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Vision transformers (ViT) detectors excel in processing natural images. However, when processing remote sensing images (RSIs), ViT methods generally exhibit inferior accuracy compared to approaches based on convolutional neural networks (CNNs). Recently, researchers have proposed various structural optimization strategies to enhance the performance of ViT detectors, but the progress has been insignificant. We contend that the frequent scarcity of RSI samples is the primary cause of this problem, and model modifications alone cannot solve it.

To address this, we introduce a faster RCNN-based approach, termed QAGA-Net, which significantly enhances the performance of ViT detectors in RSI recognition. Initially, we propose a novel quantitative augmentation learning (QAL) strategy to address the sparse data distribution in RSIs. This strategy is integrated as the QAL module, a plug-and-play component active exclusively during the model’s training phase. Subsequently, we enhanced the feature pyramid network (FPN) by introducing two efficient modules: a global attention (GA) module to model long-range feature dependencies and enhance multi-scale information fusion, and an efficient pooling (EP) module to optimize the model’s capability to understand both high and low frequency information. Importantly, QAGA-Net has a compact model size and achieves a balance between computational efficiency and accuracy.

We verified the performance of QAGA-Net by using two different efficient ViT models as the detector’s backbone. Extensive experiments on the NWPU-10 and DIOR20 datasets demonstrate that QAGA-Net achieves superior accuracy compared to 23 other ViT or CNN methods in the literature. Specifically, QAGA-Net shows an increase in mAP by 2.1% or 2.6% on the challenging DIOR20 dataset when compared to the top-ranked CNN or ViT detectors, respectively.

This paper highlights the impact of sparse data distribution on ViT detection performance. To address this, we introduce a fundamentally data-driven approach: the QAL module. Additionally, we introduced two efficient modules to enhance the performance of FPN. More importantly, our strategy has the potential to collaborate with other ViT detectors, as the proposed method does not require any structural modifications to the ViT backbone.

This study aims to comprehensively examine the transitions in household livelihood strategies within rural China, including the underlying processes, drivers and outcomes.

This study uses two waves (2010 and 2018) of longitudinal data from the China Family Panel Studies (CFPS), employing latent cluster analysis, regression models and cumulative distribution function within a dynamic household livelihood strategy framework.

The results show that (1) households’ livelihood strategies can be categorized into four distinct types, i.e. agricultural dominated, agricultural dominated with non-agricultural supplementation, non-agricultural dominated with agricultural supplementation and employment oriented. (2) During 2010–2018, approximately 60% of households underwent transitions in their livelihoods, encompassing both upward and downward trajectories, with a prevalence of upward transitions. (3) Various forms of livelihood capital significantly contribute to upward transitions, while the availability of land resources and exposure to shocks impede the potential for upward mobility. (4) The transition towards non-agricultural livelihood strategies by households leads to a notable enhancement in their livelihood welfare.

In the context of urbanization, industrialization and globalization, rural areas in China are undergoing a gradual socioeconomic transformation, which has also led to changes in rural households’ livelihood strategies. Nevertheless, a dearth of empirical investigation exists regarding the dynamics of rural households’ livelihood strategies, the determinants behind such transitions and the resulting outcomes on their livelihoods. A comprehensive understanding of livelihood transitions can provide valuable insights for policymakers in their endeavors to promote rural revitalization in China.

Based on the nationwide representative datasets in China, it examines the micro-level livelihood transitions of rural households within the broader context of socioeconomic transformation that presents both opportunities and challenges, as well as vulnerable contexts, shaped by various government policies. This exploration would offer valuable theoretical and empirical evidence to advance our understanding of the process, driver and outcome of rural households’ livelihood transition in developing countries.

Service robots with advanced artificial intelligence (AI) can collect data on customer preferences, understand complex requests, improve services, and tailor marketing strategies. This study examined how perceived relatedness, perceived warmth, and customer–AI-assisted exchanges (CAIX) of service robots affect customer service competencies and brand love through service-robot intimacy.

A brand love model was developed based on the AI device using acceptance and an emotional perspective. Data were collected from customers who had dined in robot restaurants; 415 questionnaires were completed, and partial least squares analysis was adapted to the proposed model.

The results demonstrate that the perceived relatedness, perceived warmth and CAIX of service robots affect the intimacy of robot restaurants. Customers who feel friendly and satisfied with a restaurant’s service robot will recommend it to their friends.

