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Live streaming has become an extremely popular form of online service and allows live audiences to give virtual gifts or money to their favorite streamers. This study aims to investigate the impact of the audience's multidimensional social presence on their attitudes toward live streamers and intentions to give money or gifts to streamers, based on the interactive marketing perspective. This study considers live audiences' gift-giving intentions by integrating the theory of multidimensional social presence, which includes awareness, cognitive social presence and affective social interaction, and the theory of reasoned action (TRA).

This study invited audiences who had watched live streaming from several popular live-streaming platforms to respond to a web questionnaire. The unit of analysis was at the individual level. This study applied the purposive sampling technique for data collection. A sample of 258 eligible responses to the online survey was analyzed using SPSS software and the causal relationships between the measurement variables of this research model were verified through structural equation modeling.

The results indicate that the audiences' awareness of participating in live streaming enhanced their cognitive and affective social presence, which positively affected their attitudes toward live streamers. These attitudes had a further significant effect on their gift-giving intentions. Cognitive social presence and affective social presence were found to play significant mediating roles in the relationship between awareness and attitudes toward live streamers.

This study examines audiences' intention to give gifts to their favorite live streamers, based on the interactive marketing perspective. The interactive relationship between live streamers and online audiences is developed by audience members through the process of inner psychological transformation, which is measured through the multidimensional construct of social presence. This occurs through a mutual influence relationship in which awareness simultaneously influences cognitive social presence and affective social presence, and cognitive social presence impacts affective social presence.

The study contributes three noteworthy findings to the theory development through the integrated perspective of the TRA and the theory of social presence. (1) Exploring the influence of belief factors on internal psychological responses and intention in live streaming to expand an innovative application of the TRA. (2) Adopting the multidimensional social presence can help researchers more clearly describe various live-streaming situations and extend the research scope of the social presence theory to live-streaming interactive marketing strategies. (3) From the perspective of live-streamer marketing, this study broadens the research fields of electronic commerce and interactive marketing.

This study provides four practical implications for platform managers and live streamers. (1) To induce favorable attitudes toward live streamers, live streamers initiate various interactive activities sequentially to establish a social presence with the audience. (2) Live streamers should devote themselves to forming a joyful atmosphere for their followers, as this will trigger audiences' affective social presence to generate positive attitudes and increase followers' intentions. (3) To attract and retain young followers, live streamers must devise interesting content and provide fresh services. (4) Platform managers must create useful widgets to assist live streamers in managing their channels and followers.

Building friendly real-time interaction between the live streamer and the audience is an important task in live streaming and further influences the income of the live streamer and the platform. The study provides an effective approach to building friendly real-time interaction for the live streamer and manager of live-streaming electronic commerce through the interactive marketing perspective. The approach can help the live streamer manage nice communication with their audience and obtain virtual money and gift-giving from the audience.

Despite the critical role of the policy environment in facilitating the advancement of building information modeling (BIM) as a systemic innovation to reshape traditional facility design, construction and operation processes, scant scholarly attention has been paid to systematically investigating how and why complex BIM policies are concretely and gradually implemented in different regional contexts from a dynamic policy diffusion perspective. This study aims to empirically investigate how different types of BIM policy instruments are dynamically implemented in heterogeneous regions over time and how the diffusion of BIM policies across different regions is comprehensively impacted by both internal efficiency needs and external legitimacy pressures.

This study employed a positivist research paradigm in which BIM policy data from 182 prefecture-level and above cities in China during 2011–2022 were analyzed with quantitative approaches for theory verification. Based on the content analysis of the evolutionary characteristics of the adopted BIM policy instruments in heterogeneous regions over time, the event history analysis (EHA) method was then used to further examine the mechanisms underlying the diffusion of BIM policies across different regions.

The content analysis results show that while environmental instruments (such as technological integration and goal planning) are the primary policy instruments currently adopted in China, recent years have also witnessed increasing adoptions of supply-side instruments (such as fiscal support and information support) and demand-side instruments (such as demonstration projects and tax incentives). After controlling for the impacts of regional fiscal and technical resources, the EHA results illustrate that BIM policy adoption positively relates to regional construction industry scale but negatively relates to regional industry productivity and that compared with public pressures from industry participants, vertical pressures from the central government and horizontal pressures from neighboring regions are more substantial drivers for policy adoption.

As an exploratory effort of using a dynamic policy diffusion perspective to systematically investigate how BIM policies are adopted in heterogeneous regional contexts to facilitate BIM advancement, this study not only characterizes the complexity and dynamics of BIM policies but also provides deepened understandings of the mechanisms underlying policy adoption in the conservative construction industry. The findings hold implications for how multifarious policy instruments can be more effectively and dynamically adopted to facilitate the advancement of BIM and related technologies as innovative solutions in the construction domain.

