ABSTRACT
The severe impact of global crises, such as COVID-19 and climate change, is plausibly reshaping the way in which people perceive risks. In this paper, we examine and compare how global crises and local disasters influence public perceptions of multiple hazards in Italy and Sweden. To this end, we integrate information about the occurrence of hazardous events with the results of two nationwide surveys. These included more than 4000 participants and were conducted in two different phases of the COVID-19 pandemic corresponding to low (August 2020) and high (November 2020) levels of infection rates. We found that, in both countries, people are more worried about risks related to experienced events. This is in line with the cognitive process known as the availability heuristic: individuals assess the risk associated with a given hazard based on how easily it comes to their mind. Epidemics, for example, are perceived as less likely and more impactful in Italy compared to Sweden. This outcome can be explained by cross-country differences in the impact of, as well as governmental responses to, COVID-19. Notwithstanding the ongoing pandemic, people in both Italy and Sweden are highly concerned about climate change, and they rank it as the most likely threat.
ABSTRACT
Under the background of economic globalization and professional division of labor, each link of enterprise supply chain management is facing more and more risks. Over the past two years, due to the turbulent situation at home and abroad and the repeated outbreaks of COVID-19 around the world, the normal procurement work has been greatly affected which means the purchase cost increases and even out of stock occur. The rapid development of big data, artificial intelligence and other technologies has brought new tools and means for enterprise risk management. This paper focuses on the procurement work in enterprise operation management, and analyzes how to obtain procurement related news or current review texts, and analyzes their emotional tendency to evaluate and quantify the public opinion risk of procurement by natural language processing (NLP), denoted as NLP. By combing the crawled text data, we add some purchase related words to a corpus which can analyze good and bad emotions and train a model. We use the model to score the manually labeled text data to determine the optimal threshold of positive news and negative news. Under this threshold, the accuracy of text emotion analysis is 85.4%. Finally, through a case analysis, we show the specific implementation of procurement public opinion risk score evaluation. © 2022 World Scientific Publishing Company.