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This paper aims to offer an integrated framework for branded apps (BAs) that highlights research gaps and points to areas for future research.

Using a systematic literature review approach, the authors analyzed more than 100 articles published between 2009 and the present. This research used databases such as SCOPUS, Web of Science, EBSCO and Elsevier’s Science Direct, with a particular focus on articles listed in the 2021 ABS index. The reviewed papers were coded and organized into four categories in terms of themes and concepts: antecedents, mechanisms, outcomes and moderators.

The study identified four types of antecedents (app benefits, personal traits, brand benefits and others) that influence outcomes via cognitive, affective and a mix of both mechanisms, which is termed multipaths. The authors classified outcomes into five areas (financial gains; app benefits; brand benefits; customer benefits; and others). Moderators were grouped into four types (customer individuality; app features; brand characteristics; and others). The authors concluded by recommending promising directions for future research. Specifically, the authors suggested an inverted U-shaped relationship between escapism and customer responses to BAs.

This paper focused exclusively on BAs, differentiating them from other mobile apps. The authors integrated nearly 100+ studies conducted over two decades. This integrated model serves as a guiding tool for understanding the past, present and future of BAs.

The purpose of this study is to make property price forecasts for the Chinese housing market that has grown rapidly in the last 10 years, which is an important concern for both government and investors.

This study examines Gaussian process regressions with different kernels and basis functions for monthly pre-owned housing price index estimates for ten major Chinese cities from March 2012 to May 2020. The authors do this by using Bayesian optimizations and cross-validation.

The ten price indices from June 2019 to May 2020 are accurately predicted out-of-sample by the established models, which have relative root mean square errors ranging from 0.0458% to 0.3035% and correlation coefficients ranging from 93.9160% to 99.9653%.

The results might be applied separately or in conjunction with other forecasts to develop hypotheses regarding the patterns in the pre-owned residential real estate price index and conduct further policy research.

This paper aims to develop an optimization model to enhance pipeline assembly performance. It focuses on predicting the pipeline’s assembly pose while ensuring compliance with clamp constraints.

The assembly pose of the pipeline is quantitatively assessed by a proposed indicator based on joint defects. The assembly interference between the pipeline and assembly boundary is characterized quantitatively. Subsequently, an analytical mapping relationship is established between the assembly pose and assembly interference. A digital fitting model, along with a novel indicator, is established to discern the fit between the pipeline and clamp. Using the proposed indicators as the optimization objective and penalty term, an optimization model is established to predict the assembly pose based on the reinforced particle swarm optimization, incorporating a proposed adaptive inertia weight.

The optimization model demonstrates robust search capability and rapid convergence, effectively minimizing joint defects while adhering to clamp constraints. This leads to enhanced pipeline assembly efficiency and the achievement of a one-time assembly process.

The offset of the assembly boundary and imperfections in pipeline manufacturing may lead to joint defects during pipeline assembly, as well as failure in the fit between the pipeline and clamp. The assembly pose predicted by the proposed optimization model can effectively reduce the joint defects and satisfy clamp constraints. The efficiency of pipeline modification and assembly has been significantly enhanced.

The discrepancy between the content of an article and its title is a key characteristic of fake news. Current methods for detecting fake news often ignore the significant difference in length between the content and its title. In addition, relying solely on textual discrepancies between the title and content to distinguish between real and fake news has proven ineffective. The purpose of this paper is to develop a new approach called semantic enhancement network with content–title discrepancy (SEN–CTD), which enhances the accuracy of fake news detection.

The SEN–CTD framework is composed of two primary modules: the SEN and the content–title comparison network (CTCN). The SEN is designed to enrich the representation of news titles by integrating external information and position information to capture the context. Meanwhile, the CTCN focuses on assessing the consistency between the content of news articles and their corresponding titles examining both emotional tones and semantic attributes.

The SEN–CTD model performs well on the GossipCop, PolitiFact and RealNews data sets, achieving accuracies of 80.28%, 86.88% and 84.96%, respectively. These results highlight its effectiveness in accurately detecting fake news across different types of content.

