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Timely detection of emergency events and effective tracking of corresponding public opinions are critical in emergency management. As media are immediate sources of information on emergencies, the purpose of this paper is to propose cross-media analytics to detect and track emergency events and provide decision support for government and emergency management departments.

In this paper, a novel emergency event detection and opinion mining method is proposed for emergency management using cross-media analytics. In the proposed approach, an event detection module is constructed to discover emergency events based on cross-media analytics, and after the detected event is confirmed as an emergency event, an opinion mining module is used to analyze public sentiments and then generate public sentiment time series for early warning via a semantic expansion technique.

Empirical results indicate that a specific emergency can be detected and that public opinion can be tracked effectively and efficiently using cross-media analytics. In addition, the proposed system can be used for decision support and real-time response for government and emergency management departments.

This paper takes full advantage of cross-media information and proposes novel emergency event detection and opinion mining methods for emergency management using cross-media analytics. The empirical analysis results illustrate the efficiency of the proposed method.

The proposed method can be applied for detection of emergency events and tracking of public opinions for emergency decision support and governmental real-time response.

This research work contributes to the design of a decision support system for emergency event detection and opinion mining. In the proposed approaches, emergency events are detected by leveraging cross-media analytics, and public sentiments are measured using an auto-expansion of the domain dictionary in the field of emergency management to eliminate the misclassification of the general dictionary and to make the quantization more accurate.

Despite the prevalent use of professional status and service feedback in online healthcare markets, the potential interaction relationship between two types of information is still unknown. This study used the signaling theory to examine the substitute relationship between professional status and service feedback in patients' doctor choice, as well as the moderating effect of illness severity.

To test the paper's hypotheses, we constructed a panel data model using 418 doctors' data collected over a period of six months from an online healthcare market in China. Then, according to the results of the Hausman test, we estimated a fixed-effects model of patients' choice in online healthcare markets.

The empirical results showed that the effect of a doctor's professional status and service feedback on a patient's doctor choice was substitutable. Moreover, patients' illness severity played a moderating role, in that the influence of professional status on a patient with high-severity illness was higher than that on a patient with low-severity illness, whereas the influence of service feedback on a patient with low-severity illness was higher than that of a patient with high-severity illness. In addition, we found that illness severity negatively moderated the substitute relationship between professional status and service feedback on a patient's choice.

These findings not only contribute to signaling theory and research on online healthcare markets, but also help us understand the importance of professional status and service feedback on a patient's choice when seeking a doctor online.

The purpose of this paper is to propose a framework to detect adverse drug reactions (ADRs) using internet user search data, so that ADR events can be identified early. Empirical investigation of Avandia, a type II diabetes treatment, is conducted to illustrate how to implement the proposed framework.

Typical ADR identification measures and time series processing techniques are used in the proposed framework. Google Trends Data are employed to represent user searches. The baseline model is a disproportionality analysis using official drug reaction reporting data from the US Food and Drug Administration’s Adverse Event Reporting System.

Results show that Google Trends series of Avandia side effects search reveal a significant early warning signal for the side effect emergence of Avandia. The proposed approach of using user search data to detect ADRs is proved to have a longer leading time than traditional drug reaction discovery methods. Three more drugs with known adverse reactions are investigated using the selected approach, and two are successfully identified.

Validation of Google Trends data’s representativeness of user search is yet to be explored. In future research, user search in other search engines and in healthcare web forums can be incorporated to obtain a more comprehensive ADR early warning mechanism.

Using internet data in drug safety management with a proper early warning mechanism may serve as an earlier signal than traditional drug adverse reaction. This has great potential in public health emergency management.

The research work proposes a novel framework of using user search data in ADR identification. User search is a voluntary drug adverse reaction exploration behavior. Furthermore, user search data series are more concise and accurate than text mining in forums. The proposed methods as well as the empirical results will shed some light on incorporating user search data as a new source in pharmacovigilance.

Using Transparency International’s Corruption Perceptions Index (CPI), this paper establishes a statistically significant link between CPI and foreign direct investment (FDI) flows to 54 developing and developed countries. In addition to each country’s CPI, several location and economic characteristics are also postulated to influence FDI. For a group of 22 developing countries, the paper then simulates the impact of an improvement in the CPI score on FDI. This simulation shows that a one point improvement in CPI would generate on average additional FDI of 0.5% of GDP. For instance, the gain in annual FDI would be $7.5 billion for India and $18 billion for China. The paper further simulates the effects of larger FDI on the generation of taxable income and tax revenues in each country using country-specific rates of return on US investment and the highest marginal corporate tax rate in each country. This simulation shows that a three point improvement in CPI would more than double the corporate tax take on average with the biggest beneficiaries such as India, Turkey, Egypt, South Korea, the Philippines and Thailand.

