1. Introduction

2. Materials and Methods

3. Results

3.1. Statistical Approach

Within the frames of the WeLASER project surveys involving 100 respondents in 3 countries were conducted. Because data obtained were mainly nominal, in the elaboration of the obtained results, cross tabulation analysis was used. Pearson’s Chi-square test was used to test the relationship between the two selected variables expressed on a nominal scale. [45]. The Pearson chi-square statistic is calculated according formula (2):

$${\chi }^2=\sum \frac{{\left(O_{ij}-E_{ij}\right)}^2}{E_{ij}}$$

(2)

where $O$ are observed values, $E$ are expected values and $i$ denotes row index, $j$ denotes column index of the table. Test statistic from the formula above is approximately distributed as${\chi }^2$ with ($r$ - 1) x ($c$ - 1) degrees of freedom where $r$ is the number of rows, $c$ is the number of column. The p-value was calculated based on ${\chi }^2$ distribution value. The p-value defines probability that the Null hypothesis H₀ for given pair of variables is true. If the p-value is small enough we reject the Null hypothesis, if p-value is high the null hypothesis is not rejected. The α = 0,05 was taken as significance threshold. The test for independence may be expressed as follow:

H₀: R and C are independent; no relation between R and C; Oi = Ei,

H₁: R and C are dependent; relation exist between C and R; Oi ≠ Ei,

where: R represent row, dependent variables, C represent columns, independent variables, H₀ denotes null hypothesis, H₁ denotes alternative hypothesis, the notion Oi refers to observed values in cross table and Ei refers to expected values in cross table.

Besides p-value, the upper bound for the Bayes factor $\overline{B}$ and was calculated (3). This is calculated for a given p-value, maximum coefficient indicating how many times the alternative hypothesis H₁ is more true than the null hypothesis H₀. The upper bound for the Bayes factor $\overline{B}$ and was calculated by formula [46,47]:

$$\overline{B}=-1/\left(e·p-value·\ln \left(p-value\right)\right)$$

(3)

In calculation of the strength of the association between two crosstab variables Cramer’s V was used [48]. It is defined by the following formula (4):

$$V=\sqrt{\frac{{\chi }^2}{n\times t}}$$

(4)

Where $t$ is calculated according following formula (5):

$$t=minimum\left(r-1,c-1\right)$$

(5)

where $t$ smaller result of the two subtraction and n is the number of respondents.

In interpretation of Cramer’s V coefficient Lee scale was used [49]. Lee proposes following thresholds for interpretation of association:

Cramér’s V values	Association	Cramér’s V values	Interpretation
V<0.1	negligible	0.4<V<0.6	relatively strong
0.1<V<0.2	weak	0.6<V<0.8	strong
0.2<V<0.4	moderate	V>0.8	very strong

Additionally z-test and p-value were calculated for comparisons of column proportions. The z-test value is calculated according the formula (6) [50]:

$$Z=\frac{dif}{S{E}_0}$$

(6)

where numerator $dif$ constitute difference between column proportions and is calculated according the formula (7):

$$dif=p_a-p_b$$

(7)

where $p_a$ is the first proportion of given row in given column and $p_b$ is the second proportion of the same row in another column. The denominator $S{E}_0$ is the standard error for the difference under H₀ which is calculated according following formula (8):

$$S{E}_0=\sqrt{\widehat{p}·\left(1-\widehat{p}\right)·\left(\frac{1}{n_a}+\frac{1}{n_b}\right)}$$

(8)

where $n_a$ and $n_b$ denote the sample size of columns a and b. Notation $\widehat{p}$ is an estimated proportion for both columns and is given by following equation (9):

$$\widehat{p}=\frac{p_a·n_a+p_b·n_b}{n_a+n_b}$$

(9)

Under null hypothesis both columns proportions have the same value and it is equal $\widehat{p}$. After calculation of z-test, and assuming that it follows standard normal distribution our p-value is calculated as 2-tailed significance using formula (10):

$$p-value=2·\text{Ф}\left(\mathrm{Z}\right)$$

(10)

where Φ denotes standard normal distribution and Z constitutes the value of Z-test.

Calculation of The Pearson chi-square statistic and Cramer’s V association coefficient were conducted with application of GNU PSPP software [51], which is a free alternative for IBM SPSS Statistics package. Z-tests pertaining the difference in column proportions were calculated using MS EXCEL.

