Page 205 - Hojnik, Jana. 2017. In Persuit of Eco-innovation. Drivers and Consequences of Eco-innovation at Firm Level. Koper: University of Primorska Press
P. 205
Results
N Mean St. Dev. Skew St. Err. Kurt St. Err.
Skew Kurt
Increased knowledge about 223 4.00 1.707 -0.117 0.163 -0.830 0.324 205
effective ways of managing 3.82 1.686 -0.113 -0.896
operations 223 4.10 1.786 -0.198 0.163 -0.955 0.324
223 3.59 1.679 0.000 0.163 -0.877 0.324
Improved process innova- 223 0.163 0.324
tions 4.22 1.799 -0.287 -0.896
223 4.19 1.586 -0.327 0.163 -0.657 0.324
Improved product quality 4.78 1.619 -0.628 -0.325
223 0.163 0.324
Improved product inno- 223 0.163 0.324
vations
Better relationships with
stakeholders such as local
communities, regulators, and
environmental groups
Improved employee morale
Overall improved company
reputation or goodwill
Note: N = number of observations; Mean = mean value on the Likert scale, which ranges
from 1 to 7 (1 = strongly disagree, 7 = strongly agree); St. Dev. = standard deviation; Skew =
skewness; St. Err. of Skew = standard error of skewness; Kurt = kurtosis; St. Err. Kurt = stan-
dard error of kurtosis.
Further, we conducted an exploratory factor analysis (Maximum
Likelihood Method of extraction and Direct Oblimin rotation). All
measurement items were checked for normality of distribution (see Table
60). The appropriateness of factor analysis was determined by examining
the correlation matrix of competitive benefits items. The Bartlett’s test of
sphericity showed that the correlation matrix has significant correlations
(p < 0.05), and the Kaiser-Meyer-Olkin measure of sampling adequacy
was 0.917, which indicates an excellent sample adequacy. After consider-
ation of each item’s communality index and its contribution, we retained
all the items (the lowest communality after extraction was 0.511).
Table 61: KMO and Bartlett’s test of sphericity (Competitive benefits)
KMO and Bartlett’s test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy 0.917
2697.222
Approx. chi-square
66
Bartlett’s test of sphericity df 0.000
Sig.
N Mean St. Dev. Skew St. Err. Kurt St. Err.
Skew Kurt
Increased knowledge about 223 4.00 1.707 -0.117 0.163 -0.830 0.324 205
effective ways of managing 3.82 1.686 -0.113 -0.896
operations 223 4.10 1.786 -0.198 0.163 -0.955 0.324
223 3.59 1.679 0.000 0.163 -0.877 0.324
Improved process innova- 223 0.163 0.324
tions 4.22 1.799 -0.287 -0.896
223 4.19 1.586 -0.327 0.163 -0.657 0.324
Improved product quality 4.78 1.619 -0.628 -0.325
223 0.163 0.324
Improved product inno- 223 0.163 0.324
vations
Better relationships with
stakeholders such as local
communities, regulators, and
environmental groups
Improved employee morale
Overall improved company
reputation or goodwill
Note: N = number of observations; Mean = mean value on the Likert scale, which ranges
from 1 to 7 (1 = strongly disagree, 7 = strongly agree); St. Dev. = standard deviation; Skew =
skewness; St. Err. of Skew = standard error of skewness; Kurt = kurtosis; St. Err. Kurt = stan-
dard error of kurtosis.
Further, we conducted an exploratory factor analysis (Maximum
Likelihood Method of extraction and Direct Oblimin rotation). All
measurement items were checked for normality of distribution (see Table
60). The appropriateness of factor analysis was determined by examining
the correlation matrix of competitive benefits items. The Bartlett’s test of
sphericity showed that the correlation matrix has significant correlations
(p < 0.05), and the Kaiser-Meyer-Olkin measure of sampling adequacy
was 0.917, which indicates an excellent sample adequacy. After consider-
ation of each item’s communality index and its contribution, we retained
all the items (the lowest communality after extraction was 0.511).
Table 61: KMO and Bartlett’s test of sphericity (Competitive benefits)
KMO and Bartlett’s test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy 0.917
2697.222
Approx. chi-square
66
Bartlett’s test of sphericity df 0.000
Sig.
