Search results

This study aims to examine why users search for images, how users describe their image needs and what the images are used for by analysing questions obtained from two Chinese social Q&A sites, Zhihu and Baidu Zhidao.

A total of 1,402 image questions were collected from Zhihu and Baidu Zhidao. Both quantitative analysis and qualitative content analysis were performed to identify user image needs and the potential differences on the two social Q&A sites.

Question-asker’s intention varies in different platforms. Zhihu users asked questions mainly aiming at a promotion of subsequent discussion, whereas users of Baidu Zhidao often did so to seek information. Syntactic attributes were not frequently used in both two sites. Zhihu users were more likely to express subjective evaluations on images (concept, emotion, theme and style) in their questions than users of Baidu Zhidao. In contrast, questions from Baidu Zhidao showed a tendency to more frequently include descriptive metadata (rights, format, size, quality and authenticity) and semantic attributes (generic activity, specific people, fashion and text) of the images than questions from Zhihu. Learning was an important use on social Q&A sites, especially on Baidu Zhidao. In addition, the images were primarily used to trigger emotion or served a persuasive purpose in Zhihu.

This study contributes to a better understanding of user image search behaviour, and the findings could be used to develop better image services on social Q&A sites. Meanwhile, the image attributes extracted from the questions are conducive to the improvement of image retrieval systems.

This study explored the features of image needs on social Q&A sites, especially considering image use specified in the question. The difference of image needs between two Chinese social Q&A sites (Zhihu and Baidu Zhidao) was identified.

The purpose of this study of Google and Baidu in mainland China is to identify the key contingencies to firm strategic responses to technical and institutional pressures. From an institutional logic perspective and Hirschman's model of exit, voice, and loyalty, this research proposes a few propositions which are intended to explain why foreign and local companies adopt different responses to similar institutional requirements or under similar institutional pressures.

This study applies a historical chronological method by the recognition of certain types of events and key strategic activities conducted by two sample organizations from their foundations in mainland China till March 2010 when Google exited from China's internet search market. These activities were identified as the measurement of firm strategic responses to institutional pressures. Data were gathered from various sources, including documents published by sample organizations, online and media reports, etc.

It is found that firms adopted similar responses to technical pressures which were determined by characteristics of the internet industry. However, their responses to institutional pressures, which were driven by the state logic for control of the internet, were dramatically different. As a multinational corporation, Google was faced with inherent tensions between home and host institutional requirements. When the state control pressures increased, Google eventually selected a voice and exit strategy. Baidu, as a local leading player in China's internet market, adopted a loyalty strategy through closer collaboration with local institutional constituents, including government agencies and clients, in addition to its investment in creating corporate images and reputation among local internet users.

This research explores the dynamic and diverse responses of foreign and local companies to institutional pressures and advances our understanding of political properties in firm strategies and the importance of firm nationality in strategy making.

This study aims to examine whether and when real-time updated online search engine data such as the daily Baidu Index can be useful for improving the accuracy of tourism demand nowcasting once monthly official statistical data, including historical visitor arrival data and macroeconomic variables, become available.

This study is the first attempt to use the LASSO-MIDAS model proposed by Marsilli (2014) to field of the tourism demand forecasting to deal with the inconsistency in the frequency of data and the curse problem caused by the high dimensionality of search engine data.

The empirical results in the context of visitor arrivals in Hong Kong show that the application of a combination of daily Baidu Index data and monthly official statistical data produces more accurate nowcasting results when MIDAS-type models are used. The effectiveness of the LASSO-MIDAS model for tourism demand nowcasting indicates that such penalty-based MIDAS model is a useful option when using high-dimensional mixed-frequency data.

This study represents the first attempt to progressively compare whether there are any differences between using daily search engine data, monthly official statistical data and a combination of the aforementioned two types of data with different frequencies to nowcast tourism demand. This study also contributes to the tourism forecasting literature by presenting the first attempt to evaluate the applicability and effectiveness of the LASSO-MIDAS model in tourism demand nowcasting.

This paper aims to explore the interaction between investor attention and green security markets, including green bonds and stocks.

This study takes the Baidu index of “green finance” as the proxy for investor attention and constructs several generalized prediction error variance decomposition models to investigate the interdependence. It further analyzes the dynamic interaction between investor attention and the return and volatility of green security markets using the rolling time window.

The empirical analysis and robustness test results reveal that the spillovers between investor attention and the return and volatility of the green bond market are relatively stable. In contrast, the spillover level between investor attention and the green stock market displays significant time-varying and asymmetric effects. Moreover, the volatility spillover between investor attention and green securities is vulnerable to major financial events, while the return spillover is extremely sensitive to market performance.

The conclusion further expands the practical application and theoretical framework of behavioral finance in green finance and provides a new reference for investors and regulators. Besides, this study also lays a theoretical basis for investors to focus on the practical application of volatility prediction and risk management in green securities.

Online health communities (OHCs) are attracting more and more healthy consumers, including patients, their families, caregivers and the general public. This paper aims to explore the themes and characteristics of patient-generated content (PGC) in Chinese OHCs.

Baidu Tieba for hypertension was selected as the research site. Online ethnography (netnography) approach was utilized to explore the PGC and health communication in the online hypertension community. The final database included 300 randomly sampled threads and their 3,187 reply posts and was further analyzed from three perspectives: health information needs, attitudes and psychological reactions to hypertension and social support exchange.