In analysis 4 questions from the first group and 12 key questions from the second group were selected for further analysis. The matrix below shows selected pairs of questions analyzed in the survey. A total of 110 pairs of relationships were examined. The analyses were conducted separately for Denmark, Poland and Spain.

Table 2. Matrix of performed analyses.

Table 2. Matrix of performed analyses.

	P1	P2	P3	P4	P5	P6	P7	P8	P9A	P9B	P10
A1	X	X	X	X	X	X	X	X	X	X	X
A2	X	X	X	X	X	X	X	X	X	X	X
S8	X	X	X	X	X	X	X	X	X	X	X
S9	X	X	X	X	X	X	X	X	X	X	X
P2	X
P3	X	X
P4	X	X	X
P5	X	X	X	X
P6	X	X	X	X	X
P7	X	X	X	X	X	X
P8	X	X	X	X	X	X	X
P9A	X	X	X	X	X	X	X	X
P9B	X	X	X	X	X	X	X	X	X
P10	X	X	X	X	X	X	X	X	X	X
P12	X	X	X	X	X	X	X	X	X	X	X

Analysis was divided into 2 stages. The first stage contains: (a) performing tests for existence of association by calculation of p-value of the Pearson chi-square statistic; (b) assessment of strength of the association with Cramer’s V coefficient and (c) calculation of descriptive statistics of these two parameters. Comparison of this parameters for Denmark, Poland and Spain and selection data for further analysis at the second stage.

The second stage pertain analyses performed on selected dataset and contains: (a) comparisons of column proportions for Denmark, Poland and Spain for variables describing opinion of respondents; (b) examples of crosstabs calculation with significant association (α less than 0.05) and (c) analysis of crosstab results for dominant responses and crosstabs with p-value < 0.0005 (it represents the maximum value calculated by GNU PSPP software, which the program records as 0).

3.1. Results of Tests for Existence of Association and Calculation of Strength of the Association

3.1.1. Results for Denmark

The table below presents probability of not rejecting null-hypothesis (H₀) or rejecting the alternative hypothesis (H₁) for variables of Denmark. The p-values below 0.05 shown in bold red indicate dependence and the existence of a relationship (association), and the values above 0.05 shown in blue indicate independence and no relationship (or more precisely, no proven relationship / association).

Table 3. Results of the test on independence of key variables describing respondent profile and respondent opinions for Denmark. Red bold - dependent variables, blue – independent variables.

Table 3. Results of the test on independence of key variables describing respondent profile and respondent opinions for Denmark. Red bold - dependent variables, blue – independent variables.

		P1		P2		P3		P4		P5		P6		P7		P8		P9A		P9B		P10
A1		0.000		0.000		0.000		0.061		0.411		0.001		0.155		0.011		0.372		0.272		0.000
A2		0.005		0.081		0.049		0.956		0.000		0.021		0.894		0.127		0.004		0.182		0.686
S8		0.002		0.000		0.001		0.648		0.001		0.000		0.304		0.294		0.902		0.803		0.950
S9		0.001		0.000		0.053		0.504		0.216		0.556		0.629		0.313		0.800		0.398		0.965
P2	0.000
P3	0.000		0.000
P4	0.078		0.018		0.878
P5	0.000		0.000		0.000		0.840
P6	0.000		0.024		0.006		0.856		0.000
P7	0.052		0.126		0.438		0.757		0.008		0.001
P8	0.000		0.173		0.080		0.356		0.009		0.001		0.000
P9A	0.160		0.872		0.023		0.591		0.566		0.070		0.569		0.010
P9B	0.121		0.068		0.253		0.402		0.871		0.857		0.467		0.198		x
P10	0.042		0.837		0.096		0.355		0.658		0.146		0.798		0.197		0.451		0.001
P12	0.000		0.046		0.216		0.390		0.000		0.001		0.002		0.000		0.014		0.889		0.747

Source: Authors’ calculation on the base of WeLASER project data

In the case of the Denmark, we have 46 pairs with p-value less than 0.05. Besides there are 36 cases with p-value less than 0.01 and 32 cases with p-value less than 0.005. There are 21 cases where the p-value calculation yielded 0 (in reality they are < 0.0005). We may conclude that in case of 42,2% of analyzed pairs there is relation between variables (p-value <0.05). The median of p value is 0.096 and the estimator of variability, median absolute deviation about median (MAD) is 0.142.

