Table 4.2 shows the main results of five regression analyses, i.e. when using five dependent variables.
The independent variables that are included are the same in all the five regressions. In all tables that include logistic regressions the coefficient ‘B’ refers to the ‘logit’, i.e. the logarithm of the odds ratio.21 These logits are used for estimating the probabilities illustrated in the graphs in this chapter.22
21 The odds ratio represents the odds that an outcome will occur given a particular exposure (for instance having participated in training), divided by the odds that the outcome will occur in the absence of that exposure (for example no training).
22 The estimated probabilities that are presented later in this chapter are calculated according to the formula:
P= ez/ (1+ ez) where P is the probability of being an innovative strategic learner and Z = the intercept plus the effects of the independent variables (z = B0 + B1X1 + B2X2 ....).
Table 4.2. Results of binomial regressions. Dependent variables: different aspects of being an
Table 4.2 (cont.)
Also included: controls for industrial sector, work hours, age, and gender (female=1)
* ‘F’ refers to solving complex problems at work, see Tables 2.1 and 4.1. ‘I’ refers to merging G and H, see Table 4.1. The number of respondents is somewhat higher in column J than for A, B and C since a small group with no information on ‘A’ and/or ‘B’ is coded as 0 on variable J.
** See description of education levels in Table 3.3.
*** Coefficients in boldface are significant at level p<0.05. Coefficients in boldface and italics are significant at level p<0.1.
With a few exceptions, the central explanatory variables have significant effects in the same direction on all the dependent variables that constitute variable J. This confirms that this variable (J) is an expression of, and measures, certain underlying characteristics and properties, and gives good reason
for further examination of the variable J. For example, has work-related training of own interest and/or long duration of training positive impact on all the dependent variables. The same applies to
increasing education level, with an exception for the dependent variable B (learning-by-doing).
Increased occupational level has, however, a positive effect on variable B. Otherwise, increased occupational level has no significant effect on the dependent variable H (learning strategies), whereas it has large significant impact on all the other dependent variables. On the other hand, the isolated effects of increasing education level are larger for the dependent variable H than for the preceding variables in Table 4.2.
High values on ‘autonomy and flexibility’ have a significant positive impact on all the dependent variables, in particular on variable H (learning strategies). The impact of the different work profiles overall is quite large. Of particular importance are high values on the items ‘championing’, ‘brokering’
and ‘information exchange’, though the effects of these variables are smaller on the dependent variable H (learning strategies) than on the other variables. High values on being ‘independent’ also have a positive impact on all dependent variables, but particularly on variable J. (We recall that value 1 (‘yes’) on the dependent variable J refers to a smaller and far more selected group than is the case for the other dependent variables.)
In brief, the results of the analyses of variable J tell us that people who work in organisations with a high degree of flexibility and autonomy, people who participate in work related training of own interest – and particular where it is of long duration, who score high on being brokering, independent and championing and on information exchange, are very likely to be innovative strategic learners at work.
This fits quite well by what is stated by the OECD (2010b) as cited in Chapter 1, where organizational change (flexibility), work-related training and marketing were identified as essential features for successfully developing innovations. Further, the results show that high education levels, and having skilled or semiskilled occupations, increase the probability of innovativeness (all other things being equal), This is also is in accordance with assumptions and findings in the literature presented in Chapter 1. Concerning the effects of occupational level, we see that the effects of ‘unknown occupational level’ is of approximately the same size as the effects of skilled occupation (both compared to elementary occupations). As seen in Chapter 3, this group (‘unknown’) has the same education level as the average, and probably the actual skills level of their jobs are quite high.
The effects of numeracy and literacy skills as well as problem-solving skills in technology-rich environments are different from what we expected. When controlling for all other variables the only positive effect is found in column H (learning strategies) and refers to the positive effect of increasing numeracy and literacy skills. Otherwise, this effect is negative (when controlling for other variables).
We return to the effects of skills later in this chapter.
The results of Table 4.2 imply that there are significant country differences that are not explained by the composition of the samples when it comes to work profiles and work environment
(autonomy/flexibility), demographic variables, skills, education levels, work-related training, weekly work hours and distribution of economic sectors. Norway scores lower than the Netherlands on variable A (‘keeping up to date’) when including controls for a number of independent variables, but Norway together with Finland score highest on variable B (‘learning-by-doing’). Finland has the highest rate on variable G, as in the descriptive statistics (Table 4.1), and there are no significant differences between the other three countries on variable G. Concerning learning strategies (variable H), the Netherlands scores very much lower than the other three countries, also when a number of control variables are employed.
The last variable (J), where the preceding variables are merged, and which will be analysed further in Tables 4.3 and 4.4, show that the Netherlands scores significantly lower than the other three
countries, but also that Finland stands out when controlling for a number of independent variables as those shown in Table 4.2, and which are described in Chapter 3.