated to information and communication technology (ICT) field (non-ICT-related) students in acquiring programming skills. Educational programming languages (EPLs) such as App Lab have gained popularity as they offer an accessible platform for students to learn programming fundamentals in a visual and interactive manner. This paper examines the impact of the EPL called App Lab on the development of fundamental programming skills among non-ICT-related field engineering college students. We conducted a quasiexperimental research study using a single-blinded, nonequivalent control group pretest–posttest design. The study included 56 participants, all of whom were enrolled in the Environmental Engineering and Biotechnology program at the State University of Milagro (UNEMI), Ecuador. The experimental group consisted of 26 students, while the control group comprised 30 students. The assessment process involved the administration of a battery of 200 questions before and after the intervention. The intervention involved the use of App Lab as an EPL and lasted for a duration of 3 weeks exclusively for the experimental group, while the control group followed their usual tutoring program. The study results showed that students who received EPL-mediated learning with App Lab had significant increase in their programming skills. App Lab demonstrated a positive impact, particularly among male students who reported Internet usage, as well as in advanced programming topics including loops, lists, and functions, when compared to their female counterparts.

This study explores the factors influencing consumer satisfaction among young Muslim online shopping users. Exogenous variables include celebrity endorsement, social media marketing, and promotion programs. Data were taken from 306 young Muslims from several cities in Indonesia. The analysis technique adopted structural equation modeling (SEM) with a reflective construct model. The findings reveal that celebrity endorsement through social media as the technology collection exerts the most significant influence on both direct and indirect relationships. Conversely, social media marketing demonstrates minimal impact, with insignificant effects observed in direct and indirect relationships. However, promotional programs emerge as a significant determinant of customer satisfaction, directly and as a mediating variable in purchase decisions. Additionally, importance-performance map analysis (IPMA) confirms the prioritization of celebrity endorsement to enhance consumer satisfaction. This study is the first to examine the relationship between celebrity endorsements and young Muslims when shopping online and whether it can influence their decision to buy and their satisfaction. It provides unique insights into how promotional programs and social media marketing impact this demographic, utilizing SEM for robust analysis. The findings highlight the significant role of celebrity endorsements and promotional programs in driving customer satisfaction, offering valuable implications for marketers targeting young Muslim consumers.

Researchers have developed a variety of approaches to evaluate explainable artificial intelligence (XAI) systems using human–computer interaction (HCI) user-centered techniques. This systematic literature review has been conducted to understand how these approaches are used to achieve XAI goals. The aim of this review is to explore the methods used to evaluate XAI systems in studies involving human subjects. A total of 101 full-text studies were systematically selected and analyzed from a sample of 3414 studies obtained from four renowned databases between 2018 and 2023. The analysis focuses on prominent XAI goals achieved across 10 domains and the machine learning (ML) models utilized to create these XAI systems. The analysis also explores explanation methods and detailed study methodologies used by researchers in previous work. The analysis is concluded by categorizing the challenges experienced by researchers into three types. Exploring the methodologies employed by researchers, the review discusses the benefits and shortcomings of the data collection methods and participant recruitment. In conclusion, this review offers a framework that consists of six pillars that researchers can follow for evaluating user-centered studies in the field of XAI.

The self-driving car has been developing rapidly over the past decade, with increased attention from both academia and industry worldwide. Specialized research labs are equipped with vehicles fully loaded with cutting-edge technologies. Such labs are very expensive and not accessible to students in developing economies. This work proposes using self-driving car models to enhance real-time/embedded system education. We have built two experimental low-cost self-driving robotic systems designed specifically for the classroom within an educational context. Lego Mindstorms and Arduino platforms were used as they both offer vast teaching opportunities based on interdisciplinary project-based learning. The programming languages used are compatible with professional robotic programming languages. The goals of using the proposed models as autonomous cars were, on the one hand, to encourage students to gain hands-on experiences in the field of mobile robotics and, on the other, to teach senior students programming, problem-solving, real-time systems, and embedded systems. The models successfully attracted students and motivated them to be engaged in classroom activities. Using the proposed models, real-world autodrive features exposed to automated vehicles were implemented and validated.

