Search results

Food is a key element in tourism experiences. This study aims to investigate the interplay of sensory and non-sensory factors in food tourism experiences and models their influence on satisfaction and behavioural intentions.

The study focuses on the culinary experiences of 304 tourists dining at ethnic restaurants and uses causal relationship discovery modelling to analyse data.

Sensory factors are important in tourists’ culinary experiences with cleanliness, noise levels and room temperature at the top of the causal chain. Results also indicate the interplay between sensory and non-sensory factors to explain overall satisfaction, intention to return and intention to say positive things.

Using embodied cognition theory, the study offers novel insights into the role of senses in food tourism experiences at rural destinations.

美食是乡村旅游的主要吸引物之一。本研究的目的是调查游客在用餐体验中感官和非感官因素的相互作用, 以及这些因素如何影响游客的满意度和行为意愿。

本研究使用因果关系建模的方法来分析 304 名在某地方特色餐厅用餐的游客的问卷数据。

结果显示, 对于游客的用餐体验而言, 感官和非感官因素具备同等的重要性。此外, 结果发现, 游客感知到的噪音水平、适宜的室内温度及清洁度在与其他因素的相互作用中非常重要, 并能激发游客的满意度和重游意愿。

基于认知理论, 本研究为更好地理解感官因素和非感观因素在乡村旅游情境下的游客用餐体验中的作用提供了新的知识。

La comida es un elemento clave en las experiencias turísticas. Este estudio investiga la interacción de factores sensoriales y no sensoriales en las experiencias de turismo gastronómico y modela su influencia en la satisfacción y las intenciones de comportamiento.

El estudio se centra en las experiencias culinarias de 304 turistas que cenan en restaurantes étnicos y utiliza modelos de descubrimiento de relaciones causales para analizar los datos.

Los factores sensoriales son importantes en las experiencias culinarias de los turistas con la limpieza, los niveles de ruido y la temperatura ambiente en la parte superior de la cadena causal. Los resultados también indican la interacción entre factores sensoriales y no sensoriales para explicar la satisfacción general, la intención de regresar y la intención de decir cosas positivas.

Utilizando la teoría de la cognición incorporada, el estudio ofrece nuevos conocimientos sobre el papel de los sentidos en las experiencias de turismo gastronómico en destinos rurales.

The purpose of the study is to evaluate the severity of public events in real time from the perspective of social media and to construct the early warning mechanism of public events.

This study constructed the severity assessment system of public events from the dimensions of the netizens' role, the Internet media's role, the spread of public events and the attitudes and feelings of netizens. The method of analyzing the influence tendency of the public event severity indicators was proposed. A total of 1,107,308 microblogging entries regarding four public events were investigated. The severity of public events was divided into four levels.

It is found that serious public events have higher indicator values than medium level events on the microblogging platform. A quantitative severity classification standard for public events was established and the early warning mechanism of public events was built.

Microblogging and other social media platforms provide rich clues for the real-time study and judgment of public events. This study only investigated the Weibo platform as the data source. Other social media platforms can also be considered in future.

Different from the ex-post evaluation method of judging the severity of public events based on their physical loss, this study constructed a quantitative method to dynamically determine the severity of public events according to the clues reflected by social media. The results can help the emergency management departments judge the severity of public events objectively and reduce the subjective negligence and misjudgment.

This study investigates the influence of artificial intelligence (AI) stimuli on customer stickiness (CS), the mediation effects of social presence (SP) and the moderating impacts of customer traits in this influencing process.

Drawing on the arousal theory and social response theory, a conceptual model was established and tested by a data set of 268 customers in the catering industry.

The results indicate that AI stimuli, such as perceived personalization and perceived interactivity, positively affect CS. SP partially mediates the influence of AI stimuli on CS. Customer traits such as customers' need for interaction (NFI) and novelty seeking (NS) actively moderate the mediating effects of SP.

This study advances the interactive marketing literature from three aspects. Firstly, instead of focusing on the functional aspects of AI stimuli, it extends our understanding of AI-enabled interactive marketing by examining the effects of social and emotional aspects of AI stimuli on customer response. Secondly, it extends our understanding of social response by illuminating the mediating effects of SP between AI stimuli and CS. Finally, it provides new insights and empirical evidence for the research focus on customer traits in AI-enabled interactive marketing.

