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The purpose of this study is to measure the financial reporting quality of 38 main countries (regions) in the world from 2000 to 2014.

This paper uses six accounting and auditing indicators to construct a comprehensive index for the measurement of country-level financial reporting quality. To test the validity of the methodology, the index to test institutional impacts on national financial reporting quality is used.

It was found that the results are consistent with the predictions and previous studies. The evidence suggests that the quality measure in this paper is innovative and appropriate and can provide a useful tool for researchers who are concerned with financial reporting quality at the country level.

The study is the first in the literature to use both accounting and auditing data to construct financial reporting quality indicators. The study should help international investors assess investment risks in foreign financial markets so as to make an informed decision. In addition, the diversity of financial reporting practices documented in the paper should prompt market regulators, accounting standard setters and professional accounting bodies to reinforce the efforts on international convergence of accounting and financial reporting standards and practices.

This paper is an attempt to explore the relevance and usefulness of accounting education to development needs of LDCs; and to make constructive suggestions for educational change in any areas where relevance or usefulness seem to be very seriously deficient Identification of development needs is discussed first, then the present character of accounting education as exported to LDCs is discussed in the context of development needs. Finally strategic proposals are made to close the largest gaps between development need and the supply of accounting education.

Doctor–medical institution collaboration (DMIC) services are an emerging service mode in focal online health communities (OHCs). This new service mode is anticipated to affect user satisfaction and doctors' engagement behaviors. However, whether and how DMIC occurs is still ambiguous because the topic is rarely examined. To bridge this gap, this study explores doctors' participation in DMIC services and its effects on their online performance, as well as its effect on patients' evaluation of them on OHC platforms.

The authors propose hypotheses based on structural holes theory. A unique dataset obtained from one of the most popular OHCs in China is used to test the hypotheses, and difference-in-differences estimation is adopted to test the causality of the relationship.

The results demonstrate that providing DMIC services improves doctors' online consultation performance and patients' evaluations of them but has no significant effect on doctors' knowledge-sharing performance on OHC platforms. Doctors' knowledge-sharing performance and consultation performance mediate the relationship between participation in DMIC services and patients' evaluation of doctors. Regarding doctors' participation in DMIC services, its impact on doctors' consultation performance and patients' evaluation of them is weaker for doctors with higher professional titles than for doctors with lower professional titles.

The findings clarify the value creation mechanisms of online collaboration between doctors and medical institutions and thereafter facilitate doctors' participation in DMIC services and enhance the sustainable development of OHCs.

Firms invest much money in information technology (IT) since IT support has been recognized as a critical enabler of employee outcomes. However, the value obtained by organizations and their employees is not always as much as they anticipated because of, at least partly, a poor relationship between IT staff and users. The purpose of this paper is to apply the social capital theory to examine relationship management between IT and business and explores mechanisms through which social capital between IT staff and users affect users’ employee outcomes, including job satisfaction and job performance.

Based on social capital theory and past literature, the researchers propose a research model and explore the effect of social capital on knowledge sharing, IT users’ perceived service quality, job satisfaction and ultimately job performance. Based on a survey of 289 respondents, this study applies the partial least square technique to test the research model.

Mediation test was performed to explore the effect mechanisms of social capital on employee outcomes, and the results indicate that three dimensions of social capital affect IT users’ job satisfaction and job performance in different approaches.

This study uses social capital theory to direct how to improve the poor relationship between IT staff and users and provides a useful insight into the mechanisms through which three dimensions of social capital improve users’ job satisfaction and job performance.

Although the use of instant messaging (IM) at work has been studied in the IS field, its effective use and impact on performance have not been adequately addressed. The purpose of this paper is to explore the antecedents and consequences of the effective use of IM at work by adapting Burton-Jones and Grange’s theory of effective use.

The authors introduce “Comprehensive IM policy” as a facilitator of adaptation and learning actions to improve the effective use of IM, which will improve communication quality and productivity. The impact of IM competence on effective use is also discussed. Based on a survey of 215 managers, this study applies the partial least square technique to test the research model.

