Human Behavior and Emerging Technologies最新文献

The multifaceted nature of body experience, encompassing affective, cognitive, and perceptual components, plays a critical role in the development and persistence of conditions such as anorexia nervosa (AN). Recent research attempted to address such disturbance to improve therapeutical effectiveness, using mirror exposure (ME) and body-swapping (BS) techniques in immersive virtual reality (VR). Procedures from a third-person perspective—as mirror-like experiences—were primarily used to address bodily cognitive and affective components, whereas first-person ones—as BS—were used to correct distorted body perception. This study investigated the effect of spatial reference frame on body self-consciousness and body perception during BS. Data revealed two main findings: Even though embodiment was significantly enhanced when participants engaged with a virtual body from a first-person viewpoint, both egocentric and allocentric perspectives influenced body size estimation (BSE), suggesting that both spatial perspectives critically form and sustain body self-perception. We discussed predictive coding theory to explain spatial perspective influence on body experience, highlighting the potential therapeutic benefits of incorporating first and third-person perspectives on body illusions (BIs) for more effective interventions targeting body-related disturbances.

Background: Crisis supporters at Lifeline Australia consistently engage with distressed and often suicidal help-seekers. The development of technological methods, such as machine learning (ML), in suicide prevention may complement their support work. Investigating attitudes towards the use of ML in crisis support is an important first step.

Aims: The current study is aimed at investigating crisis supporters’ attitudes towards ML in crisis support/suicide prevention, beliefs about the effect of technology on the service and help-seeking, and concerns/opinions about any future technology implementation.

Methods: Two hundred fifty-five crisis supporters aged 20–84 years were recruited through Lifeline Australia. Participants voluntarily completed an anonymous questionnaire, including measures of attitudes towards technology as well as open-text options, which provided the data for a thematic analysis.

Results: Crisis supporters were neutral to negative on an adapted measure of ML use in crisis support. Less than one-third held the belief that technology would enhance Lifeline services, and over half of the participants felt help-seekers would be less likely to contact Lifeline if technology was implemented. Thematic analysis of the open-text questions revealed loss of human connection and mistrust of algorithms to be the most prominent barriers to future technological adoption by Lifeline crisis supporters.

Limitations: Clearly defining terms of ML and technology was difficult to do in this hypothetical context, potentially impacting the attitudes expressed.

Conclusions: Any new technology to support crisis supporters needs to be carefully codesigned with the workforce to ensure effective implementation and avoid any potential or perceived negative impacts on help-seekers.

Both large and small information flows can have a significant impact on how consumers obtain trustworthy financial information, ultimately leading to an improvement in their daily lives when they interact dynamically with local geographic conditions. In economies that face both geographical and socioeconomic challenges, such as those in Africa, this kind of context is crucial. Large information flows provide significant issues such as big data challenges in the insurance sector, which calls for robust, demand-driven, and adaptive innovation solutions. In this paper, we present a geographic information system (GIS)–based location-aware recommender algorithm, called Geo-Insurance. Using some selected insurance companies in Accra, Ghana, as a point of view for location and customer data, our proposed Geo-Insurance solution addresses the big data challenges of customers finding the closest insurance companies with specific services through a web-based map created using a geodatabase file, ArcCatalog, and ArcGIS (among others). We conducted a series of benchmarking experiments. Our evaluation results show that Geo-Insurance performs better than other contemporary methods in terms of F-measure (F1), recall (R), precision (P), mean absolute error (MAE), and normalized MAE (NMAE).

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.