Human Behavior and Emerging Technologies最新文献

This study explores the adoption and societal implications of an emerging technology such as Chat Generative Pre-Trained Transformer (ChatGPT) in higher education students. By utilizing a mixed-method framework, this research combines Rogers’ diffusion of innovation theory with sentiment analysis, offering an innovative methodological approach for examining technology adoption in higher educational settings. It explores five attributes—relative advantage, compatibility, ease of use, observability, and trialability—shaping students’ behavioral intentions toward ChatGPT. Sentiment analysis offers qualitative depth, revealing emotional and perceptual aspects, and introduces a gender-based perspective. The results suggest that five innovation attributes significantly impact the adoption rates and perceptions of ChatGPT, indicating its potential for transformative social change within the educational sector. Gen Zs viewed ChatGPT as innovative, compatible, and user-friendly, enabling the independent pursuit of educational goals. Consequently, the benefits provided by ChatGPT in education motivate students to use the tool. Gender differences were observed in the prioritization of innovation attributes, with male students favoring compatibility, ease of use, and observability, while female students emphasized ease of use, compatibility, relative advantage, and trialability. The findings have implications for understanding how technological innovations such as ChatGPT could be strategically diffused across different societal segments, especially in the academic context where ethical considerations such as academic integrity are paramount. This study underscores the need for a demographic-sensitive, user-centric design in generative artificial intelligence (AI) technologies.

In today’s interconnected world, the widespread use of the Internet necessitates an understanding of factors influencing individuals’ ability to maintain a balanced relationship with technology. This study investigates digital life balance (DLB) by examining its associations with Internet social capital (ISC), loneliness, fear of missing out (FoMO), and anxiety levels. Five hundred and twenty participants (66% women; M_age = 30.12 years, SD = 12.46) took part in the data collection. Drawing upon the Psychology of Harmony and Harmonization framework, the study revealed negative correlations between DLB and ISC, loneliness, FoMO, and anxiety levels. Higher ISC was associated with lower DLB, suggesting that an extensive online network might lead to technological imbalance. Increased loneliness, FoMO, and anxiety were negatively associated with DLB, indicating possible disruptions between online and offline activities.

The significance of innovation and the expectation for employees to exhibit innovative behavior have been heightened as a result of swift technological advancements and an evolving business landscape. The present research is aimed at examining the impact of organizational justice on fostering innovation in a dynamic business environment. Extending the previous literature which generally examined the combined impact of different facets of organizational justice, we employed the social cognitive theory framework to investigate the mechanism through which the three facets of organizational justice (distributive justice, procedural justice, and interactional justice) lead to employee innovative behavior through the mediating role of employees’ creative self-efficacy. Additionally, we examined the role of age as a pertinent boundary condition, an aspect often overlooked in the literature on creative self-efficacy and innovative behavior which is likely to augment our understanding of the potential mechanism driving innovative behavior. The sample comprises 320 individuals employed in the information technology industry. The data were collected in two waves, and subsequent analysis was conducted utilizing the Warp PLS 8 software. The present investigation employed partial least square (PLS)-based structural equation modeling (SEM) to conduct analysis and evaluate hypotheses. The results indicate that all three facets of organizational justice have a positive influence on employees’ creative self-efficacy, which subsequently manifests in their innovative behavior. Additionally, age has an impact on the relationship between creative self-efficacy and employee innovative behavior, which becomes less pronounced as employees get older. Theoretical contributions and practical implications for practitioners are discussed.

Mobile devices (e.g., tablets and smartphones) have been rapidly integrated into the lives of children and have impacted how children engage with digital media. The portability of these devices allows for sporadic, on-demand interaction, reducing the accuracy of self-report estimates of mobile device use. Passive sensing applications objectively monitor time spent on a given device but are unable to identify who is using the device, a significant limitation in child screen time research. Behavioral biometric authentication, using embedded mobile device sensors to continuously authenticate users, could be applied to address this limitation. This study examined the preliminary accuracy of machine learning models trained on iPad sensor data to identify the unique user of the device in a sample of children ages 6 to 11. Data was collected opportunistically from nine participants (8.2 ± 1.75 years, 5 female) in the sedentary portion of two semistructured physical activity protocols. SensorLog was downloaded onto study iPads and collected data from the accelerometer, gyroscope, and magnetometer sensors while the participant interacted with the iPad. Five machine learning models, logistic regression (LR), support vector machine, neural net (NN), k-nearest neighbors (k-NN), and random forest (RF), were trained using 57 features generated from the sensor output to perform multiclass classification. A train-test split of 80%–20% was used for model fitting. Model performance was evaluated using F1 score, accuracy, precision, and recall. Model performance was high, with F1 scores ranging from 0.75 to 0.94. RF and k-NN had the highest performance across metrics, with F1 scores of 0.94 for both models. This study highlights the potential of using existing mobile device sensors to continuously identify the user of a device in the context of screen time measurement. Future research should explore the performance of this technology in larger samples of children and in free-living environments.

Given the phenomenon of “financing is difficult and expensive” for MSEs, this paper empirically investigated the influencing mechanism of the credit demand side characteristics on the financing constraints of MSEs based on the information conveyance perspective. The conclusions show that MSEs in China are severely suffering from financing constraints and 57.17% and 50.00% of MSEs with credit demand have not applied for loans from formal and informal financing channels, respectively. In terms of enterprise characteristics, MSEs have low asset size, short establishment history, weak profitability, and lack of tools such as fixed assets, complete financial management system, professional technicians, and private brands to convey risk information to financing institutions, which are key factors resulting in their financing constraints. In terms of owner characteristics, young owners lack financing experience and convey higher risk information to financing institutions; therefore, owners’ age negatively influences the financing constraints of MSEs. These findings suggest that banks can use big data credit technology as a tool to obtain risk information about MSEs, and the government should implement diversified interventions to improve the information environment in financial markets. These findings provide empirical evidence for banks and governments to address the financing constraints of MSEs.

