Computers in human behavior reports最新文献

Individuals with social anxiety have been shown to prefer communicating through online platforms. Part of this preference may be accounted for by their self-perceived level of social skill ability in online modalities. However, it is unclear whether perceived social skill abilities change across in-person and online contexts. Therefore, this study investigated whether specific social skills, like sociability, assertiveness, self-disclosure, and non-verbal emotion recognition vary across online and face-to-face settings. We first validated the Real and Electronic Communications Skills (RECS) questionnaire using a confirmatory factor analysis in a sample of 780 participants, who completed the survey through Qualtrics. We then conducted a series of correlations and a doubly multivariate GLM to evaluate whether perceived social skills abilities are influenced by communication medium and social anxiety. Our results indicated that we were able to validate the RECS, and that specific social skills like sociability and assertiveness are influenced by communication medium. Specifically, higher social anxiety scores were associated with decreased in-person sociability, emotion recognition, and assertiveness. The results highlight a potential perceived barrier to approaching in-person interactions, which may exacerbate avoidance of social interaction, thus resulting in social isolation and loneliness.

Much research on collaborative learning focuses on working together on tasks that are designed with collaboration in mind. However, teachers may ask children to collaborate on single player tasks as well, given a scarcity of available computers in the classroom or assuming that children can learn from each other and motivate each other. In the current study, we studied the conversations of primary school children (N = 64; 39 girls, 25 boys) working together in dyads in an adaptive single-player math learning environment (Math Garden). We aimed to investigate (1) how dyads collaborate across two math tasks aimed at practicing different math skills (arithmetic vs. reasoning skills) embedded within Math Garden and (2) whether collaborative activities differed across the two math tasks. We studied both individual utterances as well as episodes of social metacognitive interactions between the dyad members. We found that children engaged in activities important for successful collaboration, such as relational and metacognitive activities. Moreover, we identified social metacognitive episodes, meaning that children regulated their groups’ learning and that the regulatory processes which took part within the collaborative learning session cannot be reduced to individual activities. We conclude that children engage in collaborative activities when working together on single player tasks. And, in line with previous research, these collaborative activities differed across games, highlighting that collaborative learning is shaped by the task at hand.

This study explores a new way to model the adoption of AI, specifically online recommender systems. It aims to find factors that can explain the variation in usage in terms of differences between individuals and differences over technologies. We analyzed survey data from users of online platforms in the U.S. using a two-level structural equation model (SEM) (N = 1007). In this model, the dependent variable was the usage rate, which was defined as the share of time a person used a particular recommender system (e.g., “People You May Know”) when they use the platform (e.g., Facebook). The individual responses (within-systems level) were clustered in the 26 recommender systems (between-systems level). We hypothesized that three technology-specific factors, adapted from the Diffusion of Innovations (DOI) theory and the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2), could explain the variations in usage at both levels: perceived performance expectancy (PE), perceived effort expectancy (EE), and perceived hedonic motivation (HM). Our estimated model showed that usage was associated with PE and HM at the within-system level and only with PE at the between-system level. A considerable part of the variation in usage across the 26 systems could be explained by PE only (R² = 0.30). The most important contribution to practitioners is that this study provides evidence for the idea that there are inherent, measurable differences across recommender technologies that affect their usage rates, and specifically it finds usefulness to be a key factor. This is potentially valuable for app developers and marketeers who look to promote the adoption of novel recommender systems. The main contribution to the literature is that it presents a proof-of-concept of a two-level model for AI adoption, conceptualizing it as an effect of both variations over users and variations over applications. This finding is potentially valuable for policymakers, as better predictive models might enable improved assessments of AI's social implications. In future studies, the two-level approach presented here could be applied to other forms of AI, such as voice assistants, chatbots, or Internet of Things (IoT).

Simulation-based learning is being increasingly implemented across different domains of higher education to facilitate essential skills and competences (e.g. diagnostic skills, problem-solving, etc.). However, the lack of research that assesses and compares simulations used in different contexts (e.g., from design perspective) makes it challenging to effectively transfer good practices or establish guidelines for effective simulations across different domains. This study suggests some initial steps to address this issue by investigating the relations between learners' experience in simulation-based learning environments and learners' diagnostic accuracy across several different domains and types of simulations, with the goal of facilitating cross-domain research and generalizability. The findings demonstrate that used learners' experience ratings are correlated with objective performance measures, and can be used for meaningful comparisons across different domains. Measures of perceived extraneous cognitive load were found to be specific to the simulation and situation, while perceived involvement and authenticity were not. Further, the negative correlation between perceived extraneous cognitive load and perceived authenticity was more pronounced in interaction-based simulations. These results provide supporting evidence for theoretical models that highlight the connection between learners' experience in simulated learning environments and their performance. Overall, this research contributes to the understanding of the relationship between learners’ experience in simulation-based learning environments and their diagnostic accuracy, paving the way for the dissemination of best practices across different domains within higher education.

