Human Behavior and Emerging Technologies最新文献

The domestication of smart artifacts has transformed our homes into hybrid environments of physical and digital worlds. It also has been changing our mindsets, behaviors, meaning attributions to, expectations from, frustrations about, and interactions with smart artifacts. By extension, the smartness definition is reconstructed by users who are the subject of smart artifact experiences. The current study is aimed at uncovering the user experience of smart artifacts with a focus on cognitive and emotional aspects to better understand what users expect when an artifact is identified as “smart.” Therefore, an online research study is conducted to gain insight into the user experience of smart artifacts from content-rich reviews on e-commerce websites. Robot vacuum cleaners, smartwatches, and smart speakers were chosen as exemplary smart artifacts of the study. Because they offer different types of interaction with distinct aspects, our findings indicate that smartness is associated with trust in expertise, emotional engagement, exaggerated evaluation, and intriguing existence concepts about Emotional UX. In Cognitive UX, smartness relates to reducing mental workload, gratifying experience, perceived phenotype, reciprocal acquaintance, trust-building experience, tailored situatedness, shaping sociality, physical competency, and dual enhancement concepts. These findings demonstrate the potential of conceptualization in the early stages of smart artifact design processes.

There is an actual trend for humanizing technological artifacts, especially social robots. However, human-like social robots trigger negative attitudes by threatening human uniqueness as well as humanness. The present paper presents the development of the Belief in Human Nature Uniqueness Scale (BHNUS) to assess the individual tendency to deny social robots the possibility to have human features considered to be the hallmarks of humanness. The validation of the BHNUS was completed along seven studies, with a total of 1044 Portuguese participants. Results showed that BHNUS had good structural qualities (Studies 1 and 2), as well as good convergent and discriminant validities. BHNUS was correlated with negative attitudes towards robots, religiosity, and interest for science fiction (Study 3), attribution of traits of warmth to robots (Study 4), positive and negative emotional appraisal (Study 5), perspective taking (Study 6), and attitudes towards the development of robots with human features (Study 7). The importance of the BHNUS regarding the development of social robots and human–robot interaction is discussed.

If you were to insist I was a robot, you might not consider me capable of love in some mystic human sense, but you would not be able to distinguish my reactions from that which you would call love so what difference would it make?

Isaac Asimov (1982) in Foundation’s Edge, p. 420

The study examines how social and environmental innovations affect cost leadership strategies, value creation, and overall firm/business performance. It has compared how SMEs handle the ever-changing landscape of sustainability-driven business practices with large manufacturing companies. With a cross-sectional design, the study examined 179 manufacturing businesses to test the research framework. A structured questionnaire based on a five-point Likert scale with organization as a unit of analysis was used to collect data. The research’s measurement and structural models were derived from data analysis conducted using Smart PLS. Overall, the research revealed that environmental and social innovations have significant impacts on business performance through cost leadership strategies and value creation in both SMEs and large manufacturing companies. However, a direct relationship was not established between social innovation and value creation in the case of SMEs, but it was significant for large companies. Environmental innovation was found to have direct relationships with cost leadership and value creation in both SMEs and large manufacturing companies. This research shows managers and policymakers how companies of any size can balance cost leadership with environmental and social concerns to maximize resources and efficiency. It encourages sustainable practices that benefit society and the environment while boosting performance and guiding strategic decision-making. The novelty of this study is the comparative analysis of SMEs and large companies that provide empirical evidence of the intricate connection between environmental and social innovations, cost leadership, value creation, and business performance.

The “social being” perspective has largely influenced the design and research of AI virtual agents. Do humans really treat these agents as social beings? To test this, we conducted a 2 between (Cyberball condition: exclusion vs. fair play) × 2 within (coplayer type: AGENT vs. HUMAN) online experiment employing the Cyberball paradigm; we investigated how participants (N = 244) responded when they observed an AI virtual agent being ostracised or treated fairly by another human in Cyberball, and we compared our results with those from human–human Cyberball research. We found that participants mindlessly applied the social norm of inclusion, compensating the ostracised agent by tossing the ball to them more frequently, just as people would to an ostracised human. This finding suggests that individuals tend to mindlessly treat AI virtual agents as social beings, supporting the media equation theory; however, age (no other user characteristics) influenced this tendency, with younger participants less likely to mindlessly apply the inclusion norm. We also found that participants showed increased sympathy towards the ostracised agent, but they did not devalue the human player for their ostracising behaviour; this indicates that participants did not mindfully perceive AI virtual agents as comparable to humans. Furthermore, we uncovered two other exploratory findings: the association between frequency of agent usage and sympathy, and the carryover effect of positive usage experience. Our study advances the theoretical understanding of the human side of human–agent interaction. Practically, it provides implications for the design of AI virtual agents, including the consideration of social norms, caution in human-like design, and age-specific targeting.

