The authors investigate coronavirus disease 2019 (COVID-19) risk factors, suitability of online instruction, politics, and institutions’ finances as rationales guiding instructional delivery decisions for fall 2020, after COVID-19’s emergence. Contributions include estimating multinomial logit regressions with mode of delivery as a categorical variable, integrating resource dependence and crisis response as theoretical frames, and introducing new predictor variables, including a measure of local residential access to broadband Internet. Findings suggest that county populations, local political preferences, and the percentage of revenue derived from auxiliary enterprises were consistent predictors of delivery mode. Political parties of an institution’s governor and congressional representative were predictive of delivery mode for institutions in the lowest tercile of endowment per student but not for institutions in the highest tercile. Bottom-tercile institutions substituted from online to in-person reopening as reliance on revenue from auxiliary enterprises increased, but top-tercile institutions appeared only to substitute from hybrid to in-person or from online to hybrid delivery as revenue from auxiliary enterprises or tuition and fees increased.

作者调查了 2019 年冠状病毒病 (COVID-19) 的风险因素、在线教学的适用性、政治和机构的财务状况，作为指导 COVID-19 出现后 2020 年秋季教学决策的基本原理。贡献包括以交付方式为分类变量估计多项 logit 回归，将资源依赖和危机响应整合为理论框架，并引入新的预测变量，包括衡量当地居民对宽带互联网的访问。研究结果表明，县人口、当地政治偏好和辅助企业的收入百分比是交付模式的一致预测因素。一个机构的州长和国会代表的政党预测了每名学生捐赠基金最低三分位数的机构的交付模式，但对最高三分位数的机构没有预测。随着对辅助企业收入的依赖增加，底层机构从在线重新开放取代到面对面重新开放，但顶级机构似乎只是从混合到面对面或从在线到混合交付取代了辅助企业或学费和学费的收入费用增加。