To examine the nature of Chinese business negotiating style in Sino‐Western business negotiations in business‐to‐business markets involving large industrial projects from a social cultural point of view.

A conceptual approach developed from personal interviews.

This study reveals that the Chinese negotiator does not possess an absolute negotiating style but rather embraces a mixture of different roles together: “Maoist bureaucrat in learning”, “Confucian gentleman”, and “Sun Tzu‐like strategist”. The Chinese negotiating strategy is essentially a combination of cooperation and competition (termed as the “coop‐comp” negotiation strategy in this study). Trust is the ultimate indicator of Chinese negotiating propensities and role choices.

The focus of this study is on Chinese negotiating style shown in large B2B negotiations with Chinese SOEs.

Differing from most other studies on Chinese negotiating style which tend to depict the Chinese negotiator as either sincere or deceptive, this study points out that there exists an intrinsic paradox in Chinese negotiating style which reflects the Yin Yang thinking. The Chinese negotiator has a cultural capacity to negotiate both sincerely and deceptively and he/she changes coping strategies according to situation and context, all depending on the level of trust between negotiating partners.

This paper aims to analyze existing corporate governance rules which aim to regulate and control the following type of problems: to restore confidence in the financial markets, to reformulate the existing corporate governance systems and mechanisms that have been inadequate, and, finally, to rethink the relationship between ethics and economy. It also aims to identify the factors determining the corporate governance systems and mechanisms in a global economy.

The paper reports the results of a comparative analysis between different corporate governance systems and mechanisms. In addition, in order to explore the role of institutional determinants in attracting foreign direct investment (FDI) flows, this study considers variables such as an index of shareholder protection, openness to FDI and the interaction between the two above mentioned variables.

This analysis confirms the economic theory that less open countries are characterized by stronger ownership restrictions and a weak corporate governance mechanism. Conversely, open market and investment regimes are particularly powerful instruments to attract investment in general and FDI in particular.

This study provides a survey of the main system and mechanisms of corporate governance all supported by a survey of recent developments regarding the empirical analysis on the role of institutional determinants in attracting FDI flows.

The purpose of this study is to answer whether and how supervisor–subordinate instrumental or expressive ties based on enterprise social media (ESM) might enhance employee performance.

Drawing on social exchange theory, this study developed a theoretical model to explore the influencing mechanism of different supervisor–subordinate ties based on ESM on employee job performance. The model was empirically tested through 219 ESM users.

The results revealed that supervisor–subordinate instrumental ties based on ESM play a positive role in employee job performance, while supervisor–subordinate expressive ties based on ESM are not significantly related to employee job performance. Supervisor–subordinate instrumental ties and expressive ties based on ESM can positively influence employee job performance through the mediating effect of organizational trust. Besides, perceived performance climate can weaken the relation of organizational trust to job performance, and then weaken the indirect relations via the mediating of organizational trust.

Our findings advance the understanding of ESM use through various underlying mechanisms and have the potential of guiding organizations to fine-tune their social media usage strategies.

This study aims to investigate the effect of top management team (TMT) stability on outward foreign direct investment (OFDI) of Chinese firms and the moderating effects of state ownership and managerial ownership on this relationship.

An empirical analysis based on the ordinary least square regression model is conducted using Chinese A-share listed firms that engaged in OFDI from 2008 to 2021.

TMT stability has a positive effect on firms’ OFDI. Moreover, state ownership significantly strengthens the positive relationship between TMT stability and OFDI, while managerial ownership weakens this positive relationship.

The findings help firms to effectively retain TMT talents and promote the smooth internationalization of firms, thereby enhancing their long-term development capabilities and competitive advantages.

This study expands the investigation of the factors influencing OFDI at the micro level of the TMT, providing valuable decision-making insights for firms.

The prediction of by-product gas is an important guarantee for the full utilization of resources. The purpose of this research is to predict gas consumption to provide a basis for gas dispatch and reduce the production cost of enterprises.

In this paper, a new method using the ensemble empirical mode decomposition (EEMD) and the back propagation neural network is proposed. Unfortunately, this method does not achieve the ideal prediction. Further, using the advantages of long short-term memory (LSTM) neural network for long-term dependence, a prediction method based on EEMD and LSTM is proposed. In this model, the gas consumption series is decomposed into several intrinsic mode functions and a residual term (r(t)) by EEMD. Second, each component is predicted by LSTM. The predicted values of all components are added together to get the final prediction result.

The results show that the root mean square error is reduced to 0.35%, the average absolute error is reduced to 1.852 and the R-squared is reached to 0.963.

A new gas consumption prediction method is proposed in this paper. The production data collected in the actual production process is non-linear, unstable and contains a lot of noise. But the EEMD method has the unique superiority in the analysis data aspect and may solve these questions well. The prediction of gas consumption is the result of long-term training and needs a lot of prior knowledge. Relying on LSTM can solve the problem of long-term dependence.