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Although many universities have begun to provide artificial intelligence (AI)-related courses for students, the influence of the course on students' intention to participate in the development of AI-related products/services needs to be verified. In order to explore the factors that influence students' participation in AI services and system development, this study uses self-efficacy, AI literacy, and the theory of planned behaviour (TPB) to investigate students' intention to engage in AI software development.

The questionnaire was distributed online to collect university students' responses in central Taiwan. The research model and eleven hypotheses are tested using 151 responses. The testing process adopted SmartPLS 3.3 and SPSS 26 software.

AI programming self-efficacy, AI literacy, and course satisfaction directly affected the intention to participate in AI software development. Moreover, course playfulness significantly affected course satisfaction and AI literacy. However, course usefulness positively affected course satisfaction but did not significantly affect AI literacy and AI programming self-efficacy.

The model improves our comprehension of the influence of AI literacy and AI programming self-efficacy on the intention. Moreover, the effects of AI course usefulness and playfulness on literacy and self-efficacy were verified. The findings and insights can help design the AI-related course and encourage university students to participate in AI software development. The study concludes with suggestions for course design for AI course instructors or related educators.

To support achieving sustainable development goals (SDGs), we integrated science, technology, engineering and math (STEM) and extended reality technologies into an artificial intelligence (AI) learning activity. We developed Feature City to facilitate students' learning of AI concepts. This study aimed to explore students' learning outcomes and behaviors when using Feature City.

Junior high school students were the subjects who used Feature City in an AI learning activity. The learning activity consisted of 90-min sessions once per week for five weeks. Before the learning activity, the teacher clarified the learning objectives and administered a pretest. The teacher then instructed the students on the features, supervised learning and unsupervised learning units. After the learning activity, the teacher conducted a posttest. We analyzed the students' prior knowledge and learning performance by evaluating their pretest and posttest results and observing their learning behaviors in the AI learning activity.

(1) Students used Feature City to learn AI concepts to improve their learning outcomes. (2) Female students learned more effectively with Feature City than male students. (3) Male students were more likely than female students to complete the learning tasks in Feature City the first time they used it.

Within SDGs, this study used STEM and extended reality technologies to develop Feature City to engage students in learning about AI. The study examined how much Feature City improved students' learning outcomes and explored the differences in their learning outcomes and behaviors. The results showed that students' use of Feature City helped to improve their learning outcomes. Female students achieved better learning outcomes than their male counterparts. Male students initially exhibited a behavioral pattern of seeking clarification and error analysis when learning AI education, more so than their female counterparts. The findings can help teachers adjust AI education appropriately to match the tutorial content with students' AI learning needs.

The purpose of this paper is to use bibliometric analysis to identify the current state of the academic literature regarding social network analysis (SNA) and analyze its knowledge base such as research authors, research countries, document type, keyword analysis and subject areas.

Bibliometric analysis is used and furthermore, Lotka’s and Bradford’s law is applied to perform author productivity analyses in this field during 1999 and 2018, respectively, in turn, discovering historical vein and research tendency in the future.

It appears that the research on SNA has been very popular and still in the highly mature period. So far, the USA takes the lead among the published paper. The top 2 subject areas are “Computer Science” and “Business Economics.” The primary journal that SNA articles were published is Computers in Human Behavior. SNA has been related to many research areas, such as “Social network analysis,” “Computer-mediated communication,” “Online learning,” “Social Network” and “Community of inquiry.” Finally, Kolmogorov–Smirnov (K-S) test proved that the frequency indexes of author productivity distribution certainly followed Lotka’s law.

First, the productivity distribution may inform researchers and scholars of current issues and development of SNA. Second, the study proposed a theoretical model, based on Lotka’s law, for author productivity analysis of SNA, which can serve as reference for different areas of study in the evaluation of author productivity models. Also, in order to allow researchers to gain in-depth insights, this study aimed to report the most published institutions and keep track of the growth and trend of author productivity, by which scholars in related fields are provided with more opportunities for academic communication and technological cooperation.

This research on the productivity distribution of SNA may inform researchers and scholars of current issues and development of SNA. The findings report the major publication outlets and related discussion issues about SNA. Such information would be valuable for related authors, who are writing the manuscript on SNA, and also for practitioners, who may be interested in applying the theory or ideas of SNA.

This study focused on parents' health anxiety by proxy about their children when they started learning online during the COVID-19 pandemic, to explore the impact of academic stress by parent-proxy on parents' learning support services with the mediating role of health anxiety by parent-proxy and the moderating role of parental educational level.

In total, 8,940 primary school students' parents participated in the study. Bootstrapping was performed to test the constructed model.

(1) Academic stress by parent-proxy positively predicted health anxiety by parent-proxy. (2) Health anxiety by parent-proxy significantly positively predicted learning support services. (3) Academic stress by parent-proxy also significantly positively predicted learning support services. (4) Academic stress by parent-proxy positively predicted parents' learning support services through the mediating effect of health anxiety by parent-proxy. (5) Parental educational level moderated the relationship between academic stress by parent-proxy, health anxiety by parent-proxy, and learning support services. Academics and parents will benefit from the conclusions of this study in both theory and practice.

