An Empirical Study of Hotel Online Booking in O to O Commerce

Many studies have postulated the reasons for the development of the O2O business though the research of the customers’ behavioral intention is relatively unexplored. This study selects the travel industry as the research context to investigate online-offline integration between hotels and online travel agencies (OTAs). To investigate customers’ behavioral intention, this study establishes an integrated model of information systems success model and customer loyalty. The research focuses on whether customer loyalty (e.g., satisfaction and trust) can increase the booking intention, and the factors of increasing satisfaction and trust. According to the analyzed results, satisfaction directly influences customers’ booking intention while trust directly and indirectly influences customer booking intention through satisfaction. For antecedents, system quality and service quality have a significant impact on satisfaction, while brand image and size have a significant impact on trust. These findings have implications for OTAs to attract customers to book hotels through their websites

Kidney Modelling for FDG Excretion with PET

The purpose of this study was to detect the physiological process of FDG's filtration from blood to urine and to establish a mathematical model to describe the process. Dynamic positron emission tomography scan for FDG was performed on seven normal volunteers. The filtration process in kidney can be seen in the sequential images of each study. Variational distribution of FDG in kidney can be detected in dynamic data. According to the structure and function, kidney is divided into parenchyma and pelvis. A unidirectional three-compartment model is proposed to describe the renal function in FDG excretion. The time-activity curves that were picked up from the parenchyma, pelvis, and abdominal aorta were used to estimate the parameter of the model. The output of the model has fitted well with the original curve from dynamic data

Exploring doctors' willingness to provide online counseling services : the roles of motivations and costs

With the impetus of information communication technology (ICT), emerging eHealth has attracted increasing number of doctors’ participation in online health platforms, which provide various potential benefits to doctors. However, previous studies on eHealth have seldom distinguished different service modes provided by doctors. In addition, the bulk of the literature has considered doctors’ motivations based solely on online environments. To fill this gap, this study combines expectancy theory and the Bagozzi, Dholakia, and Basuroy (BDB) model to examine the relationships between anticipated outcomes, performance expectations, and effort intentions from online and offline perspectives. Doctors’ behavioral intentions are further divided into two categories: the willingness to offer free services and paid services. Using SmartPLS, this study conducts structural equation modeling (SEM) to analyze 311 sample data. The results show that extrinsic motivations (i.e., extrinsic rewards, expected relationships, and image) and intrinsic motivation (i.e., a sense of self-worth) significantly influence the desire to serve patients well, which in turn positively affects the willingness to offer free services and the willingness to offer paid services. Moreover, counseling time is confirmed as the main cost, which negatively moderates the relationships between desire and behavioral intentions. The findings provide theoretical insights for eHealth and provide practical suggestions to develop marketing strategies for online health platform providers

Buyers’ psychological situations in cross-border electronic commerce

Cross-border electronic commerce (CBEC) has emerged as an innovative business model that transforms consumer behaviour and mindsets, in the era of digitalization and globalization. Buyer situations in CBEC are complex due to the separation of global sellers and buyers in terms of geographic distance, language and cultural differences, and buyer preferences. However, few studies have explored buyers’ shopping decisions from a situational perspective. Drawing on the stimulus–organism–response framework, this study conceptualizes a CBEC buyer shopping model that theorizes four psychological situational factors (i.e., CBEC platform design, user–platform interaction, logistics evaluation, and task orientation) as stimuli, cognitive and affective states as organisms, and shopping intention as a response. The model was empirically tested using 241 data through structural equation modelling. The results indicate that all situational factors positively affect two evaluative states, which in turn positively affect shopping intention. Implications for theory and practice are discussed