Smart cities are hubs of innovation and rapid development, where technology plays a significant role in shaping the urban environment. In such settings, the ability to adapt and think flexibly is crucial for individuals to thrive. The study’s goal is to understand which factors influence how well individuals are equipped with 21st-century skills and how they apply them in the dynamic context of modern urban living. Employing a quantitative research approach, data were initially collected from 600 Thai respondents through convenience sampling. A subsequent data-cleansing process refined the focus to 568 respondents, selected based on their high-score attainment. The study utilized percentages, means, and binary logistic regression in a comprehensive data analysis. Its key findings illuminate the influence of various factors, including score, age, career trajectory, and engagement with social media platforms like Instagram, LINE, and X, on skill development within the smart city context. This research offers a nuanced perspective on the myriad elements that foster individual and collective success in technologically advanced urban environments. It moves beyond mere acknowledgment of innate abilities or age-related wisdom, highlighting the critical roles of career advancement and digital engagement in skill enhancement. The implications of these findings are far reaching, especially for policymakers, educators, and industry leaders. They underscore the need for a comprehensive approach to shaping the future of urban living and working spaces, ensuring that individuals are not just equipped with the necessary skills but are also adept at applying them effectively in the smart cities of tomorrow. This study serves as a guide for these stakeholders, emphasizing the importance of fostering environments that support continuous learning, innovation, and adaptability in the face of technological advancements.

Background: Exclusive breastfeeding (EBF) of infants during the first 6 months of their life is strongly recommended. Video-based communication is an innovative method that could promote EBF. The present study is aimed at assessing the effectiveness of video-based intervention in promoting breastfeeding practices from birth to 6 months postpartum. This was evaluated through maternal reports, as well as EBF of infants at 3 and 5 months of age, using the deuterium oxide dose-to-mother (DTM) technique.

Methods: In a cluster randomized community trial, 16 communities were assigned to receive a video-based behavior change communication (Video-Health) or standard health care messages (Control). Pregnant women in their first trimester (12 ± 2 weeks) were enrolled and followed up, together with their infants, until 6 months postpartum. Data on breastfeeding practices were collected by questionnaire, monthly, in all the participants (n = 508). Human milk intake (HMI) and nonmilk oral intake (NMOI) were measured in a subsample of 60 mother–infant pairs at 3 and 5 months postpartum using DTM. Mixed models and logistic regression were used to examine the difference in continuous and discrete breastmilk practices between the intervention and the control arms, respectively.

Results: The majority of mothers initiated breastfeeding early (92.1%), 82.4% reported colostrum feeding, and 17.5% provided prelacteal feeding. No significant differences were found between Video-Health and Control arms (p > 0.05). The intervention significantly improved reported EBF rates at 4 and 5 months postpartum (p < 0.05). DTM results showed that the proportion of women practicing EBF was 50%–67% less than reported at 3 months for both arms and at 5 months in the intervention arm. The intervention did not significantly affect measured EBF at 3 and 5 months postpartum but did improve HMI at 3 months. Non significant differences in NMOI were observed at 3 months, but at 5 months, there was a significant difference between the study arms.

Conclusions: Video-based behavior change communication did not result in significant improvements in reported breastfeeding practices, except for a higher reported adherence to EBF beyond 3 months. Additionally, the intervention had no effect on EBF as measured by the DTM. Early introduction of non-breastmilk foods and liquids persisted, despite self-reported EBF extending up to 6 months postpartum.

