Human Behavior and Emerging Technologies最新文献

This study investigated the rise of implantable or cyborg technologies, also known as insideables, which offer the potential to improve health-related conditions and enhance the capabilities of healthy individuals. This research focused on the acceptance of insideables among university students in Spain, utilizing the unified theory of acceptance and use of technology (UTAUT) framework along with analytical tools such as partial least squares structural equation modelling (PLS-SEM) and fuzzy set qualitative comparative analysis (fsQCA). The PLS-SEM analysis revealed that factors such as performance expectancy, effort expectancy, and social influence positively influenced the intention to use insideables. However, the fsQCA revealed that no single variable is a necessary condition for explaining technology acceptance or rejection. Instead, a combination of constructs is needed to understand both intention to use and rejection. Configurational analysis emphasized the importance of factors such as performance expectancy, social influence, and hedonic motivation in explaining technology acceptance, whereas effort expectancy and perceived risk were less conclusive in their impact on behavioral intention. Moreover, the research revealed that the configurations related to the acceptance and rejection of insideables are asymmetrical. This study sheds light on the complex dynamics of implantable technology acceptance and provides valuable insights into the factors influencing its adoption. From a theoretical perspective, the sequential use of both correlational and configurational methods within the UTAUT framework allows us to gain a deeper understanding of the reasons behind the adoption of emerging technology rather than using only one data analysis methodology.

Artificial intelligence (AI) tools have been used to improve the productivity of evidence review and synthesis since at least 2016, with EPPI-Reviewer and Abstrackr being two prominent examples. However, since the release of ChatGPT by OpenAI in late 2022, the use of generative AI for research, especially for text-based data analysis, has exploded. In this article, we used a critical reflection approach to document and evaluate the capacity of different generative AI tools such as ChatGPT, GPT for Google Sheets and Docs, Casper AI, and ChatPDF to assist in the early stages of a rapid evidence review process. Our results demonstrate that these tools can boost research productivity in formulating search strings and screening literature, but they have some notable weaknesses, including producing inconsistent results and occasional errors. We recommend that researchers exercise caution when using generative AI technologies by designing a thorough research strategy and review protocol to ensure effective monitoring and quality control.

The use of mobile devices has become pervasive in recent times, constituting an essential component of daily life. Mobile phones have enabled certain minorities to attain access to the Internet, news, and knowledge, thereby indicating their potential to reduce the digital divide experienced by ethnic groups and those from low socioeconomic backgrounds. This phenomenon has generated academic interest in the utilization of mobile devices to facilitate learning, as these devices merge the lines between computing and communications, giving access to both. The objective of this study is to ascertain the inclination of Peruvian higher education students to use mobile devices for learning. This will be achieved through the use of an anticipated model based on artificial neural networks (ANNs). ANNs are supervised machine learning techniques that imitate the organization and operation of the human brain to process data and render decisions. ANNs are computer systems that can learn from observation and experience, much like the human brain, and can subsequently use the acquired knowledge to recognize patterns and make predictions. The objective of this study is to assess the intention of Peruvian tertiary education students to employ mobile devices for learning by creating a predictive model that relies on ANNs. Among the main findings, it is evident that the ANN with optimal performance has 10 neurons within its hidden layer. Factors such as experience with virtual subjects, frequency of use, and coverage are crucial for the two intention variables. This enables directed prediction efforts towards the most significant variables identified by their importance.

Generalized trust has reached new lows in America, with young Americans now trusting the least. This complicates the process of interacting with new people, which formerly contributed to trust. The present study thus tested whether networked modes of social contact and social learning might add to interpersonal competence and generalized trust. Responses to a national web survey were matched to US Census percentages for sex, race, ethnicity, age, and region. The sample resembles the US population demographically and is theoretically large enough to represent it (N = 1500). Data were analyzed using SPSS and PROCESS. Diverse contact in person was unrelated to trust in general and only contributed to trust for respondents 70 or older when particular age groups were considered. Interpersonal competence, on the other hand, contributed to trust overall, and for respondents 18–29, 40–49, and 50–59. Feeling capable of interacting with new people in person has become more important than the contact itself for trusting, as a way of generating numerous diverse interactions over time. Networked efforts of sociability and posting behavior were also related to trust here. Posting related to trust for respondents 18–29, while sociability contributed to trust for those 18–29, 30–39, and 50–59. Social presence (i.e., sensing immediacy and intimacy in networked settings) related to trust overall and for those 18–29 and 40–49. Computer-mediated communication (CMC) competence contributed to trust indirectly, by way of social presence, and the indirect effect was the largest for the youngest users surveyed. CMC competence had a larger association with interpersonal competence for younger generations as well, which became a second indirect path to trusting. Different age groups draw trust from different places, and trust interventions should also differ with age.

