Search results

This study aims to assess the technological capability of Chinese internet platforms (BAT: Baidu, Alibaba, Tencent) compared to US ones (GAFA: Google, Amazon, Facebook, Apple). More specifically, this study explores Baidu’s technological catching-up process with Google by analyzing their patent textual information.

The authors retrieved 26,383 Google patents and 6,695 Baidu patents from PATSTAT 2019 Spring version. The collected patent documents were vectorized using the Word2Vec model first, and then K-means clustering was applied to visualize the technological space of two firms. Finally, novel indicators were proposed to capture the technological catching-up process between Baidu and Google.

The results show that Baidu follows a trend of US rather than Chinese technology which suggests Baidu is aggressively seeking to catch up with US players in the process of its technological development. At the same time, the impact index of Baidu patents increases over time, reflecting its upgrading of technological competitiveness.

This study proposed a new method to analyze technology mapping and evolution based on patent text information. As both US and China are crucial players in the internet industry, it is vital for policymakers in third countries to understand the technological capacity and competitiveness of both countries to develop strategic partnerships effectively.

Aiming at the lack of explanatory power of traditional industrial organization theory in cross-border competition, by introducing the idea of ecological niche, the authors aim to explore the competitive situation of platform-based enterprises when they operate in multiple fields.

With the help of ecological niche theory, construct the niche width and niche overlap index of typical enterprises in the platform economy, and find out the advantages and the intensity of competition through comparative analysis.

In an environment of cross-border competition, large enterprises have significant competitive advantages, and the fierce competition is concentrated among medium-sized enterprises.

The conclusions of this paper not only provide new insights for explaining the phenomenon of cross-border competition in the platform economy, but also provide theoretical reference for the anti-trust enforcement practice in the platform economy.

This research presents machine learning models for predicting international tourist arrivals in Indonesia during the COVID-19 pandemic using multisource Internet data.

To develop the prediction models, this research utilizes multisource Internet data from TripAdvisor travel forum and Google Trends. Temporal factors, posts and comments, search queries index and previous tourist arrivals records are set as predictors. Four sets of predictors and three distinct data compositions were utilized for training the machine learning models, namely artificial neural networks (ANNs), support vector regression (SVR) and random forest (RF). To evaluate the models, this research uses three accuracy metrics, namely root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE).

Prediction models trained using multisource Internet data predictors have better accuracy than those trained using single-source Internet data or other predictors. In addition, using more training sets that cover the phenomenon of interest, such as COVID-19, will enhance the prediction model's learning process and accuracy. The experiments show that the RF models have better prediction accuracy than the ANN and SVR models.

First, this study pioneers the practice of a multisource Internet data approach in predicting tourist arrivals amid the unprecedented COVID-19 pandemic. Second, the use of multisource Internet data to improve prediction performance is validated with real empirical data. Finally, this is one of the few papers to provide perspectives on the current dynamics of Indonesia's tourism demand.

Did Chinese cities whose public health departments are headed by medical professionals fare better in fighting coronavirus disease 2019 (COVID-19)?

The authors collected the professional background of the directors of the public health departments of 350 Chinese cities, which include 87% of the Chinese population. Excluding Wuhan, the epicenter of COVID-19, the authors analyzed the infection rates and death rates from COVID-19 between 131 Chinese cities whose public health departments are led by medical professionals and 218 cities whose public health departments are led by nonprofessionals. The authors employed a multivariate regression controlling for the number of people that traveled from Wuhan to each city, the local economic development and the number of hospital beds.

Chinese cities whose public health departments are led by medical professionals had 21 fewer confirmed cases per 10 million as of January 31, 2020 [95% CI, −40 to −3], 58 fewer cases per 10 million in the next 10 days [95% CI, −116 to 0], similar new cases between February 11 and February 20, 2020, and 3 fewer deaths per 10 million as of February 20, 2020 [95% CI, −7 to 0].

Association could not make a strong causal claim.

Local public health authorities are critical for combating a pandemic. The authors found that Chinese cities whose public health departments are headed by medical professionals were associated with lower infection rates and fewer death rates from COVID-19. The results were significant only at the start of the outbreak. This study’s results suggest that to better combat a pandemic, local public health authorities should be led by competent people who have a medical background.

The authors provide the first empirical evidence about the association between a local public health head's competence and the infection rate and death rate of COVID-19. The authors’ manually collected data also show that only 38% of the heads of the public health departments of Chinese cities have a medical background.

The purpose of this paper is to propose a new approach to retrieve similar questions in online health communities to improve the efficiency of health information retrieval and sharing.

This paper proposes a hybrid approach to combining domain knowledge similarity and topic similarity to retrieve similar questions in online health communities. The domain knowledge similarity can evaluate the domain distance between different questions. And the topic similarity measures questions’ relationship base on the extracted latent topics.

The experiment results show that the proposed method outperforms the baseline methods.

This method conquers the problem of word mismatch and considers the named entities included in questions, which most of existing studies did not.

Exploring the influencing factors on urban rail transit (URT) ridership is vital for travel demand estimation and urban resources planning. Among various existing ridership modeling methods, direct demand model with ordinary least square (OLS) multiple regression as a representative has considerable advantages over the traditional four-step model. Nevertheless, OLS multiple regression neglects spatial instability and spatial heterogeneity from the magnitude of the coefficients across the urban area. This paper aims to focus on modeling and analyzing the factors influencing metro ridership at the station level.

This paper constructs two novel direct demand models based on geographically weighted regression (GWR) for modeling influencing factors on metro ridership from a local perspective. One is GWR with globally implemented LASSO for feature selection, and the other one is geographically weighted LASSO (GWL) model, which is GWR with locally implemented LASSO for feature selection.