Switching Behavior to Cloud Enterprise Information Systems in China

Cloud computing has recently become a popular information technology in China. Several China’s enterprises tend to move from client/server enterprise information systems (EISs) to cloud EISs. However, few studies have addressed the switching issues. This study aims to investigate factors that affect switching behavior from client/server EISs to cloud EISs. The research model draws from technology-organization-environment framework. We collected data from top managers and owners of China’s enterprises to analyze six hypotheses. The results show that technological context (perceived security and compatibility), and environmental context (supplier support and consultant support) significantly influence switching behavior. The findings are useful for understanding switching issues from client/server EISs to cloud EISs

Germanium doping of Si substrates for improved device characteristics and yield

During the last decade the 300 mm Si wafer has been optimized and one is already studying 450 mm crystals and wafers. The increasing silicon crystal diameter shows two important trends with respect to substrate characteristics: the interstitial oxygen concentration is decreasing while the size of grown in voids (COP's) in vacancy-rich crystals is increasing. The first effect is due the suppression of melt movements by the use of magnetic fields leading to a more limited transport of oxygen to the crystal. This and the decreasing thermal budget of advanced device processing leads to reduced internal gettering capacity. The increasing COP size is due to the combination of decreasing pulling rate and thermal gradient leading to a decreased void nucleation and increased thermal budget for void growth. The effect of Ge doping in the range between 10(16) cm(-3) and 10(19) cm(-3) on both COP's and oxygen precipitation will be discussed

Research on China's economic model changed since the COVID-19 epidemic

The Covid-19 pandemic of 2019 was a global public health emergency, resulting in millions of deaths worldwide. The origin of pandemic was in China, where the disease was first reported in 2019 and in 2020, it became a global pandemic. Because of the economic and social effects caused by COVID-19, changes were made to China's economic model. The Chinese government wanted to protect their economy and people from the virus, so they invested a lot of money into combating the pandemic. Economic development is when a country's economy becomes more advanced than other countries. If it has good infrastructure and employment opportunities, it will become richer (McBryde et al., 2020). An economy can advance quickly through technological advancement, education, and increasing demand for goods and services. When China first experienced the pandemic, it had a rich economy and grew quickly. China was rapidly developing in terms of technology, education, and infrastructure. China's economic model took a turn for the worse when the COVID-19 pandemic hit it. The virus spread quickly, causing millions of deaths in China (Dhar, 2020). The Chinese government was forced to invest billions of RMB into research and to treat those affected by the virus. After COVID-19, China's economy fell into a slight recession. This could have been caused by a drop in exports, an increased mortality rate, and the government spending billions on stopping the pandemic. This paper will first give an overview of previous economic models that China used, then it will discuss how the COVID-19 pandemic changed China's economic model, and finally, this paper will look at the impact that COVID-19 had on China's economy

General-Purpose Multi-Modal OOD Detection Framework

Out-of-distribution (OOD) detection identifies test samples that differ from the training data, which is critical to ensuring the safety and reliability of machine learning (ML) systems. While a plethora of methods have been developed to detect uni-modal OOD samples, only a few have focused on multi-modal OOD detection. Current contrastive learning-based methods primarily study multi-modal OOD detection in a scenario where both a given image and its corresponding textual description come from a new domain. However, real-world deployments of ML systems may face more anomaly scenarios caused by multiple factors like sensor faults, bad weather, and environmental changes. Hence, the goal of this work is to simultaneously detect from multiple different OOD scenarios in a fine-grained manner. To reach this goal, we propose a general-purpose weakly-supervised OOD detection framework, called WOOD, that combines a binary classifier and a contrastive learning component to reap the benefits of both. In order to better distinguish the latent representations of in-distribution (ID) and OOD samples, we adopt the Hinge loss to constrain their similarity. Furthermore, we develop a new scoring metric to integrate the prediction results from both the binary classifier and contrastive learning for identifying OOD samples. We evaluate the proposed WOOD model on multiple real-world datasets, and the experimental results demonstrate that the WOOD model outperforms the state-of-the-art methods for multi-modal OOD detection. Importantly, our approach is able to achieve high accuracy in OOD detection in three different OOD scenarios simultaneously. The source code will be made publicly available upon publication