Page 206 - Hojnik, Jana. 2017. In Persuit of Eco-innovation. Drivers and Consequences of Eco-innovation at Firm Level. Koper: University of Primorska Press
P. 206
Pursuit of Eco-innovation
The number of factors to be extracted was determined a priori based
on previous research works that used this scale. The number of extracted
factors should be one. The scree plot of the initial run indicated that two
factors might be an appropriate number, and the latent root (eigenvalue)
criterion also indicated two factors, which in total explain 71.406% of
variance. The two factors that were extracted as a result of the explorato-
ry factor analysis are presented in Table 62, together with the 12 related
items and their factor loadings. The new competitive benefits dimension
was split into two factors, one pertaining to the various improvements
(Improvement factor) and the other to the various reductions (Reduc-
tion factor).
Table 62: Competitive benefits dimension’s item factor loadings
206
Factors
Items Factor 1 Factor 2 (Reduction factor)
(Improvement factor)
Better relationships with stakeholders such as lo- 0.872
cal communities, regulators, and environmen-
tal groups
Improved process innovations 0.820
Improved employee morale 0.820
Increased knowledge about effective ways of 0.801
managing operations
Improved product innovations 0.753
Overall improved company reputation or good- 0.753
will
Improved product quality 0.724 -0.344
Increased productivity 0.607 -0.445
Increased process/production efficiency 0.505
Reduction in process/production costs -0.992
Reduction in material costs 0.251 -0.898
Reduction in costs of regulatory compliance -0.520
N = 223
Extraction Method: Maximum Likelihood
Rotation Method: Oblimin with Kaiser Normalization (absolute factor loadings equal or
higher than 0.20 displayed)
Bartlett’s test of sphericity: Chi-square = 2697222; 66 df; sig. = 0.000
Kaiser-Meyer-Olkin measure of sample adequacy = 0.917
Variance explained = 71.406
The number of factors to be extracted was determined a priori based
on previous research works that used this scale. The number of extracted
factors should be one. The scree plot of the initial run indicated that two
factors might be an appropriate number, and the latent root (eigenvalue)
criterion also indicated two factors, which in total explain 71.406% of
variance. The two factors that were extracted as a result of the explorato-
ry factor analysis are presented in Table 62, together with the 12 related
items and their factor loadings. The new competitive benefits dimension
was split into two factors, one pertaining to the various improvements
(Improvement factor) and the other to the various reductions (Reduc-
tion factor).
Table 62: Competitive benefits dimension’s item factor loadings
206
Factors
Items Factor 1 Factor 2 (Reduction factor)
(Improvement factor)
Better relationships with stakeholders such as lo- 0.872
cal communities, regulators, and environmen-
tal groups
Improved process innovations 0.820
Improved employee morale 0.820
Increased knowledge about effective ways of 0.801
managing operations
Improved product innovations 0.753
Overall improved company reputation or good- 0.753
will
Improved product quality 0.724 -0.344
Increased productivity 0.607 -0.445
Increased process/production efficiency 0.505
Reduction in process/production costs -0.992
Reduction in material costs 0.251 -0.898
Reduction in costs of regulatory compliance -0.520
N = 223
Extraction Method: Maximum Likelihood
Rotation Method: Oblimin with Kaiser Normalization (absolute factor loadings equal or
higher than 0.20 displayed)
Bartlett’s test of sphericity: Chi-square = 2697222; 66 df; sig. = 0.000
Kaiser-Meyer-Olkin measure of sample adequacy = 0.917
Variance explained = 71.406
