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Developed countries control pandemics using smart decisions and processes based on medical standards and modern technologies. Studies on risk-reduction and humantechnology interaction are scarce. This study developed a model to examine the relationship between citizens, pandemic-related technology and official safety practices.

This study investigated the mediating role of new health regulations and moderating role of safety incentives due to COVID-19 case reduction in pandemic severity control. This study included 407 operations managers, nursing staff conducting pandemic testing and reporting, doctors and security personnel in China. An artificial neural network (ANN) was used to check nonlinear regressions and model predictability.

The results demonstrated the impact of the introduction of new technology protocols on the implementation of new health regulations and aided pandemic severity control. The safety incentive of case reductions moderated the relationship between new health regulations and pandemic severity control. New health regulations mediated the relationship between the introduction of new technology protocols and pandemic severity control.

Further research should be conducted on pandemic severity in diversely populated cities, particularly those that require safety measures and controls. Future studies should focus on cloud computing for nurses, busy campuses and communal living spaces.

Authorities should involve citizens in pandemic-related technical advances to reduce local viral transmission and infection. New health regulations improved people's interactions with new technological protocols and understanding of pandemic severity. Pandemic management authorities should work with medical and security employees.

This study is the first to demonstrate that a safety framework with technology-oriented techniques could reduce future pandemics using managerial initiatives.

The purposes of this study are to demonstrate the conditional effect of functional fit and image fit on consumer brand attitude through the altruistic attributions and identify the criteria for choosing a perfect social cause for managers.

Based on the existing research, a conceptual model is proposed, explained the moderating effect of social cause affinity on the indirect influence of functional fit and image fit on consumer brand attitude via altruistic attributions; these relationships were assessed through a bootstrap procedure. The model was estimated with a sample of 240 subjects. Two newly created, printed corporate social responsibility (CSR) information served as the basic stimulus of this article.

The main conclusions are as follows: first, social cause affinity moderates the effect of functional fit and image fit on altruistic attributions, and the combination of image fit and social cause affinity both on high levels can more easily lead to the emergence of suspicion of company's altruistic motives, contrary to functional fit. Second, contingent on social cause affinity, CSR fit has an indirect relationship with consumer brand attitude mediated by altruistic attribution. More specifically, the indirect effect will be more positive for functional fit with higher levels of social cause affinity, but for image fit with low levels of affinity.

The experiment relied on fictitious corporate name. Most of the responders were students from Changchun. This may reduce the generalizability of the results.

Besides functional fit and image fit, social cause affinity should be treated as a key criterion to choose social cause.

This work analyses and compares the indirect effect of functional fit and image fit on consumer brand attitude through altruistic attributions. The findings give some enlightenment on solutions to contradictions in existing research.

Spandex fibers have superior stretch and elastic recovery ability, which is why clothes containing spandex fibers fit well and are comfortable. The most common methods of producing elastic yarns are core-spun on a modified ring frame and hollow spindle technique. Rotor spinning has many advantages over ring spinning and has been adopted worldwide. In this paper, a method of producing the cotton/spandex composite yarns on a modified open-end rotor spinning is introduced. By changing the draft ratio and linear density of spandex filaments, different kinds of cotton/spandex composite yarns are produced on a modified rotor spinning frame, and their appearance and properties are tested and analyzed at the same time. The results show that the spandex draft ratio has great influence on the appearance and properties of rotor spun cotton/spandex composite yarns. The linear density of spandex filaments also has influence on the properties of composite yarns.

Given the significance of innovation in enabling firms to maintain a long-term competitive edge and secure excess profits, this paper aims to investigate whether and how stakeholders’ attention to innovation (SATI) influences corporate innovation.

This paper introduces a novel variable, SATI, which is achieved by segmenting stakeholders’ attention into two categories: attention to innovation and attention to other facets, using textual analysis methods. Subsequently, this paper empirically examines the influence of SATI on corporate innovation.

