*2.4. Innovation and Development*

Among the innovative strategies firms implemented to cope with the COVID-19 pandemic, refs. [77,78] identified factors, such as differentiation of products and channels in the digital market (including social media). Their studies demonstrate the effectiveness of these measures. Other indicators have also been used to measure advances in digitalization and use of innovative knowledge as strategies to adapt to changes caused by the crisis. Digitization is the measure most recommended [79,80].

Other studies evaluated the consideration of innovation in business models as a measure to mitigate the effects of the COVID-19 crisis [15,81]. Additional research studying resilience and investment in reactivation demonstrates that technology and innovation capacity contribute to sustainability in tourism SMEs [16,59,82].

#### **3. Materials and Methods**

Based on this theoretical development, we propose the following hypothesis for the data analysis:

**Hypothesis 1:** *The business situation of tourism SMEs in Colombia may influence the economic indicators caused by the COVID-19 crisis.*

**Hypothesis 2:** *The COVID-19 crisis has affected the strategic management of tourism SMEs in Colombia.*

**Hypothesis 3:** *Innovation and development in tourism SMEs have been decisive, although conditioned by the COVID-19 crisis.*

**Hypothesis 4:** *Depending on the business situation of tourism SMEs, these firms promoted innovation and development, which contribute to improving the business situation.*

**Hypothesis 5:** *Innovation and development practices support organizational management of tourism SMEs.*

Figure 1 presents the theoretical approach and the relationships between the variables.

**Figure 1.** Theoretical model.

This study developed a structural equations model following following [60,82–86]. The model relates the variables for measuring economic impacts of the COVID-19 crisis on the business situation (and vice versa), organizational management strategies, and investment in innovation and technological development. The data were obtained from a survey (see Appendix A) [62,87,88] with responses measured on a Likert scale, following [88]. Sales and other financial data were extracted from the Orbis database.

To categorize the item "Tourism subsector" according to relative importance of each subsector, we assigned 1 to the subsector with the lowest representativeness or number of companies in the population and 4 to the subsector with the highest number of companies in the population. The items "Number of workers", "Sales volume", and "Productive capacity" were categorized by ranges adapted to a scale of 1 to 5, where 1 is greatly decreased and 5 is greatly increased. Appendix B presents the ranges and criteria for all the items' categorization.

The population of tourism firms in Colombia obtained from Orbis was 4766. Of this total, 1177 firms may be considered small or medium sized, following revenue criteria for firm size in Colombia, defined by Decree 957 of 2019. We used the following equation to calculate the sample:

$$\mathbf{n} = \frac{\mathbf{N} \ast \mathbf{Z}^2 \ast \mathbf{p} \ast \mathbf{q}}{\mathbf{d}^2 \ast (\mathbf{N} - 1) + \mathbf{Z}^2 \ast \mathbf{p} \ast \mathbf{q}}$$

where:

N = population = 1177 Z = 95% confidence level = 1.96 p = expected probability of success = 0.5 q = probability of failure = 0.5

d2 = precision (maximum admissible error) = 0.05

We, thus, obtained a sample size of 289, as shown in Table 1.

**Table 1.** Population and sample.


Note: Population and sample stratified by tourism subsectors.

The data were collected through an online form. Eighteen questionnaires were found to be incomplete or to have been completed by firms that did not belong to the tourism sector, leaving useful data from 271 SMEs. Sample size, calculated based on a 95% confidence level and 5% margin of error, showed this sample to be valid.

We applied CFA following [36,38] to test the theoretical constructs proposed for the causal relationship of COVID-19 pandemic impact to firms in the tourism sector. The analysis was based on these firms' business situation and the effects on organizational and economic–financial structure and innovation and development processes, as specified in Table 2. In this table, we added, in the fourth column, authors who have researched the different items and variables.



Note: The first three variables are first-order latent variables. The fourth is the criterion variable (impact of COVID-19 on SMEs in the tourism sector). On the right side, the factors that compose each variable are defined, followed by the authors supporting the variables.

This set of constructs comprises the five hypotheses to be contrasted with the empirical data. They are H1: The business situation of tourism SMEs in Colombia may influence the economic indicators caused by the COVID-19 crisis. H2: The COVID-19 crisis has affected the strategic management of tourism SMEs in Colombia. H3: Innovation and development in tourism SMEs have been decisive, although conditioned by the COVID-19 crisis. H4: Depending on the business situation of tourism SMEs, these firms promoted innovation and development, which contribute to improving the business situation. H5: Innovation and development practices support organizational management of tourism SMEs. Table 3 presents the descriptive statistics for the variables.

**Table 3.** Statistical data on the items.


Note: Continuous variables were categorized using a Likert scale with values from 1 to 5.

#### **4. Results**
