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The application of artificial intelligence chatbots is an emerging trend in educational technology studies for its multi-faceted advantages. However, the existing studies rarely take a perspective of educational technology application to evaluate the application of chatbots to educational contexts. This study aims to bridge the research gap by taking an educational perspective to review the existing literature on artificial intelligence chatbots.

This study combines bibliometric analysis and citation network analysis: a bibliometric analysis through visualization of keyword, authors, organizations and countries and a citation network analysis based on literature clustering.

Educational applications of chatbots are still rising in post-COVID-19 learning environments. Popular research issues on this topic include technological advancements, students’ perception of chatbots and effectiveness of chatbots in different educational contexts. Originating from similar technological and theoretical foundations, chatbots are primarily applied to language education, educational services (such as information counseling and automated grading), health-care education and medical training. Diversifying application contexts demonstrate specific purposes for using chatbots in education but are confronted with some common challenges. Multi-faceted factors can influence the effectiveness and acceptance of chatbots in education. This study provides an extended framework to facilitate extending artificial intelligence chatbot applications in education.

The authors have to acknowledge that this study is subjected to some limitations. First, the literature search was based on the core collection on Web of Science, which did not include some existing studies. Second, this bibliometric analysis only included studies published in English. Third, due to the limitation in technological expertise, the authors could not comprehensively interpret the implications of some studies reporting technological advancements. However, this study intended to establish its research significance by summarizing and evaluating the effectiveness of artificial intelligence chatbots from an educational perspective.

This study identifies the publication trends of artificial intelligence chatbots in educational contexts. It bridges the research gap caused by previous neglection of treating educational contexts as an interconnected whole which can demonstrate its characteristics. It identifies the major application contexts of artificial intelligence chatbots in education and encouraged further extending of applications. It also proposes an extended framework to consider that covers three critical components of technological integration in education when future researchers and instructors apply artificial intelligence chatbots to new educational contexts.

360-degree video is recorded with omnidirectional or multi-camera systems that capture all directions at the same time in a spherical view. With immersive technologies gaining momentum and reducing educational cost, it has attracted the interest of the academic community. However, little is known about using 360-degree video in teacher education. The purpose of this study is to conduct a systematic scoping review through a systematic process based on 15 included studies to determine the characteristics, impacts, strengths and weaknesses of the 360-degree video applied to teacher education.

This study combines scoping and systematic review based on the PRISMA paradigm.

This paper explores that 360-degree videos are applicable to teacher education, specifically with their positive effects on pre-service teachers’ immersion, noticing, reflection and interpersonal competence. However, as for learners’ reactions, physical discomfort is reported, like motion sickness.

First, some recently published studies on the subjects were partially accessible, which precluded the authors from adding their findings to this study. Second, the sample of articles is constrained to the search and selection strategies described in the methods section, which increases the possibility that pertinent research may be omitted. Furthermore, this study’s summary of the selected research may be inadequate. Third, only English-language publications were included in this study. Future researchers can expand on this topic by gathering additional relevant empirical data from publications in other languages.

Practically, findings in this study reveal the positive effects of 360-degree video in teacher education. The results may help researchers and preservice teachers better understand 360-degree video and use it more frequently in teaching. Instructional video technologies have been found to have a nearly medium effect on learning effectiveness in educational practice from a broader perspective.

The findings in this study can shed light on future educational technology research on instructional video technologies and technology-enhanced teacher education.

Artificial intelligence (AI) has a large number of applications at the industry and user levels. However, AI's uniqueness neglect is becoming an obstacle in the further application of AI. Based on the theory of innovation resistance, this paper aims to explore the effect of AI's uniqueness neglect on consumer resistance to AI.

The authors tested four hypothesis across four studies by conducting lab experiments. Study 1 used a questionnaire to verify the hypothesis that AI's uniqueness neglect leads to consumer resistance to AI; Studies 2 focused on the role of human–AI interaction trust as an underlying driver of resistance to medical AI. Study 3–4 provided process evidence by way of a measured moderator, testing whether participants with a greater sense of non-verbal human–AI communication are more reluctant to have consumer resistance to AI.

The authors found that AI's uniqueness neglect increased users' resistance to AI. This occurs because the uniqueness neglect of AI hinders the formation of interaction trust between users and AI. The study also found that increasing the gaze behavior of AI and increasing the physical distance in the interaction can alleviate the effect of AI's uniqueness neglect on consumer resistance to AI.

This paper explored the effect of AI's uniqueness neglect on consumer resistance to AI and uncovered human–AI interaction trust as a mediator for this effect and gaze behavior and physical distance as moderators for this effect.

