Table S1. Response Rate of the Survey of Swiss Electricity Utilities by Language

Table S1. Response Rate of the Survey of Swiss Electricity Utilities by Language

Why some electricity utilities actively promote energy efficiency and others donot—a Swiss case study

Supplementary material

Survey results

Response rate by language

The survey used in this study was sent out in all three of Switzerland’s’ official languages. The table shows the response rate of Swiss utilities, which was similar for all languages.

Table S1. Response rate of the survey of Swiss electricity utilities by language

Decision variable / German / French / Italian / Overall
Contacted utilities / 247 / 35 / 14 / 296
Answered surveys / 96 / 11 / 7 / 114
Response rate / 38.8% / 31.4% / 50.0% / 38.5%

Size distribution and comparison to another Survey of Swiss utilities

The following table compares the size distribution of the companies participating in this survey (N=114) to the one of another survey of Swiss utilities conducted by SondereggerandSchedler (N=107)[*].

Figure S1. Size distribution of the electricity utilities participating in this study and in that of SondereggerandSchedler(2010)

Legal form

The following table shows the legal forms of the responding companies (n=101). In the study this categorical information has been transformed into a binary variable (Dependent and independent public institution Vs. the rest).

Figure S2. Legal forms of the participating electricity utilities. For the analysis the two categories dependent public institution and Independent public institution (both in grey) and the rest (in black) were each combined in an own category.

Further analysis

Regression without two outliers

With regards to company size, there are two outliers, which are considerably larger than the others (see figure below).

Figure S3. Scatterplot showing the number of employees and the EE performance of all utilities participating in the study (n=101).

Excluding these from the regression analysis yields slightly different results:

Table S2.Results of the regression analysis without the two outliers showing the effect four company characteristics (number of employees, number of large clients, share of production and legal form) on their EE performance PEE

b / Std. Error b / β
Number of employees [PEE] / .136 / .043 / .41**
Number of large clients / -.029 / .025 / -.15
Share of production [%] / -.005 / .067 / -.01
Legal form / 7.163 / 3.950 / .18

R2=.17; **=p<.01

The following figure shows the average effectiveness rating (on a 7-point scale) and the standard deviation of the utility managers (n=5) and the other experts (n=4) for all nine EE items. While there are no notably different answer patterns between the two groups, there experts rate most of the items slightly higher than the other experts. This is primarily due to one utility CEO, who is known to be a very strong promoter of EE and is responsible for most of the variance in the group of utility managers. For the item “information material”, which the reviewer mentions, there is as well one expert in the other group, who thinks that this is quite a potent measure.

Figure S4. Comparison of the effectiveness ratings (on a 7-point scale) by utility managers (n=5) and the other experts (n=4) for all nine EE items.


[*]Sonderegger, R. W., & Schedler, K. (2010). Betriebliche Steuerung von kommunalen Elektrizitätsversorgungsunternehmen - Schlussbericht zur Follow-up Studie 2009. St. Gallen: Universität St. Gallen.