The next table shows the strength of relations and presents results of Cramer’s V calculation only for pairs of variables with p-value less than 0.05. Out of total 46 pairs meeting selection criterion, 17 (37.0%) scored at least 0.4 (relatively strong association). Minimum value of Cramer’s V is 0.25. Median of Cramer’s V association coefficient for 46 pairs is 0.36, and MAD is 0.074. The strongest associations are observed for the pairs P1-A1 (0.69), P9A-A2 (0.62) as well as P1-P5 and P8-P9A (0.58).

Table 4. Results of the Cramer’s V association of key variables describing respondent profile and respondent opinions for Denmark. Orange – value below 0.4, at least 0.4 – red bold.

Table 4. Results of the Cramer’s V association of key variables describing respondent profile and respondent opinions for Denmark. Orange – value below 0.4, at least 0.4 – red bold.

	P1	P2	P3	P4	P5	P6	P7	P8	P9A	P9B	P10
A1	0.69	0.58	0.44			0.37		0.31			0.40
A2	0.33		0.25		0.42	0.30			0.62
S8	0.35	0.40	0.35		0.39	0.37
S9	0.32	0.34
P2	0.56
P3	0.45	0.39
P4		0.27
P5	0.58	0.39	0.40
P6	0.32	0.27	0.30		0.35
P7					0.32	0.33
P8	0.33				0.31	0.32	0.46
P9A			0.47					0.58
P9B
P10	0.26									0.41
P12	0.35	0.26			0.48	0.31	0.32	0.37	0.46

Source: Authors’ calculation on the base of WeLASER project data

3.1.1. Results for Poland

The table below presents probability of not rejecting null-hypothesis (H₀) for variables of Poland.

Table 5. Results of the test on independence of key variables describing respondent profile and respondent opinions for Poland. Red bold - variables dependent, blue – independent variables.

Table 5. Results of the test on independence of key variables describing respondent profile and respondent opinions for Poland. Red bold - variables dependent, blue – independent variables.

	P1	P2	P3	P4	P5	P6	P7	P8	P9A	P9B	P10
A1	0.147	0.009	0.142	0.502	0.637	0.265	0.115	0.366	0.422	0.495	0.764
A2	0.333	0.055	0.855	0.041	0.391	0.436	0.291	0.306	0.832	0.089	0.240
S8	0.149	0.025	0.765	0.440	0.072	0.962	0.357	0.340	0.653	0.225	0.405
S9	0.317	0.206	0.867	0.887	0.301	0.976	0.457	0.208	0.647	0.857	0.457
P2	0.000
P3	0.001	0.033
P4	0.025	0.816	0.685
P5	0.031	0.079	0.706	0.786
P6	0.039	0.234	0.001	0.741	0.620
P7	0.387	0.000	0.007	0.028	0.017	0.009
P8	0.039	0.481	0.660	0.243	0.139	0.812	0.000
P9A	0.351	0.371	0.008	0.689	0.769	0.331	0.701	0.726
P9B	0.358	0.225	0.958	0.752	0.552	0.587	0.123	0.507	x
P10	0.799	0.343	0.005	0.573	0.450	0.288	0.000	0.000	0.814	0.908
P12	0.927	0.012	0.419	0.976	0.331	0.445	0.000	0.003	0.041	0.239	0.175

Source: Authors’ calculation on the base of WeLASER project data

In the case of the Polish part of analysis, we have 25 pairs with p-value less than 0.05, 14 cases with p-value less than 0.01 and 9 pairs with p-value less than 0.005. There are 6 pairs where calculation of the p-value yielded 0. We may conclude that in case of 22.9 % pairs there is relation between variables (p-value <0.05). The median for p-values is 0.343 and the MAD is 0.436. The median for Poland is higher than for Denmark.

The next table presents results of Cramer’s V calculation for pairs of variables of Poland with p-value less than 0.05. In total there are 22 pairs meeting this criterion. Three of them scored at least 0.4. Minimum value of Cramer’s V is 0.24. Median of Cramer’s V association coefficient for 22 pairs is 0.31 and MAD is 0.02. The three strongest association is observed for a pair P3-P9A (0.43), P9A-P12 (0.41) and P7-P12 (0.4). The median of Cramer’s V for Poland is higher than for Denmark.

Table 6. Results of the Cramer’s V association of key variables describing respondent profile and respondent opinions for Poland. Orange – value below 0.4, at least 0.4 – red bold.

