The coronavirus (COVID-19) outbreak in the China has exposed small-and medium-sized enterprises (SMEs) to a variety of challenges, some of which are potentially life-threatening to their sustainability. Therefore, this study aims to investigate the macroeconomic lockdown effects of COVID-19 on small business in China. A survey questionnaire with 313 participants was used to collect the data. In this study, the SEM technique was used to analyse model. The data have been gathered for the study from the managers and employees of Chinese SMEs. The findings of the study show that COVID-19 has a significant negative impact on financial performance, operational performance, profitability, access to finance, and customer satisfaction. According to the study's findings, external support aids have a greater impact on SMEs' ability to survive and thrive through innovation than on their actual performance. The findings of this study have a number of important practical consequences for small-and medium-sized business owners, governments, and policymakers.
As a consequence of the COVID-19 pandemic outbreak, most commodities experienced significant price drops, which were expected to continue well into 2020. As a result, the Markov switching model is used to study the influence of policy uncertainty and the COVID-19 pandemic on commodity prices in the USA. Commodity markets are stimulated by economic policy uncertainty, according to results from a two-state Markov switching model. In both high and low regimes, economic policy uncertainty (EPU) influences the commodity market, according to the study's findings. However, in the high regime, EPU has a greater influence on the energy and metal sectors. EPU has different influences on commodity markets in highand low-volatility regimes, according to this study. There is a wide range of correlations between COVID-19 outcomes and EPU and how the prices of natural gas, oil, corn, silver, soybean, copper, gold, and steel respond to these tremors, in both high-and low-volatility tenure. Oil and natural gas, on the other hand, are unaffected by shifts in COVID-19 death rates under either regime. Results show that in both high-and low-volatility regimes, the demand and supply for most commodities are responsive to historical prices.
Innovation has been a major growing driver of sustainability. The topic addressed in this study is a much-required transition to environmental and social sustainability considering the role of innovation in pacing up those changes. Digital evolution has greatly helped in dealing with climatic changes and promoting sustainability. This has helped the entrepreneurial organizations to adopt innovative approaches to tackle the inflexible challenges. Few developed and developing countries are at the forefront regarding technological innovation that encounter significant challenges in terms of innovation and adoption of new technologies and there is still a study vacuum as to whether the influence of technical innovation on achieving social and environmental sustainability differs depending on the stage of sustainability. This quantitative study has explored these effects collecting data from the SME's (small and medium enterprises). The findings of the study show that attitude toward technological innovation has a strong role to play in organizational innovation, digital entrepreneurship, environmental and social sustainability. Organizational innovation has been found a strong mediator between technological innovation and sustainability while digital entrepreneurship could not find significant results as mediator. This study will be useful for the countries and organizations involved in adopting new technologies considering their organization's role in achieving an overall eco-friendly and social sustainability.
The application of information technology and various electronic communication equipment has grown rapidly. At the same time, information technologies such as the Internet and communication networks have become increasingly mature and widely used, making e-commerce transactions simpler and the roles of enterprises in the supply chain increasingly diversified. At this stage, supply chain finance has become an important way for small- and medium-sized enterprises to finance, and it is a key step in commercial trade. However, the risk control of this model is difficult to be effectively contained. How to control its financial risk to the lowest level is the research goal of this paper. This paper analyzes and calculates the supply chain financial risks of different enterprises through a questionnaire method, a case analysis method, and a comparison method and obtains relevant data. The data results show that the entropy value of the net interest rate is 0.97, which indicates that it has a larger market share and less risk. Through wireless multimedia communication technology and artificial intelligence algorithms, the system calculation of supply chain financial risk management is much simpler. In this regard, the research proposes a scientific system for building supply chain financial risk management.
Investors make capital investment by buying stocks and expect to get a certain income from the stock market. When buying stocks, they need to draw up investment plans based on various information such as stock market historical transaction data and related news data of listed companies and collect and analyze these data. The data are relatively cumbersome and require a lot of time and effort. If you only rely on subjective analysis, the reference factors are often not comprehensive enough. At the same time, Internet social media, such as the speech in stock forums, also affect the judgment and behavior of investors, and investor sentiment will have a positive or negative effect on the stock market. This has an impact on the trend of stock prices. Therefore, this article proposes a stock market prediction model that uses data preprocessing technology based on past stock market transaction data to establish a stock market prediction model, and secondly, an image description generation model based on a generative confrontation network is designed. The model includes a generator and a discriminator. A time-varying preattention mechanism is proposed in the generator. This mechanism allows each image feature to pay attention to the image features of other stock markets to predict stock market trends so that the decoder can better understand the relational information in the image. The discriminator is based on the recurrent neural network and considers the degree of matching between the input sentence and the 4 reference sentences and the image features. Experiments show that the accuracy of the model is higher than that of the stock pretrend forecast model based on historical data, which proves the effectiveness of the data used in this paper in the stock price trend forecast.
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