The results of real-world case study of Shenzhen Metro show that the two local models presented perform better than the traditional global model (OLS) in terms of estimation error of ridership and goodness-of-fit. Additionally, the GWL model results in a better fit than GWR with global LASSO model, indicating that the locally implemented LASSO is more effective for the accurate estimation of Shenzhen metro ridership than global LASSO does. Moreover, the information provided by both two local models regarding the spatial varied elasticities demonstrates the strong spatial interpretability of models and potentials in transport planning.

The main contributions are threefold: the approach is based on spatial models considering spatial autocorrelation of variables, which outperform the traditional global regression model – OLS – in terms of model fitting and spatial explanatory power. GWR with global feature selection using LASSO and GWL is compared through a real-world case study on Shenzhen Metro, that is, the difference between global feature selection and local feature selection is discussed. Network structures as a type of factors are quantified with the measurements in the field of complex network.

The purpose of this paper is to develop initial evidence about the nature and features of crowdfunding in China, given it is largely unregulated regulatory frameworks.

The paper used extensive desk research using data collected from the public and private sectors, after which the data was analyzed parallel to existing academic literature, that is, institutional context by Bruton et al. (2014). This paper uncovered patterns of development, profiling crowdfunding platforms, examining the regulatory landscape and providing antecedents of successful crowdfunding projects in China.

When the traditional financial markets are hard to reach, micro, small and medium enterprises (MSMEs) were starved for capital. Crowdfunding can play a major role in funding and risk sharing. It is an innovative and dynamic vehicle for MSMEs as well as enthusiastic investors in China. Since its initial introduction to China in 2009, crowdfunding has gained substantial popularity in a relatively short period. Currently, there is still not an identifiable guideline on how to delineate the significance of the crowdfunding platform. The development of crowdfunding in China faces a few unresolved key issues. As researchers exploring this phenomenon in new ways, crowdfunding platforms can be enhanced in a manner that benefits the capital seeker, investors and society as a whole.

There is a dearth of information on start-up crowdfunding in Asia. With little data available to analyze, so this paper hopes to contribute to knowledge and provide valuable information to researchers and industry representations. Crowdfunding represents a potentially disruptive change in the way that new ventures are funded. This paper represents an initial analysis in the study of new ventures in China. Finally, the authors provide recommendations for entrepreneurs, investors and policymakers as well as researchers and practitioners with suggestions about yet unexplored avenues of research.

Advanced driving assistance system (ADAS) has been applied in commercial vehicles. This paper aims to evaluate the influence factors of commercial vehicle drivers’ acceptance on ADAS and explore the characteristics of each key factors. Two most widely used functions, forward collision warning (FCW) and lane departure warning (LDW), were considered in this paper.

A random forests algorithm was applied to evaluate the influence factors of commercial drivers’ acceptance. ADAS data of 24 commercial vehicles were recorded from 1 November to 21 December 2018, in Jiangsu province. Respond or not was set as dependent variables, while six influence factors were considered.

The acceptance rate for FCW and LDW systems was 69.52% and 38.76%, respectively. The accuracy of random forests model for FCW and LDW systems is 0.816 and 0.820, respectively. For FCW system, vehicle speed, duration time and warning hour are three key factors. Drivers prefer to respond in a short duration during daytime and low vehicle speed. While for LDW system, duration time, vehicle speed and driver age are three key factors. Older drivers have higher respond probability under higher vehicle speed, and the respond time is longer than FCW system.

Few research studies have focused on the attitudes of commercial vehicle drivers, though commercial vehicle accidents were proved to be more severe than passenger vehicles. The results of this study can help researchers to better understand the behavior of commercial vehicle drivers and make corresponding recommendations for ADAS of commercial vehicles.

With the development of the modern economy, vehicles are no longer a luxury for people, which greatly facilitate people’s daily life, but at the same time bring traffic congestion. How to relieve traffic congestion and improve its capacity is a hot research area. This paper aims to propose a new simulation framework for crowd transportations to ease traffic congestion.

This paper establishes related simulation models such as vehicles, traffic lights and advisers. Then the paper describes their relationships, gives their interaction mechanism and solidifies the above into a software implementation framework.

This paper proposes a simulation framework for crowd transportations.

In this framework, traffic lights are used as a control method to control the road network and road conditions are used as an Affecter to influence individual behavior. The vehicle passing rate is defined by the correlation between endowment and the start time of the traffic lights. In this framework, members are related, dynamically adjusted according to road conditions and dynamically optimized member decisions. The optimal path is dynamic and real-time adjustments are made for each step forward. It is different from the traditional optimal path in which there is only one fixed one and it is different from the macroscopic optimal path that does not exist.

New agricultural technologies are continuously generated and promoted for adoption by farmers with the expectation that they bring about higher benefits than older technologies. Yet, depending on the perceived benefits, the user of the technology may choose to stop using it. This paper aims to analyze what drives farmers to dis-adopt climate smart sorghum varieties in Tanzania.

The study uses cross-sectional farm household level data collected in Tanzania from a sample of 767 households. The determinants of dis-adoption are explored using a bivariate probit with sample selection model.

The authors find that while farmers switch between different sorghum varieties, most farmers actually quit sorghum production. Older farmers and those facing biotic stresses such attacks by birds are more likely to dis-adopt sorghum.

These findings suggest that there is scope for improving and sustaining the adoption of sorghum varieties in Tanzania once extension services are strengthened. The findings also point to a well-founded theory on the role of markets in enhancing the overall sustainability of food systems.

The study findings have broader implications for understanding the sustainability of improved technology adoption

Dis-adoption is also positively associated with the lack of access to markets underscoring the role of markets in enhancing the overall sustainability of technology adoption and food systems.