The SEN is specifically designed to improve the representation of extremely short texts, enhancing the depth and accuracy of analyses for brief content. The CTCN is tailored to examine the consistency between news titles and their corresponding content, ensuring a thorough comparative evaluation of both emotional and semantic discrepancies.

This study aims to enhance evacuation safety and efficiency measures in primary school corridors by considering the impact of adult guidance and evacuation graphical signs on evacuation speed and density by considering different visibility conditions and corridor design.

The experiment setup involved ten carefully designed drills exploring the evacuation behavior of 6–7-year-old students in a primary school, varying factors such as adult guidance, smoke conditions and graphical evacuation signs. Kinovea software was employed for data extraction to transform video footage into frames, facilitating meticulous manual tallies of children’s movements in designated sub-areas during the drills. The research utilized statistical tests, a generalized linear model and curve-fitting techniques to analyze the extracted data.

The findings highlight the vital role of adult guidance in expediting evacuations, emphasizing the importance of trained personnel during emergencies. Additionally, graphical evacuation signs were identified as powerful tools for enhancing evacuation speed during low visibility, as they provide clear visual cues to guide evacuees effectively. Signage and adult guidance are beneficial when the classrooms’ gates opening to the passage are far from each other. In contrast, in areas with close and multiple exits, guidance strategies, especially those involving adults, are more effective in reducing population density during evacuations.

These findings have practical implications for improving emergency preparedness, guiding the design of primary school corridors and informing evacuation protocols. School administrators, architects and emergency planners can utilize these findings to inform the development of safety protocols, enhance evacuation guidance strategies and improve the design of primary school corridors. Further research can expand on these findings by exploring their applicability in diverse educational settings and evaluating the real-world implementation of evacuation measures.

The proliferation of non-fungible token (NFT)-based crypto-art platforms has transformed how creators manage, own and earn money through the creation, assets and identity of their digital works. Despite this, no studies have examined the drivers of continuous content contribution behavior (CCCB) toward NFTs. Hence, this study draws on the theory of relational bonds to examine how various relational bonds affect feelings of psychological ownership, which, in turn, affects CCCB on metaverse platforms.

Using structural equation modeling and importance-performance matrix analysis, an online survey of 434 content creators from prominent NFT platforms empirically validated the research hypotheses.

Financial, structural, and social bonds positively affect psychological ownership, which in turn encourages CCCBs. The results of the importance-performance matrix analysis reveal that male content creators prioritized virtual reputation and social enhancement, whereas female content creators prioritized personalization and monetary gains.

We examine Web 3.0 and the NFT creators’ network that characterizes the governance practices of the metaverse. Consequently, the findings facilitate a better understanding of creator economy and meta-verse commerce.

Point-of-interest (POI) recommendation techniques play a crucial role in mitigating information overload and delivering tailored services. To address limitations in conventional POI recommendation systems, constrained by sparse user-POI interactions and incomplete consideration of temporal dynamics, POI recommendation based on the spatial-temporal graph (STG-POI) is proposed.

Spatial-temporal sequence graphs from geographical locations and user interaction history data are constructed, which are used to mine spatial-temporal sequence information. Using the data filtered by the band-pass filter, graph neural networks with distance-awareness and sequence-awareness are applied to capture high-order spatial-temporal connections within diverse graph topologies. The model leverages contrastive learning for self-supervised disentanglement of graph representations, providing self-supervised signals for sequential and geographical intent perception, thereby achieving more precise POI personalization.

Compared to the baseline model GSTN, experiments on the Foursquare and Gowalla data sets reveal that STG-POI improves testing AUC by 2.0%, 2.1%, 2.0% and decreases logloss by 1.9%, 3.3%, 0.3%, respectively. These results indicate the model’s effectiveness in capturing spatial-temporal information, surpassing mainstream POI recommendation baseline models.

