Human Behavior and Emerging Technologies最新文献

Game-based learning (GBL) in a virtual reality (VR) environment provides individuals with access to high-quality learning resources that may be expensive or unavailable in real-world scenarios. For example, individuals can easily participate in target shooting through this medium, which was a popular sport during Colonial America. This article explores the potential of using GBL to enhance shooting skills through a hypercasual VR game (HVRG). The study examines the impact of progressive versus dynamic game difficulty (PGD vs. DGD) strategies on experience and performance of learners. Various factors related to both experience metrics were recorded, including playfulness (engagement, enjoyment, and anxiety) and playability (adaptability and motivation). A game score was recorded to evaluate performance. The presented research (n = 50) implicates these quantitative findings to the qualitative feedback collected from the participants during the postgameplay interview session. An analysis of the research findings concluded that participants performed significantly better during rule-based AI-enabled DGD compared with PGD.

This study investigates the interplay of green human resource management (GHRM) and green transformational leadership (GTL) on environmental behaviors (EBs) and green innovation performance (GIP) among elementary school teachers in Urmia, Iran, with environmental knowledge (EK) as a mediator. Utilizing a descriptive-correlational design, data were collected from 338 teachers across 17 schools during the 2023–2024 academic year using validated questionnaires. Structural equation modeling (SEM) via SmartPLS 3 revealed a significant positive relationship between GHRM and GIP (β = 0.228, p < 0.01), and between GTL and both GIP (β = 0.338, p < 0.01) and EBs (β = 0.516, p < 0.01). However, GHRM showed no direct link with EBs (p = 0.091). EK mediated the relationship between GHRM and EBs (β = 0.247, p < 0.01) and GHRM and GIP (β = 0.162, p < 0.01), but not between GTL and GIP or EBs. These findings underscore the pivotal role of EK in enhancing green practices in educational settings. Recommendations include implementing in-service environmental training, fostering green organizational culture, and enacting policies to promote EBs and GIP. This study fills a research gap by focusing on educational contexts, offering insights for sustainable development in schools.

Algorithmic regulation systems are increasingly used in state-led efforts to incentivize civic behaviors. They remain highly controversial, partly because citizens’ opinions of and behavioral responses to them are still poorly understood. Local government-run Social Credit Systems (SCS) in China represent the most large-scale examples of algorithmic regulation to date. Their fragmented implementation across Chinese cities presents a unique opportunity to examine how different levels of citizen involvement, defined as awareness of and participation in such systems, shapes public opinion, civic behaviors, and social trust. We conducted a survey across Chinese cities with and without local SCS, including citizens with naturally varying levels of involvement (N = 5538). We assessed participants’ perceptions of legitimacy, procedural justice, and effectiveness, and contrasted these with the normative expectations of nonparticipants characterized by varying degrees of awareness. We further analyzed how civic behaviors and social trust vary across groups with different involvement levels. Our findings reveal substantial differences in public opinion, civic engagement, and social trust across these groups. We propose that these differences might be attributable to several pathways, relating to motivation crowding effects and expectation disconfirmation mechanisms. In addition, different responses to algorithmic regulation might reflect and reinforce broader underlying sociostructural inequalities. As such, this study makes three key contributions. First, it introduces a taxonomy of citizen involvement, and extends prior research that has assumed awareness and mandatory participation. Second, it empirically demonstrates how differences in involvement shape public opinion and behavioral responses to algorithmic regulation. Third, it highlights the ethical implications of algorithmic regulation systems by suggesting that such systems may unintentionally reinforce sociostructural inequalities and digital divides. These results inform the ethical debate surrounding the governance of algorithmic systems more generally.

To improve the effectiveness and accessibility of early developmental screening in Malaysia, this study developed a mobile application for Malaysian parents to conduct developmental screening on infants and toddlers (0–23 months old) at home. During Stage 1, four subject matter experts (SMEs) collaborated with the researcher to develop a new 56-item developmental screening tool which was later developed into a mobile application named Spark Developmental Screening Mobile Application (SparkApp). Stage 2 was a pilot study to evaluate the feasibility and user-friendliness of SparkApp. Fifty-six parents (mean age = 32.59 years, S.D. = 3.00) from various ethnicities and income levels were recruited from Klang Valley, Malaysia. They conducted developmental screening on their children using SparkApp. Then, they rated the user-friendliness of SparkApp, and the researcher conducted one-on-one cognitive interviews with them. The results showed that SparkApp was easy to use and most items in it were clear and relevant. Nevertheless, some aspects could be amended to enhance the clarity and relevance of the items and the feasibility of the game tasks. In conclusion, SparkApp is a suitable medium for parents to conduct periodic developmental screenings at home to aid the screening process of neurodevelopmental disorders.