Literature has paid limited attention to narcissism’s influence on tourism. The pre-trip planning stage involves temporal psychological distance, where decision-making regarding the trip stretches over a period of time and occurs anytime during that period. Using narcissism theory and with temporal distance (6 months and 1 week before the trip) as the moderator, this study examines how tourists’ self-sacrificing self-enhancement and grandiose fantasy – grandiose narcissism facets – and travel motivations affect attraction visit intentions.

Partial least squares (PLS) analysis was applied on survey information obtained from 374 individuals (200 and 174 were six-months and one-week scenarios, respectively).

Grandiose narcissism facets affect motivations and visit intentions to various attractions at different temporal distances. The consequences of grandiose fantasy and self-sacrificing self-enhancement on motivation become evident as temporal distance decreases. At large temporal distances, grandiose fantasy positively influences social recognition motivation. At short temporal distances, it positively influences social recognition and ego and negatively influences scenery and relaxation motivations. Self-sacrificing self-enhancement positively influences socialization, exploration, scenery, relaxation and escape motivations at large temporal distances. At short temporal distances, self-sacrificing self-enhancement positively influences historical attraction visit intentions.

Grandiose narcissism facets’ influence on tourism is rarely examined. This study extends the temporal distance concept to the narcissism theory. It integrates the effects of grandiose narcissism and temporal distance on tourists’ motivations and different attraction type visit intentions and shows that motivation is temporally distance-specific.

This study aims to develop a conceptual model to understand how customer knowledge management (CKM) affects fitness club membership renewal through the mediation of relationship quality.

Data were collected outside of fitness clubs using a systematic sampling method. A total of 224 valid responses were collected. Structural equation modelling was used to evaluate the relationship between the constructs of the research model.

The results indicate that both knowledge from customers and knowledge for customers have a positive influence on customer satisfaction and customer trust. Among them, knowledge for customers has a stronger influence on customer satisfaction while knowledge from customers has a greater influence on customer trust. Additionally, three dimensions of relationship quality (customer satisfaction, customer trust and customer commitment) positively influence membership renewal intention with customer commitment exhibiting the greatest influence on it.

This study combines the theories of CKM and relationship quality management to explain why members will renew their service contracts. By using fitness clubs as an example, this research extends the authors' understanding of how knowledge from and for customers can influence customers' attitudes and behavioural intentions towards service companies.

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.

The purpose of this paper is to establish a dynamic analysis model of composite cylindrical roller bearings, investigate the effects of different working conditions on the kinematic characteristics of composite bearings and compare the differences between them and solid roller bearings.

This paper establishes a dynamic analysis model for composite cylindrical roller bearings and proves the correctness of the established model by establishing dynamic vibration experiments and contact theory for composite roller bearings. Comparative analysis was conducted on the effects of coupling changes in rotational speed, load, number of rollers and filling ratio on parameters such as bearing static stiffness, contact stress and vibration acceleration.

The composite roller can enhance the bearing’s operational stability and minimize contact stress, but that a higher filling ratio is going to increase the bearing’s stiffness. The acceleration degree of bearing vibration, the load on the outer raceway nodes and the bearing stability all decrease as inner ring speed rises.

A dynamic calculation model of composite cylindrical roller bearings is established, and the influence of multiparameter coupling changes on bearing vibration and contact is studied, which lays a foundation for the structural improvement of the bearings.

This paper examined the mediating role of boundary spanning behavior and the moderating effects of traditionality linking humble leadership and employee creative performance from the perspective of Social Exchange Theory (SET) to reveal the behavioral mechanism and boundary condition regarding the influence of humble leadership on creative performance.

A sample of 276 employees and the supervisors from 8 companies in China was taken using two-wave data.

The results indicated that humble leadership was positively related to employee creative performance, and boundary spanning behavior partially mediated the relationship between humble leadership and employee creative performance. Traditionality strengthens the mediation process when traditionality is high.

These findings provide several theoretical and practical implications for the domains of humble leadership and boundary spanning behavior. For example, human resource (HR) departments can recruit leaders with high humility and cultivate team leaders through systematic training programs about self-awareness, openness and self-transcendence; team leaders should encourage employees to participate in boundary spanning activities and hiring managers select employees with high traditionality to synergize with leader humility.

Based on the SET, this paper explored the behavioral mechanism between humble leadership and creative performance and enriched the prior research, which is from the cognitive or emotional view, and further answered the question “what are the employees' behavioral responses when they confront the humble leadership”.

