©2023 International Society of Arboriculture Arboriculture & Urban Forestry 49(6): November 2023 287 (2022), our empirical model is primarily based on the disaster theory (Torres et al. 2018) that focuses on the firm’s size and resiliency, and the resilience theory (Marshall and Schrank 2014) that focuses on post- disaster business resilience. We also included demo- graphic variables because several studies reported that gender, race, and ethnicity of business owners play a critical role in their performance, business out- comes, and risk-taking behavior (Carland and Car- land 1991; Fairlie and Robb 2009; Farlie 2020; Farrell et al. 2020). The empirical model is specified as: (1) Where, covid represents the perceived impacts of the COVID-19 pandemic on business performance collected through the question “How has the COVID- 19 pandemic impacted the urban and community for- estry segment of your business?” and were recorded on a 5-point ordinal scale: (1) very negative impact, (2) somewhat negative, (3) neutral, (4) somewhat positive, and (5) very positive. Similarly, the binary variables landscaping, nursery-tree, and nursery- stores indicate NAICS industry types related to pri- vate landscaping services, nursery and tree production, and nursery retail stores, respectively. We hypothe- sized that COVID-19 affected different U&CF indus- try types heterogeneously, depending on the level of physical interaction with customers and according to the level of restrictions they had to abide by during the pandemic. The variables large-firm, longevity, and large- sales denote various business metrics representing each respondent’s firm size in terms of the number of employees, years in business, and the total annual gross sales, respectively. These business metrics should help understand the variation in the perception of COVID-19 impacts on U&CF businesses, as they are usually a reflection of a firm’s size and success in the U&CF industry. The variable, llc, represents the legal structure or entity of the company, where the value “1” represents a limited liability corporation business or “0” otherwise. Additionally, we included a few variables represent- ing respondents’ perceptions of several challenges ..... . .............. ....... . ..... ....... . ....... ..... . ..... .......... ..... . ...... .... ......... ......... ............ .... ..... ........ ....... ....... . ..... ....... . ....... ..... . ..... .......... ..... ...... .... ......... ......... ............ .... ..... ........ ... . ....... ..... . ..... .......... ..... .. ............ .... ..... ........ ..... . .............. ....... . ..... ....... . ....... ..... . ..... .......... ..... . ...... .... ......... ......... ............ .... ..... ........ ... . ..... ....... . ....... ..... . ..... .......... ..... ...... ......... ............ .... ..... ........ related to their U&CF business—access to employee training (training), personnel turnover (turnover), and market uncertainty (uncertainty)—to assess how the variation in the COVID-19 impacts is explained by the respondents’ perceived challenges to their U&CF business. These variables were recorded on a 5-point ordinal Likert scale to gauge the perception of the severity of the challenge: (1) not at all, (2) slightly, (3) moderately, (4) very, and (5) extremely. We also incorporated a few demographic variables of the busi- ness owner that we believe reflect business owners’ level of risk aversion and perception of risk, thus affect- ing how they perceive COVID-19 has impacted their business. The variables edu, male, and white denote if the business owner has at least a bachelor’s degree of formal education, if the gender was male, and if the race was white, 0 otherwise, respectively. Table 1 presents a detailed description of the variables used in our empirical modeling. Estimation Method: Ordered Logit Model Given that the dependent variable was on a 5-category ordinal scale, we employed the ordered logistic regression model to evaluate the factors explaining the perceived COVID-19 pandemic impacts on the U&CF business of the private green industry in the SUS. With various cut points as the probabilities of negative or positive impacts of COVID-19 on U&CF business, the ordered logistic regression model esti- mates a score, which should be a linear function of the explanatory variables (Torres-Reyna 2008; Wil- liams 2021). The same relationship between each pair results in just one set of coefficients. Let be the vari- able representing the response of each respondent and let be an associate latent variable that crosses a series of increasing unknown thresholds, represented by and which will be estimated. Additionally, let j ∈ {1,2,3,4 or 5} represent the set of alternative responses such that yi = j if αj˗1 < yi* ≤ αj˗1, where α0 = ˗ ∞ and α5 = ∞. The latent response variable may be modeled as per Equation 1(Cameron and Trivedi 2005): (2) where, x′i represents a vector of the variables listed in Table 1, explaining the unknown response latent vari- able, β is a vector of parameters to be estimated, and ... . .. .. . ... .
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