This study aims to profile the government microbloggers and evaluate their roles. The results can help improve the governments' response capability to public emergencies.

This study proposes the user profiling and role evaluation model of government microbloggers in the context of public emergencies. The indicators are designed from the four dimensions of time, content, scale and influence, and the feature labels are identified. Three different public emergencies were investigated, including the West Africa Ebola outbreak, the Middle East respiratory syndrome outbreak and the Shandong vaccine case in China.

The results found that most government microbloggers were follower responders, short-term participants, originators, occasional participants and low influencers. The role distribution of government microbloggers was highly concentrated. However, in terms of individual profiles, the role of a government microblogger varied with events.

The findings can provide a reference for the performance assessment of the government microbloggers in the context of public emergencies and help them improve their ability to communicate with the public and respond to public emergencies.

By analyzing the performance of government microbloggers from the four dimensions of time, content, scale and influence, this paper fills the gap in existing literature on designing the user profiling and role evaluation model of government microbloggers in the context of public emergencies.

Multiple topics often exist on social media platforms that compete for users' attention. To explore how users’ attention transfers in the context of multitopic competition can help us understand the development pattern of the public attention.

This study proposes the prediction model for the attention transfer behavior of social media users in the context of multitopic competition and reveals the important influencing factors of users' attention transfer. Microblogging features are selected from the dimensions of users, time, topics and competitiveness. The microblogging posts on eight topic categories from Sina Weibo, the most popular microblogging platform in China, are used for empirical analysis. A novel indicator named transfer tendency of a feature value is proposed to identify the important factors for attention transfer.

The accuracy of the prediction model based on Light GBM reaches 91%. It is found that user features are the most important for the attention transfer of microblogging users among all the features. The conditions of attention transfer in all aspects are also revealed.

The findings can help governments and enterprises understand the competition mechanism among multiple topics and improve their ability to cope with public opinions in the complex environment.

The purpose of this study is to design a wearable medical device as a human care platform and to introduce the design details, key technologies and practical implementation methods of the system.

A multi-channel data acquisition scheme based on PCI-E (rapid interconnection of peripheral components) was proposed. The flexible biosensor is integrated with the flexible data acquisition card with monitoring capability, and the embedded (device that can operate independently) chip STM32F103VET6 is used to realize the simultaneous processing of multi-channel human health parameters. The human health parameters were transferred to the upper computer LabVIEW by intelligent clothing through USB or wireless Bluetooth to complete the transmission and processing of clinical data, which facilitates the analysis of medical data.

The smart clothing provides a mobile medical cloud platform for wearable medical through cloud computing, which can continuously monitor the body's wrist movement, body temperature and perspiration for 24 h. The result shows that each channel is completely accurate to the top computer display, which can meet the expected requirements, and the wearable instant care system can be applied to healthcare.

The smart clothing in this study is based on the monitoring and diagnosis of textiles, and the electronic communication devices can cooperate and interact to form a wearable textile system that provides medical monitoring and prevention services to individuals in the fastest and most accurate way. Each channel of the system is precisely matched to the display screen of the host computer and meets the expected requirements. As a real-time human health protection platform technology, continuous monitoring of human vital signs can complete the application of human motion detection, medical health monitoring and human–computer interaction. Ultimately, such an intelligent garment will become an integral part of our everyday clothing.

This paper aims to improve the wear resistance of titanium alloy using a high-hardness boride layer, which was fabricated on Ti6Al4V by a high-temperature boronizing process.

The boride layers on Ti6Al4V were obtained at 1000°C for 5–15 h. Scanning electron microscopy, energy dispersive analysis and X-ray diffractometer were used to characterize the properties of the boride layer. The tribological performance of the boride layer at room and elevated temperatures was investigated.

The X-ray diffraction analysis showed that the boride layers were a dual-phase structure of TiB and TiB₂. When the boronizing time increased from 5 h to 15 h, the microhardness increased from 1192 HV_0.5 to 1619.8 HV_0.5. At 25°C and elevated temperatures, the friction coefficients of the boride layers were higher than that of Ti6Al4V. The wear track areas of T-5 at 200°C and 400°C were 2.5 × 10^–3 and 1.1 × 10^–3 mm², respectively, which were 6.1% and 2.6% of that of Ti6Al4V, indicating boride layer exhibited a significant wear resistance. The wear mechanisms of the boride layer transformed from slight peeling to oxidative wear and abrasive wear as the temperature was raised.

