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Virtual reality (VR) offers unprecedented immersion and interactivity in education, and working and learning from home have become the norm during the COVID-19 pandemic. This study empirically investigated the factors affecting the use of a VR online learning system (VROLS).

To explore factors affecting users’ continuance behavioral intentions toward using VROLSs, a research framework was formed comprising factors that constitute benefits (i.e. pull factors) and costs (i.e. push factors); these factors included perceived value, flow and social influence. The data for this study were collected via online survey questionnaires. A total of 307 valid responses were used to examine the hypotheses in the research model, employing structural equation modeling (SEM) techniques.

Perceived value, flow experience and the number of peers using VR primarily affect the decision to adopt a VROLS. The pull factors of spatial presence, entertainment and service compatibility, along with the push factors of complexity and visual fatigue, affect perceived value. Therefore, we conclude that perceived value is a primary factor positively influencing both flow experience and the decision to adopt the service.

This study contributes to a theoretical understanding of factors that explain users’ intention to use VROLSs.

Although user stickiness has been studied for several years in the field of live e-commerce, little attention has been paid to the effects of streamer attributes on user stickiness in this field. Rooted in the stimulus-organism-response (S-O-R) theory, this study investigated how streamer attributes influence user stickiness.

The authors obtained 496 valid samples from Chinese live e-commerce users and explored the formation of user stickiness using partial least squares-structural equation modeling (PLS-SEM). Artificial neural network (ANN) was used to capture linear and non-linear relationships and analyze the normalized importance ranking of significant variables, supplementing the PLS-SEM results.

The authors found that attractiveness and similarity positively impacted parasocial interaction (PSI). Expertise and trustworthiness positively impacted perceived information quality. Moreover, streamer-brand preference mediated the relationship between PSI and user stickiness, as well as the relationship between perceived information quality and user stickiness. Compared to PLS-SEM, the predictive ability of ANN was more robust. Further, the results of PLS-SEM and ANN both showed that attractiveness was the strongest predictor of user stickiness.

This study explained how streamer attributes affect user stickiness and provided a reference value for future research on user behavior in live e-commerce. The exploration of the linear and non-linear relationships between variables based on ANN supplements existing research. Moreover, the results of this study have implications for practitioners on how to improve user stickiness and contribute to the development of the livestreaming industry.

Compensatory consumption focuses on the psychological value of products. Special agricultural products have symbolic and social functions that effectively meet psychological needs and stimulate compensatory consumption behavior. The social commerce context not only enriches consumer experience but also influences consumer purchase decisions. This study constructs a model based on the elaboration likelihood model (ELM) and the stimulus-organism-response (SOR) theory to explore the mechanism of compensatory consumption behavior of special agricultural products in a social commerce context.

This study uses a two-stage method of partial least squares structural equation model (PLS-SEM) and fuzzy set qualitative comparative analysis (fsQCA) to analyze 523 valid samples collected through random sampling. PLS-SEM was used to examine the relationships and effects between the variables; fsQCA was used to conduct a cohort analysis between the variables to further reveal the complexity and diversity of compensatory consumption behaviors.

PLS-SEM indicates that product attributes and social affordances influence consumers’ triggering of compensatory consumption behavior for control and belongingness needs. fsQCA shows that there are three different modes, and the satisfaction of belongingness or control needs is a necessary condition for triggering compensatory consumption behavior.

There is limited research on compensatory consumption behavior specifically focused on special agricultural products. This study explores the influencing factors and mechanisms of compensatory consumption behavior related to special agricultural products. The occurrence of compensatory consumption behavior is not only influenced by product attributes but also by the social commerce environment. In marketing strategies, it is important to not only consider product characteristics but also pay attention to consumers’ social and psychological needs.

This study aims to examine the effects of multidimensional factors of platform features, group effects and emotional attitudes on social media users’ privacy disclosure intention.

This study collected the data from 426 respondents through an online questionnaire survey and conducted two approaches of structural equation modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA) for theoretical hypothesis testing and configuration analysis of the data.

The results show that social media platform features (rewards of information disclosure, personalized service quality and data transparency), group effects (group similarity, group information interaction and network externality), individual emotional attitudes (trust and privacy concern) and control variable (gender) have a significant impact on privacy disclosure intention, as well as trust and privacy concern play mediating roles. Additionally, the fsQCA method reveals five causal configurations that explain high privacy disclosure intentions. Furthermore, the study reveals that male users pay more attention to platform features, while female users are more inclined to group effects.

This study attempts to construct a comprehensive model to examine the factors that affect users' intention to disclose their privacy on social media platforms. Drawing on the cognition-affect-conation model and multidimensional development theory, the model integrates multidimensional factors of platform features, group effects, trust and privacy concern to complement existing theoretical frameworks and privacy disclosure literature. By understanding the complex dynamics behind privacy disclosure, this study helps platform providers and policymakers develop effective strategies to ensure the vitality and momentum of the social media ecosystem.

This study investigated the relationship of strengths-based leadership with nurses’ turnover intention and the mediating roles of job crafting and work fatigue in the relationship.

Data comprising 318 valid participants from three hospitals in Beijing were gathered at two points in time, spaced by a two-month interval. Structural equation modeling with a bootstrapping analysis was applied to test hypotheses.

This study found that strengths-based leadership negatively relates to nurses’ turnover intention, and job crafting and work fatigue mediate the relationship of strengths-based leadership with turnover intention, respectively.