This paper finds that SATI positively affects corporate innovation input, and the result remains true after addressing possible endogeneity issues using instrumental variable regression. Furthermore, the positive effect of SATI on corporate innovation is stronger in firms facing greater financing constraints, thus verifying the financing constraints hypothesis. The positive effect is also stronger in firms with lower risk-taking levels, thus confirming the innovation failure tolerance hypothesis. Further analysis suggests that SATI increases both corporate innovation output and efficiency, thus ruling out the catering hypothesis.

This paper highlights the importance of SATI in driving corporate innovation. It enriches the literature on the repercussions of stakeholders’ attention and determinants of corporate innovation. In addition, it provides practical suggestions for further implementing China’s national innovation-driven development strategy.

This paper aims to study the interplay between a risk-averse national brand manufacturer's (NBM) selling mode decision and a risk-neutral e-platform's private brand (PB) introduction decision.

A game theory model is used to solve selling mode decision, that is whether transform the selling mode from the wholesale mode to the marketplace mode, and PB introduction decision, that is, whether introduce the PB.

The results show that for the NBM, under certain condition, the NBM's selling mode decision is not affected by the e-platform's PB introduction decision. High revenue-sharing rate is conducive only when the difference in consumer preference between the PB and the national brand (NB) is small. The NBM's risk aversion will improve the applicability of the marketplace mode. For the e-platform, high PB preference of consumers and risk-averse behavior of the NBM is not conducive to PB introduction. For the supply chain, scenarios that the NB monopolizes the market under the wholesale mode and PB introduction under the marketplace mode should be prevented. PB introduction under the wholesale mode will become the only equilibrium with the increase of risk aversion of the NBM. Finally, the authors extend the scenario that consumers prefer the PB and the e-platform is risk-averse enterprise and find that PB introduction under the wholesale mode is detrimental to the NBM but beneficial to the supply chain. The impact of consumers' PB preference on the e-platform's PB introduction is opposite to the basic model. The impact of the e-platform's risk aversion on game equilibrium is opposite to that of the NBM's risk aversion.

This paper is first to study selling mode decision and PB introduction decision when considering enterprises' risk-averse attitude.

Due to high efficiency and low carbon of natural gas, the consumption of natural gas is increasing rapidly, and the prediction of natural gas consumption has become the focus. The purpose of this paper is to employ a prediction technique by combining grey prediction model and trigonometric residual modification for predicting average per capita natural gas consumption of households in China.

The GM(1,1) model is utilised to obtain the tendency term, then the generalised trigonometric model is used to catch the periodic phenomenon from the residual data of GM(1,1) model for improving predicting accuracy.

The case verified the view of Xie and Liu: “When the value of a is less, DGM model and GM(1,1) model can substitute each other.” The combination of the GM(1,1) and the trigonometric residual modification technique can observably improve the predicting accuracy of average per capita natural gas consumption of households in China. The mean absolute percentage errors of GM(1,1) model, DGM(1,1), unbiased grey forecasting model, and TGM model in ex post testing stage (from 2013 to 2015) are 32.5510, 33.5985, 36.9980, and 5.2996 per cent, respectively. The TGM model is suitable for the prediction of average per capita natural gas consumption of households in China.

According to the historical data of average per capita natural gas consumption of households in China, the authors construct GM(1,1) model, DGM(1,1) model, unbiased grey forecasting model, and GM(1,1) model with trigonometric residual modification. The accuracy of TGM is the best. TGM helps to improve the accuracy of GM(1,1).

This paper gives a successful practical application of grey model GM(1,1) with the trigonometric residual modification, where the cyclic variations exist in the residual series. The case demonstrates the effectiveness of trigonometric grey prediction model, which is helpful to understand the modeling mechanism of trigonometric grey prediction model.

In this paper, the authors use the balance sheet data to investigate the interconnectedness and risk contagion effects in China's banking sector. They firstly study the network structure and centrality of the interbank network. Then, they investigate how and to what extent the credit shock and liquidity shock can lead to the risk propagation in the banking network.