Table 6. Results of the Cramer’s V association of key variables describing respondent profile and respondent opinions for Poland. Orange – value below 0.4, at least 0.4 – red bold.

	P1	P2	P3	P4	P5	P6	P7	P8	P9A	P9B	P10
A1		0.32
A2				0.31
S8		0.30
S9
P2	0.37
P3	0.33	0.26
P4	0.30
P5	0.30
P6	0.24		0.33
P7		0.33	0.29	0.29	0.30	0.30
P8	0.27						0.38
P9A			0.43
P9B
P10			0.32				0.35	0.35
P12		0.28					0.40	0.30	0.41

Source: Authors’ calculation on the base of WeLASER project data

3.1.1. Results for Spain

The table below presents probability of not rejecting null-hypothesis (H₀) for variables of Spain. In this case, we have 26 pairs with p-value less than 0.05, 12 cases with p-value less than 0.01 and 11 cases with p-value less than 0.005. There are 8 cases where the p-value calculation yielded 0. We may conclude that in case of 23.9% of analyzed pairs there is relation between variables (p-value <0.05). The median of p-value is 0.256 and the MAD is 0.307. These values are higher than their counterparts for Denmark and lower than their counterparts for Poland.

Table 7. Results of the test on independence of key variables describing respondent profile and respondent opinions for Spain. Red bold - variables dependent, blue – independent.

Table 7. Results of the test on independence of key variables describing respondent profile and respondent opinions for Spain. Red bold - variables dependent, blue – independent.

	P1	P2	P3	P4	P5	P6	P7	P8	P9A	P9B	P10
A1	0.253	0.224	0.034	0.327	0.836	0.411	0.335	0.594	0.537	0.096	0.762
A2	0.981	0.136	0.362	0.884	0.376	0.678	0.032	0.920	0.054	0.455	0.764
S8	0.934	0.731	0.059	0.312	0.194	0.172	0.889	0.339	0.463	0.367	0.233
S9	0.227	0.109	0.018	0.377	0.044	0.366	0.231	0.022	0.416	0.049	0.064
P2	0.021
P3	0.220	0.873
P4	0.085	0.015	0.369
P5	0.000	0.862	0.000	0.134
P6	0.466	0.887	0.215	0.033	0.613
P7	0.000	0.927	0.097	0.027	0.015	0.000
P8	0.005	0.048	0.845	0.000	0.083	0.003	0.000
P9A	0.067	0.277	0.161	0.121	0.954	0.303	0.889	0.087
P9B	0.787	0.568	0.444	0.411	0.963	0.385	0.956	0.430	x
P10	0.426	0.352	0.700	0.231	0.332	0.010	0.240	0.176	0.256	0.463
P12	0.069	0.600	0.045	0.584	0.130	0.001	0.000	0.000	0.002	0.391	0.774

Source: Authors’ calculation on the base of WeLASER project data

Table below shows results of Cramer’s V calculation for pairs of variables with p-value < 0.05.

Table 8. Results of the Cramer’s V association of key variables describing respondent profile and respondent opinions for Spain. Orange – value below 0.4, at least 0.4 – red bold.

Table 8. Results of the Cramer’s V association of key variables describing respondent profile and respondent opinions for Spain. Orange – value below 0.4, at least 0.4 – red bold.

	P1	P2	P3	P4	P5	P6	P7	P8	P9A	P9B	P10
A1			0.26
A2							0.29
S8
S9			0.29		0.30			0.27		0.45
P2	0.27
P3
P4		0.30
P5	0.39		0.40
P6				0.30
P7	0.33			0.29	0.32	0.34
P8	0.29	0.26		0.35		0.32	0.37
P9A
P9B
P10						0.30
P12			0.27			0.34	0.59	0.42	0.49

Source: Authors’ calculation on the base of WeLASER project data

At least 5 out of 26 (19%) scored 0.4. Minimum value of Cramer’s V is 0.26. Median of Cramer’s V association coefficient for 26 pairs is 0.31 and MAD is 0.052. The strongest association is observed for a pair P7-P12 (0.59). The second strongest is observed for the pair P9A-P12 (0.49) and the third is a pair P9B-S9 (0.45).

As a summary of the comparative analysis between Denmark, Poland and Spain, a complementary statistical analysis of the p-value and Cramer’s V coefficient was performed The table below shows the descriptive statistics of the p-value and Cramer’s V association coefficient for analyzed countries. Statistics of Cramer’s V only include pairs of p-value <0.05.