The findings of this study highlight the importance of strengths-based leadership in decreasing nurses’ turnover intention and reveal two potential mechanisms through which strengths-based leadership is related to nurses’ turnover intention. In order to retain nursing staff better, nurse leaders should execute more strengths-based leadership behaviors and make more efforts to promote nurses’ job crafting and to reduce nurses’ experience of work fatigue.

Adopting the context, input, process and product (CIPP) evaluation assessment model, this study aims to achieve three objectives: (1) to identify the characteristics of educational activities conducted by the International Table Tennis Federation (ITTF) museum; (2) to explore the key factors that promote and optimize the educational objectives of the ITTF museum and (3) to develop a framework that attempts to unpack the complex and dynamic processes of activities conducted by the ITTF museum.

A qualitative research using in-depth interviews was conducted to explore the functions and activities of the ITTF museum.

The path of educational activities carried out is expressed in four aspects: (1) educational background including philosophy and purpose, advantages possessed and the learning needs and readiness of the audience; (2) the resource input includes resource allocation, venue structure, funding channels and the basis of activity program design; (3) the educational activity process includes external cooperation and exchange, the specific content of the activity programs, the teaching strategies and methods during the activities and the response to the challenges of the epidemic and (4) the results of the activities include the number of audience and population distribution, evaluation and feedback of the activities, ways and means of promoting the activities and difficulties and challenges faced.

In the follow-up studies, quantitative research can be conducted by developing a questionnaire based on the current study’s findings. Evaluation scales can be set to continue to enrich and improve the uncovered research content, which is conducive to promoting the objectives of the ITTF museum and giving full play to the educational function, thus enriching the research of sports museums.

The contributions of this study are as follows. First, it enriches the application of the CIPP assessment model in the context of sports museums. It helps the ITTP museum better perform its educational function and provides a reference for future activities of sports museums.

In traditional Chinese medicine (TCM), the mechanism of disease (MD) constitutes an essential element of syndrome differentiation and treatment, elucidating the mechanisms underlying the occurrence, progression, alterations and outcomes of diseases. However, there is a dearth of research in the field of intelligent diagnosis concerning the analysis of MD.

In this paper, we propose a supervised Latent Dirichlet Allocation (LDA) topic model, termed MD-LDA, which elucidates the process of MDs identification. We leverage the label information inherent in the data as prior knowledge and incorporate it into the model’s training. Additionally, we devise two parallel parameter estimation algorithms for efficient training. Furthermore, we introduce a benchmark MD identification dataset, named TMD, for training MD-LDA. Finally, we validate the performance of MD-LDA through comprehensive experiments.

The results show that MD-LDA is effective and efficient. Moreover, MD-LDA outperforms the state-of-the-art topic models on perplexity, Kullback–Leibler (KL) and classification performance.

The proposed MD-LDA can be applied for the MD discovery and analysis of TCM clinical diagnosis, so as to improve the interpretability and reliability of intelligent diagnosis and treatment.

This study aims to examine the effects of network externalities and diffusion of innovation on users’ perceived usefulness and perceived enjoyment, and in turn subjective well-being and intention to use in the smart living context.

To explore different types of smart living services, the data collected come from surveying 512 users of smart home services and 570 users of smart living apps. Structural equation modeling is used to analyze the data, and post hoc interviews are conducted to provide insights into our conceptual model of smart living services.

Compatibility and perceived service complementarity are the most influential determinants of users’ perceived usefulness and perceived enjoyment toward smart living services. Perceived usefulness has a greater impact on users’ intention to use and their subjective well-being than perceived enjoyment. Interestingly, perceived enjoyment exerts a stronger influence on subjective well-being than on intention to use.

This study is one of the first to provide empirical evidence in the context of smart living services, contributing to transformative service literature by extending the understanding of technology use and its influence on user well-being into a less explored service context. This study also advances users’ intention to use and subjective well-being of technology adoption in the service context by integrating perspectives from network externalities and diffusion of innovation.

In this article, the authors try to determine why and under what conditions consumers intend to buy green and what the consequences are. Relying on theories of reasoned action and theory of planned behaviour (TPB), the authors offer that the green purchase intention (GPI) is impressed by environmental and personality components. Provide statements about the determinants and key implications of such market identification.

The emergence of consumerism has led to regulatory measures being integrated into business practices, but the influence of consumers in developing countries remains limited, resulting in businesses being less responsive. The digital retail landscape is undergoing a transformative revolution, driven by Industrial Revolution (IR) 4.0 technological advancements such as artificial intelligence (AI), wearables, virtual reality (VR), augmented reality (AR), and blockchain technology. This development focuses on convenience, personalisation, and emotional connections. Companies are adapting to modern consumer behaviour through various strategies, including online shopping, mobile commerce, data analytics, technology integration, user reviews, and contactless payments. The COVID-19 pandemic has accelerated this seismic shift in the retail industry, and online retail is expected to continue to grow post-pandemic, driven by these technologies. AI enhances the customer experience, wearables provide interactive engagement, VR offers immersive shopping, AR merges online and physical shopping, and blockchain ensures secure transactions in the emerging metaverse. As retail converges with the metaverse, the potential for borderless and personalised shopping experiences is enormous. Advances in VR technology could lead to interconnected virtual spaces that seamlessly connect physical and digital retail, providing immersive and personalised shopping experiences. However, challenges such as cost, learning curves, digital security, legal ambiguity, data privacy, financial risk, and ethical considerations need to be addressed through vigilant and informed consumer engagement in this evolving digital landscape.