Artificial intelligence language models are now embedded in social science workflows, yet the latent psychological and political patterns reflected in their outputs remain largely unexplored. This study examines 14 state-of-the-art large language models (LLMs) with 1000 prompt-based responses each (N = 14,000) across three linked studies. Study 1 extracts the Big Five personality traits: On average, the outputs are characterized by high levels of agreeableness (M = 6.54, SD = 0.31) and conscientiousness (M = 6.59, SD = 0.35), very low neuroticism (M = 1.57, SD = 0.48), high openness (M = 6.16, SD = 0.53), and moderate extraversion (M = 5.08, SD = 0.83). Study 2 uses hierarchical regressions to predict authoritarianism and conspiracy mentality. Personality traits add on average only around 0.4% incremental R², whereas political antecedents dominate. Study 3 measures ideological self-placement and sentiment toward Spanish leaders. Twelve models cluster at the partisan midpoint (M ≈ 4), yet Mistral Medium leans toward the Spanish Socialist Party (PSOE, center-left) (M = 3.32). Sentiment is neutral-positive for Pedro Sánchez (PSOE, center-left) and Yolanda Díaz (Sumar, far left), moderate for Alberto Núñez Feijóo (PP, center-right), and markedly negative for Santiago Abascal (Vox, far right) (sentiment = 2.98; ideology = 6.25). Overall, the outputs of LLMs converge in personality trait patterns but diverge sharply in how they translate into authoritarian and conspiratorial mentality. This study contributes the first exploratory analysis of personality traits and political orientations in AI language models and offers a replicable protocol for future comparative research.

A crossmodal correspondence (CMC) is an association between sensory features from different modalities. For example, the lightness/pitch correspondence is the tendency to pair more luminant stimuli with higher pitches and dimmer stimuli with lower pitches. There has been a recent surge in research examining how these associations are present in current generative AI models and to what degree they are language dependent. Previous research on the relationship between CMCs and AI has focused on explicit judgements of association, typically with rating scales. In contrast, other psychology subfields such as social psychology are using implicit measures of associations in AI output. In the present study, we argue for the merits of an implicit functionalist approach to generative AI research in CMCs and use two psychophysical paradigms to demonstrate this approach. Experiment 1 explores how the lightness/pitch correspondence might manifest in text-to-image models when they are prompted to visually depict auditory characteristics. The results indicate that DALL-E 3 consistently employs the lightness/pitch, as well as a contrast/pitch correspondence when attempting to visually depict auditory pitch. Experiment 2 then looks at ChatGPT-4o′s performance in classifying the images from Experiment 1 as high or low in pitch. We find that ChatGPT-4o uses both lightness and contrast information to inform its classifications of pitch. The implications of these results regarding the study of sensory associations with AI, as well as the specific future research directions of CMCs, are discussed.

This study investigates the uptake of emerging technologies—generative AI, agentic AI, and blockchain—by start-up companies in Dubai, a rapidly emerging innovation hub in the MENA region. Based on the innovation diffusion theory (IDT) and the technology–organization–environment (TOE) framework, the study investigates how technology, organizational, and environmental determinants influence technology adoption by start-ups. Specifically, it takes into account trialability, observability, relative advantage, and compatibility (technological context); stakeholder dynamics, innovation capability, and organizational resources (organizational context); and competition intensity and regulatory environment (environmental context). Citing recent criticisms of IDT, the study removes complexity as a determinant considering the growing user-centric design of technologies like GenAI. Using a mixed-methods design, the study collects data through interviews and surveys from start-up founders and technology leads. The outcomes should unveil the way start-ups view and imbed disruptive technologies and equip policymakers, entrepreneurs, and strategists for innovations with lessons to inform real-world applications. Placing the study in Dubai′s vibrant setting, this study provides theoretical and empirical contributions to technology diffusion in the emerging economies, where the flexibility of regulation, preparedness of infrastructure, and competitive stress all come together to create innovation trajectories.

Smart farming leverages advanced technologies to promote sustainable agriculture by reducing carbon emissions, conserving resources and increasing output. At the forefront of this innovation is the Internet of Things (IoT), a network of linked devices enabling efficient monitoring and control of agricultural processes. Despite its benefits, IoT adoption in agriculture remains limited. This pilot study explores factors influencing university students’ adoption of IoT technology, as they represent future agricultural innovators. Using an expanded technology acceptance model (TAM) and student survey data, the study identifies key drivers of IoT acceptance in agriculture. The findings provide valuable insights into the factors shaping IoT adoption and offer a foundation for strategies to advance sustainable farming and resource management. This research enriches the existing literature on technology adoption in agriculture. It is a reference for stakeholders aiming to enhance agricultural sustainability and productivity.