With the development of e-commerce network platforms, platform enterprises have mostly completed the first stage of user accumulation during the start-up period. How to enhance users’ stickiness and stimulate their continual participation in platform business activities through innovation and platform design has become a decisive factor for platform enterprises. To increase the motivation of e-commerce platform users, this paper explores the positive impact of gamified rewards on platform user stickiness by dividing the gamified rewards design into social and functional rewards, and studies the mediating role of self-identification and the moderating role of perceived goal progress and information disclosure.

This study applies the “S-O-R” (stimulus–organism–response) model as the theoretical basis for constructing a model of user stickiness for e-commerce platforms and subdivides gamified reward design into social rewards and functional rewards to explore how they affect platform user stickiness and the boundaries of the influencing mechanism.

It turns out both types of gamified rewards promote users’ perception of self-identification, which in turn affects the intention to continue using the platform. In addition, platforms with designs about users’ quantified self-behavior – perceived goal progress in the gaming experience can effectively enhance the effectiveness of users’ gamification rewards. Information disclosure moderates the relationship between the two types of gamification design and self-identification. For functional reward designs and social reward designs, information disclosure can improve users’ self-identification and therefore enhance users’ stickiness.

This study verifies the impact of gamification design on platform user stickiness, confirming the mediating role of self-identification and the moderating role of perceived goal progress and information disclosure, which has theoretical and practical implications for how platform enterprise can maintain user activity in the digital context.

The continuous impact of the pandemic and the downturn of the global economy have brought new challenges to the tourism industry. In this context, effectively attracting consumers and improving user stickiness are the top priorities of tourism platform companies. This study explores the impact of ethical concerns raised by new issues under the multi-governance environment on user stickiness. Based on the trust theory, the authors provide solutions for tourism platforms.

This study adopted a quantitative approach, gathering survey data via an online platform. A total of 400 participants were investigated, and 356 valid questionnaires were returned, with a recovery rate of 89%. Questionnaires that did not meet the inclusion criteria were excluded, leaving 298 valid responses.

Studies have found that consumers' ethical concerns about platform companies are key factors affecting user stickiness, and among these, consumer trust plays a mediating role. They have found that corporate social responsibility (CSR) behaviours help alleviate ethical concerns and improve trust in enterprises. At the same time, enterprises should properly control the number of platform collaborators, and excessive platform cooperation negatively moderates the impact of consumer ethical concerns on competence-based trust.

This study complements the deficiency of previous research with regard to ethical concerns in a multi-governance environment. These findings indicate that subject diversity exacerbates the negative impact of ethical concerns on consumer trust; however, CSR alleviates the impact of ethical concerns on consumer trust.

With the upgrade of natural language interaction technology, the simulation extension of intelligent voice assistants (IVAs) and the uncertainty of products and services have received more and more attention. However, most of the existing research focuses on investigating the application of theories to explain consumer behavior related to intention to use and adopt IVAs, while ignoring the impact of its privacy issues on consumer resistance. This article especially examines the negative impact of artificial intelligence-based IVAs’ privacy concerns on consumer resistance, and studies the mediating effect of perceived creepiness in the context of privacy cynicism and privacy paradox and the moderating effect of anthropomorphized roles of IVAs and perceived corporate social responsibility (CSR) of IVAs’ companies. The demographic variables are also included.

Based on the theory of human–computer interaction (HCI), this study addresses the consumer privacy concerns of IVAs, builds a model of the influence mechanism on consumer resistance, and then verifies the mediating effect of perceived creepiness and the moderating effect of anthropomorphized roles of IVAs and perceived CSR of IVAs companies. This research explores underlying mechanism with three experiments.

It turns out that consumers’ privacy concerns are related to their resistance to IVAs through perceived creepiness. The servant (vs. partner) anthropomorphized role of IVAs is likely to induce more privacy concerns and in turn higher resistance. At the same time, when the company’s CSR is perceived high, the impact of the concerns of IVAs’ privacy issues on consumer resistance will be weakened, and the intermediary mechanism of perceiving creepiness in HCI and anthropomorphism of new technology are further explained and verified. The differences between different age and gender are also revealed in the study.

The research conclusions have strategic reference significance for enterprises to build the design framework of IVAs and formulate the response strategy of IVAs’ privacy concerns. And it offers implications for researchers and closes the research gap of IVAs from the perspective of innovation resistance.