Table 9. Statistics of p-value and Cramer’s V association for Denmark, Poland and Spain.

Table 9. Statistics of p-value and Cramer’s V association for Denmark, Poland and Spain.

		p-value			Cramer’s V
Country	Denmark	Poland	Spain	Denmark	Poland	Spain
Count	109	109	109	46	25	26
Minimum	0.000	0.000	0.000	0.250	0.240	0.260
Maximum	0.965	0.976	0.981	0.690	0.430	0.590
Mean	0.269	0.382	0.340	0.388	0.322	0.338
SD	0.322	0.307	0.310	0.103	0.047	0.079
SEM	0.031	0.029	0.030	0.015	0.009	0.015
CV	1.199	0.802	0.910	0.265	0.146	0.233
Skewness	0.926	0.354	0.693	1.144	0.683	1.690
Kurtosis	-0.639	-1.130	-0.772	0.898	0.115	3.092
Q1	0.001	0.079	0.059	0.320	0.300	0.290
Median	0.096	0.343	0.256	0.360	0.310	0.310
Q3	0.467	0.653	0.537	0.435	0.350	0.365
Q3-Q1	0.466	0.574	0.478	0.115	0.050	0.075
MAD	0.142	0.436	0.307	0.074	0.020	0.052
Distribution	not defined	not defined	not defined	lognormal	normal	not defined

Source: Authors’ calculation on the base of WeLASER project data

In most cases variables representing both p-values and Cramer’s V association coefficient do not fit to normal distribution. For this reason, non-parametric estimators of location and dispersion (median, MAD) are better in comparison to classical estimators (arithmetic mean and standard deviation).

It can be observed that the median of p-value for Denmark is lower than the medians of p-value for Poland and Spain and the differences are statistically significant. The differences between Poland and Spain are rather small and statistically not significant. The MAD (median absolute deviation) of p-value, which in this case is an estimator of the dispersion, varies similarly to the median. The lowest MAD is observed in case of Denmark and much higher are observed for Poland and Spain, with Poland slightly higher than Spain. In all cases MAD is greater than median.

In case of Cramer’s V coefficient we may see that parameters of location of Cramer’s V for Denmark are higher than their counterparts for Poland and Spain (both medians and means). The differences in medians of Cramer’s V between Denmark and the other two countries are statistically significant. The differences in medians of Cramer’s V between Poland and Spain are minor and statistically not significant. Poland has the smallest Cramer’s V association coefficient variability expressed in terms of standard deviation, while Denmark has the highest Cramer’s V.

3.1. Comparisons of Column Proportions for Denmark, Poland and Spain

This part of results pertain only for variables representing respondents’ opinions. The results of column comparison proportions are presented in the table below. The frequencies of occurrence of differences with p-value < 0,1 and p-value < 0,05 were calculated and results are presented in table below.

Table 10. Results of column proportion comparison for Denmark, Poland and Spain.

Table 10. Results of column proportion comparison for Denmark, Poland and Spain.

α	Denmark		Poland		Spain		3 states
	count	%	count	%	count	%
<0.05	28	27.72	39	38.61	34	33.66	101
<0.10	26	26.26	45	45.45	28	28.28	99
Total	54	27.00	84	42.00	62	31.00	200

Source: Authors’ calculation on the base of WeLASER project data

It may be observed that the total number of significant differences in column proportions is 200 and the number of differences with α less than 0.05 (101) is generally the same as with α less than 0.10 (99). In contrast to the results of previous parameters (p-value and Cramer’s V) the highest number of significant differences pertains Poland and the lowest - Denmark. And this is also truth for total number of differences as well as for differences with both α levels.

Examples of crosstabs with significant association (α less than 0.05)

There are 9 common pairs for all 3 countries 5 of which have mean of Cramer’s V greater than 0.4 (relatively strong association). These are P9A-P12 (0.475), P1-P5 (mean of Cramer’s V is 0.423), P7-P12 (mean is 0.420), P7-P8 (mean is 0.415), and P1-P2 (0.415). Others remaining pairs are P8-P12, P6-P7, P5-P7 and P1-P8.