The results indicate that comprehensive IM policy encourages adaptation and learning actions, which improve the effective use of IM and thereafter improve communication quality and productivity. Meanwhile, IM competence has a substitutive interaction effect with IM reconfiguration and self-learning on effective use.

The results refine the general theory of effective use and provide managers with an approach to leverage IM use at work for performance gains.

The purpose of this paper is to present a method to accurately forecast the tendency of the gross amount of energy sources consumption of the country and construct a new kind of algorithm for forecasting that synthesizes the advantages of the grey model, Markov chains, and least square method.

With the application of this new algorithm, this paper have forecasted the trend of the gross amount of energy sources consumption of the country and come to the conclusions that the new algorithm is more precise than the grey model. It is proved that the improved grey‐Markov chain algorithm is effective and can be used by authorities to make decision.

It was found that combining the grey model, Markov chains, and least square method, can be a new algorithm for forecasting the trendency of the gross amount of energy sources consumption.

The new algorithm is only suitable for the short‐term forecast.

The grey forecasting method reflects the overall tendency of primitive data sequence of the gross amount of energy source, and the Markov chain forecasting method reflects the effect of the random fluctuation. The least square method reflects the tendency of increase. The new algorithm is more precise than the grey model.

This paper aims to provide a well-behaved nonlinear scheme and accelerating iteration for the nonlinear convection diffusion equation with fundamental properties illustrated.

A nonlinear finite difference scheme is studied with fully implicit (FI) discretization used to acquire accurate simulation. A Picard–Newton (PN) iteration with a quadratic convergent ratio is designed to realize fast solution. Theoretical analysis is performed using the discrete function analysis technique. By adopting a novel induction hypothesis reasoning technique, the L^∞ (H¹) convergence of the scheme is proved despite the difficulty because of the combination of conservative diffusion and convection operator. Other properties are established consequently. Furthermore, the algorithm is extended from first-order temporal accuracy to second-order temporal accuracy.

Theoretical analysis shows that each of the two FI schemes is stable, its solution exists uniquely and has second-order spatial and first/second-order temporal accuracy. The corresponding PN iteration has the same order of accuracy and quadratic convergent speed. Numerical tests verify the conclusions and demonstrate the high accuracy and efficiency of the algorithms. Remarkable acceleration is gained.

The numerical method provides theoretical and technical support to accelerate resolving convection diffusion, non-equilibrium radiation diffusion and radiation transport problems.

The FI schemes and iterations for the convection diffusion problem are proposed with their properties rigorously analyzed. The induction hypothesis reasoning method here differs with those for linearization schemes and is applicable to other nonlinear problems.

The purpose of this paper is to develop a new method to extract medical temporal information from online health communities.

The authors trained a conditional random-filed model for the extraction of temporal expressions. The temporal relation identification is considered as a classification task and several support vector machine classifiers are built in the proposed method. For the model training, the authors extracted some high-level semantic features including co-reference relationship of medical concepts and the semantic similarity among words.

For the extraction of TIMEX, the authors find that well-formatted expressions are easy to recognize, and the main challenge is the relative TIMEX such as “three days after onset”. It also shows the same difficulty for normalization of absolute date or well-formatted duration, whereas frequency is easier to be normalized. For the identification of DocTimeRel, the result is fairly well, and the relation is difficult to identify when it involves a relative TIMEX or a hypothetical concept.

The authors proposed a new method to extract temporal information from the online clinical data and evaluated the usefulness of different level of syntactic features in this task.

Previous dynamic prediction models rarely handle multi-period data with different intervals, and the large-scale patient hospital records are not effectively used to improve the prediction performance. This paper aims to focus on the prediction of cardiovascular disease using the improved long short-term memory (LSTM) model.

A new model based on the traditional LSTM was proposed to predict cardiovascular disease. The irregular time interval is smoothed to obtain the time parameter vector, and it is used as the input of the forgetting gate of LSTM to overcome the prediction obstacle caused by the irregular time interval.

The experimental results show that the dynamic prediction model proposed in this paper obtained a significant better classification performance compared with the traditional LSTM model.

In this paper, the authors improved the LSTM by smoothing the irregular time between different medical stages of the patient to obtain the temporal feature vector.