People often use audio-only communication to connect with others. Spatialization of audio has been previously found to improve immersion, presence, and social presence during conversations. We propose that spatial audio improves social connectedness between dyads. Participants engaged in three 8-min semi-structured conversations with an acquainted partner in three conditions: in-person communication, monaural audio communication, and spatial audio communication. Using Media Naturalness Theory as our theoretical framework, we found that the use of spatial audio benefited aspects of social connectedness. While in-person communication yielded the greatest social connectedness, spatial audio better facilitated social connectedness than traditional monaural communication. Spatial audio improved feelings of being physically in the same room and being on the same wavelength and produced more nonverbal behaviors associated with rapport building than monaural communication.

The Metaverse represents an innovative post-reality universe that seamlessly blends physical reality with virtual digital environments, facilitated by advanced technologies like virtual reality. As the adoption of Metaverse gains momentum, it becomes essential to comprehend individuals’ perceptions, attitudes, and the potential influence of demographic factors on their engagement with this novel digital realm. To address this gap, a comprehensive questionnaire survey was conducted in the United Arab Emirates (UAE) to investigate participants’ understanding, attention, interest, and confidence in the Metaverse. The study also sought to examine how variables such as age, gender, education level, and professional background might impact respondents’ perceptions. This cross-sectional survey involved a diverse sample of 252 individuals residing in the UAE, encompassing different age groups, educational backgrounds, and occupations. The survey instrument was carefully designed to generate responses related to four primary dimensions: understanding, attention, interest, and confidence in the Metaverse. Demographic information, including age, gender, education level, and professional background, was also collected to assess potential associations with respondents’ perceptions. The survey findings revealed disparities based on gender and age in terms of understanding, attention, and interest in the Metaverse. Particularly, male respondents demonstrated higher levels of comprehension and attention in specific aspects, while undergraduate students or higher expressed significantly greater interest in Metaverse technology compared to individuals with lower educational backgrounds.

Effective persuasive messages are indispensable when it comes to using technology to motivate behavior change. However, no scoping or systematic review has explored the landscape of using conceptual frameworks to design and evaluate persuasive messages for effective persuasive communication in different domains. The primary aim of this review is to map published articles on conceptual frameworks that aid effective persuasive message design and evaluation in technology-driven interventions with a view to uncovering their potential and unfilled research gaps. The secondary aim is to uncover how persuasive message design and evaluation frameworks have been applied to address behavior change using the United Nations' Sustainable Development Goals as an analytical lens. Our results show that about three-quarter of the articles on persuasive message design and evaluation frameworks were published in the last decade. While the reviewed user studies were not longitudinal, the presented frameworks show strong promise in fostering behavior change among the target audiences if implemented correctly. However, there is a need to (1) conduct more research among underrepresented groups (e.g., in lower-to-middle-income countries in Africa, South America, and Asia) and in unexplored domains related to other United Nations’ Sustainable Development Goals other than Good Health and Wellbeing; and (2) move away from ad hoc design principles, use of only behavior theories, and non-use of validated frameworks towards using established behavioral theory-driven frameworks to design and evaluate persuasive messages to make them more effective.

Digital voice assistants (DVAs) are application programs that can understand and interpret natural language voice commands. They are embedded in various products like smartphones and smart speakers and have thus become integral in everyday life, interpersonal communications, and social relationships. Despite users tend to humanize DVAs, emotions that arise in interpersonal interactions such as self-conscious emotions (i.e., pride, shame, guilt, and vicarious embarrassment) and their influence on human-machine interaction remain unstudied. Grounding on regulatory focus theory, we argue that these emotions are fundamental for promoting or preventing future DVA use. Additionally, drawing on social influence theory, we contend that the influence of self-conscious emotions on continuous DVA use varies across specific usage situations. Thus, we extend the expectation-confirmation model with self-conscious emotions and empirically compare user reactions between different social scenarios (alone and with friends in private vs. public places). Analyzing 860 DVA user responses through structural equation modeling and multigroup analysis, our findings reveal that pride consistently positively influences continuance intention across all social contexts. Furthermore, shame acts as an important inhibitor of continuance intentions in public, while guilt inhibits continuance intentions in private places. Vicarious embarrassment, however, does not exhibit significant effects in any scenario. These results carry valuable implications for both research and management in understanding and optimizing DVA user experiences.

Hand hygiene compliance is well known as an important measure in preventing the transmission of infectious disease. Even though general public was aware of this health message during the COVID-19 pandemic, sustaining such personal hygiene behavior is difficult. While personal hygiene virtual reality intervention via an immersive experience has been found as an effective health promotion strategy, its prior implementation was mostly among healthcare workers but not among people in the community. Using a mixed methods approach, this study draws on the theory of planned behaviors to design two experimental studies for testing this interventional approach's sustainability and applicability to the general population. The result confirmed the virtual reality scenario with both presence and embodiment demonstrated a more sustainable treatment effect in boosting individual's self-efficacy over other experimental arms. Post-hoc analysis revealed the reduction of perceived barrier after exposure to immersive environment was attributable to treatment effect. The findings were reconfirmed through triangulation in the qualitative post-intervention interviews. Finally, we highlight the theoretical and practical implication of the study for the future development of personal hygiene immersive environment-based intervention.