For the sake of conciseness, only one pair of questions was chosen for a more detailed description and it is P1-P2. The crosstab is composed of the two questions: in columns P1 constitute independent variable. The P1 question is: “Do you use innovation on your own farm?”, and possible answers are as follow: (A) I already use innovation on my farm; (B) I am considering using innovation; (C) I do not use, but I am interested in it; (D) I do not use, and I am not interested in it; (E) Hard to say. The dependent variable P2 is the question: “P2. What is your opinion on the ease of use of innovative technologies? Which of the following opinions would you subscribe to?”. The possible answers are: (i) I find it easy to implement innovative technologies on my own; (ii) Implementing innovative technologies requires me to acquire new skills and knowledge, but it is not a problem for me; (iii) I think it would be a problem for me to acquire new skills and knowledge, but I can do it; (iv) Implementing innovative technologies is a problem for me and I have to rely on external support and advice; (v) I don’t know / I have an opinion.

Below, the contingency table shows the number of cases of each pair of answers for Denmark. The number of cases also represents the percentage, since we have exactly 100 cases in total.

Table 11. Crosstab for P1 and P2 questions for Denmark.

Table 11. Crosstab for P1 and P2 questions for Denmark.

	A	B	C	D	E	Total
i	12	0	0	0	0	12
ii	29 C	4	7	0	0	40
iii	3	3	21 A	0	0	27
iv	0	0	11	1	0	12
v	0	1	1	5	2	9
Total	44	8	40	6	2	100

Source: Authors’ calculation on the base of WeLASER project data

In case of Denmark, the calculated p-value of the Pearson chi-square statistic is 0 (less than 0.0005) and Cramer’s V coefficient is 0,56. From the table above, it can be inferred that 44% of respondents are already using innovations on their farms, and 40% are not using them, but are interested in them. It can be concluded that those who use innovations it is no problem to implement innovative technologies on their own (12 cases and all answers A in group “i”). It may be observed that there is significant difference in column proportions for the group of row “ii” between columns A and C. The p-value of z-test is less than 0.05 and means that proportion of A answers is significantly higher than proportion of C answer. The reverse result is observed for the row “iii” where proportion of C is significantly higher than proportion A.

In the next contingency table pair of answers for Poland are presented.

Table 12. Crosstab for P1 and P2 questions for Poland.

Table 12. Crosstab for P1 and P2 questions for Poland.

	A	B	C	D	Total
i	19	2	0	0	21
ii	34 BCD	7	9	1	51
iii	9	1	2	4	16
iv	2	1	3	5	11
v	0	0	0	1	1
Total	64	11	14	11	100

Source: Authors’ calculation on the base of WeLASER project data

In case of Poland, the calculated p-value of the Pearson chi-square statistic is 0 and Cramer’s V coefficient is 0,37. We may observe that nobody gave answer “Hard to say” on the question P1, thus the column E is omitted. We may inferred from the table for Poland that 64% of respondents already use innovations on their farms.

For 51% of all cases it is not a problem to implement innovative technologies even though it requires to acquire new skills and knowledge. In this group of respondents (answer “ii”) we observe significant differences in column proportions. Proportions of column A are significantly higher than proportion of column B, C and D. But only difference between proportions of columns A and D are significant with α equal 0.05. For remaining two differences (marked with orange italic fonts) the significant level α was 0.10.

Next contingency table presents pair of answers for Spain

Table 13. Crosstab for P1 and P2 questions for Spain.

Table 13. Crosstab for P1 and P2 questions for Spain.

	A	B	C	D	E	Total
i	9	1	0	1	0	11
ii	25	1	6	5	2	39
iii	6	3	3	1	0	13
iv	30 CD	0	4	2	0	36
v	0	0	0	1	0	1
Total	70	5	13	10	2	100

Source: Authors’ calculation on the base of WeLASER project data

In this case, the calculated p-value of the Pearson chi-square statistic is 0.021 and Cramer’s V coefficient is 0,27. We may observe that 70% of respondents already use innovations on their farms. About half of them see no big deal in implementing innovations while other half see problems and necessity to rely on external support. In case of Spain the group of respondents who see a problem in implementing innovative technologies is the most numerous (30% of all respondents). In this group (represented by row “iv”) proportion of column of those who already use innovations significantly surpasses proportion of column of those who do use innovations (columns C and D). Though there are slight variations in significant level of the difference in column proportions between A and C and between A and D. The first comply to α equal 0.05 while the second to α equal 0.10.

4. Discussion

5. Conclusions

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