Human Behavior and Emerging Technologies最新文献

The use of collaborative robots in industries is growing rapidly. To ensure the successful implementation of these devices, it is essential to consider the user experience (UX) during their design process. This study is aimed at testing the UX goals that emerge when users interact with a collaborative robot during the programming and collaborating phases. A framework on UX goals will be tested, in the geographical context of Portugal. For that, an experimental setup was introduced in the form of a laboratory case study in which the human–robot collaboration (HRC) was evaluated by the combination of both quantitative (applying the User Experience Questionnaire [UEQ]) and qualitative (semistructured interviews) metrics. The sample was constituted by 19 university students. The quantitative approach showed positive overall ratings for the programming phase UX, with attractiveness having the highest average value (M = 2.21; SD = 0.59) and dependability the lowest (M = 1.64; SD = 0.65). For the collaboration phase, all UX ratings were positive, with attractiveness having the highest average value (M = 2.46; SD = 0.78) and efficiency the lowest (M = 1.93; SD = 0.77). Only perspicuity showed significant differences between the two phases (t(18) = −4.335, p = 0.002). The qualitative approach, at the light of the framework used, showed that efficiency, inspiration, and usability are the most mentioned UX goals emerging from the content analysis. These findings enhance manufacturing workers’ well-being by improving cobot design in organizations.

This study is aimed at formulating a contemporary regional cyber marketing model with a focus on endorsing local products and events in Semarang City, Indonesia. Utilizing the Star Model by Galbraith, the research involved 44 stakeholders in extensive interviews. Employing social network analysis (SNA) through the Net-Map tool, the study introduced an innovative cyber marketing approach, labeled as Semarang Cyber Marketing (SCyMark), designed to meet the unique requirements of the region. SCyMark demonstrated adaptability and sustainability, promoting seamless integration between local offerings and digital marketing. The findings showcased significant shifts in stakeholders’ perceptions, with 23 out of 44 experiencing noteworthy changes in their perceived influence within the SCyMark framework. SNA offered intricate insights into stakeholder relationships, enhancing the study’s credibility. This research contributes valuable perspectives to regional development, highlighting SCyMark’s potential in promoting local products and events. By fostering community engagement and supporting economic growth, cyber marketing emerges as a promising tool for advancing marketing initiatives in diverse regions.

Introduction: Digital personas (DPs) are animated human-like characters that incorporate the capabilities of conversational agents (CAs) and communicate with users via audio and voice.

Aim: This study is aimed at understanding how users perceive the DPs designed for mental well-being and perceived social support.

Methods: A total of 1485 available ratings and user reviews of the DPs from the Elysai app were analyzed using a deductive and inductive thematic analysis. Ordinal regressions were conducted to identify domains predicting users’ ratings.

Results: The average rating of the app was 4.23 out of 5. The thematic analysis identified 27.59% of comments on social support from the DP. Emotional and companionship support were the most frequently reported themes. The inductive thematic analysis identified positive themes, such as the helpfulness of DPs, anthropomorphic traits, negative experiences with DPs, and technical issues. Regression analysis showed that themes of emotional and companionship, social support, positive feedback, anthropomorphic traits, helpfulness, and recommendations significantly predicted positive ratings (odds ratio 1.51–2.86). Conversely, the themes of negative experience with DP and technical issues predicted lower ratings (odds ratio 0.05 and 0.32).

Discussion: Overall, DPs were perceived positively by most reviews analyzed. Users reported that DPs provided emotional and companionship support, were helpful, and had anthropomorphic traits, but also had negative experiences with DPs and technical issues. The types of themes reported in the reviews predicted the user ratings.

Following the COVID-19 pandemic, digital technology rapidly integrates into daily life, offering advantages in sports, including personal performance tracking, athlete-fan communication via social media, and virtual event organization. However, its comprehensive use in marathons to enhance marathoner experience remains underexplored. This study presented a technology-driven marathon service process, guiding digital technology applications across prerace communication, marathon registration, race entertainment, medical service, and postrace engagement. A mixed-method approach, utilizing the analytic hierarchy process (AHP), informed technology selection for each service dimension under expert judgment. Three groups of experts, including World Athletics marathon organizers, elite runners, and marathon medical team members, were invited for triangulated discussion, validated, and confirmed the technology selection. The results revealed the decision-making for incorporating digital technology into each marathon service process. In the realm of prerace communication, interactive content and content management were appropriated for addressing awareness, motivation, trust, and engagement. In terms of marathon registration, experts agreed that application programming interfaces (APIs) can streamline procedures for runner convenience. For race entertainment, extended reality (XR) excels can stimulate runner motivation through realistic competition simulations. Regarding medical service, digital health records emerged as a vital tool for medical operations during the marathons. Lastly, elite runners had shown a compassionate inclination towards affiliation e-rewards, enabling them to convert their achievements into tangible benefits. A service blueprint was constructed to visualize the integration of digital technologies into marathon events covering marathoner actions, front-of-house employee actions, back-of-house employee actions, and internal information technology systems. These findings offer valuable insights to marathon organizers, enhancing marathoner experiences, and service quality through digital technology that becomes more prevalent.

Mental health depends on emotional intelligence (EI), the ability to recognize, understand, and manage emotions. As emotions—positive and negative—influence our actions, decisions, and health, EI has become more important in today’s fast-paced society. Negative emotions, in particular, pose a challenge due to their complexity and the difficulty in managing them effectively. The “Motus Up” mobile app represents a significant leap in personal mental health management by merging EEG-based emotion recognition with a rule-based chatbot to aid users in managing negative emotions. Utilizing principles of EI, the app customizes the mobile interface color settings, suggesting activities and providing advice suited to the user’s emotional state confirmed by the chatbot. The emotion recognition model is trained and tested using the SEED-IV dataset, ensuring compatibility with consumer EEG headbands and accurate detection of complex emotions like sadness and fear. Thorough static and dynamic testing has validated the app’s design and operational efficacy, confirming its preparedness for practical use. By combining real-time EEG data analysis with intelligent chatbot interactions, “Motus Up” stands out as an innovative mental health tool that enhances EI and offers a responsive experience for emotional self-regulation. This integration of technology and psychology in a mobile platform is poised to improve mental well-being and EI on a personal scale.

This paper investigated the factors in the technology–organization–environment (TOE) framework that affect the decision of whether to adopt electronic commerce (EC) or not within small- and medium-sized enterprises (SMEs). To this end, a questionnaire-based survey was conducted to collect data from 60 managers or owners of manufacturing SMEs in Tunisia. Unlike the traditional regression approaches, we referred to novel machine learning (ML) techniques and reveal that ML techniques reach a higher level of performance in forecasting driving factors to EC adoption compared to the traditional logistic regression approach. The achieved results also indicate that EC adoption within SMEs is significantly affected by eight factors, namely, IT vendors’ support, the adopted technology complexity degree, chief executive officer (CEO) innovativeness, technology readiness, customers’ pressure, firm size, infrastructure compatibility, and the innovative technology-perceived relative advantage.

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).