A Machine-Learning Analysis of the Impacts of the COVID-19 Pandemic on Small Business Owners and Implications for Canadian Government Policy Response

被引：7
|
作者：
ISABELLE, D. I. A. N. E. A. [1 ,2 ]
HAN, Y. U. [3 ]
WESTERLUND, M. I. K. A. [4 ]
机构：
[1] Carleton Univ, Prott Sch Business, Ottawa, ON, Canada
[2] Univ Johannesburg, Sch Management, Dept Business Management, Johannesburg, South Africa
[3] Univ Regina, Fac Business Adm, Regina, SK, Canada
[4] Carleton Univ, Sprott Sch Business, Ottawa, ON, Canada
来源：
CANADIAN PUBLIC POLICY-ANALYSE DE POLITIQUES | 2022年 / 48卷 / 02期
基金：
芬兰科学院;
关键词：
COVID-19 crisis management; impacts; small business; topic modelling; Canada; CRISIS MANAGEMENT; RESILIENCE; ENTREPRENEURSHIP; CONSERVATION; AFTERMATH; RESOURCES; COMMUNITY; DISASTER; PATHS; UK;
D O I：
10.3138/cpp.2021-018
中图分类号：
F [经济];
学科分类号：
02 ;
摘要：
This study applies a machine-learning technique to a dataset of 38,000 textual comments from Canadian small business owners on the impacts of coronavirus disease 2019 (COVID-19). Topic modelling revealed seven topics covering the short- and longer-term impacts of the pandemic, government relief programs and loan eligibility issues, mental health, and other impacts on business owners. The results emphasize the importance of policy response in aiding small business crisis management and offer implications for theory and policy. Moreover, the study provides an example of using a machine-learning-based automated content analysis in the fields of crisis management, small business, and public policy.
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页码：322 / 342
页数：21
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