This approach constructs a dual graph from user interaction data, harnessing sequential and geographical information as self-supervised signals. It yields decoupled representations of these influences, offering a comprehensive insight into user behaviors and preferences within location-based social networks, thus enhancing recommendation accuracy and interpretability. This approach addresses the challenge in graph convolutional network where only rough and smooth features are conducive to recommendation by using band-pass filters to significantly reduce computational complexity, thereby enhancing recommendation speed by filtering out noise data that does not contribute to recommendation performance. Experimental results indicate that this model surpasses current mainstream approaches in POI recommendation tasks, effectively integrating both geographical and temporal features.

In the wake of the COVID-19 pandemic, the interest in the use of virtual reality (VR) for organizational collaboration has dramatically increased. However, empirical research on the optimal types of VR and avatar designs for effective collaboration remains limited. To address these gaps, this study investigates how VR types (immersive vs non-immersive) and avatar similarity (high vs low) impact collaboration performance, with a focus on the mediating roles of somatic, psychological and behavioral engagement.

Drawing on embodied cognition theory and grounded in user engagement theory, we developed a research model that delineates the factors influencing collaboration performance. This model was empirically tested with data collected from a laboratory experiment involving 195 participants, employing a 2 × 2 factorial design. The Engage VR was used for the experiment because it supports the same virtual environment for collaboration between immersive and non-immersive VR contexts. The data were analyzed at the team level (n = 65).

The results of this study reveal that both immersive VR and high avatar similarity significantly enhance collaboration performance by augmenting somatic engagement, which subsequently boosts psychological and behavioral engagement.

This research deepens the scholarly understanding of the intricate interplay among VR types, avatar design and user engagement. The study’s theoretical implications underscore the conceptualization of somatic engagement as a critical dimension of user engagement in virtual collaboration, enriching the extant literature on VR-based collaboration. It also provides pragmatic insights to enhance virtual collaboration in the workplace.

Drawing on the theory of memory-dominant logic, this study aims to examine how the substantive staging of the servicescape, experience co-creation, experiential satisfaction and experience intensification affect experience memorability and hedonic well-being in the case of unmanned smart hotels.

An online survey was used, with the target respondents being hotel guests people aged 18 years and older who had been recent guests of the FlyZoo Hotel in Hangzhou, China. Data were collected online from 429 guests who had stayed in the hotel between April and June 2023. Data analysis was undertaken using structural equation modelling.

The results suggest that all the proposed four constructs are positive drivers of a memorable unmanned smart hotel experience. The relationship between the memorability of the hotel experience and hedonic well-being was found to be significant and positive.

Unmanned smart hotels should ensure that all smart technologies function effectively and dependably and offer highly personalised services to guests, allowing them to co-create their experiences. This will lead to the guest receiving a satisfying and memorable experience. To enable experience co-creation using smart technologies, unmanned smart hotels could provide short instructional videos for guests, as well as work closely with manufacturers and suppliers to ensure that smart technology systems are regularly updated.

This study investigates the antecedents and outcomes of a novel phenomenon and extends the concept of memorable tourism experiences to the context of unmanned smart hotels.

This study identifies three main CSR motivations (i.e. strategic benefits, altruism and greenwashing) and explores the relationship between CSR motivations and environmental collaboration by considering the mediating role of environmental commitment and the moderating role of team autonomy.

The data collected from 336 respondents in the construction industry, structural equation modeling and bootstrapping were used to test hypotheses.

The findings show that altruistic CSR enhances environmental collaboration through enhancing environmental commitment, whereas greenwashing CSR damages environmental collaboration through reducing environmental commitment. Although there is no direct association between strategic CSR and environmental collaboration, environmental commitment mediates the effects of strategic CSR on environmental collaboration. Moreover, the positive effect of strategic CSR and altruistic CSR on environment commitment is stronger when team autonomy is stronger, whereas the negative effect of greenwashing CSR on environment commitment is weaker when team autonomy is stronger.

The findings contribute to the understanding of how CSR motivations can act as catalysts for collaborative efforts in addressing environmental issues within construction projects and offer theoretical understanding of team autonomy by illustrating its role in shaping organizational responses to CSR motivations. The findings can provide insights into why and how participating teams can collaborate better on environmental management, enriching the knowledge of environmental management practices in construction projects.