The set-union knapsack problem is one of the most significant generalizations of the Non-deterministic Polynomial (NP)-hard 0-1 knapsack problem in combinatorial optimization, which has rich application scenarios. Although some researchers performed effective algorithms on normal-sized instances, the authors found these methods deteriorated rapidly as the scale became larger. Therefore, the authors design an efficient yet effective algorithm to solve this large-scale optimization problem, making it applicable to real-world cases under the era of big data.

The authors develop three targeted strategies and adjust them into the adaptive tabu search framework. Specifically, the dynamic item scoring tries to select proper items into the knapsack dynamically to enhance the intensification, while the age-guided perturbation places more emphasis on the diversification of the algorithm. The lightweight neighborhood updating simplifies the neighborhood operators to reduce the algorithm complexity distinctly as well as maintains potential solutions. The authors conduct comparative experiments against currently best solvers to show the performance of the proposed algorithm.

Statistical experiments show that the proposed algorithm can find 18 out of 24 better solutions than other algorithms. For the remaining six instances on which the competitor also achieves the same solutions, ours performs more stably due to its narrow gap between best and mean value. Besides, the convergence time is also verified efficiency against other algorithms.

The authors present the first implementation of heuristic algorithm for solving large-scale set-union knapsack problem and achieve the best results. Also, the authors provide the benchmarks on the website for the first time.

Online influencers are increasingly used by brands around the globe to establish brand communication. This study aims to investigate the characteristics of social media content in terms of presentation style and brand communication among online influencers in China. The authors identified how characteristics of social media posts influence young consumers’ engagement with the posts.

The authors analyzed 1,779 posts from the Sina Weibo accounts of ten top-ranked online influencers by combining traditional content analysis with Web data crawling of audience engagement with social media posts.

Online influencers in China more frequently used photos than videos to communicate with their social media audience. Altogether 8% and 6% of posts carried information about promotion and event, respectively. Posts with promotional incentives as well as event information were more likely to engage audiences. Altogether 22% of the sampled social media posts mentioned brands. Posts with brand information, however, were less likely to engage audiences. Furthermore, having long text is more effective than photos/images in generating likes from social media audiences.

Combining content analysis of social media posts and engagement analytics obtained via Web data crawling, this study is, to the best of the authors’ knowledge, one of the first empirical studies to analyze influencer marketing and young consumers’ reactions to social media in China.

Artificial intelligence (AI) is experiencing growth and prosperity worldwide because of its convenience and other benefits. However, AI faces challenges related to consumer resistance. Thus, drawing on the user resistance theory, this study explores factors that influence consumers’ resistance to AI and suggests ways to mitigate this negative influence.

This study tested four hypotheses across four studies by conducting lab experiments. Study 1 used a questionnaire to verify the hypothesis that AI’s “substitute” image leads to consumer resistance to AI; Study 2 focused on the role of perceived threat as an underlying driver of resistance to AI. Studies 3–4 provided process evidence by the way of a measured moderator, testing whether AI with servant communication style and literal language style is resisted less.

This study showed that AI’s “substitute” image increased users' resistance to AI. This occurs because the substitute image increases consumers’ perceived threat. The study also found that using servant communication and literal language styles in the interaction between AI and consumers can mitigate the negative effects of AI-substituted images.

This study reveals the mechanism of action between AI image and consumers’ resistance and sheds light on how to choose appropriate image and expression styles for AI products, which is important for lowering consumer resistance to AI.

The residential sector is a principal contributor to global energy consumption, underscoring the critical importance of promoting green housing initiatives to mitigate energy use and environmental degradation. The prevalence of uncertainty in the natural environment, exemplified by phenomena like extreme weather events, highlights the urgent need for adaptive strategies and sustainable practices to mitigate the impact on human communities and ecosystems. Against this backdrop, this paper presents a theoretical framework examining the influence of natural environmental uncertainty on consumers' willingness to purchase green housing.

Through three experiments, this study modeled the mechanism by which the natural environment uncertainty affects consumers' willingness to purchase green housing, and then verified the mediating effect of the threat of ontological security and the moderating effect of the degree of consumers' natural connectedness.

This paper concludes (1) natural environmental uncertainty exerts a significant positive impact on the willingness to purchase green housing, with the threat to ontological security serving as a pivotal mediating variable; (2) the degree of natural connectedness significantly moderates the effect of ontological security threats on the purchasing intent for green housing.

This research contributes to the marketing literature by offering a novel perspective on the impact of natural environmental uncertainty on consumer behavior, augmenting the body of knowledge concerning the determinants of green housing purchase intentions, and provides new ideas for marketers.