This study draws on theory and existing literature to identify principal factors in robot restaurant service capabilities. Future research can include service robot data analysis capabilities and adoption process factors as the direction of customer relationship management research while also exploring the influence of AI computing on restaurant supply chains. Likewise, the agility of service robots in the stages of innovation can be discussed in future research based on different theories, which will bridge unique insights.

The findings of this study emphasize the relationship between service robots and restaurant brand love and propose specific practice areas for restaurants.

This study expands the main issue of current brand love research from traditional restaurant operations to the novel field of humanoid service robot restaurants. It enriches our understanding of how consumers’ emotional fondness for a brand affects their behavioural intentions.

This study aims to examine the impact of peer-to-peer (P2P) accommodation platforms’ green marketing on consumers’ pro-environmental behavioural intention through the mediation of consumer trust and engagement, following the social influence theory and the stimulus–organism–response model.

A questionnaire survey was designed to collect data from American P2P accommodation consumers. Data collection was conducted through an outsourced survey company. Partial least squares structural equation modelling was used to analyse the data.

The study reveals that P2P accommodation platforms’ green marketing orientation was positively associated with consumer trust in the platform and consumer engagement with the platform. Both consumer trust and consumer engagement positively enhanced consumers’ pro-environmental behavioural intention in the P2P accommodation consumption, serving as effective mediators between consumers’ perceptions of green marketing orientation and pro-environmental behavioural intention.

The study offers practical insights for P2P accommodation platforms and operators in engaging in green marketing and fostering consumers’ pro-environmental consumption behaviours in P2P accommodations.

The study addresses the grand question of whether business operators’ responsible production behaviour can possibly lead to consumers’ responsible consumption behaviour in the P2P accommodation sector. It contributes to the literature on P2P accommodation by providing evidence to show green marketing practices of P2P accommodation platforms can lead to consumers’ pro-environmental behavioural intention. It provides both theoretical value for knowledge advancement and practical value to guide more sustainable industry practices.

Using energy economically and efficiently is essential to ensure national energy security, reduce greenhouse gas emissions, and preventing climate change. Household appliances consume considerable energy, so choosing energy-efficient is to consuming energy economically and efficiently. The research applies the extended theory of planned behavior (TPB) to empirically investigate factors affecting consumers’ purchase intention toward energy-efficient household appliances (EEHA) in Ho Chi Minh City (HCM). The chapter develops both qualitative and quantitative methods. The qualitative method is conducted with five in-depth interviews to get more consumer insight, verify the research model, and elaborate the questionnaire. The quantitative method with 300 consumers living in HCM is used to address the research question. The primary data are collected via a web-based questionnaire and analyzed by IBM SPSS Statistics 20 and Amos 20. The research indicates that attitude, subjective norm (SN), perceived behavioral control (PBC), past purchase experience (PPE), and economic benefit (EB) have significant positive impacts on consumers’ purchase intention. EB has the most significant impact. Nonetheless, consumers’ purchase intention is not significantly impacted by their level of environmental knowledge (EK). Besides, PPE and EBs positively impact attitude and indirectly affect purchase intention, while EK does not. Based on the empirical results, some recommendations are drawn for policymakers to promote energy-efficient household appliance consumption by citizens, thereby ensuring national energy security and promoting environmental protection. Implications to help manufacturers improve products in the right direction and make wise investment decisions and distributors/retailers to understand consumers in HCM are also suggested.

This study aims to conduct a systematic literature review on mobile application experience, recognizing its growing importance in research and practical applications. The purpose is to address the factors influencing customer mobile application experience in this dynamic environment.

The study uses the theories, context, characteristics, methodology (TCCM) review framework. This research meticulously analyzes 42 articles published between 2015 and 2023. The selected articles are sourced from prominent journals in widely recognized databases, including Scopus and the Australian Business Deans Council.

The examination of the pertinent literature culminates in the formulation of the TCCM framework, shedding light on the antecedents, mediators, moderators and outcomes, related to the mobile application experience.

This study pioneers a unified framework for mobile application experience, shedding light on unexplored territories and shaping the trajectory of future mobile application experience investigations. Its emphasis on a strong academic foundation enriches technological marketing literature, offering managers actionable insights aligned with TCCM. This study marks a pivotal contribution, bridging theoretical advancements with practical strategies for the dynamic landscape of mobile applications experience.

This research uncovers overlooked areas and proposes discerning research directions to advance the understanding of mobile applications experience. It addresses a crucial gap in current knowledge and motivates further investigation into the intricate dynamics of mobile application experience.