Trial Registration: ClinicalTrials.gov identifier: NCT04414527

There remains a dearth of answers to certain inquiries regarding oral English presentations, despite the growing interest in this field in recent years. The current study examined Thai university students’ perspectives on their English presentation abilities, the difficulties they encounter when delivering English presentations in an online environment, and their preferred mode of presentation. They enrolled in a 12-week online course on English presentation and communication skills through the Zoom application. This study employed a qualitative methodology and utilized reflective writing from students in response to four open-ended questions that prompted respondents to consider their experiences. A thematic approach was applied to the reflective essays to identify recurring themes and patterns within the data. The findings of the analysis indicated that, overall, the students expressed the need for enhancement in their English presentation abilities. Furthermore, this research emphasized several challenges that students confronted when delivering online presentations, encompassing nonverbal communication, anxiety, the audience’s engagement, and addressing questions. Despite the apparent convenience of online presentations, they conveyed a preference for face-to-face presentations. Interaction, teacher and peer support, and a desire to become a professional presenter were the three factors that affected their preference. This study establishes a fundamental basis for subsequent investigations and proposes strategies that higher education instructors can implement to enhance their students’ English presentation skills.

In various domains, virtual reality (VR) has emerged as a valuable tool for simulating specific scenarios and facilitating training within controlled and secure conditions. As peripheral vision plays a crucial role in numerous contexts, it must be duly considered in VR simulations. However, while peripheral vision was examined in VR, comparisons between VR and the real world (RW) are rarely made. Therefore, this study is aimed at comparing RW and VR reaction times (RTs) to peripheral visual stimuli and the field of view (FOV). This comparison is achieved using the peripheral perception-R (PP-R) of the Vienna Test System and a programmed virtual replica. The virtual replica underwent additional testing, revealing good reliability for RTs but only moderate to poor for measurements of the participant’s FOV. The comparison between the two environments indicates slower RTs to peripheral visual stimuli in VR than in RW. This observed discrepancy is consistent with the results of previous studies investigating RTs to foveal stimuli and can be partially explained by the latencies in the hardware and software configurations used. Nevertheless, the observed correlations between the RTs in both conditions suggest comparable visual processing within the peripheral visual field and affirm the decent replication of the real PP-R in VR. Overall, the study’s results support the usage of VR as a tool for practicing and examining specific scenarios, including peripheral vision. The discrepancies revealed in the RTs between VR and RW emphasize the need to continue examining hardware and software components in VR research.

Social networks on the dark web are rich in data that provides valuable insight into the nature of the activities on the dark web and human behaviors related to these activities. It also encompasses a diversity of ideologies, interests, and thought patterns associated with illicit activities and businesses on the dark web. For this reason, social networks on the dark web constitute a powerful tool and a profuse data source for various investigative work. However, such investigations encounter considerable challenges related to the massive volumes of textual data, analyzing it effectively, and extracting knowledge from it. This knowledge can be used in various investigations and studies when representing it in ontologies as a unified and integrative data source. In this paper, we introduce a novel approach for extracting and representing knowledge hidden in dark web communities through topic modeling and ontology learning methods. We start from the conceptual design of the ontology and employ several stages of text processing and analysis to achieve the desired knowledge graph, DarkOnto. These stages include data cleaning and preprocessing, topic modeling using correlated topic model (CTM), class-topic similarity estimation, ontology construction, ontology population, and ontology evaluation, where the proposed approach achieved high results. Furthermore, we discuss the results, limitations, challenges, and future work. This paper presents a promising approach for extracting hidden valuable knowledge from dark web communities where investigating and conceptualizing criminal communities can be conducted efficiently.

Food recognition, a field under food computing, has significantly promoted people’s dietary decision-making and culinary customs. We present the design and evaluation of a sociocultural app for African food recognition using deep learning models such as transfer learning. Deep learning models have multiple processing layers that make them robust in image recognition. Based on this capability of deep learning models, we explored them in this study. A total of 3142 food image datasets were collected from three African countries: Nigeria, Ghana, and Cameroon. Using the datasets, we developed and trained a deep learning model for recognizing African foods. The model attained a test accuracy of 94.5%. The model was further deployed in a food recognition app. To evaluate the predictive ability of the app, we recruited 16 participants who were interviewed and subsequently used the app in the wild for 7 days. In a comparative evaluation between the app and human recognition capabilities, we found that the app recognized 71% of the instances of food images generated by the participants and tested with the app, while the human evaluators (participants) could only recognize 56% of the food datasets. Participants were mostly able to recognize some foods from their own country. Furthermore, participants suggested some design features for the app. In view of this, we offer design recommendations for researchers and designers of sociocultural food recognition systems.