Recognizing construction workers' activities is critical for on-site performance and safety management. Thus, this study presents the potential of automatically recognizing construction workers' actions from activations of the erector spinae muscles.

A lab study was conducted wherein the participants (n = 10) performed rebar task, which involved placing and tying subtasks, with and without a wearable robot (exoskeleton). Trunk muscle activations for both conditions were trained with nine well-established supervised machine learning algorithms. Hold-out validation was carried out, and the performance of the models was evaluated using accuracy, precision, recall and F1 score.

Results indicate that classification models performed well for both experimental conditions with support vector machine, achieving the highest accuracy of 83.8% for the “exoskeleton” condition and 74.1% for the “without exoskeleton” condition.

The study paves the way for the development of smart wearable robotic technology which can augment itself based on the tasks performed by the construction workers.

This study contributes to the research on construction workers' action recognition using trunk muscle activity. Most of the human actions are largely performed with hands, and the advancements in ergonomic research have provided evidence for relationship between trunk muscles and the movements of hands. This relationship has not been explored for action recognition of construction workers, which is a gap in literature that this study attempts to address.

Social media platforms that disseminate scientific information to the public during the COVID-19 pandemic highlighted the importance of the topic of scientific communication. Content creators in the field, as well as researchers who study the impact of scientific information online, are interested in how people react to these information resources and how they judge them. This study aims to devise a framework for extracting large social media datasets and find specific feedback to content delivery, enabling scientific content creators to gain insights into how the public perceives scientific information.

To collect public reactions to scientific information, the study focused on Twitter users who are doctors, researchers, science communicators or representatives of research institutes, and processed their replies for two years from the start of the pandemic. The study aimed in developing a solution powered by topic modeling enhanced by manual validation and other machine learning techniques, such as word embeddings, that is capable of filtering massive social media datasets in search of documents related to reactions to scientific communication. The architecture developed in this paper can be replicated for finding any documents related to niche topics in social media data. As a final step of our framework, we also fine-tuned a large language model to be able to perform the classification task with even more accuracy, forgoing the need of more human validation after the first step.

We provided a framework capable of receiving a large document dataset, and, with the help of with a small degree of human validation at different stages, is able to filter out documents within the corpus that are relevant to a very underrepresented niche theme inside the database, with much higher precision than traditional state-of-the-art machine learning algorithms. Performance was improved even further by the fine-tuning of a large language model based on BERT, which would allow for the use of such model to classify even larger unseen datasets in search of reactions to scientific communication without the need for further manual validation or topic modeling.

The challenges of scientific communication are even higher with the rampant increase of misinformation in social media, and the difficulty of competing in a saturated attention economy of the social media landscape. Our study aimed at creating a solution that could be used by scientific content creators to better locate and understand constructive feedback toward their content and how it is received, which can be hidden as a minor subject between hundreds of thousands of comments. By leveraging an ensemble of techniques ranging from heuristics to state-of-the-art machine learning algorithms, we created a framework that is able to detect texts related to very niche subjects in very large datasets, with just a small amount of examples of texts related to the subject being given as input.

With this tool, scientific content creators can sift through their social media following and quickly understand how to adapt their content to their current user’s needs and standards of content consumption.

This study aimed to find reactions to scientific communication in social media. We applied three methods with human intervention and compared their performance. This study shows for the first time, the topics of interest which were discussed in Brazil during the COVID-19 pandemic.

From a relational maintenance perspective, this study explores the impact of being envied (benignly vs maliciously) on consumers' feelings of social anxiety and its influence on their tendencies toward inconspicuous consumption, based on the resource conservation theory and the model of “Sensitivity about Being the Target of a Threatening Upward Comparison.” (STTUC)

Four studies were conducted in this paper. Studies 1a and 1b tested the main hypothesis that being maliciously envied (vs benignly) can increase consumers' inconspicuous consumption of luxury products and luxury hotel experiences. Study 2 replicated this finding and examined the mediating role of social anxiety. Study 3 investigated the moderating effect of ideal self-congruity (low vs high).

The findings reveal that being maliciously envied (vs benignly) is associated with higher levels of inconspicuous consumption and social anxiety acts as a mediating role. Moreover, when individuals have a strong sense of ideal self-congruity, the positive impact of being maliciously envied (vs benignly) on inconspicuous consumption is further amplified, confirming the moderating role of ideal self-congruity.

This study sheds light on a novel mechanism that elucidates how different types of being envied influence consumers' inconspicuous consumption and the conditions under which this impact is heightened.