Referring to the theoretical framework by Haldane and May (2011), this paper uses the network topology theory to analyze the contagion mechanism of credit shock and liquidity shock. Centrality measures and log-log plot are used to evaluate the interconnectedness of China's banking network.

The network topology has shown clustering effects of large banks in China's financial network. If the Industrial and Commercial Bank of China (ICBC) is in distress, the credit shock has little impact on the Chinese banking sector. However, the liquidity shock has shown more substantial effects than that of the credit shock. The discount rate and the rollover ratio play significant roles in determining the contagion effects. If the credit shock and liquidity shock coincide, the contagion effects will be amplified.

The results of this paper reveal the network structure of China's interbank market and the resilience of banking system to the adverse shock. The findings are valuable for regulators to make policies and supervise the systemic important banks.

The balance sheet data of different types of banks are used to construct a bilateral exposure matrix. Based on the matrix, this paper investigates the knock-on effects of credit shock triggered by the debt default in the interbank market, the knock-on effects of liquidity effects, which is featured by “fire sale” of bank assets, and the contagion effects of combined shocks.

Discovering the research topics and trends from a large quantity of library electronic references is essential for scientific research. Current research of this kind mainly depends on human justification. The purpose of this paper is to demonstrate how to identify research topics and evolution in trends from library electronic references efficiently and effectively by employing automatic text analysis algorithms.

The authors used the latent Dirichlet allocation (LDA), a probabilistic generative topic model to extract the latent topic from the large quantity of research abstracts. Then, the authors conducted a regression analysis on the document-topic distributions generated by LDA to identify hot and cold topics.

First, this paper discovers 32 significant research topics from the abstracts of 3,737 articles published in the six top accounting journals during the period of 1992-2014. Second, based on the document-topic distributions generated by LDA, the authors identified seven hot topics and six cold topics from the 32 topics.

The topics discovered by LDA are highly consistent with the topics identified by human experts, indicating the validity and effectiveness of the methodology. Therefore, this paper provides novel knowledge to the accounting literature and demonstrates a methodology and process for topic discovery with lower cost and higher efficiency than the current methods.

Dimensional variation management is a major challenge in multi‐station sheet metal assembly processes involving complex products such as automotive body and aircraft fuselage assemblies. Very few studies have explored it at a preliminary design phase taking into consideration effects of part deformation on variation propagation, since early design phase involves the development of imprecise design models with scant or incomplete product and process knowledge. The objective of this paper is to present a variation model which can be built into the preliminary design phase taking into consideration all of the existing interactions between flexible parts and tools in multi‐station sheet metal assembly process.

The paper addresses this problem by first, presenting a beam‐based product and process model which shares the same data structure of the B‐Rep CAD models, and therefore can be embedded in CAD systems for automatic product skeletal design; second, determining the influence of part deformation, for various, differing joining and releasing schemes, on variation propagation; and third, utilizing this information to generate a vector‐based variation propagation model for multistation sheet metal assemblies.

This paper presents a beam‐based product and process model which shares the same data structure of the B‐Rep CAD models, and therefore can be embedded in CAD systems for automatic product skeletal design; determines the influence of part deformation, for various, differing joining and releasing schemes, on variation propagation; and utilizes this information to generate a vector‐based variation propagation model for multistation sheet metal assemblies.

A truck cab assembly is presented to demonstrate the advantages of the proposed model over the state‐of‐the‐art approach used in industry for sheet metal assemblies.

A little-known “lottery fever” has spread to many parts of rural China over the past 10 years. This is driven by participation in underground lotteries with local bookies. It is called liuhecai, which is the name of the Hong Kong lottery, and is based on guessing the bonus number of the Hong Kong Mark Six lottery. Such lotteries are illegal, but are an open secret. This chapter seeks to understand the meaning of this apparently irrational lottery fever: why people participate in it, why they believe the conspiracy theory that it is rigged (and yet still participate), and why similar lotteries have emerged in both capitalist Taiwan and post-socialist China at this particular time.