Innovative applications of human movement analysis, for example, for mitigating/slowing down certain pathological conditions, have recently emerged from the modeling and automated measurement of full-body expressive midlevel individual and group movement qualities, at a higher complexity level than movement qualities derived directly from physical signals, still not characterizing any gesture in a specific way. More in general, the availability of automated analysis techniques of midlevel expressive movement qualities can contribute to interaction design incorporating body-based performance practices inspired by artistic theories in dance and music. This work investigates how such practices and techniques can support embodied interaction design by enabling automated measuring of cues of leadership, cohesion, and fluidity in full-body movement in group settings. In particular, the dance-inspired scientific approach, the data collection protocol, and the analysis techniques adopted for assessing movement qualities connected to leadership and cohesion within the group and fluidity of the dancers’ full-body movement are described. Finally, future developments of this research are outlined.

The exponential growth of video-sharing platforms, exemplified by platforms like YouTube and Netflix, has made videos available to everyone with minimal restrictions. This proliferation, while offering a variety of content, at the same time introduces challenges, such as the increased vulnerability of children and adolescents to potentially harmful material, notably explicit content. Despite the efforts in developing content moderation tools, a research gap still exists in creating comprehensive solutions capable of reliably estimating users’ ages and accurately classifying numerous forms of inappropriate video content. This study is aimed at bridging this gap by introducing VideoTransformer, which combines the power of two existing models: AgeNet and MobileNetV2. To evaluate the effectiveness of the proposed approach, this study utilized a manually annotated video dataset collected from YouTube, covering multiple categories, including safe, real violence, drugs, nudity, simulated violence, kissing, pornography, and terrorism. In contrast to existing models, the proposed VideoTransformer model demonstrates significant performance improvements, as evidenced by two distinct accuracy evaluations. It achieves an impressive accuracy rate of (96.89%) in a 5-fold cross-validation setup, outperforming NasNet (92.6%), EfficientNet-B7 (87.87%), GoogLeNet (85.1%), and VGG-19 (92.83%). Furthermore, in a single run, it maintains a consistent accuracy rate of 90%. Additionally, the proposed model attains an F1-score of 90.34%, indicating a well-balanced trade-off between precision and recall. These findings highlight the potential of the proposed approach in advancing content moderation and enhancing user safety on video-sharing platforms. We envision deploying the proposed methodology in real-time video streaming to effectively mitigate the spread of inappropriate content, thereby raising online safety standards.

As more of our lives are spent using electronic devices, it comes as a natural deduction that those digital tools could be used to maintain people’s health. Gamified exercise or exergames are indeed promising means to motivate the population to get physically active and even cognitively active if paired with the appropriate games. Considering the global concern of an aging population which could benefit from both physical and cognitive stimulation, these tools appear to be an encouraging solution to keep the population healthier over time. This scoping review reports on the digital tools used in publications between January 2015 and December 2023 regarding the physical and cognitive stimulation of healthy elderly people. The search was conducted in PubMed, Web of Science, and ScienceDirect databases. Of the 1579 publications retrieved, a total of 68 publications were analyzed in this review. A wide variety of digital tools were used in the corpus for the combined physical and cognitive stimulation of the elderly. These tools can be categorized into six types of hardware: pressure plates, optical motion capture, inertial motion capture, virtual reality, ergometers, and driving simulators. The apparition of publications using virtual reality and an increase in publications using inertial motion capture in 2020 could be an indicator that digital tools used for cognitive and physical stimulation of the elderly are evolving. Another finding is the wide variety in evaluation tools used to monitor the outcomes of each protocol. A standardization of the testing process might be needed in order to improve comparisons between experiments.