Table 4.

Results of the binomial logistic regression models

Odds ratio
Explanatory variablesModel 1Model 2Model 3Model 4
(N = 347)(N = 414)(N = 198)(N = 229)
Providing online services before the COVID-19 pandemic0.694
Possibility to convert a business model to an e-business model2.109*
Being impacted by COVID-19 (low impact)0.946
Being impacted by COVID-19 (low impact)0.996
Company size
Small0.4820.4950.389**0.781
Medium1.4441.5350.2150.954
Economy sector
Primary0.3100.3440.5940.594
Secondary1.1101.0671.0241.501
Notes:

Significant at ***p < 0.001; **p < 0.01; and *p < 0.05. A “very small” category is a reference for a company size variable, and “tertiary” category is a reference for an economy sector variable. If a variable was not included in the model, then it is indicated by the “–” sign. The dependent variable for Models 1 and 2 is whether a company was impacted by COVID-19 or not (0-yes, 1-no); dependent variables in Models 3 and 4 present expectations of the companies about the impact of digitalization on “Digitization of processes” and “Innovative products and services” correspondingly (1-average to high impact, 0-low/very low impact). In Models 3 and 4, the independent variable; “Being impacted by COVID-19” is a dummy variable with categories of 1-low impact and 0-average to high impact

Source: Author’s own work

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