Revisiting Small- and Medium-Sized Enterprises’ Innovation and Resilience during COVID-19: The Tourism Sector

Every day, companies are exposed to various risks arising from their environment, and small and medium enterprises (SMEs) are the most affected. This study emphasizes innovation and resilience as the starting points to understand how SMEs are influenced by external factors which are independent of the managers’ will. To date, we have seen several extreme factors that have conditioned businesses and entrepreneurs of these types of companies, such as the financial crisis of 2008. However, last year, in 2020, we saw the beginning of a worldwide pandemic: COVID-19. Thus, this research seeks to understand the extent to which this pandemic influenced the resilience and innovation of SMEs in the tourism area. Thus, eight hypotheses were raised based on four independent variables: access to finance, risk taking, working conditions, and personal network. The study of these variables was developed from an online questionnaire and in-depth interviews. After analyzing the results, it is possible to infer that the COVID-19 pandemic resulted in consequences never observed in previous crises, to such an extent that the study showed that: (1) a company’s network positively influences its resilience; (2) risk taking influences innovation and resilience of these SMEs.

PERSEPSI RISIKO ATAS IMBAUAN SOCIAL DISTANCING AKIBAT PANDEMI COVID-19

Penelitian ini bertujuan untuk menggambarkan persepsi risiko masyarakat atas pandemi Covid-19 dan imbauan social distancing di Kabupaten Sidenreng Rappang (Sidrap), Sulawesi Selatan. Temuan penelitian diharapkan memberi rekomendasi gagasan kepada pemangku kepentingan. Penelitian ini menggunakan data berupa data primer dengan menyebarkan kuesioner secara online. Jumlah sampel mempertimbangkan jumlah perumahan sebagai populasi yaitu 5.989. Jumlah responden penelitian adalah 105. Teknik sampling adalah purposive sampling. Teknik analisis data adalah deskriptif kuantitatif, menggunakan program SPSS. Hasil penelitian menunjukkan dari keenam tipe persepsi risiko menunjukkan bahwa psychological risk (risiko psikologis) termasuk kategori sangat tinggi yakni 82.34%, Physical risk (risiko fisik) termasuk kategori tinggi yakni 71.45%, dan performance risk (risiko realitas informasi), finance risk (risiko keuangan), time-losss risk (risiko kehilangan waktu), social risk (risiko sosial) berada pada kategori sedang dengan masing-masing persentasi yakni 49.63%, 57.29%, 55.94%, 44.46%. Data ini bisa dipahami bahwa semakin tinggi persentase berarti semakin tinggi persepsi risiko masyarakat dan begitupun sebaliknya.

Digital inclusive finance risk prevention based on machine learning and neural network algorithms

To improve the effectiveness of digital inclusive finance risk prevention, this paper constructs a digital inclusive finance risk prevention system based on machine learning and neural network algorithms and performs special data pre-processing for convolutional neural networks. Moreover, based on the index data arrangement with the shortest double Euclidean distance, this paper uses the principle of combining qualitative analysis and quantitative analysis to process the data set. Aiming at the characteristics of different links in the whole process of digital inclusive finance, this paper has formed a preliminary digital inclusive finance risk factor assessment of influencing factors. Besides, this paper combines the needs of digital inclusive finance risk methods to construct a digital inclusive finance risk prevention model, and design functional modules based on process analysis. Finally, this paper designs experiments to verify the performance of the digital inclusive finance risk prevention model constructed in this paper. The experimental research results show that the model constructed in this paper has a certain effect.

Educational activities of higher schools: Risk management

Subject. The article analyzes principal risks and threats in education. We substantiate scientifically that education is one of the key elements in economic security of the State since higher schools train highly qualified professionals for crucial sectors of the market. Objectives. We identify principal groups of risks associated with educational activities of higher schools and figure out whether they develop and implement risk management systems. Methods. The research draws upon methods of factual data observation and collection, historical and logic methods, analysis, synthesis, etc. Results. We examine key approaches to risk classification in education. The article also analyzes risk factors and identifies critical risks associated with educational activities of higher schools. Conclusions and Relevance. We outlined the risk map by negative consequence and probability of a risk. Using the risk map, we detect five groups of the most critical risks, i.e. rapidly outdated knowledge risk, budgetary finance risk, professional personnel risk, risk of educational program inconsistency with the labor market and risk of universities' competitiveness in the global market