The findings provide an effective strategy for improving the wear resistance of Ti6Al4V and have important implications for the application of titanium alloy in a high-temperature field.

In recent years, knowledge graph completion has gained increasing research focus and shown significant improvements. However, most existing models only use the structures of knowledge graph triples when obtaining the entity and relationship representations. In contrast, the integration of the entity description and the knowledge graph network structure has been ignored. This paper aims to investigate how to leverage both the entity description and the network structure to enhance the knowledge graph completion with a high generalization ability among different datasets.

The authors propose an entity-description augmented knowledge graph completion model (EDA-KGC), which incorporates the entity description and network structure. It consists of three modules, i.e. representation initialization, deep interaction and reasoning. The representation initialization module utilizes entity descriptions to obtain the pre-trained representation of entities. The deep interaction module acquires the features of the deep interaction between entities and relationships. The reasoning component performs matrix manipulations with the deep interaction feature vector and entity representation matrix, thus obtaining the probability distribution of target entities. The authors conduct intensive experiments on the FB15K, WN18, FB15K-237 and WN18RR data sets to validate the effect of the proposed model.

The experiments demonstrate that the proposed model outperforms the traditional structure-based knowledge graph completion model and the entity-description-enhanced knowledge graph completion model. The experiments also suggest that the model has greater feasibility in different scenarios such as sparse data, dynamic entities and limited training epochs. The study shows that the integration of entity description and network structure can significantly increase the effect of the knowledge graph completion task.

The research has a significant reference for completing the missing information in the knowledge graph and improving the application effect of the knowledge graph in information retrieval, question answering and other fields.

This paper aims to understand the facilitators and inhibitors that might influence customers' adoption of smart banking services (SBS) in Pakistan.

Semi-structured interviews were conducted with 33 bank customers in Multan and Lahore using a case study design. Cases were selected using a purposive sampling strategy with maximum variation. A thematic content analysis (TCA) was performed to analyze the qualitative data.

The findings of this study support the notion that SBS has become a sine qua non of 21st-century banking owing to performance expectancy, effort expectancy, social influence, price value, facilitating conditions, habit and hedonic motivation. However, information privacy concerns and big brother effect impair customers' cognitive, personal and social experiences, creating an obstacle toward SBS adoption.

Studying SBS adoption only in Pakistan represents the perspective of a developing country, which limits the generalizability of the findings.

Pakistani customers' demand for SBS can be linked to its performance. By upgrading technology, banks can improve the usefulness of these services.

The first scholarly inquiry explores the facilitators and inhibitors that could influence the adoption of SBS in Pakistan.

This study examines the main factors influencing banking customers' behavior toward smart banking services (SBS) adoption in Pakistan. This study extends the unified theory of acceptance and use of technology (UTAUT2) by integrating information privacy concern and big brother effect into an overarching conceptualization.

This study adopts theoretical constructs from the related literature and develops hypotheses. An analysis of 385 banking customers was performed using partial least squares structural equation modeling (PLS-SEM).

The results show that most predictors of behavioral intention, including facilitating conditions, effort expectancy, habit, performance expectancy, hedonic motivation (except for social influence and price value), big brother effect and perceived information privacy concern are significant. Indeed, all predictors of use behavior are also significant. Gender and age differences between individuals further moderate the relationship between UTAUT2 constructs. Moreover, information privacy concern mediates the relationship between big brother effect and behavioral intention.

This cross-sectional study focused only on Pakistan as a developing country, which limits the generalizability of the results.

The roles of social influence and price value must be strengthened by employing marketing strategies and personalized communication. Banks should offer incentives and rewards to customers who recommend SBS to friends and family members. In addition, banks must consider the price factor when offering these services to increase their market share.

This study contributes to the field of technology adoption by providing a unique perspective of SBS adoption in Pakistan. This empirical study is the first to extend the UTAUT2 model by integrating information privacy concern and big brother effect to assess SBS acceptance and use behavior. This study examines SBS adoption issues at the national level, especially in Pakistan.