Depending on the indicators proposed for the construction of business systemic practice development in the mountain area through IoT and ICT, it is observed that for the listed countries, a certain number of mountains (mountain peaks) can be part of the current model taking into account the values of altitude (Im1), slope (Im2), local altitude (Im3) and signal attenuation due to the mountain area (Im4), all within FAO margins (Table 1; Figs. 1). The selection of the analyzed mountain peaks was performed considering the degree of representativeness or the highest altitude, respectively, the smallest, in the mountain ranges of the studied countries. Due to climate change, global warm and other natural or human interventions, earth mountains will become more eroded. Given that the degree of erosion of the studied mountain ranges presents a medium level, the representativeness of the peaks was achieved by fractal representation, which has the quality to capture the surfaces studied in dynamics and uncertainty (current and forecasting surface representativeness) (Figs. 1 - Fractal representation of the mountain peaks for the classes considered for Im8 analysis).
As seen above, the highest degree of erosion should be met at the 1st, 2nd, and 6th mountain altitude classes. In view of this outlook, calculations in future papers about Im8 will have to consider lower altitudes for these classes. Our researches show that higher altitudine present lower IoT and ICT. This challenge will be solved when satellite telecommunications will have better coverage in the mountain area.
Specifically, for signal attenuation due to the mountain area (Im4), the forests represent an important factor of signal attenuation. (Britannica 2020)
In the European areas considered for this study, the signal attenuation index is also influenced by the structure of the component flora. Thus, in areas with low hills (up to 600 m, for example, classes 6 and 7), trees with rich leaves predominate and considerably attenuate the signal used for IoT (examples of such trees: oak, garnet, hornbeam, linden, maple and ash). In the European mountainous regions (up to 1200 m), belonging to classes 5 and 6, grow trees that have leaves and stems in considerable areas, such as beech, which mixed with sessile oak and other species oak descends in the lower hill regions. Above mixed with conifers, beech can climb to the upper limit of the forest. Softwoods (spruce, more widespread in the Carpathians, especially in the Eastern larch, fir, in some places, and pine) grow up to 1700–1800 m. The leaves of these trees are not so rich, but the stems and branches create problems to attenuate the signal. Among the grassy plants at these altitudes are characteristic ferns and, in the glades, the red fescue. In areas higher than 1800 meters above class 4 inclusive, alpine and subalpine vegetation develops, for example, dense and short meadows, juniper bushes, mountain peony, blueberry, and juniper. These are not a major signal attenuation problem, but the area itself is dominated by large cliffs that attenuate considerably the IoT signal.
Another indicator considered for the Im8 index is Internet access (Im5). To do business in a properly manner, entrepreneurs need IoT, which is also dependent on this index. The situation of internet access of enterprises for the analyzed countries presents an acceptable situation.
According to Eurostat, the percentage of enterprises with internet access (enterprises with more than ten employees including and excluding those in the financial sector) has high share in the analyzed countries, as follows: Austria 100% (2006) and 99% (2019), Czech Republic 97% (2016) and 98% (2019), Croatia 98% (2016) and 91% (2019), France 100% (2016) and 99% (2019), Italy 98% (2016, 2019), Poland 96% (2016) and 94% (2019)), Portugal 98% (2016, 2019), Romania 83% (2016) and 84% (2019), Slovakia 96% (2016) and 97% (2019). Enterprises that use a computer with internet access (enterprises with more than ten employees including and excluding those in the financial sector) have a high rate, as follows: Austria 100% (2006) and 99% (2019), Czech Republic 99% (2016) and 99% (2019), Croatia 100% (2016, 2019), France 100% (2016, 2019), Italy 99% (2016, 2019), Poland 99% (2016, 2019) and 94% (2019), Portugal 99% (2016, 2019), Romania 99% (2016) and 98% (2019), Slovakia 99% (2016) and 100% (2019). Companies that provide employees with access to organizational emails, documents and applications (companies with more than ten employees including and excluding those in the financial sector) showed considerable value in 2016, respectively: Austria 59%, Czech Republic 71%, Croatia 56%, France 43%, Italy 56%, Poland 78%, Portugal 55%, Romania 30%, Slovakia 61%. Companies that use a computer and provide employees with access to organizational email, documents and applications (companies with more than ten employees including and excluding those in the financial sector) were significant in 2016, respectively: Austria 59%, Czech Republic 72%, Croatia 61%, France 43%, Italy 56%, Poland 82%, Portugal 56%, Romania 34%, Slovakia 62%. Companies that use a computer and provide employees with access to organizational email, documents and applications (companies with more than ten employees including and excluding those in the financial sector) had high shares in 2016, respectively: Austria 60%, Czech Republic 73%, Croatia 61%, France 43%, Italy 57%, Poland 83%, Portugal 56%, Romania 35% and Slovakia 63%. (Eurostat 2021)
Degree of penetration for smart electronics (Im6) depends on the smart electronics manufactured or imported. Regarding the manufactured smart electronics, Eurostat presents some annual statistics for the manufacture industry. Thereby, computers, electronics, and optical products fabricated between 2008 and 2018 fluctuated for Czech Republic − 11.25%, France − 43.51%, Croatia − 39.66%, Italy − 30.96%, Austria 0.17%, Poland 82.53%, Portugal − 28.19%, Romania − 29.08%, Slovakia 642.91%; electronic components and boards of Czech Republic 10.48%, France − 25.03%, Croatia 72.34%, Italy − 22.80%, Austria 20.72%, Poland 102.99%, Portugal − 19.25%, Romania 6.63%, Slovakia 534.52%; computers and peripheral equipment with France − 33.10%, Croatia − 43.29%, Italy − 61.76%, Austria − 35.19%, Poland 102.54%, Portugal − 41.07%, Romania − 50.00%, Slovakia 1150.00; communication equipment with Czech Republic − 26.61%, France − 57.54%, Croatia − 53.70%, Italy − 38.73%, Austria − 5.26%, Poland − 29.19%, Portugal − 53.33%, Romania − 48.67%, Slovakia 1369.57%; consumer electronics with Czech Republic − 48.96%, France 60.12%, Croatia 1600.00%, Italy − 14.98%, Austria 16.13%, Poland 142.11%, Portugal − 60.53%, Romania 7.69%, Slovakia 405.00%. (Eurostat 2021)
Specifically, for video recording or reproducing apparatus, whether or not incorporating a video tuner (8521), the import had high values in USD thousands and tones for 2019 year, as follow, Austria 23309 USD thousands tsd (427 tones), Czech Republic 17042 (411), Croatia 3976 (91), France 84051 (2113), Italy 51773 (1718), Poland 48509 (1522), Portugal 9880 (1577), Romania 15666 (429), Slovakia 17524 (969). (Trademap 2021)
As seen, local production decreases significantly, but the imports of automatic data processing machines (computer and similar) and video apparatus from different countries (especially Asian) increase meaningful.