The members' health information needs were mainly concentrated on five aspects: causes, symptoms, measuring instrument, tests and diagnosis and treatment. Their attitudes and psychological reactions to hypertension varied with the context, for example, disease stage, health condition. Within the health communication, three types of social support – information support, emotional support and network support – were generated, transmitted and exchanged among members.

OHCs are able to serve as important source of health information and tool for health education. The implications and suggestions for health promotion of individuals, health information services optimization of OHCs and national health strategy plans were also discussed.

This is the first netnography study in information field on Chinese online hypertension community. This study provides a new perspective to explore the needs, attitudes and social support behaviors of Chinese hypertension population and also enables the Chinese experience of using OHCs to reduce health disparities to come to the world.

The purpose of this paper to investigate the effectiveness of selected search features in the major English and Chinese search engines and compare the search engines’ retrieval effectiveness.

The search engines Google, Google China, and Baidu were selected for this study. Common search features such as title search, basic search, exact phrase search, PDF search, and URL search, were identified and used. Search results from using the five features in the search engines were collected and compared. One‐way ANOVA and regression analysis were used to compare the retrieval effectiveness of the search engines.

It was found that Google achieved the best retrieval performance with all five search features among the three search engines. Moreover Google achieved the best webpage ranking performance.

The findings of this study improve the understanding of English and Chinese search engines and the differences between them in terms of search features, and can be used to assist users in choosing appropriate and effective search strategies when they search for information on the internet.

The original contributions of this paper are that the Chinese and English search engines in both languages are compared for retrieval effectiveness. Five search features were evaluated, compared, and analysed in the two different language environments by using the discounted cumulative gain method.

With the ascendance of information technology, particularly through the internet, external information sources and their impacts can be readily transferred to influence the performance of financial markets within a short period of time. The purpose of this paper is to investigate how incidents affect stock prices and volatility using vector error correction and autoregressive-generalized auto regressive conditional Heteroskedasticity models, respectively.

To characterize the investors’ responses to incidents, the authors introduce indices derived using search volumes from Google Trends and the Baidu Index.

The empirical results indicate that an outbreak of disasters can increase volatility temporarily, and exert significant negative effects on stock prices in a relatively long time. In addition, indices derived from different search engines show differentiation, with the Google Trends search index mainly representing international investors and appearing more significant and persistent.

This study contributes to the existing literature by incorporating open-source data to analyze how catastrophic events affect financial markets and effect persistence.

The development of Big Data and online searching engine provides a good opportunity for studying petition in China. This study has constructed a set of indices for predicting petitions in China by using online searching engines and further explored the predicting role of economic, environment and public life risk perception in various petitions.

Based on the study of Xue and Liu (2017), this research first re-classified offline petition by human and cluster analysis in terms of social risk perception and built online searching indices of the two sets of petition by using data from “Google Trend” and “Baidu Index.” Second, it analyzed the predicting effect of social risk perception on online searching indices of petition by using Granger causality analysis. Finally, this study integrated the results and selected significant paths from social risk perception to the two sets of petition.

The study found that the re-classification made by human was more appropriate than the categories made by cluster analysis in terms of social risk perception. For the two sets of petition, the correlations between offline petition and Baidu Index of petition were both more significant than that of Google index. Moreover, economic and finance and resource and environment risk perception had a significant predicting effect on more than one kind of online searching indices of petition.

The results have demonstrated the important role of economic issues in China on predicting petitions of the economic kind, as well as other kinds. They have also reflected the dominant social contradictions and their relationship in modern China.

This paper aims to compare the forecasting performance of different models with and without big data predictors from search engines and social media.

Using daily tourist arrival data to Mount Longhu, China in 2018 and 2019, the authors estimated ARMA, ARMAX, Markov-switching auto-regression (MSAR), lasso model, elastic net model and post-lasso and post-elastic net models to conduct one- to seven-days-ahead forecasting. Search engine data and social media data from WeChat, Douyin and Weibo were incorporated to improve forecasting accuracy.

Results show that search engine data can substantially reduce forecasting error, whereas social media data has very limited value. Compared to the ARMAX/MSAR model without big data predictors, the corresponding post-lasso model reduced forecasting error by 39.29% based on mean square percentage error, 33.95% based on root mean square percentage error, 46.96% based on root mean squared error and 45.67% based on mean absolute scaled error.

Results highlight the importance of incorporating big data predictors into daily demand forecasting for tourism attractions.

This study represents a pioneering attempt to apply the regularized regression (e.g. lasso model and elastic net) in tourism forecasting and to explore various daily big data indicators across platforms as predictors.

Mobile users increasingly employ location-based map searches in their daily lives. However, it is still relatively unknown about mobile users’ map related search behaviors. The purpose of this paper is to discover the interactions between the users and mobile map search systems, to reveal the shortcomings of existing mobile map search functions, and to propose improvement suggestions.

Based on a set of controlled user experiments performed on the Baidu mobile phone map, this paper empirically examines users’ location-based mobile search behaviors, such as timing, metering, judging and so on. This paper also conducts statistical correlation tests to generate relation tables and diagrams regarding each variable, for example, the relation between the retrieval time and the retrieval steps.

The results indicate that mobile map users have two important characteristics in their search behaviors: first, mobile map users always follow the single search path. Second, the mobile map search efficiency of users is always low.

The situation simulation testing method is mainly used for the construction of a mobile information search behavior environment, which may make the users be nervous and have some effect on the search efficiency.

Based on the identification of user behaviors, this paper provides suggestions to optimize and improve mobile map search systems.

This paper studies users’ mobile map search behavior based on location and explores the features of user behavior from the perspective of human-computer interaction.