The fourth industrial revolution is bringing artificial intelligence (AI) into various workplaces, and many businesses worldwide are already capitalizing on AI assistants. Trust is essential for the successful integration of AI into organizations. We hypothesized that people have higher trust in human assistants than AI assistants and that people trust AI assistants more if they have more control over their activities. To test our hypotheses, we utilized a survey experiment with 828 participants from Finland. Results showed that participants would rather entrust their schedule to a person than to an AI assistant. Having control increased trust in both human and AI assistants. The results of this study imply that people in Finland still have higher trust in traditional workplaces where people, rather than smart machines, perform assisting work. The findings are of relevance for designing trustworthy AI assistants, and they should be considered when integrating AI technology into organizations.

The adoption of online learning modalities has increasingly become prevalent, particularly with the advent of COVID-19, aiming to ensure student access to learning materials. This significant shift towards offering online educational formats compels educational institutions to alter their approach and develop curricula to guarantee an optimal student experience and satisfaction within the online environment. The aim of this research is to comprehensively examine the key factors that significantly impact the satisfaction of undergraduate students with online learning in Vietnamese universities. The quantitative research methodology was implemented through the collection of surveys from a total of 437 Vietnamese students. Utilizing the PLS-SEM statistical approach, the findings reveal that technology, communication, course, outcome, and motivation for learning have significant positive influences on students’ satisfaction with online education during the COVID-19 pandemic, while the effect of instructors’ attitude and the sudden change from traditional to online classes have been found with as nonsignificant. Valuable implications and practical recommendations are suggested for educational organizations and institutions in Vietnam to enhance specific activities that promote students’ satisfaction with online learning and improve teaching methods provided by instructors.

Fake news has been linked to the rise of psychological disorders, the increased disbelief in science, and the erosion of democracy and freedom of speech. Online social networks are arguably the main vehicle of fake news spread. Educating online users with explanations is one way of preventing this spread. Understanding how online belief is formed and changed may offer a roadmap for such education. The literature includes surveys addressing online opinion formation and polarization; however, they usually address a single domain, such as politics, online marketing, health, and education, and do not make online belief change their primary focus. Unlike other studies, this work is the first to present a cross-domain systematic literature review of user studies, methodologies, and opinion model dimensions. It also includes the orthogonal polarization dimension, focusing on online belief change. We include peer-reviewed works published in 2020 and later found in four relevant scientific databases, excluding theoretical publications that did not offer validation through dataset experimentation or simulation. Bibliometric networks were constructed for better visualization, leading to the organization of the papers that passed the review criteria into a comprehensive taxonomy. Our findings show that a person’s individuality is the most significant influential force in online belief change. We show that online arguments that balance facts with emotionally evoking content are more efficient in changing their beliefs. Polarization was shown to be cross-correlated among multiple subjects, with politics being the central polarization pole. Polarized online networks start as networks with high opinion segregation, evolve into subnetworks of consensus, and achieve polarization around social network influencers. Trust in the information source was demonstrated to be the chief psychological construct that drives online users to polarization. This shows that changing the beliefs of influencers may create a positive snowball effect in changing the beliefs of polarized online social network users. These findings lay the groundwork for further research on using personalized explanations to reduce the harmful effects of online fake news on social networks.

This study is aimed at exploring the impact of artificial intelligence (AI) on academic research by conducting a focus group research strategy. The focus group consists of individuals who are actively involved in academic research and have experience working with AI technologies. The purpose of the focus group is to gather in-depth insights into how AI has influenced research methodologies, findings, and overall knowledge creation. The study will begin by identifying seven participants through purposive sampling, with an aim of recruiting a diverse group of individuals from various academic disciplines. Purposive sampling, also known as selective sampling, enhances the study’s validity by ensuring that the sample consists of individuals with a high level of expertise in the subject matter. Seven is large enough to generate a diverse range of perspectives and experiences and small enough to ensure that every participating academic researcher has a chance to contribute to the conversation. The focus group is conducted using a Zoom video conferencing to gather academics from different institutions across the world. It also eliminates distance issue required for conducting an in-person session. This provides opportunity to cover a wide array research specialization representation. Data analysis is conducted using a thematic analysis approach, with a focus on identifying key themes and patterns that emerge from the data. The findings of this study contribute to a better understanding of the impact of AI on academic research and provide insights into the potential future direction of AI in academic research. While the study is aimed at providing practical recommendations for researchers who are interested in incorporating AI into their research practices, it also ignites the conversation on future incorporation of technologies into academic research activity.

Throughout the COVID-19 crisis, scientists around the globe have engaged in science communication to an unprecedented degree to convey first-hand epidemiological knowledge and information on preventive measures. The present work is aimed at empirically investigating the impact of direct exposure to scientists as compared to general COVID-19-related media consumption (N = 698) on central cognitive, affective, and behavioral variables, based on the extended parallel process model (EPPM) and its adaptations. A segment of the sample comprises individuals recruited independently, while others were sourced from an online panel. Importantly, this study sample was conducted at the outset of the COVID-19 pandemic. The results revealed that direct exposure to scientists positively affected recipients’ knowledge and self-efficacy. General media consumption, by contrast, positively affected perceived threat as well as fear and uncertainty. Both sources positively affected the adherence to protective measures.