The indicator with the greatest relevance in the context of the development of the IoT segment within the ICT sector (Rate of active ICT enterprises in a mountain area - Im7) is the population of active enterprises in t - number. This indicator shows the potential development of IoT and ICT in the mountain area.
At the European level, the population of active enterprises in the mountain area occupied in 2016 important shares in the total population of active enterprises, respectively, Bulgaria 79.39%, Italy 41.70%, Austria 38.95%, Spain 56.62%, Romania 28.30% and Slovakia 41.83%. In other countries, the population of active enterprises in the mountain area had a lower penetration of the business systemic practice sector, so that in the Czech Republic the percentage was 11.66%, in France 15.76%, Croatia 17.18%, Poland 3.84% and Portugal 13.61%. (Eurostat 2021)
The analysis of the studied countries presents specific statistics for the mountain area ICT sector. According to ANOVA (ANalysis Of Variance and Covariance) and applying regression principles, the model fit postulates that a variance has a mean of the fitted values equal to the mean itself. Thereby, model fit statistics of the mean, respectively, percentile 5 are presented in Table 2 (Eurostat 2021).
Table 2
Statistics for the ICT sector in the European mountain area
Country | Statio nar R-squa red | R-squa red | Root Mean Square Error | Mean Absolute Percentage Error | Maximum Absolute Percentage Error | Mean Absolute Error | Maximum Absolute Error | Bayesian Inform ation Criterion |
Austria | .267 | .043 | 12953.127 | 121623.14 | 943357.862 | 8086.914 | 29589.768 | 19.426 |
Bulgaria | .492 | .013 | 38470.502 | 78465.276 | 327897.026 | 29306.33 | 73049.592 | 21.529 |
Czechia | .681 | .107 | 2834.848 | 4775.996 | 18847.752 | 1964.095 | 7559.655 | 16.261 |
Croatia | .649 | .093 | 1359.930 | 4493.438 | 27017.137 | 880.431 | 3709.254 | 14.740 |
France | .631 | .038 | 32649.212 | 34898.835 | 163411.349 | 20451.381 | 90577.215 | 21.148 |
Italy | .634 | .026 | 54224.288 | 93648.089 | 498633.987 | 33708.193 | 146579.800 | 22.163 |
Poland | − .024 | .200 | 2258.946 | 4469.520 | 36259.937 | 1472.641 | 6290.453 | 15.822 |
Portugal | .645 | .036 | 2781.240 | 6900.482 | 46998.483 | 1860.552 | 6789.220 | 16.238 |
Romania | .640 | .038 | 12912.245 | 28500.516 | 240386.754 | 8023.334 | 34976.430 | 19.278 |
Slovakia | .674 | .088 | 6863.672 | 14235.786 | 123168.056 | 4412.703 | 19860.388 | 18.015 |
Spain | - | − .249 | 124044.4 | 615596.58 | 2462032.15 | 84396.176 | 119810.676 | 24.150 |
Source: Authors according to Eurostat (2021) |
The analyzed data show that the model fits correctly into the country models without errors. All presented values are in normal parameters and the variability of the returned data around its mean are explained with high precision. According to ANOVA analysis, statistics and forecasting, the studied countries will perform quickly on ICT sector in the mountain area. Only Spain, due to the depopulation of the mountain area, will perform slower than the others.
As seen in Figs. 2 (Statistics and forecasting models for the population of active enterprises
of ICT sector in mountain area of the European analyzed countries, 2019–2037) the ascending trends in the European analyzed countries will be met only for one-two years, after that the trend will be descending before 2026, except Austria where will be until 2029. Subsequent previous to 2037, the ICT sector will not develop anymore, which is explainable, because the majority of the mountain areas will be technological by then and will advance significantly 5G and satellite communications.