During the COVID-19 pandemic, offline learning has been replaced with online learning, which has brought with it many physical and mental health problems, including additional academic stress. Most studies on learning support services have focused on offline learning. However, this study explored the relationships between academic stress by parent-proxy, health anxiety by parent-proxy, learning support services, and parental educational level in the context of online learning. Results show that it is necessary to pay attention to academic stress and health to provide children with appropriate learning support services.

The purpose of this study, we present a robot used in education. Influenced by the epoch of revolutionary digital technology, the methodology of education has gone boundless. The robot programming sustainability and ability to solve problems is one an important skill that coding students require to learn programming. This educational have been integrated into curriculum instruction in clubs.

Robotics education has been regarded as a potential approach to enhance students' Science, technology, engineering, and mathematics learning competencies. The popular platform of robots diversifies educational practices by its advantages of reorganizational and logical forms. In this paper, we focus on the effects of applying blended instructional approaches to robot education on students' programming sustainability and ability.

The students of department of mechanical engineering at the University in Taipei city, who participate elective educational robot courses, prove through surveys that the problem-based leaning method with robot programming can effectively enhance students' interests and learning motivations in learning new knowledge and promote students' designing skills for a sustainable society.

In this paper, the authors focus on the effects of applying blended instructional approaches to robot education on students' programming sustainability and ability.

More hospitals and caregivers are realizing the importance of radiofrequency identification (RFID) technology in the face of increased healthcare costs, medical errors, and pressure of governmental mandates. The necessity for and awareness of RFID has yet to drive its widespread adoption in the healthcare industry. As such, this study aims to examine key factors that contribute to the intention to continue using RFID.

The paper adopts the expectation‐confirmation model (ECM) to study the intention of caregivers to continue using RFID as a vehicle to deliver emergency medical services. It is theorized that perceived usefulness of front‐end interoperability and back‐end interoperability along with performance expectancy contribute to confirmation experience, which in turn leads to satisfaction and ultimately to intention to continue to use RFID. The study surveys caregivers and staffs in the emergency room department of five hospitals in Taiwan.

Perceived usefulness of front‐end interoperability and performance expectancy both have significant relationships with confirmation experience; confirmation experience has a significant relationship with satisfaction, which in turn relates to intention to continue using RFID. The relationship between perceived usefulness of back‐end interoperability and confirmation experience is not significant.

This study explains RFID adoption behavior using the ECM. Limitations of the study and possible future research direction are discussed.

The results of this study should help hospital management to build commitment to the RFID system and help equipment vendors to build loyalty to the technology.

This study represents a novel attempt to explain RFID adoption behavior using the ECM. It is expected that the proposed framework and empirical findings can contribute to further understanding of how RFID is adopted and used in a hospital environment.

E-commerce live streaming is a new influencer advertising method that allows influencers to interact directly with consumers on e-commerce platforms. Although evidence suggests that influencer live-streaming advertisements (ads) on social media can increase consumers’ buying impulses, little research examined how this similar but new advertising method on e-commerce platforms may influence consumers’ urge to buy impulsively. This study explores the role of influencer credibility, celebrity effect, perceived entertainment, trust and perceived usefulness on consumers’ attitudes toward influencer ads and their urge to buy impulsively.

A questionnaire containing seven constructs was developed and distributed to participants using a convenient sample and snowball sampling approach. The constructs were measured based on validated measurement items from the literature and adjusted according to this study’s focus. A total of 236 valid responses were obtained from the survey and used for data analysis. A partial least squares structural equation modeling approach was employed for parameter estimation and model testing.

The empirical results show that all constructs influenced consumers’ urge to buy impulsively via attitude toward influencer ads. The proposed research model explains 61.7% of the variance in attitude toward influencer ads and 19.4% of the urge to buy impulsively.

This is an early study investigating the relationship between influencer advertising and impulse buying. The results provide valuable insights into improving the design of influencer ads and marketing strategies.

1. I-eIB model tests the mechanism of influencer ads on consumers’ buying impulse.

2. Consumers’ attitude towards influencer ads affects their urge to buy impulsively.

3. Influencer credibility affects consumer attitude via celebrity effect as a mediator.

4. Trust affects consumer attitude via perceived usefulness as a mediator.

5. Entertaining ads help develop favorable consumer attitude.

I-eIB model tests the mechanism of influencer ads on consumers’ buying impulse.

Consumers’ attitude towards influencer ads affects their urge to buy impulsively.

Influencer credibility affects consumer attitude via celebrity effect as a mediator.

Trust affects consumer attitude via perceived usefulness as a mediator.

Entertaining ads help develop favorable consumer attitude.

The concept of green buildings (GBs) in contemporary building design has risen in popularity with the rapid economic development resulting from globalization. The primary objective of this research is to provide an impetus to improve the methods of green technology adaptation, taking the Russian Federation as an example.

Globally, the first green building rating systems were implemented in the 1990s. The Russian Federation had launched its national green building rating systems apart from the international green building rating systems that were implemented in the country.

Based on qualitative analysis, a common approach in the Russian market for certifying green construction was identified. The collected information can be considered evidence of market entry and aid in preparation for possible market shocks.

Furthermore, the study determined seven significant rating measures that were generally applied by various green building rating systems, and the most influential rating measure was “energy,” followed by “site,” “indoor environment,” “land and outdoor environment,” “material,” “water” and “innovation.” Additionally, this study determined the primary research endeavors of the analyses of contemporary green building rating systems and presented the scope for future research.