Table 1:
Characteristics of the components of the studied model
| Variable | Type | Description | Source of information |
|---|---|---|---|
| AGRO_PROF | Dependent | Average annual profit of agritourism (thousand euros) | Statistical Office of the Slovak Republic |
| DIG_USE | Independent | Share of online bookings (in %) | Online platforms, questionnaire |
| ICT_LEVEL | Independent | Farmers’ digital skills index (0–1) | Questionnaire |
| EDU_RURAL | Independent | Share of rural population with higher education (%) | Statistical Office of the Slovak Republic |
| TOUR_FLOW | Control | Average number of tourists per object per year | Ministry of Tourism and Sports of the Slovak Republic |
| GDP_REG | Control | Gross regional product per capita (euro) | Eurostat, Statistical Office of the Slovak Republic |
Table 2:
Key characteristics of surveyed respondents
| Parameter | Categories | |||||
|---|---|---|---|---|---|---|
| Age of respondents | Digital experience | |||||
| Meanings | up to 35 years old | 36-55 | 56+ | Basic | intermediate | advanced |
| Share in the survey | 21% | 53% | 26% | 27% | 45% | 28% |
Table 3:
Characteristics of the components of the studied model
| Variable | Marking | Min. | Max. | Average | Standard deviation | Coefficient of variation (%) |
|---|---|---|---|---|---|---|
| Average annual profit of agritourism (thousand euros) | AGRO_PROF | 4.2 | 19.6 | 11.3 | 3.85 | 34.1 |
| Share of online bookings (in %) | DIG_USE | 13.0 | 67.0 | 39.8 | 13.4 | 35.4 |
| Farmers’ digital skills index (0-1) | ICT_LEVEL | 0.22 | 0.86 | 0.54 | 0.19 | 35.8 |
| Share of rural population with higher education (%) | EDU_RURAL | 12.1 | 36.4 | 21.5 | 5.7 | 26.5 |
| Gross regional product per capita (euro) | GDP_REG | 11 200 | 27 500 | 18 875 | 5 220 | 27.6 |
| Average number of tourists per object per year | TOUR_FLOW | 460 | 1 520 | 990 | 298 | 28.3 |
Table 4:
Checking the results for normality of distribution
| Variable | W Statistics | p-value | Interpretation of the result |
|---|---|---|---|
| AGRO_PROF | 0.942 | 0.033 | Non-normal distribution |
| DIG_USE | 0.911 | 0.017 | Non-normal distribution |
| ICT_LEVEL | 0.957 | 0.077 | Normal at the limit of significance |
| EDU_RURAL | 0.930 | 0.028 | Non-normal distribution |
| TOUR_FLOW | 0.9489 | 0.046 | Non-normal distribution |
| GDP_REG | 0.926 | 0.024 | Non-normal distribution |
Table 5:
Checking the results for normality of distribution
| Variable | VIF | Interpretation of the result |
|---|---|---|
| DIG_USE | 2.16 | Acceptable level |
| ICT_LEVEL | 2.84 | Moderate dependence |
| EDU_RURAL | 1.95 | Minor collinearity |
| TOUR_FLOW | 3.11 | Admissible correlation |
| GDP_REG | 2.42 | Without the threat of multicollinearity |
Table 6:
Correlation matrix (Pearson coefficients)
Table 7:
Results of regression analysis
| Variable | Coefficient β | Standard error | t-statistic | p-value | Interpretation of the result |
|---|---|---|---|---|---|
| Constant | 5.214 | 1.0432 | 5.01 | < 0.001 | Basic profit level excluding variables is €5,200 |
| DIG_USE | 0.088 | 0.019 | 4.59 | < 0.001 | A 1% increase in online bookings increases profits by €88 |
| ICT_LEVEL | 3.765 | 1.532 | 2.47 | 0.015 | An increase in digital competence by 0.1 increases profits by 376 euros |
| EDU_RURAL | 0.095 | 0.039 | 2.48 | 0.014 | Each additional % of rural residents with higher education adds €95 to profits |
| TOUR_FLOW | 0.00029 | 0.00009 | 3.12 | 0.002 | Insignificant impact as the number of tourists does not explain changes in income |
| GDP_REG | 0.016 | 0.011 | 1.54 | 0.128 | Every 1,000 euros in GRP per capita is associated with an increase in profits of 290 euros |