Globally and in South Africa, the beauty industry, which includes high-involvement products such as skincare, fragrance, makeup, and haircare products, is experiencing robust growth, something partially attributed to the rapidly growing influence of beauty social media influencers. While increased attention is being paid to social media influencer marketing in academic literature, most of these studies focus on the characteristics of the social media influencer rather than on the content that they deliver. Moreover, there is a paucity of studies that specifically consider YouTube vlogs in relation to beauty products and Generation Y female consumers. The purpose of this research was to determine the perceived value and purchase influence of beauty vlog content on YouTube amongst Generation Y female (females born from 1986 to 2005) consumers from the emerging market perspective of South Africa. The study adopted an explanatory research design, whereby data were collected using an electronic questionnaire from 340 Generation Y female consumers. The results of the path analysis indicate that the beauty vlog content dimensions of informativeness, entertainment, and credibility collectively account for 72% of the variability in Generation Y female consumers’ perceived value of beauty vlog content on YouTube. In turn, this perceived value, along with its predictors, accounts for almost 50% of the variance in the purchase influence of such vlogs amongst Generation Y female consumers. These findings confirm the salience of beauty brands engaging with YouTube beauty vloggers when targeting Generation Y female consumers. Moreover, when choosing which beauty vloggers to engage with, the results highlight the need to seek out those with a proven track record for delivering informative and interesting beauty product content and who consistently exercise a high degree of integrity in their beauty product review vlogs.

Employee monitoring software enables employers to oversee their workforce, even when working remotely. While it has the potential to enhance efficiency, productivity, and profitability within an organization, there are also significant risks associated with its adoption, such as violations of privacy, security intrusions, and lack of transparency. Although recent research thoroughly discusses the role of employers in the ethical use of monitoring, the role of monitoring software vendors has yet to be explored. Therefore, this research is aimed at exploring vendors’ current involvement in mitigating the risks of employee monitoring software. To achieve this, a content analysis was conducted on 15 websites selling employee monitoring software to examine how vendors address these risks. Given the lack of relevant literature and the possible reluctance of vendors to discuss the risks in surveys or interviews, content analysis enables a systematic review of the available software and the portrayal of employee monitoring on their websites. The findings show that risk-mitigating features are uncommon in the solutions offered by these companies. Furthermore, vendors tend to misrepresent and under-represent the risks of monitoring tools on their webpages compared to the benefits. With insights from this study, policymakers and advocates can develop new measures to promote the ethical use of employee monitoring. These measures could include establishing a knowledge center to provide general, evidence-based information about risks and benefits as an objective third party.

Attending to the phone while interacting face-to-face with another person, a behaviour known as phubbing, can be detrimental to the phubbed person’s psychological wellbeing. Recent research revealed that phubbing friends and partners indirectly affected the phubbed individual’s wellbeing. The aim of this study was to investigate the effect of children’s phubbing on parents’ psychological wellbeing; not the effect of parents’ phubbing on children’s psychological wellbeing, which has been the focus of most of current research. Two hundred and sixty six (266) parents of smartphone users participated in a web survey. The questionnaire measured parents’ experience of being phubbed by their children, psychological wellbeing, relationship satisfaction, loneliness, and self-esteem. The analysis showed a significant indirect effect of children’s phubbing on parents’ psychological wellbeing through the mediating roles of relationship satisfaction and loneliness. Children’s phubbing increased parents’ feelings of loneliness, and this rise in levels of loneliness worsened parents’ psychological wellbeing. Similarly, children’s phubbing decreased parents’ relationship satisfaction with their children, and this decrease in feelings of relationship satisfaction worsened parents’ psychological wellbeing. Additionally, children’s phubbing affected parents’ psychological wellbeing through the mediating roles of relationship satisfaction and loneliness in sequence (chain effect). As children’s phubbing increased parents’ feelings of loneliness, parents’ relationship satisfaction with their children dropped. The lowering of feelings of relationship satisfaction worsened parents’ psychological wellbeing. A moderated mediation analysis revealed that children’s phubbing decreased parents’ relationship satisfaction with their children, especially for parents who are low on self-esteem. This study is one of the first that offers insights into how children’s phubbing and parents’ psychological wellbeing are related.