Reusable takeaway food containers (RTFCs) are a newly emerging green packaging choice for the takeaway industry that can effectively reduce campus solid waste but are not yet well accepted. Therefore, this study aims to identify the key factors influencing university students’ intention to choose RTFCs, seeking to enhance RTFC project management practices and contribute to developing a sustainable “green university.”

In total, 316 valid respondents from a Chinese university were surveyed for data collection. A multivariate ordered logistic regression model was used to conduct empirical analysis.

The results of this study underscore the crucial role of perceived value in the relationship between perceived green attributes and students’ intention to choose RTFCs. The positive impacts of perceived green attributes on intention are direct and indirect, through the lens of perceived value. When the value is substantial, it significantly boosts the student’s intention to choose RTFCs. Conversely, the perception of lower hygienic quality or higher returning time cost dampens this intention, with a more pronounced effect than perceived green attributes. Notably, perceived publicity activities have the most significant impact on student’s intention to choose RTFCs.

This study contributes to the understanding of promoting RTFCs, a key strategy for reducing plastic waste on campuses. The findings provide actionable recommendations for the project company and the university, offering practical ways to encourage students to use RTFCs and contribute to plastic waste reduction.

This study aims to provide a timely review of hospitality and tourism live streaming (HTLS) research, aiming to understand the intellectual foundation in the past and identify opportunities for future research.

This study collected 56 papers from journals in the Social Science Citation Index database or the Australian Business Deans Council journal quality list and adopted a hybrid systematic literature review combining bibliometric analysis and the theory-context-characteristics-method framework-based approach. Additionally, a cross-tabulation analysis was conducted to compare the research on two specific forms of HTLS – tourism e-commerce live streaming (TEcLS) and travel live streaming.

The study found that the current research lacks in-depth theoretical insights, focuses on China as the national context and viewers as the research subject, mainly uses online surveys and lacks first-order knowledge. Moreover, it identified five research themes, including the impact of HTLS on viewer behavior, purchase behavior in HTLS and introduction of HTLS, within which eight categories of research constructs, such as viewer-related, streamer-related and relationship-related, are applied.

In terms of research, the study reveals various gaps in the existing literature and provides potential research directions to advance the field. As for practice, it offers valuable strategies for practitioners to optimize their marketing efforts using HTLS.

This study provides an up-to-date review of the HTLS field through a systematic and reproducible method. It offers critical discussions and suggestions for academic research and industry practices.

Yoga has become increasingly visible globally. Most of the visibility occurs through the physical (annamaya¹) kosha aspects of yoga: physical health and well-being through asana² and pranayama³ practices, in addition to the Manomaya kosha, the mental sheath, of an individual through dhyana,⁴ or meditation, and chanting of mantras.⁵ All of this is geared towards the individual and one’s ability to balance oneself in any given situation. Such practices support a bottom-up conceptualisation of yoga, giving agency to the individual. This chapter looks at the opportunities for yoga to enter organisational management, through organisational structure (chakravyuha⁶ formation), employee engagement and assessment (gunas⁷ and chakras⁸), and ethical leadership (yamas and niyamas⁹), among other aspects. It looks at how top-down systems can adapt through the utilisation of yogic principles and philosophies in everyday practice in organisations (Panchakoshas, 2021).

This study aims to identify the key characteristics of tourism live streamers (TLSers) that influence target consumers and examine how these characteristics enhance destination brand value by stimulating consumer emotional engagement and value co-creation behaviors.

A questionnaire survey was conducted with 552 respondents who had viewed tourism live streaming in China. The data were analyzed using structural equation modeling.

The results revealed that TLSers’ expertise had the strongest influence on consumers and positively affected their attachment, trust, participation and citizenship behaviors and perceived destination brand value. Attractiveness and trustworthiness exerted positive but weaker effects on these variables.

This study contributes to the theoretical development of live-streaming research by demonstrating the distinct impacts of TLSer characteristics. Furthermore, this study expands the application of the social exchange theory and value co-creation in tourism research.

本研究旨在确定影响目标消费者的旅游直播主（TLSers）的关键特征, 并检验这些特征如何通过激发消费者情感参与和价值共创行为来增强目的地品牌价值。

对在中国观看过旅游直播的552名受访者进行了问卷调查, 采用结构方程建模分析数据。

研究结果显示, TLSers的专业知识对消费者影响最大, 并积极影响他们的情感依恋、信任、参与和公民行为, 以及感知的目的地品牌价值。吸引力和可信度对这些变量的影响较弱但也是积极的。

本研究通过展示TLSer特征的独特影响, 对直播研究的理论发展做出了贡献。此外, 本研究扩展了社会交换理论和价值共创在旅游研究中的应用。