The research aspired to determine the effecting determinants of the implementation of Fourth Industrial Revolution (4IR) technologies in the manufacturing industry, examining how 4IR readiness (R) acts as a mediator to facilitate the process of 4IR adaptation (ADP) in Bangladesh. Employing the extended technology–organization–environment (TOE) framework, the research method integrated both quantitative approaches and structured surveys using random sampling approaches to explore the specific determinants that influence the adoption of 4IR technologies. A total of 500 organizations were approached with an online questionnaire, yielding 370 completed responses, a response rate of 74%. After ensuring the reliability and validity of the findings, the structural equation model was analyzed using SmartPLS 4.0 and SPSS 29 with structural equation modeling (SEM). A statistically significant positive correlation was observed among technology readiness (TR), government support (GS), and technology innovation decision-making (TIDM) on 4IR R, as well as 4IR R on 4IR ADP based on the findings. Practical implications were discussed with a focus on strategic recommendations for policymakers and industry leaders to enhance the structural and supportive infrastructure necessary for 4IR integration. To extend the R and flexibility of the manufacturing sector in anticipation of the 4IR, policymakers must urgently contemplate the expansion of government aid and the formation of expert consulting groups. The originality of this study applied in its ADP of the TOE framework to the context of a developing country, specifically targeting the R factors that facilitate technological ADP in Bangladesh. Unlike traditional models that focus solely on technological adoption, this extended model integrates organizational and environmental factors, offering a comprehensive view of industry R. The findings highlighted the significant positive correlations between technology infrastructure R, governmental support, and decision-making processes in technology innovation, which distinguish this model by its specific ADP to the developmental and economic conditions in a developing economy like Bangladesh.

Medical service digitalization not only simplifies the work of professionals but also facilitates communication with patients. However, how medical providers can implement digital transformation with limited resources and invest in digital technologies to achieve the best benefits is yet unknown. In this study, a review of the literature related to the orthodontic process and interviews with relevant orthodontics digital transformation stakeholders were conducted. Content analysis was subsequently performed to construct an evaluation framework for the digital transformation of the orthodontic service. Next, this study conducted questionnaire design, DRA model construction, and DEMATEL analysis. This research constructed an evaluation framework of the digital transformation of four procedures, namely, clinical evaluation (CE), therapeutic communication (TC), treatment procedures (TPs), and patient tracking (PT). Taking Taiwan as a case, a total of 88 valid questionnaires were obtained from orthodontic digital transformation stakeholders. The results of the DRA-NRM show that the healthcare digital transformation process in Taiwan starts at CE and that the project needs policy support or business transformation. Next, the TC project is considered to be the most important stage of the medical digitalization transformation and requires a full investment of resources. In the subsequent TP phase, marketing efforts need to be stepped up to convince stakeholders of the benefits of medical digitization. Finally, PT can be postponed during digitalization until sufficient support and resource inputs are available. This study makes strategic recommendations for orthodontic digital conversion and establishes a plan and future direction for the digital transformation of healthcare services.

This paper introduces a neural network-based model designed for classifying emotional states by leveraging multimodal physiological signals. The model utilizes data from the AMIGOS and SEED-V databases. The AMIGOS database integrates inputs from electroencephalogram (EEG), electrocardiogram (ECG), and galvanic skin response (GSR) to analyze emotional responses, while the SEED-V database continuously updates EEG signals. We implemented a sequential neural network architecture featuring two hidden layers, which underwent substantial hyperparameter tuning to achieve optimal performance. Our model’s effectiveness was tested through binary classification tasks focusing on arousal and valence, as well as a more complex four-class classification that delineates emotional quadrants for the emotional tags: happy, sad, neutral, and disgust. In these varied scenarios, the model consistently demonstrated accuracy levels ranging from 79% to 86% in the AMIGOS database and up to 97% in SEED-V. A notable aspect of our approach is the model’s ability to accurately recognize emotions without the need for extensive signal preprocessing, a common challenge in multimodal emotion analysis. This feature enhances the practical applicability of our model in real-world scenarios where rapid and efficient emotion recognition is essential.